esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Decision Tree Analysis According to Winning and Losing  
3.3.1. Winning and Losing Factors Exploration Result, Excluding Total Goals-Related Variables  
The third study's result explores winning and losing factors using game records. Concerning the first study result of the exploration of winning and losing factors, this study explored the factors affecting winning and losing, excluding total goal-related variables occurring in the games (total goals, total PPG, and total SHG). The analysis result is shown in Figure 2.  
When looking at the result, the first factor classifying winning and losing in PIH games was goals in the first period. If the goal was 1 or more in the first period, the probability of winning was 82.8%, and if the goal was 1 or less in the first period, the probability of losing was 80.9%. If the goal was 1 or more in the first period and 3 or more of SOG in the second period, the probability of winning increased to 89.5%. When the goal was 1 or less in the first period and 2 or more in the second period, the probability of winning was 83.3%. Meanwhile, if the goal was 1 or less in the first period and 2 or less in the second period, the probability of losing was 87.1%. The classification accuracy of the result was 87.1%. As a result of the importance analysis among the winning and losing factors, excluding goal-related variables, scoring goals in the first period was the most crucial factor determining winning and losing, followed by SOG in the second period.  
Figure 2: Winning and losing factors exploration result, excluding total goal-related variables.  
3.3.2 Winning and Losing Factors Exploration Result, Excluding Goal-Related Variables in Each Period  
The winning and losing factor analysis result, excluding goal-related variables in each period (goal in the first, second, and third periods, PPG in the first, second, and third periods, and SHG in the first, second, and third periods) is shown in Figure 3. The probability of winning was 81.4% if SOG was 5 or more in the first period. Meanwhile, the probability of winning if SOC in the first period was 5 or less. The result's classification accuracy was 87.1%, and the standard error was 0.029. According to the analysis of winning and losing factors excluding goal-related variables in the total period, maintaining SOG above a certain level in the first period was a factor that increased the probability of winning, followed by SOG in the second period.
## 2 Experimental Method
### Characterization of Capsules  
The morphology of calcium alginate capsules was characterized using a Leica MZ 6 light microscope (Leica Microsystems B.V., Amsterdam, NetherlandsPhilips XL30 ESEM (Philips, Eindhoven, Netherlands) was employed to evaluate the microstructure inside the capsule. The ESEM was operated at 20 kV accelerating voltage and the magnification was 1000\(\times\). The scanning capsules were pre-coated with epoxy and polished until the cross sections were reached; in this way, the inner structure of capsules can be investigated by scanning the cross-sectional area using ESEM.  
The temperature dependent mass changes of the calcium alginate capsules were evaluated with NETZSCH STA 449 F3 Jupiter TGA system (NETZSCH, Wormerveer, Netherlands). The analysis was performed in an argon (Ar) atmosphere. As shown in Figure 2, a temperature control program was employed, and the mass changes were recorded with time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mix Constituent**} & \multicolumn{4}{c}{**Percentage by Weight**} \\ \cline{2-4}  & **Without Capsules** & **2.6\% Capsules** & **2.6\% Blank Capsules** \\ \hline Sand \textless{}0.5 mm & 34.8 & 34.8 & 34.8 \\ Filler (Wigro60k) & 32.6 & 32.6 & 32.6 \\ Bitumen (70/100) & 32.6 & 30.0 & 30.0 \\ Capsules & 0 & 2.6 & 0 \\ Blank Capsules & 0 & 0 & 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix composition of asphalt mortar.  
Figure 2: Thermogravimetric analysis (TGA) temperature control program.  
The mechanical resistance of the calcium alginate capsules was evaluated by compressive strength using a micro tensile strength testing machine (TSTM) developed by Microlab (Figure 3a) [16]. The tests were performed under 20 \({}^{\circ}\)C with displacement control at a speed of 0.01 mm/s. A video camera was used to record the capsule deformation during the compression process. Prior to the compressive test, capsules were cured for 15 min at 10 different temperatures (at 20 \({}^{\circ}\)C increments from \(-\)20 \({}^{\circ}\)C to 160 \({}^{\circ}\)C) to study the mechanical property of capsules considering the multi-temperature effect. At least five capsules were studied for each condition.  
In this research, the yield strength of the tested capsule (Figure 3b) was used as the compressive strength. Below this yield point (see Figure 3c) a capsule showed elastic behavior. When the stress went higher, permanent deformation was created and the capsule gradually ruptured, along with a leaking out of the rejuvenator.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 5 Simulation Results
### The Performance of the OFDM/16-QAM System  
Figure 10 illustrates the BER performance of OFDM/16-QAM system over Rayleigh fading channels with different Doppler spread. The OFDM/16-QAM system uses comb-type pilots insertion to estimate the CFR. Compared with the ICE method under \(f_{\rm d}=20\)Hz, the ICE method with Doppler spread of 40, 60, and 80 Hz has \(E_{\rm b}/N_{0}\) degradation about 2.0, 8.0, and 10.5 dB at the target BER of \(10^{-3}\). At the target BER of \(2\times 10^{-3}\), the LS method under Doppler spread of 20 Hz provides 1.6, 7.6, and 9.7 dB \(E_{\rm b}/N_{0}\) gains compared with the LS method under Doppler spread 40, 60, and 80 Hz.  
Figure 11 illustrates the NMSE performance of the OFDM/16-QAM system over Rayleigh fading channels with different Doppler spread. When the NMSE value is 0.05, the LS method with Doppler spread of 20 Hz outperforms the LS method with Doppler spread of 40, 60, and 80 Hz by about 1.0, 3.0, and 5.0 dB \(E_{\rm b}/N_{0}\) gains. Based on the simulation results of the OFDM/OQAM system and the OFDM/16-QAM system with comb-type pilot insertion, the OFDM/OQAM system enhances the accuracy of channel estimation significantly when it utilizes the NMSE value as the measurement.  
Figure 10: BER performance of OFDM/16-QAM system over Rayleigh fading channels with Doppler spread of 20, 40, 60, and 80 Hz.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 4 Simulation and Comparative Analysis
### Normal Scheme  
In this scheme, we simulated a normal transformer condition with a variation in the load. This scheme aimed to ensure the protection of the algorithm to generate correct results in a normal condition. The summary result (see Figure 24) showed that the hybrid detection algorithm was declared a normal state in a normal scheme. All sub algorithm and hybrid algorithm declared non internal fault or normal status like shown in Figure 25. Using the hybrid detection algorithm increases accuracy more than using a single algorithm (see Figure 26). The fuzzy algorithm generated the sub algorithm's highest accuracy, 98%, when distinguishing between a fault and normal condition (accuracy one). Accuracy two distinguished between the internal and non-internal fault. The highest accuracy of the sub algorithm occurred in the differential negative sequence and EPVA. Accuracy three distinguished between internal, external, and normal conditions, where the highest accuracy of the sub algorithm occurred on the fuzzy algorithm. The fuzzy algorithm was useful in detecting the fault and distinguishing between an internal or external fault. Additionally, using the fuzzy algorithm requires the experience to design the inference rule and membership function for input or output, where the design can be different for other types of transformers. However, a hybrid detection algorithm can overcome the misdetection of the fuzzy algorithm and increase fault detection accuracy.  
Figure 24: A summary of the results from the normal scheme.  
Figure 25: The hybrid algorithm results from the normal scheme.
## 2 Materials and Methods
### Characterization
#### 2.3.4 Antiviral Activity Tests  
The _Escherichia coli_ B, living bacteriophage host (Reference: 12-4300), and the coliphage T4r (Reference: 12-4335) from Carolina (Burlington, NC, USA) were used for the antiviral tests. The sample films of alginate, CNFs0.1% were cut into 1 cm diameter discs and sterilized by washing the films for 30 min in a beaker with 50 mL of 70% ethanol under magnetic stirring at 450 r.p.m. The discs were then dried at room temperature and exposed to ultraviolet light for 1 h per side. The films' antiviral activity was analyzed by a \"contact test\" of the bacteriophages with the discs in 96-well plates for 10 min, and 18 and 48 h. The dry film disc was placed in a precision balance and drops of phosphate buffer saline (PBS) solution were added with a volumetric micropipette until the film was saturated without excess liquid. The correct volume for the saturation point (44 \(\mu\)L of liquid per disc) was determined gravimetrically by the increase in weight. For the \"contact test\", 44 \(\mu\)L with approximately 700 plaque-forming units (PFU) was added to each disc, and the 24-well plate was subsequently sealed with plastic film to prevent evaporation during the assay. After each test, the wells were filled with 500 \(\mu\)L of PBS for washing, after which the discs were extracted using sterile forceps and stored with their corresponding liquid in 15 mL tubes and sonicated for 5 min. The tubes were then vortexed for 1 min and all the discs of the samples of contact at the different times were titrated. Six discs of each material were used to provide reproducible results at the three different contact times. The infectious phage particles were determined based on the \"double layer\" assay method [26]. Briefly, 500 \(\mu\)L phage aliquots extracted from the disc were mixed with 150 \(\mu\)L of bacterial culture and 3 mL of the \"top agar\" (0.75% \(w/w\) agar in LB medium) preheated in a bath incubator to 45 \({}^{\circ}\)C. The _E. coli_ B culture was grown overnight until reaching an optical density of 1.0 at 600 nm. The samples were gently shaken and poured over Petri dishes with the \"bottom agar\" (1.5% \(w/w\) agar in LB medium). The viable PFU viral particles were calculated from the plaques appearing on the soft agar surface stained with crystal violet. Five hundred microliters of phage aliquot with approximately 700 PFU was mixed with the bacteria and used as a control.
# Assessing Database Contribution via Distributed Tracing for Microservice Systems
## 4 Experiments
### The Impact of Business Scenarios' Importance Weight
#### 4.5.1 Sock-Shop  
Based on Table 9 and Equation (10), the total numbers of records operated on databases and corresponding contribution ranking in the Sock-Shop are shown in Table 15.  
When considering business scenarios' weight (Table 8), the database contributions are shown in Figure 8 (assuming that the four basic operations have the same importance, i.e., \(\alpha=\beta=\gamma=\delta=1\)). At this time, _Catalogue_, _Cart_, _User_, and _Order_ are ranked from high to low in the order of database contribution. The main reason is that although the _Order_ has the most records to be operated, the _Historical order query_ is the least important, and it only calls the _Order_. At the same time, although the _Catalogue_ has the lowest number of records to be operated, the _Purchasing socks_ has the highest importance, and the _Catalogue_ has a higher proportion of operational records in this business scenario.  
\begin{table}
\begin{tabular}{c c c} \hline
**Database** & \(\mathbf{s_{k}}\) & **Contribution Ranking** \\ \hline User & 8 & 3 \\ Order & 20 & 1 \\ Catalogue & 5 & 4 \\ Cart & 17 & 2 \\ \hline \end{tabular}
\end{table}
Table 15: \(s_{k}\) and contribution ranking of databases in Sock-Shop.  
\begin{table}
\begin{tabular}{c c c c} \hline
**System** & **Category** & **Algorithm** & **L2-Norm** \\ \hline \multirow{4}{*}{Sock-Shop} & \multirow{2}{*}{Traditional} & Interior point & 0.1548 \\  & & SQP & 0.1546 \\ \cline{2-4}  & & SA & 0.1451 \\ \cline{2-4}  & Heuristic & PSO & 0.1432 \\  & & GA & **0.1429** \\ \hline \multirow{4}{*}{TrainTicket} & \multirow{2}{*}{Traditional} & Interior point & 0.3902 \\  & & SQP & 0.3898 \\ \cline{2-4}  & & SA & 0.3617 \\ \cline{1-1} \cline{2-4}  & Heuristic & PSO & **0.3555** \\ \cline{1-1}  & & GA & 0.3558 \\ \hline \end{tabular}
\end{table}
Table 14: Optimization effects of different algorithms. The best results are in bold, and the second best ones are underlined. The hyperparameters in the heuristic algorithm are set to empirical values that work well.
# A Novel Wideband Splitter for a Four-Element Antenna Array
## 1 Introduction
The main contributions of the paper can be summarized as follows:  
* The design of a broadband antenna array power splitter based on a novel concept of a combination of symmetrical and asymmetrical transmission lines;
* The experimental verification of the proposed power splitter in realistic conditions, on a 5G signal in the NR (new radio) band n14 with four identical log-periodic (LP) ultra-high frequency (UHF) FRACARRO LP45 FMINI antennas;
* A proof of the design with excellent impedance characteristics, a large bandwidth, a low insertion loss and excellent phase accuracy for the sub-GHz band. The rest of the paper is organized as follows. Related work is summarized in Section 2, the concept of the proposed antenna splitter is presented in Section 3, the results achieved from simulation and measurements are presented and discussed in Sections 4, and 5 concludes the paper.
## 4 Positioning Algorithms
### Inner Triangle Centroid Algorithm Model  
The inner triangle centroid algorithm (ITCA) [22], as depicted in Figure 6, is a geometric-based approach to estimate a target's location by calculating the centroid of a triangle formed within the intersection region of three circles. Each circle is centered at a known anchor node--A(x\({}_{a}\),y\({}_{a}\)), B(x\({}_{b}\),y\({}_{b}\)), and C(x\({}_{c}\),y\({}_{c}\))--and has the respective radii r\({}_{a}\), r\({}_{b}\), and r\({}_{c}\) representing the measured distances from these anchors to the unknown target location T. The three circles intersect pairwise, forming six intersection points: D, D', E,', F, and F'. Among these, three points, D', E', and F', are selected to define an inner triangle that bounds the region where the actual target T is most likely to be located.  
The triangle \(\triangle\)D'E'F' forms the core of the algorithm. The underlying assumption is that the overlapping area bounded by this triangle represents the region of highest probability for the true position, due to the convergence of all three distance constraints. To determine the estimated position T(\(-x_{T},y_{T}\)) the centroid of the inner triangle is calculated. The centroid is the average of the triangle's vertices and is mathematically given by  
\[x_{T}=\frac{\mathrm{x}_{D^{\prime}}+\mathrm{x}_{E^{\prime}}+\mathrm{x}_{E^{ \prime}}}{3} \tag{10}\]  
\[y_{T}=\frac{y_{D^{\prime}}+y_{E^{\prime}}+y_{F^{\prime}}}{3} \tag{11}\]  
Figure 5: Flowchart of triangle centroid algorithm model.
## 3 Experimental Results
### Results of Dynamic Uniaxial Compression Tests on Hard Ice
#### 3.1.2 Fracture Mode of Hard Ice  
A high-speed camera is employed to capture the failure processes of ice samples. Fracture processes of hard ice under uniaxial compression tests at moderate strain rates are shown in Figure 9. Images corresponding to five load levels--unloaded, 1/3 peak load, 2/3 peak load, peak load, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Uniaxial Compressive Strength (MPa)**} \\ \cline{3-6}
**Loading Rate** & **Serial** & \multicolumn{2}{c}{\(-5\) °C} & \multicolumn{2}{c}{\(-10\) °C} \\ \cline{3-6}
**(m-s\({}^{-1}\))** & **Number** & \multicolumn{2}{c}{**Horizontal**} & **Vertical** & **Horizontal** & **Vertical** \\  & & **Loading** & **Loading** & **Loading** & **Loading** \\ \hline  & 1 & 2.224 & 3.159 & 6.391 & 5.520 \\  & 2 & 5.187 & 3.271 & 5.462 & 7.143 \\
0.1 & 3 & 2.795 & 4.642 & 5.  
post peak load--are selected to illustrate the fracture processes. In the employed strain-rate range, fracture processes are not sensitive to strain-rate, and no clear distinguishing of fracture processes exists between horizontal loading and vertical loading. Ice samples fail in a mode which possesses features of splitting failure mode and features of crushing failure mode at the same time. Firstly, a few initial cracks emerge at the top end and bottom end of the sample. Then these initial cracks quickly propagate vertically or approximately vertically, leading to formation of full-height vertical cracks. Meanwhile, the crack density is low. As the sample is compressed further, the crack density within the sample increases and then several full-height vertical cracks form quickly. As more and more cracks emerge, a majority of intersections and link-ups of cracks form and the widths of full-height vertical cracks increase. At this moment, peak load is approximately reached. Horizontal expansion is observed with further compression, and then increase of the cracks' width, resulting from slipping among cracks, is the main course, accompanied with slow increase of crack density. The sample fails, in appearance, in the splitting failure mode and is finally separated into lots of small fragments. It seems that full-height vertical crack formation is a little easier when the sample is loaded in the horizontal direction than when it is loaded in the vertical direction. When we compare the failure processes shown in Figure 9a and that shown in Figure 9c, we find the former shows the above-mentioned characteristics more obviously. Nevertheless, there exist no significant differences in fracture processes between horizontal loading and vertical loading, according to our careful observations on the time-sequenced failure images of tests in different loading directions.
## 4 Problem Statement and Formulation
### Limitation of MILP-Based Approach  
In fact, the problem that is modeled in this section can be divided to three parts as follows:  
* Identifying a finite set of sojourn locations from a continuous trajectory. Thus, there should be a function or methodology to do this procedure. \[f:\Gamma\rightarrow\Psi\text{~{}~{}where}\] (24) \[\Gamma\rightarrow\infty\] \[\Psi\to m\]
* Selecting a suitable set of RPs for each sojourn location as described in Expressions (4) and (5).
* And finally assigning the rest of sensor-nodes to the related RPs as illustrated in Expression (6).  
Obviously, the general solution of our problem consists of solving three set assignment problems which the sets are related to each other. The set assignment is in the category of combinatorial optimization problem and its NP-hardness is proved in [49; 50]. Apparently, for small-scale networks a MILP solver can obtain the optimal solution. By increasing the scale of the network, the MILP solver cannot be used to solve the problem efficiently. Therefore, it is essential to design a polynomial-time heuristic to achieve optimal or near-optimal solution.  
In addition, there is another problem which is specific to most of the WSNs. The MILP formulation assumes that all of the information about the mobile-sink and the sensor-nodes are known. Therefore, the set assignment problem can be treated by focusing on finding a solution with all of the known parameters. However, practically, the mobile-sink is not familiar with all of the sensor-nodes. And the sensor-nodes do not know the general distribution map in the network. Thus, our problem becomes more difficult and our proposed heuristic should address this issue by being distributed and independent from complete information about sensor-nodes.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions
### Throughput Results
#### 6.2.2 Uniform Node Distribution Results  
Once again, the uniformly distributed network demonstrates resilience. Unlike the random network, it shows minimal to no observable impact on throughput under the dropping attack. The significant decrease in throughput for the random network under attack can be attributed to the dropping attack's nature. By discarding packets, this attack disrupts successful data transmission, leading to a reduction in the overall network throughput. The uniform network, on the other hand, appears to be more adept at routing around dropped packets, potentially due to its inherent redundancy in node placement.  
Both network distributions experience a significant decline in throughput under the combined attack, which discards packets and disrupts routing functionalities. The random network's throughput plummets from a baseline average of 21.21 megabits per second to a new value of 12.66, representing a 40% drop under the combined attack. Similarly, the uniform network, despite its resilience to individual attacks, suffers a substantial decrease  
Figure 11: Throughput results’ uniform distribution for all attacks.  
Figure 10: Throughput results’ random distribution for all attacks.  
in throughput, dropping from its baseline average of 36.41 megabits per second to a new value of 19.77, a 46% drop in throughput.
## 3 Results
### Detection of Free Radical Species  
In this study, pure water and filtered pure oxygen served as the raw materials for synthesizing the ozone-induced free radical solution. The gas-liquid mixing and sampling processes were completely isolated from the external atmosphere, thereby preventing impurities in water and air (notably, the formation of nitrogen oxides after nitrogen electrolysis) from interfering with the synthesis of the free radical solution.  
The interaction between gaseous free radicals and water is illustrated by the following reaction equations:  
\[\begin{gathered}\mathcal{O}_{3}+OH^{-}\rightarrow\cdot{}H \mathcal{O}_{2}+\cdot{\mathcal{O}_{2}}^{-}\\ H_{2}\mathcal{O}_{2}\leftrightarrow{}H\mathcal{O}_{2}{}^{-}+H^{+}\\ H\mathcal{O}_{2}{}^{-}+\mathcal{O}_{3}\rightarrow\cdot{}\cdot{} \cdot{}\cdot{}\mathcal{O}_{2}{}^{-}+\mathcal{O}_{3}{}^{-}+\mathcal{O}_{2}\\ \cdot{}\mathcal{O}_{3}{}^{-}+H^{+}\leftrightarrow\cdot{}H\mathcal{O}_{3} \\ \cdot{}H\mathcal{O}_{4}\rightarrow\cdot{}H\mathcal{O}_{2}+\mathcal{O}_{2} \\ \cdot{}H\mathcal{O}_{3}\rightarrow\cdot{}OH+\mathcal{O}_{2} \\ {}\mathcal{O}_{2}{}^{+}+H_{2}\mathcal{O}_{(\text{aq})}+M \rightarrow{}\mathcal{O}_{2}{}^{+}(H_{2}\mathcal{O})+M\\ {}\mathcal{O}_{2}{}^{+}(H_{2}\mathcal{O})+H_{2}\mathcal{O}_{(\text{aq})} \to H_{3}\mathcal{O}{}^{+}+\mathcal{O}_{2}+\cdot{}OH\\ \mathcal{O}_{3}+H_{2}\mathcal{O}\rightarrow\cdot{}OH+\mathcal{O}_{2} \\ \mathcal{O}+H_{2}\mathcal{O}\to 2\cdot{}OH\\ \cdot{}H\mathcal{O}_{2}\leftrightarrow\cdot{}\mathcal{O}_{2}{}^{-}+H^{+} \end{gathered}\]  
(M represents an unstable intermediate product). These aforementioned reactions lead to the production of free radicals including \(\cdot{}\mathcal{O}_{2}{}^{+}\), \(\cdot{}OH\), \(\mathcal{O}_{3}\), and \(\cdot{}\mathcal{O}_{2}\) in the solution. Of these, \(\cdot{}OH\), \(\cdot{}\mathcal{O}_{2}{}^{-}\) and \({}^{1}\mathcal{O}_{2}\) are pivotal in the oxidative degradation process owing to their high chemical reactivity, significantly influencing the solution's overall performance [38; 39; 40]. Ozone in the solution continuously transforms into other free radicals, remaining in an unstable state with a relatively minor impact on the solution's oxidation performance. Consequently, this study primarily investigates the presence of \(\cdot{}OH\), \(\cdot{}\mathcal{O}_{2}{}^{-}\), and \({}^{1}\mathcal{O}_{2}\) in the solution.  
Figure 10: Influence of liquid volume on the stability of the free radical solution concentration.