esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 3 Results
### Structural Styles from Seismic Interpretation  
Five time-migrated seismic reflection profiles were interpreted (Figure 5). The most distinguishable layer in the seismic profiles is the boundary between alluvium and limestone bedrock. A strong reflector, due to the high acoustic impedance contrast, characterizes this interface. The seismic profiles  
Figure 6: **(A)** SBAS results generated from forty descending ENVISAT ASAR images from 2003–2010 show surface deformations ranging from \(-\)5.8–1.8 mm/year in the Quetta Valley and surrounding areas. A 2D velocity profile, CC\({}^{\prime}\), was extracted from the InSAR results. The low coherence area is marked by a dotted line on the CC\({}^{\prime}\) velocity profile. **(B)** GPS rates from 2006–2016 in a vertical direction around –7.7 mm/year, which matches the InSAR vertical movement rate in the Quetta Valley. **(C)** A five-point median filter was applied on the CC\({}^{\prime}\) cross-profile (Figure 6A) to distinguish the subsidence caused by tectonic processes (shown as a red line) from subsidence caused by anthropogenic processes (water withdrawal) (shown as a green line).  
indicate that the high-angle thrust faults are mostly dipping westward. The descriptions of the interpreted seismic lines are as follows.  
Seismic reflection profile 1 shows the depth to the bedrock (red-colored horizon), which is estimated to begin at a depth of \(\sim\)380 m (Figure 5). A previous study showed that the bedrock is attributable to the Shrinab Formation, which has numerous reflectors that could be explained by alternating limestone and shale [20]. At the point where it intersects with profile 2, seismic profile 1 reveals a rising of the bedrock, which suggests complex folding in the bedrock affecting overlaying Quaternary alluvial deposits. In the middle portion of profile 2, the depth to the bedrock is \(\sim\)240 m and increases to \(\sim\)560 m at the eastern end. Profile 3 clearly shows the presence of a large asymmetric Quaternary basin up to \(\sim\)500 m deep. Following the results of previous studies [20] and adding our new interpretations from the seismic data, a geological model along seismic profile 3 has been generated (Figure 7). The eastern part of profile 3 reveals what appears to be a thrust fault serving as the boundary between the Shrinab and the Chiltan Formations. Fault 1 was mapped on seismic profile 3 and fault 2 was mapped on seismic profile 2; both were projected onto the geologic model. The soft, unconsolidated nature of alluvial sediments in the Quetta Valley masked the bedrock fault scarps, but seismic reflection profiles helped to map these blind thrust faults.  
Profile 4 intersects two blind thrust faults that dip west (Figure 5, Top). The Quaternary alluvial cover is thin along most of the line, rarely exceeding \(\sim\)140 m. Profile 5 was difficult to interpret, although there are many reflectors, most are too weak to be clearly attributed to the bedrock, except for one at a depth of \(\sim\)700 m that has been attributed to the top of the Shrinab Formation.
# An Empirical Study on Ensemble of Segmentation Approaches
## 2 Materials and Methods
### Loss Functions
#### 2.2.1 Previously Tested Loss Functions  
Our set of loss function includes the following widely adopted metric functions (the interested reader can refer to [24] for a more detailed description):  
* Generalized Dice Loss [26], \(L_{GD}(Y,T)\) is a multiclass variant of the dice loss.
* Tversky Loss [27], \(L_{T}(Y,T)\) is a weighed version of the Tversky index designed to deal with unbalanced classes, i.e., the phenomenon whereby one class prevails over another.
* Focal Tversky Loss [28], \(L_{FT}(Y,T)\) it is a variant of the Tversky loss, where a modulating factor (\(\gamma=4/3\)) is used to ensure that the model focuses on hard samples instead of properly classified examples.
* Focal Generalized Dice Loss [29], \(L_{FGD}(Y,T)\) is the focal version of the generalized dice loss.
* Log-Cosh Dice Loss \(L_{lcGD}(Y,T)\) is a combination of the dice loss and the log-cosh function, applied with the purpose of smoothing the curve of loss.
* Log-Cosh Focal Tversky Loss \(L_{lcFT}(Y,T)\) is based on the same idea of smoothing, here applied to the focal Tversky loss.
* SSIM Loss [30], \(L_{S}(Y,T)\), is obtained from the structural similarity (SSIM) index [31], usually adopted to evaluate the quality of an image.
* MS-SIM Loss \(L_{MS}(Y,T)\) is a variant of \(L_{S}(Y,T)\) defined using the multiscale structural similarity (MS-SSIM) index.
* Some combined losses: \(\circ\)\(Comb_{1}\)\((Y,T)=L_{FGD}(Y,T)+L_{FT}(Y,T)\)\(\circ\)\(Comb_{2}(Y,T)=L_{lcGD}(Y,T)+L_{FGD}(Y,T)+L_{lcFT}(Y,T)\)\(\circ\)\(Comb_{3}\)\((Y,T)=L_{S}(Y,T)+L_{GD}(Y,T)\)\(\circ\)\(Comb_{4}\)\((Y,T)=L_{MS}(Y,T)+L_{FGD}(Y,T)\)
# Binary Starling Murmutation Optimizer Algorithm to Select Effective Features from Medical Data
###### Abstract  
Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed stairling murmutation optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study.  
disease diagnosis; medical data; feature selection; binary metaheuristic algorithms; starling murmutation optimizer (SMO); transfer function +
Footnote †: journal: Applied Sciences
## 4 Proposed Implementation Techniques
### Parallel Encryption in CTR Mode  
To encrypt plain text on the GPU, we must first copy the plain text data on the CPU to the GPU. Data copy between CPU and GPU is done through PCIe. However, the speed of each CPU and GPU is very fast, but PCIe is slower than both. Thus, it takes a significant amount of time to copy data between the CPU and GPU.  
However, if we use the CTR mode of the block cipher operation mode, there is no need to copy the plain text to the GPU. All we have to do is copy the encrypted counter value generated by the GPU to the CPU and XOR it with the plain text. Threads create counter value using each thread's unique number and perform encryption in parallel. This optimization idea greatly improves performance by significantly reducing the copy time between the CPU and GPU. Algorithm 1 shows that CTR mode encryption is performed without passing plaintext as an argument to the encryption function.  
```
0: Original Key
0: Result 128-bit Ciphertext C
1: Generate _roundkey_ with Original Key
2: Generate a 64-bit random value _nonce_
3: Memory copy round key and nonce from CPU to GPU
4: Compute \(C\gets Encryption\_kernel(roundkey,nonce)\)
5: Memory copy C from GPU to CPU
6: Compute \(C\gets C\oplus plaintext\)
7:\((Return\ C)\)
```  
**Algorithm 1** CTR parallel encryption.  
Figure 6 illustrates the process of encryption on the GPU using CTR mode. When encrypting on the GPU, it can be seen that only the round key and nonce are used on the GPU.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GPU** & **GTX 970** & **GTX 1070** & **RTX 2070** \\ \hline Architecture & Maxwell & Pascal & Turing \\ SM count & 13 & 15 & 36 \\ Core count & 1664 & 1920 & 2304 \\ Memory Size & 4 GB & 8 GB & 8 GB \\ Base clock & 1050 MHz & 1506 MHz & 1410 MHz \\ CUDA & 5.2 & 6.1 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Target CUDA-enabled NVIDIA GPUs.
## 4 Experimental Verification and Analysis
### Improvement Effect Analysis  
To evaluate the effect of the improved model, we tested the images in the defect data set. The size of the input images was set to 640 \(\times\) 640, and the confidence threshold was set to 0.8. The experimental results are shown in Figure 8.  
The first is listed as one of the original images, the second as YOLOv5 test results, and the third as the result of the improved algorithm. The defects in the images of group a and group b were scattered, and there was no sign of a large overlap of defect areas. At this time, the detection accuracy of the improved YOLOv5 was higher than that of YOLOv5. The images of group c and group d had the problem of overlapping stress concentration areas. Faced with this problem, the missed detection of the improved YOLOv5 was significantly lower than that of YOLOv5.  
Among the above four groups of images, YOLOv5 has a higher missed detection rate of defects, and the detection accuracy of the improved YOLOv5 has been improved. Compared with YOLOv5, it is found that the improved YOLOv5 performs better in detecting small targets. The actual results show that precision, recall rate, F1, and mAP of the improved YOLOv5 in the detection of pressed rigging defects are 92.5%, 97.2%, 97.3%, and 97.1% respectively, which significantly improves the detection accuracy and reduces the missed detection rate after the improvement, as shown in Table 1. Therefore, the convolutional neural network model is applied to the geomagnetic flaw detection equipment, which can effectively identify the defect signal of the suppressed rigging.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Index** & **YOLOv5** & **Improved YOLOv5** \\ \hline Precision & 89.5\% & 92.5\% \\ Recall & 93.2\% & 97.2\% \\ F1 & 93.1\% & 97.3\% \\ mAP & 92.5\% & 97.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of evaluation indicators before and after improvement.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
###### Abstract  
Recently, we often see the environment where many one-to-one Wireless Local Area Networks (WLANs) exist in a small area. In this environment, the network throughput of certain WLAN reduces significantly because of the interference from other networks (i.e., inter-network interference). The inter-network interference is the effect of carrier-sensing activities when there are ongoing transmissions in neighbor networks. This paper presents analytical expressions using airtime concept, which newly take into account the inter-network interference, for network throughputs of WLANs. There are existing works that similarly address the WLAN's carrier-sensing duration. However, they either consider a simple interference model or assume the simultaneous transmission time is negligible. Different from them, we consider the significant impact of simultaneous transmission. As a result, our analytical model can precisely express each network carrier-sensing duration by subtracting the simultaneous transmission time. More specifically, we have successfully obtained each network throughput by expressing frame-existence probabilities concerning each network's End Device (ED). We also confirm the validity of the analysis by comparison with simulation. The analytical results and the simulation results agree well.  
WLAN; interference; carrier-sensing; IEEE 802.11; throughput analysis +
Footnote †: journal: Applied Sciences
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 2 Materials and Methods
### Image Feature Extraction  
The images utilized in this study exhibited variability in size due to the manual acquisition process. To mitigate this variability and standardize our analyses, we cropped three samples of the tongue images to a uniform size of 100 \(\times\) 100 pixels, as illustrated in Figure 3. From these cropped samples, one image was randomly selected, and features were subsequently extracted.  
We extracted 462 image features from the cropped tongue image, focusing on both first-order statistics and second-order statistics, which encompass textural features. First-order statistics provide insights into the statistical properties of the spatial distribution of gray levels within the image. These statistics include fundamental measures such as the mean, median, range, standard deviation, skewness, and kurtosis [23]. Textural features, on the other hand, were calculated using texture matrices that represent the spatial distribution of pixel intensities within the images [24; 25]. Specifically, we employed three primary types of texture matrices: the gray-level co-occurrence matrix (GLCM) [26], the gray-level run-length matrix (GLRLM) [27], and the gray-level size-zone matrix (GLSZM) [28; 29; 30; 31]. The GLCM characterizes the spatial relationships of pixels with predefined gray-level intensities, allowing us to quantify how often pairs of pixels with specific values occur in a specified spatial relationship. The GLRLM provides valuable information regarding the spatial distribution of consecutive pixels that share the same gray level, thus offering insights into texture continuity. The GLSZM counts the number of groups of interconnected neighboring pixels that share identical gray levels, enabling a deeper understanding of the structure and distribution of pixel intensities (Supplementary Method).  
In this study, we calculated a total of 66 features derived from these textural analyses: 11 features based on first-order statistics, 23 features extracted from the GLCM, 16 features obtained from the GLRLM, and 16 features derived from the GLSZM. Overall, we calculated a total of 462 features from the seven images generated during the image pre-processing phase.
# Wideband Circularly Polarized Antenna Based on a Non-Uniform Metasurface
## 4 Measurement Results  
The proposed antenna was fabricated and experimentally checked. The PNA Network Analyzer N5224A (Keysight, Santa Rosa, CA, USA) was used to measure the antenna's reflection coefficient, and the far-field characteristics were implemented by the Microwave Technologies Group [27]. Generally, the measured data matched well with the simulated data. There were still several discrepancies, which were caused by the tolerance in fabrication and the imperfection of the measurement setup.  
The simulated and measured \(|\)S\({}_{11}|\) results of the proposed MS-based antenna are illustrated in Figure 9. The antenna showed good matching performance in a wide frequency range from 4.7 to 7.2 GHz, equivalent to 42.1%. Figure 10 depicts the far-field radiation characteristics in terms of AR and gain in the boresight direction. As observed, the measured 3 dB AR BW was 30% (5.1-6.9 GHz); furthermore, the measured gain was from 5.6 to 7.6 dBic.  
Figure 11 plots the antenna gain radiation patterns in the \(x\)-\(z\) and \(y\)-\(z\) planes at three different frequencies of 5.2, 6.2 and 6.8 GHz. It can be seen that LHCP radiation is the dominant operating mode of the proposed antenna. In the broadside direction (\(\theta=0^{\circ}\)), the polarization isolation defined by the difference between the right-hand CP (RHCP) level and LHCP level is higher than 16 dB. Besides, the front-to-back ratio is always greater than 12 dB across the operating band.  
Figure 9: Simulated and measured \(|\)S\({}_{11}|\) of the proposed antenna.  
Figure 11: Simulated and measured gain radiation patterns at **(a)** 5.2 GHz, **(b)** 6.2 GHz and **(c)** 6.8 GHz.  
Figure 10: Simulated and measured AR and broadside gain of the proposed antenna.  
Finally, the comparisons between the low-profile, wideband CP antennas are summarized and presented in Table 1. The data indicate that, except for [19], the proposed antenna shows the best operating BW with an acceptable gain and overall size. Although the designs in [19,20] have a wide operating BW of around 30%, their lateral dimensions are larger and the maximum gains are also lower than those of the proposed antenna. The structures in [5,8,9,11,12] exhibit high gain radiation; however, a high profile is their critical drawback. Meanwhile, the antennas in [15,16] have the advantage of a lower profile, but their operating BW is significantly smaller than that of the presented design.
## 4 Implementation Details
### Pulse Generator  
The objective of the pulse generator (Figure 4) is to convert the voltage \(V_{c}\) from voltage domain to an equivalent time domain \(Enable\) pulse, which is subsequently quantized using the system clock. This time-domain measurement approach contrasts with direct voltage measurement using an ADC, which would require an accurate on-chip voltage reference and incurs area overhead, making the time-domain method a more efficient alternative. This conversion is achieved through the combination of a voltage-controlled delay line (VCDL), an inverter, and a NOR gate, as illustrated in Figure 8a. When \(V_{c}\) is settled, a trigger signal, \(t_{trigger}\), is dispatched from timing controller to the pulse generator. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{
\begin{tabular}{c} **Analytical** \\ **Model NDC** \\ \end{tabular} } & \multicolumn{2}{c}{**Circuit**} & \multirow{2}{*}{**Model Error**} \\  & & & & \\ \hline \(R_{poly}+R_{metal}\) & \(C_{metal}\) & 26 & 26 & \textless{}1\% \\ \(R_{metal}\) & \(C_{MIM}+C_{metal}\) & 32 & 32 & \textless{}1\% \\ \(R_{poly}+R_{metal}\) & \(C_{MIM}+C_{metal}\) & 277 & 270 & \textless{}2.6\% \\ \(R_{metal}\) & \(C_{metal}\) & 3.0 \({}^{\text{b}}\) & 3.1 \({}^{\text{b}}\) & \textless{}3.2\% \\ \hline \hline \end{tabular}  
* \({}^{\text{a}}\) Simulated with parasilates extracted. \({}^{\text{b}}\) Derived discharge cycle from Equation (11).  
\end{table}
Table 1: Discharge cycles of the analytical model and Cadence simulation.  
Figure 7: Simulation results of the analytical model and circuit simulations. (**a**) \(R=R_{poly}+R_{metal}\), \(C=C_{metal}\) (**b**) \(R=R_{metal}\), \(C=C_{MIM}+C_{metal}\) (**c**) \(R=R_{poly}+R_{metal}\), \(C=C_{MIM}+C_{metal}\).  
VCDL and the inverter transmit the transition to their respective outputs with distinct propagation delays, as shown as \(\tau_{VCDL}\) and \(\tau_{inv}\), respectively. Subsequently, the delay difference is converted into an \(Enable\) pulse by the NOR gate, which then proceeds to a counter for quantization. Since \(\tau_{inv}\) is negligible compared to \(\tau_{VCDL}\), the \(Enable\) pulse width is approximately equal to \(\tau_{VCDL}\).  
Figure 8b shows the schematic of the VCDL, consisting of 26 current-starved inverters followed by 26 regular inverters in an alternating pattern. By limiting the discharge current via the \(V_{c}\) voltage, the propagation delay of the VCDL can be effectively controlled. The dynamic range of VCDL, covering the voltage range from \(V_{1}\) to \(2V_{1}\), is depicted in Figure 8c and spans from 37 ns to 182 ns.
## 3 Preliminaries
### Network Model  
Assume that a WRSN consists of a set of sensor nodes \(V\), a fixed base station \(v_{0}\), a single vehicle and multiple drones. A WRSN can be represented as a weighted graph \(G=(V\cup\{v_{0}\},\ W,\ E)\), where nodes sense, generate data and transmit data to the base station through a pre-generated multi-hop path using the adopted routing algorithm [1]. A \(w_{\overline{w}}\) in the set \(W\) represents Euclidean distances between a pair of node \(s_{i}\) and \(s_{i}\), while \(E=\{e_{1},\ e_{2},\ldots,e_{|E|}\}\) represents the initial energy of sensor nodes. When the energy of a sensor node falls below a predefined threshold, it sends charging request packets to the base station. The base station communicates directly with the vehicle and drones, which are used to replenish energy for remote nodes according to the charging requests. In a WRSN scenario, a vehicle carrying \(m\) drones departs from the base station, completes its task of replenishing energy or charging remote nodes with the help of drones, and returns to the base station to reload before the next mission.
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## 5 Conclusions  
We have constructed a conventional linear-scanning GB-SAR system in the Ku-band and operated in bistatic mode where Rx follows Tx separated by several different baselines. A back-projection algorithm and range-Doppler algorithm were developed for the bistatic GB-SAR focusing.  
The results showed that the targets were images at a designed quality and resolution. The characteristic effects of bistatic configurations were identified such as the reduction in azimuth resolution and power due to the reduced coherent integration length and the uni-directional backscattering properties of CR. The use of squint mode and the necessity of more robust frequency-domain algorithms were suggested for the satellite or airborne bistatic SAR configurations.  
The advantages of bistatic SAR configurations would be numerous such as the investigation of the bi-directional scattering target properties and the systematic versatility for SAR constellation operation, which would lead to the rapid development of bistatic satellite SAR techniques in the near future.  
Conceptualization, H.L. and J.M.; methodology, H.L.; software, H.L.; validation, J.M.; formal analysis, H.L. and J.M.; investigation, H.L. and J.M.; resources, J.M.; data curation, J.M.; writing--original draft preparation, H.L.; writing--review and editing, H.L.; visualization, H.L. and J.M.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Next Generation SAR Laboratory funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.