esa-sceva/satcom-chunk-collection huggingface dataset:
# FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
## 5 Ablation Study
### Accuracy Differences Due to Changes in Layer Unfreezing Timing  
In our two scheduling algorithms, the number of base layers, \(t_{k}\), is set to 3, leading to parameter unfreezing occurring across three phases. For instance, in Vanilla Scheduling, training proceeds with the unfreezing of the conv1 layer from round \(T=0\) to 100. Subsequently, from rounds 100 to 200, both conv1 and conv2 layers are unfrozen for training. Finally, from round 200 to the final global round, all base layers, including conv1, conv2, and fc1, are unfrozen and utilized for training. While the unfreezing points in this paper are arbitrarily set for discussing the effectiveness of layer decoupling, an appropriate scheduling method is required accordingly. Therefore, this work compares the differences in outcomes when unfreezing occurs at rounds 50 and 100 instead of 100 and 200.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Number of FLOPs (\(\times\)10\({}^{9}\))** & \(T\) \\ \hline FedAvg [3] & 873.04 & 300 \\ FedBABU [20] & 865.34 & 300 \\ Vanilla Scheduling & 314.91 & 300 \\ Anti Scheduling & 838.88 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Computational Cost.  
Figure 8: Comparison of computational costs between FedAvg, FedBABU, and FedSeq. Note that FedSeq with Vanilla Scheduling significantly reduces computational costs in the initial rounds by updating only the front-end layers.  
In the CIFAR-100 dataset, with the global round \(T=1000\) and layer unfreezing round set at \(t_{1}=0,t_{2}=100,t_{3}=200\), Vanilla Scheduling achieves an accuracy of 59.52%, while Anti Scheduling achieves 60.06%. However, when the unfreezing round is changed to \(t_{1}=0\), \(t_{2}=50\), \(t_{3}=100\), Vanilla Scheduling achieves 58.68%, and Anti Scheduling achieves 59.02% accuracy, slightly lower accuracy. For the Tiny-ImageNet dataset at global round \(T=300\), with \(t_{1}=0\), \(t_{2}=100\), \(t_{3}=200\), Vanilla and Anti Scheduling achieve accuracies of 41.86% and 41.94%, respectively. Changing the unfreezing points to \(t_{1}=0\), \(t_{2}=50\), \(t_{3}=100\) results in accuracies of 41.23% for Vanilla and 41.63% for Anti Scheduling, which are also slightly lower. The insight gained here is that while the timing of layer unfreezing does not significantly affect accuracy, as seen in Section 5.2, it has a substantial impact on computational cost. Therefore, setting larger values for \(t_{k}\) is advantageous where possible.
# Mail Man Ferry: A Novel Routing Protocol in Intermittently Connected Networks
## 3 Related Works
### Data Exchange Methods  
The primary benefit of routing in ICNs is that paths between nodes do not need to be maintained. Instead, each node may exchange data in one of the three methods: opportunistic forwarding, prediction-based, or social relationship-based technique. The selection of the corresponding message carrier determines how the techniques are categorized. With the opportunistic technique, messages are routed to any discovered node. Refs. [9; 10] present two opportunistic routing protocols. In the prediction-based technique, a node calculates the probability that the encountered node will deliver a message to its intended receiver. If the discovered node is more likely to deliver a message than the present message carrier, a copy of the message is sent to it. The contact history of a node is one component used in the probability calculation. Prediction-based techniques include [11; 12; 13; 14; 15; 16] as examples. The third routing approach in ICNs is social-based. A copy of a message is given to the encountered node if it shares similar social interests or activities with the destination node. The authors of [17; 18; 19; 20; 21; 22; 23; 24; 25; 26] provide a few examples of routing protocols that are based on social interests or activities. ICN routing strategies seek to improve the delivery probability. The ratio of successfully delivered messages to messages sent is known as the delivery probability. The network will be expected to convey messages more effectively the more widely distributed the messages are.
## 2 Materials and Methods
### Optimization of Indirect iELISA Protocol Based on HEV/RBD or N Recombinant Protein  
A total of 5 negative and 5 positive serum samples, randomly selected from previously evaluated samples, were tested in two independent experiments. Optimal working dilutions of coating N or HEV/RBD recombinant proteins have been determined. Briefly, well plates were coated with 2, 5, and 10 \(\upmu\)g/mL of the recombinant protein in bicarbonate/carbonate coating buffer (100 mM) with a pH of 9.6. The microtiter plates (Maxisorp 96-well flat bottom) were coated with 100 \(\upmu\)L/well of purified coating protein and incubated overnight at 4 \({}^{\circ}\)C. After three washes with PBST, the plates were incubated with 200 \(\upmu\)L/well of blocking solution (PBST-3% [\(w/v\)] BSA) for 1 h at room temperature. Aliquots of sera diluted at 1:40 in blocking buffer were dispensed into the wells of the plates and incubated for one hour at 37\({}^{\circ}\)C. After three washes with PBST, the HRP-conjugated goat anti-human secondary antibody (ThermoFisher Scientific, Waltham, MA, USA) was added at a dilution of 1:10,000. After incubation with the secondary antibody, plate wells were washed three times before 50 \(\upmu\)L/well of the substrate solution (\(o\)-phenylenediamine, Millipore-Sigma, Munich, Germany) was added. Plates were incubated in the dark at room temperature for 20 min. The reaction was stopped with 50 \(\upmu\)L of 1 M H\({}_{2}\)SO\({}_{4}\), and the plates were read at 492 nm in an Epoch Microplate Spectrophotometer plate reader (BioTek Instruments Inc., Winooski, VT, USA). Mean positive/negative (P/N) ratio was calculated.
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 4 The Proposed Scheduling
### CEM-RM Algorithm  
The proposed scheduling algorithm CEM-RM is shown in Algorithm 2. Considering decreasing the negative effect of the diverse period, path crossing, and conflict, the algorithm adopts three approaches to improve schedulability. Assuming that \(t_{\alpha}\) and \(c_{\alpha}\) respectively represent the slot offset and channel offset allocated to the transmission \(x^{k}(s_{\alpha},d_{\alpha})\) within a hyperframe. For a flow \(F_{k}\), if there is \(e(\alpha,\beta)\), the slot allocation must satisfy \(t_{\alpha}<t_{\beta}\). Meanwhile, all the slots \(t_{\alpha}+j\cdot p_{k}(j\geq 0)\) are allocated to the transmission \(x^{k}(s_{\alpha},d_{\alpha})\) due to periodic characteristics. Since there exists node interference in the time slot, the objective of the scheduling is to avoid these interference. However, the slot allocation for transmissions has different periods. This causes more node interference, which reduces channel utilization. The given network graph is schedulable (SCH) when the slot allocation of all the flows satisfies the above constraints expressed by Equations (1)-(4) and (7); otherwise, it is unschedulable (UNSCH). As a classical scheduling strategy, rate monotonic (RM) policy preferentially assigns communication resources to the flow which possesses the shorter period [35]. We adopt RM policy as a basic strategy, where we sort all the flows in ascending period at the beginning (line 2). After individually tracing the RM-based scheduling, we notice that more transmissions need to be scheduled for a node that is close to the gateway, especially for the sink node. As shown in Figure 4, obviously, the sum of available communication resources is \(|H|\cdot|C|\), where we regard a slot with a channel as a communication resource. On the one hand, the generated transmissions of all flows cannot exceed the number of communication resources, which is expressed as follows:  
\[\sum_{k=0}^{\gamma}|X_{k}|\leq|H|\cdot|C|,F_{k}\in F \tag{9}\]  
On the other hand, the number of available communication resources is \(p_{k}\cdot|C|\) when the flow \(F_{k}\in F\) is to be scheduled. However, some of these available resources are occupied by the previous \(k-1\) allocated flows. The condition that there are enough available resources for \(F_{k}\) is expressd as follows:  
\[\sum_{i=0}^{k-1}|X_{i}|\cdot\left\lfloor\frac{p_{k}}{p_{i}}\right\rfloor+|X_{ k}|\leq\left\lfloor\frac{p_{k}}{\kappa}\right\rfloor\cdot|C|,\forall F_{k}\in F^{\prime} \tag{10}\]  
Figure 3: Generated transmissions and their transmission orders for an example flow.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 2 Related Work
### Reinforcement Learning Algorithms in Routing Schemes  
It is difficult to effectively utilize the dynamic routing information in WSN routing networks. A self-adaptive routing algorithm is needed for such dynamic routing networks. To enhance the self-adaptability of routing scheme, [PERSON] et al. were the first time to combine Q-learning algorithm with packet routing to dynamically learn the routing situation to find the shortest path [30]. Reinforcement learning is a useful tool for mining complex, dynamically updated routing network information to optimize the routing scheduling algorithms. In the traditional backpressure routing algorithm, due to the limited routing information of neighbor routing nodes, the loop routing problem is caused as shown in Figure 2 that results in a huge delay in the whole routing process. Recently, [PERSON] et al. proposed multi-agent Q-learning (QL) aided backpressure routing algorithm named QL-backpressure (BP), where each routing node only needs the local information of the neighbor routing nodes to solve this problem [34]. Their algorithm not only outperforms the BPmin algorithm in delay performance but also contains the excellent characteristics: distributed implementation, low computational complexity, and high-throughput [36]. However, when the malicious nodes appear, the throughput-optimality characteristic will no longer exist. The routing scheme based on reinforcement learning should ensure both efficiency and security. [PERSON] et al. proposed a malicious routing node detection scheme based on the reinforcement learning [37]. The core of the algorithm is to judge whether a node is malicious by dynamically learning the number of packets received by the node's neighbor nodes. But this solution can only be used to identify black hole attack nodes, which is very limited in the complicated and variable WSN routing environment.
## 6 Conclusions  
The paper presents a comprehensive investigation into improving the resilience of aviation communication systems with RED/BLACK environments through cross-domain redundancy. By developing and analyzing Markov reliability models, the paper has quantified the substantial gains achievable in the mission-critical RED domain by leveraging supplemental BLACK backups when contingencies exhaust native reserves.  
The study introduces two distinct algorithms for managing cross-domain redundancy: a unified reserve pool and a separate pool approach. Both strategies demonstrate significant improvements in reliability compared to traditional isolated architectures, with the separate pools approach showing promise in scenarios with a high number of reserve elements.  
There are some key findings of the study:  
* Cross-domain redundancy significantly enhances overall system reliability.
* The separate pools algorithm, where the RED domain accesses BLACK domain resources only after exhausting its own, proves more advantageous as the number of reserve elements increases in both domains.
* For highly reliable systems, the reliability improvement factor remains stable across varying numbers of communication channels, indicating the model's applicability across different network scales.
* The proposed strategies offer a balanced approach to redundancy, effectively improving reliability while managing system complexity and cost.  
Controlled integration, enabled through a layered reliability model and failsafe mechanisms, can significantly enhance communication resource efficiency in air traffic control systems.  
The methodology and results presented constitute a compelling argument for the sharing of resources across security domains to enhance system-wide reliability. This approach not only improves operational resilience but also optimizes resource utilization, potentially leading to cost savings in system design and implementation.  
Future work should focus on refining the model to include specific selection strategies for BLACK domain resources, exploring the impact of non-instantaneous switching mechanisms, and conducting empirical validation in operational settings. Additionally, investigating the application of these models to other critical infrastructure systems could further broaden the impact of this research.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 4 Fusion Algorithms
### Fusion Methods in Perception Systems
Distance and intensity raw data from LiDAR are transformed to high-resolution (dense) maps, which allow direct implementation on CNNs, both as single or multi-channel inputs. The results of the CNN were tested with the KITTI dataset Vision Benchmark Suite [39].
## 1 Introduction
### Research Questions  
RQ: How can an intelligent system be designed to optimize conveyor belt performance in BECS machines, enhancing separation accuracy while reducing potential damage, mitigating risks, and improving workplace safety?  
RQ1: What advanced visual analysis methods can be utilized to identify conveyor belt materials based on their shape, enhancing separation accuracy in BECS systems and preventing belt wear and tear?  
RQ2: What method can detect and correct conveyor belt misalignment in an industrial environment, and how can its accuracy be improved under different operating conditions?  
RQ3: What role does thermal imaging technology play in detecting and preventing the abnormal temperature increases in the conveyor belt that are potentially caused by ferrous materials in the BECS system?  
RQ4: What are the benefits of an IoT-based alert system in improving conveyor belt monitoring and reducing response time to potential issues?
## 2 Materials and Methods
### Compositional Data and Transformation
#### 2.3.2 Transformation of Compositional Data  
Former studies have evaluated the performance of four commonly used log-ratio transformations. The additive log-ratio (\(ALR\)) transformation [12] was the most classic and widely used, but arbitrarily choosing the divisor in \(ALR\) transformation may result in problems in compositional data analysis. To solve the problems of \(ALR\) transformation, isometric log-ratio (\(ILR\)) transformation [15] was proposed, and \(ILR\) has been evaluated with satisfactory performance in soil PSF mapping [1,15]. Therefore, in this study, \(ALR\) and \(ILR\) transformations were employed. The \(ALR\) transformation is defined as:  
\[ALR(x_{i})=\ln\frac{x_{i}}{x_{D}},\;i=1,\;2,\;\ldots,\;D \tag{2}\]  
The \(ALR\) back-transformed equation is defined as:  
\[\overline{ALR(x_{i})}=\frac{\exp ALR(x_{i})}{\sum_{i=1}^{D}\exp ALR(x_{i})} \tag{3}\]  
The \(ILR\) transformation [15] is defined as:  
\[ILR(x_{i})=\sqrt{\frac{i}{i+1}\ln\frac{\sqrt{\prod_{j=1}^{i}x_{j}}}{x_{i+1}}}, \;i=1,\;2,\;\ldots,\;D-1 \tag{4}\]  
and its back-transformed equation [47] is defined as:  
\[Y(x_{i})=\sum_{j=i}^{D}\frac{ILR(x_{j})}{\sqrt{j(j+1)}}-\sqrt{\frac{i-1}{i}} ILR(x_{i-1}) \tag{5}\]where \(ILR(x_{0})=ILR(x_{D})=0\) and \(i=1\), \(2\),..., \(D\), which is then back-transformed to the original soil PSFs via the following equation:  
\[\overline{ILR(x_{i})}=\frac{\exp Y(x_{i})}{\sum_{i=1}^{D}\exp Y(x_{i})} \tag{6}\]  
where \(x_{i}\) represents the content of the soil PSFs. \(ALR(x_{i})\) and \(ILR(x_{i})\) represent the transformed values at the sampling site \(i\). \(\overline{ALR(x_{i})}\) and \(\overline{ILR(x_{i})}\) represent the back-transformed PSFs, and \(D\) represents the dimension. The \(ALR\) and \(ILR\) functions were implemented with compositions package [48] in R 3.6.3 (R Development Core Team, 2020). The predictive results were back-transformed to three soil PSFs (i.e., clay, silt, and sand) using alrInv and ilrInv functions.
# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## 1 Introduction
[36] used a deep learning algorithm based on the convolutional neural networks of key-point regions to detect multiple key-points and locate the region of interest (RoI), and then derive the calculated bolt loosening angle or the length of the exposed bolt based on a geometric imaging theory. Although there has been much research on vision-based bolt loosening detection, all of these methods face one or more of the following problems. 1. Many of these methods often have more critical requirements on the camera angle of view or the background where the bolt is located. 2. In order to locate the initial condition of the bolts, many methods require manual marks on the bolts or connection plates. This is a significant amount of work considering the large number of bolts applied to engineering projects. Additionally, these marks will gradually fade or even eventually miss out with time, causing them to be unrecognizable. 3. For methods using deep learning algorithms for bolt detection, it is well known that establishing sufficiently large datasets is a time and labor-consuming process, especially for key-point detection. 4. The accuracy and robustness of many methods are evaluated when detecting one single bolt, while, actually, when used in buildings or mechanical structures, the bolts are often in the form of a matrix of connection plates. To solve the above problems, this paper proposes a method for the automatic detection of the bolt loosening angle based on the latest key-point detection technology. Here, the main contributions of this paper are as follows. 1. A virtual experiment platform is built based on Unreal Engine to solve the problem of laborious manual annotation in dataset establishment. The experimental platform can achieve automatic changes to the camera view, automatic saving of bolt images, and automatic labeling of key points. The platform can quickly build a large-scale synthetic dataset and significantly improve the efficiency of image dataset establishment;Based on the latest key-point detection technology YOLOv7-pose [37], using different capacities of datasets, we can verify the effectiveness of the algorithm in the bolt key-point detection and verify the effectiveness of the synthetic dataset to improve the accuracy of the model;
3. Based on the information of key points obtained from the detection, a bolt loosening angle calculation method is proposed according to the position relationship between neighboring bolts. This method avoids the necessity of extra manual marks and can correct the perspective errors caused by camera view angle changes. Additionally, the accuracy and stability of the method are verified.
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 4 Discussion  
The main influences and interacting influences of LOA in HRC in an assembly task context, considering different levels of workload is summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **QoT Execution** & **Usability** & **User Preferences** \\ \hline LWL \(|\)MWL1 & 0.858 & 0.297 & 0.038 \({}^{\star}\) \\  & & & Low LOA \(>\) High LOA \\ \hline  & & 0.03 \({}^{\star}\) & \\ LWL \(|\)MWL2 & 0.88 & Low LOA: Low \(<\) MWL2 & 0.089 \\  & & High LOA: Low \(<\) MWL2 & \\ \hline  & 0.004 \({}^{\star}\) & & \\ LWL \(|\)High & Low LOA: LWL \(>\) HWL & 0.059 & 0.956 \\  & High LOA: LWL \(=\) HWL & & \\ \hline  & & 0 \(<\) 0.001 \({}^{\star}\) & \\ MWL1 \(|\)MWL2 & 0.1 & Low LOA: MWL1 \(<\) MWL2 & 0 \(<\) 0.001 \({}^{\star}\) \\  & & High LOA: MWL1 \(<\) MWL2 & Low LOA \(<\) High LOA \\ \hline  & 0.042 \({}^{\star}\) & 0 \(<\) 0.001 \({}^{\star}\) & 0.008 \({}^{\star}\) \\ MWL1 \(|\)HWL & Low LOA: MWL1 \(>\) HWL & Low LOA: MWL1 \(<\) HWL & Low LOA \(>\) High LOA \\ \hline  & 0.033 \({}^{\star}\) & & \\ MWL2 \(|\)HWL & Low LOA: MWL2 \(>\) HWL & 0.782 & 0.242 \\  & High LOA: MWL2 \(=\) HWL & & \\ \hline \hline \end{tabular} \({}^{\star}\) green depicts comparison with statistical significance; similar trends are marked with identical colors.  
\end{table}
Table 2: Comparison of assessment (with \(p\)-values) within the workload groups \({}^{\star}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Constituent Measures** & **Significant Effects** & **Finding** \\ \hline  & & LOA (\(p\) \(<\) 0.001); workload (\(p\) \(<\) 0.001); & LOA and workload had significant effect on \\
**QoT execution** & Efficiency; effectiveness & (\(p\)\(<\) 0.001); & the QoT execution. \\  & & LOA\({}^{\star}\)workload (\(p\) = 0.002) & The QoT execution was higher at the \\  & & high LOA. \\ \hline  & QoT execution measures; & & The usability was higher at high LOA. The \\
**Usability** & perceived ease of use, perceived reliability, perceived workload & LOA (\(p\)\(<\) 0.001); Workload (\(p\)\(<\) 0.001) & workload had more influence on the \\  & & perceived reliability, perceived workload & (\(p\)\(<\) 0.001) & constituent variables, with the LWL resulting \\ \hline  & & & Most of the participants preferred the high \\
**User preferences** & User choices regarding & & \\ LOA modes & Workload (\(p\)\(<\) 0.001) & Most of the participants preferred the high \\  & & LOA for both LWL and HWL. In the medium \\  & & & workload levels, the low LOA was preferred \\  & & & for the MWL1 where some task complexity \\  & & & was involved \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of findings.  
Figure 7: LOA preference for the different workload levels.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 4 Combination Protocol Proof Method  
In this section, we interpret the sequential composition of the protocol as syntactic operations on the basic sequence and propose relevant methods for proving the security properties of composite protocols. Clearly, the sequential composition of protocols does not yield a unique result. Typically, when we perform sequential composition of protocols, we consider specific role combinations. For instance, in the case of two-party protocols, we define corresponding initiator and responder roles. The ordering rules and sequential composition rules are the primary rules used to construct modular correctness proofs for protocols. Composite protocols must respect each other's invariants. The method for proving properties of the sequential composition of two protocols involves the following steps:  
1. Construct and prove the security properties of each protocol \(Q_{1}\) and \(Q_{2}\) separately.
2. Identify the invariant sets \(\Gamma_{1}\) and \(\Gamma_{2}\) used in the proof of security properties for the two sub-protocols. The formulas contained in these sets are typically proven using honest rules in both protocols.  
Figure 1: Protocol Authentication Proof Flowchart.  
3. Independently verify that the security properties of the two sub-protocols are preserved in the invariant set \(\Gamma_{1}\cup\Gamma_{2}\). If the postcondition of protocol \(Q_{1}\) matches the precondition of protocol \(Q_{2}\), apply the ordering rule to sequentially compose them. 4. Prove that the invariant set \(\Gamma_{1}\cup\Gamma_{2}\) holds for the security properties of the composite protocol \(Q\). The specific process of proving the protocol combination based on Logic of Events is shown in Figure 2.