esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical Assessment of Customized 3D-Printed Wrist Orthoses
## 2 Materials and Methods
### Immobility Test in Phase I  
An experiment was conducted to determine if the plaster cast and the customized wrist brace exhibited significant differences in immobilization capability. In addition, we introduced a design variation to the customized wrist brace by adding ventilation. Ventilation, or its lack, might be the key contributor to discomfort when wearing a wrist brace. A 3D-printed customized brace with ventilation can provide relief. A plaster cast, customized wrist brace, and vented customized brace were tested with 10 participants with neutral and arm sling postures. A 2 mm clearance for venting and without venting for the customized braces was used.  
The non-parametric Friedman test was used to assess the experimental results. As shown in Table 2, both medians of pressure data based on time and four postures showed a significant difference, \(p\) < 0.05. Further analysis, shown in Table 3, compared two braces in three combinations. A significant difference was observed between the plaster cast and the customized braces posture-wise, but there was no significant difference between the vented and unvented customized braces. This implies that the presence of vents had no effect on the pressure exerted by the customized braces under different postures. Although differences between the vented and unvented customized braces were observed timewise, both customized braces did not display hardening or deformation, as the plaster casts did. Therefore, only the vented customized wrist brace was used in the next experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Medians Pressure (gw/cm\({}^{2}\))**} \\ \hline  & \multicolumn{1}{c}{**Plaster Cast**} & \
\begin{tabular}{c} **Unvented** \\ **3D Brace** \\ \end{tabular} &
\begin{tabular}{c} **Vented 3D** \\ **Brace** \\ \end{tabular} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \hline  & 10 min & 56.46 & 36.24 & 34.41 & \\  & 30 min & 55.90 & 36.44 & 35.99 & \\ Time & 60 min & 53.90 & 34.55 & 32.65 & \\  & 90 min & 56.60 & 34.64 & 33.33 & \\ \hline  & Arm Sling & 43.87 & 30.14 & 30.00 & \\ Postures & Extension & 48.86 & 34.27 & 26.94 & \\  & Flexion & 76.95 & 43.76 & 46.72 & \\  & Neutral Position & 63.89 & 38.90 & 39.78 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Median pressure at different time points and postures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{1}{c}{**Friedman Test**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \hline  & \multicolumn{1}{c}{Plaster cast vs. unvented 3D brace} & 0.01627 \\ Time & Unvented 3D brace vs. vented 3D brace & 0.01627 \\  & Vented 3D brace vs. plaster cast & 0.00044 \\ \hline Postures & \multicolumn{1}{c}{Plaster cast vs. unvented 3D brace} & 0.031 \\  & Unvented 3D brace vs. vented 3D brace & \textgreater{}0.999 \\  & Vented 3D brace vs. plaster cast & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of two braces at different time points and postures.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 4 Proposed Resource Allocation Techniques
### User Pairing Approach Based on HNG Algorithm
The resulting marked circles are the optimal solutions for all marked users' pairs with maximized sum rate or energy efficiency. Figure 2 shows an example of the user pairing process based on the proposed HNG solution to maximize the system rate with six users available at the BS. The aforementioned steps are conducted to find the optimal user pairing, which is (UE\({}_{1}\), UE\({}_{6}\)), (UE\({}_{2}\), UE\({}_{9}\)) and (UE\({}_{3}\), UE\({}_{8}\)).
# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 4 Experiment Results and Evaluation
### Office Dataset  
In this experiment, we evaluate our method on the Office dataset [41]. This is the most popular benchmark dataset for object recognition in the domain adaptation field. This dataset has 4410 images across 31 classes of everyday objects in three domains: amazon (A), webcam (W), and dslr (D). The amazon contains product pictures with no background from the Amazon website, and images in webcam and dslr contains similar real-world objects with different resolution. Following previous domain adaptation work [26], we use ResNet-50 as the model structure for the classifier. Other sub-parts of the model are the same as those shown in Figure 2. We adopt the common \"fully-transductive\" training protocol [8; 9; 26], using all available labeled source examples and unlabeled target examples. Optimization is done on Pytorch using Adam for 10 epochs with the learning rate set as 0.0001 and the weight decay set as 0.0001. The \(\alpha\), \(\beta\) and \(\gamma\) in Equation (8) are 1.0, 1.0 and 0.1. We evaluate our method on all six domain adaptations and compare our method with other reported domain adaptation approaches (some of them only have experiment results on three domain adaptations). We also implement two other methods on Pytorch based on share-latent space assumption, which are CoGAN and UNIT. Note that in the original papers of these two methods, the classifier is gained by attaching a softmax layer to the last hidden layer of the discriminator. Whereas in our implementation of these methods, we train a stand-alone classifier with the same structure of our method for a fair comparison. The experiment results in Table 2 show that our method is a competitive method and achieve state-of-the-art results compared with previously-reported methods, except for D\(\rightarrow\)W. The method proposed in this paper aims to solve the problem of domain adaptation when the high-level features in the two domains are different and the shared high-level latent space cannot be established. As shown in Figure 7, the images of webcam (W) and dslr (D) are actually very similar, only having differences in the illumination and the image resolution. In other words, their high-level features are the same. Therefore, our method did not achieve better results than other methods in this particular task, but obtained better results in other more challenging tasks with obvious high-level feature differences, such as W\(\rightarrow\)A and D\(\rightarrow\)A.  
Figure 6: Confusion matrices of the classification accuracy on target domain in facial expression recognition experiment.
###### Abstract  
We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn--the two most important metrics--can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.  
solar-induced chlorophyll fluorescence; reflectance; phenology; fall phenological events 2018
# Performance Comparison of Machine Learning Disruption Predictors at JET
## 2 Materials and Methods
### Machine Learning Methods
#### 2.3.1 Multi-Layer Perceptron Neural Networks (MLP-NN)  
An MLP-NN consists of layers of units, where the units in a layer are connected with all the units in the next layer. Referring to the binary classification of disrupted or non-disrupted samples, an MLP can model the non-linear relationship among the input feature vector \(\mathbf{x}\) and the corresponding output \(y\). Usually, a three-layer MLP-NN is used, where the input-output relationship is described by the following algebraic system:  
\[\begin{array}{l}\text{Input Layer}\left\{\begin{array}{l}\mathbf{W}_{1}\mathbf{ x}+\mathbf{b}_{1}=\mathbf{g}\\ \mathbf{h}=f(\mathbf{g})\\ \mathbf{o}\mathbf{\cdot}\mathbf{W}_{2}\mathbf{h}+\mathbf{b}_{2})=y\end{array}\right.\end{array} \tag{1}\]  
where \(\mathbf{W}_{1}\), \(\mathbf{b}_{1}\) and \(\mathbf{W}_{2}\), \(\mathbf{b}_{2}\) are the weights matrices and the biases of the input and output layers, respectively, \(\mathbf{g}\) and \(\mathbf{h}\) are the input and output vectors of the hidden layer, and \(f(\cdot)\) and \(\sigma(\cdot)\) are the hidden and output layer activation functions, respectively. For the classification task, \(\sigma\) is generally a SoftMax function while \(f\) is a sigmoid function.  
During the training, the weights and biases are adjusted in order to minimize the prediction error, using a back-propagation algorithm [39]. In order to avoid overfitting, the training is stopped when the validation error begins to rise. Any numerical optimization algorithm could be used to optimize network performance, but the most applied use either the gradient of the performance or the Jacobian of the network errors with respect to the weights. Once the MLP-NN model is trained, the output related to a new discharge provides its disruptive likelihood.
## 6 Conclusions  
(1) The study indicates that the coal pillar beneath the close-distance goaf is subject to double compression due to the synchronous rotation of key blocks. It is proposed that during the side face's mining, the roof near the pillar be cut off in advance. This measure aims to block the action path through which the key block generated by the side--working--face mining exerts pressure on the pillar.  
(2) When the pillar width reaches 8 m and beyond, the stress assumes the configuration of \"double peak and one valley.\" The abutment pressure curve undergoes a transformation from a single-peak parabolic form to a double-peak saddle form, and an elastic area with robust supporting capacity comes into existence. Based on the comprehensive stress situation and resource saving analysis, the pillar is determined to be 8 m.  
(3) The roof-cutting's comparative simulation shows that after the implementation of roof-cutting, the roof-caving degree in the working face's end area increases significantly, the vertical stress's peak value decreases to 16.46 MPa, the maximum shear stress's peak value decreases to 5.93 MPa, and the \(J_{2}\) peak value decreases to 7.23 \(\times\) 10\({}^{13}\) Pa\({}^{2}\). Roof-cutting effectively cuts off the mining stress's transmission path in the lateral face, and the pressure on the pillar is significantly alleviated.  
(4) Two-way cumulative tension blasting was applied to cut the haulage roadway's roof, and 5.90 m of sandy mudstone was successfully cut off, which reduced the influence of mining stress. Meanwhile, the ventilation roadway's roof was close to the pillar side and the two sides were reinforced with anchor cables, which successfully guaranteed stability.  
(5) The roof-cutting and roadway-protection technology for the narrow pillar beneath the close-distance goaf can effectively achieve pressure-relief mining. The future research direction lies in the parameters' sensitivity analysis and the schemes' refinement, to meet the requirements of geological environments such as varying burial depths, coal-seam thicknesses, and interlayer spacings.  
Conceptualization, G.Z., C.L. and Y.W.; methodology, C.L., Y.W. and H.L; software, C.L.; formal analysis, Y.W.; investigation, G.Z., C.L. and H.Z.; data curation, H.Z.; writing--original draft preparation, G.Z. and Y.W.; writing--review and editing, G.Z. and Y.W.; supervision, H.L.; project administration, H.Z.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Ph.D. Foundation of Henan Polytechnic University (Grant No. 760207/050).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 HMADM Modeling of a High-Speed Press Structural Optimization Problem  
As shown in Figure 1, the structural optimization of a high-speed press aimed at achieving high punching precision, good operational reliability and low manufacturing cost involves benefit, cost, deviation and fixation attributes. Any of the attributes can be described as real numbers, fuzzy linguistic terms, interval numbers and TFNs due to the multi-source uncertainties that must be considered in the optimization process. Benefit attributes must be the largest possible, while cost attributes must be the smallest possible. A fixation attribute must be the closest possible to a given value. A deviation attribute must be the most different possible from a given value.  
The alternative and attribute sets for the high-speed press structural optimization with \(m\) alternatives to be evaluated from \(n\) attributes can be described as \(S=\{s_{1},s_{2},\ldots,s_{m}\}\) and \(A=\{a_{1},a_{2},\ldots,a_{n}\}\), respectively. The attribute set \(A\) can be divided into three subsets \(A_{1}=\left\{a_{1},a_{2},\ldots,a_{j_{1}}\right\}\), \(A_{2}=\left\{a_{j_{1}+1},a_{j_{1}+2},\ldots,a_{j_{2}}\right\}\), \(A_{3}=\left\{a_{j_{2}+1},a_{j_{2}+2},\ldots,a_{n}\right\}\); \(1\leq j_{1}\leq j_{2}\leq n\), the corresponding attribute values of which are expressed as fuzzy linguistic terms, interval numbers and real numbers, respectively. Supposing that \(N_{1}=\{1,2,\ldots,j_{1}\}\), \(N_{2}=\{j_{1}+1,j_{1}+2,\ldots,j_{2}\}\) and \(N_{3}=\{j_{2}+1,j_{2}+2,\ldots,n\}\) are the subscript sets of subsets \(A_{1},A_{2},A_{3}\), it holds that \(N_{1}\cup N_{2}\cup N_{3}=\{1,2,\ldots,n\}\). Let the rating of the \(i\)th alternative structural configuration \(s_{i}(i=1,2,\ldots,m)\) on the \(j\)th attribute \(a_{j}(j=1,2,\ldots,n)\) be denoted by \(f_{ij}\), the decision matrix for the HMADM model of the structural optimization problem can be obtained as  
Figure 1: Heterogeneous attributes defined in the structural optimization of high-speed press. TFNs: triangular fuzzy numbers.  
\[\mathbf{F}=\left[f_{ij}\right]_{m\times n}=\left[\begin{array}{cccc}f_{11}&f_{12}& \cdots&f_{1n}\\ f_{21}&f_{22}&\cdots&f_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ f_{m1}&f_{m2}&\cdots&f_{mn}\end{array}\right], \tag{1}\]  
where \(f_{ij}\) is a fuzzy linguistic term denoted by \(f_{ij}=y_{ij},y_{ij}\in\{\text{Poor, Fair, Good, Very good, Excellent}\}\) when \(j\in N_{1}\), which can be transformed into a TFN according to the membership functions in Figure 2; \(f_{ij}\) is an interval number that can be described as \(f_{ij}=\left[{f_{ij}}^{L},f_{ij}{}^{L}\right]\) when \(j\in N_{2}\) and \(f_{ij}\) is a real number when \(j\in N_{3}\).
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 4 Experiment and Results
### Discussion
The effectiveness of prediction models depends on their capacity to match the complexity of the task at hand and the amount of training data they are given. During training, will more complex models with higher capacities memorize the properties of the clustered traces and overfit, leading to poor performance on test data? Lastly, how much does the prediction depend on the network trace used? In the case of the fixed UE scenario, the results were similar, but in the case of mobile UE devices, there were differences in prediction accuracy. It would be interesting to find the contributory features influencing these results--whether it be a mobility pattern; particular attributes of the environment (e.g., antennas, location, landforms); or a particular UE model used for the measurements. Though we used the NRMSE (2), a comparison with other research results was almost impossible because other authors have used different measures for prediction accuracy, namely the MSE (Mean Squared Error), RMSE (Root MSE), or MAPE (Mean Absolute Percentage Error). Some of these metrics, like the MSE or RMSE, are not normalized, and the NRMSE is defined differently [11] and cannot be compared. It is worth noting that NRMSE values can vary greatly between different studies. For instance, in [7], the NRMSE ranged from approximately 0.1 to 5.5, depending on the dataset and LSTM ANN, while in [13], the NRMSE was consistently below 0.1 across all experiments. Comparing the results with our previous study [3], our approach obtained a slightly lower NRMSE than the universal model based on bi-directional LSTM. Bi-directional LSTM, which considers additional backward dependencies in data, is a more advanced version of an LSTM ANN. The obtained results fit the needs of DASH systems. The relatively long video sessions are susceptible to concept drift affecting network traffic. Environmental circumstances like UE number fluctuation within a cell or UE location change are likely to periodically impact the video trace characteristics. Replacing a single predictive model with several simpler models facilitates adaptation to the video player's streaming algorithm. Aside from DASH systems, the presented results have several potential applications involving short-term traffic prediction, such as network adaptive services; surveillance (e.g., traffic anomaly detection); and 5G network optimization. However, the proposed solution's implementation in production systems would present a trade-off between accuracy and the computational resources needed to train an ensemble of deep learning models.
## 3 Research Results and Discussion
### Measurement of the Coefficient of Friction  
Figure 6 presents a change in the coefficient of friction depending on the number of fatigue cycles. In the tribological kinematic pair under analysis, the change of the coefficient of friction is strongly influenced by the condition of the surface of the mating elements, which follows from the destruction of the top layer as a result of developing wear. The coefficient of friction measured was 0.764 before the wear tests. After the first fatigue cycle, the coefficient increased insignificantly to 0.773. That increase may be due to the intensive shearing of surface microirregularities on the mating surfaces during joint assembly and then during operation in rotational bending moment conditions. The movement of the torn-off microirregularities takes place at that time, and the microholes occurring on the shaft and sleeve surfaces are filled, and thus, the surfaces become \"smoother\". In this case, smoothing will create the surface area of the mating elements larger, and thus the value of the coefficient of friction will increase. After the next fatigue cycle, the decrease in the value of the coefficient of friction is noted. That phenomenon should be explained by the partial removal of the wear products, which had filled the microholes until that time, and by the creation of build-ups on the surface of the elements. Then the contact surface area of the mating elements decreases, and there is a smaller risk of the creation of adhesive bonds. Therefore, the value of the coefficient of friction diminishes. The smallest value of the coefficient of friction was noted after the last fatigue cycle. As a result of the repeated loading of the tribological kinematic pair with the rotational bending moment and of the next joint disassembly and assembly, the process of creating wear products generates  
Figure 5: Shaft sleeve removal force pattern after successive fatigue cycles.  
more build-ups on the surface or the increase of the volume of the existing build-ups becomes more intensive. Moreover, as a result of shaft bending during fatigue tests and of the occurrence of microvibrations, some wear products are removed to the outside of the contact surface area [30].
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
###### Abstract  
This paper presents the spectral gap-based topology control algorithm (SGTC) for wireless backhaul networks, a novel approach that employs the Laplacian Spectral Gap (LSG) to find expander-like graphs that optimize the topology of the network in terms of robustness, diameter, energy cost, and network entropy. The latter measures the network's ability to promote seamless traffic offloading from the Macro Base Stations to smaller cells by providing a high diversity of shortest paths connecting all the stations. Given the practical constraints imposed by cellular technologies, the proposed algorithm uses simulated annealing to search for feasible network topologies with a large LSG. Then, it computes the Pareto front of the set of feasible solutions found during the annealing process when considering robustness, diameter, and entropy as objective functions. The algorithm's result is the Pareto efficient solution that minimizes energy cost. A set of experimental results shows that by optimizing the LSG, the proposed algorithm simultaneously optimizes the set of desirable topological properties mentioned above. The results also revealed that generating networks with good spectral expansion is possible even under the restrictions imposed by current wireless technologies. This is a desirable feature because these networks have strong connectivity properties even if they do not have a large number of links.  
topology control; wireless backhaul networks; simulated annealing; Laplacian Spectral Gap; 5G and beyond; expander graphs +
Footnote †: journal:  
002020203