esa-sceva/satcom-chunk-collection huggingface dataset:
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 4 The Fabrication of the LHP Based Devices
### Device Protection  
Due to the tendency to degradation in LHP, device protection is often necessary for most of the LHP devices. It has been shown that device protection such as encapsulation and introduction of barrier layers is very effective when compared to the unprotected devices. For instance, in the LHP/MoS\({}_{2}\) photodetector [95], both octadecyltrichlorosilane (OTS) and PMMA were used to encapsulate the device which could provide a good optical transparency simultaneously. The device without encapsulation deteriorated in photoresponsivity and detectivity after 48 h; the encapsulated device, on the other hand, deteriorated less than 30% after 200 h. The degradation due to the exposure of moisture and oxygen could be considerably reduced by the protections above. On the other hand, the light-induced degradation forms the major bottleneck limiting the practical usage of  
Figure 11: SEM images of various LHP structures after a complete lithography process. (**a**) Unetched area, top view; (**b**) Lines, top view; (**c**) Squares, top view; (**d**) Squares and unetched film, tilted view; (**e**) Lines with width <1 μm; (**f**) 250 nm wide lines. The scale bars in the Figure correspond to 1 μm. Reproduced with permission from [121]; published by Wiley, 2018.  
Figure 10: A lithography scheme for the LHP. (**a**) Preparation of thin LHP films; (**b**) Imprint resist spin coating. (**c**) Pattern imprinting into the resist. (**d**) Removal of the residual resist to reach the LHP film. (**e**) Argon ion beam milling of the unprotected LHP areas. (**f**) Removal of the residual imprint resist. Reproduced with permission from [121]; published by Wiley, 2018.  
LHP devices for long working lifespan, as it could be avoided by the protections mentioned above. It is expected that a better understanding on the mechanism of light-induced degradation could benefit the solving of this problem.
## 4 Hierarchical Cooperative MIMO Clustering
### Clustering
#### 4.2.3 Choosing the Cooperative Node (CN)  
Choice of cooperative node is made in a way to possibly maximize the network lifetime. This is achieved depending upon the distance from CCS and the clusterhead. Cooperative nodes are chosen in such a way that they lie in close geographic vicinity of clusterhead. Second criteria in selection of CN is residual energy of the cooperative nodes. If the clusterhead can directly communicate with the CCS, no cooperative node is selected in the cluster. Such a case occurs when cluster and CCS lie at a small distance. In this case there is Single Input Multiple Output (SIMO) communication between the cluster and the CCS. This is illustrated in Figure 1. When both the clusterhead and cooperative nodes communicate with the CCS, the communication operates in MIMO mode. Cooperative node is selected each time clusterhead wants to send data. Clusterhead is aware of residual energies of nodes in the cluster. If a node lying in close geographic vicinity starts getting drained off, the clusterhead selects another node with a higher amount of residual energy.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Invasion of the Host
#### 2.2.3 BtsR1  
Bacteria sRNA can also be managers of resource optimization ensuring the timely expression of their target only in the presence of the stimuli as exemplified by the _Bacillus thuringiensis_ strain YBT-1518. This strain harbors three nematicidal _cry_ genes (_cry5 SAa_, _cry6 Aa_, and _cry5 Ba_), with high toxicity against _Caenorhabditis elegans_[104]. The 51-nt sRNA BtsR1 directly regulates _cry5 Ba_ expression by binding to the ribosome binding site (RBS) of Cry5 Ba mRNA, preventing translation. During free-living conditions, the suppression of Cry5 Ba by BtsR1 reduces toxin production, minimizing energy expenditure and avoiding host detection. Interestingly, once the bacterium is ingested by _C. elegans_, BtsR1-mediated silencing is alleviated, leading to the activation of Cry5 Ba toxin expression within the host. The toxin then subverts the nematode's defenses and ultimately causes its death. This dynamic regulatory mechanism enables the bacterial pathogen to optimize toxin production in response to environmental conditions. Cry proteins are energetically costly, constituting over 25% of the bacterial dry mass, and their production during free-living conditions would waste resources. By silencing toxin synthesis via BtsR1, YBT-1518 avoids triggering host avoidance behaviors and ensures efficient ingestion by nematode hosts. Once inside the host, the reduced transcription of BtsR1 allows the bacterium to activate Cry5 Ba production, securing nutrients critical for its survival. This mechanism of toxin silencing highlights the ecological significance of sRNA-mediated regulation in balancing energy conservation with survival strategies. BtsR1 exemplifies how regulatory sRNAs enable pathogens to fine-tune gene expression in response to environmental and host-derived signals. This sRNA-mediated strategy reflects the co-evolutionary arms race between _B. thuringiensis_ and nematode hosts [104].
## 6 Simulation of Meshing  
The tooth contact analysis (TCA) implemented in this work is based on the method presented in detail in Section 2 and is used to simulate the gear meshing and study the bearing contact between the crowned tooth surfaces by using the Wolfram Mathematica environment. TCA's key objectives are to define the path of contact and the TE caused by misalignments of the gear shafts. During the simulation, the geometric parameters of the gear pair are given as well as the orientation and misalignment of the gear shafts. For all simulation cases, the pinion tooth surface was crowned in the longitudinal direction with one of the three types of modifications (parabolic, circular, or logarithmic) and meshed with an involute (not modified) gear tooth surface.  
As stated in the introduction, the selection of the type and amount of crowning is critical for gear meshing. For instance, excessive crowning leads to an increase in TE, while insufficient crowning will cause the gear teeth to contact close to the flank edges. Therefore, the amount of crowning should be optimized to give a contact path close to the center of tooth surfaces or at a certain distance from the flank edges with a continuous TE function.  
In this work, the three different types of crowning were designed to meet these requirements in case of various in- or out-of-plane misalignments. The parameters of each type of modification applied to the lead tooth flank (Figure 5) are selected so as to obtain a continuous TE function. This can prevent shock loads and rattling and diminish noise and vibrations. The function of TE is defined as the actual rotation angle of the output gear minus the theoretical rotation angle. The TE vary from zero due to surface mismatch or misalignment and can be calculated using the equation below:  
\[\Delta\varphi=(\varphi_{2}-\varphi_{20})-\frac{N_{1}}{N_{2}}(\varphi_{1}- \varphi_{10}), \tag{43}\]  
where \(\varphi_{10}\) and \(\varphi_{20}\) are the rotational angles of the pinion and the gear when tooth surfaces are in contact at an initial contact point. Here, \(\varphi_{2}\) is the gear rotational angle corresponding to the pinion rotational angle \(\varphi_{1}\), which is an independent variable. The function \(\Delta\varphi\) is a periodic function with the period \(T=2\pi/N_{1}\). \(\Delta\varphi\) has different shapes depending on conditions such as the angle of misalignment or applied crowning. Figure 6 displays the undesirable result \(\Delta\varphi\) caused by gear misalignment without applying any modification.  
Figure 6: The function of TE with angular misalignment.  
In the case where meshing gears without crowning are misaligned, their axes are crossed or intersected. In that case, the edge of the pinion tooth surface will be in tangency with the gear tooth surface, which will lead to the path of contact located at the edge of the tooth flank as in Figure 7, accompanied by the discontinuous function of TE. In this circumstance, gear transmission at the end of the meshing cycle is inevitably accompanied by a jump in the angular velocity, vibration, and noise.
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Crack Growth and Linkage in Pre-Cracked Rock Disks
#### 3.5.1 Crack Growth and Fracture Mode Transition  
The evolution of the fracture processes in the CSCBD specimens with varying pre-crack inclinations is presented in Figure 17, demonstrating the distinct transition between tensile and shear-dominated failure modes. Crack initiation consistently occurred at the pre-crack tips and progressed along the principal stress trajectories. The failure mechanisms exhibited the following characteristics:  
* Mode I tensile failure was dominant at \(\beta=0^{\circ}\) and \(\beta=15^{\circ}\), where crack propagation extended vertically under maximal tensile stress conditions. This aligns with the typical fracture patterns observed in brittle materials under purely tensile loading conditions.
* Mixed-mode fracture (I + II) occurred at \(\beta=30^{\circ}\) and \(\beta=45^{\circ}\), where tensile and shear interactions jointly governed crack propagation, leading to complex stress redistribution effects. This fracture mode represents the transition zone, where shear contributions significantly influence crack evolution.
* Mode II shear-dominated failure emerged at \(\beta=60^{\circ}\), characterized by a substantial crack path deviation due to localized shear stress concentration. The crack trajectory exhibited pronounced branching and irregular fracture surfaces, indicative of shear-driven instability.  
These observations are in close agreement with the findings of [PERSON] et al. [69], who demonstrated that mixed-mode fractures (\(\beta=30^{\circ}\)-\(45^{\circ}\)) exhibited the lowest fracture toughness values owing to the simultaneous tensile and shear contributions. The present numerical results reaffirm this trend, with the minimum failure load occurring during \(\beta=45^{\circ}\), thereby reinforcing the predictive accuracy of the model.  
Figure 16: Simulated outcomes for scenarios with various pre-crack dip angles: (**a**) load–displacement relation; (**b**) Comparison of normalized failure load vs pre-crack dip angle between the simulated method and the findings of [PERSON] et al. [68].  
The fracture surfaces produced in the CSCBD specimens exhibit self-affine scaling properties, as predicted by [PERSON] et al. [48]. Their study showed that brittle fractures follow a roughness exponent of approximately 2/3, which is a characteristic that is replicated in the current numerical model. Additionally, finite-size effects influence crack coalescence dynamics, particularly in specimens exhibiting shear-dominated failure at larger inclination angles. The integration of scaling principles reinforces the robustness of the SW-DVIB approach in capturing fracture roughness and energy dissipation mechanisms.
# Reliability by Using Weibull Distribution Based on Vibration Fatigue Damage
## 2 Fatigue Damage  
### Fatigue Damage Accumulation  
Fatigue damage can be described as a failure mechanism that is manifested when a material tends to fail or break under repeated deflections [14; 15; 16]. Thus, a nonlinear model to accumulate the random vibration fatigue damage has been proposed [17] with the purpose of evaluating the fatigue damage of different dynamic loads in mechanical components and structural elements. The acceleration response of the analyzed vibration system is determined by stress as in Equation (1). The applied vibration cycles are determined by the rainflow method [18]. From the S-N material's curve, the corresponding life cycles are determined by using the Basquin Equation [19] as is in Equation (4).  
\[\sigma(vib)_{i}=\sigma_{dynamic}\times A_{res} \tag{1}\]  
\[\sigma_{dynamic}=\left(\frac{Km_{e}Lc}{I}\right)A \tag{2}\]  
\[A_{res}=\frac{2\pi^{2}F^{2}D_{2}}{G} \tag{3}\]  
\[N_{i}\times\sigma(vib)_{i}^{b}=a^{b} \tag{4}\]In Equation (1), \(\sigma_{dynamic}\) and \(A_{res}\) are the dynamic load factor and the acceleration response, respectively. In Equation (2) [20], \(K\) is the stress concentration factor in the mechanical component, \(m_{e}\) is the effective mass, \(C\) is the distance to the neutral axis, \(\hat{L}\) is the distance from the fixed point of the component to the point of application load, A is the constant of gravity, and \(I\) is the moment of inertia. In Equation (3), \(F\) is the frequency applied by the vibration power spectral density (PSD) and \(G\) is the gravity constant. In Equation (4), \(N_{i}\) is the maximum number of cycles that the material's component can sustain at a vibration load with stress amplitude \(\sigma(\textit{vib})_{i}\) and the parameters \(a\) and \(b\) are constant variables that represent the intercept and the slope of the S-N curve, respectively.  
As shown by Equations (1)-(4), because a vibration has a nonlinear behavior [21], the generated fatigue damage also presents a nonlinear behavior. Consequently, the fatigue damage is determined by using Equation (5), where the damage is described by a curve that represents the effect under two-level loading conditions [22], where \(n_{i}\) represents the applied vibration cycles at the stress level \(\sigma(\textit{vib})_{i}\).  
\[D=\sum_{i=1}^{2}D_{2}=\Biggr{[}\frac{n_{2}}{N_{2,f}}\Biggr{]}^{\frac{N_{2,f}} {N_{1,f}}}|^{\frac{\sigma(\textit{vib})_{1}}{\sigma(\textit{vib})_{2}}}_{ \frac{1}{2}} \tag{5}\]  
Now that the random fatigue damage generated by the vibration environment is determined, a Weibull formulation is presented that let us use the generated damage in the Weibull \(Y\) vector (see Equation (13)) to determine the reliability of the analyzed element.
## 1 Introduction  
Compared with PIN diodes and FETs, RF MEMS switches are smaller in size, lighter in weight, less sensitive to acceleration, no DC power at microwave frequencies and have excellent isolation and insertion loss [1]. [PERSON] et al. [2] address the fundamentals of RF switches, providing a comparison between semiconductor and RF MEMS switches. RF MEMS switches are widely used in radar [3], satellite communication systems [4], smart antennas with beam forming and phased array capabilities [5], tunable filters [6], phase-shifting networks [7] and other important fields. [PERSON] et al. [4] describe the potential applications of RF MEMS switch matrices in the satellite industry, wherein mass reduction and performance improvement are crucial. [PERSON] [5] tells us that RF MEMS switches and transmission lines can realize the function of phase shift so that they can point out the antenna beam to a desired location with high precision.  
However, there are a few issues to be resolved. First, the driving voltage of the MEMS switch increases as the beam height increases. Secondly, the capacitance ratio is not very high. The actuation voltage represents the switch's integration performance, which contributes to the development of a monolithic microwave-integrated circuit (MMIC). A larger off/on capacitance ratio is advantageous for achieving high isolation and outstanding RF performance. For example, the capacitance ratio of the switch controls the adjustable range of the center frequency of the resonant unit in the tunable filter.  
There has been some research done in larger capacitance off/on ratios to date. [PERSON] et al. [8] employ warped-beam, and [PERSON] et al. [9] and [PERSON] et al. [10] use materials with a high dielectric constant to get a larger capacitance ratio. The other approach of achieving a high capacitance ratio is to widen the distance between the MEMS beam and the dielectric layer [11]. However, the capacitance ratio is constrained by the minimum dielectric layer thickness, the maximum dielectric constant value and the maximum distance between the beam and the signal transmission line. As a result, the ways used in [8; 9; 10; 11; 12] are not the most suitable.  
Based on the floating metal membrane, this research increases the capacitance ratio of the RF MEMS switch. Therefore, a low insertion loss, high isolation and larger capacitance ratio RF MEMS switch has been designed and fabricated.
## Materials and Methods
### Data Description and Selection of Variables  
The study area selected for this study was two motorways in Belgium named E-313 and E-314 (Limburg Province Sections with a total length of 103 km). Each Motorway has segments, traffic-related characteristics, and road network segmentation derived from the FEATHERS model [53]. In this study, a segment with at least one crash was considered as a decision-making unit (DMU) to analyze the road safety condition. According to this criterion, 67 segments are selected for these two motorways. The crash data used in this study consisted of a geographically coded set of crash data that occurred between 2010 and 2012, which was provided by the Flemish Ministry of Mobility and Public Works, as shown in Table 1. The first and very critical step in conducting an analysis is the selection of inputs and outputs variables. For this purpose in the first stage (DEA), those variables which were the exposure variables and could not be directly affected by a traffic engineer/decision maker were selected to calculate risk, while in the second stage (ANN) those variables (i.e., Horz and Vert Curve design, speed, and flow) which could be altered or improved by directly changing certain parameters, were selected. So, the target while calculating risk was to reduce the number of crashes (NoC) and casualties (NoAP) with the increase of average volume to capacity on each segment (V/C), total daily vehicle miles travelled on each segment (VMT) and total daily vehicle hours travelled on each segment (VHT). A traffic engineer cannot directly change V/C, VMT, or VHT; however, the geometric design (Horz and Vert Curve), speed (speed limit) and flow (by controlling access) so practically, a selection of variables was targeted according to the feasibility of the problem's solution. To confirm the validity of the DEA model condition, an isotonicity test [54] was conducted. An isotonicity test comprises the intention of all inter-correlations between inputs and outputs for detecting whether increasing amounts of inputs lead to greater outputs. As positive correlations were established, the isotonicity test was accepted and the presence of the inputs and outputs was reasonable. However there are no diagnostic checks for improper model specification detection in DEA [55]. However, a general rule of thumb, the minimum number of DMUs is higher than three times the number of inputs plus outputs [56]. In our study with a total of three inputs and two outputs, so a set of 15 data points would be optimal; we have 67 data segments.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 4 Experiments
### Datasets and Experiment Setup
#### 4.1.2 Experiment Setup  
In the training stage, we adopt TSM as the baseline network, utilizing ResNet50 pre-trained on ImageNet [34] as the backbone. The same data augmentation strategy used in TSM is adopted. Each video is sampled with 8 or 16 frames as input. Training is performed on 8 NVIDIA GeForce RTX 3090 GPUs using distributed learning, with a per-GPU batch size of 16. The server includes an Intel Xeon CPU, 128 GB of RAM, and SSD storage for fast data loading. The experimental software includes Python 3.9, PyTorch 1.10, and CUDA 11.1, running on Ubuntu 20.04. Optimization is conducted via stochastic gradient descent (SGD) with a momentum of 0.9 and a dropout rate of 0.5. For the Something-Something V1 and V2 datasets, the initial learning rate is set to 0.02 with a weight decay of \(5\times 10^{-4}\). Training spans 60 epochs, with the learning rate reduced by a factor of 10 at epochs 30, 40, and 45. For Kinetics-400, the initial learning rate is set to 0.01 with a weight decay of \(5\times 10^{-4}\). Training extends to 100 epochs, with the learning rate decreasing by a factor of 10 at epochs 50, 70, and 90.  
In the evaluation stage, the experimental software and hardware conditions remain consistent with the training stage. For the Something-Something V1 and V2 datasets, we sample a single clip with \(T\) frames from each video as input. Frames are uniformly sampled with a center crop of size 224 \(\times\) 224 (center crop \(\times\) 1 clip). For the Kinetics-400 dataset, 10 clips are sampled with \(T\) frames from each video. Each frame is uniformly sampled using three crops of 256 \(\times\) 256 (three crops \(\times\) 10 clips).
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 4 DPTracer Logger
### Process Tree Construction
#### 4.1.1 Adding Log Entries  
Log addition begins when the logger receives an upstream report from a node. Upon receipt, the logger assigns a new version number \(i\) to these data and converts them into a log entry \(X_{i}\), which is then added to the tree.  
The locations of leaf elements are grouped based on the roles of the nodes. The log entries from collector nodes are stored in Group 1. Similarly, the logger stores the log entries from aggregator nodes and the submitter in Groups 2 and 3, respectively. For example, as illustrated in Figure 3, log entries such as \(X_{1}\), \(X_{2}\), \(X_{3}\), and \(X_{5}\) from collector nodes are placed in Group 1. Among the aggregator nodes, two entries, \(X_{4}\) and \(X_{6}\), are located in  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(X_{i}\) & Version-\(i\) (\(i\)-th) log entry \\ \(C_{i}\) & Version-\(i\) commitment \\ \(\mathbb{C}\) & Set of commitments \\ \(\pi_{i,j,k}\) & Interval proof for versions \(i\), \(j\), and \(k\) \\ \(r\) & Round number \\ \(N\) & Total number of nodes \\ \(n_{\alpha}\) & Node with identifier \(\alpha\) \\ \(R_{\alpha}\) & Upstream report of \(n_{\alpha}\) \\ \(\mathbb{R}\) & Set of upstream reports \\ \(\
u(n_{\alpha})\) & Version number of \(n_{\alpha}\) \\ \(\mathbb{V}\) & Set of input versions \\ \(\mathbb{L}\) & Engagement list of nodes contributing to the final result \\ \(\sqcap\) & Aggregation operator \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 3: Process Tree at version 7.  
Group 2. When a new log entry is entered into Process Tree, it is placed at the leftmost available slot within its group. Thus, log entries within a group are arranged in the order of increasing version number, though the numbers may not be sequential.  
Specifically, when a new log entry is received, the logger performs the following steps:  
1. **Assign version number**: increment the version counter in the logger to obtain the new version number \(i\).
2. **Create leaf element**: make a leaf element for \(X_{i}\) and calculate its hash value as \(h_{i}=\text{Hash}(X_{i})\), where the contents of \(X_{i}\) are as described in Section 4.2.
3. **Place in group**: determine the appropriate group based on the node's role and place the leaf element in the leftmost available slot within that group.
4. **Update tree**: recompute the hash values of the nodes along the path from the new leaf element up to the root, updating any affected interior elements.  
For instance, Figure 4 illustrates an earlier version (version-4) of the Process Tree from Figure 3. In the version-3 tree, which precedes version-4, only three entries--\(X_{1}\), \(X_{2}\), and \(X_{3}\) are placed in Group 1 in order. When \(X_{4}\) is added, assuming it comes from aggregator nodes, it is then located at the leftmost slot of Group 2, skipping the free slot of Group 1.