esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 3 Power Control in MANETs  
The transmission power determines the signal strength at the receiver, the transmission range, and the interference it creates for other receivers in the network. Therefore, transmission power control affects the sharing of the wireless medium and influences many aspects of the operation of wireless ad hoc networks [13]. Transmission power control is a key factor in several performance measures such as throughput delay and energy efficiency. For this reason, intensive research has been conducted to improve the network performance in terms of network throughput, delay, and energy efficiency with transmission power control. Nevertheless, these early works only consider the interplay between power control and a single layer. Hence, they may not necessarily lead to performance improvement when combining power algorithms across multiple layers. Some of the studies focused on power control using a single layer are [13]:  
* Power control focused on the MAC layer, it focuses on sending packets at the minimum power needed such that the SINR in the receiver is only above the predefined threshold for successful transmission. The methods studied have been: contention-based MAC Scheme, Interference-Based MAC Schemes, Scheduling-Based MAC Schemes [14].
* Power control focused on Network layer: Power control that operates at the MAC layer does not give routing protocols the opportunity of choosing optimal next-hop node. This means that the MAC approach only leads to local optimization of network performance, and hence it is necessary to add transmission power control at the network layer (or higher) to obtain the global optimization [13]. Authors considering the interaction between power control and network layer propose COMPOW, CLUSTERPOW, and LOADPOW schemes. The difference between these mechanisms with DPCM is that our mechanism depends on the number of neighbors detected.  
Since transmission power control can affect several layers of the protocol stack, it is a typical cross-layering design problem in wireless ad hoc networks. Crosslayering design, in general, should be approached with some caution, keeping in mind the longer-term architectural issues. [PERSON] and [PERSON] [15] enumerate several principles that guide the design of transmission power control algorithms such as load effect, throughput and retransmission effect. In this paper, we consider the cross-layering technique in the mechanism for obtaining the interaction of two layers (physical and network).
# A Review of the Network Arch Bridge
## 3 Network Arch Bridges of Different Structural Systems around the World
### Troja Bridge in Prague  
Another unique NAB is Troja Bridge in Prague, Figure 11. Troja Bridge has a main span of 200.4 m, supported by a bowstring arch-type structure [21] with a network system for the hangers. The bridge carries four road lanes, two tram tracks and two wide pedestrian lanes for both pedestrian and cyclist traffic [22]. The rise of the arch in only 20 m, thus having a rise to span ratio of only 1/10. The arch is made of steel, with a pentagonal box cross-section that can be seen in ref. [21]. Near the sides, the arch divides into two sections, called \"the legs\", that are on both sides of the tram tracks. The road and pedestrian lanes are on the \"exterior\" of the arch. The tie of the arch if also made of steel box in the shape of the letter omega encased in concrete. The tie carries six prestressing cables. The deck is suspended from the arch through 200 hangers of S520 grade steel [21] that were tensioned in two phases. The deck of the bridge is made of prestressed concrete: precast prestressed beams C70/85 spaced at 4000 mm [23], and a thin prestressed concrete slab C50/60 with a thickness of 28 mm. The total width of the deck is 30 m. Some aspects related to the numerical analysis and the construction process are presented in refs. [22; 23]. The cross-section of the network arch span can be seen in ref. [22]. The bridge also has a smaller span, 40.35 m, independent of the NAB span.  
The Troja Bridge combines both efficiency and elegance in a large-span network arch. As already stated, it carries all traffic categories on the same superstructure and has a very low arch--the rise-to-span ratio is only 1:10. This was possible due to the 200-hanger network that connects the arch and the deck. The hanger network made possible a height of the arch of just 20 m, compared to a classical tied arch where the height would have probably been around 40 m. In the design of the Troja Bridge, both engineers and architects were involved, leading to a new landmark for the city of Prague.  
Figure 11: Street view of the Troja Bridge, Prague, Czech Republic, from Google Street view.
## 3 Results and Discussion
### Fracture Mechanism
#### 3.4.2 Microscopic-Fracture Characteristics
Additionally, the fracture modes of both fracture C2 in specimen C-0-40 and fracture U2 in specimen U-30 are dominated by the shear fracture along the foliation plane, but the fracture surface of C2 is smoother, as shown in the magnified-100-times image. The difference between fractures C2 and U2 is amplified at 5000-times magnification, showing that the fracture C2 exhibits a higher integrity compared to the fracture U2, with the foliation planes in fracture C2 more visible. This can be explained by the difference in the formation mechanism of fractures U2 and C2, as illustrated in Figure 22. When the angle between foliation plane and loading direction (\(\beta_{\text{J}}\)) is smaller than the angle between shear plane and loading direction (\(\beta_{\text{S}}\)), the edges of broken foliation planes are more susceptible to interlaced extrusion during shear sliding, and thus abundant rock fragments are formed in the fracture surface, as observed in fracture U2. On the contrary, when \(\beta_{\text{J}}\) is larger than \(\beta_{\text{S}}\), a fracture morphology  
Figure 20: Microscopic morphology of different fractures in specimens under monotonic loading by SEM scanning: (**a**) U1; (**b**) U2; (**c**) U3; (**d**) U4; and (**e**) U5. similar to the fracture C2 can be formed. Similar to the fracture U3, obvious crystal and IG-fracture characteristics are observed in the fracture C3. Furthermore, the characteristics of IG fracture are also found in the fracture C4, but, compared with the fracture C3, more thin foliation-section tips are formed in the fracture C4, which is an important feature for distinguishing the tensile fracture along the foliation plane from one through the foliation plane. Figure 21: Cont. Figure 21: Microscopic morphology of different fractures in specimens under cyclic loading by SEM scanning: (**a**) C1; (**b**) C2; (**c**) C3; and (**d**) C4. Figure 22: The formation mechanisms of the fractures (**a**) U2 and (**b**) C2.
## 3 Results and Discussion
### Model Architecture and Embedding Configuration  
In Table 8, we summarize the key parameters of the Sentence Transformer base model and the sequential head (GRU, LSTM, or Transformer). These parameters define how text inputs (such as job descriptions and resumes) are encoded into embeddings, how many hidden units are in the sequential layer, how many layers are stacked, and how the sequence of embeddings is aggregated (e.g., using an attention mechanism).  
Figure 4: Transformer final t-SNE visualization (1000 triplets). Blue points correspond to Job Description, green points to Positive Resume, and red points to Negative Resume.  
Figure 3: LSTM final t-SNE visualization (1000 triplets). Blue points correspond to Job Description, green points to Positive Resume, and red points to Negative Resume.  
Table 8 outlines the primary settings for both the base embedding model and the sequential head. Base Model shows the pretrained model name (distiluse-base-multilin qual-cased-v2), which is responsible for initial text embedding. The _Input Embedding Size_ indicates the dimensionality of those embeddings. The rows under Head Model specify the hidden size (_Hidden Size_), the depth of the sequential network (_Number of Layers_), the dimensionality of the final output vector (_Output Embedding Size_), and the aggregation strategy (_Output Aggregation_) used to combine the sequential outputs into a single embedding vector.
# Planning-Level Optimisation of Headway Regularity
## 4 Case Study
### Evaluation of Passenger Travel Time (Phase IV)  
The evaluation of passenger travel time in the PPT was performed for different primary headway disturbance combinations based on the difference in passenger travel time.  
Figure 8 shows the difference in average passenger travel times for the two analysed variants. The data are presented for two observation periods and both line directions. If the difference in passenger travel time is less than zero, the optimised (split) line design (the second variant) is more efficient than the current (diametrical) line (the first variant).  
The observed optimisation effects, in terms of average passenger travel times, vary significantly depending on the observation period and the primary headway disturbance duration. The reduction in average passenger travel times is only achieved during the peak period when the primary headway disturbance on the diametrical line is at least 2 min. If the primary headway disturbance in the peak period is 4 min, the optimisation achieves a 1.5-min and 1.8-min reduction in travel time in directions A and B, respectively. In the optimised variant for the off-peak period, the average travel time would be up to 4 min longer irrespective of the primary headway disturbance value.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 4 System Architecture
### Application Scenario  
The proposed system focuses on using images from the UAV in conjunction with the sensor nodes utilizing the information measured to determine if any changes have occurred in an area. If the sensors report any results exceed a predefined threshold, a UAV could then be dispatched to the area for images to be captured. There are two types of nodes. The first type, the monitoring nodes, they keep collecting data from the sensor, form packets and forward the data to the relay nodes. The second type of nodes, the relay nodes, they do not have any sensing capabilities, but they are able to receive packets and forwards them towards the control room. The system contains the following seven steps:  
1. The monitoring node collects environmental information on the plant or the immediate vicinity where the node is located.
2. The monitoring node transmits the collected information in a packet toward the destination node through the relay nodes.
3. The collected data is then stored in a system containing a database of previous information collected at that location. The system can then analyze the new data with historical records and with existing models which are known for the different diseases in order to determine if conditions are favorable for the development of a disease or the presence of a pest.
4. If attention is required the UAV is ordered to the area for surveillance images to be taken. To find the exact location, the UAV uses its GPS.
5. Once the UAV reaches the required location it takes images of the area and then it returns to the control room.
6. The images taken by the UAV are forwarded to the control room and then to the system administrator.
7. Any further actions that need to be taken for the plant are done by the system administrator.  
In extreme weather conditions, the drone might not be able to fly and the system might not operate as expected.
# Dispersion Curve Interpolation Based on Kriging Method
## 3 Methods
### Procedure of Applying Kriging to Dispersion Curves  
The main procedure of the application is as follows. Firstly, dispersion curves were randomly drawn among the original ones obtained by forward modeling. These dispersion curves were regarded as the results with poor ray density (or accuracy), and the rest were used as input data. Secondly, all dispersion curves were discretized into scattered points in different periods. Then, dispersion points of the same period were grouped and interpolated by kriging. In this step, two restrictions were proposed. One is the deviation angle, which is used to determine whether the data come from different areas. If the deviation angle becomes larger, the predicted point is farther from the boundary, which  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Period (s)** & **Method** & **the Best Variogram** \\ \hline
2 & Ordinary Kriging & Gaussian \\  & Universal Kriging & Spherical \\ \hline
4 & Ordinary Kriging & Spherical \\ \hline
6 & Ordinary Kriging & Spherical \\  & Universal Kriging & Spherical \\ \hline
8 & Ordinary Kriging & Spherical \\  & Universal Kriging & Spherical \\ \hline
10 & Ordinary Kriging & Gaussian \\  & Universal Kriging & Spherical \\ \hline \hline \end{tabular}
\end{table}
Table 1: The best variogram under different kriging approaches in each period.  
means that the result is available. The deviation angle is determined as shown in Figure 3. Points A and B are known, and the star indicates the predicted point. If \(\alpha\) and \(\beta\) denote the minimum and maximum azimuth angles, respectively, the deviation angle is \(\theta=\beta~{}-~{}\alpha\).  
The other restriction involves the distance between the predicted and input points. In kriging, points farther away have less weight; therefore, we only use data within a certain range to participate in the interpolation. We repeated the second step in different periods, and thus obtained the predicted dispersion curves. Fourth, in order to make the dispersion curves smoother, we smoothed the spikes. Finally, we compared the predicted dispersion curves with the original dispersion curves.
## 4 Discussion
### Research Gaps Identified in the Literature
A noticeable gap in the reviewed literature is the lack of localization frameworks specifically tailored for nuclear power plants. While detection methods are frequently addressed, few studies extend the analysis to fault origin tracing or spatial diagnostics in such safety-critical systems. This represents a vital direction for future research, considering the stringent fault containment requirements in nuclear environments. Another notable research gap is the lack of distinction between AC and DC protection strategies in fault detection literature. With the growing integration of DC-based systems--such as photovoltaic arrays, battery storage, and HVDC links--diagnostic methods need to account for fundamentally different fault behaviors, propagation mechanisms, and isolation techniques compared to AC systems. Future research should explore topology-specific approaches tailored to the dynamics of DC grids. While the current taxonomy did not explicitly distinguish between AC and DC systems due to inconsistent reporting in the reviewed studies, this dimension is increasingly important for future diagnostic models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Type** & **Advantages** & **Limitations** \\ \hline Traditional (PCA, FDI) & Interpretable, efficient, low data needs & Inflexible, poor generalization \\ \hline Machine Learning (SVM, RF) & Scalable, works on structured data & Requires labeled datasets, sensitive to noise \\ \hline Deep Learning (CNN, LSTM) & High accuracy, handles & Data-hungry, low explainability \\ \hline Hybrid (Physics + ML) & Combines domain knowledge and learning & Complex integration, underused in practice \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative Summary of Fault Detection Methodologies: Strengths and Limitations.
## 3 Materials and Methods
### Automatic Pig Face Detection  
In developing our two-stage pig face recognition model, we implemented a decoupled approach that distinctly separates the tasks of detection and recognition. This design choice significantly reduces the burden of annotating new data. In this stage, we employed the advanced capabilities of YOLO-v8 to automatically detect pig faces within the dataset. YOLO-v8, known for its speed and accuracy, is well-suited for real-time applications and serves as the backbone for our automated pre-processing.  
Yolo-V8 includes CSPDarknet backbone, YOLO-v8 PAFPN neck, and head blocks. The feature extraction part uses a network architecture called CSPDarknet, which is an improved version based on Darknet. CSPDarknet uses a Cross-Stage Partial Network (CSP) structure that divides the network into two parts, each containing multiple residual blocks. This structure can effectively reduce the number of parameters and calculation amount of the model and improve the efficiency of feature extraction. The object detection part adopts a detection head structure named YOLO-v8-Head. The structure consists of multiple convolution layers and pooling layers for processing and compressing feature maps. Then, the feature map is converted into the target detection result through multiple convolution layers and fully connected layers. Finally, the detected pig faces are then cropped and fed into the recognition model.  
YOLO-v8 total loss consists of class loss, objectness loss, and location loss:  
\[\mathcal{L}_{Total}=\lambda_{1}\mathcal{L}_{cls}+\ \lambda_{2}\mathcal{L}_{obj}+ \lambda_{3}\mathcal{L}_{loc}, \tag{1}\]  
\(L_{cls}\) is the class loss, representing the error in classifying objects into their correct categories. \(L_{obj}\) is the objectness loss, which measures the error in detecting whether a certain region contains an object or not. \(L_{loc}\) denotes the location loss, which quantifies the error in determining the precise location and size of the detected objects.  
To balance the contribution of each loss component to the total loss, they are weighted by their respective coefficients, \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\). These coefficients allow for fine-tuning of the importance of each component in the final combined loss.
## 3 Proposed Method
### Lightweight AI Model
#### Model Quantization  
The principle of quantization involves reducing the precision of the bits used to represent model parameters (typically 32-bit floating-point (float32) numbers) [40]. This approach results in smaller model sizes and faster computations. Model quantization involves approximating the continuous values (or a large number of possible discrete values) of the floating-point model weights with a limited set of discrete values (usually 8-bit integer (int8) numbers) at a lower inference accuracy loss, as shown in Figure 15[40]. A lower-bit data type is used to approximate the finite-range floating-point data, which  
Figure 14: Weight adjustment process in the three-step training pipeline for pruning.  
Figure 13: Model pruning retains important weights and parameters while reducing the number of connections between neural network layers.  
leads to a reduced model size, decreased memory consumption, and faster inference speed. The calculations are expressed as:  
\[Q=\frac{R}{s}+z \tag{25}\]  
\[s=(R_{max}-R_{min})/(S_{max}-S_{min}) \tag{26}\]  
where \(R\), \(Q\), \(z\), and \(s\) denote the real floating-point value, fixed-point value after quantization, fixed-point value after quantization of the 0 floating-point value, and minimum scale that can be represented after fixed-point quantization, respectively.  
The model-pruning approach is applied using weight sparsity, where weights close to zero are removed from the original model. Subsequently, the model is retrained to adjust its performance. Additionally, quantization techniques are combined to convert the weights from float32 to int8, thereby significantly reducing the model size and enhancing the computational speed.
## 3 Results and Discussion
### Visual Analysis of P. tricornutum Cell Morphology and Lipid Droplets  
We next examined _P. tricornutum_ lipid droplets using confocal microscopy (Figure 4). Our initial expectation based on a gravimetric analysis of lipids was to observe a change in the lipid droplet size. The results showed that the selected light conditions were not responsible for any changes in the morphology, size, or number of lipid droplets of _P. tricornutum_ cultures grown in the studied light conditions (Figure 4a,b). The morphological changes in cells are important for microbes to respond to various environmental conditions such as the state of media, the temperature, or the salinity. Previous studies have reported the presence of three morphotypes in _P. tricornutum_ culture: oval, fusiform, and triradiate [48]. It is also known that light absorption is the function of the cell shape, size, and pigments [49]. Microscopic analyses (Figure 4c,d) revealed a high occurrence of fusiform cells, a limited number of triradiate shapes, and no appearance of the oval form in all of our growth conditions. It has been reported that the fusiform type is dominant in suspension culture over the other forms [43; 50], which is in agreement with our tested conditions. It has also been reported that the triradiate _P. tricornutum_ cells are rarely found in laboratory growth conditions [1; 43]. However, our study detected the presence of a limited number of triradiate cells (Appendix A--Figure A2). _P. tricornutum_ can be grown in both silicon or silicon-free medium; we studied silicon-free media, as it showed better growth for our strain, and it has been reported that fusiform cells have a greater lipid percentage as compared to other forms [43; 51].  
Figure 4: Microscopic fluorescent images (top) and transmitted with fluorescent overlay (bottom). Visualization of lipid droplets in _Phaeodactylum tricornutum_ using Nile red staining. Each panel represents Nile red staining of lipid droplets in white light conditions (**a**,**c**) and in red light (**b**,**d**). Scale represents 100 nm.  
Figure 3: Analysis of _P. tricornutum_ biomass (**A**) and lipid accumulation (**B**) in autotrophic conditions under continuous white (W) light, red (R) light, and red with light shift (RS), which is red light during the exponential phase and then a shift to white light. Experiments were done in triplicate in 50 mL liquid culture, and cells were collected at day 10. Results are expressed as mean value \(\pm\) standard deviation. An asterisk (*) indicates significant differences compared to the white light (W) used as control. Statistical analysis was done using one-way ANOVA.
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 3 Methodology
### Filtering and Boundary Extraction  
Each robot generates a dynamic map of the explored area and its corresponding position using odometry and LIDAR data through SLAM. Subsequently, this map undergoes filtering to eliminate noise or imperfections that may arise during the mapping process, such as small gaps in boundary walls. This filtering primarily involves image processing techniques, including boundary dilation and erosion. To simplify the control process, each robot is considered to be a point, and thus the boundaries are inflated by the robot's radius. Computer vision algorithms are then employed for edge detection and boundary coordinate extraction. The extraction of the explored region's boundaries, including the identification of frontiers, is essential for the construction of the HMT.