esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Creation and Data Processing
### The Problem of a Sudden Change in Data Continuity and Its Solution in Matlab  
The task was to use the computational core of CoppeliaSim Edu and apply it to the inverse kinematic task, to calculate the joint coordinates of \(\varphi\) = [\(\varphi_{1}\), \(\varphi_{2}\),..., \(\varphi_{6}\)]T for the entered trajectory. When the joint coordinates data were imported, in some cases, the above-mentioned problem with value repolarization emerged as a result of the software-imposed limitation on the joint rotation. In other words, in the case of a requirement arising from the calculation, the range of the degree of freedom upon exceeding the rotation values in the \(<\!\!-\!\pi\), \(+\!\!\pi\!\!>\) interval must be switched to the value with the opposite sign to remain in the defined working interval. The values above the upper limit \(+\pi\) will rise again, but this time starting with the lower value of \(-\pi\). Moreover, the opposite values below the lower limit \(-\pi\) will fall again, this time starting from the upper value of \(+\pi\). This sudden change causes deformation or even computational instability of the \(p_{7}\) trajectory of the effector, see  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Diameters** & **(mm)** & **Diameters** & **(mm)** & **Diameters** & **(mm)** \\ \hline r1 & 243.215 & a1 & 73 & b1 & 128 \\ r2 & 212.430 & a2 & 42.215 & b2 & 128 \\ r3 & 212.430 & a3 & 42.215 & b3 & 128 \\ r4 & 212.430 & a4 & 42.215 & b4 & 128 \\ r5 & 212.430 & a5 & 42.215 & b5 & 128 \\ r6 & 212.430 & a6 & 42.215 & b6 & 128 \\ r7 & 42.215 [FOOTNOTE:]Footnote : Effector (last part of the passive joint).[ENDFOOTNOTE] & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions table of the robotic arm of Figure 2 and also Figure 3, Figure 4, respectively.  
Figure 4: (**a**) Model visualization with hints of dimensions in Matlab-Simulink using the Simscape/Multibody toolbox. (**b**) Detail URM with a passive joint.  
Since the precision of the trajectory traveled by any manipulator is critical in practical applications, it was necessary to somehow address this problem. Either through a labor-intensive rewriting of the imported data upon each change in the defined trajectory subject to the presence of those repolarizations in the joints or through a suitable solution. One such solution is the so-called repolarize, described below.
# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 2 Methodology
### Case Study  
This paper aims to evaluate the quality of various electrical equipment in a substation. Data from a 110 kV substation, operated by a power supply company was used to analyze the effectiveness of data mining-based state evaluation for electrical equipment. The 110 kV substation is equipped with: (1) Two 110 kV incoming lines with GIS, including three sets of circuit breakers and isolation switches on both sides, three sets of current transformers, two capacitive line voltage transformers, one set of 110 kV bus voltage transformers and lightning arresters. (2) A 50 MVA main transformer (3) Seven 10 kV outgoing lines with supporting electrical equipment using armored movable indoor AC metal-enclosed switchgear. Each outgoing cable is equipped with a parallel lightning arrester at its front end. (4) A 10 kV bus is connected to station power transformers, bus voltage transformers, and two sets of capacitor banks. (1) Effectiveness analysis  
The GIS was used to validate the proposed electrical equipment state evaluation methods. Data from various sources, including online monitoring, testing, operation, maintenance, and environmental factors, were used to assess its operational status. This approach assumes that all data accurately reflect the real state of the equipment. The dataset was split into a training set (70%) and a test set (30%). The training set was used to identify key factors influencing equipment quality and to uncover the influence mechanisms and strong association rules. The test set was used to evaluate the quality assessment methodology, thereby validating its accuracy. The study dataset includes GIS data samples from three sources: routine tests, online monitoring, and inspections. The correct functioning of GIS depends on four main components: conductors, insulators, cooling medium and mobile contacts. In the data samples, beyond general environmental factors such as time, weather, temperature, and humidity, loop resistance and external contact temperature are used to represent conductive factors. The insulation resistance characteristics are used to account for the insulating factors. And  
Figure 5: Flowchart for state evaluation of new samples. the SF\({}_{6}\) moisture content and air pressure features accounts for cooling medium factors. The moving contact factor is reflected by operating time characteristics. Table 3 summarizes the features included in each data sample. To investigate the interrelations among various GIS status data in the substation environment, raw data samples were preprocessed using Gaussian mixture models before applying the a priori algorithm. Table 4 shows the distribution of data processed by the Gaussian mixture model. The internal structure and features of the data are described based on the center position and distribution shape of each cluster [34].
# Thermal Evaluation of a Hybrid Dryer with Solar and Geothermal Energy for Agroindustry Application
## 1 Introduction
At the Ecuadorian agro-industrial level, the drying of product is usually done in square or rectangular chambers, made with masonry materials in its structural part (bricks and cements). They are used as trays, jute bags, or plates of perforated stainless steel. The ducts and pipes for the entry of air are built with stainless steel plates. On the other hand, there are circular camera dryers, which work with a LPG burner and an extractor, where the dimensions of the equipment depend on the drying capacity. Additionally, rotary dryers and electric dryers (operating temperature between 100 to 120 \({}^{\circ}\)C) are also used. It is necessary to indicate the reality of the agricultural sector that for small products, drying of the product is carried out by spreading out the product on a concrete or plastic floor and exposing it to the sun (tendals) for several days. The use of technology is not cost effective for this sector given the cost of fuel and electricity that is needed to dry the product for 6 or 12 h. The added problem of the use of tendal is the contamination of the agricultural product by dust, emissions of vehicle gases, animals, and people stepping on the product. During the study presented by [PERSON] 2018, it indicated that the problem of drying technology is caused by the inadequate distribution of hot air throughout the chamber (0.3 m/s to 4 m/s) and lack of control of flame temperature (use of LPG, 280 \({}^{\circ}\)C to 600 \({}^{\circ}\)C), which generates an over-sizing of an air extractor (air speed between 8 to 13 m/s), so that the drying temperature of the product reaches between 50 to 60 \({}^{\circ}\)C. In economic terms, it is estimated that drying 450 kg of product with about 40% initial moisture content to 10% moisture content after 8 h consumes 250 kWh electric ($25 dollars) and between 45 kg and 90 kg of gas ($12 dollars). An estimate of the electricity and gas consumption is presented below in Table 1 of the different drying capacities [5]. Finally, the above values vary from the drying time and the initial and final moisture of the product. Inappropriate energy management in the drying process can influence the product's marketing chain, causing fluctuations in the final price. As a result, industries where food products are processed will need to establish energy efficiency actions for their processes in the future in order to improve their operations and thus ensure the competitiveness of their products. For this reason, the objective of this work was to assess the function of a hybrid dryer with solar and geothermal energy for agroiondustry application as a technological alternative to allow for energy savings and ensure that a final quality product is obtained, based on the control of the temperature and air speed of the equipment [4; 5].
## 3 Case Study
### Dataset and Computation
#### 3.2.2 Hydrogeological Parameter Dataset
The hydraulic conductivity varies from 15 to 300 m/d. Topography refers to the slope of the land surface, which is generated in the DEM. The slope ranges from 0% to >100%. The impact of human activities on groundwater vulnerability is also reflected in the land use. The land use types were used as a contamination indicator in this study. The land use data were derived from the work of [PERSON] et al. [57] and [PERSON] et al. [58] and include six land use types: winter wheat/summer maize, vegetables, orchards, woods, cotton, and residential area (village). The mean nitrate concentration in the groundwater of different land use types was used as criterion for the rating scores for the land use types. No samples were obtained from the cotton growth area. However, according to the survey, cotton planting uses the least amount of chemical fertilizer, that is, 40% of the amount used for wheat/maize. Therefore, the lowest score (1, the same score as that used for woods) was assigned to the cotton area (Table 2). \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{2}{c}{**Net Recharge (According to [11])**} & \multicolumn{3}{c}{**Soil Media**} \\ \hline
**DRASTIC Range (mm)** & **Range in This Paper (mm)** & **Score** & **Infiltration Coefficient** & **Score** \\ \hline
0–51 & 0–50 & 1 & 0.2 & 1 \\
51–102 & 50–100 & 3 & 0.226 & 1.52 \\
102–178 & 100–150 & 6 & 0.275 & 2.5 \\ – & 150–200 & 7 & 0.285 & 2.7 \\
178–254 & 200–250 & 8 & 0.3 & 3 \\ >254 & >250 & 9 & 0.4 & 5 \\  & & & 0.45 & 6 \\  & & & 0.55 & 8 \\  & & & 0.65 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Rating scores for the recharge and soil media for Aller’s common and pesticide models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{4}{c}{**Landforms**} & \multicolumn{4}{c}{**Land Use**} \\ \hline
**Geomorphic Units** & **NO\({}_{3}\)\({}^{-}\) (mg/L)** & **Score** & **Land Use Types** & **NO\({}_{3}\)\({}^{-}\) (mg/L)** & **Score** \\ \hline River belts & 12.06 & 1 & Cotton & – & 1 \\ Alluvial and flood plains & 13.21 & 1.24 & Woods & 12.19 & 1 \\ Lakes and depressions & 21.12 & 2.9 & Residential area & 16.32 & 1.88 \\ Alluvial-proluvial fans & 44.44 & 7.78 & Orchards & 21.67 & 3.02 \\ Proluvial fans & 55.03 & 10 & Wheat/maize & 29.91 & 4.78 \\  & & Vegetables & 54.33 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rating scores for geomorphic units and land use types of Aller’s common and pesticide DRASTIC models.
# Triterpennoids Biosynthesis Regulation for Leaf Coloring of Wheel Wingnut (_Cyclocarya paliurus_)
## 1 Introduction  
_Cyclocarya paliurus_ (Batalin) Iljinsk. is a tree species only distributed in subtropical areas of China, and the tree leaves contain abundant health benefits such as secondary metabolite [1, 2, 3]. Among them, triterpenoids and flavonoids have been found to exhibit strong bioactivities. Health-benefitting triterpenoids including cyclocarioside I, cyclocarioside II, cyclocarioside III, cyclocaric acid A, and cyclocaric acid B are unique ingredients contained in _C. paliurus_ leaves [4]. Furthermore, high levels of oleanolic acid and arjunolic acid also improved the bioactivities of total triterpenoids. In previous research, _C. paliurus_ triterpenoids have shown positive results in the treatment of diabetes, antioxidant, scavenging free radicals, and improving drinking flavor [5, 6].  
Recently, _C. paliurus_ germplasm with red new leaves--with high content of total flavonoids and triterpenoids--was selected for targeted breeding [7]. It was found that the red new leaves in plants always accumulate more flavonoids (anthocyanins) [7, 8], while the triterpenoids accumulation pattern has not been previously studied in _C. paliurus_ resources with red new leaves. The synthesis pathway of plant triterpenes is mainly the mevalonic acid pathway (MVA) and mevalonate pathway (MEP) [9], while flavonoids biosynthesis is mainly through the phenylpropane pathway [10]. According to a previous study, triterpenoids and flavonoids exhibited accumulating differences between different _C. paliurus_ genotypes [6]. Therefore, comprehensive and systematic studies are needed for analyzing the total triterpenoids and triterpenoid variation pattern in leave coloring stages.  
Transcriptomic and metabolomic methods have helped progress the analysis of plant tissue metabolites dynamics [11; 12; 13]. Screening and selection of plant evaluation are directly dependent on differences in chemical composition, metabonomics method (which has unique advantages in the evaluation of medicinal plants for testing multiple ingredients at the same time) [14], comprehensive testing data [13], directly fitting the metabolic pathway, and conducting association analysis with transcriptomic results [12]. In this research, for the purpose of studying triterpen dynamics during _C. paliurus_ leaf coloring, transcriptomic and metabolomic methods were conducted in different leaf color development stages. Information regarding the triterpenoids biosynthesis mechanism and related genetic basis will help make progress on _C. paliurus_ targeted breeding processes, and provide evidence on the accumulation pattern of target triterpenoids related to leaf quality and bioactivity.
## 2 Symmetrical Component Analysis of Electrical Winding Faults
\right]=\frac{1}{3}\left[ \begin{array}{c}i_{r}+S_{90}i_{i}\\ i_{r}-S_{90}i_{i}\end{array} \right],\] (6a) where: \[i_{r}=i_{sA}-\frac{1}{2}(i_{sB}+i_{sC}) \tag{6b}\] \[i_{i}=\frac{\sqrt{3}}{2}(i_{sB}-i_{sC})\]In the next step of the ISCA algorithm, the FFT analysis of the calculated instantaneous symmetrical components can be performed, and then on the basis of the spectrum, the fault symptoms may be sought [25]. It is known that the FFT has limitations because it assumes the stationarity of the analysed signals, i.e., that the harmonic components they contain occur all the time and are unchangeable over time. However, in the case of electric faults, analysed in steady-state motor operation, with adequate recording time and high resolution, the assumption of stationarity does lead to major errors in the separation of frequencies (symptoms) characteristic of these failures. The elimination of the basic FFT defect, i.e., the loss of information about the time of the occurrence of individual fault harmonics, is possible through the use of time-frequency analyses, e.g., STFT or WT. In the case of monitoring drives with high dynamics, in conditions of rapid changes of velocity or load moments, the variability of signal states containing components with variable time amplitudes, it becomes necessary to use more complex signal analysis methods. This, however, is associated with an increase in the complexity of calculations and an increase in the time of receiving diagnostic information. Therefore, in most industrial drives, the FFT analysis is a sufficiently accurate tool to obtain diagnostic information and is the most commonly used method of fault diagnosis in electrical machines. Thus the procedure used in this research for the calculation of the stator and rotor fault symptoms using instantaneous symmetrical components and FFT analysis is presented in Figure 1.  
To fulfil the stationarity requirement of the tested signals, the appropriate conditions of the data recording should be met. In the case of a rotor bar failure analysis (whose symptoms depend on motor slip frequency), the data were recorded within 5 s, thus ensuring the spectral resolution at the level of d\(f\) = 0.2 Hz, which already allows the extraction of appropriate harmonic components with high accuracy. For a damage to the stator winding, the case is simpler because then the change in the amplitude value of supply frequency \(f_{s}\) is analysed, thus the data recording time can be at the level of 1 s (or even less). The instantaneous symmetrical components of the positive and negative sequence of the stator current are used in the further part of this work to evaluate the condition of IM winding with the application of the Kohonen self-organizing maps.
## 4 The Implementation of the Proposed System
### The Web Application Section  
1. A login process is undertaken by the admin (the manager or medical employee) to enter the donor's initial data (name, blood type, etc.) in the reception unit;
2. A clinical test of the donor's blood sample is conducted to ensure that the blood is eligible for donation (free from transitional blood diseases);
3. If the blood test result is positive, the donor is not qualified to donate, and their information is recorded in the database. If the blood test result is negative, the donor can donate and complete the rest of the steps;
4. After the actual donation, the donor completes the registration process, including all the information required by the receptionist employee;
5. A donor QR code is generated in the form of a label to be affixed to the blood bag of that donor;
6. The QR code label is affixed to the blood bag.  
The reading of the QR code label on the blood bag was carried out by the code reader for inclusion in the database and then the blood bag was kept in the refrigerator. The saving of the blood bag information via QR code was an automated process (more accurate and free from human error, obtaining accurate statistics and reports at any time). When the blood bag was removed to provide blood to the person in need, the code reader performed the opposite process.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Fingerprinting  
A device fingerprint is the collected information from the software and hardware of a remote computing device for the purpose of identification. Device fingerprints play an important role in identity authentication. Based on device characteristics, researchers have designed efficient and accurate fingerprint algorithms.  
[PERSON] and [PERSON] [108] investigated how to use a generic attribute (GATT) profile from Bluetooth-low-energy devices to create fingerprints. After analyzing a dataset of more than 13,000 profiles, they proved that the GATT profile can be used to identify devices uniquely. They also mentioned that sensitive information (e.g., device type, device model, device manufacturer, and user's name) can be inferred from the values of some characteristics. Finally, they suggested mitigating those issues by restricting access to values of characteristics or minimizing the exposure of the GATT profile.  
[PERSON] et al. [11] proposed two methods to generate unique fingerprints for devices in industrial control system (ICS) networks to enhance current intrusion detection methods. The first method leverages cross-layer data processing times, and the second uses their unique physical properties and operation times. Through a combination of the dataset from a live power substation and controlled lab experiments, the accuracy of the first and second methods achieve 99% and 92%, respectively. Both methods resist simple forgery attacks alongside traditional IDS systems.  
BleScope [109] is a tool for generating BLE device fingerprints by extracting static UUIDs from companion mobile apps. As the devices' UUIDs and their hierarchies are hardcoded in plaintext in apps, static analysis on an app can be used for UUID fingerprinting. Through evaluations on all the free BLE IoT apps from the Google Play store, BleScope identified 1757 vulnerable mobile apps. Among the 5822 real BLE devices identified in a region, 94.6% were fingerprintable by attackers, while 7.4% of them were vulnerable to unauthorized access. Moreover, the authors proposed methods to mitigate these attacks at the app level, channel level, and protocol level, such as obfuscating the app, broadcasting disrupting signals, or generating dynamic UUIDs.  
[PERSON] et al. [110] presented an approach that uses IoT device behavioral fingerprinting to identify devices. Certain command and response sequences in protocols describe a device's behavior. The authors uses machine learning tools to learn local features and detect similar device types. They further validated the approach with multiple machine learning classifiers, and the classifiers reported an identification rate of 93-100% and a mean accuracy of 99% across all the experiments.
## 2 Materials and Methods
### Experimental Measurement  
For further validation of the beam model, a quasi-static free bending experiment was carried out and the associate measurements of maximum deflection at the mid-span of the tested strand under different combinations of axial and lateral loads were recorded to compare with the FE simulation results. A test setup was designed and manufactured to conduct the free bending test for the single-layered strand as presented in Figure 9. In Figure 9a, \(a\) represents the tested strand with a length of 0.93 m, nearly seven pitches; \(b\) is a laser displacement sensor to measure the deflection of the mid-span of the strand; \(c\) is a force transducer to measure the axial tension acting on the strand; \(d\) is a data acquisition card to record the electronic signal from the sensors; \(e\) is a mass block to apply lateral force on the strand by suspending to the strand, and \(f\) is a hand jack to adjust the tension exerted on the strand. One end of the tested strand specimen is fully constrained, and the other end can only move freely in the axial direction.  
The deflection of the strand depends on the loading path due to the effect of the nonlinear interwire friction during bending. The displacement will form a hysteretic loop under the action of cyclic lateral force, which means that the similar displacement may correspond to different loadings. Therefore, only a one-way loading method is adopted here.  
The description of the experimental measurement is given. Firstly, axial tensile force is applied to the tested strand at a specified value, such as 500 kg, by manually adjusting the jack. Then, the distance, denoted by \(y_{0}\), between the laser displacement sensor and the middle point of the tested strand is recorded as a reference. After that, a mass block is suspended to the middle position of the tested strand and the corresponding measured distance is written down, denoted as \(y_{1}\). At the same time, the real-time tension \(T\) subjected to the tested strand should be recorded. Subsequently, turn down the tension of the strand to a new value whose difference with respect to the last value is limited to a range of 25-50 kg. Simultaneously, the values of the deflection and the tension are recorded. Repeat the previous step until the tension reaches the minimum value.  
The deflection, \(y_{max}\), is the difference between these two recorded distance values, that is \(|y_{1}-y_{0}|\), as shown in Figure 9b. The experimental data are listed in Table 2. The transverse loads range from 4 kg to 20 kg with an increment of 4 kg. The axial loads are limited in range from 100 kg to 530 kg.  
Figure 9: Test setup for the free bending deformation tests: (**a**) experimental setup; (**b**) schematic diagram of the experimental principle.