esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Information
### Data Collection, Aggregation and Dissemination  
In WSNs, the data gathering and dissemination process aims to gather data from nodes and convey it to the sink/ BS in such a way that the highest data rate is accomplished  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline ANN & Artificial Neural Network. \\ \hline ACO & Ant Colony Optimization. \\ \hline ABC & Artificial Bee Colony. \\ \hline AI & Artificial Intelligence. \\ \hline BA & Bat Algorithm. \\ \hline CI & Computational Intelligence. \\ \hline DC & Data Collection. \\ \hline DL & Deep Learning. \\ \hline DNN & Deep Neural Network. \\ \hline GA & Genetic Algorithm. \\ \hline IoT & Internet of Things. \\ \hline PSO & Particle Swarm Optimization. \\ \hline RL & Reinforcement Learning. \\ \hline SI & Swarm Intelligence. \\ \hline WSN & Wireless Sensor Network. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of main Abbreviations.  
while the total WSN lifespan is maximized. The data might be plain raw data or data that has been processed using basic techniques like aggregation or compression. The act of aggregating data from various nodes and sending it to the BS for further analysis is known as data aggregation. WSN gathers data from environment for a long duration and direct the delivery of collected data from nodes to sink (or BS), which may contain highly redundant data and increases the communication load. Data coming from different sensors are redundant and huge for the BS to process. Data aggregation is carried out to deal with this problem and the BS obtains only the significant information. The key purpose of data fusion is to reduce duplication of data from multiple sources and also decrease the transmissions' count and conserve resources [12]. On the other hand, data dissemination refers to the process for distributing the data and queries for data to be routed in network. Source node in WSN is considered as node that generates data in relation to an event, and node that is interested in data is considered as the sink node [13]. Event is information that needs to be reported while interest refers to the descriptors for events that node is interested in.
## 3 Results and Discussions
### Workability
#### 3.1.5 Influence of Extra Water  
The mixes of group C, with 35% AAS content, exhibited poor workability characteristics when compared with their counterparts in group A (40% AAS content). To improve the workability with the same AAS content, the group E mixes were prepared with additional water (12 kg/m\({}^{3}\)). As shown in Figure 2, all the mixes of group E have higher slump values as compared to the corresponding mixes of group C. The slump values of E1-S10 (150 mm), E2-S20 (135 mm), E3-S30 (115 mm), and E4-S50 (100 mm) were increased by 53%, 56%, 61%, and 65% respectively as compared to C1-S10 (70 mm), C2-S20 (60 mm), C3-S30 (45 mm), and C4-S50 (35 mm). Hence, additional water can be used to improve the workability of GPC mix in situations where high workable mixes are desired. However, the effect of the extra water on the mechanical properties needs to be studied as well.
## 2 Materials and Methods
### Screening and Selection  
Two independent reviewers ([PERSON] and [PERSON]) screened study titles, abstracts, and full texts. Disagreement over eligibility was resolved through discussion. The search was restricted to RCTs and clinical trials in humans. In detail, the inclusion criteria are as follows: publication date from January 2004 to the present; must be English language; full-text article; split- or full-mouth design; participants with ASA (Physical Status Classification System) I health status and periodontitis; in good health condition; without systemic, organic, or psychiatric diseases; administration of HA in monotherapy as an adjunct to non-surgical periodontal treatment; HA formulations from 0.2% to 1.6% were considered (but only studies from 0.2% to 0.3% and those from 0.8% to 0.9% were included in the meta-analysis in order not to compromise heterogeneity too much); minimum 3-month follow-up. The primary outcome was changes in pocket probing depth (PPD); the secondary outcomes were changes in clinical attachment level (CAL), bleeding on probing (BOP), microbiological, and biochemical parameters.  
The PICO elements considered in this review were as follows:  
P (Population): patients diagnosed with periodontal disease;  
I (Intervention): adjunctive use of HA gel in support of SRP;  
C (Comparison): SRP alone;  
O (Outcome): clinical, microbiological, and biochemical parameters.  
The exclusion criteria were as follows: patients with systemic disease and/or substantial functional limitations with one or more moderate to severe diseases (ASA II, III) and patients with systematic disease (ASA II, III) who had received the following: surgical therapy for periodontitis; dental implants; antibiotic prophylaxis; and topical agents active on biofilm (systemic administration of drugs, antibiotics, probiotics, laser, ozone therapy). The following types of studies were excluded: reviews, editorials, systematic reviews, meta-analyses, PhD theses, case reports, in vitro trials, and studies that did not report clinical outcomes.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CWN & Cognitive Wireless Network \\ CRN & Cognitive Relay Network \\ SE & Spectrum Efficiency \\ EE & Energy Efficiency \\ SWIPT & Simultaneous Wireless Information and Power Transfer \\ SNR & Signal-to-Noise Ratio \\ BMR & Bidirectional Multi-Relay \\ DL & Direct Link \\ BRT & Bidirectional Relay Transmission \\ BDT & Bidirectional Direct Transmission \\ TTPT & Total Transmit Power Threshold \\ OPA & Optimal Power Allocation \\ PU & Primary User \\ SU & Secondary User \\ PTU & Primary Transmit User \\ PDU & Primary Destination User \\ EH & Energy Harvesting \\ SP & Signal Processing \\ CSI & Channel State Information \\ PS & Power Splitting \\ AWGN & Addictive White Gaussian Noise \\ AF & Amplify and Forward \\ AB & Adaptive Bidirectional \\ EEAB & Energy-Efficient Adaptive Bidirectional \\ BRS & Best Relay Selection \\ NC & Non-Collaborative \\ \end{tabular}
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 4 Consecutive Error Prevention Performance: Evaluation
### Duration of Consecutive Collisions
Note that the SPS + colFeedback scheme falls back to SPS when there is no feedback provider available in the vicinity of the two vehicles whose packets collide. It will mostly happen under very low traffic densities where the resource collisions will be rare events anyway. Even in the absence of the broadcast feedback, they will still be resolved by the reselections in the standard SPS algorithm, although not as promptly as with the feedback.
## 4 Conclusions  
The main electrical variables of a DLSC, modelled as a capacitance linearly varying with the internal voltage, when charged or discharged through a voltage source, have been obtained. The set of analytical expressions presented is equally useful for the case in which the DLSC is discharged through a constant resistance, as occurs in industrial applications such as instantaneous heaters, or if it is desired to analyze the self-discharge process. Furthermore, from the new set of equations, the time constant of the DLSC has been defined by resemblance to conventional, constant capacitance capacitors. Unlike other more sophisticated models, which are valuable and very accurate, even in the representation of the physical structure of the DLSC, the set of analytical equations presented here is not restricted to the use of numerical calculation tools. It also allows analytical solutions to be easily obtained without the complexity involved in the models mentioned, where complex tools, from distributed parameter circuits to artificial neural networks, are used.  
It is often claimed that the series RC model does not accurately model the behavior of a DLSC. Therefore, in this paper, a comparative study between the model with variable capacitance (k\({}_{0}\) < 1) and the conventional model with constant capacitance (k\({}_{0}\) = 1) has been carried out and several conclusions have been drawn. It can be finally concluded that when a DLSC is connected to a voltage source, the differences between the models with k\({}_{0}\) < 1 and with k\({}_{0}\) = 1 are not too relevant, and the traditional RC series model, which is simpler and more widely used, provides, in most cases, sufficiently good values so that it is not necessary to treat it as a variable capacitance SC. In the case of needing to improve accuracy and/or perform direct analytical calculations, the new set of analytical equations developed here can be applied in a straightforward and very simple way.  
Conceptualization, J.F.P.; methodology, J.F.P. and M.G.M.; software, M.G.M., A.S.G. and M.F.Q.; validation, M.G.M. and M.F.C.; formal analysis, J.F.P. and A.S.G.; investigation, M.G.M., J.F.P. and M.F.C.; writing--original draft preparation, J.F.P.; writing--review and editing, M.F.Q., M.F.C., G.A.O. and A.S.G.; supervision, M.F.Q. and G.A.O.; project administration, G.A.O. and M.G.M.; funding acquisition, G.A.O. and M.G.M. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Principality of Asturias through the FICYT foundation, under research grant AYUD/2021/51047.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Analysis and Processing  
Raw data acquired with the marker-based MoCap system were initially processed using the proprietary software, i.e., SMARTtracker (version: 1.10.465.0; BTS Bioengineering, Milan, Italy). This step involved labeling each marker so as to assign it to its corresponding anatomical landmark. In particular, for this evaluation the attention was focused on the following markers: C7, left and right acromion, left and right antero-superior iliac spines, sacrum.  
Subsequently, the processed data were imported into SMARTanalyzer software (version: 1.10.465.0; BTS Bioengineering, Milan, Italy) for further analysis through custom routines designed to extract quantitative measurements. The 3D marker coordinates underwent linear interpolation and were filtered using a 5 Hz low-pass Butterworth filter to minimize noise. Task-specific routines were employed to calculate relevant rotation angles and their corresponding ROM. In particular, trunk orientation was calculated with respect to the fixed laboratory coordinate system, providing an absolute measure of the movement. The angles were computed using Euler angles, ensuring a precise evaluation of trunk kinematics within the global reference frame.  
Repetitions for each task were identified by locating the maximum and minimum values along the curve corresponding to the anatomical angles. For each repetition, ROM was determined as the difference in degrees (\({}^{\circ}\)) between these maximum and minimum values. These values were extracted from the time series of angular data by applying a peak detection method to identify local extrema.  
IMU data were processed through an ad hoc and custom MATLAB (R2025a) (Mathworks Inc., Natick, MA, USA) routine. Raw data, including accelerometer and gyroscope readings preprocessed by a Digital Motion Processor (DMPTM), which provided rotational angles (i.e., roll, pitch, and yaw), were processed to obtain Cardan angles referred to the global reference system. Cardan angles about the IMU axes were calculated using the YZX sequence (i.e., flexion-extension around Y; lateral bending around Z axis; axial rotation around X axis) as generalized by [PERSON] [32] using as a reference the mean angles calculated during the stabilization phase defined as a 5-second window in which acceleration variations fell below 0.2 m/s\({}^{2}\). Functional angles were then extracted so as to quantify lateral bending, flexion/extension, and axial rotation, from which the ROM was calculated as the difference in degrees (\({}^{\circ}\)) between the maximum and minimum values.
## 5 Numerical Validation and Discussion
### Results in the IEEE 34-Node Test Feeder  
Table 4 presents the numerical results provided by the BONMIN solver in the GAMS optimizer, the comparative DCCBGA, and the solution reached by the proposed NMA in the IEEE 34-node test feeder.  
The numerical results in Table 4 show that: (i) the proposed NMA finds the best objective function value with an objective function value of USD 3,354,676.16/year which corresponds to a reduction of 26.89% with respect to the benchmark case, while the best solution reached by the DCCBGA is USD 35/year more expensive; (ii) the small differences between the solutions of the NMA and DCCBGA are mainly related to the nodes selected  
Figure 5: Implementation of the proposed methodology in the MATLAB programming environment.  
for the location of the PV sources since nodes 10 and 24 in the NMA are replaced by neighborhood nodes 11 and 25 in the DCCBGA case; and (iii) the total power injections in the peak load cases are 4483.45 kW for the BONMIN solver, 4460.58 kW for the NMA, and 4460.52 kW for the NMA; this implies that with similar levels of power injections, the NMA finds a better set of nodes to locate all the generators owing to the objective function which is minimum for this algorithm when compared with the DCCBGA and the BONMIN solver, respectively.  
An important fact when the DCCBGA and the NMA are applied to solve the studied problem is that the former algorithm finds 11 solutions with the better objective function than the BONMIN solver, and the proposed NMA finds 12 additional solutions with better numerical performance with regard to the exact solver. This is particularly important since having multiple options for the location and sizing of the PV sources in the AC distribution network allows the utility to select the most adequate solution as a function of its grid needs and intervention possibilities.  
To ensure that all the voltages are between their assigned regulation bounds, in Figure 6,the maximum and minimum voltage magnitudes experienced in the IEEE 34-node test feeder along the operation day are presented, where all the voltage magnitudes are contained in all the nodes of this AC grid.  
The main result in Figure 6 is that for all the periods of time, all the voltages are between the assigned regulation bound, i.e., \(\pm 10\%\). In the worst case, hours 20 and 21, the active power injection in the PV sources is zero and the demand is maximum, the minimum voltage is 0.9417 pu; while in the period of time when the active power injections in the PV sources are maximum (hour 14), the maximum voltage in the grid has a magnitude of 1.0197 pu.
## 2 Materials and Methods
### Model Evaluation  
Evaluation of our models was carried out using classification metrics to calculate the accuracy, sensitivity, and AUC score of each model. Shown as a confusion matrix (Figure 4), classification metrics contain binary values of four prediction categories: true positives (TPs), false positives (FPs), true negatives (TNs), and false negatives (FNs) [37].  
We divided the number of correct predictions (TP + TN) by the total number of predictions to obtain the accuracy of our models. Accuracy is the ratio of correctly predicted instances to the total instances and gives a straightforward measure of how often the model is correct [38].  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Hyper-Parameter** & **Values** \\ \hline \multirow{8}{*}{Random Forest} & Bootstrap & True, False \\  & Max depth & 10, 20, 30, None \\  & Max feature & 2, 3 \\  & Min samples leaf & 3, 4, 5 \\  & Min sample split & 2, 5, 10 \\  & N-estimators & 100, 200, 300, 500 \\ \hline \multirow{8}{*}{XGBoost} & Max depth & 3, 5, 7, 10 \\  & Learning rate & 0.2, 0.15, 0.1, 0.01, 0.001 \\ \cline{1-1}  & Subsample & 0.5, 0.7, 1.0 \\ \cline{1-1}  & N-estimators & 50, 100, 150, 200, 300 \\ \cline{1-1}  & Colsample bytree & 0.5, 0.7, 1.0 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: Confusion matrix used for classification metrics.  
We also measured the proportion of actual positives that were correctly identified by the model. This parameter is usually called sensitivity but also may be referred to as recall or True Positive Rate (TPR). To obtain this evaluation parameter, we divided the true positives (TP) by the sum of true positives (TP) and false negatives (FN) [37].  
\[Sensitivity=\frac{TP}{TP+FN}\]  
Moreover, we calculated the area under the curve (AUC), which represents the area under the Receiver Operating Characteristic (ROC) curve. This parameter is a performance metric for binary classification problems that plots the True Positive Rate (sensitivity) and the False Positive Rate (Specificity) at various threshold settings [39]. The AUC score ranges between 0 and 1.0, with an AUC of 0.5 suggesting no discrimination ability, meaning that the model's performance is equivalent to random guessing and has no ability to discriminate between the positive and negative classes [40]. Figure 5 is an example of an AUC graph and its score.  
To ensure robustness, we validated the models by stratified cross-validation with 10-fold [41], a variation in k-fold cross-validation stratified cross-validation that ensures that each fold is representative of the overall class distribution. Unlike regular \(n\)-fold cross-validation, stratification ensures that each fold has the same class distribution as the original dataset, providing a more accurate evaluation [42]. In this study, we calculated the AUC score for the cross-validation parameter to validate the possi  
Figure 5: Receiver Operating Characteristic curve example.  
the models, which can be identified by comparing the AUC score of a baseline model with that of the 10-fold cross-validated model.
## 2 Theoretical Background
### Convolutional Neural Network  
Convolutional neural networks (CNN) have become one of the most commonly used deep network models, as they can automatically extract feature information from the original data [19]. CNN is mainly composed of a convolutional layer, a pooling layer and a fully connected layer. Its typical structure is displayed in Figure 2.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 7 Results on the Impact of Increasing DVs on 5G Network EE
### Impact of Increasing DVs on the Energy Consumption of 5G Network  
Since trends in the necessity of transferring increasing DVs impact both EE metrics and power consumption of 5G networks, an increase in DVs will also have an influence on the total 5G network energy consumption. In the performed analyses, the total network energy consumption refers to the energy consumption of all (small and macro) BSs located in the area of the analyzed device density class during the time period of one year. The impacts of the DV increase through 2020s on coverage EE metrics and total yearly energy consumption of 5G network for indoor-hotspot, dense-urban, urban and rural device density classes are presented in Figure 4a-d, respectively.  
According to Figure 4a-d, for analyzed Approaches 1, 2, 4 and 5, an increase in DV of each device density class will impose an increase in the yearly energy consumption of the 5G network. As explained in the previous section, this is due to the fact that transferring larger DVs requires the exploitation of more BSs and BSs resources, thus resulting in the higher total instant power consumption. This consequently results in an increase of the yearly energy consumption of the analyzed device density class area (Figure 4a-d). Due to explained reasons related to lack of any deployment and RRM approach dedicated to improving EE, only in the case of Approach 3 will the total yearly energy consumption be equal to maximal energy consumption for all years in every device density class.  
Figure 3: Impact of DV increase on data EE metrics and instant 5G network power consumption during the 2020s for (**a**) indoor-hotspot, (**b**) dense-urban, (**c**) urban and (**d**) rural device density class.  
Although analyzed Approaches 1, 2, 4 and 5 are based on the adjustment of BSs deployment dynamics and/or operation activity with respect to the increasing DVs trend, this DV increase will have a negative impact in terms of increase of the yearly energy consumption of the 5G network. This negative impact also negatively impacts the coverage EE metrics, which will decrease with the increase of DV in all analyzed device density classes (Figure 4a-d). This is the consequence of the increase of network energy consumption when the DVs that must be transferred in the network increase. Consequently, larger energy consumption for the same size of the device density class area will, according to Equation (2), result in the degradation of the coverage EE metric.
## 4 Results and Discussion
### Immersion Marshall Test and Spring-Thawing Stability Test Results  
Figures 11 and 12 present the spring-thawing stability (SS), immersion Marshall stability (MS\({}_{1}\)) and immersion residual stability (MS\({}_{0}\)) for three mixtures. The MS\({}_{1}\) value is obtained by performing Marshall test after the specimen has been immersed in water at 60 \({}^{\circ}\)C for 48 h, and the MS\({}_{0}\) value is obtained by the Equation (4):  
\[MS_{0}=\frac{MS_{1}}{MS}\times 100. \tag{4}\]  
As shown in Figure 11, the SS value of SM-OGFC was the largest of the three mixtures, and the SS value of SM-OGFC was 42% higher than OGFC, even 1.6% higher than SBS-OGFC. The results indicated that SM-OGFC had excellent performance in resisting moisture damage during spring-thawing season. One reason for the increase of the SS value of SM-OGFC is that silane coupling agent with two different types of functional groups has a strong tendency to bond asphalt and aggregate, which can make the content of moisture that penetrates into the interface between asphalt and aggregate decrease as a result. This improves the performance of SM-OGFC against moisture damage during spring-thawing season. As shown in Figure 12, the MS\({}_{1}\) value of SM-OGFC was 41.4% higher than OGFC, reached 98.7% of SBS-OGFC. The results showed that SM-OGFC had a high Marshall stability after immersion at 60\({}^{\circ}\)C for 48h. However, the MS\({}_{0}\) value of SM-OGFC was the smallest of the three mixtures, which indicated that Marshall stability attenuation of SM-OGFC was the largest among three mixtures after immersion at 60 \({}^{\circ}\)C for 48 h. The main reason for this is the high water absorption of OSW. Nevertheless, the higer MS\({}_{1}\) value proved that SM-OGFC had a higher bearing capacity relative to OGFC after immersion at 60 \({}^{\circ}\)C for 48 h.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types** & \begin{tabular}{c} **Bending-Tensile** \\ **Strength/R\({}_{\text{B}}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum Bending** \\ **Strain/\(\epsilon_{\text{B}}\)** \\ \end{tabular} &
\begin{tabular}{c} **Bending Stiffness** \\ **Modulus/S\({}_{\text{B}}\)** \\ \end{tabular} \\ \hline OGFC & 5.202 & 2886 & 1802 \\ SBS-OGFC & 7.465 & 4155 & 1796 \\ SM-OGFC & 6.842 & 4077 & 1678 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Low-temperature beam bending test results of all OGFC mixtures.  
Figure 10: Cantabro losses of all OGFC mixtures.