esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous Works
### Motivation and Contributions  
Most of the depth based forwarding schemes in the literature just consider depth plus energy of the node itself to forward the packet. There are couple of schemes that consider link quality and hop count. However, they need to exchange a lot of beacons or short messages to get the current link quality and to update the hop count in the link breakage situation. This beacon control overhead has a drastic impact on a node's battery and the overall network lifetime.  
Inspired from the above discussion, we proposed a novel EDOVE for UASNs. EDOVE computes the holding time of candidate forwarders by keeping the following points under consideration.  
Figure 3: WDFAD-DBR routing scenario.  
1. Avoid using constant weighting factor(s) to compute the holding time because the estimation of the optimal value quite difficult in the unpredictable and error prone topology network, e.g., \(\alpha\) weighting factor was used by WDFAD-DBR.
2. Select the forwarder from the 1-hop neighbors of the data _Sender_ node (also known as the _data receiver nodes_) that has smaller depth relative to the sender node and has more residual energy. To achieve this, we use depth difference between sender and its 1-hop neighbors (the _first hop depth difference parameter_) and it is prioritized using the normalized residual energy of the potential forwarder. The main objective of prioritizing the smaller depth potential forwarder with larger residual energy is to achieve energy efficiency, energy balancing, and disseminating data further in the network. In result, it extends the network lifetime and minimizes the data broadcast overhead.
3. Next, choose the forwarder that has larger depth difference relative to its neighbors, which is the _second hop depth difference_, and has more depth variance among its neighbors. The second hop depth parameter is scaled with the depth proximity variance of the neighbors of the potential forwarder node. Any potential forwarder that has the large depth proximity variance becomes the potential forwarder and it intends to avoid the packet collision and prevents the void region when it further forwards the packet.
4. The adaptive weighting factors, based on 2 the residual energy and 3 the depth variance, with one and two hop depth difference parameters, significantly scale the holding time of the receivers to avoid multiple data packet transmissions.  
In the next section, we briefly discuss our proposed EDOVE scheme and next we discuss the simulation results, which shows that EDOVE improves energy efficiency, increases packet delivery ratio, and network lifetime.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 5 Design of Executor Model
### Conclusions and Further Research  
The developed method consists in the decomposition of a relatively complex task, which is load balancing in web servers, into many small tasks. For each of these subtasks, a neural network is designated that estimates the response time to requests. The best results were obtained when separate elements predicting times for different servers and request categories were made (i.e., one neural network provides only service times for one server and one type of request). Both the types of requests and the load on the servers change in the simulated environment. Each neural network specializes in a small portion of the tasks reaching the load balancer--this helps it adapt more accurately.  
In future studies, the authors intend to perform tests in a real distributed environment. An additional research direction will be an attempt to use neural networks that will adapt not only by changing weights but also by changing the structure of connections.
## 2 Methods  
Several issues affect the precision of a speech to text system when it is used to transcribe recordings from real call centers, which may result in large degradation of the results compared to those obtained in a controlled scenario. There are possibly many factors causing this degradation. In this article, we have identified two of the main reasons for the degradation in our particular call center recordings, the low bit-rate codecs used, and the packet losses during transmission in Voice-over-IP (VoIP) networks. This article tries to analyze these issues and to propose possible techniques to reduce such degradation.  
The method followed to achieve these goals has been the following. First, each possible scenario has been simulated to analyze the effects produced, thus assessing how the original system behaves in each situation and how much degradation is found in each case. Next, several types of data augmentation models have been applied to build a more robust speech to text system, with the goal of analyzing the effect of data augmentation for each possible degradation and trying to find the best one to apply in real data. Finally, we have applied this more robust speech to text system to real data to check if the system selected using simulated data also works better in real data.  
The detailed steps taken to train the robust speech to text models and to evaluate them are the following:  
1. Encode and decode each audio using codecs commonly used in the industry to record calls in call centers (GSM-FR, MP3-8 Kbit/s, MP3-16 Kbit/s,...).
2. After encoding and decoding, remove several packets using different packet loss simulation strategies. Three different strategies have been applied. The first one includes only single packets losses; the second contains only burst packet losses (three contiguous packets losses); and the third one includes individual and burst packet losses comprising two to three contiguous packet losses.
3. Each speech to text system trained has been evaluated in test data sets which contain each simulated degradation separately to analyze the results obtained in each case and each circumstance, and also to make a decision about which data augmentation techniques could be used to improve the performance obtained.
4. ITU-T G.711 packet loss concealment standard has been applied in test data sets that contain packet losses and each speech to text system has been evaluated to analyze if it can be a good approach to have a better performance of the system.
5. The last step has been to evaluate from real call center recording data the best system obtained based on our study on simulated data.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 3 GAN-Enhanced CNN for Voiceprint Recognition
### GAN Model Construction  
GAN produces various samples through the trained generator and lets the discriminator determine whether the generated samples are real samples or fake samples; subsequently, the discriminator produces feedback. The audio quality generated by the adversarial generator gradually increases, and the discriminant ability of the discriminator gradually improves; this mechanism allows for samples generated by the GAN to have high-quality output. It can also help us improve the problem of low accuracy caused by insufficient samples when building voiceprint recognition models. The GAN model runs in the TPU environment of Colab, and the TPU is more efficient than the GPU and CPU for such a small but very large amount of data processing. To mitigate pattern collapse and instability, the Wasserstein GAN with gradient penalty (WGAN-GP) architecture was utilized. Additionally, hyperparameters such as learning rates and batch sizes were optimized, and diversity was monitored by visualizing and evaluating the statistical distribution of generated samples.
## 7 Qualitative Evaluation and Open Discussion
### SSFXG Challenges and Open Discussion
#### 7.4.2 SSFXG and Quantum Computing Attacks  
The SSFXG framework, though innovative with its blockchain-based decentralized management, is vulnerable to quantum attacks due to its use of classical cryptographic algorithms. Quantum computers can rapidly solve complex problems like factoring large numbers and calculating discrete logarithms, potentially compromising the cryptographic infrastructure of blockchain transactions and identity data [25]. This vulnerability underscores the need for quantum-resistant cryptographic algorithms to secure such systems against quantum threats.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 5 Enhancements for M-Rep
### ABR Extension  
The PIM-DM protocol, which is used by the M-REP mechanism, assumes that all connected end stations are interested in receiving the multicast traffic. Therefore, the PIM-DM router delivers multicast packets simply by flooding the packets to all active PIM-DM neighboring routers. PIM-DM routers that do not have receivers for a given multicast or have received multicast packets on interfaces, which do not pass RPF control, will be pruned from the distribution tree.  
These processes take place in the beginning of multicast broadcasting and periodically later on, so they cause an unnecessary network load [68]. As the EM-REP mechanism uses these PIM-DM processes (flood and prune), they are only carried out until the network convergence process is complete. Subsequently, the routing protocol then takes control of the router's routing logic. From this point of view, networks consisting of several administrative areas appear to be problematic. In this case, the multicast (S, G) flow flooded by router S will be delivered to all routers in all administrative areas (Figure 9).  
Here, we propose the modification of the M-REP behavior applied on border routers of administrative areas (ABR). If we consider a network with applied OSPF routing, the area boundary routers are called the Area Border Router or the Autonomous System Boundary Router (ASBR). In this case, if a failure occurs in a given area, the ABR/ASBR router will act as a decapsulating router instead of the original D router. It means, that the ABR/ASBR router will decapsulate a specific M-REP multicast (S, G) flow back to the original unicast communication. The ABR behavioral design in OSPF is shown in the diagram (Figure 10).  
Let us explain this process using the topology shown in Figure 11. The source sends its packets to the destination. R01 detects a link failure to the next-hop router and begins to encapsulate the unicast flow on the M-REP specific multicast flow (S, G). In this case, the boundary routers (ABR/ASBR) are  
Figure 9: The process of PIM-DM flooding in multi-area Open Shortest Path First (OSPF).  
This principle also removes the original M-REP mechanism design requirement, which assumes that the router D is directly connected to the destination. This solution requires that two different areas are connected over one link and only one failure has occurred there.  
Routers R12 and R21 will behave according to Action no. 1 (Figure 10). This means that the M-REP multicast will not be forwarded to the next area. The R02 router, however, will behave according to the Action no. 2 (Figure 10), which causes the decapsulation of the multicast M-REP flow and its further delivery to the destination.
## 2 Adaptive Spatial Feature Fusion Depth Completion
### Network Architecture  
This section presents an encoder-decoder network called DepthNet. The architecture of this network is shown in Figure 1.  
The input of this network consists of a sparse depth map \(D_{k}\) and an RGB image \(I_{k}\) in the same coordinate system, and the output is the predicted dense depth map \(D_{pred}\). Before processing, \(I_{k}\) and \(D_{k}\) are first fed to a convolution with a kernel size of 3 \(\times\) 3 for simple feature extraction, respectively, and then the two feature maps are concatenated and input to the encoder. A block module similar to resnet34 [31] is used at the encoder to downsample the image by 16 times. This block module is able to extract deep features in the feature map and avoid the problem of gradient disappearance.  
The decoder upsamples the feature map that has been downsampled by 16 times through the transpose convolution to obtain a feature map of the same size as the input original image. At the same time, the feature information corresponding to the scale at the encoder is fed to the decoder through a skip connection, which helps to decode the image  
Figure 1: The network architecture of DepthNet, where Conv means convolution, Tconv means transpose convolution, Bn means batch normalization, Relu means rectified linear unit activation function, k3s1c16 means convolution operation with a step size of 1 using 16 convolution kernels of size 3 \(\times\) 3, and other similar representations and so on. The output of DepthNet is passed through the ASFF-ED module to obtain the \(D_{pred}\).  
information better and prevents the gradient from disappearing. Finally, the output results \(Y_{1}\), \(Y_{2}\) and \(Y_{3}\) are three kinds of feature maps of different scales.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 2 Proposed Asymmetrical Bidirectional Rendering Approach  
The flowchart of the proposed method is shown in Figure 3. Our framework mainly contains four parts: depth image preprocessing, asymmetric bidirectional rendering, color-correction-based merging, and depth-guided postprocessing. In the following, these approaches will be described in detail.
## 5 Conclusions  
1. The stress field distribution characteristics of a single oil well in the formation were theoretically calculated, revealing that the plastic zone radius extends to 1.43 m. A mechanical analysis of the casing and cementing cement demonstrated that their primary function is to enhance the cohesion of the rock mass and increase the internal friction angle. After casing anchorage, the cohesion of the surrounding rock increased by 5.51 MPa, while the increment in the internal friction angle was negligible. The casing anchorage mechanism improves the compressive and shear strength of the rock mass and modifies its elastic modulus and Poisson's ratio. Within the plastic zone, the uniaxial compressive strength of the casing-anchored solid increased by 23.06 MPa, the elastic modulus reached 13.245 GPa, and Poisson's ratio was 0.257. These findings indicate a significant reinforcement effect of casing anchorage on the surrounding rock mass, contributing to enhanced stability and mechanical performance.
2. Based on the FLAC3D 7.0 numerical model, the stress peak around the oil wells gradually increases with burial depth, rising from 0.37 MPa to 2.24 MPa. Simultaneously, the displacement influence range of the oil well cluster in each monitored stratum also exhibits a progressive expansion, extending from 18 m to 43 m. Furthermore, the subsidence displacement of the rock strata in proximity to the oil wells is significantly smaller compared to that of more distant formations, indicating that oil wells exert a restraining effect on stratum subsidence. These findings underscore the mechanical stabilization role of oil wells in mitigating excessive deformation within the surrounding strata.
3. Oil well anchoring technology ensures well wall stability by regulating stress distribution, supporting the overburden, and enhancing the compressive and shear strength of the well structure. Through the synergistic effect of cementing and casing support, the anchoring system effectively mitigates the risk of well wall instability. The oil well casing functions analogous to an embedded anchor within the formation, thereby enhancing the structural integrity and damage resistance of the surrounding rock mass.
# Transformer Self-Attention Change Detection Network with Frozen Parameters
## 2 Methodology
### Pooling Refinement Unit (PRU)  
As the depth of the ResNet increases, the receptive field of the model also expands. In the feature maps extracted by the lower-level networks, a single pixel often represents the result of convolutions over a spatial region in the original image. Therefore, these feature maps contain rich spatial position information. To extract and refine this spatial position information, we propose the Pooling Refinement Unit (PRU). The PPM [23] module applies different convolutions on the original feature map to obtain multiple feature maps of different sizes. These feature maps are then concatenated together. This approach utilizes the mutual guidance of context to enhance the model's prediction accuracy. However, it tends to overlook pixel-level local details. Building on its advantages, we utilized four pooling layers to extract the contextual information from the feature maps at the lowest layer of the network. At this stage, the feature maps themselves are at the pixel level, thereby mitigating the drawback of overlooking local details. Furthermore, we strengthened the guidance of context by concatenating the output of the pooling layer with a smaller pooling kernel and the original input along the channel dimension. This concatenation effectively guides the pooling layer with a larger pooling kernel, enabling it to capture richer contextual information.  
A schematic diagram of the PRU is shown in Figure 5, and the input of the PRU is set to \(f\in R^{8\times C\times H\times W}\); we sum \(f_{1}\) and \(f_{2}\) to obtain \(f\). First, we send \(f\) to the pooling layer with a core of 1; then, the result is concatenated with \(f\) and sent to the pooling layer with a core of 2 and a convolution of \(1\times 1\). After that, the result is concatenated with \(f\) and sent to the pooling layer with a core of 4 and a convolution of \(1\times 1\). Similarly, the result is concatenated with \(f\) and sent to a pooling layer with a core of 6 and a convolution of \(1\times 1\). Finally, the results of the four convolutional layers are concatenated in the channel dimension and added to the original input to obtain the final output \(f_{out}\in R^{8\times C\times H\times W}\). The specific arithmetic expression is as follows:  
\[f_{1}=\text{Conv}_{\text{bll}}(\text{AvgPool}\,2d_{1}(f)) \tag{10}\]  
\[f_{2}=\text{Conv}_{|k|}(\text{AvgPool}\,2d_{2}(\text{Concat}(f,f_{1}))), \tag{11}\]  
\[f_{3}=\text{Conv}_{|k|}(\text{AvgPool}\,2d_{4}(\text{Concat}(f,f_{2}))), \tag{12}\]  
\[f_{4}=\text{Conv}_{|k|}(\text{AvgPool}\,2d_{6}(\text{Concat}(f,f_{3}))), \tag{13}\]  
\[f_{out}=f+\text{Concat}(f_{1},f_{2},f_{3},f_{4}), \tag{14}\]  
where \(\text{Concat}(\cdot)\) indicates splicing in the channel dimension; \(\text{Concat}\,2d_{a}(\cdot)\) indicates the average pooling with the pooling core as \(a\).
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Network Security Design  
A key pillar of this solution is a robust, layered network security strategy designed to reduce the attack surface and enforce least-privilege access. By leveraging Azure-native networking features such as subnets, delegated subnets, Private Endpoints, and Private DNS Zones, the deployment ensures that all resources communicate securely over Azure's backbone network, avoiding exposure to the public internet wherever feasible.
## 3 Case Study and Numerical Modeling  
### Construction Site  
This study takes a high-rise building in Hangzhou, Zhejiang Province as a case study. The nearest distance between the building under construction and the existing subway line is only 20 m. Therefore, the requirements for the construction and monitoring of the foundation pit are strict and of high quality. The layout and the monitoring scheme of the foundation pit are shown in Figure 2. The inclinometer monitors the deformation of the sidewall, and the total station monitors the surface settlement. There are four small foundation pits and one large foundation pit according to the braced excavation plan. The sequence for the excavation is shown in the figure, marked with 1 to 5. The high-rise building has a three-story basement, and the designed excavation depth of the foundation pit is 13.9 m. According to the design, the supporting system of the foundation pit is composed of a diaphragm wall with a thickness of 800 mm and a height of 44.1 m. Within the excavation depth, there are three concrete internal supports with a horizontal spacing of 8 m (Figure 3). Each concrete internal support is arranged 0.5 m above the excavation face.  
The excavated soil layers from top to bottom are fill, clayey silt, sandy silt, sandy silt mixed with silt, muddy clay, silty clay, and rounded gravel (Figure 3). The groundwater level of the construction site is located 0.5 m below the ground surface. The groundwater level had been artificially reduced to the bottom of the foundation pit before the excavation.  
In addition, the in situ SPTs were carried out at the stage of the field investigation (Figure 2). The blow counts of the soil layer at every 2 m were measured. The SPT results at borehole S2 are shown in Figure 4. In the same soil layer, the SPT blow count increases with the increase in buried depth. There are no data for the muddy clay layer and the rounded gravel layer as the SPT was not suitable.  
Figure 2: Layout of the foundation pit and its monitoring scheme.  
Figure 4: The soil layer and SPT results at borehole S2.  
Figure 3: Profile of the foundation pit and the soil layers (section A-A).
## 4 Data Analysis of Gas-Liquid Separation Efficiency in Two-Phase Flow
### Gas-Water Two-Phase Flow Separation Efficiency with 10% Gas Holdup  
The gas-phase to water-phase ratio changes without changing the total flow research range. We studied the change in the DGLS's gas separation efficiency when the gas phase increases. As shown in Figure 5, the gas holdup is 10%, and the water holdup is 90%. With the increase in total flow, the bubble size increases as well. We divided the fluid flow pattern at the inlet into three situations: (1) VFD G/W bubble flow; (2) flow pattern domination by VFD G/W bubble flow with supplemental D G/W bubble flow; (3) flow pattern is supplemented by VFD G/W bubble flow and dominated by D G/D bubble flow. The volume of the gas phase in the central pipe also increases with the increase in the total flow rate. Both the content and the size of the bubbles in Figure 5 are larger than those in Figure 4, and the gas ceiling formation is faster than that in Figure 4. The gas expands, while the gas phase's volume in the central pipe is also greater than that in Figure 4.  
As shown in Table 3, the calculation results for the numerical simulation show that as the total flow increases, the gas-liquid separation efficiency gradually decreases. Only when the total flow rate is 20 m\({}^{3}\)/d does it meet the condition assumption (e).  
Tables 2 and 3 provide a comparison of the gas-water separation efficiency when the gas content is 5% and 10%, respectively. The DGLS shows a good gas-water separation efficiency at a flow rate of 20 m\({}^{3}\)/d, and the separation efficiency is similar. When the flow rate exceeds 20 m\({}^{3}\)/d, the gas-water separation effect is gradually weakened. When the gas holdup is 5% and the flow rate is 30 m\({}^{3}\)/d-60 m\({}^{3}\)/d, the separation efficiency is much lower than the gas holdup of 10%. In summary, under the premise of gas-water two-phase flow, the applicable range of DGLS is the total flow rate at 0-20 m\({}^{3}\)/d. High gas holdup and low flow rates lead to higher gas-water separation efficiency.  
Figure 4: Cloud diagram of gas-phase distribution at gas holdup 5% when total flow is at (a) 20 m\({}^{3}\)/d; (b) 30 m\({}^{3}\)/d; (c) 40m\({}^{3}\)/d; (d) 50 m\({}^{3}\)/d; (e) 60 m\({}^{3}\)/d.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total Flow (m\({}^{3}\)/d)** & **20** & **30** & **40** & **50** & **60** \\ \hline \(\eta_{g}\)(\%) & 91.2 & 80.9 & 75.2 & 69.1 & 51.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gas-water separation efficiency at gas holdup 10%.