esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 3 Deep Learning Techniques  
Machine learning systems use algorithms that improve their output based on experience. In the future, machine learning will replace traditional optimization methods in many fields because ML models can expand to include new restrictions and inputs without starting from scratch and they can solve mathematically complex equations. ML models are readily adapted to new situations, as we are currently witnessing with computer systems.  
In the last decade, a subset of machine learning called deep learning (DL) has garnered much attention in computer vision [20; 21] and has discovered new optimal strategies for games [22; 23] without the costly hand-crafted feature engineering previously required. Deep learning uses neural networks to perform automated feature extraction from large data sets and then use these features in later steps to classify input, make decisions, or generate new information [24].  
Research on deep learning for computer vision exploded after the release of ImageNet, a curated database of over 10 million images across 10,000 categories, which helped train ML image classification models [25]. Because 5G implementations are new and deployed in select regions, there are few representative data sets for 5G network traffic and many authors rely on simulations. 5G and slice-based networking may change the models of service demand. However, few authors have representative and detailed data from telecommunication companies [26; 27] because of the risk of leaking proprietary or customer information.  
Combined with the adaptation of SDN/NFV techniques within 5G networks, deep learning presents an opportunity for accurate identification and classification of mobile applications and automates the creation of adaptive network slicing [28], among other possibilities. Figure 3 shows examples of four common deep learning models, which are explained in the following subsections.  
Figure 3: Examples of four common deep learning models.
# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 2 Proposed Method
### View Projection
Thus, an occupancy map expressing how many pixels were projected in the coordinates of the height map was generated using Equation (7) [15]:  
\[O_{(x,y)}=\sum\frac{Z_{cam}}{f^{2}} \tag{7}\]  
where \(Z_{cam}\) is the \(Z\)-axis value of the camera coordinate system, \(O\) is the occupancy map, and \(f\) is the focal length. Figure 9c shows the occupancy map. The brighter the color, the more projected the point. This shows that a large number of pixels were mainly projected around the head. The height map and occupancy map can be used to generate a likelihood map modeled as a Gaussian distribution, as shown in Equation (8) [15]. The likelihood map represents the pixel with the highest probability considering the Gaussian distribution of height and the Gaussian distribution of occupancy. When generating a likelihood map, the average of the Gaussian distribution for height was set to a person's average height of 160 cm, and the standard deviation was set to 40 cm. In a likelihood map, a pixel with a corresponding height and deviation has a high probability value. The average and variance of the Gaussian distribution for the occupancy were set to 2500 and 100, respectively, which were the optimal values through experiments. \[L_{(x,y)}=\frac{\exp\biggl{(}-\biggl{(}\frac{\left(O_{(x,y)}-\mu_{b}\right)^{ 2}}{2\sigma_{g}^{2}}+\frac{\left(H_{(x,y)}-\mu_{b}\right)^{2}}{2\sigma_{h}^{2} }\biggr{)}\biggr{)}}{2\pi\sigma_{o}\sigma_{h}} \tag{8}\]where \(O\) and \(H\) are the occupancy map and height map, respectively, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. Figure 9d shows the likelihood map. It shows the head with the highest value and that the farther away from the head, the more the value decreases. The likelihood map is scanned in a rectangular unit of a certain size, and the head was detected when the center value of the rectangle was the local maximum value. Figure 9e shows the results of the head detection.
## 4 Results and Discussion
### Effect of the Sliding Speed on the Friction and Wear Characteristics
At this  
Figure 20: Three-dimensional topography of the wear surface of the hemispherical-pit surface friction pair at different speeds (\(P\) = 300 N): (**a**) CF/PEEK, \(n\) = 1000 r/min; (**b**) 316L, \(n\) = 1000 r/min; (**c**) CF/PEEK, \(n\) = 1750 r/min; (**d**) 316L, \(n\) = 1750 r/min. stage, oxidation wear dominates, friction occurs between the oxidation films, and the wear rate decreases. When the sliding speed rises to 2.75 m/s (\(n=1750\) r/min), the friction coefficient and the specimen contact temperature are further increased. As illustrated in Figures 19d-f and 20c,d, the grooves on the CF/PEEK and 316L wear surfaces become shallow and uneven. A significant increase in friction heat greatly increases the oxidation speed and degree of the 316L surface, generates filamentous oxide Fe\({}_{3}\)O\({}_{4}\), and also makes the CF/PEEK surface material easy to soften and peel off [32]. The friction changes from oxidation wear to adhesive wear. Due to the high surface temperature and the large plastic deformation of the contact peak point, the strength and area of the adhesive node increase, and the shear failure occurs in the deep layer, resulting in the dramatic increase in adhesion wear and wear rate. The friction surface temperature is high, the material migration is significant, and the grooves are filled by the wear debris through the daub effect, such that the roughness of the specimen's wear surface decreases.
## 4 Results and Discussion
### FEM Results  
#### 4.2.1 The BRBs  
The different values of the friction coefficient (0.05, 0.1, and 0.2) for the tangential behavior in the numerical analysis were used to observe their effects on the numerical cyclic response of BRB2. In addition, the material models [27; 41] were calibrated by matching the test result of BRB2. Figure 16 shows the effects of both calibrated material models and friction coefficients on the numerical hysteretic behavior of BRBs. The numerical cyclic behavior of BRB2 exhibited good agreement with the test results when the value of the friction coefficient between the CP and CM was taken to be 0.05. Both calibrated material models gave approximately identical numerical results for all BRBs [2] (see Figure 17).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Slip (mm)**} & \multicolumn{3}{c}{**Out-of-Plane Displacement (mm)**} \\ \hline  & \multicolumn{3}{c}{LVDTs (in Figure 6)} & \multicolumn{3}{c}{LVDTs (in Figure 6)} \\  & 10 & 11 & 12 & 13 & 16 & 17 \\ Max. & 0.065 & 0.075 & 0.098 & 0.08 & 1.08 & 1.08 \\ Min. & \(-\)0.005 & \(-\)0.033 & \(-\)0.048 & \(-\)0.072 & \(-\)1.06 & \(-\)1.06 \\ \hline \end{tabular}
\end{table}
Table 4: Out-of-plane displacement and slip between the BRB and gusset plate.  
Figure 15: High mode buckling of CP.  
Figure 16: Calibration of material models and value of friction coefficient between the CP and the CM [2; 36; 46].  
Figure 17: Prediction of hysteretic behavior of BRBs using the calibrated material models based on their test results [36,46].
# A Review of Sound Field Control
## 3 Personal Audio Systems
### Applications of Personal Audio Systems  
Personal audio systems, designed to meet the different listening needs of different listeners, improve the privacy of audio transmission and reduce interference to others significantly. Such applications potentially include directional audio guides in museum and galleries, private communication devices in banks and hospitals, and personalized entertainment devices in cabins and other public places.  
Figure 5 shows several typical microphone and loudspeaker arrays for personal audio systems. Figure 5a shows a microphone array for sound field recording, and Figure 5b-d  
Figure 4: Frequency response at center of the microphone array in the bright zone with traditional acoustic contrast control (TACC), broad acoustic contrast control (BACC), and BACC-response variation (RV) methods [45].  
Figure 3: Acoustic contrast versus frequency with traditional acoustic contrast control (TACC), broad acoustic contrast control (BACC), and BACC-response variation (RV) methods [45].  
show several different personal audio systems. In Figure 5b, a linear loudspeaker array is used to control the sound field. The bright and dark zones are generated at the two microphone arrays in this figure, respectively. Figure 5c shows a circular array, and the bright and dark zones are inside the circular area. Another circular array provided in Figure 5d is able to achieve sound field radiation in a certain direction.  
[PERSON] and [PERSON] investigated the application of the ACC method in a headrest [47]. [PERSON] et al. implemented a two-source personal audio system on a mobile phone [48]. The personal audio systems in a car cabin were also implemented [49] using two loudspeaker arrays to achieve independent listening zones over the wide frequency ranges. A significant level of acoustic contrast was achieved when a bright zone was produced in the front seats. The bright zone of this system was expanded to either the front or rear seats by [PERSON] et al. [50]. In this study, a loudspeaker array was mounted on the ceiling to generate independent listening zones at higher frequencies, and the array locations are selected from some of the configurations under study. Compared with the system reported in [49], the improved one achieved a significant boost in contrast performance between 1 and 4 kHz, and more consistent contrast when generating either a front or a rear bright zone.
## 3 Results and Discussion
### Development of Seismic Microzonation Map  
This phase underlines the first objective, which was to develop a seismic microzonation map for the Malay Basin. Since this was a large scope to cover, two sub-objectives were introduced--determination of the peak surface acceleration and plotting the seismic microzonation map. Details on each sub-objective are presented in the following subsections.
# Predicting Fruit's Sweetness Using Artificial Intelligence--Case Study: Orange
## 4 Conclusions  
We conclude that color values can be used to predict fruit's sweetness. Different algorithms were used, namely logistic regression, KNN, tree, SVM and neural network methods. We applied a logistic regression algorithm to an orange fruit dataset in order to train our model. After the prediction, we concluded that logistic regression gave us a prediction accuracy of 97%, KNN gave us an accuracy of 82%, the tree method gave us an accuracy of 96%, SVM gave us an accuracy of 93% and the neural network gave us an accuracy of 88%. All of the tested algorithms are good for generating predictions; however, in this project, we can say that logistic regression gave us better results for predicting the sweetness of oranges. The results suggest that there is a direct relationship between the value of the red color of orange fruits and their sweetness. Based on the above results, it can be concluded that artificial intelligence technology can be used for predicting the sweetness of orange fruits and, thereby, reduce their sorting time and provide consumers with the best-tasting fruit, especially in convenient and affordable forms--an improvement that is likely to increase their consumption.  
Conceptualization, M.A.J.A.-S. and L.G.; methodology, M.A.J.A.-S., L.G. and K.P.; software, M.A.J.A.-S. and L.G.; validation, M.A.J.A.-S., L.G. and K.P.; formal analysis, M.A.J.A.-S., L.G. and K.P.; investigation, M.A.J.A.-S., L.G. and K.P.; resources, M.A.J.A.-S., L.G. and M.S.; data curation, M.A.J.A.-S. and L.G.; writing--original draft preparation, M.A.J.A.-S., L.G. and K.P.; writing--review and editing, M.A.J.A.-S., L.G., K.P. and H.M.B.; visualization, M.A.J.A.-S., L.G. and J.B.; supervision, L.G., K.K. and H.M.B.; project administration, L.G.; funding acquisition, H.M.B., L.G. and K.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# The Preparation of Active Support-Based Sealing Material and Sealing Effect Analysis
## 1 Introduction
When the polycarboxylic acid superplasticizer content is between 1.8 and 2.4%, the fluidity of the experimental cement is greatly increased. [PERSON] et al. [29] studied the effect of geopolymer sealing materials on the rheological properties of cement slurry. The rheological properties of cement slurry decreased with the increase in the water/binder ratio. [PERSON] et al. [30] studied the sealing performance of the sealing material used in pulverized coal drilling, and the research showed that the smaller the water/material ratio, the greater the expansion degree. According to the expansibility, strength and cost of the pulverized coal sealing material, the optimum composition ratio of the pulverized coal drilling sealing material was determined to be 70% fly ash and 30% dosage. At this time, the expansion rate and maximum compressive strength of the sealing material are 6% and 7.30 MPa, respectively. However, in the process of the development of sealing materials, no modification method has been found to improve the fluidity, strength and expansion of the sealing material at the same time. In the past, the development of sealing materials focused on improvements in a certain aspect of performance, and no sealing materials with high fluidity, high strength and expansion properties were developed. Therefore, in view of the problems existing in conventional sealing methods, this study started by studying the source of sealing gas leakage and proposed the concept of the active support sealing of gas drainage boreholes. On this basis, a new type of sealing material with permeability, fluidity, expansibility, early strength and compressive resistance was developed, which could provide active support for boreholes. Using this new material, crack development around the borehole could be inhibited, thus improving the efficiency of gas drainage in the later stage.
# Effect of Space Configurational Attributes on Social Interactions in Urban Parks
###### Abstract  
Urban parks are one of the most common spaces for social interactions in modern cities. The design of park spaces, especially space configuration, has significant influences on people's social behaviors in parks. In this study, the associations between space configurational attributes and social interactions were investigated using space syntax theory. An observation analysis of social behaviors was carried out in two urban parks in Beijing, China. Nine space configurational attributes, including depth to the gate, depth to the main road, connectivity, normalized angular integration (NAIN), and normalized angular choice (NACH) with three radii, were calculated using a segment model. The variance analysis and regression analysis reveal the strong joint effect of space type, space scale factors, and space configurational attributes on social interaction behaviors in parks. The personal interaction group contained 23% of the total observed people involved in social interactions. Pathway length, zone area, and NACH-10K (NACH with a radius of 10,000 m) are positively associated with the number of people involved in personal interactions. For the social interaction group (77% of the total observed people), the space scale and depth to main city road were found to have a positive and negative influence on social interaction intensity.  
Keywords:space syntax; personal interaction; social interaction; behavior mapping +
Footnote †: journal:
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 6 Discussion
### Addressing Real-Time Threats  
While blockchain strengthens data integrity and access control, it does not inherently prevent cyberattacks at the device or network level, where most IIoT vulnerabilities reside. The proposed system integrates complementary security mechanisms, such as Zero Trust architecture and AI-driven anomaly detection, to address the network vulnerability gap.  
Under the Zero Trust model, every access request is rigorously authenticated and authorized, minimizing the risk of unauthorized access. This approach assumes that no user or device is inherently trustworthy, even if they are already inside the network. Meanwhile, AI-based anomaly detection models analyze patterns in network traffic and sensor data to identify suspicious activities in real time. These models effectively detect threats such as DDoS attacks, sensor spoofing, and firmware tampering. By combining blockchain with advanced security measures, the system achieves robust protection against evolving cyber risks [47].
## 3 Experiment Results
### Experimental Setting  
The focus of this study is to analyze the effectiveness of the proposed algorithm primarily from a data science perspective. To better evaluate the algorithm, we consider the following three issues in the experimental setup:  
1. Evaluation metrics In this paper, common metrics such as overall accuracy (OA), feature number (FN), and execution time are used to evaluate feature selection (FS) algorithms. Additionally, we propose two novel metrics: relevance (Rel) and number of redundant features (NRF). The Rel metric represents the ratio of the relevance between the selected feature subset \(F_{s}\) and the target class \(c\) to that between the original feature set \(F\) and the target class \(c\). It is calculated using the following equation: \[Rel=MI(c;F_{s})/MI(c;F)\] (10)  
The Rel value ranges from 0 to 1. The NRF metric represents the number of redundant features contained in the selected feature subset. These two metrics serve to evaluate the quality of the selected feature subset and conduct a more in-depth analysis of the relationship between feature quality and classification accuracy.  
In practical terms, OA and FN are crucial for gauging the performance and complexity of models in classification tasks, while Rel and NRF are essential for elucidating and interpreting the significance and effectiveness of the selected features.  
2. Comparison with existing methods Since the proposed FS algorithm is typically a filter-based model, we introduce representative filter-based FS methods for comparison. We consider two types of methods: IEMs and SEMs. For IEMs, we choose InfoGain [25], GainRatio [13], SU [26], ChiSquare [12], and Fisher [27] as the representative methods. For SEMs, we choose CIFE [19], CMIM [20], JMIM [21], mRMR [23], and maxMIFS [24] as the representative methods. These selected comparison methods cover a wide range of filter-based techniques to assess the performance of the proposed algorithm effectively.  
3. ML algorithms for classification To demonstrate that our FS algorithm is not biased towards specific ML algorithms, we used four widely used ML algorithms: Naive Bayes (NB) [28], SVM [29], C4.5 [30],and Random Forest (RF) [31], to evaluate the classification performance of our FS algorithm. These four ML algorithms represent different ways of solving supervisory learning problems. Their parameter settings are shown in Table 2.  
A 10-fold cross-validation was applied for performance evaluation. In this process, the dataset was split into ten parts, with 90% used for training and 10% for testing in each iteration. Each subset was used once as the test set, mitigating data variability and providing a reliable estimate of the model's performance. Finally, all algorithms are executed on a Windows computer with an Intel Core i7 3.6 GHz 8-thread, 8 GB RAM, and the Matlab R2018a implementation.
## 3 Throughput Performance Limits of Multimode Underwater Acoustic Networks
### Modeling of Throughput Performance Limits in Multimode Networks  
#### 3.1.1 System State Modeling  
In the previous introduction to the network node scheduling model, the states of nodes are denoted as \(s_{j,t}\), and the time slot \(t\) indicates the current time. In an \(N\)-node network, each node has \(2N-1\) states, which include sending signals to other nodes (the number of states is \(N-1\)), receiving signals from other nodes (the number of states is \(N-1\)), and an idle state. The states of the sending and receiving nodes of each link correspond to each other, for example, in link \((i,j)\), node \(i\) transmits signals that reach node \(j\) through a propagation delay \(D_{ij}\). This corresponding relationship can be utilized to reduce the number of node states and simplify the MDP process. Specifically, only the sending state and idle state are considered individual node states, while the receiving state of network nodes is jointly determined by both the sending state of source nodes and the propagation delay \(D_{ij}\). The receiving status of the node is no longer considered, and the status of the node is reduced to \(N\).  
The propagation delay corresponding to the maximum transmission range in the network is denoted by \(G\). The decision of time slot \(t\) is related only to the node state of the time slot \(t-G\) to \(t-1\), so the network state \(\hat{S}\) can be determined by the network node state of the first \(G\) moments. Therefore, the state can be modeled as a matrix of \(N\times G\), where \(\hat{s}_{i,k}\) represents the state of the \(i\)-th node at slot \(t-G-1+k\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Symbol** & **Significance** \\ \hline The set of all states of the system & \(S\) & The stable state of the system over some time \\ The set of all possible actions of & \(A\) & Actions taken by the system in the \\ the decision process & & current state \\  & & The probability of moving to state \(s^{\prime}\) by \\ System state transition probability & \(P_{a}(s,s^{\prime})\) & performing action \(a\in A\) in the current state \\  & & \(s\in S\) \\ Return function & \(R_{a}(s,s^{\prime})\) & The reward function or reward function for \\  & & transferring the system state from \(s\) to \(s^{\prime}\) \\  & & under action \(a\) \\ Damping coefficient & \(\gamma\in[0,1]\) & The discounting or decay of the return \\  & & function over time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter meaning in the Markov decision process.