esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### Taxonomy of Energy Consumption Solutions
#### 3.7.2 Scheduling Algorithms
The proposed scheme included a fast CoreH and an ultra-low-power CoreL processor. This technique assigns the tasks between these two processors and performs multi-tasks at the same time. However, the problem with the multi-core processor system is that requires a large memory to hold the packets during processing. Additionally, overheating due to the use of two processors can cause the damage to the device [145]. Surveys such as that conducted by [PERSON] et al. [146] have introduced a good survey paper on some specific category of duty-cycling mechanism in WSNs. The paper also summarized important directions and challenges that duty cycling will face with new emerging WSNs. Another study [147] revealed a new forwarding method for WSNs based on a simple splitting procedure able to enhance the lifetime of the network. The paper investigates a trade-off between energy efficiency and reliability of the proposed forwarding scheme when duty-cycling techniques are considered too. This paper organizes the most important proposals into a taxonomy and provides insights into their strengths and weaknesses in relation to important characteristics of applications, more's hardware and network deployments. The authors in [148] proposes a new scheduling algorithm for IEEE 802.15.4e MAC protocol is called time synchronized channel hopping (TSCH). This approach fits for multihop WSNs based on the highway addressable remote transducer. The proposed scheme promotes wireless sensor networks by improving low latency and duty cycle and thus utmost power efficiency. With the density of nodes increases, scheduling different types of data packets such as low or high priority data at CH nodes is essential for decreasing energy depletion, capacity and end-to-end delay. For example, if the queue is not prioritized over others, then the packets drop might happen and retransmission is required. This problem can be serious for border nodes which access many devices and use several hops to achieve the required target. This means that these nodes consume more energy than other nodes in the network located close to the destination. Therefore, scheduling at CH nodes which gives priority to the data that comes from further nodes is crucial for energy-saving.
## 3 Numerical Simulation and Results
### Finite Element Model
#### 3.1.3 Mesh Convergence Analysis  
In the cutting simulation, the element size is considered very influential [35]. Compared with the cutting length and cutting width, the geometry of the cutting depth direction is the smallest. So, it is determined to be the reference element size of 0.4 mm (not exceeding the tool's edge radius value of 0.43 mm). The cutting width and length direction sizes are determined according to the solid element aspect ratio of 1:2:2. Therefore, four groups of different element sizes are set as follows: 0.4 \(\times\) 0.8 \(\times\) 0.8 mm, 0.2 \(\times\) 0.4 \(\times\) 0.4 mm, 0.15 \(\times\) 0.3 \(\times\) 0.3 mm, and 0.2 \(\times\) 0.2 \(\times\) 0.2 mm.  
The force-displacement curves for different element sizes are shown in Figure 5. The cutting force of group 1 is significantly higher than those of other groups. In contrast, groups 2 and 3 are close to each other, indicating that the element size has converged, and further refinement will not significantly improve the calculation accuracy. The comparison between group 2 and group 4 suggests that elements have also converged on the aspect ratio. At last, the element size of 0.2 \(\times\) 0.4 \(\times\) 0.4 mm is adopted to balance the computational accuracy and efficiency. The cutting area element size in the simulation model is set to 0.2 \(\times\) 0.4 \(\times\) 0.4 mm, and the remaining non-cutting area is 1 \(\times\) 2 \(\times\) 2 mm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(A\) & \(B\) & \(C\) & \(n\) & \(m\) & \(D_{1}\) & \(D_{2}\) & \(D_{3}\) & \(D_{4}\) & \(D_{5}\) \\ \hline
448 & 782 & 0.0247 & 0.562 & 0.988 & 1.36 & 0 & 0 & 0.0252 & 1.8019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: [PERSON]–[PERSON] material parameters for S355 ([32; 33]).  
Figure 5: Force–displacement curves for different element sizes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & **S355** & **HSS** \\ \hline \multicolumn{2}{c}{Specific heat capacity (\(J/Kg\cdot K\))} & 469 & 220 \\ \multicolumn{2}{c}{Thermal conductivity (\(W/m\cdot K\))} & 61.1 & 41 \\ \multicolumn{2}{c}{Melting temperature (\(K\))} & 1773 & - \\ \multicolumn{2}{c}{Room temperature (\(K\))} & 293 & 293 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material thermal properties [34].
## 4 Results and Comparison
### RUL Estimation for Test Instance Bearings  
The spectrogram and its corresponding PC feature sequences for the learning instance Bearing1-2 are shown in Figure 8a,b, respectively. In Figure 9, we show the first five PCs of the learning instance Bearing1-2. They each account for data variability of 49.78%, 37.45%, 9.24%, 1.71%, 0.98%, respectively. As shown in Figure 9, we can observe that the 1 st PC mainly reflects the variability of the vibration signal at two frequency ranges 0-3 kHz and 10-12 kHz, the 2 nd PC at two frequency ranges 0-3 kHz and 3-5 kHz, and so on.  
Figure 8: The spectrogram (**a**) and PC feature sequences (**b**) of learning instance Bearing1-2.  
Recall that the first five PC sequences of Bearing1-1 have been illustrated in Figure 4 in the previous Section 3.3. For comparison, Table 2 summarizes the PCs for data variability with respect to Bearing1-1 and Bearing1-2.  
Applying the proposed IBL approach to the bearing datasets, we compare the five PC feature sequences between each pair of a test instance and the augmented feature sequences of the learning instance. The best similarity matching sequence pair can be identified from the test instance vs. each learning instance. It should be noted that we only present the best matching sequence pair and the corresponding RUL estimate results for each test instance here.  
As a result, the 2 nd PC feature sequences were chosen for test instances Bearing1-5, 1-6, and 1-7 paired with the learning instance Bearing1-2. The 1 st PC feature sequences are used for Bearing1-4 paired with the learning instance Bearing1-2. Figure 10 shows the five PC feature sequences of the test instances Bearings 1-4, 1-5, 1-6, and 1-7, which are generated by projecting their spectrograms on the PCs of learning instance Bearing1-2.
## 5 Experiments and Validation
### Effectiveness Validation of the Proposed DI Dataset Extraction Scheme  
To validate the effectiveness of the DI dataset for improving prediction accuracy, the interpolation and position prediction of two test paths (Figures 12 and 13) are conducted by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Data Type** & **Number** & **Data Type** \\ \hline
1 & Reference joint velocity & 10 & Simulated joint position \\
2 & Simulated joint velocity & 11 & Simulated motor position \\
3 & Reference EE velocity & 12 & Reference normal acceleration of \\
4 & Simulated EE velocity & 13 & Simulated motor acceleration \\
5 & Simulated motor velocity & 14 & Simulated joint acceleration \\
6 & Simulated motor torque & 15 & Reference joint acceleration \\
7 & Reference EE position & 16 & Simulated tangential acceleration of EE \\
8 & Simulated EE position & 17 & Reference tangential acceleration of EE \\
9 & Reference joint position & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data categories in \(\Phi_{DI}\).  
the proposed HDPP where RECs are trained by the DI dataset and ETE dataset, respectively. The training configuration parameters are completely consistent. The prediction results are shown in Figures 14 and 15, and the detailed comparisons are illustrated in Table 3. The maximum error, MAE, and RMSE of the prediction are calculated. As can be seen, the prediction results of joint 2 are generally superior to those of joint 1 due to the simple load characteristics. By comparison, the maximum prediction errors and MAEs of the joint and contour errors are reduced by more than 50% when using the DI dataset. Meanwhile, the decreased RMSEs of the joint and contour errors indicate the effectiveness of the DI dataset in improving prediction stability. Consequently, the DI dataset involves more dynamic features of the predicted system with identical experiments and can improve the prediction accuracy, adaptability, and stability
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 2 Methodology
### Ship Collision Avoidance Behavior Recognition
#### 2.2.2 Curvature-Based Ship Collision Avoidance Behavior Recognition
In summary, the Curvature-Based Ship Collision Avoidance Behavior Recognition algorithm relies on the sliding window algorithm, which calculates the curvature of the ship's AIS trajectory points through a fixed window, and it uses the curvature threshold \(C_{L}\) and the turning factor \(\Delta k\) in the window to judge whether there is turning behavior in the trajectory and to identify the direction of turning; at the same time, the variable window records the recovering factor R to judge whether there is course recovery behavior in ship's subsequent trajectory, and then it determines whether the turning point of the ship is a collision avoidance point. The Curvature-Based Ship Collision Avoidance Behavior Recognition algorithm is shown in Algorithm 1. Input: _traj_: ship's trajectory; _w_: sliding window; \(C_{L}\): curvature limit;  
Initial: \(\Delta k=0\); \(R=0\); \(W=\{P(t_{1}),P(t_{2}),P(t_{3})\}\); consecutive_turns = [ ]; turning_point  
= null; recovery_point = null; avoidance_point = null;  
1. _for_ i = 2 to n - 1  
2. Update the points in \(w\)  
3. Calculate curvature at point \(P(t_{i})\) using points \(\{P(t_{i-1})\,,\;P(t_{i}),\;P(t_{i+1})\}\)  
4. _if_\(|C(t_{i})|\geq\ C_{L}\)  
5. \(\Delta k\) += 1  
6. _if_\(C(t_{i})>\ 0\ R_{i}\)= 1 _else_\(R_{i}=-1\)  
7. consecutive_turns.append (P(\(t_{i}\)))  
8. _if_\(\Delta k<\ 6\) _continue_  
9. _else_ turning_point = consecutive_turns[0]  
10. _else_  
11. _if_\(0\ <\ \Delta k<6\) consecutive_turns = [ ]  
12. _else_\(0\ <\ \Delta k<6\) turning_point = consecutive_turns[0]  
13. _end_if_  
14. _end_if_  
15. end_i = i  
16. consecutive_turns = [ ]  
17. _for_] = end_i to n - 1  
18. Update the points in w  
19. Calculate curvature at point \(P(t_{j})\) using points \(\{P(t_{j-1})\,,\;P(t_{j}),\;P(t_{j+1})\}\)  
Figure 7: Variable sliding window to determine the ship’s recovery behavior.
## 2 Experiments
### Characterization Techniques
#### 2.3.2 Fourier-Transform Infrared Spectroscopy (FTIR)  
The surface functional groups and structure were investigated using FTIR (Thermo Nicolet AVATAR FTIR 360 instrument). The FTIR spectra of the raw materials and biocomposites were recorded from a wave number 400-4000 cm\({}^{-1}\), at a spectral resolution of 4 cm\({}^{-1}\). Before the test, a 2-3 mg sample and 200-300 mg KBr were ground together while using an agate crucible and dried sufficiently; and then made into a tablet on a tablet press. The spectrum of dry KBr served as the background and it was subtracted from the experimental spectrum.
# A Prototype Mechatronic Device for Upper Limb Rehabilitation and Analysis of Its Functionality
## 3 Concept and Design Solution for the Rehabilitation Device
### Prototype Solution of the Device
AR-L and AR-R define the manipulation zones for the left and right arms, respectively, aimed at transferring objects into the left and right containers. AR-C illustrates the central manipulation area designated for retrieving objects from the storage container. This provides additional training for finger extension and whole-arm coordination. To reduce habituation to a fixed button location, alternative control button positions were incorporated into the housing design (see Figure 1). The developed device concept has been granted a patent by the Patent Office of the Republic of Poland under the title \"Device for Upper Limb Rehabilitation\" [84]. Based on numerical simulations of the working space, a CAD model of the prototype was created (Figure 5). A critical design criterion was achieving a compact overall size to allow for easy transport and convenient placement on a standard table or desk. Depending on the individual's capabilities, the shape and dimensions of the exercise objects can be modified. The prototype was designed to accommodate a variety of object shapes, without restricting the form factor. Both lightweight and heavier objects may be used. For the purposes of prototyping and the initial validation, lightweight plastic components were employed.
## 5 Simulation Experiments  
This paper uses the MATLAB 2019a platform to perform simulation experiments on the feasibility of the detection and repair algorithms in this paper. DHD-MEPO with 3D-Voronoi partitioning for WSNs (3D-VPCA) proposed in [33], a novel confident information coverage hole-healing algorithm (C-CICHH) proposed in [41], the random algorithm (RA), an enhanced mobile sink-based coverage optimization and link-stability estimation-based routing protocol (EMSCOLER) proposed in [31], and an improved energy-efficient routing protocol (IERP) proposed in [42] were compared. The validation is carried out in the small-scale region \(10\times 10\times 10\) m\({}^{3}\) and the large-scale region \(100\times 100\times 100\) m\({}^{3}\), respectively. A total of 35 static nodes and 15 mobile nodes are randomly thrown for a target region of \(10\times 10\times 10\) m\({}^{3}\), and 70 static nodes and 30 mobile nodes for a target region of \(100\times 100\times 100\) m\({}^{3}\). The parameters set in the simulation experiments are presented in Table 3.
## 5 Results and Discussion
### Discussion and Result
#### 5.2.3 Smart Contract  
Smart contracts are a very important concept in the field of blockchains. They provide many important features to enhance system functionality and to increase the speed of operations. In the current review, only seven research papers did not use smart contracts in their proposed solutions: [15; 21; 25; 28; 30; 32; 35]. On the other hand, 18 research papers adopted smart contracts to provide more efficient solutions for the privacy problems in IDMS: [12; 13; 14; 16; 17; 18; 19; 20; 22; 23; 24; 26; 27; 29; 31; 33; 34; 36].  
The analysis of statistics related to the previous research shows that there has been an increase in the number of publications over recent years that adopted blockchain technology in the field of IDMS, as depicted in Figure 2. In terms of the blockchain type, the analysis results presented in Figure 3 show that Ethereum has been more frequently used than the other types of blockchain. There are several reasons for this. The smart contract is one of the most important components of an Ethereum system's development and improvement. The Solidity Language is another important reason, along with the fact that Ethereum is involved in several applications, the most important of which is the DApp.  
There has been a significant increase in identity control on the proposed blockchain-based systems because of the third-party limitations caused by the decentralization feature. The system is powerful and operates faster when it is using smart contracts as they are self-executed codes, but there is some uncertainty about the security of the stored data. As  
Figure 2: Years and number of publications.  
a result, there have been many research papers on identity management systems that are trying to reduce the different risks and to mitigate cyberattacks encountered in this field.  
It can be seen from the research articles shown in Tables A1 and A2 that blockchain technology, the underlying technology for decentralized IDMSs, has been proposed as an effective solution for privacy and security issues in a variety of fields, such as IOT, supply chains, ad-hoc networks, cloud IDM, healthcare, internet of connected vehicles, and access control. Previous research has illustrated that blockchain is a powerful technology and has many features that may effectively contribute to enhancing user privacy and increasing the level of self-control over personal data in the field of IDM and relevant applications.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 2 Related Work
### Forest Fire Detection Based on the Deep Learning Approach  
Recently, deep-learning-based object detection has become more popular than sensor-based object detection. In [13], [PERSON] et al. proposed the ELSTIC-YOLOv3 model to detect small objects and, in the same study, they mentioned the dynamic fire tube, which is a characteristic of fire. The research team in [14] proposed a CNN-based model with an average precision accuracy of 98.7%. Furthermore, in [15; 16; 17; 18; 19; 20; 21; 22], an approach to improve fire detection technology was presented. Fast region-based convolutional neural networks (R-CNN) for object detection utilized high-quality region proposals created by the Region Proposal Network (RPN), which was trained in the end-to-end process [23]. [PERSON], [PERSON] et al. introduced a single-shot detector (SSD) for multiple categories that was faster and significantly more accurate than the previous traditional works for single-shot detectors (YOLO), as they claimed in their paper [24]. Fast YOLO employs a neural network with 9 as opposed to 24 convolutional layers, and fewer filters in those layers. The only difference between YOLO's and Fast YOLO's training and testing parameters is the network's size [25; 26; 27]. Automated deep learning and IoT algorithms are becoming very active and attractive approaches in agriculture and industrial appliances because of accurate detection and predictions [28; 29; 30]. In CNN, the challenge is to achieve high accuracy by training with a large dataset, which is an expensive process. To overcome this problem, we prepared a large dataset publicly available on the Internet.  
In summary, the main contributions of this study are as follows:  
* The most common method to detect fire is the smoke alarm, which can commonly be found in a lot of projects or even at a store. However, having only smoke sensors can cause false alarms.
* Remote cameras are probably the most common methods used by park rangers. However, these require 24/7 human interactions. This can lead to human errors by falling asleep during a shift.
* R-CNN is now advancing more and more and is being introduced as a product. This allows real-time surveillance and it can detect whether there is a fire or not. This can be very useful in furthering our society in the future.
* The goal of this research is to converge Internet of Things devices with AI methods in the field of global wildfire prevention. Therefore, our research saves lives and government property.
## 3 Case Study: SmartLab Concept and 3D Printing Environment  
The 3D printing environment of the Lapland UAS was connected with the SmartLab concept via integrated product development functions, in which the first part of the SmartLab path (from idea to product) was handled. The environment consists of three polymer printing technologies: (1) FDM = material extrusion, (2) SLA = stereolithography, and (3) SLS = selective laser sintering of polymers. These represent the three most used AM technologies worldwide [45]. The 3D printing environment focuses on education in AM technologies, where various AM-related phenomena can be learnt and researched. The main outputs are functional prototypes, end-use products, and various tooling products for manufacturing.
## 4 Results
### Spatial Concentration and Distribution of Vulnerable Populations
#### 4.3.1 Resident Population Status  
The analysis began by examining the residential locations of the older adult population, revealing that their residences are predominantly concentrated around Gayang Station, Songjeong Station, and Hwagok-Kkachisan Station. As depicted in Figure 6, these areas show significant clustering of the older adult population, indicating potential zones where healthcare and social services should be prioritized.  
For children under 14 years old, the analysis showed that their residences are primarily distributed around the Sinbanghwa-Magok Station and Yeomchang Station-Jungmi Station areas. This pattern, represented in Figure 7, suggests that these neighborhoods are particularly important when considering the placement of educational and recreational facilities for younger population.  
The intensity of residential concentration is visually represented in Figures 6 and 7, with areas of higher population densities indicated by darker, more saturated shading. This color gradient effectively highlights densely populated areas in each demographic group.  
Figure 6: Residential concentration of older adult population (aged 65 and over) around Gayang, Songjeong, and Hwagok-Kkachisan Stations.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
###### Abstract
Various deep learning models, such as recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and deep belief network (DBN), provide high accuracy in forecasting solar irradiance for both short-term and long-term forecast horizons. Nevertheless, no studies applied deep learning models to forecast the solar irradiance using cloud cover data obtained from sky images. In this study, we proposed a method to estimate solar irradiance in high variability of solar irradiance such as partly cloudy days. The proposed method combines the cloud cover obtained from sky images, the deep learning model to forecast the cloud cover ten minutes ahead, and the physical model to estimate GHI with the forecasted cloud cover. The LSTM was selected out of deep learning algorithms because it is recommendable for predicting time-series data compared with other deep learning models [8]. The forecasting was conducted for clear, cloudy, and overcast days on a minute basis and validated by the comparison with GHI measurement at a ground station in Seoul, South Korea. Because no study used the deep learning model to forecast the cloud cover from sky images, this study will investigate its applicability.