esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Tools and Resources Supporting CRISPR-Cas Experiments
## 1 Introduction
Consequently, the essential task that the researcher must face in most of the CRISPR-Cas experiments is to design an experiment and to choose tools that lead to an acceptable ratio between on-target and off-target activity. The rapid development in the field of genome engineering is reflected by the constantly expanding ecosystem of computational tools aimed at facilitating experimental design and result analysis. The main group of CRISPR-Cas computational tools is dedicated to aid in gRNA design by prediction of their efficiency (on-target activity) and specificity (off-target activity). These tools historically range from simple sequence alignment-based approaches to elaborate scorings of mismatches in any position of the gRNA and evaluation of the sequence features and are expanding progressively towards incorporation of the chromatin context and genetic variation data into the scoring algorithms. The relatively new group of tools exploits the observed biases in repair outcomes to predict the most likely results of a given CRISPR-Cas edit. The third class of tools is developed to assist in the evaluation of the editing results by analysis of the Sanger/next-generation sequencing (NGS) data. We present a schematic overview of CRISPR-Cas genome editing experiment as well as main groups of involved computational tools in Figure 1.  
The large number of divergent tools inevitably leads to difficulty in finding the appropriate one for a specific type of experiment. There are several excellent reviews summarizing advancements in the field [29; 30; 31], but they are limited in their scope mainly to gRNA design. Furthermore, the rapid evolution and divergence in the area of CRISPR-Cas-related tools and in genome editing itself generates the need for constant updates. Here, we present an updated overview of the currently available CRISPR-Cas-related tools from the perspective of a user who needs a convenient and reliable means to facilitate genome editing experiments at every step, from gRNA design to analysis of editing outcomes.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network
### Integrated Routing Strategy
The fixed routing table is obtained based on the open shortest path priority protocol (OSPF). Additionally, the routing update module can quickly inform all nodes of the current state in the network. Each node counts periodically the queue length in turn and updates its global dynamic routing table. 3. Each node has two queues that store two levels of services. When the packet polling scheduler polls the queue, if the queue length of the node \(\mathrm{n_{i}}>\mathrm{C_{i}}\), the \(\mathrm{C_{i}}\) packets are put in the polling  
Figure 4: The process of packet processing. Figure 3: Fundamental structure of the node queue. QM: Queue Management; QS: Queue Scheduling. scheduler or else, n\({}_{\text{i}}\) packets are put in the polling scheduler. For an incoming packet, it can be routed to the destination through two outgoing ports depending on different packet levels. * Recalculate the queue length of nodes in the network and update the dynamic routing table. The flowchart of integrated routing is shown in Figure 6.  
Figure 5: Data flow at the hub node. Figure 6: The flowchart of integrated routing.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 5 Hierarchical Manipulation Planning  
We plan the motions to manipulate an object on which information is available. We propose hierarchical planning, which includes task and motion level planning. The manipulation planner constructs a graph. In task level planning, we design the discrete object poses on a table, which along the grasp poses are saved in the nodes. In motion level planning, we plan the motions for moving the object and changing the grasp configurations. Primitive motions, such as pivoting, tumbling, and regrasping, are represented by the edges of the graph. Further, given the initial and goal configuration of the object, we search the graph and find a motion sequence to manipulate the object to the goal position.  
Different from previous planners [2; 3; 19], we consider tumbling and regrasping in addition to the pivoting gait. Moreover, we consider the SP and USP of the object. Object motion in USP allows object movement within a limited table area. However, the object may easily fall if the robot regrasps it in USP. To avoid this problem, we design the regrasping motion in USP, such that the robot sequentially changes the contact position of each EEF.
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 3 The Proposed Fault Information-Based Sparse Low-Rank Algorithm
### Convexity Condition  
As mentioned above, to maintain the amplitude of a denoised signal, and to further induce sparsity of the solution of optimization problem (2), the penalty function 'atan' is utilized. Therefore, in order to obtain the global optimal solution, the non-convex penalty parameter \(\alpha\) is studied to ensure the convexity of optimization function \(F(\mathbf{x})\). In the optimization function \(F(\mathbf{x})\), \(\sum_{i=1}^{k}\sigma_{i}(X)\) is essentially \(l_{1}\) norm of the SVs of the matrix \(X\), so it is only necessary to ensure that the remaining part of the optimization problem (2) is convex to guarantee the convexity of whole function, since the convexity  
Figure 3: Penalty function \(\varphi\).  
of the sum of two convex functions does not change. According to Lemma 2 in [38], the convexity of optimization function \(F(x)\) can be ensured if:  
\[0<a\leq\frac{1}{\lambda_{1}} \tag{3}\]
## 4 Conclusions  
As the pyrolysis temperature increased, the biochar yield decreased while the adsorbability of the biochar increased and reached a peak at a pyrolysis temperature of 600 \({}^{\circ}\)C.  
The biochar yield and adsorbability had a similar trend as the heating rate increased. The biochar yield reached a peak at a heating rate of 1 \({}^{\circ}\)C/s, and the adsorbability of the biochar reached a peak at 1.5 \({}^{\circ}\)C/s.  
The biochar yield and adsorbability both increased when the temperature holding time was increased, reaching the highest value at 15 min. Then they both decreased when the holding time was longer than 15 min.  
Additives decreased the biochar yield. There was no significant influence on the adsorbability of the biochar whether carbon (or nothing added) was used as an additive. The addition of Na\({}_{2}\)CO\({}_{3}\) (metal carbonate) or ZnCl\({}_{2}\) (metal chloride) could improve the adsorbability of the biochar. The highest adsorbing capacity was reached when Na\({}_{2}\)CO\({}_{3}\) (metal carbonate) was used as an additive.  
For the biochar adsorbability, the order of influence for the four factors is additive > pyrolysis temperature > heating rate > temperature holding time. The optimal conditions were pyrolysis temperature 550 \({}^{\circ}\)C, heating rate 2 \({}^{\circ}\)C/s, temperature holding time 15 min and without additive. The SEM results also indicate the reasons for the differences of adsorbability from the structures.  
**Author Contributions:** Formal analysis, K.L., H.L. and W.Y.; Investigation, K.L.; Methodology, X.L. and H.E.M.; Project administration, H.L.; Resources, C.G.; Supervision, W.Y.; Writing-original draft, H.L.; Writing-review and editing, H.E.M., H.L. and W.Y.  
**Funding:** This research was funded by Jiangsu Provincial Engineering Laboratory for Biomass Conversion and Process Integration Open Research Funded Projects (JPELBCPI2015002) and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18-0737).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 3 System Architecture, Scenarios of Multi-Hop LoRa, and Results
### Flat Topology Calculations
#### 3.1.3 Temperature and Pressure Sensor Calculations  
We performed another set of tests on temperature and pressure sensors and compared the results to determine the optimal inner distance between the sensing devices (sensor) and the suitable number of bytes for both sensor types. We set the packet size to be 255 bytes, and the pipeline length to be 10 km. The temperature sensors were assumed to transmit a sample every 3 h, and the pressure sensors sent a measurement every 1 h for one day. The test calculated the network density, the energy transmitting and receiving packets, and the total number of bytes at different distances between sensors. Each type of sensor generates a different number of bytes, The pressure sensors need a higher sampling rate and lower distances between sensors than temperature sensors [48; 49]. Figure 7 presents the energy consumed by all nodes located within the inner distances specified by the x-axis, for both pressure and temperature sensors. As can be observed from the figure, the pressure sensors need more energy than the temperature sensors for the same network density. For inter-sensor distances between 200 m and 500 m, the energy consumption is low for both sensor types. Figure 8 shows the total number of bytes for all nodes located within the inner distances specified by the x-axis, for both pressure and temperature sensors. Pressure  
Figure 6: The total energy consumption in the multi-hop scenarios.  
sensors have a higher number of bytes than temperature sensors, as can be seen in Figure 8. Therefore, pressure sensors require high energy consumption.
### Summary  
Advancements in ECG SQA have demonstrated promising results, particularly in controlled environments. Traditional ML methods, such as SVM classifiers with hand-crafted SQIs (e.g., pSQI, bSQI), achieve high accuracy (up to 99%) by leveraging noise augmentation and multi-stage pipelines. DL approaches, including CNNs and xResNet architectures, further automate feature extraction, attaining state-of-the-art performance (e.g., 98.97% accuracy for multi-class SQA). However, critical limitations persist. First, models often overfit to curated datasets (e.g., PhysioNet challenges), resulting in poor generalization to unseen data (e.g., 57% accuracy on MIT-BIH). Second, reliance on short, static ECG segments and synthetic noise simulations fails to address the dynamic noise profiles inherent in wearable devices, such as motion artifacts during physical activity.  
Methodological trade-offs exist between interpretability (traditional ML) and performance (DL), with the latter posing challenges for real-time deployment on resource-constrained wearables. Dataset heterogeneity further complicates reproducibility, as studies often use device-specific or narrowly annotated data (e.g., Lenovo H3 wearable ECG device, SensEcho (Table 1)), limiting cross-domain applicability. While emerging datasets like ScientiISST MOVE are beginning to address dynamic conditions, their integration into SQA frameworks remains underexplored.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)  
SANET is a wireless multi-hop network composed of nodes (e.g., ships, offshore buoys, and beacons). As shown in Figure 6, connections can be established between these nodes, or with coastal base stations and satellites [85; 86]. SANET has characteristics that are similar to those of other wireless networks: composition made up of mobile nodes; high processing capacity; no energy restrictions; and high data storage capacity. However, there are some differences in many areas, such as power consumption, topology, speed, and deployment changes. SANET can be used for maritime communications, navigation, and emergency response. The challenges of SANET include:  
* Quality of Service (QoS): maintaining the best forwarding quality of service is important because it involves certain requirements, latency, throughput, and packet error rates associated with the application. * Security: confidentiality and integrity are important for information transmission. * Scalability: the most important step in achieving effective ship communication is to ensure a stable routing mechanism on SANET. Due to the variation in ship mobility and network topology, the routing path is often interrupted, so it is difficult to ensure its stability. * Routing protocol: has large end-to-end latency and reduced packet delivery rate. Figure 5: (**a**) Four link modes; (**b**) structure diagram of FANET. Comparison with other ad hoc networks  
Ad hoc networks are also known as infrastructure-free networks, non-central networks, multi-hop networks, and self-organizing wireless networks. Ad hoc networks have no fixed infrastructure, and their nodes can be vehicles, mobile devices, aircraft, and ships, which can dynamically maintain contact with other nodes in any way. Ad hoc networks include MANET, VANET, FANET, and SANET. VANET is a subset of MANET, FANET is a subset of VANET, and SANET is a popular type of self-organizing network. Table 2 summarizes the differences between different ad hoc wireless networks. An ad hoc network is an ad hoc wireless network composed of communication nodes that do not depend on the infrastructure, in which each node can act as a host and router. Ad hoc networks have been gradually applied to many key scenarios, such as emergency communication, rescue and disaster relief, and expanding the coverage of existing cellular networks, due to their fast network speed and robustness. Therefore, ad hoc networks are an important part of next-generation wireless communication network systems.
# Dynamic Stability of Orthotropic Viscoelastic Rectangular Plate of an Arbitrarily Varying Thickness
###### Abstract  
The research object of this work is an orthotropic viscoelastic plate with an arbitrarily varying thickness. The plate was subjected to dynamic periodic load. Within the Kirchhoff-Love hypothesis framework, a mathematical model was built in a geometrically nonlinear formulation, taking into account the tangential forces of inertia. The Bubnov-Galerkin method, based on a polynomial approximation of the deflection and displacement, was used. The problem was reduced to solving systems of nonlinear integrodifferential equations. The solution of the system was obtained for an arbitrarily varying thickness of the plate. With a weakly singular Koltunov-Rzhanitsyn kernel with variable coefficients, the resulting system was solved by a numerical method based on quadrature formulas. The computational algorithm was developed and implemented in the Delphi algorithmic language. The plate's dynamic stability was investigated depending on the plate's geometric parameters and viscoelastic and inhomogeneous material properties. It was found that the results of the viscoelastic problem obtained using the exponential relaxation kernel almost coincide with the results of the elastic problem. Using the Koltunov-Rzhanitsyn kernel, the differences between elastic and viscoelastic problems are significant and amount to more than 40%. The proposed method can be used for various viscoelastic thin-walled structures such as plates, panels, and shells of variable thickness.  
Keywords:parametric vibrations; rectangular plate; orthotropic; viscoelasticity; variable thickness; weakly singular relaxation kernel; integrodifferential equation; Kirchhoff-Love hypothesis +
Footnote †: journal: Applied Sciences
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Throughput  
Throughput refers to the rate at which the transmission of deduplicated healthcare data toward a cloud repository takes place. It can be defined as in Equation (2), where \(\alpha\) represents the transaction process, \(f_{s}\) is the size of the data, and \(Avg_{f}\) is the average transaction size. \(N_{t}\) is the time for nodes in seconds.  
\[\alpha=\frac{f_{s}}{Avg_{f}}*\frac{1}{N_{t}} \tag{2}\]  
Figure 6 shows the throughput of the different schemes for different data sizes. The proposed DICA scheme's better chunking procedure contributes to smooth healthcare data processing, which ultimately results in high throughput during deduplicated data transfer to the cloud repository. Along with this, the high throughput is credited to the algorithm's efficient deduplication process, which reduces the data volume and accelerates transmission. Thus, the increased throughput guarantees the timely delivery of critical health data, improving real-time monitoring and decision-making in IoMT systems. The WCA-BF + ASE scheme maintains high throughput to alleviate the redundant data volume by employing a Bloom filter, which eventually enhances the throughput. The RAM has comparatively low throughput because of its byte-by-byte comparison. The scheme scans every byte, which lowers its transmission speed. The DSW technique has low throughput due to managing two sliding windows simultaneously. Each window requires continuous hash value calculations and comparisons to determine the breakpoints, which increases the processing time. Moreover, managing a larger number of smaller chunks impacts the throughput, as more resources are needed to handle the chunks. For a data size of 400 KB,  
Figure 5: Likelihood of breakpoint failure.  
the RAM has a throughput of 466, while the CWCA, WCA-BF + ASE, DSW, and DICA schemes have values of 490, 950, 935, and 954 bits/sec, respectively.
## 6 Comparison with the Classical PD and PID Controllers  
Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) controllers have been extensively used in exoskeleton control due to their simplicity and effectiveness in addressing various challenges. Applications range from lower-limb rehabilitation to upper-limb assistance, as demonstrated in [63], where a PID controller enhanced stability and tracking for a lower-limb exoskeleton, and [64], which showcased its relevance in therapeutic settings. These controllers provide robust solutions for stability and performance, making them a popular choice in exoskeleton applications.  
Advancements in PID control include adaptive approaches, such as the particle swarm-optimized adaptive PID controller for multi-joint exoskeletons in [17], which improved trajectory tracking and handled dynamic uncertainties. Combining PD controllers with advanced methods has further expanded their capabilities. For instance, [65] proposed a PD-based fuzzy sliding mode control for a wheelchair upper-limb exoskeleton, integrating PD control with sliding mode and fuzzy logic to address unmodeled dynamics and external disturbances, achieving smooth motion for tasks like eating and drinking.  
PD controllers have also demonstrated their effectiveness in lower-limb exoskeletons for gait trajectory tracking and sit-to-stand motion. In [66], a gravity-compensated PD controller with a kinematic model derived using the Newton-Euler equations and the Lyapunov stability theorem reduced gait trajectory errors effectively. Similarly, a comparative study in [67] highlighted the advantages of an adaptive PD controller over an iterative LQR, showcasing its adaptability to dynamic uncertainties. Collectively, these studies establish PD and PID controllers as a robust foundation for addressing the complexities of human-exoskeleton interactions.
## 2 Background and Related Work
### ECG Algorithm Review
With the intervention of computer technology, ECG data compression technology is increasingly showing its importance. The Holter data compression algorithm is one of the most fruitful hotspots of current international research in the field of biomedical signal processing [13]. Data compression is possible with a variety of methods. Early predictive coding methods, such as Differential Pulse Code Modulation (DPCM), directly encode the amplitude variation of the adjacent sample values. The principle of these methods is simple and easy to implement, but the compression rate is relatively low. Run-length coding (RLC) uses the correlation among the symbols, by recording the length of each symbol to achieve compression. Shannon-Fano codes and Huffman codes are based on the frequency with which each signal appears [14]. Then they assign the most economical code length so as to achieve compression. With a flat distribution of the signal in the time domain, after orthogonal transformation, the energy will be concentrated on the low-frequency component so the high-frequency component can be omitted, or we can use only a few bits to encode them. These transform compression methods include the Karhunen-Loeve transform (KLT), Fourier Transform (DFT, FFT), and discrete cosine transform (DCT) [15]. New compression techniques include the neural network and wavelet transform (DWT) methods and others [16]. Two algorithms are described that are suited for real-time biomedical signal compression, these being amplitude threshold compression and SQ segment compression [17].
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 1 Introduction
### Related Work
In [17], the authors proposed a joint iterative detection and decoding scheme (JIDD) based on a PC-SCMA system, which achieved a competitive performance and effectively reduced the computational complexity by updating messages between MPA and SCAN algorithms. In [18], the authors proposed a JIDD scheme based on serial message updating S-JIDD, and the scheme can reduce the complexity by speeding up the convergence rate while maintaining an ideal error performance. In the references, the proposed algorithm only focuses on the construction of the joint system and the acceleration of iterative convergence through the structural deformation of the joint factor graph. However, there is still great potential to improve the performance of the joint system and reduce the computational complexity. In this paper, we propose the PI-JDD scheme to effectively reduce the computational complexity of the PC-SCMA system, and the proposed C-JIDD algorithm can achieve significant performance gain while maintaining ideal complexity. Finally, we combine the two proposed schemes to achieve an overall improvement and trade-off between the performance and complexity.
# A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge
## 2 Methodology  
### Vortex-Induced Vibration of a Bridge Girder  
Vortex-induced vibration is a classic aeroelastic behavior of fluid-structure interaction which is caused by negative aerodynamic damping and usually is of larger amplitude than buffeting. During the VIV event, the frequency of the vortex shedding generated by the wind is close to a certain natural frequency, and the corresponding vortex-excited force can stimulate the resonance of the bridge girder. During the VIV event, there is only one main significant frequency, which indicates that the VIV response can be modeled with an SDOF equation, and the classic VIV model is presented as follows [15; 18]:  
\[\ddot{y}+2\xi\omega\dot{y}+\omega^{2}y=\frac{F}{m}=f \tag{1}\]  
\[F=\frac{1}{2}\rho U^{2}(2D)\bigg{(}Y_{1}(K)\bigg{(}1-\lambda\frac{y^{2}}{D^{2 }}\bigg{)}\frac{\dot{y}}{U}+Y_{2}(K)\frac{y}{D}+\frac{1}{2}C_{L}(K)\sin(\omega t +\theta)\bigg{)} \tag{2}\]  
where \(\xi\) and \(\omega\) denote the modal damping and frequency, respectively; \(y\), \(\dot{y}\) and \(\ddot{y}\) denote the displacement, velocity and acceleration response of the girder, respectively; \(F\) denotes the aerodynamic force, which consists of motion-induced force and flow-induced force; \(\rho\), \(U\) and \(D\) denote the air density, mean wind speed and cross-flow dimension of the section, respectively; \(K=\omega D/U\) and \(\omega\) denote the reduced frequency and frequency of the vortex shedding, respectively; \(Y_{1}\), \(Y_{2}\), \(\lambda\) and \(C_{L}\) denote the aerodynamic parameters; \(m\) denotes the mass per unit span; and \(f\) denotes the normalized aerodynamic force.