esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Since the assessment of bone age is seen in many clinical applications amongst children, in order to make it easier to assess bone age, an automatic bone age assessment system  
Figure 4: Bone age assessment results for each stage on Stage\({}_{cs}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE on Females** & **MAE on Males** & **Input** \\ \hline [PERSON] et.al [8] & 1.028 & 0.831 & Entire image \\ [PERSON] et.al [10] & 0.767 & 0.747 & Entire image \\ [PERSON] et.al [17] & 0.998 & 1.057 & ROI-based \\ [PERSON] et.al [18] & 0.644 & 0.689 & ROI-based \\ Proposed method & 0.532 & 0.560 & ROI-based \\ (MSCS-CNN) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: MAE comparison with other bone age assessment methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE on Females** & **MAE on Males** \\ \hline Config. 1 & 0.497 & 0.51 \\ Config. 2 & 0.475 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 8: MAE under perfect maturity stage classification.  
is necessary. A CNN can meet these requirements. In order to make a CNN model more accurate for the assessment of bone age, this article proposes a multi-step framework. First, for the stage classifier, through a two-step method of rough stage classification and fine stage classification, stage identification can be as accurate as possible. In addition, due to the ambiguity between the stages, we also propose a cross-stage class strategy, combining cross-stage classes and cross-step strategies. Among females and males, the accuracy is 1.0 and 0.96, respectively, which makes the establishment of the subsequent bone age regression model more accurate. In order to prove that cross-stage classification has its advantages, we have also proven its effectiveness. This article is based on children in Taiwan. Generally speaking, children in different regions will have different developmental conditions due to factors such as race and diet. A simple solution is to build models for specific races. However, there are several shortcomings to this approach. First, it is time-consuming and labor-intensive, due to having to establish one's own model and database. In addition, as the years go by, diets and living environments will change. This will cause the model to fail over time; therefore, building a universal bone age prediction model will be an important focus of future work.  
Software and Writing--original draft, C.-T.P.; Methodology, Supervision and Writing--review & editing, Y.-K.C. and S.-S.-Y.; Resources, Validation, Y.-S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Cheng Hsin General Hospital, Taipei City 112, Taiwan (CHGH109- (N)15).  
This research was approved by the Institutional Review Board of the Cheng Hsin General Hospital, Taipei City 112 (IRB No. CHGH-IRB (735)108A-51).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Contributing this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 2 Review of Related Works and Technological Trends
### Trends in Truck Platooning Technology  
Research on truck platooning began with Europe's \"CHAUFFEUR\" project in 1996, which was part of the EU initiative called T-TAP. Subsequently, various European projects have promoted the development of truck platooning technology, such as Safe Road Trains for the Environment (SARTRE) [6], which aimed to operate truck platooning while preserving existing road infrastructure and traffic flow, and Enabling Safe Multi-brand Platooning for Europe (ENSEMBLE) [2], which aimed to develop a cloud-based embedded platooning system [10].  
In the United States, attempts have been made to introduce platooning to increase highway capacity while minimizing infrastructure construction. The government-led Partners for Advanced Transit and Highways (PATH) project demonstrated the technical feasibility of platooning by maintaining a 3 m gap for two trucks and a 4 m gap for three trucks [6]. The Federal Highway Administration conducted the Exploratory Advanced Research project, where three trucks maintained a time gap of 0.6-1.5 s while traveling at 90 km/h using V2V communication based on Wireless Access in Vehicular Environment technology. In this experiment, the platooning vehicles were equipped with various perception sensors, such as cameras and radars, and were designed to automatically adjust the intervehicle distances in response to merging vehicles [3].  
Japan conducted the Energy ITS project from 2008 to 2012 to reduce fuel consumption by suppressing unnecessary acceleration and deceleration during truck platooning and alleviating traffic congestion. In 2010, a real-road experiment successfully demonstrated truck platooning at a speed of 80 km/h with a 4 m intervehicle distance [11].  
From 2018 to 2021, a national project led by the Ministry of Land, Infrastructure, and Transport in Korea developed truck platooning technology, safety systems, and operational services. This project confirmed that truck platooning contributed to improving fuel efficiency, road capacity, logistics competitiveness, and safety. In the final demonstration conducted in 2021, three trucks traveled an 80 km stretch at speeds of up to 90 km/h while maintaining a time gap of 0.5 s.
# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
## 3 Our Proposed Method
### The Location Selection Algorithm of 5G Edge Nodes Based on Hotspots and Benefit Constraint
When \(S_{i}=0\), it means that the candidate hotspot area \(i\) does not place a base station. Then, according to decision variables \(S_{i}\), \(S_{i}*U^{\prime}(i)\) and \(S_{i}*B(i)\) can be calculated. Finally, by introducing weighting factors: \(\alpha\) and \(\beta\), the problem of selecting the deployment location of 5G base stations can be formalized into Formula (3). \[\underset{s_{i},1\leq i\leq M}{\text{Maximize}}\left(\alpha{\sum}_{i=1}^{M} \frac{S_{i}*CF_{i}^{T}}{U_{all}}+\beta{\sum}_{i=1}^{M}\frac{S_{i}*d_{i}*CF_{i}^ {U}}{B_{all}}\right) \tag{3}\]  
subject to:  
\[\text{C1}:\sum_{i=1}^{M}S_{i}\ \leq N\]  
\[\text{C2}:\ \ \ \alpha+\beta=1,\ \ \alpha,\beta\in(0,1)\]  
\[\text{C3}:\ \ S_{i}\in\{0,1\},\ 1\leq i\leq M\]  
In the above formula, the objective function is to maximize the investment return of Telcos by deploying base stations in hotspots with high demand of computing and data storage. Constraint C1: ensure that the number of base stations deployed is less than or equal to the number that can be built by Telcos. Constraint C2: \(\alpha\) is the weight of the high load utilization rate, \(\beta\) is the weight of the core network bandwidth reduction rate, the value of both ranges from 0 to 1, and the sum of \(\alpha\) and \(\beta\) is 1. If the Tecom operator expects the base station to be in a state of efficient computation for a long time, \(\alpha\) should be set to a relatively large value. If the operator wants the base station to be able to handle more data traffic and reduce the load on the core network, the \(\beta\) should be set to a relatively large value. Constraint C3: it specifies the value of the decision variable. As we can see from Formula (3), this problem is actually a 0-1 integer linear programming problem, which is also an NP-hard problem. Although it is possible to find the optimal solution by exhaustion, this method is obviously not desirable when \(M\) is large enough. Therefore, we design a heuristic greedy algorithm to solve this formula. This heuristic greedy algorithm is shown in Algorithm 3.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Labeled Dataset  
While existing datasets are available for fake image detection, there is a significant limitation of labeled datasets in deep learning methods. The datasets we have today are not always up-to-date with the latest real-world events, and they often focus on only a limited number of situations [51; 129; 131]. For instance, datasets that contain fake news data from platforms like Twitter, Weibo, or News websites may not cover the full scope of rapidly evolving multimedia information. This limitation hampers the progress of research in this field. Authors believe that creating new and continuously updated datasets is essential to address this issue. Some recent datasets, like Fakeddit and GSR-Net [127; 136], from Reddit, show promise, as they come from a web aggregator that captures a wide range of content. However, the pace of advancement in image tampering techniques requires constant adaptation. Furthermore, deep learning models for fake image detection need regular retraining to stay effective. If they are not updated with the latest data, they can become too specialized and make incorrect predictions, a phenomenon known as overfitting. This presents a significant challenge in the research field.
## 4 Experimental Setup  
### Experiments  
The aim of the experiments was to evaluate the effectiveness of the proposed solution with respect to anomaly detection and threat identification, as well as to assess the potential for transferring the trained models between different networks. For this reason, two datasets were selected, which were similar in terms of the recorded cyber threats (e.g., both datasets consisted of similar attacks, such as network scan or DDoS). However, both datasets differed in terms of network size, the volume of recorded traffic, etc.
# Data Chunks Placement Optimization for Hybrid Storage Systems
## 4 Heuristics for Finding an Optimal Data Chunk Placement
### Data Chunk Placement Design Algorithm  
To provide full-scale search, the availability probability of the storages was checked first. If the availability is \(\text{p}(\theta^{\prime})>\text{p}^{\prime}(\theta^{\prime})\), then set the initial cost of the transfer \(\text{W}_{\text{i}}=0\).  
Start to search for the chunk to rebuild the object. If the chunk \(\Lambda_{\text{m}}\) was transferred, then the overall transfer cost changes. In this case, the state matrix update for the given transfer storage updates from \"0\" to \"1\".  
Check the conditions (8), (9), and (10); if the conditions are fulfilled, then update the overall minimum cost for the data transfer, \(\text{W}_{\text{i}}=\text{W}_{\text{i}}{}^{\prime}\). If, in the next iteration, the new overall cost \(\text{W}_{\text{i}}{}^{\prime\prime}<\text{W}_{\text{i}}\), then \(\text{W}_{\text{i}}=\text{W}_{\text{i}}{}^{\prime\prime}\); otherwise, the old one keeps continuing to search. There are three conditions for the termination of the algorithm:  
* Availability probability of the data transfer storages decreases to less than 0.999. This case is violating the first reliability condition of the system.
* Probability of the successfully rebuilding object became less than the probability of the unsuccessfully rebuild \(\text{P}(\Upsilon^{\prime})<\text{p}^{\prime}\).
* There is no more transfer storage to provide search.
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Extended Kalman Filter (EKF)  
Extended Kalman Filter (EKF) is an improvement of a conventional Kalman filter that has been widely used to solve non-linear estimation system problems. EKF works in higher-order terms, which include the Taylor expansions. It can approximate high non-linearity using first-order information. Many proposals have been given to improve the EKF. In addition, various applications have been used by EKF based on geolocation measurements. For example, Multilateration (MLAT) was used to overcome the problems caused by the drawbacks of Automatic Dependent Surveillance-Broadcast (ADS-B), which is considered one of the most important technologies in maneuvering and air traffic control [35]. The authors studied the problem of position precision and enhancing the robustness of the monitoring systems. Therefore, they proposed EKF based on hybrid TDOA/AOA measurements for the ADS-B/MLAT positioning system. Numerous experiments in various scenarios, such as TDOA individual or hybrid AOA/TDOA, were implemented. From the simulation results, one can observe the improvement of the geolocation accuracy and enhancement of the robustness of the surveillance systems [109].  
Figure 20 illustrates the geometry distribution of static sensors and moving targets in the simulation. In the simulation scenario, four (04) stationary sensors distributed around a route were considered to receive signals from two spatially separated stations. Using time-delay and direction of arrival between transmitters (emitters) and receivers (sensors), three measurements of TDOA were obtained, as well as AOA, which was implemented as input parameters into EKF [109].  
Figure 19: Kalman Filter prediction and correction algorithm.
## 2 FMCW LiDAR
### FMCW LiDAR Principle of Operation
#### 2.2.2 Sawtooth-Shaped FMCW  
Sawtooth FMCW technology facilitates the determination of both distance of a target through the noncoherent accumulation of signals over multiple periods. This technique generates a two-dimensional dataset, subsequently analyzed via a two-dimensional FFT to obtain detailed information on the target's distance, as shown in Figure 4. The figure illustrates the variation of the laser optical frequency over time after modulation. \(B\) represents the modulation range of the laser, denoted as \([f_{0},f_{1}]\). The two sawtooth waves correspond to the optical frequencies of the two light beams, where the solid and dashed lines represent fixed frequency values at a given moment. Due to a time delay \(\tau\) between the two light beams, a frequency difference is observed upon reception. The employed formula is structured as follows [8]:  
\[R=\frac{OPD}{2n}=\frac{T\cdot c}{2 nB}\cdot f_{b}. \tag{7}\]  
The formula includes various variables: \(f_{b}\) represents the absolute value of the frequency difference between the signal light and the local oscillator light; it contains crucial information regarding the distance to the target. Optical path difference (\(OPD\)) stands for the optical path difference between two coherent beams. \(B\) signifies the laser's frequency  
Figure 4: Principle of Sawtooth waveform FMCW ranging method. Among them, the blue line represents the waveform of the frequency of the transmitted signal changing with time.  
Figure 3: Principle of triangular waveform FMCW ranging method.  
modulation range (the bandwidth). \(T\) represents the modulation period. \(c\) refers to the speed of light. \(n\) indicates the refractive index. \(R\) is the distance to the detection target.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 4 Proposed Method
### Route Reply Phase-2  
Next, we show an additional case, in which intermediate nodes on the route generate RREPs instead of \(\mathsf{D}\).  
1. When the intermediate nodes \(\mathsf{N}_{r}\) receive an RREQ from \(\mathsf{S}\) and generate an RREP instead of \(\mathsf{D}\), \(\mathsf{N}_{r}\) adds the RREP generated by itself to the RREP that it has received from \(\mathsf{D}\) in the past (defined below as \((\mathsf{D}^{\prime}\mathsf{s}\,\mathrm{RREP})_{\mathrm{ Sig}_{\mathsf{D}}}\)). This can be derived by \[\mathsf{N}_{r}\rightarrow\mathsf{N}_{r-1}:(\mathrm{RREP},(\mathsf{D}^{\prime} \mathsf{s}\,\mathrm{RREP})_{\mathrm{ Sig}_{\mathsf{D}}})_{\mathrm{ Sig}_{\mathsf{N}_{r}}}\] (7) where \[\mathrm{RREP}=(\mathrm{RREP}_{\mathrm{AODV}},\,\mathrm{LT},\,\mathrm{LI}_{r}).\] (8) \(\mathrm{LI}_{r}\) is the location of \(\mathsf{N}_{r}\).
2. When \(\mathsf{N}_{r-1}\) receives the RREP defined in Equation (7), this node verifies the signatures of the responders \(\mathsf{N}_{r}\) and \(\mathsf{D}\) in the packet. Subsequently, the RREP is forwarded along the route to \(\mathsf{S}\). At this time, \(\mathsf{N}_{r-1}\) adds its own signature and address to the received packet. The contents of the RREP at this time are provided by \[\mathsf{N}_{r-1}\rightarrow\mathsf{N}_{r-2}\;\;:((\mathrm{RREP},(\mathsf{D} ^{\prime}\mathsf{s}\,\mathrm{RREP})_{\mathrm{ Sig}_{\mathsf{D}}})_{\mathrm{ Sig}_{\mathsf{N}_{r}}},\,\mathrm{ID}_{\mathsf{N}_{r-1}})_{\mathrm{ Sig}_{\mathsf{N}_{r-1}}}\] (9)
3. \(\mathsf{N}_{r-2}\) verifies the signatures of the respond \(\mathsf{N}_{r}\), \(\mathsf{D}\), and the latest intermediate node \(\mathsf{N}_{r-1}\) in the packet. Subsequently, \(\mathsf{N}_{r-2}\) discards the ID and signature of \(\mathsf{N}_{r-1}\) from the received RREP and adds its own ID and signature to the packet in their place. The packet is forwarded, and this forwarding process continues until the RREP reaches \(\mathsf{S}\) and a route to \(\mathsf{D}\) is established.  
The relaying procedure is stopped when the distance between a node and \(\mathrm{LI}_{r}\) exceeds a threshold, which is similar to the RREQ phase.
# Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
## 3 Active Disturbance Rejection Controller  
The Active Disturbance Rejection Controller (ADRC) consists of three core components: the Tracking Differentiator (TD), the Extended State Observer (ESO), and Nonlinear State Error Feedback (NLSEF). The primary function of the TD is to effectively extract the original signal and its differential signal from discontinuous or noisy signals in practical engineering applications. This enables fast and overshoot-free tracking of a given reference signal, thereby enhancing the control system's performance. The ESO is the heart of the ADRC, responsible for real-time observation of the system's output state variables and total disturbances. It compensates for detected disturbances to ensure the system maintains stability and efficient control performance even under external interference. The NLSEF receives the error signals and their derivatives during transient states and computes the integral of the error signal. By performing a nonlinear combination of these three signals, NLSEF generates the final control law, achieving optimal system control. The structure of the controller is presented in Figure 2.
## 4 An IGA Design Based on Tabu Search Applied to HFSP
### Proposed IGA Based on Tabu Search
#### 4.3.3 Genetic Manipulation  
In the IGA of this paper, the crossover operator, mutation operator and selection operator constitute the genetic operation of the genetic algorithm, which simulates the process of biological evolution and gradually eliminates the solutions with low fitness function values and increases the solutions with high fitness function values. In this way, individuals with high fitness function values will evolve after iterating for \(N\) generations. This section designs the genetic manipulation of the improved genetic algorithm.  
1. Crossover Operator  
The crossover operator is an important operation of the genetic algorithm, which promotes the evolution of a population by generating a new generation of chromosomes through crossover operation on the population. In this paper, the algorithm uses the partial mapping crossover method (PMX). Through this crossover strategy, no duplicate genes will appear in a chromosome, as shown in Figure 5.  
Randomly select a pair of paternal chromosomes with two crossover regions as shown in Figure 5a, where the two chromosomes are selected at the same position, and exchange the genes of the two sets of paternal chromosomes in the crossover region to form the offspring chromosomes as shown in Figure 5b.  
Figure 5: Chromosome crossover. (**a**) is a pair of paternal chromosomes; (**b**) is a pair of crossover-generated proto-child chromosomes; (**c**) is a pair of offspring chromosomes without gene conflict.  
At this time, there are identical gene sequences in the formed offspring chromosomes, and the identical genes need to be transformed according to the mapping relationship by conflict detection. Finally, a new pair of offspring chromosomes without gene conflict is formed, as shown in Figure 5c.  
2. Mutation Operator  
Following the production of offspring chromosomes by the genetic algorithm, anisotropic transformations are performed on certain genes in the chromosomes to form new chromosomes in order to improve the local search ability of the algorithm and maintain population diversity. In this paper, the exchange mutation operator is used, as shown in Figure 6.  
Two gene fragments in the chromosome are randomly selected as shown in Figure 6a, and the two genes exchange their values to form a mutant chromosome as shown in Figure 6b. The mutation operation can effectively prevent the GA from falling into a local optimum solution during the optimization process.  
3. Selection Operator  
The selection operator simulates the mechanism of meritocracy in nature. Under this mechanism, chromosomes that are more adapted to the scheme model are retained to survive, and chromosomes that are poorly adapted perish. In this paper, chromosomes are selected in the algorithm by using the competition selection method, and two chromosomes are randomly selected from chromosomes based on Equation (9). The chromosome with high adaptation is selected according to the value of the adaptation function, while the contraindication table is updated.
## 3 Results
To understand the relationship between the thrust and velocity of the prototype, Figure 4c is applied. The thrust of the propeller system and velocity of the vehicle are in a direct proportional relation. The more the thrust, the faster the vehicle moves. Finally, the velocity of incoming flow is measured to be 2.722 m/s. Using Equations (5) and (6), the actual efficiency of the whole vehicle is found to be 18.55%. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Efficiency (\%)** & **Thrust (N)** & **Velocity (m/s)** & **SolidWorks Motion (m/s)** & **Error Percentage (\%)** \\ \hline
0 & 0 & 0 & 0 & 0 \\
5 & 0.000002719 & 0.0453 & 0.032 & 41.6 \\
10 & 0.000163244 & 0.111 & 0.126 & 11.9 \\
30 & 0.013622846 & 1.014 & 1.136 & 10.7 \\
50 & 0.148246405 & 3.345 & 3.151 & 6.16 \\
70 & 0.536264756 & 6.362 & 6.194 & 16.8 \\
90 & 1.54482245 & 10.798 & 10.235 & 5.5 \\
100 & 1.96201222 & 12.169 & 12.613 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated Results with Efficiency. Figure 3: SolidWorks Motion Analysis.