esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Transport Layer  
The Transport Layer plays a crucial role in ensuring the successful delivery of data packets to the destination. The Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are the two traditional transport protocols. TCP is a reliable, connection-oriented protocol that guarantees the delivery of data packets to the intended destination. It is a heavyweight protocol with high packet overhead. Conversely, UDP is a connectionless protocol that lacks reliability in data transmission. While UDP does not provide guaranteed packet delivery, it is known for its speed and is used for applications that can tolerate packet loss during transmission. With the introduction of innovative transport protocols like SCTP [67], DCCP [68], and QUIC [69], the transport layer has attracted significant research  
Figure 8: HTTP REST Architecture.  
Figure 9: (**a**) WebSocket Req/Res Architecture, (**b**) WebSocket pub/Sub Architecture.  
interest. Additionally, there have been efforts focused on the performance optimisation of traditional transport protocols [70] as well as congestion and error control [71]. The development of new protocols and upscaling of the traditional transport layer protocols are crucial in enabling reliable and efficient communication in the rapidly evolving IoT paradigm.
## 3 Method  
### Feature Selection Using Genetic Algorithms  
In this study, we performed feature selection using GAs. Figure 1 shows a flowchart of the GA. According to [PERSON] [79], diversity decreases if the population size is less than 30, and if the population size is more than 120, the GA converges more slowly. Accordingly, the population size was set to 100 so that diversity was guaranteed without slowing down the convergence speed. In addition, the number of features was set to 25, 50, 75, and 100 out of 17,850 for comparison. In order to select the best number of features, the experiment was conducted with various numbers of features. We executed experiments using two fitness functions. The fitness functions are represented in the following [80,81]:  
\[w\times\Big{|}\overline{\rho(X,Y)}\Big{|}+\Big{|}\rho^{\prime}(X,P)\Big{|}^{-1} \tag{5}\]  
\[\frac{k\times\rho^{\prime}(X,P)}{\sqrt{k+k\times(k-1)\times\rho(X,Y)}} \tag{6}\]  
where \(w\) is a constant as an intercorrelation weight factor, which was set to 2 in this experiment, and \(k\) is the number of features. \(\Big{|}\overline{\rho(X,Y)}\Big{|}\) is the correlation value between features \(X\) and \(Y\), and \(\big{|}\rho^{\prime}(X,P)\big{|}^{-1}\) is the correlation value between feature \(X\) and label \(P\). The genetic algorithm using Equation (5) (GA1) converges as the value decreases, and the genetic algorithm using Equation (6) (GA2) converges as the value increases. In each generation, the top 50% were selected as parents based on fitness. For a pair of parent chromosomes, one-point crossover with a 100% probability was applied, and two offspring were created. After the crossover operation, a 10% probability of bit-flip mutation was applied. The bottom 50% of chromosomes were replaced with the newly created offspring.  
Figure 1: Flow chart of the genetic algorithm.  
Figures 2 and 3 shows the best fitness of each generation of genetic algorithms using Equation (5) (GA1) and genetic algorithms using Equation (6) (GA2). The appropriate number of generations was determined based on this result. Both algorithms converge without improvement after the 80 th generation. Therefore, we set the number of generations to 100. The whole experimental results of machine learning models according to the number of features selected by GA1 and GA2 are provided in Appendix A.  
Figure 2: Fitness values for each generation (GA1).
# Predictive Current Control for Switched Reluctance Motor Based on Local Linear Phase Voltage Model
## 2 Predictive Current Control
### Local Linear Phase Voltage Model  
Mutual coupling between phases is usually ignored [29], and thus, the instantaneous voltage of phase A can be given as  
\[v_{\text{A}}=R_{\text{s}}i_{\text{A}}+L_{\text{inc,A}}\frac{di_{\text{A}}}{dt }+i_{\text{A}}\omega\frac{\partial L_{\text{A}}}{\partial\theta} \tag{1}\]  
where \(i_{\text{A}}\) is the phase current; \(R_{\text{s}}\), \(L_{\text{A}}\) and \(L_{\text{inc,A}}\) are the phase resistance, inductance and incremental inductance, respectively; \(\theta\) and \(\omega\) are the rotor position and speed, respectively; the first and third items on the right are the resistance voltage drop and back EMF, respectively. As can be seen from Figure 1, the instantaneous phase voltage can be \(U_{\text{dc}}\), \(-U_{\text{dc}}\) or 0, depending on the switching states of the power switches.  
Figure 1: Asymmetric half-bridge converter for phase A.  
It is observed that the phase current slope is nearly constant with a constant phase voltage in a short period. Therefore, we assume that the phase voltage can be regarded as a linear function related to the current slope in a short period, as expressed in Equation (2).  
\[v_{\rm A}=P_{\rm A}\frac{di_{\rm A}}{dt}+Q_{\rm A} \tag{2}\]  
where \(P_{\rm A}\) and \(Q_{\rm A}\) are the slope and intercept of the linear function, respectively. \(P_{\rm A}\) and \(Q_{\rm A}\) can be expressed as Equations (3) and (4), respectively.  
\[P_{\rm A}=L_{\rm inc,A} \tag{3}\]  
\[Q_{\rm A}=R_{\rm s}i_{\rm A}+i_{\rm A}\omega\frac{\partial L_{\rm A}}{ \partial\theta} \tag{4}\]  
In a digital system, the average phase voltage in a short period can be derived as Equation (5).  
\[\overline{v}_{\rm A}=P_{\rm A}\frac{\triangle i_{\rm A}}{\triangle t}+Q_{\rm A} \tag{5}\]
## 5 Experimental Validation
### Experimental Validation of the Voltage Regulation  
The final experiments were conducted to validate the behavior of the external voltage loop. In this way, Figure 31a shows the HESS operation without the voltage controller, where the DC bus current stars at \(-3\) A, increasing in steps of 1 A each 4 s, until 3 A is reached. In this first experiment, the supercapacitor voltage decreases for each DC bus current change, which is caused by the power extraction from the supercapacitor to provide each high-frequency transient. Figure 31b shows the HESS operation with the external voltage loop, where the supercapacitor voltage is successfully regulated at 9 V (boost mode) with null steady-state error.  
Figure 31: Operation (**a**) without and (**b**) with the voltage controller at \(v_{sc}=9\) V and \(v_{dc}=12\) V (boost mode). Magenta: converter current (\(i_{zz}\)); yellow: reference current (\(i_{ref}\)); cyan: supercapacitor voltage (\(v_{sc}\)); green: bus current (\(i_{dc}\)); white: battery current (\(i_{b}\)).  
Figure 30: Performance of the current controller LQG-FF with \(v_{sc}=15\) V and \(v_{dc}=12\) V (buck mode). Magenta: converter current (\(i_{zz}\)); yellow: reference current (\(i_{ref}\)); cyan: supercapacitor voltage (\(v_{sc}\)); green: bus current (\(i_{dc}\)); white: battery current (\(i_{b}\)).  
The same load profile is used in two additional experiments, where the supercapacitor voltage is regulated at 12 V (unitary gain) and 15 V (buck mode), respectively. The results of these experiments are reported in Figures 32 and 33, where the supercapacitor voltage is successfully regulated at the desired value.  
Figure 33: Operation (**a**) without and (**b**) with the voltage controller at \(v_{sc}=15\) V and \(v_{dc}=12\) V (buck mode). Magenta: converter current (\(i_{zz}\)); yellow: reference current (\(i_{ref}\)); cyan: supercapacitor voltage (\(v_{sc}\)); green: bus current (\(i_{dc}\)); white: battery current (\(i_{b}\)).  
Figure 32: Operation (**a**) without and (**b**) with the voltage controller at \(v_{sc}=12\) V and \(v_{dc}=12\) V (unitary gain). Magenta: converter current (\(i_{zz}\)); yellow: reference current (\(i_{ref}\)); cyan: supercapacitor voltage (\(v_{sc}\)); green: bus current (\(i_{dc}\)); white: battery current (\(i_{b}\)).  
The previous experiments demonstrate the correct operation of the adaptive gain-scheduling strategy under real operation conditions, thus ensuring the correct tracking of the reference current and the regulation of the supercapacitor voltage and SoC. These conditions ensure that the battery is not exposed to high-frequency transients during charging or discharging modes, but the load current is always provided by the HESS.  
Finally, the satisfactory agreement between the simulated and experimental data confirms the correctness of the models used in this work to reproduce the system behavior (circuital model), to calculate the adaptive parameters (switched model), and to design the adaptive controller (linear model).
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Integrated Topologies  
This section reviews the various integrated multi-port converter topologies for application in satellites. In integrated multi-port topologies, switches and storage elements are shared during various operating modes, reducing component count and conversion stages. They are compact and have improved reliability compared to several independent converters performing the same task. A classification of integrated DC-DC topologies is provided in Figure 17a.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 4 Key Properties of Kaolin: Chemical, Structural, and Functional Insights  
Kaolin's unique properties, including its chemical inertness, adsorbent capabilities, non-swelling nature, and soothing and detoxifying effects, make it a valuable material for diverse applications. This clay appears bright white upon extraction, but it can develop yellow or rust-colored stains due to contact with iron oxide. Naturally, kaolin exists as a finely textured powder with the following characteristics [37]:  
* Chemistry: Unprocessed kaolin has the chemical formula Al2 Si2O5(OH)4. Kaolinite, an aluminum silicate mineral, is formed through the chemical weathering of aluminum-rich feldspars found in pegmatites and granites.
* Structure: Kaolin consists of hexagonal crystals ranging from 0.1 to 10 \(\upmu\)m in size. The layered structure of these crystals enhances the desirable properties of kaolin.
* Chemically Inert: Kaolin's lack of reactivity makes it ideal for use in pharmaceuticals, cosmetics, and various industrial applications. Its neutral pH ensures it does not alter the chemical composition of products or promote bacterial growth.
* Adsorbent: Kaolin's natural absorbency allows it to remove unwanted pollutants, pathogens, and other components from mixtures, thereby enhancing its effectiveness in various applications.
* Non-swelling: The hydrogen bonds between kaolinite crystals prevent water molecules from infiltrating the layers, resulting in kaolin's non-swelling properties. This makes it a stable additive that does not alter the physical properties of mixtures or products.
* Molecular Stability: Kaolinite exhibits non-expanding behavior and minimal isomorphous replacement due to its high molecular stability. While kaolinite is the least reactive clay, its sensitivity to pH can influence metal adsorption. Metal adsorption occurs through the release of hydrogen (H\({}^{+}\)) ions at the mineral's edge sites and on the exposed planar surfaces of the silica and alumina sheets.
* Soothing Properties: Kaolin's calming properties make it useful for soothing red, irritated skin, benefiting those with sensitive or sunburned skin by reducing redness and irritation.
* Detoxification: Kaolin's cleansing properties help remove dirt, oil, and impurities from the skin, revitalizing and freshening the complexion by eliminating dead skin cells and debris.
* Gentle Exfoliation: The slightly abrasive texture of kaolin clay enables it to exfoliate the skin gently, promoting a smoother, more luminous complexion by clearing away dead skin cells and unclogging pores.
* Healing Qualities: Kaolin clay is believed to have healing properties and can help treat skin issues such as psoriasis, eczema, and acne. While more research is needed to confirm its efficacy for specific conditions, kaolin clay is widely used in the cosmetic and skincare industries due to its versatility, gentleness, and beneficial properties for various skin issues.
# Array Gain of a Linear Array in an Ocean Waveguide
## 4 Numerical Simulation Results and Discussion
### Array Gain in the Shallow Water  
In simulation B, we consider the gain of the HLA in shallow water. The water depth is 229 m with the corresponding SSP as shown in Figure 2a. The source radiating a narrow-band signal with center-frequency 190 Hz is fixed at 110 m. The corresponding \(ATLs\) are calculated as follows: the transmission loss corresponding to all frequencies within the narrow band is calculated by Kraken program [22] which is developed based on the horizontal-invariant normal model [23]; then \(ATL\) can be obtained by Equations (7) and (8). The \(ATLs\) in shallow water within 80 km is shown in Figure 2b.  
The HLA is suspended on a depth of 120 m at the direction of end-fire. We investigate the gain of the HLA at 49 km away from the source, and the corresponding \(ATLs\) and acoustic channel coherence. The acoustic channel transfer functions corresponding to the HLA are also obtained by Kraken program.  
The curve of \(ATLs\) on the distance 49 km as a function of element number is displayed in Figure 3a. It is observed from Figure 3a that at the 49 km distance, \(ATLs\) have slight fluctuation with the element number. The variance of \(ATLs\)' fluctuation on the HLA is calculated and equal to 0.03 dB. Submitting the acoustic channel transfer functions into Equation (12), we calculate the spatial correlation coefficients between all pairs of the acoustic channels as a function of element number, and the result is shown in Figure 3b. It is observed that at the 49 km distance, the spatial correlation coefficients are almost greater  
Figure 1: \(AG_{\text{U}}\) (colorful solid line) and \(AG_{\text{W}}\) (dash line) as a function of the number of elements for different \(\rho\), with the black solid line showing the ideal value of AG.  
than 0.7, which can be considered as completely coherent [24]. Next, we will investigate the gain of the HLA from transmission loss and acoustic channel coherence.  
Firstly, ignoring the fluctuation of \(ATL\)s (for Case 2), i.e., \(\sqrt{p_{i}}=\sqrt{p_{k}}\), the expression of array gain can be simplified as Equation (19) that specifies \(AG_{\text{U}}\). We calculate \(AG_{\text{U}}\) as the function of element number, and the result is shown by the blue solid line in Figure 3c. Then, using Equation (18) or Equation (20), \(AG_{\text{W}}\) is also calculated as the function of element number, as shown by the red dash line in Figure 3c. The ideal value of \(AG\) (\(10\log_{10}M\)) is shown by the black solid line for comparison. It is observed that both \(AG_{\text{U}}\) and \(AG_{\text{W}}\) are close to the ideal value since the acoustic channels are almost completely coherent. In addition, \(AG_{\text{U}}\) is almost equal to \(AG_{\text{W}}\), as the result of the slight fluctuation on \(ATL\)s (in this case, Equation (18) is equivalent to the Equation (19)).  
Figure 3: _Cont._  
Figure 2: Simulation parameters and sound propagation in shallow water: (**a**) The sound speed profile; (**b**) The average transmission loss.
## 3 Computed Tomography and Mitral Valve
### CT Assessment of the Mitral Apparatus for Transcatheter Procedures  
Imaging modalities are necessary to provide information on mitral valve anatomy complexity and obtain a detailed plan for transcatheter procedures. Although echocardiography has represented the gold standard for pre- and post-treatment grading, diagnosis, and monitoring, MDCT has become widespread for interventional planning in patients who are candidates for TMVI. It allows rapid image acquisition time and wide availability, excellent spatial resolution, allowing high-quality 2D and 3D reconstructions of the entire mitral apparatus at any moment of the cardiac cycle, high reproducibility and relative operator independence, visualization, quantification of calcifications, and a complete view of the heart in its entirety and the chest wall [72]. CT provides relevant information on mitral leaflet pathology. Compared with echocardiography, CT provided the highest accuracy in identifying prolapse (sensitivity of 96%; specificity of 93%) [73].  
Furthermore, cardiac CT is an excellent technique for diagnosing annular disjunction, a remodeling process associated with mitral valve prolapse, since observation of the entire cardiac cycle allows us to evaluate not only the degree of disjunction but also the dynamics of the ring [74]. In the context of secondary or functional MR, CT also provides essential information regarding the geometry of the valve and the remodeling process (including flap tenting heights and coaptation depth). Knowing that these data are essential for docking the device, CT provides details on the anatomy of the left ventricle, which is also crucial in assessing feasibility. A ventricular diameter greater than 70 mm is often considered an exclusion criterion for transcatheter procedures [75].  
Exact measurements of the mitral annulus are critical for choosing the correct device size and, consequently, for an effective TMVI procedure [76]. Equally important is the understanding of the modifications of the annulus during the various phases of the cardiac cycle (modifications that vary according to the type of etiology of the insufficiency); based on this, the landing zone and the type of device are decided [77]. It appears evident that the annulus is larger in patients with mitral regurgitation than in controls, with a greater remodeling in an anterior-posterior, rather than lateral, direction [76]. Furthermore, in functional MR, the annulus reduces movement compared to primary MR [78]. According to [PERSON] et al., some annular geometry changes in patients with severe mitral regurgitation were detected using CT. They demonstrated that the mitral valve annulus was larger in patients with severe MR, especially in patients with prolapse (Carpentier type II). It is curious how, in these patients, the largest dimensions were found in the systolic phase, unlike controls in which the largest area is found in the early diastolic phase [79].
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 4 Evaluation Setup of ID-Based Authentication
### Leakage Model in Authentication
#### 4.2.1 Non-Profiled Model: HD Model  
As for the non-profile model, we used the well-known Hamming distance model. Since the key is known to the verifier, the Hamming distance of the 128-bit intermediate value rather than a single byte was used. For the Hamming distance model proposed in [2], side-channel information, denoted by \(W\), is modeled as  
\[W=kH(D\oplus E)+b\]  
where \(H(D\oplus E)\) is the Hamming distance between \(D\) and \(E\), which are intermediate values for an AES round, and \(k\) and \(b\) are constants. For the HD model, the intermediate values are the ones stored in registers, i.e., \(D\) is stored in a register and is replaced with \(E\) after a round operation. The HD model assumes that there is a linear dependency between the side-channel leakage \(W\) and the Hamming distance value \(H(D\oplus E)\).  
In [1], it was shown that the 128-bit intermediate values can be used in the HD model because AES-comp implementation [7] performs each AES round in 1 cycle. In this work, we also considered the authentication using \(n\)-round AES, which is modified from the AES-comp implementation. The \(i\)-th round leakage model \(\mathbf{W^{i}}\) and measured side-channel information \(\mathbf{S^{i}}\) are denoted as \((W_{1}^{i},W_{2}^{i},\ldots,W_{N}^{i})\), and \((S_{1}^{i},S_{2}^{i},\ldots,S_{N}^{i})\), respectively. Here, \(N\) is the number of total plaintexts. The correlation coefficients  
Figure 5: Experimental Environment for Side-Channel Authentication.  
are derived by \(\rho(\mathbf{W^{i}},\mathbf{S^{i}})\) and classified into acceptance trials and rejection trials. In the acceptance trial, it is assumed that the prover who registered the pre-shared secret key in the verifier is authenticated, i.e., legitimate prover authentication. On the other hand, if it is authenticated using the unregistered secret key, it is considered to be the rejection trial.
# Evaluation of the Nutritional Quality and Shelf Life of
## 3 Results
### Lactic Acid Bacteria Growth Analyses throughout Shelf Life  
The results obtained from laboratory analyses of the lactic acid bacteria present in assays S2 and S3 of the salami-type fermented sausages containing probiotics are expressed in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**pH**} \\ \hline
**Maturation Time** & **S1** & **S2** & **S3** \\ \hline
0 & 5.19 \(\pm\) 0.03 \({}^{\text{\text{\text{\text{\text{\  
It was observed from Table 7 that the S2 and S3 assays had viable and active lactic acid bacteria counts during all 84 days of the shelf life of these products, showing values higher than 10\({}^{7}\) CFU/g. According to the National Health Surveillance Agency (ANVISA), values must be between 10\({}^{7}\) and 10\({}^{9}\) CFU/g, which correspond to the minimum amount of active and viable probiotic microorganisms that have to be present for the product to be classified as functional and to guarantee that the strains effectively promote their benefits.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results
### End-to-End Delay  
End to end delay depends on the speed of the acoustic signal and the transmission distance. Here, \(s\) denotes the distance between the source node to the MS/CN; the speed of acoustic signal varies from 1450 m/s to 1500 m/s, depending on the depth of the water. As \(t=s/v\), end-to-end delay is directly proportional to the communication distance. In Figure 13, end-to-end delay of three schemes is presented. On the x-axis, the lifetime of the network is shown. On the y-axis, cumulative values are plotted. DDRP has greater end-to-end delay because nodes wait until the MS arrives at a nearby stop to receive data; otherwise, data are forwarded via multi-hopping. In AUV-PN, nodes transmit the sensed data to their respective PN's. When the AUV starts the data gathering tour, first, it obtains the list of PNs from CHs, and afterwards, it visits each PN for data gathering according to the list. This procedure increases the delay. In our proposed scheme, 3D-SM, the MS gathers data form the nodes after a regular interval. In the rest of the regions, CNs transmit the collected data to the MS, which leads to relatively lower end-to-end delay. After the stability period ends, nodes start to drain their energy. Once a node drains its energy, it is no longer able to participate in sensing and transmission; we call it a dead node. As the number of dead nodes increases in the network, the sparsity also increases, which leads to end-to-end delay. Interestingly, the end-to-end delay seems inappropriate for delay-tolerant applications. However, our solution is appropriate, as the calculated value (140,000) of end-to-end delay is cumulative; its average value is 190 s.  
Figure 12: TL of network. (**a**) when all nodes die; (**b**) when first node dies.