esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Results and Discussions  
### Identification of Stiffness and Damping  
The stiffness of SDOF was determined after extracting the frequency and by using the expression \(K_{h}=mw_{h}^{2}\) for heave and \(K_{\alpha}=mw_{\alpha}^{2}\) for pitch. The damping values were obtained using the logarithmic decrement method (LDM), the exponential decay curve fitting method, and the Hilbert transform method for both the heave and pitch responses, as shown in Figure 21. For the heave response, a good agreement can be observed between the polynomial fit of the exponential decay curve and the Hilbert transform. On the contrary, a better match can be seen between the Hilbert transform and the LDM values in the case of the pitch. The corresponding frequency values of the HT are presented in Figure 22.  
Figure 21: Damping values: (—) Hilbert transform method, (O) polynomial fit of the exponential decay curve fitting method, (\(\Delta\)) polynomial fit of logarithmic decrement method. (**a**) Heave response; (**b**) pitch response.  
Figure 22: Frequency values: (—) Hilbert transform method, (O) frequency obtained through FFT of filtered acceleration response. (**a**) Heave response; (**b**) pitch response.
## 2 Biologically Active Polysaccharides--Structure and Function
### Plant polysaccharides  
Among the chief components of herbal medicines, polysaccharides are responsible for various pharmacological immuno-stimulatory, antiviral, antioxidant, antitumor, hepato-protective, neuro-protective, and other potentials [109].  
Numerous natural polysaccharides found in herbs, often components of the plant cell wall, are known to stimulate the human immune system. The activation of macrophages by plant polysaccharides results from recognition of polysaccharide by TLR4 (toll-like receptors), CR3, and scavenger receptors. The studies on the polysaccharides from _Aloe barbadenis_ showed that acetylated mannan isolated from aloe has the ability to increase the production of monocytes [110]. In addition, _Astragalus membranaceus_ polysaccharides (\(\alpha\)-(1-4)-d-Glucans with \(\alpha\)-(1-6)-branches) have the ability to inhibit the proliferation of tumor cells by regulating p53 expression. Additionally, they strongly induce the process of erythropoiesis through gene modulation, expression of \(\gamma\)-globin mRNA, and the synthesis of fetal hemoglobin [28]. Further, angelan is a pectin polysaccharide isolated from the roots of _Angelica gigas_, which reduces the adhesive capacity of neoplastic cells and has the ability to induce the maturation of dendritic cells [28], while fructan isolated from the root of _Achyranthes bidentata_ shows a dose-dependent effect on the growth of [PERSON] lung cancer in vivo. Lower doses of this polymer inhibited tumor growth, as anti-tumor activity of _A. bidentata_ is associated with inhibition of the tumor cell cycle. However, in higher doses, it can also cause stimulation of tumor growth due to NK dysfunction and an increase in the amount of interleukin-6 and TNF-\(\alpha\)[111]. Polysaccharides isolated from _P. ginseng_, co-responsible for the biological activity of ginseng preparations, are mainly composed of starch-like glucans: \(\alpha\)-D-(1,4)-glucans, 6-branched \(\alpha\)-D-(1,4)-glucans, 3-branched \(\alpha\)-D-(1,6)-glucans without side chains, and pectin, mainly composed of homogalacturonan, rhamnogenalacturons, and arabinogalactans [112; 113].  
It was reported that the PS, isolated from the root of _Panax ginseng_, can inhibit the proliferation of human bladder cancer T24 tumor cells and lactate dehydrogenase release. In addition, they have also significantly hampered cell migration and invasion. It was reported that polysaccharides isolated from _P. ginseng_ induced the peritoneal macrophages (PMs) activity against K562, HL-60, and KG1\(\alpha\) cells. The mechanism was involved with increasing the expressions of CD68, increasing the level of TNF-\(\alpha\), interleukin-1 (IL-1), and IL-6 [114; 115; 116]. In a like manner, the polysaccharides and glycoproteins are compounds partly responsible for the immunostimulatory effect of _Echinacea_ sp. The polysaccharides and glycoproteins, which have been isolated from _E. purpnea_ and _E. angustifolia_, are inulin-type fructans, acidic arabinogalactans, arabinogalactan-protein complexes, rhamnogalactans, and xyloglucans [117].
## 2 Materials and Methods
### Test Equipment and Methods  
#### 2.3.1 Freezing-Thawing Test Apparatus and Procedure  
The custom-developed freezing-thawing test apparatus is illustrated in Figure 3. The system mainly consists of a DC power supply (12 V), a temperature control chamber (ranging from \(-50\) to \(150\)\({}^{\circ}\)C), a voltmeter, an ammeter, and temperature-humidity sensors. During the test, the prepared cylindrical soil specimen with a known initial volumetric water content is placed inside the specimen chamber of the temperature control box. A temperature-humidity sensor is then inserted at the center of the soil column. To avoid damaging the soil specimen, three small-diameter holes, each slightly smaller than the sensor probe diameter, are pre-drilled on the soil sample to facilitate sensor insertion. The temperature during the freeze-thaw cycle experiment ranged from \(-28\)\({}^{\circ}\)C to \(22\)\({}^{\circ}\)C. Throughout the experiment, temperature increments and decrements were set at \(2\)\({}^{\circ}\)C, with a constant temperature maintained for \(1\) h at each step. The total duration to complete one freezing or thawing test was \(26\) h.  
Copper electrode plates were connected to both ends of the soil specimen, which were then linked to the voltmeter and the positive and negative terminals of the power supply, respectively. To prevent moisture evaporation during the test, the cylindrical soil sample was tightly wrapped with plastic wrap. The soil resistivity (\(\rho\)) during the freezing-thawing process was calculated using Equation (1) [32; 33]. Variations in the content of unfrozen water and temperature in the soil sample were recorded via the temperature-humidity sensor at \(30\) s intervals.  
\[\rho=\frac{\pi UD^{2}}{4 IL} \tag{1}\]  
where \(\rho\) is the resistivity (\(\Omega\cdot\)m), \(U\) is the voltage across the soil specimen (V), \(I\) is the current passing through the specimen (A), and \(L\) is the length of the soil specimen (m).
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 3 Proposed Framework
### Feature Analysis  
For efficient learning, it is crucial to understand the data form of the level set and the variables used in its computation. Considering the characteristic of the level set, which suggests that \"the more neighboring particles a target particle has, the more likely it is to be inside the fluid\", it is evident that the distribution of particles is important. The distribution of particles can be represented by density, and the key idea of this paper starts from the assumption that density will significantly contribute to the inference of the level set.  
As the number of layers and the input data in the learning process increase, the training time grows, and the model becomes more complex. Therefore, if the input data and the number of layers can be appropriately reduced, accuracy and learning speed can be improved, and unnecessary resource consumption can be minimized. Figure 3 displays a heat map representing the correlation of the input features. A value close to 1 or \(-1\) for an input feature when the level-set increases indicates a positive or negative correlation, respectively. Conversely, a value close to 0 implies a nonlinear relationship with the level-set value, which could adversely affect model accuracy during training. The selection criteria for the frame to determine the correlation is a scene where the ratio of interior particles to surface particles is balanced. As the variance of particles increases along the \(Y\)-axis, the number of surface particles also increases. A balanced ratio between surface particles and interior particles can prevent learning biases caused by imbalanced data, aiding in the model's generalization. Hence, scenes with widely distributed particles are used. Table 1 lists the features used for training.  
Figure 3: Correlation heat map of input features.  
Figure 2: The network architecture of the proposed model. \(x\) and \(y\) are the positions of the particles, \(p\) is the pressure, \(\rho\) is the density, and \(N\) is the number of neighboring particles. It runs from left to right.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 2 Related Work  
Cyber-physical systems, including SCADA systems, are threatened by classical and quantum attacks [1]. Researchers and organizations have proposed various standards and algorithms to provide resistance against these threats. We briefly review part of related work from four aspects, namely, current standards for SCADA, quantum key distribution, and post-quantum digital signature.
## 3 Derivation of Water Quality Criteria and Relevant Fish Assay
### Comparison of Acute and Chronic Toxicity for Fish and Priority Substances in WQS
Biochemical oxygen demand, chemical oxygen demand, total organic carbon, suspended solids, dissolved oxygen, pH level, and total coliforms directly affect living organisms in aquatic environments [1; 2; 3; 4; 5]; these can have significant impacts on the WQS derivation. By comparing the maximum acceptance level of common priority substances in each country and organization [53; 54; 55], different values for the chemical species were described (Table 6). These indicate that the WQS derivation systems have different calculation protocols considering various indicators, which result in different values for certain priority substances. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multicolumn{2}{c}{**Acute Toxicity**} & \multicolumn{2}{c}{**Chronic Toxicity**} & \multirow{2}{*}{**Guideline Reference**} \\ \cline{2-2} \cline{4-6}  & **Stage** & **Duration** & **Stage** & **Duration** & **Reference** \\ \hline \multirow{3}{*}{United States} & \multirow{3}{*}{Juvenile/adult} & \multirow{3}{*}{4 days} & Embryo, 48 hpf larvae \({}^{*}\) & \(\geq\)28 days & \multirow{3}{*}{[1]} \\ \cline{3-3} \cline{5-6}  & & & Juvenile & \(>\)28 days & \\ \cline{3-3} \cline{5-6}  & & & Embryo \(\rightarrow\) larvae \(\rightarrow\) early juvenile & 28–32 days & \\ \hline \multirow{3}{*}{Canada} & \multirow{3}{*}{Not stated} & \multirow{3}{*}{4 days} & Embryo/larvae & \(\geq\)7 days & \multirow{3}{*}{[2]} \\ \cline{3-3} \cline{5-6}  & & & Juvenile/adult & \(\geq\)21 days & \\ \cline{3-3} \cline{5-6}  & Embryo & 4 days & Embryo & 28–32 days & \\ \cline{3-3} \cline{5-6}  & Adult & 4 days & Embryo \(\rightarrow\) sac-fry & 16 days & \\ \hline Australia \& New & Embryo/larvae & \(<\)7 days & Embryo/larvae & \(\geq\)7 days & \\ \cline{3-3} \cline{5-6} Zealand & & Juvenile/adult & \(<\)21 days & Juvenile/adult & \(\geq\)21 days & \\ \hline Japan & Adult & 2–4 days & Embryo/larvae & 28–32 days & [5] \\ \hline \hline \end{tabular}  
* 48 h post fertilization (hpf).  
\end{table} 
Table 5: Classification of acute and chronic toxicity of fish, depending on the developmental stages used for deriving water quality standards for protecting aquatic ecosystems in each country or continental union.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
###### Abstract
For example, according to [PERSON] et al. [30], the k-nearest neighbors (k-NN) is one of the best algorithms for data mining because of its simplicity when compared to other machine learning techniques; it is the one used in this work. All in all, the main objective of this work is to develop a low-cost, real-time, field-applicable method to determine the presence and the severity of bovine mastitis. To tackle this issue, the behavior of a recent portable low-cost NIR spectrometer has been analysed in combination with the four most common preprocessing techniques used with NIR spectral data in the literature, and their possible combinations. These preprocessing techniques were optimized for each of the two K-NN models which compose the processing pipeline, to assess the presence of mastitis and its severity on a raw sample of milk.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 3 The ECG Circuit Design
### The Schematic Diagram of the ECG Circuit
#### 3.2.7 The Peak Detector  
The circuit shown in Figure S2 (in Supplementary Material) is a peak detector that is used for R-peaks detection to determine the heart rate from a measured ECG signal. The circuit simply consists of a BJT transistor that works as a switch in two regions either saturation or cutoff. The resistors R\({}_{12}\) and R\({}_{14}\) are used to ensure that the waves smaller than the R-wave are below 0.7 V by attenuating the signal, this will result in counting the R-peaks only and generate a negative pulse at each R-peak.
## 2 Materials and Methods
### Defense System Adaptive Design
#### 2.3.4 Examples of Adjusting Weights on Different Applications  
In this section, we use several application scenarios to show why they need different cost weights in our adaptive design and the above optimization Equation (1). In different image classification applications, users can define the concrete values for the other cost factors according to their expert opinion and application scenarios. Four applications are introduced in the following, and Table 2 in the following section presents the outcomes of this adaptive method.  
* **Autonomous driving**: We can define \(C_{a}\) = 0.3, \(C_{b}\) = 0.1, and \(C_{c}\) = 0.5. The value of \(C_{c}\) is higher than \(C_{a}\) because in autonomous driving, it is more important for us to detect an adversarial attack than to correctly identify a normal roadside sign image. Similarly, we can define \(C_{d}\) = 0.1, \(C_{e}\) = 0.3, and \(C_{f}\) = 0.8. We define \(C_{f}\) as having a significantly higher value than others because \(Dec_{f}\) means autonomous driving is compromised under a deliberate adversarial attack. For example, we could treat a STOP sign image as a right-turn-only sign, which could result in serious accident consequences. The value of \(C_{e}\) is higher than \(C_{b}\) in detecting misclassified images by the model due to the risk value we assume.
* **Healthcare**: Although deep-learning-based healthcare systems could achieve high accuracy in disease diagnosis, few such systems have been deployed in highly automated disease screening settings due to a lack of trust. Therefore, the human-based double-check process is usually used, and hence, the deep learning healthcare system can be tolerated in the security. Example values of the weights are \(C_{a}\) = 0.7, \(C_{b}\) = 0.4, \(C_{c}\) = 0.1, \(C_{d}\) = 0.4, \(C_{e}\) = 0.1, and \(C_{f}\) = 0.3. \(C_{a}\) is the highest cost weight because the physician will most likely discover failure in other decisions during manual double checking.
* **Face recognition in checking work attendance**: Misrecognition or adversarial impact is low because the potential of utilizing these challenges by the employees is rare.  
Therefore, we can obtain higher positive gain values with \(C_{a}=0.7\), \(C_{b}=0.4\), and \(C_{c}=0.2\). In contrast, we can value the negative decisions as \(C_{d}=0.4\), \(C_{e}=0.2\), and \(C_{f}=0.2\).
* **Detecting inappropriate digital content**: Mispredicting nudity images to protect children is another example where the costs of an AML attack are medium--not as risky as in autonomous driving, nor as tolerable as in face recognition. Hence, we can choose \(C_{a}=0.7\), \(C_{b}=0.1\), \(C_{c}=0.2\), \(C_{d}=0.3\), \(C_{e}=0.1\), and \(C_{f}=0.1\).
## 2 Geolocation and Target Tracking System  
The geolocation and target tracking system can be described within five main components as:  
1. The front end equipment for RF signal sensing.
2. The geolocation equipment that allows the measurement and determination of the relative position of the RF emitter.
3. The optimization approaches that allow optimization of geolocation and target tracking algorithms, as well as organizing the non-linear equations within a set of linear equations.
4. The optimal state estimation tools, which allow the data smoothing filtering using Kalman filtering.
5. The display system that allows a display of the position estimation and trajectory of the stationary or mobile RF emitter.  
Moreover, consideration must be given to whether the system runs in urban (crowded) areas or rural (open) areas. More details of these components will be exhibited in this survey. Figure 2 illustrates the flow chart of geolocation and target tracking systems.  
\begin{table}
\begin{tabular}{c c c} \hline
**Survey Ref.** & **Survey Date** & **Survey Contribution** \\ \hline
[28] & 2008 & Study various techniques for wireless position estimation. \\
[7] & 2016 & Study positioning systems for wireless systems deploying dynamic sensors. \\
[27] & 2017 & Study solutions of the accuracy and reliability navigation for the 5G positioning system. \\
[29] & 2017 & Study indoor positioning and navigation systems and technologies. \\
[5] & 2019 & Study interference types in GNSS systems, and management solutions of the interference. \\
[26] & 2019 & Study solutions of network design for accurate vehicle localization that have considered the 5G position system. \\ \hline \end{tabular}
\end{table}
Table 1: Recent surveys tackling similar topics to our survey.
# DYMatch: Semi-Supervised Learning with Dynamic Pseudo Labeling and Feature Consistency
## 4 Experiments
### Results
#### 4.3.1 Experimental Results for Standard SSL Datasets
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**CIFAR-100**} \\ \hline
**Model** & **400 Labels** & **1000 Labels** & **2500 Labels** & **10,000 Labels** \\ \hline Mean Teacher [27] & - & 60.99 \(\pm\) 0.21 & 53.91 \(\pm\) 0.57 & 36.21 \(\pm\) 0.19 \\ Pseudo-Label [16] & - & 62.78 \(\pm\) 0.33 & 57.38 \(\pm\) 0.46 & 36.21 \(\pm\) 0.19 \\ MixMatch [7] & 67.61 \(\pm\) 1.32 & 54.73 \(\pm\) 0.24 & 39.94 \(\pm\) 0.37 & 28.31 \(\pm\) 0.33 \\ ReMixMatch [8] & 44.28 \(\pm\) 2.06 & 37.86 \(\pm\) 0.55 & 27.43 \(\pm\) 0.31 & 23.03 \(\pm\) 0.56 \\ UDA [18] & 59.28 \(\pm\) 0.88 & 45.20 \(\pm\) 0.71 & 33.13 \(\pm\) 0.22 & 24.50 \(\pm\) 0.25 \\ FixMatch [9] & 48.85 \(\pm\) 1.75 & 40.55 \(\pm\) 0.09 & 28.29 \(\pm\) 0.11 & 23.03 \(\pm\) 0.56 \\ FlexMatch [10] & 39.94 \(\pm\) 1.62 & 38.44 \(\pm\) 1.20 & 26.49 \(\pm\) 0.20 & 21.90 \(\pm\) 0.15 \\ Semi-Clustering [48] & 66.57 \(\pm\) 0.23 & 54.01 \(\pm\) 0.12 & 44.35 \(\pm\) 0.03 & 35.99 \(\pm\) 0.23 \\ DoubleMatch [23] & 42.61 \(\pm\) 1.15 & 42.05 \(\pm\) 0.34 & 27.47 \(\pm\) 0.19 & 21.69 \(\pm\) 0.26 \\ FeatMatch [22] & 48.70 \(\pm\) 0.42 & 41.85\(\pm\)0.23 & 30.43 \(\pm\) 0.04 & 26.83 \(\pm\) 0.04 \\ MPL [19] & 44.49 \(\pm\) 0.99 & 38.13 \(\pm\) 0.23 & **27.43 \(\pm\) 0.22** & 22.79 \(\pm\) 0.18 \\ SimMatch [47] & 48.82 \(\pm\) 1.51 & 37.12 \(\pm\) 0.52 & 32.54 \(\pm\) 0.27 & 26.42 \(\pm\) 0.18 \\ SoftMatch [11] & 49.64 \(\pm\) 1.46 & 36.42 \(\pm\) 0.34 & 33.05 \(\pm\) 0.05 & 27.26 \(\pm\) 0.03 \\ FreeMatch [12] & 49.24 \(\pm\) 2.16 & 37.15 \(\pm\) 0.44 & 32.79 \(\pm\) 0.21 & 27.17 \(\pm\) 0.11 \\ \hline DYMatch (ours) & **44.03 \(\pm\) 0.62** & **35.42 \(\pm\) 0.25** & 31.13 \(\pm\) 0.10 & **20.34 \(\pm\) 0.08** \\ \hline \end{tabular}
\end{table} 
Table 3: The Error Rates for CIFAR-100 with Four Different Numbers of Labelled Images. The best result is in bold.
# The Role of Delays in the Performance of Blasting
## 2 Materials and Methods  
This research was conducted at the Experimental Mine of the University of Sao Paulo, Brazil. The research constants were as follows:  
* The rock and rock mass: dolomitic marble, moderately fractured. * The explosive: emulsion explosive under the commercial name DINAPEX, by manufacturer DINCON (Estrela, Brasil). Density of 1.15 g/cm\({}^{3}\), nominal VOD of 5200 m/s. In cartridges, 64 mm in diameter. * The hole diameter: 76 mm. * The initiation system: detonating cord, 10 g/m. * The sequencing system: pyrotechnic connectors for det cord with a delay of 42 ms.
* The height of the benches: approximately 9 m; consequently, the depth of the holes was approximately 10 m, with a 0.9 m underdrilling design (taking into account field conditions that may affect the effective height of the benches and thus the length of the holes). * The charge per hole: 30 kg of cartridge emulsion. It must be noted that the scattering of the real-time delays around their nominal times was considered an unavoidable systematic experimental error present throughout all experiments. Therefore, analyses such as those detailed in [18] were not conducted. Figures 1 and 2 show two examples of blast plans with a typical distribution of delays, as used during the research. All the experimental blasts took place at the last level of the benches above the quarry floor; a panoramic view of this sector of the quarry is shown in Figure 3.  
All the blast tests took place at short distances one from one another; therefore, the experimental medium (rock mass) can be considered a constant. The characteristics of the intact rock are shown in Table 1, in which the ultimate compression strength (UCS), Schmidt hammer rebound, and corrected point load test value (Is50) are reported. The main sets of discontinuities present in the rock mass are shown in Figure 4.  
Figure 1: Example of a blast with a low number of delays compared to the number of blastholes. Each double triangle represents one delay element, resulting in 0.25 delays per hole. Not to scale. Figure 3: Image of the site where the analyzed blasts took place. Figure 2: Example of a blast with an average number of delays compared to the number of blastholes. Each double triangle represents one delay element, resulting in 0.41 delays per hole. Not to scale. Key performance indicators (KPIs) were collected and used to analyze the results. Said KPIs are reported in Table 2, where detailed descriptions are given. Similar KPIs were used by [13;14].
## 3 Experimental Results and Discussion
The tilt angle to the northwest is set to negative; because the water level increases, it will make the buoyancy of the cylindrical float on the two FBGs increase so the FBG reflection center wavelength will shift to a short wavelength band. That is, the sensor was tilted to the southeast or northwest direction; the buoyancy changes are the same for the two cylindrical floats. As a result, the fiber grating is simultaneously stretched or contracted. From the experimental results, in this azimuth angle, the sensitivities of FBG1 and FBG2 are 0.1 nm/\({}^{\circ}\)and 0.095 nm/\({}^{\circ}\), respectively. When the sensor is tilted toward the northeast or southwest, the measurement results of the center wavelength shift of two FBGs are shown in Figure 8. The tilt angle of the sensor towards the northeast is negative and the tilt angle towards the southwest is positive. Because the sensor is tilted toward the northeast or southwest, the water level of one of the floats will increase and the water level of the other float will drop. That is, the two fiber gratings in the container will change. One is stretched and the other shrinks. As a result, the reflected center wavelength of the contracted FBG will be shifted to shorter wavelength, whereas the reflected center wavelength of the stretched FBG will be  
Figure 6: Measured wavelength shift of FBG1 and FBG2 in the south–north direction when the tilt angle is increased from \(-5^{\circ}\) to \(5^{\circ}\). Figure 7: Measured wavelength shift of FBG1 and FBG2 in the southeast–northwest direction when the tilt angle is increased from \(-5^{\circ}\) to \(5^{\circ}\). shifted to longer wavelength. In this azimuth, the sensitivities of FBG1 and FBG2 are 0.093 nm/\({}^{\circ}\) and 0.088 nm/\({}^{\circ}\), respectively.