esa-sceva/satcom-chunk-collection huggingface dataset:
# Evidence-Based Investigation of Coronary Calcium Score in Cardiac Computed Tomography
## 3 Results
### Phantom Study  
The total calcium score for the six calcium phantoms was 2% higher with the bolus than without the bolus (Figure 6a). For the three calcium phantoms below 130 HU, the total calcium scores were all 0 without the bolus, however, with the bolus, a calcium score of 1.9 was measured at C3. Of the three calcium phantoms above 130 HU, C4 increased by 0.56%, C5 by 1.01%, and C6 by 10.54% with the bolus compared to without the bolus (Figure 6b).  
Decreasing the tube voltage from 120 to 100 kV with a slice thickness of 3 mm decreased the total calcium score for the six calcium phantoms without a bolus by 1.61%. For the three calcium phantoms below 130 HU, all values were zero at 120 kV, but a calcium score of 3.1 was measured for C3 at 100 kV. Among the three calcium phantoms above 130 HU, C4 decreased by 0.54%, C5 by 2.84%, and C6 by 1.86% at 100 kV compared to 120 kV. Decreasing the tube voltage from 120 to 100 kV with a slice thickness of 3 mm decreased the total calcium score for the six calcium phantoms with a bolus by 0.13%. For the three calcium phantoms below 130 HU, a calcium score of 1.9 was measured in C3 at 120 kV, whereas calcium scores of 14.8 and 0.7 were measured in C3 and C2, respectively, at 100 kV. Of the three calcium phantoms above 130 HU, C4 decreased by 1.10%, C5 by 0.96%, and C6 by 2.76% at 100 kV compared to 120 kV (Figure 7).  
Decreasing the slice thickness from 3 to 1 mm at a tube voltage of 120 kV increased the total calcium score for the six calcium phantoms without a bolus by 12.06%. For the three calcium phantoms with values below 130 HU, all calcium scores were 0 at 3 mm, however, at 1 mm, a calcium score of 6.35 was measured at C3. For the three calcium phantoms above 130 HU, C4 increased by 17.02%, C5 by 7.15%, and C6 by 23.02% at 1 mm compared with 3 mm. Decreasing the slice thickness from 3 to 1 mm at a tube voltage of 120 kV increased the total calcium score for the six calcium phantoms with a bolus by 14.76%. For the three calcium phantoms below 130 HU, a calcium score of 1.9 was measured in C3 at 3 mm; calcium scores of 20.3, 2.8, and 0.8 were measured in C3, C2, and C1, respectively, at 1 mm. For the three calcium phantoms above 130 HU, C4 increased by 18.72%, C5 by 10.24%, and C6 by 19.74% at 1 mm compared with 3 mm (Figure 8).  
Figure 6: (**a**) Total calcium scores of the phantoms with and without a bolus. (**b**) Total calcium scores of the phantoms C4, C5, and C6 with and without a bolus.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 2 Problem Statement  
Change detectors are techniques to identify changes in data streams that may indicate shifts in their underlying distribution. In many applications, the ability to quickly detect changes and adapt to them is critical. The problem of change detection becomes even more challenging when dealing with concept drifts, where the concept can change with time. When a concept drift occurs, the relationships between the input features and the output variable may change, leading to a shift in the data distribution. This means that the model trained on the previous data may become less accurate or even useless for making predictions on the new data. Therefore, detecting and adapting to concept drifts in a timely and effective manner is crucial for maintaining model performance and avoiding costly mistakes.
## 4 Statistical Results
### Medium-Period Region  
Figure 6 illustrates the comparison between the benchmark model and the scaled model for the EPP system with a radius (\(R\)) of 4 and a period (\(T\)) of 1.0 s. The ground  
Figure 5: Potential distortion caused by the scaling of ground motion for EPP in the short-period range.  
motions in the scaled model are adjusted using scaling factors of \(\
abla S_{\text{a}}\) and \(\
abla SI\), respectively. Figure 6 shows the comparison between the benchmark model and scaled model for the EPP system with \(R\) = 4 and \(T\) = 1.0 s, when the ground motions are scaled with \(\
abla S_{\text{a}}\) and \(\
abla SI\), respectively. The results for unscaled ground motions and fitted benchmark model can be found in Figure 1. Different from the system with \(T\) = 0.2 s (Figure 4), the platform section still appears in the scaled model, indicating that the minimum \(\
abla IM\) in the scaled database is below the intensity threshold, and some aftershock records in the scale database would induce no additional damage (\(\
abla DI\) = 1.0) in the medium-period region. Using \(\
abla SI\) as the scale indicator induces lower bias (i.e., 14%) than using \(\
abla S_{\text{a}}\) as the scale indicator (20%).  
In the medium-period range of 0.6-1.5 s, the average biases resulting from the scaling of ground motions are calculated for EPP systems. Figure 7 displays the findings for eight different scale indicators. In comparison with the results in Figure 5, significantly lower biases are observed in Figure 7, especially for the scale indicators of \(\
abla SI\), \(\
abla PGV\), \(\
abla S_{\text{a}}\), and \(\
abla I_{\text{C}}\). The biases are generally within 20% even for \(SF\) being 10.0, when \(\
abla SI\) and \(\
abla I_{\text{C}}\) are used as the scaled indicators, and corresponding biases for \(\
abla PGV\) and \(\
abla S_{\text{a}}\) are generally within 30%. It is interesting to note that \(\
abla I_{\text{C}}\) shows superior performance as the scale indicator compared to other intensity measures, because it induces the lowest biases among eight different scale indicators. Although \(I_{\text{C}}\) is computed based on acceleration [54], the dimension of \(I_{\text{C}}\) is between acceleration and velocity. Consequently, \(I_{\text{C}}\) is capable of effectively capturing the potential for damage caused by ground motion on systems that are sensitive to both acceleration and velocity. Moreover, the computation of \(I_{\text{C}}\) incorporates the duration of ground motion, which contributes to the additional damage. The dependence of bias on the strength reduction factor \(R\) varies across the eight different scale indicators. For example, using \(\
abla SI\) as the scaling parameter tends to produce higher bias for stronger systems (e.g., \(R\) = 2), while using \(\
abla S_{\text{a}}\) as the scale indicator tends to produce higher bias for weaker systems (e.g., \(R\) = 6).
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 2 Related Work  
### Crowd Counting  
In crowd-analysis scenarios, crowd counting is the essential component when aiming to calculate the crowd number. In the last decade, several methods have emerged to solve the problem of crowd counting. Many traditional algorithms have applied hand-crafted features to detect people from images. Ref. [15] introduced the Hough forest to perform a generalized Hough transformation for object detection. Ref. [16] boosted several weak part detectors based on extracted features, and all detector responses were combined for counting. Ref. [17] combined mosaic images with a foreground segmentation module and head-shoulder detector to accurately estimate pedestrian counts. While early methods can satisfactorily solve the occlusion problem, they are conducted at the expense of spatial information. Various density estimation-based methods are proposed [18]. These methods do not need to detect every object, as they estimate the image density and calculate the area in the density map to obtain the quantity within that area. Ref. [19] proposed a patch-based method to learn patch features and the nonlinear mapping of corresponding objects in the patch. To improve estimation accuracy and speed, they used random forest regression. Ref. [20] combined deep and shallow, fully convolutional networks, for which the high-level and low-level semantics complemented each other to predict higher-quality density maps. Ref. [21] proposed solving accuracy problems in the generation of density maps through multi-scale averaging. Ref. [6] attempted a multi-column-based architecture (MCNN) used with images of dense crowds and an angle of view. In a different approach, Ref. [22] presented a universal model for crowd counting across scenes and datasets. The model learns to obtain the optimal image rescaling factors for alignment, by minimizing the distances between their scale distributions. Ref. [23] proposed an unsupervised domain adaptation problem for video-based crowd counting.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 2 Formation Principle of PPWJ
### Operational Principle of the PPWJ Device  
One operational cycle of the device involves the forward and backward motion states of the piston, as shown in Figure 1a,b, respectively. When surface A\({}_{2}\) of the piston moves to chamber 3, chambers 9 and 4 are connected, allowing chamber 9 to be linked to the oil outlet. The hydraulic force \(F_{\mathrm{v}}\) acting on the valve core fulfills Equation (1) as follows:  
\[F_{\mathrm{v}}=\frac{\pi}{4}\left(d_{\mathrm{v1}}^{2}-d_{\mathrm{v0}}^{2} \right)p_{2}-\frac{\pi}{4}\left(d_{\mathrm{v4}}^{2}-d_{\mathrm{v0}}^{2}\right) p_{2}<0 \tag{1}\]  
where \(p_{2}\) is the inlet pressure. At this point, the valve core shifts to the left, causing the piston to begin its backward stroke due to the hydraulic thrust \(F_{\mathrm{P}}\). The low-pressure oil in chamber 5 then flows sequentially through chamber 7 and chamber 8 before reaching the oil outlet. Meanwhile, the low-pressure water enters chamber 1 through the check valve.  
When surface A\({}_{1}\) of the piston moves to chamber 3, chambers 9 and 2 are connected, allowing chamber 9 to be connected to the oil inlet. The hydraulic force \(F_{\mathrm{v}}\) acting on the valve core fulfills Equation (2) as follows:  
\[F_{\mathrm{v}}=\frac{\pi}{4}\left(d_{\mathrm{v1}}^{2}-d_{\mathrm{v0}}^{2} \right)p_{2}+\frac{\pi}{4}\left(d_{\mathrm{v3}}^{2}-d_{\mathrm{v2}}^{2}\right) p_{2}-\frac{\pi}{4}\left(d_{\mathrm{v4}}^{2}-d_{\mathrm{v0}}^{2}\right)p_{2}>0 \tag{2}\]  
At this point, the valve core shifts to the right, and the piston starts to stroke under the action of hydraulic thrust \(F_{\mathrm{P}}\). As the piston moves, the pressure in chamber 1 increases, causing the check valve to shut. The pressurized water generates kinetic energy as it passes through the nozzle, and is subsequently ejected at high velocities. During the stroke stage of the piston, the high-pressure oil in chamber 2 sequentially flows through chamber 6 and chamber 7 to reach chamber 5, forming a differential connection.  
Under continuous hydraulic oil, the position of the valve core is shifted to alter the flow direction of the hydraulic oil that drives the piston to move back and forth, intermittently expelling the water in chamber 1, leading to periodic changes in the pressure of chamber 1. Furthermore, the jet pressure exceeds the inlet pressure as a result of the disparate piston action areas in the oil chamber and water chamber. Therefore, under the continuous injection of low-pressure oil, consecutive high-pressure pulse jets can be generated without extra power and high-pressure equipment, with low cost and a simple operation.
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 4 Network Selection
### Metrics and Evaluation Scenario  
We compared NetSel-RF to SSF and AHP-TOPSIS regarding the number of handovers and throughput drops. The quantity of handovers is the number of transfers an end-device makes when it moves from a place to another one [13]; this number is affected by the ping-pong effect that occurs when the mobile device does not stay within the coverage of the selected AP and returns to the associated AP. The throughput drops represent the times that the number of bytes transmitted falls to zero due to handover.  
With the aim of evaluating NetSel-RF, we used Mininet-WiFi to emulate a network formed by four APs with 802.11 g and 17 end-devices distributed in four rooms (see Figure 1). We analyzed the performance of NetSel-RF, SSF, and AHP-TOPSIS regarding the metrics mentioned above when \(dev1\) carried out a linear movement at a speed of 1 m/s.
## 3 Experiments and Results
### Custom Models
#### 3.3.3 Light-XSimpleNet  
We now aim at building an even smaller and simpler NN architecture than XSimpleNet. To do so, however, it would be risky to decrease the depth--i.e., number of layers--of such architecture. Indeed, XSimpleNet depth is set to the bare minimum needed to manipulate visual information. Therefore, to build a lighter version of the XSimpleNet architecture (Light-XSimpleNet), we here rely on depthwise separable convolution layers [39]. Previously used, standard convolutions perform the channel-wise and spatial-wise computation in one step. Conversely, depthwise separable convolution splits the computation into two steps: a single convolutional filter is applied for each input channel (depthwise), whereas a pointwise convolution is applied to create a linear combination of such output. The splitting allows for a smaller and faster convolutional layer. The Light-XSimpleNet architecture is shown in the bottom section of Figure 1.  
Table 6 shows the ablation study and performance of the NNs designed from scratch. Remarkably, the complexity and performance of the SimpleNet architecture is comparable to the ones of the MobileNet-based NN. On the other hand, XSimpleNet and its lighter version outperform all SOTA-based models in terms of inference speed, while retaining a satisfactory accuracy on the test set at hand. Those results represent an encouraging answer to the application of AI into constrained devices issue. Indeed, many simple AI-based scenarios are similar to our case study, and the performance of super simple NN architectures (like XSimpleNet and its lighter version) prove their suitability for solving such tasks.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 3 Controller Design
### Sliding Mode Control
\[\dot{\sigma}=0\to u=u_{eq}\]  
The equivalent control is found as  
\[u_{eq}=\frac{1}{b}\left(\dot{x}_{2}^{r}-f+C\dot{e}\right) \tag{25}\]  
From (24), the following equations are obtained for \(\dot{\sigma}\). \[ \begin{array}{l}\dot{\sigma}=b\left(u_{eq}-u\right)\\ b\left(u_{eq}-u\right)+D\sigma=0\end{array}  \tag{26}\]  
Discretizing (25) and (26), they are obtained as follows:  
\[\frac{\sigma(k)-\sigma(k-1)}{T}=b\left[u_{eq}(k-1)-u(k-1)\right] \tag{27}\]  
\[b\left[u_{eq}(k)-u(k)\right]+D\sigma(k)=0 \tag{28}\]  
\(u_{eq}\) (\(k-1\)) and \(u_{eq}\) (\(k\)) can be solved using (27) and (28). \[u_{eq}(k-1)=u(k-1)+\frac{\sigma(k)-\sigma(k-1)}{bT} \tag{29}\]  
\[u_{eq}(k)=u(k)-\frac{D}{b}\sigma(k) \tag{30}\]  
Assuming the equivalent control is an average control, and it would not change in one period,  
\[u_{eq}(k-1)=u_{eq}(k) \tag{31}\]Manipulating (29) and (30), the control input for the system can be derived as follows:  
\[u(k)=u(k-1)+\frac{1}{bT}[\sigma(k)(1+DT)-\sigma(k-1)] \tag{32}\]
## 4 Simulation Results and Analysis
### Noise and Data Loss Analysis  
When transmitting information online, it is important to consider the potential for it to become lost or overwhelmed by unrelated and unimportant data, known as noise. If digital images are not properly managed, this can negatively impact their usefulness when attempting to analyze, interpret, or retrieve the information they contain. It is important to handle digital information with care to ensure that it remains clear and meaningful. As shown in Figure 9, we evaluated the decryption outcomes of encrypted images that underwent varying levels of data loss. The recovery test was conducted across a range of data loss from 0.01% to 0.9%, and the results revealed that data loss up to 0.5% can be effectively recovered. Figure 10 shows the effectiveness of the proposed mechanism in handling various forms of noise, as demonstrated through the noise effect test. The test covered a range of gap sizes from 0.01% to 0.2% when applied to encrypted images, and the recovery results indicated that data up to 0.19% could be successfully recovered. However, beyond that point, the output quality deteriorates, as evidenced at the 0.2% level. The results demonstrate the capability of the mechanism to deal with various degrees of noise as well as data loss to a considerable extent. Our approach proves to be highly efficient in recovering lost or corrupted data during transmission. The design of the method aims to be highly effective in retrieving information, even in the presence of significant distortion during transmission.  
Figure 8: Key sensitivity.  
Figure 9: Data loss and recovery in different degrees.
## 3 Key Findings
### What Security Enhancement Could the Quantum Mechanism Offer the IIoT Ecosystem?
#### 3.3.2. SOTA of Quantum Mechanism as an Alternative Security Measure
Hence, in this adaptation, the proposal was made to explore mathematical modeling and analytics approaches to adopt quantum mechanisms for the security of WSNs. The author henceforth encouraged the research community to intensify the integration, as the literature depicts the fast metamorphosis from quantum computing to a proposed new era of quantum Internet. Relatively, [PERSON] et al. [105] proposed a quantum cloud for the security of healthcare management systems based on its sensitivity and necessity to all. The study revealed that quantum features such as entanglement and no-cloning theory are most appropriate to safeguard the sensitivity of medical data, while the deployable quantum cloud system (QCS) would be appropriate to take care of the data's processing complexity, molecular simulation, drug discovery, diagnostic exercise, among others. Another industrial realm was considered in the work of [106]. In the study, several security approaches for the automotive industry were considered and compared based on KPIs. Of the approaches, some forms of quantum algorithms take precedence in securing the industry. In recognition of the studies in a quantum ecosystem, a bibliometric study of a decade's research output was conducted by [107]. The authors queried and analyzed 15,911 publications on QC-based security for the IoT environment using VOSViewer's algorithm, ranging from the notable contributing nations, organizations, researchers, citation networks, and collaboration networks to publishing sources. The result showed the researchers' interest in exploring quantum mechanisms for IoT security to the fullest, as it has significant capability over conventional cryptography. Based on this, developed countries such as India and the US have a concentrated leading record in the proposal and deployment of quantum approaches to both quantum attacks and non-quantum attacks, while other countries such as China, Egypt, the UK, South Korea, Saudi Arabia are also in the top following layer in terms of contribution frequency to the creation of, and improvement on, theories and protocols underlying QC-based IoT security. Also, in the same year, [108] decried a lot of optimization problems in an IIoT ecosystem. While considering a variety of available quantum algorithms expressed by [106], the authors conceded to the adoption of a quantum annealing (QA) processor for the optimization problems. The outcome was satisfactory, and the authors suggested the efficacy of the quantum approach as a preventive measure for present and future quantum attacks in an IIoT ecosystem. In a tabular presentation, Table 6 reveals the SOTA on quantum techniques deployment toward diverse industrial settings.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 6 Conclusions  
This paper overviewed how matrix theory can be applied in wireless communications and signal processing applications. In particular, this paper illustrated how matrix transformations, matrix decompositions and random matrix theory could be exploited in the implementation, characterization and optimization of MIMO communication systems, OFDM signaling schemes and signal estimation applications. This paper also reviewed the significant role played by matrices in representing and processing digital images and signals acquired by an array of sensors. Furthermore, a recent research activity termed as compressive sensing was briefly discussed herein paper. It will require continued efforts from researchers in wireless communications and signal processing to ultimately exploit the full potential applications of matrix theory. Furthermore, the complex phenomena in nature and engineering applications require higher-dimensional matrices for their modeling, characterization and understanding. In this regard, tensors have been used in mathematics, physics and engineering since they are a generalization of scalars, vectors and two-dimensional arrays to represent more complex quantities and properties in higher dimensions. Therefore, a promising research direction is to investigate new applications of tensors in wireless communications and signal processing, and to extend the arsenal of mathematical tools available for storing, extracting and processing information from tensors. Finally, we would like to mention that linear algebra plays a very important role in developing powerful algorithms in bioinformatics and systems biology applications. Numerous algorithms for the inference of gene and proteomic regulatory networks as well as for the inference of transcription factor networks have been proposed in literature (see, e.g., [48; 49]) by exploiting linear algebra tools. However, reviewing these computational biology applications as well as numerous other machine learning applications where linear algebra plays a key role is beyond the scope of this paper.  
This work was supported by NSF Award CCF-1318338.  
[PERSON] contributed to draft writing, [PERSON] proposed the framework of the paper and helped to improve the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
###### Abstract  
In this paper, a novel ultra-wideband UWB antenna element with triple-band notches is proposed. The proposed UWB radiator element operates from 2.03 GHz up to 15.04 GHz with triple rejected bands at the WiMAX band (3.28-3.8 GHz), WLAN band (5.05-5.9 GHz), and X-band (7.78-8.51 GHz). In addition, the radiator supports the Bluetooth band (2.4-2.483 GHz). Three different techniques were utilized to obtain the triple-band notches. An alpha-shaped coupled line with a stub-loaded resonator (SLR) band stop filter was inserted along the main feeding line before the radiator to obtain a WiMAX band notch characteristic. Two identical U-shaped slots were etched on the proposed UWB radiator to achieve WLAN band notch characteristics with a very high degree of selectivity. Two identical metallic frames of an octagon-shaped electromagnetic band gap structure (EBG) were placed along the main feeding line to achieve the notch characteristic with X-band satellite communication with high sharpness edges. A novel UWB multiple-input multiple-output (MIMO) radiator is proposed. The proposed UWB-MIMO radiator was fabricated on FR-4 substrate material and measured. The isolation between every two adjacent ports was below \(-\)20 dB over the FCC-UWB spectrum and the Bluetooth band for the four MIMO antennas. The envelope correlation coefficient (ECC) between the proposed antennas in MIMO does not exceed 0.05. The diversity gains (DG) for all the radiators are greater than 9.98 dB.  
ultra-wideband communication; microstrip antenna; sharpness edges; MIMO 202323475. [[https://doi.org/10.3390/s23094475](https://doi.org/10.3390/s23094475)]([https://doi.org/10.3390/s23094475](https://doi.org/10.3390/s23094475))  
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