esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameter Assessment for Optimization
### Motor Efficiency  
Figure 3: Linear fitting of BPMSM performance. (**a**) Linear fitting of axial levitation force, tilting levitation torque, and radial levitation force; and (**b**) relative fitting error of \(F_{z}\), \(T_{b}\), and \(F_{r}\).  
Motor power losses mainly include the stator iron-core loss (iron loss, \(P_{Fe}\)), the winding loss (copper loss, \(P_{Cu}\)), the permanent magnet eddy current loss (\(P_{eddy}\)), and the mechanical loss (\(P_{fu}\)). The motor efficiency is calculated according to:  
\[\eta=\frac{P_{out}}{P_{in}}=\frac{P_{out}}{P_{out}+P_{Fe}+P_{Gu}+P_{fw}+P_{eddy} }\times 100\% \tag{3}\]  
Since the BPMSM has no mechanical bearings, the mechanical loss (\(P_{fu}\)) is out of consideration. The BPMSM model is utilized to determine the motor loss at a full load and rated speed, and the results are shown in Figure 4.  
According to Figure 4, \(P_{Fe}\) and \(P_{Cu}\) are calculated to be 1.26 W and 51.46 W. The value of \(P_{eddy}\) is 10.6 mW, which is too small to consider. The initial motor efficiency (\(\eta\)) is 69.45% according to (3). To improve the efficiency of the BPMSM, \(P_{Fe}\) and \(P_{Cu}\) are selected as the optimized performance.
# The Influence of Copper Oxide Particle Size on the Properties of Epoxy Resin
###### Abstract  
This study examines the relationship between the size of copper particles and the properties of epoxy resin. Epoxy resin is a type of thermosetting resin commonly used as a matrix in polymer matrix composite materials reinforced with glass or carbon fibers. As part of this study, three microscale and two nanoscale composite samples modified with copper oxide particles of varying sizes were produced. This study included mechanical property tests such as static tensile tests, static bending tests, and impact tests. The results of the strength tests were compared to modeling results. Additionally, an accelerated thermal aging process was conducted to determine the impact of external conditions on the behavior of the produced composites. This study concluded with an analysis of thermal conductivity. The test results revealed that the size of the copper particles significantly impacted the tested properties. The composites with copper oxide particles on the nanoscale demonstrated the best results. These composites have promising applications in the automotive and aviation industries due to their strength, resistance to external factors, and increased thermal conductivity, suggesting their potential for producing materials that effectively dissipate heat.  
epoxy resin; copper particle size; polymer matrix composite materials; mechanical properties; ageing +
Footnote †: journal: Applied Sciences
# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
## 3 Results
### Intra-Observer Agreement without and with the Aid of the DLA  
The observers' performance results are summarized in Table 3, with visual exemplification in Figure 3 showcasing sensitivity, specificity, and the true-positive rate on a case level. Considering that the participants were asked to perform routine reporting based on general complaints (such as acute abdominal pain or elevated liver enzymes), only one out of six radiologists did not include spinal findings in the first round (without DLA). Another radiologist solely identified degenerative changes without detecting any significant or suspicious findings that aligned with the ground truth. The participant with the highest experience level (11 years) initially reported two true positives in the first round. However, when aided by the DLA, this number increased to 14 true-positive and two false-positive findings. The two least experienced radiologists (one year) did not report any spinal findings without relying on the algorithm. Following the predictions in round 2, only one included spinal findings in the report. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Total** \\ **Number of** \\ **Cases** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Detected** \\ **Detected** \\ **Objects** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Detected** \\ **Spinal** \\ **Lesions (TP)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Undetected** \\ **Spinal** \\ **Spinal** \\ **Lesions (FN)** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Falsely Detected** \\ **Spinal** \\ **Lesions (FP)** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Gold \\ Standard \\ \end{tabular} & 16 & 27 & 27 & N/A & N/A \\ \hline DLA & 11 & 40 & 12 & 15 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithm performance in comparison to the gold standard (count). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Sensitivity (TP Rate)**} & \multicolumn{2}{c}{**Specificity (TN Rate)**} & \multicolumn{2}{c}{**Accuracy**} \\ \hline
**Case Level** & **Object Level** & **Case Level** & **Object Level** & **Case Level** & **Object Level** \\ \hline
75.00\% & 44.44\% & 56.25\% & N/A & 65.63\% & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm performance in comparison to the gold standard (metrics). Figure 2: DLA predictions of osteolytic (**a**) and oseoblastic (**c**) lesions are shown in corresponding images (**b**,**d**). Considering a potential maximum of 162 true positives (27 lesions multiplied by 6 participants), the radiologists reported 11 true-positive objects alone. However, when assisted by the DLA, this number increased to more than three times that value, with 35 true-positive findings in the study's second phase. The DLA predicted 13 false-positive objects. When interpreting these predictions, on average, the radiologists included less than one false positive in the report, resulting in four false-positive reports across all participants.
## 3 Proposed Algorithm
### Control routing overhead  
_Ad Hoc_ On-demand Distance Vector with Controlled Flooding (AODV-CF) [22] uses two new messages to determine an alternative route in order to prepare for link failure. The first is a Controlled message, and the second is a Controlled-ACK message. Once the alternative node is set, there is no periodical update until a link failure occurs. This can cause critical problems when nodes are moving fast. In such an environment, the alternative nodes no longer perform their function as alternative nodes. In this circumstance, in the suggested scheme the main nodes send a beacon message containing information pertaining to the location of the destination to their one-hop neighbor nodes periodically every second.  
Figure 5: Packet forwarding.  
Figure 6: Limitation in sending beacon messages.  
The black node is the main route node described in Figure 6. The gray node, the one-hop neighbor from the main node, receives information regarding the location of the destination as periodically transmitted from the main route nodes. The main route nodes continue to send location information to their new one-hop neighbors unless gray nodes move far away from the main route nodes. This mechanism works effectively with a large network. In existing hybrid algorithms, every node sends a beacon to its one-hop neighbor, which creates a tremendous amount of routing message overhead. Although the proposed scheme has some routing message overhead, it has small number of messages compared to the previous hybrid routing protocols.
## 2 The Principle of Structural Reliability Analysis
### The Limit State of the Structure  
To accurately describe the working state of a structure in the analysis and design of structural reliability, a clear definition of the boundaries between structural safety, durability, functionality, and failure is required. These boundaries are collectively referred to as the limit state of the structure. The limit state is defined as the specific condition beyond which the entire structure, or a part of it, can no longer meet the functional requirements specified in the design. This specific condition is mathematically represented by the limit state function.  
The limit state of a structure essentially represents the critical transition point between its functional and failed states. When this value is exceeded, the structure is considered to be in an unsafe, non-durable, or inapplicable state. Conversely, if this value is not exceeded, the structure remains in a safe, durable, and applicable state. Let the basic random variables of the structure be represented by the random vector \(\mathbf{X}=(X_{1},X_{2},\ldots,X_{n})\). The function that describes the working state of the structure can then be expressed as Equation (1), referred to as the limit state function.  
\[Y=g(\mathbf{X}) \tag{1}\]  
Then, the working state of the structure can be shown by Equation (2).  
\[Y=g(\mathbf{X})\left\{\begin{array}{ll}<0&failure\ state\\ =0&limit\ state\\ >0&reliable\ state\end{array}\right. \tag{2}\]  
The limit state of a structure can be defined either objectively based on the actual condition of the component or subjectively by experts, who take into account experience, specific requirements, and human control factors.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 6 Conclusions
For a better validation, the proposed analysis has been conducted on two datasets, acquired in two distinct temporal intervals by sensors with different parameters (ERS and Sentinel-1). A study of the parameters' influence on the PS detection process is presented in this paper. The diversity of dataset temporal intervals confirms the temporal consistency of the experiment. The highest similarity between the detected PS and an LDA evolution class is present in case of single-look analysis of the ERS dataset. This is a consequence of the fact that the spatial resolution of the ERS images is better compared to Sentinel-1 acquisitions. The multi-looking process presents two main consequences: noise effect mitigation and spatial resolution loss. The second effect is predominant in the case of theERS images (since their spatial resolution is better), leading to a PS-LDA similarity decrease. Noise suppression is dominant in the Sentinel-1 set, leading to an increase of PS-LDA similarity. The presented experiment can also serve as a validation process for PS detection without georeferencing and visual interpretation of the results. If an LDA analysis is conducted using a limited number of classes (visual-based approach) the vast majority of the detected PS should belong to the same evolution class. The experiments herein are some of the first attempts in the literature concerning this subject. All the coauthors made significant contributions to the experiments, preparation, analysis, review, and editing of the manuscript. The main idea on which this work is based belongs to [PERSON]. She also conducted the LDA classifications and the statistical analysis between the LDA's output classes and detected PS. The dataset images preprocessing, multi-looking, and PS detection were carried by [PERSON] He also implemented the statistical analysis of the amplitude's mean correlation index. [PERSON] provided scientific support due to background knowledge on SAR image processing, SITS analysis, and helped with the manuscript review. Part of his work has been conducted within the frame of the research project \"SPERO--Space technologies used in the management of disasters and major crises, manifested at local, national and regional levels\", funded by the Minister of Research and Innovation, UEFISCDI, project reference: PN-III-P2-2.1-SOL-2016-03-0046 and part within the HGF project \"Automated Scientific Discovery\". The idea of this paper and first experimental results were presented at the Multitemp2017 conference. The authors would like to thank [PERSON] for her helpful advice regarding the evolutionary character hidden inside the persistent class. The authors declare no conflicts of interest.
## 5 Conclusions  
Aiming at the shortcomings of existing photon-counting UOWC time slot synchronous clock recovery methods, a time slot synchronous clock recovery scheme for photon-counting UOWC based on deep learning is proposed in this paper. By establishing an underwater channel model and SPAD receiver model, a large amount of training data is produced by using the Monte Carlo method based on these two models, and two neural network models based on the classification problem and regression problem are designed to predict the phase value of the time slot synchronous clock. When the network training is complete, we use the trained network to recover the time slot synchronous clock in the photon-counting UOWC system, and then recover the data. Both simulation and water tank experiment results show that the deep learning method we proposed is better than the existing pulse counting method and can effectively reduce the BER of the photon-counting UOWC system. Another point is that the method of using regression-based CPR-Net to reconstruct the time slot synchronous clock phase is better.  
Conceptualization, H.Y. and Q.Y.; methodology, M.W.; validation, H.Y., Q.Y. and M.W.; formal analysis, H.Y.; investigation, Y.W.; resources, P.L. and W.W.; data curation, H.Y. and M.W.; writing--original draft preparation, H.Y.; writing--review and editing, Q.Y.; visualization, Y.W.; supervision, P.L. and W.W.; project administration, Y.W., P.L. and W.W.; funding acquisition, Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 61565012), China Postdoctoral Science Foundation (No. 2015T80691), the Science and Technology Plan Project of Jiangxi Province (No. 20151 BBE50092), the Funding Scheme to Outstanding Young Talents of Jiangxi Province (No. 20171 BCB23007).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 14: The influence of different time slot synchronous clock recovery methods on the communication BER of the water tank experimental system.
## 3 Results and Discussion
### WGCNA of the DEGs  
To investigate the gene regulatory network, a WGCNA was performed to identify candidate hub genes associated with certain functions or traits ([PERSON] et al., 2020). The WGCNA was used to identify related genes that regulated monoterpene metabolism, using FPKM >1 of all the sequenced points and the total monoterpene profile contents (Figure 8). Genes having the same expression pattern were clustered into the same module to form a cluster dendrogram (Figure 8a). In total, 10 distinct modules (MEblack, MEblue, MEbrown, MEgreen, MEgreenyellow, MEmagenta, MEpink, MEpurple, MEred, and MEtan) were revealed for individual monoterpenes, with module sizes ranging from 55 to 1932 (Figure 8; Table S5). Among them, the MEblue module contained the most genes (1932 genes), whereas the MEtan module had the fewest genes (55 genes). Each module's eigengene was regarded as a representative of all the gene expression profiles in a given module. The correlations between module eigengenes and monoterpenes were detected. The analysis revealed that the MEbrown, MEgreenyellow, and MEgreen modules showed significant correlations with most of the monoterpene profiles, indicating that the genes in these  
Figure 6: Number and Gene Ontology (GO) classification enrichment of differentially expressed genes (DEGs) among the different E-L 35, E-L 36, and E-L 38 stages, respectively (**a–c**). The DEGs were assigned into three classifications of the cellular components, molecular function, and biological process.  
modules, especially MEbrown, play important roles in monoterpene synthesis in grapes (Figure 8b).  
Figure 7: _Cont._  
There were 492 candidate genes in the MEbrown module, which might be associated with monoterpene metabolism (Table S5). To understand the functional distribution of these genes, the KEGG pathway functional classifications and enriched GO terms were determined. The eggNOG classification indicated that 46.27% of the genes in the MEbrown module had unknown functions, followed by those associated with \"posttranslational modification, protein turnover, chaperones\" (16.87%), \"transcription\" (7.71%), \"signal transduction mechanism\" (5.3%), and \"carbohydrate transport and metabolism\" (3.61%) (Figure S3). Of the 492 genes, 140 were obviously enriched in the MEbrown module (Figure S4). These annotated genes were distributed in \"cellular processes\", \"genetic information processing\", \"metabolism\", \"organism systems\", and \"environmental information processing\". \"Protein processing in the endoplasmic reticulum\" was the most significant pathway in genetic information processing. In metabolism-related classifications, \"pentose and glucuronate interconversions\", \"starch and sucrose metabolism\", and \"galactose metabolism\" were the most significant pathways.
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 5 System Completion
### Early Warning Completion  
For every single landslide monitoring sites, we analyzed the relationship between rainfall, deformation value/velocity and landslide, then set the threshold for starting landslide forecasting at this site according to the analysis result. When certain data streams change and meet the threshold value, the system automatically initiates its landslide early warning procedure.  
By combining past and new real-time monitoring data with deformation early warning conditions (e.g., accumulated accelerated speed, rainfall, and groundwater level), the system can automatically select an appropriate early warning model and calculate the landslide transformation trend and instability time. After observing the calculation results, the system can automatically transmit relevant forecasting information to target users (e.g., disaster control experts and monitoring supervisors). After checking the monitoring data with experts, the system finally determines whether or not to publish the warning information, at which point it will transmit the relevant emergency plan and action suggestions to target people at disaster sites. In summary, this model integrates both the early  
Figure 7: System interface: (**a**) the 3D map page; (**b**) the monitoring data page.  
warming results and expert experience, so as to ensure that the published information is as accurate as possible in order to avoid negative social influences from model flaws (Figure 8).
## 5 Simulation and Analysis
### Acquiring the System Transfer Function  
After collecting the data of the control voltage of the proportional relief valve amplifier and pillar pressure in the time domain, the system transfer function was acquired through the above step 2.1 in Section 4. The result of the system identification was shown in Figure 4. The best fit was 94.56% and the system model can be represented as:  
\[G(s)=K_{p}\ \times\frac{1+T_{z}\ \times\ s}{1+2\ \times\ Zeta\times\ T_{w}\ \times\ s+(T_{w}\ \times\ s)^{2}}exp(-T_{d}\ \times\ s) \tag{11}\]  
where \(K_{p}=2.6649,\ T_{w}=0.94635,\ Zeta=0.59862,\ T_{d}=0,\ T_{i}=-0.02786\).  
After the compilation of the above equation, the mathematical model of the system was described as:  
\[G(s)=\frac{2.6649-0.0742s}{0.8956s^{2}+1.133s+1} \tag{12}\]  
Figure 3: Panorama of the experiment platform.
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 4 6G Technologies
### Wireless-Communication-Technology and System  
Each every generation of communication networks introduces novel and captivating characteristics. The use of 6G technology is now imminent. Given its early level of development and ongoing research, 6G technology is now in the phase of investigation. This study explores the speculation around the possibilities and tactics for the implementation of 6G communication.  
In [7], the possible operations and the technologies are to be stationed for 6G communication. They also described the potential obstacles and research trajectories needed to achieve 6G aspirations. In addition to describing the innovative technology that may be used in 6G, they also explained the idea of 6G transmissions. This article provides a viewpoint on the network infrastructure and new wireless 6G networks. In this article, new technologies such as machine learning, broadband, free-space optic systems, blockchain, 3D networking, communication systems, autonomous drones, mobile free communication, incorporation of wireless energy, the transmission of information, associated networks, switching network slicing, hologram beamforming, and scattering are explored.  
In [40], gaining an understanding of research done in various optical wireless systems is highly anticipated for the establishment of future networks. Implementing IoT and 5G/ 6G expectations on a tactile internet foundation is difficult. The supply of high capacity, huge connection, low quiescence, extreme safety, a minimal energy footprint, high quality of experience, and the most dependable connection for 5G communications networks are the most crucial and challenging concerns. Only RF-grounded systems are unable to fulfil the significant demands of future 5G/6G and IoT networks. RF networks' chic equivalent outcome is OWC technology. The cohabitation of RF and optical wireless communication technologies can enable the creation of similar networks. This study included a complete analysis of how OWC technologies including VLC, LiFi, OCC, and FSO will support the successful rollout of the next 5G/6G and IoT networks. The properties of 5G, 6G, and IoT systems as well as the features of OWC technologies have been consolidated here for that purpose. Here, the performance advantages of OWC systems over characteristics of a similar kind are explored for each of the 3 standards.  
Future communications beyond 5G and 6G will heavily rely on reconfigurable intelligent surfaces (RIS). The RIS is expected to be used to help establish a smart wireless environment for forthcoming communications, but it can also be used to construct the  
Figure 4: Key technologies of 6G network.  
transmitter and receiver, creating a smart and intelligent transmitter and receiver. As a result, and this is a very crucial aspect, the latency of forthcoming communications may be greatly decreased by utilizing RIS. The next generation of networks will largely embrace artificial intelligence since it facilitates communication.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 4 Implemented Attacks and Relative Mitigations
### Impersonation Attack
#### 4.2.4 Use of Asymmetric Cryptography  
In this case, each sensor node will keep a couple of keys: a public key and a private key. A packet, sent by a node, will be composed of the message \(m\) (as a plaintext) along with the digital signature, obtained from the plaintext, and the private key of the sender, see Figure 8.  
As we explained in the previous sections, the server will use the public key of the sender to verify the signature. Also, in this case, an attack attempt has been considered. As the previous example related to the symmetric cryptography, also here the attack will fail since the signature verification will fail due to the signature generated not with the private key of the actual legacy sensor but with the private key of the attacker node. Thus, when the server will retrieve the public key of the legacy sensor in order to use it to verify the tag, the verification process will fail since the key is not the right one. Of course, regarding asymmetric cryptography, either using RSA or ECC, the aforementioned process is the same, but in the latter case, using ECC, the only different procedure is in regard to the generation of keys: a procedure that involves the use of the geometric theory and the related geometric element called elliptic curve.