esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 BM-RAGAM Retrieval Scheme in the Vertical Domain of LLMs
### Cloud Deployment of the BM-RAGAM for Knowledge Retrieval and Generation  
If the BM-RAGAM is deployed using cloud architecture, it will benefit complex knowledge retrieval tasks with more efficient scaling, an enhanced accessibility, and real-time processing. This cloud-based architecture combines advanced retrieval methods and large language model (LLM) integration, enabling precise and interpretable outputs, even for extensive domain-specific knowledge bases [46].  
The cloud-based deployment of the BM-RAGAM framework employs RESTful API to facilitate interactions, as in the work reported in [47]. The deployment leverages distributed vector databases, such as Milvus or Chroma, for scalable storage and retrieval. The data that are uploaded are subjected to preprocessing and embedding generation using domain-optimized models (e.g., text2 vec-large-Chinese). These embeddings are subsequently indexed within the vector database, thereby supporting efficient similarity searches across extensive datasets. This framework integrates with LLMs using cloud APIs or self-hosted models, such as Qwen2-72B, facilitated by the LangChain framework. The Flask-based back-end handles all requests, routing them to the retrieval and generation pipeline. The operational workflow includes the following: (1) Receiving user queries through a Web or API interface; (2) conducting hybrid retrieval from the vectorized database using the BM-RAGAM scheme; (3) scoring the retrieval results with an attention mechanism to prioritize highly relevant content; (4) generating responses via the LLM, tailored to the user's query context. The retrieval flow chart of the cloud-deployed LLM is shown in Figure 5.  
To ensure scalability and reliability, the system can be deployed on cloud platforms such as AWS, Azure, or Google Cloud, with Kubernetes used for orchestrating containerized services. This setup supports autoscaling based on demand and fault-tolerant operations, ensuring minimal downtime and a consistent performance. Monitoring tools, such as Prometheus and Grafana, are integrated to track the query loads, database performance, and API response times, enabling real-time optimization.
## 4 Results and Analysis of Three-Dimensional Forward Modeling
### Single-Anomaly Model
#### 4.2.2 Individual Anomalies with Different Distances and Resistivities  
The resistivity of the target anomaly body, such as water-bearing structures or air-filled goals, typically varies. Therefore, target anomalies can be designed to be low-resistance or high-resistance. In this example, the resistivity of low-resistance anomalies was 10 \(\Omega\cdot\)m, and that of high-resistance anomalies was 1000 \(\Omega\cdot\)m, with the background resistivity set to 100 \(\Omega\cdot\)m. The anomalies were positioned at different locations along the centerline between the transmitters and receivers (Figure 8), and the relationship between the anomaly response and distance was investigated.  
As shown in Figure 9a,b,e,f, the electric field responses were opposite for the locations of high- and low-resistance anomalies. The differential curves show a negative region for the high-resistance cube with positive regions on both sides and a narrower value range compared to for the low-resistance cube. Furthermore, it is evident from Figure 9 that both the electric field value range and the differential value range gradually decreased as the distance increased. At a distance of 50 m, the electric field closely matched the half-space model, but the difference curve still shows a noticeable disparity. At 75 m, the difference between the two models became minimal, indicating that the differential parameter was more sensitive to anomalies. Moreover, the regions of variation in the model's electric field parameters and the differential boundaries corresponded well with the anomaly locations.  
Figure 8: Cubic anomalies located at (**a**) 25 m (resistivity of 10 \(\Omega\cdot\)m or 1000 \(\Omega\cdot\)m), (**b**) 50 m, and (**c**) 75 m in the midline of the transmitters and receivers.  
Figure 7: Multi-channel electric field and differential curves for cubes with (**a,e**) 5 m side length; (**b,f**) 7.5 m side length; (**c,g**) 10 m side length; and (**d,h**) 12.5 m side length.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 3 Results
### Positioning of Pruning Points  
As can be seen in Figure 14, the algorithm in this paper can correctly identify the location of the pruning point, but there are still some cases where it is difficult to identify the correct location of the pruning point. For example, the location of the second bud of Figure 15a is at the back of the shooting direction and the bud cannot be observed from the front of the branch. The position of the second bud in Figure 15b is obscured by the fixing wire and the location of the clipping point cannot be recognized either. The bud in Figure 15c is on the side and back of the branch, which leaks out to a smaller area and is also difficult to recognize. In response to Figure 15a,c, we propose that in practical applications, both the left and right sides of the same vine can be photographed to address the effect of the growth position of the buds on the localization of the pruning point.  
We recognize 102 pruning points, including the cases mentioned above. The total running time is 157.19 s, with an average time of 1.54 s. And 84 detections are successful  
Figure 12: (**a**) RGB image. (**b**) The result image of the Zhang–Suen thinning algorithm. (**c**) The result image of the Hilditch algorithm. (**d**) The result image of the Stentiford algorithm.  
Figure 13: The thinning process for an RGB image. First, the image is converted into grayscale; next, inversion processing is performed; then, binarization is applied; subsequently, inversion is performed again; finally, the skeletonization algorithm is applied.  
in determining the correct location of the pruning points with a success rate of 82.35% as shown in Table 7.
# Influence of Drivetrain Hybridization on Transmission Lifetime
###### Abstract  
Parallel hybrid drivetrains for passenger cars have additional electric drives compared to conventional drivetrains. In the event of vehicle deceleration, the electric drives are operated as generators, thus recovering kinetic energy through regeneration. If these drives are positioned upstream of the transmission input, regeneration power must be transferred by the transmission. This creates additional loads on the individual machine elements, which has a negative effect on the transmission lifetime. This paper investigates the influence of hybridization in terms of regeneration on the lifetime of bearings as highly critical elements in a dual clutch transmission. The vehicle simulation model employed in this study consists of an internal combustion engine, an electric motor, a mechanical drivetrain and the vehicle body, as well as a driver and a simple operating strategy. In this model, a detailed transmission model including its controls is embedded to determine its component loads. The resulting load spectra are used in a methodical approach to calculate the bearing lifetime of the transmission. The results show that the additional regenerative power flow reduces the bearing lifetime so that additional loads must be taken into account in the development and operation of transmission systems.  
Keywords:dual-clutch transmission; bearing lifetime; vehicle simulation; hybrid electric vehicle; fatigue damage; regeneration +
Footnote †: journal: Applied Sciences
## 1 Introduction
The performance of the MLP was compared with machine learning algorithms such as Random Forest (RF), Generalized Linear Models (GLM), and Gradient Boosting Machine (GBM). The MLP model had the best performance, with the mean square error ranging from 0.0369 to 0.1215. [PERSON] et al. [18] used three CNNs with different structures to make daily predictions for ETo. Comparisons were made between the CNN, the seasonal ARIMA (SARIMA), and Seasonal Naive (SNAIVE). The CNN model performed better in terms of variance, accuracy, and computational cost. However, traditional machine learning cannot store and learn from the past observations of a time series. Under deep learning, LSTM models are designed to model time series data. [PERSON] et al. [11] developed a Neural Network approach to model temporal soil moisture. In the model evaluation, a value of \(R^{2}\) above 0.94 was obtained at all test sites. [PERSON] et al. [19] used an LSTM model to predict agricultural water table elevation. They used 14 years of time-series data such as water diversion, precipitation, reference evapotranspiration, temperature, and water table depth. The proposed model achieved higher values of \(R^{2}\) than the model Feed-Forward Neural Network (FFNN) in predicting water table depth. As indicated earlier, measuring reference evapotranspiration volume and soil water volume is time and labor-intensive. In this paper, a Long Short-Term Memory (LSTM) was developed to predict these two critical parameters. The model forecasts one day- ahead ET and SWC using eight days of historical data. The various evaluation metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), and \(R^{2}\) were used to demonstrate the effectiveness of the models. Each metric has its advantages and disadvantages, and as the results of this paper will show, using only one metric can lead to misleading results. The Convolutional Neural Network (CNN) is capable of extracting features from raw data [20]. A CNN model is superimposed on the LSTM model to investigate the performance of CNN- LSTM models compared to LSTM. The performance of the model is also compared with CNN and traditional machine learning algorithms such as Support Vector Regression (SVR) and Random Forest [21; 22]. Finally, the impact of the loss function on the training of the model is investigated. The most commonly used loss functions for regression problems are selected, including MSE, RMSE, MAE, and the Huber function. The Huber function is basically MAE, which becomes MSE when the error is small. Unlike MAE, the Huber function is differentiable at 0 and is less sensitive to outliers in the data than MSE [23]. Each of these functions yields a different error for the same prediction and affects the performance of the model on the test set.
## 1 Introduction
### The Motivation of Long-Term Temporal Encoding  
The first challenge lies in the relatively large temporal search space, resulting from long-range temporal dependencies and flexible temporal length, compared to action recognition (also known as action classification). In general, action alignment is more challenging than action recognition, for the following reasons:  
* In action recognition, temporal localization is not taken into account, where input samples are previously truncated to contain exactly the temporal span of a certain target action, leading to relatively short inputs (e.g., 2\(\sim\)20 s in UCF101 datasets). In frame-wise action alignment, inputs generally last minutes or hours. In action recognition, background class is not taken into account, where input samples may not contain any of the target actions.
* In action alignment, dependencies can also last temporally across seconds or even minutes. The types of temporal dependencies include individual action durations, pairwise compositions between consecutive actions, and long-term compositions lasting across multiple sub-actions. As an example of a cooking instance, when cutting a potato, it is difficult to recognize what is being cut because it tends to be occluded by hands holding it. The recognition of frames where the potato is being cut shares dependencies with previous frames where the potato is being taken out before being cut.
* In action recognition, only one label needs to be assigned to the whole video, whereas the action alignment task needs to densely assign a label to each frame. Consider a video instance consisting of 20 frames sharing the same class.
* In action recognition, even if a convolution network correctly predicts only for 10 frames, it is still very likely to correctly predict the whole video. A powerful temporal encoder, illustrated in Figure 2, on top of a convolution network would not bring any improvement in this case, because per video labels do not change whether or not per frame labels are neighboring. In action alignment, 10 accurate but not neighboring predictions would lead to over-segmentation error with 20 segments. Bidirectional temporal encoders would be motivated to predict neighboring segments, because they encode late samples together with the early ones for final judgement, leading to fewer false positives compared to a purely convolutionnetwork (see Figure 3).  
Figure 1: The training/testing settings in the problem of action labeling through temporal alignment learning. (**Left**) During training, only the order of the occurring actions is given, the model is learned by maximizing the probability of all possible frame-to-label alignments. (**Right**) During testing, no annotation is given, as the learned model already encodes the temporal structure of data, it predicts the actions frame by frame without any further information.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### System with Separated TX and RX  
In order to show the potential of the methodology and the concept of the hybrid TX-RX lens, a second optical wireless link based on the principle shown in Figure 2a is developed. A concave lens is chosen for TX to widen up the beam. A convex RX lens is used for optical concentration. The distance \(d\) between the TX and RX determines the maximum size of both lenses. The RX lens must not be too small to enable a sufficient optical gain \(g\). Therefore, \(d\) will be larger compared to the hybrid lens approach. The exact optical parameters are determined by numerical optimization. The hybrid TX-RX lens and the fully separated TX-RX lens system are denoted by H TX-RX and S TX-RX respectively.
## 2 The CNES PPP-WIZARD Demonstrator  
The PPP-WIZARD (PPP with Zero-difference Ambiguity Resolution Demonstrator) was first designed to promote the undifferenced phase ambiguity resolution technique developed at CNES [9; 20]. This proof-of-concept for the aforementioned original ambiguity resolution technique aims at achieving a Precise Point Positioning (PPP) accuracy of 1 cm in real time. It relies on the means available at the IGS [13; 21] and in particular its Real-Time Service (RTS) [22], such as the access to a network of GNSS stations or the standards for the dissemination of the corrections. The demonstrator is also a good laboratory to test new ideas related to PPP with ambiguity resolution.  
The overall PPP approach is depicted in Figure 1 and involves the following steps:  
1. on the network side, also named server side, raw data are collected thanks to a global network of stations, then the main processing software Orbit Determination and Time Synchronization (ODTS) computes all the necessary corrections (orbits, clocks, biases, ionosphere) that are compatible with the ambiguity resolution on the user side;
2. the corrections are disseminated over the network in an open standard, RTCM, or SSR;
3. on the user side, the PPP-User software estimates the position of the receiver by means of a stochastic filter, leading to centimeter-level PPP by fixing the integer ambiguities of the phase measurements.  
Figure 1: The PPP-WIZARD overall architecture.
## 2 Materials and Methods
### Remote Sensing Data  
#### 2.3.1 PlanetScope Imagery  
PlanetScope is a commercial satellite constellation operated by Planet Labs, a company headquartered in San Francisco, California, USA. The constellation consists of approximately 180 satellites. Each satellite is a CubeSat 3U form factor (10 \(\times\) 10 \(\times\) 30 cm), capable of imaging the entire Earth's surface at a daily time step. The latest generation of PlanetScope sensors, known as Super Dove or PSB.SD, were used in this study. This generation produces near-daily images from March 2020 at 3 m spatial resolution and over 8 spectral bands (Supplementary Material: Figure S1 and Table S2) [49]. The band radiometry, the band spectral response, and the image sharpness have been significantly improved compared to the previous generations.  
To develop this study, the Analytic Ortho Scene product was downloaded from October 2022 to August 2023 considering a total of 100 cloud-free surface reflectance images. The Ortho Scene product is an orthorectified surface reflectance product (level 3B), radiometrically, geometrically, and atmospherically corrected. Orthorectification was performed to correct terrain-induced distortions using high-quality digital elevation models (DEMs) with a post-spacing of 30-90 m. Ground control points (GCPs) were integrated to enhance geolocation accuracy, with precision depending on the density and quality of available GCPs in the region. For geometric corrections, computer vision algorithms, including OpenCV's STAR keypoint detector and FREAK keypoint extractor, were utilized to detect feature points [50]. Radiometric calibration was applied to ensure the pixel values accurately represented surface reflectance. The imagery was captured in 16-bit depth, providing a high level of detail for spectral analysis. Atmospheric corrections were performed using the 6 SV2.1 radiative transfer code to account for effects such as aerosol scattering and water vapor absorption [51]. Key atmospheric inputs, including aerosol optical depth (AOD), water vapor, and ozone levels, were retrieved from MODIS near-real-time datasets (MOD09 CMA [52], MOD09 CMG [53], and MOD08-D3 [54]) Analysis using PlanetScope images was performed at the pixel level due to the small size of the experimental plots (Figure 2). However, given the limited size of the experimental plots and their rectangular geometry that was not aligned with the pixel orientation, none of the PlanetScope pixels purely observed any of them, providing a mixed signal. Thus, only the pixels observing >85% of a given experimental plot were selected, resulting in a total of 21 pixels, covering at least one pixel each treatment.
# ML-Based 5G Network Slicing Security:
## 3 Network Slicing and Its Paradigms
#### Architecture
**ONF-SDN standardized network slicing architecture. ** Open Network Foundation (ONF) defines orchestration as a selection process of resources and optimally managing the client's request. The orchestrator is governed by an administrative body that tends to follow specific policies and fulfill clients' service requests. The SDN supports the slicing phenomenon, which needs to ensure the clients' service requests are fulfilled effectively and swiftly. The two main components in ONF-SDN architecture are controllers and resources, as shown in Figure 6. The controller mediates between the clients and resources as a centralized body pictured in the control plane. The administrator controls and configures the entire controller, including the client and server context and its policies for effective functioning [16]. **Network Function virtualization (NFV) standardized architecture. ** ONF-SDN architecture discussed the control plane features that enable network slicing architecture but lack effective management of network slicing and its essential resources. NFV architecture is comprised of the following: NFV Infrastructure (NFVI), a collection of resources; virtual network functions run on NFVI and MANO, consisting of a VNF Manager, Orchestrator and Virtualized Infrastructure Manager; and a Network Management System (NMS), which is comprised of Element Management (EM) and Operation/Business Support Systems (OSS/BSS) [56; 51]. The NFV architecture works on the management of infrastructure resources and directs the allocation of the slices concerning the needs of the virtual networks and network services. **ETSI standardized architecture. ** The European Telecommunications Standards Institute (ETSI) standards led NFV in 2012, and since then, it has collaborated with industries to set up a standard to support the concept of network virtualization. The ETSI standard sets up an architectural framework for NFV; it focuses on the changes in the network which are likely to happen due to the NFV virtualization process. It never focuses on network functions, packet flow, end-to-end network services, or physical infrastructure control. Figure 6: ONF- SDN architecture. Instead, it adds new reference points and functional blocks. With NFV, generic hardware can handle the software [12]. ETSI works on three domains, as shown in Figure 7.
# Multi-Strategy-Improvement-Based Slime Mould Algorithm
## Appendix A \begin{table}
\begin{tabular}{l c c c} \hline
**C** & **Function** & **\(\mathbf{D}\)** & **Range** & \(\mathbf{fopt}\) \\ \hline US & \(f_{1}(x)=25+\sum_{i=1}^{n}\left|x_{i}\right|\) & 5 & \([-5.12,5.12]\) & \(-5\) \\ US & \(f_{2}(x)=\sum_{i=1}^{n}(\left|x_{i}+0.5\right|)^{2}\) & 30 & \([-100,100]\) & 0 \\ US & \(f_{3}(x)=\sum_{i=1}^{n}x_{i}^{2}\) & 30 & \([-100,100]\) & 0 \\ US & \(f_{4}(x)=\sum_{i=1}^{n}{x_{i}}^{2}\) & 30 & \([-10,10]\) & 0 \\ US & \(f_{5}(x)=\sum_{i=1}^{n}{x_{i}}^{4}+random[0,1)\) & 30 & \([-1.28,1.28]\) & 0 \\ UN & \(f_{6}(x)=(1.5-x_{1}+x_{1}x_{2})^{2}+(2.25-x_{1}+x_{1}x_{2}^{2})^{2}+\left(2.625-x_{1}+x_{1}x_{2}^{2}\right)^{2}\) & 5 & \([-4.5,4.5]\) & 0 \\ UN & \(f_{7}(x)=-\cos(x_{1})\cos(x_{2})e^{-(x_{1}-x^{2})^{2}-(x_{2}-x^{2})^{2}}\) & 2 & \([-100,100]\) & \(-1\) \\ UN & \(f_{8}(x)=0.26(x_{1}^{2}+x_{2}^{2})-0.48x_{1}x_{2}\) & 2 & \([-10,10]\) & 0 \\ UN & \(f_{9}(x)=100(x_{1}-x_{2})^{2}+(x_{1}-1)^{2}+(x_{4}-1)^{2}+90(x_{3}^{2}-x_{4}) ^{2}\) & 4 & \([-10,10]\) & 0 \\  & \(+10.1\Big{[}(x_{2}-1)^{2}+(x_{4}-1)^{2}\Big{]}+19.8(x_{2}-1)(x_{4}-1)\) & & & \\ UN & \(f_{10}(x)=\sum_{i=1}^{n}(x_{i}-1)^{2}+\sum_{i=2}^{n}x_{i}x_{i-1}\) & 6 & \([-D^{2},D^{2}]\) & \(-50\) \\ UN & \(f_{11}(x)=\sum_{i=1}^{n}(x_{i}-1)^{2}+\sum_{i=2}^{n}x_{i}x_{i-1}\) & 10 & \([-D^{2},D^{2}]\) & \(-210\) \\ UN & \(f_{12}(x)=\sum_{i=1}^{n}x_{i}^{2}+(\sum_{i=1}^{n}0.5 ix_{i})^{2}+(\sum_{i=1}^{n }0.5 ix_{i})^{4}\) & 10 & \([-5,10]\) & 0 \\ UN & \(f_{13}(x)=\sum_{i=1}^{n}\left(x_{i-3}+10x_{i-2}\right)^{2}+5(x_{4i-1}+10x_{4}) ^{2}+\) & 24 & \([-4,5]\) & 0 \\ UN & \(f_{44}(x)=\sum_{i=1}^{n}\left|x_{i}\right|+\prod_{i=1}^{n}\left|x_{i}\right|\) & 30 & \([-10,10]\) & 0 \\ UN & \(f_{15}(x)=\sum_{i=1}^{n}\left(\sum_{j=1}^{i}x_{j}\right)^{2}\) & 30 & \([-100,100]\) & 0 \\ UN & \(f_{16}(x)=\sum_{i=1}^{n}\left(100(x_{i+1}-x_{i})^{2}\right)+\left(x_{i}-1 \right)^{2}\) & 30 & \([-30,30]\) & 0 \\ UN & \(f_{17}(x)=(x_{1}-1)^{2}+\sum_{i=1}^{n}i\left(2x_{i}^{2}-x_{i-1}\right)^{2}\) & 30 & \([-10,10]\) & 0 \\ MS & \(f_{23}(x)=-\sum_{i=1}^{n}\left(x_{i}\sin\sqrt{\left|x_{i}\right|}\right)\) & 30 & \([-500,500]\) & \(-12,569.5\) \\ \hline \end{tabular}
\end{table} 
Table 1: Fifty benchmarks’ function information. D: Dimensions, C: Characteristics, U: Unimodal, M: Multimodal, S: Separable, and N: Non-separable.
## 4 AODV-EM Routing Protocol Design
### Frame Structure Design
In the subsequent simulation process, the moving speed \(v_{node}\) of network nodes is set within the range of 5 m/s-50 m/s, so the weight coefficient \(\alpha\) can be expressed as follows:  
\[\alpha=0.2+(\frac{50-v_{node}}{45})\times 0.3 \tag{11}\]  
It can be observed that the value of the weight coefficient \(\alpha\) decreases with the increase in the node movement speed \(v_{node}\), which indicates that the faster the node movement speed, the higher the influence weight of the node relative mobility on the calculation of the total routing cost. When \(v_{node}\) is set to 5 m/s, indicating that the nodes in the network move slowly, and the weight coefficient \(\alpha=0.5\), both routing metrics have an equal impact on the total routing cost. In contrast, when \(v_{node}\) is set to 50 m/s, reflecting nodes in the network move rapidly, and the weight coefficient \(\alpha=0.2\), the relative mobility between nodes becomes the primary influencing factor.