esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### ECA-ATCNet on MI-EEG Classification
#### 4.1.2 Performance Metrics  
In this study, model performance is evaluated using Accuracy (Acc) and \(\kappa\)-score.  
\[\text{Acc}=\frac{1}{n}\sum_{i=1}^{n}\frac{\text{TP}_{i}}{\text{H}_{i}} \tag{18}\]  
Here, \(\text{TP}_{i}\) is the true positive, which refers to the number of correctly predicted samples in class i, \(\text{H}_{i}\) is the number of samples in class i, and n represents the number of classes.  
\[\kappa=\frac{1}{n}\sum_{a=1}^{n}\frac{\text{P}_{a}-\text{P}_{e}}{1-\text{P}_{ e}} \tag{19}\]  
Here, n is the number of classes, \(\text{P}_{a}\) is the observed agreement, and \(\text{P}_{e}\) is the expected agreement [61].
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 1 Introduction
### Motivation and Contributions  
From the above analysis, we can see the enormous advantages of the certificateless cryptosystem and the feasibility of constructing a certificateless scheme based on the traditional signature scheme. However, current research is mostly limited to the implementation of the most basic signature schemes, while some signature algorithms with special functions have not yet emerged. For example, in systems with high throughput and low latency requirements, batch verification of signatures is also a crucial attribute that directly affects the availability of the entire system. Currently, there is no batch verifiable certificateless signature algorithm based on the national security algorithm. The primary contributions of this study include:  
1. We propose a certificateless signature algorithm with batch verification based on the Chinese national cryptographic standards, in particular with the SM2 algorithm;
2. Our scheme supports batch verification of multiple signatures, thereby accelerating the algorithm in high throughput scenarios.
## 4 Conclusions  
The following conclusions were formulated on the basis of the test results:  
1. Both the ASTM C1138 test and the waterborne sand flow impact test were successful in evaluating the relative performance in abrasion resistance of the concrete of hydraulic structures sited on a high-sand percentage river.
2. For the ASTM C1138 underwater method, friction wear involved mainly abrasive action from the rolling water and steel balls. The principal resistances were shear strength and surface hardness.
3. For the concrete impacted by waterborne sand flow at 90\({}^{\circ}\), the abrasive action mainly involved pre-abrasion peeling by water molecules and its associated hydraulic pressure, solid particle impact, edge effect, and prising. The principal resistances are tensile strength and the matrix/aggregate bond. For the concrete impacted at 45\({}^{\circ}\), the aforementioned resistances applied, in addition to abrasion wear by waterborne sand flow and the principal resistances of shear strength and surface hardness.
4. Concrete attacked by waterborne sand flow impact abrasion exhibited greater abrasion damage than that subjected to waterborne particle friction abrasion with the same abrasion time. The average IARs of the concrete were approximately 8-17 times the average FARs.
5. In both the underwater and waterborne sand flow methods, the abrasion resistance of concrete without steel fibers increased as the w/cm ratio decreased, and the compressive strength of the concrete also increased. As the compressive strength decreased from 50 to 31 MPa, the average FAR, IAR-45\({}^{\circ}\), and IAR-90\({}^{\circ}\) increased to 0.08, 0.80, and 1.53 per MPa, respectively, and they increased to only 0.04, 0.21, and 0.52 per MPa, respectively, as the compressive strength decreased from 91 to 50 MPa.
6. Concrete containing steel fibers and silica fume exhibited improved abrasion resistance. For concrete subjected to friction abrasion and impacted at 45\({}^{\circ}\) and 90\({}^{\circ}\), the improvements in friction and impact abrasion loss were nearly 17%, 8%, and 9%, respectively, relative to those of the reference mixture (C36).  
Figure 14: Relationship between average abrasion rate and compressive strength of concrete without steel fiber.  
**Author Contributions:** Conceptualization, Y.-W.L.; methodology, Y.-W.L.; formal analysis, Y.-Y.L.; investigation, Y.-W.L.; resources, Y.-Y.L.; data curation, S.-W.C.; writing--original draft preparation, S.-W.C.; writing--review and editing, Y.-W.L.; project administration, Y.-W.L. All authors have read and agree to the published version of the manuscript.  
**Funding:** This research was funded by National Science Council of Taiwan, grant number NSC-96-2221-E-415-004.  
**Acknowledgments:** The generous donation of materials from Jack & Vicky Co., Ltd., China Hi-Ment Corporation, and Hi Con Chemical Admixture Taiwan, Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methods
### Transmission of the Multilayer Photonic Crystal  
We used the two refractive indexes \(n_{eff1,\omega}\) and \(n_{eff2,\omega}\) for the ITO:TiO\({}_{2}\):air layers and for the SiO\({}_{2}\):air layers, respectively, to study the light transmission through the photonic structure by employ the transfer matrix method [8,26,27]. For a transverse electric (TE) wave the transfer matrix for the \(k\)th layer is given by:  
\[M_{k}=\left[\begin{array}{cc}cos\Big{(}\frac{2\pi}{\lambda}n_{k}d_{k}\Big{)} &-\frac{i}{n_{k}}sin\Big{(}\frac{2\pi}{\lambda}n_{k}d_{k}\Big{)}\\ -in_{k}sin\Big{(}\frac{2\pi}{\lambda}n_{k}d_{k}\Big{)}&cos\Big{(}\frac{2\pi}{ \lambda}n_{k}d_{k}\Big{)}\end{array}\right] \tag{9}\]  
with \(n_{k}\) the refractive index and \(d_{k}\) the thickness of the layer. In this study the thickness of the ITO:TiO\({}_{2}\):Air layers is 185.63 nm, while the thickness of the SiO\({}_{2}\):Air layers is 238.46 nm.  
The product \(M=M_{1}\cdot M_{2}\cdot\ldots\cdot M_{k}\cdot\ldots\cdot M_{s}=\left[\begin{array} []{cc}m_{11}&m_{12}\\ m_{21}&m_{22}\end{array}\right]\) gives the matrix of the multilayer (of \(s\) layers). The transmission coefficient is:  
\[t=\frac{2n_{s}}{(m_{11}+m_{12}n_{0})n_{s}+(m_{21}+m_{22}n_{0})} \tag{10}\]  
with \(n_{s}\) the refractive index of the substrate (in this study \(n_{s}=1.46\)) and \(n_{0}\) the refractive index of air. Thus, the light transmission of the multilayer photonic crystal is:  
\[T=\frac{n_{0}}{n_{s}}|t|^{2} \tag{11}\]  
In this work the number of bilayers selected was 10 (i.e., the ITO:TiO\({}_{2}\):air/SiO\({}_{2}\):air bilayer repeated 10 times), which means 20 layers and a total thickness of about 4,241 nm.
## 5 Materials and Methods
### Molecular Identification and Phylogenetic Analysis  
The DNA of strain ETR-B22 was extracted using FastPure Bacteria DNA Isolation Mini Kit (Vazyme Biotech Co., Ltd., Nanjing, China) following the manufacturer's instructions. The universal primers 27F (5\({}^{\prime}\)-CAGAGTTTGATCCTGGCT-3\({}^{\prime}\)) and 1492R (5\({}^{\prime}\)-AGGAGGTGATCCAGCCGCA-3\({}^{\prime}\)) were used to amplify and sequence the 16s rRNA gene. Based on 165 rRNA sequencing, ETR-B22 was initially identified as _Burkholderia_ sp. To identify it at the species level, the primers BUR1 (5\({}^{\prime}\)-GATCGARAAGCAGTTCCGCAA-3\({}^{\prime}\)) and BUR2 (5\({}^{\prime}\)-TTGTCCTTGCCCTGRCCGAT-3\({}^{\prime}\)) were used to sequence the housekeeping gene _recA_[55]. PCR products were sequenced at Sangon Biotech in Shanghai, China. The obtained sequences were analyzed to identify the strain with the BLAST searchesin public databases (NCBI, USA). Type strains of the _Burkholderia_ group were collected to construct a phylogenetic tree and the taxonomic status of strain ETR-B22 was analyzed. Briefly, partial sequences of _recA_ (518 bp) and 16S rRNA (1334 bp) genes of the _Burkholderia_ strain were concatenated to yield an alignment of 1852 sites. A concatenated phylogenetic tree supplemented with sequences of the _Burkholderia_ group was constructed using Bayesian inference (BI) analysis with optimal substitution model GTR+G+I selected by MrModelTest 2.4 according to the Akaike Information Criterion (AIC). All BI analyses were run for 2,000,000 generations with four chains in two parallel runs and sampled every 5000 generations with a burn-in of the first 5000 trees. The convergence of the two parallel runs was determined by a splitting frequency of less than 0.005. All other parameters were set as default. Sequences of the 16S rRNA (accession numbers: MT712217) and _recA_ gene (accession numbers: MT708133) were deposited.
## 2 Methodology
### Analysis of Input Values for Local Fatigue Design Methods
#### 2.1.3 Requirements for ESED Method  
The elastic strain energy density approach involves the calculation of the sum of the elastic strain energy in the control volume, related to the volume. As the control volume can be assessed for sharp or blunt notches (the latter depending on the notch opening angle and the notch radius), it is convenient to adopt relatively small element sizes for changing control volume definition. Alternatively, one may apply a sub-modeling-based technique to obtain the ESED value. The number of elements within the control volume does not affect the ESED result significantly [20]; at least three elements were used in the cited study as the lower limit value. As the control volume definition changes rapidly along the defect circumference, it is possible to use an unchanged coarse mesh pattern and compute the aggregated strain energy within each control volume, based on the related element fractions. For each point of the defect contour, the control volume is computed based on the current notch opening angle and notch radius. The subsequent spherical control volume clips the mesh in the vicinity of the chosen evaluation defect point and the element fractions are stored accordingly for fatigue post-processing. This does not require any re-meshing or sub-modeling of the whole input model, but the change in accumulated elastic strain energy has to be checked for different mesh-seed settings.  
As the control volume is affected by the local notch geometry, the following key parameters are required for ESED-based fatigue strength assessment:  
* Notch opening angle 2\(\alpha\) (sharp notch or blunt notch case study),
* Notch radius \(\rho\) (blunt notch only),
* Notch bisector vector (in the case of blunt notches, the center of the control volume is shifted by \(r_{0}\) from the notch tip),
* Mesh-based evaluation of element cells within the control sphere, or cells across sphere boundary and their corresponding fractions.  
Especially due to the last point, it is important that the mesh in the vicinity of the defect does not change with varying load angle. Otherwise, the computation of lists containing elements that are within or across, as well as their fractions, must be performed for each simulation run, as visualized for different meshes in Figure 2a.
## 4 Matheuristics Development
### Step 1. Pickers (Buffers) Free Time Minimization Problem  
In this research, we define the following additional decision expressions.  
Decision expressions:  
\[t_{p}^{pkr}=\sum_{i=1}^{N}\sum_{j=1}^{H}\sum_{b=1}^{R}\min(y_{ijpb},1)\cdot t_{ ij}t_{b}^{bfr}=\sum_{i=1}^{N}\sum_{j=1}^{H}\sum_{p=1}^{W}\min(y_{ijpb},1)\cdot t_{ij}\]  
Depending on the active matheuristics (Matheuristics 1 or Matheuristics 2), we define the two different criteria functions. The set of constraints is the same for each of the matheuristics. From the main model from Section 3, we take the constraints that steer the assignment of containers to pickers and buffers, excluding time sequencing constraints. Therefore, constraints (19) and (20) correspond to constraints (2) and (3), constraints (21)-(23) correspond to constraints (12)-(14). We define only the quantity decision variable (24) which corresponds to the decision variable (16).  
Minimize the pickers' free time if there is Matheuristics 1:  
\[\min\sum_{p=1}^{W}\big{(}\max_{l=1,\ldots,W}t_{l}^{bfr}-t_{p}^{bfr}\big{)}, \tag{17}\]  
Figure 4: Procedure “Solve order picking problem”.  
Minimize the buffers' free time if there is Matheuristics 2:  
\[\min\sum_{b=1}^{R}(\max_{g=1,\dots,R}\max_{g}b_{g}^{bfr}-t_{b}^{bfr}), \tag{18}\]  
Subject to:  
Pickers to buffers assignment, i.e., the picker does not pick products in buffers to which the picker is not assigned.  
\[\forall(p)\forall(i)\forall(j)\forall(b:v_{pb}=0)[y_{ijpb}=0], \tag{19}\]  
Products to buffers assignment, i.e., the picker does not pick products in buffers in which the product is not located.  
\[\forall(p)\forall(i)\forall(j)\forall(b:z_{bj}=0)[y_{ijpb}=0], \tag{20}\]  
Enough products are available at each buffer to be picked by all pickers for each order.  
\[\forall(i)\forall(j)\forall(b)[\sum_{p=1}^{W}y_{ijpb}\leq q_{bj}], \tag{21}\]  
Enough products have been picked.  
\[\forall(i)\forall(j)[\sum_{p=1}^{W}\sum_{b=1}^{R}y_{ijpb}=c_{ij}], \tag{22}\]  
Each product is processed in one buffer by one picker.  
\[\forall(i)\forall(j)\forall(p)[\sum_{b=1}^{R}\min(y_{ijpb},1)\leq 1], \tag{23}\]  
Decision variable:  
Quantity  
\[y_{ijpb}=\left\{0,\mathbb{Z}^{+}\right\}, \tag{24}\]
## 2 Impacts on the UK EV Market  
### Positive Impacts of Global EV Expansion  
The rapid expansion of EV markets in countries like China and Japan is expected to positively influence the UK EV market in several significant ways:  
* Increased Model Availability: As Chinese automakers scale up production, a wider variety of EV models--particularly affordable compact and mid-range vehicles--are becoming available in overseas markets. Brands such as MG (a Chinese-owned manufacturer) have already established a presence in the UK, offering lower-cost EV options to British consumers [13].
* Battery Cost Reductions: China is the global leader in lithium-ion battery production. Economies of scale and technological advances in Chinese battery manufacturing are contributing to a steady decline in global battery costs. This trend benefits the UK both directly--through cheaper imported EVs--and indirectly, by lowering production costs for UK- or EU-based manufacturers using imported Chinese battery components.
* Global Competitive Pressure: Strong sales performance in Asia has incentivized traditional automotive manufacturers to accelerate their electrification strategies. This  
Figure 8: Japan’s annual EV sales from 2015 to 2021 [10].  
Figure 9: UK annual EV sales for 2015–2025 [12].  
includes more aggressive product rollout plans in international markets, including the UK. As a result, British consumers may gain access to a wider variety of competitive EV options at lower prices.
* Technology Spillovers: Heavy R&D investments by Asian manufacturers are driving advancements in areas such as battery longevity, fast charging, and integrated vehicle software. These innovations are likely to spill over into global markets, raising the overall technological standard of EV offerings in the UK.
* Market Awareness and Momentum: Success stories from China and Japan increase global public confidence in EV technology and accelerate consumer readiness in markets like the UK. The normalization of EV adoption abroad may enhance social acceptance at home.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 3 The Cyber Kill Chain
### Phases of Cyber Kill Chain  
There are a total of seven different phases in a cyber kill chain, which are as follows:  
1. **Reconnaissance:** Methods of investigating, acknowledging, and identifying targets.
2. **Weaponization:** Constructing an attack vector by amalgamating remote access software with a known security vulnerability, for example, Adobe PDF and MS Office files.
3. **Delivery:** Transference of weaponry to its intended recipient (via email attachments, websites, or USB drivers).
4. **Exploitation:** Upon delivery, the weapon's code is activated, exploiting systems or applications that are susceptible to attack.
5. **Installations:** Installation of a backdoor on a target's system permits continual access.
6. **Command and Control:** The external server facilitates communication between the weapons and the target network by granting \"direct keyboard access\" within the latter.
7. **Actions on Objectives:** The perpetrator endeavours to realize the objective of incursion, which could involve the extraction or annihilation of data or the infiltration of another objective.  
Example: WannaCry malware attack by [PERSON] on MS Users in May 2017, demanding ransom in Bitcoin currency [16].
## 3 Validation  
The classification and identification of radiation emission components were performed on the same test platform, as shown in Figure 5. The whole platform was placed in a darkroom to avoid unnecessary interference. The test platform was mainly built with reference to the test platform of RE102 in GJB151b-2013 [6] or in MIL-STD-461G [5]. It includes three main parts: a spectrum analyzer, a pre-amplifier, and a receiving antenna for the corresponding frequency band. In this paper, only the part of the spectrum data between 30 MHz and 200 MHz was intercepted for discussion.  
The FSW-26 signal and spectrum analyzer from Rhodes and Schwartz were adopted during validation, which has high sensitivity and can effectively meet the requirements of the radiation emission spectrum tests. The biconical antenna required by the corresponding frequency band in the RE102 test was selected, and the model was BCA-9522. This type of antenna is a linearly polarized antenna, which is mainly composed of a cage-shaped biconical antenna and a matcher of a portable umbrella structure.  
In order to ensure the repeatability of the experiment, it was necessary to ensure that the state of the pre-amplifier and the RBW of the spectrometer remained unchanged in all experiments. In the following experiments, the pre-amplifier was off, and the RBW = 1 kHz.  
Figure 5: A test system modeled after the RE102 experiment.
## 4 Analysis of Experimental Results
### GTSP-Based Algorithm Performance Evaluation
#### 4.1.1 Algorithm Parameter Configuration  
This paper explored multiple parameter combinations to achieve a compromise between optimal performance and computing economy across different scenarios. Table 2 displays the parameters selected for the GA, the IWOA, the ABC Algorithm, and the WSHA. The table's first column displays the parameter names, the second column shows the GA parameter values, the third column lists the IWOA parameter values, the fourth column presents the ABC Algorithm parameter values, and the fifth column showcases the WSHA parameter values.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Neighborhood State Management
Different categories of data packets transmitted over the same link incur varying transmission delays. By redefining the method of delay computation, this issue can be effectively addressed. Specifically, the announcement messages record queuing and processing delays of packets at different QoS levels on the port. The network node computes the transmission delay based on the fixed propagation delay of the physical link and the queuing and processing delays on the ports recorded in the message. Figure 3: The Two-Step Construction Process of NSIB. (**a**) Example Topology; (**b**) Neighbor State Information Advertisement; (**c**) NSIT Advertisement.
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies
#### 3.4.2 Intelligent Reflective Surfaces (IRS)  
Another key enabling 6G technology is the novel IRS technology. Often also referred to as reconfigurable intelligent surface (RIS) or large intelligent surface (LIS), the IRS plays an important role in the developing 6G network [52]. IRS allows for dynamic electromagnetic (EM) response in wireless networks by controlling radio channel features such as scattering, reflection, phase, amplitude, frequency, polarization, and refraction [52]. IRSs are intelligent EM surfaces that operate automatically to achieve reconfiguration for effective wireless communications. Typically, most IRSs are made of three surfaces: conventional reflective arrays, liquid crystals surfaces, and software-defined meta surfaces [52; 89; 90]. IRS surfaces achieve automated reconfiguration through a two-dimensional planar meta surface connected to an electronic circuit [15; 39; 91]. These electronic smart surfaces respond in relation to user mobility (UE) in the propagation pathway of a communication network [38]. Initially deployed for radar and satellite applications, IRSs have now been applied for mobile communication systems.  
For the 6G network, the demand for low-complexity and energy-efficient communication has necessitated the paradigm shift to IRS systems [63]. Primarily, the development of IRS technology was implemented to overcome the challenge of NLOS propagation in 6G wireless communication. IRS assists in redirecting EM waves from base stations (BS) to potentially desired receivers during communication in an NLOS scenario, thereby enhancing the transmission quality [15]. Technically, they are an improved version of the massive MIMO system in 5G [63; 92]. The IRS system still requires support to further enhance the QoS of the 6G network. Some of the issues still needing to be addressed include channel state information (CSI), phase shift configuration, beamforming, power and spectral efficiency, and physical layer security, among others [15; 52]. Researchers believe that ML integration can provide an adequate intelligent solution to addressing these concerns [15; 38].
# Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method
## 3 Proposed G-DCNN Method Based on Modified AlexNet  
### Modified AlexNet Structure for Classification  
Transfer learning is an important machine-learning tool for applying knowledge learned in a domain to different but related domains or issues [33]. Thus, transfer learning thought is used in this study to modify the original AlexNet structure for spare parts classification task. During transfer learning, only the dense layers are restructured and  
Figure 1: The original AlexNet structure (extracted from [32]).  
retrained to identify features that are specific to the spare parts classification. The modified structure and dimensions of crucial layers are shown in Figure 2.  
Specially, the input dataset are the spare parts hierarchical structure images with the preprocessed resolution of 227 \(\times\) 227 pixels. Additionally, since the spare parts are usually classified into four classes (A, B, C and D), we mainly modify the final softmax layer and change the 1000 neurons to four, which is the same size as the number of the target categories, as shown in Figure 3.  
The spare part's hierarchical structure images are processed by convolutional layers and pooling layers which can recognize the graphic features through trained weighted matrices. During the process, the filters expand overall dimensions of the input data through the creation of multiple feature maps. Additionally, max-pooling approach is used to down-sample the input information and extract the features. Each extracted feature from the convolutional layers is assigned weights with respect to each classification category. Then, the fully connected layers use the weight matrices to compute the confidence level of the input belonging to each category.