esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Enabling Technologies for FSO Communication
### Modulation Schemes
#### 4.3.1 Traditional Modulation Techniques  
The performance impairments caused by scintillation can be alleviated through various methods, including enhanced error control coding [165; 10], advanced modulation schemes [166], and diversity techniques [167; 10; 168]. In modulation schemes, the three main optical signal parameters that can be adjusted to achieve high data transmission rates are phase, polarization, and intensity [169]. Choosing the appropriate modulation scheme is crucial for mitigating scintillation in FSO communications. Various FSO modulation techniques, such as PSK, OOK, PAM, and Frequency Shift Keying (FSK), have been extensively studied and analyzed over the years [147; 93].  
Traditionally, OOK using IM/DD is the most commonly used modulation scheme due to its simplicity in design and implementation, as well as its cost effectiveness [170]. However, OOK-based FSO systems are suboptimal in AT channels and provide a lower SE compared to PAM [171]. The high SE of the PAM scheme comes with a trade-off of considerable performance degradation, as higher-order PAM is highly sensitive to Rx noise and AT [147]. Consequently, PPM has been extensively employed in FSO systems, despite its poor bandwidth efficiency and requirement for symbol synchronization between transceivers, adding significant complexity to the system [146]. Phase modulation schemes, including standard and differential PSK (DPSK), are prone to phase noise [172]. For instance, DPSK is frequently employed but experiences fluctuations in the phase of the encoded signal as a result of scintillation. Differential Quadrature PSK (DQPSK) offers double the SE of binary modulations like DPSK and OOK, as it transmits two bits per symbol with four potential optical phase fluctuations in successive symbol periods [169]. Further advancements have utilized coherent digital modulation schemes such as QAM. While coherent modulations have shown improved data rates, their power efficiency limitations and complexity become more significant at higher data rates [158].
# Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo
## 4 Experiments
### Implementation Details  
The parameters used to train networks are as follows: For CIFAR dataset, the baseline networks were trained for 300 epochs using the stochastic gradient descent (SGD) algorithm; the learning rate was initialized to 0.1 and decremented by a factor of 10 at 120, 180, and 240 epochs; and batch size, momentum, and weight decay were set to 256, 0.9, and \(5\times 10^{-4}\), respectively. During subnet search, the subnet was not fine-tuned for fast evaluation on the validation set. At the end of network pruning, we initialized the learning rate to 0.01 and set other parameters to the same values as in the baseline network to fine-tune the pruned network. For ImageNet, we used ResNet-50 to evaluate the performance; the baseline network was trained for 50 epochs using the SGD algorithm; the learning rate was initialized to 0.1 and adjusted using the cosine descent method; and batch size, momentum, and weight decay were set to 256, 0.9, and \(5\times 10^{-4}\), respectively. During the training process, data enhancement methods such as random cropping, random PCA noise, and random flipping were employed. In the validation set, we performed a random center crop of the images by first enlarging the image to a size of 256 and then cropping a \(224\times 224\)-sized region in the center of the image. We conducted all experiments using a single NVIDIA TITAN V GPU on a machine with 128 GB RAM and implemented our method with the TensorFlow framework.  
The baseline network was divided into several blocks. The VGGNet consists of convolutional layers and fully connected layers. ResNet is composed of multiple residual blocks, each of which includes multiple convolutional layers and a shortcut connection. For VGG-16, we directly searched a subnet by treating the whole baseline network as a block. Every 3 residual blocks for ResNet-20 constitute a block \(S\) to prune. For ResNet-56 and ResNet-110, we treated every 9 residual blocks as a block \(S\) to prune. ResNet-50 consists of 5 stages, where the 1 st stage is a single convolutional layer and the remaining 4 stages consist of 3, 4, 6, and 3 bottlenecks, respectively. In the pruning process, the 1 st stage was not pruned, and each of the remaining 4 stages was used as a block to prune. For MCMC sampling, we set the number of restarts, burn-in periods, thinness, and length of each MCMC chain to 10, 3000, 1, and 6000, respectively, and randomly initialized the state \(Z_{0}\) of each restart to meet a FLOPs' reduction of \(\sim\)50%. The pruning results with hyperparameter \(\alpha=0.1\) and \(\alpha=0.2\) were analyzed.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### LiDAR  
LiDAR is another active RS system that estimates the physical characteristics of the seafloor mainly using a green laser pulse (532 nm), with a laser repetition frequency of approximately 10 KHz [328; 329]. LiDAR emits a light pulse that can penetrate through the water column and then receives the returning signal to measure the range of the seabed [330]. As such, the returning signal from the ocean can be analyzed to provide high-resolution ocean floor topography. LiDAR systems have proven to be remarkably useful in mapping seamless, coastal topographic-bathymetric due to the high penetration in ocean water (nearly three times deeper than passive RS systems) [329; 331].  
Both airborne and spaceborne LiDAR systems have been employed for bathymetric mapping. Regarding accuracy and speed, airborne LiDAR systems are very promising for obtaining underwater topography in coastal areas [332]. They can measure the ocean floor topography with a spatial resolution smaller than 1 m and vertical resolutions of around 15 cm [333]. However, they are not relatively cost-efficient, reducing their applications, especially over areas where airplanes have difficulties getting close to them [334; 335]. Although spaceborne LiDAR systems have been developed since 2003, their applications were limited until 2018 when the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)), which carries the photon- counting LiDAR sensor (i.e., Advanced Topographic Laser Altimeter System (ATLAS)) was launched [336]. This spaceborne LiDAR system has been employed to determine ocean floor topography, as well as the mass balance of the polar ice sheets and their contributions to global sea-level change [337]. For example, [PERSON] et al. (2020) produced the bathymetric maps based on empirical linear and the band ratio models using ICESat-2 and Sentinel-2 imagery. [PERSON] et al. (2020) also employed the bathymetric points derived from ICESat-2 instead of the in situ auxiliary bathymetric points to train the traditional empirical linear and band ratio models. Then, bathymetric maps for an area in the South China Sea were produced using the trained empirical models and Sentinel-2 multispectral images. These studies provided evidence that ICESat-2 has the potential to reach a sub-meter bathymetric accuracy (nearly 10% of the maximum depth of water) in shallow and clean water areas after calibrating the bathymetric errors [336]. However, compared to the other RS bathymetric techniques, spaceborne LiDAR systems have multiple disadvantages. For example, they are subject to bathymetric inaccuracies in turbid water [329]. Moreover, some other error resources, including the SL variability persuaded by tides and waves, the non-nadir incidence, the refraction effect of sloping sea surface and water column, and inherent water properties reduce the accuracy of these systems [338; 339].
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 2 System Model
### Deployment Model  
We consider highway deployment of sub-terahertz 6G BSs as shown in Figure 1. We assume that the BSs are deployed on both sides of the road at the constant height \(H_{A}\). To reduce CAPEX, it is possible to use existing road infrastructure, e.g., lamp-posts. We denote the road length between two adjacent BSs by \(d\), the number of lanes in both directions by \(N_{l}\), and the lane width by \(w\). Observe that BSs form isosceles triangles with the base of \(2d\) and height \(N_{l}w\). We also assume that the speed of the vehicles is constant and equals \(v\) km/h, while their positions along the lanes follow the shifted Poisson process of intensity \(\lambda\). The shift is introduced to reflect both the vehicle length and minimum separation distance \(d_{s}\) required for safe driving of automated cars or dedicated control systems [13].
## 4 Naturalistic Driving Data-Based Longitudinal Driver Model Parameter Design
### Driver Model Parameter Adaptation from the Analysis of Driving Data  
In Sections 4.1-4.3, parameters expressing the driving characteristics of human drivers are derived. These parameters are the maximum acceleration (\(a_{max}\)), the maximum deceleration or minimum acceleration (\(a_{min}\)), and the deceleration at the sailing start point (\(a_{sail}\)). The three parameters are analyzed in terms of the mean and standard deviation in the previous sections. A typical human driver can be regarded as having the mean value of each parameter, and the more atypical the driver is, the further their behavior is from the mean. Therefore, the driving situations of various drivers can be simulated using the parameters derived from the analysis results. This means that atypical scenarios and various human driving patterns can be expressed according to changing parameters, such as the constraint for acceleration/deceleration and the weights of the driver model in the proposed longitudinal motion scenario design method. In other words, it is possible to create various longitudinal scenarios by defining the mode transition conditions of the driver model, as shown in Figure 9, and changing the constraint settings presented in Equation (6) for the required acceleration/deceleration corresponding to each mode.  
Figure 8: Distributions of braking/sailing start point w.r.t. relative distance/velocity: (**a**) the case of the braking start point; (**b**) the case of the sailing start point.  
There is a limit to expressing the driving patterns of various drivers only by setting the constraints. Thus, as shown in Equation (11), they can be expressed by changing the weights of the MPC used in the driver model design.  
\[Q=\begin{bmatrix}q_{1}&0\\ 0&q_{2}\end{bmatrix},\;\;R=r_{1} \tag{11}\]  
\(q_{1}\) and \(q_{2}\) are weights for the distance error and velocity error, respectively, and \(r_{1}\) is a weight for acceleration, which is a control input. In linear quadratic-based optimal control, suitable control performance is derived with the ratio of each weight. Therefore, in this study, the driving pattern is simulated by changing only \(q_{1}\) and \(r_{1}\), while fixing the weight of the velocity error \(q_{2}\) as 1 [46]. When the driver's velocity to be imitated in the simulation is \(v_{velo,data}\), the weights \(q_{1}\) and \(r_{1}\) of the driver model can be derived using the following equation. Here, \(T\) is the length of the time interval to be imitated, and \(v_{model}\) is the speed of the driver model.  
\[q_{1}^{*},\;r_{1}^{*}=\underset{q_{1},\;r1}{\text{argmin}}\left(\sqrt{\frac{1 }{T}\sum_{k=1}^{T}(v_{velo,data}(k)-v_{model}(k))^{2}}\right) \tag{12}\]
# Profile Control Using Fly Ash Three-Phase Foam Assisted by Microspheres with an Adhesive Coating
## 1 Introduction
[35] demonstrated that nanoash could be utilized to stabilize nitrogen foam in the presence of crude oil at high temperatures and pressures. [PERSON] et al. [36; 37] showed that combining fly ash nanoparticles with a surfactant mixture of alpha-olefin sulfonate and lauramidopropyl betaine results in excellent foamability and stability and highly enhanced oil recovery in microfluidics. [PERSON] et al. [38] generated nanoash-stabilized CO\({}_{2}\) foam for CO\({}_{2}\) mobility control in sandstone cores and sand packs. Their results suggested that the nanoash produced by the ball-milling method can greatly improve foam stability and the resistance factor. [PERSON] et al. [39] found that the adsorption of fly ash at the CO\({}_{2}\)-liquid interface may lead to a solid-like bubble film, and the viscosity of FATPF was thrice that of the pure surfactant foam. They further examined the micro-flow behavior and oil-displacement efficiency of the generated FATPF [40]. Given its practicability, fly ash is selected as the foam booster in our formula. In addition to fly-ash particles, we developed a special micro-sized plugging material (MCL) that comprises a rigid internal core and an adhesive coating layer, which combines the merits of both organic and inorganic particles, including good injectivity, selective plugging, and high plugging intensity. The internal core is made of ultralight ceramsite, and the hot-molten adhesive layer coating out of the inner core is composed of cardanol-modified thermo-setting phenolic resin. When reaching a critical softening temperature, the coating layer starts to melt, and the particles tend to bond with each other under the interactions of these adhesive layers. The thickness of the adhesive outer layer is about 1.5 \(\upmu\)m. A series of MCL with a broad range of bonding temperatures can be produced by modifying the ratio of cardanol and phenolic resin as well as the reaction temperature [41; 42]. These MCL particles have been substantiated to be efficient in plugging high-permeability channels in porous media [43]. By adjusting the mixture ratio of MCL and fly ash, we can obtain TPF of various plugging rates. In this study, modified starch gel, sodium dodecyl sulfate (SDS), and consolidation agents incorporating fly ash and MCL are used to generate a novel fly ash three-phase foam system assisted by high-temperature resistant MCL particles. Static evaluation tests are carried out to assess the foamability, stability and suspendability of the generated TPF. Single- and parallel-core floods are performed to test the plugging performance and oil-displacement efficiency.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 2 Shielding Grounding Optimization Method
### CST Modeling and Simulation
The error obtained by subtracting it from the simulation result after parameter extremization is shown in Figure 4.  
Figure 3: CST cable simulation example model. Figure 4 shows that, the larger the resistance value of \(R_{2N}\), the smaller the error between the simulation results for parameter extremization processing and the simulation results for an ungrounded shield. When \(R_{2N}\) is taken to be \(10^{8}\)\(\Omega\), the error drops by an order of magnitude of \(10^{-8}\), which allows the state of an ungrounded shield to be completely simulated. In addition, it can be seen from the graph that, the lower the frequency of the interference source, the slower the decrease in the error curve of parameter extremization. This means that, at low frequencies, greater resistance and a deeper degree of parameter extremization are required to simulate the state of an ungrounded shield. The reason is that the low-frequency signal interferes less with the line that is subjected to interference compared to the high-frequency signal; after the double-ended well-grounded shield (a smaller grounding resistance means better grounding) was introduced, the signal improvement amplitude was also small, and when the signal frequency increased, the well-grounded shield caused a drastic improvement, so the curve decreased faster. After the parameter extremization was complete, CST could be used to perform a full-parameter scan simulation of the interference voltage of the target cable, and the interference voltage was recorded as \(U\). The final output in terms of the simulation data is shown in Table 1. The method has good scalability, and the \"other parameters\" alluded to in Table 1 could be the shielding material, thickness, incidence angle, intensity of external radiation sources, cable spacing, etc., according to any additional needs. This paper only focuses on grounding resistance. Table 1 shows \(i\) cables, and each cable shield has four possible grounding states: \(R_{iN}=R_{iF}=0\)\(\Omega\) for the cable \(C_{i}\) double-ended ground shield; \(R_{iN}=R_{iF}=10^{8}\)\(\Omega\) for the ungrounded shield; \(R_{iN}=0,\ R_{iF}=10^{8}\)\(\Omega\) for the grounded shield at the near end; and \(R_{iN}=10^{8},\ R_{iF}=0\)\(\Omega\) for the shield grounded at the far end. Therefore, a total of \(4^{i}\) kinds of permutations are possible. The CST model shown in Figure 3 is used as an example to illustrate the results of the parameter extremization and parameter scans, as shown in Table 2. When additional parameters were introduced, the number of states exponentially increased, which required MATLAB to process large amounts of data. Figure 4: Error due to different resistance values at different frequencies.
## 4 Security Attacks
### Network Layer
#### 4.2.1 Sybil Attack  
A Sybil attack [18; 27; 29; 31; 32; 42] occurs when an adversary creates multiple fake identities (or nodes) within the network. By doing so, the attacker can gain disproportionate influence over the network, disrupting consensus and manipulating the flow of information. To prevent Sybil attacks, robust identity management and reputation systems are essential.  
Figure 2: Transaction malleability attack where the transaction ID is modified to enable double-spending.  
These systems help detect and mitigate the presence of fake nodes, ensuring that only legitimate participants contribute to the network.
## 4 Analysis of Computational Complexity  
We analyze the computational complexity of the proposed method in terms of the number of multiplications and additions per iteration for single-channel and multichannel simulations. The computational cost is compared with conventional FXAPL-I, filtered-x alternating affine projection (FXA-AP) and FXLMS algorithms. The notation used is as follows: \(M\) is the length of the secondary path, \(N\) is the length of the adaptive filter and \(L\) is the order of the projection.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Components of Telemedicine
#### 3.1.2 Communications Technology Unit  
Short-Range Communication  
The vital signals collected by the sensors are transmitted via short-range wireless devices, such as Bluetooth, Wi-Fi, and ZigBee [59; 60; 61; 62], to a local server. A Local Area Network (LAN) can be constructed for networked devices within nearby locations. It is highly desirable that the reach of such communications be spread over a wide locale. A Wide Area Network (WAN) can be established to make connection between a Care Seeker Unit (CSU) and Caregiver Unit (CGU) easier.  
Long-range communication is employed to transmit information from the CSU to the CGU. A number of relevant technologies have previously been outlined in the literature. With respect to the type of information intended to be transmitted, choices must be made as to which communications technology is most ideal. The size of the information, its bandwidth requirements, and the cost of execution may provide useful insights during the design of a telemedicine communication network. Long-range communication technologies that have been used in telemedicine include, but are not limited to, satellite, GSM, 3G, 4G, 5G, WAN, LoRaWAN, and internet technologies [63; 64; 65; 29; 66; 67; 68].  
A set of rules and guidelines are necessary for consistency and universality in order for the successful transmission and reception of a message or information from the source to the sink for two or more communication devices. Communication protocols and standards include authentication, Error Detection and Correction (EDC), signaling, modulation, and demodulation techniques. Among the common communication protocols are:  
1. Bluetooth stack of protocols;
2. Zigbee stack of protocols;
3. Wireless Application Protocol (WAP);
4. File Transfer Protocol (FTP);
5. Hypertext Transfer Protocol (HTTP);
6. TCP/IP;
7. UDP.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System
### Dataset Description and Data Preprocessing  
Within this research, we use two different datasets to evaluate the effectiveness of the proposed framework.  
The MNIST dataset consists of a training set of 20,000 images and a testing set of 10,000 images, each of which is \(28\times 28\) pixels in size, totaling 784 pixels per image. Each pixel has a value ranging from 0 to 255. By dividing each pixel value by the maximum value (255), the pixel values in each image are normalized to a range of 0 to 1. This normalization helps ensure that the data are on the same scale, which can improve the model's performance. The dataset is reshaped into a three-dimensional array with dimensions \(28\times 28\times 1\). Since the images are grayscale, the 1 at the end indicates the number of color channels. To augment the dataset, random noise is added to the 20,000 training images to create a new set of 80,000 training images.  
The CIFAR-10 [33] dataset consists of 60,000 color images across 10 classes. Each CIFAR-10 image is \(32\times 32\) pixels in size. Similar to the MNIST dataset, CIFAR-10 pixel values range from 0 to 255 and are typically normalized to a range of 0 to 1 by dividing by 255. CIFAR-10 images are in RGB format, resulting in data dimensions of \(32\times 32\times 3\). The CIFAR-10 dataset contains 50,000 training images and 10,000 testing images.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 3 Results  
Results achieved applying the RST-FLARE approach on the Niger Delta region will be presented and described in detail, pulling apart those related to the detection of onshore and offshore areas affected by gas flaring (Section 3.1) by the ones concerning the emissive power sources estimation (Section 3.2) and the gas flared volumes computation (Section 3.3). All the results, aggregated at national and state scale, refer to each year of the investigated temporal period (2000-2016).