esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 3 Large Language Models for Networking
### Network Implementation  
Network implementation focuses on the deployment and configuration of network infrastructure, ensuring that network policies and designs are accurately executed. LLMs can enhance this phase by automating configuration tasks, translating high-level policies into actionable commands, and providing robust validation mechanisms. This leads to more precise and efficient implementation processes. For configuration, several innovative frameworks have been introduced. Verified Prompt Programming (VPP) in [135] combines GPT-4 with verifiers to automatically correct errors in router configurations. S-Witch in [144] uses a LLM combined with a network digital twin to generate and verify command line interface commands for commercial switches based on natural language requests. NETBUDDY in [137] utilizes LLMs to translate high-level network policies into low-level network configurations. Ciri [136], a generic LLM-based configuration validation framework, employs effective prompt engineering and few-shot learning to address hallucination and nondeterminism in LLMs during configuration validation. Additionally, the work in [142] proposes an LLM-based intent translation system that converts user-defined business network requirements expressed in natural language into Network Service Descriptors. GeNet [143], a multimodal copilot framework, leverages LLMs to streamline enterprise network design workflows by interpreting and updating network topologies and device configurations based on user intents.  
Other significant contributions include an LLM-centric intent lifecycle management architecture for configuring and managing network services using natural language [145]. The study in [146] utilizes LLMs to automate network configurations through IBN, ensuring data privacy and configuration integrity. Emergence [138; 181], an intent-based management system, employs the few-shot capability of LLMs with a Policy-based Abstraction Pipeline to create a closed control loop for intent deployment and application management. ChatAFL [141], an LLM-guided protocol implementation fuzzing engine, generates machine-readable information about protocols for enhanced security testing and coverage. In the context of federated learning, GPT-FL in [139] is a generative pre-trained model-assisted federated learning framework that leverages synthetic data generated by LLMs to train downstream models on the server, which are then fine-tuned with private client data. LP-FL in [140] combines few-shot prompt learning from LLMs with efficient communication and federating techniques, using Low-Rank Adaptation to reduce computation and communication costs and enabling iterative soft-label assigning to expand labeled data sets during the federated learning process.
## 5 Conclusions and Future Directions  
This study examined the effects of codebook size and dataset split ratios on the performance of four ML algorithms, namely KNN, SVM, DT, and NB, for GPS-aided mmWave V2I beam prediction. The datasets used in this study were based on the real-world mmWave V2I DeepSense6G experimental datasets. The results show that the performance of the ML algorithms declines as the codebook size increases. This means that lower codebook sizes (i.e., with lower number of beams) lead to more accurate beam pairing predictions as the probability of beam alignment is higher. Additionally, the algorithms are mostly unaffected by the data split ratios used in the ML algorithms, such that 20, 30, and 40% testing ratios of the dataset give comparable beam prediction accuracy results. Generally, for the combined scenario, the results show that DT outperform the other classifiers, followed by KNN, SVM, and NB. The future research direction is to investigate other ML algorithms, explore the impacts of other system parameters, and consider multi-modal approaches employing a combination of sensor data such as LiDAR, radar, and camera in addition to the position data employed as ML features in this study.  
Conceptualization, K.K.B. and S.A.B.; methodology, K.K.B. and S.A.B.; software, K.K.B. and S.A.B.; validation, S.A.B.; formal analysis, K.K.B.; resources, K.K.B. and S.A.B.; writing--original draft preparation, K.K.B.; writing--review and editing, S.A.B., F.G.-C. and J.R.; visualization, K.K.B. and S.A.B.; supervision, J.R. and F.G.-C.; project administration, S.A.B.; funding acquisition, S.A.B. and J.R. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the Fundacao para a Ciencia e a Tecnologia (FCT-Portugal)/MEC through national funds under the MATIS project with reference number 2022.07313.PTDC.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & \multicolumn{2}{c}{\(M=32\)} & \multicolumn{2}{c}{\(M=16\)} & \multicolumn{2}{c}{\(M=8\)} \\ \cline{2-7}
**Number** & [16] & **This Work** & [16] & **This Work** & [16] & **This Work** \\ \hline
1 & 0.7134 & 0.6494 & 0.8617 & 0.8050 & 0.9024 & 0.9149 \\
2 & 0.6002 & 0.5690 & 0.7899 & 0.7778 & 0.8805 & 0.8838 \\
5 & 0.5591 & 0.5478 & 0.7473 & 0.7283 & 0.8402 & 0.8522 \\
6 & 0.6391 & 0.5082 & 0.7943 & 0.7377 & 0.9063 & 0.8579 \\
7 & 0.4182 & 0.3647 & 0.6253 & 0.6177 & 0.7623 & 0.8059 \\ Combined & 0.5250 & 0.4159 & 0.5815 & 0.5820 & 0.8020 & 0.7275 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results’ comparison betweeen the KNN in this work and the top-1 accuracy results in [16].  
Data Availability Statement: For this study, the datasets used are based on the real-world experimental datasets known as DeepSense6G. The dataset can be found at [[https://www.deepsensee6.net](https://www.deepsensee6.net)]([https://www.deepsensee6.net](https://www.deepsensee6.net)) accessed on 12 March 2024.  
The authors declare no conflict of interest.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 2 Signal Model and Problem Statement
### Neural-Induced Support Vector Machine (NSVM)  
In this paper, the NSVM is introduced for the automatic classification of wireless signals based on their modulation formats by combining Convolutional Neural Networks and the Support Vector Machine. The architecture of NSVM is shown in Figure 4. As it can be seen from Figure 4, the softmax layer of the Convolutional Neural Networks is replaced by the Support Vector Machine. The Support Vector Machine takes the entire feature layer as input and outputs the label of the input sample. The output expression can be written as:  
\[f(x)=\sum\limits_{i=1}^{l}\big{(}\alpha^{*}_{i}-\alpha\big{)}K(\varphi(x_{i} \big{|}\theta),\varphi(x\big{|}\theta))\ +b \tag{22}\]  
where \(K(.,.)\) is the kernel function of the Support Vector Machine.  
Figure 3: Support Vector Machine (SVM) separating the hyperplane for Binary Classification.  
To find a suitable \(f\), the system must find a representation of the input data in \(z\) that codes the feature most relevant for estimating the desired output. The training samples \(x_{i}\) is replaced by the feature vector \(\varphi(x_{i}\big{|}\theta)\). The primal problem of SVM can be written as:  
\[\begin{array}{l}\mathcal{P}1:\quad\min\limits_{w,\ b,\ \xi}\ \frac{1}{2}w^{2}+C\sum\limits_{i=1}^{n}\xi_{i}\\ s.t.\quad\quad y\big{(}w^{T}\sigma(x_{i}|\theta)+b\big{)}\geq 1-\ \xi_{i}\\ \xi_{i}\geq 0,\ i=1,\ \ldots,\ n\end{array} \tag{23}\]  
Corresponding to the dual problem formulates as:  
\[\begin{array}{l}\mathfrak{D}1:\ \min\limits_{\theta}arg\max\limits_{ \alpha_{i}\geq 0}\sum\limits_{i=1}^{M}\ \alpha_{i}-\ \frac{1}{2}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{M}\alpha_{i} \alpha_{j}y_{i}y_{j}\varphi^{T}(x_{i}|\theta),\ \varphi\big{(}x_{j}\big{|}\theta\big{)}\\ =\min\limits_{\theta}arg\max\limits_{\alpha_{i}\geq 0}\sum\limits_{i=1}^{M} \alpha_{i}-\ \frac{1}{2}\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{M}\alpha_{i} \alpha_{j}y_{i}y_{j}\Big{(}x_{i}\big{|}\theta,\ x_{j}\big{|}\theta\big{)}\\ s.t\ 0\geq\alpha_{i}\geq C\ \forall i,\ \sum\limits_{i=1}^{M}\alpha_{i}y_{i}=0 \end{array} \tag{24}\]  
The two goals of training are to find \(\alpha^{*}\) that maximizes the above problem and also the weights \(\theta\) of the neural network which minimizes Equation (24). This hybrid model is trained as single architecture rather than training them separately and then combining them offline for classification.
## 2 Background
### Frequency and RMS Histograms  
#### 2.3.1 Frequency Histogram Calculation  
The frequency histogram is the waveform's dominant frequency PMFs over time. The dominant frequency is calculated by generating the Power Spectral Density (PSD) across a user-defined bandwidth, which is a small window (i.e., smaller than \(\pm\hat{f}_{s}/2\)) around the fundamental transmission frequency (e.g., 60 Hz). This enables the calculation of an Alternate Discrete Fourier Transform (A-DFT) instead of the Fast Fourier Transform (FFT). The FFT calculates the frequency response from \(\pm\hat{f}_{s}/2\), which leads to the processing of additional frequencies not used for histogram generation and wastes computational resources. The A-DFT processes frequencies within the desired and much smaller user-defined bandwidth, resulting in a more efficient implementation. The A-DFT-based PSD is  
Figure 3: Representative cyclic, residual, frequency, and RMS histograms generated using one hour of a 161 kV bus line with a transmission frequency of 60 Hz.  
\[\hat{X}_{w}[f]=\left|\sum_{t=0}^{N_{w}}x_{w}(t)\exp(-j2\pi ft)\right|, \tag{5}\]  
where \(x_{w}\) are the waveform samples in window \(w\), \(N_{w}\) is the window length used to calculate the PSD, \(f\) is the dominant frequency in the user-defined bandwidth at which \(\hat{X}_{w}\) is the maximum, and \(t\) is the user-defined bandwidth-determined time vector. The function \(\exp(-j2\pi ft)\) is the same value for all calculations of \(\hat{X}_{w}[f]\); thus, it is calculated once and the resulting array is stored for later use. This is advantageous because the exponential function is computationally expensive, so the precalculation of \(\exp(-j2\pi ft)\) significantly reduces the time needed to compute Equation (5). This process repeats until the dominant frequency is calculated for each window. The frequency histogram is the PMF of all dominant frequencies within the histogram's bin limits, equal to the user-defined bandwidth. A representative frequency histogram is shown in Figure 3c.
# Concealed Conduit Routing in Building Slabs
## 4 Discussion and Future Work
Another potential application of the proposed method is to design tunnels in cooling jackets, especially incorporated with AM technology [24]. The tunnels of a cooling jacket are concealed inside the walls of the jacket. Filled with running liquids, they are used to transfer the heat produced by electronic devices or machines. Their application is different from the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Researches**} & **Routing** & **Data** & **Working** & \multirow{2}{*}{**Applications**} \\  & **Algorithms** & **Structures** & **Environment** & \\ \hline [PERSON] et al. [3] & Genetic & Graph & Vehicles & Cables \& pipes of vehicles \\ \hline [PERSON] et al. [6] & A* method & Graph & Buildings & MEB systems \\ \hline [PERSON] et al. [10] & ACO algorithm & Octree & Engine surfaces & Aero-engine pipes \\ \hline [PERSON] et al. [12] & Expert system & Graph & Ships & Pipes of ships \\ \hline [PERSON] [15] & ACO algorithm & Graph & Communities & Water supply systems \\ \hline Ours & Two-stage Dijkstra & Graph + 3D voxel & & \\ method & images & Building slabs & MEB systems \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons among various piping research. concealed pipes in a building. However, their geometric characteristics are similar to those of the concealed pipes. By using the concept of the distance field and voxelization process, the proposed method is suitable for routing cooling tunnels.
# An Observation Scheduling System for Radio Telescope Array
## 1 Introduction
However, the Effelsberg software does not offer a detailed predictor for target source observation or similar functionality, only providing an observation scheduling interface, while DSS is not publicly available. Furthermore, both systems lack a visual interface and can only fit one target at a time. The 4 \(\times\) 4.5 m radio telescope array at Guizhou Normal University is primarily intended for preliminary research on the search and localization of transient sources, such as fast radio bursts (FRBs) and giant pulses from pulsars. This array is responsible for the observation tasks of multiple distinct targets daily. Given the large number of such tasks, it is required to arrange them rationally and guarantee the validity of the data collected during each observation. Moreover, the scale of the array will be upgraded in the future as well. Based on the observational and upgrade needs of the array, this paper focuses on the design and development of an observation scheduling system for the telescope array at Guizhou Normal University, aiming to create a fully functional and highly adaptable target prediction system. This paper adopts a modular design approach using Python and PyQt5 (PyQt5-5.15.2), proposing features such as simulating and visualizing the positional changes in targets over the upcoming 24 h, intelligent scheduling for multiple target observations, calibration source observations, and array layout simulation based on maximizing \(uv\) coverage.
## 1 Introduction
Many researchers applied various forms of artificial neural networks to accurately model SOC estimation in E-mobility systems [27; 28; 29]. [PERSON] et al. [30] applied fuzzy neural networks in conjunction with a reduced form of the genetic algorithm for the same. [PERSON] et al. [31] achieved 95% prediction accuracy of SOC models using artificial neural networks. [PERSON] et al. [22] minimised SOC prediction errors to 1.75% using recurrent neural networks. [PERSON] et al. [27] used unscented Kalman filtering to improve neural network prediction accuracy of SOC estimation. Investigations were also carried out with regard to the fault diagnosis and motor design of FSEV [32; 33; 34]. Similar research was carried out for the EV battery state of energy (SOE) estimation. [PERSON] et al. [35] implemented the covariance matching method to update the battery model parameters on a real time basis. In a similar study, [PERSON] et al. [36] followed the adaptive unscented Kalman filter approach to estimate the SOE of a lithium ion battery. [PERSON] et al. [37] developed a Gaussian model for SOE prediction, whereas [PERSON] et al. [38] applied the moving window energy integral method for the same. [PERSON] et al. [39] followed a unique methodology that combined an analytical model with an electrical model to improve SOE estimation. [PERSON] et al. [40] applied a multimodel-probability-based state fusion methodology for SOE estimation. [PERSON] et al. [41] established a multi time scale observer for SOE assessment. [PERSON] et al. [42] explored fractional calculus-based SOE modelling for LiFePO\({}_{4}\) battery. Fewer investigations were carried out for power-loss (PL) modelling in EV. In an interesting study, [PERSON] et al. [43] analysed EV PL as a function of charging rate and SOE. This brief literature review indicates that the past investigations did not include SOC/SOE/PL soft sensor designs for FSEV applications.The exploration of effective PL estimation and modelling strategies is an open research area. Therefore, a novel research scope was identified for the current study to estimate the SOC, SOE, and PL of a formula student electric vehicle (FSEV) battery pack based on neural-network-enabled soft-sensor designs. The following section gives details of the battery-pack modelling and neural-network-based soft-sensor design methodology.
## 5 Results and Discussion
### Path Hop Count  
This measures the path hop-count through which MAs move to collect data records and return the results. Path hop-count is used to study the protocol's capacity in minimising hop-count and/or establishing optimal MA migration paths. This results in reduced power consumption and decreased ETE. That way, MAs should avoid randomly moving and blindly visiting the source sensor to capture recordings.  
According to Figure 7, MINDS outperforms ZMA. This is because MINDS forms a minimum hop-count data-centric tree. That way, MAs avoid blindly moving throughout the network to collect data records which match the consumer's queries. Indeed, this tree allows MAs to move toward event regions which already have the requested data samples. Moreover, the clustering technique limits MA migration only into a restricted area which is formed according to a data-centric manner. That way, MAs do not need to visit all LDSs which have the data records; they only visit CHs to collect the aggregated results. However, ZMA plans MA itineraries using all LDSs which have data samples according to the consumer's query. For this, MAs need to visit all LDSs at each data zone one by one until the sink is reached.  
TBID reduces PHC as compared to MINDS and ZMA, especially in sparse networks. This is because TBID is not able to detect event regions and/or LDSs which have sensory data matching the consumer's query when the network deployed is sparse. However, this increases when the network deployed is crowded and data density if full. This is rooted in that a greater number of LDSs join to form a longer address-centric path. This results in increased path length as MAs blindly move throughout the network to visit LDSs and capture data records, especially when the network is crowded.  
Figure 6: Data robustness percentage.
## 2 Sensors
### Camera
#### 2.3.1 Camera Calibration  
Camera calibration is the process of estimating internal, structural, external, radiometric, and systematic characteristics of a single or multi-camera. These calibration methods could be classified in two separate schemes: geometric [65] and radiometric [66].  
Geometric camera calibration is the process of determining intrinsic, structural, extrinsic, and systematic parameters of a camera to accurately connect 3D object points to their corresponding 2D image points [67]. Intrinsic parameters involve the camera's lens characteristics, such as focal length, principal point, and distortion coefficients, which account for lens distortion and other optical aberrations [68]. Structural parameters deal with the internal configuration of a multi-camera. Stability analysis of multi-cameras deals with studying structural changes of a multi-camera during the time [69]. This analysis is essential for efficiently employing a multi-camera in an MMS. Extrinsic parameters describe the camera's position and orientation in the environment, defining how it is situated relative to the objects it captures. Systematic calibration involves aligning the cameras with respect to other MMS components. This aspect of the calibration ensures a robust fusion of different information, allowing data from MMS sensors to be fused into a cohesive representation. Accurate calibration is critical because it ensures that each data point captured by other components aligns with its corresponding pixel location in the captured images. Geometric camera calibration usually involves capturing images of a known calibration body, such as a checkerboard or coded targets, from different angles [70]. By analyzing these images, algorithms can calculate parameters of complex transformations that map 3D points in the world coordinate system to their corresponding 2D projections in the images.  
Radiometric calibration is the process of adjusting and modeling a camera's response to light to accurately represent the intensity values of the scene [66]. Unlike geometric calibration, which focuses on the spatial arrangement of pixels, radiometric calibration addresses how pixel intensities correspond to actual physical radiance values [71]. Radiometric camera calibration includes correcting for non-linearities in the camera's response,exposure settings, lens vignetting, and sensor noise. By modeling these factors, radiometric calibration ensures that each pixel's intensity in an image accurately reflects the real-world brightness, allowing for consistent comparisons across images taken with different exposure settings or cameras. Radiometric calibration usually requires a special test field that brings the calibration process under a constraint-controlled environment [72].
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 2 Mathematical Model
### Solving Matrix-Fracture Interface Saturation  
Consider a manifold and its corresponding truncated control volumes (Figure 3), volume conservation written for phase fluxes exchanged between fracture and matrix without sink or source terms is \(\sum F_{w}^{m}(S_{w}^{m})+F_{w}^{f}(S_{w}^{f})=0\). [PERSON] et al. [51] utilize a saturation-based iteration solver, where the saturation value in matrix finite volume sectors relates to the value in fracture sectors via the condition (8). This method particularly suites the drainage process in which the capillary curves are flat. However, at the saturation front, for spontaneous imbibition, the sharp gradient of capillary pressure significantly compromises solver efficiency. This motivated us to develop a new capillary pressure-based interface solver with more flexibility and a wider trust region. In this approach, capillary  
Figure 2: The extended pressure condition corresponding to different fracture capillary pressure curves: (**a**) the Brooks-Corey type and (**b**) the linear type, Romm [62].  
pressure is the primary variable. Inserting the inverse of capillary pressure, \(S_{w}=S_{w}^{-1}(p_{c})\), into Equation (10)  
\[\sum_{i=1}^{n}S_{w,i}^{t+\Delta t}\left(p_{c}^{m,t+\Delta t}\right)\Delta V_{i}= \sum_{i=1}^{n}\left(S_{w,i}^{t}\Delta V_{i}+\Delta t\sum_{j=1}^{m}F_{w,j}+ \Delta tq_{i}\right) \tag{12}\]  
achieves these objectives. It can be solved by iterative methods such as Newton-Raphson, _Regula Falsi_ or [PERSON]'s method [64]. While updating capillary pressure, these methods automatically evaluate the saturation values at the interface, enforcing condition (8).  
As the water saturation decreases to its residual value, matrix capillary pressure approaches infinity, causing numerical instability. We eliminate this issue by using the linear extension of the curve to a finite value approximating a dry matrix region, see [PERSON] [65], [PERSON] et al. [66], [PERSON] [67].  
Importantly, by contrast to the common Richardson equation-based simulation of flow in the vadose zone, we apply the described two-phase flow model to model water infiltration and air flow in fractured unsaturated rock. Beyond the benefit that the volume and flow of air is resolved explicitly, this approach has the major advantage that the resulting flow and transport equations are better behaved than when the water-content dependent hydraulic conductivity is used.