esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### In-Field Test Room Analysis
#### 3.1.3 Equivalent Enthalpy and Latent Enthalpy  
The measurement results of the equivalent enthalpy, latent enthalpy, and areal enthalpy are summarized in (Table 8). The equivalent enthalpy (h) and the areal enthalpy (\(h_{A}\)) were calculated considering the sum of the partial enthalpies (or the maximum cumulated enthalpy values) that were determined over the 16-32 \({}^{\circ}\)C range.  
The specific heat (\(c_{p}\)) was determined by considering the partial enthalpy values (h) divided by the thermal gradient of 1 K (in this test) and measured in temperature steps of (31.5-32.5 \({}^{\circ}\)C) and (15.5-16.5 \({}^{\circ}\)C), in which it was assumed that the PCM conditions were fully liquid (\(c_{\text{PM}}\)) or fully solid (\(c_{\text{PF}}\)), respectively.  
The obtained results show that the three analyzed PCMs have different storage properties:  
* The maximum difference between the three PCMs is 39% (melting test) and 51% (solidification test).
* PCM X (PCM 21) shows the lowest latent enthalpy. This can be explained by considering that the PCM shows the lowest enthalpy (h) and the highest specific heat (\(c_{p}\)) in fully melted/fully solid PCMs, which is directly linked to the sensible heat capacity.
* PCM Y (PCM 23) and PCM Z (PCM 25) show similar heat capacity values. However, a significant difference in the latent enthalpy (\(h_{\text{,}alt}\)) is observed (PCM Y shows a value that is about 51% higher than PCM Z).  
As mentioned in the previous section, a difference of \(\sim\)8-16% is observed for the total enthalpy values between the melting and solidification tests. This, as previously mentioned, is caused by the non-complete control of the velocity of the temperature variation.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 2 The Parallel Drone Scheduling Traveling Salesman Problem  
The PDSTSP can be represented on a complete directed graph \(G\ =\ (V,A)\), where the node set \(V=\{0,1,...,n\}\) includes the depot (node 0) and a set of customers \(C=\{1,...,n\}\) to be served. The set \(A\) contains ordered pairs of nodes, representing connections. A truck and a set \(D\) of identical drones are available to deliver parcels to the customers. The truck starts from the depot 0, visits a subset of the customers, and returns back to the depot. The drones operate back-and-forth trips from the depot to customers, delivering one parcel per trip. Not all the customers can be served by a drone due to the weight of the parcel, excessive distance of the customer location from the depot, or adverse morphological configurations of the terrain. Let \(C^{D}\subseteq C\) denote the set of customers that can be served by drones. These customers are referred to as _drone-eligible_ in the remainder of the paper. The travel time incurred by the truck to go from node \(i\) to node \(j\) is denoted as \(t_{ij}^{T}\), while the time required by a drone to serve a customer \(i\) (back-and-forth trip) is denoted as \(t_{i}^{ll}\). The truck and the drones start from the depot at time 0; the objective of the PDSTSP is to minimize the time required to complete all the deliveries and to have the truck and all the drones back at the depot. Note that, since the truck and drones work in parallel, the objective function translates into minimizing the maximum time required by the vehicles. In the remainder of the paper, we refer to this quantity as _cost_.  
An example of a PDSTSP instance is provided in Figure 1.
## 3 Materials and Methods
### Laboratory Stand Equipment  
Measuring instruments:  
* Tektronix MDO3014 oscilloscope used in dynamic and transient states.  
Figure 6: Scheme of the measuring setup with marked measuring points. Source: author’s development.  
Figure 7: View of the measuring stand. Source: author’s development.  
Voltage measurement accuracy: \(\pm\)1.5% for 5 mV/div and above, derated at 0.1%/\({}^{\circ}\)C above 30 \({}^{\circ}\)C \(\pm\) 2.0% for 2 mV/div, derated at 0.1%/\({}^{\circ}\)C above 30 \({}^{\circ}\)C.  
Time base accuracy: \(\pm\)10 ppm over any \(\geq\)1 ms interval.  
Frequency measurement accuracy: \(\pm\)(([Reference Frequency Error] \(\times\) [Marker Frequency]) + (span/750 + 2)) Hz; Reference Frequency Error = 10 ppm (10 Hz/MHz).
* FLUKE 435 analyzer of electric energy quality. Power quality measurements: IEC61000-4-30 Compliance, Class A. Voltage Vrms (AC + DC): \(\pm\)0.1% of nominal voltage. Current Arms (AC + DC): \(\pm\)0.5% \(\pm\) 5 counts. Frequency: \(\pm\)0.01 Hz.  
Watts (VA, W, var): \(\pm\)1% \(\pm\) 10 counts.  
A block diagram of the test stand with the designation of the drive control systems (combustion engine simulator) and the generator (along with the converter system and the on/off-grid operating mode selector) is shown in Figure 8.  
Equipment of the test stand:  
* cabinet with built-in converters;
* a drive with an induction motor, M, and an inverter-diesel drive simulator;
* adjuster of generator G and drive system parameters;
* Load: (1) adjustable resistive unbalanced load with a power of 16.5 kW, consisting of 9 resistors with a power of approx. 2 kW each. The resolution of the load power regulation is approx. 0.5 kW for each phase separately. The load power value is true for the rated phase voltage of 230 V; (2) non-linear load: three-phase 6-pulse rectifier with resistive load.  
The induction motor, M, was powered by an inverter. The inverter is controlled by specifying the torque or rotational speed of the motor. The generator converter system is controlled by setting the power at the connection to the network (only in on-grid mode), the load torque or the rotational speed of the induction generator G, as well as the on-grid or off-grid operating modes. It is also possible to limit battery current and voltage. The motor and the generator system are connected to the same grid, therefore, a separating transformer Tr_sep was used to power the drive motor.  
Figure 8: Block diagram of the test setup with the drive control system, the generator converter system, and the operating mode selector (on/off-grid). Source: author’s development.
## 2 Background
### ISI and ITI Estimator  
We considered the desired signal \(d[j,k]\) in Figure 1 as follows:  
\[d[j,k]=\sum_{m=-1}^{1}\sum_{j=-1}^{1}a[j-m,k-n]g_{m,n}. \tag{8}\]  
Figure 1: Block diagram for estimating the coefficients of target \(\mathbf{G}\) and equalizer \(\mathbf{F}\)[16].  
With the 2D ISI in the HDS channel, we can achieve \(g_{0,-1}=g_{0,1}=q\), \(g_{-1,0}=g_{1,0}=p\), and \(g_{1,-1}=g_{-1,-1}=g_{1,-1}=g_{1,-1}=g_{1,1}=\beta\) as the interferences from the horizontal, vertical, and diagonal directions, respectively. We replaced \(q\), \(p\), and \(\beta\) in (8) as follows:  
\[\begin{array}{l}d[j,k]=\sum\limits_{m=-1}^{1}\sum\limits_{n=-1}^{1}a[j-m,k- n]g_{m,n}\\ =a[j+1,k+1]\beta+a[j+1,k]p+a[j+1,k-1]\beta\\ +a[j,k+1]q+a[j,k]g_{0,0}+a[j,k-1]q\\ +a[j-1,k+1]\beta+a[j-1,k]p+a[j-1,k-1]\beta\end{array}. \tag{9}\]  
In Equation (9), \([\beta\ q\ \beta]^{T}\), \([\beta\ p\ \beta]\), \([q\ g_{0,0}\ q]\), and \([p\ g_{0,0}\ p]^{T}\) are assigned as the left-right interference (LRI), upper-lower interference (ULI), HI, and VI, respectively. In addition, the terms in (9) can be grouped into vectors as follows:  
\[\big{[}n[j,k+1]\quad n[j,k]\quad n[j,k-1]\big{]}, \tag{10}\]  
where  
\[n[j,k+1]=a[j+1,k+1]\beta+a[j,k+1]q+a[j-1,k+1]\beta, \tag{11}\]  
\[n[j,k]=a[j+1,k]p+a[j,k]g_{0,0}+a[j-1,k]p,\text{ and } \tag{12}\]  
\[n[j,k-1]=a[j+1,k-1]\beta+a[j,k-1]q+a[j-1,k-1]\beta. \tag{13}\]  
Because we used \(\{-1,1\}\) to represent the signal \(a[j,k]\), \(n[j,k+1]\), \(n[j,k]\), and \(n[j,k-1]\) have six values. With six values of \(n[j,k]\), the trellis can be used to detect \(n[j,k]\). The LRI was then calculated as follows:  
\[LRI[j,k]=n[j,k+1]+n[j,k-1], \tag{14}\]  
Similarly, we can group the terms in (13) as follows:  
\[\big{[}m[j+1,k]\quad m[j,k]\quad m[j-1,k]\big{]}^{T}, \tag{15}\]  
where  
\[m[j+1,k]=a[j+1,k+1]\beta+a[j+1,k]p+a[j+1,k-1]\beta, \tag{16}\]  
\[m[j,k]=a[j,k+1]q+a[j,k]g_{0,0}+a[j,k-1]q,\text{ and } \tag{17}\]  
\[m[j-1,k]=a[j-1,k+1]\beta+a[j-1,k]p+a[j-1,k-1]\beta. \tag{18}\]  
Using Equation (15), after \(m[j,k]\) is detected, the ULI can be calculated as follows:  
\[ULI[j,k]=m[j+1,k]+m[j-1,k], \tag{19}\]
## 3 Motivation
### Impact of Cluster Head Selection on QoS Routing  
A cluster head acts as the source node to send compressed data. Not only the distance between a cluster head and the place where the event occurs but also the potential energy consumption should be considered in cluster head selection. Due to the existence of PUs, the channel availability of each node is different. If a cluster head is located in PUs' transmission range and the PUs are occupying its data channel, its data transmission process is postponed, and many packets may be stacked on a cluster head waiting to be sent. Once the channel is detected to be released by the PUs, the cluster head may generate a large amount of traffic in a short time. These backlogs exacerbate network congestion and make it difficult for a routing protocol to meet QoS requirements or to consume more energy to achieve this goal.
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology
### Carrier Frequency Offset Estimation (CFO)  
We estimated the CFO and calculated the degradation of signal-to-noise ratio (SNR) resulting from frequency offset in Orthogonal Frequency Division Multiplexing (OFDM) modulation. The data rate per OFDM symbol corresponds to the number of subcarriers for Binary Phase Shift Keying (BPSK). The signal-to-noise ratio (SNR) spans from 0 to 30 dB, with frequency increments occurring at intervals of 10 MHz within the range of \(-200\) to 200 MHz. Figure 4 illustrates the relation between CFO on the \(X\)-axis and the error magnitude in decibels on the \(Y\)-axis. The red trajectory shows theoretical values, and simulated values are represented in green.
## 2 Materials and Methods
### Nonlocal Means (NLM) Noise Reduction Approach  
The NLM filter takes advantage of image redundancy and obtains pixel values using the spatial information of the image. Each pixel in the NLM-filtered image identified and weighed an area similar to the surrounding area, thereby effectively eliminating noise.  
\[\text{NLM}(\text{I}(\text{i}))=\sum_{\text{i}\in\text{N}_{\text{i}}}^{\text{n}} \text{w}\left\{\text{N}_{\text{i}},\text{N}_{\text{j}}\right\}\text{I}(\text{j}) \tag{1}\]  
here, \(\text{I}(\text{i})\) is the noise intensity of \(\text{i}\), and \(\text{N}_{\text{i}}\) is the peripheral pixel of I. The weight \(\text{w}\left\{\text{N}_{\text{i}},\text{N}_{\text{j}}\right\}\) depends on the similarity between \(\text{i}\) and \(\text{j}\) and satisfies the general value [20]. The NLM algorithm can improve image quality by solving signal distortion and blurring effects, which are problems with existing filters; the degree of distortion of pixels with noise removed from the original image is small [20].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pitch & 2.5 (high pitch) \\ Tube voltage (kVp) & 100 \\ Average tube current (mAs) & 92 \\ Additional filter & Tin \\ Rotation time (s) & 0.25 \\ Slice thickness/increment (mm) & 1/1 \\ Detector collimator (mm) & 192 \(\times\) 0.6 \\ \hline \end{tabular}
\end{table}
Table 1: CT image acquisition parameters.  
Figure 1: Scan line for each area to acquire CT images: aortic arch, 4 chambers, and end of the heart.
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 3 Methods
### Multi-Band Graph Neural Network
#### 3.3.2 Graph Pooling  
Based on the node-level representations produced by the GAT layer, a graph-level representation is required for the downstream classification task. The DIFFPOOL [32] is a hierarchical graph representation algorithm that maps graph nodes to a set of clusters using a differentiable soft cluster assignment method. Unlike the commonly used global pooling algorithms, the DIFFPOOL sequentially collapses nodes into smaller clusters until there is only a single node with the final embedding, which incorporates the topology information of the graph. Thus, the DIFFPOOL is used in the proposed MBGNN model to produce a coarsened input for the subsequent band-wise attention layer.  
Suppose a graph \(\mathcal{G}\) is represented by a dense adjacency matrix \(\mathbf{A}\in\mathbb{R}^{N\times N}\) and a feature matrix \(\mathbf{h}\in\mathbb{R}^{N\times F}\), where \(N\) is the number of nodes and \(F\) is the feature dimension. The DIFFPOOL operator at layer \(l\) computes the updated versions of \(\mathbf{A}\) and \(\mathbf{h}\) as follows:  
\[\mathbf{A}^{(l+1)},\mathbf{h}^{(l+1)}=\text{DIFFPOOL}\Big{(}\mathbf{A}^{(l)}, \mathbf{h}^{(l)}\Big{)} \tag{12}\]  
Specifically, a GNN architecture is duplicated to compute a new embedding \(\mathbf{Z}\in\mathbb{R}^{N_{(l)}\times F^{\prime}}\) and an assignment matrix \(\mathbf{S}\in\mathbb{R}^{N_{(l)}\times N_{(l+1)}}\) as follows:  
\[\begin{split}\mathbf{Z}^{(l)}=\text{GNN}_{l,embed}\Big{(} \mathbf{A}^{(l)},\mathbf{h}^{(l)}\Big{)}\\ \mathbf{S}^{(l)}=\text{softmax}\Big{(}\text{GNN}_{l,pool}\Big{(} \mathbf{A}^{(l)},\mathbf{h}^{(l)}\Big{)}\Big{)}\end{split} \tag{13}\]where \(N_{(l)}\) is the number of nodes at layer \(l\), \(N_{(l+1)}\) is the new number of nodes, and \(F^{\prime}\) is the new feature dimension. The operator ends with the creation of the new node embedding matrix and the adjacency matrix, to be entered into the next layer:  
\[\begin{split}\mathbf{h}^{(l+1)}=\mathbf{S}^{(l)}{}^{T}\mathbf{Z}^ {(l)}\\ \mathbf{A}^{(l+1)}=\mathbf{S}^{(l)}{}^{T}\mathbf{A}^{(l)}\mathbf{S }^{(l)}\end{split} \tag{14}\]  
where \(\mathbf{h}^{(l+1)}\in\mathbb{R}^{N_{(l+1)}\times F^{\prime}}\) and \(\mathbf{A}^{(l+1)}\in\mathbb{R}^{N_{(l+1)}\times N_{(l+1)}}\).
## 4 Non-Destructive Investigation
### Structural Investigation
#### 4.2.6 Sonic-Pulse Velocity Tests  
SPV testing was used to provide a qualitative assessment of the masonry and to obtain quantitative values of mechanical parameters, such as Poisson's ratio (v) and dynamic modulus of elasticity (E\({}_{\text{d}}\)), based on the measured wave velocities [16,17]. Given the visible heterogeneity in the morphology of different sections of the tower, all walls were tested at three different heights, as seen in Figure 12. The average velocity of the primary (P)  
Figure 11: Graph showing variation in monitored parameters from February 2020 to April 2021. Positive values indicate that the crack has closed since the beginning of the monitoring, and negative values indicate that the crack has opened.  
waves obtained from sonic testing was 1300 m/s, resulting in an average dynamic modulus of elasticity ranging between 2.72 and 3.32 GPa. The P-wave velocities varied between 350 m/s and 2500 m/s. As there was no variation in the masonry typology within the tower, low velocities correspond to very irregular masonry, with a high percentage of voids within the cross section, while higher velocities indicate a more regular, solid masonry wall. The notation of the walls of the north-eastern tower is seen in Figures 12 and 13, which presents the variation in the mean value of the dynamic modulus of elasticity in different portions of the tower.  
Walls NW1-EW3-EW2 have similar mechanical parameters, with the highest dynamic modulus of elasticity measured at the topmost level of the central wall (EW3), possibly confirming the conjecture that this part of the wall is a later addition to the structure. Although the curtain wall on the southern side of the tower (EW1) and the perpendicular wall connected to the northern side of the tower (NW2) had similar mechanical parameters, they were noticeably lower than the ones obtained at walls NW1-EW3-EW2. Similar mechanical parameters obtained in tests conducted at different locations could indicate common construction or intervention dates, matching construction quality or, possibly, a matching state of degradation. At the northern wall orthogonal to the tower (NW2), both direct and indirect sonic tests were carried out. Velocities obtained from the direct tests were higher than the velocities obtained with indirect tests. This could be an indicator of  
Figure 12: Location of non-destructive tests carried out at the tower.  
Figure 13: Unfolded tower elevation showing mean \(\mathrm{E_{d}}\) values calculated.  
a consistent material present throughout the whole section of this wall and the absence of a differentiated internal core.
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 5 Experiments and Evaluation
### Experimental Results  
The experiment includes two aspects. First, analyze the influence of different curvature spaces on the prediction of abnormal events, and then analyze the prediction effect of CDSTAEP and the baseline model. We use the AUC score to evaluate the prediction performance of the proposed method.
## 4 Experimental Methodology
### Relay Chain Length Analysis  
Experiments were conducted meticulously with different lengths of the relay chain, specifically evaluating configurations consisting of 2-hop, 5-hop, 8-hop, and 20-hop relay nodes. Each node chain configuration started from a high-performance source node, a laptop equipped with AMD Ryzen(tm) 7 8845H, progressing downstream through nodes with heterogeneous computing capacities, including multiple low-power Intel(r) N100 mini PCs and Intel(r) Celeron(r) J1900 embedded computing terminal devices, and desktop computers with 12 th generation Intel(r) Core(tm) i3 hardware, 5 th generation Intel(r) Core(tm) i5 processors with NVIDIA(r) Quadro(tm)600, 8 th generation Intel(r) Core(tm) i7 processors, legacy Intel(r) Xeon(tm) E3 series workstation processors, or AMD(tm)Pro A-Series processors (often referred to as APUs). This strategic choice was made to reflect the inherent heterogeneity found in real-world deployments, effectively simulating realistic relay-chain conditions and allowing precise measurements of cumulative end-to-end (E2E) latency variations associated with chain length increments. The devices are deployed across multiple cities in different provinces, spanning regions such as East China (Shandong) and Northwest China (Ningxia). Their placement varies from being just meters apart in the same lab to hundreds of kilometers apart on separate campuses connected via dedicated fiber-optic LAN, or even thousands of kilometers apart on the Internet under different ISPs, simulating real-world network conditions and geographical distribution.  
A further objective of the experimental design was to evaluate the cost-effectiveness and hardware adaptability of the proposed decentralized chain-based architecture. Specifically, experiments were planned to assess system performance on low-specification hardware, including typical household or personal laptops or desktops, or even lower-performance devices that cost less than USD 150 each on average. The goal was to determine whether reliable real-time video broadcasting could be achieved without reliance on high-performance servers. This aspect of the experimental methodology was intended to validate the practical feasibility of large-scale deployments under constrained hardware budgets and operational costs and to compare the resource utilization efficiency of the decentralized approach against traditional centralized solutions.
## 3 Results and Discussion
### Fusion Model Building and Discriminant
#### 3.3.1 Discriminant Model Based on Spectral Data Layer Fusion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wavelength Range**} & \multicolumn{3}{c}{**Kernel Function**} & \multirow{2}{*}{**Operation Time(s)**} & \multirow{2}{*}{**Identification Accuracy(\%)**} \\  & & **Number of Variables** & & & \\ \hline
500–1000 & RBF\_Kernel & 665 & \(\gamma=135.62\)\(\sigma^{2}=12\),704.14 & 5.44 & 90\% \\  & Lin\_Kernel & 665 & \(\gamma=115.60\) & 3.97 & 94\% \\  & RBF\_Kernel & 1555 & \(\gamma=25.14\)\(\sigma^{2}=31.65\) & 6.27 & 92\% \\  & Lin\_Kernel & 1555 & \(\gamma=8.29\) & 2.23 & 96\% \\  & RBF\_Kernel & 2220 & \(\gamma=2090.26\)\(\sigma^{2}=27\),965.65 & 9.48 & 92\% \\  & Lin\_Kernel & 2220 & \(\gamma=306.04\) & 4.16 & 98\% \\
500–2500 after & RBF\_Kernel & 2220 & \(\gamma=124.73\)\(\sigma^{2}=19\),820.81 & 4.50 & 100\% \\ second derivative & Lin\_Kernel & 2220 & \(\gamma=0.09\) & 3.06 & 100\% \\
500–2500 s derivative & RBF\_Kernel & 2220 & \(\gamma=586.13\)\(\sigma^{2}=69\),463.80 & 6.02 & 98\% \\ normalization & Lin\_Kernel & 2220 & \(\gamma=0.09\) & 3.26 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of LS-SVM model for data fusion under three strategies. It is shown in Table 3 that the identification accuracy of each model is 100%. Among them, 156 samples were randomly divided into calibration set and prediction set according to 2:1, and the model prediction effect was the best. At this time, the lowest _RMSEC_ was 0.65%, and the highest \(R_{\rm c}\) was 0.94. Therefore, the subsequent sample division was carried out according to the above.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 1 Introduction
### Population-Based Metaheuristic Approaches to TSP-D  
[PERSON] et al. [30] proposed a solution to the FSTSP: a hybrid approach combining a genetic algorithm (GA) [31] and [PERSON]'s savings algorithm (CW) [32]. In this approach, the GA is used to assign the drone and truck to the customers. Following that, the sequence of the customers is determined using the CW. The aforementioned process continues until a specified number of iterations is reached. The authors proposed multiplespecific mathematical assumptions for the problem. The most consequential assumption is that the drone must launch and return to the truck at the same node, resulting in an increase in the waiting time of the truck. [PERSON] et al. [33] used another hybrid GA, called the HGA, to solve the TSP-D with both minimum time (min-time) and minimum cost (min-cost) objectives. To improve the HGA's quality, the authors educated their offspring using LS methods. The authors designed a hill-climbing and first-improvement LS to address the min-time and min-cost objectives. In addition, the authors developed a restoration method to \"educate\" the TSP-D solution. [PERSON] et al. [34] used K-means clustering to help to find the truck stops, which were also the drone launch points. A GA was used to solve the truck route as a TSP. [PERSON] [35] implemented an extension of the classical ant system [36]. Using ant pairs is essential for the TSP-D, as a truck route and a drone route are needed every time the tour is constructed. When constructing its own path, every ant in the pair lays down a pheromone which is different from those the others. Every ant type is attracted to its own pheromone.