esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Metrics and Environmental Conditions for 5G MIMO OTA
## 3 Experimental Results and Analysis
### Variation Analysis per 12 Azimuth Positions between TRMS in FS DML and MARSS  
For smartphone applications, it is expected that the radiation performance variance across all angles of arrivals (AoAs) is close to zero. Traditionally, performance metrics, i.e., TRMS and MARSS, are the average values of 12 AoAs, which cannot reflect the variation between different AoAs. In this section, two new FoMs, i.e., Peak-Null and Variance, are introduced to analyze the variations in the 12 azimuth positions between TRMS in FS DML and MARSS. Peak-Null is determined by evaluating the total peak-to-peak variations between the 12 azimuth positions, which emphasizes the delta in the AoAs of signal. Variance stands for the variance of the measured values at each azimuth position.  
It can be seen from Figures 4 and 5 that the Peak-Null results and the Variance results show similar trends between different thresholds under each combination of channel models and environmental conditions. However, the Peak-Null results are at least 1.5 dB higher than the Variance results for the same DUT, which indicates that Peak-Null can better reflect the difference between AoAs. In addition, the maximum variations for both Peak-Null and Variance between DUTs are also analyzed. It can be seen from Figure 4 that the maximum variations are 2.6 dB and 1.6 dB between DUTs for Peak-Null and Variance respectively. A similar delta of around 1 dB can also be achieved in other plots, which indicates that Peak-Null can better reflect the difference between the 12 azimuth positions than Variance.  
Figure 4: A comparison between Variance and Peak-Null for (**a**) the UMi and (**b**) the UMa channel models for TRMS under 3 GPP UE noise-limited environmental condition.
## 2 Establishment of the Model
### Mathematical Model
#### 2.2.2 Initial and Boundary Conditions  
Melting Process  
Initial conditions:  
\[t=0:\ \ \ T(r,0)=T_{\rm m},\ f_{1}=0\]  
Boundary condition:  
\[\begin{array}{l}r=R_{\rm o}:\ \ T(R_{\rm o},t)=T_{\rm a}\\ r=R_{\rm i}:\ T(R_{\rm i},t)=T_{\rm a}\end{array} \tag{13}\]  
In the formula, \(R_{\rm i}\) is the inner radius of the cylinder, \(R_{\rm o}\) is the outer radius of the cylinder, and \(T_{\rm a}\) is the seawater surface temperature.  
Solidification Process  
Initial conditions:  
\[\begin{array}{l}t=0:\ \ \ T(r,0)=T_{\rm a},\ f_{1}=1\end{array}\]  
Boundary condition:  
\[\begin{array}{l}r=R_{\rm i}:\ -k\frac{\partial T(R_{\rm i},t)}{\partial r}=h_{ \rm conv}[T(R_{\rm i},t)-T_{\rm f}]\\ r=R_{\rm o}:\ -k\frac{\partial T(R_{\rm o},t)}{\partial r}=h_{\rm conv}[T(R_{\rm o },t)-T_{\rm f}]\end{array} \tag{14}\]  
In the formula, \(T_{\rm f}\) is the external seawater temperature; and \(h_{\rm conv}\) is the surface average heat transfer coefficient (convective heat transfer coefficient) can be calculated according to the Zhukauskas correlation, \(h_{\rm conv}=\frac{N_{\rm f}+k_{\rm f}}{k_{\rm f}}\), where \(k_{\rm f}\) is the thermal conductivity of seawater, \(L\) is the characteristic size (the height of the cylindrical phase change heat exchanger is taken here), and \(Nu\) is the Nusselt number. The Nusselt number can be calculated according to the laminar flow type of the plate [31]: \(Nu=0.664 Re^{\frac{1}{2}}\cdot Pr^{\frac{1}{3}}\), where \(Re\) is the Reynolds number, and \(Pr\) is the Prandtl number. In order to facilitate the effective underwater measurement of the platform, the underwater motion speed of the platform is \(u=0.1\) m \(\cdot\) s\({}^{-1}\). The physical parameters of seawater used in the calculation are as follows [40]: \(T_{\rm f}=10\)\({}^{\circ}\)C, \(c_{p}=3.97\) kJ \(\cdot\left(\rm{kg}\cdot\rm{K}\right)^{-1}\), \(\rho=1025\) kg \(\cdot\rm{m}^{-3}\), \(Pr=9.52\), \(k_{\rm f}=0.58\) W \(\cdot\left(\rm{m}\cdot\rm{K}\right)^{-1}\), \(a=1.37\times 10^{-6}\) m\({}^{2}\cdot\rm{s}^{-1}\), and \(\
u=1.188\times 10^{-6}\) m\({}^{2}\cdot\rm{s}^{-1}\).
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 3 Experimental Study  
### Milling Experiments  
Milling experimental data come from [PERSON]'s team of Singapore Institute of Manufacturing Technology [3]. The data set contains tool wear, cutting force, AE signals and vibration signals. The milling experiment scheme is introduced in this section. Milling experimental data come from [PERSON]'s team of Singapore Institute of Manufacturing Technology [3]. A high speed computer numerical control (CNC) machine, whose spindle speed up to 42,000 rpm, was selected to achieve the experiment. The workpieces, whose material used in the milling experiment was stainless steel, were cut off from original stock and their surfaces were prepared through face milling to get rid of the original skin layer containing hard particles. Three piezo accelerometers were mounted on the workpiece to measure the machine tool vibrations of cutting process in \(X\), \(Y\), \(Z\) direction respectively. The outputs of these sensors were conditioned through corresponding signal conditioning accessories such as charge amplifiers or couplers. The voltage signals were captured by a NI DAQ PCI 1200 board with 12 KHz frequency. The DAQ board generates 16-bit digitized data and directly streamed to a hard disk of an Intel Core 2 Quad 2.66 GHz based industrial PC with 8 GB RAM.  
The acquisition and installation of cutting vibration and schematic diagram for the experiment are shown in Figure 3. The machining tests were carried out with cutting speeds 4.7 m/min and spindle speed 23,600 rpm. In the experiment, tool life-cycle experiments were made by inspecting the tool wear. The cutter's wear was measured after a complete cutting distance using a LEICA MZ12 microscopy system (Leica Microsystems, Wetzlar, Germany). In order to reduce the influence of other factors on the vibration signals, the same cutting parameters were selected in the experiments of three tools. Experimental data was acquired at 50 KHz/channel. Experiments data set contains three groups tool and 315 times milling experiments a group, and each experiment include vibration signals in X, Y, Z dimensions; cutting force signals in X, Y, Z dimensions; and AE signals. Among the data set, two groups are chosen as training set that used for learning, and one group is chosen as test set that used only to assess the performance.
## 3 System Model
### Network Model  
The SIN resource management and scheduling center and the single-domain controllers realize the dispatching of various resources. This paper takes an LEO communication satellite and GEO data relay satellite as examples to analyze. Let _la_, _lga_, _ca_, _ma_, and _ua_ represent the LEO communication satellite, GEO data relay satellite, cache device, MEC server, and user in the underlying physical resources, respectively. Let \(la=\{1,\ldots,L\}\), \(lga=\{1,\ldots,Lg\}\), \(ca=\{1,\ldots,C\}\), \(ma=\{1,\ldots,M\}\) and \(ua=\{1,\ldots,U\}\), where \(L\), \(Lg\), \(C\), \(M\), and \(U\) represent the number of LEO satellites, GEO data relay satellites, caches, MEC servers, and users, respectively [28].
## 2 Related Work  
### Deep Learning for MI-EEG Classification  
The adoption of DL has marked a significant paradigm shift in MI-EEG classification, moving the field beyond traditional machine learning pipelines that rely on handcrafted features [35]. As systematically reviewed by [PERSON] et al. [36], this transition has been driven by the increasing availability of large public datasets and the inherent ability of DL models to learn relevant hierarchical features directly from raw or minimally processed EEG data. Convolutional neural networks (CNNs) have been the dominant architecture in this domain. Foundational models like _ShallowConvNet_[37] and the more compact _EEGNet_[38] were specifically designed to capture the unique spatiotemporal characteristics of EEG signals and have become standard benchmarks in the field. These models and their variants are the most frequently used architectures in recent literature, demonstrating robust performance across various MI paradigms [36]. Subsequent innovations have focused on enhancing temporal modeling capabilities. For instance, _TCNet Fusion_[39] integrated Temporal Convolutional Networks (TCNs) to better handle long-range dependencies, while other hybrid models have combined CNNs with recurrent neural networks (RNNs) like LSTMs to process EEG as sequential data [40,41]. More recently, the field has begun to explore architectures inspired by advances from other domains. Graph convolutional networks (GCNs) have been employed to explicitly model the spatial relationships between EEG electrodes based on their physical proximity, aiming to learn more neurophysiologically plausible spatial filters [42]. The most current trend, as highlighted by multiple recent studies, is the adaptation of Transformer models for EEG analysis. Architectures like CNet [43] and others [44,45] leverage self-attention mechanisms to capture global dependencies across both time and space, showing great promise for improving decoding accuracy. Despite this rapid architectural innovation, [PERSON] et al. [36] note that consistent challenges related to overfitting, reproducibility, and interpretability remain central concerns. Our work, therefore, does not aim to propose a new backbone architecture, but rather to enhance the most established and widely used CNN-based frameworks by introducing a unified solution for robustness and interpretability, two of the most critical hurdles for the clinical translation of these powerful models.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### Performance Result of the Proposed Model with UIR-FAll Dataset  
We performed a fold-wise cross-validation and experimental evaluation of the proposed model on the UR-Fall multimodal dataset, as shown in Table 6. The model achieved an average accuracy of 99.21%, a precision of 98.98%, a recall of 99.07%, and an F1-score of 98.99%, respectively. These results demonstrate that the proposed model generalizes well to other datasets.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author** & **Data Modality** & **Method Name** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** \\ \hline [PERSON] et al. (2016) & Multi-Sensor & SVM (IMU) + EEG System & 90.77 & - & - \\ \hline [PERSON] et al. (2014) & Multi-Sensor & MS-DLD System & 88.75 & - & - \\ \hline [PERSON] et al. (2016) & Multi-Sensor & Naive Bayes Classifier & 88.61 & - & - \\ \hline [PERSON] et al. (2015) & Skelton & JDM & 88.10 & - & - \\ \hline [PERSON] et al. (2017) &
\begin{tabular}{c} Skeleton + Multi-Sensor \\ \end{tabular} & Logistic Regression (LR) & 91.51 & 90.00 & 91.00 \\ \hline Our Proposed System & Sensor + Skeleton & Two-Stream DNN & 99.09 & 97.06 & 97.18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: State-of-the-art comparison of the proposed model on the UP Fall multimodal dataset.
### Radar System  
The airport category depends on many aspects. Among various security elements, a short-range radar ensures on-line detection of intruders. However, each radar system (in terms of end-user software application) has functionalities, strengths, and weaknesses. In our example, the wide area protection (airport prone) is implemented by a commercial microwave radar with the following parameters:  
* Operating range up to 1400 m;
* One second period (a differential \"image\" where the detection of obstacles is possible). To improve and enhance the information obtained, radar software can accomplish the following:
* Reduce reflected signals (echoes);
* Automatically select objects moving at a speed of at least 0.2 m/s;
* Integrate the radar (signal) map with the terrain map;
* Control CCTV (IP), visible and infrared, cameras to provide more accurate automatic motion detection and tracking of selected objects.  
The radar system provides 0.25 m (radial) and 1\({}^{\circ}\) azimuthal accuracy of a tracked object. These values are \"constant\" within the radar's operating range and are therefore significantly better than typical GPS accuracy [15].  
The system operator sets the appropriate status of individual objects detected with the system. This is conducted on the basis of the image transmitted via the user interface and other information on the CCTV monitor. Usually, two types of statuses are implemented:  
* Friend--the object is authorized to be present at a certain place and time;
* Foe--when there is _no certainty_ that an object is authorized. In summary, the location of an object is accurate and automatically found via the radar system, but the status of the object must be set manually, which is _inconvenient and leads to oversights_. In addition, when a large group of objects needs to be classified immediately, the operator must concentrate hard to avoid misclassification. System operators claim that in most cases (over 90%), the system generates false alarms by detecting workers with valid access to a restricted area. As a result, the operator typically reassigns the _friend_ status. Such routine scenarios reduce security.
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 3 The Facilities  
This case study involves three facilities (System 1, System 2, and System 3) located slightly to the north from the center of the Spanish autonomous region of Castile and Leon, at the city of Palencia (System 1), at Torquemada (Palencia) (System 2), and at Los Balbases (Burgos) (System 3). The map locations are shown in Figure 1. The facilities are subject to very similar environmental conditions, in terms of temperature, solar irradiation, humidity, and wind speed. Solar irradiation is estimated at approximately 1450 kWh/m\({}^{2}\) year [9]. The ambient temperature range is between 4 \({}^{\circ}\)C and 20 \({}^{\circ}\)C and the number of cloudy days is very low [43]. Figures 2 and 3 and Figure 4 show photographs of the three facilities.  
As can be seen in Figures 2 and 3 and Figure 4, all the facilities in this study have different types of sun-tracking technology. System 1, located at Palencia City, is a 38.88 kW\({}_{\text{p}}\) peak power fixed roof solar facility. It is composed of 218 PV panels (model FOTONA 180D) tilted at 20\({}^{\circ}\) and has an electrical performance of 12%. System 1 uses six 7 kW\({}_{\text{p}}\) model SMC 7000 TL inverters.  
System 2, located at Torquemada (Palencia), is a 101.01 kW\({}_{\rm P}\) power plant on the ground. It has a support structure, with which the panels can be manually adjusted in steps of 5\({}^{\circ}\) to inclinations between 15\({}^{\circ}\) and 55\({}^{\circ}\) thereby optimizing the angle of incidence throughout the year, approximately once per month with the optimal tile angle for each month. System 2 is composed of 546 PV panels (model BP-71855), 15% electrical performance. Only one 100 kW inverter was selected, Ingecom Sun 100 Model.  
System 3, located at Los Balbases (Burgos), is a 2-axis sun-tracker facility with 51 modules and an electrical performance of 14% that can generate 14.28 kW\({}_{\rm P}\) (model Support 72M). It has three 4.4 kW\({}_{\rm P}\) Sunny Boy 4200 TL HC inverters. The technical specifications of each of the three systems are summarized in Table 2.
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 3 Algorithm Framework  
In this section, our proposed DWT_AE_BiLSTM deep learning algorithm framework is shown in Figure 2, which includes three modules: data processing and denoising module, feature extract module and forecast module. Its implementation details are described as follows:  
1. Data processing and denoising module: Firstly, the missing data on a wind farm is interpolated and corrected. Then, the discrete wavelet transform technology is used to decompose the nonstationary wind power time series data into low-frequency component and high-frequency component. These components exhibit a greater degree of stationarity and may be forecasted more easily. Input data mainly includes wind tower observation data, wind farm total active power and numerical weather prediction (NWP) data. Wind tower observation data and NWP data include 12 meteorological elements, i.e., wind speed and direction at heights of 10 m, 30 m, 50 m and 70 m, and turbine hub, temperature, humidity and pressure. All of the data time resolutions are 15 min.
2. Feature extract module: Based on step (a), in addition to the actual power of the power station, there are 13 elements in total. Each element takes five elements in chronological order to form a 65-dimensional vector, which is input into the autoencoder. The features are compressed into a 30-dimensional vector through the training of the autoencoder.
3. Forecast module: The compressed features in step (b) are input into the bidirectional LSTM to predict the short-term power generation of the wind farm combined with NWP. Bidirectional, two-layer stacked LSTMs are used. We apply the Adam optimization method for training. The grid search method is used to determine the hyper-parameters, and the optimal configuration of model parameters is obtained from the validation set. The final optimal parameters learn rate = 1 \(\times\) 10\({}^{-3}\) and batch size = 128. The dataset utilized in this study comprises data from the calendar year 2018, which has been divided into training and validation sets consisting of 70% and 30% of the data, respectively. In order to evaluate the predictive performance of the model, data from four representative months of the year 2019 were handpicked for comparison against the forecasted outcomes. The results are illustrated in Figure 3, which displays the respective losses of the training and validation sets for Wind Farm #1.  
Figure 2: Proposed flowchart of DWT_AE_BiLSTM deep learning algorithm framework. Numerical weather prediction (NWP) and historical observation data are used as input data, discrete wavelet transform (DWT) is used to denoise the data, autoencoder (AE) is used for feature extraction and Bi-LSTM is used for prediction.
# Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design
## 5 Conclusions  
In recent years, the development of quantum devices has been very significant. Considering this, it is also necessary to create a specific backbone based on quantum entanglement and teleportation in order to interconnect QCs on Earth reaching an extremely high computational capacity. With the deployment of a QSNs, it is possible to overcome the limitations of fiber optic networks, and the recent technological developments in terms of quantum satellite communications encouraged us to study an efficient management system for a LEO quantum satellite backbone. Given the difficulty in being able to create entanglement over long distances, SDN technology could be useful to control the operations necessary to create it. Specifically, we have designed a specific architecture consisting of SDN controllers positioned both on the ground and in the constellation itself with different roles, which is capable of controlling operations on the entire E2E path. Moreover, we have developed a specific communication protocol through which it is possible to obtain an E2E entanglement.  
Figure 6: Time required to establish an entanglement E2E.  
The performance of the protocol was evaluated considering the time required to establish an E2E entanglement as the conditions of the E2E path change. From the tests conducted, it is possible to deduce that as the number of satellites increases, the performance tends to improve, considering that the shorter distance increases the probability of success in order to obtain an entanglement. In fact, the time needed to obtain the entanglement strongly depends on this probability, and therefore the time needed to obtain it decreases.  
Future development could consist of the integration within the MCS of a specific path selection algorithm designed for quantum networks, which would take into account the issues of quantum networks and contribute to further improve performance. In addition, more accurate analysis in terms of scalability could also provide additional elements in order to allocate controllers in the constellation even more efficiently.  
Conceptualization, R.P. and F.C. and L.P.; methodology, R.P. and F.C.; software, R.P.; validation, R.P. and F.C. and L.P.; formal analysis, R.P.; investigation, R.P. and F.C. and L.P.; resources, F.C. and L.P.; data curation, R.P.; writing--original draft preparation, R.P. and F.C. and L.P.; writing--review and editing, R.P. and F.C. and L.P.; visualization, R.P.; supervision, R.F.; and project administration, R.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable, as the study does not report any data.  
The authors declare no conflict of interest.
## 3 Results  
### Training and Validation Loss Analysis for Epochs  
To evaluate the cloud segmentation model's performance across different channel inputs, this study analyzed the training and validation loss per epoch for the various channel combinations derived from the KOMPSAT-3 and 3A imagery. Figure 5 illustrates these loss curves, with the left side depicting the training data and the right side representing the validation data.  
For all the channel configurations, the training loss decreased steadily with increasing epochs, as depicted in Figure 6, which illustrates the training and validation loss per epoch for different channel combinations using KOMPSAT-3 and 3A data, with training data on the left and validation data on the right. Correspondingly, validation loss also declined consistently without rising, even at higher epochs, suggesting that the model avoids overfitting to the training data. However, certain configurations, particularly those incorporating multiple spectral bands, exhibited fluctuating validation loss curves. In the context of  
benchmark for assessing the model's generalization and real-world applicability. The results indicate that simply altering the combination of multispectral channels can influence performance. Accuracy ranges from a minimum of 86.5% to a maximum of 87.5%, while the mIoU scores vary from 43.3% to 46.5%. Figure 7 further demonstrates that the TOA reflectance-based channel combinations consistently outperform those using DN values. Notably, incorporating the NDVI with TOA yields the highest accuracy and mIoU among all the tested configurations.  
Table 3 summarizes the number of parameters for each channel combination. While the total parameter count varies slightly depending on the input data configuration, these differences are relatively small compared to the overall model size. These results indicate that adding the NDVI as an additional channel slightly increases the parameter count but does not significantly affect the overall model complexity.