esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
Advancements in science and technology have led to new research on telemedicine. Data about a user that are measured by a wearable device can enable doctors more accurately to determine the physical condition of the user. This study proposes a mechanism for preserving medical information that considers the entire process of data from data generation through transmission by wearable devices to mobile devices and then to a medical center server. When the data that are generated by a wearable device on the user are transmitted to a hospital to be viewed by medical staff, this process is protected in a manner that complies with HIPAA privacy and security regulations. This goal of this work is to improve the security and privacy of users' physiological data in transmission, and to provide a secure data transmission channel for telemedicine, supporting further developments in the medical industry. As part of future work, the scalability issue for healthcare big data must be taken care of seriously in order to make blockchain popular and to integrate artificial intelligence features in the process of analyzing collected health data and healthcare diagnosis decisions.  
Conceptualization, T.-F.L., I.-P.C. and T.-S.K.; methodology, T.-F.L. and T.-S.K.; writing--original draft preparation, T.-F.L., I.-P.C. and T.-S.K.; Writing--review and editing, T.-F.L. and I.-P.C. All authors have read and agreed to the published version of the manuscript.  
Supported in part by grants from the Ministry of Science and Technology of the Republic of China (Grant No. MOST110-2221E320-005-MY2).  
Not applicable.  
[PERSON] is appreciated for his editorial.  
The authors declare no conflict of interest.
## 5 Experimental Results and Analysis
### Results Analysis of the Lbop-Arima Model  
The prediction accuracy of the ARIMA model is affected by the length of the training and testing data set. To investigate this influence, here we set the prediction length as 10 s and run the model prediction using different data set length and the results are shown in Figure 12, in which the length changes from 10 s to 70 s. It can be seen that changes of the residual and relative errors behave in almost the same trend. Here, we select 20 s as the sampling length of the data set. The length of prediction is set as 2, 4, 6, 8, 10, 12, 14 s here, and the residual and relative errors for different prediction lengths are shown in Figure 13. It can be seen from Figure 13 that both the residual and relative errors vary for different prediction lengths. The optimal prediction length can be taken as 6 s.  
Now we have determined that the prediction model is developed as ARIMA (2,2,2). The optimal sampling length is 20 s, and the optimal prediction length is 6 s. In order to improve the prediction accuracy, we have proposed to improve the ARIMA model by combining the Savitzky-Golay filtering and the sliding window method to improve the data stationarity. To verify this improvement, the bandwidth occupancy sequence data of a randomly selected link \(l_{j}\) in the LoRaWAN downlink network is selected, and the prediction comparison results have been illustrated in Figure 14, in which the red solid line represents the original sequence data, the blue dash line represents the predictions with existing ARIMA model, and the black dot line represents predictions with the proposed model. The root mean square error (RMSE) is adopted to evaluate the prediction accuracy. It is seen that average value of RMSE\({}_{2}\) for the existing ARIMA prediction is 9.4363 and for the proposed method, the average value of RMSE\({}_{1}\) is 1.1883. The prediction error is 87% reduced.  
Figure 12: Prediction error vs. the data set length.  
Figure 13: The residual and relative errors for different prediction lengths.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
###### Abstract  
There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.  
visible light communication; indoor positioning system; localization scheme; system design; performance comparison +
Footnote †: journal: Sensors
## 10 Research Challenges and Future Directions
### Human Displacement and Evolving Roles  
The integration of AI annotation agents raises important questions about the future employment landscape for human annotators, many of whom are part of global workforce networks. In the near term, human roles are shifting from direct labeling to responsibilities such as exception handling, quality control, and more complex judgment tasks. This transition requires annotators to acquire advanced skills for effective collaboration with AI systems, including the interpretation of agent outputs, the ability to provide actionable feedback to improve agent performance, and the discernment needed to decide when to accept or override AI-generated annotations. A related challenge is automation bias, where annotators may uncritically accept AI suggestions, leading to the endorsement of inaccurate outputs.  
Specialized training programs for HITL workers and well-designed interfaces that promote critical oversight are essential. One interface strategy involves occasionally concealing AI suggestions to ensure that human assessment capabilities remain active and independent. From a workforce perspective, although some entry-level annotation jobs are becoming less common, new specialized roles are emerging. These include positions such as \"annotation manager\" or \"AI auditor\", where human experts supervise AI-based annotation systems. A sustainable approach assigns repetitive annotation tasks to AI agents while human contributors focus on complex decisions and strategic enhancements to the pipeline, including guideline development and data quality analysis. Ensuring responsible deployment will depend on close monitoring of this transition and investment in fair and effective re-skilling initiatives.
An orthogonal fine-meshed volume was generated to allow precision handling of the dynamic phase changes of matter, due to the high temperature gradients, in the center of the irradiated target [7]. However, this locally adaptive mesh approach cannot be used for downscaling, since the size of the elements does not remain constant. Therefore, in order to keep the interpolation level of the finite elements constant and at the same time obtain the optimal spatial geometry description, a uniform mesh was here generated. The dimensions of the computational domain were reduced to include the laser-heated area and the corresponding section of the roughness periodic profile. Initially, the rectangular computational domain was modeled with a flat up-front surface, where roughness was neglected, and was further used as a reference. The small and steady size of the elements used to discretize the computational domain allowed for sequential downscaling of the initial model. Following the Gaussian filtered geometry of an experimentally measured average roughness profile, a linear approximation on the depth of roughness geometry was performed and the original flat surface was gradually downscaled to model the spatial roughness profile with increasing accuracy. As a result, a step-like linear profile was developed according to the experimentally determined characteristics of the geometry of the rough periodic surface pattern of our target, while no changes on the boundary and the loading conditions were imposed compared to the original model. Furthermore, experiments based on white light interferometry highlight the value of the proposed methodology. The proposed FEM multiscale model will be easily upgraded in the future using local adaptivity on the roughness level that was achieved here and expanded to describe computational domains of higher dimensions. This is of great impact for the field of material characterization, when, for example, the influence of the surface roughness and micro/nano-defects on the generation and propagation of surface acoustic waves under pulsed laser irradiation is required to be investigated with high accuracy or for industrial applications such as laser polishing, engraving, and cutting [18; 19], and the manufacturing and response of the solid targets in inertial confinement fusion [20], where the influence of the surface micro-defects on the laser-matter interaction processes should be well determined.
## 2 Methodology
### Feature Selection Methods Based on Machine Learning  
#### 2.1.1 Feature Selection with Support Vector Machine  
Support vector machine based on recursive feature elimination (SVMRFE) is a multi-variable wrapper feature selection algorithm, and it can keep relevant features and remove relatively insignificant feature variables in order to achieve higher classification performance. SVMRFE was first proposed for gene selection [33], and it has been widely applied to MRI data research, text analysis and biological information processing [34; 35; 36].  
For SVMRFE, the scoring function for each feature \(i\) is defined as:  
\[Score(i)=|\omega_{i}|\quad\text{or}\quad Score(i)=\omega_{i}^{2} \tag{1}\]  
where \(\omega_{i}\) is the weight for feature \(i\) as obtained from the SVM training. Thus, features that contribute the most to discriminating the two classes are represented by \(|\omega|\) with the highest values, and features with small scores are generally considered as noise, redundant or irrelevant to the problem. Therefore, eliminating features with smaller scores does not bring about great changes of the optimization problem, which is the essence of the algorithm [37; 38]. The SVMRFE algorithm is briefly described as below.  
**Input:** Dataset \(D\)  
**Process:**  
1. Initialization  
Let the current feature subset \(Current_{-}D\) contain all features, and the optimal feature subset \(Best_{-}D=\emptyset\);  
2. Training the classifier  
Train a SVM on the training set with the \(Current_{-}D\), and evaluate the classification accuracy on the test set;  
3. Updating \(Current_{-}D\)  
Calculate the importance of each feature in \(Current_{-}D\) by the scoring function (1), and eliminate features with the smallest score;  
4. Updating \(Best_{-}D\)  
If the accuracy rate of \(Current_{-}D\) is greater than that of \(Best_{-}D\), then let \(Best_{-}D=Current_{-}D\);  
5. Repeat Steps 2-4 until the stop condition is satisfied.  
**Output:** The optimal feature subset \(Best_{-}D\)  
The stopping criterion can be a desired dimensionality, a pre-specified number of iterations or a generalization of the performances, etc.
## 3 Results and Discussion
### Comparison of Different Training Results  
A comparative visualization of the training trajectories for the proposed CNN architecture with and without transfer learning implementation is presented in Figure 9.  
The proposed CNN was trained and tested on the asphalt macro-texture RGB-D dataset, and the change in the loss value with respect to the number of iterations is shown in Figure 10. The results indicate that the loss values of this model generally show three stages of rapid decrease, slow decrease, and levelling off, and by comparing Figure 10, it is found that good convergence results are achieved for both training strategies with and without migration learning. The baseline CNN architecture achieves convergence at epoch 20, whereas its transfer learning-enhanced counterpart requires 30 epochs to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**REL**} & \multicolumn{3}{c}{**Accuracies**} \\ \cline{2-11}  & \multirow{2}{*}{**V2**} & \multirow{2}{*}{**RGB**} & \multirow{2}{*}{**V2**} & \multirow{2}{*}{**RGB**} & \multicolumn{3}{c}{\(\delta\) = 1} & \multicolumn{3}{c}{\(\delta\) = 2} & \multicolumn{3}{c}{\(\delta\) = 3} \\ \cline{5-11}  & & & & & **V2** & & **RGB** & **V2** & **RGB** & **V2** & **RGB** \\ \hline [PERSON] [25] & 0.824 & 1.012 & 0.230 & 0.421 & 0.614 & 0.492 & 0.883 & 0.832 & 0.971 & 0.828 \\ [PERSON] [26] & 0.745 & 0.623 & 0.220 & 0.465 & 0.605 & 0.548 & 0.890 & 0.810 & 0.970 & 0.914 \\ [PERSON] [27] & 0.555 & 0.715 & 0.127 & 0.221 & 0.841 & 0.726 & 0.966 & 0.892 & 0.991 & 0.957 \\ [PERSON] [28] & 0.592 & 0.668 & 0.139 & 0.275 & 0.826 & 0.879 & 0.946 & 0.927 & 0.987 & 0.933 \\ [PERSON] & 0.630 & 0.491 & 0.135 & 0.102 & 0.801 & 0.931 & 0.951 & 0.979 & 0.986 & 0.990 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results compared with other models on different data sets.  
stabilize. Through analysis, the main reasons for this phenomenon are as follows: there is a big difference in the depth distribution between the NYU Depth V2 data set and the asphalt macro-texture RGB-D data set created in this paper, and the units of the two data sets are not in the same order of magnitude. In addition, when the domain differences between the two tasks are large, the knowledge learned in transfer learning may not be applicable to the new task, which has a negative impact on the training effect [29]. Thus, the strategy without transfer learning was selected in the following study.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing  
### The Main Idea  
The main idea is that the RREQ message delivery is delayed according to the trust of the previous hop node. The goal of the trust-aware route discovery is to prefer routes consisting of more trusted nodes. We will call this method TARA (Trust-Aware Reactive Ad Hoc routing).  
In the rest of the article, we will use the AODV protocol as the reactive ad hoc routing protocol. However, the proposed adaptivity of route discovery on trust values of nodes is independent of routing protocol implementation. It should work with any reactive ad hoc routing protocol that accepts the first obtained route.  
To make the TARA method independent of the particular ad hoc routing protocol, the delaying of RREQ messages needs to be implemented as an interlayer of the ISO-OSI model. Said interlayer needs to be placed between the link and the network layer, and its job is to filter the RREQ messages coming from the link to the network layer. For each RREQ message, this interlayer decides how long the message will be delayed before sending it to the network layer. The delay value is derived from the estimated _PDR_ value of the neighbor node that sent or resent the RREQ message. In this article, we consider three functions to calculate these delays, see Section 3.5.  
The method description follows. Section 3.2 outlines the assumptions for the distribution of trust values that are necessary for computation of the delay. Section 3.3 introduces the time model for delay and metrics related to time. Section 3.4 describes the method performance metrics. Finally, Section 3.5 provides algorithms for the delay functions.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
###### Abstract
The combined application of optical and electrical methods in apple core browning detection aims to fully exploit the complementary information they provide. Optical methods, especially hyperspectral image information, offer a detailed description of the surface color, appearance features, and tissue structure of apples [31]. In contrast, electrical methods focus on the dielectric properties and internal moisture content of fruits [32; 33]. Tissue changes and moisture distribution variations resulting from apple core browning can be identified through the measurement of electrical characteristics. Conducting research on non-destructive methods that integrate optical and electrical approaches is expected to further improve the accuracy and sensitivity of apple core browning detection. This research provides a theoretical basis and technical guidance for the development of non-destructive detection devices for apple core browning. The main contributions of our article can be summarized as follows:  
1. We constructed a dataset for apple core browning, which includes two-view data containing both hyperspectral and dielectric information. The dataset has a total of 265 apple samples and serves as a valuable resource for future research endeavors. 2. A non-destructive apple core browning detection method via combining hyperspectral imaging and dielectric parameters was proposed for accurate and efficient discrimination of apple core browning disease. Figure 1: Healthy and core browning apples. (**a**) Healthy apple. (**b**) Core browning apple. Experimental results demonstrated that the proposed non-destructive detection method outperforms traditional discriminant methods based on single detection data in terms of performance.
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
## 3 Proposal for PLC Control Logic Embedding and Anomaly Detection
### Control Logic Embedding
At this stage, the embedding dimension is set, and each opcode and operand are transformed into a vector representation. 4. If an opcode has multiple operands, the average of the operand embeddings is calculated and used as the final embedding representation. 5. The embedded opcode and operand are combined while preserving the original IL code structure. Figure 3: Control logic embedding example. The proposed embedding method effectively maintains the relationships between opcode and operands within IL code, making it highly beneficial for anomaly detection and code analysis. This approach offers two key advantages over conventional embedding techniques. First, the proposed method clearly distinguishes the semantic differences between operators and operands, allowing the model to better preserve contextual information in the code. Traditional single vector embedding approaches use a single embedding model without differentiating between opcodes and operands. As a result, if the same text appears in both an opcode and an operand, it is interpreted with the same meaning. However, the proposed embedding method separates opcodes and operands and applies different embedding models to each. This ensures that even if identical text appears, its semantic meaning is correctly distinguished. Second, the proposed method preserves the original IL code structure. By sequentially combining the embedded opcode and operand, the method maintains the structural integrity of the IL code. This approach enables the model to effectively capture both the context and structure of IL code.
## 5 Results and Discussion
### Quantitative Results  
We use two Cartesian sampling patterns (random and equispaced) and four acceleration factors (4\(\times\), 8\(\times\), 10\(\times\), 12\(\times\)), and measure the performance based on three evaluation metrics (PSNR, SSIM, RLNE). In Table 1, we show the results of three different acceleration factors using random Cartesian sampling on the fastMRI datasets. The results show that our method performs best in all cases. Specifically, in the T1 WI, the corresponding PSNR values of the three acceleration factors are 42.969 dB, 39.896 dB, and 35.571 dB, respectively; while in the T2 WI, the corresponding PSNR values are 38.815 dB, 35.235 dB, and 32.607 dB, respectively. This proves that our proposed method can effectively utilize the complementary information between different modalities, thereby achieving effective performance improvement in both modalities. In addition, when using the U-Net [51] for experiments, we simply input images of two different modalities into two independent branches for processing. However, the performance of this method is not ideal. Similarly, DuDoRNet [14] combines images of different modalities by alternating learning in the k-space domain and the image domain, and performing multi-cascade operations. Although the results are better than those of a single fusion method, they are still slightly inferior to those of our proposed method. Compared with DuDoRNet, our method improves PSNR by 3.052 dB, 5.613 dB, and 4.185 dB, SSIM by 0.011, 0.056, and 0.049, and RLNE by 0.011, 0.033, and 0.038, respectively, under different acceleration factors (4\(\times\), 8\(\times\), and 12\(\times\)) in the T1 WI. In the T2 WI, the PSNR gains are 2.856 dB, 4.691 dB, and 4.706 dB, the SSIM increases by 0.013, 0.041, and 0.064, and the RLNE decreases by 0.017, 0.048, and 0.064, respectively. In addition, as the acceleration factor increases, the performance of DuDoRNet shows a gradually weakening trend. In contrast, our method exhibits better robustness. Similarly, we also adopted a 1D equispaced undersampling pattern and conducted experiments on three acceleration factors on the brain dataset. From the comparison between Tables 1 and 2, when using the one-dimensional Cartesian sampling pattern, the performance difference between the two is not significant. At a 12\(\times\) acceleration factor, our method outperforms DuDoCAF [27] in multi-modal reconstruction performance, with PSNR improving from 34.481 dB to 36.277 dB and from 31.828 dB to 32.302 dB. At the same time, SSIM improves from 0.911 to 0.913 and from 0.917 to 0.922, respectively, while RLNE decreases from 0.067 to 0.055 and from 0.097 to 0.092, corresponding to the T1 WI and the T2 WI, respectively. DuDoCAF [27] is slightly lower than our method in PSNR and RLNE but is fairly close to ours in SSIM. We speculate that this may be due to the advantage of its dual-domain alternating learning architecture.