esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Soil-Pile Analytical Model  
Slab foundations are frequently used to support structures built on poor soil conditions. In order to widen their field of application, a soil improvement can precede the slab construction. Nowadays, this soil improvement can be done with rigid inclusion, which is cemented columns or piles that increase soil stiffness and bearing capacity.  
The model described in this paper studies the interaction between the slab, the rigid inclusions, and the surrounding soil, regarding the benefices in increasing soil stiffness, and hence the settlement reduction.  
Soil-pile interaction can be simulated by a single cell model (Figure 5), which takes into account the differential settlement between the two material and the friction stresses generated between them [9]. The following model was used:  
Figure 4: \(N_{SPT}\) representation: (**a**) Example of a statistical representation of the SPT-parameter corrected by method and statistical uncertainty; (**b**) Artificial SPT profile generated by probabilistic Monte Carlo Method.  
* Concrete slab foundation load (Fo) is transmitted to the piles through the transfer layer or capping. This is made of a granular material less deformable and with a higher friction angle than the soil underneath. This layer tends to concentrate the distributed load on the piles.
* Soil and pile are connected by elastoplastic springs which limit the maximum friction load transmitted to pile up to a value equal to the maximum skin friction
* Soil column transmits vertically the load taking account of the soil vertical deformation modulus, considered as an elastic material.
* Pile elements are also connected by elastic springs.
* At the pile base, an elastoplastic spring is placed to simulate pile base resistance.
* Below pile base, soil still settles according to a one-dimensional deformation model  
Site investigation allows doing prediction on soil deformability and pile-soil interaction. For granular soil, SPT index can be used to support that prediction, following the procedure below:  
1. SPT index is related to relative density. Gibb and Holtz give a well-known correlation between them, and the vertical effective pressure.
2. Relative density is related to the ultimate skin friction and base resistance according to ISO 19901-4:2003 (API RP 2 GEO) [14].
3. The pile can be split into same-length slices. At each slice, ultimate skin friction is associated depending on its depth. The deepest slice has additionally a base resistance.
4. For each pile slice, its curve t-z is calculated and for the deepest the base bearing capacity curve, according to the aforementioned code.
5. Soil deformation modulus can be obtained also from the relative density and SPT index. It can be transformed into spring when area and slice thickness is known.
6. That analytical model gives a load-settlement prediction curve, which later can be compared to, for example, a real-scale load test.  
The following sections describe this procedure in more detail
## 4 Finite Element Analysis
### Optimization Analysis of Purlin Hangers
The results showed that the steel consumption of one L-shaped purlin hanger was 0.324 kg, while that of one Z-shaped purlin hanger and one \(\Pi\)-shaped purlin hanger was 0.418 kg and 0.742 kg, respectively, representing increases of at least 1.29 times compared to the L-shaped purlin hanger. Z-shaped and \(\Pi\)-shaped purlin hangers seem uneconomical from this perspective. However, when comparing the total steel consumption of the photovoltaic supports of Array A, the amount of steel required for the Z-shaped purlin hanger only increased by 0.55% compared to the L-shaped purlin hanger, and the \(\Pi\)-shaped purlin hanger showed a mere 2.4% increase. These marginal increments become negligible when considering the significant improvement in joint mechanical performance. Furthermore, although L-shaped purlin hangers are more conventional, both Z-shaped and \(\Pi\)-shaped purlin hangers can be manufactured with comparable ease in factory settings. When purlin hangers are installed with three bolts, the on-site installation difficulty of Z-shaped and \(\Pi\)-shaped purlin hangers is comparable to that of L-shaped purlin hangers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Stress (MPa)** & **Ratio of Stress** & **U1 (mm)** & **Ratio of U1** \\ \hline A2 LO & 359.4 & 1 & 11.42 & 1 \\ A3 LO & 351.0 & 0.98 & 1.92 & 0.17 \\ A3 ZO & 283.8 & 0.79 & 1.71 & 0.15 \\ A3 TIO & 258.8 & 0.72 & 1.03 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis results for stress and deformation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Purlin Hanger** & \begin{tabular}{c} **Steel Consumption of** \\ **One Purlin Hanger** \\ \end{tabular} & \begin{tabular}{c} **Ratio of One** \\ **Purlin Hanger** \\ \end{tabular} & \
\begin{tabular}{c} **Steel Consumption of** \\ **Array A** \\ \end{tabular} &
\begin{tabular}{c} **Ratio of Array A** \\ \end{tabular} \\ \hline L-shaped & 0.324 kg & 1 & 482.372 kg & 1 \\ Z-shaped & 0.418 kg & 1.29 & 485.004 kg & 1.005 \\ \(\Pi\)-shaped & 0.742 kg & 2.29 & 494.076 kg & 1.024 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of steel consumption for different types of purlin hangers.
## 1 Introduction
The study used three criteria: heavy vehicle accident incidents, the number of heavy vehicles involved in accidents, and accident severity index values. Spatial autocorrelation (Moran's I) and the Getis-Ord Gi statistic are used to detect clustering and assess the probability of risk. This approach guides the development of targeted countermeasures for identified hotspots, prioritizing segments with a high risk of heavy goods vehicle accidents. The prediction of traffic incidents based on coordinates is a challenging task, resulting in scarce literature on this topic. To overcome this drawback, the geographical information system was used to convert past accident coordinates (latitude and longitude information) into geohash codes to predict traffic incident locations. It is not possible to determine the exact coordinates of traffic accidents, so appropriate geohash codes and defined regions were utilized to determine their location. These regions also consider the areas in close proximity to the accident location. The main purpose of our study is to make more accurate predictions by using geographic data and machine learning algorithms to determine the locations of traffic events. Including predictions of traffic incident locations in studies related to traffic predictions will offer significant insights into traffic control and potential traffic incidents. In the study, a three-stage model was utilized to estimate the location of traffic incidents requiring intervention by traffic teams. First, we analyzed the regions with the highest frequency of traffic incidents, identified potential incident areas in the selected pilot region, and converted the coordinates of potential incident locations into geohash fields through ArcGIS. During the second stage, a group of variables were identified as primary components that can impact traffic accidents and incidents. These variables consist of time-dependent factors that influence traffic events, variables linked to traffic density, accidents, road conditions, vehicle-related variables, and meteorological variables. Data on all variables were then collected for defined incident locations to create a comprehensive dataset. Finally, traffic incident locations were predicted for certain regions and time periods with the help of various machine learning algorithms such as DT, k-NN, RF, and SVM. Including geographical data and geohash coding in machine learning algorithms as input variables can enhance prediction accuracy and contribute to traffic management and security. Improving the accuracy of predicting the location of traffic incidents has the potential to enhance traffic management and flow, decrease travel times, prevent accidents, ensure traffic safety, provide alternative routes, bolster public transportation, diminish fuel consumption and air pollution, and improve traffic management and planning. One of the primary benefits of this approach is its positive impact on both drivers and traffic management teams, as it improves traffic flow and mitigates the adverse effects of traffic [48; 49].
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 3 UAV Formation Control Technologies
Under three key rules--separation, cohesion, and velocity alignment--UAVs can maintain a desired formation shape and move uniformly toward a target direction, while avoiding collisions and ensuring safe inter-UAV spacing. Moreover, flocking supports dynamic splitting and merging of UAV groups, offering excellent scalability and adaptability. The control rules are summarized as follows [45; 46]:  
* Separation: Each UAV is repelled by the sum of repulsive forces from neighboring agents to avoid collisions. * Cohesion: Each UAV is attracted toward neighboring agents to maintain team compactness. * Velocity alignment: Each UAV adjusts its velocity to match the average velocity of its neighbors, ensuring synchronized motion across the swarm. Based on the aforementioned rules, considering the realistic swarm flight environment, the flocking algorithm adopted in this work is designed as follows.
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
###### Abstract  
In recent years we have been assisting a radical change in the way devices are connected to the Internet. In this new scope, the traditional TCP/IP host-centric network fails in large-scale mobile wireless distributed environments, such as IoT scenarios, due to node mobility, dynamic topologies and intermittent connectivity, and the Information-Centric Networking (ICN) paradigm has been considered the most promising candidate to overcome the drawbacks of host-centric architectures. Despite bringing efficient solutions for content distribution, the basic ICN operating principle, where content must always be associated with an interest, has serious restrictions in IoT environments in relation to scale, performance, and naming, among others. To address such drawbacks, we are presenting ndInIoT-FC, an NDN-based architecture that respects the ICN rules but offers special treatment for IoT traffic. It combines efficient hybrid naming with strategies to minimize the number of interests and uses caching strategies that virtually eliminates copies of IoT data from intermediate nodes. The ndInIoT-FC makes available new NDN-based application-to-application protocol to implement a signature model operation and tools to manage its life cycle, following a publisher-subscriber scheme. To demonstrate the versatility of the proposed architecture, we show the results of the efficient gathering of environmental information in a simulation environment considering different and distinct use cases.  
Internet of Things; Smart City; Information-Centric Network; Named Data Networks; publish-subscribe +
Footnote †: journal:  
_Article_  
ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
# Experimental Study on Shear Strengthening of RC Beams with an FRP Grid-PCM Reinforcement Layer
###### Abstract  
This paper investigates the shear strengthening effect of a number of reinforced concrete (RC) beams strengthened by a reinforcement layer which combines carbon fiber reinforced polymer (CFRP) grid and polymer cement mortar (PCM). A total of ten RC beams, including three types of specimens as Series A and seven kinds of specimens as Series B, were prepared and investigated. The test variables in both series of experiments included various reinforcement ranges and different reinforcement amounts that consisted of CFRP grids' spacing and cross-section areas. The experimental results suggest that the shear strengthening effect of the CFRP grid-PCM layer for RC beams is obvious and adequate. Meanwhile, better performance is observed if the CFRP grid-PCM reinforcement layer is used for the full sectional reinforcement of RC beams with an I-shaped profile, in contrast to RC beams with reinforcement of the web only. In addition, a new evaluation method based on the effective strain of the CFRP grid is developed to determine the shear capacity of RC beams strengthened by a CFRP grid-PCM layer.  
Keywords:fiber reinforced polymer (FRP) grid; polymer cement mortar (PCM); shear strengthening; reinforced concrete (RC) beams; evaluation method +
Footnote †: journal: Applied Sciences
## 4 Discussion
The comparison between this investigation and our previous works confirms the role of the metal precursor and preparation method in the change of the crystallographic of the product. In other words, this study resulted in the preparation of cubic spinel Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) as a single phase. The nano regime range of the synthesized Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) NPs with certain agglomerations has been verified by SEM results. Figure 5f indicates that the nanoparticles were clustered or very aggregated. The TEM results gave the same results that were obtained using the SEM technique. From the EDS spectrum, it was evident that the Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) NPs were successfully synthesized in accordance with the main components of the sample prepared as Cu, Mn, and O elements with carbon traces. Nevertheless, the EDS mapping results were compatible with that of the XRD of the as-synthesized material. The biosynthesized Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\)NPs exhibit magnetic hysteresis loops with coercivity (215.16 Oe) and saturation magnetization (0.2986 emu/g). In order to understand the mechanism responsible for ferromagnetism in Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) NPs at room temperature, the origin of ferromagnetism is analyzed by different possibilities that can be present in this system. In other words, Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) NPs have a room temperature ferromagnetic-like state (RTFM) depending upon the competition between ferromagnetic and antiferromagnetic sublattices [44]. This competition could be attributed to the presence of a complex magnetic structure with the existence of several magnetic sublattices in the oxides. Some authors observed the Mn\({}^{2+}\)-Mn\({}^{3+}\) antiferromagnetic interactions at low temperatures [41]. These authors reported Mn\({}^{3+}\) and Mn\({}^{4+}\) ferromagnetic interactions at a higher temperature close to the magnetic transition. The interaction between Cu\({}^{2+}\) and Mn\({}^{3+}\) ions has been suggested to be ferromagnetic [45]. From this point, we can add that the ferromagnetic behavior of the prepared sample with 5 mL egg white is also due to the interaction between Cu\({}^{+}\) and Mn\({}^{2+}\) ions at the tetrahedral site involved in the Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) lattice. It is reasonable to suspect that the indirect coupling between Mn ions mediated by O and Cu ions leads to the stabilization of ferromagnetism [46; 47]. Additionally, Cu-O-Cu interactions may also play a role. In addition, the investigated system has superparamagnetism properties depending on its nano particle nature. Moreover, the squareness of the prepared Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) NPs was below 0.5, indicating a decrease in anisotropy in the crystal lattice [27]. This behavior could be attributed to magnetostatic interaction between these particles [27]. Therefore, the prepared Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) system can be used in the manufacture of spintronic devices depending upon its room temperature ferromagnetic behavior.
## 3 Results and Analysis  
### IR and AE Time Series Distribution of Different Coal Impact Types  
The average and maximum infrared radiation temperature (AIRT and MIRT) and AE count and energy are commonly used for the analysis of IR and AE time series signals. This paper uses the AE energy and MIRT variation as examples to obtain the time series distribution of IR and AE for three coal impact types: no, weak, and strong, as shown in Figure 3.  
As the load increases, the particles inside the no-impact coal undergo sliding and friction, and the IRT and AE energy slowly increase synchronously (Figure 3a). Near the stress peak, both IRT and AE energy reach their maximum values. After reaching the peak stress, the coal body continues to experience compression and friction, accompanied by fluctuations in IRT and AE energy. Weak-impact coal exhibits fluctuating changes in AE energy before coal body failure. At the moment of stress drop, the AE energy suddenly increases, corresponding to an increase in IRT fluctuations (Figure 3b). Strong-impact coal exhibits fewer AE signals and no significant changes in IRT during the early stages of loading. A small amount of stress drop occurs before the main failure, which generates a large amount of AE energy and increasing IRT fluctuations (Figure 3c).  
There is a good correlation between the IRT and AE energy of the different impact types of coal. The IRT increases slowly when AE energy occurs intensively and increases rapidly when AE energy increases abruptly. At the moment of main failure, the IRT and AE signals reach their maximum values instantaneously. With the increase in coal impact tendency, the intensity of the IR and AE signals gradually increases. The MIRT variation in strong-impact coal is 1.71 and 8.72 times that of weak-impact coal and no-impact coal, respectively. The maximum AE energy of strong-impact coal is 6.18 and 20.87 times that of weak-impact coal and no-impact coal, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Numbers**} & \multicolumn{3}{c}{**Coordinates**} \\ \cline{2-4}  & x & y & z \\ \hline
1 & 0 & 25 & 90 \\
2 & 0 & 25 & 50 \\
3 & 0 & 25 & 10 \\
4 & 10 & 50 & 90 \\
5 & 40 & 50 & 10 \\
6 & 50 & 25 & 90 \\
7 & 50 & 25 & 50 \\
8 & 50 & 25 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordinates of AE sensors.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband
### Performance Figure Derivation
#### 3.2.1 Noise and Channel Estimator  
The channel and noise estimation block's primary purpose is to estimate the minimum mean square error (MMSE) channel equalization tap weights, achieved by uniting several operations. Its noise estimates are also an important factor in soft-decision demapping. The noise and channel estimation tasks include:  
* Using the fast Golay correlation (FGC) algorithm [34] to calculate the cross-correlation between the received signal and the two known complementary Golay sequences \(Gv_{512}\) and \(Gu_{512}\). The process is repeated twice--once for each Golay sequence--and ultimately yields the channel input response (CIR).
* Converting the FGC results to the frequency domain via a fast Fourier transform (FFT) block, weighing them with \(\frac{1}{2}\), and adding them together, forming the channel frequency response (CFR).
* Calculating the signal-to-noise ratio (SNR) using the CFR and the frequency-domain correlation results.
* Finally, obtaining the MMSE matrix using the CFR and SNR.  
The above tasks are illustrated in Figure 5, which roughly outlines their time delays and parallel execution possibilities. Based on these, the study assumes the FFT is the most time-consuming factor in the noise and channel estimation block. The FFT's throughput and propagation delay are derived from [35], where the authors report a 2.64 Gsps output sample rate and a latency from the input to the output of 63 cycles. Using the same clock frequency as in their work--330 MHz--results in a 191 ns propagation time delay.  
Figure 4: Component time delay performance figures, illustrated on an example.
## 4 Proposed Method
### PDNet  
#### 4.2.1 Theoretical Basis  
CNNs and absolute positions are rarely discussed together. Since specific tasks have different requirements, it is generally believed that CNNs are translation-invariant (for classification tasks) or that translations are equivalent (for segmentation and detection tasks). The network does not need to know the absolute position of an object, and the position information was used as an a priori learning case for coordinate conversion and in pre- and postprocessing.  
According to the literature, U-Net is more suitable for medical image segmentation than other frameworks. However, the experimental results showed that for small databases and high-resolution images, even if the network is trained to enhance the network segmentation ability through rotation, scaling and other methods to increase the amount of data, the segmentation accuracy of displaced and rotated images is not ideal. Moreover, the absolute position information is valuable in many tasks. For example, information about the object and the absolute position can distinguish between multiple instances.  
The neural network attempts to describe the image accordirng to Equation (4):  
\[Y=WX+B \tag{4}\]  
where \(X\) represents the input image, \(Y\) represents the segmentation result, and (\(W\), \(B\)) represents the relationship between the image and the result.  
The image is stored in the computer in pixels. During network training, pixels were used as the unit of \(X\). However, the pixel set cannot describe the relationship between the image and the object, as shown in Equation (5):  
\[S(X)\#F(X) \tag{5}\]  
where \(S(X)\) represents the set of pixels of the imaged object, and \(F(X)\) represents the relationship between the object pixels. \(X\) represents the image itself. The network attempts to train a stable weight (\(W\), \(B\)) to describe the relationship \(F\). (\(W\), \(B\)) should describe the relationship \(F\) as much as possible. However, (\(W\), \(B\)) is not truly equal to \(F\), while denoting  
Figure 3: Convolution block. (a) Convolution block used in this paper. (b) Original convolution block.  
that the two are not exactly equal. Therefore, the relationship F does not necessarily have linear invariance, as shown in Equations (6) and (7):  
\[F(D(X)+dD(X))\#F(D(X))+F(dD(X)) \tag{6}\]  
\[F(A*D(X))\#A*F(D(X)) \tag{7}\]  
In the formulas, \(F()\) represents the image relationship function, \(D(X)\) represents the coordinate of \(X\), \(dD(X)\) represents the increase or decrease in the coordinate of \(X\), and \(A\) represents a coefficient. The symbol denotes that the two sides of the equation are not always equal.  
In addition, in the convolution method, the feature graph after the CNN convolution is a feature vector, and its position information is lost. Even if U-Net and other network feature graphs are no longer represented as a single column of feature graphs, the convolution kernel does not change as the image position and rotation angle change, and the influence of the position and rotation angle on the segmentation result cannot be eliminated.