esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Method  
### Rough Set Theory  
Rough set theory was proposed by Polish professor [PERSON] [31] in 1982. It can effectively analyze all kinds of incomplete information, discover the implicit knowledge behind a large amount of data, reveal the significance of conditional attributes to decision-making attributes, and remove redundant or irrelevant properties.  
In rough set theory, an information system is defined as being four-tuple \((U,A,V,f)\), where \(U\) is a non-empty finite object set, representing the domain; \(A\) is a set of non-empty finite attributes; \(V\) is the range of attribute set; and \(f\) is an information function. For \(x\in U\) and \(a\in A\), there is \(f(x,a)\in V\). If there is \(A\in C\cup D\) and \(C\cup D\
eq\varnothing\), the information system is called a decision system, where \(C\) is the set of conditional attributes and \(D\) is the set of decision attributes.  
**Definition 1**.: _Given a decision system \(S=(U,C\cup D,V,f)\), let \(ind(p)\) denote the indiscernibility relation on attribute set \(p\subseteq A\), then:_  
\[ind(p)=\{(x,y)\in U\times U|\forall a\in P,a(x)=a(y)\} \tag{1}\]  
_where \(a(x)\) represents the value of attribute \(a\) of object \(x\). \(ind(p)\) means that object \(x\) and object \(y\) in the information system cannot be distinguished according to the attribute values on attribute set \(P\). Symbol \(U/ind(p)\) denotes the partition based on indiscernibility relation \(ind(p)\) on domain \(U\)._  
**Definition 2**.: _Given a decision system \(S=(U,C\cup D,V,f)\), the lower and upper approximations of set \(X\subseteq U\) with respect to its indiscernibility relation \(ind(p)\) are:_  
\[P(X)=\cup\{Y_{i}|Y_{i}\in U/P,Y_{i}\subseteq X\} \tag{2}\]  
\[\overline{P}(X)=\cup\{Y_{i}|Y_{i}\in U/P,Y_{i}\cap X\
eq\varnothing\} \tag{3}\]  
**Definition 3**.: _Given a decision system \(S=(U,C\cup D,V,f)\), let \(P\) denote a conditional attribute set in a decision system and \(D\) denote a decision attribute set, then the \(POS_{P}(D)\) of \(P\) relative to \(D\) is:_  
\[POS_{P}(D)=\underset{X\in U/Q}{\cup}P(X) \tag{4}\]  
If there is \(POS_{C-c_{i}}(D)=POS_{C}(D)\) for \(\forall c\in C\), attribute \(C\) is a redundant attribute in the decision table. If \(POS_{C}(D)\
eq POS_{C-c_{i}}(D)\), then \(c_{i}\) is said to be important about \(D\) in \(C\), and the set of all important knowledge about \(D\) in \(C\) is called the core of \(C\) relative to \(D\), denoted as \(CORE_{D}(C)\).
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 2 Materials and Methods
### Energy Balancing  
The benchmark method was chosen to represent the most simplistic operation of the ESS in the given setup. The idea of the basic energy balancing was very simple: the energy storage charges when production exceeds load and discharges when power was imported from the grid [27]. This control method minimized the exchange of energy with the external grid and did so without considering tariffs or even energy prices, thus foregoing possibilities for price arbitrage. On the other hand, such a method was guaranteed to use as much of the local RES overproduction as possible, in which case it potentially managed to increase self-consumption. In periods with low production from RES, the energy storage may not have a chance to charge. In our model, which assumed PV sources, this occurred in times with less sunshine, and, as a consequence, the HESS spent most of the winter time in a discharged state, thus not helping to decrease the costs of energy. On the upside, not using the battery implied that there is no degradation cost--the model is simplified and did not consider any degradation that might be caused by leaving the battery in an extremely low state of charge.  
Figure 4: The price tariff for the selling and purchase of energy to/from the grid for months October–March.
# Model-Based Test Suite Generation Using Mutation Analysis for Fault Localization
## 3 Model-Based Test-Suite Generation Using Mutation Analysis
### Test-Suite Generation
#### 3.3.1 Initial Test-Case Generation  
The generated test cases and test suites are test inputs based on paths; therefore, they must be generated such that the FTPs can be processed in the appropriate transition order. Each test case must be able to pass transitions that match its order, and each test suite must be able to pass through the TP. To achieve this, we use a boundary value as the initial value. The boundary information is checked based on the transition information, and one of the boundary values is selected at random. However, if test cases are generated solely through a boundary value to ensure that transitions are traversed, a problem occurs whereby some mutants are not handled. For example, when a guard with the condition \(x>1\) exists, the value of \(x\) as the boundary test-case value for passing through this guard becomes 2. However, when a test case is executed for testing \(x\geq 1\), which is a mutant of guard \(x>1\), the test case passes without any problem. If a transition for the else branch (in this case, \(x\leq 1\)) does not exist, the mutant cannot be killed. To resolve this problem, failing test cases are inserted between the initial test suites. Table 2 shows examples of failing test cases. An input value of 1, which cannot pass the condition, is entered as a failing test case for condition \(x>1\) to verify whether the test-case execution fails. Then, the mutant coverage is increased, and the fault-localization accuracy is improved.
## 2 Related Work
### SDN and NFV in 5G VANETs  
SDN decouples the control and user plane functionalities of the RAN, providing a flexible and programmable framework for managing diverse QoS requirements for URLLC applications in 5G VANETs. The RAN Intelligent Controller (RIC) is an initiative of the Open-RAN (O-RAN) Alliance, combining Extensible RAN (xRAN) and Cloud RAN (C-RAN) to enable more dynamic and intelligent network provisioning and management. This allows for the functional split of the Next Generation Node B (gNB) baseband functions into Real-Time RIC, responsible for dynamic resource allocation, radio admissions control, and RAN data measurements, and Non-Real-Time RIC, which is responsible for inter-cell RRM, resource block control, and connection monitoring [16]. NFV complements SDN through the virtualisation of network functions that traditionally run on dedicated proprietary hardware [1]. NFV allows network functions to be deployed and scaled on demand to support the rapidly growing IoT and VANETs services in next-generation networks.  
The integration of an SDN Application Programming Interface (API) Controller into the edge points of an SDN overlay network was explored in [14] in the design of a reliable mesh network via the OmNET modelling system to develop traffic management practices for SDN and SD-WAN communication infrastructures. Their proposed framework achieved a maximum packet delivery rate of approximately 96%, compared to the 83-90% achieved using traditional protocols such as Advanced On-Demand Distance Vector (AODV), Optimised Link State Routing (OLSR), and Better Approach to Mobile Ad Hoc Networking (B.A.T.M.A.N).  
The SDN-IoT architecture proposed in [12] includes the integration of IoT devices into the user or data plane of the SDN architecture. Technically, this aligns with our proposed framework, although no experimental results were reported. The SDN-IoT architecture creates a technical change in requirements when designing and implementing communication infrastructures for IoT connectivity on mobile networks. The integration of IoT connectivity into the user plane of the SDN architecture differs from traditional frameworks, with a consolidated structure for communication protocols like the Transmission Control Protocol/Internet Protocol (TCP/IP) and architectures that cannot be disintegrated.  
The integration of SDN and NFV into IoT and VANET frameworks faces challenges in supporting dynamic, layered communication architectures. The study conducted in [9] highlights the lack of comprehensive frameworks featuring new layers in communication infrastructures and protocols. Although their proposed IoTNetSim framework allows for the end-to-end simulation of IoT services, including device interactions and communication protocols like Message Queueing Telemetry Transport (MQTT), it lacks support for SDN integration. This is a significant gap as SDN's centralised control is essential for optimising QoS in 5G VANETs.
# Biomechanical Effects on Lower Extremities in Human-Robot Collaborative Agricultural Tasks
## 2 Materials and Methods
### Experimentation Setup
#### 2.1.2 Data Collection  
A modified marker set was employed consisting of twenty-four retroreflective markers positioned bilaterally over selected anatomical landmarks and locations of the pelvis and lower limbs [59], based on Schwartz and Rozumalski [60]. Lifting kinematics were captured by using 10 infrared cameras (Vicon T-series, Oxford, UK), sampling at 100 Hz, whereas lifting kinetics were captured by two force platforms (Bertec 4060-10, OH) embedded in the laboratory floor and sampling at 1000 Hz. EMG data were collected at 1000 Hz with a Myon MA-320 wireless EMG system (Myon AG, Switzerland) from the Vastus Medialis (VM), the Biceps Femoris (BF), the Medial Gastrocnemius (MG), and the Erectus Spinae (ES) of the same side.  
The dominant limb was defined as the participant's preferred limb when kicking a ball [61]. The skin was shaved and meticulously cleaned with alcohol before placing bipolar surface electrodes (Noraxon USA, Scottsdale, AZ, USA) on the selected muscles (2 cm interelectrode distance) based on the SENIAM guidelines. Finally, kinematic, kinetic, and EMG data were synchronized utilizing Vicon Nexus software.
## 5 Classification of Smart Grid Cyberattack
### Data Availability Attacks  
#### 5.1.1 DoS and DDoS  
A denial-of-service (DoS) attack is one of the most common cyberattacks against smart grids and smart energy systems in general. Attacks of this type work by jamming communication lines and causing delays through targeting routing protocols and electronic maneuvers. Therefore, a DoS attack can restrict authorized users' access to resources and services by oversaturating the communication network bandwidth with traffic. Regarding smart energy systems, DoS has the potential to paralyze communication between smart power electronics in a smart grid and can interfere with communication between the control system and all agents within a system, delaying the distributed control framework for microgrids. However, if the source of the DoS attack is known and can be found, it is simple to protect the system from a DoS attack by blocking the malicious website [81].  
DoS attacks on smart grids SCADA systems keeps on rise in last years. A complex piece of malware called Industroyer, was deployed in the 2015 BlackEnergy attack against the Ukraine power grid. The malware was designed to disrupt the operation of the industrial control system (ICS) used in electrical substations by launching a DoS attack and temporarily blocking serial COM lines, causing a large denial of control. This resulted in an energy blackout that affected about 225,000 customers for several hours [82]. Compared to DoS, the distributed DoS (DDoS) attack is a more severe form of DoS in which numerous hosts simultaneously attack the victim site. By taking advantage of widespread vulnerability to hack numerous hosts throughout the communication infrastructure, DDoS attackers prepare their breach in advance by recruiting these hosts. Then, they flood the victim site with all compromised hosts [83].
## 6 Results and Discussion
### OWC Performance Evaluation  
For the assessment of the performance of the suggested airflow-control on the OWC system, the optimized PID controller using the parameters found of the mean case of the SGHS optimization results. The evaluation compares the performance of the uncontrolled OWC, the OWC using a traditionally tuned PID using the well known Ziegler-Nichols method (ZN-PID) and the OWC using the optimized PID with SGHS algorithm (SGHS-PID) with the parameters given in Table 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Proportional Gain (\(K_{p}\))** & **Integral Gain (\(K_{i}\))** & **Derivative Gain (\(K_{d}\))** \\ \hline HS & 2088 & 103.50 & 77.33 \\ IHS & 1806 & 112.87 & 66.88 \\ GHS & 2115 & 132.18 & 78.33 \\ SGHS & 2364 & 147.75 & 87.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PID parameters obtained by optimization algorithms.  
Figure 12: Convergence histories of the HS, IHS, GHS and SGHS algorithms for Problem (18).
# A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind
## 3 Materials and Methods  
The objective of this research is to develop a portable and flexible navigational solution that caters to both cognitive and spatial awareness of VIB people, thus, a smartphone with a depth camera is used. The smartphone chosen for the research is the Samsung Galaxy A80 (Samsung Corporation in Seoul, South Korea) running on Android v11 Operating System with a Qualcomm Snapdragon 730 G processor, 8 GB RAM, 6.7\" infinity display, 48 MP + 8 MP rotating rear camera that supports 3D depth estimation with an embedded Time-of-Flight (ToF) sensor. The 3D depth camera has a pixel size of 10 \(\upmu\)m with a field of view at 72\({}^{\circ}\) capable of calculating distances of up to 10 m away with a constant accuracy of <1%. The developed user interface for the mobile application is a combination of different accessibility features of a smartphone that helps users with vision impairment through the use of different gestures and voice commands. The main workflow of the system is shown in Figure 1 where the process starts off with specific voice commands to open the features. The grey area shows the 2 main components, which are the object detection and the obstacle detection features with an output of vibration and audio feedback.  
The object detection feature utilizes TensorFlow Lite framework with a custom trained COCO SSD MobileNet v2 model and an additional unique interactable feature for VIB people to use. The obstacle detection takes advantage of the generated depth map on ARCore by Google that is overlaid with different coordinates on multiple directions to locate nearby obstacles. The output for both features will be a vibration and audio feedback. The application requires Android version 7 as the minimum SDK version. Figure 2 shows the proposed wearable system. The smartphone is inserted on a portable, foldable fabric sling pouch that is hung around the neck of the user.  
Figure 1: The proposed system workflow mobile application using voice command (**left**), mobile application interface and features (**middle**), and feedback to users (**right**).
## 1 Introduction
Besides, the functions of this architecture can be divided into three parts, namely border network functions, identification functions and data processing functions. Figure 1: Vision of 6G. 5G is an architecture for edge computing that has been conceptualized through the enhancement of Software Defined Network (SDN) and Network Function Virtualization (NFV) [10]. Additionally, 5G technologies promote the concept of dividing networks into network resources and network functions. In 5G technology, a centralized infrastructure can be maintained while improving wireless communication at the same time [11]. This allows service providers to build a single physical network, with the potential to take into consideration high-bandwidth applications (e.g., broadcasting) and low-bandwidth (e.g., Internet of Things (IoT)) applications with time-low-latency connectivity and internal corporate networks. Increasingly development of 5G technologies enables sophisticated applications and services such as IoT, online gaming, Augmented Reality AR, Virtual Reality VR and acceleration of intelligent video [12]. The integration between IEC and 5G provides important improvements, such as enabling data processing at the network edge to reduce latency and deliver tangible business results.
## 2 Methods
### Multi-Objective Optimization of the Satellite Selection Problem
The parameter \(\overrightarrow{a}\) is a convergence factor that linearly decreases from 2 to 0 as iterations progress. Additionally, \(\overrightarrow{r}_{1}\) and \(\overrightarrow{r}_{2}\) are randomly generated values within the range [0, 1]. In the encircling prey phase, the WOA sets the current best search agent as the target prey position, or the best solution closest to the target prey. Once the best search agent is defined, the other search agents update their positions in the direction of the best search agent, iteratively determining the optimal solution \(\overrightarrow{x}^{*}\). The bubble-net foraging phase, also known as the exploration phase, updates the whale's position by probabilistically selecting between the shrinking encircling mechanism and the spiral position update model with equal probability, as expressed by  
\[\overrightarrow{x}^{(t+1)}=\left\{ \begin{array}{l}\overrightarrow{x}^{(t)}-( 2\cdot\overrightarrow{a}\cdot\overrightarrow{r}_{1}-\overrightarrow{a}) \cdot\left|2\cdot\overrightarrow{r}_{2}\cdot\overrightarrow{x}^{(t)}- \overrightarrow{x}^{(t)}\right|\text{p<0.5}\\ \\ \left|\overrightarrow{x}^{*}-\overrightarrow{x}^{(t)}\right|\cdot\text{e}^{ b\lambda}\cdot\cos(2\pi\lambda)+\overrightarrow{x}^{(t)}\text{p}\geq 0.5\end{array} \right. \tag{8}\]  
where \(\lambda\) represents a random number uniformly distributed in the interval [\(-\)1, 1] and \(b\) is a constant that defines the logarithmic shape.
## 3 Results and Discussion
### Effect of the pH Media on the Adsorption
Other authors proposed that when these species are adsorbed within the interlayer clay, they arrange at the surface so that the positively charged group is located close to the surface sites and their negatively charged part is driven as far away as possible from the surface [42; 43]. The lowest TC adsorption was obtained at pH values higher than 9.7, where the main species was the TC\({}^{2-}\) and it was probably because of the repulsion with the negatively charged surface of the NC. The TC adsorption on Si-PILC 75 was considerably lower than that obtained for the NC in all the pH range except to values up to 8.5. No greater differences in the amount of TC adsorbed on pillared clay were observed in the whole pH range. This could suggest that the interactions between the TC species and the pillared clay surface are low, or that the porous structure of this material could be avoiding the molecule access to the adsorption sites. The adsorption of CPX on natural clay showed a similar behavior to the TC adsorption, were the highest adsorptions were obtained at low pH values with a maximum at pH 6; decreasing when the pH increases. These results can be explained similarly to those above; at low pH values the CPX\({}^{+}\) is present and is adsorbed on the negative surface of the natural clay by cation exchange. After that, at pH values of 7.5, CPX\({}^{\pm}\) and CPX\({}^{-}\) are present in  
Figure 5: TC solubility curve and distribution of species as function of pH media. the solution, decreasing the adsorption and resulting in other adsorption mechanisms [16]. Results shown for the adsorption of CPX on Si-PILC 75 evidenced that there are no variations in the amount adsorbed at different pH values, decreasing with an increase in the pH. However, the adsorption for this material under basic conditions (up to 7.5 pH) was higher than for the natural clay, suggesting that Si-PILC interacts with the CPX species by another mechanism, like the formation of an inner sphere complex [25; 44].
## 4 Limitations of Past Research and Implementation Barriers
### Policy and Technological Shortcomings  
Routine infrastructure planning set aside, it is often expected of the government agencies to provide a platform, encourage discussion and dialogue between stakeholders, and commission research projects promoting equity, safety and technological integration and development policies as the machine-brawn artificial intelligence revolution engulfs transportation industry. Algorithms able to crunch big data and slicker transportation promised by autonomous vehicles opens a new window, yet due to the substantial uncertainties associated with their long-term deployment impacts, partial information makes policies difficult to draft. [PERSON] et al. [88], through a metropolitan case study from the Netherlands, found that despite the uncertainties associated with AV implementation, there is a need for encouraging systematic, approach-based pilot projects to study the long-term impacts. Researchers such as [PERSON] et al. [1] asserted the need for developing a consensus on the transformation of the traditional transport systems through the emergence of vehicle automation technologies. Future research on the autonomous vehicle, and particularly autonomous PT as a mobility solution, should address how these alternate vehicle technologies can be adopted to reduce overburden on personalised vehicle transit and achieve city-wide sustainability goals of municipal authorities in congested and heavy traffic density cities around the world.
# Noise Attenuation Based on Wave Vector Characteristics
## 2 Principles
### Attenuation of Random Noise and Other Non-Ground-Roll Related Coherent Noise  
Unlike ground roll, to suppress random noise and other non-ground-roll related coherent noise, we use smaller moving windows to maintain valid signal amplitudes. As shown in Figure 2b, the moving time window can be set to:  
\[T_{2}=\frac{1}{2f_{\text{max}}} \tag{13}\]  
where \(f_{\text{max}}\) is the high cutoff frequency of the valid signal wavelet. Within the moving time window, we adopted a three-stage smoothing approach to extract pure valid body wave vectors. In the first stage, we use mean filtering to get the mean vector \(\mathbf{M_{1}}(t)\):  
\[\mathbf{M_{1}}(t)=\frac{1}{T_{2}+1}\sum_{\tau=t-T_{2}/2}^{t+T_{2}/2}\mathbf{C }(t) \tag{14}\]  
Mean filtering superposes all the wave vectors within the moving time window and takes the mean vector for the centre. The superposition of wave vectors in the moving time window can highlight body-wave vectors and suppress noise vectors [29].  
As shown in Figure 3, in the second stage, we perform median filtering over time (blue rectangle in Figure 3) at each trace for all wave vectors to suppress random noise vectors:  
\[\mathbf{M_{2}}(t)=\text{argmin}\left(\sum_{i=t-T_{2}/(2\Delta t)}^{t+T_{2}/(2 \Delta t)}\left\|\mathbf{M_{1j}}-\mathbf{M_{1i}}\right\|_{2}\right)\quad j=t- \frac{T_{2}}{2\Delta t},\cdots,t+\frac{T_{2}}{2\Delta t},\quad i\
eq j \tag{15}\]  
Then, in the third stage, we perform median filtering over trace (green rectangle in Figure 3) for the same sample to suppress non-ground-roll related coherent noise:  
\[\mathbf{M_{3}}(l)=\text{argmin}\left(\sum_{i=l-L/(2\Delta l)}^{l+L/(2\Delta l )}\left\|\mathbf{M_{2j}}-\mathbf{M_{2i}}\right\|\right)\quad j=l-\frac{L}{2 \Delta l},\cdots,l+\frac{L}{2\Delta l},\quad i\
eq j \tag{16}\]  
where \(l\) denotes the output trace of the median filtering and \(L\) is the size of the moving trace window. The median filtering over trace should be performed within the same seismic receiving line because only adjacent seismic traces in the same receiving line have similar wave vector characteristics. Then, we derive the pure valid signal vector by scaling the median vector \(\mathbf{M_{3}}\) as:  
\[\mathbf{B}(t)=\gamma_{2}(t)\mathbf{M_{3}}(t) \tag{17}\]  
and  
\[\gamma_{2}(t)=\frac{\sum\limits_{\tau=t-T_{2}/2}^{t+T_{2}/2}\mathbf{C}(\tau) \cdot\mathbf{M_{3}}(\tau)}{\sum\limits_{\tau=t-T_{2}/2}^{t+T_{2}/2}\left( \mathbf{M_{3}}(\tau)\right