esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Results  
The bearing fault diagnosis is performed on three fault types (IR, RE, and OR) and the normal condition (NC). In this process, the ratio of the validation dataset is set to be 0.2. The Deep Network Designer embedded in Matlab is employed for the construction, training, and validation of DCNN using a single CUDA-enabled NVIDIA Quadro P4000 GPU.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Pursue Mobility Model (PMM)
#### 6.4.2 End-to-End Delay  
In Figure 18, which illustrates E2 ED, the \(x\)-axis represents the number of nodes ranging from 0 to 60, while the \(y\)-axis measures E2 ED in milliseconds (ms) from 0 to 7 ms. The graph depicts eight distinct lines, each representing different combinations of mobile and attack nodes. The simulations are conducted in a mobile environment using the PMM mobility model, with and without the Hello Flood attack. Here, mobile nodes denote those operating in the mobile environment without the attack, while attacker nodes represent malicious nodes within the same environment. Notably, in all scenarios, there is a consistent pattern of E2 ED rising as the number of nodes increases. For instance, setups without attack nodes and with only 10% mobile nodes exhibit minimal E2 ED, contrasting with the highest E2 ED observed in configurations featuring a larger proportion of both mobile and attack nodes. As the percentage of mobile and attack nodes grows, E2 ED tends to increase, with the highest values likely linked to scenarios with a significant proportion of both types of nodes in the network. Moreover, it is notable that as the proportion of mobile nodes increases, E2 ED follows a similar upward trend.  
Through a variety of interconnected mechanisms, the PMM mobility model and the Hello Flood attack contribute to an increase in E2 ED. Firstly, the PMM model's pursuit-based movement pattern can cause network congestion as nodes chase a target. This can  
Figure 17: PDR without and with attack using PMM.  
cause packet collisions and longer transmission times. Concurrently, the Hello Flood attack floods the network with excessive traffic, saturating network channels and exacerbating interference, further delaying packet transmission and increasing E2 ED. Secondly, the pursuit-based movement pattern may introduce routing instabilities, leading to suboptimal routing decisions and increased packet loss. When combined with the disruption caused by the Hello Flood attack to routing protocols, the overall impact on E2 ED is amplified. Additionally, the heightened network activity caused by both the PMM model and the Hello Flood attack increases contention for limited network resources, such as bandwidth and processing power. This contention increases the workload on network components, leading to higher transmission delays and contributing to an increase in E2 ED. Moreover, the Hello Flood attack may introduce additional processing overhead and network congestion, further delaying packet transmission and increasing E2 ED. The combination of the PMM mobility model and the Hello Flood attack results in a significant increase in E2 ED, driven by congestion, routing instability, resource contention, and increased network activity.
# Applying Reinforcement Learning for AMR's Docking and Obstacle Avoidance Behavior Control
## 3 Autonomous Mobile Robot Design
### Software Architecture  
The software design architecture of the AMR is based on ROS (Robot Operating System) communication and consists of three main layers: the drive layer, the control layer, and the application layer. As shown in Figure 7, the drive layer is responsible for handling the robot's hardware actuation and sensory input, including the DC servo, which controls the movement of the wheels, the camera for perception and object recognition such as AprilTag identification, the BMS (Battery Management System) for power distribution and battery monitoring, and the 3D laser and ultrasonic sensors, where the Velodyne VLP-16 LiDAR provides high-resolution 3D mapping and obstacle detection, while ultrasonic sensors enhance close-range safety by stopping the robot when obstacles are detected. The control layer is responsible for decision-making, navigation, and adaptive control, integrating multiple algorithms to enhance autonomy. It includes AMCL (Adaptive Monte Carlo Localization) and Deep Q Network (DQN), where AMCL enables probabilistic localization within a map, and DQN optimizes navigation and path planning through reinforcement learning. The AprilTag docking module utilizes the Realsense D435 RGB-D Camera for AprilTag recognition, enabling precise docking and positioning. Additionally, the adaptive motion controller adjusts the robot's movement based on environmental feedback, improving stability and responsiveness. The ROS application layer serves as the communication backbone, facilitating real-time data exchange between the drive and control layers, ensuring seamless operation of the robotic system. The overall architecture ensures that the AMR can efficiently navigate, avoid obstacles, and accurately dock using AprilTags, making it well-suited for autonomous operations in dynamic environments.
###### Abstract  
The bactericidal effects of fluorescent metal nanoclusters have impeded their bacterial bioimaging applications due to the reactive oxygen species (ROS) generation that is induced by the nanoclusters in bacteria to cause bacterial death. Herein, an ROS scavenger of cysteine was exploited as a surface ligand to prepare cysteine-conjugated gold nanoclusters (Cys-AuNCs) and cysteine-conjugated silver nanoclusters (Cys-AgNCs) using a facile hydrothermal approach. The structural and optical characterizations demonstrated successful syntheses of Cys-AuNCs and Cys-AgNCs. With the same weight concentration, the bactericidal effect increased in the order of Cys-AuNCs, Cys-AgNCs, and silver nanoparticles (AgNPs), according to the results of the bacterial growth curves. Furthermore, based on the results of the standard colony-counting method, the Cys-AuNCs revealed the best biocompatibility compared to those of the Cys-AgNCs and AgNPs in _Escherichia coli_ (_E. coli_). The superior biocompatibility of the Cys-AuNCs can be attributed to the use of the ligand of cysteine as an ROS scavenger to reduce ROS in _E. coli_. Electron paramagnetic resonance (EPR) analyses indicated that the use of the ROS scavenger cysteine as the surface ligand of the Cys-AuNCs eliminated the ROS production induced by the Cys-AuNCs in _E. coli_. The biocompatible Cys-AuNCs were also confirmed as a fluorescent probe using confocal microscopy. Highly biocompatible Cys-AuNCs could be a potential fluorescent probe in the application of bacterial bioimaging.  
cysteine; reactive oxygen species; scavenger; nanoclusters; bactericidal effect +
Footnote †: journal:  
00 April 2021
# Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation
## 1 Introduction
In this paper, a space coding representation model based on an icosahedral spherical rhombus discrete grid and a space CD method based on matrix Hadamard product operation are established. A set of methods for transforming the longitude and latitude of the airspace boundary to the positioning code is designed, which can uniquely and accurately locate the AG code. Realize that under different latitude and longitude, the grid scale is moderate, suitable for airspace representation (AR) and matrix operation, and there is no obvious deformation. In this paper, the method of matrix Hadamard product is used to calculate the airspace conflict, which solves the defect of inaccurate spatial representation of the existing detection method. It achieves the goals of global coding of airspace and the accuracy and security required for airspace CD, and provides a data basis for the implementation of subsequent airspace conflict resolution algorithms.
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 6 Approximation Algorithm
Nodes are taken from the head of the resulting list one at a time in order and assigned a group in a round robin fashion (lines 10-15), that is, cycling through the groups in order and assigning one node to each before repeating the process. The most complex operation in the algorithm is sorting the nodes, which is performed twice sequentially. Efficient sorting algorithms, such as quick sort and merge sort, can perform this operation in \(O(KlogK)\) time, where \(K=|\mathcal{K}|\) is the number of nodes to be sorted. The two sequential for loops each have a complexity of \(O(K)\), which is lower than that of the sorting operation so it does not increase the overall complexity class. All other operations are basic operations that are executed in \(O(1)\) time. Thus, the total computational complexity of the approximation algorithm is \(O(KlogK)\). It should also be noted that in cases where the angular spectrum spreads are low, such that the resulting angular shifts are lower than the angular differences between nodes' dominant directions for most nodes, the list of nodes will already be nearly sorted, assuming it is initially provided in the order of the nodes' dominant directions. In this case, the complexity could be reduced even further by using bubble sort, which has good performance of \(O(K)\) for an almost-sorted list where no node is out of place by more than one position. In such a case, the complexity of our approximation algorithm also becomes \(O(K)\). Since node partitioning based on angles, as proposed in this paper, is intended for cases where node channels have a strong dominant component and thus interference correlates strongly with the directional characteristics of the channel, cases where the angular spectrum spread is low enough to use bubble sort are quite likely to occur. Checking whether a given problem instance fulfils the needed conditions can be performed in \(O(K)\) time, for example, by performing the first pass of bubble sort and, in case the list is not yet sorted, switching to quick sort or merge sort. Thus, this added optimization for such cases can be included at no additional cost in terms of computational complexity.
# HVdc Circuit Breakers: Prospects and Challenges
## 4 HVdc Circuit Breaker Topologies
### Electro-Mechanical HVdc Breakers
#### 4.1.2 Electro-Mechanical Active Resonance HVdc Breakers  
In this mode, a current oscillation supported by the pre-charged commutation capacitor \(C_{c}\) would increase instantly. It will rise to suppose the main CB's current when the current is commutated to the LC branch. This topology is also presented as a hybrid interruption technique. Typically, a thyristor is employed as a disconnector and commutator in this type of breaker. This notion is also termed a two-stage interruption method. As this concept has various variants, the two main topologies are introduced in [92; 93]. The first variant of electro-mechanical active resonance CB is depicted in Figure 3[94; 95].  
Under a steady-state condition in this first variant, the breaker switches \(S_{2}\), \(S_{3}\), and \(S_{4}\) are open while the only main breaker switch \(S_{1}\) is closed. The capacitor \(C_{c}\) is pre-charged with a negative initial voltage \(V_{c0}\). When fault current is encountered, \(S_{1}\) is opened, while \(S_{2}\) and \(S_{3}\) would close simultaneously. Then the fault current is opposed by the reverse current rising through the LC branch, and a zero-crossing will occur, and thus the current is commutated to parallel branches. Whence the \(C_{c}\) is charged, \(S_{2}\) opens, and the current through it will reduce to zero. Subsequently, the switch \(S_{4}\) will close, and discharging of \(C_{c}\) will happen in the loop \(C_{c}\)-\(S_{4}\)-\(S_{3}\)-\(L_{c}\)- \(C_{c}\). When a current-zero happened in \(S_{3}\), it turns off, and the main breaker current again commutates into the loop \(R_{lim}\)-\(L_{c}\)-\(C_{c}\)-\(S_{4}\). However, \(S_{4}\) opens upon current-zero creation, resulting in a new power equilibrium in which the capacitor is completely charged.  
In the second variant of active resonance dc breakers, current interruption is realized by closing the switch \(S_{3}\) only, as shown in Figure 4. When a fault is detected in the second variant, \(S_{3}\) is closed, and \(S_{1}\) is opened simultaneously to generate a current-zero in the main breaker. Eventually, \(S_{3}\) will open when the capacitor is completely charged [94].
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 4 Experiments
### Experimental Results
#### 4.2.2 Experimental Results on AwA2  
**Performance comparison**. Table 2 presents the experimental outcomes on the AwA2 dataset. Among the teacher CBM models, the joint training approach attains the highest task performance. Utilizing this joint model as the teacher, we contrast the performance of the Vanilla-KD method with the proposed VICO-KD approach. The findings illustrate that the VICO-KD method notably surpasses the Vanilla-KD approach, successfully transferring visual-concept knowledge from the teacher to the student model.  
**Intervention**. The intervention experiment, as outlined in [4], examines the impact of correcting misclassified concepts on the performance of the teacher model, Vanilla-KD, and VICO-KD. As depicted in Figure 5, augmenting the intervention ratio enhances concept and task accuracy for all three methods. Nevertheless, VICO-KD consistently surpasses the teacher model and Vanilla-KD across all intervention ratios, underscoring its superior capacity to utilize corrected concept information and transfer this knowledge to enhance final task performance. Moreover, the performance disparity between VICO-KD and the other methods widens as the intervention ratio escalates, underscoring its scalability and robustness. In essence, the intervention experiment conducted on the AwA2 dataset underscores the efficacy of VICO-KD in enhancing both concept and task accuracies, outperforming the teacher model and Vanilla-KD across diverse intervention ratios.
## 3 Proposed Method
### Overall Architecture
#### 3.2.2 Multi-Level Feature Fusion Module (MFFM)  
In order to solve the disadvantages of different feature maps mentioned above with different sizes, and to improve the detection effect, we adopted some strategies to adaptively fuse multi-scale feature maps at multiple levels, that is MFFM, which is composed of its sub-module MFFU.  
MFFU  
The internal structure of MFFU is shown in Figure 2. First of all, we use step-by-step upsampling and fusion, and also step-by-step downsampling and fusion, but we only do it in \(R3\), \(R4\), and \(R5\). Specifically, firstly, \(R3\), \(R4\), and \(R5\) are convoluted by 3 \(\times\) 3 kernels.  
Figure 1: Overall architecture of Adaptive Feature Fusion Detector (AFFDet). The red line represents the route with weights \(W_{n}\) representing a series of weights that can be learned, and it is actually three routes with weights (corresponding to \(R3\), \(R4\) and \(R5\) respectively).  
Then the convoluted \(R3^{\prime}\), \(R4^{\prime}\), and \(R5^{\prime}\) are bottom-up and top-down, respectively, and then connected horizontally. To solve the problem of original information loss after an MFFU, we add a shortcut structure. At the same time, for each process of feature fusion in the MFFU and the shortcut, how they achieves the best effect of feature fusion, we add the weight parameters that can be learned to every branch, which is denoted as \(w_{n}\) in Figure 2, so that we can get the best fusion weights through training. In this way, more advantageous information can be contained in both the lower-level feature maps and the higher-level feature maps, and the feature map of each scale has a more consistent degree of feature fusion.
## 3 Results and Discussion
### Flow Field
#### 3.1.5 Derivation of the Air Return Velocity  
According to the previous discussion, the return wind speed is not significantly affected by \(L_{0}\) or \(\theta\), but is more significantly affected by \(L_{d}\) and most significantly affected by \(u_{0}\). Considering that there are fewer \(L_{d}\) variables, only the effect of \(u_{0}\) is considered, and the experimental results are fitted to obtain a functional relationship between \(u_{0}\) and \(u_{\rm{return}}\). As shown in Figure 10, the \(u_{\rm{return}}\) is a primary function of \(u_{0}\), and it increases linearly with \(u_{0}\). The fitting function is specified as follows:  
\[u_{\rm{return}}=0.03355u_{0}+0.03632, \tag{2}\]  
where \(u_{\rm{return}}\) is the air return velocity within the model, m/s; \(u_{0}\) is the velocity of the wind duct outlet, m/s.  
Figure 10: Linear fit equation for \(u_{\rm{return}}\) vs. \(u_{0}\).  
Figure 9: Influence of \(\theta\) on the flow field: (**a**) \(u_{0}=2\) m/s and (**b**) \(u_{0}=10\) m/s.
# Predicting the Life of Varistors via a Nonlinear Coefficient Based on a Small-Scale Data Model
## 2 Materials and Methods
### BiLSTM Network  
Long short-term memory network (LSTM) is a special recurrent neural network (RNN) which was proposed by [PERSON] and [PERSON] in 1997 [PERSON] and [PERSON] in 1997 [PERSON] and [PERSON] (1997). It solves the problem of long-term dependence on information in time series learning through network design and is widely used in engineering practices.  
The structure of the network in the single-layer LSTM is shown in Figure 5. xt is the input at the t training, and yt is the output at the t training. The time-series information is transmitted between each training through two transmission states: cell state C and hidden state h. For example, during the t training, the states Ct-1 and ht-1 obtained from the t-1 training will participate in the training, and the new states Ct and ht will be calculated to participate in the t + 1 training.  
Figure 4: The variation in the nonlinear coefficients of three samples (the values were normalized).  
Figure 5: Schematic diagram of LSTM layer structure.  
The LSTM network contains three gate structures and four intermediate state parameters \(f\), \(t\), \(\widetilde{C}\) and \(o\).  
* Forgotten gate This structure is responsible for controlling the influence of historical information on the current training. Its state parameter \(f_{t}\) represents the degree of forgetting the cell state information at the previous time. When it is equal to 1, it means that the cell state information at the previous time is completely retained, and when it is equal to 0, it means that the cell state information at the previous time is completely forgotten. \[f_{t}=\sigma(W_{f}\times[h_{t-1},x_{t}]+b_{f}),\] (4) where \(\sigma\) is sigmoid activation function.
* Input gate This structure is responsible for controlling the new information in the cell state. \[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i}),\] (5) \[\widetilde{C}_{t}=\tanh(W_{C}\times[h_{t-1},\ x_{t}]+b_{c}),\] (6) \[C_{t}=f_{t}\times C_{t-1}+i_{t}\ast\widetilde{C}_{t},\] (7)
* Output gate This structure is responsible for controlling the output information in the cell state. \[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o}),\] (8) \[y_{t}=o_{t}\times\tanh(C_{t}),\] (9)  
The weight matrix \(W\) and the bias matrix \(b\) are obtained through the supervised learning of the training data and updating with the loss function as a metric.  
Bi-directional long short-term memory (BiLSTM) is a complex LSTM model proposed by [PERSON] and [PERSON] [23]. A single BiLSTM layer is composed of two LSTM layers, one forward processing sequence and one reverse processing sequence, as shown in Figure 6. Together, these two LSTM outputs form the BiLSTM output. Compared with the LSTM structure, the BiLSTM structure increases the transmission of reverse information and enhances the calculation accuracy by paying attention to the relationship between the before and after moments and the present moment at the same time.  
Figure 6: Structure of BiLSTM.
# Study on Stress-Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan-Chang Model
## 2 Materials and Methods  
### Test Materials  
In this paper, the test soil was taken from a school soil in Tianxin District, Changsha City, Hunan Province. After the soil samples were taken back to the laboratory, the basic physical properties were measured according to the \"Highway Geotechnical Test Procedures\" (JTG3430-2020). The results are shown in Table 1 and Figure 1. To ensure the homogeneity of the reinforced soil samples made, the soil collected on site was air-dried and crushed, and then passed through a 2 mm sieve. Coir fiber is a type of long fiber with a multi-cell aggregation structure. Its diameter is 100-450 \(\upmu\)m, length is 10-25 cm, density is 1.12 g/cm\({}^{3}\).
## 2 Materials and Methods
### RNA Extraction, cDNA Synthesis and qRT-PCR  
To obtain total RNA, liver tissue was crushed and processed following the protocol of the Thermo-fischer RNA extraction kit, and further analyzed for the purity and concentration on Nano drop (Nano Spectrophotometer BSANA-101).  
cDNA was synthesized using the Thermo-fischer cDNA synthesis kit in a thermocycler following incubation of the 2 \(\upmu\)L RNA and primer mixture at 65 \({}^{\circ}\)C for 15 min, snap-chill for 5 min and incubation at 42 \({}^{\circ}\)C for 1 h. cDNA formed was stored at \(-\)80 \({}^{\circ}\)C till further use. Primers were designed using NCBI Blast. The criteria used for primer selection were: Tm (58-60 \({}^{\circ}\)C), length (18-24 nt), GC content (50-60%) and amplicon size (100-200 bp). Primers were checked for secondary structure, primer-dimer and spanning intron-exon junction. Gene sequences used for mRNA expression studies were HSP70 (F-GCTGGAGAAGTCTGCAATCC, R-TCCCTGGACCATTCATTCCT, Accession No.-XM_019074376.1) and CYP1A (F-AGTGGCCTACCCTGGAGATCC, R-TCCATTAGAGGCAGC GTGTCTT, Accession No. XM_019064218.1). 18S (F-GAATTAGCGGAAGGGCACCACC, R-AACCAGACAAATCGCTCCACCAAC, [48]) was considered as normalizer.  
For quantitative differential expression, cDNA samples from the control and treated group were run on Roche Light Cycler(r) 480 (Roche Life Science, Basel, Switzerland). PCR reaction mix was comprised of 5 \(\upmu\)L of Q-PCR Master mix (Kapa Sybr) (Fast), forward and reverse primers (1 \(\upmu\)L each), 1 \(\upmu\)L of cDNA and 2 \(\upmu\)L of nuclease free water. PCR conditions followed initial denaturation (94 \({}^{\circ}\)C for 2 min), 35 cycles of denaturation (94 \({}^{\circ}\)C), annealing (59-61 \({}^{\circ}\)C), and extension (72 \({}^{\circ}\)C) for 30 s each. Relative fold expression of genes was determined with the 2\({}^{-\Delta\Delta\mathrm{CT}}\) method [49].