esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Threshold Self-Corrected Minimum Sum Algorithm for 5G LDPC Decoders
## 3 Proposed DT-SCMS Algorithm
### Thresholds Setting  
If \(th1\) deviates far from \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\), only a small number of node messages are modified so that the performance improvement is limited. Conversely, if \(th1\) deviates near from \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\), a large number of node messages are modified, which expands the range of unreliable messages and causes degradation in the decoding performance. The purpose of adding \(th2\) is to identify \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\) as a reliable message when it deviates very far from \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\). Here, the thresholds \(th1\) and \(th2\) are defined as:  
\[th1 = \theta 1\cdot L^{(l-1)}\Big{(}q_{ij}\Big{)} \tag{11}\] \[th2 = \theta 2\cdot L^{(l-1)}\Big{(}q_{ij}\Big{)} \tag{12}\]  
where \(\theta 1\) and \(\theta 2\) are adjustment factors, \(\theta 1\) ranges from \(-0.5\leq\theta 1\leq 0.5\), and \(\theta 2\) ranges from \(-1.5\leq\theta 2\leq-0.5\), so that the value of \(th1\) does not deviate too far or too near from \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\), and \(th2\) can filter out certain reliable message nodes. At the same time, both \(th1\) and \(th2\) are the relative values of \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\). As \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\) is changed in each iteration, \(th1\) and \(th2\) will also change as iterative updates, making sure that the criteria for judging reliability are adaptively updated.  
In different LDPC codes, different thresholds can be set variously according to the requirements of the applications. The method of density evolution can be used to choose the optimal values of \(\theta 1\) and \(\theta 2\), as the NMS algorithm uses [17].  
Figure 1: Schematic diagram of reliable message determination for the dual threshold self-corrected minimum sum (DT-SCMS) algorithm.
Table 1 introduces some typical existing studies and evaluates them in terms of optimization goals, adoption methods, network structures, and advantages and disadvantages. Most of the research is aimed at improving the lifetime of network systems, and the strategies to maximize network lifetime can be divided into deployment optimization, data processing, and protocol design. Among them, routing protocol design optimization is a more effective and widely used strategy, and protocol design can be divided into single-hop, multi-hop, and clustering methods. However, for a typical linear network such as a high-speed railway operating environment monitoring network, it is obviously more appropriate to use a multi-hop routing protocol. In some studies, the network lifetime is extended by minimizing the total network energy consumption (MTECR). However, its shortcomings are also obvious. As mentioned above, the problem of unbalanced network  
energy consumption while minimizing energy consumption leads to network energy holes, resulting in the premature death of some nodes, which affects the service life of the network. In addition, some other research focuses on balancing the energy consumption of nodes in the network, such as MVECR and AUMRP, and proves that their balancing scheme based on the residual energy of nodes is more effective for prolonging the network lifetime. To the best of our knowledge, there are few studies on the co-optimization of lifetime and delay for high-speed railway operational environmental monitoring networks. Traditional routing algorithms cannot ensure stable transmission services in dynamic environments because they cannot cope with network dynamics and voids. The maintaining of global routing information incurs great overhead, and the complex structure also reduces
###### Abstract  
A 5G metasurface (MS) transmitarray (TA) feed by compact-antenna array with the performance of high gain and side-lobe level (SLL) reduction is presented. The proposed MS has two identical metallic layers etched on both sides of the dielectric substrate and four fixed via connecting two metallic layers that works at 28 GHz to increase the transmission phase shift range. The proposed planar TA consisting of unit cells with different dimensional information can simulate the function as an optical lens according to the Fermat's principle, so the quasi-spherical wave emitted by the compact Potter horn antenna at the virtual focal point will transform to the quasi-plane wave by the phase-adjustments. Then, the particle swarm optimization (PSO) is introduced to optimize the phase distribution on the TA to decrease the SLL further. It is found that the optimized TA could achieve 27 dB gain at 28 GHz, 11.8% 3 dB gain bandwidth, \(-\)30 dB SLL, and aperture efficiency of 23% at the operating bandwidth of 27.5-29.5 GHz, which performs better than the nonoptimized one. The advanced particularities of this optimized TA including low cost, low profile, and easy to configure make it great potential in paving the way to 5G communication and radar system.  
Keywords:high gain; transmitarray (TA) antenna; metasurface (MS); PSO; side-lobe level (SLL) reduction +
Footnote †: journal: Accepted: 6 August 2020; Published: 10 August 2020
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 2 Methodology
### Gain Estimation  
The MATLAB 2014 software provides a tool for fine-tuning PID controllers in PID tuners based on optimization techniques and response, employing Ziegler-Nichols and frequency response methods [24]. The process, designated the PID tuner, adheres to a prescribed sequence of steps for its preliminary tuning, which encompasses the following:  
1. System modeling: The application can accept a transfer function as an input or estimate it. The previously estimated transfer function (see Equation (5)) was input in the present case;
2. The initial parameters of the PID controller are calculated by MATLAB using frequency-based techniques based on the principles set forth by the Frequency Response Theorem and Ziegler-Nichols. This approach permits the identification of an initial configuration that offers an optimal equilibrium between stability, response speed, and damping;
3. The objective of performance-based optimization is to achieve optimal performance. Once the initial PID parameter values have been obtained, the PID Tuner employs optimization algorithms to fine-tune the Kp, Ki, and Kd parameters;
4. The following performance criteria will be considered: In the preceding step of the optimization process, the application also considers performance criteria, including minimizing overshoot, settling time, response speed, and steady-state error;
5. Controller validation: The tuned controller is subsequently validated through simulations to guarantee that it fulfills the design specifications, including stability and the desired performance regarding response time and frequency analysis.  
In accordance with the aforementioned methodology, the PID Tuner facilitates the acquisition of the preliminary tuning parameters, for example, for a PI-type controller. In light of the results mentioned above derived from the first-order plant approximation, the proportional gain \(k_{p}=0.9\) and the integral gain \(k_{i}=0.001\) were estimated.  
The use of a P.I. controller is supported by its ability to correct steady-state errors and improve system stability. Previous studies have demonstrated its effectiveness in peristaltic pumps, emphasizing its capability to reduce fluctuations in high-precision applications such as hemodialysis [25; 26]. In this case, the inclusion of the integral term ensures adequate control even under variable load conditions, while the proportional term responds quickly to transient errors, optimizing the overall system performance. This combination makes the P.I. controller particularly suitable for maintaining precise flow rates in medical applications.  
The controller incorporates the constants, and the peristaltic pump is characterized by establishing a correlation between the revolutions per minute (RPM) and the delivered volume and mass. This methodology is delineated in the following section.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 2 Background
### Security in Real DTN Implementations  
Several existing DTN implementations have been released with varying compliance regarding the proposed standards [19]. More specifically, the main implementations considering security are listed below.  
* _DTN2_[20]--This is the most well known implementation of the BP, which implements partially the Bundle Security Protocol (BSP) specification. It uses OpenSSL library to perform the cryptographic operations, although most functionality for cryptographic operations is not yet implemented [19].
* _ION_[21]--JPL's implementation of the BP. It implements the BSP Bundle Authentication Block (BAB), Bundle Confidentiality Block (PCB) and Bundle Integrity Block (PIB) security blocks in versions greater than 3.0.0.
* _IBR-DTN_[22]--BP implementation for embedded systems, which relies on the BSP specification.
* _ByteValla_[23]--BP implementation for Android devices. It implements the BSP PCB security block with AES in Galois Counter Mode (GCM).
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 4 Experimental Results
### Parameter Settings
When the \(T\) increases, the transformed image quality will decrease because the number of image blocks used to match by traditional RIT method is reduced, while the amount of AI will decrease because the number of image blocks used to hide partial AI is increased. To choose an appropriate \(T\), we maintain the parameter \(M\) such as \(M=3\), and change T. In the average values of the table, when \(T\) is larger than 3, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Block Size**} & **Transformed Image** & **Stego-Image (RMSE)** & **AI (bpp)** \\  & **(RMSE)** & **4** & **4** \\ \hline \(3\times 3\) & 10.725 & 14.990 & 0.852 \\ \(4\times 4\) & 13.001 & 13.310 & 0.488 \\ \(6\times 6\) & 16.000 & 16.060 & 0.227 \\ \(8\times 8\) & 17.918 & 17.947 & 0.134 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results with different block sizes. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Example 1**} & \multicolumn{2}{c}{**Example 2**} & \multicolumn{2}{c}{**Example 3**} & \multicolumn{2}{c}{**Example 4**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-10}  & **AI** & **RMSE** & **AI** & **RMSE** & **AI** & **RMSE** & **AI** & **RMSE** & **AI** & **RMSE** \\ \hline \(T=1\) & 0.852 & 18.868 & 0.738 & 12.841 & 1.070 & 16.631 & 0.973 & 11.139 & 0.908 & 14.870 \\ \(T=3\) & 0.541 & 19.131 & 0.494 & 13.023 & 0.584 & 16.756 & 0.495  
the average RMSE of the transformed image will increase rapidly but the average AI increases slowly. Thus, \(T=3\) is an appropriate value. As mentioned in Section 3.2, the parameter \(M\) is used to express the number of secret segments represented by each image block. When \(M\) increases, the AI will decrease because each image block can represent more secret bits to be patched into the composite image, while the transformed image quality is not changed too much. To choose an appropriate \(M\), we maintain the parameter \(T=3\), and change the value of \(M\). In the average values of the table, when \(M\) is larger than 3, the average AI is decreased very slowly because the amount for recording the class index is also increased, while the transformed image quality is increased. Thus, \(M=3\) is an appropriate value.
## 2 Theoretical Background
### Fundamental Concepts of Uncertainty Theory  
As a vital concept of uncertainty theory, uncertain measure is interpreted as the human belief degree of an event that may occur.  
**Definition 1** (Uncertain measure [31]).: _Let \(\Gamma\) be a nonempty set and \(\mathcal{L}\) be a \(\sigma\) -algebra over \(\Gamma\). A set function \(\mathcal{M}:\mathcal{L}\rightarrow[0,\,1]\) is defined as an uncertain measure if it satisfies normality, subadditivity, and duality axioms._  
**Definition 2** (Uncertain variable [31]).: _The uncertain variable is a measurable function \(\xi\) from an uncertainty space \((\Gamma,\,\mathcal{L},\,\mathcal{M})\)to the set of real numbers, i.e., \(\{\xi\in B\}\) is an event for any Borel set \(B\)._  
**Definition 3** (Uncertainty distribution [4]).: _Uncertainty distribution \(\Phi:R\rightarrow[0,\,1]\) of an uncertain variable \(\xi\)can be defined by \(\Phi(x)=\mathcal{M}\{\xi\leq x\}\)._  
In general, regular uncertainty distribution is defined as a continuous and strictly increasing uncertainty function \(\Phi(x)\) with respect to \(x\) with \(0<\Phi(x)<1\). For example, a normal uncertain variable \(\xi\sim\mathcal{N}(m,\,\sigma)\) has a normal uncertainty distribution:  
\[\Phi(x)\ =\ (1+\exp(\,\frac{\pi(m-x)}{\sqrt{3}\sigma}))^{-1},x\in\mathbb{R}, \tag{1}\]  
where \(m\) and \(\sigma>0\) are the expected value and standard deviation, respectively.  
A linear uncertain variable \(\xi\sim\mathcal{L}(a,\,b)\) has a linear uncertainty distribution:  
\[\Phi(x)\ =\ \left\{\begin{array}{ll}0,\text{ if }x\leq a\\ \frac{x-a}{b-a},\text{ if }a<x\leq b\\ 1,\text{ if }b<x\end{array}\right., \tag{2}\]  
where \(a\) and \(b\) are real numbers with \(a<b\).  
**Definition 4** (Inverse uncertainty distribution [4]).: _Let \(\xi\) be an uncertain variable with regular uncertainty distribution \(\Phi(x)\). The inverse function \(\Phi^{-1}(\alpha)\) is called the inverse uncertainty distribution of \(\xi\)._  
**Theorem 1.**_Consider independent uncertain variables \(\xi_{1}\), \(\xi_{2}\), \(\cdots\), \(\xi_{n}\) with regular uncertainty distributions \(\Phi_{1}\), \(\Phi_{2}\), \(\cdots\), \(\Phi_{n}\), respectively. Suppose that \(\xi=f(\xi_{1},\cdots,\xi_{n})\) is continuous, strictly increasing for \(\xi_{1}\), \(\xi_{2}\), \(\cdots\), \(\xi_{m}\), and strictly decreasing for \(\xi_{m+1}\), \(\xi_{m+2}\), \(\cdots\), \(\xi_{n}\). Then, inverse uncertainty distribution of \(\xi=f(\xi_{1},\cdots,\xi_{n})\) can be calculated as follows [4]:_  
\[\Psi^{-1}(\alpha)=f(\Phi_{1}^{-1}(\alpha),\Phi_{2}^{-1}(\alpha),\cdots,\ \Phi_{m}^{-1}(\alpha),\Phi_{m+1}^{-1}(1-\alpha),\Phi_{m+2}^{-1}(1-\alpha), \cdots,\ \Phi_{n}^{-1}(1-\alpha)) \tag{3}\]  
**Definition 5** (Uncertain expected value [31]).: _Let \(\xi\) be an uncertain variable. Uncertain expected value of \(\xi\) can be defined as follows:_  
\[E[\xi]\ =\int_{0}^{+\infty}\mathcal{M}\{\xi\geq x\}\mathrm{d}x-\int_{-\infty}^{0} \mathcal{M}\{\xi\leq x\}\mathrm{d}x \tag{4}\]
## 2 Proposed Circuit Arrangement
### Inverting Behavior  
In inverting mode of operation, the polarity of the output voltage is always opposite with respect to the polarity of the input voltage. It means that output voltage is always out of phase with respect to the input voltage. There is no change in the voltage at the output of the first converter either there is a non-inverting or inverting mode, this voltage always remains in pulsating dc form (absolute value of the input voltage). The behavior of the output voltage is governed by the switching scheme of the transistors of the second converting stage. For example, during the interval in which input voltage is positive, the turning on of the switching transistors \(Q_{3}\) and \(Q_{4}\) realized negative (inverting) output. It is demonstrated in the highlighted power transfer loop of Figure 6c.  
Similarly, the turning on of the switching transistors \(Q_{1}\) and \(Q_{2}\) is responsible for positive output once the input voltage is negative. The power transfer loop for inverting output during the negative input is highlighted in Figure 6d. Inverting output predicts the negative voltage gain that can be evaluated with the help of inductor voltage second product phenomena.  
The value of inductor voltage during the overlapping interval is the same as computed for non-inverting operation.  
\[v_{L}(t)=v_{s} \tag{8}\]  
However, the value of inductor voltage during the non-overlapping interval is larger as it is computed for non-inverting operation. Because in inverting mode, the inductor voltage is the sum of the input and output voltage as the output is always inverted with respect to the input.  
\[v_{L}(t)=v_{s}+v_{o} \tag{9}\]  
The average value of the inductor voltage should be zero during any switching interval that may be formulated as:  
\[v_{s}\cdot t_{ov}+t_{con}\cdot(v_{s}+v_{o})=0 \tag{10}\]  
\[\frac{v_{o}}{v_{s}}=-\frac{t_{ov}+t_{con}}{t_{con}}\simeq-1 \tag{11}\]  
This result validates the inverting operation for any polarity of the input voltage, as can be seen as:  
\[v_{o}(\omega t)=-V_{m}sin(\omega t) \tag{12}\]  
The summary of the operating modes (inverting and non-inverting) with either polarity of the input voltage is explored in Table 1 by detailing the operating states of all the solid-state devices employed in the proposed topology.  
It can be observed that there is no change in the operating states of the diodes once the voltage at the output is changed from non-inverting to inverting and vice versa. However, the operating states of the transistor's pairs \(Q_{1}\)-\(Q_{2}\) and \(Q_{3}\)-\(Q_{4}\) are changed. For example, during positive input voltage, the switching transistors \(Q_{1}\)-\(Q_{2}\) are on and off for non-inverting and inverting operations, respectively. In addition, there is the conduction of two transistors and two diodes that realized the output in any desired operating mode (non-inverting or inverting).
## 2 IoV Distributed Intelligence
### Intelligence at the Edge for IoV
#### 2.4.2 Aspects and Advantages  
Intelligence at the edge in the context of the IoV refers to the deployment of computational and analytical capabilities directly within the vehicles or at the network's edge, rather than relying solely on centralized cloud-based processing. In IoV applications, as mentioned above, the need for real-time decision-making, decreased latency, increased efficiency, and improved privacy is what motivates this strategy. Here are key aspects and advantages related to intelligence-at-the-edge in IoV:  
1. **Real-time Decision-Making:** By embedding intelligence at the edge, vehicles can make local, real-time decisions without relying on a centralized cloud server. This is critical for applications such as emergency braking and collision avoidance that require quick reactions [49].
2. **Reduced Latency:** Edge computing minimizes the delay in processing the data, since computations occur closer to the source of the data. This is particularly crucial in the IoV, where accurate and timely responses to dynamic traffic conditions depend on low-latency communication [50].
3. **Bandwidth Efficiency:** Processing data at the edge reduces the need to transmit large amounts of raw data to a central server for analysis. Instead, only relevant or summarized information can be sent, optimizing bandwidth usage in IoV networks [51].  
4. **Enhanced Privacy and Security:** Edge intelligence allows data processing to occur locally, addressing concerns related to privacy and security. Sensitive information can be processed within the vehicle, minimizing the exposure of personal data to external networks [52].
5. **Distributed Computing:** Edge computing in IoV involves a distributed computing paradigm where intelligence is distributed across vehicles and road infrastructure. This decentralized approach enables collaborative decision-making and more efficient utilization of resources [53].
6. **Scalability:** Edge computing supports scalability in IoV applications. As the number of connected vehicles increases, edge devices can handle processing tasks locally, preventing bottlenecks on centralized cloud servers [54].
7. **Adaptive learning:** Intelligent edge devices can employ machine learning algorithms to adapt and improve their performance based on the data they process. This adaptability is valuable in IoV scenarios where traffic patterns and conditions can change dynamically [55].
8. **Offline operation:** Edge intelligence allows vehicles to perform certain tasks even when not connected to the central network. This offline operation is beneficial in scenarios where network connectivity may be intermittent or unavailable [56].  
In summary, deploying intelligence at the edge in IoV applications offers the above-mentioned advantages. This approach aligns with the dynamic and distributed nature of IoV, which contributes to more efficient and responsive connected vehicle systems.
# A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
## 5 Experimental Results
### Image Verification
#### 5.3.2 Image Similarity Siamese Network  
Figure 11 shows the receiver operating characteristic (ROC) curve of the image similarity measure results. The proposed similarity measure shows better performance than that of MSE and SSIM. Since handcrafted image quality metrics such as MSE and SSIM take an average of local similarities for their final similarity scores, it is insensitive to capture local differences. Specifically, SSIM is robust to linear transforms such as contrast enhancement and mean shift, but its similarity score is greatly reduced when nonlinear transforms are such as JPEG compression or blurring occur. On the other hand, since the proposed network is designed from a cognitive perspective on geometric structures, it is able to handle color manipulations better than the other similarity metrics. Table 5 also provides F1-scores and AUC (Area Under the Curve) of Figure 11, showing the great performance of the proposed similarity measure.  
In addition, to show efficiency of similarity measure based on significantly different patches, the results of patch-based MSE and SSIM are also provided. MSE of sampled patches is measured as the same as MSE of an image due to its consistent similarity statistics. The patch-based SSIM reflects only local patches with large structural differences  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Clipping Proportion** & **\(\sim\)10\%** & **\(\sim\)15\%** & **\(\sim\)20\%** & **\(\sim\)25\%** & **\(\sim\)30\%** \\ \hline Avg. IoU & 0.998 & 0.998 & 0.998 & 0.998 & 0.997 \\ Min. IoU & 0.991 & 0.989 & 0.992 & 0.986 & 0.990 \\ \hline \end{tabular}
\end{table}
Table 4: Image alignment results according to clipping proportions.  
Figure 11: Receiver operating characteristic curves of similarity metrics with geometric coincident image pair or patch pairs. Patch means a similarity metric uses multiple patches sampled from an image as inputs. The pink curve (ROC of mse) is same as the cyan curve (ROC of mse_patch).  
and seems very sensitive to subtle differences. However, due to the sensitivity to nonlinear transforms, the results of SSIM are worse than those of the proposed similarity measure.