esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Lithium Distribution  
#### 3.3.1 Calibration of LIBS Spectrum  
For the quantitative analysis of LIBS spectra, a calibration procedure is necessary. The detected Li peak intensity in LIBS correlates with the real lithium amount [18]. According to the data from cyclic voltammetry measurement in Figure 2b, the lithium intercalation process takes place below 0.3 V. Therefore, voltages of 0.01, 0.05, 0.06, 0.085, 0.1, 0.11, 0.15, 0.19, and 0.3 V were chosen for electrochemical titration. Graphite electrodes with the same constitution were assembled and lithiated to the above-mentioned state of charge (SoC). After disassembly of these cells, half of the electrode was analyzed by inductively coupled plasma-optical emission spectrometry (ICP-OES), which enables to determine the quantitative amounts of elements in the electrode. Figure 3 demonstrates the titration curve of the ratio of lithium to carbon measured by ICP-OES at a chosen SoC. At 0.01 V, the graphite electrodes were completely lithiated, and the maximal ratio of amount of substance Lithium compared to carbon (nLi/nC) was 0.14. For the delithiated sample, the ratio was 0.035. The detected lithium amount is attributed to the formation of solid-electrolyte interphase (SEI).  
The other halfs of the electrodes were as prepared for LIBS measurements. Nine laser spots were irradiated in the center of the electrode with a distance of 0.5 mm six times. The measured spectra were analyzed by using a data filter for the generation of the calibration associated with the measured element concentration obtained by ICP-OES. Figure 4 exhibits a typical LIBS-spectrum detected after one laser pulse on a lithiated graphite electrode using filters such as Savitzky-Golay smoothing [12]. The representative peaks of carbon, lithium, and CN-bonding are labeled in the diagram. The representative wavelength of the element is extracted from the National Institute of Standards and Technology (NIST) database [19].  
Figure 3: Quantitative ratio of lithium to carbon content measured by inductively coupled plasma–optical emission spectrometry (ICP-OES) as a function of voltage.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 6 Strategies for Eradication of Biofilms
### Downregulation of Biofilm Formation Pathways  
As described previously, QS represents the master coordinator of all bacterial virulence behavior, especially biofilm formation. The ability to control transcription of genes involved in biosynthesis of biofilm components is of upmost importance for suppression of biofilm formation. Considering this approach, the interruption or downregulation of essential proteins that are involved in biofilm matrix secretion could lead to weaker biofilm consistency. Another example is observed in the ATP-binding cassette (ABC) transporters which are required for the initial steps of biofilm formation in _Pseudomonas aeruginosa_[165]. Targeting of this system by the synthetic antimicrobial peptide (Nal-P-113) results in decreased biofilm formation [166]. On a similar basis, the downregulation of genes responsible for anaerobic biofilm formation (e.g., _mirS_, _f\({}_{\beta}\)B_, _norC_, and _nosZ_), fucose binding of lectin (_lecB_), and secretion system Type IV pilus (_fimX_) would reflect biofilm inhibition in different bacterial spp. [167; 168; 169]. Other factors that are essential for biofilm initiation, such as motility and adhesion, represent a logical target when aiming for biofilm inhibition, based on the fact that non-motile bacterial cells or pili-deficient mutants cannot form biofilms [170; 171; 84]. Hence, downregulation of the genes that encode flagellar or fimbrial proteins leads to crippling of bacteria and suppression of biofilm formation. Several agents were tested in this regard. Sitagliptin and prazosin showed the ability to downregulate the genes _fimA_, _fimC_ and _bsmB_ that encode fimbria biosynthesis, and genes _fhlD_, _rssb and rsmA_ that control _S. marcescens_ swarming. As a side effect, biofilm formation was significantly hindered as well [144; 153; 145].
## 2 Methods
### Data Analysis
#### 2.3.3 Hilbert-Huang Transform, HHT  
The Hilbert-Huang Transform (HHT, [PERSON] et al. [31]) can be used to analyze complex and nonlinear signals. It is particularly useful for interpreting seismic and acoustic signals. HHT consists of Empirical Mode Decomposition (EMD) and Hilbert Transform (HT). EMD decomposes the signal into several Intrinsic Mode Functions (IMFs) and a residual component. The IMFs can then be processed with the HT to obtain a time-frequency representation. This time-frequency spectrum shows the relationship between spectral magnitude (i.e., instantaneous energy), instantaneous frequency, and time, which can help identify the signal characteristics during the erosion process of the slope models. This study conducted the HHT analysis using Visual Signal Ver. 1.6 software (AnCad, Inc., Taipei, Taiwan [33]).
## 3 The Proposed Method
### Overall Structure  
The structure of GEA-MSNet is shown in Figure 4. GEA-MSNet is named by the added GEA attention module and is based on the improvement of the Res-UNet [51] model. This model encompasses an encoding and decoding structure based on residual networks, a GEA attention module, and a multi-scale feature fusion module. Firstly, within this model, the encoder stage employs a 50-layer residual network structure [52] for efficient feature extraction. The features of different levels are correspondingly blended, with the inclusion of shallow features in advanced ones to facilitate information restoration at a detailed level. Secondly, the GEA attention module is devised to establish connections between semantic context features while prioritizing global information. The aggregation of global feature information is increased to efficiently extract image features to optimize the learning ability of the model. Thirdly, the multi-scale feature fusion module is used in the decoder to restore the resolution of the image layer by layer. The multi-scale feature fusion module addresses the limitation of convolution operation by mitigating overfitting caused by solely focusing on local features. It enhances the recovery of water body edge information and improves the detection of small-scale spatial details, thereby alleviating these issues.  
Figure 4: Overall structure of the GEA-MSNet network.
# Privacy-Preserving Data Sharing in Telehealth Services
## 4 Analysis and Evaluation
This comprehensive threat management strategy makes it a reliable solution for safeguarding sensitive healthcare information. **Innovative Aspect**: The innovation of the proposed model lies in its integration of homomorphic encryption with secure multi-party computation (SMPC) and blockchain technology. This combination allows secure data sharing and analysis while maintaining the privacy of patient information. Unlike traditional models, the proposed solution enables computations on encrypted data without compromising confidentiality. This unique approach allows for collaborative research and analysis without exposing individual patient records, making it a forward-thinking solution in the telemedicine landscape. The integration of homomorphic encryption with blockchain technology represents a significant scientific advancement in securing telehealth data. This approach not only ensures data privacy but also enables a transparent and verifiable means of managing health records. In the comparative analysis, we will assess the efficiency, durability against quantum threats, and energy consumption of our framework compared to those of traditional systems. This analysis will highlight the practicality and future readiness of our approach, considering the evolving landscape of cyber threats and computing capabilities. **Performance in Real World**: The real-world applicability of the proposed model is demonstrated by its ability to securely manage patient data in telemedicine systems, ensuring compliance with regulations such as the HIPAA. Its use of blockchain and SMPC allows it to handle large volumes of encrypted data efficiently, making it suitable for diverse healthcare environments, from small clinics to large hospital networks. The model's performance in securing data transmission and providing detailed access control has proven effective in maintaining trust between patients and healthcare providers, offering a scalable and secure solution for modern telehealth services. Table 1 emphasizes different aspects of each work, such as encryption methods, access control, scalability, and their unique contributions to the field. It offers a broader perspective on how these models compare to our proposed solution, showcasing the strengths and focus areas of each approach. Our proposed model, as highlighted, offers a balance of encryption, scalability, and comprehensive threat defense.
## 3 Results and Discussions  
### Practicality Experiment Results of Micro Reacting Pipe  
#### 3.1.1 Metal AM Printing and Compression Resistance Test of Micro Reacting Pipe  
In this study, two kinds of materials, IN718 and 316L stainless steel, were used for forming the micro reacting pipe. Because 316 LSS is relatively cheap and used extensively as metal materials in L-PBF, on the contrary, IN718 is expensive but has better corrosion-resistance. The optimization parameters of L-PBF forming IN718 are as follow: spiral angle 30\({}^{\circ}\), scanning distance 0.08 mm, laser power 200 W, scan speed 1200 mm/s; the L-PBF optimized parameters for 316L stainless steel material: inter-laminar spiral scanning, the effect of spiral angle 30\({}^{\circ}\), scanning distance 0.08 mm, laser power 140 W, scanning speed 800 mm/s. The formed parts made of IN718 and 316L stainless steel are shown in Figure 5. The printed molded parts were cut from the substrate by wire cutting and polished. In this study, the inner and outer diameters of the molded parts were measured by electronic vernier calipers, and each dimension data was measured three times. The maximum dimension deviation value is 0.2 mm.  
To facilitate the physical experiment of mixing efficiency, the shape of the micro reacting pipe was adjusted, the groove was added to the outer wall of the micro reacting pipe according to the size of the determining size, and the experimental sample was molded by IN718 and the molding parameters obtained in Section 2.3. After obtaining the molded part, the tee coupling was welded to import the two reaction solutions, and the final sample is shown in Figure 6.  
The channel size of the micro reacting pipe is small so the pressure in the micro reacting pipe drops dramatically, and it is used in a large flow rate to increase the output in the industrial production, which requires the compression resistance of the micro reacting pipe. In this study, the parameters of L-PBF were optimized by booster test, and the parameters of the high-quality pipe could be obtained by the test. The test pressure was dynamic and gradually increased. During the test, horizontal flow pump was used to inject water into the formed micro reacting pipe and the output flow rate of the pump was continuously increased. As the pressure increases up to the limited pressure 5 MPa in industrial application, if there is no leakage on the external wall of the pipe, the compression resistance of the formed parts can meet the industrial requirements.
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
## 2 Materials and Methods
### Experiments  
On 23 January 2023, at 16:11, the buoy was deployed by the Antarctic krill vessel \"Shen Lan\" at coordinates 46.1322\({}^{\circ}\) W, 60.036498\({}^{\circ}\) S. The buoy operated normally until 30 January 2023, at 18:39, at which point it was located at coordinates 45.6795\({}^{\circ}\) W, 60.70572\({}^{\circ}\) S. The buoy maintained normal operation for a duration of seven days, and its movement trajectory is shown in Figure 3. The buoy moved from east to 46.53\({}^{\circ}\) W, west to 45.68\({}^{\circ}\) W, south to 60.73\({}^{\circ}\) S, and north to 60.68\({}^{\circ}\) S. The spatial movement of the buoy was relatively small. During this period, a total of 157 sets of data were transmitted back via satellite, including ambient temperature (Figure 4), speed, and location information in 157 sets, as well as sonar data and sea surface temperature (Figure 5) in 82 sets.  
Figure 4: Line chart of ambient air temperature.  
Figure 3: Krill distribution.  
When the buoy was powered on, it was initially on the ship before being placed into the sea. Consequently, the temperature readings collected at the first two points were higher. Figures 4 and 5 illustrate that the ambient temperature fluctuates within a range of two degrees. Additionally, the sea surface temperature remains relatively low, mostly below one degree, with minor fluctuations.
## 2 Data
### Data Processing  
Rectangular intercept boxes can be either rectangular or square. Given that a difference in side length has the potential to interfere with the experimental results, using a square intercept box is recommended, as this is considered more conducive to analysis of the results. The procedure for intercepting the data is outlined below.  
To explore the anisotropy of the roughness correction coefficient, we used a square intercept box to intercept the fracture surface data and the gap width data, respectively, from the spatial coordinate data for each sample described above.  
Based on the rectangular frame defined in Section 2.1, we established the centroid of this frame as the reference point and conducted a series of interception operations using the maximum size as the basis, with the interception ratios set to 10%, 20%, 30%, 50%, 60%, 70%, 80%, 90%, and 100% of the frame's dimensions. The aim was to investigate how variations in size impact the roughness correction coefficient. Subsequently, the interception frame was kept at a constant size while being rotated counterclockwise. During the rotation, a set of data samples containing information on the fracture surface and gap width was intercepted every 5 degrees until the rectangular frame had rotated a full 360 degrees, signifying successful completion of the data collection phase for the interception model. Figure 3 is a clear schematic diagram illustrating this operational procedure.  
Figure 3: Schematic diagram of interception of the fractured data. Taking the largest square that the sample can intercept as the maximum size (i.e., the proportions of the interception frame are set to 100%), the proportions of the interception frame are gradually reduced for spatial data interception. For each interception, we rotated at the level (0\({}^{\circ}\)) and intercepted every 5\({}^{\circ}\) up to the end of a full rotation (360\({}^{\circ}\)).  
During the data organization phase, we found that, using the method mentioned, each sample generated a total of 72 specific data points. These points could be further consolidated into 36 sets of sample data, with each set comprising one top surface and one bottom surface, resulting in a total of 720 independent data points in this study. However, given the symmetry between the data from 0\({}^{\circ}\) to 180\({}^{\circ}\) and from 180\({}^{\circ}\) to 360\({}^{\circ}\), we limited our calculations to the 0\({}^{\circ}\) to 180\({}^{\circ}\) range. Subsequently, leveraging the principle of symmetry, we expanded this range of data and plotted them onto a comprehensive chart spanning the full 360\({}^{\circ}\) spectrum. Ultimately, we utilized a total of 1080 sets of data (taking into account the efficient utilization of symmetry) for the subsequent analysis.
# Optimizing Finite-Difference Operator in Seismic Wave Numerical Modeling
## Appendix A FD Operator
### Fourier Transform  
The Fourier transform in the wavenumber domain is  
\[F(k_{x})=\int_{-\infty}^{+\infty}f(x)e^{-ik_{x}x}dx, \tag{100}\]  
\[f(x)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}F(k_{x})e^{ik_{x}x}dk_{x}, \tag{101}\]  
where \(k_{x}\) is the wavenumber, and \(i\) is the imaginary number.  
Substitute Equation (101) into Equations (7) and (8) as follows. The first derivative is  
\[\begin{split}\frac{\partial f}{\partial x}&\approx \frac{1}{\Delta x}\sum_{n=1}^{N/2}b_{n}(f_{n}-f_{-n}),\\ \frac{1}{2\pi}\int_{-\infty}^{+\infty}ik_{x}F(k_{x})e^{ik_{x}x} dk_{x}&\approx\frac{1}{\Delta x}\frac{1}{2\pi}\sum_{n=1}^{N/2}b_{n} \int_{-\infty}^{+\infty}F(k_{x})(e^{ik_{x}(x+n\Delta x)}-e^{ik_{x}(x-n\Delta x )})dk_{x},\\ \Delta x\int_{-\infty}^{+\infty}ik_{x}F(k_{x})e^{ik_{x}x}dk_{x} &\approx\sum_{n=1}^{N/2}b_{n}\int_{-\infty}^{+\infty}F(k_{x})e^{ ik_{x}x}\times 2 isin(k_{x}n\Delta x)dk_{x},\\ \int_{-\infty}^{+\infty}e^{ik_{x}x}F(k_{x})\Bigg{[}ik_{x}\Delta x -2i\sum_{n=1}^{N/2}b_{n}isin(k_{x}n\Delta x)\Bigg{]}dk_{x}\approx 0.\end{split} \tag{102}\]  
Hence,  
\[k_{x}\Delta x\approx 2\sum_{n=1}^{N/2}b_{n}isin(k_{x}n\Delta x). \tag{103}\]  
The second derivative is  
\[\begin{split}\frac{\partial^{2}f}{\partial x^{2}}& \approx\frac{1}{\Delta x^{2}}\Bigg{[}c_{0}f_{0}+\sum_{n=1}^{N/2}c_{n}(f_{n }+f_{-n})\Bigg{]},\\ -\frac{1}{2\pi}&\int_{-\infty}^{+\infty}k_{x}^{2}F (k_{x})e^{ik_{x}x}dk_{x}\\ &\approx\frac{1}{\Delta x^{2}}\Bigg{\{}\frac{c_{0}}{2\pi}\int_{ -\infty}^{+\infty}F(k_{x})e^{ik_{x}x}dk_{x}+\frac{1}{2\pi}\sum_{n=1}^{N/2}c_{n }\int_{-\infty}^{+\infty}F(k_{x})(e^{ik_{x}(x+n\Delta x)}+e^{ik_{x}(x-n\Delta x )})dk_{x}\Bigg{\}},\\ &\approx\frac{1}{\Delta x^{2}}\frac{1}{2\pi}\Bigg{\{}c_{0}\int_ {-\infty}^{+\infty}F(k_{x})e^{ik_{x}x}dk_{x}+\sum_{n=1}^{N/2}c_{n}\int_{- \infty}^{+\infty}F(k_{x})e^{ik_{x}x}\times 2 cos(k_{x}n\Delta x)dk_{x}\Bigg{\}},\\ \approx\frac{1}{\Delta x^{2}}\frac{1}{2\pi}\int_{-\infty}^{+ \infty}F(k_{x})e^{ik_{x}x}(c_{0}+2\sum_{n=1}^{N/2}c_{n}cos(k_{x}n\Delta x))dk _{x},\\ \int_{-\infty}^{+\infty}F(k_{x})e^{ik_{x}x}\Bigg{[}(k_{x}\Delta x )^{2}+(c_{0}+2\sum_{n=1}^{N/2}c_{n}cos(k_{x}n\Delta x))\Bigg{]}dk_{x}\approx 0.\end{split} \tag{104}\]  
Hence,  
\[-(k_{x}\Delta x)^{2}\approx c_{0}+2\sum_{n=1}^{N/2}c_{n}cos(k_{x}n\Delta x). \tag{105}\]
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 2 Original Survey
### Captured Images  
Black and white images captured by UMK 6.5/1318 camera were stored on glass plate negatives (Figure 3). The used frame had four fiducial marks with calibrated coordinates. We also found information about calibrated camera constant of the used lens in mm on the frame. The used lens had camera constant c = 64.41 mm. On the frame, there were also found marks which signify the left/right position of an image in a stereo-pair (left image marked by \"A\" and right image marked by \"B\"). Another mark on the frame was a rotation angle \(\omega\) (Figure 4). Most of the images were marked as \(\omega\) = 0, so it means that zenith angle of the optical axis was z = 90\({}^{\circ}\). Images of stereo-pair A1_15-B1_15 were marked as \(\omega\) = +15\({}^{\circ}\). Images of this stereo-pair were captured in exactly the same  
Figure 2: Sketch of original photogrammetric survey in 1991.  
position as the stereo-pair A1-B1 (Figure 2) but they were rotated up by 15\({}^{\circ}\) so the zenith angle was z = 75\({}^{\circ}\). Stereo-pairs (A1-B1, A1_15-B1_15, A2-B2, A3-B3) were captured approximately 8 m from the wall so their approximate scale was M = 1:124.  
Figure 4: Details of the frame: (**a**) one of the four fiducial marks; (**b**) camera constant in mm written on the frame; (**c**) mark “A” indicates that the image is the left image of a stereo-pair; (**d**) mark “\(\omega=+15^{\circ}\)” indicates that the axis of the lens is rotated by 15\({}^{\circ}\) upwards.  
Figure 3: Images of one stereo-pair of the south wall of Padise Abbey: (**a**) Left image A1; (**b**) Right image B1.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 6 Results and Discussion
### Normal Operation  
First, we look at the result of measuring the actual throughput produced by each of the four schemes when tested under normal operation, where no challenge is presented. The results are shown in Figure 11(a). As can be seen, MPResiSDN(\(k=3\)) produces triple the throughput produced by MPResiSDN(\(k=1\)), while MPResiSDN(\(k=2\)) produces twice the throughput. This is because MPResiSDN(\(k=2\)) and MPResiSDN(\(k=3\)) use additional paths to deliver data to the sink, which obviously increases the actual throughput.MPResiSDN(\(k=1\)) and Spanning Tree produce the same amount of data; however, Spanning Tree takes a much longer time to discover the topology before it starts to produce throughput.  
Now, we look at the results of measuring the goodput produced by the four schemes evaluated in the case of no challenge (Figure 11(b)). Differing from the actual throughput, the goodput of all schemes is approximately the same. These results are expected given that the goodput filters out all redundant data and only keeps one copy of all required packets.  
Figure 11(c) shows the cumulative distribution functions (CDF) of the delay of each scheme under normal operation. For the most part, the delay values range between 5 and 6 ms, regardless of the schemes used. It is evident that increasing the number of alternative paths used in MPResiSDN does not affect the delivery time of the information when the environment does not contain a challenge.  
The difference in the actual throughput and goodput results when tested under normal operation leads us to look at the actual overhead caused by the four schemes (Figure 11(d)). The overhead is calculated as a percentage of the actual throughput divided by the goodput, which represents the redundancy in the data. MPResiSDN(\(k=3\)) results in an increase in overhead of up to 200%, while using MPResiSDN(\(k=2\)) results in an increase of nearly 100%.  
As can be seen, using additional paths to deliver data under normal operational conditions when no challenge is presented creates massive overhead without providing any  
Figure 12: Comparison of the four strategies under normal operation.  
benefits. Thus, it is crucial to consistently use only one path to deliver data under normal operation. This result reinforces the importance of the Challenge Detector component. When the detector reports no challenge, the MPResiSDN should use \(k=1\) to avoid unnecessary overhead on the sensor network.
# LabConcept--A New Mobile Healthcare Platform for Standardizing Patient Results in Telemedicine
## 2 The LabConcept's Architecture
### Sending the Results  
Telemedicine is a process that implies sending data across a communication channel between the end-users. Thus, the results must be sent to the medical doctor to be analyzed in order to provide a correct medical diagnostic.  
This application is designed to store the entire medical record, which provides an enhanced diagnostic information thanks to the multiple factors that need to be taken into account from the complete stored data. This information is sent via e-mail from a specific menu called \"Send results\".  
Nowadays, a very important issue is territorial coverage. Supposing a scenario where a patient living in a city wants to be consulted by a doctor that has his office at a very large distance in another city, when using the LabConcept application, the teleconsulting process will happen without any locomotion and with no time consumption due to transportation.  
Another benefit is that teleconsultation can take place also for patients that cannot attend the doctor's office due to locomotion issues.  
Although it is a software application and a concept designed to improve the clinical diagnosis procedure and to enhance the consultation flow, the clinical judgement for teleconsultation is disclaimed on the patient's own consent.