esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Classification Model
#### 2.3.2 Vegetation Indices  
Two vegetation indices were used namely, \(SR\) and \(NDVI\), calculated as in Equations (2) and (3).  
\[Simple\leavevmode\
obreak\ Ratio,\leavevmode\
obreak\ SR=\frac{NIR}{Red} \tag{2}\]  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{3}\]  
These indices were constructed using wavelengths with the highest mean reflectance difference between healthy and infected seedlings taken from the red and \(NIR\) regions.  
Each of the datasets was divided into two groups which were 70% for training and 30% for testing. SVM algorithms with different kernel types available in MATLAB machine learning toolbox (2019b, The MathWorks Inc., Natick, MA, USA) as tabulated in Table 1  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Kernel Type** & \multicolumn{2}{c}{**Model Flexibility**} \\ \hline  & Linear SVM & \(\bullet\) & Low \\  & & \(\bullet\) & Makes a simple linear \\  & & & separation between classes. \\ \hline  & Quadratic SVM & \(\bullet\) & Medium \\  & & \(\bullet\) & Makes a simple separation \\  & & \(\bullet\) & between classes. \\ \hline  & Cubic SVM & \(\bullet\) & Medium \\  & & \(\bullet\) & Makes a simple separation \\  & & & between classes. \\ \hline  & & & High but decreases as kernel \\  & Fine Gaussian SVM & \(\bullet\) & scale decrease. \\  & & \(\bullet\) & Makes finely detailed \\  & & & distinctions between classes, \\  & & & with kernel scale set to sqrt(P)/4, \\  & & & where P was the number of \\  & & & predictors. \\ \hline  & Medium Gaussian SVM & \(\bullet\) & Medium \\  & & \(\bullet\) & Medium distinctions, with \\  & & & kernel scale set to sqrt(P). \\ \hline  & Coarse Gaussian SVM & \(\bullet\) & Low \\  & & \(\bullet\) & Makes coarse distinctions \\  & & & between classes with kernel \\  & & & scale set to sqrt(P)*4. \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of classifiers with its corresponding type and model flexibility [53].  
were used to develop the models. A five-fold cross-validation technique was applied to test the output of the established model, where the cross-validation method selected five disjoined sets to partition the data. While only one set was used for the validation of the model, the other four sets were used for training. This process was repeated five times, and the resulting confusion matrix was obtained by using the arithmetic means of the results arising from each of the iterations.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 3 Experimental Datasets
### Rain Rate Time Series and Specific Attenuation of the Site  
A LabVIEW software was designed at NASA GRC to monitor and process the data that were obtained by the Thies Cima Laser Precipitation Monitor. The measured rain rate time series and the DSD data are retrieved from the output files provided by such a software. In particular, the drop size distribution \(N(D)\)[15], which represents the number of particles per cubic meter having diameter \(D\), can be used to derive the specific attenuation of the site, \(\gamma_{R}\)[16]:  
\[\gamma_{R}=4.343\cdot 10^{3}\int_{0}^{\infty}N(D)C_{ext}(D,f)dD\;[\mathrm{dB/km}], \tag{1}\]  
where \(f\) is the frequency of interest and \(C_{ext}\) is the extinction section of the raindrops with diameter \(D\), which were computed while using the T-Matrix method [17].  
The regression coefficients \(k\) and \(\alpha\) (reported in Table 1) were derived by fitting the power law relationship \(\gamma=kR^{\alpha}\)[16] on the points of the scatter plot shown in Figure 6, where the 1-min values of the specific rain attenuation are plotted against the rain rate with the same integration time. Figure 6 also shows the specific rain attenuation calculated while using recommendation ITU-R P.838-3 [18] (the corresponding \(k\) and \(\alpha\) coefficients are reported in Table 1), which tends to overestimate the specific attenuation derived from the dis  
Figure 5: Rain attenuation time series on 7 th September 2017.  
Figure 6: Specific rain attenuation as a function of the rain rate in Milan (19.701 GHz).
## 3 Simulation Tests and Analysis
### Reconstruction Error Analysis  
Equation (9) shows that the accuracy of the proposed method is associated with the number of POD basis vectors, sensor placement, and measurement noise. In the simulation test, measurement data were extracted from the CFD result. The measurement noise was artificially simulated by adding Gaussian noise to the sensor signal. These effects are discussed in this section.
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
## 2 Background and Related Works
### Related Works
Their study involved discovering PLC vulnerabilities, developing exploits to understand attack methodologies, and subsequently modifying PLC memory using the developed attack techniques. The proposed detection framework detects cyberattacks on PLCs by utilizing memory structures such as stacks to determine whether the control logic has been manipulated. However, existing PLC anomaly detection methods cannot identify the root cause of control logic anomalies and are vulnerable to attacks that manipulate process data, such as Stuxnet. Existing studies on detecting control logic tampering require a separate control logic repository and a verification system. Additionally, they detect tampering by comparing hashes or individual lines of code. As a result, even minor modifications that do not affect functionality, such as changing the order of variable declarations, can falsely detect that normal control logic has been tampered with. For example, if [A = 1, B = 2] is changed to {B = 2, A = 1}, it is detected as tampering and classified as anomalous control logic. In other words, existing methods increase false positives by flagging slight variations in control logic within the normal operating range. Moreover, they require dedicated storage for control logic or hash values. For this reason, an AI-based anomaly detection model is needed to analyze logic without requiring additional storage. Instead of detecting changes, it should determine whether the control logic operates within a normal range.
## 3 Results and Discussion
### Recovery of Tomato Seed Oil  
#### 3.2.1 Oil Yield  
The impacts of different extraction methods on the tomato seed oil yield are shown in Figure 5. Conventional (Con) stirring, Soxhlet extraction, UAE-MAE, and PLE were used to extract oil from tomato seeds, with hexane as the solvent. Overall, no statistically significant differences were observed between conventional stirring, Soxhlet extraction,  
Figure 4: Response surface and contour plots showing the effects of MW power, US power, and the solid/liquid ratio on \(EY\) (%). (**a**) US power vs. ratio (MW power: 200 W); (**b**) US power vs. MW power (ratio: 0.03 g/mL).  
and the UAE-MAE method. The highest oil yield (19.66 \(\pm\) 0.71%) was obtained through conventional stirring, followed closely by UAE-MAE (19.53 \(\pm\) 0.83%) and Soxhlet extraction (19.18 \(\pm\) 0.62%) The PLE method yielded the lowest amount of oil (17.17 \(\pm\) 0.63%). These results are consistent with previous research on tomato seed oil extraction, although data for the UAE-MAE and PLE methods are still limited. For instance, [PERSON] et al. (2021) reported a yield of 19.1 \(\pm\) 0.2% for tomato seed oil extraction using continuous stirring for 2 h at room temperature [32]. In another study, [PERSON] et al. (2021) found a higher yield of 21.6 \(\pm\) 1.5% using Soxhlet extraction with n-hexane for 8 h [33]. [PERSON] et al. (2023) used the UAE-MAE method with a solvent system of ethanol and ethyl acetate, yielding 23.07% oil [20]. Lastly, [PERSON] et al. (2010) employed accelerated solvent extraction (PLE) with hexane, obtaining a 20% oil yield., which is slightly higher than the result from this study [21].  
In conclusion, both Soxhlet extraction and conventional stirring are well-established methods for extracting oil from seeds; however, they are time-consuming techniques. The UAE-MAE method produced a comparable oil yield to these traditional methods, but in a significantly shorter time--10 min. Although the PLE method resulted in the lowest yield, it was still efficient, achieving oil extraction in only 6 min.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 4 Android System Architecture and Attacks
### Hardware Abstraction Layer-Based Attacks  
This layer contains library modules for the implementation of different devices such as Wi-Fi, Bluetooth, Camera, GPS, and other components of Android devices. Here, the attacker targets the interface and uses these components. Most of the devices contain a Wi-Fi chipset designed by the M/S Broadcom company, and these chipsets are highly vulnerable to a Bobby-Trapped Signal. The same kind of vulnerability was present in Wi-Fi security protocol WPA2 in Android version 6.0 and higher, IOS, OpenBSD, and Windows. This vulnerability was exploited using an advanced attack method named KRACKs, i.e., Key Reinstallation Attack. To exploit the vulnerability, the attacker needs to be in the Wi-Fi range. This attack can steal confidential data, decrypt encrypted data, and undertake spying activity using a camera, voice recording, or GPS. The GooglePlay service also offered common vulnerability challenges, e.g., default service enabling and disabling, which affects the normal functioning of the android device.  
Attackers also use rooting and jailbreaking tools, i.e., Aircrackng, to generate passwords. After gaining kernel control, the attacker gains full access to all the data and system resources. The solution to this issue is a stronger encryption algorithm. This process is slightly challenging to execute when considering brute force attacks due to the requirements of memory, computation resources, and intensive knowledge.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 1 Introduction and Purpose of the Study
The authors reported some basic recommendations for designing a micropump such as drug delivery and blood transport [21]. The piezoelectric micropump was designed and simulated on microneedle architecture. Microneedle is optimized for overcoming the skin resistance and producing a high flow rate at low operating voltages and frequencies [22]. The pentagonal-shaped microneedle gave minimum pain at insertion into the skin [23]. Further, based on the shape effect of the microneedle, the flow characteristics inside the piezoelectric bimorph-actuated micropump were studied and analyzed [24]. In this paper, a new design of a piezoelectric-based micropump is presented. Four quarter piezoelectric bimorph (FQPB) actuator was used for micropump actuation. On actuation at the natural frequency, each quarter can vibrate separately and acts as a triangular cantilever beam, giving maximum possible deflection. On the actuation of the FQPB, the sucked volume into the pump chamber was channelized towards the biosensor location, instead of allowing it to spread all over in the pump chamber. For the same, the microneedle was extended inside the pump chamber with a hole (flat edge orifice) towards the biosensor location. The new design blocked the flow path elsewhere and required less volume to be sucked, thereby reducing the time of actuation as well as actuation frequency. The research aimed to design a wrist watch dial-like wearable device, consisting of seven such micropumps. Each micropump would be used for one-time blood sampling. The holding fixture that would hold micropumps would be replaceable so that the next sequence of sampling may be carried out.
## 3 Experiments and Comparison
### Noise Modeling Analysis  
The causes of nonuniformity in infrared images are very complicated. From the perspective of signal transmission, the nonuniformity of sensors is mainly caused by two reasons. One is the inconsistent response of the photosensitive element of the infrared detection unit, while the other is the impact of the readout circuit's noise. According to the analysis in [PERSON] [3], the detector response model can be approximated as a linear model when the infrared detector operates normally, as indicated in the Formula (13):  
\[Y(i)=g(i)R(i)+o(i)+\sigma(i) \tag{13}\]  
where \(Y(i)\) denotes the response value output by the \(i\)-th unit in the direction of the detector column. \(R(i)\) indicates the actual amount of infrared radiation received by the detection device. \(g(i)\) and \(o(i)\) are the gain coefficient and bias coefficient of the infrared detection device, respectively. \(\sigma(i)\) represents random noise. Random noise \(\sigma(i)\) can be ignored when the signal-to-noise ratio of the infrared detector is high. For infrared scanning detectors, the output values of the same row of images have the same gain  
Figure 3: The overall flow chart of the proposed algorithm for nonuniformity correction.  
and bias coefficients, so the nonuniformity of the detector appears as visible horizontal stripes. In conventional infrared detector nonuniformity correction, the black body is typically employed for image acquisition first, followed by the linear model for two-point correction [3], as illustrated by Equation (14). The objective is to ensure that each detector unit has an identical response curve.  
\[I(i,j)=k(i)\times v(i,j)+m(i) \tag{14}\]  
where \(\hat{I}(i,j)\) represents the image after correction, \(k(i)\) represents the gain correction coefficient of the linear model, and \(m(i)\) represents the bias correction coefficient. Analyzing the linear correction model of nonuniformity noise, this paper proposes an analog nonuniformity model of infrared detectors, as demonstrated by Equation (15).  
\[I_{1n}(i,j)=g(i)\times I_{1}(i,j)+b(i) \tag{15}\]  
where \(I_{1}(i,j)\) is the infrared image without nonuniformity, \(I_{1n}(i,j)\) is the image with added analog horizontal stripes, \(i\in[1,H]\). \(H\) is the image's height. \(g(i)\) is a sequence of Gaussian distribution with a mean value of 1, and \(b(i)\) is a sequence of Gaussian distribution with a mean value of 0. The horizontal stripe of an image is more apparent as the variance of the Gaussian sequence increases.
## Methods
### Classification-Based Tree Matching and Co-Registration
#### 3.6.4 Co-Registration Method  
Since the tree matching method presented in Section 3.6.4 only gives information about the estimated matching probability for single tree pairs, a further method is required which finally identifies the most probable set of tree pairs \(r_{after}\).  
The proposed co-registration algorithm initially selects all candidate pairs \((s,d)\in(S\times D)\) with a matching probability \(p_{s,d}\) (Section 3.6.3) greater than zero. For each of these candidate pairs, the tree set \(S\) is shifted by the preliminary displacement vector \(d-s\) to derive the tree pair sequence \(r^{\omega^{C}}(T_{d-s}S,D)\). The weights \(\omega^{C}(T_{d-s}S,D)\) (Equation (14)) correspond to a distance-based tree assignment (Section 3.6.2) in ascending order of matching probability.  
\[\omega^{C}(A,B)=\left(\omega^{C}_{a,b}=\begin{cases}0,&\text{if }\|b-a\|_{c}>1\\ p_{a,b},&\text{otherwise}\end{cases}\right)_{a\in A;\ b\in B} \tag{14}\]  
As such a tree pair sequence might include pairs with low probabilities, the pair with lowest probability is omitted iteratively until the probability \(p^{correct}(r)\) (Equation (15)) of this subsequence \(r\) exceeds 0.5. If this threshold is not exceeded, the subsequence with highest probability is kept.  
\[\begin{split} p^{correct}(r)&=p^{total}_{\geq 50\%}( \{p_{a,b}\mid(a,b)\in r\})\\ p^{total}_{\geq 50\%}(P)&=\sum_{\begin{subarray}{c}\hat{p} \subset P\\ 2:|\hat{P}|\geq|P|\end{subarray}}\prod_{p\in\hat{P}}p\prod_{q\in\hat{P}\setminus Q }(1-q)\end{split} \tag{15}\]  
Finally, the point pair sequence or subsequence \(r_{after}(S,D)\) with highest achieved co-registration probability \(p^{correct}(r_{after}(S,D))\) is returned by the algorithm. The result is classified as _Co-Registration successful_, if the co-registration probability exceeds 0.5, but _Co-Registration failed_ otherwise. The final displacement vector \(\vec{T}_{after}(S,D)\) (Equation (16)) is calculated by coordinate weighting, which results in a predicted GNSS error of \(|\vec{T}_{after}(S,D)|\) m.  
\[\vec{T}_{after}(S,D)=\sum_{(s,d)\in r_{after}(S,D)}p_{s,d}\cdot(d-s) \tag{16}\]
###### Abstract  
In the context of increasing energy demands in Vietnam, and as a result of the limited supply of domestic energy (oil/gas/coal reserves are exhausted), the potential for renewable energy sources in Vietnam is significant. Thus, building wind power plants in Vietnam is necessary. Access to this type of renewable energy not only contributes to society's energy supply but also helps to save energy and reduce environmental pollution. Although some works have reviewed applications of the Multi-Criteria Decision Making (MCDM) model in wind power plant site selection, little research has focused on this problem in a fuzzy environment. This is the reason why a hybrid Fuzzy Analytic Hierarchy Process (FAHP) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are developed for wind power plant site selection in Vietnam. In the first stages of this research, an FAHP model is proposed for determining the weight of each potential location for building a wind power plant, based on qualitative and quantitative factors. A TOPSIS is applied for ranking all potential alternatives in the final stage. The authors collected data from seven locations, which have good conditions for investment in a wind power plant. The results indicate that Binh Thuan (Binh Thuan Province is located on coast of South Central Vietnam) is the best place for building a wind power plant in Vietnam. The contributions of this work proposed an MCDM approach under fuzzy environments for wind power plant location selection in Vietnam. This paper also resides in the evolution of a new approach that is flexible and practical for a decision-maker. This work also provides a useful guideline for wind power plant location selection in others countries.  
renewable energy; location selection; wind power plant; MCDM; TOPSIS; FAHP; fuzzy environments
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 1 Introduction
### Beam Management Technique Framework for 5G Mobility Management  
Multiple-beam within a cell need to be carefully managed to maintain high-quality data communications and seamless intra-cell mobility [16]. There are various schemes for supporting beam management, e.g., beam sweeping, beam measurement, beam reporting, beam determination, beam maintenance, and beam failure recovery [17; 18]. To improve mobility performance in beam management, various techniques have been proposed [17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. The framework of beam management and the antenna structure are summarized in [17]. Reference [19] focuses on beam management at the initial access (IA) stage. Reference [20] summarizes the existing techniques for beam-switching methods in IA, where a new searching method to detect a beam to reduce outage probability and power consumption was proposed. Reference [21] shows experimental throughput results under indoor scenarios with a beam channel environment. Reference [22] proposes a new physical channel model to report beam measurement. Reference [23; 24] focuses on a beam tracking method for supporting beam-level mobility. Reference [18] described the spectral efficiency result in a single-cell and the multiple-beam environment with three different beam reporting schemes. Reference [25] proposed a simplified downlink command for beam-mobility, and a measurement report for a beam refinement scheme was also reported. Furthermore, [26] investigated a beam-selection method to enhance the throughput; however, a realistic mobility procedure was not considered. Although these studies propose effective beam reporting and beam switching schemes for mobility enhancement in a multi-beams environment, further research is necessary for practical implementation such as ultra-dense-networks (UDN) of a typical 5G environment. Especially because [18; 25] described two main categories in terms of beam reporting methods, the mobility performance of further research needs to be compared to that of these two main categories. Both beam-level mobility and cell-level mobility occur in practical mobile communication systems; therefore, the performance evaluation of mobility management in the practical environment of 5G networks is required [27; 28].
## 3 Results and Discussion  
### Thermal Analysis  
DSC measurements were carried out to determine the thermal properties of the MPs. According to the van't Hoff theory, peak integration and melting temperature values are used to compare the consistency of formulations from different batches and to determine impurities that will change the melting profiles of the materials. Melting temperature is a strong indication of drug purity and therefore allows not only for a quick screening of the melting process, but also for the identification of drug populations that may be in a different conformation or interacting with an excipient, resulting in shoulder regions in the thermograms. The thermograms of insulin, mucin, and PEG-4000 showed endothermic peaks at 94.88, 95.0, and 62.4 \({}^{\circ}\)C, respectively (Figure S1), while batches A1-A5 showed peaks at 94.9 \({}^{\circ}\)C (Figure 1). In comparison, insulin and mucin presented broader peaks, while PEG-4000 showed a very sharp endothermic peak. The peaks observed for the MPs are consistent with a thermal-induced transition. PEG-4000 is a nonionic amphiphilic polymer with thermotropic phase behavior, including melting temperature and enthalpy changes, that is highly affected by the presence of mucin due to the interactions between both polymers. All insulin-loaded MPs showed endothermic peaks around the melting temperature of mucin and insulin (\(\approx\)95 \({}^{\circ}\)C), independently of the PEG-4000 concentration. A transition peak was observed for each batch due to the solubilization of PEG into the mucin. Therefore, the thermal behavior of the matrixes depends on the interactions between the constituents, as well as their concentrations. The sharpness and symmetry of the endothermic peaks of the insulin-loaded MPs, corresponding to the mucin, are indicative of matrixes with high purity (Figure 1). Furthermore, batches A5, A4, and A3 showed sharper peaks, while batches A1 and A2 displayed slightly broader peaks, suggesting an amorphous nature for the latter. This amorphous feature was observed at lower enthalpy and less crystallinity of the matrixes, which could lead to retention of the entrapped drug over time. The reduced crystallinity may indicate imperfect lattices leading to a special pocket that can accommodate a large number of drug molecules [23]. Hence, an increase in the quantity of PEG-4000 was observed to increase the crystallinity of the formulations, the order being as follows: A5 > A4 > A3 > A2 > A1. The thermogram of batch A0 (blank) clearly showed the expected glass transition (Figure 1).