esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 2 Key Features in the Energy Internet
### Security and Privacy
This trade-off is an ongoing challenge for energy internet systems, as in one hand, the more information that is transferred among IoT components, the more benefit they could gain; and, on the other hand, the more information end-users share with third parties, the more they could be threatened, and in this manner system may become more vulnerable. This uncomfortable feeling can be balanced by incentive offers from service providers. In IoT-based systems, a tremendous amount of heterogeneous objects is connected together. As the number of things rises, traditional methods used for cyber security and control systems become insufficient. For example, for a large IoT system in which a large geographical area and many components are included, it is more practical to implement distributed or decentralized control system than centralized control systems, for the sake of more reliable decision-making [69]. On the other hand, in distributed and decentralized system architectures with different levels of controllability, different levels of security may be applied. In this manner, advanced machine learning and data mining methods are developed with respect to of cyber security [70]. This happens in IoT framework because data is collected from different sources, which may be located in different security but the same physical level [71; 72]. In this regard, many aspects of security in energy internet arise such as privacy and trust of the end-users, authentication, access control, etc. [73]. As an example, in [74], a method is developed to give access to the end-users' position in emergency conditions. In summary, security in energy systems includes three main parts: (a) data confidentiality; (b) privacy; (c) and trust area [71]. Related parts should be defined in detail and in an understandable way for both end-users and service providers. For instance, it is not beneficial to explain encryption of data for end-users, but they should be convinced that their information is safe with the system.
# Galileo Receiver Performance Analysis with New I/NAV Improvements Live Data +
###### Abstract  
This work presents the results showing the improvement of the performance of a Galileo receiver processing the new I/NAV words 16-20 and SSP in comparison to the performance using the previous legacy I/NAV configuration. The results were obtained by replaying GNSS signals recorded in Urban and Open Sky mobile environments when at least four satellites broadcasting the new words were visible. The results show an improvement of Time to First Fix values using RedCED (word 16) and FEC2 (words 17-20) in cold start conditions and a reduction in time needed to converge using SSP when coarse time (within 3 s error) is available.  
Gallieo; E1 band; I/NAV improvements; secondary synchronization pattern; Reed-Solomon; reduced CED; Time to First Fix +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## 3 Results  
### Data Acquisition  
Because of the experimental need, the calibration data set, control points set, and the test data set are acquired. The calibration data set is used to calibrate the pose sensor, and the control points set is selected from the calibration data set for calculating the radial basis function interpolation. The test data set is used to verify the accuracy of the calibration results.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 3 Qualitative Benchmark for Analysis
### Criteria for Requirement Mapping for Dependability  
The criterion for meeting the DL1 parameter will assist in satisfying the 11 th requirement of Table 1 in Section 2. Similarly, the DL3 parameter contains significant associations toward satisfying requirements 3 and 7, as mentioned in Table 1. Likewise, the DL3 parameter also ensures that data path changes due to service replication do not lead to delays. Next, the DL4 parameter will help to achieve requirements 4, 5, and 8 as mentioned in Table 1. Here, the capability to offer seamless handover support in providing mobility between multiple RATs is mentioned in requirement 8 of Table 1. Likewise, improving localization proficiencies to complete the same in a dense and urban environment is mentioned in requirement 5 of Table 1, and lastly, correlating multi-connectivity and thus attaining the dependability is mentioned in requirement 4 of Table 1. Lastly, the criterion for providing redundant flows and connections may be met by satisfying the parameter DL5, which can fulfill requirement 4 mentioned in Table 1.
## 3 Results and Discussion
### Results  
In this section, we present and evaluate the obtained results. Although the ns-3 simulator provides some tools for data analysis, they are mainly designed to work with protocols that operate at the network layer. As this work is focused on a protocol (HWMP) that operates at the data link layer, a new tool was designed. In the following sub-sections, the obtained results are evaluated in terms of packet delivery ratio, network transit time, throughput, routing tables size, and control channel utilization. All the modifications made to the HWMP ns-3 model can be downloaded from [18].
# A Dynamic Numerical Simulation on the Grouting Timing in Retained Rib of Pillarless Mining
## 6 Conclusions  
According to multiple simulations conducted in this study, the following conclusions were drawn.  
1. A dynamic numerical method was established and adopted to simulate the surrounding rock stability of the retained roadway in FLAC3D.
2. In this study, the double-yield constitutive model can be used effectively to simulate the compaction process after the gangue falls into the goat while the strain-softening constitutive model can effectively simulate the strain-softening behavior of coal and grouted coal blocks after peaks in FLAC3D.
3. The grouting slurry diffusion range, the mining affecting stage behind the working face and the retained coal rib deformation are closely interrelated. Severe mining-induced stresses lead to the destruction of the integrity of the surrounding rock of the roadway. During this process, many cracks are produced in the surrounding rock and the cracks gradually expand. The roadway deformation increases rapidly. At the same time, the grouting diffusing range increases gradually.
4. An optimal timing in the grouting process can be identified to maximize the grouting efficiency in the retained coal rib of pillarless mining. The optimum support effect can be achieved by grouting in the sections before and after the working face where the roadway surrounding rock is affected by high mining-induced stress. A timely grouting can lead to a surrounding rock-bolting-grouting support system with active support capabilities and maintain the stability of the retained roadway.  
Conceptualization, X.Y.; methodology, Y.W. and Q.W.; software, J.X.; validation, Y.W.; formal analysis, Y.W. and Z.Z.; investigation, H.W.; resources, J.X.; data curation, Z.Z.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y. and J.X.; visualization, H.W.; supervision, X.Y.; project administration, X.Y.; and funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Science Foundation of China, grant number 51904102 and Open Foundation of Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, grant number E21733 and Scientific Research Fund of Hunan Provincial Education Department, grant number 21C0353.  
Not applicable.  
The data in this article are available from the corresponding author.  
The authors declare no conflict of interest.  
Figure 10: Statistical graph of crack development in boreholes around working face.
## 4 Experiments  
### Experimental Setup  
Our experimental setup is intended to address the challenges posed by poor lighting and low spatial resolution in order to comprehensively assess the performance of anomaly detection in low-quality surveillance videos. The trimmed UCF-Crime dataset, which includes eight anomaly identification classes, is used in two different types of experiments.  
The Trimmed UCF-Crime dataset has original video dimensions of \(320\times 240\). To simulate real-world scenarios with poor lighting and low spatial resolution, we intentionally reduced the spatial resolution by a factor of eight, resulting in low-resolution videos with dimensions of \(40\times 30\). It is important to note that this intentional downsampling is only for experimental purposes and is not a component of the proposed anomaly detection system. Moreover, we recognize that the term \"low quality\" can be broad: our research focuses on a specific aspect, low spatial resolution, to evaluate the robustness of our proposed methodology under these conditions.  
In the first experiment, the downscaled dataset is upscaled to its original spatial resolution using bicubic interpolation. This experiment allows us to assess the performance of our proposed anomaly classifier under standard upscaling conditions and serves as thebaseline for comparison. In the second experiment, we use an advanced GLEAN-based model for super resolution. This model upscales low-resolution videos, resulting in super-resolved video frames. These frames are then used for classification modeling with our proposed anomaly classifier. This experiment addresses the issue of low spatial resolution by employing advanced super-resolution techniques. The experiments are carried out in TensorFlow with the Keras 2.4.0 backend on a Windows 10 machine. Table 1 shows the detailed system setup used for the experiments. During the model's training phase, a random search is used to select specific hyperparameters. To ensure the best model performance, we use the Adam optimizer with a piecewise learning rate. If no progress is seen after minimizing the learning rate for three consecutive validation checks, the training is stopped. Table 2 summarizes the hyperparameter tuning process and offers insights into optimizing different parameters for the best results.  
This experimental setup was designed to simulate and effectively address the technical limitations associated with poor lighting and low spatial resolution in real-world surveillance scenarios. This was to ensure a thorough evaluation of TempoFuseNet, our proposed anomaly identification framework.
## 6 Results Validation
### External Validation  
We have attempted to validate the results obtained from the Sehaa system against external sources, including reports from various relevant organizations, news media, and results reported in research articles. This involved comparing the statistics reported in various sources and the statistics reported from Sehaa. Unfortunately, we found limited information related to health and diseases in Saudi Arabia in the various media. The information is either incomplete or is old. Moreover, the existing information is limited to communicable diseases only. The sources we have looked at include the official website of the Saudi Ministry of Health [80], published articles from various organizations including the Institute for Health Metrics and Evaluation (IHME) (an independent global health research center at  
Figure 11: Sehaa: numerical evaluation of first-level classifiers and feature extraction methods.  
Figure 12: Sehaa: numerical evaluation of second-level classifiers and feature extraction methods.  
the University of Washington) [82], the official website of the World Health Organization (WHO) [83], and the Centers for Diseases Control and Prevention (CDCP).  
According to the latest report published by CDCP [84], heart disease was considered among the top causes of death in Saudi Arabia in 2018. The Institute for Health Metrics and Evaluation reported that the risk of death from heart diseases increased by 36% from 2007 to 2017 [82]. These data confirm the findings of the Sehaa tool, where heart disease was detected as the second top disease in Saudi Arabia (see Section 5.3).  
[PERSON] et al. [85] reported in 1997 that more than a quarter of Saudi adults were suffering from hypertension. [PERSON] reported, based on a study considering all hospitalized patients in one of Jeddah's hospitals, that hypertension was the sixth most widely prevalent disease in 2010 [86]. The Saudi Ministry of Health reported in 2017 that there was a remarkable increase in hypertension cases among Saudi adults [87]. These findings from as early as 1997 until recently show the high prevalence of hypertension in Saudi Arabia and hence a good correlation with the Sehaa findings.  
MOH announced 1452 reported cases of Tuberculosis (TBC) in 2018 [88]; 451 of these cases were from Jeddah, 338 from Riyadh, and 21 from Taif. While this news item does not relate directly to the findings of the Sehaa tool, the low TBC numbers from Taif are in agreement with our findings for the low levels of diseases in Taif.
# Research on Data Transmission of Laser Sensors for Reading Ruler
###### Abstract  
A coding ruler is a device that marks position information in the fordigital signals, and a code reader is a device that decodes the signals on the coding ruler and converts them into digital signals. The code reader and encoder ruler are key devices in ensuring the positioning accuracy of coke oven locomotives and the safety of coke production. They are common information transmission and positioning detection devices that can provide accurate monitoring and information feedback for the position and speed of coke oven locomotives. Four encoding methods were studied, namely, binary encoding, Gray code encoding, shift continuous encoding, and hybrid encoding. The application scenarios and encoding characteristics of each encoding method are summarized in this paper. Hybrid encoding combines the advantages of two different encoding methods, absolute and incremental encoding, to achieve higher accuracy and stability. Hybrid coding has high positioning accuracy in the long-range coke oven tampering tracks, ensuring the accuracy and high efficiency of the tampering operation. A certain number of opposing laser sensors are installed inside the code reader to obtain 0/1 encoding and read the movement displacement of the code reader on the ruler. In order to effectively detect the swing of the coding ruler, a certain number of distance sensors are installed on both sides and on the same side of the code reader. Ruler swing is accurately detected by the sensors, which output and process corresponding signals. Timely adjustment and correction measures are taken on the production line according to the test results, which not only improves detection accuracy but also enhances the stability and reliability of the system.  
Therefore, in-depth research on automatic positioning technology for coke oven locomotives is not only aimed at improving production efficiency but also at ensuring production quality and preventing potential risks.  
Accurate sensor measurement and monitoring technology is crucial for ensuring product quality and improving production efficiency in modern industrial production. A small swing deviation may have a significant impact on the production process, especially in situations involving precision equipment such as coding rulers. Therefore, an efficient and reliable method is required to monitor whether the coding ruler is swinging or offset [4].
# Short-Term Load Forecasting Based on Spiking Neural P Systems
## 3 Experiments
### Experimental Results
#### 3.3.2 Case B  
In this case study, we used the load and temperature historical data from 2009 to 2013 in ISO [26] as the experimental data set. The data from 2009 to 2012 were used to cultivate the prediction model, and the data used in the test set are from 2013.  
Figure 5 shows the prediction curves based on the LF-NSNP model tested between the 5000 th and 5400 th hours. The upper figure is the predicted absolute error curve, and the lower figure is the original load curve and the forecast load curve. From Figure 5, we can see that the predicted load curve of the proposed LF-NSNP model can well coincide with the actual load curve, and the exploited LF-NSNP model has a very low absolute error that stably remains between 0 and 4. This is due to the fact that the exploited LF-NSNP model is capable of capturing the temporal correlation of short-term load characteristics better.  
In this case study, the exploited LF-NSNP model was compared with ten baseline models, including TA, WA, OLS, LAD, PW, CLS, IRMSE, EWAFS, and ML-poly. Table 2 gives the MAPE metric value comparison between the exploited LF-NSNP model and ten baseline models for case study B. The MAPE of the baseline models can be retrieved from Ref. [27]. It can be clearly seen from Table 2 that the MAPE measurement value under the developed LF-NSNP model is far ahead of the other ten prediction baseline models. Quantitatively, the exploited LF-NSNP model has a very low MAPE metric value. The performance comparisons reveal the strength of our exploited LF-NSNP model for short-term load forecasting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Models** & **Metrics** & **CT** & **ME** & **NH** & **RI** & **VT** & **NEMASS** & **SEMASS** & **WCMASS** & **SYS** \\ \hline \multirow{3}{*}{CRM} & RMSE & 171.82 & 49.06 & 59.74 & 40.60 & 34.81 & 133.21 & 94.54 & 91.85 & 582.09 \\  & MAE & 127.25 & 37.44 & 44.22 & 29.95 & 25.79 & 99.01 & 68.99 & 69.06 & 433.44 \\  & MAPE & 3.78 & 2.95 & 3.36 & 3.28 & 4.21 & 3.55 & 4.17 & 3.68 & 3.16 \\ \hline \multirow{3}{*}{LF-NSNP} & RMSE & 75.95 & 26.90 & 27.07 & 19.21 & 14.43 & 48.39 & 40.87 & 36.03 & 251.59 \\  & MAE & 54.77 & 18.84 & 19.88 & 14.10 & 10.39 & 35.33 & 31.414 & 27.06 & 172.68 \\ \cline{1-1}  & MAPE & 1.56 & 1.45 & 1.51 & 1.52 & 1.65 & 1.22 & 1.89 & 1.40 & 1.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison between the exploited LF-NSNP model and CRM model for case study A.  
Figure 4: The total load prediction results and AE curves of the proposed LF-NSNP model for 2016.  
As mentioned earlier, load forecasting plays an extremely important role in power system planning and operation. Accurate prediction of future loads in different time horizons will be able to considerably enhance the stable and efficient management and coordinated dispatching of power grids. Due to its advantages in structure and performance, the proposed LF-NSNP model must provide a potential choice for the power industry sector.
## 4 Discussion  
Since the Nuss procedure was announced by Dr. [PERSON] in 1997, many Nuss procedures have been carried out and various related studies have been conducted. However, the Nuss bars have been formed by naked eye and experience in the operation room. This study verified the effectiveness of the CAD-based patient-specific Nuss bar design technology for the optimal Nuss procedure.  
The CAD-based patient-specific Nuss bar design technology was verified by measuring the Haller index before and after the Nuss procedure. In particular, an increase of the minimum AP distance after the procedure shows that the depressed sternum that pressured the organs inside the chest has been successfully corrected by the patient-specific Nuss bar designed with CAD technology and sufficient internal space has been attained. This method is expected to improve the accuracy and reliability of surgery because the optimal insertion point and correction result of the Nuss bar can be predicted before the actual Nuss procedure.  
Furthermore, this study demonstrates the possibility and utility of fabricating the patient-specific Nuss bar through a CNC bending machine using the CAM-based manufacturing technology, and it shows that patient-specific Nuss bars with a dimension error range within \(\pm\)5% can be fabricated. Using the CAM data for correcting the springback phenomenon for each material and shape will improve productivity because the same products with a certain performance can be manufactured accurately within a short time. However, new CAM data must be constructed if the product shape is changed, or if the material thickness, bending degree, and components are changed even if the product has the same shape. This characteristic will act as a disadvantage when manufacturing diverse products.
# Liquid Crystal Tunable Dielectric Metamaterial Absorber in the Terahertz Range
## 3 Conclusions  
In summary, we introduced an active THz absorber composed of a silicon pillar array integrated with LC. Through spatially reorienting the LC directors with bias, its absorption peak degenerated into two small peaks, and the absorption at the resonance frequency half reduced. The proposed design adds a new candidate beyond traditional broadband and frequency tuning absorbers. The switchable absorption will benefit various applications such as energy harvesting and spatial light modulations. Also, the polarization sensitivity of the proposed device may have potential in polarization detectors. This work may supply a practical approach for active THz metadevice fabrication and may further inspire various advanced THz apparatuses in sensing, switching, imaging, and filtering.  
S.Z. conceived the idea of tunable metamaterial absorber and performed the numerical simulations; Z.S. and R.K. analyzed the simulations and modified the article; [PERSON] and [PERSON] supervised the whole work.  
This research was supported by the National Key Research and Development Program of China (2017 YFA0303700), the National Natural Science Foundation of China (NSFC) (Nos. 61575093, 61490714, and 61435008), the Distinguished Young Scholars Fund of Jiangsu Province (BK20180004), the Fundamental Research Funds for the Central Universities, and Jiangsu Donghai Silicon Industry Science and Technology Innovation Center.  
The authors declare no conflict of interest.  
Figure 4: (**a**) Simulated absorption spectra along with the LC orientation changes from 0\({}^{\circ}\) to 90\({}^{\circ}\). (**b**) The absorption at 0.79 THz when the LC orientation changed from 0\({}^{\circ}\) to 90\({}^{\circ}\).
## 2 Proposed VO System
### Solution Implementation  
This section presents the flow of the algorithm, covering the flow analysis process to track feature movement across frames, the estimation of the movement using least squares, and the sub-pixel accuracy method.  
The system continuously collects a sequence of images that can be represented as \(l_{0,k}=\left\{l_{0},...,l_{k}\right\}\), where \(l_{k}\) describes the image taken at time \(k\). Once a new frame is taken, \(T_{k+1}\), an L-K algorithm is employed to extract the features of the image and match them to those of the previous frame in the sequence, \(l_{k}\). This process results in a field of movements that describe how each key point moves across frames, which will be used to estimate the global motion of the robot in the next step.