esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Statement  
Our algorithm is applicable to WSANs that involve sensors and actors. Sensors are inexpensive and have scarce resources, whereas actors are more powerful nodes in terms of energy, communication and computation power (processing and memory). The communication range of an actor refers to the maximum Euclidean distance that its radio can reach and is assumed to be larger than that of sensors. Both sensors and actors are deployed randomly in an area of interest. After deployment, actors are assumed to discover each other and form a connected inter-actor network. An actor is assumed to be able to move on demand and is aware of the positions of its 1-hop neighbors.  
The impact of an actor's failure depends on the position of that actor in the network topology. For example, losing a leaf/non-critical node, such as \(K\) or \(D\) in Figure 2, does not affect inter-actor connectivity. Meanwhile, the failure of a critical node such as \(F\) partitions the network into disjoint segments. In order to tolerate critical node failure, three approaches are identified: (i) proactive; (ii) reactive and (iii) hybrid. Proactive approaches establish and maintain bi-connected topology in order to provide fault tolerance. This necessitates a large actor count that leads to higher cost and becomes impractical. On the other hand, in reactive approaches the network responds only when a failure occurs. Therefore, reactive approaches might not be suitable for mission-critical time-sensitive applications. In hybrid approaches, each critical actor proactively designates another appropriate actor to handle its failure when such a contingency arises in the future. We argue that a hybrid approach will better suit autonomous WSANs that are deployed for mission-critical time-sensitive applications due to the reduced recovery time and overhead.  
Despite the fact that sensor networks have attracted considerable attention from the research community in recent years, however, \"we are still nowhere near the production of industrial grade WSAN software that can be relied upon for mission-critical applications\" [8]. The success or performance of WSANs in these applications is strongly dependent on the correctness of the algorithms/protocols running on the underlying network. Therefore, the criticality of the applications, distributed, dynamic and complex operation of the network necessitates for rigorous and reliable validation of recovery schemes.  
Figure 2: Graphic representation of a connected inter-actor network.
## 1 Introduction  
Terahertz (THz) radiation is generated by using various techniques such as ultrafast photoconductive antennas [1; 2; 3; 4], rectification of optical pulses, second order nonlinear optical effect [5; 6], quantum-cascade lasers [7], and coupled double quantum-well structure's carrier oscillations [8; 9]. Using of photoconductive antennas is one of the most common sources of THz radiation due to its high intensity efficiency and large bandwidth. Semi-insulating gallium arsenide (SI-GaAs) is widely applied to semiconductor material. It is often used to make ultrafast photoconductive devices because of its special material properties such as the short carrier-lifetime (\(\sim\)10 ps), the ultra-high dark resistivity, and ultra-high photogenerated carriers mobility [3; 10].  
Semi-insulating gallium-arsenide-based photoconductive antennas (SI-GaAs PCA) have two working modes: linear mode and avalanche multiplication mode. When the SI-GaAs PCA operates in the linear mode, each photon that is absorbed induces the formation of one electron-hole pair, which means that the photocurrent waveform is similar to laser waveform [11]. The avalanche multiplication mode is also known as the \"nonlinear\" or \"high-multiplication\" mode. Because it is mainly caused by the avalanche effect of photogenerated carriers [12], the rising edge of the photocurrent is much faster than the rising edge of the triggering laser. In addition, the rise-time decreases with increasing (external) bias voltage [13]. Due to the influence of negative differential mobility (NDM), an optimal time jitter is produced about 15 ps [14]. More importantly, under certain external conditions such as triggering laser energy and bias electric field, the characteristics of terahertz radiation (such as radiant power, frequency, waveform and spatial distribution) generated by SI-GaAs PCA are mainly determined by the spatial distribution and dynamic transmission of photogenerated carriers. A work has focused on the inter-valley scattering of photogenerated carriers with the increasing of bias electric field [15]. However, the relationship between the photocurrent and terahertz wave radiation caused by the carriers' inter-valley scattering of SI-GaAs PCA is not studied in the experiment. In order to obtain the terahertz radiation characteristics of the photoconductive antenna, the relationship between the terahertz radiation and the spatial distribution of photogenerated carriers is very important.  
Therefore, the experiments are carried out by comparing the photocurrent of terahertz field integrating with respect to time with the photocurrent measured by oscilloscope. The properties of multi-energy valley scattering for carriers in a SI-GaAs PCA are investigated. It provides a theoretical basis for generating high power terahertz radiation using GaAs PCA's nonlinear working mode.
## 4 Results and Discussion
### Results of Deep Learning with Four Feature Transformation Methods for 2D Data  
The experimental results of our study are presented in Table 7. We evaluated the results of two DL models, a 2D CNN and 2D FCN, using four different feature transformations: PR, GAF, SWT, and RP. The 2D CNN model outperforms the 2D FCN model on all four feature transformations, except for the PR feature transformation, where the 2D FCN model achieves a slightly higher accuracy. The results demonstrate that the preference for feature transformation has a significant influence on the performance of DL models. The RP feature transformation results in the best performance, achieving an accuracy of 0.9340. Next is the SWT feature transformation, achieving an accuracy of 0.8868. The GAF feature transformation results in moderate performance, while the PR feature transformation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline ML Models & & & & \\ RF & 0.9242 & 0.9308 & 0.9242 & 0.9169 \\ SVM & 0.9091 & 0.9058 & 0.9091 & 0.9061 \\ ET & 0.9091 & 0.9077 & 0.9091 & 0.9025 \\ GB & 0.9091 & 0.9183 & 0.9091 & 0.8979 \\ AdaBoost & 0.8485 & 0.8408 & 0.8485 & 0.8436 \\ DT & 0.8333 & 0.8208 & 0.8333 & 0.8248 \\ LR & 0.7727 & 0.8378 & 0.7727 & 0.7914 \\ SGD & 0.6970 & 0.8142 & 0.6970 & 0.7269 \\ KNN & 0.6061 & 0.7488 & 0.6061 & 0.6461 \\ NB & 0.5455 & 0.7501 & 0.5455 & 0.5901 \\ DL Models & & & & \\
1D CNN & 0.8491 & 0.8491 & 0.8491 & 0.8491 \\ MobileNet & 0.8208 & 0.8255 & 0.8208 & 0.8213 \\ EfficientNetB4 & 0.8302 & 0.8335 & 0.8302 & 0.8307 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ML and DL models on 1D raw data.  
Figure 5: Performance of ML and DL models on 1D raw data.  
results in lower performance. The 2D CNN model outperforms the 2D FCN model on most feature transformations.
## 1 Introduction
This method requires no iterative computation and always yields a convergent solution; however, the updated system matrices often lose their physical meaning, and nodal continuity cannot be ensured [35]. Moreover, the direct modification approach operates directly on the system matrices; it typically requires complete experimental modal data at multiple measurement points. To address these limitations, [PERSON] and [PERSON] [36] proposed a sensitivity-based updating method, which adjusts material properties, geometric parameters, and boundary conditions of the model in accordance with measured responses. However, traditional sensitivity-based model updating methods require repeated calls to FE software (e.g. ANSYS 2022 R1), which is computationally inefficient. Furthermore, ill-conditioned sensitivity matrices may result in convergence difficulties [37]. To overcome these shortcomings, [PERSON] and [PERSON] [38] introduced the response surface model, which utilizes explicit surrogate functions to approximate the implicit relationships in FE models, thus avoiding repeated FE computations and enabling more efficient convergence. Based on ambient vibration data from a six-span continuous beam bridge, [PERSON] et al. [39] performed response surface-based FE model updating and compared the results with traditional sensitivity-based methods. The findings indicated that the response surface-based approach significantly improved updating efficiency and convergence speed. [PERSON] [40] applied the response surface method to FE model updating and damage identification for the I-40 bridge; the results demonstrated that second-order polynomial and first-order linear models are capable of characterizing the relationship between structural parameters and responses. Although FE model updating has been widely applied in bridges and high-rise buildings, studies on its application to flexible PV support structures remain limited. The remainder of this paper is organized as follows. Section 2 provides a detailed explanation of modal property identification based on multi-sensor data fusion and the response surface-based FE model updating method for flexible PV support structures. Section 3 introduces the flexible PV support's structure composition and the field modal testing scheme. Section 4 presents the field-measured high-order modal frequencies, damping ratios, and spatial mode shapes of the flexible PV support, and it fits the response surface to obtain the updated FE model. Section 5 concludes with a summary of the key findings.
## 1 Introduction  
### Motivation  
With the rapid penetration of distributed energy resources (DERs) in the distribution network (DN), conventional electricity consumers who own these small-scale generation facilities become prosumers, that is, they not only consume energy from but also generate to the utility grid. Active prosumers, who allow for bi-directional flows of energy, are increasing, accordingly enabling more grid operation flexibility. This transition is driven not only by environmental awareness but also the desire of prosumers at the residential level to get benefit through efficient energy transactions with the grid [1].  
Meanwhile, it is estimated that there will be 26 billion Internet of Things (IoT) devices connected by 2020 [2]. The IoT brings a new era where various end-devices and sensors are linked in a wired or wireless manner with the aid of modern communication and Internet technologies [3]. With the increasing number of IoT devices and the application of energy internet technology in almost all aspects of the power grid, the information and data exchange among different parties becomes more frequent, and increasingly controllable units participate in the energy operation and management process of the DN. This increased penetration of smart things and renewable energy resources (RESs) in the DN have paved the way for the next-generation distribution grid. Thus, the real-time energy management of these next-generation distribution grid is attracting increasing attention [4; 5; 6; 7; 8].
## 2 LSDF-Net and Structural Optimization of the Network
### Convolutional Pooling  
In convolutional neural networks, there are three commonly used pooling operations: max pooling, mean pooling, and convolutional pooling. Maximum pooling takes the maximum value of the pixel in the pooling area [31]. The feature map obtained in this way is more sensitive to rough texture features, but it discards the non-extreme information, which may cause a loss of detail in the features of dim small targets. Mean pooling takes the average value of pixels in the pooling area [32]. The feature map obtained in this way is more sensitive to background information, but it will smooth the prominent features of dim small targets. Convolutional pooling is achieved by adjusting the stride of the convolution, resulting in a performance comparable to traditional pooling methods. The parameters of the convolutional pooling kernel are automatically learned during network training. Convolutional pooling can transmit the overall information of the target to the next layer and achieve different pooling effects for different channels, which provides high flexibility in extracting the features of dim small targets. Convolutional pooling combines the convolution layer and pooling layer, which can reduce the number of network layers. The schematic diagram of the three pooling methods is shown in Figure 5. In the following chapter, a comparison of the convolutional pooling experimental results is provided.  
Figure 5: Comparison of the three pooling methods: (**a**) max-pooling; (**b**) mean-pooling; (**c**) conv-pooling.
## 1 Introduction
Thus, void regions in UASNs can impact network operations in terms of energy efficiency and reliability by interrupting the communication between acoustic nodes. Figure 1: Underwater Acoustic Sensor Network architecture. Therefore, routing protocols must establish alternative paths for transmitting data to the surface sink node for addressing the void region issue as well as preserving energy. Consequently, many routing protocols have evolved recently to optimize the energy utilization of sensor nodes and reduce the occurrence of void regions [9; 24; 25; 26]. However, in all these mentioned studies, the acoustic nodes are deployed randomly in the networking area. This random deployment in the networking region may lead to some areas with denser node populations and other areas with sparser node populations. This eventually becomes the reason for imbalance energy utilization and higher chances of void occurrence [23]. In this research paper, a unique approach for deploying the acoustic sensor nodes is proposed along with a method of optimal clustering and routing based on a cost function. The deployment approach is adopted to mitigate the randomized sensor node deployment which leads to a reduced likelihood of void existence. Furthermore, the clustering and routing approach adopted in this research paper aims to curtail energy usage and increase network reliability. The main contributions of this work are as follows. * A depth-aware strategic deployment technique is proposed which focuses on the even distribution nodes in the monitoring region in all depths. This deployment technique mitigates the need for an excessive number of sensor node deployments to cover a monitoring region and reduces the chance of void node issues. * Introduces a clustering-based routing protocol that generates the energy optimal clusters to maximize the UASN life expectancy and avoid routing paths that involve void nodes. * The proposed Reliable, Energy Optimized and Void-Aware (REOVA) protocol undergoes a thorough evaluation by performing extensive simulations to observe network performance comparing it to the state-of-the-art protocols and analyzing its performance. The succeeding sections of the paper are prepared in the following manner: Section 2 presents an indication of the related approaches proposed to mitigate the void issue along with excessive energy utilization in UASNs. Section 3 focuses on the problem description followed by Section 4, which provides a detailed discussion related to underwater energy usage and propagation models. Section 5 presents a comprehensive explanation of the proposed Reliable, Energy Optimized, and Void-Aware (REOVA) protocol. Section 6 focuses on the simulation environment. Section 7 provides an evaluation of the pro  
Figure 2: Routing void hole issue. posed protocol by comparison with some of the existing studies. Section 8 provides a comprehensive conclusion.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Accuracy Assessment of RD Orbits by Using the Three New Methods
#### 3.3.1 Carrier Differential Validation  
The carrier differential validation is a method based on the data on DOY 266 in 2021 for the external validation of orbit, and the period for the receiver to observe the GPS satellite was about 30 min. At the beginning and end of the observations, the elevation angle was less than 40\({}^{\circ}\) and the impact of various errors was too large, so data in the middle 1200 epochs with a sample interval of 1 s (20 min) of observations were used for the validation. The data of particular satellites used for the validation does not participate in the POD. The GPS satellite numbers G05, G15, G25, G31 on DOY 266 in 2021 were taken as an example. The results of these satellites are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Stations** & **Min (m/s)** & **Max (m/s)** & **RMS (m/s)** \\ \hline
42 & \(-\)0.0585 & 0.0258 & 0.0085 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Seven days summary results of DORIS orbit check.  
Figure 4: _Cont._  
The results of the G11 and G28 satellites are not listed in the Table 7 because there were no observations of either on DOY 266 in 2021. The RMS value of the residuals for each satellite was better than 0.009 m, and the total RMS value was 0.0068 m. When using each satellite for validation, the fluctuation range of residuals obtained was constant and the mean value was close to 0, indicating that the carrier differential validation method could effectively eliminate the influence of errors on the carrier, so as to obtain reliable accuracy evaluation results.
# A Review on the Importance of Microbial Biopolymers Such as Xanthan Gum to Improve Soil Properties
## 2 Biological Processes in Geotechnical Engineering
### Application of Biopolymers in Soil Treatment  
Three main typical types of main biopolymers are considered: polynucleotides (for example, RNA and DNA), polypeptides (amino acid compounds), and polysaccharides, which have been the type most commonly applied in various engineering practices [6,8,10]. However, the use of biopolymers is not, in fact, an entirely new method in geotechnical engineering, since in a broad sense, organic polymers such as natural bitumen, straw, and rice have already been used in ancient civilizations and can also be classified as biopolymers.  
Studies have shown that the direct use of biopolymers in soil can have several advantages over traditional biological treatment methods. This fact can be explained because different types of biopolymers have shown a good interaction with the soil. Additionally, biopolymers have distinct properties such as good viscosity, resistance to shear degradation, and stability over wide pH and temperature ranges, which can promote significant strengthening effects in soils by enhancing cohesion, strength and resistance to erosion, and reducing its permeability [10].  
The effect of biopolymers on the soil is related to their ability to make a stable gel matrix within the soil without damaging the local ecosystem. Moreover, they have the potential to promote vegetation growth [11], which is also beneficial in terms of stabilizing shallow soil, since it contributes to increasing soil resistance to erosion and to the satility of slope. The industrial production of biopolymers of microbial origin, such as chitosan and xanthan gum, creates biopolymers with better workability in terms of material rheology. Table 1 summarizes some characteristics of the common biopolymers used in geotechnical engineering [6; 8; 12].
# Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems
## 3 Time Series Classification Using Convolutional Neural Networks (CNN)
### Model Architecture  
By applying the modifications to the ordinary convolutional network, we construct two models. One model for online and one for offline classification of scenarios. Both models share the same architecture but differ on how the input data is processed. The model architecture consists of a combination of two residual and two separable convolutional blocks, which are shown in Figure 3. The eight convolutional layers are then followed by four dense layers. The parameters for each layer can be seen in Table 1.  
The input to the models is split into windows of equal length and move, with a fixed stride, through the time series data, as shown in Figure 4. The online model is not able to \"look\" into the future and its input window consist of past measurements followed by the current stride. The model then uses this data to predict the classes associated with the current stride. When applied to real measurements on a vehicle, this scheme would result in a prediction for each stride duration, e.g., every half a second. The offline model has all the data available and thus can consider behavior of the vehicle before and after the current stride which leads to higher prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer** & **Kernel** & **Stride** & **Dilation** & **Activation** & **Input Online Model** & **Input Offline Model** \\ \hline conv1 & \(1\times 3\) & \(1\times 1\) & \(1\times 1\) & selu & \(10\times 60\) & \(10\times 80\) \\ conv2 & \(1\times 3\) & \(1\times 1\) & \(1\times 2\) & linear & \(10\times 60\) & \(10\times 80\) \\ conv3 & \(1\times 3\) & \(1\times 2\) & \(1\times 1\) & selu & \(18\times 30\) & \(18\times 40\) \\ conv4 & \(1\times 3\) & \(1\times 2\) & \(1\times 1\) & linear & \(18\times 15\) & \(18\times 20\) \\ conv5 & \(1\times 3\) & \(1\times 1\) & \(1\times 2\) & selu & \(18\times 15\) & \(18\times 20\) \\ conv6 & \(1\times 3\) & \(1\times 1\) & \(1\times 4\) & linear & \(18\times 15\) & \(18\times 20\) \\ conv7 & \(1\times 3\) & \(1\times 2\) & \(1\times 1\) & selu & \(20\times 8\) & \(20\times 10\) \\ conv8 & \(1\times 3\) & \(1\times 2\) & \(1\times 1\) & selu & \(20\times 4\) & \(20\times 5\) \\ \hline \multicolumn{5}{c}{**Output Size Conv8**} & **Dense1** & **Dense2** & **Dense3** & **Dense4** \\ \hline online & 80 & 64 & 32 & 20 & 13 \\ offline & 100 & 64 & 32 & 20 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model Architecture.