esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Microstructure of Laminated Shale Specim and Micro-Constitutive Models
### Complexity Measure of Micro-Cracking Morphology  
Micro-cracking morphology in shale formation can be comprehensively measured using the fractal dimension of the crack network. A bigger fractal dimension indicates more complex fracture morphology. Recently, fractal analysis has been successfully used to characterize the pore structures or fracture networks in an investigation of unconventional gas reservoirs [39; 40]. The pore structures and fracture complexity of shale gas reservoirs control the capacity of gas flow during hydraulic fracture treatments [41]. According to the simulated final fracture morphology of the shale specimen, the effects of bedding strength parameters on fracture complexity are quantitatively analyzed through crack fractal dimension. The crack fractal dimension is calculated using the box-counting method with the following formula.  
\[D=\lim_{n\rightarrow\infty}\frac{\text{lg}M(n)}{n\text{lg}2} \tag{10}\]  
where \(M(n)\) is the number of boxes containing cracks, and \(2^{n}\) represents the total number of square boxes which fully cover the binary image.  
The calculation procedure is stated below. A binarization algorithm is conducted first before the image segment. After that, the binary image will be covered by \(2^{n}\) square boxes. By changing the size of the square box, the surface of the binary image (micro-cracking morphology after binarization treatment) can be covered by a different number of square boxes. The total number of square boxes is always an exponential of base two. Among these square boxes, there are \(M(n)\) square boxes containing cracks. As the box size decreases, the slope is the crack fractal dimension \(D\) when \(\text{lg}M(n)\) and \(n\text{lg}2\) are projected in an orthogonal logarithmic coordinate system [42]. In this study, the crack fractal dimension is used to measure the complexity of micro-cracking morphology under different bedding strength parameters. This is to describe the complexity of the crack network.  
Figure 3: Different particle motions in rock matrix and bedding-rock matrix interface: (**a**) particle moves around one another in matrix; (**b**) particle slips along beddings (modified from [PERSON] et al. [32]).
## 2 Theoretical Background
The coupling can be enhanced if either the lines are placed very close to each other or lines with a large area and a material with a high dielectric constant are used. However, the standard technologies such as 130-nm SiGe BiCMOS have some limitations in terms of minimum separation between two conductors, and they must be separated by some distance. Additionally, the dielectric constant of the material and thickness of the conductors (which in the case of edge coupling, defines the area of the conductors) are fixed. This makes the design of a wideband edged coupled balun based on two lines highly challenging. Alternatively, three asymmetric coupled lines can be used to design the edged coupled balun, which can assist in attaining the capacitive coupling needed for wideband performance without violating the design rules of standard technologies. The coupling between asymmetric coupled lines can be adjusted with two different sizes of gap separation. The two gaps together with the width of the coupled lines provide extra design flexibility in controlling the even/odd mode capacitances (see Equation (7)).
## 4 WirelessHART Scheduling Schemes
### The Zhang Algorithm  
The work in [79] proposes a scheduling algorithm that uses graph coloring for the graph routing only and aims to optimize latency and provide high reliability. The edges represent the links from the network, and their color (an integer number) represents the slot time in which that link should be scheduled. Two adjacent edges must not have the same color. Furthermore, a channel number is used to differentiate links with the same color (thus being in the same slot time). All edges in the network must be dyed in at least one color.  
In order to provide reliability, the algorithm uses ORMGR [80] to determine an optimal transmission path and uses a retransmission policy. This policy considers two data retransmission opportunities on the optimal route. Edges on the optimal route have priority for being dyed and have two colors along with different channels. Thus, if the data transmission fails through a link on the best route for the first time, then the device sends the data through another channel to the same neighbor. The second retransmission occurs on another link. However, these alternate routes do not have an additional slot arranged in order to improve the network throughput.  
In the algorithm, the graph is traversed from source to destination coloring (staining) the edges based on the source's degree \(d\). When the links related to the nodes of degree \(d\) are scheduled, \(d\) is decreased and the algorithms proceed until the last degree (zero, from the gateway) is reached. The algorithm builds two groups of devices, E and E'. E is the group of all links in the network, and E' is the group of stainable devices in an iteration. Group E' is formed by the links that connect nodes from degree \(d\) and their upper or lower-layer nodes. A node can appear at most once in E', and dyed links of the optimal route should be included. In each iteration, if there is no non-dyed edge \(e\) from the optimal path, the variable \(d\) is incremented, and the algorithm moves to the next degree. The edges are dyed at the end of each iteration, and the available channels are distributed to the allocated links. The colors are determined by a variable \(s\), which is incremented at the end of each iteration.  
In that paper, there are tests for validation purposes and a comparison with the algorithm presented in [78], but no further details about those tests are revealed. The paper claims that its algorithm has a better success rate than both the algorithm proposed in [78] and a non-retransmission alternative. This advantage grows with increasing network depth.
# Material Composition Testing Related to Measurement Instrument Enclosure Design and Safety
## 3 Results
### Classification of Test Samples by Category and Thickness  
Table 4 classifies the samples according to both burning categories and thicknesses (1.5 mm and 3.0 mm). Figure 6 visually summarizes these results, clearly indicating the number of materials per category and thickness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material Thickness**} & **Material** & **Material** & **Material** & **Cannot Be Categorized** \\  & **CategoryHB** & **CategoryHB40** & **CategoryHB75** & **Cannot Be Categorized** \\ \hline \multirow{8}{*}{1.5 mm} & S8\_1.5 & S12\_1.5 & S2\_1.5 & S1\_1.5 \\  & S9\_1.5 & S13\_1.5 & S4\_1.5 & S3\_1.5 \\  & S10\_1.5 & S20\_1.5 & S5\_1.5 & \\  & S15\_1.5 & S23\_1.5 & S6\_1.5 & \\  & S16\_1.5 & S24\_1.5 & S7\_1.5 & \\  & S17\_1.5 & S26\_1.5 & S11\_1.5 & \\  & S18\_1.5 & S27\_1.5 & S14\_1.5 & \\  & S19\_1.5 & S29\_1.5 & S28\_1.5 & \\  & S21\_1.5 & & S30\_1.5 & \\  & S22\_1.5 & & & \\  & S25\_1.5 & & & \\  & S31\_1.5 & & & \\  & S32\_1.5 & & & \\ \hline \multirow{8}{*}{3.0 mm} & S8\_3 & S2\_3 & S1\_3 & \\  & S9\_3 & S7\_3 & S3\_3 & \\ \cline{1-1}  & S10\_3 & S11\_3 & S4\_3 & \\ \cline{1-1}  & S13\_3 & S12\_3 & S5\_3 & \\ \cline{1-1}  & S14\_3 & S23\_3 & S6\_3 & \\ \cline{1-1}  & S15\_3 & S27\_3 & S30\_3 & \\ \cline{1-1}  & S16\_3 & S28\_3 & & \\ \cline{1-1}  & S17\_3 & S29\_3 & & \\ \cline{1-1}  & S18\_3 & & & \\ \cline{1-1}  & S19\_3 & & & \\ \cline{1-1}  & S20\_3 & & & \\ \cline{1-1}  & S21\_3 & & & \\ \cline{1-1}  & S22\_3 & & & \\ \cline{1-1}  & S24\_3 & & & \\ \cline{1-1}  & S25\_3 & & & \\ \cline{1-1}  & S26\_3 & & & \\ \cline{1-1}  & S31\_3 & & & \\ \cline{1-1}  & S32\_3 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification of test samples according to burning categories and sample thickness.  
Figure 5: Number of materials in each burning category.  
This classification indicates which material mixtures consistently achieve the same burning category at specific thicknesses, highlighting groups of material mixtures such as PC Makrolon, PA6 Bergamid, PA66 Ultramid combined with RAL 7021 pigment, and Masterbatch for polyamide.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 3 Experiments and Discussions
### 3D Displacement Measurement of Log Specimen
#### 3.3.2 3D Reconstruction Error  
In order to ensure the measured three-dimensional displacement field is credible, the correctness of the reconstructed panoramic three-dimensional profile of the log with the known three-dimensional laser point clouds was first investigated. This was achieved by matching the measured log surface topography with the wood surface topography obtained by laser scanning using the ICP algorithm. To do this, the measured three-dimensional point cloud of the logs was mapped onto the laser point cloud data and the Euclidean distance between each measured three-dimensional point and the corresponding point on the laser point cloud data was calculated to obtain the error map shown in Figure 6. As can be seen from Figure 6, the measured three-dimensional shape of the logs is identical to the true shape, with an average error of less than 2 mm. The average error for each single surface is shown in Table 4. The validation results prove the correctness of the proposed  
Figure 5: Real scale three-dimensional reconstruction results. (**a–c**) are the reconstructed surfaces of camera pairs C\({}_{1}\), C\({}_{2}\) and C\({}_{3}\), respectively. All units in the diagram are in mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Camera Pairs**} & **Scale Factor** & **Rotation Angle** & **Translation Vector** \\  & **(mm/Pixel)** & **(\(\circ\))** & **(mm)** \\ \hline No.1 & 158.55 & (91.94, 0.57, \(-\)98.22) & (1052.54, \(-\)130.97, \(-\)119.16) \\ No.2 & 160.72 & (93.63, 0.50, 8.47) & (\(-\)180.12, 1046.81, \(-\)91.92) \\ No.3 & 154.94 & (93.50, \(-\)0.44, 137.71) & (\(-\)717.77, \(-\)809.85, \(-\)91.84) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scale factor, relative rotation and translation vector determined in the measurement.  
measurement principle. As the three-dimensional displacement calculation is strongly dependent on the reliability of the three-dimensional shape reconstruction, validation from a geometrical point of view is reasonable and necessary. Combined with the validation results of camera calibration and surface merging, the proposed panoramic displacement measurement system can be considered reliable and can provide correct and reliable 360-deg three-dimensional displacement measurements for complex shaped objects.
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 2 Materials and Methods
### Analysis of Volatile Compound Profile  
The headspace solid-phase microextraction (HS-SPME) technique was applied before gas chromatography-mass spectrometry (GC-MS) to determine the volatile compounds of _Tentrohic molitor_ larvae oil samples. For HS-SPME extraction of volatiles from 6 g of oil sample, a triple-coated fiber DVB/CAR/PDMS (divinylbenzene/carboxy/polydimethylsiloxane; 10 mm length, 50/30 mm thickness, Supelco, Bellefonte, PA, USA) was used. The fiber was exposed to the sample's headspace for 40 min at 45 \({}^{\circ}\)C. Chromatographic separation was performed on a GC-MS instrument (6890N GC, 5975 MS Agilent Technologies, Santa Clara, CA, USA) equipped with an HP-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; 5%-diphenyl-95%-polydimethylsiloxane; Agilent, Santa Clara, CA, USA). The detailed conditions of the oven temperature program were described previously [34]; the carrier gas was helium at 0.9 mL/min, splitless injection. The MS was operated at 70 eV, source temperature at 230 \({}^{\circ}\)C, and mass range 33-350 \(m/z\). The identification of the volatile compounds was based on comparing the Kovats' retention indexes and mass spectrum with those of NIST.08 Mass Spectral Search Program and Wiley 8 th Ed. libraries. The determination of the linear retention indices (LRIs) was performed, using a mixture of n-alkanes (C6-C20; Sigma-Aldrich, Poznan, Poland) dissolved in n-hexane, according to [PERSON] and [PERSON] (1963) [35]. The quantities of volatile compounds were presented as a relative percentage of the total peak area [34]. The analyses were carried out in triplicates.
## 3 Results
### \(e_{t}^{\prime}-I_{C}^{-1}\) Curves  
The Figure 5 has been created from the data contained in Table 11 (Appendix C):  
In Figure 5, and in contrast to the \(I_{E}-I_{C}^{-1}\) curves (Figure 3), now \(e_{t}^{\prime}\) has replaced \(I_{E}\). It must be noted that \(e_{t}^{\prime}\) can be calculated as \(E_{t}^{\prime}\) divided by \(m_{tot}\) or \(I_{E}\) divided by \(L_{t}\). This means that all of the tendencies observed before are still valid. Now there are two additional tendencies, explained next.  
In the first place, \(E_{t}^{\prime}\) decays as \(L_{t}\) augments, as Table 11 proves. This is due to the fact that accelerating the vehicle requires the provision of a high amount of kinetic energy and this energy is better used for longer routes.  
Secondly, \(I_{C}\) worsens as \(L_{t}\) grows. It is simple to understand this by reviewing Equations (A12) and (A25): as \(L_{t}\) grows, \(t_{tot}\) does too and \(I_{C}\) decreases (or \(I_{C}^{-1}\) increases). Shorter routes allow a higher throughput because, for the same period of time, more containers can be dispatched.  
After having seen the different trends involved, it can be concluded that the best option is \(L_{t}=750\) km. 750 km (point/run 14 above in Figure 5 and Table 11) is the only one that optimizes \(e_{t}^{\prime}\) (associated with both \(I_{E}\) and \(E_{t}^{\prime}\)) and \(I_{C}\). In addition, 500 km (point/run 13) improves \(I_{C}\) and its counterpart but worsens \(e_{t}^{\prime}\), while 1000 km (point/run 15) has the contrary effect.
## 5 Simulation and Results
### Performance Metrics  
The experiments are performed to evaluate the efficiency of the proposed system in differentiating between benign and malicious nodes. The following metrics provide a quantitative measure of the system's accuracy, sensitivity, specificity, and overall discriminatory power. Each metric plays a crucial role in comprehensively evaluating the system's performance and is fundamental to the subsequent analysis.  
* True Positive (TP): the number of intruder nodes correctly recognized as malicious by the detection system.
* True Negative (TN): the number of benign nodes correctly identified as non-intruders by the detection system.
* False Negative (FN): the number of malicious nodes mistakenly categorized as benign by the detection system.
* False Positive (FP): the number of benign nodes mistakenly classified as malicious by the detection system.
* True Positive Rate (TPR): also known as sensitivity, this represents the proportion of actual positive instances (intruder nodes) that are correctly identified by the system.  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{11}\]
* False Positive Rate (FPR): the proportion of actual negative instances (benign nodes) that are incorrectly identified as positive (misclassified as intruders).  
\[\text{FPR}=\frac{\text{FP}}{\text{TN}+\text{FP}}. \tag{12}\]  
FPR can also be represented as  
\[\text{FPR}=1-\text{Specificity}, \tag{13}\]  
where specificity is known as true negative rate, defined as follows.  
* True Negative Rate (TNR): also known as specificity, this signifies the proportion of actual negative instances (benign nodes) that are correctly identified by the system.  
\[\text{TNR}=\frac{\text{TN}}{\text{TN}+\text{FP}}. \tag{14}\]
* Receiver Operator Characteristic (ROC) Curve: The ROC curve is a graphical representation that illustrates the trade-off between TPR and FPR at various thresholds. It helps evaluate the performance of a classification model across different operating points.
## 2 Materials and Methods
### InSAR Analysis
#### 2.2.2 Method and Techniques
Wrapped interferograms (deformation in radians) have to be unwrapped and transformed from phase radians into motion (meters). In these products (unwrapped interferograms), phase fringes have disappeared and absolute motion in the line of sight (LOS) in meters is recovered. To do this, the w  
Figure 4: The two wrapped interferograms that resulted from SARscape ((a): ascending geometry, (b): descending geometry) and SNAP ((c): ascending geometry, (d): descending geometry) software. They are of good quality and contain the phase difference between the master and slave images due to the 30 October 2020, Mw = 7.0, Samos earthquake. imported to the unwrapping processing step. Finally, the result of the interferometric unwrapped phase is converted to a displacement map in meters and geocoded. In the case of SNAP, the wrapped interferogram is exported in the form of SNAPHU software for the unwrapping process in the Linux environment. Finally, the result of the interferometric unwrapped phase is re-entered into SNAP to create the LOS displacement map in meters. The displacement decomposition tool calculates the vertical, east-west, and, optionally, north-south components of the displacement. In this case, the vertical and east-west (2D) displacement was calculated. The minimum input data requirement in order to calculate the 2D measurements is to obtain from different acquisition geometries DInSAR measurements in ascending and descending orbit over the same study area. As SNAP software cannot generate the displacement decomposition, further calculations were performed through mathematical operations between the two layers in the LOS. The generation of the Z vertical (up-down) and E horizontal (east-west) displacement can be calculated using the LOS displacement products from the ascending and descending image pairs together with the incidence angle through equations [21]:  
\[\text{de}=[\text{Dd}\cos\theta\alpha-\text{D}\alpha\cos\theta\text{d}]/\sin \left(\theta\alpha+\theta\text{d}\right)\]  
\[\text{dz}=[\text{Dd}\sin\theta\alpha+\text{D}\alpha\sin\theta\text{d}]/\sin \left(\theta\alpha+\theta\text{d}\right)\text{,}\]  
where Dd and Da are the LOS displacement and \(\theta\)a and \(\theta\)d are the incidence angles of each geometry.
# Federated Learning: A Comparative Study of Defenses Against Poisoning Attacks
## 4 Attack Model
### Classification of Attacks  
Malicious clients in a federated learning environment can attempt to use their inclusion in the training process to influence the final global model. However, different adversaries may have different goals for the global model. Here are the main categories of poisoning attacks against federated learning:  
**Untargeted Attacks (Byzantine Attacks).** These attacks aim to prevent the global model from converging to an effective classifier. Techniques include data poisoning, where false labels are introduced during training to negatively affect the performance of the global model [18]. A model update poisoning attack can be more powerful because it exploits the fact that the agent has direct control over the model updates it sends to the server. It can thus increase the amount of its contribution in order to dominate the global model after aggregation.  
**Targeted Attacks.** Also known as \"backdoor\" attacks, targeted attacks seek not to degrade the performance of the global model, but to insert a backdoor subtask into the global model. For example, an organization might attempt to inject marketing messages into a next word prediction model. This can also be accomplished through data poisoning or model poisoning [1].  
There are two main approaches to model poisoning for a targeted attack--boosting the entire contribution of the malicious client to replace the global model [2], or boosting only the malicious component of the update [17].  
In this paper, we only consider a targeted model update poisoning attack with selective boosting.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 4 Challenges and Research Directions
### Challenge 1: LBT Is Mandatory to Operate in the Unlicensed or Shared Band
The eNB sets a 3 ms DL transfer and leaves 1 ms for the UL before the MCOT reaches its maximum period. As there is a time gap between the last DL transmission and the first UL subframe, the UE only conducts a 25 us CCA(clear channel assessment) LBT because it is still part of the eNB's initiated transmit opportunity. As the previously distributed opportunity (i.e., 3 DL subframes \(+\) 1 UL subframe) exceeded the MCOT limit, a new LBT should be followed for a new transmission. In this regard, the proper allocation of the number of UL and DL frames in MCOT is still a challenge for a stable network.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 3 Proposed Routing Optimization Algorithm
### Principle and Implementation
#### 3.1.3 Selection of the Next Hop  
Based on the determined routing optimization parameters, the fusion contribution degree of each forward neighbor is calculated according to the routing selection strategy in Section 3.2. When the sensor node detects or forwards data, the next hop \(g\) is the node that has the maximum fusion contribution degree among the forward neighbor set \(FN(i)\).