esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm
## 2 Basic Formulations of HS and JAYA Algorithms
### The JAYA Algorithm  
The JAYA method is very simple to implement because it has only one equation for perturbing design. Let \(X_{\text{j,k,it}}\) be the value of the jth design variable (j = 1,..., \(NDV\)) for the kth design of population (k = 1,..., \(N_{\text{POP}}\)) at the \(i\)th iteration. The \(X_{\text{j,k,it}}\) value is modified as follows:  
\[X_{\text{j,k,it}}^{\text{new}}=X_{\text{j,k,it}}+r_{\text{1,j,it}}\Big{(}X_{ \text{j,best,it}}-\left|X_{\text{j,k,it}}\right|\Big{)}-r_{\text{2,j,it}}\Big{(} X_{\text{j,worst,it}}-\left|X_{\text{j,k,it}}\right|\Big{)} \tag{2}\]  
where: \(X_{\text{j,k,it}}^{\text{new}}\) is the updated value of variable \(X_{\text{j,k,it}}\); \(r_{\text{1,j,it}}\) and \(r_{\text{2,j,it}}\) are two random numbers in the interval [0,1] for the jth variable; \(X_{\text{j,best,it}}\) and \(X_{\text{j,worst,it}}\), respectively, are the values of the jth variable for the best design \(\mathbf{X_{\text{best,it}}}\) and worst design \(\mathbf{X_{\text{worst,it}}}\) of the population in the current iteration.  
The \(r_{\text{1,j,it}}\Big{(}X_{\text{j,best,it}}-\left|X_{\text{j,k,it}}\right| \Big{)}\) term describes the JAYA's tendency to approach the best design \(\mathbf{X_{\text{best,it}}}\), while the \(-r_{\text{2,j,it}}\Big{(}X_{\text{j,worst,it}}-\left|X_{\text{j,k,it}}\right| \Big{)}\) term refers to the tendency of moving away from the worst design \(\mathbf{X_{\text{worst,it}}}\). Random factors \(r_{1}\) and \(r_{2}\) allow design space to be well explored while the absolute value \(\left|X_{\text{j,k,it}}\right|\) in Equation (2) further enhances exploration [12].  
The new trial solution \(\mathbf{X_{k}}^{\text{\emph{new}}}\) generated with Equation (2) is compared with its counterpart \(\mathbf{X_{k}}^{\text{\emph{pre}}}\) stored in the population. If \(\mathbf{X_{k}}^{\text{\emph{new}}}\) is better than \(\mathbf{X_{k}}^{\text{\emph{pre}}}\), the population is updated by replacing \(\mathbf{X_{k}}^{\text{\emph{pre}}}\) with \(\mathbf{X_{k}}^{\text{\emph{new}}}\). This process is repeated until reaching the limit number of iterations/analyses. Similar to HS, the computational cost of the JAYA search may be \(N_{\text{POP}}\times N_{\text{itermax}}\) structural analyses. [PERSON] et al. [54; 55; 56] attempted to limit the number of analyses by directly rejecting heavier designs than those stored in the population. However, such a strategy did not allow to find the global optimum in all structural design problems (see, for example, Ref. [53]).
# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges
### Ethical Challenges in Using LLMs in Computer Science Studies
#### 3.3.1 Accuracy and Reliability  
An important concern with the use of LLM tools in various fields, including computer science and among computer science students, is the accuracy of the information they generate.  
[PERSON] [5] reviewed the challenges of accuracy and reliability associated with ChatGPT in the educational context, as well as issues related to the prevention of plagiarism. Key concerns include ChatGPT's reliance on biased or outdated data, its tendency to produce incorrect or fabricated information, and its inability to verify its own accuracy. Problems such as inaccurate programming code and fake bibliographic citations have been reported, rendering its outputs unreliable in fields such as mathematics, sports science, psychology, and health. These issues highlight the importance of the cautious use and thorough validation of ChatGPT-generated content.  
[PERSON] et al. [37] further examined the performance of LLMs--including ChatGPT-3.5, GPT-4.0, BingAI, and LLMa--in undergraduate computer science tasks. Their evaluation of these models on lecture materials, exercises, and exams revealed mixed results. While ChatGPT-3.5 achieved a relatively high success rate of 79.9%, other models demonstrated significant limitations. Notably, none were able to complete tasks requiring mathematical precision, suggesting that while LLMs can be valuable tools, their application should be carefully constrained in contexts demanding exact calculations.  
[PERSON] et al. [33] have addressed the risks of over-reliance on LLMs and advocated for an educational approach that emphasizes teacher training and the development of critical thinking skills. They proposed the use of LLMs as complementary tools alongside traditional resources and encouraged creative, student-led projects. To mitigate risks, [PERSON] et al. emphasized the necessity of human oversight, including critical analysis and fact-checking of AI-generated outputs, to ensure the reliable and effective integration of LLMs in educational settings.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 3 Experimental Results and Analysis
### Effect of Training Set Size  
A robust recognition model should be able to maintain good recognition performance even with a small number of training samples. To test the effect of the size of the training sample on the recognition rate, six size training sets were used in the experiments, namely 4320, 8640, 10,800, 12,960, 17,280 and 19,440, corresponding to 20%, 40%, 50%, 60%, 80% and 90% of the data set, respectively. The results of the six types of models on different training sets are shown in Table 3.  
Figure 7: Two-dimensional ISOMAP projection. a-h are the two-dimensional visualization results of the extracted features of several models. (**a**) original data, (**b**) RNN, (**c**) LSTM, (**d**) Transformer, (**e**) R-Transformer, (**f**) R-DeLighT, (**g**) R-DeLighT + Autoclip.  
As can be seen from the table, with only 20% of the training samples, the local RNN module, GLTs combined with the Transformer model can achieve 81.83% accuracy, which is a 16% raise in accuracy compared to the Transformer. In the process of changing the training set from 90% to 20%, the accuracy of Transformer model decreased by 27.53%, while the local RNN module, GLTs combined with Transformer only decreased by 15.3%, indicating that the model is more stable in the process of changing the training set. Transformer combined with the local RNN module can raise the recognition accuracy by 1-3% when the training set is reduced compared to Transformer model. Therefore, in comparison, the GLTs can make the Transformer model more robust when the training set is reduced.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 1 Introduction
### Contributions  
In this paper, we investigate the uplink synchronization issue of NB-IoT in LEO satellite scenarios. The terminal obtains observation values by detecting the NPSS, formulates a TDOA equation to determine its position, and then estimates the TA and Doppler offset using satellite ephemeris. The main contributions of this work are summarized as follows:  
1. Position-aided uplink synchronization for NB-IoT terminals: To address the challenges of large Doppler shifts and varying numbers of visible satellites in LEO satellite scenarios, we propose a position-aided uplink synchronization method. Observation values are obtained through NPSS detection, and a TDOA equation is constructed to estimate the terminal's position, which is then combined with satellite ephemeris to complete uplink synchronization.
2. Multiple Segment Auto-correlation (MSA) detection method for NPSS: To cope with the detection of NPSSs under the high-dynamics and large Doppler shift conditions of LEO satellites, we propose the MSA algorithm, which exploits the symmetry of NPSSs. This algorithm leverages the time-domain repetition property of the NPSS to achieve robust acquisition of timing and frequency observation values under severe Doppler conditions.
3. Terminal positioning via NPSS-based delay observations: Based on the time delay observations from NPSSs, we establish TDOA equations under different numbers of visible satellites to estimate the terminal position. Furthermore, we derive the Cramer-Rao lower bound (CRLB) and analyze the power consumption characteristics of IoT terminals. Under an SNR ratio of \(-2\) dB, the positioning error can be maintained at around 200 m.
4. Simulation under the Iridium constellation: We simulate the performance of the proposed algorithm under the Iridium satellite constellation configuration. The simulation results demonstrate that the approach proposed in this paper reduces terminal power consumption by 15-40% while still meeting the uplink synchronization requirements of the NB-IoT system.
## 3 Results
### Factors Influencing Changes in Livestock Numbers  
#### 3.2.1 Principal Component Analysis (PCA)  
The primary (PC1) and secondary (PC2) components in the PCA explained 39.1% and 25.6% of the total variance, respectively, or 64.7% of the variation in the data. The tertiary component (PC3) explained 11.5% of the variance but had an eigenvalue smaller than one. PC1 was strongly positively correlated (i.e., \(|\) Loading\(|\)\(\geq\) 0.4) with NDVI\({}_{\rm s}\) and P\({}_{\rm s}\) and negatively with T\({}_{\rm s}\) and dryness. PC2 was strongly positively correlated with T\({}_{\rm w}\) and T\({}_{\rm s}\) and negatively with P\({}_{\rm w}\). Thus, a high PC1 value indicates viable vegetation growth and a high PC2 value indicates a warm and dry winter.  
In the score plot, we highlighted annual changes in climate conditions in each _soum_ (Figure 5a). The annual differences were captured well by PC2. In 2009 (i.e., 2009-2010), when a particularly severe \(Dzul\) occurred, PC2 was low for all _soum_s, which indicates a cold winter with heavy snowfall. In contrast, the years with low livestock loss such as 2005 and 2006 had a high PC2. These patterns are apparent in the score plot colored according to the rate of livestock loss (Figure 5b). High livestock loss (i.e., change \(<-30\%\)) was observed where PC1 and PC2 were both negative (i.e., cold and snowy winter with low vegetation) and vice versa. Livestock loss occurred when either PC1 or PC2 was negative, although it was greatest when both components were negative.  
Figure 4: Spatial patterns of the rate of change in livestock numbers and environmental variables: the first column: rate of change in livestock numbers (%) from 2003 to 2010; from second to fifth columns: scores of summer and winter environmental variables from 2003 to 2009, respectively. \(03\)–\(04^{\prime}\) means ‘livestock change rate between 2003 and 2004’, while ‘03’ means ‘summer and winter environmental variable in 2003’. Red and blue areas correspond to negative and positive values, respectively.
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 2 YOLOv5 Target Detection Network  
YOLOv5 is the fifth version of the YOLO series of algorithms and is a target detection algorithm further optimized and improved based on YOLOv4, with the advantages of a fast detection speed, high detection accuracy, easy deployment of edge devices, etc. YOLOv5 is divided into five different models according to the sub-module width and depth, namely, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. These five models gradually improve with the increase in model volume. With the increase of the model volume, the detection accuracy gradually improves in these five models. Considering the real-time detection and accuracy in real scenarios, the YOLOv5s-6.1 model was chosen as the benchmark model for insulator multi-defect detection in this paper. The YOLOv5s network structure is shown in Figure 1.  
YOLOv5, as a whole, consists of four parts: the input side, the backbone feature extraction network, the neck network, and the prediction header. The input side uses mosaic data  
Figure 1: YOLOv5s network structure.  
enhancement, adaptive anchor frame calculation, and adaptive image scaling operations to enrich the dataset, alleviate model overfitting, and improve the overall model performance. For the backbone feature extraction network part, YOLOv5 version 6.1 replaces the Focus module, the first layer of the network, with a 6 \(\times\) 6 sized convolutional layer, both of which are equivalent in feature extraction but are more efficient for training on certain GPU devices: the C3 module achieves feature fusion and improves the computational speed by residual linking and embedding multiple bottleneck modules; the SPPF module uses multiple small-sized pooling. The SPPF module uses multiple small-size pooling kernels cascaded instead of a single large-size pooling kernel in the SPP (spatial pyramid pooling) module, which further improves the operation speed while retaining the original functionality. Neck feature fusion adopts the FPN (feature pyramid network) and the PANet (path aggregation network) to realize the fusion of deep semantic features and shallow location features and to enhance the fusion of information at multiple scales. The prediction head part, which receives the feature maps of different scales delivered by Neck, uses the GIoU (generalized intersection over union) bounding box loss function to speed up the model convergence and employs non-maximal inhibition to filter the target frames to form three prediction layers, which are used for predicting and regressing the targets. The components of the YOLOv5s network are shown in Figure 2.
## 2 Materials and Methods
### Model Hypothesis  
**Hypothesis 1:**_Both the supplier and distributor are rational and make great efforts to sustainably supply eco-products, while their objectives are maximizing profits. \(\pi_{S}\) represents the supplier's profit, \(\pi_{R}\) represents the distributor's profit._  
**Hypothesis 2:**_The market information obtained by the supplier and distributor is completely symmetrical; that is, a game of perfect information._  
**Hypothesis 3:**_The market demand for ecological products is influenced by price and consumer preferences, which can be calculated as \(q=a-bp+k\theta\). In the formula, \(a\) is the potential market size, \(b\) is the coefficient of sensitivity of price, \(k\) is the consumer preferences for eco-products, and \(\theta\) is the ecological level of the supply chain reflecting the supply level of eco-products; \(a\), \(b\), \(k\), and \(\theta\) are all bigger than 0, and \(a\) is bigger than 0 bp._  
**Hypothesis 4:**_According to the beneficiary pays principle, the eco-product supplier and distributor are obliged to contribute back into the ecosystem for more and better eco-products as a \"realization-feedback\" path. The invested cost of ecological conservation and restoration is determined with reference to the supply chain's ecological level, and calculated as \(\frac{\mu\theta^{2}}{2}\), where \(\mu\) is the cost coefficient of ecological conservation and restoration. The supplier and distributor bear the cost by a ratio of \(r\) and \(1-r\), respectively. Then, the cost-sharing contract coefficient is defined as \(\delta=r/1-r\)._  
**Hypothesis 5:**_The wholesale price of the supplier is \(w\), and the market price of the distributor is \(p\). In this respect, the condition \(p\geq w\geq c\) is assumed to be at work to make it possible for the supply chain to achieve positive earnings._  
Figure 1: Ecological product supply chain system.  
**Hypothesis 6:** _The operating subsidy is defined as E(f), which will subsidize the supplier and distributor by the coefficientsf and \(1-f\), respectively (0 < f < 1). The consumption subsidy is defined as E(\(\varphi\)), and \(\varphi\) (\(\varphi\) > 0) is the subsidized price for lowering each eco-product's price in the market._  
The meaning of all parameters is listed in Table 1.
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.2 Software Correlator
Then, this zero-code-phase segment is correlated with zero-phase PRN code and carrier tables for whole code period. Figure 9(a) shows the architecture of IF data buffer. When new data comes in, the oldest data will emerge, the second entry is shifted to the first entry, and the new data is loaded to the second entry. Figure 9(b) shows the correlation window which is set by current code phase measurement. However, code phase would shift forward or backward due to Doppler frequency. A strategy is adopted to account for code phase movement. According to the direction of code phase movement, there are three possible cases as shown in Figure 9(c-e) and listed as follows  
1. Forward phase movement to zero (phase Np-1 in 1 st entry and phase 0 in 2 nd entry): perform correlation two times for the first and the second buffers. 2. Backward phase movement to Np-1 (phase 0 in 1 st entry and phase Np-1 in 2 nd entry): perform a correlation for the second buffer and skip next correlation. Figure 8: Assembly code example of software correlator using SIMD instructions.
## 3 Methodology
### G-R Index Analysis  
In the initial set of tests, both the complex shear modulus (\(\mid\)G*\(\mid\)) and the phase shift angle (\(\delta\)) were measured for samples at 15 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. The G-R index was calculated to assess the damage potential of the bitumen under various conditions. The phase shift angle (\(\delta\)) directly reflects the balance between the material's elastic and viscous behaviors.  
The G-R index was calculated using Equation (1):  
\[G-R=G^{*}\frac{cos^{2}\delta}{sin\delta} \tag{1}\]where \(\mid\)G*\(\mid\) is the complex shear modulus (in Pa), and \(\delta\) is the phase angle (in degrees). This parameter quantifies the binder's brittleness and its susceptibility to fatigue and thermal cracking, especially at intermediate service temperatures.  
These results were used to generate Glover-Rowe (G-R) parameter plots, which delineate areas with no block cracking, potential damage zones, and block cracking risks. The complex shear modulus (\(\mid\)G*\(\mid\)) was plotted against the phase shift angle (\(\delta\)), with different regions indicating the material's vulnerability to damage under stress. Higher G-R values (above 180 kPa) indicate a brittle binder more susceptible to low-temperature and fatigue cracking, while lower G-R values (below 100 kPa) reflect a more flexible binder prone to rutting at elevated temperatures. The intermediate zone (100-180 kPa) represents a balance where the binder shows moderate resistance to both cracking and rutting.  
The 15 \({}^{\circ}\)C results represent cooler climatic conditions, where binders with higher G-R values are more likely to experience thermal and fatigue cracking. In contrast, the 25 \({}^{\circ}\)C results simulate warmer environments where rutting is more critical; binders with low G-R values in this range may be more vulnerable to permanent deformation. This plot offered valuable insights into the bitumen's ability to resist cracking and rutting, particularly under different climatic and loading conditions. The G-R index also helped to determine the material's performance limits, highlighting when it transitions from an undamaged state to a damage-prone zone.
# Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study
## 5 Comparison between MODBUS TCP and MQTT
The result of Figure 8 is illustrated in Figure 9 for values between 2 bytes and 106 bytes. The figure demonstrates the overhead percentage with respect to the total message size according to Equation (3):  
\[\text{Overhead}(\%)=\frac{\text{Overhead}}{\text{Overhead}+\text{ Payload}}\times 100\%. \tag{3}\]  
The percentage overheads of the aforementioned function codes are calculated and plotted in Figure 9. The figure shows values for fewer than 106 bytes because the message size in industrial applications and SCADA-like systems is small. MQTT has the highest overhead; the function code \"read input registers\" (0x04) has the least overhead (21 bytes), and the function code \"write multiple registers\" (0x10), which is covered by the curve of the function code \"read/write multiple registers (0x17), has 25 bytes of overhead. However, if the application client reads and writes registers in the same message, the function code \"read/write multiple registers\" (0x17) shows lower overhead than the function codes \"read input registers\" (0x04) and \"write multiple registers\" (0x10) if both are used separately for the same purpose. Accordingly, the MQTT protocol is suitable for IoT-based publish-subscribe applications, but it is unable to replace the MODBUS TCP, which fulfills the industrial requirements and uses an optimized frame-format for request-response communications between industrial clients and servers. These properties make it suitable for SCADA-like systems, automation, monitoring, and control. In Section 6, performance of MQTT and the MODBUS TCP are tested and compared based on the Round-Trip time (RTT) measurements and the central processing unit (CPU) usage.
###### Abstract
[32] combined the improved gravity search algorithm and particle swarm optimization algorithm to optimize a BPNN and successfully applied it to the fault diagnosis of motor drive systems. [PERSON] et al. [33] combined the harmonic search algorithm and a BPNN to form a new hybrid model and applied it to the inverse analysis of rockfill parameters. The example study shows that compared with the original BPNN algorithm, the hybrid model has the advantages of high convergence accuracy, fast convergence speed, and strong stability. The dung beetle optimization (DBO) algorithm [34] is a global optimization algorithm with search information as its objective function. The natural behaviors exhibited by dung beetles, such as rolling, dancing, foraging, breeding, and stealing, are the basis of the DBO algorithm. The design of these different update strategies shows good competitiveness in convergence speed, scalability, optimization accuracy, and elasticity; the DBO algorithm divides the initial population into four subpopulations and searches for the optimal location, respectively. By comparing each subpopulation, the global optimal position is obtained. The weights and thresholds of the BPNN are used to solve the optimal position of the dung beetles. By using DBO to optimize the BPNN's weights and thresholds, the convergence speed of the BPNN is improved, the training time is reduced, and the optimal global solution is obtained in the global search. Since the DBO algorithm has a good balance between global search and local development, it has significant advantages in optimizing the BPNN. The existing research shows that using DBO to optimize the initial parameters of the BPNN can improve the convergence speed of the BPNN, reduce the training time, and obtain the optimal global solution [35; 36]. Although the DBO-BPNN has been applied in engineering, there is still room for improvement in this method. Therefore, this paper will improve the existing DBO algorithm and combine the enhanced DBO (EDBO)algorithm [37] with a BPNN to obtain the so called EDBO-BPNN model. The model will optimize the initial parameters of the BPNN and update the weights and thresholds of the BPNN iteratively to find the optimal solution in the range of solutions. In the present study, the stress-strain curves of the Inconel 625 superalloy AB samples and HT-1200 samples fabricated by WAAM were measured experimentally. On the basis of the experimental data, the constitutive equation was established using the established EDBO-BPNN model. Finally, the prediction results of the EDBO-BPNN model established in this study are compared with the experimental results and some existing BPNN models in the literature.