esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The cellulose paper industry, detergents, adhesives, water treatment agents, sensors, carbon dioxide capture, batteries, and cosmetics are some of the other areas where PEI and its composites have been used in the past [24; 25; 26; 27]. Figure 1: Types of polyethylene imine (PEI) architectures. The use of non-crosslinked PEI (polychelatogen) for removing pollutants such as dyes and heavy metals has also been investigated in a system called polymer-enhanced ultrafiltration (PEUF). PEI interacts with pollutants by complexation, hydrogen bonding, and other electrostatic interactions [28]. A flat membrane filter with high permeability in a \"dead end\" operating mode is used to filter out the PEI-pollutant complex. The PEI is finally recovered by decomposition [29; 30]. Clearly, PEUF is an alternative way where pristine PEI is used to capture heavy metals from water samples. However, while the preparation of crosslinked PEI has an additional crosslinking reaction step, the low solubility of the crosslinked material in water will allow its use as an adsorbent and in the recovery of the material by a filtration step without the need to use high pressures for separation. Moreover, crosslinking increases the stability of PEI and hence it avoids/minimizes the chance of low-molecular-weight organic fragments entering the environment during filtration. Studies also show that the crosslinked PEI is more selective toward copper than it is toward zinc, which can be used to separate the two ions efficiently. In the presence of equimolar copper and zinc in water samples, crosslinked PEI adsorbed copper selectively while a trace amount of zinc got adsorbed. On the other hand, in non-crosslinked PEI, a larger amount of zinc is adsorbed [16]. The present review starts by critically discussing the various crosslinking reactions of PEI and possible reaction mechanisms reported in the literature. In addition to presenting the reactions, this review discusses the properties of the resulting crosslinked materials. Then, this review discusses the applications of crosslinked PEI, such as in heavy metal adsorption, and its release with an acid treatment and electrochemical processes. The preparation of a thin coating (by spin and spray coating techniques) and subsequent crosslinking reactions are also presented. The thin film properties, adsorption, and electrochemically triggered release of copper are discussed with a great emphasis, as they are deemed to have importance in anti-fouling systems in the marine environment without impacting the environment with a high level of copper. The roles of crosslinked PEI in CO\({}_{2}\), dye, and aldehyde adsorption are also briefly discussed. This review has also surveyed the adsorption kinetics and adsorption isotherms of heavy metals and dyes onto crosslinked PEI.
## 3 Results and Discussion
### Topic 3: Innovative Models of Healthcare Delivery
#### 3.4.1 Telehealth and Telemedicine
In evaluating the success of telehealth systems in remote operations, no validated indicators have been documented [54]; however, a recent systematic review [54] suggested a number of key performance indicators based on the current evidence. It found that quality telemedicine in a remote setting should include reliable, transmittable, and continuous equipment-based monitoring available within 15 min, high-quality video systems available within 15 min, electronic records accessible via telemedicine, a major hospital available for specialist consulting, appropriately qualified teleconsultant physicians and offshore personnel, clear protocols for managing common cases, and appropriate training of all personnel in using telemedicine [54]. While innovative telehealth/telemedicine models have advantages for the oil and gas industry, research also shows that less-sophisticated, more cost-effective measures can also add value. For example, [PERSON] et al. [55] reported on a range of e-health strategies which had been effective for an international oil and gas company. One approach was a 24 h call centre which connects employees to a doctor via telephone. Also discussed was a health database which supports ready access to confidential health records, along with software for real-time medical stocks (medication/consumables), and a health education portal with resources and e-learning modules for employees. These various strategies provide opportunities for cost-effective solutions which may reduce unnecessary strain on local health services and proactively improve the health behaviours and health status of oil and gas workers.
## 5 Conclusions  
It is known that characterizing the PD inception and endurance of insulating materials and systems must take real electrode geometrical characteristics carefully into account. To quantify this speculation, a specific electrode configuration meant to trigger surface PD on polymeric specimens was considered. Electrode geometrical characteristics (contour curvature radius and electrode contact shape/dimensions) were varied to assess their impact on the computation of the electric field profile and PD inception field estimates, the latter based on the three-leg approach (methodology that gathers coherent field computations, inception modeling, and experimental activity supported by SRI software).  
Simulation and parametric analysis results, validated by experimental testing, show that small electrode curvature radius variations (lower than 300 \(\upmu\)m) do not impact significantly on the estimated and measured PDIV, as well as on PD typology identification.  
Figure 12: Electrode B: PD acquisition for the first measurement of Table 4 (applied voltage of 1.6 kV). (**a**) Global cluster and automatic separation map; (**b**) Noise cluster with the relevant sub-pattern for recognition; (**c**) PD cluster with the relevant sub-pattern for recognition and identification: the PD are identified as 100% surface discharges.  
However, small deviations in the shape of the electrode contact area that lies on the specimen from the theoretically flat one can impact drastically on PD inception evaluation. This affects insulation system design, which must be based on criteria that allow PD-free insulation system operation as well as the type of defect generating PD and, thus, its harmfulness (thus diagnosis). As an example, if the purpose of testing is to characterize surface discharge inception and surface PD resistance, a lack of accuracy in electrode profile can trigger gas discharges rather than surface ones, which will provide misleading results for both design and diagnostic purposes.  
Conceptualization, G.C.M.; Methodology, G.C.M.; Software, G.G.; Investigation, G.G.; Writing--original draft, G.G.; Writing--review & editing, G.C.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions.  
The authors declare no conflicts of interest.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 3 Proposed Intrusion Avoidance Protocol
### Optimize Clusters
In the end, the queue length factor is incorporated in the selection mechanism of cluster head, thus a node is given higher priority to be elected as a cluster head if its transit queue length is higher than a certain threshold. After the selection of primary cluster heads, they advertised their status in a precise manner. All the normal nodes join their adjacent cluster head for the formation of clusters, upon receiving the status messages. Normal nodes might receive status messages from more than one adjacent cluster heads and associate themselves with those cluster heads, having the strongest RSSI value. At the end of the clusters formation process, ESR protocol assigns a unique ID for all generated clusters in order to specify their boundaries. The set of nodes selected as cluster heads announce channel access schedules based on time-division multiple access (TDMA). Thresholding Based Secret Sharing Scheme (SSS) for Secure Data Routing Against Intrusions of Malicious Nodes  
In the proposed approach, the BS generates a secret key \(S\), which is to be partitioned among a set of \(n\) cluster head using \((t,n)\) threshold-based [PERSON]'s secret sharing scheme where any \(t\) subset of cluster heads are enough to reconstruct the secret key \(S\). It must be noted that for [PERSON]'s secrete sharing scheme, the following two conditions must be satisfied:  
1. Any combination of \(t\) or higher number of subkeys \((S_{0},\ S_{1},\ldots,S_{t-1})\) can reconstruct the secret key \(S\). 2. Less than \(t\) or fewer number of subkeys can not reconstruct the secret key \(S\). In SSS, for construction of \(t\) subkeys, a \(t-1\) degree polynomial is constructed. For creating a \((t,n)\) threshold scheme, \(t-1\) random numbers \((b_{1},\ b_{2},\ldots,b_{t-1})\) greater than zero are selected. If \(b_{0}=S\), the numbers \((b_{1},\ b_{2},\ldots,b_{t-1})\) are coefficients of the polynomial as given in Equation (4). \[f(x)=b_{0}+b_{1}x+b_{2}x^{2}+b_{3}x^{3}+\ldots,\ b_{t}x^{t-1} \tag{4}\]  
The reconstruction of the secret keys \(S\) requires computing the Lagrange basis polynomial given in Equation (5). \[l_{j}(x)=\prod_{ \begin{array}{c}0\leq m\leq t\\ m\
eq j\end{array} }\frac{x-x_{m}}{x_{j}-x_{m}} \tag{5}\]  
After computing \(t-1\) Lagrange values, these are put in Equation (6) for the computation of secret key \(S\). \[f(x)=\sum_{j=0}^{t-1}y_{j}l_{j}(x) \tag{6}\]  
Each share of the key \(S_{i}\) is diffused towards the cluster heads, which is flooded towards an individual node in the particular cluster. When the node forwards the sensory data \(D_{i}\) towards the cluster head, it is encrypted by applying the Exclusive OR (XOR) operation with the key \(S_{i}\) as given in Equation (7). \[E_{i}=S_{i}\oplus D_{i} \tag{7}\]  
After receiving the encrypted data \(E_{i}\) from the member nodes, the cluster head transmits it to the BS for further processing. Similarly, on the arrival of encrypted data, the BS decrypts the data using the decryption key \(S\) and transmits it to the end user.
# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 5 Experimental Setup
### System Evaluation  
Automatic evaluation of results on MT is a difficult job. Each sentence has multiple possible translations that are good enough to be used, but we use only one as reference to compare the translations generated. By extension, this is a problem that we encounter in our simulation of the IPNMT session. The simulated user will always try to produce the ground truth, although other translations could involve a lower effort. We have to deal with this problem when measuring the user effort and evaluating the quality of the translations, so the results we obtain will be pessimistic.  
To evaluate the performance and robustness of our CMs, we use the metric classification error rate (CER) [18]. This metric calculates the number of classification errors of the confidence model divided by the total number of words classified. The main objective of the CMs is to classify the words accurately, using the confidence estimation score and the threshold set. If the model classifies the words correctly, the number of errors will be near zero, as the CER score.  
Secondly, we want to analyze the applicability of our CMs in a real-world interactive translation workflow. For this reason, we study the relationship between human effort reduction and the quality of the translations. To evaluate the human effort of the translation process, we selected the metric Word Stroke Ratio (WSR) [21], and to estimate the quality of the translation, we used the metric BiLingual Evaluation Understudy (Bleu) [22].  
WSR is computed as the number of word strokes that the user performs to correct a translation, divided by the length of the sentence translated. In this context, we define word stroke as the complete action of correcting one word, but it does not take into account the cost of reading the new suffix provided by the system. Bleu computes a geometric mean of the precision of n-grams multiplied by a factor to penalize short sentences.
## 4 Discussion
The precision in time and resolution for assessing areas of land, as demonstrated in this research, clearly offer huge potential as primary evidence for the MRV of habitat and other benefits that are needed for the correct distribution of the incentives associated with ELM policy. Capitalising on this potential requires improved coverage in both time and area at these levels of resolution and at acceptable cost. EO data are becoming more and more widely used in governance and planning at all scales, and the demonstration of its cost-effectiveness in similar applications [34; 110] provides good reason to be optimistic that ongoing R&D and applications development enable the near-term use of VHR imagery in MRV for ELM and related policy. Finally, we wish to reiterate the usefulness of EO data to inform biodiversity conservation and environmental management as highlighted here and by remote sensing specialists and ecologists in recent years [37; 47; 64; 66]. When EO is used in this field, it requires ongoing collaboration between ecologists and EO experts to develop a common, shared understanding about the relationship between the species selected and their habitats, terminology (e.g., regarding land-cover classes), guidance for understanding the opportunities to create/improve/extend suitable habitat for the species, and recognition of ecological and environmental pressures on the species. Such collaboration is essential to realising the outstanding potential for EO data to effectively support (i) ecologically informed environmental management decisions and (ii) the provision of habitat as a much-needed 'public good'.
## 4 Experimental Results, Analysis, and Evaluation
### Visualization Experiments  
To evaluate the visualization experiments, we evaluated the perceptual encryption results of the 3D printing models. The aim of the proposed algorithm is to alter and distort the shape of 3D printing models. Experimental results are shown in Figure 5. The number of facets in each 3D triangle mesh is different. The encryption process changes the values of the vertices of facets. Thus, the shape  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**\# Facets**} & \multicolumn{4}{c}{**Entropy (dB)**} & \multirow{2}{*}{\begin{tabular}{c} **Computation** \\ **Time (ms)** \\ \end{tabular} } \\ \cline{3-6}  & & \begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]’s** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]’s** \\ **Method** \\ \end{tabular} & \
\begin{tabular}{c} **[PERSON]’s** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Time (ms)** \\ \end{tabular} \\ \hline Wheel Camping & 544 & 4981 & 2754 & 2218 & 8.0 & 17.5 \\ Knife & 1176 & 12,032 & 6705 & 5430 & 8.0 & 29 \\ Aperture Face & 2026 & 22,292 & 12,485 & 10,137 & 8.0 & 46 \\ Flower & 2986 & 34,508 & 19,390 & 15,766 & 8.0 & 72 \\ Gun & 3961 & 47,378 & 26,681 & 21,714 & 8.0 & 90 \\ Castle & 7466 & 96,096 & 54,362 & 44,322 & 8.0 & 209 \\ Batman & 13,566 & 186,268 & 105,767 & 86,360 & 8.0 & 591 \\ Rabbit & 21,056 & 302,443 & 172,161 & 140,704 & 8.0 & 1312 \\ Moto-bike & 22,034 & 317,932 & 181,022 & 147,959 & 8.0 & 1421 \\ Car & 32,426 & 485,937 & 277,229 & 226,767 & 8.0 & 2951 \\ Yoda & 49,844 & 777,860 & 44,4681 & 364,021 & 8.0 & 6763 \\ Lion & 79,162 & 1,288,203 & 737,923 & 604,535 & 8.0 & 17,486 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results.  
of the facets is also altered. After the encryption process, facets are distorted into big facets (see \"Encrypted Wheel Camping\") or small facets (see \"Encrypted Car\" and \"Encrypted Knife\") or changed location, positioned disorderly (see \"Encrypted Yoda, Encrypted Lion\"). Thus, the shape of the 3D triangle meshes is altered. The content of the 3D triangle meshes is completely altered after the perceptual encryption process. Pirates or unauthorized users cannot extract or view the content of 3D triangle meshes.  
In [PERSON]'s method [9], he permuted the vertices of 3D meshes. According to his permutation, the shape of the 3D mesh changed, but he did not show the experimental results of his method. Moreover, the decryption of this method cannot fully restore the original 3D meshes from the encrypted 3D objects. In [PERSON]'s method [12], the encrypted CAD model was slightly changed (see Figure 6a,c). Anybody can view the content of the encrypted CAD model. We compared [PERSON]'s model with our proposed algorithm. Results are shown in Figure 6b,d. No unauthorized user could view or steal it, but the entire model was altered. Thus, the perceptual results of our proposed method are superior to those of [PERSON]'s method.  
Figure 5: Experimental results with test models.
## 4 Imaging System Characterization and Performance
### Photonic to Digital Relationship
Considering the measured energy density per pulse at the object plane for the LED illumination system (c.f. Figure 5), the energy going through a virtual pixel at the object plane is slightly more than 3.6 \(\times\) 10\({}^{-6}\) nJ. On the other hand, the energy going through a virtual pixel at the object plane with the VCSEL illumination system is 1.1 \(\times\) 10\({}^{-5}\) nJ, a significant gain compared to LED lighting. To understand what these quantities represent in front of the energy necessary to illuminate a pixel at the image plane, the Planck-Einstein relationship can be used as it relates fundamental quantities such as the photon energy E, at a particular wavelength, and is expressed as  
\[\mathrm{E=\frac{h\cdot c}{\lambda}.} \tag{3}\]  
where h is the Planck constant, c is the speed of light in vacuum, and \(\lambda\) is the illumination wavelength. With the illumination wavelength taken as the center wavelength of the LED radiation (455 nm), or at the specified VCSEL radiation wavelength (860 nm), each photon carries an energy on the order of 4.37 \(\times\) 10\({}^{-19}\) J for LED and 2.31 \(\times\) 10\({}^{-19}\) J for VCSEL illumination. The respective number of photons going through a virtual pixel at the object plane was on the order of 8200 for the LED illumination, while it was over 48,000 for the VCSEL light source. Assuming a conversion gain of 1 with the sensor's ADCs operating under 8-bit mode, the conversion factor is 0.0187 bit/e-, meaning that each pixel would reach digital saturation (255 Cts) by accumulating a total of nearly 32,000 photons at the LED illumination wavelength, and over 85,000 photons at the VCSEL wavelength. The number of photons necessary to reach full dynamic range of the camera sensor is approximately four times higher than the measured LED light energy at over a virtual pixel at the object plane, and a little under twice the energy delivered by the VCSEL illumination system. These quantities assume that the light between the object and image planes suffer no loss. Comparing these results to the digital level measured by the camera indicates that \(\sim\)80% of the energy with LED lighting is being lost between the object and image planes. The losses are likely explained by a higher than anticipated dispersion of the diffuse illumination distribution, resulting in lower light energy entering the microscope objective, as well as losses within the microscope objective as a result of the diffuse illumination distribution. Conversely, the VCSEL lighting only appeared to suffer an energy loss below 10% between the object plane and the sensor, demonstrating the efficient energy transmission beyond the probe volume and inside the microscope objective. In both cases, some amount of gain needs to be applied to the camera sensor to maximize signal-to-noise ratio and effective dynamic range. We applied a gain level of 3 for the LED illumination, and a 40-% gain when using the VCSEL illumination system.
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
## 3 Results
### Enzymatic Activity and Feather Degradation Capacity of Mutants  
In this study, we obtained mutation sites and substituted amino acid residues through sequence alignment and homologous protein mutation experiments. We selected the amino acid residue V299 near the S1 substrate binding pocket and the residue A350 in the loop region linking the catalytic triad for mutagenesis. The most frequently encountered amino acid residues in the corresponding sites 299 and 350 of the homologous protease are phenylalanine and serine, respectively. Thus, A350S and V299F were selected for site-directed mutagenesis of DgoKerA.  
The enzymatic activities of DgoKerA mutants of were measured with 1% soluble keratin and feather powder as substrates. As shown in Table 2, the enzymatic activity of the mutant A350S was higher than that of DgoKerA (66,872.84 \(\pm\) 137.41 U/mg vs. 51,147.65 \(\pm\) 442.80 U/mg) using 1% soluble keratin as the substrate, with an increase of about 30%. In addition, mutant V299F also exhibited high keratinocyte activity on soluble keratin and feather powder.  
The degradation of whole-feathers by each mutant at 60 \({}^{\circ}\)C is shown in Figure 6. The mutants A350S and V299F all exhibited better feather degradation compared with that of DgokerA. The feather branches were clearly shed by the treatment of V299F for 40 min and the solution changed from clarified to turbid. Subsequently, more feathers were decomposed to small debris in the reaction system. After 90 min of incubation, the whole-feathers were degraded by V299F to just rachis.
## 2 Materials and Methods
### Spectral Analysis  
Before sample measurements, dark (non-reflective standard, 0.07% relative reflectance) and white references (reflective standard, Spectralon, SRS-99, Labsphere Inc., North Sutton, NH, USA, 99% relative reflectance) were measured within the wavelength region 320-2453 nm. The reflection from the drop surface can be neglected as the contact between the probe and sample drop was always ensured. Each measurement was repeated 6 times for each substance. A 20 \(\mu\)L drop of an aqueous solution was pipetted onto the reference material and the spectrum was measured. The measured data is provided as supplementary material (Table S1: Data sheet). The reflective material and the probe were then wiped clean, and the protocol was repeated. In total, 18 spectra for each concentration of aqueous L-fucose and L-proline solutions were acquired. An average of all 18 measurements was calculated from the raw data. Absorbance for transfection mode was calculated using the Beer-Lambert law (Equation (1))  
\[A=-\log_{10}\frac{S-D}{W-D}, \tag{1}\]  
where \(S\) is the spectrum of the sample, \(D\) is the dark reference spectrum, and \(W\) is the white reference spectrum.  
The values calculated using Equation (1) were normalized using standard normal variate (SNV). A Savitzky-Golay filter (degree = 3, window = 49 for 319-1100 nm, and degree = 3, window = 19 for 943-2500 nm) was used to smooth the data and calculate the 2 nd derivative for each averaged spectrum (MATLAB R2019a, The MathWorks, Natick, MA, USA, 2019). Area normalization was used with the derivative spectra. The area normalization was calculated as (Equation (2))  
\[S_{AN}=\frac{S}{\sum\lvert S_{p}\rvert}, \tag{2}\]  
where \(S_{AN}\) is area normalized spectra and \(S_{p}\) is point of spectrum. The area normalization was done over 455-2370 nm. If solutions appeared in the order of F1, F2, F3, F4, and water, or P1, P2, P3, P4, and water, meaning an increase in water content, a black rectangle was used to mark those wavelengths. If the solutions appeared in the order of F1, F2, F3, and F4, or F1, F2, F3, and water, or P1, P2, P3, and P4, or P1, P2, P3, and water, assuming the F4 and P4 solutions and pure water having almost equal water content, these wavelengths were marked with a light green rectangle.  
Figure 1: The NIRS set-up (**a**), and schematic presentation of the drop on top of reflective standard with the probe touching the drop (**b**).