esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Developed Model  
The BERT methodology was adopted to develop this study's ABSA model. In this process, the BERT Tokenizer was employed with several vital functions, including separation [SEP], classification [CLS], padding [PAD], and handling unknown words [UNK]. These tokenization steps ensured effective processing of the input text, with each component accurately identified. The [CLS] function served as a marker for classification, while [SEP] acted as a separator between text segments, instrumental in analyzing sentences containing multiple aspects or entities. Padding [PAD] maintained consistent input length, and the [UNK] symbol was applied to handle words not recognized by the model's vocabulary, thereby minimizing the risk of losing critical information during analysis [12].  
Following the tokenization process, additional encoding and attention masks were applied to improve the model's understanding of the text [15]. Encoding enhanced the model's ability to recognize each word's context, which is particularly valuable in ABSA, where specific aspects and sentiments in student reviews need detailed analysis. The attention mask ensured that each relevant word in a sentence received adequate focus, allowing BERT to concentrate on crucial information and disregard irrelevant elements or padding. These steps aimed to enable BERT to capture the relationships between words in the text more effectively, resulting in a more accurate and contextually nuanced ABSA [16].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentiment** & **Number of Reviews** & **Percentage (\%)** \\ \hline Positive & 1000 & 40\% \\ \hline Negative & 1000 & 40\% \\ \hline Neutral & 400 & 20\% \\ \hline Total & 2000 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Sentiment distribution in test dataset (20% of 10,000 reviews).  
This study utilized the pre-trained IndoBERT model due to its superior performance in Indonesian language tasks. IndoBERT has been trained on a comprehensive Indonesian dataset, including terms, phrases, and colloquial expressions, providing a robust foundation for analyzing nuanced feedback in student reviews. Its ability to handle complex Indonesian language structures ensured a high-quality baseline for ABSA tasks [9]. Moreover, leveraging IndoBERT facilitated savings in time and resources as the model already possesses a deep understanding of Indonesian grammar and syntax, reducing the need for extensive retraining [22].
## 3 Gray Value Compression
### Lz-Family Coding
#### 3.4.1 LZ77  
The LZ77 algorithm is also known as the _sliding window compression algorithm_. This encoding algorithm holds \(n-L_{S}\) symbols (\(L_{s}\) is the maximum string length to be compressed, and \(n\) is the size of the input buffer) to find a substring within \(n-L_{S}\) that corresponds to the prefix of the input stream to be encoded. The found prefix is encoded with the position of the substring, its length, and the first symbol of the prefix. The length of the prefix can vary from 0 to \(n-L_{S}\), and the greater it is, the greater the coding efficiency.
## 4 Performance Prediction
### Scene Length Dependency  
A second simulation was performed to estimate the influence of the scene length in azimuth on the performance of the pointing estimation. Again, the mean operator has been used to derive the \(\gamma\)-profile. The simulation parameters are given in Table 2 and the results are shown in Figure 2. As expected, the standard deviation decreases with scene length. For the SNR = 20 dB case (\(\times\)- and \(\square\)-sign) the standard deviation is already much lower for 1000 azimuth lines than for a SNR of 0 dB (+- and \(\blacktriangle\)-sign) as analyzed in the last section (c.f. Figure 1). The mean pointing error saturates at 0.5 mdeg very quickly with the chosen simulation parameters. The standard deviation further decreased asymptotically to about 0.1 mdeg.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Scene length in azimuth & 1000, \(\dots\), 10,000 lines (\(\approx\)4.7, \(\dots\), 47 km) \\ Scene length variation step size & 1000 lines \\ Scene length in elevation & 7728 samples \\ No. of Monte Carlo impl. & 250 \\ SNR & 0 dB, 20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used for the Monte Carlo simulation studying the dependency of the pointing estimation for different scene lengths.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Scene length in azimuth & 12,000 lines (\(\approx\)57 km) \\ Scene length in elevation & 7728 samples \\ No. of Monte Carlo impl. & 100 \\ SNR range & 0, \(\dots\), 30 dB \\ SNR resolution & 1 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for the Monte Carlo simulation analyzing the influence of noise (expressed as SNR) on the pointing estimation.  
Figure 2: Performance estimation for the antenna retrieval algorithm w.r.t. azimuth size (number of azimuth lines) using simulated scenes with an SNR of 0 dB and 20 dB.  
It can be concluded that the SNR of the scene has an effect on the performance of the proposed technique, but can be controlled and partly compensated for by the length of the scene in azimuth. The actually required scene length and SNR strongly depend on the underlying scene and its variability.  
A detailed treatment of range and azimuth ambiguities which can emerge due to improper timing and which are neglected in Equation (2) is out of scope of this paper. The notch beam is, in general, more prone to range ambiguities as the maximum gain of a notch pattern lies on the edges of the illuminated scene while there is low gain in the central part. This stays in contrast to a nominal boresight beam and has to be considered in the timing of the SAR instrument, namely the PRF (pulse repetition frequency) and the range start time to limit range ambiguities. The impact of ambiguities on the antenna pointing estimation is found to be low, as long as the emergence of ambiguities is well controlled by adapting the timing and the length of the scene in azimuth is sufficient.
## 6 Conclusions
\begin{tabular}{l l} CGS & Central Geological Survey \\ COSI-Corr & Co-registration of Optically Sensed Images and Correlation \\ CSC & Coulomb Stress Change \\ CWB & Central Weather Bureau \\ DEM & Digital Elevation Model \\ ENVI & Environment for Visualizing Images \\ ERS & European Remote-Sensing Satellite \\ ESA & European Space Agency \\ gABIC & Generalized Akaike's Bayesian Information Criterion \\ GCP & Ground Control Point \\ GPS & Global Positioning System \\ IDL & Interactive Data Language \\ IESAS & Institute of Earth Sciences, Academia Sinica \\ InSAR & Interferometric Synthetic Aperture Radar \\ MOEA & Ministry of Economic Affairs \\ MOI & Ministry of Interior \\ M-PSO & Multi-peak Particle Swarm Optimization \\ PSOKINV & Particle Swarm Optimization and Okada Inversion package \\ RMS & Root Mean Square \\ SNAP & Sentinel Application Platform \\ SNAPHU & Statistical-cost, Network-flow Algorithm for Phase Unwrapping \\ SNR & Signal-To-Noise Ratio \\ SPOT & Satellite Pour l'Observation de la Terre \\ SRTM & Shuttle Radar Topography Mission \\ TWD67 & Transverse Mercator Two-degree Grid 1967 \\ USGS & United States Geological Survey \\ WGS84 & World Geodetic System 1984 \\ \end{tabular}
## 4 Results
### IESM Responses
#### 4.2.1 Scenario 1  
The collected data were statistically analysed by two scenarios with a paired sample \(t\)-test. In the Slower car ahead scenario there were no significant differences between the actions overtaking the car ahead (\(M\) = 3.66, \(SD\) = 1.43) and slowing down to follow the car ahead (\(M\) = 3.11, \(SD\) = 1.41), \(t\) (31) = 1.17, p \(<\) 0.25, even though overtaking the car ahead scored higher than slowing down. However, driver satisfaction (IESM2) (\(M\) = 3.48, \(SD\) = 0.61), \(t\) (31) = \(-\)2.49, \(p\) \(<\) 0.018 was significantly higher than driver expectation (IESM1) (\(M\) = 3.30, \(SD\) = 0.51) using a paired sample \(t\)-test, which means that satisfaction about AV action is not influenced by drivers' expectations in a Slower car ahead scenario. They prefer the AV to overtake the slower car ahead even if they do not usually perform that action in their own driving.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input** & & **Mental** & **Physical** & **Temporal** & **Performance** & **Effort** & **Frustration** \\ \cline{2-8}
**Interaction** & & **Demand** & **Demand** & **Demand** & **Performance** & **Effort** & **Frustration** \\ \hline \multirow{2}{*}{Gaze-Head} & Mean & 10.68 & 5.2 & 7.83 & 10.2 & 7.93 & 7.89 \\ \cline{2-8}  & SD & (7.83) & (5.98) & (6.46) & (9.85) & (6.59) & (10.51) \\ \hline \multirow{2}{*}{Touch} & Mean & 6.47 & 9.1 & 8.97 & 12.22 & 6.89 & 2.95 \\ \cline{2-8}  & SD & (6.16) & (8.47) & (7.5) & (12.37) & (6.58) & (4.89) \\ \hline \multirow{2}{*}{Voice} & Mean & 7.43 & 5.2 & 7.7 & 11.43 & 6.37 & 5.89 \\ \cline{2-8}  & SD & (5.91) & (5.95) & (5.77) & (10.69) & (4.59) & (8.23) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mean (M) and standard deviation (SD) values for the six dimensions of NASA-TLX for the three input interactions.  
Figure 10: The six dimensions of NASA-TLX for the three input interactions.
## 3 Results and Discussion
### Morphology Analysis
#### 3.2.3 Thermal Properties  
Figure 6 shows the TGA curves of synthesized MIP and NIP. The TGA was performed to study the thermal stability, uniformity, and binding affinity effects of the polymer materials in case that they are subjected to high temperatures. The analysis was run over a 0-500 \({}^{\circ}\)C temperature range. Two disintegration peaks are observed at slightly different temperatures for both the MIP and NIP, suggesting the collapse of the material at both points. The initial backbone decomposition of both these polymers is observed at 275 \({}^{\circ}\)C and 278 \({}^{\circ}\)C, respectively, as seen in Figure 6a,b, the backbone of both the washed MIP and NIP disintegrated, resulting in a considerable weight loss of nearly 90%. A similar polymer backbone collapse was observed at comparable temperatures of 280 \({}^{\circ}\)C in a study by [PERSON] et al. [13] when they synthesized the polymers under the same circumstances utilizing the bulk polymerization process. The difference between the curves produced for washed NIP and washed MIP is that the MIP has a second decomposition peak between 371 and 444 \({}^{\circ}\)C that is below 15 mass % (compared to the same peak in the NIP that is over 30%). The discrepancy might be due to structural differences imposed during the template removal process of the MIP; there might be residual template molecules or template-related impurities in the MIP. These remaining molecules may interact with the polymer, affecting its thermal stability and resulting in the observed disintegration phases. During the imprinting process, the binding of template molecules to functional monomers may result in chemical bonds or interactions that affect the thermal stability of the polymer. These interactions might be missing from the NIP. It is possible that the molecular imprinting process introduces structural heterogeneity within the MIP, leading to varying decomposition behaviors for different portions of the polymer. Thermal breakdown for both polymers is observed at 445 \({}^{\circ}\)C. These findings are consistent with those found in the literature [11; 13]. As a result, the thermal stability of these polymers was regarded as adequate because their applications were made at ambient temperature, which revealed slight irregularities in both materials but showed uniformity in the synthesis process of these materials.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Polymer**} & **Surface Area** & **Total Pore Volume** & **Average Pore Diameter** & **Total Area in Pores** & **Average Particle Size** \\  & **(m\({}^{2}\)g\({}^{-1}\))** & **(cm\({}^{3}\)/g)** & **(Å)** & **(m\({}^{2}\)g\({}^{-1}\))** & **(nm)** \\ \hline MIP & 425 & 0.345 & 44.2 & 258 & 14.1 \\ NIP & 347 & 0.393 & 45.3 & 201 & 17.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BET polymer analysis.  
Figure 6: Thermal decomposition of (**a**) MIP and (**b**) NIP by TGA.
## 4 Refinement of the Parameters of the Interstitialcy Theory  
As reviewed earlier [15; 16] and discussed in the present work, the IT provides a good description of different aspects of MGs relaxation behavior. At the same time, some model parameters of the IT were introduced in a phenomenological way. It is therefore desirable to clarify their physical meaning and relationship with material parameters. In the initial model, an interstitial defect was considered by [PERSON] as an elastic string. At the same time, a split interstitial can be treated as an elastic dipole [47; 50]. The corresponding \"dipole\" approach is based on the expansion of the energy into a series in powers of the elastic strain created by the dipoles [50]. It was found that that the Granato and \"dipole\" approaches give practically identical expressions for the elastic energy and shear modulus [47]. A comparison of these approaches leads to an expression for the parameter \(\alpha\) introduced in the original version of the IT (see Equations (1) and (2)) as \(\alpha=\int(\varepsilon_{ij}\varepsilon_{ji}-\frac{1}{3}\varepsilon_{ii}^{2}) dV/\Omega\), where \(\varepsilon_{ij}\) is the elastic strain field created by the interstitial. Thus, the parameter \(\alpha\) characterizes the \"strength\" of the defect. The shear susceptibility within the \"dipole\" approach was calculated as \(\beta=-\gamma_{4}\Omega_{t}/\mu\), where \(\mu\) is the shear modulus of the maternal crystal and \(\Omega_{t}=1.38\) is a parameter characterizing the elastic anisotropy of the interstitial [50]. This estimate is about two times smaller than that given by [PERSON]. It should be noted that the \"dipole\" approach was found very useful upon further IT development, especially in the part that accounts for the effect of the concentration of interstitials on their interaction (see Section 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Metallic Glass** & \(\beta\) \\ \hline
1 & La\({}_{55}\)Al\({}_{25}\)Co\({}_{20}\) & 21 \\
2 & Zr\({}_{46}\)Cu\({}_{46}\)Al\({}_{8}\) & 19 \\
3 & Zr\({}_{46}\)Cu\({}_{45}\)Al\({}_{7}\)T\({}_{2}\) & 21 \\
4 & Zr\({}_{56}\)Co\({}_{28}\)Al\({}_{16}\) & 17 \\
5 & Zr\({}_{65}\)Al\({}_{10}\)Ni\({}_{10}\)Cu\({}_{15}\) & 22 \\
6 & Pd\({}_{40}\)Ni\({}_{40}\)P\({}_{20}\) & 15 \\
7 & Pd\({}_{41.25}\)Cu\({}_{41.25}\)P\({}_{17.5}\) & 21 \\
8 & Pd\({}_{40}\)Cu\({}_{30}\)Ni\({}_{10}\)P\({}_{20}\) & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Determination of the shear susceptiblity \(\beta\) on the basis of calorimetric and shear modulus data taken on MGs in the initial and relaxed state using Equation (6) [49]. With permission from Elsevier, 2019.  
Figure 4: Temperature dependences of the quantities \(\Delta W\) and \(\partial\Delta G/\partial T\) entering Equation (6) derived from calorimetric and shear modulus measurements [49]. The data correspond to structural relaxation below the glass transition. With permission from Elsevier, 2019.
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
## 1 Introduction
An amount of 16 circularly connected VTSAs with wide fractional BW (159.97%) are proposed in [26] to provide omnidirectional radiation for wireless communication systems. With the Internet of Things' (IoT) rapid expansion and the connectivity of all devices, the primary concern in designing a modern wireless communication system is how to make it more reliable, compatible with recent application requirements, and suitable for customer needs. To that end, straightforward integration of equipment such as power dividers with antennas will aid in decreasing the overall size and power budget of the system. For this purpose, as a novelty of this work, at the UWB frequency band (3.1-10.6 GHz), the proposed compact UWB unequal split NTL WPD [27] is exploited as a feeding network for the compact UWB VTSA [1] and novel Vivaldi nonuniform slot antenna (VNSA) [28] to design, fabricate, and test simple compact Vivaldi arrays. Array 1 is achieved by integrating the designed compact UWB VTSA [1] and 2:1 unequal split NTL WPD [27]. Then, 52% compactness improvement with 6.76% BW enhancement is achieved in Array 2 by integrating the compact UWB VNSA and 3:1 unequal split WPD designed in [28] and [27], respectively. This work is regarded as proof of concept for the effectiveness of applying the NTL [27, 29] and VNSPA [28] theories for compactness while maintaining or improving performance. Due to the good characteristics of the proposed arrays in terms of ease of integration, small size, wide BW, high gain, and stable directive radiation patterns, they are good candidates for breast cancer detection (BCD) applications. To prove that, a simple BCD scenario based on CST simulation is included in this work. The major contributions of this work are arranged in Figure 1 below. In addition to this section, Section 2 describes the design of the proposed arrays. Section 3 discusses the result. A simple BCD simulation scenario using the proposed arrays is addressed in Section 4. Finally, Section 5 outlines the research conclusions.
## 3 Test Results and Analysis
### Microstructural Characteristics of Rubberized Concrete After Freeze-Thaw Cycles  
Figure 13 shows the microstructure after different numbers of freezing and thawing cycles. From the figure, it can be seen that the internal structure of ordinary concrete (group A) is relatively simple. There are tiny cracks before the freezing and thawing cycles, but the overall densification is better. After 125 freezing and thawing cycles, the specimen has more internal cracks and more holes, and densification deteriorates. This shows that the concrete is damaged internally after freeze-thaw cycles, the number of cracks and holes increases, and the concrete becomes loose. This is due to the damage caused by the expansion of internal water icing after the specimen is frozen, and the continuous freeze-thaw cycle intensifies the internal damage, resulting in more and larger cracks and a looser texture.  
Figure 13 also shows the microanalysis diagrams of rubber concrete with a 20% admixture of 10 mesh (group B), 20 mesh (group C) and 30 mesh (group D) before freeze-thaw and after 125 freeze-thaw cycles, respectively. It can be seen from the micrographs that the rubber is mainly in the form of flaky and irregular distribution, and it does not participate in the hydration reaction in the concrete without the generation of new substances. Before freeze-thaw cycles, there are more cracks and holes inside the specimen. This is due, on the one hand, to there being less formation of C-S-H gel hydration product between rubber and cement, resulting in weak joints and easy to produce pores, and on the other hand, to the rubber mixing introducing more gas and playing the role of air-entraining agent [44; 45]. After 125 freezing and thawing cycles, the internal cracks of the specimen increase significantly, but the increase in internal pores relative to ordinary concrete is less, and the change in compactness is relatively small, which indicates that the addition of rubber can improve the frost resistance performance of concrete to a certain extent, thus reducing the damage caused by the expansion of the internal water icing of the specimen.  
The specimens were damaged to different degrees after freezing and thawing, and the higher the number of freezing and thawing cycles, the more internally damaged the specimens were. Compared with ordinary concrete, rubber concrete after the same number of freezing and thawing cycles has less internal damage, indicating that the mixing of rubber can improve the frost resistance of concrete.  
Figure 13: Microstructure of the specimen after different numbers of freeze–thaw cycles.
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 3 Results and Discussion  
### Homogeneity Analysis of Air Temperature and Precipitation Data  
The results of the Pettitt test and the SNHT applied separately to detect inhomogeneities in annual, seasonal and monthly minimum and maximum air temperature and precipitation time series for the whole 1982-2020 period are summarized in Table 1. The inhomogeneity was assessed for a significance level of 95% and the datum of the breakpoints was determined. The Pettitt test showed that maximum air temperature time series were inhomogeneous at annual, seasonal and monthly time scales. However, for January, February and December, which refer to the winter season, the change points were not statistically significant. The significant breaks were upward shifts and generally observed between 1998 and 2002 and in 2006. Relatively similar results were registered by the SNHT with inhomogeneities structure recorded between 1998 and 2007. Indeed, during the 1994-2011 period, the Nabeul region, as one of the competitive triangle areas surrounding the capital (Tunis), has seen the emergence of a metropolization, mainly of an industrial origin [45]. This industrial development has resulted in an increase in urban areas in the region, which would contribute to global warming. For the annual and seasonal minimum air temperature datasets, no significant shifts were detected by either the Pettitt or SNHT methods. In the time series of Spring Tmin, a significant breakpoint was detected based on the SNHT. The breakpoint was an upward change and occurred in 1987. Regarding monthly Tmin, a significant break point was found in April using both tests. In addition, starting from 1987, a significant upward change point in March was detected based on the SNHT. The homogeneity results are influenced by several factors, among them, the adopted homogeneity test [35]. As described before, contrarily to the Pettitt method, the SNHT is appropriate for detecting abrupt changes at the beginning and at the end of the records [33]. According to the Pettitt test and the SNHT, the annual and seasonal time series of precipitation are homogenous (Figure 2).
## 4 Autopilot System
### PD Controller  
In Figure 5, the conventional PD control system is generally introduced in the following equations including two control gains, i.e., proportional gain (\(K_{P}\)) and derivative gain (\(K_{D}\)).  
\[\delta_{V}(t)=K_{P1}(\Psi(t-1)-\Psi_{d}(t-1))+K_{D1}\dot{\Psi}(t-1) \tag{24}\]  
\[\delta_{H}(t)=K_{P2}(\theta(t-1)-\theta_{d}(t-1))+K_{D2}\dot{\theta}(t-1) \tag{25}\]  
where \(\delta_{V}(t)\) and \(\delta_{H}(t)\) denote control outputs of the rudder plane angle and the stern plane angle, and they can also be recognised as \(\delta_{r}\) and \(\delta_{s}\), respectively. \(K_{P1}\) and \(K_{D1}\) are the yaw and yaw rate gains by the rudder plane, whereas \(K_{P2}\) and \(K_{D2}\) are the pitch and pitch rate gains by the stern plane. \(\dot{\Psi}(t-1)\) and \(\dot{\theta}(t-1)\) imply the yaw rate and pitch rate, respectively.  
Figure 4: The schematic diagrams of the line-of-sight (LOS) guidance algorithm in (**a**) the horizontal plane and (**b**) the vertical plane, respectively.  
Figure 5: The flow chart of the autopilot system for the submarine free-running model (SFRM) including the LOS guidance algorithm, the PD controller, the PID tuner, and the 6-DOF manoeuvring mathematical model.  
In the control system, the trial-and-error method has been adopted to determine an initial set of control tuning parameters. For an extensive variety of applied methods, it might be the most familiar one. Sometimes, the trial-and-error method does not offer fair tuning and tends to provide an evident overshoot. Hence, it usually needs returning before application. In order to tune the control constraints in demand for an optimal performance, the PID tuner in MATLAB/Simulink [33] has been suggested to achieve the design objectives by means of a balance between robustness and performance. Some important characteristics parameters that need to be considered in the PID tuner are the rise time, settling time, overshoot, and peak, as shown in Figure 6.