esa-sceva/satcom-chunk-collection huggingface dataset:
The constitutive equations, often being nonlinear, has to be implemented in the FEM, requiring most of the time the experimental characterization of the material parameters. Another key point for getting a consistent modeling is the contact formulation and its characteristics: friction coefficient, contact state (stick/slip). In a recent work, [PERSON] et al. studied the impact response of a ball onto targets corresponding to the polymeric layers of a table tennis paddle [22]. Normal impacts with varying incident linear velocities and no spin were solely considered and joint experimental measurements and 3D FEM were performed. The linear coefficient of restitution corresponded to the quantitative metric chosen for comparison and fair agreement was obtained. The buckling of the ball was confirmed numerically meaning that the sample/ball contact zone corresponded to a ring with its mean radius increasing till the maximum crushing. It is worth noting that the material properties, the geometry and the local contact zone were considered in the model, so that parametric studies allowed to evidence their importance. Nevertheless, the explored impact conditions were away from realistic conditions where angular velocity (ball spin) and non-normal incidence usually prevail. Thus, the present research aimed at exploring an enhanced set of incident loading conditions, as well as challenging the finite element model in matching the experimental observations. Along with the large set of impact tests, new metrics had been defined, monitored and confronted.
# Logitwise Distillation Network: Improving Knowledge Distillation via Introducing Sample Confidence
###### Abstract  
While existing knowledge distillation (KD) methods typically force students to mimic teacher features without considering prediction reliability, this practice risks propagating the teacher's erroneous supervision to the student. To address this, we propose the Logitwise Distillation Network (LDN), a novel framework that dynamically quantifies sample-wise confidence through the ranking of ground truth labels in teacher logits. Specifically, LDN introduces three key innovations: (1) weighted class means that prioritize high-confidence samples, (2) adaptive feature selection based on logit ranking, and (3) positive-negative sample adjustment (PNSA) to reverse error-prone supervision. These components are unified into a feature direction (FD) loss, which guides students to selectively emulate trustworthy teacher features. Experiments on CIFAR-100 and ImageNet demonstrate that LDN achieves state-of-the-art performance, improving accuracy by 0.3-0.5% over SOTA methods. Notably, LDN exhibits stronger compatibility with homogeneous networks (2.4% gain over baselines) and requires no additional training costs when integrated into existing KD pipelines. This work advances feature distillation by addressing error propagation, offering a plug-and-play solution for reliable knowledge transfer.  
Keywords:image classification; knowledge distillation; logit information; sample confidence
## 2 UWB Radar System  
Figure 1 shows the diagram of the utilized UWB radar system. First, the pulse generator produces narrow pulses with a pulse repetition frequency of 128 kHz. The pulses are sent to the transmitter and shaped into bipolar pulses to excite the transmitting antenna (TA). The TA transmits bipolar pulses at the center frequency of 500 MHz with a bandwidth of 500 MHz Subsequently, owing to the high reflectivity of the body-to-radiofrequency signals, the transmitted pulses from the TA impinges on the human body, and some of them are subsequently reflected and received by the receiving antenna (RA). Therefore, the human target's position can be determined according to the time-of-flight (ToF) of the pulses from TA to RA. Simultaneously, parts of the narrow pulses from the pulse generator are sent to the delay unit to generate a range gate, which triggers the receiver to receive echoes at selective ranges. The received pulses are then sampled by a sampler at selective ranges according to the range gate and are accumulated by the integrator. Finally, the output from the integrator is amplified and filtered by a signal processor, and it is then sent to a computer for further processing through a high-speed analog-to-digital converter (ADC).  
The echo signals after ADC are stored in the form of a two-dimensional matrix \(R(M,N)\), where M indicates the sampling point during the propagation time period, namely, \"fast time.\" Each fast-time instance corresponds to a specific detection range. Herein, \(M=2048\) range points, and the corresponding detection range is 0-9 m. N denotes the number of recorded sampling points in the measurement time, or \"slow time\"; the sequence number of the data sampled in conjunction with the slow time t = \(nT_{i}\) (\(n=1,2,\ldots,N\)) at a specific distance. \(T_{i}\) = 0.0156 s is the time interval between each successively received echo signal. Thus, the sampling frequency at the slow time was \(\text{Fs}=1/T_{i}=64\) Hz, which satisfies the Nyquist sampling rate for measuring human respiration (0.2-0.4 Hz). The key parameters of the UWB radar used in this study are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Parameter** & **Value** \\ \hline Center frequency & 500 MHz \\ Bandwidth & 500 MHz \\ Detection range & 0–9 m \\ Pulse repetition frequency & 128 kHz \\ Range points & 2048 \\ Sampling frequency along slow time & 64 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of the utilized ultrawideband (UWB) radar.  
Figure 1: Diagram depicting the utilized ultrawideband (UWB) radar (TA: transmitting antenna, RA: receiving antenna, ADC: analog-to-digital converter).
## 2 Methodology of Data Acquisition
### Data Acquisition of Track Longitudinal Level (LL)  
Track geometric conditions are determined based on the spatial location and orientation of the rail within a three-dimensional framework. This framework is represented by a coordinate system comprising three axes: (1) the x-axis parallel to the direction of train movement, (2) the y-axis perpendicular to the rail horizontally, and (3) the z-axis extends perpendicular to the track vertically. According to the European standard EN 13848-1 [50], which evaluates track geometric quality, there are five geometric parameters used to assess the track: longitudinal levels (LLs), horizontal alignments (ALs), track gauge (GAU), cross-level (XLV), and twist (TWI). The longitudinal levels refer to the vertical deviations of the rail level from the reference track serving as a fundamental metric in gauging track geometry.  
In this study, the data were collected by an EM120 track recording car, as depicted in Figure 2. This sophisticated equipment enables thorough and efficient data collection, facilitating comprehensive analysis and informed decision-making in track maintenance and management. In these machines, data acquisition components are mobilized along both the vertical and horizontal axes by pneumatic cylinders, enabling the measurement of geometric parameters. Parameters such as longitudinal levels (LLs) are determined by assessing the displacement of the middle point with respect to the two initial and final points [46]. The track geometric parameters are automatically measured and logged along the referenced railway line.
# Generative Image Steganography via Encoding Pose Keypoints
## 4 Evaluation and Comparisons
### Image Generation Quality  
Unlike traditional steganography methods, namely, the ones that modify the original cover image to embed the message, generative steganography approaches directly generate the stego-images driven by the secret information. Consequently, such methods typically cannot quantify the similarity between the original cover image and the stego-image and may not even involve the original cover image. Hence, in this paper, frechet inception distance (FID) was adopted as the metric for evaluating the quality of stego-images. FID can quantify the distribution difference between the generated and real images in the feature space. A lower score indicates that the quality of the generated image is closer to that of the real image. Experimental data comparisons revealed that the proposed method generally outperforms the existing generative steganography methods regarding the FID metric (refer to Table 3 in detail). This advantage is partially attributed to the sequence generation model trained with the MLE strategy, which can map the secret information into pose keypoints conforming to the real data distribution during the sequence generation process, thereby synthesizing stego-images with natural poses. It should be noted that only when the set threshold is increased overall will the payload of this method significantly improve. This might result in the generated pose keypoints deviating from the real data distribution, ultimately generating stego-images with relatively unnatural poses, causing the FID metric to decrease.
## 4 Simulation Results and Discussion
### Influence on Fault Indicator Configuration Scheme by Environment  
There is a large amount of electromagnetic interference in the power system. In our proposed method, the acquisition unit adopts LoRa wireless communication technology and it is installed on the wire, and the collection unit adopts LoRa and NB-IoT wireless communication technology and it is installed on the pole as shown in Figure 3. Therefore, the communication qualities of the acquisition unit and collection unit will be affected by the electromagnetic interference environment and the weather conditions, such as temperature.
## 4 Optimization
### Optimization Results at Design Point  
Figure 14 shows the locations of Planes 1 and 2 in the calculation domain. Plane 1 is the mid-span of the rotor and Plane 2 is the passage area between the blade pressure side and blade suction side. Figures 15 and 16 show the entropy distribution of the rotor, and Figures 17 and 18 show the relative velocity distributions at Planes 1 and 2. As shown in the figures, the entropy of the original model at the suction side of the rotor was larger than that of the optimized model.  
Through the optimization, the blade height was increased and the blade \(\beta\) angle was distributed smoothly from the blade leading edge to the trailing edge. The increased rotor blade height induced an increase in the passage area. Therefore, the flow velocity of the optimized model was slightly lower than that of the original model.  
As shown in Figure 17, in the original model, the flow area which is not the main passage flow component near the suction blade surface was formed wider than the optimized model. That is, through optimization, the flow of the main passage flow could be smoother than the original model.  
Figure 16: Comparison of entropy distribution at Plane 2: (**a**) original model and (**b**) optimized model.  
Figure 17: Comparison of relative velocity distribution at Plane 1: (**a**) original model and (**b**) optimized model.  
Figure 18: Comparison of relative velocity distribution at Plane 2: (**a**) original model and (**b**) optimized model.  
As shown in Figure 18, in the case of the original model, a strong tip leakage flow occurs from the pressure blade side to the suction blade side. In the original model, a stronger tip leakage flow occurred in the relatively narrow passage area than in the optimized model, resulting in increased losses and consequently affecting efficiency.
## 6 Conclusions  
We studied network planning and techniques for heterogeneous or mixed cell architecture to deliver the required data rate. We used the appropriate link budget calculation process to deploy a minimum number of \(\text{RRU}_{\text{m}}\) and \(\text{RRU}_{\text{M}}\). We used a metaheuristic approach to perform simulations using GWO, SSA, WOA, MPA, PSO, and ALO for comparative studies. MPA performed in the best-optimized location, as results show. Two 5G frequencies, FR1 (3.5 GHz) and FR1 (28 GHz) were evaluated for the link budget. Combining the frequencies gave better results for capacity and coverage, and complements each other in urban areas. In suburban areas, using millimeter-wave was not cost-effective, and only macro-RRUs were used to maintain the required average data rate. Using both frequencies, the average data rate was achieved 50 Mbps at any time, with excellent coverage of at least 98%. For 5G deployment, using proper optimization methods and existing 4G infrastructure, such as backhaul and civil infrastructure, the resources can be minimized up to 33.7% and 54.2% in urban and suburban areas, respectively. Aggregating two carrier frequencies 3.5 GHz and 28 GHz in urban areas, resources can be minimized further up to 41.42%. The deployment solution is suited for urban areas and outdoor scenarios. Further research will focus on modeling actual fading, indoor scenarios, and rural and hard-to-reach areas without adequate fiber infrastructure. Artificial intelligence and machine learning techniques will be suitable for additional research while designing telecommunication infrastructure in diverse territories.  
Conceptualization, N.R.K., B.R.D., and S.R.J.; methodology, N.R.K. and B.R.D.; software, N.R.K.; validation, N.R.K. and B.R.D.; formal analysis, N.R.K.; investigation, B.R.D. and S.R.J.; data curation, N.R.K.; writing--original draft preparation, N.R.K. and B.R.D.; writing--review and editing, N.R.K., B.R.D., and S.R.J.; visualization, N.R.K. and B.R.D.; supervision, B.R.D. and S.R.J. All authors have read and agreed to the published version of the manuscript. This research was supported by UGC Nepal Collaborative Research Grants (ID: CRG-078/79-Engg-01). Program codes and simulation environment details can be provided upon request.  
We acknowledge [PERSON] for providing the LESIM lab at the University of Sannio, Italy, under the Erasmus Mobility Program (KA171). The authors declare no conflicts of interest.
## 6 Scenario and Methodology
### Performance Metrics
#### 6.1.2 Fairness Index  
Fairness metrics determine whether the nodes are getting a fair share of the available resources. Jain's fairness index is one of the most commonly used metrics for fairness evaluation. This is independent of its population size and the values lies in 0 to 1. Jain's fairness index is calculated as  
\[f(x_{1}.x_{2}.x_{3}.\ldots\ldots x_{n})=\frac{\Big{(}\sum_{i=1}^{n}x_{i}\Big{)} ^{2}}{n\Big{(}\sum_{i=1}^{n}x_{i}^{2}\Big{)}} \tag{2}\]  
In Equation (2), \(x\) is normalized throughput and \(n\) is the number of nodes. \(J\) equals to 1 means the fairest allocation of resources, and all the users can enjoy the resources.
# Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
## 2 Antenna Design and Operating Principle  
Antenna geometry comprises a substrate, annular ring DRA, four metallic rods, eight copper strips, a defected ground structure, and two microstrip lines as feeds. First guess of dimensions of CDRA for resonant frequency (8.5 GHz) was calculated from design equations mentioned in [1; 3]. Insertion of copper rods in CDRA changed its radiation characteristics, which were tuned by performing parametric analysis in the commercially available electromagnetic solver ANSYS HFSS. Geometry of antenna is shown in Figure 1. Rogers RT Duroid 5870 high frequency laminates with dielectric constant \(\varepsilon_{r}=2.33\), thickness 0.8 mm, and \(\tan\delta=0.0012\) has been used as substrate. DRA is constructed with material having electrical properties as \(\varepsilon_{r}=10\), \(\tan\delta=0.001\). Defected ground structures (DGS) are one of solutions to increase isolation between ports and reduce mutual coupling. Here, DGS is utilized by the introduction of tilted slits in the ground plane. Aperture-coupled feed is used to excite DRA with introduction of 45\({}^{\circ}\) titled long slit, which combines both the apertures in the ground plane. Eight copper strips on the surface of DRA are used to enhance gain of antenna. Four copper rods of 1 mm diameter each, have been inserted in the DRA and substrate. These rods are joined by four metallic clips on both sides to provide mechanical stability to DRA, as shown in the Figure 1. Comparison between simulated parameters of three cases: (i) DRA without copper strips; (ii) DRA with glue and copper strips; and (iii) proposed DRA, which is shown in Figure 2. Here, glue is applied in simulation with a 0.1 mm thick bonding film of Rogers R4450B (\(\varepsilon_{r}=3.5\), \(tan\delta=0.004\)) [24]. It is analyzed from Figure 2 that application of glue deteriorates the performance of DRA. Novel mechanism of insertion of thin copper rods not only catapulted the antenna's performance positively, but also provided desired mechanical stability. These thin rods act as dipoles, which improve radiation characteristics of the antenna at higher-order modes. To dig deep into the modal characteristics of the proposed design, an eigen mode analysis has been done in HFSS. Figure 3 shows simulated E-field of proposed design in the eigen model solver highlighted with arrows, which resembles higher-order TM mode (TM\({}_{416}\)) at 8.8 GHz. It was observed by eigen mode analysis that effect of thin rods become significant at higher order TM modes. However, careful optimization of dimensions and position of metallic rods has been done on EM simulator software (HFSS) to achieve coveted results.  
Figure 1: Geometry of proposed Antenna.  
Figure 2: Comparison of simulated \(S11\), \(S12\) and realized gain of different Antennas.
## 5 Results and Analysis
### 3D Visualization of DNN Performance  
This research made use of 3D visualizations to provide a more profound understanding of the relationship between training functions, network architecture, and SER performance [22]. This helped optimize DNN configurations for particular signal detection applications in MIMO systems. These graphs depict the impact of variations in the number of hidden layers and the number of neurons per layer on SER across different SNR levels. This information is instrumental in selecting optimal network configurations for specific signal detection applications in MIMO systems.  
Figure 14 showcases the influence of the number of hidden layers on both SER and SNR using the traincgp training function. A similar visualization was generated for the traingdx function, as shown in Figure 15.  
Figure 13: DNN vs. MLD and MMSE while applying traincgp function.  
These 3D plots reveal intricate relationships between the number of hidden layers, the number of neurons per layer, and the resulting SER performance at varying SNR levels. It is important to acknowledge that, within the DNN architecture, several parameters are interconnected and exert a mutual influence. These parameters encompass the number of antennas employed in the MIMO system, the SNR itself, and system performance metrics like SER [22]. By visualizing these factors in three dimensions, we can achieve a more comprehensive understanding of how they interact with each other. This visualization serves as a valuable tool for optimizing system parameters, developing algorithms, and making informed decisions to improve the performance of the designed DNN, as shown in Figure 16.  
For instance, the 3D plots can guide us in determining the optimal number of hidden layers and neurons per layer that minimize SER for a specific range of SNR values. This information can be crucial in tailoring the DNN architecture to specific MIMO communication scenarios.  
Figure 14: SER and SNR vs. no. of hidden layers while applying traincgp.  
Figure 15: SNR and SER vs. no. of hidden layers while applying traindx.
## 9 Experimentation
### Hardware Requirements  
To ensure optimal performance and efficient execution of the experimental setup, the following hardware specifications were used during experiments. A PC with a multi-core processor with a clock speed of 2.4 GHz, 8 GB RAM, and 128 GB SSD with an additional 512 GB HDD was used to handle the computational workload effectively. The experimental setup is designed to run on a Linux-based platform; therefore, a Linux distribution such as Ubuntu, Fedora, or Debian is ideal for compatibility and optimal performance. These hardware parameters ensured the efficient execution of the simulations. In this experiment, the Cooja simulator, running on a Linux-based platform, provided the necessary tools and resources for simulating and evaluating the GBG-RPL methodology in a CPS network. Furthermore, three types of nodes used during the simulation are described in Table 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameters** & **Value** \\ \hline OS/Platform & Linux \\ Simulation Software & Cooja 3.0 \\ Nodes Used & 30–90 \\ Simulated Attacks & Blackhole and Greyhole \\ Attacker to Normal Node Ratio & 1:10 \\ Receive Ratio & 30–100\% \\ Transmit Ratio & 100\% \\ Transmission Range & 50 m \\ Range of Interference & 50 m \\ Protocol Used for Routing & RPL \\ Routine Trust Calculation & 10 msec \\ Trickle Timer & 5 msec \\ Initial Node Energy & 100 J \\ Tx Energy & 0.0010875 mJ/bit \\ Rx Energy & 0.0009 mJ/bit \\ Standby Energy & 0.708 mJ/s \\ Used Networking protocol & Internet Protocol-based \\ Time for Simulation & 60 min \\ Reference Technique & BCPS-RPL [31] \\ Proposed Technique & GBG-RPL \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental setup.
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
###### Abstract  
Accurate cell type annotation is a critical step in single-cell RNA sequencing (scRNA-seq) analysis, enabling deeper insights into cellular heterogeneity and biological processes. In this study, we conducted a comprehensive comparative evaluation of various machine learning techniques, including support vector machine (SVM), decision tree, random forest, logistic regression, gradient boosting, k-nearest neighbour, transformer, and naive Bayes, to determine their effectiveness for single-cell annotation. These methods were evaluated using four diverse datasets comprising hundreds of cell types across several tissues. Our results revealed that SVM consistently outperformed other techniques, emerging as the top performer in three out of the four datasets, followed closely by logistic regression. Most methods demonstrated robust capabilities in annotating major cell types and identifying rare cell populations, though naive Bayes was the least effective due to its inherent limitations in handling high-dimensional and interdependent data. This study provides valuable insights into the relative strengths and weaknesses of machine learning methods for single-cell annotation, offering guidance for selecting appropriate techniques in scRNA-seq analyses.  
Keywords:cell annotation; single-cell; clustering; machine learning; scRNA-seq _Article_
## 3 Parameter and Performance Comparison
### Recent Algorithm Implementations  
In the current landscape of algorithmic research, there is considerable effort devoted to the enhancement of standardized algorithms through specific implementations. As illustrated in Table 6, a review--conducted based on the authors' estimation for relevance to the topic of this paper--of publications from the years 2022 and 2023 indicated a substantial concentration of studies focused on Dilithium.  
However, active research efforts regarding all three algorithms will warrant their further development, particularly in the context of applicability on performance-constrained platforms, as discussed in next section.