esa-sceva/satcom-chunk-collection huggingface dataset:
# Rheology, Hydration, and Microstructure of Portland Cement Pastes Produced with Ground Acai Fibers
## 2 Materials and Methods
### Materials and Mixes
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Composition (\%)** & **Physical Properties** \\ \hline SiO\({}_{2}\) & 18.87 & Specific gravity (g/cm\({}^{3}\)) & 3.12 \\ Al\({}_{2}\)O\({}_{3}\) & 4.29 & Blaine fineness (m\({}^{2}\)/kg) & 447.4 \\ Fe\({}_{2}\)O\({}_{3}\) & 2.97 & 28-day compressive strength (MPa) & 51.2 \\ CaO & 58.92 & \\ \cline{2-3} K\({}_{2}\)O & 0.85 & **Mineralogical Composition (wt.\%)** \\ \cline{2-3} Na\({}_{2}\)O & 0.27 & C3S & 54.3 \\ MgO & 5.90 & C2S & 14.9 \\ SO\({}_{3}\) & 3.10 & C3A & 1.8 \\ TiO\({}_{2}\) & 0.63 & C4 AF & 8.97 \\ BaO & 0.19 & Calcite & 10.1 \\ PbO & \textless{}0.05 & Dolomite & 1.7 \\  & & Peridase & 5.4 \\ LOI & 4.17 & Gypsum & 2.4 \\ \hline \hline \multicolumn{3}{c}{LOI: loss on ignition (CO\({}_{2}\))} \\ \hline \end{tabular}
\end{table} 
Table 1: Composition and properties of the Portland cement used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mix** & **Cement** & **Fibers** & **Quartz Powder** & **Water** & **Description** \\ \hline REF & 1.00 & - & - & 0.50 & Plain cement paste \\
5 IN & 0.95 & 0.05 & - & 0.50 & 5\% dry in nature fibers addition \\
10 IN & 0.90 & 0.10 & - & 0.50 & 10\% dry in nature fibers addition \\
5 IN-S & 0.95 & 0.05 & - & 0.50 & 5\% saturated in nature fibers addition \\
10 IN-S & 0.90 & 0.10 & - & 0.50 & 10\% saturated in nature fibers addition \\
5 T & 0.95 & 0.05 & - & 0.50 & 5\% dry chemically-treated fibers addition \\
5 T-S & 0.90 & 0.10 & - & 0.50 & 5\% saturated chemically-treated fibers addition \\
5 QTZ & 0.95 & - & 0.05 & 0.50 & 5\% quartz powder addition \\
10 QTZ & 0.90 & - & 0.10 & 0.50 & 10\% quartz powder addition \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed composition of the pastes investigated (in weight basis). The materials were mixed using a hand mixer (with 6000 rpm capacity) in batches of 15 mL. Firstly, the dry materials and water were added to the mixer container and mixed by hand for one minute. Then, the paste was mixed for 2 min.
## 2 Relevant Theoretical Basis
### Principles of Polarization Imaging in Electroluminescence
As is evident from Figure 2, the surface of photovoltaic cells and the surface with scratch defects exhibit marked disparities in polarization images, primarily due to the fact that the surface of non-defective photovoltaic cells is relatively smooth, whereas the surface of scratched defective cells is comparatively rough. Hence, polarization imaging is employed to enhance the contrast of small defective targets within cell surface images, resulting in enhanced texture details and edge contours of the surface image of such small defect targets.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band ON and K/Ka-Band OFF
#### 7.2.2 Noise Performance  
Figure 22 illustrates the noise figure (NF) of the receiver front-end with the K/Ka-band arm turned off. The X-band arm's noise figure remains below 10 dB over the frequency range of interest (8-12 GHz). Since the K/Ka-band path is off, its contribution to NF and gain in this mode is negligible.  
Figure 21: Gain performance of the switchable wideband receiver front-end with the X-band arm ON and K/Ka-band OFF.  
Figure 22: Noise performance of the switchable wideband receiver front-end with the X-band arm ON and K/Ka-band OFF.
## 3 Results and Discussion  
### Bulk Density Results  
(1)  
Table 2 assesses the significance of the factors CPP (A), HSO (B), and MDI (C), along with their interaction and quadratic effects, on the model throughout the biocomposite production process. The total variance explained by the model (968.60) indicates that the model is highly statistically significant (\(p\) < 0.0001). This result demonstrates that the model is an accurate representation of the dataset and can be relied upon for further analysis. Upon examination of the individual effects of the factors, it is evident that CPP (A), HSO (B), and MDI (C) all exert a pronounced and statistically significant influence on the model (\(p\) < 0.0001). It is noteworthy that the effect of the CPP (A) factor is the most dominant, with a high F-value of 27,379.65. This finding highlights the crucial role of CPP in influencing the characteristics of biocomposites. However, the binary interactions (AB, AC, BC) were found to be insignificant for the model (\(p\) = 1.0000). This indicates that there is no synergistic interaction among the factors. In consideration of these findings, it can be concluded that the combined effects of these factors do not result in greater changes than their individual effects. Among the quadratic effects, only B\({}^{2}\) (the square of HSO) has a significant impact (\(p\) < 0.0001), while the other quadratic effects (A\({}^{2}\) and C\({}^{2}\)) are not significant (\(p\) > 0.05). The model's exceedingly low error rate (residual SS = 0.1742) and the absence of significance in the \"Lack of Fit\" test (Pure Error = 0, \(p\) > 0.05) demonstrate that the model accurately represents the data and provides reliable results.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 3 Network Model and Charging Scheme
### Energy Consumption Model  
In this paper, we assume the network is an event-driven network [6; 13]. There are some events appear independently at random locations and random times in the network field. For simplicity, whenever an event appears in the sensing range \(d_{s}\) of sensor nodes, these nodes consume \(e_{s}\) energy to capture the event and transmit the sensory data to the base station via a dynamic routing path. Each node has the same maximum transmission range \(d_{r}\), and consumes \(e_{t}\) and \(e_{r}\) energy to transmit and receive a data message, respectively.
###### Abstract  
The response to perturbations in the gait of elderly and young individuals can differ due to various factors, such as age-related changes in sensorimotor function, muscle strength, and balance control. This study aimed to identify and compare compensatory kinematic and kinetic gait strategies in response to sudden treadmill perturbations applied during the Pre-Swing phase in young and older adults. The analysis focused on determining age-related differences in joint behavior and force production under perturbation stress, with implications for fall prevention. Twenty-one young and an equal number of elderly healthy females walked on a treadmill in a virtual environment (GRAIL, Motek). Unexpected perturbations were applied five times. Principal Component Analysis (PCA) and k-means clustering identified three distinct compensatory strategies per limb. Young adults primarily employed Strategies I (42.2%) and II (40%), while older adults most often selected Strategy II (45.5%). Statistical analysis (SPM and Mann-Whitney U test, \(p=0.05\)) showed significant between-group differences in joint angles and torques across the gait cycle. For instance, in Strategy I, young participants had significantly lower ankle plantarflexion angles (\(p<0.01\)) and hip extension torques (\(p<0.05\)) compared to the elderly. Strategy II in older adults showed significantly higher vGRF minimums (\(p<0.01\)) and anterior-posterior GRF peaks (\(p<0.001\)). The elderly adopted strategies compatible with their neuromuscular capacity rather than those minimizing joint load, as observed in the young group. These findings offer novel insights into age-related compensatory mechanisms and highlight the importance of tailored fall-prevention strategies based on biomechanical response patterns.  
Keywords:gait; perturbations; treadmill; provoked trips; ankle joint
# Dynamic Multi-Attribute Decision-Making Method for Risk-Based Ship Design
## 2 A Dynamic Multi-Attribute System for RCO Ranking
### Dynamic Multiple-Attribute Indices System  
A multi-attribute assessment model is used to rank RCOs. As analyzed in Section 1, if the RCOs were assessed only by using the abovementioned CEA indices, options that exhibit excellent performance in controlling risks would likely be screened only because of their high costs. Thus, more performance indices are added to the multi-attribute system, such as \(\triangle\)PLL, \(\triangle\)PLO, and \(\triangle\)PLP, where \(\triangle\)PLL represents the decrease in life loss risk (potential loss of lives) by carrying out an RCO; \(\triangle\)PLO denotes the decrease in oil spill risk (potential loss of oil spill); and \(\triangle\)PLP represents the decrease in property loss risk (potential loss of property).  
Furthermore, a dynamic multi-attribute system is proposed to rank RCOs, especially for FSA projects based on the ALARP principle. This system has two typical alternative multi-attribute index sets: cost-effectiveness indicators (GCAF, NCAF, and CATS) and performance indices (\(\triangle\)PLL, \(\triangle\)PLO, and \(\triangle\)PLP). Figure 2 shows the dynamic selection procedure of multi-attribute sets: if the risk levels (life loss risk and oil spill risk) are in the ALARP area (as shown in Figure 1), the cost-effectiveness index set could normally be used. If any of the risks (life loss risk or oil risk) are high and lie in the intolerable area,  
Figure 1: The standard line in the F-N diagram represents the absolute value of intolerable and negligible risks, between which is the ALARP area, to which the cost-effectiveness criterion applies [34].  
the performance index set should be used because the risks should be reduced as much as possible regardless of the cost factors. Thus, the decision-makers' aversion to high risks can be reflected in the selection procedure of RCOs.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 5 System Architecture and Design Scheme
### Louvain Algorithm  
The Louvain algorithm is a community detection method for complex networks based on modularity maximization [31]. It has an approximate linear time complexity and is regarded as one of the best-performing clustering algorithms. The modularity function \(Q\) is defined by the following equation:  
\[Q=\frac{1}{2\tau}\sum_{i,j}\left[a_{ij}-\frac{k_{i}k_{j}}{2\tau}\right]\delta(c _{i},c_{j})=\frac{1}{2\tau}\left[\sum_{i,j}a_{ij}-\frac{\sum_{i}k_{i}\frac{ \sum k_{j}}{j}}{2\tau}\right]\delta(c_{i},c_{j}) \tag{13}\]  
where \(k_{i}=\sum\limits_{j}a_{ij}\) is the sum of the weights of all edges connected to node \(i\). \(\tau=\frac{1}{2}\sum\limits_{i,j}a_{ij}\), \(c_{i}\) is the cluster containing node \(i\). When \(c_{i}=c_{j}\) and \(c_{i}=c_{j}\), \(\delta(c_{i},c_{j})=1\); otherwise, \(\delta(c_{i},c_{j})=0\). The value of \(Q\) reflects the quality of community clustering. The closer it is to 1, the more distinct the clustering structure.  
The basic idea of the algorithm is to first treat each node in the network as a cluster. Then, node \(i\) is moved into the cluster \(c\) of its neighboring node \(j\), and the modularity increment \(\Delta Q\), expressed by Equation (14), is calculated to determine the movement method that has the greatest positive impact on modularity:  
\[\Delta Q=\left[\frac{\sum_{in}+k_{i,in}}{2\tau}-\left(\frac{\sum_{tot}+k_{i}}{ 2\tau}\right)^{2}\right]-\left[\frac{\sum_{in}}{2\tau}-\left(\frac{\sum_{tot}} {2\tau}\right)^{2}-\left(\frac{k_{i}}{2\tau}\right)^{2}\right] \tag{14}\]  
where \(\sum_{in}\) is the sum of the weights of the internal edges within cluster \(c\), \(k_{i,in}\) represents the sum of the weights of the edges connecting node \(i\) to the nodes within cluster \(c\), and \(\sum_{tot}\) is the sum of the weights of all edges connecting to the nodes within cluster \(c\). The similarity measure is based on the distribution of load data that groups users, which manifests as community clustering at the level of the complex network.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 5 Conclusions  
This paper has proposed a novel and regularized fused Swish-ELU EfficientNet-B4 model to classify the different types of wound images accurately. The proposed model fuses the two customized EfficientNet-B4 models to extract better competent features, which are then classified via a regularized prediction module. Experimentation was performed on the Medetec dataset, the AZH dataset, and their extended versions, to demonstrate the efficacy of our SEEN-B4 model. To evaluate the generalization ability of the proposed model, we have also conducted a cross-corpora evaluation. We have generated and analyzed the heatmaps to strengthen the trustworthiness, and we demonstrate the explainability power of the SEEN-B4 framework. The results indicate that our proposed model can successfully classify the different wound types and outperform existing methods.  
Conceptualization, Z.A.A. and H.M.; methodology, Z.A.A., H.M. and R.M.; software, Z.A.A.; validation, Z.A.A., H.M. and R.M.; formal analysis, Z.A.A.; investigation, H.M.; resources, H.M. and R.M.; data curation, Z.A.A.; writing--original draft preparation, Z.A.A.; writing--review and editing, Z.A.A., H.M. and R.M.; visualization, Z.A.A.; supervision, H.M. and R.M.; project administration, H.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Dataset** & **Testing Dataset** & **Accuracy (\%)** & **Balanced Accuracy (\%)** \\ \hline AZH & Medetec & 68.89 & 61.51 \\ Medetec & AZH & 59.15 & 59.68 \\ Extended AZH & Extended Medetec & 77.05 & 76.56 \\ Extended Medetec & Extended AZH & 58.06 & 58.06 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross-Corpora Evaluation.  
Data Availability Statement: We used the publicly available dataset, and links are already provided in the Reference section.
## 4 Experiment and Analysis
### Results  
Figure 9 presents results of our proposed method based on appearance, data augmentation with appearance, and data augmentation with appearance with license plate verification. Data augmentation and data augmentation with license plate verification improved the mean average precision (\(mAP\)) by 0.75% and 3.37% compared to appearance results, respectively. We have used \(mAP\) and cumulative matching characteristics (CMC) for evaluation of the technique; the \(mAP\) metric evaluates the overall performance for Re-Id. \(mAP\) was calculated for each probe image as follows:  
\[AP=\sum_{k=1}^{n}p(\mathrm{k})*\mathrm{f}(\mathrm{K})/n_{total}\]  
where \(n\) denotes the total number of entries in the result list, \(p(\mathrm{k})\) is precision, _f(k)_ is a function, it is equal to 1 if the result is correct, otherwise 0. \(N_{total}\) is number of vehicles images in test set that are same as a probe vehicle image.  
where \(T\) denotes the size of input set.  
CMC calculates the probability of the input vehicle image appearing in different lengths of the result list. Meanwhile, the effectiveness of the contribution can also be seen in CMC curves, as shown in Figure 9.  
As shown in Figure 9, our proposed complete architecture with license plate verification achieved the best performance.
## 3 System Design and Implementation
### Client Server Model
#### 3.4.2 Client-Side Application  
A brief overview of the main Python libraries used in this client-side application is as follows:* **Socket**: This module provides low-level inter-process communication on Layer 3 (Network layer) of the TCP/IP (Transmission Control Protocol/Internet Protocol) architecture by implementing socket system calls. A socket, in the simplest term, is a gateway that provides point-to-point, two-way communication between two processes running on two separate endpoints.
* **RPi_.GPIO**: This is a Python module that controls the general-purpose I/O pins on a Raspberry Pi. The configuration of GPIO pins on a Raspberry Pi can be examined in Figure 4. We can retrieve the values inserted by the user through an attached keypad, by setting up the rows and columns of the 4 \(\times\) 4 Matrix keypad as the input and output.
* **Threading:** A thread of execution is the smallest sequence of programmed instructions that can be independently managed by a system scheduler, which is typically a part of the Operating System (OS) [30]. The thread waits for the user to press a button, while the GPS monitors the current location. Both of these activities need to be performed simultaneously, and this can be achieved by opening two parallel threads that can work independently.  
A separate \"GpsController\" class works with a GPS daemon to periodically collect the latitude and longitude values from the satellite. These values can be pulled out whenever required by declaring an object of this class and calling a \"next0\" method on it. To wrap it up, whenever a client-code is executed, the node gets connected to either the nearest intermediate node or directly to the server, provided that the node is in the vicinity. Once a TCP-based connection is up and running, the data transmission can take place. Since the data can have a random size depending upon the input fed by the rescue team member, it can just be an emergency call that requires you to dispatch an ambulance or give some additional information regarding the collapse of a building or a blockage of some nearby road. In order to bring flexibility into the picture, a Java Script Object Notation (JSON) wrapper is used, which can adequately manage a message of variable length. After all the data is nicely compiled and packaged, it is sent through the socket interface.
# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
## 2 Related Works  
ADT comprises a large body of research, of which [PERSON] et al. [1] conducted an extensive review up to 2018. In their work, they analyzed three types of ADT with increasing levels of complexity: drum transcription of drum-only recordings (DTD); drum transcription in the presence of percussion (DTP) with additional, non-transcribed, percussion instruments; and drum transcription in the presence of melodic instruments (DTM), with \"full-mixture music such as pop, rock, and jazz recordings\". [PERSON] et al. reported that \"reliable performances can be expected from the state-of-the-art systems\" only for DTD, the simplest of the three. Indeed, as the tasks become more complex, as with DTP and even more so with DTM, there is \"room for future improvement\". The authors also noticed that, among all the evaluated algorithms, deep learning (DL) methods seemed \"to be the most promising approaches\", as long as sufficient training data are provided. This is something that [PERSON] and [PERSON] also realized while comparing multiple methods for DTM [8].  
Since 2018, many other attempts at improvement (already identified by [PERSON] et al.) have been pursued. A summary of the works concerned with enhancing ADT is provided in Table 1, and they are described below by focusing first on the tasks and vocabulary included and then, separately, on the architectures, training procedures, and training data employed.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Year** & **Work** & **Task** & **Voc.** & **Architecture** & **Training Proc.** & **Training Data** \\ \hline
2018 & [PERSON] and [PERSON] [9] & DTM + BD & 14 & CRNN & Supervised & ENST, MDB, RBMA, SDDS 1, etc. \\
2018 & [PERSON] and [PERSON] [8] & DTM & 3 & CNN & Supervised & ENST, RWC 1  \\
2018 & [PERSON] et al. [2] & DTM & 18 & CRNN & Supervised & ENST, MDB, RBMA, TMDT 1  \\
2019 & [PERSON] and [PERSON] [10] & DTP & 11 & CRNN & Unsupervised & In-house dataset \\
2019 & [PERSON] and [PERSON] [11] & DTM & 3 & CNN & Supervised & MIREX 2018  \\
2020 & [PERSON] et al. [12] & DTD + V & 7 & CRNN & Supervised & E-GMD 1,2  \\
2020 & [PERSON] et al. [5] & DTM & 3 & CRNN, LM & Supervised & RWC 3,4  \\
2020 & [PERSON] et al. [13] & MIT + SS & 88 & RNN & Supervised & MAPS, Slakh 1, GuitarSet \\
2020 & [PERSON] et al. [14] & DTM & Open & CNN & Few-shot & Slakh 1  \\
2021 & [PERSON] et al. [15] & MIT & 88 & CNN-SelfAtt & Semi-supervised & MAPS 1, MusicNet \\
2021 & [PERSON] et al. [16] & MIT & 128 & SelfAtt & Supervised & Cerberbast 1, Slakh 1, etc. \\ \hline \end{tabular}
\end{table}
Table 1: Overview of works related to ADT since 2018.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 3 Simulation Results  
### Environment Characterization  
First, some characterization and analysis of the environment was performed. Basic parameters such as building height and line of sight were calculated.  
Figure 4: Examples of cell assignment in the case of 24 and 45 sectors per base station at an altitude of 35 m AGL.  
A comparison between the resulting building height distribution and a fitting to the Rayleigh distribution suggested in [28] can be seen in Figure 4(a). We found a minimum building height of 4 m. As can be seen, the distribution overestimates the amount of buildings with a height up to 10 m and underestimates the amount of buildings with a height larger than 10 m. The calculated mean building height is 13.32 m, which was calculated with respect to the base area of every building.  
A probability of line of sight was also calculated to characterize the environment. The probability of line of sight is presented as a function of UE height and indicates whether the user is in LOS with any base station. When determining whether a ground or aerial user is in LOS with a base station, the 3D map is used, as explained in Section 2.1.2. The resulting LOS probability can be seen in Figure 4(b). In our example, the simulations showed that a UAV is always in LOS with at least one BS if the UAV flies higher than 30 m AGL.
## 2 Background and Literature Review
### SDVN Architectures  
Based on the level of control of the SDN controller, there are three SDVN architectures: centralized, distributed, and hybrid [24]. Nodes in the data plane carry out actions in accordance with the traffic rules provided by the SDN controller in a centralized architecture where control is totally conceptually centralized. The control plane connection between the nodes and the centralized controller in this design, however, has resulted in greater latency [25]. Additionally, expansion is limited under this design, and there is a propensity for error when the control plane connection becomes unavailable or interrupted. In a distributed control architecture, control is split among a number of controllers, with the end nodes operating under the direction of local controllers, while the local controllers may be subordinate to a global controller. This design avoids the centralized control architecture's sole point of breakdown and expansion capability problems. However, because of consistency difficulties, this design takes longer than the centralized architecture to optimize and make choices [26]. To surpass the drawbacks of both distributed control and centralized control systems, a hybrid control architecture has been developed. According to the needs, the centralized controller in this architecture may change the nodes' level of control from maximal to minimal, acting as a hybrid of centralized and distributed control [27].
## 4 Research Methodology
### Description of the Benchmark Algorithm (BRDFS)
#### 4.3.1 Demand Forecasting System (DFS) of BRDFS  
Most of the existing resilient demand forecasting systems use a single Long-Term Short-Term Memory (LSTM) for forecasting the point estimate of the demand [9; 17]. For comparison purposes, the DFS of BRDFS consists of two LSTMs for forecasting the interval estimate of the demand. LSTM1 is trained to forecast the conditional mean of demand and LSTM2 is trained to forecast the conditional variance of demand. LSTM1 is trained on the original aggregate demand time series and LSTM2 is trained on the squared residuals of LSTM1. Most of the existing resilient demand forecasting systems do not perform differencing or wavelet decomposition on the demand time series to deal with non-stationarity. Hence, BRDFS does not apply these measures as well.