esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 1 Introduction
First, it is designed for dynamic and heterogeneous traffic conditions, where each station has a different packet transmission interval that may change over time. Second, it only uses information readily available on the AP side, estimating station-side variables based on available data. Third, it can be executed in real time, not relying on at-runtime evaluation of complex mathematical models. The combination of these three factors allows the algorithm to be deployed in realistic environments, executing it at the start of each beacon interval for instantaneous adaptation to changes in traffic conditions. To the best of our knowledge, this is the first real-time IEEE 802.11 ah station grouping algorithm that can cope with dynamic and heterogeneous traffic. The algorithm is thoroughly evaluated using our previously presented 802.11 ah ns-3 simulator [2]. The remainder of this article is structured as follows. Section 2 introduces related research on IEEE 802.11 ah station grouping. Section 3 provides a brief overview of the IEEE 802.11 ah RAW feature. The real-time station grouping algorithm for dynamic traffic is presented in Section 4. Section 5 describes the derivation of the optimal value of two input parameters used by the proposed algorithm. In Section 6, we provide a comparison of the algorithm to enhanced distributed channel access/distributed coordination function (EDCA/DCF) and static grouping using simulation results. Finally, conclusions and future work are discussed in Section 7.
## 5 Experimental Results
### Brief Report of Some Additional Experiments  
Here, we proceed by doing conducting additional experiments on a number of additional datasets in the same line as before to better understand the difference between accuracy in English and non-English datasets when BERT+GCN is used. On the Arabic-twitter-corpus-AJGT dataset, we experimented with BERT and GCN. We believe that taking some further steps in the pre-processing stage can lead to a better accuracy in this dataset. However, this is out of the scope of the present paper and we leave further investigation on it to future work. We report that, by using BERT and GCN, we reached a 98% accuracy in train data and a 83% accuracy in test data.  
Similarly, the number of data instances in the MR dataset (an English dataset) is much smaller than that in the Digikala dataset (in Persian), yet significantly better results were achieved compared to the Digikala dataset. In fact, BERT+GCN has poor accuracy when operating on Digikala-2 Class compared to the MR dataset. Despite having more data instances, the accuracy obtained on the Digikala dataset was lower than the accuracy achieved on the MR dataset (it is worth mentioning here that, in a limited experiment on the DeepSentiPers dataset using ParsBERT, the result improved compared to when using BERT). These phenomena of differences in the results on English and Persian datasets can be attributed to the strength of BERT in English language data that we used to obtain the features of the nodes in the graph. As mentioned, BERT models have been trained on larger datasets, including the entire English Wikipedia and a vast collection of English books,whereas BERT models for Persian, e.g., ParsBERT, may have been trained on smaller-scale datasets. This extensive pre-training allows English BERT to capture a wide range of language patterns and semantic relationships, making it a powerful model for English text processing. On the other hand, tokenization, the process of dividing text into smaller units such as words, differs between English and Persian due to the linguistic variations. In English, words are usually separated by spaces, which makes tokenization relatively straightforward. In Persian, words are connected without clear spacing, making the tokenization process more challenging. The summary of the experiments on AJGT and MR is reported below.  
As was mentioned before, the MR and AJGT datasets are both balanced and the number of positive and negative comments in them is equal. In the above experiments, we allocated 85% of the data points for training and the rest for testing. Similar to our previous experiments in the previous sections, we repeated the above experiments several times, each time with a different model weights initialization. As mentioned in Table 5, the means of the results (accuracies) were 86.3 and 82.8 on the MR dataset and the AJGT dataset, respectively. Moreover, the standard deviations of the results were less than 0.1 and 0.5 on the MR dataset and the AJGT dataset, respectively. We recall that both the MR and AJGT datasets are smaller than the Digikala dataset.
## 2 Multisensor Array Layout Scheme Based on the Spatial Distribution Characteristics of Rail Strain
### Establishment of Simulation Model
#### 2.1.2 Refined Rail Response Simulation Model  
A refined numerical model (shown in Figure 5) is developed with finite element method (FEM) software Abaqus to investigate the strain responses of rails under the previous calculated wheel-rail forces. The rails, sleepers, and slab track are modeled with solid elements according to their respective nominal geometry and material properties. The fastener system is modeled with multiple discrete linear springs and viscous damping to simulate the effect of rail pads and clamps.  
The boundary settings are shown in the picture. To avoid the influence of boundary conditions on the calculation results, the model is divided into a calculation zone and two transition zones. The rail in the calculation zone is more precisely meshed. The mesh size in the longitudinal direction is 5 mm because the per unit time movement of the load on the rail is 5 mm at the given sampling frequency of 4 kHz. Since the sensors are usually mounted at the rail web or near the rail foot, due to the limitation of installation condition, the meshes at these regions are refined in the cross-section.  
Figure 4: Wheel-rail force curves with wheel flats of different depths.  
Figure 5: Refined rail responses simulation model.  
To transmit the wheel-rail forces to the FEM model, the user subroutine VDLOAD of Abaqus is deployed. The role of this subroutine is to define nonuniformly distributed loads as a function of position, time, and velocity, etc. According to the Hertz contact theory [28], the elastic deformation of the steel of the wheel and the rail creates an elliptic contact area. Assuming that the contact elliptic does not change during the movement, the effect of the vehicle can be regarded as a moving surface load applied to the tread of the rail. In the VDLOAD, the contact elliptic is simplified to a rectangular area (\(15\) mm \(\times\) 5 mm). The velocity of the moving load is 20 m/s and the load history are specified by the former section. The steps of the VDLOAD, at each point in time, are as follows: (1) Determine the center position of the load based on the speed of the vehicle and the time of the movement, (2) determine the contact area according to the predefined contact elliptic size, and (3) traverse all the nodes on the rail tread and calculate the stresses exerting on the nodes within the contact area in accordance with the load history.
# Finding Patterns in Signals Using Lossy Text Compression
## 2 Problem Statement
### Recursive Run-Length Encoding (RRLE)  
In this subsection we suggest a novel generalization of the classic run-length encoding compression, called recursive run-length-encoding (RRLE) which is more suitable to our applications.  
**Definition 1** (Recursive run-length encoding (RRLE)): _An RRLE \(s=(t_{1},s_{1},\cdots,t_{k},s_{k})\) is defined recursively as a 2k-tuple, where \(k\geq 1\) is an integer, \(t_{i}\geq 1\) is an integer, and \(s_{i}\) is either a string or an RRLE, for every \(i\in[1..k]\). The size\(|s|\) of \(s\) is recursively defined as \(k+\sum_{i=1}^{k}|s_{i}|\). Similarly, \(S(s)=u_{1}{}^{t_{1}}\cdots u_{k}{}^{t_{k}}\) where \(u_{i}=s_{i}\) if \(s_{i}\) is a string, and \(u_{i}=S(s_{i})\); otherwise, for every \(i\in[1..k]\), if \(S(s)=Q\), then \(s\) is RRLE of the string \(Q\). We define \(\operatorname{rcost}(Q)\) to be the size of the smallest RRLE of \(Q\), \(\operatorname{rcost}(Q)=\min_{\{s|S(s)=Q\}}|s|\). Such an RRLE is called an optimal RRLE of \(Q\) and is denoted by \(s^{*}(Q)\)._  
For example, consider the string \(M=(aaabbaaabb)\). One RLE of \(M\) is \((3,a,2,b,3,a,2,b)\). Another one is \((2,aaabb)\). A possibly shorter description of the string may be as a couple of repetitionsof the string \(aaabb=(3,a,2,b)\). This gives the recursive description, \((aaabbaaabb)=(2,saabb)=(2,S(3,a,2,b))\).  
A less trivial example is the string  
\[abcccabcccgghhekmffffccdcccd=\] \[S(2,abccc,2,g,2,h,1,ekml,3,f,2,cccd)=\] \[S(2,S(1,ab,3,c),2,g,2,h,1,ekml,3,f,2,S(3,c,1,d)).\]  
While the last expression seems longer than the first one, it can actually be represented efficiently using a RRLE tree, which is a tree, where each edge corresponds to a counter (number of repetitions), and each of its leaves corresponds to a string; see Figure 1.  
A natural problem statement that follows Definition 1 is: how to compute the optimal compression of a given string.  
**Problem 2**.: _Given a string \(Q\), compute the optimal RRLE \(s^{*}(Q)\) of \(Q\). That is, \(s^{*}(Q)\) is the tuple that minimizes \(|s|\) over every tuple \(s\) which is a compression of \(Q\); i.e., \(S(s)=Q\) and \(|s|=\operatorname{rcost}(Q)\). Here, \(S(s)\) is the string that corresponds to \(s\) as in Definition 1._
## 3 Results and Discussion
### Conductivity
#### 3.3.2 Temperature Dependencies of Conductivity
Thus, dense ceramics based on perovskite LaScO\({}_{3}\) are obtained at 1650 \({}^{\circ}\)C [33], and Zn\({}^{2+}\)-containing phases have good ceramics when sintered at 1300 \({}^{\circ}\)C.  
These studies have shown that the phase La\({}_{2}\)ScZnO\({}_{5.5}\) is promising for further investigations, both when varying the concentration of the dopants, and when the nature of the latter changes. Probably, the selection of the optimal concentration of the dopant with its optimal size will increase the ionic conductivity. From this point of view, the new phases capable of low-temperature sintering, having chemical stability and high ionic conductivity could be used as electrolytes for fuel cells.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Methods
#### 5.3.1 Comparative Analysis of Demand Forecasting Dataset Based on Delay  
The UDAF model's effectiveness in demand forecasting is evaluated by comparing it with other methodologies using MAE, MSE, RMSE, and RMSLE, in line with the delay of 25.  
According to Figure 4a, the UDAF model surpasses NARX in demand forecasting, with the lowest error of 5.46 and an MAE of 1.73.  
Furthermore, Figure 4b demonstrates the UDAF model's proficiency in demand forecasting through MSE, outperforming NARX by 91.39% with a 4.14 MSE for a delay of 25.  
Regarding demand forecasting, Figure 4c showcases the superior performance of the UDAF model compared to NARX. It achieves a 38.66% improvement with an RMSE of 2.03 for a delay of 25, surpassing previous techniques.  
Similarly, in demand forecasting, as shown in Figure 4d, the UDAF model exhibits superior performance compared to NARX, achieving a 27.73% improvement with a 0.019 RMSSE for a delay of 25, surpassing previous techniques.  
Regarding demand forecasting, Figure 4e showcases the superior performance of the UDAF model compared to NARX. It achieves a 4.62% improvement with an R-squared of 0.936 for a delay of 25, surpassing previous techniques.
## 4 Results
### KEGG Pathway Enrichment Analysis for the DEGs  
The R package _Enrichr_ was employed to discover cancer pathways for the significant DEGs. _EGFR_ and _PIK3 RI_ were involved in most of the pathways, and they can be further investigated for biological discoveries. Moreover, by ranking the pathway terms using a combined score, we noticed that focal adhesion, ECM-receptor interaction, melanoma, and prostate cancer contains many of our genes (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**GO Term** & _p_**-Value** & **Odds Ratio** & **Combined Score** \\ \hline collagen-containing extracellular matrix (GO:0062023) & \(4.04\times 10^{-12}\) & 4.231518 & 111.0119 \\ microtubule (GO:0005874) & \(5.27\times 10^{-7}\) & 4.317358 & 62.40844 \\ spindle (GO:0005819) & \(1.2\times 10^{-6}\) & 4.065692 & 55.43982 \\ polymeric cytoskeletal fibre (GO:0099513) & \(1.93\times 10^{-6}\) & 3.489522 & 45.92285 \\ cell-substrate junction (GO:00300555) & \(3.54\times 10^{-6}\) & 2.853014 & 35.81027 \\ spindle microtubule (GO:0005876) & \(4.96\times 10^{-6}\) & 7.18054 & 87.70782 \\ focal adhesion (GO:0005925) & \(7.4\times 10^{-6}\) & 2.792398 & 32.98852 \\ cell-cell junction (GO:0005911) & \(1.54\times 10^{-4}\) & 2.779697 & 24.40338 \\ platelet alpha granule (GO:0031091) & \(1.56\times 10^{-4}\) & 4.570755 & 40.05538 \\ actin-based cell projection (GO:0098858) & \(3.98\times 10^{-4}\) & 4.44022 & 34.76533 \\ \hline \end{tabular}
\end{table}
Table 4: The top 10 enriched GO terms in which the DEGs were significantly enriched, in the cellular component group.  
\begin{table}
\begin{tabular}{c c c c} \hline
**GO Term** & _p_**-Value** & **Odds Ratio** & **Combined Score** \\ \hline protein homodimerization activity (GO:0042803) & \(1.82\times 10^{-6}\) & 2.459593376 & 32.51094586 \\ platelet-derived growth factor binding (GO:0048407) & \(5.68741\times 10^{-6}\) & 30.30218069 & 365.9672024 \\ microtubule motor activity (GO:0003777) & \(1.7457\times 10^{-5}\) & 7.00721241 & 76.69830849 \\ microtubule binding (GO:0008017) & \(1.89485\times 10^{-5}\) & 3.29533099 & 35.83274576 \\ tubulin binding (GO:0015631) & \(3.43055\times 10^{-5}\) & 2.857481542 & 29.37549443 \\ motor activity (GO:0003774) & \(6.71537\times 10^{-5}\) & 5.769550996 & 55.43688284 \\ actin binding (GO:0003779) & \(9.57553\times 10^{-5}\) & 3.403527337 & 31.49526963 \\ receptor ligand activity (GO:0048018) & \(9.98683\times 10^{-5}\) & 2.712680383 & 24.98828531 \\ cell-cell adhesion mediator activity (GO:0098632) & \(1.19521\times 10^{-4}\) & 7.288930582 & 65.8337387 \\ metalloendopeptidase activity (GO:0004222) & \(3.63166\times 10^{-4}\) & 4.501276991 & 35.65303972 \\ \hline \end{tabular}
\end{table}
Table 3: The top 10 enriched GO terms in which the DEGs were significantly enriched, in the molecular function group.
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 4 Results
### Point Anomalies--SESD (Seasonal ESD)
#### 4.3.2 Heatmap Saturation Percentage Series  
In Figure 16, we can observe the different point anomalies that have been detected for the saturation percentage series of the heatmap, after applying the ESD algorithm to the STL decomposition of the series. Despite not considering the intervals considered as collective anomalies, the algorithm is able to detect anomalies in these intervals, reinforcing the validity of the methodology used.  
To validate the proposed point anomalies, the anomaly with the highest residue is verified with the available previous or subsequent instances. In Figure 17, we can observe the capture corresponding to the anomalous value of '2 October 2023 10:45:00' compared to captures from the previous and subsequent day. The difference in saturation is evident,  
Figure 16: Plot of heatmap saturation percentage residual with point anomalies.  
Figure 14: Plot of detection residual with point anomalies.  
Figure 15: Justification of anomalies in detection series. (**a**) 11 October 2023 10:15:00 (Anomaly). (**b**) 4 October 2023 10:15:00 (Previous week). (**c**) 20 September 2023 10:15:00 (3 weeks earlier).  
obtaining a higher saturation value (0.016843) in the one considered anomalous. Checks have been carried out at more time instances and with different anomalies, but for the sake of article length, they are omitted here.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments  
### Data Description and Pre-Processing Methodology  
#### 3.1.1 Grayscale CT Imaging Dataset  
This work sourced publicly available organ data from \"Multi-Atlas Labeling Beyond the Cranial Vault--Workshop and Challenge\" (BTCV) [17]. The BTCV dataset contains annotations for various organs, including the spleen, right and left kidneys, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal and splenic veins, pancreas, and both adrenal glands. It also includes images of organs without annotations.  
To preprocess the CT images, pixel values are limited to a range between \(-135\) and \(215\), as well as normalized. Additionally the images are resized to dimensions of \(256\times 256\). The dataset is allocated \(75\%\) for training and the remaining \(25\%\) for testing, based on individual patients. Given that no single image encompasses all organs, the focus was placed on those that contained seven or more organs. This resulted in a selection of \(821\) training images and \(228\) testing images.  
To train the U-Net-based [15] target segmentation model, the entire training dataset was utilized. For the detection of adversarial attacks, the \(821\) training images were partitioned evenly into a dedicated training set and a validation set. The former facilitated the training of a binary classifier tailored for adversarial attack detection, while the latter was leveraged to select the best filter to detect the adversarial sample.
## 3 Experimental Verification and Analysis
At the same time, this paper introduces a lateral suppression mechanism in the network iteration process to further ensure the sparsity of pulse delivery and the specificity of learning features, which reduce network complexity and training calculations. In addition, the introduction of adaptive thresholds also enables the model to suppress the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Model** & **Learning Algorithm** & **Classification Accuracy (\%)** \\ \hline SNN & STDP with Fixed Threshold & 88.5 \\ SNN & STDP with Adaptive Threshold & 95.4 \\ SNN & SNN with Adaptive Threshold & 96.6 \\  & Lateral Suppression & 96.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithm Comparison. Figure 8: Graph of Weight Reconstruction. (**a**) result of Figure 7a; (**b**) result of Figure 7b; (**c**) result of Figure 7c; (**d**) result of Figure 7d; (**e**) result of Figure 7e. Figure 7: Comparison Chart of Pulse Threshold. (**a**) fixed high threshold; (**b**) fixed low threshold; (**c**) variable threshold for “1” with lateral suppression algorithm; (**d**) variable threshold for “2” without lateral suppression algorithm; (**e**) variable threshold for “2” with lateral suppression algorithm. occurrence of overfitting under limited resources without resorting to additional dynamic learning rates and random inactivation schemes. Compared with SNN model with other algorithm, classification accuracy of the SNN model fused with the lateral suppression algorithm is 96.6. Therefore, the model proposed in this paper can effectively reduce occupancy while ensuring good classification performance, which is more suitable for the realization of edge computing solutions of small intelligent hardware systems with high efficiency and low power consumption.
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
###### Abstract  
Wireless sensor networks (WSNs) have been applied in networking devices, and a new problem has emerged called source-location privacy (SLP) in critical security systems. In wireless sensor networks, hiding the location of the source node from the hackers is known as SLP. The WSNs have limited battery capacity and low computational ability. Many state-of-the-art protocols have been proposed to address the SLP problems and other problems such as limited battery capacity and low computational power. One of the popular protocols is random path routing (RPR), and in random path routing, the system keeps sending the message randomly along all the possible paths from a source node to a sink node irrespective of the path's distance. The problem arises when the system keeps sending a message via the longest route, resulting because of high battery usage and computational costs. This research paper presents a novel networking model referred to as calculated random path routing (CRPR). CRPR first calculates the top three shortest paths, and then randomly sends a token to any of the top three shortest calculated paths, ensuring the optimal tradeoff between computational cost and SLP. The proposed methodology includes the formal modeling of the CRPR in Colored Petri Nets. We have validated and verified the CRPR, and the results depict the optimal tradeoff.  
wireless sensor network; source-location privacy; calculated random path routing; modeling; Petri nets; security system +
Footnote †: journal: Applied Sciences  
**Copyright** © 2022 by the authors. Liconssee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 5 Conclusions
It also receives aggregated consumptions from intermediate elements of the grid (substations at district level, city distribution level). A cybersecurity attack could target both the billing system and the individual smart meters, preventing the electrical utility from knowing how much electricity has been consumed by the customers. An electrical company would lose a significant amount of money if it was not able to bill its customers, and the attacker could ask for an important ransom. However, thanks to machine learning, the electrical company could estimate the consumption made based on historical data, and therefore, minimize the incentive of the attacker to ask for a ransom, and if the attack is executed in any case, make a provisional billing based on an accurate estimation. With the current dataset, estimations are made at the small city level, because consumption data from individual smart meters are much more restrictive from a General Data Protection Regulation (European Union) data privacy perspective. As a future line of study, research could be extended with anonymized personal identifiable information. Considering the experience gained with deep learning models applied to time-series forecasting from the perspective of a multivariate multi-output regression problem, we plan to extend the experiments with novel deep learning sequential models, such as transformers [62]. Another future work will be to build controlled perturbation-based experiments to demonstrate the capabilities of STLF as a security/threat indicator. Conceptualization, methodology, formal analysis, and software, M.L.-M.; validation, A.S.-E. and L.H.-C.; resources, B.C., J.I.A., A.S.-E. and L.H.-C.; data curation, L.H.-C.; writing--original draft preparation, M.L.-M.; writing--review and editing, L.H.-C., J.I.A. and A.S.-E.; visualization, M.L.-M.; supervision, B.C., J.I.A. and A.S.-E.; project administration, B.C. and J.I.A. funding acquisition, B.C. and J.I.A. All authors have read and agreed to the published version of the manuscript. This research was funded with grant RTI2018-098958-B-I00 from Proyectos de l+D+i <<Retos investigacion>>, Programa Estatal de l+D+i Orientada a los Retos de la Sociedad, Plan Estatal de Investigacion Cientifica, Tecnica y de Innovacion 2017-2020, Spanish Ministry for Science, Innovation, and Universities; the Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER). Not applicable. Restrictions apply to the availability of these data. Data was obtained from Iberdrola and are available from the authors with the permission of Iberdrola. Code will be shared upon properly justified requests. The authors declare no conflict of interest.