esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distributed Hotspot Network Architecture
### IoT Device Layer  
This layer includes various IoT devices from smart home, smart health, smart transportation, smart agriculture, and smart industry. All types of IoT devices along with sensors can participate in this layer. Standard 8-bit, 16-bit. and 32-bit microcontrollers and microprocessor-based IoT hardware pools can be used in this layer. The main role of this layer is to transmit data packets through intermediate layers to remote application servers/service providers/users. For example, a farmer is a remote end user who can visualize, monitor, or analyze the status of his farm through this architecture. They can place many devices that are equipped with IoT sensors at different locations on his farmland. These IoT devices are equipped with Wi-Fi and/or LoraWAN, depending on the data transmission capacities that are required (see above). However, it must be assumed that such IoT devices should be placed near a hotspot so that the sensor data can be easily transmitted. The farmer can set up his own dew server-centric hotspots (Wi-Fi or  
Figure 3: IoT-aware dew computing enabling blockchain-assisted, distributed hotspot network architecture.  
LoraWAN) or rent the hotspot services of others who have already set up their hotspots in the geographical coverage area of the farmer's IoT devices. In this way, the farmer's IoT devices can communicate with the remote application layer by either using the hotspot services of their own hotspots or renting the network access services of other hotspots that have been systematically placed in the nearby coverage areas of such IoT devices.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 3 Algorithm
### Algorithm Improvement
With a probability of 0.0119, the solution of RLF algorithm was found to be greater than the approximate optimal solution, while in all experiments, the optimal solution was obtained in 6000 iterations with the improved-RLF algorithm. From the experiment, we found that the improved RLF algorithm can get a smaller approximate optimal solution within a certain time period. As can be seen from Figure 6, with a probability of 0.0119, the approximate optimal solution of RLF algorithm was less than that of the improved-RLF with 6000 iterations. As the number of satellites increased, this happened more and more. Figure 8 shows how the minimum PRN codes of the RLF algorithm and improved-RLF algorithm changed with the number of iterations. We found that, compared with the improved RLF algorithm, the RLF algorithm required more iterations to obtain an approximate optimal solution and, in some cases, did not obtain it. Thus, the improved RLF algorithm can obtain a smaller approximate optimal solution within a certain period of time, and it is more suitable for calculating the minimum number of PRN codes. In the following section, we describe the use of the improved RLF algorithm to conduct experiments on satellite PRN code assignment. Figure 8: Comparison of minimum PRN codes between the RLF algorithm and the improved-RLF algorithm. The satellite configuration was the walker 288/12/6 at an altitude of 1000 km and the walker 576/24/12 at an altitude of 900 km. Both the RLF and improved-RLF were run once with 10,000 iterations. Figure 7: The probability of not approximate optimal solution varying with the number of iterations. The experimental method is the same as that in Figure 6.
## 2 Research on UAV Post-Disaster Path Planning Problem
### Path Smoothing Algorithm Based on Cubic B-Spline Curves
#### 2.3.1 Smoothing of Path Using Basis Functions and Control Points  
By providing spatial vertices \(P_{i}(i=0,1,\cdots,m+n)\), \(n\)-degree curve segments can be obtained.  
\[P(t)=\sum_{i=0}^{n}P_{i}F_{i,k}(t) \tag{13}\]In the equation, \(P_{i}\) represents the curve equation corresponding to the \(i\) control point, and \(F_{i,k}(t)\) represents the \(k\) order B-spline basis function. Since the value of \(k\) represents the smoothness of the curve, a higher value of \(k\) results in a smoother curve but also increases the computational complexity. The parameter \(t\) represents the nodes used to divide the curve into segments and control the shape of the curve. To balance smoothness and complexity, this study selects \(k=3\), resulting in the cubic B-spline basis function as follows:  
\[F_{i,\ k}(t)=\frac{1}{k!}\sum_{m=0}^{k-i}\ (-1)^{j}C_{k+1}^{j}(t+k-m-j)^{k} \tag{14}\]  
The set of points obtained by the algorithm calculation is referred to as the path planning points \(M\). Assuming there are \(n+1\) ordered spatial pose vectors \(V_{i}(i=0,1,\cdots,n)\) in \(M\), the linear combination of \(k+1\) adjacent position vectors can be used to derive the curve equation for the \(i\) segment of \(k+1\) adjacent control points as follows:  
\[P_{i}(x)=\sum_{j=0}^{k}F_{i,k}(x)\mathbf{V}_{i+j} \tag{15}\]  
In the equation, \(P_{i}(x)\) represents the \(i\) B-spline curve function. \(x\) is the data point. By substituting Equation (13) into Equation (15), the matrix form of the curve is obtained as follows:  
\[P_{i}(x)=\frac{1}{6}\big{[}1\quad x\quad x^{2}\quad x^{3}\big{]}\begin{bmatrix} 1&4&1&0\\ -3&0&3&0\\ 3&-6&3&0\\ -1&3&-3&1\end{bmatrix}\begin{bmatrix}V_{i-1}\\ V_{i}\\ V_{i+1}\\ V_{i+2}\end{bmatrix} \tag{16}\]
## 2 Materials and Methods
### Measurement Configuration  
In this study, we fabricated a solid CZT detector, with a metal-semiconductor-metal planar structure (Figure 1a) and electrodes attached to both sides of the CZT crystal. When high-energy radiation with a wavelength that exceeds the bandgap of the CZT layer is incident, electrons in the valence band are excited to the conduction band because of the generation of electron-hole pairs. These electrons and holes are collected by the electrodes upon the application of a bias, which can lead to the generation of a signal. In this study, an external bias of 100 V/mm was applied to the electrodes by using a Keithley 236 high-voltage source measurement unit to facilitate the collection of the generated electrons and holes. The current generated by the collected electrons and holes was measured using a B2911A Precision Source/Measure unit at a sampling rate of 1000 points/s. Figure 1b illustrates the measurement setup used to extract the reference dose data. The sample was positioned at a source-to-surface distance (SSD) of 100 cm and depth of 10 cm in a solid water phantom and irradiated with therapeutic 6 and 10 MV beams using a LINAC device (Trilogy Clinac Ix, Varian, Palo Alto, CA, USA). The final results were obtained by averaging those of five measurements. The characteristics of the CZT detector, including dose linearity, dose rate dependence, energy dependence, angular dependence, SSD dependence, field size dependence, reproducibility, and percentage depth dose (PDD), were evaluated under defined conditions.
## 1 Introduction
[25] optimized the feature extraction network of RFBNet by abandoning the ordinary regular convolution and using self-correcting convolution, which can extend the perceptual field. Using the idea of a multi-scale and dense prediction module to make shallow information richer and integrate contextual information, [PERSON] et al. [26] added an attention mechanism to the network and combined it with contextual features to make the network model more focused on objects with low resolution and deliver more targeted objects. Inspired by the above research methods, in order to improve the performance of the network model for small object detection in high resolution remote sensing images, and to solve the problems of its diverse scale and dense distribution, this paper proposes a new solution called \"Enhanced YOLOX Network Model with Different Convolutions\" (DCE_YOLOX), using YOLOX as the baseline model, which uses depth-separable deconvolution for upsampling, combines dilated convolution and CoTNet to extract local contextual features, adds EcaNet to the enhanced feature extraction network of the original network model, and, based on the characteristics of remote sensing images, i.e., the input of the network model, the size is optimized. Through these methods, the network model is made to focus more on information-rich object features and on small target objects with stronger feature extraction capability and higher small object detection accuracy. Finally, the angular loss is calculated by CSL to realize the detection of rotated objects for remotely sensed imagery. The main contributions of this article are as follows:  
1. We propose an upsampling method using depth-separable deconvolution. With this special way of expanding the feature map, the pixel values around the central pixel can be diversified when the resolution is expanded, so that the obtained feature map is closer to the original map. 2. We replace the conventional convolution in Contextual Transformer Networks with dilated convolution, which can make better use of local context information to extract the feature information of small objects in remote sensing images and use EcaNet to improve the feature extraction ability of the network while maintaining the parameters of the original network model. 3. We studied the impact of network input size on the performance of the network model and optimized the network input size. At the same time, we used circular smooth tags to achieve more timely remote sensing image rotation object detection. 4. By comparing the performance of the related algorithms of horizontal object detection and rotationally invariant object detection, the proposed method has the best performance in remote sensing image object detection. The remaining chapters of this paper are organized as follows: Section 2 introduces the selection of baseline models, Section 3 describes the methods we proposed and used, and Section 4 introduces and analyzes the experimental environment, data, and results in detail, including horizontal object detection and selected object detection. Section 5 summarizes the research contents of this paper.
## 2 Methods
### Model Evaluation Metrics  
In the prediction process, the evaluation of model performance is crucial [25; 26; 27]. Common evaluation metrics include: mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). Among them, MAE and RMSE are the two most commonly used metrics to measure the accuracy of variables. MAE is the average of absolute errors, while RMSE is the square root of MSE. Its unit aligns with the original data, making it easier to evaluate the model. By substituting the actual values and the model output values into the MAE and RMSE formulas, the larger the value, the greater the prediction error of the model. Based on the aforementioned two indicators, the mean square error (MSE) is additionally selected as an evaluation metric. MSE can display the degree of variation in the evaluation data, thereby reflecting the stability of the model. If the MSE value is smaller, the variation in the residuals is reduced, making the results of the model more stable. The detailed formulas for the three indicators can be found in Equations (10)-(12).  
\[\text{MSE}= \frac{1}{n}{\sum_{i=1}^{n}}{\left({{\hat{y}}_{i}}\ -\ y_{i}\right)^{2}} \tag{10}\]  
\[\text{RMSE}= \sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left|{{\left({{\hat{y}}_{i}}\ -\ y_{i}\right)}}\right|^{2}}} \tag{11}\]  
\[\text{MAE}= \frac{1}{n}{\sum_{i=1}^{n}}{\left|{{\hat{y}}_{i}}\ -\ y_{i}\right|} \tag{12}\]  
In the equation, \({\hat{y}}_{i}\) represents the predicted value for the \(i\)th sample; \(y_{i}\) represents the actual value for the \(i\)th sample; and \(n\) represents the total number of samples.
## 4 Results and Discussion
### Experimental Setup and Evaluation Metrics
#### 4.1.2 Hyperparameter Settings  
To ensure generalizability and stability, the dataset was divided using a temporal split strategy to prevent future information leakage inherent in standard random cross-validation schemes. Specifically, data collected from January to September 2023 were used for training, while data from October to December 2023 were reserved for testing. This setup simulates real-world deployment scenarios in which pricing strategies must generalize to future unseen market conditions. In addition, since the four behavior data subsets (i.e., browsing behavior, inventory status, product pricing records, and sales feedback) span different temporal ranges, each subset was treated as an independent experimental task. For each task, training and testing splits were performed within the respective subset's available time range (e.g., browsing data from January-June were split into training on January-May and testing on June), thereby ensuring consistency in temporal ordering and eliminating the risk of information leakage over time. Several hyperparameters were configured to ensure effective training and convergence of the DRL model. The learning rate \(\alpha\) was set to 0.001, a value determined through empirical tuning to balance training speed and convergence stability. The discount factor \(\gamma\) was set to 0.99, reflecting a strong emphasis on long-term rewards and improving the stability and accuracy of pricing policies. The experience replay buffer size was configured to store 100,000 samples to ensure a rich and diverse history of interactions. A batch size of 64 was selected to balance computational efficiency and learning diversity across different states. The neural network architecture adopted in the deep Q-network (DQN) consisted of three fully connected layers, each containing 256 neurons. The ReLU activation function was applied to each layer to enhance the model's capacity for nonlinear representation. The target network was updated every 10,000 steps during training to stabilize learning.
## 3 Results and Discussion
### Photosynthetic Pigments  
Figure 4 shows the concentration of CHA (A), CHB (B), and TCA (C) from basil seedlings as a function of each PhAC at different doses and of MIX at same doses. The trend of CHA content was quite similar among the studied PhACs, and from 100 ppb to the highest dose, its content decreased in all treatments, although to a different extent, with CLZ, NPX, TCS, and MIX resulting in the largest reduction in CHA concentration. Finally, CBZ, RET, and MTP showed slightly higher content at the single dose of 25 ppb. Instead, all PhACs resulted in the highest CHB concentration when applied at a dose of 25 ppb, while its concentration decreased from 100 ppb with increasing concentration of the dose.  
Figure 4: _Cont._  
The worst results were recorded with the application of 600 ppb of MIX, CLZ, NPX, and TCS, which resulted in an almost zero concentration of both chlorophylls. In fact, the latter are pigments involved in photosynthesis and, therefore, responsible for plant growth, with CHA being more abundant because it is the principal pigment involved in photosynthesis, while CHB is an accessory pigment [45]. Changes in their concentration are indicative of the cumulative impact of environmental stress [46] and mainly attributed to metabolic disorders or ROS accumulation in chloroplasts [47]. Therefore, all contaminants tested, at doses higher than 50 ppb, induced a stress condition in basil seedlings as the reduction in chlorophyll content is a typical stress response [48]. Similarly, [PERSON] et al. [49] tested the effects of non-steroidal anti-inflammatory drugs (NSAIDs) on green leafy vegetables and found a reduction in chlorophylls, carotenoids, polyphenols, and flavonoids as a function of the dose applied. [PERSON] et al. [50] also confirmed the toxicity of NSAIDs in green alga with a reduction in the concentration of photosynthetic pigments.  
A significant reduction in the content of TCA was observed mainly with the application of the highest dose of contaminants, that is, 600 ppb and, only in some cases, 200 ppb. Therefore, carotenoids appeared to be less sensitive than chlorophylls to the addition of different contaminants in the basil growth medium. Carotenoids are secondary metabolites that help absorb light and protect elements against photodamage and ROS-induced oxidative stress [51, 52]. Therefore, the reduction in carotenoids occurred only in the case of severe stress [49] and, in general, their trend resembled those of chlorophylls, confirming the highest toxic effect of CLZ, NPX, TCS, and MIX on basil seedlings.
## 5 Results and Discussion  
To verify the effectiveness of the proposed thrust allocation method, comprehensive simulation studies were conducted in the MATLAB (R2021a) environment, focusing on two configurations: a 5-unit and a 30-unit MATC system. The parameter values used in the simulations are as follows:  
(1) The initial states: \(u_{1}=u_{2}=\cdots=u_{30}=2\) N; \(v_{1}=v_{2}=\cdots=v_{30}=1\) knot; \(v_{\rm p}=1\) knot; \(x_{12}=x_{23}=\cdots=x_{2930}=0.13\) m.  
(2) The target states: \(x_{12}=x_{23}=\cdots=x_{2930}=L_{b}=0.12\) m; \(v_{\rm o}=1.5\) knots.  
(3) Parameters of the MATC system: \(n=5\) or \(n=30\); \(m_{1}=m_{2}=\cdots=m_{30}=5\) kg; \(L_{a}=0.9\) m; \(w_{1}=2.3\) N, \(w_{2}=2.25\) N, \(w_{3}=2.15\) N, \(w_{4}=w_{5}=\cdots=w_{30}=2.1\) N; \(h_{12}=h_{23}=\cdots=h_{2930}=25\); \(T_{\rm max}=3.5\) N.  
(4) Parameters of the proposed method: \(T_{\rm s}=0.75\) s; Rolling optimization iterations \(k=15\); When using the GA, the sample size \(N_{1}=500\), the number of replacements \(N^{\prime}=15\), the crossover probability \(p_{i}=0.5\), the mutation probability \(p_{v}=0.02\), the distance deviation weighting factor \(\alpha=130\), the overall speed deviation weighting factor \(\beta=110\), and the penalty factor \(\gamma=10000\); When using the HHO algorithm, the population size \(N_{2}=20000\), the distance deviation weighting factor \(\alpha=190\), the overall speed deviation weighting factor \(\beta=30\), and the penalty factor \(\gamma=10000\); When using the neural network algorithm, the number of training iterations \(D=15000\), and the learning rate \(\sigma=0.01\).  
(5) Mathematical description of current disturbances: The disturbance force (\(f_{\rm c}\)) varies with time and can be expressed as:  
\[f_{\rm c}=1.35\text{sin}\left(\frac{2\pi}{T_{\rm s}}\cdot t\right) \tag{17}\]
# Hardware-in-the-Loop Test of a Prosthetic Foot
## 5 Summary and Conclusions  
This paper provided a proof-of-concept about whether HiL is generally suitable to represent the dynamic interaction of a human with a prosthesis. For that purpose, a HiL setup of a modeled amputee and a prosthetic foot was developed, which performed one stride. The amputee was modeled based on the Virtual Pivot Point (VPP) model. The modifications of the VPP model included (i) the insertion of a mass interface point at the ankle joint, (ii) the insertion of a damper along the leg axis to dampen oscillations of the mass interface point, and (iii) the implementation of a forward shifting center of pressure, which is the point where the ground reaction forces are applied. The results of the HiL test showed the center of mass motion and velocity, the ground reaction forces, and the hip torque of the amputee during one stride. All these quantities qualitatively emulated human gait characteristics well and revealed the potential of HiL to emulate the dynamic interaction between the human and the prosthesis. Differences from the VPP model can be explained by the modifications made to model the amputee and the limited performance of the used Stewart Platform. This work therefore forms the basis for further work and to develop HiL as a test method for prosthetic feet. There are still many research questions to be answered before HiL can be used as a test method. In the future, more complex gait models will be implemented. Additionally, different prosthetic feet will be mounted and the parameters of the human gait model altered to investigate how gait characteristics change. This analysis will help to understand the dynamic interplay between a human and prosthetic feet better.  
Conceptualization, C.I. and D.J.R.; methodology, C.I. and L.-M.B.; software, L.-M.B. and F.L.; validation and formal analysis, C.I., L.-M.B. and F.L.; investigation, C.I.; resources, D.J.R.; data curation, C.I.; writing--original draft preparation, C.I., L.-M.B., F.L. and D.J.R.; visualization, C.I., L.-M.B. and F.L.; supervision, project administration, and funding acquisition, D.J.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Part of this research was funded by the German Research Foundation (DFG), Project Number 450801414.  
**Acknowledgments:** We would like to express special thanks to the students involved in preliminary implementations for this work. [PERSON] and [PERSON] contributed with their ideas to model the amputee based on the VPP model. Part of this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 450801414.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Quantized Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Reference** & **Precision** & **Complexity** & **Response** & **Disadvantages** \\ \hline \multirow{8}{*}{KF} & [60] & Medium & Medium & Medium & 2-D targets \\ \cline{2-6}  & [70] & Medium & High & Quick & Increase the computation burden \\ \cline{2-6}  & [74] & High & Low & Quick & The linearity is limited to tracking target in long-distance \\ \cline{2-6}  & [75] & High & Medium & Quick & Sensitive to the accuracy of the estimation of the initial target. \\ \cline{2-6}  & [81] & High & Low & Quick & Extra energy consumption \\ \cline{2-6}  & [84] & Medium & Medium & Medium & Being suitable for deep underwater scene \\ \cline{2-6}  & [85] & Medium & Medium & Quick & \begin{tabular}{c} ignore the energy consumption of UWSNs \\ Have poor performance for maneuvering targets \\ \end{tabular} \\ \hline \multirow{4}{*}{PF} & [86] & Medium & Medium & \
\begin{tabular}{c} Poor performance for maneuvering target \\ \end{tabular} \\ \cline{2-6}  & [87] & High & Medium &
\begin{tabular}{c} The performance for experimental data is not as good as simulation \\ results \\ \end{tabular} \\ \cline{2-6}  & [90] & Medium & Medium & Medium & Only has simulation results \\ \cline{2-6}  & [91] & High & Medium & Quick & Choosing KF as the comparison is not proper \\ \hline Arithmetic & [70] & Medium & Low & Quick & The proper order of pre-processor is hard to decide \\ \cline{2-6}  & [71] & High & Medium & Quick & Being sensitive to the measurement error \\ \hline \multirow{8}{*}{Sensor} & [61] & Medium & Low & Quick & Waking up all senors with the probability of detecting the target \\ \cline{2-6}  & [63] & Medium & Medium & Quick & Only validated for non-maneuvering target \\ \cline{2-6}  & [66] & Medium & Medium & Medium & The sampling interval set is small \\ \cline{2-6}  & [84] & Medium & Low & Quick & The ratio of waking sensor is determined without theory support \\ \cline{2-6}  & [96] & High & High & Medium & The fusion center is ambiguous. \\ \cline{2-6}  & [98] & Medium & Low & Medium & Assuming the communication radius is adjustable \\ \hline Quantized & [67] & Medium & Low & Medium & The quantized threshold is fixed \\ \cline{2-6}  & [103] & High & Medium & Medium & The fusion center is ambiguous \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of tracking optimization methods.