esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Experimental Setup  
We divide the noise sources into two categories to simulate the actual working conditions of circuits in a noise-filled environment: input noise and supply noise. Considering the low-pass characteristics of logic gates and the central limit theorem, these two types of noise are modelled as random variables following a normal distribution. The input noise represents external noise effects, such as single event transient and electromagnetic coupling, and the amplitudes of these noises are relatively large. Additionally, as the probability of error in actual circuits is very small, an extremely long time is required for the MC simulation to obtain the overall transient fault rate. Therefore, the standard deviation of the noise on the input signal is set to 300 mV such that the MC simulation can be implemented within a reasonable time to verify the effectiveness of the PVTC method. Supply noises, which represent noise effects inside the VLSI circuit system, such as power supply ripple and amplifier noise of the linear regulator, are also considered; the amplitudes of these noises are relatively small, and thus, their standard variation is set to 20 mV. The supply noise and the input noise are assumed to be independent. In our experiment, \(dV_{out}\) and \(dV_{in}\) are set to the same value to simplify the experiment, and \(dV_{out}\) and \(dV_{in}\) are set to the small value of 30 mV to guarantee accuracy.  
Figure 10: Changes in the SVTC of an inverter in the fresh and degraded states.  
Figure 9: Change of \(V_{th}\)_vs._ work time under different duty cycles of an input signal.  
The predictive technology model (PTM) [22] for 45 nm general-purpose pMOS and nMOS transistors is exploited to evaluate the PVTC method, and the power supply of the logic circuit is set to 0.9 V. In NBTI effect simulation, the duty cycle for the pMOS transistor is set to 0.95, as in [23] by the MDS method, temperature \(T\) is set to 300 K. In the transient error rate calculation, \(V_{high,min}\) in Equation (5) is set to 0.7 V, which represents logic \"1\", whereas \(V_{low,max}\) is set to 0.2 V, which represents logic \"0\". The number of iterations for MC simulation is set to 10,000. The above configuration is the same for all of the experiments.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 7 Evaluation
### RQ1: Identification Accuracy  
We first measure how completely ErrorExplainer recovers transaction-reverting statements. Across the 335 contract pairs, ErrorExplainer extracted a total of 6284 normalized errors. Each corresponds to one transaction-reverting statement. Of the 1161 statements that SC-Bench intentionally removed in the mutated variants, ErrorExplainer correctly detected 96.99% of those deletions, missing only 3.01% of them. This high recall indicates that few transaction-reverting statements are missed by the static analysis.  
Figure 2 reports the mean number of errors per contract: 18.76 for original versions and 15.4 for mutated versions. The drop in the mutated version matches the ground-truth removals, which validates the precision of ErrorExplainer.  
Further analysis is provided in Figure 3, which breaks the results down by compiler version. Three quarters of the smart contracts target \(\geq\)0.8.4, i.e., after custom errors became available. ErrorExplainer handles both cases uniformly. The largest contract analyzed (Solidity 0.8.1) contained 49 distinct transaction-reverting statements.  
Lastly, Figure 4 plots the number of identified errors against the source-code length. The correlation is weak (0.29), but an upward trend confirms that contracts with more logic naturally embed more guard conditions. Figure 4 also shows that ErrorExplainer can scale with the complexity increase.  
Figure 2: Errors identified vs. ground-truth removals. For every contract pair, ErrorExplainer counts the number of transaction-reverting statements in the original and mutated versions. The difference between the two bars is compared with the exact number of deletions recorded in the mutation log.  
Figure 4: Relationship between identified errors and lines of code (LOCs) Each point represents one contract. \(x\)-axis is LOCs, and \(y\)-axis is number of errors detected by ErrorExplainer. Transaction-reverting statements increase almost linearly with LOCs.  
Figure 3: Average errors per contract by Solidity version. Bars report mean number of transaction-reverting statements that ErrorExplainer detects in original and mutated versions of every contract, grouped by compiler versions. Solidity versions show rise in error counts.
## 3 Finite Element Analyses  
### Introduction to the Analyses  
Aiming at reducing the additional Joule losses potentially occurring in high-frequency operations, the relations between several slot parameters and the total loss distribution have been sought by the 2D FEA analysis. The clearance between conductors inside the slot has not been varied and the total MMF is kept at a constant value. The parameters under investigation are the stator slot opening \(w_{\infty}\), the tooth width \(w_{t}\) and the polar shoe thickness, see Figure 6. The FEA analysis is not intended to reduce the additional Joule losses through elimination of the closest conductor to the slot-opening, since this technique has been already studied in [11].  
A single stator slot with four series-connected conductors was studied. Furthermore, two slot shapes were analysed: the rectangular and the trapezoidal shapes. Initially, the slot parameters were varied one by one, keeping the other constants. Then, two or three parameters were investigated  
Figure 5: Current Density module for multiple conductors.  
simultaneously: this is consistent with the multi-factorial analysis promoted by the _Design of Experiments_ approach of [12].  
The base dimensions in millimeters for the slot are shown in Figure 7.  
Two key performance indicators (KPIs) were employed to compare different simulation results:  
* The percentage reduction, or the increment, in the total power losses;
* The percentage difference between the greatest and the smallest impedance \(\Delta\)Z% between the conductors inside the slot, see Equation (13).  
\[\Delta\)Z% = \frac{Z_{max}-Z_{min}}{Z_{min}} \tag{13}\]  
The first KPI expresses which slot geometry leads to the lowest additional Joule losses, while the second KPI shows which configuration causes the slightest percentage impedance difference between all conductors inside the slot. The percentage difference impedance is of paramount importance in the  
Figure 6: Stator slot parameters.  
Figure 7: Rectangular slot, reference main dimensions in mm.  
case of parallel conductors because if \(\Delta Z\%\) is too high, the parallel paths will have an uneven current distribution. Since the BEV and the HEV usually work at high operating frequencies, the FEA analysis was carried out at 1000 Hz.
## 3 Test Results and Analyses
### Strain Field Evolution Characteristics of the Free Surface
#### 3.3.2 Uniaxial Static Compressive Load
The eight curves in Figure 7a at different moments intersect at the same point, where the strain is zero, at \(x=28\) mm. This shows that the boundary between the tensile and compressive strains perpendicular to the loading direction does not change with time or increase in strain at the center point. Under the uniaxial static load, only tensile cracks parallel to the loading direction appear (Figure 8), as determined by the shape of the tensile strain concentration zone and variations in strain direction and magnitude. When microcracks initiate and propagate in the high-strain zone near the center point, leading to the formation of microcracks, other parallel microcracks are restrained. Finally, only one macrocrack parallel to the loading direction emerges. The monitoring results also indicate expansion-induced tensile cracks at 32 \(\upmu\)s after the explosion. The corresponding maximum dynamic tensile strain is 0.00319, approximately equal to that without static load. The dynamic tensile fracture strain of the sandstone under such blasting conditions can be determined by integrating the experiment results for the two cases  
\[\varepsilon_{td} =0.00322 \tag{6}\]
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 3 Lattices
### Computational Lattice Problems
#### 3.2.3 Shortest Independent Vector Problem (Sivp)  
An additional significant computational problem in lattices that has great significance in cryptography is the shortest independent vector problem (Sivp). The main question in this problem is how to minimize the length of the longest vector of the basis. Viewing it from a different angle, the basic task is to find a new basis that generates the same lattice and has the ability to minimize the length of the longest vector. This problem can be visualized in Figure 5.  
Figure 5: Shortest independent vector problem (Sivp).  
**Definition 22**.: _Shortest independent vector problem (SIVP). Given a basis \(B=\{b_{1},b_{2},\ldots,b_{n}\}\) of a lattice \(\mathcal{L}\), find \(n\) linearly independent vectors \(\{v_{1},v_{2},\ldots,v_{n}\}\) that belong to the lattice such that \(\|v_{i}\|\leq\lambda_{n}\) for \(1\leq i\leq n\)._  
The approximate shortest independent vector problem (SIVP\({}_{\gamma}\)) is presented below.  
**Definition 23**.: _(SIVP\({}_{\gamma}\)). Given a basis \(B=\{b_{1},b_{2},\ldots,b_{n}\}\) of a full-rank lattice \(\mathcal{L}\), output a set \(V=\{v_{i}\}\subset\mathcal{L}\) of \(n\) linearly independent lattice vectors, such that \(\|v_{i}\|\leq\gamma(n)\cdot\lambda(n)(\mathcal{L})\) for all \(i\)._  
This problem has been studied and proven to be NP-hard by a reduction from the closest vector problem to the shortest independent vector problem [25].
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 4 Performance Analysis
### Protocol Specification
#### 4.2.1 Link-Adaptive Beaconless Forwarding  
Link-adaptive beaconless forwarding (LBF) protocol is a modified version of ILBFC, and it does not contain road adaptive features present in ILBFC. Here, \(\delta_{eff}=\delta_{link}\) and is computed using Equation (3) with maximum reservation time (\(\Lambda\)) value set to 100 s which represents the maximum duration for the data packet exchange among nodes in the simulated scenario. Also, the relay node stays in the \"winner\" state and forwards all the incoming data packet for an entire duration of \(\delta_{link}\) s since there is no winner relay management condition (\(d_{c}\geq d_{p}\)) as present in ILBFC. The rest of the features (redundant packet elimination, forwarding zone and loop protection) of LBF are similar to ILBFC. The performance of LBF is compared with ILBFC to analyze the impact of location information of road intersections and urban road traffic adaptive winner relay management on the overall routing and network performance.
## 2 Materials and Methods
### AAPH-Induced Oxidative Stress against RBC  
Ovis aries (sheep) red blood cells (RBC) were used to evaluate hemolysis induced by oxidative stress. Blood samples from a healthy animal were purchased from _EBE Pharma Biologica e Agropecuaria_ (Rio de Janeiro, RJ, Brazil). The RBCs were collected by centrifugation (2000 rpm/5 min) after four washing cycles with cold phosphate buffer saline (PBS, pH 7.4). Subsequently, a 10% RBC suspension was prepared in cold PBS. Aliquots (1 mL) from the RBC suspension were distributed into 10 mL Erlenmeyer flasks containing 4 mL of a 2,2\({}^{\prime}\)-azobis(2-methylpropionamidine) dihydrochloride solution at different concentrations (1 to 12 mM AAPH in PBS, final concentrations). The cells were incubated in a Dubnoff shaking water bath (QUIMIS\({}^{\circledR}\), Sao Paulo, SP, Brazil) at 37 \({}^{\circ}\)C for 4 h. AAPH decomposes at 37 \({}^{\circ}\)C into peroxyl (ROO\({}^{\bullet}\)) and alkoxyl (RO\({}^{\bullet}\)) radicals [PERSON] et al. (2009). Then, 0.5 mL were transferred from each flask to microcentrifuge tubes containing 1 mL of cold PBS to inhibit AAPH decomposition. Positive (100%) and negative (0%) controls of hemolysis were performed using untreated cells added with distilled water or PBS, respectively. The tubes were centrifuged, and the supernatants (0.2 mL) were transferred to 96-well microplates. The hemoglobin released after RBC breakdown was used to determine the hemolysis caused by oxidative stress. For this, a spectrophotometric assay was carried out at 540 nm by using a microplate reader (Multiscan FC, Thermo Fisher Scientific, Inc. Waltham, MA, USA). The percentage of hemolysis was calculated as follows.  
\[(\text{Abs}_{\mathbf{A}}-\text{Abs}_{\mathbf{B}})*100/(\text{Abs}_{\mathbf{C}}- \text{Abs}_{\mathbf{B}}), \tag{1}\]  
where \(\text{Abs}_{\mathbf{A}}\): supernatant of AAPH-treated cells; \(\text{Abs}_{\mathbf{B}}\): supernatant of negative control; and \(\text{Abs}_{\mathbf{C}}\): supernatant of positive control.  
AAPH 50% oxidative concentration (OC\({}_{50}\)) was determined by linear regression fit of the dose-response curve.
# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
###### Abstract
Extreme events are successfully separated as a special pattern from normal, anomalous, and unknown patterns. At the stage of the post assessment of earthquakes, [PERSON] et al. proposed ensemble models [20] and a hybrid wavelet scattering network-based model [21] to identify damage in reinforced concrete structures after earthquakes, demonstrating higher accuracy compared to single models. Although the aforementioned anomaly detection methods have demonstrated adequate performance in various scenarios, two common issues exist: Firstly, most of the method's process either sensor faults (as anomalies) or extreme events (as anomalies). The concurrence of two types of data is hardly considered. Secondly, these algorithms, especially the deep learning-based methods, only output results as black-boxes, leading to insufficient trust in their decisions. To open the black-boxes of deep neural networks (DNN), interpretation algorithms for deep models have been researched in recent years. Overall, DNN's interpretability is categorized into pre-hoc and post hoc interpretability, with the cut-off point being the completion of the network's training. The research subject of pre-hoc interpretability is neural network models, and it focuses on understanding the inner mechanism of the model architecture; post hoc interpretability has developed as a subfield of machine learning (ML) that seeks to augment the learned representations and the decisions with human-interpretable explanations [22]. [PERSON] et al. [23] input the perturbations into the sample so that the prediction result can represent the importance of each location in the image. [PERSON] et al. [24] employed gradient information in the neural network's backpropagation to show the depth model's concentration on areas of the image. [PERSON] et al. [25] used an up-convolutional network to visualize CNNs. As for the application of the interpretability algorithm, [PERSON] et al. [26] used a DNN combining the Grad-CAM algorithm to detect COVID-19, and the diagnosis was indicated by showing the patient's symptoms. [PERSON] et al. [27] used FCSNet to detect surface scratch defects and embedded Grad-CAM algorithm to visualize its interest region. To further the research on the two aforementioned problems, we proposed a deep learning approach capable of effectively identifying seismic responses amidst faulty data interference, while also providing trustworthy visual interpretability. The main innovations of this article are as follows:  
1. The seismic responses of a cable-stayed long-span bridge are successfully identified under the interference of the multi-class monitoring system's faulty data. 2. Transfer learning technique is utilized for efficient learning of monitoring data, especially the small-sample patterns. 3. An interpretation algorithm, termed Grad-CAM, is embedded into the DNN, enabling the model to provide interpretable visual evidence while outputting classification results.
###### Abstract  
The VX gasket is an important part of the wellhead connector for a subsea Christmas tree. Optimization of the gasket's structure can improve the connector's sealing performance. In this paper, we develop an optimization approach for the VX gasket structure, taking into consideration working load randomness, based on the Kriging surrogate model-NSGA-II algorithm. To guarantee the simulation accuracy, a random finite element (R-FE) model of the connector's sealing structure was constructed to calculate the gasket's sealing performance under random working load conditions. The working load's randomness was simulated using the Gaussian distribution function. To improve the calculation efficiency of the sealing performance for individuals within the initial populations, Kriging surrogate models were constructed. These models accelerated the optimization speed, where the training sample was obtained using an experimental method design and the constructed R-FE model. The effectiveness of the presented approach was verified in the context of a subsea Christmas tree wellhead connector, which matched the 20\" casing head. The results indicated that the proposed method is effective for VX gasket structure optimization in subsea connectors, and that efficiency was significantly enhanced compared to the traditional FE method.  
VX gasket; wellhead connector; random load; Kriging; sealing performance +
Footnote †: journal:  
Article Design Optimization of a VX Gasket Structure for a Subsea Connector Based on the Kriging Surrogate Model-NSGA-II Algorithm Considering the Load Randomness
## 1 Introduction
Field-level yield data for training and validation is difficult to obtain as it is often collected in sample plots (crop cuts) or obtained from governmental regional/district level datasets. The objective of this study was to use actual field-level grain yield data in coordination with the local farmers in order to develop empirical models for predicting bread wheat grain yield at the field-level using European Space Agency (ESA) Sentinel-2 imagery in Navarre (Northern Spain). We also aimed to explore different spatial factors influencing wheat grain yield regionally. In order to achieve these objectives, we developed a combination of techniques to take maximal advantage of the still novel aspects of the recent full operationality of Sentinel-2 a + b constellation in terms of its temporal, spatial, and spectral resolution. We aimed to maximize the benefits of improved Sentinel-2 temporal resolution by matching satellite data with phenology over a diverse geographical region. Increased spatial resolution improvements were used for an adequate accuracy of areal crop estimates, and also used for improving empirical models with zonal statistical summaries (maximum, minimum, mean, and median) that take advantage of the increased number of total pixels per field in small- and medium-scale agricultural parcels. We also took advantage of the improved spectral characteristics, which are pivotal for a refined crop monitoring, with stepwise multilinear equations to explore zonal statistic combinations of ten different vegetation indices calculated per field. Furthermore, wheat-growing parcels in the region were mapped using the random forest classification algorithm and the most suitable Sentinel-2 spectral reflectance bands. Finally, the model developed for estimating grain yield was applied to each of the classified wheat parcels, and then spatial topographic factors (aspect, altitude, and slope) and rainfall effects on grain yield were investigated for their correlations with grain yield across all bread wheat fields for the entire Navarre region of Spain.
## 5 IPv6 in Networking
### Support for Quality of Service (QoS)  
IPv6's support for Quality of Service (QoS) is vital in optimizing load-sharing and link-failure systems. QoS mechanisms in IPv6 allow for the prioritization of different types of network traffic based on factors like latency, jitter, and bandwidth requirements. In load-sharing scenarios, this ensures that critical applications or services receive priority, preventing congestion and improving overall network performance. Similarly, in link-failover systems, QoS maintains an uninterrupted user experience by prioritizing essential traffic even during link transitions. IPv6's QoS capabilities provide enhanced control over network resource management, creating a more flexible, efficient, and reliable network infrastructure.
## 2 Methodology
### Solar Data  
The data gathered from the State of Sonora, specifically the city of Hermosillo, were obtained with the support of the University of Sonora (UNISON), while the ones from Mexico City were issued by the Centro de Investigacion y de Estudios Avanzados (CINVESTAV) campus Zacatenco, with the solar radiation (global-horizontal), outside temperature, wind speed, and daylight hour (time) as the input meteorological variables and the electric power as the output for both PV systems. Considering that both the solar plants are from different locations and do not have the same arrangement, their respective output power magnitudes vary among them; Hermosillo site has an electric power maximum around 2500 \(W\), while Mexico City site reaches values around 45 kW.  
The matrix \"\(X\)\" and the vector \"\(y\)\" from Equation (3) are shown in Equation (8).  
\[\begin{array}{c}X=\left[\begin{array}{cc}\mathit{Solar\ radiation}&\mathit{Temperature}&\mathit{Wind\ speed}&\mathit{Time}\end{array}\right]\\ y=\left[\mathit{Electric\ Power}\right]\end{array} \tag{8}\]  
By using the same variables from both locations, the aim is to achieve an acceptable statistical estimation of the electric power even if those variables are gathered from distant geographic areas.
## 6 Simulation and Results Discussion  
### Simulation Setup  
To simulate our routing protocol for MANET with secured dynamic broker selection, to provide composite services for a particular user according to their requirement, we usedNS2 [34]. The required simulation parameters and their values are provided in Table 2. The proposed system is implemented in Network Simulator version 2.35 (NS2) under the Ubuntu 11.10 Linux operating system with 4 GB of RAM. We simulated 53 intermediate nodes and 35 mobile nodes moving into an area of 1000 m \(\times\) 1000 m according to the random-mobility model [35]. We have considered 12 brokers at the data rate of 1 Mbps with a transmission range of 250 m and bandwidth of 1000 kHz, and nodes are scattered in the simulation area randomly throughout 450 s at each simulation. The traffic follows the Constant Bit Rate (CBR) model, and the authors have used 25,600 bits for the buffer size. Our model with dynamic broker and cluster formation is described in Figure 5.  
The performance evaluation metrics such as throughput, end-to-end delay, delivery ratio, average path lifetime, and routing control overhead are used to measure the performance of the algorithms. The results of the proposed QoSDSBS algorithm are compared with existing algorithms such as the energy efficient routing based on the hierarchical rout  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **Parameter** & **Value** \\ \hline
1. & Number of nodes & 53 \\
2. & Number of mobile nodes & 35 \\
3. & Number of brokers & 12 \\
4. & Number of packets & 8 \\
5. & Area size & \(1000\times 1000\) (m\({}^{2}\)) \\
6. & Mobility & 0–15 m/s \\
7. & Data rate & 1 Mbps \\
8. & Transmission range & 250 m \\
9. & Routing protocol & AODV \\
10. & Speed & 10 m/s \\
11. & Buffer size & 25,600 Bits \\
12. & Bandwidth & 1000 KHz \\
13. & Simulation time & 450 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 5: Dynamic broker and cluster formation.  
ing algorithm (EE-OHRA) [36], energy efficient demand routing protocol (EE-DRP) [37], and novel energy efficient trust aware routing (NETAR) [38].