esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Placement of Nodes
### Placement of Ordinary Nodes
In contrast, [PERSON] _et al._[123] developed a general sensor deployment scheme, which can efficiently determine sensor positions for an arbitrary sensing field, probably with obstacles. Studies on objectives other than minimising the total number of sensors are also available. [PERSON] [124] studied the placement of a given number \(n\) of nodes in a grid sensor field. The aim was to minimise the distance between sensors for fault tolerance reasons, subject to the constraints that all the \(n\) sensors are placed on grid points, and all the grid points are sensed by some sensors. Other work adopting grid-based deployment include [PERSON] _et al._[125], and [PERSON] _et al._[126]. [PERSON] _et al._[125] tried to find the node placement scheme that minimizes the cost of sensors to meet the complete coverage constraints. In particular, [PERSON] _et al._[126] developed an algorithm to solve the sensor placement problem on a grid field. Each grid point was coded according to the coverage relationship to sensors. The algorithm aimed to find the set of grid points that can minimise the maximum coding distance between any pair of grid points, subject to the cost limitation. The above mentioned work [114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126] was based on deterministic sensor placement. However, for large scale WSNs, random placement has also received much attention. [PERSON] and [PERSON] [127] proposed three types of random placement models. The authors studied the fault-tolerance and sensing coverage through simulations, and suggested that the placement model which assumes that sensors are uniformly distributed in terms of the network radius and angular direction from the sensor field centre is the best candidate for random node placement. Others (see [128] for an example) assume that all nodes follow identical Gaussian distribution. Models of random placement usually assume that nodes are independently and uniformly distributed in space. It is not clear that this is a suitable model, although its tractability is appealing. Moreover, it has been shown that several models of random motion of nodes eventually yield a uniform distribution of nodes. [PERSON] _et al._[129] showed by simulation that a uniform distribution results if the initial distribution of nodes is uniform distribution, all the nodes move according to the random waypoint model and all the nodes move for a large number of steps of Brownian-like motion, which represents unintentional movement. [PERSON] [128] showed that a uniform distribution results even if the initial distribution was Gaussian instead of uniform. Note however that these mobility models are no less arbitrary than the initial assumption of uniform placement. There is a dearth of literature on the actual distributions yielded by the proposed methods of random deployment.
# Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time
###### Abstract
Section 4 contains a short explanation of prefix sums and its parallel computation algorithm. Section 5 describes an extension of the prefix sums for the correction of overlapping jobs and its parallel computation algorithm. In Section 6, a parallel method for a quick calculation of the makespan for the considered problem is proposed. Section 7 describes the proposed metaheuristic solving method. Section 8 contains the computer experiments using the developed solving method. Finally, Section 9 contains the conclusions and future work. Moreover, Table 1 contains the overview of the notation used throughout the paper. \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(\mathcal{J},\mathcal{M}\) & Set of jobs, set of machines \\ \(n,m\) & Number of jobs, number of machines \\ \(p_{t}^{a}\) & Processing time of job \(i\) on machine \(a\) \\ \(r_{a}\) & Minimal allowed idle time on machine \(a\) \\ \(d_{a}\) & Maximal allowed idle time on machine \(a\) \\ \(\pi\) & Processing order of jobs (solution) \\ \(\pi(i)\) & Job processed as \(i\)-th in solution \(\pi\) \\ \(\pi^{\prime}\) & A neighbor solution of solution \(\pi\) \\ \(\pi^{*}\) & Optimal solution or best-known solution \\ \(\rho_{t}^{a}\) & Shorthant for \(p_{t+1}^{a}\) (solution-adjusted processing time) \\ \(S_{t}^{a}\) & Starting time of job \(\pi(i)\) on machine \(a\) \\ \(C_{t}^{a}(\pi)\) & Completion time of job \(\pi(i)\) on machine \(a\) \\ \(C_{\max}(\pi)\) & Makespan for solution \(\pi\) \\ \(\oplus\) & Binary operator of the job shift scan \\ \(t,E\) & Current temperature and epoch (solving method parameters) \\ \(T_{p},T_{G},T_{C}\) & Running time of solving method variants \(P,G\) and \(C\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of notation.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 3 Experiment Result and Analysis  
The numerical experiment for validating the CAPS performance was carried out by analyzing the noise of the code and carrier-phase measurement based on zero-baseline data and by analyzing the accuracy of the relative positioning and velocity estimation in post-processing and real-time cases respectively based on short baseline data.
# A Lightweight Forest Test Image Recognition Model Based on Improved YOLOv8
###### Abstract  
In response to the shortcomings of traditional pest detection methods, such as inadequate accuracy and slow detection speeds, a lightweight forestry pest image recognition model based on an improved YOLOv8 architecture is proposed. Initially, given the limited availability of real deep forest pest image data in the wild, data augmentation techniques, including random rotation, translation, and Mosaic, are employed to expand and enhance the dataset. Subsequently, the traditional Conv (convolution) layers in the neck module of YOLOv8 are replaced with lightweight GSConv, and the Slim Neck design paradigm is utilized for reconstruction to reduce computational costs while preserving model accuracy. Furthermore, the CBAM attention mechanism is introduced into the backbone network of YOLOv8 to enhance the feature extraction of crucial information, thereby improving detection accuracy. Finally, WIoU is employed as a replacement for the traditional CIOU to enhance the overall performance of the detector. The experimental results demonstrate that the improved model exhibits a significant advantage in the field of forestry pest detection, achieving precision and recall rates of 98.9% and 97.6%, respectively. This surpasses the performance of the current mainstream network models.  
forest pest; YOLOv8; object detection Article
# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 4 Two-Cycle Methodology to Build a Semantic Model
### The Vocabulary Cycle
The reference ontologies in this task are the basic formal ontology (BFO) [44], the product-service system (PSS) ontology [45], and the product lifecycle (PLC) ontology. These were selected because they all have the same philosophy as the BFO, and the PLC and PSS ontologies were created in reference to each other. For an ontology to be a generic and applicable semantic model, the names of entities should use the standard vocabulary in the scope. All of the vocabulary should be explicitly defined and refined (this occurs during vocabulary definition and refinement), and definitions should be described in the ontology. After vocabulary refinement, all of the selected terms are given definitions. In this study, the definitions were made based on the  
Figure 2: Logical diagram of the maintenance semantic model. definitions in the reference ontologies. If a term was missing in the reference ontologies, we have elaborated it, so its definition matches existing contexts in the references.
## 3 Results and Discussion
### Optical Properties  
For the investigation of optical characteristics, absorption and emission spectra were used. The absorption of the rare earth/dopant (here holmium), depending on the range of UV (ultraviolet) to (near infrared) nitrogen was estimated. The greatest intensity of absorption wavelength in the spectrum was seen for sample B0H spectrum; the same wavelength as excitation wavelength was reported.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 5 SA-Based 5G V2X Services
### System Support  
The next generation (5G) communication support is without a doubt the most valuable and significant support for V2X services offered by 3 GPP. This introduced enhanced supports, which play important roles in realizing 5G V2X services i.e., non-safety (comfort), safety-related, and in multiple RATs (including non-3 GPP V2X technology e.g., ITS-G5, DSRC, etc.). Obviously, for the evolved presentation of system support, the previously discussed service and system supports are considered for 5G V2X services. We now present a significant overview of 5G-based service and system support for V2X services.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 4 Data Rate Optimization  
In our research, logical channels are defined from the perspective of the system, that is, a logical channel contains different mappings of physical channels, so different resource blocks correspond to different channel noise and raw resource allocation. Therefore, when solving the optimization problem, the MS needs to solve \(R\) separately in several different physical mappings and get the final optimal scheme.  
Thus, the problem **P2** can be conducted in these two process named as:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline
**R** & Reference transmission rate matrix of services \\
**Z** & Package size matrix of users \\
**T** & Service type matrix of users \\
**S\({}_{\textbf{p}}\)** & Priority score matrix of users \\
**R\({}^{\prime}\)** & Actual allocated data rate matrix \\
**P\({}_{kb}(t)\)** & Transmitting power matrix of users \\ \(M\) & Number of MS per unit time \\ \(B_{kb}\) & Allocated bandwidth of a certain user \\ \(P_{kb}(t)\) & Transmitting power of a certain user \\ \(h_{kb}(t)\) & Channel state array of a certain physical link \\ \(\sigma_{k}^{2}(t)\) & Noise power of a certain channel \\ \(L_{i}\) & Location parameter of a certain user \\ \(D_{ij}\) & Distance between two access node in Km \\ \(\rho(t)\) & MS density of a certain location area \\ \(C\) & Cost function of an allocation decision \\ \(C_{ch}\) & Capacity of a certain slice \\ \(\textbf{A}(t)\) & Slice allocation states(allocated or not) \\ \(\boldsymbol{\Theta}\) & Resource availability of a certain slice \\ \(\textbf{S}(\textbf{t})\) & User real time parameter (e.x. data amount) \\
**PW\((\textbf{t})\)** & Penalty function of DP-DQN \\
**DA** & Congestion Rate of slices \\ \(\textbf{L}_{\textbf{c}}\) & The distance from MS to cellular access node \\ \(\textbf{L}_{\textbf{u}}\) & The distance from MS to UAV access node \\ \(AS\) & The successful access ratio of several MS \\ \(Bd_{c}\) & The network overhead of control panel \\ \(Bd_{u}\) & The network overhead of user panel \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of parameters.  
* **P4**: First, it is assumed that in all physical mapping cases, the transmitted power of MS is the same. On this basis, **R** is calculated and the optimal allocation resource block amount is selected.
* **P5**: Then, when the allocated resource block is determined, the transmission power \(P_{kb}(t)\) is changed to obtain the optimal transmitting rate **R**.
## 4 Analysis and Verification
### Path Planning Experiment
#### 4.3.2 Real Vehicle Test Verification and Analysis  
In the real vehicle test, the basic indoor environment was simulated by adding a card-board box as an obstacle. The ROS smart car with sensors was used to build an environment map, and the real-time operation status of the intelligent robot was observed in the Rviz visualization window. According to the constructed real-time environment information, the path planning algorithm proposed in this paper was verified and analyzed, as shown in Figure 12.  
As shown in Figure 13a, both the traditional ant colony algorithm and the improved ant colony algorithm were able to avoid obstacles and reach the target position in the grid graph formed in the real closed environment in a safe and stable manner. However, by  
Figure 11: Intelligent robot control parameters: (**a**) linear speed parameters of the intelligent robot based on path planning feedback; (**b**) intelligent vehicle angular velocity parameters fed back by path planning.  
Figure 12: Labyrinth-type environment real scene: (**a**) simulation of the basic indoor environment; (**b**) adding the location obstacle environment map.  
improving the heuristic function of the traditional ant colony algorithm and the hybrid strategy, the shortest path effect in the global path planning was found to be better, and the smooth and safe passing speed of the intelligent robot was accelerated. The trajectory and error parameters of the intelligent robot are shown in Figure 14. It can be seen from the figure that when the intelligent robot travelled to the range of 1.6 to 2 m, the error between the actual path and the planned path was about 4 cm at most due to the influence of low obstacles, indicating that the improved ant colony algorithm is more accurate. In order to verify the robustness and accuracy of the algorithm in this paper, the global path planning of the intelligent robot was completed according to the sensors carried by the intelligent robot to detect the driving environment, as shown in Figure 13a. Then, position obstacles were set on the global path, as shown in Figure 12b. When there was a positional obstacle, the intelligent robot adopted the dynamic window method to complete the local obstacle avoidance during the driving process, and always travelled along the target direction of the global optimal path, which improved the efficiency of path planning and the driving stability of the robot, as shown in Figure 13b.  
Figure 14: Improved ant colony algorithm path analysis: (**a**) path analysis; (**b**) improved path trajectory error of the ant colony algorithm.  
Figure 13: Real-time environment drawing experiment: (**a**) comparison between basic ant colony and ant colony algorithm in this paper; (**b**) the result graph of path planning based on the fusion algorithm in this paper.
## 3 Methodology  
### Controlled Source Audio-Frequency Magnetotellurics for Goaf Detection  
#### 3.1.1 Principle of Controlled Source Audio-Frequency Magnetotellurics  
Based on the different skin depths of electromagnetic waves with different frequencies in the underground propagation, the controlled source audio-frequency magnetotellurics (CSAMT) method can obtain geoelectric parameters at a depth corresponding to the frequency of each EMF by measuring the EMF intensity, thereby reaching the goal of depth sounding [30].  
CSAMT is a frequency-domain electromagnetic depth-sounding method. By taking the finite length ground electric dipole as the field source, the electric- and magnetic-field parameters can be simultaneously observed at a certain distance from the dipole center, the Cagniard resistivity and impedance phase can be calculated, and the geological objects can be detected at different burial depths. The EMF components measured by CSAMT and the coordinate system are displayed in Figure 3. The CSAMT-based scalar measurement was realized using the power from an electric dipole source, and the observation point was located in the sector area formed by 30\({}^{\circ}\) angles at two sides of the middle perpendicular to the electric dipole source (Figure 4) [31].
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Flow-Based Attack Detection
#### 4.2.3 Traffic Grouping-Based Detection  
Flow includes all traffic within a period, and many types of traffics may act as white noise in attack detection. Training machine learning models with such data probably leads to overfitting. One natural approach is to group traffic to decrease heterogeneity. The grouping methods include protocol-based and data-based methods.  
The traffic features of various protocols have significant differences; thus, grouping traffic by protocol is a valid step toward improving accuracy. [PERSON] et al. [52] proposed an SVM detection method based on protocol grouping using the data of the KDD99 dataset, which involves various protocols. They first divided the dataset based on protocol type, and considered only TCP, UDP, and ICMP protocols. Then, according to the characteristics of these different protocols, they selected features for each subdataset. Finally, they trained SVM models on the 3 subdatasets, obtaining an average accuracy of 89.02%.  
Grouping based on data characteristics is another traffic grouping approach. One typical method is clustering. [PERSON] et al. [53] proposed a DNN and spectral clustering-based detection method. The heterogeneity of flow may cause low accuracy. Therefore, they first divided the original dataset into 6 subsets, in which each subset was highly homogeneous. Then, they trained DNN models on every subset. The accuracy of their approach on the KDD99 and the NSL-KDD datasets reached 92.1%.
## 5 Application to Case Study and Discussion  
To verify the reliability of the solutions provided and their versatility, we applied the type curves to two real case studies: the discharge of the Guadalfeo River to the Motril-Salobrena aquifer and the artificial recharge of the Agua Amarga coastal aquifer in southern Spain. These hydrogeological systems have many uncertainties related to their geological features (heterogeneity, anisotropy, etc.); these uncertainties lead to some constraints and shortcomings in the analytical solutions [35]. Consequently, some assumptions and hypotheses had to be made regarding geometry and the mechanical or thermal properties. These assumptions are decisive in the verification of the accuracy of the validation.  
The procedure applied is outlined in Figure 9. In the case that there is a reliable value for both the hydraulic conductivity and its anisotropy, the value of the thermal conductivity of the saturated porous media can be obtained from Figure 6. If only the type of material is known and only a range of thermal conductivities and hydraulic conductivities is available, it is preferable to use thermal conductivity as input data since its range of variation is much smaller [8].
## 4 The Proposed PUL-DUCG Model  
### Definition of the PUL-DUCG  
In this section, a new type of DUCG based on PULSs and the EDAS method [47] is presented for KRR.  
The events are represented by some graphical symbols in the classical DUCG. A square is used to represent the \(B\)-type variable, which could only be an independent cause. A circle is used to represent the \(X\)-type variable, which can be divided into two types: \(X_{c}\) and \(X_{r}\). The \(X_{c}\)-type variable can represent a consequence; the \(X_{r}\)-type variable can represent both a consequence and a cause. A certain state \(j\) of variable \(V_{i}\) (\(V_{i}\) can be a \(B\)- and \(X_{r}\)-type), referred to as \(V_{ij}\), represents a parent event. A certain state \(k\) of variable \(X_{n}\)(\(X_{n}\) can be \(X_{c}\) and \(X_{r}\) type), referred to as \(X_{nk}\), represents a child event. The connection event variable \(F_{nk;ij}=(r_{n;i}/r_{n})\,A_{nk;ij}\), denoted by the arrow \(V_{ij}\)\(\rightarrow\)\(X_{nk}\), represents the cause-and-effect relationship between \(V_{ij}\) and \(X_{nk}\). The causal mechanism of \(V_{ij}\) independently causing \(X_{nk}\) is quantified using the virtual event \(A_{nk;ij}\), with all knowledge parameters given in a virtual event matrix \(A=\left[a_{nk;ij}\right]_{p\times q}\) for \(p\) child events \(X_{nk}\) and \(q\) states of child events. A typical PUL-DUCG is established as shown in Figure 2. Similar to the normal DUCG model, the variables of the PUL-DUCG model can be expressed as  
\[X_{n}=\sum\limits_{i}F_{n;i}V_{i}=\sum\limits_{i}(r_{n;i}/r_{n})\,A_{n;i}V_{i}. \tag{10}\]  
When there are multiple states for a variable, the model can be expressed as  
\[X_{nk}=\sum\limits_{i}\sum\limits_{j}F_{nk;ij}V_{ij}=\sum\limits_{i}(r_{n;i}/ r_{n})\sum\limits_{j}A_{nk;ij}V_{ij}. \tag{11}\]
# A Clutter Loss Model for Satellite Communication Systems
## 7 Model Validation
In fact, for the comparison, it must be taken into account that the ray-tracing simulation considers a limited area of the city, i.e., a limited number of buildings with their actual heights and distances: the derived statistics are expected to have a more limited stability if compared to the Monte Carlo simulation, which considers a very high number of realizations (several rays, each of which will interact with a random building scenario) generated on the basis of the building height and distance statistics. Indeed, the purpose of the comparison between the Monte Carlo simulation and the ray tracing calculations was to confirm the same order of magnitude for the clutter loss by using two very different approaches.
## 2 Test and Result Analysis
### Test Results Analysis
#### 2.3.5 Vertical Acceleration  
Figure 15 shows the temporal evolution curves depicting the vertical acceleration at the center of the baseplate in box chamber 2 for all specimens. The acceleration reaches its peak 42 ms after detonation, followed by oscillation and a gradual decay to zero. The recorded peak accelerations for specimens G, PCG, and PCGR were 36.5 km/s\({}^{2}\), 10.4 km/s\({}^{2}\), and 48.44 km/s\({}^{2}\), respectively. Specim PCG showed a 71.4% reduction in peak acceleration compared to G, owing to the protective effect of the polyurea coating, which mitigated the blast shockwave and decreased vertical acceleration at the baseplate. In contrast, the peak acceleration for PCGR rose by 366.3% compared to PCG under repeated explosive loads. This is attributed to the damage and defects formed in the box girder after the first explosion, which weakened its blast resistance. Furthermore, a comparison of specimens G, PCG, and PCGR indicates that the acceleration curve for specimen G exhibits the largest oscillation amplitude, which further validates the capability of the polyurea coating for reducing explosive shockwaves, thereby enhancing the overall stability of the structure.