esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Heat Dissipation Performance Simulation and Analysis
### Simulation Parameter Settings  
#### 4.2.1 Material Selection  
The metallic components used in this study were the upper/middle/lower plates and the straight/curved heat pipes. Consideration of the null end of heat pipes and the physical characteristics of all materials are as shown in Table 1. The three types of materials utilized in this study include aluminum (6061-T4), red copper (C1100), and oxygen-free (C1020).
## 3 Hybrid Multi-Agent Reinforcement Learning (Hybrid MARL)
### Algorithm Workflow  
The overall Hybrid MARL algorithm workflow is presented in Figure 4, outlining the sequential process of data collection, spatio-temporal feature extraction, reinforcement learning updates, and execution.  
Figure 4: Algorithm workflow for Hybrid MARL.  
The algorithm follows these key steps:  
1. Traffic Data Processing: Data is collected and preprocessed into structured state embeddings.
2. Spatio-Temporal Feature Extraction: CNNs, LSTMs, and GATs are used to extract relevant spatial and temporal patterns.
3. Policy Learning: Each sub-region agent optimizes its traffic signal policy using MADDPG.
4. Global Coordination: The central agent aggregates information and provides coordination signals.
5. Execution Phase: Trained policies are deployed for real-time traffic signal control.
# Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
## 2 Literature Review
### Related Works
Another RTLS based on Bluetooth low-energy beacons showed that the error decreases for short beacon-smartphone distances; it was concluded that numerous beacons should be used in indoor environments in order to achieve a low positioning error [36]. Furthermore, an additional RTLS based on Bluetooth technology and the RSSI method with an accelerometer and a barometer was proposed, and the estimated position error was about 1.95 m, which is still not appropriate for industrial applications [37]. In the case of the Wi-Fi RTLSs, the total accuracy is still at a very low level. The main advantage of Wi-Fi technology is that it does not need extra hardware appliances as it can be based on existing Wi-Fi routers. Some researchers tried to implement a Wi-Fi-based RTLS for real-world construction and, based on their experiment, the positioning system's accuracy has an average error of 5 m [38]. Furthermore, a Wi-Fi RTLS based on a deep neural network called WiDeep had an average accuracy of 2.64 m and 1.21 m in large and small environments, respectively [39]. Due to the increased error levels, a RTLS based on dual-band Wi-Fi at 2.4 and the 5 Ghz bands was proposed; the researchers concluded that this method provides better results (a mean distance error of 1.7 m) compared to the single Wi-Fi band (a mean distance error of 3-5 m), but it needs multiple dual band access points [40]. Even in cases where the Wi-Fi technology is combined with other state-of-the-art technologies such as geo-magnetism, the errors in the measurements were still high [41]. Additionally, research that aims to increase Wi-Fi position error has been published by using CNNs, and the mean error was still over 3 m (3.5 m mean error and 5.1 m 75 th percentile) [42]. Another technique based on Wi-Fi technology is the fine-time measurement (FTM) round-trip time (RTT). This technique uses the IEEE 802.11 mc protocol incorporated in IEEE 802.11-2016, which allows computing devices to measure the distance to nearby Wi-Fi access points (APs) and determine their indoor location with a precision of 1-2 m [43]. In 2D ranging tests in a recent study, an average root mean square error (RMSE) of 1.1 m was accomplished [44]. In another study, a new method was proposed that reduces the positioning RMSE error to 0.79 m [45]. Another study demonstrated that the Wi-Fi FTM RTT technique can be utilized in outdoor areas surrounded by buildings with available access points (AP), providing more accurate smartphone localization than GNSS [46].
## 3 Results
### Segmentation Tasks
#### 3.1.5 Edge Detection  
Edge detection methods in OCTA images are used rarely as the main segmentation method (n = 1, 2.1%). Briefly, numerous edge detection methods exist, and are based on computing the image gradient, which highlights the sections of the image that present a transition from dark to light or from light to dark along a specific direction.  
The study that employed an edge detection method used the Canny method [74], which calculates the gradient using the derivative of a Gaussian filter. The Canny method exploits two thresholds to detect strong and weak edges, including weak edges in the output if they are connected to strong edges. Thanks to the use of these two thresholds, this method is robust to noise and is likely to detect true weak edges. The study using edge detection was found to be employed for determining the FAZ [75] in ocular applications, showing a Jaccard index equal to 0.82. Another study focusing on dermatological applications also employed an edge detection method, but as a preprocessing stage, that is, for determining the tissue surface in skin burn scars [76]. Hence, this type of segmentation method has not been found to segment vasculature, which can be explained by the vasculature complexity and difficulty of detecting connected edges at each angle of the image.
# Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition
## 3 Materials and Methods  
### Dynamics Modeling  
The schematic model is shown in Figure 3. The hoisting platform has four degrees of freedom, and the power device is composed of a Gantry driver and a Trolley driver. The driving force in each direction has no coupling effect. The state equation of hoisting equipment can be expressed as follows:  
\[\dot{\eta}=\mathrm{T}(\eta_{b})\mathrm{v} \tag{1}\]  
where \(\eta=[x,y,z,\varphi,0,0]^{\mathrm{T}}\in\mathbb{R}^{6}\) represents the state vector of the end-effector, which contains the displacement vector \(\eta_{a}=[x,y,z]^{\mathrm{T}}\) and the angle vector \(\eta_{b}=[\varphi,0,0]\). \(\mathrm{v}=[v_{x},v_{y},v_{z},\omega_{\varphi},0,0]^{\mathrm{T}}\in\mathbb{R} ^{6}\) represents the velocity vector. \(\mathrm{T}(\cdot)\) is the transformation matrix related to the body-fixed velocities and the global pose rates [25].  
In an ideal state, the dynamic equation of the hoisting mechanism is expressed as follows:  
\[M(\eta)\dot{\mathrm{v}}+C(\eta,\mathrm{v})\mathrm{v}+G(\eta)=Q \tag{2}\]  
\(M(\cdot)>0\) is the positive definite inertial matrix of the system. \(C(\cdot)\) is the corresponding Coriolis and centripetal matrices. \(G(\cdot)\) represents the gravitational vector and \(Q\) represents the control vector, respectively.  
However, in the actual hoisting positioning project, considering the nonlinear, uncertain and external interference factors in the model, the dynamic equation of hoisting equipment is described in the following form:  
\[M(\eta)\dot{\mathrm{v}}+C(\eta,\mathrm{v})\mathrm{v}+G(\eta)+\mathrm{D}( \mathrm{v})\mathrm{v}= Q+\tau \tag{3}\]  
\(D(\cdot)\) represents the damping matrix [21], and \(\tau\) is the external disturbance.  
Figure 3: Schematic model.
# Experimental Study of the Formation and Evolution of Gas Jets in Supersonic Combustion Chambers
## 2 Test and Observation Setup  
### Test System  
#### 2.1.1 Direct-Connect Tests  
The ground direct connection test system of the National University of Defense Technology, shown in Figure 2, was used to perform the experiments. The setup includes an air heater, a rectangular supersonic flow channel (isolation section, test chamber, expansion section), a jet generator, and a measurement and control system. The three-component combustion heater (alcohol/oxygen/air) produced an oxygen mass fraction in the gas of about 0.23. The gas was accelerated to Ma 2.6 by a Laval nozzle at the end of the heater, which was used to simulate a supersonic mainstream at the entrance of the isolation section. The jet generator was used to provide a sonic nitrogen jet, and the diameter (d) of the jet outlet was 3 mm. Two windows were opened on lateral walls of the test chamber for observation. Various parameters such as pressure (air heater and jet generator) and mass flow rate (air heater) were measured during the test. The pressures at the air heater and jet generator were measured by high-pressure sensors (range 0-6 MPa, error \(\pm 0.5\%\)). The mass flow rate of each component of the air heater was measured by turbine flowmeters with different ranges and an error of \(\pm\)0.2%. The whole test process was controlled by a dedicated measurement and control system, which also increased the operational safety.  
The test simulated a flight condition with Mach number of 6 and altitude of 25 km. Table 1 shows the simulated gas parameters of the incoming supersonic flow at the entrance of the isolation section.
# A \(3/2\)-Approximation Algorithm for the Graph Balancing Problem with Two Weights
## 2 Algorithm
### Proof of Approximation Algorithm  
Finally we prove our algorithm is indeed a \(3/2\)-approximation algorithm for GBP2W.  
Theorem 4.1: _There is a \(3/2\)-approximation algorithm for the graph balancing problem with two weights._  
Proof: Recall that in Step 1, algorithm GB2W scales the edge weights and vertex dedicated loads by \(T\), then sets \(T=1\). Step 3 of algorithm GB2W invokes the algorithm of [PERSON] _et al._[2]. This algorithm finds a fractional solution using linear programming, and then it performs a two-step rounding procedure. If the value of an optimum solution is \(OPT\leq 1\), the first step guarantees that the load of any vertex is at most 1; the second step orients at most one more edge towards each vertex. Hence, either an orientation with maximum load \(1+max\{r,s\}\leq 1+1/2=3/2\) is found, or \(OPT>1\) and an orientation is not produced. For Steps 4 and 5 of algorithm GB2W, Lemmas 1-4 ensure that either a solution of value at most \(3/2\) is found or \(OPT>1\). Therefore, if \(OPT\leq 1\), Step 6 of the algorithm returns an orientation \(\gamma\); otherwise it returns FAIL.  
The binary search then is guaranteed to find a value \(T\leq OPT\) and an orientation with load at most \(3T/2\). For a weighted multigraph \(G=(V,E,\mathbf{p},\mathbf{q})\), since \(OPT\leq|E|s\), the number of iterations of the binary search is at most \(O(\log|E|+\log s)\) and since algorithm GB2W has polynomial running time, the overall running time is also polynomial in the size of the input.
# Enhancing Wind Energy Harvesting Using Passive Turbulence Control Devices
## 2 Physical Mathematical Model
### Solution and Verification of the Model
The theoretical model was verified by experimental results as shown in Figure 3, where Figure 3a shows the effect of load resistance \(R\) on the critical wind speed \(U_{cr}\). It can be found that for the galloping energy harvester, \(U_{cr}\) firstly increases and then decreases with the increase of \(R\). Figure 3: Verification of the theoretical model: (**a**) the critical wind speed \(U_{cr}\) versus \(R\); (**b**) the output voltage versus \(R\); (**c**,**d**) are respectively theoretical and experimental time-domain output voltage range of the GEH-PTC at \(U=2.464\) m/s and \(R=1.1\) M\(\Omega\). Figure 2: Experimental setup: (**a**) the wind tunnel with the bluff body with attachments (PTC), which is made of (**b**) sanding belts with a very coarse surface, (**c**) the laser sensor, and (**d**) the data acquisition system. The model maximizes at the optimum load of 1.1 M\(\Omega\), \(U_{crmx}\) = 1.485 m/s. Figure 3b is the output voltage amplitude along with the wind speed in the open circuit condition. The voltage increases along with increases in the wind speed, which is a common characteristic of the galloping energy harvester. Figure 3c,d are respectively the time-domain output voltage range of the GEH-PTC with \(R\) = 1.1 M\(\Omega\) at \(U\) = 2.464 m/s. The stable theoretical and experimental voltage amplitudes are 13.2 V and 12.5 V, respectively. Figure 3 shows that both the critical wind speed and the output voltage are slightly smaller than the theoretical values when the wind speed is very small, but the divergence is small. In general, the results from the theoretical model are very close to the experimental results, proving its accuracy.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 2 System Structure
### DL-Based CSK-SS UWA Communication System Structure
In the training process, quantifying the difference between the expected probability distribution of the output result and the predicted probability distribution, iterate continuously on the weight parameters and bias parameters in the model to gradually reduce the difference. Through the softmax activation function that maps the eigenvector to the effective real space of \([0,1]\) to represent the probability of the category, the cross-entropy loss function is selected to calculate the loss value to quantify the difference. The cross-entropy loss function [31] is as  
\[L(p,q)=-\sum_{i}p(Y_{i}(n))\log(q(f(x_{i}(n)))), \tag{16}\]  
where \(p(Y_{i}(n))\) is the true probability distribution of the \(i\)-th sample and \(q(f(x_{i}(n)))\) is the predicted probability distribution of the \(i\)-th sample. In the online test stage of scheme A and scheme B, the unknown source data is modulated into the CSK-SS signal, and then the UWA channel sample space for the test is established. It should be noted that the above test channel samples are different from the training channel samples. In scheme A, the modulated CSK-SS test signal is directly received by the receiving end after testing the channel. In scheme B, it is received by the receiving end after preprocessing. The sufficiently trained neural network models will realize the direct demodulation of the received signal according to the mapping relationship between the waveform in the time domain and the information bits carried by each symbol.
## 4 Experiment and Results  
The development of an automatic emotion recognition system using a new methodology for 3D filtered bispectrum feature extraction and the ANN classifier has been presented in Section 3; to verify the performance of the proposed methodology, five experiments were carried out (Exp. #1-Exp. #5). In Exp. #1, to find the best feature extraction type based on bispectrum analysis, four feature extraction types were compared. In Exp. #2, two types of 3D pyramid filters were compared, namely the overlap and the non-overlapping filters. To reduce the computation cost, in Exp. #3, the number of EEG channels used in the system was reduced. In Exp. #4, to increase the recognition rate, the number of samples in the training data was increased to 90%. In the final experiment, Exp. #5, two types of ANN classifiers were compared, the BPNN and the PNN, in order to find the best classifier.  
The DEAP database [4] provides EEG signals from 32 people and 40 video stimuli, forming 1280 person-video samples. Originally, the DEAP database used four emotion classes, which are the HAHV (Class 1), the LAHV (Class 2), the LALV (Class 3) and the HALV (Class 4) emotions, by way of H = high, L = low, A = arousal and V = valence, with a balanced sample size (320 samples) resulting from 32 people and 10 video stimuli per class. As indicated in the data acquisition section, this research principally takes two binary classes of emotion in 2D arousal and valence planes: high arousal (Class 1 + Class 4)/low arousal (Class 2 + Class 3) and high valence (Class 1 + Class 2)/low valence (Class 3 + Class 4), with 640 samples for each class, accordingly.  
In the following experiments (apart from Exp. #4), 50% of the samples were used as the training data, while the remaining data were used as the testing data, producing 320 samples per class for each of the training and testing parts. All thirty-two people were represented in the training data, but the video stimuli were presented to only a half. For instance, Videos 1-5 (from Class 1) and Videos 31-35 (from Class 4) were chosen as high arousal training data, while Videos 6-10 (from Class 1) and Videos 36-40 (from Class 4) were chosen as testing data. Similarly, Videos 11-15 (from Class 2) and Videos 21-25 (from Class 3) were chosen as low arousal training data, and Videos 16-20 (from Class 2) and Videos 26-30 (from Class 3) were chosen as testing data.  
For Exp. #1-Exp. #4, BPNNs were used as the benchmark classifier, with the setting parameters of \(\alpha\) (learning rate) = 0.2 and \(\beta\) (momentum) = 0.3. The number of input neurons was equal to the dimension of the feature vectors, the number of hidden neurons is equal to half of the input neurons, and the output of the neurons equals one. In Exp. #5, the PNN was compared with the results of the benchmark classifier.
# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 6 Conclusions  
In this paper, a progressive dynamic registration (PDR) method is proposed for tile maps. With the PDR method, we can perform registration between any two tile maps of the same type without knowing the coordinate system conversion formula between the maps. Moreover, the PDR method has strong versatility; it can not only be used to register the tile maps but also the corresponding vector data. When there are many image feature elements, we use feature points for matching and calculation, and when there are few image feature elements, we use feature surfaces for matching and calculation. We combine the characteristics of the tile map and propose a matching filtering strategy, which improves the correct matching rate. The values of adjacent levels are used as references in the registration process to progressively optimize the results. The root mean square error of the PDR method is below 2.5 in different maps. Compared with registration using feature points only, the accuracy of the PDR method is significantly improved; compared with the method using coordinate system conversion, the registration effect of the PDR method isbetter in remote sensing maps and vector maps in nonurban areas, while it needs to be improved in vector maps of urban areas.  
Conceptualization, D.Z. and J.D.; data curation, D.Z.; formal analysis, J.D.; funding acquisition, J.D.; investigation, D.Z.; methodology, D.Z. and J.D.; project administration, D.Z.; software, D.Z.; supervision, J.D.; validation, D.Z.; visualization, D.Z.; writing--original draft, D.Z.; writing--review and editing, J.D. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (Grant Number: 42172330).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Hand Orthosis Development
### Design Requirements
#### 3.2.3 Patient-Specific  
Patient **comfort** is a key factor influencing adherence to orthosis use. The orthosis should be designed to match the patient's anatomical features and include a soft cushioning layer to prevent pressure sores and discomfort. Ensuring comfort can involve using padded straps, ergonomic shapes, and well-selected materials. **Ventilation** helps maintain skin health by preventing heat build-up and sweating, thereby reducing the risk of skin irritation and infection. This can be achieved through features that allow airflow, such as perforated materials or breathable fabrics. Additionally, the orthosis should be easy to clean with water, soap, or other commonly accessible detergents to maintain **hygiene**, especially for devices worn over long periods or shared among multiple patients. Materials that are resistant to bacteria and fungi growth and designs that allow easy disassembly for cleaning are important considerations.  
**Ease of use** is another important aspect. The orthosis should be user-friendly, accommodating different levels of independence and mobility. It should be easy to put on and take off with one hand, and allow for quick adjustments. One-hand donning can be achieved by utilizing Velcro bands with a loop-through fastening system or the Boa Fit system [33]. Moreover, the orthosis should be adjustable to meet the specific needs of each patient. This includes accommodating changes over time, such as swelling, recovery stages, or different activities. Features like adjustable straps, modular components, and flexible materials can enhance **customizability**.  
During the design process, **aesthetic considerations** must also be taken into account. The appearance of the orthosis can impact patient morale and willingness to use the device. An aesthetically pleasing design can reduce stigma and increase motivation [34]. This can be achieved through stylish design, customizable colors, and a modern, sleek appearance. Moreover, each patient has unique comfort levels and expectations regarding a device. It is essential to consider individual preferences and lifestyle factors, as what is acceptable for one patient may not be for another. Personalized fittings and involving patients in the design process can help in achieving a positive **patient-subjective opinion** about the orthosis.  
This overview emphasizes the most important aspects to consider when designing splints, noting that the requirements often intertwine and influence one another. Moreover, there are other considerations that, while less critical, may also be relevant. Although the focus is on hand orthoses, these points can be extended to the design of all types of orthoses. The list is universal and not tied to any specific technology, ensuring that the principles outlined can be applied across different manufacturing methods.
## 3 Signal Demodulation
### Architecture of Demodulator  
Figure 3 shows the demodulation flowchart of this paper. The input signal is first resampled and pre-processed via a band pass filter, then reshaped into the symbol matrix \(\mathbf{X}_{in}\). The rows of \(\mathbf{X}_{in}\) correspond to the received symbols \(\mathbf{u}_{m}^{\prime}\), and the columns to the sampling points. Thirdly, \(\mathbf{u}_{m}^{\prime}\) are associated to the units of a detector array, respectively. The units of detector array are essentially binary classifiers, which score the similarity degree between \(\mathbf{u}_{m}^{\prime}\) and the symbol candidate \(\mathbf{u}_{n}\). The output of the detector array is a score matrix denoted using \(\mathbf{Y}_{out}\), whose rows correspond to the symbol candidates and the columns to the received symbols. The element of the score matrix \(a_{nm}\) represents the similarity between \(\mathbf{u}_{n}\) and \(\mathbf{u}_{m}^{\prime}\). Finally, the transmitted message is recovered through a decision function and ASCII code mapping. This paper supposes that a higher score means better similarity; the decision function, therefore, returns the indices of the maxima of the columns of \(\mathbf{Y}_{out}\):  
\[M_{m}=max\{\mathbf{a}_{m}^{T}\} \tag{10}\]  
where \(\mathbf{a}_{m}^{T}=[a_{1m},a_{2m},\dots,a_{nm},\dots]\).