esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analytical Solution to Two-Region Flow Induced by Constant-Head Pumping in an Unconfined Aquifer
## 4 Verification
In order to verify the reliability of Equation (15a,b), the \(R\)-\(t\) curves based on the parameters in Case A by both analytical and numerical solutions are simulated, as shown in Figure 3. The results suggest that the fitting degree by different solutions is relatively high. Hence, the \(R\) value calculated by two implicit functions of Equation (15a,b) should be considered to be accurate. Otherwise, \(R\) value is found to be decreased with time, indicating that the influence of non-Darcian in the two-region flow is weakened as pumping continues. It is explained that after the pumping starts, the hydraulic head of the aquifer gradually declined and is approached to be \(h_{w}\). Its further results in a reduction in hydraulic gradient and specific discharge. The smaller specific discharge results in a slower flow rate and smaller Reynolds number, which ultimately leads to the degeneration of region N [28]. When the pumping time is long enough, region N can disappear completely, and the two-region flow can be transformed into pure Darcian flow. Afterward, the constant-head pumping test in Wisconsin, USA, reported by [PERSON] et al. [39], is used to demonstrate the engineering application of the proposed solution. The unconfined aquifer with a thickness of 2.5 m is composited of shallow weathered glacial till of low clay content and high sand content [39]. The pumping well with an effective radius of 0.051 m is fully penetrating the unconfined aquifer, of which the bottom is filled by pea gravel. During the 24-h pumping period, the hydraulic head inside the constant-head well is 1.5 m [34]. The two drawdown data series have been collected in observation wells with a radial distance of 0.87 m and 3.62 m, respectively. Based on the work from [PERSON] and [PERSON] [3], the ranges of gravity storage and hydraulic conductivity in the horizontal direction can be considered as 0.014-0.042 and 0.241-0.362 m/day, respectively. Therefore, after careful investigation, the best-fitting results based on Equation (12a,b) can be obtained when \(S_{1}=S_{2}=0.031\), \(K_{1}=0.285\) m/day, \(K_{2}=0.32\) m/day and \(n_{1}=1.3\). The comparison curves shown in Figure 4 matched well with each other, suggesting the acceptability of the use of the proposed solution in practice. However, similar to the results in Figure 2, the simulation curves by the analytical solution rarely deviate from the measured data at the beginning of pumping, and such deviation is gradually disappeared with time. It  
Figure 3: Comparison of the interface distance—time curve of analytical and numerical solutions. further verifies that the proposed solution is applicable even at a 40% relative drawdown (\(\frac{b-h_{w}}{b}=40\%\)) inside the pumping well in the aquifer investigated.
## 3 Results  
### PGA Module Fabrication  
Laser power for fabrication was carefully optimized to achieve a high filling factor. According to Table 1, with an increase of laser power, porosity showed an increasing trend. However, when the laser power exceeded 7.29 W, excessive energy caused the burn-through and burn-out phenomenon, which made the canal structure in the scaffold hard to maintain, and even collapse. Therefore, a power of 7.29 W was selected. The fabricated rectangular layers are shown in Figure 2a.  
Under the selected laser power, PGA layers ranging from 200 to 500 \(\upmu\)m were successfully fabricated. However, current modules were designed to have transverse pores on the side wall, and it was difficult to complete this structure with a 200 \(\upmu\)m thick side wall, which proved too fragile. Furthermore, due to the in vivo oxygen diffusion limitation of 200 \(\upmu\)m and flow supply provided from both the outer and inner wall surfaces, the wall of a hollow canal structure had a limiting thickness of 400 \(\upmu\)m. Since the diameter of the laser beam caused the actual thin wall structure to be thicker than the designed size, a thickness of 300 \(\upmu\)m was selected for module fabrication.  
Three types of fabricated PGA modules are shown in Figure 2b: solid cylinder (cylindrical module); cylinder with hollow through the center (Raschig ring module); and cylinder with hollow and canal placed perpendicularly towards the hollow on the side wall (transverse-pore module). The porosity of all modules was determined to be around 60% (n = 15). The proposed design could be clearly distinguished on the top and side views for all modules.
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 3 The Proposed Method
### Optimization of SVR Parameters
#### 3.2.6 Circulation  
After mutation, the fitness of each individual in the population is calculated according to Equation (12), and the individual with the smallest fitness value is recorded. This evaluation-selection process is iteratively repeated, ensuring the continuous improvement of the population over successive generations. The iteration continues until a predefined termination condition is satisfied. Upon completion of the optimization process, the individual corresponding to the minimum fitness value is selected as the optimal estimation result of the \(C\) and \(\gamma\) parameters, thereby ensuring the best possible performance of the model.
## 2 Materials and Methods
### Characteristics of the Measured CER Spectra
When the bunch is assumed to have a Gaussian distribution, the form factor can be expressed with a root-mean-square (RMS) bunch length \(\sigma_{z}\) by the following equation [24]:  
\[f(\omega)=\exp\Bigl{(}-\frac{\omega\sigma_{z}}{c}\Bigr{)}^{2} \tag{4}\]  
The RMS bunch length can be evaluated by fitting the calculating \(U_{CER}(\omega)\) with Equations (2) and (4) to the observed CER spectrum. As shown in Figure 3a, the observed CER spectrum was affected by the absorption of the water vapor. Therefore, the RMS bunch length was evaluated with the measured spectrum in a frequency set of 14 points that were not affected by water vapor absorption; namely, 0.30, 0.45, 0.60, 0.70, 0.80, 0.90, 1.05, 1.27, 1.35, 1.47, 1.55, 1.82, 1.97, and 2.12 THz. The dotted curve shown in Figure 3b is obtained by applying the least squares approximation to these data. The coefficient of determination of this fitting curve is as high as 0.987, and it is considered that the electrons in the bunch were in a Gaussian distribution. The RMS bunch length for \(\Delta=+15^{\circ}\) was evaluated to be 42.6 \(\pm\) 1.1 \(\upmu\)m. As the RMS bunch length was 53.7 \(\upmu\)m at the electron energy of 27.5 MeV, as reported previously [12], it was found that the RMS bunch length is shortened by increasing the electron energy.
## 2 Design and Geometry of the Circular Minkowski UWB Antenna
The characteristic impedance of the microstrip feedline is calculated using the following equations [18; 19]:  
\[Z_{C}=\left\{\frac{60}{\sqrt{\varepsilon_{reff}}}ln\left[\frac{8h}{W_{0}}+ \frac{W_{0}}{h}\right]\text{for }\frac{W_{0}}{h}\leq 1\right. \tag{6}\]  
\[Z_{C}=\left\{\frac{120\pi}{\sqrt{\varepsilon_{reff}}\left[\frac{W_{0}}{h}+1. 393+0.677\ln\left(\frac{W_{0}}{h}+1.44\right)\right]}\text{ for }\frac{W_{0}}{h}\geq 1\right. \tag{7}\]  
The length of the partial ground is given by the relation:  
\[l_{gg}=l_{f}+p \tag{8}\]  
where \(l_{f}\) and \(p\) represent the length of the feedline and the feed gap, respectively. Self-similarity is one of the characteristics of the Minkowski fractal, which indicates that at various sizes, distinct portions of the curve resemble the entire curve. The specific construction technique and the applied parameters determine the Minkowski fractal's precise fractal dimension. The following equations have been used to estimate the initial size [20]:  
Figure 2:0  
Figure 2:1  
where \(L\) is the length of the Minkowski Square, \(a_{1}\) and \(a_{2}\) are the indentation ratios, \(K_{1}\) and \(K_{2}\) are the length and width of the indentation, and \(n\) is the iteration number. The equivalent circuit of the proposed CMFA is depicted in Figure 2\(f\). This circuit comprises several elements. Specifically, \(R_{1}\), \(L_{1}\), and \(C_{1}\) represent the resistance, inductance, and capacitance of the patch, respectively. Furthermore, \(C_{s}\) and \(L_{s}\) indicate the mutual capacitance and inductance between the patch and the ground plane. The ground plane, on the other hand, is defined by \(R_{2}\), \(L_{2}\), and \(C_{2}\), which signify its resistance, inductance, and capacitance. Lastly, \(Z_{in}\) represents the input impedance of the circuit.
## 4 Results and Discussion
### Results: Salient Terms Extraction  
We evaluated our trained models: decision tree, rule induction, and gradient boosted tree with and without feature selection on the hypertension [33], rhinusitis [37] and chapter 4 of asthma guideline [38]. The models achieved classification accuracy as given in Figure 5. Where graph (a) represents model accuracies when features selection was not performed and (b) represents accuracies with features selection. Based on the results shown in Figure 5, the accuracy of the model increases with feature selection. Also, the final generated model changes the extracted salient terms.
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 2 Materials and Methods
### Software Algorithm
#### 2.2.3 Design of the Fuzzy PID Controller
When the deviation change rate is large, \(K_{d}\) should be smaller; on the contrary, \(K_{d}\) should be larger. 2. When the deviation and the deviation change rate are medium, the value of \(K_{p}\) should be smaller, and the value of \(K_{i}\) should be appropriate. 3. When the deviation is large, in order to prevent the deviation from becoming large instantaneously and causing the differential saturation, take the larger \(K_{p}\) and the smaller \(K_{d}\). The value of \(K_{i}\) should be small to avoid integral saturation and large overshoot in the system response. For example, according to Table 1, if the rotational speed of the pulling rollers deviation is NL and the deviation change rate is NL, it means that the actual rotational speed is far below the target rotational speed and the deviation tends to increase. Therefore, \(K_{p}\) is PL, \(K_{i}\) is PS, and \(K_{d}\) should choose NL. If the rotational speed deviation is ZE and the deviation rate of change is ZE, it means that the actual rotational speed is close to the target rotational speed, but the system has a static error. Therefore, \(K_{p}\) is ZE, \(K_{i}\) is NS, and \(K_{d}\) is ZE. The center of gravity method was used for defuzzification to convert the output variable into a numerical value. Similarly, the operating speed controller receives the target speed from the main controller and the actual speed from the vehicle speed sensor. It then calculates the deviation \(v_{e}\) between the target and actual operating speed and the deviation change rate \(v_{ec}\). This controller also applies the proportional, integral, and differential coefficients adjusted by fuzzy control as the control inputs to regulate the operating speed error to approach 0. Figure 6: Membership functions of input and output: (a) Membership function of the input variables \(n_{e}\), \(n_{\alpha}\); (b) Membership function of the output variables \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\).
## 4 Multifrequency Backscatter Mosaics and Texture Analysis Features
### Image Texture Features  
Various texture-based sonar seafloor classification software have been popular among researchers during the recent years, including academic ones, such as TexAn [49; 50; 51] and SonarClass [52], and commercial ones, such as QTC Side and Multi-view [53; 54], Geocoder [18] and others [55; 56]. Image texture relies on the relative positions of grey levels within an image neighborhood, and describes the roughness or the smoothness, the variability or the homogeneity and the repeatability or the randomness of different areas on the swath sonar backscatter [51]. In the present work, a set of 11 textural features (Figure 5) was extracted through SonarClass from each one of the three backscatter mosaics.  
SonarClass is a Matlab tool used with success in various benthic habitat mapping and target detection works [25; 37; 40; 47; 52; 57; 58; 59; 60]. SonarClass employs three feature extraction algorithms, based namely on first order grey-level statistics, metrics on grey level co-occurrence matrices (GLCMs) originally defined in [61] and 2D power spectrum specifications, all fully described in [62] (Figure 5). The textural features were extracted from each backscatter mosaic using 30 \(\times\) 30-m windows, with a 20 m sliding step, leading to 11 feature maps with 10 m spatial resolution each. The 30 \(\times\) 30 m patch size was decided after visual interpretation of the main image textures in the mosaics, so that primitive texture elements from different bottom types (e.g., seagrass patches, minute reefs, sand ripples) could completely fit into a single image patch. Smaller image patches would lead to incomplete analysis of bottom textures while bigger patches would increase the likelihood that different bottom types are included in a single patch.  
The nadir patches were excluded from analysis as they are affected by strong acoustic artifacts [7; 8], making them inappropriate for use in automated classification processes. Likewise, transmitting two frequencies with the same system but with one at half the ping rate of the other, induced a persistent interference, expressed as a parallel to the nadir and at a distance equal to the high frequency (400 kHz) slant range linear strip noise (Figure 6). In this work, automated localization of both above linear artifacts was performed using Independent Component Analysis (ICA) decomposition of the feature vector. ICA holds the advantage of separating irregularities in the image scene, usually captured in just one independent component [58]. The selection of the IC corresponding to the artifact relied on visual inspection of the ICs. The theory and principles of ICA have first been applied in SSS images in [58], to which readers are directed for further details. Figure 6 shows an example of artifact exclusion through ICA decomposition.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 6 Conclusions  
### Theoretical Contributions  
* This work proposes a novel methodological approach for the characterization of cookies by combining web traffic analysis techniques, cookie analysis, and unsupervised clustering, which represents a significant contribution to the study of privacy and security in student web browsing.
* The experimental browser BOOKIE was introduced, which integrates cookie-based visualization, classification, and vulnerability prediction, showing how data analysis can be applied directly in a browsing environment.
* The results obtained with the clustering algorithms (K-Means, DBSCAN, and hierarchical clustering) confirm that it is possible to identify behavior patterns and user segmentation based on the cookies collected, which theoretically supports the hypothesis that cookies can be used as vectors for vulnerability analysis and digital behavior.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Background  
Various localization techniques have been applied in indoor and outdoor environments. In outdoor environments, the _global positioning system (GPS)_ is available. However, it cannot cover indoor ones [12, 13]. Then, to successfully cover indoor environments, several wireless technologies have been explored for _indoor localization systems_.  
_Fingerprinting_ has obtained great interest due to the reasonable accuracy capability by adopting the _radio map pattern matching_[14]. Each location in the target field is assumed to have its own unique radio pattern called the _fingerprint_. The value should be different from the one for other locations. This method consists of the _calibration phase_ and the _detection phase_. The _calibration phase_ collects the _radio signal map_ and generates the _fingerprint_ for every location in the field, and stores it in the database. The _detection phase_ compares the received radio signal with every _fingerprint_ and selects the closest one as the current location. When considerable calibration efforts are made, this method can achieve robust detection capabilities [15].  
Based on this method, we are currently studying the _fingerprint-based indoor localization system_ using the _IEEE802.15.4_ protocol, called _FILS15.4_[1, 16]. _FILS15.4_ uses the _IEEE802.15.4_ devices in _Mono Wireless_[2]. The transmitter device is suitable for use to be worn by a user. It is inexpensive (USD 30), is small (2.5 mm \(\times\) 2.5 mm), is light (0.93 g), and can work with a coin battery for a long time. The radio signal from the transmitter will be received at multiple receivers allocated in the field, and the _LQI (link quality indicator)_ vector is compared with the _fingerprint_ for each location.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 3 SARDIMM: The Proposed NMP Architecture for RDA
### Data Storage Order for FFT Operation  
Figure 7 shows the operation flow and state of the data buffer during the performance of a 16-point DIF FFT using an FPBF module. Figure 7a depicts the data buffer, sequentially storing 16 input data points from x0 to x15 for the first stage of the 16-point DIF FFT. The width of the data buffer is 64 bits, capable of storing two 32-bit complex number data within a storage space corresponding to one address. Initially, for the butterfly operation, 64-bit Rdata0 and Rdata1 are read from the data buffer across the two clocks. Rdata0 corresponds to input data x0 and x1 in the first stage, while Rdata1 corresponds to x8 and x9. In the butterfly operation of the first stage, x0 and x8, as well as x1 and x9, should be processed as pairs. Thus, as depicted in Figure 7b, the most significant 32 bits from both Rdata0 and Rdata1 are input to the first operator, while the least significant 32 bits from both Rdata0 and Rdata1 are input to the second operator.The first operator's high path output is represented as y0, and the low path output as y8, whereas the second operator's high path output is denoted as y1, and the low path output as y9. The FPBF module separately concatenates the outputs of the high and low paths. Subsequently, two data points are selected individually per clock cycle through the toggle signal, and Wdata is written into the data buffer. The data buffer, utilizing the dual-port SRAM, reads the input data of the first stage while simultaneously writing the output. As depicted in Figure 7c, the output data of the first stage are sequentially stored in the data buffer from y0 to y15, as shown in Figure 7a. This process ensures that SARDIMM sequentially stores the results in the data buffer at each stage of the FFT to facilitate the correct reading of the input data for the subsequent stage.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 3 Methodology
### Conventional Fuzzy C-Means Imputation Algorithm
\[\min J=\sum\limits_{k=1}^{n}\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{p}u(\mathbf{x }_{k},y_{ji})^{m}\cdot d(\mathbf{x}_{\mathbf{k}},y_{ji}) \tag{2}\]  
\[d(\mathbf{x}_{\mathbf{k}},y_{ji})=\sum\limits_{g=1}^{p}\left(x_{gk}-y_{ji}\right)^{1/t} \tag{3}\]  
\[u(\mathbf{x}_{\mathbf{k}},y_{ji})=1/[\sum\limits_{a=1}^{c}\left(\frac{d(\mathbf{x}_{\mathbf{k} },y_{ji})}{d(\mathbf{x}_{\mathbf{k}},y_{ji})}\right)^{1/(m-1)}] \tag{4}\]  
\[\sum\limits_{j=1}^{p}u(\mathbf{x}_{\mathbf{k}},y_{ji})=1 \tag{5}\]  
\[y_{ji}=\frac{\sum\limits_{k=1}^{n}\left[u(\mathbf{x}_{\mathbf{k}},y_{ji})^{m}\cdot x_{ jk}\right]}{\sum\limits_{k=1}^{n}u(\mathbf{x}_{\mathbf{k}},y_{ji})^{m}} \tag{6}\]  
\[x_{jk}^{*}=\sum\limits_{i=1}^{c}u(\mathbf{x}_{\mathbf{k}},y_{ji})\cdot y_{ji} \tag{7}\]  
It should be noted that although the terminating condition of the conventional FCM is met, the final estimation result may not be optimal, because the number of the cluster centroids \(c\) and the parameter of the membership degree \(m\) are predetermined. Moreover, the clustering performance of the FCM is sensitive to both. Thus, this is the main drawback of the FCM. The way to overcome this shortcoming is to optimize \(c\) and \(m\) in each condition.
## 3 Results
### Different Cell Lines Displayed Different Responses to Cold Plasma  
Treatment duration in the range of seconds produces significant inhibitory effects in the majority of cancer cell lines.  
The MTT assay shows a decrease in metabolic activity in several cell lines, particularly after 120 s CAP exposure, with OE19 being an obvious exception (see Appendix A Figure A1). In the SRB assay (see Appendix A Figure A2), after 60 s of CAP exposure, cells show a statistically significant decrease in viability, and maximal effects were seen using plasma exposures of 120 s.  
Most plasma-treated cell lines displayed a parallel decrease in metabolic activity and viability in a time-dependent manner, as evaluated by MTT and SRB assays. Treatment durations required to reduce metabolic activity and viability by 50% (IT50) were calculated for each of the cell lines. Calculated IT50s in responsive cancer cells were lower than 1 min, as seen in Table 3.  
In order to allow a better comparison between different cell lines, Figure 5 confronts calculated IT50 with a confidence interval of 95% for all cell lines. This also allowed for the classification of the cell lines into susceptible, stressed, stunned, and resistant to CAP. Most cell lines studied are sensitive to CAP; this is the case of A375, HT1376, TFK-1, LnCap, ECC-1, and H1299. OE19, WiDR, HCC1806, and HFF-1 are among the cells resistant to plasma treatment while PC3 and MNNG/HOS become stunned and MCF7 become stressed.
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
## 4 Experiments
### Ablation Studies  
This section explores the impact of different parts of the Transformer architecture on the effectiveness of distillation through ablation experiments.  
The ablation experiment results in Table 6 demonstrate that removing different distillation objectives significantly affects the learning performance of AMKD. The removal of attention distillation (w/o Atten) has the most substantial impact on overall model performance, particularly on the MNLI task. Similarly, removing hidden layer distillation (w/o Hidden) also results in a significant performance reduction. In contrast, removing embedding layer distillation (w/o Embed) causes a smaller decrease in performance.