esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Models  
Human biomechanical models perform better than dummy models in studying passengers injuries in out-of-seat position, particularly in reclined postures. Because dummies such as THOR and WorldSID were designed for the assessment of occupant injury with a standard sitting posture, the stiffness of the lumbar spine in dummies makes it difficult to position the dummy in a natural lying configuration. Subsequently, the 50 th percentile THUMS V6 model was simulated. THUMS is a biomechanical model that depicts the human skeleton, muscles, and organs based on the finite element method, as shown in Figure 1. The model consists of the head, neck, torso, and upper and lower limbs. The most vulnerable parts of the reclined occupants of the abdomen, pelvis, and spine were also modeled in detail according to human anatomy. The element length of the THUMS was 3-5 mm, and the reference values for the quality of the element geometry were set as follows: warpage, 50\({}^{\circ}\) or less; aspect ratio, 5\({}^{\circ}\) or less; skew, 60\({}^{\circ}\) or less; and Jacobian, 0.3 or more. The skeletal parts of the THUMS were assumed to have elastoplastic properties, and a hyperelastic material was assumed for the soft tissues. The impact responses of the THUMS were verified by simulating 38 cadaver impact tests described in the literature (refer to the Documentation of Total Human Model for Safety (THUMS) [[https://www.toyota.co.jp/thums/](https://www.toyota.co.jp/thums/)]([https://www.toyota.co.jp/thums/](https://www.toyota.co.jp/thums/)), accessed on 25 December 2023).  
The original posture of the THUMS was the driving state, and it was positioned in a reclined posture via single-point constraint (SPC) boundaries in the Oasys Primer for sitting in a zero-gravity seat, as shown in Figure 2. Ninety-nine springs and SPC boundaries were used to reposition the THUMS model. Points A and B in the THUMS model represent the original and target postures, respectively. A spring with a stiffness of 500 N/mm connected point A and fixed point B, and an Ls-DYNA analysis was conducted to position the THUMS. The seat was modeled with rigid slabs to eliminate any influence of materials or seat features. The crash pulse was collected from the left side of the vehicle's B-pillar during a frontal sled test at 50 km/h. The maximum acceleration of the pulse was 27.6 g, and the occupant load criterion (OLC) used for evaluating vehicle deceleration during impact was 21 g.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
###### Abstract  
This work details the theory and implementation of a multi-wavelength quantum key distribution (QKD) emulation system with a physical unclonable function (PUF). Multi-wavelength QKD can eliminate the need to share a subsection of the final key for eavesdropper detection and allow for ternary and quaternary data transmission. The inclusion of the PUF adds an additional layer of security. We provide preliminary error analysis of our emulation system. To support this work, we introduce a bitwise transform operator that enables binary output of the PUF to satisfy the ternary and quaternary input requirements of the QKD system.  
quantum key distribution (QKD); multi-wavelength QKD; physical unclonable functions (PUFs); bitwise transform; quantum-resistant cryptography; cryptographic systems +
Footnote †: journal: _Cryptography_  
B.; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function. _Cryptography_  
202226633  
multi-wavelength QKD protocols with a physical unclonable function (PUF) to increase security and improve data throughput.
_Toxins_**2015**, \(7\), 299-321; doi:10.3390/toxins7020299  
_toxins_  
**ISSN 2072-6651**  
www.mdpi.com/journal/toxins  
_Article_  
**Orphan Toxin OrtT (YdcX) of _Escherichia coli_**  
**Reduces Growth during the Stringent Response**  
**[PERSON] 1, [PERSON] 2 and [PERSON] 1,3,***  
1 Department of Chemical Engineering, the Pennsylvania State University, University Park, PA 16802-4400, USA; E-Mail: [EMAIL_ADDRESS]  
2 Department of Biology, Texas A&M University, College Station, TX 77843-3258, USA; E-Mail: [EMAIL_ADDRESS]  
3 Department of Biochemistry and Molecular Biology, the Pennsylvania State University, University Park, PA 16802-4400, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-814-863-4811; Fax: +1-814-865-7846.  
Academic Editor: [PERSON]  
_Received: 22 December 2014 / Accepted: 20 January 2015 / Published: 29 January 2015_  
**Abstract:** Toxin/antitoxin (TA) systems are nearly universal in prokaryotes; toxins are paired with antiotoxins which inactivate them until the toxins are utilized. Here we explore whether toxins may function alone; _i.e._, whether a toxin which lacks a corresponding antitoxin (orphan toxin) is physiologically relevant. By focusing on a homologous protein of the membrane-damaging toxin GhoT of the _Escherichia coli_ GhoT/GhoS type V TA system, we found that YdcX (renamed OrtT for orphan toxin related to tetrahydrofolate) is toxic but is not part of TA pair. OrtT is not inactivated by neighboring YdcY (which is demonstrated to be a protein), nor is it inactivated by antitoxin GhoS. Also, OrtT is not inactivated by small RNA upstream or downstream of _ortT_. Moreover, screening a genomic library did not identify an antitoxin partner for OrtT. OrtT is a protein and its toxicity stems from membrane damage as evidenced by transmission electron microscopy and cell lysis. Furthermore, OrtT reduces cell growth and metabolism in the presence of both antimicrobials trimethoprim and sulfamethoxazole; these antimicrobials induce the stringent response by inhibiting tetrahydrofolate synthesis. Therefore, we demonstrate that OrtT acts as an independent toxin to reduce growth during stress related to amino acid and DNA synthesis.  
**Keywords:** toxin/antitoxin; orphan toxin; stringent response
# Architecting Software for the Internet of Thing Based Systems
## 2 Background and Related Work
### Reference Architectures and Survey-Based Studies for IoTs
#### 2.2.2 Styles and Patterns for the IoTs  
In the literature, the terms patterns and styles are considered as interchangeable terms and they are often used synonymously, which refer to reusable knowledge and the best practices for architecting software systems [6; 12]. Patterns represent recurring solutions from a technical perspective, while architectural styles can be considered as a blue-print or a layout to organize or structure the system as per a given style [13]. We now discuss the existing survey-based studies on software architectural styles for developing the IoT based software systems.  
* Microservices for IoTs  
Microservices, also known as microservice architecture, is an architectural style that structures a software system in terms of loosely coupled software services for a rapid and continuous delivery and deployment of large and complex systems. In [11], the authors present the results of a mapping study,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Study ID**} & \multirow{2}{*}{
\begin{tabular}{c} **Total** \\ **Reviewed** \\ \end{tabular} } & \multirow{2}{*}{**Research Focus**} & \multirow{2}{*}{**Publication Year**} \\ \hline
[9] & & &
\begin{tabular}{c} Analyze the main characteristics \\ of reference architectures. \\ \end{tabular} & 2015 \\ \hline
[10] & N/A &
\begin{tabular}{c} Impact of Reference Architectures \\ on Industrial IoTs. \\ \end{tabular} & 2016 \\ \hline
[11] & 364 &
\begin{tabular}{c} Mapping of research on \\ microservices for IoT systems. \\ \end{tabular} & 2018 \\ \hline
[12] & 63 & \
\begin{tabular}{c} Classification of architectural \\ styles for IoT based software. \\ \end{tabular} & 2018 \\ \hline Proposed & 88 &
\begin{tabular}{c} Mapping of challenges and \\ solution for architecting IoT \\ software. \\ \end{tabular} & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing secondary studies and their research focus.  
which analyzes the state-of-the-art for microservice architectures as the building blocks for IoT systems and cloud computing solutions. The results of the mapping study in [11] highlight the role of reusable frameworks to support microservice architectures for IoT software.  
* Architectural Styles for IoTs  
In [12], the authors have conducted a mapping study to investigate the role of styles and patterns for the IoT systems. The authors have reviewed a total of 63 research studies to analyze the architectural styles for the IoT based software. The study highlights the research progress and existing solutions, with a discussion regarding future research needs on architectural styles and patterns for the IoT systems. The study in [12] focuses on classifying the existing and highlights the possible futuristic research that can help to develop the patterns and styles for existing and futuristic solutions.
## 2 The Establishment of CWM-UM Evaluation Modeling
### Determination of Index Weight
#### 2.1.2 CRITIC Method to Determine the Objective Weight  
The CRITIC method is a technique that uses the standard deviation and correlation coefficient to calculate objective weights, which comprehensively takes into account the variability and conflict between indicators. It can be more reasonable and comprehensive for the assignment of weights, and the calculation process is as follows [17; 18]:  
Assuming that there are \(m\) evaluation objects and \(n\) construction quality evaluation indicators, the value for the evaluation object to the indicator is recorded as \(x_{ik}(i=1,2,\cdots,n;k=1,2,\cdots,m)\).  
The evaluation data can be standardized using Equation (8).  
\[y_{ik}=\frac{x_{ik}-\min_{i}(x_{ik})}{\max_{i}(x_{ik})-\min_{i}(x_{ik})} \tag{8}\]  
Calculate the variability among indicators using Equation (9).  
\[V_{i}=\frac{\sigma_{i}}{\overline{r}_{i}} \tag{9}\]  
where, \(V_{i}\) is the coefficient of variation of the \(i\)th indicator; \(\sigma_{i}\) is the standard deviation of the i-th indicator; and \(\overline{r}_{i}\) is the mean of the i-th indicator.  
The conflict between indicators can be calculated using Equations (10) and (11).  
\[\mathrm{h}_{ij}=\frac{\sum\limits_{k=1}^{m}{(x_{ik}-\overline{r}_{i})\left(x_ {jk}-\overline{r}_{j}\right)}}{\sqrt{\sum\limits_{k=1}^{m}{(x_{ik}-\overline{ r}_{i})^{2}\sum\limits_{k=1}^{m}{(x_{jk}-\overline{r}_{j})^{2}}}}} \tag{10}\]  
\[R_{i}=\sum\limits_{i=1}^{n}{\left(1-\mathrm{h}_{ij}\right)} \tag{11}\]  
where \(\mathrm{h}_{ij}\) is the correlation coefficient between indicators \(i\) and \(j\); and \(R_{i}\) is the value of the quantitative indicator of conflictiveness for indicator \(i\).  
The amount of information \(C_{i}\) for the i-th indicator can be calculated using Equation (12).  
\[C_{i}=V_{i}R_{i} \tag{12}\]  
The objective weight values of construction quality evaluation indicators are calculated using Equation (13).  
\[\beta_{i}=\frac{C_{i}}{\sum\limits_{i=1}^{n}{C_{i}}} \tag{13}\]
## 3 Analysis of the Switching Transients  
The switching process of the MOSFET will be studied stage by stage in this section. A double-pulse test circuit is adopted for the modeling of MOSFET switching transients, as shown in Figure 3. The input is a constant voltage source \(V_{DD}\) and the load is a clamped-inductive load which can provide a constant  
Figure 1: Simplified physical view of a power MOSFET with the package.  
Figure 2: Equivalent circuit of MOSFET.  
current \(I_{DD}\). The gate signal \(V_{pulse}\) is assumed to flip between \(V_{SS}\) and \(V_{GG}\) with zero rise time and fall time in the analysis. In order to investigate the switching process better, two different external gate resistances \(R_{g\_on}\) and \(R_{g\_off}\) are used in the turn-on transients and turn-off transients, respectively. Similar to the MOSFET, the freewheeling diode is modeled by two parasitic inductances \(L_{c\_1}\) and \(L_{a\_1}\) and a parasitic resistance \(R_{f}\) in series with an ideal diode \(D_{f}\), and then in parallel with a nonlinear junction capacitance Cf. In addition to these power devices, there also are a lot of stray inductances result from the PCB traces of the power loop, which are represented by \(L_{bus1}\), \(L_{bus2}\), \(L_{c\_2}\), \(L_{a\_2}\), \(L_{d\_2}\), \(L_{g\_2}\) and \(L_{s\_2}\). In order to simplify the model and the equations, some parameters are merged in the equivalent circuit. \(L_{d1}=L_{d\_ext}+L_{d\_int}\), \(L_{g1}=L_{g\_ext}+L_{g\_int}\) and \(L_{s1}=L_{s\_ext}+L_{s\_int}\) are denoted as the total parasitic inductances at each terminal of the MOSFET, respectively. It should be noted that the stray inductance of the ground lead of a measuring loop is hard to quantitatively analyze and its influence on the loss assessment can be ignored. Therefore, it was not considered in this model.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 2 Problem Statement  
### Virtual Linkage  
The virtual linkage is a reconfigurable formation control method proposed by the authors in Reference [18]. The main idea of virtual linkage is to consider the entire formation as a mechanical linkage which is a collection of rigid bodies connected by joints. It can be defined as a collection of virtual structures connected by virtual joints. Instead of specifying each agent's desired position relative to a single reference frame, as in the virtual structure, the virtual linkage approach tells each agent the virtual link it belongs to and the relative position in the corresponding virtual link. In this way, the designed virtual linkage can be reconfigured into different formation patterns by coordinating the value of each virtual joint.  
**Definition 1** (Virtual Joints [18]): _A virtual joint is a connection between two virtual structures and imposes constraints on their relative movement._  
**Definition 2** (Virtual Linkage (VL) [18]): _A virtual linkage is an assembly of virtual structures (named \"virtual link\") connected by virtual joints._  
Figure 1 shows the comparison of an intuitive example of a virtual linkage. Three agents are designed into a virtual linkage composed of two virtual links. They are able to be configured into line formation and arrow formation by only changing the virtual joint angle.
## 3 Results
### Clinical Outcomes Variables
#### 3.4.3. Bleeding on Probing (BOP)  
Statistically significant intergroup differences were detected in the changes between baseline and week 6 for the overall (reduction of 0.19 in the test group and of 0.12 in the control group, \(p\) = 0.031) and buccal areas (reductions of 0.16 and 0.07, respectively, \(p\) = 0.028) (Table 6). No other statistically significant differences were found between the groups at any visit, or in the changes from baseline to week 3. In terms of intragroup assessments (Table 6), both groups demonstrated significant reductions after 3 and 6 weeks, in all areas.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Test Group** & **Control Group** & **Mean** & **95\% CI Bounds** & \(p\) \\ \cline{2-7}  & **Mean (SD)** & **Mean (SD)** & **Difference** & **Lower** & **Upper** & \\ \hline
**n** & 29 & 26 & & & & \\ \hline \multicolumn{7}{l}{**All sites**} \\ \hline Baseline & 0.46 (0.13) * & 0.42 (0.16) * & 0.045 & \(-\)0.03 & 0.12 & 0.274 \\ \hline
3 weeks & 0.29 (0.13) & 0.28 (0.19) & 0.002 & \(-\)0.08 & 0.09 & 0.961 \\ \hline
6 weeks & 0.26 (0.13) ** & 0.30 (0.14) ** & \(-\)0.031 & \(-\)0.10 & 0.04 & 0.412 \\ \hline Change BL-3w & 0.17 (0.14) & 0.13 (0.11) & 0.042 & \(-\)0.02 & 0.11 & 0.238 \\ \hline Change BL-6w & 0.19 (0.13) & 0.12 (0.10) & 0.075 & 0.00 & 0.14 & **0.031** \\ \hline
**Buccal** & & & & & \\ \hline Baseline & 0.36 (0.16) * & 0.31 (0.18) * & 0.050 & \(-\)0.04 & 0.14 & 0.287 \\ \hline
3 weeks & 0.21 (0.13) & 0.22 (0.15) & \(-\)0.007 & \(-\)0.08 & 0.07 & 0.857 \\ \hline
6 weeks & 0.20 (0.12) ** & 0.23 (0.12) ** & \(-\)0.037 & \(-\)0.10 & 0.03 & 0.290 \\ \hline Change BL-3w & 0.15 (0.14) & 0.09 (0.14) & 0.057 & \(-\)0.02 & 0.13 & 0.148 \\ \hline Change BL-6w & 0.16 (0.12) & 0.07 (0.16) & 0.087 & 0.00 & 0.16 & **0.028** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Bleeding on probing (BOP) analyzed by means of repeated measures ANOVA and Student’s \(t\)-test.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Test Group** & **Control Group** & **\(p\)** \\ \cline{2-5}  & **Mean (SD)** & **Median (IQR)** & **Mean (SD)** & **Median (IQR)** & \\ \hline
**Lingual** & & & & \\ \hline Baseline & 0.35 (0.18) & 0.31 (0.23) * & 0.38 (0.25) & 0.36 (0.29) * & 0.704 \\ \hline
3 weeks & 0.22 (0.18) & 0.17 (0.17) & 0.27 (0.23) & 0.20 (0.21) & 0.362 \\ \hline
6 weeks & 0.19 (0.15) & 0.15 (0.17) ** & 0.28 (0.23) & 0.21 (0.27) & **0.049** \\ \hline Change BL-3w & 0.12 (0.17) & 0.13 (0.17) & 0.11 (0.18) & 0.05 (0.25) & 0.292 \\ \hline Change BL-6w & 0.16 (0.15) & 0.14 (0.21) & 0.09 (0.16) & 0.08 (0.28) & 0.159 \\ \hline
**Proximal** & & & & \\ \hline Baseline & 0.36 (0.21) & 0.31 (0.33) * & 0.37 (0.27) & 0.29 (0.25) * & 0.906 \\ \hline
3 weeks & 0.22 (0.22) & 0.14 (0.15) & 0.24 (0.21) & 0.20 (0.21) & 0.197 \\ \hline
6 weeks & 0.18 (0.18) & 0.12 (0.24) ** & 0.25 (0.22) & 0.21 (0.22) ** & 0.093 \\ \hline Change BL-3w & 0.14 (0.20) & 0.12 (0.19) & 0.12 (0.17) & 0.11 (0.26) & 0.433 \\ \hline Change BL-6w & 0.18 (0.15) & 0.14 (0.26) & 0.11 (0.17) & 0.11 (0.22) & 0.152 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 4 Design of the Outdoor Positioning System
### Proposed Extended Kalman Filter
Basically, the matrix \(\mathbf{K}_{k}\) tries to minimise the a posteriori error covariance \(\mathbf{\tilde{P}}_{k}\) by giving more weight in the a posteriori estimate either to the UWB ranging estimate or to the a priori estimate computed from the RC car on-board data. Figure 3 shows a basic architecture of the proposed Extended Kalman Filter. In this algorithm both the measurement and process noise matrices \(\mathbf{R}\) and \(\mathbf{Q}\) have a constant value over the filter iterations. Meaning that, for obtaining an optimal performance of the filter, their value must be correctly tuned depending on the application. An incorrect selection of these matrices can lead to inaccurate positioning results or even to the filter divergence.
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 3 Proposed Methodology
### Training Process and Creation of DNNs  
In order to train the models discussed herein, the following tasks were performed:  
* Data preparation included several format conversion tasks aimed at converting the data into a proper form;
* Data normalization, wherein data were linearly normalized in the range of \([-1,\,1]\);
* Class imbalance handling, dealt with the imbalance of the dataset using the NearMiss method [31];
* Train-test split, where the available data were split in training and testing by means of a generic approach of an 80-20% split, so we could use enough data to train the models.  
In order to create the classification models based on each of the aforementioned datasets (Sections 3.3 and 3.4), we utilized the TensorFlow [32] and Keras [33] frameworks. Both these frameworks are renowned for their high performance. In detail, the used classification model is a long short-term memory model [34], which contains five layers for training and validation:  
* A hidden LSTM (long short-term memory neural network) [35] layer with 64 units and a \(return\_sequences=True\) parameter, which returns the full sequence of outputs for each input sequence and allows stacking additional recurrent layers;
* A 'flatten' layer [2], which flattens the 3D output from the LSTM layer into a 2D tensor; this is typically done to connect the LSTM layer to a standard feed-forward neural network;
* Two dense layers [36]: The first layer comprises 32 units and the second of 16 units, which are fully connected, and each neuron is connected to every neuron in the previous layer. Both dense layers use the ReLU (rectified linear unit) [37] as activation functions;
* An output layer, which is also a dense layer that represents the output of the model. The activation function used in this case is the sigmoid function [38], which outputs a probability score between 0 and 1.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 2 Related Work
### Hybrid Learning Methods  
Deep CNNs have shown potential in skin cancer classification. Inspired by the recent performance of deep-learning-based approaches, researchers are now working on developing hybrid methods, in which an attention mechanism is developed and integrated with numerous deep-learning models for various purposes including image classification [31] and machine translation [32]. This attention module has been used as an advanced approach to acquire long-range feature interactions and increase the CNN feature representation capabilities [33]. This attention mechanism plays a significant role in human visual perception because it can allocate available resources to selectively focus on processing the salient part instead of the entire scene [34,35]. Skin cancer is a global issue, and dermoscopy-based classification is an effective method for diagnosing skin lesions.  
However, challenges such as interclass similarity, intraclass variation, high-class imbalance, and lack of focus on lesion areas affect the classification results. To address these issues, [PERSON] et al. [36] proposed a deep CNN dermatoscopic image classification method. Their method extracts multiscale fine-grained features and uses class-specific loss weighting to address category imbalance. The ACC and AUC models were evaluated on the HAM10000 dataset, achieving an accuracy and AUC of 91.6% and 97.1%, respectively, demonstrating their potential for dermatoscopic classification tasks. [PERSON] et al. [37] presented a deep learning model using VGG19 and self-attention for skin cancer classification and obtained improved performance using the HAM10000 dataset. However, its accuracy requires further improvement. [PERSON] et al. [38] proposed an approach to improve the performance of an existing lightweight MobileNetV2 model for skin cancer monitoring that included an attention mechanism to enhance the model learning capability. A fine-tuning strategy was adopted including pretraining weights with an autoencoder using the AM10000 dataset. Their approach achieved an ideal performance with a condensed MobileNetV2 model and a coordinate attention mechanism, achieving an accuracy of 83.93%. Methods based on conventional machine learning, deep learning, and hybrid models have achieved promising results; however, studies in this field require more attention to increase the model performance for early and effective skin cancer detection to save human lives.