esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Experiment Study on Vertical Bearing Characteristics of Large-Diameter Ring Piles
## 6 Conclusions  
Variables in the LDRP mechanical property model experiments were strictly controlled. The similarity theory was used to compare LDRPs and LDSPs. We determined that LDRPs are distinguished by \(K\), and the mechanical properties and bearing characteristics of LDRPs were revealed. The conclusions of this study are summarized below:  
1. The smaller the value of \(K\), the greater the settlement of the pile top, and the lower the ultimate bearing capacity of the LDRPs. The ultimate bearing capacity of the LDRPs with the same shape and size is the same as that of the LDSPs for a thickness-to-diameter ratio, \(K\), in the range of 0.4-1; and for \(K\) in the range of 0.2-0.4, the bearing capacity is 80% that of a large-diameter solid pile. However, enough safety reserves remained.  
Figure 18: Comparison of pile compression.  
2. The wall thickness and the stress area of the pile section decrease with \(K\); however, the compression of the pile increases. This is the main reason for the increase in the settlement of LDRPs.
3. The strain of the LDRPs increases with a decrease in \(K\), but it is far less than the material strength of the pile. The position of the maximum pile strain moves up the pile as \(K\) decreases.
4. When \(K=0.2\), the large-diameter ring-shaped pile can save up to 64% of concrete usage. Despite this, the bearing capacity can reach 80% that of a large-diameter solid pile and has sufficient safety reserves.  
Due to the limitations of conditions, only one parameter, \(K\), was researched in this paper. The physical and mechanical properties of LDRPs may be more accurately described if more parameters can be combined, such as the pile length, material, and back cover form. In addition, this paper conducted only a vertical static load experiment of the pile. If the horizontal load and combined load can be increased, the mechanical response law of LDRPs may be better summarized.  
Conceptualization, A.Y. and Y.S.; methodology, Y.S.; software, L.D.; validation, Y.S., Y.L. and Y.G.; formal analysis, Y.S.; investigation, A.Y.; resources, L.D.; data curation, Y.L. and Y.G.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; visualization, Y.S.; supervision, Y.S.; project administration, Y.S. 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.
## 5 Case Study  
As previously discussed, our methodology was tested using the case of an electric substation in Israel. The power-plant systems in the research region are widely dispersed and exposed to terror events, while an increasing local demand for electricity--combined with lack of access to backup power from neighboring countries--has created an urgent need for an effective protection strategy for this critical infrastructure. The case substation has been deemed a First Degree Strategic Facility by the Israel government, being the main and almost only electric power supplier to this region, and it is under constant terror threat in the form of steep-path shooting with projectiles containing up to 20 kg of TNT each [9].
## 4 Joint Message-Passing and Convex Optimization Framework for UAV Scheduling
### Algorithm Details
#### 4.2.1 MWIS-Based UAV Scheduling via Message-Passing
If the number of iterations increases, the comparison propagates, i.e., observing more information in the graph is possible. ```
1:Input: \(\mathcal{A}_{(i,j)}\) by (6), and \(w_{i}\) by (12), where \(s_{i}\in\mathcal{S},s_{j}\in\mathcal{S}\)
2:Output: \(\mathcal{I}_{i}\)
3:\(t\gets 0\), \(w_{i}\gets Q_{i}(0)\) for all \(s_{i}\in\mathcal{S}\)
4:while Each unit time \(t\in\{0,1,\cdots,T-1\}\)do\(\triangleright\)\(T\) is the maximum number of time iteration. 5: Initialize \(m^{1}_{i\to j}(t)=0\) and \(m^{1}_{j\to i}(t)=0\) for all \(s_{i}\in\mathcal{S},s_{j}\in\mathcal{S}\)
6:\(\blacktriangleright\)Update Phase
7:while Each iteration time \(n\in\{2,3,\cdots,K\}\)do\(\triangleright\)\(K\) is the number of message-passing iteration. 8: Calculate the message \(m^{n+1}_{i\to j}(t)\leftarrow\max\{0,w_{i}-\sum_{k\in\{\mathcal{N}(i)-j\}}m^{ n}_{i\to j}(t)\},\ \forall s_{j}\in\mathcal{N}(i)\). 9: UAV \(s_{i}\) sends \(m^{n+1}_{i\to j}(t)\) to all \(s_{j}\in\mathcal{N}(i)\). 10: Update \(n\gets n+1\). 11:endwhile
12:\(\blacktriangleright\)Estimation Phase
13: Calculate the belief \(b^{K}_{i\to j}(t)\leftarrow\sum_{s_{k}\in\mathcal{N}(i)}m^{K}_{k\to j},\ \forall s_{j}\in \mathcal{N}(i)\).\(\triangleright\) Belief is the lastly received message. 14:if\(b^{K}_{i\to j}(t)<w_{i}\)then
15:\(\mathcal{I}_{i}=1\), where \(s_{i}\in\mathcal{S}\), then \(s_{i}\in\mathcal{S}^{*}\)\(\triangleright\) UAV \(s_{i}\) is scheduled (i.e., its camera is turned on). 16:else
17:\(\mathcal{I}_{i}=0\), where \(s_{i}\in\mathcal{S}\), then \(s_{i}\in\mathcal{U}\)\(\triangleright\) UAV \(s_{i}\) is not scheduled (i.e., its camera is turned off). 18:endif
19: Update \(t\gets t+1\). 20:endwhile
```  
**Algorithm 1** Message-passing for MWIS-based energy-efficient surveillance schedulingWe use the Jaccard similarity index [68], one of the standard measures used to calculate the similarity between two data points, in order to numerically determine the overlapping areas. The Jaccard similarity index is the ratio of cardinality of co-rated items to that of all items rated by both UAVs; thus, it can be calculated, as follows:  
\[Sim(i,j)\triangleq\frac{|a_{i}\cap a_{j}|}{|a_{i}\cup a_{j}|}=\frac{|a_{i}\cap a _{j}|}{|a_{i}|+|a_{j}|-|a_{i}\cap a_{j}|}, \tag{13}\]  
where \(a_{i}\) and \(a_{j}\) are the monitoring areas that contain spots by \(s_{i}\) and \(s_{j}\), respectively. If the Jaccard similarity index is high among two adjacent UAVs, the UAVs observe overlapping areas, spatially and temporally at the same time. Therefore, the edge can be set to 1 in our considered conflict graph when the UAVs are assumed to be vertices in the conflict graph.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges
### Security  
Security is very important in routing for UAWSNs because sensor nodes and their communications are extremely vulnerable to various security problems [63]. Encryption, identity verification, link verification, and authentication broadcasting can protect the sensor network's routing protocols against external attacks, bogus routing information, flooding attacks, and acknowledgment spoofing.  
In summary, the design of an efficient routing protocol to meet different design criteria is a promising direction to enhance the routing performance in UAWSNs. Further research should be performed on UAWSNs to achieve high routing performance.
# 3D Facial Plastic Surgery Simulation: Based on the Structured Light
## 3 Results
### Results of 3D Face Model Evaluation  
Firstly, generating facial esthetic key points based on interactive selection, which shows several key points at the right zygomatic bone of the face. In our case, these key points form a symmetrical 3D face, which can assist doctors to quickly infer whether the key parts meet the beauty canons and mark the interesting surgical parts.  
The system provides relevant esthetic evaluation figures, including golden face ratio (mainly three courtyards and five eyes), eyebrow and eye evaluation, and nose evaluation. It is worth noting that these esthetic standards are mainly for females, and there are slight differences between females and males. Taking 3D face shape, eyebrows, eyes, and nose as examples, the evaluation results are shown in Figure 3a.  
The evaluation results of the model can draw the following conclusions: (1) Comparing the zygomatic mandibular face value to the standard value, it exceeds the 3% gradient value, which means the mandibular of the model is a little large, and it is recommended to reduce it slightly. (2) By comparing the zygomatic mandibular face value with the standard value, which means that the jaw of the face model is a little large, and it is estimated that the face model belongs to the square face. (3) The nasal frontal angle is larger than the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Devices** & **Resolution** & **Precision (mm)** & **Working Distance (m)** & **Scanning Time (s)** & **Size (mm)** \\ \hline Kinect v2 & \(512\times 424\) & 2\(\sim\)10 & 0.5–4.5 & \(>\)60 s & 250 \(\times\) 66 \(\times\) 67 \\ \hline RealSense D435 & \(1280\times 720\) & 5 & 0.8–3 & 30 s & 90 \(\times\) 20 \(\times\) 23 \\ \hline Orbber persee & \(640\times 480\) & - & 0.6–8 & - & 172 \(\times\) 63 \(\times\) 56 \\ \hline Our system & \(1280\times 800\) & 0.4 & 0.3–0.6 & 4 s & 101 \(\times\) 26 \(\times\) 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The technical parameters of several 3D scanning systems.  
Figure 3: 3D evaluation results of key parts of face. (**a**) follow the results obtained “three courtyards”, “five eyes”, eyebrows and eyes, and nose. (**b**) comparison of evaluation results.  
standard value, which indicates that the nose of the model is relatively straight as a whole, and rhinoplasty is not recommended. However, the nose tip angle is quite different from the standard value. It is recommended to pull down the nose.  
The above esthetic evaluation presents the indicators that need to be referred to in the beauty canons with intuitive numerical values, which can assist doctors in effectively judging whether the face meets the relevant standards.
## 2 Background
### Work Design in HRCS
#### 2.1.4. Precise Positioning  
Industry requirements for compact-scale assemblies have been frequently allocated to specialized machinery due to its repeatability and accuracy features (i.e., electronics industry). Nevertheless, small- and medium-sized assemblies that demand precise positioning of elements were indeed found in the current HRCS praxis. A supportive strategy similar to mounting tasks is displayed in panel [64] or weight [65] manipulation, where robots are hand-guided to an exact coordinate using auxiliary references like bolt holes and laser pointers, respectively. Therefore, leader roles were mostly identified as humans assigned to the control of robot actions. Intuitive interfaces for safe interaction with the robot companion are needed as precise positioning is generally based on the joint manipulation of workpieces. Mixed reality hardware [11], hand-tracking systems [66], and effective teleoperation devices [67] are fitting work design strategies where a clear allocation of responsibilities was previously assessed [68].
# Implementation of the SoftMax Activation for Reconfigurable Neural Network Hardware Accelerators
## 4 Implementation of the SoftMax Activation in RCE  
### Challenges of the Hardware Implementation of SoftMax Activation  
The SoftMax is a neuron activation widely used in the output layers of neural networks for classification tasks if the number of output classes exceeds one. It is also known as the softargmax or normalized exponential function. The result of this activation is the probability distribution of the input vector values among several classes. Consequently, SoftMax is a multi-class alternative to the sigmoid activation. The SoftMax activation has the following form:  
\[S(x_{1},\ldots x_{N})_{i}=\exp(x_{i})/\sum_{1}^{N}\exp(x_{n}) \tag{1}\]  
where \(N\) is the number of inputs.  
As can be seen, there are several challenges to hardware implementation of this function:  
* It is based on the non-linear fast-growing exponential function (Figure 6);
* In general, the input values of the activation can lie within a wide range, limiting possible optimizations;
* It utilizes division to produce the final result.  
Figure 5: Pipeline processing in RCE.  
Figure 6: The exponential function.  
The wide usage of the SoftMax activation has led to numerous studies addressing these difficulties.
## 3 Results
### XRD Results  
Investigations were conducted with a Bruker D8 Advance powder X-ray diffractometer. The Bruker DIFFRAC.EVA program was used to determine the qualitative and quantitative phases of the analysis. Figure 6 shows the diffractogram of the 3D-printed titanium sample. Two phases were found, namely aluminum titanate (Al\({}_{0.15}\)Ti\({}_{0.85}\)) and less aluminum titanate vanadate (Ti\({}_{2}\)VAl). The established hexagonal phase of Al\({}_{0.15}\)Ti\({}_{0.85}\) has a crystallite size of 77 nm, and the cubic phase of Ti\({}_{2}\)VAl has a crystallite size of 18.5 nm. Figure 7 shows the diffractogram of a milled titanium sample. Two phases were found: mainly aluminum titanate (Al\({}_{0.15}\)Ti\({}_{0.85}\)) and less titanium vanadate (Ti\({}_{0.8}\)V\({}_{0.2}\)). A hexagonal phase of Al\({}_{0.15}\)Ti\({}_{0.85}\) with crystallite sizes of 106 nm and a cubic phase of Ti\({}_{0.8}\)V\({}_{0.2}\) with crystallite sizes of 22.8 nm were found.  
The aluminum titanate phase is predominant in both samples, with the differences being a small amount of aluminum titanate vanadate in the printed titanium sample and a small amount of titanium vanadate in the milled titanium sample. With small quantities of chemical elements and compounds in the studied samples and insufficiently manifested peaks, the software detects phases as close to the characteristic peaks from the diffractograms as possible. There was a significant difference in the size of the crystallites of the Al\({}_{0.15}\)Ti\({}_{0.85}\) main phase, with the crystallites (106 nm) in the milled titanium sample being 37% larger than the crystallites in the printed titanium sample (77 nm). For the secondary phases, the milled titanium sample had approximately 20% larger crystallites.  
Figure 5: SEM image of the surface of (**a**) milled Ti disc specimen (\(\times\)200); (**b**) disk sample of 3D-printed Ti (\(\times\)200).
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
###### Abstract  
Internet of Things (IoT) has become one of the key factors that enables, drives and accelerates the digital transformation all over the world. The vision of the IoT is not only the immediate access to the data but also the ability to turning data into intelligence. As such, there is a growing number of public cloud computing providers offering IoT related services, including data processing, data analyzing and data visualization. However, with tens of billions of microcontroller-powered devices getting involved in the era of IoT, the concerns for overall security, privacy and cost are rising constantly and exponentially. Furthermore, these issues cannot be solved by public cloud computing providers since they mainly focus on the software and services rather than on the end devices. In this article, an integrated solution including Azure Sphere devices and Azure cloud services is proposed to provide a comprehensive and efficient way to ensure security that starts in the device and extends to the cloud with limited budgets. Moreover, the implementation details including hardware components, software design and Azure cloud integration are presented to demonstrate the feasibility and efficiency of the proposed solution.  
Internet of things; Azure Sphere; Azure cloud services; IoT Hub; ZigBee; WSN +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
This paper considers four factors when studying SDVN controller deployment: network traffic (network load), number of RSUs, RSU location, and delay. The deterministic annealing clustering algorithm is used to solve the model. After determining the number of SDVN controllers to be four, namely A, B, C, and D, the network traffic is further analyzed and predicted. In network traffic prediction, the sequential model neural network algorithm, XGBoost classifier algorithm, random forest algorithm, logistic regression algorithm, support vector machine algorithm, and ensemble algorithm are used. The average accuracy, standard deviation of accuracy, precision, and recall of the above algorithms are compared. Finally, the prediction differences between the integrated model and the neural network algorithm for the number of future traffic occurrences of A, B, C, and D controllers are compared. In the traffic prediction after the optimization of SDVN controller deployment, the performance of the machine learning algorithm is better than that of the neural network algorithm, because the judgment of network traffic is based on the binary classification of 'high' and 'low'. In terms of the performance of the binary classification problem, machine learning algorithms are often not weaker than deep learning neural network algorithms.  
Figure 12: Traffic Prediction of Sequential Model Neural Network and Ensemble Model on Test Set, Respectively.  
**Author Contributions:** Conceptualization, H.L. ([PERSON]) and H.L. ([PERSON]); methodology, H.L. ([PERSON]) and H.L. ([PERSON]); software, H.L. ([PERSON]); validation, H.L. ([PERSON]); formal analysis, H.L. ([PERSON]) and H.L. ([PERSON]); investigation, [PERSON] ([PERSON]) and [PERSON]; resources, [PERSON] ([PERSON]) and [PERSON]; data curation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON] and [PERSON]; visualization, [PERSON] ([PERSON]) and [PERSON]; supervision, [PERSON] ([PERSON]) and [PERSON]; project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation, grant number 2024M762090.  
Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 5 Performance Evaluation
### Transmissions  
Figures 8 and 9 respectively depict the total number of transmissions for all compared algorithms when the number of message copies varies from 100-500 under the circumstances of urban and highway. As is evident from these two figures, EDDA performs significantly fewer transmissions than other algorithms. As the number of message copies increases, data transmissions will increase rapidly until the network converges, thus more redundant transmissions would occur. Especially, GPSR and epidemic need more transmissions to achieve ideal data dissemination performance. SW sprays first and then waits for another spray, which results in fewer transmissions. Likewise, CCR can also achieve better performance by taking advantage of the available network capacity. Benefiting from the bounded number of message copies and distributed averaging operations, EDDA outperforms the other compared schemes.  
Figure 6: Delivery ratio vs. number of vehicles in urban VANET.  
Figure 7: Delivery ratio vs. number of vehicles on the highway.  
Figures 10 and 11 show the impact of the number of vehicles on transmissions for the compared schemes in urban and highway, respectively. It is obvious to see that the number of transmissions increases greatly when the vehicle density is larger. This is because when more vehicles participate in data dissemination, more communications happen among vehicles. However, the trends of the transmission amount for all the compared algorithms are similar. EDDA always consumes fewer transmissions when compared with other algorithms. We can get such a result that the proposed algorithm can achieve better results even in a traffic scenario with lower vehicle density.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 3 Practical Implementation and Case Study  
### Project Overview  
For the implementation case study, a large underground powerhouse was selected. Situated in a geologically challenging region, the cavern is subjected to high overburden stresses, requiring advanced stability monitoring and rock support strategies. The excavation process involved sequential bending and the installation of shotcrete, rock bolts, and steel ribs to ensure structural integrity. For DT implementation, the powerhouse cavern serves as an ideal case study for integrating real-time geotechnical monitoring, predictive deformation modeling, and BIM-driven stability optimization. The DT framework enables continuous synchronization between physical site conditions and virtual simulations, improving deformation prediction, excavation sequencing, and adaptive rock support design. By leveraging sensor data, numerical analysis, and IFC-based BIM integration, the DT-driven approach enhances risk assessment, decision-making efficiency, and the long-term structural stability of the underground powerhouse cavern.
## 5 Conclusions  
1. The atomistic representations of the coking coal model and anthracite coal model considering the influence of minerals were constructed, which is an approach to characterize the methane adsorption and mechanical characteristics in coal.
2. The amount of methane adsorption follows the order anthracite coal > coking coal, and the presence of minerals increases the methane adsorption capacity; the increased amount of methane adsorption follows the order calcite > kaolinite.
3. The presence of calcite and kaolinite greatly increased the shear modulus of compound coal and mineral models. Notably, after the addition of calcite and kaolinite minerals, the brittleness of the model increases, and the brittleness index increases with increasing mineral content.
4. Only calcite and kaolinite were considered typical co-associated minerals in coal, which is limited to explaining the methane adsorption and mechanical properties in all the coal-rock compound models. In addition, there are limited coal molecular structure models for molecular dynamic simulation work, and the construction of diversified coal molecular structure models is difficult but imperative. Thus, based on Graph Representation Learning and Graph Neural Networks, our team is committed to building diverse molecular structure models for almost all coal and rock, providing a theoretical basis for coal and rock dynamic disasters.  
Methodology, writing--original draft, X.Z.; writing--review and editing, Y.L.; investigation, T.Z.; conceptualization, writing--review and editing, Y.C.; formal analysis, B.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the youth project of National Natural Science Foundation of China (No. 52204220, No. 52304088, No. 42302202), the Science and Technology Innovation and Entrepreneurship project of TDTEC (No. 2022-QN001, No. 2022-2-MS003, No. 2023-TD-MS010), the Natural Science Foundation of Liaoning Province, China (No. 2022-KF-23-09), the Independent Research fund of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining(Anhui University of Science and Technology) (No. EC2022021).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Authors [PERSON], [PERSON], [PERSON], [PERSON], were employed by the company Chinese Institute of Coal Science. The remaining author [PERSON] declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 6: Change in mechanical properties after the addition of calcite and kaolinite.