esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Profile Harmonic Transponder for IoT Applications
###### Abstract  
This paper proposes a novel single-layer, low-profile harmonic transducer to be integrated with the printable diodes based on Si particles. The introduced prototype consists of two bowtie dipoles that are directly matched to the harmonic generation element at a fundamental 2.45 GHz frequency and also at the corresponding second harmonic 4.9 GHz frequency. Therefore, the antennas and T-matching parts can be manufactured as separate inlays using a single layer-substrate. Besides the new prototype, the harmonic conversion loss (CL) is theoretically and experimentally investigated. In this regard, the equivalent circuit is driven and utilized to analyze the CL performance with variations in fundamental frequency and input power. The introduced transponder outperforms the state-of-the-art work from the printability, size, and CL point of views.  
RFID; harmonic transponder; IOT +
Footnote †: journal: Electronics
# Ranking of Electricity Accumulation Possibilities: Multicriteria Analysis
## 2 Comparative Description of Selected Energy Storage Technologies
### Pumped Hydro Storage  
Accumulation of electrical energy is also possible in the reservoirs of hydro stations, considering that the principle of water potential energy is used for the accumulation of electrical energy. In such a system, during periods of low demand and high availability of electricity, water is pumped and stored in upper reservoirs. By releasing energy in accordance with demand, the electricity is obtained in a shorter reaction time. The difference between peak loads and off-peak periods is balanced, ensuring grid stabilization. Weighing several criteria, the use of water storage is the most mature electricity storage system, when taking into consideration the installed power, capacity, the ability to provide additional frequency, and voltage control to the power grid. The ability of such an accumulation system to adapt and switch to different operating modes is also important, providing particularly efficient pumping power even at low capacities when asynchronous motor generators are engaged. It is predicted that the use of water storage for electricity storage, as a concept, will be the main driving factor to help countries achieve their goals in reducing GHG emissions [18].
## 2 Materials and Methods
### Simulation Setup for Extended Scenarios
#### 2.3.2 Cross-Industry Scenario (Furniture Example)  
To demonstrate generality, we outline a second simulation scenario. Consider a furniture supply chain, e.g., for dining tables. We chose furniture given that traceability complexity is comparable: similar main components in a dining table vs. a shirt, clear assembly hierarchy, and identifiable quality checkpoints. Other industries could be selected, but they would likely add further simulation complexity without benefit. For modeling, we map analogous roles like raw material (lumber mill) and component manufacturer (hardware manufacturer), while duplicating others like the retailer and third-party auditor. The network topology remains largely similar, as shown in Figure 2, barring the inclusion of a refurbisher in the furniture production channel in place of a buying agent in the apparel network.  
A further motivation for selecting furniture lies in the markedly different end-of-life challenges, market scale, and regulatory pressures faced by this sector compared with apparel. Furniture is one of the fastest-growing bulky-waste streams in Europe, prompting dedicated extended-producer-responsibility (EPR) requirements under the 2023 amendments to the EU Waste-Framework Directive and the French AGEC law [30]. Tracking component provenance and take-back performance is therefore immediately salient for manufacturers and retailers. Economically, the global furniture market is projected to generate a revenue in excess of USD 700 billion in 2025 [31]. Technically, disassembly complexity between furniture and apparel differs sharply: a typical dining table combines engineered-wood substrates, metallic fasteners, and surface coatings that must be separated and graded before material recovery, whereas commodity garments are mostly single-material textile assemblies. Demonstrating that WEave can map such heavier, multi-material flows--and record both refurbishment and part-harvesting events--thus stresses the generality of the asset model and smart-contract logic beyond what the apparel case alone can reveal. Finally, our furniture scenario aligns with recently published circular-design frameworks calling for blockchain-enabled product passports in the furniture industry.  
We simulate a batch of dining tables produced, sold, and returned. In our simplified simulation, the recycler will extract components and create recycled material assets for those that re-enter the production stream. The new production cycle uses those recycled metals in making new components. On the other hand, a returned good that is refurbished by a refurbisher can simply be resold once handed over to the retailer. The entire state transition of furniture simulated is diagrammed in Figure 4b. We aim to compare how the system performs vs. the apparel case and portray the effects of including a robust refurbishment process otherwise absent in the apparel scenario.
## 2 Background
### Nelder-Mead Algorithm
#### 2.2.2 Disadvantages of the Nelder-Mead Algorithm  
Some disadvantages of the Nelder-Mead algorithm are:1. **Sensitivity to initial conditions:** The performance of the Nelder-Mead algorithm is highly dependent on the initial simplex configuration. Poor initial setups may result in slow convergence or even failure to converge.
2. **Lack of global convergence guarantee:** Unlike some other optimization algorithms, the Nelder-Mead algorithm does not have a guaranteed global convergence property. It can converge to a local minimum or even get trapped in non-optimal regions of the search space.
3. **Inefficiency in high-dimensional spaces:** The performance of the Nelder-Mead algorithm deteriorates as the dimensionality of the problem increases, known as the \"curse of dimensionality\". It may struggle to converge or require significantly more function evaluations in high-dimensional spaces.
###### Abstract  
The in situ stress distribution is one of the driving factors for the design and construction of underground engineering. Numerical analysis methods based on artificial neural networks are the most common and effective methods for in situ stress inversion. However, conventional algorithms often have some drawbacks, such as slow convergence, overfitting, and the local minimum problem, which will directly affect the inversion results. An intelligent inverse method optimizing the backpropagation (BP) neural network with the particle swarm optimization algorithm (PSO) is applied to the back analysis of in situ stress. The PSO algorithm is used to optimize the initial parameters of the BP neural network, improving the stability and accuracy of the inversion results. The numerical simulation is utilized to calculate the stress field and generate training samples. In the application of the Shuangjiangkou Hydropower Station underground powerhouse, the average relative error decreases by about 3.45% by using the proposed method compared with the BP method. Subsequently, the in situ stress distribution shows the significant tectonic movement of the surrounding rock, with the first principal stress value of 20 to 26 MPa. The fault and the lamprophyre significantly influence the in situ stress, with 15-30% localized stress reduction in the rock mass within 10 m. The research results demonstrate the reliability and improvement of the proposed method and provide a reference for similar underground engineering.  
in situ stress; underground engineering; inversion method; numerical simulation; neural network; particle swarm optimization algorithm +
Footnote †: journal: Applied sciences
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Solution of eMBB Allocation Problem
#### 4.1.3 Alternating Iteration-Based eMBB Allocation Algorithm  
Based on the results in the previous two subsections, an alternating iteration-based eMBB allocation algorithm is proposed for problem \(P1\). Specifically, in the \((r+1)\)th iteration, the optimal RB allocation is achieved in the first stage based on a fixed power allocation and RIS phase shift matrix, i.e., \(\left\{P_{eb}^{(r)},\Phi_{\epsilon}^{(r)}\right\}\), to produce the gNB traversal capacity \(C_{gNB}(P_{eb}^{(r)},\Phi_{\epsilon}^{(r)},x_{eb}^{(r+1)})\). Then, based on \(\left\{x_{eb}^{(r+1)}\right\}\) determined in the previous stage, \(\left\{P_{eb}^{(r+1)},\Phi_{\epsilon}^{(r+1)}\right\}\) and the pseudo-optimal gNB traversal capacity, i.e., \(C_{gNB}(P_{eb}^{(r+1)},\Phi_{\epsilon}^{(r+1)},x_{eb}^{(r+1)})\), are obtained in the second stage. When the change in the target value is below the threshold \(\epsilon\), the algorithm stops iterating, denoted as \((C_{gNB}^{(r+1)}-C_{gNB}^{(r)})/C_{gNB}^{(r+1)}\leq\epsilon\). Algorithm 2 summarizes the process of the alternating iteration-based eMBB allocation algorithm.
applied sciences Article  
Effect of Dietary Enrichment with Hempseed (Camanabis sativa L.) on Blood Pressure Changes in Growing Mice between Ages of 5 and 30 Weeks  
[PERSON] 1,*(c), [PERSON] 2 and [PERSON] 3  
Department of Nutrition and Dietetics, Idaho State University, Pocatello, ID 83209, USA College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80521, USA; [EMAIL_ADDRESS] Department of Biology, Colorado State University, Pueblo, CO 81001, USA; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
Dietary hempseed (Camanabis sativa L.) reduces blood pressure in adults and animal models of hypertension; however, whether hempseed consumption throughout early life reduces adult blood pressure is not known. This study tested the hypothesis that hempseed enrichment versus a control diet modifies the age-dependent pattern of blood pressure changes in growing female C57 BL/6 mice and results in lower adult blood pressure. From ages 5 to 30 weeks, early post-weaning to mid-adulthood, mice were fed either a control AlN-93C (0%), 50 g/kg (5%), or 150 g/kg (15%) hempseed-supplemented diet (\(n=8\) per group). Biweekly measurements of systolic, diastolic, and mean arterial pressure were collected using the tail-cuff method. Mice fed 5% or 15% hempseed versus the control diet exhibited no significant differences in systolic, diastolic, or mean arterial blood pressure (repeated measures ANOVA main effect of diet, \(p>0.05\)). Blood pressure did not differ significantly between diet groups in adulthood (\(p>0.05\)). However, mice fed a control or 5% hempseed, but not 15% hempseed, diet exhibited blood pressure changes across age marked by significant increases during early adulthood (weeks 11-17) versus early post-weaning (week 5) (\(p<0.05\)). In conclusion, long-term dietary hempseed enrichment at 5% and 15% concentrations during development does not reduce adult blood pressure, but a 15% dose blunts the temporary increase in blood pressure during early adulthood seen in mice fed a control diet.  
hempseed; blood pressure; mice; C57 BL/6
# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 3 Methodology
### Bio-Inspired Coordination Mechanisms
Figure 1 illustrates the coordination process with three AMRs executing assigned tasks simultaneously. Figure 1 displays their movement paths, highlighting a collision between two robots near the end of their routes. The subsequent figures demonstrate the sequential application of our bio-inspired mechanisms: initial movements decrease path costs (Figure 1), collision locations are assigned higher costs (Figure 1), paths are then reinforced or penalized based on the resulting delays (due to the collisions, shown in Figure 1), and, finally, evaporation gradually returns costs toward the neutral value of 0.5 (Figure 1). For clarity, neutral costs are omitted from the figure. **Algorithm implementation:** Building on these weight modification mechanisms, the complete algorithm, presented in Algorithm 1, operates continuously during system runtime. For each AMR, it combines path planning with real-time adaptation of the environment representation. The algorithm processes new tasks as they arrive, plans and executes movements for each AMR, and updates edge weights based on system feedback. This continuous adaptation creates emergent behavior where the system learns to avoid congestion and minimize delays while maintaining efficient paths. The algorithm maintains a balance between exploitation of known good paths (through movement rewards) and exploration of alternatives (through evaporation). This core ACO-inspired behavior is enhanced with mechanisms specifically designed for multi-robot coordination. Immediate collision avoidance is achieved through dynamic weight penalties that redistribute traffic away from congested areas, while the delay feedback mechanism serves a dual purpose: it both optimizes task execution times and reinforces the avoidance of potentially problematic paths where delays might occur due to congestion or frequent conflicts. Together, these mechanisms create an adaptive system that learns to anticipate and prevent delays by steering AMRs towards efficient conflict-free paths. Figure 1: Illustration of the bio-inspired mechanisms. (**a**) AMR movements and the corresponding weights after applying (**b**) movement rewards (pheromone deposition), (**c**) collision handling, (**d**) delay feedback, and (**e**) pheromone evaporation.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 4 Experimental and Analysis
### Comparison with State-of-the-Art Methods  
To further test the adequacy of our proposed GFSR model, we compare it with seven CNN-based SR methods: SRCNN [30], VDSR [6], LapSRN [21], IDN [25], DPSR [22], IMDN [26], PAN [35], LAPAR-A [27], SMSR [28], DASR [24] and DeFiAN [23]. Inspired by [57], a self-ensemble strategy is introduced to further improve the proposed GFSR, and the improved model is named as GFSR+. The best average PSNR and SSIM of the reconstructed images obtained by the above SR methods using diverse scaling factors (\(\times 2\), \(\times 3\), \(\times 4\)) on the four standard datasets of Set5, Set14, BSD100 and Urban100 are listed in Table 6, where the optimal and suboptimal values marked in bold and underline, respectively. It can be seen from Table 6 that both the best average PSNR and SSIM metrics of the reconstructed images obtained by the proposed GFSR+ are optimal for different scale factors on all datasets. Even for the proposed GFSR that does not incorporate the self-ensemble strategy, its best average PSNR and SSIM metrics of the reconstructed image are \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1:1** & **2:1** & **3:1** \\ \hline PSNR & 32.83 & 33.24 & 32.97 \\ Param. & 2018 K & 2152 K & 2220 K \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effects of different super-parameter of \(P\). We report the best average PSNR(dB) on Set14 (\(\times 2\)) in \(2.5\times 10^{4}\) iterations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Base** & \(R_{a}\) & \(R_{b}\) & \(R_{e}\) & \(R_{d}\) & \(R_{e}\) & \(R_{f}\) & \(R_{g}\) \\ \hline ACAB & & ✓ & & & ✓ & ✓ & & ✓ \\ RFFB & & ✓ & & ✓ & & ✓ & ✓ \\ GB & & & ✓ & & ✓ & ✓ & ✓ \\ \hline PSNR & 33.02 & 33.07 & 33.12 & 33.15 & 33.11 & 33.18 & 33.19 & 33.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of different modules. We report the best average PSNR(dB) on Set14 (\(\times 2\)) in \(2.5\times 10^{4}\) iterations. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1:1** & **2:1** & **3:1** \\ \hline PSNR & 32.83 & 33.24 & 32.97 \\ Param. & 2018 K & 2152 K & 2220 K \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effects of different super-parameter of \(P\). We report the best average PSNR(dB) on Set14 (\(\times 2\)) in \(2.5\times 10^{4}\) iterations. also optimal for different scale factors on all datasets compared to other state-of-the-art methods, which indicates that the proposed method achieves leading performance in terms of overall super-resolution performance. To visually compare the reconstructed images obtained by different SR methods from subjective vision, Figures 8-11 show the reconstruction results of different methods in the same region with a scaling factor of 4, and the corresponding original HR image is given as a reference. From Figures 8-11, it can be seen that the vast majority of methods cannot precisely reconstruct edge and texture details, and even generate severe artifacts. Conversely, our GFSR can reconstruct a clearer image with richer edge and texture details.
# Handwritten Signature Verification Method Based on Improved Combined Features
## 4 Experiment Results and Discussion  
Since this article hopes to realize the combination of offline images and online data, it is still difficult to obtain publicly available data sets that can simultaneously exist offline images and corresponding online data. Therefore, this paper uses the collected offline images and online data of 1200 signatures for experiments. We use FAR, FRR, AER and Accuracy as evaluation indicators. FAR is the percentage of forged signatures that are judged to be real signatures among the forged signatures used for testing. FRR refers to the percentage of real signatures that are judged to be forged signatures among the real signatures used for testing. AER is the average value of FRR and FAR. Accuracy refers to verification accuracy, that is, whether the predicted result is consistent with the actual situation. The current deep learning methods can achieve a good signature verification effect, but they need a lot of data to carry out experiments, and are not very practical. If considering the practical application, we think it is more necessary to solve the small sample in the actual situation. Through the study of small sample problems, most scholars choose samples within ten for training. Normally, ten samples will get the best experimental results. For 1200 signatures, this paper selected three, five, eight and ten real signature samples for training, and conducted experiments and comparisons on different features. For the offline signature training set, we randomly selected three, five, eight and ten real  
Figure 4: Sample distribution when using five training samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Features** & **FAR** & **FRR** & **AER** & **Accuracy** \\ \hline SVM & GLCM & 8.83\% & 10.50\% & 4.83\% & 90.33\% \\  & HOG & 7.00\% & 6.33\% & 6.67\% & 93.33\% \\  & Geometric & 16.83\% & 25.33\% & 21.08\% & 78.92\% \\  & GLCM + HOG & 4.83\% & 5.83\% & 5.33\% & 94.67\% \\  & Texture + Geometric & 4.17\% & 5.50\% & 4.83\% & 95.17\% \\ DTW & & 7.67\% & 7.50\% & 7.59\% & 92.42\% \\ SF-L & & 2.50\% & 2.33\% & 2.42\% & 97.58\% \\ SF-A & & 1.00\% & 3.33\% & 2.17\% & 97.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The result of ten training samples.  
Figure 3: Sample distribution when using three training samples.
# Deep Directional Network for Object Tracking
## 1 Introduction
Inspired by the above observations, we propose a novel deep direction network for object tracking that classifies positive samples into 11 direction and scale categories: upper-left, left, lower-left, lower, lower-right, right, upper-right, upper, small-scale, large-scale and true-sample. We use end-to-end training, and the final output layer of the network has two branches. One branch classifies positive samples into 11 direction categories, and the other classifies positive and negative samples. To enable the network to learn the ability to describe the difference between target and background and to judge whether the target is lost in the tracking phase, the existence of the second branch is necessary. In the tracking phase, the network obtains the information on target direction according to the single sample extracted from the previous position and uses the sliding window operation to approach the target. Our algorithm uses a single-sample tracking strategy, thereby reducing the computational burden on the network and improving the tracking speed. Our algorithm cleverly classifies the target's positive sample into 11 categories, uses the constraint box size to limit 11 classes, and uses a deep network to learn the differences between categories. Using a deep network can better describe the target and improve the robustness of tracking. Another interesting aspect of our algorithm is that we design a sliding window mechanism for direction classification network. In summary, we make the following contributions:  
* We propose a network for the classification of target direction based on CNNs, and this network has a good adaptability to target tracking. * Our network successfully incorporates scale variation in a deep network that is robust to scale variation sequences. * In the online tracking stage, the network only calculates a single sample, which guarantees a low computational burden on the network. * The positive and negative sample redetection strategies can successfully ensure that the samples are not lost. The rest of the paper is organized as follows. Section 2 briefly summarizes our related studies. Section 3 describes our direction network mechanism. Section 4 illustrates our online tracking implementation. Section 5 demonstrates our experimental results with direction network in the Visual Tracker Benchmark.