esa-sceva/satcom-chunk-collection huggingface dataset:
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## Appendix A Architectures of 1D LeNet5, 1D AlexNet, and 1D VGG16 Models  
The architectures of 1D LeNet5, 1D AlexNet, and 1D VGG16 models used as feature extractors in Section 4.1 are presented in Tables 13-14. Note that, for the AlexNet and VGG16 models, we added a batch normalization layer between the 1D convolutional layer and its activation layer to normalize the output of the convolutional layer before passing it to the activation layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Year** & **Reference and Model Name** & **Accuracy** \\ \hline \multirow{6}{*}{PAMAP2} & 2018 & [19] CNN-IMU & 93.13 \\  & 2020 & [22] GlobalFusion & 90.86 \\  & 2021 & [26] Multi-Input CNN-GRU & 95.27 \\  & 2022 & [28] Multibranch CNN-BiLSTM & 94.29 \\  & 2023 & [23] Multi-ResAtt & 93.19 \\  & 2023 & Proposed MSE Feature Fusion & 99.17 \\ \hline \multirow{3}{*}{DaLiAc} & 2018 & [44] Iss2 Image & 96.40 \\  & 2021 & [45] DeepFusionHAR & 97.20 \\  & 2023 & Proposed MSE Feature Fusion & 98.32 \\ \hline \multirow{3}{*}{DSAD} & 2020 & [22] GlobalFusion & 94.28 \\  & 2021 & [45] DeepFusionHAR & 96.10 \\ \cline{1-1}  & 2023 & Proposed MSE Feature Fusion & 98.04 \\ \hline \hline \end{tabular}
\end{table}
Table 20: The accuracy scores (%) of related work who were evaluated on classifying the PAMAP2, DaLiAc, and DSAD datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Name**} & \multirow{3}{*}{**Layer**} & \multirow{3}{*}{**\# Filters**} & \multirow{3}{*}{**K Size**} & \multirow{3}{*}{**Stride**} & \multirow{3}{*}{**Pad**} & \multirow{3}{*}{**Activ**} & \multicolumn{6}{c}{**Size of Output**} \\ \cline{5-12}  & & & & & & & & & **W** & **C** & **W** & **C** & **W** & **C** \\ \hline In & Input & - & - & - & - & - & - & 300 & 12 & 600 & 6 & 125 & 9 \\ C1 & Conv1D + BatchNorm & 96 & 11 & 4 & valid & ReLU & 73 & 96 & 148 & 96 & 29 & 96 \\ S2 & MaxPooling1D & - & 3 & 2 & valid & - & 36 & 96 & 73 & 96 & 14 & 96 \\ C3 & Conv1D + BatchNorm & 256 & 5 & 1 & same & ReLU & 36 & 256 & 73 & 256 & 14 & 256 \\ S4 & MaxPooling1D & - & 3 & 2 & valid & - & 17 & 256 & 36 & 256 & 6 & 256 \\ C5 & Conv1D + BatchNorm & 384 & 3 & 1 & same & ReLU & 17 & 384 & 36 & 384 & 6 & 384 \\ C6 & Conv1D + BatchNorm & 384 & 3 & 1 & same & ReLU & 17 & 384 & 36 & 384 & 6 & 384 \\ C7 & Conv1D + BatchNorm & 256 & 3 & 1 & same & ReLU & 17 & 384 & 36 & 384 & 6 & 384 \\ S8 & MaxPooling1D & - & 3 & 2 & valid & - & 8 & 256 & 17 & 256 & 2 & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Architecture of 1D LeNet5. The column names # Filters, K Size, Pad, and Activ are short for number of filters, kernel size, padding, and activation, respectively.
## 4 Discussions Alongside Numerical Experiments
### LTN-Based Intrusions Detection in Network Traffic
Figure 4 shows it is possible to have an accuracy metric (number of correct predictions divided by total number of predictions), specific to deep learning but also have an interactive accuracy consisting of both the provided formulas and the queried constraints. Spikes in first two graphs also make sense, as an incorrect prediction leads to a drop in the truth values of the axioms. While these are related, the accuracy level is almost perfect while the satisfaction level can see some improvements. A higher satisfiability level means a better performance of the model. The hybrid network can also generalize better and use learned knowledge when querying constraints with impressive results. This way overfitting can be avoided by providing accurate logic constraints in axioms. On the other side, some tasks may need extensive logic defined and the large number of axioms affects the computational power, therefore the scalability of such a hybrid system could be an issue. **Deep Neural Network approach for KDD99. ** The same experiment presented before is conducted again, without the symbolic part. As such, any kind of reasoning over data is conducted only with the accuracy measurement, making deductive reasoning, interactive accuracy, and the ability to add constraints via real logic impossible. For the sake of consistency, in this experiment, the following aspects were kept the same as the ones in the LTN implementation. Data normalization, one-hot encoding of the categorical data, hidden layers number, and the number of neurons on each hidden layer have all not been altered. Notable differences between these two approaches are in the batching of the dataset, for LTN data was first stored in tensors and then batched; in the output layer, for the LTN we used a \(sigmoid\) activation function and for DNN we used a \(softmax\) one and lastly, the hidden layers in the neuro-symbolic case have \(elu\) as an activation function as opposed to \(relu\) in the other case. Figure 2.2 Comparison of accuracy, satisfiability and querying of constraints. As Figure 5 shows, the results of the intrusion detection classifier implemented with a Deep Neural Network are very good. With accuracy values reaching almost perfect values, 0.98863 for both train data and 0.9869 for validation data.
## 2 Materials and Methods
### Methods
#### 2.2.2 Proposed Crossvalidation Method  
Given the seasonality of the meteorological variable rainfall, with higher values in autumn-winter months and lower ones in the spring-summer months [9], it was decided to carry out the imputation process month by month. Data loss patterns observed in a particular month/year were applied in different years to the same calendar month.  
To compare the different imputation methods, experimental data sets were created using the following crossvalidation method (Figure 8):1. Select several stations in Galicia that have different characteristics, for example, a station located on the coast, another in the interior, another high in the mountains, etc. We call these stations target stations.
2. For each target station, select 9 neighbouring stations by proximity, with the aim that they surround the target station and have a sufficient number of data point. We will call these stations candidate stations.
3. Repeat the following steps for each month of all years for which records are available: 1. Read ten-minute rainfall data of the corresponding month for the target station. 2. If that month is complete for the target station, that month becomes part of the data set we can experiment with. If that month is incomplete for the target station, we must save its data loss pattern, that is, in which positions its NAs are located (value used to indicate missing data). 3. Having read the rainfall data for that month for all the years for which we have records, we end up with two sets of data: a set of full months and a set of months with loss patterns. 1. We apply one of the loss patterns to each month of the complete data set for the target station, thus generating a month with missing data, which we call an experimental month. 2. For that experimental month, we must choose, among candidate stations to be imputed, those that are going to be the predictors, which are computed based on the following criteria: * Complete ten-minute data for that month. * Crosscorrelation with the target station. 3. Perform a time shift of the series to improve correlation. 4. Carry out preprocessing at the experimental month, thus filling the NAs with 0s when 0s appear in all the predictor stations for that specific position. 5. Carry out data imputation with _missForest_, _MICE_, _Amelia II_, and _IDW_.
4. Calculate the precision and fit metrics between full data sets that have been generated and original full data sets from the target station.
5. Compare the results obtained.  
Figure 8: Crossvalidation method flow chart.
# Multiband and Lossless Compression of Hyperspectral Images
## 5 Conclusions and Future Works  
In this paper, we have investigated on the lossless compression of hyperspectral images by introducing a multiband three-dimensional predictive structure, we named as 3D-MBLP.  
Because of its configurability, it is possible to implement the algorithm on different typologies of sensors, by using appropriate configuration for each type of sensors. Moreover, the proposed approach can be also easily scaled for future generation sensors which will have better hardware capabilities. The experimental results we achieved are comparable and often outperform the other state of the art lossless compression techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods/Scenes (Yellowstone Uncalibrated)** & **Hawaii** & **Maine** & **Average** \\ \hline _3D-MBLP + PAQS_ & 2.76 & 2.84 & 2.80 \\ _(N = 16, \(B\) = 2)_ & 2.84 & 2.90 & 2.87 \\ _(N = 8, \(B\) = 2)_ & 2.90 & 2.98 & 2.94 \\ _3D-MBLP + PAQS_ & 2.80 & 2.90 & 2.85 \\ _(N = 16, \(B\) = 2)_ & 2.87 & 2.95 & 2.91 \\ _3D-MBLP + AC_ & 2.95 & 3.07 & 3.01 \\ _(N = 8, \(B\) = 1)_ & 2.64 & 2.72 & 2.68 \\ FL & 2.58 & 2.68 & 2.63 \\ LUT\# & 3.26 & 3.45 & 3.35 \\ LAIS-LUT\# & 3.05 & 3.19 & 3.12 \\ TSP-W1 & 2.61 & 2.71 & 2.66 \\ TSP-W2 & 2.62 & 2.74 & 2.68 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison with other lossless compression methods (12-bit uncalibrated images). The results are reported in bits-per-sample (BPS).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods/Scenes (Yellowstone Uncalibrated)** & **0** & **3** & **10** & **11** & **18** & **Average** \\ \hline _3D-MBLP + PAQS_ & 6.34 & 6.22 & 5.63 & 5.97 & 6.29 & 6.09 \\ _(N = 16, \(B\) = 2)_ & 6.40 & 6.28 & 5.71 & 6.04 & 6.35 & 6.16 \\ _3D-MBLP + PAQS_ & 6.60 & 6.50 & 5.86 & 6.17 & 6.58 & 6.34 \\ _(N = 8, \(B\) = 1)_ & 6.41 & 6.31 & 5.78 & 6.03 & 6.37 & 6.18 \\ _3D-MBLP + AC_ & 6.46 & 6.36 & 5.84 & 6.09 & 6.42 & 6.23 \\ _(N = 8, \(B\) = 2)_ & & & & & \\ _3D-MBLP + AC_ & 6.79 & 6.69 & 6.06 & 6.30 & 6.79 & 6.53 \\ _(N = 8, \(B\) = 1)_ & & & & & \\ FL & 6.23 & 6.10 & 5.65 & 5.86 & 6.32 & _6.03_ \\ FL\# & 6.20 & 6.07 & 5.60 & 5.81 & 6.26 & 5.99 \\ LUT\# & 7.14 & 6.91 & 6.26 & 6.69 & 7.20 & _6.84_ \\ LAIS-LUT\# & 6.78 & 6.60 & 6.00 & 6.30 & 6.82 & 6.50 \\ TSP-W1 & 6.23 & 6.09 & 5.59 & 5.83 & 6.28 & 6.01 \\ TSP-W2 & 6.27 & 6.13 & 5.64 & 5.88 & 6.32 & 6.05 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison with other lossless compression methods (16-bit uncalibrated images). The results are reported in bits-per-sample (BPS).  
In future works, we will include a pre-processing stage before the compression of the hyperspectral image, which substantially reorders the bands by considering their correlation. This will possibly improve the compression performance as in [28; 29].  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  
**Author Contributions:** All the authors worked together and contributed equally.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 TRPES Results  
Here we present a few representative results from our recent TRPES studies using 9 th harmonic pulses [11; 12; 13; 14; 15; 16; 17]. Figure 4 shows TRPES spectra of furan (C4H4O), where the time profile measured at each eBE has been normalized to unity. This helps to reveal the changes in spectral shape. It is evident that the spectral peak (see dashed curve, which is a guide for the eye) gradually shifts toward higher eBE by several electronvolts within the first 150 fs, as was discussed in the Introduction. We attributed the kink of the spectral shift at \(\tau\)\(\sim\)90 fs (eBE \(\approx\) 5 eV) to a CI mediating a nonadiabatic transition to the ground state. Also shown as the dotted line is the result we reported previously [9] using DUV probe pulses (\(lv_{\mathrm{probe}}\) = 4.7 eV). Notably, the kink at eBE \(\approx\) 5 eV was outside of the observation window using the DUV probe pulses, demonstrating the utility of the VUV probe pulses for TRPES studies.  
Figure 3: (**a**) Normal-incidence reflectivity of aluminum. (**b**) Spectrum of the 9 th harmonic after a couple of bounces by bare aluminum mirrors. (**c**) Spectral shape of the 9 th harmonic.  
Figure 5a shows a TRPES spectrum of nitromethane (CH\({}_{3}\)NO\({}_{2}\)) measured at a pump-probe delay of \(\tau\) = 1000 fs. In the displayed eBE region, a strong one-color ionization signal of nitromethane from the ground electronic state is observed. We evaluated the one-color signal from the TRPES spectra averaged for negative delays and already subtracted in Figure 5a. A negative peak at 11-12 eV represents a \"bleach\" signal, which is negative signal intensity frequently observed in transient absorption spectroscopy due to depletion of the ground state population, of nitromethane. Meanwhile, positive peaks at 9.8 and 10-11 eV are assigned to nascent CH\({}_{3}\) and NO\({}_{2}\) fragments, respectively, created as a result of nitromethane photodissociation. The observed TRPES spectrum was well reproduced [Figure 5b] by a linear combination of four static photoelectron spectra [nitromethane, CH\({}_{3}\), and NO\({}_{2}\) in the electronic ground (X) and excited (A) states]. In Figure 5c, individual contributions from the four species are separately shown. Our results suggest that the populations of NO\({}_{2}\)(X) and NO\({}_{2}\)(A) are comparable. We successfully identified the electronic state of the NO\({}_{2}\) product [16], which has been a central subject in experimental and theoretical studies on the photodissociation dynamics of nitromethane.
# Profiling of Physical Qualities of Highly Trained Portuguese Youth Soccer Players
###### Abstract  
Background: A comprehensive understanding of variations in physical attributes both between and within young players is crucial for accurately identifying talent based on physical performance. This study aimed to compare maximum strength, jump, and sprint test results among young soccer players from different age categories and playing positions. Secondarily, this study aimed to analyze the association between maximum strength, jump, and sprint performances. Methods: A total of 103 players were categorized as U23, U19, U17, and U16. The players were placed into these age categories based on their football abilities. All participants completed standardized warm-ups, and testing procedures followed protocols established in previous studies. Results: Significant differences were found between age categories regarding the Isometric Mid-Thigh Pull (\(p<0.001\); \(\eta^{2}_{\text{P}}\)= 0.33), Countermovement Jump (CMJ) (\(p<0.001\); \(\eta^{2}_{\text{P}}\)= 0.50), Squat Jump (SI) (\(p<0.001\); \(\eta^{2}_{\text{P}}\)= 0.29), and sprint (\(p<0.001\); \(\eta^{2}_{\text{P}}\)= 0.30) tests. No significant differences were detected in Broad Jump results between age categories. Moreover, no significant differences were observed in any physical capacities between playing positions. Furthermore, significant moderate-to-strong correlations (\(r\) = 0.30-0.86) were observed between all physical tests. Conclusions: Age categories can distinguish soccer players' performance in different physical tests while no differences were observed between playing positions. Similar magnitude correlations were observed across all tests with only the CMJ and SJ being strongly correlated. Coaches and strength and conditioning professionals should apply a variety of tests to assess different physical qualities since they have different patterns between age categories.  
s _Article_  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], R. Profiling of Physical Qualities of Highly Trained Portuguese Youth Soccer Players  
1 Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), University of Maia (UMaia), 4475-690 Maia, Portugal, [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (F.Y.N.); [EMAIL_ADDRESS] (A.R.S.)
## 1 Introduction
Such a model has been denominated _Algebraic Model_ since the output variable, namely the bearing restoring force, is computed by solving an algebraic equation in closed form. To allow designers and researchers to adopt the Algebraic Model and fully exploit its appealing features, we have recently developed a computationally efficient identification algorithm capable of calibrating the five model parameters from experimental or numerical tests [24]. In addition, such a model has been implemented in the Open System for Earthquake Engineering Simulation (OpenSees), that is a powerful object-oriented, open source software currently adopted by many researchers in the field of structural engineering [25]. In this work, we employ, for the first time, a novel OpenSees material object, called _HystereticPoly material model_, to perform some nonlinear time history analyses on a seismically base-isolated structure with elastomeric bearings. In particular, the numerical accuracy of the proposed hysteretic material model is verified by comparing the results of several nonlinear analyses with the ones obtained by modeling each elastomeric bearing by means of the OpenSees Bouc-Wen material model. The present paper is organized into three parts. The typical transverse hysteretic behavior displayed by elastomeric and sliding bearings, under an axial compressive load, is illustrated in the first part (Section 2). In the second part (Section 3), the formulation of the Algebraic Model is summarized and the related OpenSees hysteretic material object is illustrated. Finally, in the third part (Section 4), the accuracy of the HystereticPoly material model is validated by means of numerical simulations.
# MINC-NRL: An Information-Based Approach for Community Detection
## 2 Related Work
Such type of methods is often used for the detection of overlapping communities, but the local optimums as the final solutions usually have significant difference with the ground-truths. Aiming at improving the accuracy of the results and reducing the convergence time while searching optimal partitions, recent research is trying to add some pre-processing steps before community transforming to collect more structural information from the network. For example, the community detection method based on positive/negative connections [15] runs a random walking process in the network and performs statistical analysis on the random-walking sequence. Then, the relationships of the nodes are evaluated as positive/negative for further detection. With a similar idea, the EdMot algorithm [16] uses a motif-based hypergraph of the target network to enhance the edge, and applies other state-of-the-art algorithms to partition the network. The DEMON [17] methods builds an EgoMinusEgo network from the original network, by combining the ego network extraction and the graph-vertex difference operation. In addition, based on an ego network, SONIC-MAN [18] use moderator nodes to integrate the local structural information in distributed online social networks. Such pre-processing steps are verified to be effective in improving the community detection performance of existing approaches. In recent years, with the development of representation learning algorithms in areas such as natural language processing, researchers have adopted the technique on deep learning to community detection. The idea of these approaches, in general, is to compress the high-dimensional structural information of the network (e.g., an adjacency matrix) into a set of low-dimensional vectors. Such vectors are defined as a low-dimensional representation of the network, which brings two main advantages: (1) Distance and density of nodes can be easily defined based on their representations; (2) The compression of structural information greatly reduces the time and space costs on detecting communities. For example, the ComE algorithm constructs the \"Community Detection-Community Representation-Node Representation\" closed-loop framework [6] to optimize both the node embedding and community embedding. The model improves the accuracy of community detection on multiple real-world datasets, together with better results in node classification and graph visualization. Based on a two-level representation learning strategies, MemeRep [19] adopts a genetic framework to optimize the representation which preserves the topology structure of the network. It is shown that the algorithm is effective on community detection for that it can make full use of the modularity density to preserve communities. Different from improving the quality of representation learning, our research pays more attention on getting community partitions with high scalability and accuracy, and preserve the structural information of the network at the same time.
# Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone
###### Abstract  
In this paper, an adaptive fuzzy tracking control method is proposed to address the issues of dead-zone and unobservable states in a flexible robotic arm system. The control design process begins with the utilization of a fuzzy logic system to approximate the nonlinear functions present in the flexible robotic arm system. To estimate the unobservable states of the system, a state observer is then designed. To alleviate the computational complexity during controller design, the command filtering technique is introduced. Additionally, the Nussbaum function is incorporated to address the unknown control gain problem. The stability of the system can be verified through the design of a Lyapunov function. This study's simulation results demonstrate that the designed control system can closely track the specified reference signals. The closed-loop system effectively controls the flexible robotic arm, as verified through experimentation.  
Keywords:dead-zone; observer; adaptive fuzzy tracking control; flexible robotic arm +
Footnote †: journal: Applied
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 4 Material and Methods
### WiMAX IEEE 802.16e Performance Metrics Used
#### 4.1.3 Jitter  
Jitter is the delay variance as the transmission sends packets through the network [26]. Jitter mostly happens when the network experiences congestion, interference, some router path changes, etc. As the transmission sends packets, they are continuous and fragmented as they arrive at the receiver, mainly because of congestion.  
\[\text{Jitter J}=\text{D}_{i+1}-\text{D}_{i} \tag{3}\]  
where:  
D = forwarding delay  
\(i\) = packets received