esa-sceva/satcom-chunk-collection huggingface dataset:
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 3 Girth and Cycle Structure Analysis of BIBD-LDPC Codes
### Cycle Structure of BIBD-LDPC Codes  
According to the definition of cycles in the Tanner graph [1,11], a cycle in the BIBD-LDPC code can be represented as an ordered sequence of positions of alternate nonzero elements in the parity-check matrix. Based on the structure of the incidence matrix of BIBD\((\kappa,1;v)\), a cycle of length \(2i\) in the Tanner graph of the BIBD-LDPC code can be expressed as the following sequence:  
\[x_{j_{0}},B_{l_{0}},x_{j_{1}},B_{l_{1}},x_{j_{2}},B_{l_{2}},\ldots,x_{j_{i-1}},B_{l_{i-1}},x_{j_{0}},B_{l_{0}} \tag{1}\]  
where for \(0\leq k\leq i-1,j_{k}\
eq j_{k+1},j_{i}=j_{0},l_{k}\
eq l_{k+1},l_{i}=l_{0},x_ {j_{k}}\in\mathbf{X}\), and \(B_{j_{k}}\subset\mathbf{B}\), \(x_{j_{0}},x_{j_{1}},\ldots,x_{j_{i-1}}\) are distinct points in \(\mathbf{X}\), and \(B_{l_{0}},B_{l_{1}},\ldots,B_{l_{i-1}}\) are distinct blocks in \(\mathbf{B}\). Each consecutive pair \(((x_{j_{i}},B_{l_{i}})\) and \((x_{j_{i+1}},B_{l_{i}})\), where \(j_{i}=j_{0}\) and \(l_{i}=l_{0}\)) corresponds to a nonzero entry in the incidence matrix (**I**), ensuring the cycle is closed. This sequence represents a closed path in the Tanner graph, where each point \((x_{j_{i}})\) is connected to block \(B_{l_{i}}\) and each block \((B_{l_{i}})\) is connected to the next point \((x_{j_{i+1}})\), forming a cycle of length \(2i\).  
In other words, the adjacent points (or blocks) in the sequence (1) are distinct from one another. Additionally, each block in (1) contains its two adjacent points, and each point in (1) is included in its two adjacent blocks. For a BIBD\((\kappa,1;v)\), any pair of points in \(\mathbf{X}\) is contained in exactly one block. Consequently, for two points adjacent to the same block in (1), no other block contains both of them. Therefore, the point-and-block sequence (1) can be simplified and represented by the following point sequence:  
\[x_{j_{0}},x_{j_{1}},x_{j_{2}},\ldots,x_{j_{i-1}} \tag{2}\]  
where for \(0\leq k\leq i-1,j_{k}\
eq j_{k+1}\), that is, a cycle of length \(2i\) can be fully represented by the point sequence (2), making it unnecessary to explicitly consider the blocks in the cycle. Therefore, a cycle of length \(2i\) corresponds uniquely to an ordered point sequence of the following form:  
\[x_{j_{0}},x_{j_{1}},x_{j_{2}},\ldots,x_{j_{i-1}},\]  
where \(x_{j_{0}},x_{j_{1}},x_{j_{2}},\ldots,x_{j_{i-1}}\) are distinct points in \(\mathbf{X}\) and each consecutive pair \((x_{j},x_{j+1}\), where \(x_{i}=x_{0})\) is contained in exactly one block (\(B_{k}\in\mathbf{B}\)). This representation simplifies the analysis of cycles by focusing solely on the point sequence, leveraging the property of BIBD\((\kappa,1;v)\) according to which each pair of points appears in exactly one block. As a result, the cycle structure is entirely determined by the arrangement of points, eliminating the need to explicitly track the blocks in the cycle.
# A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation
## 1 Introduction
The platform is used together with our previously developed muscle strength assessment system [24; 25]. The assessment system allows therapists to assess the status of patients and adjust the training parameters of various training devices. The contribution of this paper is that with the therapist-in-the-loop platform, human therapists are capable of appropriately adjusting the training strategy and amount of kinesthetic guidance over the work session. In particular, both the assistive and resistive force generated by the therapist can be transferred into a patient with an exoskeleton device for delivering training. The exoskeleton for patients is safe to use with a specially designed torque limiter mechanism [26]. In particular, the use of virtual reality (VR) technology can provide goal-directed tasks with rewards and motivate the patient to undertake extended rehabilitation. Through the visually shared model, the motion of the therapist and patient are measured and mapped to the corresponding object's motion. The force generated by the therapist can be transmitted to the patient for delivering training with an exoskeleton device, while real-time force feedback can be provided to a therapist with a haptic device to know the amount of force being applied to the patient. This paper is organized as follows. The background and related research are introduced in Section 1. In Section 2, the schematic of the implemented training platform and the visually shared model are introduced. In particular, the 2-port network model is introduced for transparency between the master and slave sides of the proposed platform. In Section 3, the models for patient-exoskeleton interaction and therapist-haptic device interaction are given. The experiments and results are given in Section 4 including patient-exoskeleton, therapist-haptic device interaction, and a therapist-in-the-loop test. Lastly, the conclusion and future works are discussed.
## 2 Related Works  
### Enhancement of Haze Images  
In the early stages of research, [PERSON] et al. [5] introduced the concept of Markov Random Fields, aiming to enhance haze images by maximizing local contrast. Subsequently, [PERSON] et al. [6] proposed the widely acclaimed dark channel prior estimation method, which gained significant popularity. Following this, dark channel-based dehazing techniques underwent continuous improvements, such as the fast image dehazing method based on the dark channel introduced by [PERSON] et al. [7], the sky-constrained dark channel prior-based dehazing method presented by [PERSON] et al. [8], and the remote sensing image cloud detection algorithm based on dark channel by [PERSON] et al. [9]. [PERSON] et al. [10] introduced the color-line method based on the one-dimensional distribution of image blocks in the RGB color channels. Despite the notable achievements of traditional algorithms in dehazing, there still exist several limitations, including issues related to poor robustness.  
DehazeNet [11] was the pioneering model that utilized deep learning for dehazing, employing Convolutional Neural Networks (CNNs) for feature extraction, followed by the application of the Atmospheric Scattering Model (ASM) to obtain clear images through transmission map estimation. On the other hand, AOD-Net [12] aimed to eliminate the complex transmission map and atmospheric light estimation steps in previous models, achieving simultaneous estimation of transmission map and atmospheric light through a redesigned ASM. However, these ASM-based models tend to exhibit color distortion issues when handling dust and underwater images, prompting a shift towards end-to-end restoration approaches. [PERSON] et al. [13] introduced the FFA-Net with attention modules, achieving promising results. [PERSON] et al. [14] employed multiple UNet networks to output different scale features and then combined them for multi-scale learning. [PERSON] et al. [15] proposed the Fast Deep Multi-Patch Hierarchical Network, aggregating multiple features from various spatial regions with fewer parameters to restore non-uniform hazy images. [PERSON] et al. [16] used a dual-branch neural network to address different aspects of the dehazing process, enhancing different aspects of hazy image restoration with distinct network branches. Additionally, several other models [17; 18] have been dedicated to enhancing the clarity of haze images, yielding commendable outcomes.
###### Abstract  
A broadband coplanar waveguide (CPW)-fed monopole antenna based on conventional CPW-fed integration with an organic solar cell (OSC) of 100% insolation is suggested for Ku band satellite communication. The proposed configuration was designed to allow for 100% insolation of the OSC, thereby improving the performance of the antenna. The device structure was fabricated using a Leiterplatten-Kopierfrasen (LPKF) prototyping Printed circuit board (PCB) machine, while a vector network analyzer was utilized to measure the return loss. The simulated results demonstrated that the proposed antenna was able to cover an interesting operating frequency band from 11.7 to 12.22 GHz, which is in compliance with the International Telecommunication Union (ITU). Consequently, a 3 GHz broadband in the Ku band was achieved, along with an enhancement in the realized gain of about 6.30 dB. The simulation and experimental results showed good agreement, whereby the proposed structure could be specifically useful for fixed-satellite-services (FSS) operating over the frequency range from the 11.7 to 12.22 GHz (downlink) band.  
CPW-fed monopole antenna; satellite communication; organic solar cell; P3 HT:PCBM 2020, 13, 142; doi:10.3390/ma13010142
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 2 Experimental Procedures
### Characteristics of Experimental Raw Materials
#### 2.1.4 Characteristics of Fly Ash  
Fly ash is generated in the combustion of coal and is an important material used to fill voids left after mining. Fly ash is mainly sourced from solid particles in the emissions associated with the combustion of coal in coal-fired power plants. To avoid the environmental pollution of discharged fly ash, fly ash was collected as an additive of the coal slime based backfill material to enhance the strength and stability of the filling bodies, so that they can bear geological stress. The chemical composition of fly ash is displayed in Table 6.  
SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) generally account for large proportions by mass of fly ash. This exerts important influences on the strength and stability of the specimens of backfill  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **TiO\({}_{2}\)** \\ \hline
55.8 & 22.1 & 11.9 & 3.4 & 2.3 & 3.2 & 0.6 & 0.3 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 6: Chemical composition of fly ash (mass fraction) %.  
Figure 4: The particle size distribution of cement.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **TiO\({}_{2}\)** \\ \hline
22.5 & 5.3 & 4.7 & 61.7 & 2.1 & 2.2 & 0.8 & 0.4 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 5: Chemical composition of cement (mass fraction) %.  
materials prepared with fly ash and helps to improve the early strength and fire resistance thereof.  
It can be seen from Figure 5 that the particle size of fly ash is mainly between 10 and 80 \(\upmu\)m, in which the material shows the following physical properties:  
An ability to fill voids and stability: the fly ash with an appropriate particle size range can fill in voids in the coal slime based filling bodies, enhance the stability, and avoid the collapse of mined-out areas;  
Inhibiting the dispersivity: some active constituents in fly ash can suppress the dispersion of harmful materials and play a role in protecting the environment around mines;  
Lowering the cost: use of fly ash, as a by-product, can reduce the cost of backfill materials and improve the economics of engineering/mining operations.
## 2 Approach to Study Selection
### Managing Bias in the Review  
To uphold the objectivity and methodological rigor of the review, several strategies were implemented to manage and minimize bias throughout the assessment process:  
1. Diverse Assessors: Each study was reviewed by two assessors with different academic backgrounds and expertise in both assistive robotics and healthcare. This diversity ensured a broad range of perspectives and minimized the risk of individual biases influencing the evaluation.
2. Clear Assessment Criteria: The studies were analyzed using predefined criteria, including Clarity of Rationale, Study Design Appropriateness, methodological rigor, Result Presentation, Justification of Conclusions, and disclosure of conflicts of interest. Furthermore, data were presented based on a standardized checklist, reducing the risk of subjective interpretation.
3. Scoring System: Each parameter was rated on a scale from 1 to 5, while the disclosure of conflicts of interest was assessed using a binary evaluation (Yes/No). This quantitative approach ensured consistent evaluations across studies and provided a transparent mechanism for comparing study quality.
4. Independent Review: The primary assessors reviewed the studies independently, assigning scores without prior discussion. This independence ensured that individual judgments were based solely on the study's merit and predefined criteria, minimizing groupthink or shared biases.
5. Dispute Resolution: In cases where the two assessors disagreed on scores or study inclusion, a third assessor was consulted to provide an impartial judgment. This adjudication helped resolve conflicts fairly and ensured balanced decision making.
6. Structured Mechanism for Disagreements: The process for resolving disagreements was formalized and structured. The third assessor reviewed the initial evaluations and provided a reasoned judgment to reconcile differences. This structured approach ensured that conflicts were systematically addressed and that final decisions were based on a comprehensive evaluation.
7. Transparency: The use of a standardized checklist for data presentation and a clear scoring system enhanced transparency in the assessment process. By documenting the criteria and scoring rationale, the review process became more traceable and reproducible, reducing the potential for undisclosed biases.  
By incorporating these strategies, this review aimed to provide a thorough and balanced evaluation of the literature. The multi-assessor approach, combined with structured criteria and a formal dispute resolution mechanism, was designed to minimize bias and enhance the reliability and objectivity of the review process.
## 5 Experiments and Analyses
### Performance on Different Numbers of Requests  
In the fourth experiment, different numbers of requests were submitted to Resource Broker, including 30, 60, 90, 120, and 150 rather than 60, and each experiment lasted 10 s rather than 200 s listed in Table 11. The experimental results are shown in Figure 13, in which Figure 13a illustrates the drop rates. The greater the number of submitted user requests, the higher the drop rates. The reason is mentioned above. Due to establishing multiple routing paths to deliver packets, the SPA-Multipath drop rates are lower than those of the SPA-WP.  
As illustrated in Figure 13b, when the number of user requests increases, the throughputs are lower, since generating a huge number of packets will contest the wireless AP and the wireless channel contention will be serious. The Random's and FCFS's throughputs are lower than those of the SPA-WP and the SPA-Multipath. This is because the former two do not balance the loads of resource nodes, and their drop rates are higher.  
Figure 13c shows their turnaround times. When more requests were sent, due to higher probabilities of channel contention, job preemption (with the SPA-WP and the SPA-Multi), packet collision, job re-enqueue (with Random) and longer global queue, the turnaround times were longer. Theoretically, the turnaround time when number of requests = 30 is 186 s (\((=(\sum\limits_{i=1}^{30}12\times i)/30)\)). Others' theoretical values can be calculated in the same way. With the FCFS, the performance of nodes 1 and 4 was better than that of nodes 5 and 6 so that the FCFS's turnaround times were shorter than those of the Random, since about 66% of requests were served by the two nodes. However, if nodes 1 and 4 were the latest two nodes entering the system, the turnaround times, denoted FCFS' in Figure 13c, were longer than those of the Random. The turnaround times with the SPA-WP and the SPA-Multipath were shorter than those with the Random and FCFS. The reason is described above.  
Figure 13: Performance of the fourth experiment given different numbers of requests per second. (**a**) Drop rates; (**b**)Throughputs; (**c**) Turnaround times.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation
### Throughput Performance Varying the Number of Malicious Nodes  
The simulation was run varying the number of malicious nodes from 5 to 50 with a constant number of 200 nodes and a flooding rate of 50 packets per second.  
Figure 5 shows that increasing the number of malicious nodes from 5 to 25 sharply reduces the throughput of AODV from 0.27 Mbps to around 0.054 Mbps. In contrast, there was a slight decrease in the TAODV case.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 4 ArPico VBL
### ArPico to ArUco Transformation  
An example of the ArPico marker conversion for pictorial objects is shown in Figure 4. ArPico are always displayed with a black frame (Figure 1b). This black frame should be cropped off from the picture before the marker identification (the upper-left picture on Figure 4). Then convert the inner picture to greyscale (the upper-right picture on Figure 4), and divide it into blocks. In each block, we use the average of grey value as its feature, thus an average grey block matrix is created (the lower-left picture on Figure 4). The median of the matrix is set as the threshold to define each block as 0 or 1 (black or white). In this way, an ArPico pictorial object is converted to a 2D code pattern (the lower-right picture on Figure 4) just like an ArUco marker (Figure 1c).
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture
### Layer Normalization  
Layer normalization is a normalization technique that normalizes the inputs to a layer across the feature dimension, which can help improve the stability and performance of deep learning models. In our proposed model, we used layer normalization as a critical component to enhance the stacked normalized recurrent neural network (SNRNN) performance for earthquake detection. While batch normalization is another commonly used normalization technique, we chose layer normalization in our model due to its ability to normalize the inputs across different samples in a batch. This can be particularly useful when working with earthquake data.
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 3 Performance Evaluation and Results
### ADR vs PSNR  
Figure 9 presents the ADR of the proposed D2C model with respect to the PSNR values for all three mechanisms of data embedding. We performed a real-world experiment and image rotation simulation respectively, with and without considering the optical wireless channel. We can observe that the system yields relatively higher ADR values at a lower PSNR level. This is because the ADR is directly related to the BER, i.e., \((1-BER)\times m\). For all three data-embedding mechanisms, a maximum ADR of approximately 460 bps was achieved under real-world conditions for all perspective parameters.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 4 Balanced Realization/Truncation Technique for 2-D IIR Digital Filters
### Design Procedures  
For a 2-D IIR filter with a separable denominator, it is assumed that either \(A_{12}=0\) or \(A_{21}=0\). The design steps proposed for this type of 2-D filter are described as follows:  
1. Design a linear-phase 2-D FIR digital filter that approximates the required frequency response.
2. Realize the designed 2-D FIR filter \(H(z_{1},z_{2})\) in state space using [PERSON]'s model [6,41] as follows: \[\begin{bmatrix}X^{h}(i+1,j)\\ X^{v}(i,j+1)\end{bmatrix}=\begin{bmatrix}A_{11}&A_{12}\\ A_{21}&A_{22}\end{bmatrix}\begin{bmatrix}x^{h}(i,j)\\ x^{v}(i,j)\end{bmatrix}+\begin{bmatrix}b_{1}\\ b_{2}\end{bmatrix}u(i,j)\] (14) \[y(i,j)=\begin{bmatrix}c_{1}&c_{2}\end{bmatrix}\begin{bmatrix}x^{h}(i, j)\\ x^{v}(i,j)\end{bmatrix}+du(i,j),\] (15)where \(A_{11},A_{12},A_{21},A_{22},b_{1},b_{2},c_{1},c_{2}\), and \(d\) are real matrices.
3. Compute the structured controllability Gramian \(P^{s}\) and the structured observability Gramian \(Q^{s}\) using the LMI-based algorithm proposed in Section 3. Note that since either \(A_{12}\) or \(A_{21}\) is zero, the equations are simplified and the computational cost of these Gramians is reduced. The obtained structured controllability and structured observability Gramians are block-diagonal matrices, i.e., \(P^{s}=diag(p_{1},p_{2})\) and \(Q^{s}=diag(q_{1},q_{2})\).
4. Find the invertible matrices \(T_{1}\) and \(T_{2}\) such that \[T_{i}^{-1}p_{i}(T_{i}^{-1})^{T} =\Sigma_{i}=T_{i}^{T}q_{i}T_{i}\] (16) \[=diag(\sigma_{1i},\sigma_{2i},\ldots\sigma_{n,i}),\;\;i=1,2.\]
* Compute the matrices \(M_{1i}=p_{i}q_{i},\;\;i=1,2\).
* Decompose the matrices \(M_{1i}\) as \([u_{i},s_{i},v_{i}]=svd(M_{1i}),\;i=1,2\). where the full singular value decomposition of an m-by-n matrix \(M\) involves: m-by-m matrix \(u\). m-by-n matrix \(s\). n-by-n matrix \(v\).
* Compute the matrices \(T_{i}=u_{i}\sqrt{s_{i}},\;\;i=1,2\).
5. Obtain the similarity transformation matrix \(T:=diag(T_{1},T_{2})\).
6. Form the balanced realization model \(\Sigma_{b}=(T^{-1}AT,T^{-1}b,cT)\).
7. Obtain the reduced-order \((r_{1},r_{2})\) filter by partitioning the balanced realization obtained in the above step \(\Sigma_{r}=(A_{r},b_{r},c_{r})\).  
Figure 1 shows the flowchart of the proposed method.  
The following section provides some design examples and a discussion of the implementation and evaluation details of this algorithm.  
Figure 1: Flowchart of the proposed method.
## 3 Results and Discussion
### Sensitivity Analysis  
To assess the stability of the model, a sensitivity analysis is conducted using data from the Tangjiashan Barrier Lake. This involves changing one influencing parameter at a time while keeping the others constant and evaluating the impact of the main parameters in each module on the breach's development and discharge.