esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method
###### Abstract  
The peak shear strength of a rock joint is an important indicator in rock engineering, such as mining and sloping. Therefore, direct shear tests were conducted using an RDS-200 rock direct shear apparatus, and the related data such as normal stress, roughness, size, normal loading rate, basic friction angle, and JCS were collected. A peak shear strength prediction model for rock joints was established, by which a predicted rock joint peak shear strength can be obtained by inputting the influencing factors. Firstly, the study used the correlation analysis method to find out the correlation coefficient between the above factors and rock joint peak shear strength to provide a reference for factor selection of the peak shear strength prediction model. Then, the JRC-JCS model and four established GA-BP neural network models were studied to identify the most valuable rock joint peak shear strength prediction method. The GA-BP neural network models used a genetic algorithm to optimize the BP neural network with different input factors to predict rock joint peak shear strength, after dividing the selected data into 80% training set and 20% test set. The results show that the error of the JRC-JCS model is a little bigger, with a value of 11.2%, while the errors of the established GA-BP neural network models are smaller than 6%, which indicates that the four established GA-BP neural network models can well fit the relationship between the peak shear strength and selected input factors. Additionally, increasing the factor number of the input layer can effectively improve the prediction accuracy of the GA-BP neural network models, and the prediction accuracy of the GA-BP neural network models will be higher if factors that have higher correlation with the output results are used as input factors.  
rock joint; peak shear strength; JRC-JCS model; genetic algorithm; BP neural network
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Recharging  
AIRDROP utilizes far-field wireless power transfer (WPT), employing specialized transmitter UAVs (tUAVs) equipped with multiple input-multiple output (MIMO) antennas to wirelessly transfer power to receiver UAVs (rUAVs) during missions. The tUAVs optimize energy transfer gain by adjusting their distance to the rUAVs, while MIMO antennas enhance energy reception by focusing the energy beam. Additionally, separate charging stations for UAVs, UGVs, and USVs are provided, enabling autonomous navigation and docking for recharging.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 7 Standardization of QKD Systems  
Standardization is crucial for the industrialization of QKD systems in order to develop interoperable multi-vendor products and mitigate the risk of individual companies developing proprietary solutions that may never succeed. Considerable standardization efforts toward QKD systems have been undertaken by the standardization sector of the International Telecommunication Union (ITU), ETSI, and Internet Engineering Task Force (IETF).  
At the standardization sector of ITU, referred as ITU-Telecommunications (ITU-T), a Focus Group on Quantum Information Technology for Networks (FG-QIT4N) was established in September 2019 as a collaborative platform for investigating quantum communication networks, with the main objectives of studying the evolution and applications of communication networks based on quantum technologies; the definition of terminology and use cases for quantum networks; and the preparation of technical background information to support related standardization work in ITU-T study groups. As a result, standardization work on QKD networks started with ITU-T Study Group 13. Besides an overview of networks supporting QKD [146], covered topics in the Y.QKDN draft recommendation series are functional requirements and architecture; business role-based models; quantum key management; control and management, including software defined networks (SDN) control; and quality of service (QoS) aspects.  
At ETSI, an industry specification group (ISG) on quantum key distribution for users was established to enable digital keys to be shared privately without relying on computational complexity. The scope of the group encompasses the specification of QKD system interfaces, implementation of security requirements, and optical characterization of QKD systems and their components [144, 147, 148, 149, 150, 151, 152, 153].  
At IETF, the Quantum Internet Proposed Research Group (QIRG) was established to investigate and engineer the new communication and remote computation capabilities offered by quantum technologies. Cryptographic functions are within the groups scope, including quantum key distribution and quantum byzantine agreement, as well as routing schemes, dynamic resource allocation, connection establishment, interoperability, and design of application programming interfaces (APIs). The QIRG intends to understand the applications of quantum Internet, specifying its characteristics (e.g., the data rate) and addressing multi-party states and multi-party transfers (e.g., network coding) rather than simple, independent point-to-point transfers. The QIRG will define an architectural framework delineating network node roles and definitions to build a common vocabulary and serve as the first step toward a quantum network architecture.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 2 Materials and Methods
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 2.4.2 Fourier Transformer Infrared Analysis  
The functional groups in the seaweed crude extracts that were responsible for the reduction of the zinc ions were discovered using a Fourier transformer infrared (FTIR) spectrophotometer (tensor 27 Bruker, Germany) carried out at the Scientific Research Center and Measurement-Tanta University.
## 2 Materials and Methods
### Acoustic Emission Analysis Methods
#### 2.2.2 RA-AF Analysis  
The RA value (rise angle, the ratio of rise time to amplitude) and AF value (average frequency) are critical parameters in AE signal analysis for distinguishing damage modes. RA values are typically associated with shear-type cracks (e.g., steel-concrete interface debonding), while AF values correspond to tensile-type cracks (e.g., concrete matrix cracking). Research demonstrates that RA-AF two-dimensional correlation plots can effectively identify damage types in concrete columns [18; 28].  
As shown in Figure 4a, traditional methods use a single-threshold segmentation to classify RA-AF data: regions with high RA and low AF (upper boundary) are identified as tensile cracks, while regions with low RA and high AF (lower boundary) are classified as shear cracks. However, the failure process of eccentrically loaded reinforced concrete columns often involves the coexistence of tensile and shear cracks, termed mixed-mode cracks. Traditional single-boundary classification methods struggle to accurately characterize such complex scenarios.  
Figure 4: RA-AF classification: (**a**) traditional RA-AF boundary division; (**b**) optimized RA-AF boundary division.  
To address this, improved methods such as the GMM have been introduced for crack classification. As illustrated in Figure 4b, GMM employs mixed-distribution modeling to simultaneously identify tensile, shear, and mixed-mode cracks. Its multimodal analysis capability significantly enhances classification accuracy, aligning more closely with the complex crack evolution patterns observed in real-world engineering applications.
## 2 Materials and Methods
### Data Processing
#### 2.6.4 Anomaly Map  
As the \(\rho_{a}\) color map can show some anomalous behaviors at specific depths (space-time variations), sometimes this phenomenon cannot be seen clearly. Therefore, it is necessary to create an anomaly map that represents the same time interval and depth ranges.  
The values of the anomaly map (shown in the lower part of Figure 5) are the result of finding how anomalous each value of the \(\rho_{a}\) map is, that is, quantifying how much each one varies according to the average of the total window. First, the average value of the window is found, which will be a column of \(N\) rows resulting from the average resistivity for each period:  
\[\begin{array}{ccccc}\rho_{a_{(0,0)}}&\rho_{a_{(0,1)}}&\cdots&\rho_{a_{(0,M-1 )}}&\sum\limits_{i=0}^{M-1}\rho_{a_{(i)}}/M&\overline{\rho_{a_{0}}}\\ \rho_{a_{(1,0)}}&\rho_{a_{(1,1)}}&\cdots&\rho_{a_{(1,M-1)}}&\sum\limits_{i=0}^ {M-1}\rho_{a_{(1,i)}}/M&\overline{\rho_{a_{1}}}\\ \vdots&\Rightarrow&\vdots&=&\vdots\end{array} \tag{7}\]  
Starting from the matrix, which represents the apparent resistivity map (left part), then (\(\Rightarrow\)) we average each row (middle part), which is equivalent (=) to an average value corresponding to each row (period); we get:  
\[\rho_{a_{(N-1,0)}}\;\rho_{a_{(N-1,1)}}\;\cdots\;\rho_{a_{(N-1,M-1)}}\;\sum \limits_{i=0}^{M-1}\rho_{a_{(N-1,i)}}/M\;\overline{\rho_{a_{N-1}}},\]  
thus, obtaining an average \(\rho_{a}\) value for each period.  
Finally, each value of the initial apparent resistivity matrix (\(\rho_{a_{(i,j)}}\)) is operated with its corresponding average (\(\overline{\rho_{a_{i}}}\)) as follows:  
\[Anomaly_{(i,j)}=A_{(i,j)}=\frac{\rho_{a_{(i,j)}}-\overline{\rho_{a_{i}}}}{\rho _{a_{i}}}\cdot 100, \tag{8}\]  
to obtain a percentage of the anomaly. This value means how different each \(\rho_{a}\) value (\(\rho_{a_{(i,j)}}\)) is from the average (\(\overline{\rho_{a_{i}}}\)) of the total window, e.g., getting an anomaly value of 300% means that we have a \(\rho_{a}\) value three times greater than the average.  
By obtaining all values of the anomalies, we will be able to plot a map with the same dimensions as the \(\rho_{a}\) map. This map is presented in grayscale (Figure 5) and allows us to see more clearly the area where an anomalous resistivity value occurs according to the average value of the total window.
## 2 Beidou-Binocular Cooperative Navigation Model
### Model Framework and Implementation
#### 2.4.2 Data Flow  
The data flow of the BBCN model is divided into three stages: input, processing, and output. The specific steps are as follows:  
1. Input Stage: The Beidou navigation module receives signals from the Beidou satellites and calculates the current position and attitude through differential positioning (RTK). It outputs global positioning information and Beidou state estimation errors. The binocular vision module's left and right cameras capture synchronized images, calculate disparities, and generate depth maps. Object detection and semantic segmentation algorithms are applied to output the positions, categories, and local environmental maps of obstacles.
2. Processing Stage: The UKF fuses the global positioning data from Beidou with the local environmental data from vision, generating a unified robot state estimate. The depth map output by the binocular vision and the object detection results are converted into a 2D grid map, marking obstacles and free space. Based on the fused data, the path is planned by combining global path and local obstacle information, generating the dynamically adjusted optimal path.
3. Output Stage: The robot's motion control commands are output, including linear velocity and angular velocity. If Beidou signals are lost, the system switches to visual odometry and local path planning mode.
# Analytical Modeling for Underground Risk Assessment in Smart Cities
## 6 Implementation of Approximation Schemes for Underground Risk Index  
### Linear Approximation  
We have risk index of individual contributing factors \(M_{k}\forall k=1,2,3,4\) on location\((x_{i},y_{i})\). Final risk index can be calculated by linear approximation as below  
\[Risk_{T}(x_{i},y_{i},t)=\beta_{0}+\sum_{s\in s_{1},s_{2},s_{3},s_{4}}\beta_{s}. M_{s}(x_{i},y_{i},t) \tag{18}\]  
Using multiple linear regression analysis, estimated values for \(\beta\) are as follows: \(\beta_{0}=1.42\), \(\beta_{1}=0.11\), \(\beta_{2}=-0.05\), \(\beta_{3}=0.21\), and \(\beta_{4}=0.36\). Putting these \(\beta\) values into Equation (18), we get  
\[Risk_{T}(x_{i},y_{i},t)=1.42+0.11.M_{1}+(-0.05).M_{2}+0.21.M_{3}+0.36.M_{4} \tag{19}\]  
Thus, we calculate final risk index as a linear combination of the four risk contributing factors by linear approximation scheme.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Transfer Learning in Network Measurement  
Learning a task's model and applying it to other tasks that are related is referred to as transfer learning, and the basis of transfer is the requirement that two learning activities be connected [58]. In other words, we can apply the lessons we choose to learn in one situation to another. A model must be built for transfer learning and then turned into a feature extraction module. The model is then applied immediately to a different task after being trained on a comparable task. The model can be fine-tuned to a new task after training so that it can more easily adapt to the new activity. Transfer learning is comparable to mimicking how the human brain thinks, in other words. If we can solve one problem, we can solve others that are related to it more effectively and quickly. Transfer learning is given a source domain and a source task, a target domain, and a target task. When the source domain or source task is not the same as the target domain or the target task, the purpose of transfer learning is to use the knowledge of the source domain and source task to enhance the prediction of the target task learning function. The definition of transfer learning, domain data, and transfer methods are only a few of the criteria that can be used to categorize transfer learning.  
Transfer learning is frequently used in a variety of contexts. Transfer learning is an option if the issue falls inside the scenario. [PERSON] et al. [31] thought that without real measurements, network delays could be correctly anticipated. They used datasets to train the model, freeze the CNN layer of the predictor, and load out-of-date weights when data were entered. The network delay predictor was used in the real world with 93% accuracy thanks to transfer learning in an upgraded model of ML-assisted transfer learning that was built in the lab. The main problems of transfer learning are data-adaptive distribution, feature selection, and subspace learning, and it is demonstrated that transfer learning is superior to begining from scratch in terms of both accuracy and training time. It can address issues with a variety of duties, fewer data in the new office, and differing data distributions between the new office and the old office. The transfer cannot be used if there is no connection between the source and target domains. In the worst circumstances, this interferes with the target field's task learning. Negative transfer is a circumstance that needs to be avoided as much as feasible.
## 5 Results
### Estimated Parameters and Parameter Sensitivity
The values from the matrix show that \(k_{31}\), \(k_{51}\), \(c_{21}\), \(c_{31}\), \(c_{51}\) and \(c_{3}^{T}\) are the most significant parameters to the appearance and magnitude of the resonance frequency. Table 9 summarizes the order of sensitivity of these parameters for the examined responses. We utilized the sensitivity matrix score to update the bounds in the mGA repeatedly and extracted the final optimized parameters. This process allowed us to obtain numerically stable values for each of \(k_{21}\), \(k_{22}\), \(k_{31}\), \(k_{32}\) and \(k_{51}\), whose estimated values remained unchanged at 0.5 m/s\({}^{2}\) rms regardless of the body segment transmissibility data used in the objective function (Figure 12). Similarly, at 2.0 m/s\({}^{2}\) rms, all the \(k_{ij}\) terms remained consistent except \(k_{31}\), whose value reduced slightly for all body segments, confirming the'softening effect' observed in the experimental results. The reduction in \(k_{31}\) as the excitation magnitude increased, suggested the softening in the stiffness of the tissue beneath the ischial tuberosities, as reported in the literature [27; 33]. As observed with the \(k_{ij}\) terms, values of \(c_{22}\), \(c_{32}\) and \(c_{51}\) were consistent, while the variances in values of \(c_{21}\) and \(c_{31}\) for some of the body segments can be associated with their sensitivity to the magnitude of resonance frequency. Also, as presented in Figure 13, the torsional parameters for HOM showed nearly the same values across all body segments and forcing magnitudes. Particularly, the torsional stiffness coefficients, \(k_{i}^{T}\) were unchanged. The changes in the torsional damping, especially \(c_{3}^{T}\) is associated with its high significance in determining the magnitude of the resonance frequencies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Examined Responses** & **Decreasing Order of Sensitivity** \\ \hline First resonance frequency & \(k_{31},\ k_{51},\ c_{21}\) \\ \hline Second resonance frequency & \(k_{31},\ k_{51}\) \\ \hline Peak magnitude of resonance frequency & \(c_{31},\ c_{21},\ c_{3}^{T},\ c_{51}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of sensitivity of key parameters to the appearance and magnitude of the resonance frequency at \(\pm\)30% perturbation.
# Acceleration Techniques for Teeth Movements in Extractive Orthodontic Therapy
## 4 Discussion
### Dentalveolar Ostedistraction  
The study by [PERSON] et al. (2017) aimed to evaluate the effects of the DAD device on OTM compared to conventional methods in patients with Class II malocclusion [46]. Thirty-three patients were divided into the DAD group and the Distalisation Group (DG). The patients who underwent a DAD have applied a retraction force of approximately 800 g for the upper canine. The rate of canine movement was measured in millimetres per month, and the dentoskeletal effects were assessed via an analysis of panoramic and lateral skull radiographs [86].  
Furthermore, the dentoskeletal effects of DAD were limited to the treatment area, with no adverse effects on the position of the other teeth. The DAD group showed significantly faster canine movement (0.87 mm/month) concerning average speed experimented with conventional orthodontic technique (0.35/month) and no significant changes in the other maxillary and mandibular parameters except for maxillary canine retraction.  
The DG group showed significant changes in the vertical dimension and mesial movement of the maxillary first molars, indicating anchorage loss [46, 87]. No significant difference was found between the two groups in maxillomandibular measurement difference or root resorption. The study concludes that DAD can benefit patients with increased skeletal vertical dimensions and may reduce anterior tooth retraction time during fixed orthodontic therapy [46].  
Osteedistraction, as exemplified via the DAD device, offers advantages in OTM for patients with Class II malocclusion [88]. The DAD group exhibited notably faster canine movement than conventional methods, benefiting those with increased skeletal vertical dimensions. Significantly, dentoskeletal effects were localised, avoiding negative impacts on the neighbouring teeth. However, some limitations were observed, including altered maxillary canine retraction and potential vertical dimension changes in the distalisation group. Root resorption and maxillomandibular measurements remained unaffected. While advantageous for specific cases, a careful consideration of possible anchorage loss and anatomical factors is essential.  
Furthermore, this work [89] asserts that the absence of periodontal defects or endodontic lesions characterises the rapid distraction of the periodontal ligament during canine retraction. So, it is possible to rapidly distract the periodontal ligament without complications.
# On the Energy Performance of Iridium Satellite IoT Technology
## 3 Iridium Overview
Iridium offers different types of communication services including dial-up, direct Internet access, paging, router-based unstructured digital inter-working connectivity solution (RUDICS), and short burst data (SBD). For IoT purposes, that is, infrequent transmission of short payloads (e.g., corresponding to sensor readings) with relaxed latency requirements (e.g., in the order of tens of seconds), SBD is the most suitable service. SBD is a packet-switched-oriented service that uses forward error correction (FEC) encoding, along with a selective automatic repeat request (ARQ) mechanism. In SBD, the user duplex timeslot comprises 160 SBD information bits, along with 20 SBD header bits, and 234 bits that correspond to other overheads. The 160 SBD information bits are protected by BCH(31,20) FEC encoding [23]. The theoretical user data capacity of a SBD channel (i.e., 160 user data bits are carried every 90 ms) is 1.78 kbit/s. However, the actual user capacity defined for a SBD channel is 125 byte/s (i.e., 1 kbit/s). The maximum size of a message sent by a user (e.g., a sensor device equipped with Iridium satellite connectivity) is 1960 bytes. However, commercial devices may support a smaller maximum message size (e.g., 340 bytes in the RockBLOCK Mk2 model [24]). In fact, IoT applications typically handle small user payloads. An Iridium IoT device can send and receive data by means of the aforementioned SBD duplex timeslots. When a message is sent to the device, it is stored in a queue at the gateway. If the gateway receives a message from the Iridium IoT device, the first message in the queue is then sent to the device. Therefore, downlink data transmission requires a previous uplink transmission performed by the Iridium IoT device.