esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Endocrine Disrupting Chemicals Analysis  
EDCs were analysed by gas chromatography coupled to a mass detector prior to solid phase extraction. For solid phase extraction, a Vac Elut manifold was used. First, the Bond Elut Plexa PAX type extraction cartridges were placed and conditioned by adding 2 mL of analytical grade methanol and 2 mL of MiliQ water; then, 500 mL of the sample passed through the extraction column supported by a vacuum pump, at a rate of about 40 drops per minute. The compounds retained in the solid phase were eluted twice with 5 mL of dichloromethane. Afterwards, a eluted volume of dichloromethane was evaporated with a gentle flux of nitrogen (N\({}_{2}\)); the tube in which the eluent had been evaporated was rinsed several times with 250 \(\upmu\)L of toluene, and then the accumulated volume of toluene was poured into a 1 mL vial insert; finally, the poured extract in the vial was analysed in Agilent 7890B Series Gas Chromatograph (GC) linked to an Agilent 5977B Mass Selective Detector (MSD) equipped with a DB-5 ms capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA). High purity helium (99.9999%) was used as carrier gas. One complete run lasted 34.17 min. A programmed-temperature vaporiser (PTV) injector worked in solvent vent mode. The inlet temperature was held at 100 \({}^{\circ}\)C for 0.5 min, raised to 280 \({}^{\circ}\)C at 900 \({}^{\circ}\)C/min, and finally decreased to 100 \({}^{\circ}\)C at 900 \({}^{\circ}\)C/min until the analysis was finished. The oven temperature was held at 100 \({}^{\circ}\)C for 1.5 min, raised to 180 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/min, then raised to 310 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min for 4 min. MS transfer line and the ion source temperature were both set at 280 \({}^{\circ}\)C. The MSD was operated in full scan mode from 50 to 500 m/z. Data acquisition and processing were based on deconvolution technologies, which were accessed in ChemStation Software (version E.0200.493; Agilent Technologies, Santa Clara, CA, USA). The database used for screening analysis included 926 pesticides and endocrine disruptors as in Wylie [17]. Deconvolution was run to identify endocrine disruptor compounds according to the following criteria: the automated mass spectral deconvolution and identification system match values were higher than 60%. Chemical analyses were performed at the facilities of the Biotechnology and Natural Resources Laboratory at the Institute for Regional Development (IDR), University of Castilla-La Mancha (UCLM).
# Interferences of Electromagnetic Pulses on Microcontroller Units
## 2 Experimental Environment
### EMP Simulator  
The EMP simulator was a compact EMP simulator, which was composed of a power source and a flat plate radiator (Figure 2). The radiator consisted of three components; namely, the front transition section, the rear transition section, and the workspace. Figures 3 and 4 present an image of the radiator and its dimensions. The TEM wave propagated in the workspace, so the workspace was also referred to as the TEM cell. The output voltage indices of the simulator were as follows: the rise time was 7.6 ns, the half-pulse width changed with the capacitance of the power source, and the maximum FWHM was 1730.8 ns. The maximum amplitude of the EMP voltage was about 30 kV, and the maximum field strength generated in the TEM cell was 200 kV/m. Figure 5 (Left) shows the coordinate system of the TEM cell, and the typical time-domain waveform of the electric field measured at the origin is shown in Figure 5 (Right).  
Figure 1: Image of the MCU test plate (left: front view, right: back view).  
Figure 4: Dimensions of the TEM cell.  
Figure 5: Left: Coordinate system of the TEM cell. Right: Typical electromagnetic time-domain waveform at the origin.  
Figure 3: Image of the flat-type EMP simulator.  
Figure 2: Plate-type EMP simulator.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.4 Objective Function  
The objective function is used to place sensors such that the differences between the maximal responses (for example equivalent strain or temperature) and detected responses by the sensor are minimized [28]. For this case, the objective function is formulated as follows:  
\[f\left(\mathbf{x}_{j},\theta_{j},\phi_{j}\right)=\sum_{i=1}^{n}w_{i}\underset {j=1..m}{min}\left[\left(\left|R_{max_{i}}\right|-\left|R_{det_{i}}\right.\right. \left.\left(\mathbf{x}_{j},\ \theta_{j},\ \phi_{j}\right)\right|\right)^{2}\right] \tag{7}\]  
where \(n\) is the total number of load cases, \(m\) is the number of sensors, \(R_{max_{i}}\) is the maximum response of the structure (maximum equivalent strain or temperature) in the domain for the \(i\)th load case, \(R_{det_{i}}\left(\mathbf{x}_{j},\theta_{j},\phi_{j}\right)\) is the value detected by sensor \(j\) under the load case \(i\), and \(w_{i}\) is the weight of the \(i\)th load case that is the inversed maximal response of the structure to the applied load and given as follows:  
\[w_{i}=\
icefrac{{1}}{{R_{max_{i}}}} \tag{8}\]  
To illustrate how to determine the objective function value, let us consider the example structure presented in Figure 4. The number of load cases is \(n=3\), while the number of sensors is \(m=2\). For the subsequent load cases where the same amount of load is applied in different locations, the maximal responses of the structure are \(3\), \(5\), and \(8\), respectively. The sensor measurements for each load case are presented in the figure. For this case, the objective function value would be calculated as follows:  
\[f=\frac{1}{3}\text{min}[(3-2),(3-1)]+\frac{1}{5}\text{min}[(5-3),(5-2)]+\frac{ 1}{8}\text{min}[(8-3),(8-5)]\approx 1.11 \tag{9}\]
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Performance Evaluation and Validation  
Due to the principal design of the considered algorithms, the algorithms start by generating random solutions and then proceed with the optimization. Moreover, due to the multimodality aspect of the path planning problem, the outputs of the algorithms will not always be the same. Hence, we ran the simulations for each scenario 50 times (independently) and then analyzed the results statistically. The effectiveness of the solutions under different algorithms and scenarios was evaluated in terms of the median value, standard deviation (denoting the stability of the algorithms), and convergence speed of the algorithms.  
To further compare the simulation results of different scenarios and algorithms, some other metrics, such as the ratio of path reliability, \(R_{reliability}\); the ratio of infeasible solutions, \(R_{infeasible}\); and execution time were considered, which can be calculated as  
\[R_{reliability}=\frac{L_{reliable}}{L_{total}}, \tag{27}\]  
where \(L_{total}\) and \(L_{reliable}\) can be calculated based on Equations (4) and (5), respectively,  
\[R_{infeasible}=\frac{n_{infeasible}}{n_{it}}, \tag{28}\]  
where \(n_{infeasible}\) is the number of infeasible solutions generated during the optimization procedure. Infeasible solution refers to the solutions that the penalty terms in Equations (9)-(11) are not zero.  
Although the execution time of algorithms depends on the hardware and software configuration of the utilized computer, and it is not the same for other devices, as a metric to compare the run time of algorithms, the considered scenarios and algorithms also were compared in terms of execution time. All the simulations in this study were implemented in MATLAB R2022a, and the simulation test environment is; OS: Windows 10 64-bit; Processor: Intel(r) Core i5-4430; central frequency: 3 GHz; RAM: 32 GB.  
To assess the effectiveness of the algorithms, the problem was simulated under three scenarios presented in Table 2. Finally, to validate the performance of the proposed optimization method, the simulation results of Scenario I were compared with the conducted aerial drive test on the 4G network at the UKM campus.
## 2 Literature Review
### CBCT Image Processing  
After the acquisition, the CBCT needs some technical adjustments before exporting the final images. First, the best axial slice (spline or panoramic base curve) for the annotations of the canal has to be identified. A curve is drawn on the selected axial slice, and it is usually placed at the equidistant point between the lingual and buccal cortices of the mandible. Since the mesial component of the IAC is usually clinically more relevant due to the presence of teeth, this curve is often shorter than the actual length of the IAC, which instead reaches up to the mandibular ramus. Once the annotation is complete, a coronal image similar to an OPG is generated. It is based on the axial slice selected and almost the entire 2D course of the IAC can be annotated on it. From here, the 2D annotation performed on this slice can be back projected to the original 3D volume, thus providing a 2D sparse annotation of the IAC on the original CBCT scan. Unfortunately, all these annotation steps are performed manually by a dedicated radiology technician, which is a waste of time and resources. The possibility of a machine masking these adjustments with great accuracy is therefore intriguing.  
Moreover, 2D annotations fail to identify a considerable amount of inner information about the IAC position and the bone structure, i.e., the accurate contour, position, and diameters of the canal. An incomplete detection of the nerve positioning is often sufficient to facilitate a positive surgical outcome, but it is not an accurate anatomical representation of the IAC, and as such, it may lead to failure. On the other hand, manually producing a 3D annotation of the IAC would be practically unfeasible in the daily medical routine. In fact, this experimental operation requires almost one hour for every patient, and therefore, it is not applicable in the clinical practice. Automatic algorithms could certainly exceed this limitation.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Graphene and Graphene Derivatives  
Graphene offers, among other features, large surface area and high electrical conductivity. It has been widely used in electrochemical sensor devices in which it participates to increase current densities and contact area capture probe and target molecules. The advantage that graphene (but not graphene oide) could have compared to CNTs or related carbon NPs is its intrinsic conductivity as well as, if correctly prepared (that is, in a well-dispersed form and not as bundles) a higher surface-to-volume ratio.
# Additive Manufacturing for Surgical Planning and Education: A Review
## 2 Workflow
### Imaging  
In many hospitals, the pioneers in adopting 3D-printing technology are closely linked to medical imaging. Utilizable data from MRI, CT, and certain ultrasounds serve as a foundation for 3D printing [11]. However, not all scanning data is equal; low-resolution images can lead to disparities between the generated model and actual anatomy. Precision is crucial, as 3D models derived from scan data aid healthcare providers in comprehending a patient's condition. Accurate data enhance the potential of 3D-printed models for personalized clinical education and patient-specific planning, significantly impacting healthcare [5].  
Radiologists, with their expertise in various imaging modalities, play a pivotal role in ensuring the highest-quality models are produced from the scans. Their skills are integral to the development and seamless integration of this transformative technology in medicine. These specialists need to ensure that the imaging is done with the specific intent of creating an accurate 3D model, with closely spaced scan slices. Figure 2 represents a single MRI image of a spine with a cancer tumor that has been detected next to it.  
An additional level of difficulty can be introduced when using scan data from multiple systems. In many cases, bone data, for example, are easier to obtain from CT scans, whereas soft-tissue data are easier to obtain from MRI scans. If both are needed, and if the scanning facility produces the scans on different machines, so with the patient in slightly different positions, data-fusion techniques need to be developed in order to fuse the two different data into a cohesive model.
## 3 The SOMM Model
### The Main Idea: Achieving a Dynamic Network Using SOA  
Most early WMSN deployments have adopted _ad-hoc_ and application-specific architectures. They were closed networks which typically were deployed by a single party (e.g., a government agency, a research institute or a private company) and there were no need to link them with each other. Only the owners of these networks used them and there were no other clients for them. In recent years, wide usages and different applications of WMSNs, the need for some kind of WMSNs which can perform multiple tasks and serve multiple clients at the same time raised. These networks are called GWMSNs (Generic WMSNs). They are expected to be open, multi-purpose, ubiquitous, and interoperable networks. A GWMSN can be replaced by multiple WMSNs. For example, consider two parties in a town which both of them deployed a WMSN throughout the town (e.g., traffic control organization and police station). Each network only can perform a special task for its owner and one party cannot use other one network. Also, if another party needs to monitor the town, it must deploy another WMSN. A GWMSN is able to replace these two networks and can service all parties at the same time. But, the design and development of a GWMSN using current design methods and programming frameworks, if it is possible, is a complex and costly task. Thus, new programming frameworks must be defined to enable the development of GWMSNs with reasonable cost.  
The proposed middleware (SOMM) consists of some service registry and several servers. Servers register the specifications of their services, such as type and quality range of service, in the nearest service registry. A client can refer to appropriate service registry, find the address of server and communicate with server via message passing. Also, there are some special servers named code repository which are used as a place for storing different implementations of services that servers can provide to their clients. An overall view of a GWMSN which designed based on SOMM is depicted in Figure 1.  
Figure 1: Overall view of a GWMSN designed based on SOMM.  
In this network, there are three types of video sensor nodes with different capabilities. Each video node registers specifications of its service in nearest service registry using API provided by SOMM. Clients can connect to base station via the Internet and query the SOMM for a specified service with a given QoS. After that, SOMM checks to see if it is possible for network to provide desired service with specified QoS. SOMM informs client if network cannot serve the client, otherwise, the SOMM connects the client to proper server node after reserving needed resources for that service. The details of these operations are described later.  
It is clearly observable that using SOA concepts in SOMM lead to a fully dynamic and scalable network of server nodes which can provide different services to clients. A network can serve multiple clients at the same time and new functionalities can be added to the network easily (as described later).
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 2 Related Background
### Requirements for VSS-QR  
The VSS-QR has a different setting as ordinary VSS because the decoders are different. For ordinary VC, the decoder consists of a stacking operation and a HVS. In contrast, for VSS-QR, the decoder consists of stacking operation and a QR code decoder.  
Taking the \((n,\;n)\)-threshold scheme as an example, ordinary VC should satisfy two conditions. (1) Contrast condition: If all \(n\) shares are stacked, then the secret image should be revealed with sufficient contrast. (2) Security condition: If less than \(n\) shares are collected, no information about the secret bits should be revealed. For a VSS-QR scheme, in order to define the contrast condition and security condition, we must determine the computational ability of the attacker and define the meaning of \"contrast\" for a QR code decoder.  
Security should be defined under an assumption of the ability of a potential attacker. This ability is quantified by the computational resources the attacker may have. In this paper, we assume that the attacker has a standard QR code decoder which may help him to reveal the embedded data in a QR code.  
Contrast in VSS-QR is different from contrast in ordinary VSS. In ordinary VSS, one focuses on the difference between white region and black region. The larger the difference, the better the contrast. However, this contrast is defined for HVS, not for a QR code decoder. Using a QR code decoder as an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Color 1** & **Color 2** & **Color 3** & X \\ **(c1)** & **(c2)** & **(c3)** & **(c1**\(\sqcap\)c2**\(\sqcap\)c3)** \\ \hline R & G & B & W \\ K & G & B & C \\ R & K & B & M \\ R & G & K & Y \\ C & M & Y & W \\ C & K & Y & W \\ C & B & K & C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Color stacking of three shares.  
observer, if the number of codeword errors is within ECC capability, then the \"contrast\" is acceptable for the QR code decoder. Otherwise, the \"contrast\" is not large enough for QR code decoder. So, here 'contrast' is defined as correct decoding.  
In summary, a VSS-QR should satisfy the following conditions.  
1. _Contrast condition:_ If all \(n\) shares are collected and stacked, then the recovered secret QR code should be decoded correctly by a standard QR code decoder.
2. _Security condition:_ If \(k\) shares \((k<n)\) are collected, then the secret QR code should not be decoded by stacking any combination of these \(k\) shares.
## 3 Results and Discussion
### Assessment of the TMA-N Value  
The TMA-N content has been reported to be a valid index for quality loss assessment during the refrigerated storage of fish species on the basis of the strong relationship between its value and the freshness loss [26; 47]. Volatile amine compounds such as TMA have been shown to originate partially as a result of endogenous enzyme activity but mostly as a result of microbial growth [48; 49].  
In the present study, a marked increase (\(p<0.05\)) in the TMA-N value in the fish muscle was detected as the chilling time increased (\(r=0.94\), quadratic fitting) (Figure 1). In agreement with the above-mentioned data on microbial count evolution, this increase can be explained on the basis of the microbial activity development during refrigerated storage. Thus, good correlation values were detected for the TMA-N value with the above-mentioned microbial counts (mesophiles, \(r=0.93\); psychrotrophs, \(r=0.95\); _Enterobacteriaceae_, \(r=0.90\)).  
Previous research has already shown remarkable TMA formation during the refrigerated storage of pouting. Thus, [PERSON] and [PERSON] [50] observed exponential TMA-N formation during storage at 2 \({}^{\circ}\)C. In this study, the 1.0 mg TMA-N\(\cdot\)100 g\({}^{-1}\) value at day 9 rose after 15 days up to a 31.4 mg TMA-N\(\cdot\)100 g\({}^{-1}\) value. Similarly, marked TMA-N formation was reported by [PERSON] et al. [23] during 2 \({}^{\circ}\)C refrigerated storage. Thus, values in the range of 11.0-15.7 mg\(\cdot\)100 g\({}^{-1}\) were detected in the pouting muscle after 7-8 d of storage.  
TMA-N formation has also been detected in related lean fish species during chilled storage. Thus, no relevant TMA-N formation was detected in hake (_M. merlucius_) muscle during chilling storage for up to 12 d. Then, a sharp increase up to 11.5 g\(\cdot\)100 g\({}^{-1}\) muscle was observed on day 19 [43]. Likewise, iced wild turbot (_Scophthalmus maximus_) attained a 38.9 mg TMA-N\(\cdot\)100 g\({}^{-1}\) muscle value after 19 days of storage [51], while a 19.6 value was detected after 8 days of storage. In the case of farmed turbot (_Psetta maxima_),[PERSON] et al. [30] reported low TMA-N formation (4.5 mg\(\cdot\)kg\({}^{-1}\) muscle) after 19 d of chilled storage. Low TMA-N formation (0.9 and 2.2 mg TMA-N\(\cdot\)100 g\({}^{-1}\) muscle) were also described by [PERSON] et al. [45] in chilled sea bream (_S. aurata_) and sea bass (_D. labrax_), respectively, after 12 d of storage. Relatively low TMA-N values were also detected in the dorsal and ventral zones of Atlantic pomfret (_Brama brama_) [52] after 15 d of storage, and thus TMA-N values of 1.0 and 2.1 mg\(\cdot\)100 g\({}^{-1}\) muscle were assessed, respectively.
## 3 Results
### Evaluation of the Economic Performance of the Optimal EMS  
Figure 3 shows the results of 42 out of the 46 simulated scenarios, presented in a 2 \(\times\) 3 matrix. In the figure, the scenarios differ in three parameters: spark gap (country), FiT (column), and degradation level (row). The numerical results, as well as the parameter values for each scenario, are listed in Table 4.  
Figure 2: Results of the preliminary study conducted to determine the HP. The sequences are WWF WWC WWC (winter), TWF TWF TWC (transition), and SWX SWX SWX (summer).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{Sce.} & \multicolumn{1}{c}{City} & \multicolumn{1}{c}{\(P_{td}\)} & \multicolumn{1}{c}{\(P_{tdg}\)} & \multicolumn{1}{c}{\(\mathbf{Smg}\)} & \multicolumn{1}{c}{\(\mathbf{FIT}\)} & \multicolumn{1}{c}{D} & \multicolumn{1}{c}{CZ} & \multicolumn{1}{c}{HPR} & \multicolumn{1}{c}{\(\mathbf{SmS}\_{t}\)} & \multicolumn{1}{c}{\(\mathbf{EMS}\_{t}\)} &Figure 4 shows the effect of varying the HPR of the demand (scenarios 43 and 44), and Figure 5 shows the effect of a change in climate zone (scenarios 45 and 46). Once again, the optimal EMS is the best in all scenarios, but by a small margin. The numerical values are listed in Table 4.  
The optimal EMS is the best in all scenarios. This is indeed what was to be expected. However, the difference between the savings achieved by the optimal EMS and those achieved by the best rule-based EMS in each scenario is small. This difference (\(\Delta\) in Table 4) ranges from 0 to 2 percentage points. In absolute terms, assuming an annual energy bill of EUR 2500 with the reference system, it would amount to less than EUR 50.  
Figure 4: Economic performance of the optimal EMS compared to those achieved by the rule-based EMSs, scenarios 43 and 44. Effect of a change in the HPR of the demand.  
Figure 3: Economic performance of the optimal EMS compared to those achieved by the rule-based EMSs, in 42 out of the 46 simulated scenarios. The colored rectangles indicate which of the rule-based EMSs is the best.  
Figure 6 shows a histogram with the \(\Delta\) numbers from Table 4. In 65% of the cases, the increase in economic performance achieved by the optimal EMS, compared to the best rule-based EMS, does not exceed 1%. Obviously, if the selected rule-based EMS is not the most suitable, the optimal EMS achieves a much greater cost reduction.