esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 4 LoRa Network Performance Evaluation
### LoRa Network ED Latency Analysis
Assuming there are \(n\) EDs in the LoRa network, and a total of \(m\) different data rates are used, with \(n_{i}\) EDs for each data rate, the average latency \(T_{avg}\) of the EDs in the entire LoRa network can be calculated using a weighted average, as shown in Equation (38):  
\[T_{avg}=\sum_{i=1}^{m}\frac{n_{i}}{n}\cdot T_{avg\_i} \tag{38}\]  
Figure 8 shows the relationship between the number of EDs and the average latency at different data rates. As the SF increases (resulting in a lower data rate), the time required for the ED to send a data packet increases, leading to a longer average latency. Additionally,  
Figure 7: Comparison of ED transmission power consumption at different data rates and transmission power levels. as the number of EDs using lower data rates increases, the probability of packet collisions rises, further increasing the average latency. Therefore, to optimize the average latency, it is necessary to reasonably control the number of EDs using the same data rate.
## 3 Results
### Analysis of Weighted Results
On average, the SVM + XGBoost classifier achieved an accuracy of 93.1%, representing a 1.5% improvement over the SVM classifier (\(p<0.05\)) and a 3.7% improvement over the XGBoost classifier (\(p<0.001\)). These results indicate that the ensemble classifier combining the SVM and XGBoost significantly enhances the overall classification performance. This improvement can be attributed to the complementary strengths of the two classifiers: the SVM is particularly effective for binary spatial features extracted by CSPs, while XGBoost performs better on multi-frequency features derived from CCA. To further validate this observation, the classifiers were also applied to the raw EEG signals, and the results are shown in Figure 10.  
Figure 10: Classification accuracy of the original signal under the combined classifier. Figure 9: Classification accuracy of combined features under combined classifiers. As shown in Figure 10, for most participants, the highest classification accuracy was achieved using the SVM classifier. Only participant S1 showed a 1.9% improvement in accuracy when using the ensemble classifier. On average, the SVM classifier outperformed both the ensemble and the XGBoost classifiers, with the SVM + XGBoost ensemble achieving a mean accuracy of 74.9%, which was 2% lower than that of the SVM classifier alone (\(p<0.05\)). These findings suggest that the ensemble classifier does not provide additional benefits when applied directly to raw EEG data and may, in fact, reduce model performance. Furthermore, a comparison between classification results using fused features and those using raw EEG data demonstrates a significant performance gain. Specifically, the accuracy improved by 23.7% under XGBoost (\(p<0.001\)), 14.8% under SVM (\(p<0.001\)), and 18.3% under the SVM + XGBoost ensemble classifier (\(p<0.001\)). These findings provide strong evidence that using fused FB(CCA + CSP) features as classifier inputs substantially improves classification accuracy compared to using raw EEG signals.
# A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation
## 2 System Overview  
### Schematic of the Implemented Training Platform  
The schematic diagram of the therapist-in-the-loop rehabilitation training system for elbow joint motor function recovery is shown in Figure 1. The platform is motivated by the current challenge regarding the need for tuning the strategy and intensity of robotic rehabilitation systems in a patient-specific manner. It also enables therapists to share their time with several patients. The platform is actually used together with the muscle strength tele-assessment platform, which can record the activation level of skeleton muscles with surface electromyogram (sEMG) at home and predict the muscle strength with a prediction function derived from a musculoskeletal model [27]. The hardware of the system consists of a haptic device (Phantom Premium 3.0, 3D Systems, Rock Hill, SC, USA) on the therapist's side, an exoskeleton device on the patient's side, and a specially designed visually shared training model. The visual training model is rendered with Open Graphics Library (OpenGL, Khronos Group, Beaverton, OR, USA). On the therapist's side, the motion of the therapist is measured and mapped to the corresponding object in the training model. Previously, we designed a coordinative motion-based training system for bilateral rehabilitation with an exoskeleton and haptic devices [28]. Different from that, in this study, the system is designed for patient-specific training, and the motion and force between therapist and patient are transmitted with a visually shared model. The therapist cooperates with the patient to achieve a goal-oriented task. There are also two phases: the online training phase and the regeneration phase. In the online training phase, the training intensity and strategy can be recorded when a therapist delivers the training. During the regeneration phase, the academic therapeutic experience can be regenerated. Therapy strategies may be adjusted between assistance and resistance according to patients' experience and adjustment. The therapists' motion will be regenerated with the exoskeleton device. The intensity is correlated with the selection of control parameters (such as the stiffness of the practical guidance) considered for delivering therapy.
## 2 Methodology
### Coordinate Attention  
The core logic of the attention mechanism is to focus on the key issues, meanwhile some less important information would be overlooked. [PERSON] [20] et al. analyzed previous excellent attention modules such as SE (squeeze-and-excitation attention) and CBAM (convolutional block attention module), and came to the conclusion that their spatial location information is lost in the process of modeling channel relations. However, other attention modules without this problem are also effective, but the number of parameters is too large to be applied to the network of mobile devices; thus, this coordinate attention mechanism is also proposed.  
For coordinate attention mechanism, in order to obtain the attention on the width and height of the image and encode the precise location information, the input feature map is firstly divided into width and height directions for global average pooling, respectively, to obtain the feature maps in the width and height directions, respectively.  
\[z_{c}^{h}(h)=\frac{1}{w}\sum_{0\leq i\leq w}x_{c}(h,i) \tag{1}\]  
\[z_{c}^{h}(w)=\frac{1}{H}\sum_{0\leq i\leq H}x_{c}(j,w) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **FPS** & **FLOPs** & **Params** \\ \hline Conv2D & 491.43 & 38.789G & 295.68K \\ GSConv2D & 473.33 & 19.881G & 151.42K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Convolutional model data comparison.  
Based on the two features generated above, the two feature maps are further subjected to a combining operation, followed by a transform operation using a \(1\times 1\) convolution, as follows:  
\[f=\delta\Big{(}F_{1}\Big{[}z^{h},z^{w}\Big{]}\Big{)} \tag{3}\]  
where \(F_{1}\) is the \(1\times 1\) convolutional transform function, square brackets denote the combining operation along the spatial dimension, and \(\delta\) is the nonlinear activation function \(h\)-Swish. The intermediate feature mapping \(f\) is decomposed into two separate tensors \(f^{h}\in R^{C/r\times H}\) and \(f^{w}\in R^{C/r\times W}\), and \(r\) is the module size reduction rate. Transforming \(f^{h}\) and \(f^{w}\) into tensors with the same number of channels and undergoing sigmoid activation, the resulting \(g^{h}\) and \(g^{w}\), respectively, are the following:  
\[g^{h}=\sigma\Big{(}F_{h}\Big{(}f^{h}\Big{)}\Big{)} \tag{4}\]  
\[g^{w}=\sigma(F_{w}(f^{w})) \tag{5}\]  
Finally, the output of the attention module is obtained as follows:  
\[y_{c}(i,j)=x_{c}(i,j)\times g_{c}^{h}(i)\times g_{c}^{w}(i) \tag{6}\]  
In this research, the coordinate attention mechanism was added to the last layer of the neck part to strengthen the feature extraction ability and improve the network accuracy.
## 2 Normal Cloud Model Theory  
### Entropy Weight Method  
In the application of the normal cloud model, the different evaluation indexes have contributions to the evaluation object. The weight, expressed in a quantitative value that reflects the relative importance of each evaluation index in the set of factors, directly affects the resulting comprehensive evaluation. As such, scientific and reasonable assessment of the evaluation index weights is very important for building an objective evaluation model.  
The entropy weight method can be used to weigh multiple evaluation indicators and does not depend on the subjective judgment or experience of decision-makers, which is more objective and accurate for weight calculation. Moreover, the calculation process of this method is simple, and it easily solves the weighting problem in multi-index target evaluation scenarios. The entropy weight method assigns weights according to degree of difference of each evaluation index. If there is a large difference in a specific index, it means that the index contains a lot of information and has a great influence on the comprehensive evaluation results. Thus, it is given a larger weight value. There are three main steps of the entropy weight method, as follows:  
(1) Dimensionless processing of the data. According to the property of each index, the profitability index and the cost index can be divided as:  
\[t_{ij}=\left\{\begin{array}{l}\frac{x_{ij}}{\max_{ij}}\ (i=1,\ 2,\ldots,n)\ \text{the profitability index}\\ \frac{\min_{ij}}{x_{ij}}\ (i=1,\ 2,\ldots,n)\ \text{the}\ \cos\text{t}\ \text{index}\end{array}\right. \tag{1}\]  
where \(j=1,2,\ldots,m\); \(n\) is the number of data, and \(m\) is the number of evaluation indicators. The larger the profitability index and the smaller the cost index, the better they are. Then, the data can be normalized as:  
\[t_{ij}{}^{\prime}=\frac{t_{ij}}{\sum\limits_{j}\sum\limits_{i}t_{ij}} \tag{2}\]  
(2) Calculate the information entropy of each indicator. The information entropy of the \(j\)-th evaluation index is expressed as:  
\[H_{j}=-l\sum\limits_{i=1}^{n}u_{ij}\ln u_{ij},\ (j=1,\ 2,\ \ldots,\ m;\ i=1,\ 2,\ \ldots,\ n) \tag{3}\]  
where \(u_{ij}=\frac{t_{ij}{}^{\prime}}{\sum\limits_{i=1}^{n}t_{ij}{}^{\prime}}\), \(l=\frac{1}{\ln n}\), and \(u_{ij}=0\), \(u_{ij}\ln u_{ij}=0\).  
(3) Calculate the weight. The weight of each evaluation index is calculated as:  
\[w_{j}=\frac{1-H_{j}}{m-\sum\limits_{j=1}^{m}H_{j}} \tag{4}\]
# Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool
## 2 Materials and Methods
### KTP Selection Method
The updated equations for the three gates are as follows:  
\[i_{t}=sigmoid\left(x_{t}U^{i}+h_{t-1}W^{i}\right)+b^{i} \tag{5}\]  
\[f_{t}=sigmoid\left(x_{t}U^{f}+h_{t-1}W^{f}\right)+b^{f} \tag{6}\]  
\[o_{t}=sigmoid(x_{t}U^{o}+h_{t-1}W^{o})+b^{o} \tag{7}\]  
\[\widetilde{c}_{t}=\tanh(\ x_{t}U^{g}+h_{t-1}W^{g})+b^{g} \tag{8}\]  
\[c_{t}=sigmoid(\ f_{t}\times c_{t}+i_{t}\times\widetilde{c}_{t}) \tag{9}\]  
\[h_{t}=\tanh\ (c_{t})\times o_{t} \tag{10}\]  
where \(x_{t}\) is the input value of the \(t^{\text{th}}\) datum. Variables \(i_{t}\), \(U^{i}\), \(W^{i}\), and \(b^{i}\) denote the output, input weight, previous output weight, and bias of the input gate, respectively. Variables \(f_{t}\), \(U^{f}\), \(W^{\theta}\), and \(b^{\prime}\) are the output, input weight, previous output weight, and bias of the forget gate, respectively. Variables \(o_{t}\), \(U^{g}\), \(W^{o}\), and \(b^{\prime}\) represent the output, input weight, previous output weight, and bias of the output gate, respectively. Variable \(\widetilde{c}_{t}\) is the current neural output, while \(U^{g}\) is the neural input weight, \(W^{g}\) is the previous output weight of the neuron, \(b^{g}\) is the bias of the neuron, \(c_{t}\) is the memory cell output, and \(h_{t}\) is the LSTM unit output. With the three-gate mechanism, LSTM architecture can store old data on a memory cell for deep learning. This technique has advanced in fields such as text generation, machine translation, speech recognition, and image description generation. Because the data of temperature increase and spindle displacement resemble time series data, the LSTM scheme is an ideal option for modeling. In this study, LSTM modeling was adopted to create a z-direction spindle thermal displacement model to accurately estimate the spindle displacement with various rotation speeds. The temperature increase \(\Delta T\) of the KTP combination and the spindle rotation speed \(S\) were the inputs, and the z-direction displacement of the spindle was the output. Based on input data complexity, the control parameters, including several hidden-layer neurons, several hidden layers, neuron bias, and nonlinear transfer functions, were designed for LSTM training. The LSTM estimation results were later compared to those of an MLR and BPNN.
## 3 Measurement and Result Analysis
### Parametric Analysis for the Radiating Patch  
The split gap is one of the essential criteria to evaluate the effectiveness of any metamaterial-based structure. The frequency of any structure depends on inductance and capacitance values. The inductance value depends on the length, width and thickness of the metal, and the capacitance value depends on a split gap (g) in the design. Depending on the split gap, the capacitance values are changed. It should be noted that the split can be represented as a capacitance. As the split gap of any SRR grows, the frequency also increases due to a decrease in capacitance value. Parametric analysis for the split gap of the proposed circular type SRR with a lower limit of 0.4 mm and an upper limit of 0.8 mm with 0.2 mm step width is shown in Figures 11 and 12.  
Figure 11: (a) S\({}_{11}\) and (b) S\({}_{12}\) parameters plots for the variation of split gap (g) of the proposed unit cell.  
Figure 10: Unit cell’s (a) S\({}_{11}\) and (b) S\({}_{12}\) simulated plots for both CST and ADS tools.  
When the split gap of the proposed unit cell is varied for the split gaps 0.4, 0.6 and 0.8 mm, the corresponding return loss plot resonates at two different frequencies. For a 0.4 mm gap, the S\({}_{11}\) resonates at 9.77 GHz with a magnitude of \(-31.97\) dB. For  
Figure 12: (a) E-field distribution, (b) H-field distribution and (c) surface current distributions of proposed structure at 9.84 GHz and 11.46 GHz frequencies.  
0.6 mm, the S\({}_{11}\) resonates at 9.95 GHz with a magnitude of \(-\)32.12 dB, and for the 0.8 mm gap, the S\({}_{11}\) resonates at 10.02 GHz with a magnitude of \(-\)32.11 dB. Similarly, the second S\({}_{11}\) frequency for 0.4, 0.6 and 0.8 mm split gaps resonates at 11.48 GHz, 11.63 GHz and 11.71 GHz with \(-\)22.42 dB, \(-\)22.74 dB and \(-\)22.81 dB, correspondingly. The unit cell transmission coefficient (S\({}_{12}\)) possesses three different frequencies. For a 0.4 mm split gap, the S\({}_{12}\) resonates at 8 GHz, 8.44 GHz and 11.10 GHz frequencies with amplitudes of \(-\)33.20 dB,\(-\)37.25 dB and \(-\)26.31 dB, correspondingly. For a 0.6 mm gap, the S\({}_{12}\) resonates at 8.23 GHz, 8.64 GHz and 11.25 GHz frequencies with S\({}_{12}\) amplitudes of \(-\)35.44 dB,\(-\)35.86 dB and \(-\)26.25 dB, correspondingly. For a 0.8 mm gap, S\({}_{12}\) resonates at 8.29 GHz, 8.72 GHz and 11.31 GHz frequencies with S\({}_{12}\) amplitudes of \(-\)34.99 dB, \(-\)36.50 dB and \(-\)26.64 dB, correspondingly. According to X-band frequency applications, a final split gap of 0.2 mm was selected for the proposed unit cell.
## 6 Experiments and Results
### Performance Evaluation of the Heuristic Scheduling Method  
To further validate the effectiveness of the proposed heuristic scheduling method in solving the DDFJSP-VPT, we compare it with two other approaches: the rules-based reactive scheduling method and the hybrid-shift strategy, which comprises left-shift and right-shift strategies. The reactive scheduling method is implemented by applying different rules to factory assignment, machine selection, and operation sequencing; in other words, the combination of three different types of rules is adopted at every scheduling decision point. In this study, we use two factory assignment rules, three machine selection rules, and four operation priority rules, making a total of 24 combinations. Details of the rules are as follows:  
Factory Assignment Rule: The first one selects the factory with the fewest unfinished operations for processing. The second selects a factory at random. We refer to them as FAR1 and FAR2, respectively.  
Machine Selection Rules: The first selects the machine with the shortest processing time from the available machines. The second selects the machine with the minimum load from the available machines, while the third randomly selects the machine. For convenience, we call them MAR1, MAR2, and MAR3, respectively.  
Operation Priority Rules: We employ four prevalent operation priority rules: the shortest processing time (SPT), earliest delivery date (EDD), lowest completion rate (LCR), and random allocation.  
The performance of each method is evaluated based on three objectives: the completion time of all jobs, total tardiness, and total factory load, over the entire dynamic scheduling process. Using MK07 as the test instance, each method is independently executed 20 times, with the average value of each objective computed. The results are presented in Table 7.  
The results shown in Table 7 clearly demonstrate that our proposed dynamic scheduling method significantly outperforms the other dynamic scheduling methods in terms of reducing the makespan and minimizing tardiness. Additionally, with regard to the total factory load, the combination of the MAR1 rule highlights its superior performance compared to other methods and combination rules. This phenomenon is primarily attributed to the MAR1 rule's preference for selecting machines with the shortest processing times in terms of task allocation, which effectively reduces the overall factory load. However, this approach may result in an excessive concentration of operations on a limited number of machines, leading to longer waiting times for some machines while leaving others idle. This imbalance may negatively affect the other two objectives. Overall, the proposed scheduling method offers a more efficient solution to dynamic scheduling problems, optimizing both resource utilization and operational performance.  
Figure 10: Average performance scores of four algorithms across all rescheduling points.
## 5 Evaluation
### Warm starts
#### 5.4.2 Measurement Analysis  
Warm starts are less likely to have high latency issues when compared to cold starts, since they do not have to deal with cold booting operations as often. Therefore, the percentage of best records to be taken into consideration when calculating the average was increased from 75% to 85%. Because we are triggering the same function multiple times, cold boots can still occur due to auto-scaling, which causes a new instance to be created. The warm starts test results can be found in Figure 15.  
The test results show minor differences in execution times, despite the greater abstraction provided by cloud-agnostic libraries, which also simplifies code complexity. Even though there is no guarantee that the state of serverless functions is preserved between consecutive invocations, the execution environment can be often recycled during warm starts. QuickFaaS libraries take advantage of this characteristic by caching certain objects that may be expensive to recreate on each function invocation [72]. For instance, whenever a cold start happens, the JSON text of the configurations file is cached in a global variable, avoiding the need to repeat the file I/O operation in subsequent warm starts. By adopting  
Figure 14: Cold start memory usage.  
this strategy, we are able to close the gap in execution times between the two function definitions, which was much higher in cold starts.  
It is also worth mentioning the difference in execution times between cloud providers. GCP functions took approximately the double the amount of time of MsAzure functions to finish execution. One of the factors that may have contributed to this time difference is the amount of memory allocated to MsAzure containers, which is much higher than in GCP. Low memory capacity can increase the function's execution time.  
As shown in Figure 16, there was also a considerable difference in memory usage. No records were excluded this time when determining the average memory consumption.  
The measurements reveal that cloud-agnostic functions made a better use of the available memory when compared to the cloud-non-agnostic ones. We were unable to find a reasonable answer to justify these differences in memory consumption. Despite the difference, both functions from GCP reached a maximum of 145 MB in memory consumption during testing. The same analysis cannot be made for azure functions, given that _Function execution units_ are presented as a time-based aggregation and not individually for a particular execution. Despite the noticeable increase in memory usage when compared to cold starts, there were no signs of memory leaks. This increase is a natural consequence of warm starting the same container instance several times.  
Figure 16: Warm start memory usage.  
Figure 15: Warm start execution time.
## 2 Materials and Methods
### Methods
#### 2.3.1. Impervious Surface Coverage (ISC) Construction Method  
To construct an Impervious Surface Coverage (ISC) dataset with temporal continuity and sub-pixel expression capability, this study selected the 30 m resolution annual binary impervious surface products (1985-2020) provided by GAIA (Global Artificial Impervious Area), where pixel values of 1 represent impervious surfaces and 0 represents non-impervious surfaces. The GAIA dataset demonstrates high accuracy with overall accuracies ranging from 89% to 97% across validation years, with a mean overall accuracy higher than 90% globally and 89% specifically in arid regions using improved algorithms [10]. Considering the low-density development characteristics in urban expansion processes and macro-scale analysis requirements, spatial aggregation methods were employed to convert these into 1 km resolution proportional impervious data. The 1 km resolution was specifically chosen to maintain spatial consistency with other variables in our EVI assessment framework (MODIS-derived FVC and LST data, population density data), while providing an optimal balance between capturing regional urbanization patterns and computational efficiency for the 35-year time series analysis across the 216,000 km\({}^{2}\) study area. Using 33 \(\times\) 33 pixels (corresponding to approximately 1 km \(\times\) 1 km) as aggregation units, the number of impervious pixels with values of 1 within each unit was counted and divided by the total number of pixels to calculate the impervious surface proportion within that 1 km pixel. While this aggregation process inevitably smooths fine-scale heterogeneity and may underestimate urban fragmentation in areas with highly dispersed settlement patterns, it preserves the essential spatial gradients needed for regional-scale urbanization analysis and policy-relevant applications. The high accuracy of the source GAIA data (>90% mean overall accuracy) ensures the reliability of our spatially aggregated ISC dataset. All annual data were processed in the same manner, ultimately generating continuous ISC proportion data sequences from 1985 to 2020. This ISC dataset provides high-resolution, long-term temporal spatial support for subsequent urbanization intensity identification,  
Figure 2: Research methodology flowchart.  
spatial pattern evolution analysis, and ecological vulnerability assessment, with good regional applicability and explanatory power.