esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Data Synchronization Algorithm for IoT-Oriented Low-Power Wide-Area Networks
## 4 Adaptive File Synchronization
### Adaptive Chunking Algorithm
Specifically, given \(p_{m}\) and \(p_{m-1}\) so that \(ud_{t}<\Phi_{q}\leq(u+1)d_{t}\), with \(u=1,2,...,\left\lfloor\frac{L-m}{d_{t}}\right\rfloor\), we measure the minimum number of updated bytes \(N_{UD}\) as:  
\[N_{UD}=\left\lceil\frac{\Phi_{t}}{d_{t}}-1\right\rceil \tag{2}\]  
Therefore, in this case, the adaptation rule for chunk size decrease is given by:  
\[d_{t+1,w}=d_{t}-\mu N_{UD} \tag{3}\]Equations (1)-(3) return a partial metric of the new chunk size that refers only to the considered file section \(w\). Depending on the update distributions along the file \(F_{DIoT}\), the proposed algorithm provides \(W\) partial measures corresponding to the \(W\) identified file portions, so that the final resulting averaged chunk size \(d_{t+1}\), to be used in the next \((t+1)\)-th synchronization procedure, is obtained by:  
\[d_{t+1}=\frac{1}{W}\sum_{w=1}^{W}d_{t+1,w} \tag{4}\]  
As introduced before, the step size \(\mu\) defines the adaptation speed of the algorithm. An opportune choice of \(\mu\) should be driven by the _temporal_ features of the file \(F_{DIoT}\). In fact, for applications where the update distribution along \(F_{DIoT}\) changes quite slowly in time (that is, considering the synchronization events at time \(t\) and \(t+1\), the updated bytes in \(F_{DIoT,t}\) and \(F_{DIoT,t+1}\) are localized in the same portion of the file) it would be convenient to select a high-valued \(\mu\), making the new chunk size the optimal one as quickly as possible. A similar behavior can be found in medical device networks where the output files to be exchanged are usually formed by a header, reporting barely variable control information, and a payload containing the actual measurement. On the other hand, if there is no correlation between the consecutive update distribution in \(F_{DIoT,t}\) and \(F_{DIoT,t+1}\), working with a relatively small \(\mu\) is preferable since by doing so the chunk dimension varies slowly, therefore avoiding large deviations from the optimal value. This happens especially when the rough data contained within the file are larger than the data header.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 6 Conclusions  
This study aimed to develop a machine learning-based ground subsidence risk prediction model and visualize the risk map. The data pertaining to the ground subsidence history of the target area, underground facility attribute information, permeability coefficient, stratigraphic thickness, and height were used to develop the machine learning model. Owing to the inclusion of data on permeability coefficient, stratigraphic thickness, and height, the ground subsidence risk prediction model developed herein exhibited better performance and reliability compared to a previous model that utilized only the underground facility attribute information.  
The developed model was used to create a ground subsidence risk map for the target area. The areas with a high density of past subsidence occurrences were predicted as high-risk areas. The cavities within the ground can be prioritized for investigation based on the areas identified as risk zones in the ground subsidence risk map presented in this study. However, although the performance and reliability of the model are better than those of previous studies, the model developed in this study does not offer the very high levels of reliability required in computer engineering. This limitation can be attributed to the fact that the ground subsidence over a wide area depends on multiple factors. Therefore, to enhance the reliability of the ground subsidence risk map, predictive models that use various data included in the underground space must be developed. The machine learning-based ground subsidence risk prediction model development protocol presented in this study is expected to help improve the performance and reliability of models with various additional influencing factors.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); developed the models and carried out the model simulations, S.L.; writing--original draft preparation, S.L.; writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** Research for this paper was carried out under the Korea Institute of Civil Engineering and Building Technology (KICT) Research Program (project no. 20240306-001 Establishment of a Disaster and Hazard Database for 3D Risk Assessment of Underground Living Facilities) funded by the Ministry of Science and ICT.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## Appendix A An Example of Gridding the Gravity Observation Data
### Gridding of Gravity Observation Data before and after 135 m Water Imounding Stage  
Figure 14a shows the distribution of gravity stations from October 2002 to October 2003. We chose riverside Area A and mountain Area B near the Three Gorges Dam for credibility testing. Figure 14b,c show the distribution of stations and their observed values after removing the stations in Areas A and B, respectively. The observation values are represented by different colors.  
In the Matlab environment, we used the observation data of Figure 14 and the DACE toolbox to perform Kriging interpolation, and the result is shown in Figure 15. Obviously, there is no noticeable change in the overall gravity field after removing the interpolation results of the stations in Area A and when all stations are used, suggesting that the overall error of the Kriging interpolation results in the area along the river being relatively small. After the stations in Area B were removed, the contours in the northern part of the study area changed significantly, although the overall shape was still maintained. It also shows that the interpolation accuracy of the mountain Area B might be low, and the interpolation results depend on the distribution of stations to a relatively large extent.  
We then used the station observation values in Figure 14 to compare with the Kriging interpolation results after subtracting Areas A and B, as shown in Figure 15a. It can be seen that the observation value \(\mathsf{v}_{\text{ob}}\) at the five stations in Area A was relatively consistent with the
## 5 Experiments
### Baseline Methods  
* FC-LSTM: The Long Short-Term Memory Network is suitable for dealing with long-term dependencies in time series [60].
* DCRNN: The Diffusion Convolutional Recurrent Neural Network uses bidirectional random walks on the graph to capture spatial correlations and an encoder-decoder architecture with predetermined sampling to capture temporal correlations [39].
* STGCN: The Spatial-Temporal Graph Convolutional Network employs a temporal gated convolutional module to capture temporal correlations and a graph neural network to capture spatial correlations [38].
* ASTGCN(r): The ASTGCN constructs recent, daily, and weekly modules. A spatial-temporal attention module is used to capture spatial-temporal dynamic features, a graph convolution module is used to capture spatial features, and a standard convolution is used to capture temporal features. The comparison algorithm has recently adopted the recent modules in the model [32].
* STSGCN: The Spatial-Temporal Synchronous Graph Convolutional Networks construct a spatial-temporal synchronization graph to capture spatial-temporal relationships simultaneously and obtain prediction results by stacking multiple modules to aggregate long-range spatial-temporal relationships and heterogeneity [57].
* STFGNN: The Spatial-Temporal Fusion Graph Neural Networks constructs a temporal graph based on time series similarity that can learn long-range dependencies, and it employs a gated dilated convolution module whose large dilation rate can capture long-range dependencies [59].  
The spatial-temporal feature extraction methods used by different baseline models are shown in Table 2.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 4 Dynamic Spectrum Access Model
### Dynamic Spectrum Access Strategy
In other words, large bandwidth ratios for PU and CU calls may result in drastic spectrum inefficiency. The system can be protected by restricting the maximum number (= \(r\)) of CU calls that can be in service at the same time. That is, the excessive delay of CU calls caused by the suddenly arriving PU calls can be protected by setting some restrictions on the CU calls. Therefore, it is natural to choose the cutoff values \(r\) that make the available channels for CU calls in licensed spectrum bands a multiple of the spectrum channel of a PU call. The cutoff value \(r\) needs to be adjusted in order to find good tradeoff between more spectrum availability and excessive delay experience for CU calls.
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 1 Introduction
This is performed by implementing two steps:  
First, we formulate an ILP optimization problem for BBU placement for C-RAN over a 5G optical aggregation network. We consider a multi-objective optimization problem, which minimizes (i) the number of BBU pools and (ii) the number of wavelengths used. The ILP results in the optimum location of the BBU pools' traffic routing. In addition, it pairs each RRH with a corresponding BBU pool. Second, after knowing the ILP outputs (i.e., BBU pool locations, traffic routing and RRHs/BBU pools pairing), we propose a heuristic algorithm to reduce the number of users undergoing inter-CoMP services. This is performed as follows: For the RRHs engaged in inter-CoMP service, we reconfigure their serving BBU pools and reroute them to different BBU pools such that the new serving BBU pool enables the RRH to be engaged in intra-CoMP services. The heuristic algorithm changes the serving BBU of the RRH dynamically to decrease the number of inter-CoMPs. The new serving BBU pool is chosen based on three criteria: (i) BBU computational capacity; (ii) the number of newly activated links; (iii) path delay. Decreasing the number of inter-CoMP users will save the X2 interface signalling overhead as well as provide better coordination between RRHs engaged in CoMP. The algorithm performance is a measure in terms of the number of users transferred from inter-CoMP to intra-CoMP, as well as the number of activated BBU pools and the number of used wavelengths. The results show that at low latency (50 usec), up to 97% of inter-CoMP users migrated to be intra-CoMP users. On the other hand, at higher latencies (80 usec), up to 78% of inter-CoMP users migrated to be intra-CoMP users. On the other hand, optical link utilization increases up to 94% compared to the optimum solution. The research is divided into six sections. Related studies are discussed in Section 2. The proposed ILP BBU placement problem is discussed in Section 3. The proposed heuristic placement algorithm for CoMP is tackled in Section 4. The results are shown in Section 5. A conclusion of the work is provided in Section 6.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Rights Delegation
#### 6.3.2 Integrity  
Evaluating integrity protection, it was found that all protocols are secure against \(\mathcal{A}_{weak}\), as this adversary lacks corruption capabilities and is, therefore, unable to tamper with the data.  
\(\mathcal{A}_{middle}\) faces significant challenges in tampering with data integrity in most of the considered protocols, as these protocols are designed with strong encryption and authentication mechanisms. Many of them are based on blockchain technology, smart contracts, and secure encryption schemes to protect data from unauthorised modification [65; 66; 67; 60; 67]. However, some protocols may still be vulnerable to \(\mathcal{A}_{middle}\)'s attacks if internal accomplices are able to tamper with access tokens and thereby influence the delegation process [63; 64; 68]. The same holds true for \(\mathcal{A}_{strong}\). Although this adversary has more advanced capabilities than \(\mathcal{A}_{middle}\), they cannot compromise the integrity of systems based on blockchain, as any modification must pass a network-wide validation process. To manipulate the entire network, \(\mathcal{A}_{strong}\) would need to control more than 50% of it, which is considered an unlikely scenario.
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 3 System Overview and Identity Resolution  
The authors present the overview of the proposed framework in this section. Illustrated in Figure 1, our framework outlines a factory production line comprising multiple devices operational within the system, with each device being assigned a unique ID via an identification resolution technique. To track the real-time status of a device, these devices are equipped with various sensors that transduce pertinent parameters. These sensor-derived signals are transmitted in real time to an edge server embedded in the system, which performs data interpretation based on the deployed state awareness model, yielding the health status of each device as an output. Moreover, leveraging sequence prediction models, the edge server seeks to anticipate parameter trends in the upcoming period using historical parameter trends from previous periods before transmitting the results to the state awareness model for determining the health status of a device. Terminal monitoring is provided for the managers to view results, thereby enhancing timeliness and effectiveness for the maintenance decision-making process. Simultaneously, the edge server employs a sequence prediction model that utilizes historical parameter trends from previous periods to forecast future parameter trends for the next period. Thus, the predictions obtained are then relayed to the state awareness model in order to ascertain the health status of each device during the upcoming period. By integrating these predictive and preventive maintenance strategies, real-time maintenance can be performed proactively, thereby preventing potential losses and accidents within the plant. To implement this approach, the state awareness model and sequence prediction model are developed and trained offline in a cloud server prior to being deployed to their respective counterpart edge servers.
## 3 Results
### Results from Questionnaires  
The on-board operators' responses to the questionnaires on work context, acceptance, trust, system transparency, and suitability of HMI elements for their tasks are reported below. First, applicable subscales from the Work Context category of the Work Design Questionnaire (WDQ) are stated in order to assess the on-board operators' workspaces and HMIs. Table 5 provides a summary of the operators' self-report data by living lab.  
Second, acceptance of the HMI was measured with [PERSON] et al.'s [12] Acceptance Scale. As shown in Table 6, the scale splits into two subscales, Usefulness, delineating usability, and Satisfaction, referring to user experience.  
Third, two scales from [30] were utilized in order to capture perceived system transparency and trust in the workspace and its HMI. The results are stated in Table 7.  
Fourth, the suitability of a number of HMI components that differed by living lab was measured on a Likert scale. The results are stated in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Laboratory** & \multicolumn{2}{c}{**HEAT**} & \multicolumn{2}{c}{**RealLabHH**} & \multicolumn{2}{c}{**TaBuLa**} \\ \hline Scale & \(M\) & \(SD\) & \(M\) & \(SD\) & \(M\) & \(SD\) \\ \hline Overall & 0.17 & 0.42 & 0.13 & 0.56 & 0.89 & 0.80 \\ \hline Usefulness & 0.67 & 0.52 & 0.27 & 0.54 & 1.2 & 0.75 \\ Satisfaction & \(-\)0.33 & 0.32 & 0 & 0.59 & 0.58 & 0.85 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline
**Laboratory** & \multicolumn{2}{c}{**HeAT**} & \multicolumn{2}{c}{**RealLabHH**} & \multicolumn{2}{c}{**TaBuLa**} \\ \hline Scale & \(M\) & \(SD\) & \(M\) & \(SD\) & \(M\) & \(SD\) \\ \hline System Transparency & 1.73 & 0.87 & 2.33 & 0.73 & 4.60 & 0.57 \\ Trust & 2.69 & 1.02 & 2.63 & 0.69 & 4.17 & 0.46 \\ \hline \hline \end{tabular}
\begin{tabular}{c} Adapted from [30]; low: 1 to high: 5 (center: 3). Range of values: 1: “strongly disagree” to 5: “strongly agree”. \\ \end{tabular}.  
\end{table}
Table 6: Subjective acceptance of the on-board-operation workspace measured with the Acceptance Scale.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Laboratory** & \multicolumn{2}{c}{**HEAT**} & \multicolumn{2}{c}{**RealLabHH**} & \multicolumn{2}{c}{**TaBuLa**} \\ \hline Scale & \(M\) & \(SD\) & \(M\) & \(SD\) & \(M\) & \(SD\) \\ \hline Overall & 2.15 & 0.65 & 3.19 & 0.96 & 3.07 & 1.36 \\ \hline Ergonomics & 1.44 & 0.47 & 1.67 & 0.94 & 2.78 & 1.53 \\ Physical Demands & 1.78 & 0.90 & 3.44 & 1.15 & 3.11 & 1.61 \\ Equipment Use & 3.22 & 0.59 & 4.44 & 0.79 & 3.33 & 0.94 \\ \hline \hline \end{tabular}
\begin{tabular}{c} Adapted from [28]; low: 1 to high: 5 (center: 3), 1: “strongly disagree” to 5: “strongly agree”. \\ \end{tabular}.  
\end{table}
Table 5: Subjective ratings of the work context of the on-board operators’ workspaces measured with WDQ.
## 4 Results and Analysis
### Identification of Signal App Traffic
#### 4.2.5 Call Received by User A (Callee)  
When another user dialed a call to target device A, certain patterns were observed. Initially, the server sent a TCP segment of reassembled PDU to the client with packets of length 1454 bytes and a payload size of 1388 bytes. In response, the client sent 66 bytes of packets to the server. Later UDP packets were observed flowing between the sender and the receiver. It was also noticed that the IP address of the caller was also captured during this activity. The UDP packets of 64 bytes with a payload size of 22 bytes were observed between the receiver and the caller. As shown in Figures 13 and 14, the server's IP was 76.223.92.165, the IP of User A was 192.168.137.252, and the IP of User B was 192.168.10.3. The traffic patterns of incoming calls to User A is shown in Figure 13. The flow of UDP packets during a call is shown in Figure 14.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 3 The FH Dual One-Way Ranging System  
### The Concept of Modulation and Demodulation  
Figure 4a shows the design of a transmitter for an FH DOWR system. A pseudorandom sequence generator provides a frequency control sequence. The frequency selector selects a specific frequency modulation frequency based on the control sequence. The spread-spectrum signal is mixed with the FH signal using a multiplier. After filtering, the FH DOWR signal is output.  
Figure 4b shows the receiver of the FH DOWR system. At the receiving end, the filtered signal selects the frequency point at which there is signal input according to the hopping pattern. Then, the receiver acquires and tracks the signal and extrapolates it using the frequency results and orbital elements obtained from previous tracking results. In this way, we can obtain multiple frequency carrier phase measurements using only one transmission signal.
## 3 Artificial Intelligence Approach
### Supervised ML
#### 3.1.3 Partial Least Square (PLS)  
PLS represents a multivariate statistical approach intended for both dimensionality reduction and regression analysis. It is specially fitted for the analysis of spectral data, in which datasets are usually big and collinear. So far, PLS has been the most frequent application in the food industry, with the aim of predicting chemical composition among other attributes in high-dimensional datasets. Moreover, the combination of PLS-DA and a PLSR model for fish freshness classification was able to predict successfully storage time under different temperature treatments, indicating the versatility of this technique in the food industry [81]. In another similar study, the detection of fungal contamination in maize kernels using HIS was carried out by PLS-DA, showing how PLS can be adapted for food safety [113].  
The flexibility of the PLS model is also shown through various key components in several food products: the prediction of protein content in black fly soldier larvae [114], the quantification of oil and fatty acid content in Brassica seeds [102], or assessing adulteration in Attactylodes rhizoma powders [115]. Although it may lag behind in nonlinear relationships for which other methods like the Support Vector Machine Regression or deep learning can provide superior performances [116], it is a powerful approach in food quality and safety assessments because of its complexity in dealing with collinear data, in addition to reducing dimensionality.
# Research of Adaptive Extended Kalman Filter-Based SOC Estimator for Frequency Regulation ESS
## 2 Design of Operation Algorithm for FR ESS and Power Characteristics Analysis
### Operation Algorithm Design of Frequency Regulation ESS
(**a**) Normal pattern that used the ESS near annual average; (**b**) Severe pattern that used an ESS that is larger than the annual average. 60.08 Hz and a minimum frequency of 59.94 Hz, and the severe pattern showed a maximum frequency of 60.10 Hz and a minimum frequency of 59.91 Hz. The frequency characteristics of the power grid can vary from day to day, depending on the type of load and the time of use. Since the FR ESS operates according to the grid frequency input to the algorithm, the power pattern of the ESS varies for each grid frequency. In addition, since ESS complexly charges and discharges in a very short time, it is very difficult to accurately estimate the SOC of the ESS. The EKF algorithm can accurately estimate the SOC, even for the complex test pattern of the FR ESSs, and can improve the safety and reliability of ESSs.