esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 FE Analysis Procedure  
The method described in this paper can be implemented using any FEA software. The MS FEA problems do not require particular attention in the mesh definition, since no induction phenomenon has to be observed. Normal triangular mesh is used with first order elements. To get more reliable results, the machine air-gap has to have at least three elements in the radial direction, and the iron regions, near the air-gap, to have a higher element density.  
The fundamental condition to find the proper rotor current to set in the MS FEA model is defined in (8). If the rotor current follows this relationship, the flux \(\lambda_{\text{rq}}\) is automatically set to zero, which is the checking result to verify that the rotor current amplitude, set in the simulation, is correct.  
In MS FEAs, the rotor current is imposed using the equivalent rotor winding, introduced in Section 2. This is a three-phase winding, then similar to the stator, but the conductor allocation in each slot leads to a sinusoidal distribution of the current along the rotor periphery.  
Considering again condition (8), the rotor current depends upon the ratio \((L_{\text{m}}/L_{\tau})\), while \(i_{\text{sq}}\) is imposed. The following FEA procedure allows a precise estimation of the inductance, carefully considering the iron saturation.
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Binary Transformation  
The extracted files are transformed into grayscale matrices suitable for image-based analysis. Each file is read as raw bytes, converted into a hexadecimal string, split into 8-bit segments (0-255), and mapped to pixel intensities. To ensure consistent input dimensions, each component is resized to a fixed resolution of 256 \(\times\) 256 pixels.  
This resolution strikes a practical balance: it is large enough to preserve discriminative byte-level patterns from varying file sizes, yet small enough to keep the model lightweight and training efficient. Moreover, 256 \(\times\) 256 is a common resolution in deep learning pipelines, facilitating compatibility with pre-trained convolutional backbones.  
To validate this choice empirically, we evaluated performance using three image sizes: 64 \(\times\) 64, 128 \(\times\) 128, and 256 \(\times\) 256. The 256 \(\times\) 256 resolution achieved the highest F1-score and precision while maintaining comparable training time (see Section 4.2). These findings support the use of 256 \(\times\) 256 as a standardized resolution throughout the image generation process.  
Additionally, we analyzed the file size distributions of the extracted components. DEX files ranged from 0.29 KB to 35,468.52 KB (mean: 1897.40 KB), Manifest files from 0.00 to 2298.60 KB (mean: 1.70 KB), and Resources.arsc files from 0.55 to 33,297.56 KB (mean: 262.27 KB). This heterogeneity reinforces the need for a fixed-size format, normalized through zero-padding or truncation.
## 4 Results
### Predictive Performance of Classifier Models  
The performance of the ML models was analyzed using confusion matrices (Figure 7a-c), which quantify key performance indicators, such as sensitivity, specificity, accuracy, precision, and F1-score, as aggregated in Figure 7d. Additionally, ROC curves and corresponding AUC values are illustrated in Figure 7e. Among the evaluated algorithms, the RF classifier demonstrated the strongest performance, attaining a test accuracy of 95.5%, marginally exceeding XGBoost and LightGBM, which both achieved 94.9%. This represents a 0.6% improvement over other ensemble methods. RF also attained the highest precision (96.4%), specificity (96.5%), and F1-score (95.4%), with XGBoost trailing closely in precision (95.4%). While all classifiers exceeded 94% across metrics--signifying robust predictive capability--RF exhibited a slight edge in overall accuracy. Further analysis of AUC values (Figure 7e) highlights the strong discriminatory power of all models, with RF achieving the highest AUC (98.8%), followed by LightGBM (98.5%) and XGBoost (98.3%). Higher AUC values reflect a model's ability to minimize misclassification, with RF showing marginally superior class separation in this case study.  
Figure 7: The performance evaluation of trained ML models for SSM: (**a**–**c**) present confusion matrices for RF, XGBoost, and LightGBM, respectively. (**d**) consolidates key metrics, including sensitivity, specificity, accuracy, precision, and F-1 score. (**e**) showcases ROC curves and Area Under the Curve (AUC) results.  
Analysis of the classifiers' confusion matrices highlighted the RF model's precision in distinguishing sinkhole and non-sinkhole instances within the test dataset. In the sinkhole category, RF correctly identified 187 out of 198 samples while achieving 191 correct classifications among 198 non-sinkhole samples. This corresponds to classification accuracies of 94.5% for sinkholes and 96.5% for non-sinkholes, underscoring RF's balanced proficiency across both classes. Owing to its consistent superiority in accuracy and reliability, the RF model was selected to generate the final SSM and to undergo SHAP-based interpretation, enabling deeper insights into its decision logic.
## 2 Materials and Methods
### Grid Analysis
#### 2.3.2 Lineation Detection  
The phase symmetry algorithm [68; 69; 70] considers a line to be symmetrical across the line, e.g., perpendicular to the line. By locating axes of symmetry, this attribute helps in the detection of line-like patterns. It is also well known that a signal's symmetry and the periodicity of its spatial frequency are tightly related. As a result, it makes sense to use a frequency-based method to find axes of symmetry.  
Figure 10: Flowchart of the processing method.  
The limits of the majority of magnetic and gravitational anomalies are not clearly defined step edges but rather slowly moving bands, whereas standard gradient-based edge detection approaches find step edges. The phase congruency transform is a contrast-invariant edge detection technique based on detecting the local spatial frequencies, much like the phase symmetry transform [64]. It takes advantage of the fact that edge features appear when the local frequency components are most in phase with one another [70]. This finding can be compared to symmetry points where components are at extremes.  
Applying non-maximal suppression (NMS) to data in conjunction with amplitude thresholding is helpful for identifying ridges, since low values are suppressed while points of local maxima are kept [71]. Our NMS solution considers the local feature orientation to maximize feature continuity, which can be used to reduce noise and emphasize linear features [64].  
Skeletonization  
The skeletonization algorithm [72] is a morphological operation that takes a binary grid and skeletonizes each foreground object by iteratively eroding away its boundary cells until the object is only 1 cell wide.
# A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival
## 4 System Performance and Constraints
\[f_{frame}\cdot N_{y}\cdot\left(2T_{pulse}+T_{guard}\right) \leq D_{y}+1\] \[2n_{pulse}+n_{guard} \leq D_{y}+1 \tag{4}\]  
As mentioned above, the light source's projected size plays a capital role on the system's performance. Concretely, \(D_{y}\) follows Equation (5) [51]. \[D_{y}=\frac{2N_{y}}{FOV_{v}}\arctan\left(\frac{H}{2d}\right) \tag{5}\]  
where \(H\) is the apparent physical height of the radiant source, \(FOV_{v}\) is the receiver's vertical field of view, and \(d\) is the actual separation distance between transmitter (lamp) and receiver (camera). Figure 6: Geometrical scheme for spot size calculation. All these elements are depicted in Figure 6. Assuming the BS-to-MS ranging is carried out without errors, \(T_{pulse}\) can be defined as \(T_{pulse}=\gamma\cdot d/v_{2}(T)\). \(\gamma\) is a scaling factor introduced by the authors as a way of improving the system's performance. It modifies the pulse duration at MS-side to allow measurements at longer distances or to increase accuracy. In long-distance mode (\(\gamma<1\)), the generated pulse is shorter than the corresponding to the actual distance. In this way, whole pulses can fit into the resulting smaller Region Of Interest (ROI) due to distance. For instance, \(\gamma=1/2\) would double the maximum measurement range. As a restriction, the receiver must be aware of this mode in order to compensate the effect of \(\gamma\) and to calculate the actual pulse duration. On the other hand, when in short-distance mode (\(\gamma>1\)), the generated pulse is wider than the expected one. Thus, very short distances can be measured because the pulse will cover enough lines in the image. As an example, \(\gamma=2\) will double the resolution and the minimum-reachable distance to the half. Again, the receiver obtains the actual pulse duration compensating the \(\gamma\) factor. Introducing this into Equations (5) and (4) yields a new distance-dependent restriction (Equation (6)). \[f_{frame}\cdot N_{y}\cdot\left(2\gamma\cdot\frac{d}{v_{2}(T)}+T_{guard} \right)\leq\frac{2N_{y}}{FOV_{v}}\arctan\left(\frac{H}{2d}\right)+1 \tag{6}\]  
Figure 7 depicts the critical distance at which the condition of Equation (6) is violated. Beyond this distance, a correct recovery of the pulse duration (as a difference between rising and falling edges) is not ensured, and several runs of the measurement procedure (capturing more frames) must be carried out and statistical treatment must be applied. Typically, error uncertainty is reduced by averaging. However, for the presented scenario, this approach does not offer the best performance, as discussed in Section 5.
## 2 Review of Related Scientific Works  
### Forecasting Time Series: Local vs. Global Approach  
For many decades, most research on time series forecasting assumed that the time series are generated by independent processes and can be tackled as a regression problem, creating a single model per time series (local models) [8; 11]. Growing empirical evidence suggests that creating a single model to forecast multiple time series (under the assumption of forecasts' independence for different time series) known as global models can outperform local ones. Seminal research on using global models developed decades ago [12], and the concept was further explored by many researchers afterwards [13]. The first approaches towards global models considered pooling similar time series, which improves the overall models' accuracy. By having more training data of similar time series, algorithms can better distinguish common data patterns, while also minimizing distortions introduced by outlier data points [14]. On the other side, this approach requires defining some time series grouping criteria, which can lead to suboptimal groupings [15]. Among pooling strategies, we can find model-based clustering [14; 16; 17], random clustering [8], grouping based on similarity measures [18; 19; 20], or expert judgement [21]. Recent research has explored creating global models considering all available time series, regardless of their heterogeneity, obtaining promising results [3; 8; 22]. Furthermore, it has been demonstrated that, for every dataset, some global model exists that can equal or outperform a local model, regardless of how heterogeneous the data may be [8]. Such models make the strong assumption that some relationship exists between time series, though the forecasts are independent of each other [11].  
These insights, the good results obtained by applying global neural network models for time series forecasting [11; 23; 24; 25; 26; 27], and success of global models at the M4 and M5 competitions [5; 6], have renewed the research interest on global models for time series forecasting [3]. In such models, the relationship between time series is not very well understood, and understanding why and when do global time series forecasting models work remains an open research topic [3]. We envision that anomaly detection algorithms can be used to detect when the GTSMLFM provides accurate forecasts or not, and XAI approaches can provide insights to understand better factors affecting the forecasts, and provide prototype (local) explanations [28], and counterfactual examples.
## 2 Device Setup and Mechanism
The first one is that, as mentioned above, the LCCT relieves the stress of the AlGaN layer, turning out a reduction of the polarization charges; whereas the other one, which has been depicted before as well, is that negative charges are introduced into the remaining SiN during the formation of the LCCT, which attracts the E-field generated from the positive polarization charge and as a result, partially squeezes out 2 DEG underneath. This functionality of LCCT could be illustrated explicitly by simulations as well. As shown in Figure 4, the conduction band of conventional HEMT only with a thin AlGaN barrier is higher than that of the device with SiN passivation. In addition, as aforementioned, the LCCT could gently lift the band structure by introducing the negative charges and partially canceling out the polarization. Figure 4: The simulated conduction band structure (**a**) perpendicular to the wafer through the LCCT region and (**b**) along the two-dimensional electron gas (2 DEG) channel of conventional HEMT, UTB-HEMT, and LCCT-HEMT. Figure 3: The schematic charges and electric field distribution of (**a**) conventional ultra-thin barrier HEMT (UTB-HEMT), (**b**) LCCT-HEMT with a short LCCT, and (**c**) LCCT-HEMT with a longer and deeper LCCT (red arrow represents electric field). Besides, before all detailed investigations, the Silvaco-ATLAS simulator, models, and corresponding settings were verified with the experimental data from previous UTB-HEMT works. The simulation models used in ATLAS were the Shockley-Read-Hall (SRH) recombination model, Fermi-Dirac statistic model, and the parallel electric field dependence model. The calibration commenced with the establishment of the device structure, which is strictly generated in ATLAS with the same geometry parameters of the experimental one. Then, the calibration is done by revising the polarization parameters, such as charge density and relaxation degree. As shown in Figure 5, the voltage-current curves match well not only regarding linear region, but also the subthreshold region, indicating the settings of simulation are reasonable. Furthermore, the breakdown voltage in simulation is adjusted to the value of the experiment, which is around 1200 V and omitted in these figures. Thus, this suggests the simulation is accurate and reasonable.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
###### Abstract
Utilize the ACO approach to find the best tracking route between the fire station and the fire area, to achieve the best QoS;
3. Create a mathematical model to demonstrate the magnitude of the QoS improvement obtained after implementing the proposed approach. \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Reference** & **Limitation** \\ \hline
[6] & Some research has employed VADD and A-STAR to address the problem of the QoS, but it remains a theory that is not appropriate for dealing with dynamic networks, such as VANETs. \\ \hline
[7] & Other research employed SADV to redirect packets and increase the QoS in real-time. However, this strategy might cause a network congestion, especially during peak hours. \\ \hline
[4,5] & Some researchers have employed local motion characteristics, such as the GyTAR and TADS protocols to predict the next junction, although neither the density of the chemicals nor their distribution can reflect the overall pattern of the QoS improvement. The increased vehicle density can improve the network connectivity, but it also can cause an increase in data throughput on the network. \\ \hline
[8] & The CAR protocol for the routing packets employs the idea of blind flooding in its schemes, rendering it ineffectual in improving the QoS and potentially burdening the network. \\ \hline \hline \end{tabular} The references represent the main targeted references for the study.  
\end{table} 
Table 1: The prior studies along with their limitations. The rest of the article is structured as below:  
The next section proposes the methodology of the suggested technique. The third and fourth sections describe the results and discussion, then the article ends with the conclusion.
# Diffusion Model as a Base for Cold Item Recommendation
## 2 Related Work
### Recommendation with Diffusion Models  
Recent advancements in DMs have demonstrated significant potential in improving recommender systems by addressing issues such as data sparsity, noisy interactions, and user preference modeling.  
As one of the pioneering works in this area, DMs have been effectively used to generate robust user-item interaction representations. [PERSON] et al. [37] introduced a perturbation recovery paradigm to model user-item interactions, similar to score-based generative models but applied to collaborative filtering. [PERSON] et al.'s work [33] extended traditional DMs to tackle high resource costs in large-scale item prediction and temporal shifts in user preferences. It introduced latent-space clustering and temporal weighting to enhance recommendation performance. Similarly, the Denoising Diffusion Recommender Model [27] applied DMs to denoise user and item collaborative embeddings, improving robustness against implicit feedback noise.  
Graph-based approaches leveraged DMs to enhance collaborative filtering. [PERSON] et al.'s work [34] applied heat equation-based smoothing on an item-item similarity graph to refine user preferences. [PERSON] et al. [35] integrated multi-hop cross-attention autoencoders to capture high-order collaborative signals, significantly improving user-item interaction modeling.  
DMs were also applied to the sequential recommendation. [PERSON] et al. [43] combined multi-scale CNNs and residual LSTMs to extract local and global user behavior patterns. They effectively improved short-term and long-term sequence modeling by conditioning the diffusion process on user interaction sequences, significantly enhancing sequential recommendation accuracy. [PERSON] et al. [41] employed a time-interval DM to infer users' preferences across all items, addressing the data sparsity issue. Similarly, [PERSON] et al. [42] reshaped sequential recommendation as a learning-to-generate problem using guided diffusion, removing the reliance on negative sampling.  
In multi-modal recommendation, [PERSON] et al. [39] and [PERSON] et al. [40] integrated diffusion models to align multi-modal content features (e.g., text, image, and audio) with user-item interactions, leading to improved recommendation accuracy.  
For the cold start problem, [PERSON] et al. [44] integrated a variance-scheduled diffusion process with a classifier to enhance cold start multi-scenario recommendations. To improve recommendation accuracy, they effectively learned domain-specific and shared representations to balance knowledge transfer between cold start and rich data domains. However,no notable works have been published in the cold item recommendation domain that seek to improve start-time cold item recommendations.
## 4 Analysis of the Magnetic Nanoparticles
### Thermal Analysis of MNPs in Magnetic Stimulations  
Cell proliferation is sensitive to temperature. Due to the side effects of MNPs in high-frequency magnetic fields, temperature rises should be avoided.  
In this section, the rise in temperature of MNPs is calculated using the bioheat equation [16]:  
\[\rho c\frac{\partial H}{\partial t}=\
abla\cdot(k\
abla H)+cw(H-H_{a}), \tag{7}\]  
where \(H\) is the actual temperature (\"C) and \(H_{a}\) is the temperature of the environment (assumed as a constant 37 \({}^{\circ}\)C), \(k\) is the thermal conductivity of the culture medium, \(c\) is the specific heat, and \(w\) is the mass flow rate (kg/m\({}^{3}\)/s).  
Figure 6: (**a**) Theory of the Maxwell stress tensor method. (**b**) Calculation of the magnetic force.  
Based on the previous research, the frequency of the applied magnetic field for hyperthermia ranges from 100 kHz to 450 kHz, and for magnetic-actuated ranges from 0.01 kHz to 3 kHz. The concentration of MNPs inside the cells ranges from 5 pgFe/cell to 25 pgFe/cell [17; 18; 19]. As shown in Figure 7, the thermal effect in high and low-frequency conditions and at different MNPs concentrations is studied by numerical calculation (with the same specification of MNPs). To determine the appropriate frequencies and concentration of MNPs, we have carried out various cell experiments with different frequencies and concentrations, and the response surface is established to reflect the relationship among concentrations of MNPs, frequency of the applied magnetic field, and thermal effect. The thermal effect of temperature rise was normalized for comparison. The response surface indicates that the higher concentrations of MNPs were in cells, the higher the thermal effect occurred. Moreover, the higher the frequency of the magnetic field applied, the higher the thermal effect was that occurred.  
Thus, based on numerical calculation and cell experiments, the concentration of MNPs in the culture medium is specified as 5 \(\mu\)g/mL (corresponding to 16.51 pgFe/cell) and the frequency of the applied magnetic field is 2 kHz.
## 2 Friction Phenomenon
### Rolling Resistance  
Rolling resistance, which can also be referred to as rolling drag or rolling friction, is a force applied to the moving tire due to macro deformation of the tire and pavement [12]. In normal conditions, the rolling resistance of a tire depends on three factors: the energy loss of rubber and reinforcements due to cyclic deformation of the rolling tire; frictional energy in the contact area; and the air resistance of the tire, where energy loss of rubber and reinforcements generates most of the drag [13]. The determination of rolling resistance is a relatively trivial problem that can be solved using simple testing techniques or finite element modeling. Examples of rolling resistance calculations for road vehicle tires can be found in various studies [14; 15]. Similarly, a rolling resistance evaluation for aircraft landing and take-off has also been performed. First, in a study performed by NASA in 1937 [10], the influence of different tire and wheel configurations on the rolling resistance of an aircraft, and the effect of rolling resistance during take-off were analyzed. It was concluded that the rolling resistance of a tire mostly depends on the properties of the pavement surface, such as the macrotexture and viscoelastic properties [16]. In the case of unsurfaced runways and runways covered with snow, rolling resistance can be high enough to affect aircraft landing and take-off and there are a number of techniques for the calculation of rolling resistance in snow based on testing data [17]. This reflects the importance of aircraft operations on temporary snow runways in the Artic and Antarctic regions [18].  
As well as the surface characteristics, rolling resistance also depends on the ground speed of an aircraft. Figure 1 shows the influence of aircraft ground speed on the rolling resistance on unsurfaced airfields with different grass lengths [19]. Since most commercial airports have paved runways with concrete or asphalt surfaces, the rolling resistance is very small and its calculation is not important, especially in moderate and warm climates where thick snow contamination is unlikely to occur. Consequently, rolling resistance is not considered further in this review.