esa-sceva/satcom-chunk-collection huggingface dataset:
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 2 Problem Description
### Input Data and Optimization Criteria  
The input instances that are used in current work are the Mandl's Swiss transit network [25] and four transit networks that were contributed by [PERSON] [13]. The data used by the CSO-based algorithm as input are described in the following:  
* Data that concern the connections among the network's nodes (road network's structure).
* Data that concern times needed to go from one node of the network to another (travel times).
* Data that concern travel demands between any two nodes of the road network (see Table 1 as an example).  
The optimization criteria--in order of significance--on which the performance evaluation of the presented CSO algorithm is based, are described as follows ([13; 14; 26; 31; 36; 38; 43; 46; 47]):  
1. The percentage of unsatisfied demands (should be as low as possible, ideally equal to zero).
2. The average travel time in minutes per transit user (should be as low as possible).
3. The percentage of \"with no transfers\" satisfied demands (should be as high as possible). Except for these three major criteria, there also some other constraints that should be fulfilled [47]:  
* A minimum and a maximum number of nodes (length) must be defined for each route (defining that each route must have minimum and maximum length ensures route network's connectivity and assists bus schedule adherence, respectively).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
0 & 400 & 200 & 60 & 80 &  
* In order passengers to be able to travel between any two nodes of the road network, there should be a path connecting any two of them (the road network should be a connected graph).
* Individual routes should not have any cycles or backtracks.
* To limit transportation cost, the service provider has usually predefined the number of routes in the route set.  
The constraints mentioned above are considered as hard constraints that should never be violated by any acceptable solution, and are also considered by the presented CSO approach.
## 4 The Compressive Acquisition Model for Gas Data Flow
### Two Different Networks for the Transmission of Gas Data Flow
#### 4.1.2 The Multi-Hop Mode  
As illustrated in Figure 4, in addition to the single-hop mode, data can be transmitted through a multi-hop mode when multiple mobile gas detection instruments move between two wireless access points. Data were aggregated as follows.  
Data were acquired though \(M\) time-independent multi-hop communication links with different initial nodes. Each multi-hop network randomly selected an initial node. At each node, the measured gas value was multiplied by the generated random coefficient; then, the product was transmitted to the next node according to the preset routing algorithm. At the next node, the received signal was added to the local product, and the sum was transmitted to the next node. By analogy, the \(i\)-th multi-hop signal received by the wireless access point can be written as [25]:  
\[y_{i}=\sum_{j\in P_{i}}\Phi_{i,j}x_{j} \tag{7}\]  
where \(P_{i}\) denotes the node index set of the \(i\)-th multi-hop communication link, \(\Phi_{i,j}\) denotes the random coefficient at the \(j\)-th node, and \(x_{j}\) denotes the gas data measured at the \(j\)-th node. Similarly, based on the spatial correlation of the measured gas data, the signal vector received by the wireless access point \(\mathbf{y}=[y_{1},\cdots,y_{M}]\) can be written as follows:  
\[\mathbf{y}=\mathbf{\Phi\
abla s} \tag{8}\]  
where \(\mathbf{\Phi}\) denotes an \(M\times N\) matrix (when \(j\in P_{i}\), the element at the \(i\)-th row and the \(j\)-th column equaled \(\Phi_{i,j}\); otherwise, equaled 0). Accordingly, it can be regarded as a standard compressed sensing problem.
## 4 Results and Discussions
### Analysis of Load Bearing Characteristics of Adjacent Pile Bases without Immersion
#### 4.1.3 Pile Tip Force  
In the loading process, the values of pile tip force (PTF) are shown in Table 5. As Figure 10 demonstrates, with the increase in the load, the pile shaft friction increased and accounted for about 80% of the total vertical load, and the pile tip force increased slowly and accounted for about 20% of the total vertical load. The bearing type of pile bases was end-friction pile (load is mainly borne by shaft friction, and pile tip force cannot be ignored). When the load borne by the pile bases exceeded its ultimate bearing capacity, the pile shaft friction decreased significantly, while the pile tip force increased quickly. The load shared by the shaft friction decreased from about 80% to about 40%. At this time, the soil around the pile had a shear failure, and the pile bases became a friction-end-pile (load is mainly borne by pile tip force, and shaft friction cannot be ignored).
## Materials and Methods
### Questionnaires  
The PSQI [15] was used to assess sleep quality and disturbances over a 1-month period. This questionnaire is composed of 19 self-rated questions and 5 questions that should be answered by bedmates or roommates. The 19 questions are separated into 7 categories and there is a score from 0 to 3. The components of this questionnaire are subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of medication, and daytime dysfunction. The maximum PSQI score is 21 points. Higher scores indicate poor quality of sleep [37].  
The ESS [16] is composed of 8 self-reported questions and it is used to assess the self-reported level of daytime sleepiness [38]. These items have a four-point scale (0 indicating \"would never nod off\" and 3 indicating a \"strong chance of nodding off\"). The questionnaire questions are related to daily activities. The questionnaire score ranges between 0 and 24. Higher total scores are related to more sleepiness [39].  
The Berlin Questionnaire [17] has been proposed as a tool in screening OSA and validated in primary care. The subjects were classified into high risk (if there were 2 or more categories where the score was positive), and low risk (if there was only 1 or no categories where the score was positive). The percentage os individuals with high risk to OSA was calculated in both groups [40; 41].  
The PSQI, ESS, and Berlin Questionnaire were only measured before and after the 6-week intervention in both groups.
## 3 Results and Reports
### Anomaly Detection Results  
In this section, we applied the SVM classification algorithm. After obtaining a reliable classification model with adequate accuracy using SVM kernels, the algorithm was used to classify the sensed medical data from the WBAN device. We first randomly generated multiple datasets based on the rules presented above. For visualization of the decision function, we retained only two classes, making this a 'binary classification problem'. The training datasets used '1' as a decisional value for the ideal blood pressure (IBP) interval and '0' for the HBP, PHBP and LBP intervals. This means that any value of systolic or diastolic BP that fell within the ideal BP range was a normal value; otherwise, the decisional value was considered as abnormal. The SVM model was using first the default values for the hyper parameters c and gamma. Then we tried different combinations to enhance the performance of the proposed classification model. Figure 11 shows the numerical method to classify the data according to medical rules, as well as the plot obtained for the different datasets. Blue and yellow colors represent normal and abnormal entries respectively. The datasets were sorted vertically from Dts_1 to Dts_3.  
Figure 11: (**a**) Numerical example of used rules and (**b**) graphical representation of the classification.  
Figure 10: (**a**) Systolic box-plot representation and (**b**) diastolic box-plot representation.
###### Abstract
Recently, researchers discovered new types of cells, which can alternate existing stem cells, induced pluripotent stem cells (iPSCs) or directly reprogrammed somatic cells [24; 25]. In 2006, [PERSON] and [PERSON] induced the reprogramming of murine and human fibroblasts, resulting in the generation of iPSCs with self-renewal and pluripotent abilities. Following the discovery of iPSCs, numerous studies accelerated research related to the direct reprogramming of somatic cells (also known as direct conversion) of somatic cells into other types of somatic cells, through various approaches, such as genetic modification, biomaterials, growth factors, small molecules, and cocktail methods [26; 27]. As a result of this direct reprogramming, somatic cells can be differentiated into specific cells without going through the intermediate pluripotent stage. Therefore, direct conversion by various transcription factors and biomolecules can be considered as a promising strategy for regenerative medicine, since it bypasses the intermediate pluripotent stage and, thus, the required time can be reduced in clinical applications [5; 28]. Therefore, direct conversion is applied for differentiating fibroblasts into various somatic cells, such as cardiomyocytes [29; 30], neurons [31; 32], chondrocytes [33; 34], myoblasts and myotubes [35; 36], and more. To induce direct reprogramming of the somatic cells into intended types of cells, transcription factors are delivered through various methods. According to recent studies, the protein-based approaches, using cell-penetrating peptides or proteins for direct conversion of somatic cells, have been found to be practical and suitable for clinical use with a low risk of genomic integration [37; 38; 39; 40]. Regarding the viral-based approaches, using retrovirus and lentivirus for direct conversion of somatic cells has been found to be practical in terms of high efficiency [41; 42]. However, those studies are limited in in vitro results utilizing human cells and, thus, direct conversion in other species, such as bovine-derived somatic cells, remain relatively unexplored, which is an obstacle in research for domestic animal applications. In this study, we tried to determine an efficient method for the direct conversion of bovine dermal fibroblasts (BDFs) into myogenic cells using a transcription factor, bovine myoblast determination protein (bMyoD), comparing a protein-based method and retrovirus-based method. Therefore, we performed genetical and immunocytochemical analysis to investigate the direct reprogramming of cells in the early stage (myoblasts) and late stage (myotube) of myogenesis [43; 44]. It is anticipated that this genetic engineering strategy, with high efficiency, could be a useful tool for regenerative therapy for muscle-related diseases, such as sarcopenia or cachexia [45]. In addition, if sufficient efficiency can be achieved with the transgene-based approach without chromosomal integration, such cell-based therapies would be beneficial in food industries for the generation of cultured meat products [46; 47].
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
###### Abstract  
This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm's key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.  
information security; correlation electromagnetic analysis; particle swarm algorithm; cryptographic algorithm 2021 11 839 110.399 110.3990/app11020839
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 3 Multi-Objective Path Planning
### Optimization Method
```
Inputs: G: Road network graph, \(\mathbf{n_{\text{start}}},\mathbf{n_{\text{goal}}}\): Start and goal nodes (waypoints, avoid_points, avoid_zones): Pathway points and avoidance areas (traffic_lights, road_conditions): Traffic light status and road conditions \(\mathbf{T_{\text{max}}},\mathbf{C_{\text{max}}},\mathbf{R_{\text{max}}},\mathbf{A_{\text{max}}}\): Constraint thresholds for time, congestion, risk, and avoidance areas \(\mathbf{\alpha}(t),\mathbf{\beta}(t),\mathbf{\gamma}(t),\mathbf{\epsilon}(t),\mathbf{\eta}(t)\): Dynamic weight factors for different objectives at time \(t\) Output: optimized_path: The optimized path from \(\mathbf{n_{\text{start}}}\) to \(\mathbf{n_{\text{goal}}}\)
1 Initialize openSet, closeSet
2 Add \(n_{\text{start}}\) to openSet with priority 0
3 whileopenSet is not empty do
4\(n\leftarrow\) node in openSet with lowest \(F_{\text{total}}(n)\)
5 if\(n==n_{\text{goal}}\)
6\(global\_path\leftarrow\) trace back from \(n_{\text{goal}}\) to \(n_{\text{start}}\)
7 break
8 endif
9 Remove \(n\) from openSet; add to closeSet
10 for each neighbor \(m\) of \(n\)
11 if\(m\) in closeSet or \(m\) in avoid_points or \(m\) in avoid_zones
12 continue
13 endif
14 Compute dynamic weights \(\alpha(t),\beta(t),\gamma(t),\epsilon(t),\eta(t)\)
15 Compute multi-objective cost \(F_{\text{total}}(m)\): \(F_{\text{total}}(m)=\alpha\cdot f_{\text{length}}+\beta\cdot f_{\text{ congestion}}+\gamma\cdot f_{\text{time}}+\) \(\eta\cdot f_{\text{risk}}+\epsilon\cdot f_{\text{avoidzone}}\)
16 Set parent of \(m\gets n\)
17 Add \(m\) to openSet
18 endfor
19 endwhile
```  
**Algorithm 1**Dynamic-weighted A* with DQN for multi-objective path optimization. The agent employs an \(\epsilon\)-greedy strategy to balance exploration and exploitation, continuously sampling training samples from the experience replay buffer to iteratively update the Q-network. This process enhances the ability of the path to respond to unexpected situations. The DQN model not only improves the adaptability and safety of the path at the local level, but also allows the path to avoid potential risk areas at specific nodes, enabling the fine-tuning of the path to adapt to real-time environmental changes. The combination of A* and DQN in the path planning task achieves a collaborative effect of \"global optimality + local adaptation\". The A* algorithm ensures the multi-objective feasibility and efficiency of the path in terms of overall structure, making it suitable for global optimal path search. On the other hand, DQN leverages the advantages of reinforcement learning in dynamic environment modeling and decision optimization, compensating for A*'s slower response to environmental changes. The synergy between the two enhances the path planning system by providing strong objective control while maintaining the flexibility to adapt to dynamic and complex traffic environments, thus improving the system's intelligence, practicality, and robustness, see Algorithm 2.
# A Study on the Preference of Electric Vehicle Front Design Elements
## 1 Introduction  
### Research Background and Purpose  
According to the German market research company Statista Research Department [1], global sales of electric and battery electric vehicles are anticipated to experience significant growth between 2020 and 2025. While hybrid vehicles remain popular due to their enhanced mileage and cost-effectiveness, the competitiveness of electric vehicles is projected to surge owing to ongoing advancements in battery storage technology. It is forecasted that by 2025, electric vehicles will capture approximately a quarter of the global market share, with pure electric vehicles constituting around 7.4% of total global car sales. Furthermore, projections suggest that the market share of electric vehicles will soar to approximately 80% by 2050. However, in Korea, electric vehicle sales are experiencing a downward trend, evidenced by the sale of fewer than 10,000 units in August 2023, marking a 5.7% decline in sales from January to August 2023 [2].  
Presently, various research endeavors related to electric vehicles are underway in Korea, predominantly focusing on technology, performance, and autonomous driving. However, research on electric vehicle design remains inadequate, necessitating a differentiation strategy to stimulate consumer interest and drive purchases through innovative design. The front fascia, being the crux of car design, is deemed a pivotal component influencing not only brand perception, but also consumer attitudes and purchase decisions. Hence, this study aims to delve into consumer preferences regarding the front-end elements of electric vehicles and propose future design directions.  
Furthermore, this study undertakes an analysis of the key factors prioritized by consumers, alongside investigating preferences for electric vehicle design styles. It elucidates the significance and priority of various factors pertinent to electric vehicles' characteristics, thereby facilitating the alignment of consumer needs with product offerings. By doing so, we aim to contribute to informed decision making and service provision. The structure of this study encompasses theoretical background, research methodology, findings and discussion, and conclusion sections. With the escalating prominence of electric vehicles, there arises a pressing need for intensified design research, and this paper endeavors to yield meaningful insights in this context.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 3 Experimental Results and Analysis  
In this section, detailed experimental results and analysis are presented. More specifically, Section 3.1 introduces a parameter study regarding the proposed method so that the justification of the proposed method's design choices is possible. Next, a comparison to the state of the art is shown on the CLIVE [50] and KonIQ-10k [4] databases in Section 3.2.
## 3 Dataset Construction
### SysML Model Format Transformation  
In this paper, the MagicDraw modeling tool is used to construct SysML model sets for the spacecraft-related domains. Since the graphical content of the models is difficult to process directly for data processing, the graphical models are transformed into XML format by the MBSE modeling tool. The advantages of XML data are in terms of its readability, scalability, cross-platform, self-descriptiveness, support for multiple data formats and simplification of data processing and improvement of data availability. In XML documents, the data structure is clear. Moreover, XML documents are text-based, so XML data can be easily exchanged and shared among different devices and systems. This cross-platform nature greatly simplifies the complexity of data exchange and processing. These advantages make XML ideal for handling scenarios such as network data transfer, configuration files and storing structured data.
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 4 Fundamentals of Smart Polymeric Materials: Focus on Magneto-Active Elastomers and Hydrogels
### Three-Dimensional Printing of Magneto-Active Polymers
This procedure was described, accounting for six printing degrees of freedom, due to the capability of the magnetic particles to move freely and controllably throughout the polymeric matrix. Another notable example of 4D printing of magneto-active materials was carried out by [PERSON] et al. [125]. In this case, the fabrication of fast-transforming structures was achieved by DIW of a silicon matrix filled with neodymium-iron-boron (NdFeB) hard magnetic particles. These particles were oriented at the extrusion nozzle by using an electromagnet at the tip of the dispenser, capable of applying an oriented magnetic field. This way, the extruded material had a magnetic orientation, which was then used to achieve fast transformation of the structure by application of a magnetic field. Another example of soft magneto-active polymers printing was explained by [PERSON] et al. [143]. In this case, they used soft Fe\({}_{2}\)O\({}_{4}\) magnetic particles dispersed in a modified polyethylene glycol and cyclic trimethylolpropane formal acrylate photocurable resin to print, using DLP, different structures combining magnetic and not magnetic regions. These structures were able to achieve bending, deformation and cargo transportation. All these examples of printed magneto-active soft polymers serve as a proof-of-concept of an exciting technique and its application in the field of soft robotics bioengineering. Regarding magneto-active hydrogels, the main objective for these materials is their use as soft actuators capable of working inside the human body, for specific biomedical applications. In this regard, [PERSON] et al. [144] developed a complex structure through 3D printing capable of undergoing volume collapse due to the magnetothermal effect. Using Fe\({}_{3}\)O\({}_{4}\) magnetic particles dispersed in a poly(N-isopropylacrylamide) (PolyNIPAm), they developed a shape transformative structure capable of encasing and killing cancer cells through hyperthermia. Another example was developed by [PERSON] et al. [145]. In this case, they were able to print, using a rapid 3D printing technology (microscale continuous optical printing), poly(ethylene glycol) diacrylate (PEGDA)-based biomimetic microfish with embedded Fe\({}_{3}\)O\({}_{4}\) magnetic particles. These microfish were able to propel through an aquatic environment, and were magnetically guidable, which proved to be a viable approach for drug delivery and toxin neutralisation systems.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 3 Types of Physical Covert Channels
### Power-Based Channels  
Power, and, subsequently, supply current consumption, can be used to establish a covert channel through an on-chip power delivery network or even an electrical outlet that a device is plugged into. Similar to thermal channels, malicious software on the transmitter runs CPU intensive instructions in order to encode the data into voltage drops along the power delivery network. Figure 5a shows an example of an attack model of a power covert channel occurring on a multi-tenant FPGA, where voltage fluctuations along the shared power delivery network are caused by the transmitter [54]. Custom logic in the receiver, such as ring oscillators (ROs), is able to detect the fluctuations and decode the data being transmitted. Similarly, Figure 5b shows a power covert channel occurring in a data center between two FPGAs that share the same power supply unit (PSU) [38]. Activity in FPGA 2 causes fluctuations in voltage supplied by the PSU, which can be detected by FPGA 1 (a malicious user in the data center).  
Figure 5: Voltage-based covert channels as a threat between (**a**) a multi-tenant FPGA [54], (**b**) two FPGAs in a data center sharing the same power supply unit (PSU) [38].