esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### A Socially Assistive Robot with an Air Quality Sensor  
We utilized a SAR named 'Hyodol', developed by the Hyodol Corporation in Seoul, Korea. Hyodol is a doll-shaped companion robot resembling a 7-year-old child (Figure 1). It is equipped with touch sensors on the head and back, buttons on the ears and hands, and an electronic control unit with antennas inside [33]. The care robot provides daily routine reminders for tasks such as taking medication, eating meals, and attending appointments. It also offers cognitive enhancement activities like singing, stretching, quizzes, and religious practices. Despite its doll-like appearance, Hyodol can maintain an upright seated position independently. Once fully charged, its battery lasts approximately 48 h, allowing elderly individuals to carry it to different locations within their living space. The SAR is equipped with a built-in speaker that delivers a maximum volume of 110 dB and an average volume of approximately 92.8 dB, ensuring that the sound is both loud and clear, making it suitable for elderly patients. This configuration enables Hyodol to be easily transported and used effectively in various living spaces.  
One significant advantage of using a SAR as a mediator is that it introduces an element of play for the elderly. For older adults, interacting with a care robot is akin to engaging in a playful activity. The SAR is designed to be constantly interactive, initiating conversations in a kind and friendly manner on various topics. With around 4000 different phrases, Hyodol (version 1) can effectively capture the attention of elderly patients. This interaction helps reduce loneliness, providing a companion to talk with and simulating the experience of hugging, patting, and stroking a grandchild [34]. Moreover, the care robot responds to gestures such as hugging, patting, and touching. This interaction is crucial in allowing the elderly to experience hedonic motivation through the care robot [22].  
We connected a \"PMS5003T\" air quality sensor (Plantower, Beijing, China) to the SAR. This real-time air quality monitoring device can measure temperature, humidity, and the concentrations of fine and ultrafine dust particles [35]. It measures airborne dust using laser particle detection and can detect particles with a minimum diameter of 0.3 \(\upmu\)m. The sensor's patented six-sided shielding structure ensures high anti-interference performance. It also includes options for different air inlet and outlet directions to accommodate various installation designs. Table 1 summarizes the measurement range and accuracy of the sensor. Although the sensor contains an air-circulating fan to reduce measurement errors, we attached it to the back of the SAR to ensure that diffusible dust raised during hugging or touching the SAR would not distort the measurements.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 3 System Model  
### Network Model  
The assumptions underlying the design of the proposed FQ-UCR are outlined as follows:  
1. A total of \(N\) sensor nodes are dispersed randomly across an \(M\times M\) region.
2. All sensor nodes are initialized with the same amount of energy upon deployment.
3. Once deployed, it is assumed that both the sensor nodes and the BS stay in fixed positions.
4. The sensor nodes operate on a limited energy supply, while the BS has an unlimited energy source.  
The network model of the proposed FQ-UCR is shown in Figure 1.
## 1 Introduction
These methods can solve the computational cost problem caused by a large number of forward simulations in the inversion process, especially for Bayesian inversion, because the number of forward simulations involved in Bayesian inversion can reach a million [22]. Secondly, deep learning algorithms can also be directly applied to the imaging [23; 24] and inversion [25; 26] of airborne electromagnetic data. These methods directly establish the mapping relationship between electromagnetic response and model parameters through deep learning network models so as to quickly obtain the distribution of underground media and almost realize real-time inverse imaging processing. Although some attempts have been made to carry out airborne electromagnetic data inversion based on deep learning algorithms, the models considered have certain complexity. After the observation data are preprocessed, the ATEM data to be inverted are converted into a one-dimensional sequence, so a one-dimensional CNN is more suitable. In addition, the length of one-dimensional ATEM data series obtained via the use of different measuring instruments and preprocessing methods is different. Therefore, in this paper, we propose the use of a CNN structure to construct an effective and general one-dimensional convolutional neural network to invert ATEM data. Results from tests involving synthetic data and two forms of survey data are provided to demonstrate the effectiveness of the CNN inversion method presented in this paper.
# Machine Learning and Deep Learning Based Model for the Detection of Rootkits Using Memory Analysis
## 2 Background  
Memory analysis is a critical aspect of cybersecurity, involving the examination of a computer's volatile memory to identify malicious activities, unauthorized processes, and potential threats. It has emerged as a promising approach for detecting and classifying malware, surpassing the limitations of static and behavioral analysis. Traditionally, memory analysis has been a time-consuming and intricate manual process, requiring expert analysts to sift through memory dumps and detect anomalies. However, the integration of ML and DL techniques has revolutionized this field, enabling the automation of memory analysis for faster and more accurate threat detection. More precisely, the benefits of this automation include:  
* ML- and DL-powered automation significantly accelerate memory analysis, allowing security teams to detect threats faster and allocate resources more effectively.
* These technologies reduce the risk of human error and increase the accuracy of threat detection by considering a wider range of patterns and behaviors.
* Automated memory analysis can handle a high volume of memory dumps simultaneously, making it suitable for large-scale environments.
* ML and DL models can provide real-time or near-real-time analysis of memory dumps, enabling a rapid response to ongoing attacks.
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 4 HAR Datasets and Experiment Settings
### HAR Datasets
#### 4.1.2 UCI Datasets  
The UCI dataset is a dataset proposed by the University of California, Irvine, specifically designed for machine learning purposes. the UCI HAR dataset includes accelerometer and gyroscope data for six activities performed by 30 participants, namely walking, jogging, climbing stairs, descending stairs, sitting, and standing. Researchers have widely utilized this dataset to evaluate the accuracy of various machine learning and deep learning algorithms in identifying human behavior. Furthermore, the dataset has been divided into training and testing subsets, which makes it a valuable resource for researchers to test and measure the accuracy of different machine learning and deep learning algorithms in the field of human activity recognition.
## 2 Related Work
### Trail-Like Routing  
Some approaches have been proposed to take advantage of actor mobility to improve routing efficiency in ad hoc networks. In [23; 24], the transmission path along the trail has been proved to be asymptotically optimal, and scales as the shortest path. HILS in [25] proposes a history information based light location service, using the temporal relationship among historical locations of mobile nodes in the network. In HILS, location information of mobile nodes is propagated by Hello beacons locally, and the location query is performed with the aid of historical locations. In this way, HILS can eliminate the tremendous periodical location updates and significantly reduce the overhead for location service. The eTrail [26] proposes a clustered trail-based protocol. When the actor broadcasts its location information, it does not just broadcast to its neighbor nodes, but a cluster with a largerarea. The transmission path in a cluster is also determined when the actor makes this location update. However, all these work do not consider the characteristics of event report transmission in WSAN and the challenges in searching the trail, which are mainly addressed in our research.  
Compared with related works, the TS protocol proposed in this paper distinguishes them in the following aspects: (1) TS is based on the search algorithm, which performs better than location service in the event report transmission. (2) TS takes advantage of the trail information to improve search efficiency which has not been considered in existing search algorithms. (3) This paper makes in-depth study about the trail including its connectivity and redundancy.
## 4 Results and Discussion
### DA-OA-ICOS Combined with White Noise Perturbation
#### 4.1.2 Effect of White Noise Perturbation on Absorption Spectroscopy  
The gas mixing system was equipped with 1000 ppm CH\({}_{4}\) and the absorption spectroscopy of the DA-OA-ICOS was measured with the white noise perturbation of different modulation depths (0%/5%/10%/15%/20%/25%/30%/35%). The DAS of 1000 ppm CH\({}_{4}\) with white noise perturbation of different modulation depths showed different waveforms, as shown in Figure 4. The results showed that the fluctuation characteristics of the DAS exhibited significant differences with different white noise perturbation [32]. In particular, the absorbances of the absorption spectroscopy at different white noise perturbations were 0.390, 0.387, 0.378, 0.378, 0.382, 0.381, 0.378, and 0.376, respectively. The absorbance showed little significant difference. The system noise levels (1\(\sigma\)) at different white noise perturbations were 0.09884, 0.05215, 0.04361, 0.03444, 0.02982, 0.02541, 0.02338, and 0.02324, respectively. Based on the noise levels, the calculated SNRs were 4.0, 7.4, 8.7, 11.0, 12.8, 15.0, 16.2, and 16.2, respectively. Since the improvement in SNR was minimal after the modulation depth beyond 30%, the modulation depth of 30% was chosen. Compared with the original system, the SNR of the white noise perturbation was increased 4.1 times. The effect of white noise perturbation on absorbance was minimal, but the cavity mode noise level of the system was effectively reduced.  
Figure 4: DAS of 1000 ppm CH\({}_{4}\) with the white noise perturbation of different modulation depths (0% (black)/5% (red)/10% (green)/15% (purple)/20% (blue)/25% (pink)/30% (yellow)/35% (gray)).  
Figure 3: Sawtooth wave signals with the white noise perturbation of different modulation depths (0% (black)/5% (red)/10% (green)/15% (purple)/20% (blue)/25% (pink)/30% (yellow)/35% (gray)).
# An Experimental Study of Turbulent Structures in a Flat-Crested Weir-Type Fishway
## 2 Experimental Facility and Methodology  
The experimentation was conducted in a large-scale fishway flume in the Hydraulics Laboratory at the Zhejiang University of Technology. The experimental setup of fishway flume is shown in Figure 1, which consists mainly of inflow, working, and outflow sections. The flume was 20.0 m long, 0.6 m wide, and 1.0 m deep. The two sides were made of tempered glass. The working section consisted of 4 pools separated by baffles, each pool was 0.9 m long, and the length to width ratio of pool was 1.5. The baffle material was polyethylene plate. Baffle height over each step was 0.65 m. Step heights for 4 pools were 0.20, 0.15, 0.10, and 0.05 m, respectively. The flow rate was measured by a rectangular sharp-crested weir at the end of the flume, and the water level was measured by an automatic water level measuring system. Two flow rates \(Q_{1}\) = 20.79 L/s and \(Q_{2}\) = 30.04 L/s were selected, the corresponding weir flow velocity were \(U_{1}\) = 49.49 cm/s and \(U_{2}\) = 62.50 cm/s, respectively. Three-dimensional velocities at each measuring point were measured with SonTek Micro ADV.  
The coordinates of the fishway pool can be defined as \(x,y,\) and \(z\) axes along longitudinal, transverse, and vertical directions as shown in Figure 2, respectively. The origin is located at the left lower corner of intersection between \(x\) and \(y\) axes. In the experiment, 2 flow rates were selected, 5 different planes were taken for each flow rate, 11 transverse lines (\(x\) = 5, 10, 15, 20, 25, 35, 45, 55, 65, 75, and 85 cm) were measured on each horizontal plane, and 10 measuring points with spacing 5 cm were arranged at each transverse line as shown in Figure 3, respectively. Based on the ADV measurements and the theory of turbulent flow, variations in longitudinal velocity along flow direction and depth, distribution of vertical velocity along depth, characteristics of vertical vortex, longitudinal and vertical turbulence intensity distributions, and Reynolds stress distribution were analyzed.  
Figure 1: Setup of the flat-crested weir-type fishway flume.  
Figure 3: Layout of measuring points.  
Figure 2: Sketch and coordinates definition of the flat-crested weir-type fishway flume.
## 3 Results and Discussion
### Thermodynamic Analysis of Hydrogen Dissociative Adsorption on Pipeline Steel Surfaces  
Understanding the adsorption behavior of hydrogen within natural gas pipeline systems is essential for maintaining the structural integrity of these pipelines and improving transport efficiency [52]. This, in turn, ensures the safety and efficacy of the energy supply chain. The Gibbs free energy offers a thermodynamic framework for elucidating the interactions between hydrogen molecules and the iron surface [53]. As illustrated in Figure 10, when examining the adsorption process of gas molecules on solid surfaces, our primary focus is on energy changes. These changes are predominantly driven by two factors: the alteration in adsorption energy and the variation in chemical potential,  
Figure 7: Fe’p orbital density of states before and after the reaction.  
which stems from the fluctuation in the number of gas molecules [54; 55]. As shown in Equation (5).  
\[\Delta G=\Delta E_{\text{ adsorption}}+\Delta\ n_{\text{gas}}\cdot\mu(T,P) \tag{5}\]  
where \(\Delta G\) represents the total change in Gibbs free energy during the adsorption process, \(\Delta E_{\text{ adsorption}}\) is the change in adsorption energy, \(\Delta\ n_{\text{gas}}\) is the change in the number of gas molecules, and \(\cdot\mu(T,P)\) is the chemical potential at a specific temperature T and pressure P. This equation explicitly states that the spontaneity of the adsorption process under given temperature and pressure conditions is determined not only by the change in adsorption energy but also by the influence of changes in chemical potential due to the adsorption of gas molecules on the surface. By calculating \(\Delta G\), we can determine whether the adsorption process is spontaneous. If \(\Delta G<0\), this indicates that the process occurs spontaneously, and the more negative the value of \(\Delta G<0\), the higher the stability of the products.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 4 Detection Algorithm and Its Applicability
### Performance Measurement
#### 4.2.4 Precision & Recall (P&R)  
Meanwhile, [PERSON] et al. (2019) used a different approach and proposed a quality evaluation method that utilizes two different factors: Precision and Recall. While Precision shows how close the generated image is to the real image, Recall measures the differences in the distribution of the generated and real images.
## 2 Mesh Relationship Modeling of Internal Spur Gears with Tooth Wear  
Generally speaking, we can use mesh relationship to describe the gear relative kinematic relationship when the gears are meshing. In this section, an analytical model is proposed to evaluate the new mesh relationship of internal spur gears caused by tooth wear. Firstly, the single-tooth analytical model of the internal spur gears is established. Subsequently, we establish the single-tooth meshing analytical model with tooth wear through two analytical models. Finally, solving the geometric equations, the new mesh relationship can be derived. Moreover, the results of the new mesh relationship are discussed in the typical case of non-uniform wear.
## 2 The Evolution of Vehicle-Mounted Media
### Vehicle-Mounted Media
#### 2.2.1 Hearing Media  
The sense of hearing is regarded as the main channel to receive the outside signal. In-car auditory information acquisition is originated from the car phone. After the evolution of wired to wireless telephony [42; 43; 44], the car phone can be regarded as the first medium in the cabin, which could enable occupants to exchange information during commuting. Until now, the function of a car phone still exists and becomes a standard module in vehicle-mounted media scenarios.  
The actual sense of audio-based IVI experience starts from car radio. Since the first crystal radio invented in the early 20 th century [45], vacuum and transistor radios are viewed as important carriers and installed to the cabin. As shown in Table 1, Chevrolet firstly put the Westinghouse-made radio in their products, which was popular at that time. With the successful promotion of radio modes, such as MOTOROLA 5T71 and Blaupunkt's Autoradio AS5, the car radio became an indispensable part of the IVI ecology and accelerated the penetration of broadcast media into people's lives [46]. From the 1950s to 1980s, some fashionable audio-based carriers, e.g., vinyl record, cassette tape recorder, eight-track tapes and audio Compact Disc (CD) were deployed in vehicles successively. Until the digital stereo system was installed in 1990s, the drivers or occupants could switch quickly between radio, tape recorder and CD player. As a result, the user experience of auditory IVI was greatly improved. With the evolution of audio-based IVI carriers, such as car telephones, car radios and other devices with stereo, users gradually get used to hearing in vehicle-mounted scenarios.  
However, the information provided by aural IVI devices is always viewed as \"low definition\", which relies on imagination to draw the whole picture. If drivers and passengers want to receive stronger sensory stimulus in the vehicle space, the integration of audio-visual presentation becomes a good choice.
## 1 Introduction
### Wave-Based Modeling of Joints: Scattering Relations  
#### 2.2.1 Coupling Conditions between the Beam Elements and Scattering Relations at the Interior Joints  
At any interior joint \(J\) (\(J=2,3,\cdots,N-1\)) where two adjacent structural members in the current unit cell are rigidly connected, the spectral equations of compatibility of displacements and equilibrium of forces should be:  
\[-\phi^{II}(0)=\hat{u}_{Y}^{I}=\vartheta^{IK}(0)\text{, }\phi^{II}(0)=\hat{ \theta}_{Z}^{J}=\phi^{JK}(0)\text{: }-\hat{Q}^{II}(0)+\hat{Q}^{IK}(0)=0\text{, }\hat{M}^{II}(0)+\hat{M}^{IK}(0)=0. \tag{6}\]  
Substitute the solutions to the generalized displacements and forces of the members, as given respectively in Equations (3) and (5), into Equation (6), and one obtains the local scattering relation of interior joint \(J\) as:  
\[\mathbf{A}^{I}(\omega)\mathbf{a}^{I}+\mathbf{D}^{I}(\omega)\mathbf{d}^{I}= \mathbf{0}\text{ }(J=2,3,\cdots,N-1), \tag{7}\]  
where \(\mathbf{a}^{I}=[a_{2}^{II},a_{3}^{II},a_{2}^{IK},a_{3}^{IK}]^{\mathrm{T}}\) and \(\mathbf{d}^{I}=[d_{2}^{II},d_{3}^{II},d_{2}^{IK},d_{3}^{IK}]^{\mathrm{T}}\) are the arriving and departing wave vectors of interior joint \(J\), respectively, with the superscript T signifying the transpose of a vector or a matrix here and after. The corresponding coefficient matrices are:  
\[\mathbf{A}^{I}=\left[\begin{array}{cccc}1&1&1&1\\ -\beta_{2(i)}&-\beta_{3(i)}&\beta_{2(j)}&\beta_{3(j)}\\ -\zeta_{2(i)}&-\zeta_{3(i)}&\zeta_{2(j)}&\zeta_{3(j)}\\ \mu_{2(i)}&\mu_{3(i)}&\mu_{2(j)}&\mu_{3(j)}\end{array}\right],\mathbf{D}^{I}= \left[\begin{array}{cccc}1&1&1&1\\ \beta_{2(i)}&\beta_{3(i)}&-\beta_{2(j)}&-\beta_{3(j)}\\ \zeta_{2(i)}&\zeta_{3(i)}&-\zeta_{2(j)}&-\zeta_{3(j)}\\ \mu_{2(i)}&\mu_{3(i)}&\mu_{2(j)}&\mu_{3(j)}\end{array}\right] \tag{8}\]  
where \(\Gamma_{r(i)}\) and \(\Gamma_{r(j)}\) (\(\Gamma=\beta,\zeta,\mu,\)\(r=2,3\)) are the influence coefficients of rotation, shear force and bending moment for members \((i)\) and \((j)\) associated with \(r\) wave mode, respectively.
## 1 Introduction
### Meteorological Conditions Associated Issues and Solutions
Moreover, it is obvious that by developing a VLC receiver that is highly resilient to light interferences, the SNR is enhanced and so, the overall system performances are improved, including here the performances achieved in adverse meteorological conditions. Based on this analysis, this article continues with the aspects associated to the development and the evaluation of a new VLC receiver concept based on a logarithmic transimpedance solution. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Perturbation Source** & **Effects** & **Solutions** & **Solution Complexity** \\ \hline \multicolumn{4}{c}{_Perturbations introduced by Natural and artificial light sources_} \\ \hline Incandescent lighting sources & \begin{tabular}{c} 100 Hz perturbation and \\ its harmonics \\ \end{tabular} & High pass filters; & Low \\ \hline Fluorescent lighting & \begin{tabular}{c} 100 Hz perturbation and \\ its harmonics \\ \end{tabular} & High pass filters; & Low \\ \hline \multirow{6}{*}{Sunlight} & Photoelement saturation & Difficult to address; & High \\ \cline{2-4}  & Strong DC component & High pass filters; & Low \\ \cline{2-4}  & & Optical filters [16; 36]; & Low \\ \cline{2-4}  & & \begin{tabular}{c} Lowpass/bandpass filters; \\ Noise subtractor [39]; \\ Optical interference \\ suppression based on LCD filtering [40]; \\ Complex digital signal \\ processing; \\ \end{tabular} & Moderate - high \\ \hline \hline \multicolumn{4}{c}{_Perturbations introduced by Whether/atmospheric phenomena_} \\ \hline Rain and Fog & \begin{tabular}{c} Reflection, refraction, \\ absorption, scattering - \\ all leading to Signal \\ Attenuation \\ \end{tabular} & \begin{tabular}{c} Fresnel lens and multiple \\ photodiodes [11]; \\ \end{tabular} & Low \\ \cline{2-4}  & \begin{tabular}{c} Low \\ all leading to Signal \\ Attenuation \\ \end{tabular} & \begin{tabular}{c} Environment-adaptive \\ features [22]; \\ \end{tabular} & High \\ \hline Snow & Light blockage, & Large reception area, \\ \hline Dust & Attenuation & focal lens, multiple \\ \hline \multicolumn{4}{c}{_Perturbations introduced by the vehicular environment_} \\ \hline \multicolumn{4}{c}{Strong variations of the} \\ Mobility & \
\begin{tabular}{c} emitter – receiver \\ distance, \\ Loss of connectivity; \\ \end{tabular} &
\begin{tabular}{c} Field of View Adaptivity; \\ Automatic gain control; \\ \end{tabular} & Low to Moderate \\ \hline Unpredictability & Loss of connectivity; & Context-adaptivity [22]. & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the perturbation sources and their effect on automotive VLC systems.