esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Ablation Experiment  
#### 4.2.1 Verifying the Validity of the Modules  
In this section, in order to further validate the effectiveness of each improved module of Slim-YOLO, we design a series of ablation experiments to analyze the effects of different components on the model performance. Also, to validate the effectiveness of the modules in the ELANSlimNeck neck structure, we added the core modules RepNCSPELAN4 and GSConv in the ELANSlimNeck neck structure to the ablation experiments. The following groups of experiments are conducted on the basis of the improved loss function UIoU, and the results are shown in Table 3.  
Figure 13: Comparative visualization of different models: (**a**) YOLOv8n; (**b**) YOLOv9t (**c**) YOLOv10n; (**d**) YOLO11n; (**e**) YOLO11s; (**f**) YOLO11m; (**g**) YOLO11l; (**h**) RTDETR-l; (**i**) ours.  
From the results of the ablation experiments, it can be seen that each of the improvement modules contributes significantly to the performance improvement of Slim-YOLO. First, the introduction of RepViT alone (Model A) significantly improves mAP50 and mAP50-95 from 0.84 and 0.421 to 0.906 and 0.46, and also reduces the GFLOPS to 4.1, indicating that RepViT as a lightweight backbone network not only enhances the feature extraction capability but also improves the computational efficiency. Second, RepNC-SPELAN4 (Model B) was able to significantly improve mAP50-95 to 0.47, indicating that the module optimizes feature fusion, resulting in more accurate target detection. GSConv (Model C) slightly improves the recall (R) while improving mAP50-95, which indicates that this module optimizes the efficiency of feature extraction. Further, combining both modules (A+B, A+C, and B+C) leads to performance improvements, while the full Slim-YOLO (A+B+C) optimizes on almost all metrics, where the mAP50-95 improved to 0.482, the GFLOPS reduced to 3.5, and the number of parameters reduced to 1,331,835, which verifies the synergistic effect of the modules in improving the detection accuracy while keeping the computational overhead low.
## 1 Introduction
More studies using ANN to predict M\({}_{\rm r}\) for other pavement applications can be found in [15; 16; 17; 18]. In addition, similar applications for ANN in predicting concrete strength and the mechanical properties of materials can be found in [19; 20]. However, when using a gradient-based approach during the learning process of single hidden layer feed-forward neural networks (i.e., ANN/ELM), the network may fall into local minima, thus necessitating a long training time and some terminating criteria [21]. Therefore, an evolutionary algorithm may be used to find an approximate global solution for better network prediction and to maintain good generalization capability for the network. Although PSO and ELM have been found to be powerful methods in modeling different engineering applications [22; 23], very few studies have been conducted on modeling M\({}_{\rm r}\) in pavement applications. [PERSON] and [PERSON] [24] used and evaluated ELM for predicting M\({}_{\rm r}\) for subgrade soils, and concluded that high correlation in terms of coefficient of determination, r\({}^{2}\) (0.991), could be observed between the measured and predicted values of M\({}_{\rm r}\). In addition, the Kernel ELM (KELM) approach performed well in predicting the M\({}_{\rm r}\) of subgrade soils as compared to the sigmoid ELM and SVR approaches. [PERSON] and [PERSON] [25] used hybrid algorithms, ANN, Support Vector Machine (SVM), and hybrid adaptive neuro-fuzzy inference system methods with PSO algorithm for the M\({}_{\rm r}\) prediction of fine-grained soils. They found that the PSO-ANN was superior to the other methods for M\({}_{\rm r}\) prediction. This research study aims to develop and design a hybrid algorithm (PSO-ELM) for predicting M\({}_{\rm r}\) of stabilized base aggregates subjected to W-D cycles based on the M\({}_{\rm r}\) data presented in [PERSON] et al. [4]. Furthermore, the developed PSO-ELM was compared with other methods, such as PSO-ANN and KELM. The performance of these models was statistically assessed and validated for predicting the M\({}_{\rm r}\) of stabilized base aggregates based on the data presented in [PERSON] et al. [4]. The following sections present the background of the developed methods, the data and performance evaluation criterion, and the results and discussion of the models' performance.
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 4 Experiments
### Path Planning for Conformal Antenna Surface Detection
During the test, three rows of features on both sides and in the middle of the conformal antenna were selected for scanning. The scanning times of the original path and the path optimized by the CHOP-IGA were compared, and the comparison results are shown in Table 5.  
Figure 14: **(a)** 3D coordinate distribution of feature points before leveling; **(b)** 3D coordinate distribution of feature points after leveling. Figure 15: Changes in height of feature points during leveling. As can be seen from the results, the path optimized by the algorithm saved the time spent on feature leveling and improved the scanning efficiency by 48.44%, while also providing a guide for the path planning of AOI, coating, mounting, and welding.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### Visual Navigation Algorithms for Grasping Targets  
#### 4.2.1 Vision-Guided Laser Range Measurement  
The visual guidance-based laser range measurement used in this paper is shown in Figure 6. First, the two-dimensional image information of the target object is capturedwith the D435 depth camera with a 120\({}^{\circ}\) wide field of view to facilitate rapid target object identification. Next, the edge detection algorithm is applied using OpenCV to extract the contour information of the target object's edges. The NO. 2 TFrini sensor is employed to collect real-time height information for the UAV and target object to determine the height information \(d1\) of the UAV relative to the target object. Finally, by transforming the coordinates between the target object and the camera coordinate system, the position relationship between the UAV and the target object is established, facilitating the adjustment of the manipulator's various joint servo angles to achieve precise grasping.
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 2 Criteria for Evaluating Solutions of Noise Elimination in a GNSS Receiver
### Diagram of the Processing of Navigation Signals in a GNSS Receiver
The acquisition is based on the signal from the output of only one element of the antenna array. Later in this work, it was assumed that this is the first antenna element whose phase center is the reference point for measuring the phase delay of signals reaching other elements. The signal tracking block in the anti-noise system is also very similar to its counterpart in a standard receiver. The complete tracking procedure is performed only for the signal received by the first antenna element. However, the reconstructed replica of this signal multiplies not only itself but also the outputs of the remaining array elements. This makes it possible to determine the carrier wave phases of all signals received by all antenna elements and, consequently, their phase delays and the differences of these delays between the signals modulated by different \(C/A\) sequences. The phase delay differences are the input to the actual noise interference detection algorithm. The decision to detect interference noise is made based on a comparison of the phase delay differences with a threshold value, depending on the number of received signals and their quality. Noise originating from LTE system signals is judged when the measured phase delay differences from at least four satellites are less than a predetermined threshold. The largest number of satellites for which the detection criterion is satisfied is considered to be the number of false signals transmitted by the base station of the LTE system. The phase delays from signals considered to be erroneous are averaged and passed to the spatial filtration block.
## 2 Numerical Model
### Mesh Model and Grid Independence Verification  
According to the structural parameters of the bearing chamber, Gambit software is used to build up the geometric model of the flow region, hexahedral grids are used to discrete the flow region, and the grid of air inlet and oil inlet is refined. The grid model is shown in Figure 2.  
In order to eliminate the influence of mesh on the calculation, three groups mesh with different size are generated on the gambit platform, and the number of meshes is 487,585, 609,719, and 1,530,258, respectively. Under the condition of air inflow 12 g/s, the air phase velocities at four position are extracted on the section z = 0, and the four coordinate points are as follows; (-0.11,0,0), (-0.09,0,0), (0.09,0,0), (0.11,0,0). The calculation results are shown in Figure 3. From the figure, the velocities at four position obtained by three groups mesh are approximate. In order to save computing cost, the grid model of 609,719 is chosen in this paper.  
Figure 2: Grid model of the bearing chamber.
## 7 Discussion  
### The Use and Experiences with the Scheduling Software Tool  
Medical doctors who have used CIPLAN consider it an intuitive, rapid, and efficient software solution that can make easier the scheduling task. With the manual method, the confidence level of not exceeding the available time is unknown before building the schedule. However, with CIPLAN, they find an important advantage that the input parameter is the confidence level and the tool maximizes the occupation rate respecting this confidence level. Moreover, they said that the time spent for scheduling is drastically reduced, a check of the obtained solution being necessary.  
Figure 5: Comparison of the time execution of the surgical blocks. The time execution of the 24 time blocks scheduled (**a**) manually and (**b**) using CIPLAN are compared. Numbers inside the boxes represent the preference order of the patients. In case (**b**) color green/orange/red means that the patient has been advanced or delayed at most 1/2/3 time blocks with respect to the manual scheduling.  
Using CIPLAN, from an economic point of view, the improvement in the occupation rate using the manual method is 2.83%. Only considering the OSD, this 2.83% implies an increment \(\Delta_{Oc}\) of 95.65 effective hours per year in the use of the ORs:  
\[\begin{split}\Delta_{Oc}=0.0283\text{-}6.5\left[\frac{hours}{ block}\right]\cdot 2\left[\frac{block}{team\cdot week}\right]\\ \cdot 52\left[\frac{week}{year}\right]\cdot 5[team]=95.65\left[ \frac{effe.\ hours}{year}\right]\end{split} \tag{8}\]  
Assuming an occupation rate of 77.86% and time blocks of 6.5 h, these 95.65 effective hours are equivalent to an increment \(\Delta_{Tb}\) of 18.89 blocks per year:  
\[\Delta_{Tb}=\frac{95.65\left[\frac{effe.\ h}{year}\right]}{0.7786\left[\frac{ effe.\ h}{h}\right]\cdot 6.5\left[\frac{h}{block}\right]}=18.89\left[\frac{blocks}{ year}\right] \tag{9}\]  
Considering that the cost to open an OR during 6.5 h is 5850 euros, the use of CIPLAN in the surgical scheduling could suppose a saving of about 110,000 euros per year. The savings could be really greater extending the use of CIPLAN to other Departments.
## 4 Results
### HEV Control Level Decisions: Example  
In Figures 6 and 7, an example of the HEV control trajectories identified by DP during the WLTP is shown considering two different vehicle layouts embedding a two-speed and a four-speed gearbox, respectively. These two differ not only in the transmission, but also in the powertrains: the sizes of the ICE and EM are 68 kW and 14 kW, respectively, in the two-speed HEV and 99 kW and 29 kW, respectively, in the four-speed vehicle.  
More precisely, concerning the figures, on the top half of each, the SOC and the fuel rate are reported, providing information on the battery and ICE usage, whereas on the bottom, the speed profile of the drive cycle together with the engaged gear is shown. Focusing on the activation time of the ICE, it is roughly 29% of the overall drive mission when referring to the four-speed layout, against 27% with the two-speed HEV. Despite the longer running time of the former, it operates the ICE at higher efficiency points, obtaining an average fuel rate of 0.34 g/s in comparison with 0.38 g/s computed with the two-speed HEV (i.e., 10% higher in the average value throughout the WLTP). Thus,  
Figure 6: Example of the DP control results for a two-speed HEV driving the worldwide harmonised light vehicles test procedure (WLTP), equipped with a 68 kW ICE and a 14 kW EM.  
it allows a lower amount of battery power to be recharged by the ICE to satisfy the SOC constraints. More details about the operative points in the brake specific fuel consumption map of the two ICEs mentioned above will be provided in Section 4.2. It might also be noted that the instantaneous fuel rate of the two-speed HEV reaches lower peaks, yet this is expected because the two ICEs are diverse in size. Moreover, a visible difference in SOC trend is found, especially focusing on the behavior of the two HEVs from 1200 s until the cycle ending. In this particular time window, the two-speed vehicle prefers to charge the battery at first, then discharge it to approximately 40%, and then it uses the ICE to bring the battery up to the initial SOC. Instead, with the four-speed vehicle, the battery is kept in a thinner SOC window and there is neither substantial usage nor recharge in electric energy.  
A laptop computer equipped with Intel Core i7-9850H (2.6 GHz) and 16 GB of RAM was used to virtually simulate the vehicles. Regarding the two-speed HEV, it needed about 3 min for running the DP-based algorithm, whereas the four-speed took more than 16 min.
## 3 Depth Safety Path Planning Method
### Path Planning Algorithms Considering Depth Hazard
The distance cost from the \(N_{i}\) node to the starting point S. \(h(N_{i})\) is the heuristic distance cost from the \(N_{i}\) node to the goal node, \(\sum\limits_{i=1}^{N_{i}}r(N_{i})\) is the water depth hazard cost sum from the starting node to the current node, and \(\alpha\) is the constant greater than 0 used to control the weight of the risk cost in the total cost, thus controlling the impact of the water depth hazard on the final path. The pseudo code chart of the water depth risk level A*(WDRLA*) algorithm considering depth hazard is shown in Figure 11.  
\[r(N_{i})=\frac{S_{\min}}{D(N_{i})},(0<r(N_{i})<1), \tag{12}\]0 A* path planning algorithm schematic diagram. (**a**) Schematic of adjacent node expansion; (**b**) The shortest path of A* algorithm. The green star represents the starting point, and the yellow diamond represents the goal point. The shortest path is the black line between the starting point and the end point. The color grids are expanded nodes. The deeper the blue is, the closer the starting point is, and the deeper the red is, the closer the end point is. A set of white grids represents a feasible space, and black grids are obstacles.
## 1 Introduction  
LTE is an abbreviation for Long-Term Evolution, which is a fourth generation mobile communication technology. LTE is designed for high-speed transmission, reduced cost per bit, low transmission delay and applicability to existing frequency bands. It is currently implemented worldwide.  
LTE technology is not only used as the base technology of smartphones, which store, send and receive the sensitive personal information of individuals [1; 2], but as the base technology of mobile communication technology to be used in the future cyber world, such as M2M, IOT [3; 4], _etc._, and as the research base technology of the fifth generation mobile communication technology to be used in the future cyber world [5].  
However in the current LTE technology, vulnerability of the identification parameter values of UE (user equipment) exists, being exposed as clear text in the initial attach process [6; 7; 8; 9]. The vulnerability has existed since the initial release of the LTE standards and is still present in Release 12. In the LTE technical documentation, according to the \"Technical Specification Group Services and System Aspects; Rationale and track of security decisions in Long-Term Evolved (LTE) RAN/3 GPP System Architecture Evolution (SAE) (Release 9)\", there is a vulnerability during the initial attach process in the access to the LTE network, in which the UE identifying parameters are transmitted in plain text. Problems, such as tracing and privacy infringement, could occur.  
This paper proposes a plan for safely transmitting identification parameters by classifying the initial attach processes into two tasks, initial attach with the International Mobile Subscriber Identity (IMSI) and initial attach with the Global Unique Temporary Identifier (GUTI).  
The proposed paper consists of six sections. Section 2 analyzes the structure of the LTE, initial attach process, security process and threats. Section 3 proposes a security protocol by classifying the initial attach process into multiple cases in order to safely transmit identification parameters. Section 4 carries out a security analysis of the proposed protocol, and Section 5 compares and evaluates the performances between the proposed process with a security protocol and the existing process. Section 6 concludes the discussion.  
Please see Table 1 for definitions and terms used in this paper.
## 2 Related Work
[16] provided a composite energy-aware strategy for data forwarding in IoT, considering the need to extend IoT technologies and the NP-hardness of power management in diverse and dispersed IoT networks. By integrating the support vector machine (SVM), a popular machine learning (ML) approach, and the meta-heuristic heat transfer optimizer (HTOA) approach, this paper tried to implement an optimal method for routing and transmitting data. The results demonstrate that merging ML and HTOA has produced the best possible energy-conscious routing for the IoT.However, it should be taken into account that HTOA, like another optimizer, could become trapped in local optima. In 2022, [PERSON] et al. [4] briefly discussed low-energy adaptive clustering hierarchy (LEACH)-based and bioinspired protocols, their advantages and disadvantages, their underlying presuppositions, and the selection criteria for the CH to comprehend routing protocols with various structures, innovative tactics, and improved efficiency in the WSN environment. The scalability, durability, and packet-delivery rates of different protocols are contrasted and considered as performance aspects. Additionally, the exploration of developing cryptographic techniques for verified encryption in WSNs that support privacy and network safety is an option. In 2023, ref. [20] the WSN is responsible for gathering and arranging sensed data before sending it to the base station (BS). As the battery power of sensor nodes is limited, it is crucial to employ effective techniques for data collection and transmission to ensure the extended operation of the sensor network. In this study, the researchers utilized the particle swarm optimization (PSO) method to establish the cluster in WSN. Additionally, they proposed an energy-efficient routing protocol (E-FEERP) based on fuzzy logic. The E-FEERP algorithm considers various factors such as battery energy, the average distance between sensor nodes (SN) and the BS, node density, and communication quality to transmit data from the cluster head (CH) to the BS optimally. Other works study the problem of clustering and energy efficiency from a game theory perspective [21; 22]. Indeed, game theory is routinely used as a framework for the modeling and analysis of performance and security networked systems [23; 24; 25; 26; 27; 28; 29]. The work by [PERSON] et al. [30] addresses the issue of energy efficiency by proposing a protocol, called GREET,based on non-coalitional game theory: the protocol is not power-aware, but it improves over LEACH in terms of network lifetime. The current research increases throughput rates and decreases the packet loss rates, energy usage, defective routes, and packet delay of the WSN as compared to the existing energy-efficient protocol (Table 1), as indicated in the prior research.
## 5 Applications of Thermal Images to Future Space Missions
### Detection of Meteoroids and Moons  
As was mentioned in 5.1, the fact that an object is seen as the only bright spot in thermal image enables detection of the meteoroids or the orbiting moons which encounter nearby the spacecraft. Attempts to detect the meteoroids around the Sun-Earth Lagrange points were conducted in April 2016, using the optical imagers the ONC and TIR. The ONC has higher spatial resolution by one order of magnitude and the lower detection limit of the size of meteoroids by about one order of magnitude,  
Figure 5: Close-up thermal image taken by TIR on 25 October 2018, during the descent operation of TD1-R3 Campaign. The width of this image is 20 m across. Most of boulders are the same temperature but a cold boulder (in a red circle) is seen as a “Cold Spot”. The temperature scale is in Kelvin. (Image: hyb2_tir_20181025_022416_12.fit, ©JAXA).  
but the ONC images have numerous bright spots other than the target. The TIR can detect meteoroids confidently if they exist within the distance for its detectable size; within 1.5 x 10\({}^{4}\) km for 1 km-sized body, 1.5 \(\times\) 10\({}^{2}\) km for 10 m-sized body, or 1.5 km for 10-cm sized body, provided that the meteoroids have similar geometric albedo and emissivity to Ryugu [23]. For the case of S-type asteroids which have several times higher geometric albedo, the detection range is a little bit smaller, but there is no substantial difference in the detection range. A similar situation has occurred during the approach and proximity phases for the survey of orbiting moons around the asteroid for the both purposes of asteroid science and spacecraft safety. It is difficult to detect natural moons with optical images because of saturation by the presence of the very bright main large asteroid and its smearing and blooming effect. Thermal images have an advantage of searching moons, since there is no need to change the exposure time to avoid saturation, smearing, or blooming effect. Unfortunately, TIR has never found any meteoroids at the Sun-Earth Lagrange points nor orbiting moons around Ryugu.