esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Related Work  
Smart environment monitoring is a well-explored topic in the literature, with many works focused on different goals and technologies. A comparison with the current state-of-the-art is given in Table 1, which shows, for each related work, the monitored air component, hardware devices used, an indication of whether they are implemented in a real-time environment or used just for simulations and test-beds, an explanation of the type of computation and level of intelligence, and the application scenario.
## 3 Results and Discussion  
### Micromorphology and Composition Characterization  
The morphologies of the samples are shown in Figure 3. Figure 3a,c shows the morphology of Al nanoparticles and their corresponding size distribution, respectively. The size of Al nanoparticles ranged from 25 nm to 150 nm. Figure 3b,d displays the morphology and width distribution of MoO\({}_{3}\) nanostrips, which ranged from 75 nm to 200 nm. The morphology of the mechanically mixed composite Al/MoO\({}_{3}\) is shown in Figure 3e. As seen, the Al nanoparticles were uniformly distributed on the MoO\({}_{3}\) nanostrips, but the contact was rather loose compared with the electrospray counterparts (Figure 3g). With the help of NC, Al and MoO\({}_{3}\) could be bound into an intact structure resembling a bird nest, as shown in Figure 3g. And the size of the composite particle was around 12 \(\upmu\)m. From the enlarged image of Figure 3h, it is evident that NC filled the hollow part between Al and MoO\({}_{3}\), and more importantly, it served as the network to bond them together. Additionally, the FTIR spectra in Figure 3f indicate that they were merely a physical combination since no new bonds were generated during the electrospray process. To characterize the distribution of each component, the typical element Mo, Al and N, O, C of the components MoO\({}_{3}\), Al and NC were scanned and mapped throughout the composite particle, and the typical results are shown in the Figure 4. From the elemental mapping results, we could learn that each component uniformly dispersed all over the composite particle. Furthermore, the NC binder homogeneously dispersed between oxidant and fuel, which will benefit the heat accumulation and the transfer of heat and mass during the thermite reaction.  
Figure 3: (**a,b**) SEM images of Al nanoparticles and MoO\({}_{3}\) nanostrips, respectively; (**c,d**) size distribution of (**a,b**), respectively; (**e**) SEM image of mechanically mixed Al/MoO\({}_{3}\)-M; (**f**) FTIR spectra of NC and the electrospray Al/MoO\({}_{3}\); (**g,h**) SEM images of Al/MoO\({}_{3}\)-E.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 4 Experimental Evaluation
### Experimental Results
#### 4.2.1 Varying the Number of Processing Nodes  
We assessed the impact of varying the number of processing nodes on the overall time-based performance of the big data processing system (Spark) deployed on the HPC. Sometimes, this impact is referred to as the speed up of the algorithm. Specifically, we varied the number of nodes that execute the K-means workloads from one to eight. We ran experiments for the three different datasets. Figure 3 plots the results using different colors to highlight the different dataset compositions. The first observation is that the processing cost significantly differs in the three cases. This effect is expected and is consistent with the existing literature. In particular, the highest number of dimensions (1000) corresponds to the best performance, but also to the lower speedup, due to an increased data shuffling overhead. In the other case, the shuffling cost is reduced, and this leads to a better speedup. In turn, however, the overall cost of the computation is increased, because larger datasets with smaller records induce a higher computational load on the system. To substantiate this claim with a quantitative performance evaluation, we included a heatmap (Figure 4) showing CPU utilization across a subset of cores during a representative execution window. This visualization highlights the distribution of workload among Spark workers and supports the analysis of resource usage during this variant of the K-means execution.  
Figure 4: A K-means _CPU_ utilization heatmap from all the cores in the cluster for the 1000M10d case, detailing a representative window of the computation.  
Figure 3: K-means speedup: the workload execution time for different dataset compositions. Error bars represent the variance across different experiment runs.
# Influence of Random Modulated Power Converter on G3 Power Line Communication
###### Abstract  
Power Line Communication (PLC) technologies are being used in many applications and offer the advantage of utilizing existing power cables for both power and data transmission, thus minimizing cost and complexity. Nevertheless, PLC technology requires further investigation to solve possible co-existence issues. Indeed, recent studies confirmed that alternative modulation schemes such as Random Pulse Width Modulation (RPWM), applied to switching-mode power converters to minimize conducted emissions, detrimentally interfere with the PLC system. This paper presents an experimental test campaign aimed at investigating the effects of RPWM on the G3-PLC system, with the final goal of understanding the conditions under which RPWM schemes can be considered as an effective alternative to conventional Pulse Width Modulation (PWM) in applications involving PLC systems. In details, the effects of different RPWM parameters such as switching frequency, modulation index, and Random Number Update Rate (RNUR) on the G3-PLC is investigated. In addition, different RPWM schemes such as Random Frequency Modulation (RFM) and Random Pulse Position Modulation (RPPM) are compared in terms of performance so as to highlight which RPWM is best suited to assure coexistence with PLC systems. The impact of RPWM on the communication channel is evaluated in terms of Frame Error Rate (FER), Channel Capacity, and Channel Capacity Loss metrics. Experimental results confirmed that randomly modulated converters with switching frequencies near the G3-PLC bandwidth cause more significant disturbance and possible coexistence issues than the switching frequencies out of this range. Results also show that the modulation index and the RNUR of RPWM have a direct effect on the communication channel. Moreover, a trade-off between Electromagnetic Interference (EMI) reduction and coexistence issues is observed: RFM, which is very effective for EMI reduction, is found to be very disruptive for G3-PLC, compared to alternative random modulation techniques such as RPPM.  
conducted emission (CE); Electromagnetic Compatibility (EMC); Electromagnetic Interference (EMI); Frame Error Rate (FER); Power Line Communication (PLC) +
Footnote †: journal: _Sciences_  
_Article_
## 2 Materials and Methods
### Fabrication of Nanopipets  
Borosilicate glass capillaries (O.D. 1.0 mm, I.D. 0.58 mm, Sutter Instruments, Novato, CA, USA) were pulled using a P-2000 laser puller to produce nanopipets with a tip diameter of approximately 600 nm. The inner walls of the nanopipets were silanized with chlorotrimethylsilane. Silanization was performed by placing up to 14 pipets on a custom tray inside a desiccator connected to a vacuum pump. After achieving a vacuum, 300-500 uL of chlorotrimethylsilane was introduced into the desiccator for 30-60 min, with the exact time dependent on the ambient humidity and temperature. Following silanization, the nanopipets were filled with the organic phase, consisting of 1,2-dichloroethane (DCE), 10 mM DB24C8 or DB18C6 as the ionophore, and 0.1 M tetradodecylammonium tetrakis(pentafluorophenyl)borate (TDDATFAB) as the electrolyte. TDDATFAB was synthesized according to established protocols [30]. Between testing, the electrodes were stored at 19 \({}^{\circ}\)F in a refrigerator to minimize the loss of the organic phase from the nanopipets.
# Analyzing Demand with Respect to Offer of Mobility
## 4 Model and Analyzer
### Beginning and Final Stops' Analysis
To cope with these aspects, we defined a provider weight vector \(W_{sp}[1,\ldots,|\mathbb{P}|]\), where the weights as above have been set in the basis of their capability of serving. Formally, we have:  
**Definition 4**. : _Let \(P_{bgg}(s,aft)\Big{(}P_{fin}(s,mng)\Big{)}\) denotes the probability that \(s\) is a beginning \(s_{bgg}\) (final \(s_{fin}\)) stop in the afternoon (morning). It is calculated as in (13)._  
\[P_{bgg}(s,aft)=P_{fin}(s,mng)=e^{(-\frac{1}{s_{sp}(s)})} \tag{13}\]  
_It is noted that \(\alpha_{sp}(s)\) is the service provider level of stop \(s\) which indicates the normalized weighted sum of service providers in circle \(c(s,r)\) and calculated as in (14)._  
\[\alpha_{sp}(s)=\frac{u_{sp}(s)-u_{sp}(min)}{u_{sp}(max)-u_{sp}(min)} \tag{14}\]  
_where:_  
\[u_{sp}(s)=\sum_{\mathbb{P}\in\mathbb{P}}^{|\mathbb{P}|}N_{sp}(\mathbb{p}).\ W _{sp}[\mathbb{p}]\]  
\(N_{sp}(\mathbb{p})\) _is the service providers number with type \(\mathbb{p}\in\mathbb{P}\) in circle \(c(s,r)\) and the operator \".\" denotes the multiplication between scalars. Additionally, \(u_{sp}(min)\) and \(u_{sp}(max)\) respectively  
Figure 7: Density of services around stops **A** and **B**. Then, the amount of pick-ups \(pck(s,mmg,k)\) (drop-offs \(drp(s,mmg,k)\) ) at the stop \(s\) in the morning interval time \(H(mmg)[k]\) is equal to the number given by the sum of the commuters who begin (end) their trip and the ones that start their next trip stage (which finish their current trip stage) at \(s\), as in (17) and (18). \[pck(s,mmg,k) = pck(mmg).W(mmg)[k].P_{tra\cup\mathit{h}eg}(s,mmg,k) \tag{17}\] \[drp(s,mmg,k) = drp(mng).W(mng)[k].P_{tra\cup\mathit{fin}}(s,mng,k) \tag{18}\]  
Analogously, the amount of pick-ups \(pck(s,aft,k)\) and drop-offs \(drp(s,aft,k)\) at stop \(s\) in the afternoon interval time \(H(aft)[k]\) can be calculated as in (19) and (20). \[pck(s,aft,k) = pick(aft).W(aft).P_{tra\cup\mathit{h}eg}(s,aft,k) \tag{19}\] \[drp(s,aft,k) = drp(aft).W(aft).P_{tra\cup\mathit{fin}}(s,aft,k) \tag{20}\]  
where \(pick(t)\) and \(drp(t)\) are the number of pick-ups and drop-offs in the time \(t\in\{mng,aft\}\), respectively. Moreover, considering all stops in the studied area, the sum of pickup \(pck(s,aft,k)\) (and drop-off \(drp(s,aft,k)\)) probabilities in each time interval \(H(t)[k]\) must be equal to 1. Therefore, the amount of commuters that are picked-up and dropped at a given stop \(s\), on each vehicle arrival, in time interval \(H(t)[k]\) can be calculated as in (21) and (22):  
Figure 7.0 Figure 7.1  
where \(\mathfrak{t}(s,t,k)\) denotes the arriving vehicles number at stop \(s\) in the interval \(H(t)[k]\).
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 3 Machine Learning in Wireless Communication
### Deep Learning with Artificial Neural Networks (DL-ANN)
Each neuron contains a modifiable value called _bias_, which is denoted as \(b_{3}\) in this case and is applied to the prior sum. The end result is known as _logit_, and it is commonly represented by the letter \(z\). Some basic models simply return the logit value as an output, and most of them apply a non-linear function to it. The output is traditionally indicated by the letter \(y\). The most common non-linear function is the logistic function, which receives logit \(z\) and returns as an output \(\sigma(z)=\frac{1}{1+e^{-z}}\). This function \"narrows\" the field and returns a value between 0 and 1, and it can be given the intuitive probability calculation interpretation of the output given a certain input. Different layers may have different non-linearities, but all neurons in the same layer apply the same non-linearity to their logits. Weights and biases are essential components: the goal of a neural network is to discover the best set of weights and biases possible. This is accomplished through training, which is done with backpropagation. Its idea is to calculate the network error and then adjust the weight to minimize it as much as possible. Intuitively, the error backpropagation is nothing more than a method of descending the gradient; that is, mathematically speaking:  
\[w_{updated}=w_{old}-\eta\
abla E\]  
where \(w\) denotes the weight, \(\eta\) is the learning rate, and \(E\) denotes the cost function measuring the performance of a prediction operation in terms of a mean square error between the perdition results and the target values [18]. In the hidden layer, the weights and biases are initialized at random, multiplied by the relevant inputs, summed together, and then reduced to values between 0 and 1 using logistic regression; and in the end these steps are repeated.
## 1 Introduction
Among them, the transmission resource depends on the coverage time of low Earth orbit (LEO) satellite to users, the transmission state of geostationary orbit (GEO) data relay satellite, and the communication link state. 3. The dynamic allocation of multi-dimensional resources in the SIN is modeled mathematically. A SIN resource allocation method based on the A3C algorithm is proposed. 4. The expected benefits of unit resources under different conditions are simulated and analyzed. The simulation results show that the proposed scheme of unified management of transmission resources, caching resources, and computing resources has better expected benefits, and can effectively improve the efficiency of the SIN resources. The rest of this article is arranged as follows: Section 2 analyzes the related research of the SIN and SDN-based SIN. Section 3 proposes an SDN-based SIN architecture and builds the system model. In the Section 4, the dynamic allocation of multi-dimensional resources in the SIN is modeled as a deep reinforcement learning process based on the algorithm of A3C. The Section 5 simulates and analyzes the scheme proposed in this paper. Section 6 summarizes and discusses the full text.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 3 Details of Damst
### Updating DAMST Angle List and DAMST Point List  
#### 3.3.1 Definition of DAMST Angles  
As described in Section 3.2.1, once the mobile sink designates a new footprint, it immediately updates the DAMST point list and DAMST angle list. For detailed description of these two lists update, definition of DAMST Angles is firstly provided as follows in conjunction with Figure 2. There is a correspondence between the DAMST points and the DAMST angles, specifically, the (n)th DAMST point in the DAMST point list is a vertex of the (n)th DAMST angle in the DAMST angle list.  
**Definition 1**.: _The (n\(-\)1) th DAMST angle \(A[n{-}1]\) in the DAMST angle list is an angle between the edge from the (n\(-\)1)th DAMST point P[n\(-\)1] to a RA and the edge from the (n)th DAMST point P[n\(\,\)1] to the (n\(-\)1)th DAMST point P[n\(-\)1], which is less than or equal to 180\({}^{\circ}\)._  
After each footprint appointment, the new footprint becomes the last element in the DAMST point list and a new DAMST angle is calculated accordingly based on Definition 1 and then put into the  
\begin{table}
\begin{tabular}{c|c} \hline
**Packet Type** & **Sink ID** \\ \hline \multicolumn{2}{c|}{Sink coordinate} & RA ID \\ \hline Node Collection radius & Collection period & Data type \\ \hline \multicolumn{2}{c|}{DAMST point list} \\ \hline \multicolumn{2}{c|}{Route list} \\ \hline \multicolumn{2}{c|}{Data} \\ \hline \end{tabular}
\end{table}
Table 1: Packet format.  
\begin{table}
\begin{tabular}{c c} \hline
**Notations** & **Definition** \\ \hline \(P\) & The DAMST point list \\ \(A\) & The DAMST angle list \\ \(S_{A}\) & Size of the DAMST angle list \\ \(N_{c}\) & The neighbor list \\ \(N_{c}^{0}\) & The neighbor having the minimum route deviation angle \\ \(DA\) & Route deviation angle list of neighbors \\ \(R\) & The route list \\ \hline \end{tabular}
\end{table}
Table 2: Definition of notations.  
Figure 2: A DAMST angle.  
DAMST angle list. How to cancel a DAMST point on a detour via analysis its corresponding DAMST angle is described in Section 3.3.2.
## 3 Experimental Results and Analysis
### Measurement Experiments of 21 Geometric Errors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Translational Error (\(\upmu\)m)**} & \multicolumn{3}{c}{**Angular Error (\(\arcsec\))**} \\ \cline{2-7} \multirow{2}{*}{\(X\)-Axis} & \(\delta_{x(X)}\) & \(\delta_{y(X)}\) & \(\delta_{z(X)}\) & \(\varepsilon_{x(X)}\) & \(\varepsilon_{y(X)}\) & \(\varepsilon_{x(X)}\) \\ \cline{2-7}  & 2.57 & 6.94 & 3.49 & 7.03 & 4.72 & 6.24 \\ \hline \multirow{2}{*}{\(Y\)-Axis} & \(\delta_{x(Y)}\) & \(\delta_{y(Y)}\) & \(\delta_{z(Y)}\) & \(\varepsilon_{z(Y)}\) & \(\varepsilon_{y(Y)}\) & \(\varepsilon_{x(Y)}\) \\ \cline{2-7}  & 0.35 & 10.59 & 0.66 & 1.86 & 1.31 & 1.15 \\ \hline \multirow{2}{*}{\(Z\)-Axis} & \(\delta_{x(Z)}\) & \(\delta_{y(Z)}\) & \(\delta_{z(Z)}\) & \(\varepsilon_{z(Z)}\) & \(\varepsilon_{y(Z)}\) & \(\varepsilon_{x(Z)}\) \\ \cline{2-7}  & 1.13 & 0.46 & 5.12 & 1.53 & 0.47 & 1.42 \\ \hline \multicolumn{7}{l}{Squareness error} & \multicolumn{3}{c}{\(S_{XY}\): 20.59\({}^{\prime\prime}\); \(S_{YZ}\): 12.71\({}^{\prime\prime}\); \(S_{ZX}\): 2.11\({}^{\prime\prime}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurement results of 21 geometric errors. Figure 1: Measurement of 21 geometric errors for the vertical machine tool: (**a**) developed measurement system; (**b**) vertical machine tool TH5656; and (**c**–**e**) measurement of six errors of the \(X\)-, \(Y\)-, and \(Z\)-axes, respectively.
## 4 Discussion
### Physical Activity  
The implementation of machine learning algorithms for physical activity analysis has demonstrated significant potential in revolutionizing how we measure and understand preschool children's movement patterns. Random Forest algorithms have emerged as particularly effective, achieving accuracy rates up to 86.4% in free-living conditions [7]. This superior performance can be attributed to the algorithm's sophisticated handling of complex, non-linear relationships in movement data while maintaining robust protection against overfitting. The effectiveness of Random Forest algorithms aligns with findings from both laboratory and free-living studies in preschool settings [19; 22].  
Sensor placement has emerged as a critical factor influencing measurement accuracy, with notable variations between hip-mounted (69.4-81%) and wrist-mounted (59.1-80%) accelerometers [19; 22]. This variation reflects broader challenges in pediatric activity monitoring, where movement patterns are typically more sporadic and varied than in adults [5; 29]. Research in preschool populations has demonstrated the importance of considering these placement effects when designing physical activity monitoring protocols [20; 25].  
The evolution of activity classification methodology demonstrates a clear trajectory of improvement. [PERSON] et al. [17] established initial benchmarks with accuracy rates of 69.7-82.6% using neural networks, while subsequent studies by [PERSON] et al. [7; 18] achieved enhanced performance through refined algorithms and improved data processing techniques. This progression aligns with recent advances in machine learning applications for childhood physical activity assessment [12; 30].
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Testbed-Friendly Ad Hoc Routing Protocols
#### 2.1.3 Hwmp  
Using AODV as a foundation, a Hybrid Wireless Mesh Protocol (HWMP) has been developed. HWMP is a hybrid routing protocol, capable of using both proactive and reactive path selection algorithms. HWMP offers a higher packet delivery ratio, at the expense of a higher packet overhead. Similarly to AODV, HWMP uses path request (PREQ) messages to obtain optimal packet paths. Authors of [48] engage in performance comparison of AODV, DSDV, DSR, OLSR, AOMDV and HWMP alike: their measurements and simulations prove that under their test conditions, HWMP offers the best performance, followed by OLSR. HWMP's better performance is comparison to AODV stems from the aforementioned fact: usage of both proactive and reactive route path discovery methods. HWMP can route on layer 2 of the Open Systems Interconnection (OSI) stack using Media Access Control (MAC) instead of Internet Protocol (IP) addresses.  
Both AODV and HWMP exist as installation for Linux-based platforms such as Raspberry pi. Due to AODV's library age, between the two, HWMP is generally preferred as a routing scheme.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Prediction Intervals Evaluation
The PICAW and PINAW values present AQRA as the best model followed by an interchange of SGB and BNN, respectively, while GAM remains constantly the least considering these two confidence values. Using PINAD, BNN has the best degree of deviation, and then AQRA followed by an interchange between SGB and GAM, respectively. The number of target values outside of the lower and upper limit range keeps reducing as the confidence level increases. AQRA is thus the best model at 90% confidence value; however, best models are evaluated based on what these metrics measures such as validity, reliability, quality and degree of deviation at a particular confidence value. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PINC\%** & **Model** & **\%PICP** & **\%PINAW** & **\%PICAW** & **\%PINAD** & **ABLL** & **AAUL** \\ \hline
90 & BNN & 90.4956 & 3.7853 & 8.3489 & 0.0130 & 717 & 727 \\  & SGB & 90.6997 & 3.8336 & 8.4560 & 0.1101 & 701 & 712 \\  & GAM & 90.6334 & 3.8259 & 8.4432 & 0.1109 & 702 & 721 \\  & AQRA & 90.7194 & 2.8583 & 6.3140 & 0.0650 & 707 & 703 \\ \hline
95 & BNN & 95.2807 & 4.7673 & 10.5152 & 0.0388 & 358 & 359 \\  & SGB & 95.4058 & 4.1536 & 9.1076 & 0.0659 & 348 & 341 \\  & GAM & 95.3992 & 5.1263 & 11.3586 & 0.0665 & 345 & 354 \\  & AQRA & 95.3926 & 3.6725 & 8.1101 & 0.0534 & 342 & 358 \\ \hline
99 & BNN & 99.0654 & 7.3491 & 16.1999 & 0.0083 & 72 & 70 \\  & SGB & 99.0983 & 7.8238 & 17.9690 & 0.0298 & 66 & 71 \\  & GAM & 99.0259 & 8.8693 & 19.6512 & 0.0198 & 78 & 70 \\  & AQRA & 99.0588 & 7.1009 & 15.8021 & 0.0133 & 70 & 73 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction Interval Evaluation.