esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 4 Experimental Design
### Benchmark Methods  
To verify the performance of the ITACIE-GP method, this paper compares it with several benchmark methods. These benchmark methods include manual feature-based methods, CNN-based methods, deep feature-based methods, and a GP-based method.  
For feature-based methods, this paper employs several image classification techniques that combine various handcrafted feature methods with the classification algorithm Support Vector Machine (SVM), namely SVM + Histogram, SVM + DWT [43], SVM + Gabor [44], SVM + SIFT [6], SVM + HOG [7], and SVM + uLBP [5]. These methods demonstrate relatively low computational complexity and good performance across multiple datasets.  
For CNN-based methods, two widely used network architectures in computer vision tasks are selected for experimentation: CNN-5 and LeNet-5 [45].  
For deep feature-based methods, this paper selects several deep models pre-trained on the ImageNet dataset [46], namely ResNet [47], VGG [48], AlexNet [49], MnasNet [50], and SqueezeNet [51], for performance evaluation. The ResNet, VGG, and MnasNet models have their last fully connected layer removed, while AlexNet has all its fully connected layers removed, thereby obtaining network structures for feature extraction. The extracted features are then fed into the SVM classification algorithm to complete the final classification task.  
For the GP-based method, EFLGP [16] is considered to be representative in this field. The GP program of this method generates a set of feature vectors, which are then used as input to an SVM. This method has demonstrated superior performance on datasets with issues like blurriness, low contrast, and noise.  
These benchmark methods were chosen because their performance on various publicly available datasets has been widely validated and is commonly used in this domain. The experimental design strictly accords with parameter configurations from the existing literature and assesses the classification performance of all methods on selected datasets.
## 3 Proposed Methodology  
In this study, the primary focus was the improvement of the quality of medical images, particularly for diagnosing coronary disease. The main goal was to enhance images that have low contrast and may contain noise, making it difficult for medical professionals to accurately diagnose heart-related conditions. The aim was to make these images clearer and cleaner, ultimately aiding in more effective and reliable diagnoses for coronary diseases.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 3 Proposed Schemes
### Packet Types  
Before proceeding to the packet types used during the network operation, some of the definitions need to be described for avoiding ambiguities.  
Area towards the destination (ATD): This is an area where the neighbors of the sender node are closer to the destination.  
Potential neighbor number (PNN) is defined as nodes deployed in ATD are nearer to the destination than the sender node. In order to initiate the network process, a few control messages need to be exchanged among the network nodes to allow the communication between the network nodes. After the deployment of the node, for establishing routes between and the destination, neighbor query (NQ) and neighbor Ack (NA) packets are exchanged. NQ is broadcast by the sender node to find out PNN in ATD. The format of NQ is a tuple (type ID, sender ID, SX, SY, SZ, PNQ where type ID is the type of packet and its value is \"00\", sender ID denotes the address of the node sending NQ and SX, SY,  
Figure 4: Algorithm for QF-AHH-VBF.  
SZ are the \(x\)-coordinate, \(y\)-coordinate and \(z\)-coordinate of the sender node, respectively. To reduce the overhead of the control message, Boolean values are used to represent the types of packets. NA packets are sent back from neighbor nodes to the sender node after receiving NQ. The NA format has three fields (type ID, sender ID, X, Y, Z, \(PN_{n}\)), where type ID is \"01\" that shows NA, sender ID is the address of the node sending NA and X, Y, Z are the \(x\)-coordinate, \(y\)-coordinate and \(z\)-coordinate of the sender node, respectively. The data packet header format consists of (type ID, sender ID, destination ID, SX, SY, SZ, FX, FY, FZ, PID, PR, TR where PID is packet ID, PR, TR is pipeline radius and transmission radius, respectively.  
These fields have the following meanings: sender ID is the ID of the broadcasting node; destination ID shows the address of the destination node; SX, SY, SZ and FX, FY, FZ are the \(x\)-coordinate, \(y\)-coordinate and \(z\)-coordinate of the sender node and the forwarding node, respectively. The PSN field is for packet sequence number, and PR and TR show the pipeline radius and transmission radius, respectively. The message exchange procedure repeats when the void hole occurs; else, the same routes are used. The unnecessary use of the control message is avoided, which ensures that the energy dissipated is minimal, resulting in efficient network performance. The changes in the function are handled through the composite function defined in Section 3.4. The algorithm of proposed scheme is presented in Figure 4.
# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 4 Machine Learning Applications in UOWC
### Supervised Learning in UOWC Systems
Results show that the photon-counting UOWC based on the ML time slot synchronous clock recovery succeeded in achieving a data rate of 1 Mbps and a BER of \(5.35\times 10^{-4}\) at eight photons per time slot. Figure 10: Schematic diagram of the proposed TFDNet [128; 112].
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 2 Related Works
[11] and [PERSON] et al. [13] apply a genetic-algorithm-based solution to solve a multi-objective version of the RPP-3d problem for, respectively, elastic and WDM optical networks. Based on the forecast traffic matrix, the algorithm seeks to find the regenerator placement solution which achieves the best trade-off between the total number of deployed regenerators and the network blocking probability. These algorithms reach lower blocking probabilities than other placement algorithms in the literature. The traffic information and the total network blocking probability are used to influence the placement decision itself, but a network simulator engine is needed to assess each solution proposed by the genetic algorithm during the regenerator placement procedure. [PERSON] et al. [21] propose analytical models for computing the blocking performance of translucent optical networks with sparsely located regeneration nodes. The analytical models developed to estimate the call blocking probability in an optical network are applied to assess two regeneration node allocation policies. The proposed analytical models are suitable to be used to assess the translucent optical network performance, but they are computationally complex enough to prevent its use during the translucent network design (regenerator placement decision) because several different placement combinations must be investigated during the designing procedure. The results from the above-reviewed papers consistently show that traffic-based placement approaches achieve lower blocking probabilities in comparison to topology-based approaches. Traffic-based approaches can also satisfactorily solve the placement problem for the network under both uniform and non-uniform traffic patterns, whereas the topology-based approaches give the same result regardless of the offered traffic pattern. All reviewed traffic-based approaches require a network simulator engine to simulate a realistic per node regenerator demand (in the case of the application of RPP-3h) [1; 4; 29] and its resultant network blocking probability (in the case of the application of RPP-3d) [11; 13]. Different from the other algorithms in the literature, we propose in this paper an approximate analytical model to evaluate the network blocking probability and we employ this model during the placement procedure. Our heuristic proposals use the blocking probability information to guide the regenerator placement. The proposed heuristics are traffic-based placement algorithms that can, at the same time, heuristically solve RPP-3d, deals with uniform and non-uniform traffic patterns, and dismiss the use of a simulator engine during the regenerator placement decision.
# Automated Assessment of Inferences Using Pre-Trained Language Models
## 4 Discussion  
### Effects of Pre-Trained Language Models on the Classification Accuracy  
The accuracy of inference classification is significantly influenced by the choice of the base pre-trained language model. The comparison of the F1 scores based on the BERT-base and RoBERTa-base models shows the effect of the training strategies used during the pre-training phase of a language model on the classification accuracies of the fine-tuned models. The more advanced training strategies used for the RoBERTa-base model resulted in significantly higher F1 scores for the inference classification than that of the BERT-base model of the same size. This is consistent with previous findings which indicate that models trained with RoBERTa-base models outperform models trained with BERT-base models for various downstream tasks [22].  
Furthermore, the comparison of the F1 scores based on the RoBERTa-base and RoBERTa-large models suggests that the larger model is not necessarily better for classifying inference in the current dataset. The RoBERTa-base and RoBERTa-large models share the same Transformer architecture and training objective (masked language model with dynamic masking), but the main difference between the two models is the number of layers (12 vs. 24) and the model sizes (110 million vs. 355 million parameters). Despite the greater representational capacity, the RoBERTa-large model did not perform significantly better than the smaller model with the same architecture. The average F1 score became even lower, and the SEM of the F1 scores became larger. The error analysis using the inference subtypes shows that the largest language model (RoBERTa-large) made more errors in classifying evaluative comments, paraphrases, and meaningless responses.  
This lower accuracy of the larger model is probably due to overfitting. In Table 4, the optimal weight decay value was zero for RoBERTa-base and non-zero for RoBERTa-large. This suggests that regularization with weight decay worked for RoBERTa-large, yielding a higher F1 score than RoBERTa-large without weight decay. Without weight decay, the F1 score of RoBERTa-large would be even lower than that of RoBERTa-base. More data are needed to train the larger model, and a well-trained small model (RoBERTa-base) would be the preferred choice for inference classification with a relatively small dataset.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 1 Introduction
### Motivation and Contributions  
Channel modeling is the basis for establishing reliable communication links. With the use of IS, wireless communication channels can be configured and controlled. Hence, the channel characteristics can be remodeled. The idea is to control the incident radiations on the surface, with constructive and destructive interference at the receiver's location. ISs can reflect and phase shift the incident radiations to maximize the signal at the intended receiver while minimizing it to an unintended receiver.  
In this context, this paper surveys the research works on IS-aided wireless communications, emphasizing channel modeling at the THz frequency band. In addition to reviewing the state-of-the-art and the IS principles, this paper outlines THz channel modeling and IS-aided THz channel models. This paper also includes an in-depth discussion and points out research issues related to THz channel modeling with the implementation of IS-aided wireless networks.
# An Earthquake Early Warning Method Based on Bayesian Inference
## 2 Earthquake Data Selection and Preprocessing  
We used KiK-net data (1 October 2007 to 1 October 2017) obtained from the Japan Institute of Disaster Prevention Technology and Science. The earthquake magnitudes in this dataset range between 3.0 and 8.0, and the majority exceed 4. Earthquake data were processed and screened according to the following standards:  
1. We selected data from earthquakes within 10 km of the focal depth and 200 km from the epicenter.
2. The short-term average to long-term average (STA/LTA) and Akaike information criteria (AIC) were combined to automatically detect the arrival of the P- wave and manually correct it.
3. The acceleration record was corrected at the baseline, and the acceleration records were integrated to obtain the velocity and displacement records. Following this, 0.075 Hz Butterworth high-pass filtering was performed to eliminate the low-frequency drift caused by integration.
4. Earthquake records should meet certain Signal to Noise Ratio(SNR) requirements during the calculations. In this paper, velocity amplitudes greater than 0.05 cm/s within 3 s after P-wave triggering were selected as the SNR criteria. Noise reduction of the selected records was also performed to reduce the high-frequency interference of the acceleration records. A total of 145 earthquakes were screened out. Figure 1 presents the distribution of the magnitude, epicenter distance, and earthquake frequency of the dataset.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 3 System Model
### Multi-DAG Task Deployment Process  
In certain DAG tasks, there are public subtasks that can reuse the same processing results, such as the recognition of remote sensing images for the same geographical location. These public subtasks only need to be executed once, thereby reducing unnecessary resource consumption. To meet the requirements of DAG satellite network task scheduling, public subtasks can be processed uniformly. We deploy a task decomposition module, task deployment management module, and public task management module in the SDN controller. The task decomposition module primarily decomposes incoming tasks, storing the decomposed tasks in the DAG pool. The task deployment management module provides edge computing node monitoring and task deployment functions. The public task management module is primarily used to identify and execute public subtasks in the DAG pool. Due to the extensive coverage of GEO synchronous satellites, they naturally provide an advantage for deploying public subtasks. A schematic diagram of multi-DAG task deployment is shown in Figure 2.  
Multiple different users on the ground send task deployment requests to their corresponding coverage satellites. The receiving satellites uniformly send task information to the SDN controller. The task decomposition module of the SDN controller decomposes the tasks into DAG subtasks and stores them in the current DAG pool. The public task management module in the SDN controller scans the subtasks in the DAG pool. When a subtask has been executed a certain number of times, it is marked, and its execution result is cached on the GEO satellite. This cached result is then made available as a public resource for all tasks. Then, based on the resource information obtained from satellite nodes, the task deployment module schedules multi-DAG tasks through an algorithm to select the optimal deployment locations. The SDN controller sends the corresponding control information  
Figure 2: Multi-DAG task deployment diagram.  
to the LEO satellite and forwards tasks to the specified deployment locations through a flow table.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 2 Related Work
### Main Features of K3P Algorithm  
The K3P algorithm, the approach we propose for generating the walking gait for legged robots, is based on a centralized kinematic planner. This algorithm performs a steadily fast gait cycle while also being able to drive the robot at an arbitrary speed by dynamically adjusting the duty factor \(\beta\). Also, K3P blurs the differentiation between slow, medium, and fast walks under static stability conditions. Basically, K3P moves the robot's limbs in tripod configurations, keeping a support tripod while swinging the second tripod to a new location, according to the actual desired speed and trajectory of the robot's center of mass.  
The main differences between K3P and other approaches are as follows:  
1. K3P is self-contained and makes it possible for a legged robot to walk at an arbitrary speed along an arbitrary trajectory over uneven terrain, within the physical limitations of the robot.
2. K3P does not require any precomputed limb trajectories for straight or turning maneuvers; instead, it computes the limb trajectories in real-time, to make the robot move straight ahead or in a sharp or wide curve, according to the terrain level.
3. Since K3P moves the robot's center of mass from a supporting tripod to the next one, K3P guarantees static equilibrium during the march while controlling the clearance of the robot to ground level.
4. The kinematic planner behind K3P also guarantees slip-free locomotion and collision avoidance among limbs.  
Despite the mathematical complexity behind K3P, its use is fairly simple: it requires as input the physical dimensions of the robot and some PD controller gains. We must highlight that none of the limb displacements is computed beforehand, as in most of the mathematical or heuristic approaches; instead, K3P can change robot displacement at any moment. K3P takes into account the actual position and velocity of the center of mass of the robot, as well as its target trajectory, in order to compute the best position for landing the swinging legs to place the next supporting tripod. Furthermore, K3P verifies the stability of the gait by measuring how close the projection of the center of mass is to the support polygon's edges.  
In comparison to precomputed approaches, K3P exhibits flexibility by allowing the dynamic adjustment of the gait pattern during execution in accordance with the specified trajectory. For example, it enables modifications to the body clearance over varied terrain. Moreover, while precomputed methods enforce minimal fixed curvature for the robot's trajectories, K3P overcomes this constraint by adapting the gait pattern to ensure that the instantaneous turning radius of the robot matches the specified trajectory. Unlike many heuristic methods that demonstrate comparable performance, K3P distinguishes itself through its ease of tuning, facilitated by the concrete nature of all its parameters.
## 5 Seismic Performance of the SSSI System
### Acceleration Maps of the SSSI System  
The final analysis shifts the focus from the displacements to the accelerations. Specifically focusing on Frame 2 (refer to the second row of Tables 1 and 2), the absolute accelerations \(a_{1}\) and \(a_{2}\) for both stories of the 2-DOF equivalent system are computed. Subsequently, two acceleration performance indexes are introduced according to Equation (16):  
\[\alpha_{a1}=\frac{\max\lvert a_{1}(t)\rvert}{\max\lvert\tilde{a}_{1}(t)\rvert}, \qquad\alpha_{a2}=\frac{\max\lvert a_{2}(t)\rvert}{\max\lvert\tilde{a}_{2}(t) \rvert}. \tag{16}\]  
where \(\tilde{a}_{1}\) and \(\tilde{a}_{2}\) represent the absolute accelerations of the stories in the 2-degree-of-freedom (2-DOF) model corresponding to the standalone structure. The effectiveness of the SSSI in reducing absolute accelerations improves as the values of \(\alpha_{a1}\) and \(\alpha_{a2}\) decrease relative to unity. The parametric investigation entails plotting the values of the acceleration performance indexes, \(\alpha_{a1}\) and \(\alpha_{a2}\), in the \(\eta-\gamma\) parameter space under a single earthquake record to generate acceleration performance maps. In these maps, lighter shades of gray correspond to lower values of the acceleration performance indexes.  
Figure 10 shows the acceleration performance maps referring to Frame 2 under the three selected earthquakes. It is evident that the external system significantly diminishes the absolute acceleration of the first story, \(a_{1}\). Moreover, the contour patterns observed in the \(\alpha_{a1}\) maps exhibit notable distinctions from those in the \(\alpha_{1}\) maps. In contrast, the contour lines in the \(\alpha_{a2}\) maps closely resemble those in the corresponding \(\alpha_{2}\) maps (refer to Figure 7). Nevertheless, the reduction in absolute acceleration experienced by the second story of the 2-DOF equivalent system in comparison to the standalone frame structure is notably less than the reduction observed for the first story. Despite this discrepancy, the SSSI system delivers an overall reduction in absolute acceleration across the entire frame structure.  
Figure 10: _Cont._
## 3 Results
### Controlled Release Study of \"Free\" RIS, Ca-RIS and Sr-RIS
No drop in RIS concentration was observed, and the release profiles were as expected, showing a rise in drug concentration during the first hours that eventually led to a plateau. The release of RIS in the presence of PAMAM-2 showed an initial rate of 0.08 umol/min, reaching a plateau at \(\sim\)5% after 10 h. The release of RIS in the presence of LPEI was slightly faster, and showed an initial rate of 0.10 umol/min, reaching a plateau at \(\sim\)35% after 250 h. Since LPEI was proven to be a more effective RIS  
Figure 8: Schematic structures of PAMAM-2 and LPEI, used as solubility enhancers of RIS. The amine groups that are protonated in acidic solutions are highlighted red. Figure 7: Controlled release of “free” RIS, showing the onset of its crystallization in acidic solution. Inset: calculated (red) and measured (blue) powder X-ray diffractograms (calculated and measured) of RIS acid. solubilizer, the next experiments were focused on using this additive for the evaluation of the metal-containing systems. After proving the beneficial effects of the cationic additives as drug solubilizers, experiments were set up, using Ca- and Sr-RIS as the CDSs in the presence of LPEI. The release curves are shown in Figure 10. In contrast to other metal-containing BP systems [11], these experiments revealed that the metal-containing tablets exhibited faster RIS release than those containing the \"free\" drug (no metals). Specifically, the initial rate observed for Ca-RIS was 0.44 umol/min, and for Sr-RIS, it was 0.52 umol/min. These initial rate values can be compared to that for the \"free\" RIS system, 0.10 umol/min. The final % release values were also higher for the Ca-RIS (~75%) and the Sr-RIS (~60%) systems, compared to the \"free\" RIS system (~35%). An explanation for the difference in the plateau values in the Ca- and Sr-RIS CDSs is not immediately obvious. Based on these and previous results [11], it seems to be dependent on the metal cation and the released drug itself since all other variables (pH, geometrical features of the tablet, and amount of active drug) are the same. Apparently, further experimentation is needed to delineate this point. Interpretation of these results is presented in the following discussion section. Figure 9: Controlled release of “free” RIS in the presence of cationic additives, LPEI (red) and PAMAM-2 (light blue) at pH = 1.3. **Upper**: long-term RIS release. **Lower**: initial stages of RIS release.