esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Sample Preparation and Pelletization  
RDF was selected and separated from a landfill area in Pattalung Province, Thailand, where the municipal solid waste (MSW) used in this study had been landfilled for 2 years and managed by the Provincial Administrative Organization of Pattalung Province. Firstly, MSW was recovered and separated into combustible and incombustible materials, using a separating machine and manual selection by workers. Then, the combustible materials were converted by a machine into refuse-derived fuel (RDF) level 3, which only has fluff RDF, such as plastics, papers, and fibers. Eventually, the RDF wastes were shredded to a size suitable for pelletization.  
Rubberwood sawdust, a waste product supplied by BNS Wood Industry Co., Ltd., a sawmill located at Mueang, Surat Thani, Thailand, was collected. The material was controlled through its moisture content reaching 10-15% in a laboratory oven. The oversized sawdust was screened using a sieve with 18 meshes before mixing with RDF.  
The composition of sawdust/RDF materials with four different types of pellets was prepared in ratios of 100/0, 70/30, 60/40, and 50/50 by weight, respectively. A rotary drum mixer was used to completely mix the two materials for 5 min before pelletization. The mixed materials were pelleted in an electric flat die wood pellet mill, ZhengZhou Known Imp. & Exp. Co., Ltd, KN-D-200, Zhengzhou, Henan, China, with 7.5 hp (380 v), 50 Hz, with the extruded temperature of 90-110 \({}^{\circ}\)C and a pellet mill die of 6 mm to produce 10 kg of each condition. Five samplings of each condition were randomly collected for the testing (Figure 1), while the pellets test had five replications of each property.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 3 Results
### Prediction Results of the Network Model
#### 3.1.3 Computational Time for Network Prediction and Numerical Simulation  
The time costs for both the numerical simulation and the network prediction are listed in Table 2. The second column is the time required for CNN model training, the third column is the prediction time and the fourth column is the time OpenFOAM takes to solve the two-pulse simulation of drug flow at any injection position. Although considerable time is needed for training, the network model prediction time is nearly four orders of magnitude faster than that obtained with numerical simulation. The prediction time of the proposed CNN-based model is less than 0.1 s, which can achieve the purpose of real-time assisting clinical treatment. It is worth noting that from the straight and bifurcated blood vessels to the human hepatic artery, the vascular geometry becomes more complex and, therefore, finer meshes are required to achieve convergence in the numerical simulations, resulting in increased time consumption. However, the network model does not have the same convergence issue and the prediction time remains almost constant as the geometry varies.
# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 1 Introduction
### Embedding Parameters for RQA Analysis  
RQA requires reconstructing the state space of a dynamic system from a single scalar time series through an embedding process [26; 27]. The embedding method uses time-delayed versions of the original time series to reconstruct the state space, which involves the choice of time delay (tau) and embedding dimension (D). The mathematics behind this technique was presented in detail by [PERSON] [26]. The embedding dimension (D) is the minimum number of variables required to create a new state space from a given time series. The vectors in this new embedding space are built from the time-delayed values of the scalar measurement [27; 28]. Many methods have been developed for estimating embedding dimensions [29; 30; 31; 32]. Of all the possible techniques, the most prominent are the correlation dimension [33] and the false nearest neighbors (FNN) [34; 35]. The average mutual information function (AMIF), time series autocorrelation (ACF), or average displacement (AVD) method is a common and classical approach to time lag selection [36; 37; 38]. However, with both the choice of delay time and the embedding dimension, it is unclear which method is the best. The methods mentioned are briefly described below.
# Technique Variables Associated with Fast Bowling Performance: A Systematic-Narrative Review
## 4 Discussion
### Performance Parameters Associated with Fast Bowling Performance
#### 4.1.5 Trunk, Pelvis and Centre-of-Mass Kinematics  
Trunk and pelvis kinematics have been reported by several studies (\(n=10\) high-quality and \(n=5\) medium-quality studies). Trunk and pelvis rotation contributed most to high BRS following upper arm rotation contribution [12]. Increased trunk flexion allows for large rotational energy to be transferred to distal segments and eventually to the ball [6]. Specifically, the magnitude of upper trunk flexion between FFC and BR was reported to be associated with higher BRS [2]; the mechanism leading to increased upper trunk flexion remains unexplained. Additionally, the hip-shoulder separation angle, defined as the angle between the vector joining the hip joint centres and the vector joining the shoulder joint centres, has also been considered an important variable associated with bowling speed (\(n=4\); two high quality and two medium quality). A relationship between maximum hip-shoulder separation angle during the delivery stride was observed, with bowlers with larger separation angles demonstrating higher BRS [16]. However, [PERSON] et al. [6] found no correlation between hip-shoulder separation angle and BRS.  
The kinematics of the mass centre were considered in several studies across the review (\(n=6\); five high-quality and one medium-quality study). The kinetic link principle specifically indicates that, for effective transfer of full-body momentum gained during the run-up to the bowling arm, a sequential deceleration of proximal segments during the delivery stride is required [8]. Therefore, for efficient transfer of momentum, COM deceleration promotes rapid accelerations of the distal segments (via trunk flexion and bowling arm circumduction), generating high ball speed. [PERSON] et al. [8] found that COM deceleration in the delivery stride phase was the most important kinematic factor associated with ball speed. [PERSON] et al. [10] also stressed the importance of quick COM deceleration in addition to a quick run-up for generating high BRS. Supporting previous findings, [PERSON] et al. [22] observed that COM acceleration during the delivery stride and FFC to BR phase had a large negative correlation with ball speed. However, rather than during the entire delivery stride, [PERSON] and [PERSON] [9] found a significant negative relationship between BRS and average horizontal COM acceleration during FFC to BR only.
## 3 The Proposed Approach of SCDNN  
Several classification engines (BPNN, SVM, RF, Bayes and DNN) perform well given the advantages of their algorithms, which can efficiently handle complex classification problems; hence, these models can be successfully applied to intrusion detection. They usually perform poorly when facing the complex randomness and camouflage of network intrusion dataflow. Therefore, in this section, the proposed approach is employed to solve the above problems. First, the training data subsets divide the training process and calculate centre points by SC from each training point. Second, each training data subset is trained by the corresponding DNNs, where the scale of DNNs is the same as the number of clusters. In this way, the DNNs have learned different characteristics from each subset. Third, the testing data subsets are divided into test datasets by SC, which uses the previous cluster centres in its first step, and these subsets are applied to detect intrusion attack types by pre-trained DNNs. Finally, the output of every DNN is aggregated for the final result of the intrusion detection classifiers.
## 2 Description of the Research Systems  
The S-CO\({}_{2}\) Brayton cycle based on solar energy has the same structure as that of a conventional heat source. Core components of the power cycle include the turbine, generator, heat regenerator, cooler, and compressor. The thermal cycles studied in this paper include three types, simple recuperated cycle, intercooling cycle, and the recompression cycle. Solar receivers are adopted to absorb solar radiation heat to warm S-CO\({}_{2}\).  
The simple recuperated cycle is shown in Figure 1, which has been presented by [PERSON] [28]. The working fluid S-CO\({}_{2}\) is cooled before compressing to an applicable pressure for the compressor. In the recuperator, cold S-CO\({}_{2}\) absorbs heat from hot S-CO\({}_{2}\) exiting from the turbine outlet. Moreover, before expanding in the turbine, the pre-heated S-CO\({}_{2}\) further absorbs heat from the solar receiver.  
Figure 2 shows the layout of the intercooling cycle, which has been previously presented by [PERSON] et al. [29]. Compared to the simple recuperated cycle, the intercooling cycle adopts another cooler to help reduce the compression work. It can be accomplished since the temperature of the compressed S-CO\({}_{2}\) is further reduced. This arrangement can also generate a larger pressure ratio. Moreover, other components in the intercooling cycle have high similarity with those in the above cycle.  
Figure 1: Configuration of the simple recuperated cycle.  
Figure 2: Configuration of the intercooling cycle.  
Figure 3 presents the recompression cycle, which has also been adopted by [PERSON] et al. [30]. This kind of cycle has high similarity with the simple recuperated cycle. Nevertheless, the components are of different numbers and arrangements. Before the cooler, there are two kinds of recuperators, which are used in high-temperature and low-temperature, respectively. After regeneration, S-CO\({}_{2}\) splits into two parts. Part of the fluid flows through the cooler and then enters the main-compressor while the other part flows into the re-compressor. The fluid from the main-compressor outlet flows into the low-temperature recuperator and the high-temperature recuperator. The fluid from the outlet of the re-compressor flows only into the high-temperature recuperator. In addition, other components are similar with the above two configurations.  
It should be noticed that due to the inexistence of a thermal energy storage device, the systems would mostly work under off-design conditions. As shown in previous studies, the performance of systems change dramatically [31; 32]. Therefore, we can add a thermal energy storage device to the systems to ensure that the systems operate in a steady state. However, the discussion of a thermal energy storage device is not the focus of this paper, so the relevant description is omitted.
## 3 Doherty Power Amplifier Circuit Design
### Tunable Power Allocation by Driver Amplifier  
As shown in Figure 6, the driver stage biases are dynamically tuned to vary the power division ratio from 2 dB to 5 dB, optimizing the load modulation across the operating power range. This adaptive power division scheme, combined with the asymmetric carrier and peaking amplifier biases, enables a flatter AM-AM/AM-PM response and higher back-off efficiency. This technique is crucial for maintaining low ACLR performance under wide modulation bandwidths.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 2 GSP Model
### Image Group Construction  
The construction of the image group in GSR model is illustrated in Figure 1. The image \(x\) with the size of \(\sqrt{N}\times\sqrt{N}\) is divided into \(n\)_overlapped_ patches with the size of \(\sqrt{b}\times\sqrt{b}\). The overlapped patches are denoted by \(x_{i}\) where \(i=1,\ 2,\ \ldots,\ n\). For each \(x_{i}\), its \(m\) similar matched patches are constructed using the Euclidean distance as the similarity metric in a training window with the size of \(W\times W\). The similar matched patches form a set denoted by \(S_{i}\). All the similar patches in \(S_{i}\) form a matrix with the size of \(b\times m\), denoted by \(X_{i}\in R^{b\times m}\). Each element in \(S_{i}\) is a column of the matrix \(X_{i}\), i.e., \(X_{i}=\{x_{i,1},x_{i,2},\ldots,x_{i,m}\}\). The column \(x_{i,j}\) denotes the \(j\)th similar patch of the \(i\)th image group corresponding to \(x_{i}\). The matrix \(X_{i}\) includes all the similar patches as columns, and hence is referred to as a group.
## 4 Results and Discussion
### Comparison between the Dynamics-Based and the Structure-Based Clustering
Another difference regards the heat-shock proteases S1C, which include proteins from bacteria, chloroplasts, and mitochondria; even though structurally similar in the proteolytic core, members of this subfamily appear very scattered in the dynamics-based clustering. Specifically, the observed similarities in the dynamics accentuates the structural relatedness already observed between some eukaryotic S1C proteases and different viral protease subfamilies, in that these similarities are stronger than the similarity within the S1C subfamily itself. This relatedness has been previously explained on the basis of exchanges of protease genes between eukaryotic viruses and their hosts [34]. In the structure-based distance tree, proteases from flavivirus (families S29 and S7) and from togavirus (family S3) are grouped together, even though the two viruses belong to different families; on the other hand, S29/S7 and S3 are placed in different clusters when their dynamics is included in the analysis. This distinction might arise from the difference in function: the S3 protein togavirin, in fact, does not only function as a viral protease, but plays also the structural role of the capsid protein of the virus [82]. S29 and S7 proteases, on the other hand, possess only proteolytic function and do not work as structural components. Overall, the inclusion of dynamics in the comparison of the proteases from the PA clan adds therefore an additional level of classification, which seems appropriate to bridge structural and functional similarities.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 3 Clinical Applications  
### Periodontal Tissue Regeneration  
#### 3.1.1 Membranes for Periodontal Tissue Regeneration  
Periodontitis is the inflammation of the periodontium and causes damage to the surrounding tissues, such as the alveolar bone and periodontal ligament. Without treatment, loss of the supporting structures around the tooth eventually results in tooth loss. To reconstruct periodontal tissue, the development of membranes for guided tissue regeneration (GTR) and guided bone regeneration (GBR) with adequate mechanical and biological properties have been explored [7].  
Membranes must satisfy several important criteria, such as biocompatibility, space maintenance, cell exclusion, tissue integration, and clinical manageability. On one hand, nonresorbable membranes require additional surgery for their removal and increase the risks of post-operative infection. On the other hand, resorbable membranes have unpredictable degradation rates and often lack sufficient strength. Synthetic membranes combine strong mechanical properties to prevent collapse within the bony defect and biological properties to deliver biomolecules and cells to promote tissue regeneration. Polycaprolactone (PCL) membranes synthesized using the electrospinning technique can be loaded with anti-inflammatory drugs (such as ibuprofen) and growth factors (such as bone morphogenetic protein-2 (BMP-2)) to enhance periodontal regeneration [25]. Biomimetic fish collagen/ bioactive glass/ chitosan composite nanofiber membranes (Col/BG/CS) have good hydrophilicity, higher porosity and surface area promoting cell-cell and cell-matrix interaction, adequate tensile strength, and limited antibacterial properties [26]. Clinically available collagen membranes may be functionalized by electrospinning only-DL-lactic acid (PDLLA) polymers to enhance periodontal regeneration. In fact, membranes functionalized with antibiotics and growth factor coated PDLLA nanofibers can facilitate healing and regenerative processes while reducing the risk of bacterial infection [27].
# Active Control of Broadband Noise Inside a Car Using a Causal Optimal Controller
## 2 Secondary Path Analysis  
### Influence of a Secondary Path on Control Performance  
The block diagram of a control system is shown in Figure 1, where \(z=e^{j2\pi f}\), \(P(z)\) and \(S(z)\) correspond to the primary and secondary paths, \(W(z)\) is the controller, and \(d(n)\) and \(\hat{d}(n)\) represent the undesired noise and output signal of the control system, respectively. If the output signal is designed to cancel the undesired noise completely, the resulting optimal controller \(W(z)\) will have the following expression:  
\[W_{opt}(z)=P(z)/S(z) \tag{1}\]  
Apparently, the optimal controller is a result of the convoluting transfer functions of the primary path and the inversion of the secondary path. Unfortunately, most of the secondary paths in the engineering are nonminimum-phase and their inversions may be unstable [20]. To circumvent the  
Figure 1: The block diagram of an active noise control (ANC) system.  
problem, we expand the inversion of \(S(z)\) as a noncausal filter and use a causal filter \(C(z)\) to approximate it as [22], as follows:  
\[S(z)C(z)=z^{-D}, \tag{2}\]  
where \(z^{-D}\) denotes \(D\) sampling times delay. Thus, the optimal controller can be rewritten as \(W_{opt}(z)=z^{D}C(z)P(z)\). It is assumed that \(H(z)=P(z)C(z)\) and the impulse response of \(H(z)\) is \(h_{n}\), \(n=0\),...,\(N_{h}-1\); then, the optimal output signal of the controller \(W_{opt}(z)\) is [20], as follows:  
\[y_{opt}(n)=h_{n}*x(n+D)=\sum_{i=0}^{D-1}h_{i}x(n+D-i)+\sum_{i=D}^{N_{h}-1}h_{i }x(n+D-i), \tag{3}\]  
where \(n\) denotes current sampling time. If \(n+D-i<n\), it means \(x(n+D-i)\) is a past input signal. Similarly, if \(n+D-i>n\), it means \(x(n+D-i)\) is a future input signal. It can be seen from above equation that the first part of the optimal output signal \(y_{opt}(n)\) is a linear convolution of the future input signal with the system impulse response \(h_{n}\) and the second part is a convolution of the past input signal with the impulse response \(h_{n}\). For the controller \(W(z)\), the second part of the optimal output signal can be generated by using the past input signal and causal part of system response. However, to produce the first part of the optimal output signal, it needs the future input signal, which is impossible for the controller to obtain. Therefore, the best noise-reduction performance, which is achievable for a physical system, can be derived by designing a controller with the causal part of the system impulse response.
## 3 Numerical Models
### Loading Applying  
In this study, the pre-stress coefficient \(k_{s}\) is defined as the ratio of the pre-stress to the static uniaxial compressive strength of the specimen. Six impact speeds are considered, namely 4 m/s, 6 m/s, 8 m/s, 10 m/s, 12 m/s and 14 m/s. For each given impact speed, the impact tests were conducted nine times at different pre-stress levels with the pre-stress coefficient \(k_{s}\) increasing from 0.0 to 0.8 with an interval of 0.1. When \(k_{s}\) is 0.0, it means that there is no pre-stress. Overall, 54 coupled static and dynamic SHPB tests with different impact speeds and pre-stress levels were carried out in the numerical simulations.  
Figure 2: Numerical model of SHPB test system (1: Striker, 2: Fixed board, 3: Incident bar, 4: Specim, 5: Transmitted bar, 6: Absorbing bar).
# Smart Hospitals and IoT Sensors: Why Is QoS Essential Here?
## 5 Discussion
### Lesson 2: Consider the Complexity of the Environment in Your System Design  
Medical settings are highly critical environments that encompass complex processes. Such complexity includes a variety of equipment and medical roles performed by several persons simultaneously. On top of that, the patients are the central focus, and their well-being is the ultimate goal of the process. As a result of all this complexity, implementing new techniques requires awareness of multiple aspects. On the one hand, depending on the technology, it is crucial not to have an adverse effects on the current technologies already used in the hospitals. On the other hand, as medical processes involve legal issues, privacy concerns should also be considered. Thus, there is a limitation on what we can do or not do in the hospital facilities.  
Considering all of that, we deployed our solution as carefully as possible. Hence, we opted to install our network infrastructure. We did that for two main reasons. First, as we consider that some sensors generate a considerable amount of data, this could impact the network and other services already running in the operating room. Second, a private  
Figure 9: Comparison of resource consumption of each node with the QoS Service Elasticity enabled and disabled.  
network only for the middleware data exchange prevents raw data transmission through the hospital network due to several privacy concerns. Moreover, this also prevents network sniffers from capturing the middleware packets and corrupting them. As a bonus, we have more control over the experimental environment, enabling customization and improving our tests' reliability.
# High Throughput Data Relay in UAV Wireless Networks
## 3 Problem Formulation  
For efficient transmission, whilst considering fairness among all the victims, we aim to maximize the minimum average data rate relayed by the UAV among all \(N\) victims. That is  
\[\max_{\mathcal{A},\mathcal{C}}R, \tag{5}\]  
subject to  
\[R_{n}\geq R,\ \forall n \tag{5a}\] \[\sum_{n=1}^{N}a_{n}[k]\leq N+2,\ \forall k\] (5b) \[a_{n}[k]\in\{1,2\},\ \forall n,k\] (5c) \[Ph_{i}[k]=Ph_{j}[k],\ \forall k,\left(a_{i}[k]=a_{j}[k]=2,i\
eq j\right)\] (5d) \[||\mathbf{c}[k+1]-\mathbf{c}[k]||\leq V_{\max}\delta_{t},\ \forall k\in\{1,\ldots,K\} \tag{5e}\]\[\mathbf{c}[1]=\mathbf{c}_{I},\ \mathbf{c}[K+1]=\mathbf{c}_{F}, \tag{5f}\]  
where \(R\) is the objective average data rate to be maximized. (5a) represents the minimum average data rate among all the victims. (5b) assumes that one pair of victims can be supported by the DS method during each time slot. (5c) considers that a victim can be either supported by the DS method or not. (5d) means that for the DS method supported victims, their received signal power at the UAV are kept the same [22]. (5e) means that the maximum traverse distance of the UAV is limited by its maximum flying speed during each time slot. In addition, (5f) shows the pre-determined initial and final locations of the UAV trajectory.
## 2 Related Work
### Transmission Efficiency
To address these challenges, they proposed a dynamic scheduling strategy that uses DRL to solve the problem, especially in situations with random task arrivals. In order to optimize the benefits of IoT applications while guaranteeing equitable resource distribution, [PERSON] et al. [33] presented a novel Q-learning system.