esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Modeling and Solution  
### Overall Problem Description  
As mentioned earlier, in SDN, we can specify paths between node pairs within a logical topology. This allows us to construct each flow table (logical topology) by combining paths. In a complex mesh network, there are many paths between a source node \(s\) and a destination node \(t\). All simple paths from \(s\) to \(t\) can be obtained using Depth-First Search (DFS) or Breadth-First Search (BFS) algorithms. However, since these paths may share overlapping links, having more paths does not necessarily increase the overall transmission capacity. Given that mobile devices may move to various nearby locations, the constructed multi-topology should include multiple paths from the source node to multiple destination nodes. Our goal is to construct a multi-topology consisting of a subset of all possible paths between node pairs that ensures high and balanced transmission capacity for each node pair. We  
Figure 1: Multi-topology routing in software-defined networking (SDN) and the corresponding processing logic in Switch B. Switches are labeled A, B, C, and D, while ports 1, 2, and 3 represent the three ports of Switch B.  
formulate the multi-topology construction problem as a mathematical model. The main symbols used in the model are shown in Table 1.  
Objective Function: The goal of efficient multi-topology construction for scheduling mobile data flows is to maximize the average total flow capacity that can be transmitted through multiple paths from the source node \(s\) to each destination node \(t\) within a unit of time. \[Maximizeavg(F_{t})=\frac{\sum_{t\in T}\sum_{p\in R_{t}}f_{p}}{n_{T}} \tag{1}\]  
where \(T\) is the set of all destination nodes, \(P_{t}\) is the set of all simple paths from node \(s\) to node \(t\), \(f_{p}\) is the amount of traffic transmitted by path \(p\) in a unit of time, and \(n_{T}\) is the number of destination nodes. Constraints:  
Link Bandwidth Constraint: For each link \(l\), the amount of traffic that can be transmitted in a unit of time must not exceed its available bandwidth. \[\sum_{p\in R_{l}:l\in L_{p}}f_{p}\leq b_{l},\forall l\in L,\forall t\in T \tag{2}\]  
where \(L\) is the set of all links in the physical topology, \(b_{l}\) is the available bandwidth of link \(l\), and \(L_{p}\) is the set of links contained in path \(p\). Non-Negativity Constraint: Ensure that the amount of traffic is non-negative. \[f_{p}\geq 0,\forall p\in P_{t},\forall t\in T \tag{3}\]  
It should be noted that \(f_{p}\) may equal 0, indicating that path \(p\) is not in use. Flow Entry Capacity Constraints: For each switch node, the number of active paths traversing it cannot exceed its flow entry capacity constraint. \[\sum_{p\in PT_{n}}x_{p}\leq c_{n},\ \ \ \forall n\in N \tag{4}\]  
where \(P_{n}\) is the set of all paths traversing switch node \(n\), and \(c_{n}\) is the flow entry capacity constraint for switch node \(n\).
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 2 Related Works  
### Deep Learning-Based Spacecraft Pose Estimation from Photorealistic Renderings  
The deep learning framework that is applied in the experiment of this work is derived from [11]. This deep learning framework involves end-to-end PE, the drawback of which is that it does not handle multiple objects concurrently. The end-to-end PE method involves feeding images into the model, which directly computes the pose information of interest. Another method is based on the key points of the target object. However, the end-to-end PE method should be employed whenever possible to mitigate the computational overhead and inference inefficiency of other approaches.  
This deep learning framework is for pose estimation. It is based on orientation soft classification, which enables modeling orientation ambiguity as a mixture of Gaussians which is a proposed method in [11] by performing continuous orientation estimation via classification with soft assignment coding [40]. This deep learning framework used ResNet architecture with pretrained weights as its network backbone. The last fully connected layer and the global average pooling layer of the conventional network were removed and retained for spatial feature resolution, effectively leaving only one pooling layer in the second layer. The global pooling layer was replaced by one extra 3 \(\times\) 3 convolution with a stride of 2 (bottleneck layer) to compress the CNN features. For 3D location estimation, the framework adopts a simple regression branch with two fully connected layers. It minimizes the relative error via its loss function, which was developed on its own to be suitable for the constructed framework, instead of the Euclidean distance measure. Another study presented a mathematical model for the loss function of spacecraft PE [10,22]. This deep learning framework has also been studied to find alternative modifications or adjustments [21] because of its low number of pooling layers and good accuracy-complexity tradeoff, which are the highlights of ResNet architectures.
## Materials and Methods
### Design of a Wireless Carotid Wearable Ultrasound Doppler System
After quadrature demodulation, 40-MHz in-phase and quadrature components in complex baseband data are separately accumulated with a hanning window whose length is determined from the selected sample volume size in the sample volume accumulator, and these accumulated complex baseband data are gathered with a given PRF (e.g., 4 kHz). To remove clutter components, in wall filtering, a 128-tap FIR filter is applied. After wall filtering, a commercial Bluetooth 2.0 module (i.e., HC-06, Guangzhou HC Information Technology Co., Ltd., Guangzhou, China) is used for wirelessly transmitting the complex baseband data to the external smartphone (Galaxy S7, Samsung Electronics Co., Suwon, South Korea). In the smartphone, a complex FFT with varying window sizes (e.g., 128) is first performed; then, the amplitude of the output of the FFT operation is calculated. These operations were implemented with the Java running on OpenGL ES 3.0. To remove the noise, the threshold operation based on the comparison between the input data and the predetermined value is performed. Log-compression is applied to lower the dynamic range of the processed data, and the \(PSV\) and \(RI\) are measured. Lastly, these values are displayed along with a spectral Doppler waveform on the screen of the smartphone. An in-house power module is used for supplying necessary analog and digital power to the designed wireless carotid wearable ultrasound Doppler system.
## 2 Background Literature Review
### Assistive Technology in Assessments for BVI Students
#### 2.2.3 Refreshable Braille Display  
The BrailleSense device--a standard refreshable Braille display used by BVI students in KSA [2]--offers significant benefits by integrating Braille literacy with digital functionalities [2; 15; 20; 21; 22; 29]. It supports essential academic tasks such as note-taking and research, which are crucial for academic success [2; 15; 20]. Research indicates that Braille displays can enhance independent learning and increase engagement in educational settings [25]. However, notable limitations can impact its effectiveness in educational settings despite its strengths.  
One of the primary challenges associated with BrailleSense is the increased cognitive load it demands from users. While the device supports various assessment formats, such as short answers and essays, BVI students may find BrailleSense taxing during timed assessments. The need to read and interpret Braille while simultaneously formulating responses may hinder performance, especially under pressure [16; 21]. This complexity may discourage some students from fully engaging with their assessments.  
Inclusivity is another concern, as the reliance on specialized devices like BrailleSense can inadvertently create disparities among BVI students. For instance, students who need access to BrailleSense or similar technology may face significant assessment barriers, leading to inequities in educational outcomes. Additionally, using BrailleSense can result in distractions, both for the user and other students, particularly in environments where the tactile nature of the device might draw attention [33].  
Furthermore, BrailleSense may not effectively support certain question types, such as those requiring complex formatting or diagrams, which are not easily translatable into Braille. This limitation can restrict the scope of assessments and fail to capture the full range of a student's knowledge and skills [34].  
Other issues, such as software compatibility, the high cost, and the need for specialized training, can limit the effective use and broader adoption of Braille displays [2; 15; 23].  
In summary, while BrailleSense (HIMS, Inc., Incheon, Korea) is a valuable tool for BVI students, it is essential to address these limitations to foster a genuinely inclusive educational environment. Efforts should focus on ensuring equitable access to technology and designing assessments that consider the unique needs of all learners.  
Table 1 summarizes key aspects of traditional and assistive technologies, highlighting related benefits and limitations.
## 6 Conclusions  
In this paper, the deformation of the Futian District of Shenzhen City from 2008 to 2010 was obtained by the high-resolution TerraSAR-X data and PSI techniques. For a visual expression of the results, we exported the PS points of the buildings to Google Earth for 3D visualization after ortho-rectification of the PS height. Based on the Google Earth 3D model, the temporal and spatial characteristics of the building deformation became obvious. The deformation analysis results showed that during the study period, the deformation rates of the Futian area were between -10 and 10 mm/year. Most of the regions were stable, and the deformation was mainly distributed in a few buildings. We found that the Shenzhen International Convention and Exhibition Center showed seasonal deformation, which was related to changes in local temperature. A rise in the temperature results in an expansion of the building, while a decrease in temperature compresses it. In addition, high-rise buildings are vulnerable to strong winds, which are regular and acceptable because winds are considered at the beginning of construction. There were also some discordant phenomena. For example, two buildings displayed obvious settlement. Since only the TerraSAR-X data can be acquired for the study area, the definition of driving mechanism of buildings' deformation is not within the scope of this study. Deformation causation investigation can be conducted in the future when the additional data is available.  
Since only the ascending data were available, this experiment did not involve the other side of buildings, and some buildings were missed due to their materials and shadows. Furthermore, widespread layover and shadow in urban areas can easily lead to the lack of building shape, and we'll  
Figure 9: Deformation time series of four feature points in Figure 8. (**a–d**) The deformation time series of p1, p2, p3, and p4, respectively.  
get nothing from the missing part of the buildings. Therefore, fusing multi-source data to obtain comprehensive building deformation information will be the next stage in our work.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the results. [PERSON] wrote the paper. All authors reviewed and approved the manuscript.  
This research received no external funding.  
We appreciate Infoterra GmbH and USGS for providing TerraSAR-X SAR images and SRTM DEM. This work was supported by the National Key Basic Research and Development Program of China (No. 2018 YFC1505101), the National Natural Science Foundation of China (Nos. 41674010, 41774023), the Science and Technology Project of Hunan Province (Nos. 2018 JJ3347, 2017 RS3001), and the Project of Education Department of Hunan Province (No. 16K053).  
The authors declare no conflict of interest.
## 3 Discussion
### Mixing Height Retrieval
These findings highlight the potential of the CNN algorithm to improve boundary layer height detection and its application to improve air quality assessments and regional weather forecasting systems. In 2021, [PERSON] et al. [67] investigated the retrieval of PBLH using satellite lidar data and compared the results with weather model reanalysis data and radiosonde measurements. The study primarily focused on observations obtained from the Ice, Cloud and Land Elevation Satellite (ICESat-2) and CATS instruments, using two retrieval techniques: a classic threshold method and a CNN method. The main findings of the study showed an ability to retrieve the PBLH at a global scale from spaceborne lidars. The threshold method, which sets a backscatter threshold value and analyzes lidar profiles, demonstrated good agreement with radiosonde data, confirming the conventional definition of the PBL top based on temperature inversion and moisture decrease. Furthermore, a comparison with the MERRA-2 reanalysis showed spatial correlations but also highlighted differences, particularly over oceans and specific land areas. The CNN method, based on machine learning algorithms, provided a higher horizontal resolution and showed promising results in retrieving the PBL height. Although some discrepancies were observed compared to the threshold method, it was suggested that the CNN technique may be more accurate in certain cases, as it can work with raw, uncalibrated data. The limitations of satellite lidar data were also stated, including challenges associated with cloud attenuation, daytime noise, and sparse spatial coverage. Despite these limitations, the study emphasized the potential of lidar instruments, especially with improvements to algorithms, in future PBL observing systems. In summary, the threshold method and CNN technique demonstrated their effectiveness, with the former exhibiting good agreement with the radiosonde data and the latter offering a higher horizontal resolution. Although discrepancies were observed compared to the MERRA-2 reanalysis, the general spatial patterns were found to be similar. The limitations of lidar data were recognized, but the study emphasized the potential of lidar instruments to contribute significantly to future PBL observation systems.
## 5 Analysis and Verification of Text Events Categorizer Approach--Classification Results
### Obtained Results
#### 5.3.1 Classification Efficiency  
Table 4 and Figure 7 demonstrate the efficiency of classification for each of the experiments performed. The data show that the lowest classification efficiency values were achieved in both series of experiments for the \(Z_{1}\) dataset. The efficiency for the \(Z_{2}\) set was higher than for the \(Z_{1}\) set but much lower than the classification efficiency obtained for the \(Z_{3}\) set, which significantly exceeded 90% for both sets of experiments.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### The Stability of Free and Immobilized Laccase
#### 3.2.4 Kinetic Parameters  
By varying the substrate concentrations and measuring the initial reaction rates, according to the Lineweaver and Burk plots, we calculated the kinetic parameters (maximum velocity (V\({}_{\text{max}}\)) and Michaelis constant (K\({}_{\text{m}}\))) for free laccase, Fe\({}_{3}\)O\({}_{4}\) immobilized laccase, and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase. The kinetic parameters for the free and immobilized laccases are presented in Table 1. The affinity between the substrate and the enzyme is represented by the K\({}_{\text{m}}\) value. According to Table 1, the Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase has the highest K\({}_{\text{m}}\) value, which is denoted by its requirement for a greater concentration of the substrate to reach the V\({}_{\text{max}}\). The interaction between the enzymatic active sites and the substrate was hindered in the case of the immobilized enzymes, due to the steric hindrance that resulted from the nanoparticles laccase linkage and, subsequently, the K\({}_{\text{m}}\) values are higher for the immobilized laccase than the free one [37]. The irregular results of the V\({}_{\text{max}}\) and K\({}_{\text{m}}\) values in the case of the CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase, when compared to the free laccase, resulted from the increased activity of laccase, due to the presence of copper ions. Also, the Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase showed the lowest value for V\({}_{\text{max}}\), due to the reaction inhibition resulting from the accumulated product on the surfaces.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### NIST Statistical Test Suite  
The need for random and pseudo-random numbers arises in many cryptographic applications. Standard cryptographic systems use keys that must be generated randomly. Many cryptographic protocols also require random or pseudo-random inputs at various points, this article's focus. The NIST states that these procedures help detect binary sequence deviations from randomness. However, the tester should note that apparent deviations from randomness may be due to either poor generator design or deviations that appear in the binary sequence tested (i.e., a certain number of failures are expected in the random sequences produced by a particular generator). The tester needs to interpret the test results accordingly. The two basic types of generators used to produce random sequences are random number generators (RNGs) and pseudo-random number generators (PRNGs) [31].
###### Abstract  
A signal transmitted in an Underwater Acoustic Communication (UAC) system operating in a shallow-water channel suffers from strong time dispersion due to multipath propagation. This causes the Inter-Symbol Interference (ISI) observed in the received signal, which significantly limits the communication system's reliability and transmission rate. In such propagation conditions, the Direct-Sequence Spread Spectrum (DSSS) method is one of the solutions that make reliable data transmission possible. In systems with one-to-one communication, it ensures communication with a satisfactory Bit Error Rate (BER). Additionally, it makes it possible to implement the Code-Division Multiple Access (CDMA) protocol in underwater acoustic networks. This paper presents the results of simulation and experimental communication tests on a DSSS-based UAC system using three types of spreading sequence, namely m-sequences, Kasami codes and Gold codes, and occupying different bandwidths from 1 kHz to 8 kHz around a carrier frequency equal to 30 kHz. The UAC channel was simulated by impulse responses calculated by the virtual sources method and the UAC channel models available in the Watermark simulator. The experimental tests were conducted in a model pool. Based on the obtained results, a transmission rate was estimated, which is possible to achieve in strong multipath propagation conditions, assuming reliability expressed as BER less than 0.001.  
underwater acoustic communications; UAC; direct sequence spread-spectrum; DSSS; m-sequences; Kasami codes; Gold codes; multipath propagation
## 2 Methods
### SDVN Framework
#### 2.1.1 Registering Phase  
In this framework, each vehicle uses hello messages, including its velocity, location, pseudo-ID, and channel state information (CSI) etc. to periodically advertise status information to the nearest local controller at a fixed interval. After the corresponding controller receives these \"State Update\" messages, it will update the vehicles' state information tables. Note that each local controller separately maintains a vehicular history trace database which records the state information of all vehicles in its control area, and all vehicles' state information from these local databases could be integrated into a global network state vector via cloud computing.
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 4 Engineering Application and Progress of Ultrasonic Excitation of Coal and Rock Mass
### Strength Characteristics of Specimen after Ultrasonic Vibration Excitation  
With the development of ultrasonic excitation, the edge of the specimen first produces radial cracks which gradually extend to the radial centre of the specimen with the extension of the excitation time. After 50 s of excitation, the radial cracks basically run through the whole diameter surface of the specimen, and then with the extension of the excitation time, new cracks are generated on the upper surface of the specimen; these cracks continue to expand and connect, and they distribute in a divergent manner around the excitation contact surface.  
As the selected material is hard and dense red sandstone, the specimens have high compressive strength (shown in Table 4), with a compressive strength value of 53.6 MPa. After 140 s of ultrasonic excitation at a frequency of 20 kHz, the compressive strength value is reduced from 53.6 MPa to 36.8 MPa, as shown in Figure 8, and the compressive strength is reduced by 45.6%. The compressive  
Figure 7: Mineral grain nucleus within the rock using SEM: (**a**) prior to ultrasonic vibration excitation; and (**b**) after ultrasonic vibration excitation.  
strength of the specimen decreases linearly with an increase of time, and the fitting curve formula is y = \(-0.0971\)x + 50.343. An obvious breaking of the rock can be seen as the effect of the ultrasonic vibration excitation.