esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wireless body area networks (WBANs) can be used to realize the real-time monitoring and transmission of health data concerning the human body based on wireless communication technology. With the transmission of these sensitive health data, security and privacy protection issues have become increasingly prominent. Fine-grained authentication allows physicians to run authentication checks of another specific entity according to their identifying attributes. Hence, it plays a key role in preserving the security and privacy of WBANs. In recent years, substantial research has been carried out on fine-grained authentication. However, these studies have put considerable effort into WBAN performances, resulting in weakened security. This paper proposes a fine-grained user authentication and key agreement protocol based on physical unclonable functions (PUFs) while maintaining robust security and performance. This will allow physicians to perform mutual authentication and obtain key agreements with authorized body area sensor nodes according to their identity parameters, such as occupation type and title. We then provide comprehensive security and heuristic analyses to demonstrate the security of the proposed protocol. Finally, the performance comparison shows that the proposed protocol is more robust in security, cost-effective communication, and computational overheads compared to three leading alternatives.  
authentication; physical unclonable function (PUF); wireless body area networks (WBANs) +
Footnote †: journal: Applied sciences  
However, the openness of WBANs communication will undoubtedly lead to illegal network intrusion. If adequate security protection measures for health data involving personal privacy are not implemented, serious consequences will arise, including personal privacy disclosure, data tampering, and unauthorized access [2; 3]. Meanwhile, given the different identity authentication requirements for users of different professional levels, it is important to perform personalized authentication according to corresponding levels and permissions to ensure that each user can only access the resources for which they have permission. Naturally, realizing efficient, safe, and credible data transmission and personalized authentication mechanisms in WBANs is essential.  
Fine-grained authentication [4] is an authentication technology designed to provide detailed and precise identity verification in order to identify and authorize users in detail according to the users' unique attributes and permissions. The advantage of this authentication technology is that it can provide a higher level of security and precise control and meet WBANs' requirements for real-time accuracy and personalization in the authentication process.  
This paper aims to provide an efficient and reliable fine-grained authentication solution to ensure the privacy and security of medical data. By carrying out this study, we expect to provide a useful reference and guidance for developing fine-grained authentication technology in order to promote the expansion and application of authentication in WBANs.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Head Selection Phase
#### 5.1.4 Cluster Head-Assisted Clustering Optimization  
The cluster heads can collect updated information from the cluster members through regular communications in the data transmission stage. The CHs can rely on this information during the next round of CH selection to balance the clusters and avoid extra energy usage in the re-clustering stages. The schemes in this category are classified into balanced clusters and energy expenditure mitigation in re-clustering [56].
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 2 Strategies
### Changes to RTKLIB Settings
The statistical changes to the configuration file were made to increase the weighting given to the L5 pseudorange measurements, as well as to account for lower confidence in the carrier phase bias due to the increased possibility of missed slips. The L5 phase error ratio was reduced from 300 to 100 due to L5's higher signal strength and longer codes relative to L1, which results in smaller pseudorange errors. The carrier-phase bias was set to 0.01 cycles. Some of these values were decided upon by trial-and-error, using a select number of datasets from the training set that showed improvement to the overall solution using the values defined above.
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
###### Abstract  
With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CloU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management.  
crowd person detection; YOLOv5; CBAM; BiFPN; emergency management Article
## 5 Experimentation and Performance Evaluation of Obfuscation Methods
### Evaluation of Obfuscation Features
#### 5.3.2 Preservation of Compiler Indicative Comments  
Compiler indicative comments differ from typical explanatory comments in that they provide guidance to the EDA tool during simulation, synthesis, and other processes involving the RTL code. Deleting these comments can significantly impact the results of subsequent RTL code operations, and such action is inconsistent with the precision and rigor demanded in engineering practices. Preserving indicative comments is essential for ensuring the accurate processing of the code by EDA tools.  
To prevent errors in the project, the proposed obfuscation methods filter the original code content based on the indicative comments provided in Table 3. These comments are intentionally preserved in the obfuscated code. This approach ensures that crucial guidance and instructions for the EDA tool are maintained, contributing to the accuracy and reliability of the obfuscated code during subsequent project phases.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**IP Core**} & **Occupancy** & \multirow{2}{*}{**Equivalence**} & \multicolumn{6}{c}{**Circuit Performance Changes (\%)**} & \multirow{2}{*}{**Obfuscation**} \\  & & & & **Critical Path Obfuscation U 1** & & & **Critical Path Obfuscation N U 2** & & **Rate (\%)** & **Consume** \\  & & & **Area** & & **TIMING** & **Power** & **Area** & **Timing** & **Power** & **Rate (\%)** & **Time 1 (s)** \\ \hline MCPU & 27.3 & \(\surd\) & 3.7 & 4.6 & 2.6 & 0.0 & 0.0 & 0.0 & 100.0 & 1.23 \\ DUSB & 148.6 & \(\surd\) & 3.1 & 5.5 & 3.3 & 0.0 & 0.0 & 0.0 & 100.0 & 2.35 \\ DAHB64 & 410.4 & \(\surd\) & 8.1 & 4.1 & 5.0 & 0.0 & 0.0 & 0.0 & 100.0 & 3.11 \\ ALTOR & 298.5 & \(\surd\) & 4.6 & 4.7 & 3.8 & 0.0 & 0.0 & 0.0 & 98.4 & 3.55 \\ \hline \hline \end{tabular} \({}^{1}\) Critical path obfuscation used. 2 Critical path obfuscation not used. \(\surd\) Meets the corresponding description.  
\end{table}
Table 2: Obfuscation effect of 4 sets of IP cores.
## 2 Results
### MeJA Promotes TIA Accumulation and UrGES Upregulation  
MeJA is an endogenous phytohormone with multiple physiological functions and is crucial for regulating the accumulation of plant secondary metabolites [28]. To investigate whether exogenous MeJA affects alkaloid accumulation in _U. rhnchophylla_, we quantified the contents of rhnchophyllline, isorhynchophyllline, corynoxine, and isocorynoxine in leaves at different treatment times using HPLC. The chromatographic peaks of the leaf samples and standards are shown in Figure 5a, and MeJA treatments induced the accumulation of all four TIAs. Compared with the control at 0 h, the content of all TIAs did not change significantly at 1 h, peaked at 3 h, and subsequently declined progressively to approach the near-original values after 12 h (Figure 5). To investigate whether the _UrGES_ gene responded to MeJA induction, we analyzed its relative expression levels by qRT-PCR. Similar to TIA accumulation, _UrGES_ transcript levels peaked at 3 h and then declined slowly, returning to near-original levels, suggesting that MeJA effectively upregulated _UrGES_ expression. In addition, correlation analysis showed a significant positive relationship between _UrGES_ expression pattern and the content changes in these four TIAs (Pearson correlation coefficient \(r>0.8\), \(p<0.01\)) (Figure 5). Taken together, these results suggest that in _U. rhnchophylla_ plants, _UrGES_ is likely to contribute to TIA biosynthesis by catalyzing the formation of intermediates, a process regulated by MeJA signaling transduction.  
Figure 4: _UrGES_ gene transcription patterns in different organs of _Uncaria rhnchophylla_. Values indicate means \(\pm\) SD from triplicate assays. Double asterisks show a highly significant difference (\(p<0.01\)) compared to the root.
## 8 Routing Protocols of Multi-UAV Systems
### Topology-Based Routing Protocols  
Topology-based protocols [23; 75] are classic routing protocols that employ a routing table that contains all the data transfer rules on a network; each of them is identified by its IP address (IPv4 or IPv6). For two UAVs in the network, the routing algorithm computes the appropriate route to send and receive packets between them based on the rules defined in the routing table. As shown in Figures 15 and 16, topology-based protocols are divided into four sub-categories: static [73; 81; 82], proactive [83; 84], reactive [23; 85; 86; 87], and hybrid [88] routing protocols.  
Figure 14: Roadmap of multi-UAV communication architectures.  
Figure 15: Classification of FANET routing protocols discussed in this paper.
# Real-Time Cloth Simulation Using Compute Shader in Unity3D for AR/VR Contents
## 3 Implementation of Cloth Simulation in Unity3D
### Constraint Enforcement on the GPU  
From Equation (11), we classify the operation into three parts, constructing a sparse linear system, solving a sparse linear system, and computing constraint force. So, we use three different compute shaders, and each computes shader consists of multiple kernels, as shown in Table 1:  
Figure 3: Scheme of accessing buffer through the index of the thread. A Jacobian matrix (J) is obtained by partial differentiation of the distance constraint equation and consists of lots of zero values. In this case, the sparse matrix format is the most suitable in terms of memory allocation and performance, since it contains only non-zero (nnz) values. The J matrix has m \(\times\) 3n size, and each row possibility consists of 6 nnz values. As a result, the nnz elements are arranged uniformly near the diagonal of a matrix J. General operation, such as sparse matrix-matrix multiplication (SpMM), sparse matrix-vector multiplication (SpMV), and transpose sparse matrix-vector multiplication (SpMVT), is the bottleneck in the simulation. Under these circumstances, ELLPACK and Compressed Sparse Row (CSR) format are considered for the structure of the J matrix. ELLPACK format obtains a good result of the performance as compared to CSR format, but CSR format has an advantage over ELLPACK in being faster at performing the SpMVT operation [27]. In CSR format, there are three lists to represent the non-zero values, a list of the non-zero elements (A), a list of the column indices of the non-zero elements (JA), and a list of the compressed row indices of the non-zero elements (IA). Note that the list of the compressed row indices is of length m + 1, and each element of the IA points to row starts in the JA and A. Another advantage of the CSR format is a self-transpose operation, which does not require an additional operation to transpose each element of the J matrix. From the idea of CSR format, the new transpose of J matrix can now be stored in Compress Sparse Column (CSC) that has three lists similar to the CSR format. The transpose of CSR format, denoted by \(B\), obtains the same order of the three lists, the IB list now represents the row indices of the non-zero elements, and the JB list is a list of the compressed column indices of the non-zero elements. The size of the system matrix in Equation (11) is m \(\times\) m, but it is a sparse matrix, so it can be represented by coordinate sparse matrix format (COO), and it stores each non-zero element in a list of triplets (nnz value, row index of the non-zero value, column index of the non-zero value). Table 2 shows the list of buffer objects used in constraint enforcement. Since the size of the buffer to store data is static and cannot be changed during the simulation process, each buffer is initialized with a defined number.
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 4 Experiments
### Analysis of Results
#### 4.3.1 Experiment on Different Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Set** & **TNB** & **MSTrA** & **HYDRA** & **DM-CPDP** \\ \hline \multicolumn{5}{c}{NASA to NASA} \\ \hline CM1 & 0.089 & 0.047 & 0.041 & **0.039** \\ PC1 & 0.071 & 0.035 & 0.039 & **0.027** \\ PC2 & 0.002 & 0.002 & 0.001 & **0.001** \\ PC3 & 0.083 & 0.056 & **0.019** & 0.022 \\ PC4 & 0.114 & 0.087 & **0.058** & 0.061 \\ PC5 & 0.172 & 0.103 & 0.069 & **0.057** \\ KC1 & 0.126 & 0.097 & 0.053 & **0.047** \\ KC2 & 0.075 & 0.074 & 0.039 & **0.035** \\ KC3 & 0.147 & 0.079 & **0.072** & 0.084 \\ KC4 & 0.204 & 0.265 & 0.185 & **0.142** \\ MC1 & 0.087 & 0.068 & 0.040 & **0.039** \\ MC2 & **0.113** & 0.152 & 0.144 & 0.119 \\ MW1 & 0.084 & 0.089 & 0.083 & **0.076** \\ JM1 & 0.096 & 0.074 & 0.074 & **0.036** \\ \hline \multicolumn{5}{c}{average} & 0.105 & 0.088 & 0.066 & **0.056** \\ \hline \multicolumn{5}{c}{NASA to SOFTLAB} \\ \hline ar3 & 0.062 & 0.045 & 0.041 & **0.040** \\ ar4 & 0.057 & 0.046 & 0.046 & **0.042** \\ ar5 & 0.049 & 0.034 & 0.032 & **0.031** \\ \hline \multicolumn{5}{c}{average} & 0.056 & 0.042 & 0.040 & **0.038** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of performance indicators cost-effectiveness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Set** & **TNB** & **MSTrA** & **HYDRA** & **DM-CPDP** \\ \hline \multicolumn{5}{c}{NASA to NASA} \\ \hline CM1 & 0.642 & 0.674 & **0.793** & 0.782  
the average value of the cost-effectiveness for the DM-CPDP method is 1%-4.9%, which is lower than other algorithms. In the NASA to NASA experiment, the average value of DM-CPDP method on F-measure and AUC are higher than other methods: 4.4%-28.8%, 5.5%-13.0% respectively. The average value of DM-CPDP method on cost-effectiveness is lower than other methods 0.2%-1.8%. For the three metrics of F-measure, AUC, and cost-effectiveness, they are 9, 9, and 10 datasets respectively showing the best performance on the DM-CPDP method. For the HYDRA, there are 4, 4, and 3 datasets respectively showing the best performance. For the MSTrA method, only one dataset shows the best performance on the F-measure indicator. For the TNB method, there is one dataset performs optimally on AUC and cost-effectiveness respectively. The reason for the above phenomenon is the influence of data distribution. Different machine learning methods behave different on the same data set, and the same machine learning method performs differently on different datasets. DM-CPDP is still superior to other algorithms in general. The reason is that this method uses multi-source datasets and dissimilarity representation method. By comparing with these three classic CPDP methods, it can be proved that DM-CPDP method performs well in the field of CPDP.
# Fault Localization Using TrustRank Algorithm
## 2 Related Work
### TrustRank Algorithm  
TrustRank is a link-ranking algorithm proposed by [PERSON] and [PERSON] from Stanford University and [PERSON] from Yahoo (Hong Kong, China), which is used to optimize query speed and quality [32]. It is a variant of the PageRank algorithm used in Google search engine. In the traditional PageRank algorithm, the ranking of a page is based on the link relationship between pages, and pages with more links are more likely to be considered important. However, this method is vulnerable to link manipulation and spam pages, which can lead to a decrease in the quality of search results. TrustRank mitigates these issues by introducing the concept of trusted sources. TrustRank aims to combat spam by filtering out unreliable networks. This method requires selecting a small group of seed pages that are evaluated by experts. Once reputable seed pages are manually identified, crawling that extends outward from the seed set will look for similar reliable and trustworthy pages. The reliability of TrustRank decreases as the distance between documents and seed sets increases.  
The intuition behind the TrustRank algorithm is that for every node in a network, if it is an important node, then nodes linked to it should also be equally important due to its influence, and this importance relationship will weaken with increasing distance between nodes. In other words, nodes with closer links to important nodes are relatively more important than those linked to less influential nodes in the same network.  
[PERSON] et al. [33] used the TrustRank algorithm to differentiate citation values of different-level journals by leaving journals as seeds for evaluating journal rankings. [PERSON] et al. [34] proposed an auditor suspicious ranking algorithm based on community discovery and TrustRank to solve the problem of lack of supervised learning data for detecting spamsenders. The experimental results show that this algorithm performs better than baseline methods in ranking spam senders and can effectively improve their ranking performance in unsupervised environments.  
Inspired by complex network link ranking algorithms, TrustRank simulates the propagation of program faults by considering the link relationship between nodes. In network analysis, this simulation can help us better understand the structure and behavior of networks. Moreover, TrustRank propagates the degree of influence of a node to its adjacent nodes through influence propagation algorithms. This propagation mechanism can help us evaluate the entire network and better identify important nodes in the network. In addition, different versions of programs have different link relationships, which makes the complex network to be analyzed more diversely. By using the TrustRank algorithm, the influence propagation relationship between these networks can be better identified and their structure better understood. This study starts with the degree of impact of software faults and analyzes the impact of program entities' importance on software suspiciousness using the TrustRank algorithm.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.9 Smartspaces  
The project \"Smartspaces\", which involved participants from nine countries, also referred to energy saving issues in Europe's public buildings through ICT solutions. The scope has been to utilise ICT in order to help the city councils to observe the energy consumption in public buildings. As a first step, energy consumption information has been provided and this has been used for analysis and alert systems in order to provide energy decision support and energy management services. The telecommunication technologies used for data transferring from the smart meter to the data collector vary from site to site and included ZigBee, GSM/GRS or Ethernet [89].
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 5 Conclusions  
A defect size recognition method based on DRT is proposed in this paper for the defect whose size is close to the diffraction limit of the MSDI system. To obtain the far-field simulation light intensity distribution of the defect as the training data set of the DRT, the FDTD electromagnetic simulation model of the MSDI system is built. Since the diffraction effect caused by the limit of the aperture to the scattered light isn't calculated in the simulation model, the simulation light intensity distribution needs to be revised. Hence, the PSF of the actual MSDI system is measured, and the simulation light intensity distribution is convolved with the PSF as the revision. The mean value of the distance between three groups of feature points, whose intensity is 0.75, 0.5, and 0.25 of the peak value of the revised simulation light intensity distribution, is taken as the feature parameter, and the machine learning algorithm of DRT is applied to find the mapping relationship between the feature parameter and the defect size from the training data set. To validate the DRT, the standard glass plates with the process of electron beam exposure and the ion beam etching are taken as the tested defect samples. The experiment results show that the relative error of DRT is within 10%, which is far better than the conventional Otsu method and exponential regression method. In future work, the simulation database of more defect types will be built, and the defect depth recognition will be carried out.  
Conceptualization, Y.Y., PC., and W.L.; methodology, W.L.; software, W.L.; validation, W.L., P.C., and D.Z.; formal analysis, W.L.; investigation, D.Z.; resources, P.C.; data curation, D.Z.; writing--original draft preparation, W.L.; writing--review and editing, Y.Y.; visualization, P.C.; supervision, Y.Y.; project administration, P.C.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC,61627825, 61875173) and supported by the State Key Laboratory of Modern Optical Instrumentation of Zhejiang University and Zernike Optics Co., Ltd.  
The authors declare no conflict of interest.