esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The performance of sensor nodes is adversely affected when the battery level falls below a pre-defined battery threshold level. Energy presents a main challenge for designers while designing sensor networks. Each node in this network has restricted energy resources due to a partial amount of power. So, the routing protocol should be energy efficient. Different routing protocols for wireless sensor networks (WSNs) have been proposed in recent years. Routing protocols for WSN may be classified into three categories: flat, location-aware, and hierarchical. The first category has a high energy consumption due to the methodology of work in which the whole traffic is transferred to the base station. In the case of hierarchical routing protocols, energy consumption decreases and thus the lifetime of the network increases. This category relies on establishing a cluster, and a head node is allocated to each cluster. Data from each cluster is gathered and consolidated at the head node before being transferred to the base station. Finally, the location-aware category forwards the data to the base station using geographic forwarding to trace the node's position. This paper proposes a model to address two of the major challenges mentioned, namely, security and energy in WSN. Security is considered while data is being transferred between authorised nodes by means of a distributed key management scheme using elliptic curve cryptographic operations. In addition, we proposed an enhanced version of the LEACH routing protocol that has been redesigned to provide better and more efficient power consumption, thus maximizing the life of the network in addition to less message overhead. The proposed model is scalable and depicts the primary response patterns of a network node. The proposed model has been evaluated, showing better performance figures on WSN's. The major contributions of this research can be identified as follows:  
* Propose a method for key generation, distribution, and management using an elliptic curve approach with fewer overheads and time for secure, fast and reliable data exchange between nodes;
* Propose a dynamic approach to shared key regeneration based on sensing network topology changes or attacks to prevent intrusion and ensure that data integrity is not compromised;* Propose an enhanced low energy consumption adaptive routing protocol to reduce overall network energy consumption and thereby extending the lifespan of the network.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 5 Experiment Results and Analysis  
In different weather conditions such as sunny, foggy, and rainy days, pictures of the verification site were captured. Since both fog and rainfall can lead to low visibility, no distinction is made between fog and rainfall in practice. Approximately 600 verified images under different visibility conditions have been collected.  
Figure 6: Horizontal brightness histogram of the target.  
In some pictures, cars and pedestrians are set on the road at various distances to test the robustness of the method. Some samples of the original images are given in Figure 7. The laser transmission scheme mentioned in Section 3.1 is used to estimate the atmospheric transmissivity of the current state; the image analysis mentioned in Section 3.2, to obtain the image characteristics; and the method mentioned in Section 3.3, to assess visibility as a whole. Table 3 shows the estimated visibility of the images in Figure 7 and the errors between the estimated and measured values. The results show that the visibility estimation errors are within 20% when there are cars and pedestrians on the road, which has a certain degree of credibility.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **TSVE** & **Visibility Meter** & **Error** \\ \hline Figure 7a & 8828 & 10,000 & 12\% \\ Figure 7b & 8992 & 10,000 & 10\% \\ Figure 7c & 3138 & 3791 & 17\% \\ \hline \end{tabular}
\end{table}
Table 3: Visibility estimation results of the images in Figure 7.  
Figure 7: Images grabbed on the validation site with cars and pedestrians on the road. (**a**) cars and pedestrians; (**b**) pedestrians; (**c**) pedestrian with an umbrella.  
We evaluate the proposed visibility algorithm by calculating the mean absolute error (MAE) [2]. The MAE is defined as:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|d_{i}-y_{i}\right| \tag{24}\]  
\(d_{i}\) is the estimated visibility, \(y_{i}\) is the measured value by visibility meter, \(\left\{d_{i},y_{i}\right\}\in\left[0,\ 10000\right]\), \(N\) is the number of samples. The final experimental results in Table 5 show that the proposed algorithm made fewer errors than V1 and V2. Some of the experimental images were captured in various common nighttime conditions (clear day, foggy, and rainy conditions), which shows that our algorithm has high accuracy under common nighttime weather conditions. In addition, experiments have shown that our approach provides better performance when data size increases.  
Indeed, experimental verification of these methods is difficult. Taking photos in natural fog is a daunting task because weather conditions and fog uniformity cannot be controlled. Increasing the fog concentration of the environment through high-pressure spray equipment is also uneven. Our implementation is done through a target, which has some minor flaws. Using a purely \"black\" and larger target will result in a better estimate, but this is not realistic. In the future, we will test these methods with better cameras, measure the atmospheric extinction coefficient in a more precise way, and minimize or not use the target.
## 2 Literature Review
### Identification Algorithms
Bottom-up is to first classify the original data nodes, calculate the clusters of different categories of data nodes and select the nearest category to merge, and then form a new classification layer and form the lowest number of categories by clustering layer by layer. The top-down approach is to first merge all nodes into one category and then gradually divide them into different categories until the clustering effect is below a pre-required threshold. Like other algorithms, the HC algorithm is also based on similarity, but this multilayer clustering approach is computationally complex and susceptible to individual singular values, which is clearly not sufficient for the study of industrial clusters driven by big data [53]. The DBSCAN algorithm is a conventional density-based unsupervised node clustering technique. Its methodology involves the progressive formation of new clusters by evaluating connectable samples while also considering the density of samples as a constraint. Compared to the KM algorithm, the DBSCAN algorithm eliminates the need for the manual determination of cluster quantity and can identify irregularly shaped clusters [54]. Industrial cluster research aims to cluster spatially dispersed nodes to identify clusters with improved indicators. The DBSCAN algorithm surpasses other algorithms in terms of algorithm principles, ease of use, and ability to visualize clustering effects. However, the research of the DBSCAN algorithm in industrial clusters often only considers the spatial distance and the number of single nodes. This limited approach fails to consider other important indicators of overall performance, which is insufficient for identifying multi-technology industrial clusters. While some scholars have proposed using the HDBSCAN algorithm to address the consistency of different cluster densities, this algorithm still lacks the ability to implement the multivariate input function [55].
## 3 Method
### ECG and Respiration Recording  
ECG and respiration were recorded at 1 kHz using the ML866 Power Lab data acquisition system and analyzed using its software LabChart8 (ADInstruments Pty, Bella Vista, New South Wales, Australia) and MATLAB (R2015b, MathWorks, Inc., Natick, MA, USA). ECG was assessed using disposable electrodes (Ambu, Blue sensor VL 00 S/s5 Cardiology) and respiration using a strain gauge over the chest.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 5 Contributions to the Ocean Observatories Development and Open Access to the System
Design and engineering services were offered for the development of an underwater strain gauges data acquisition module to study the ageing of composites materials, a very critical factor for composite underwater structures that operate for prolonged periods of time. Several batches of composite filament wound ring, arc and straight and two filaments wound Carbon Fiber Reinforced Plastics CFRP housings were deployed in a depth of about 900 m at the E1M3A obs. Approximately every 10 months, the mooring line was recovered, the logger data were retrieved and a batch of specimens was collected in order to be tested in the laboratory [38]. The LETS_SAT TNA project aimed to test the OpenLknos tracking technology in fixed-point oceanographic platforms under the influence of a wide range of environmental conditions (winds, waves, temperature, sea sprays, etc.) The simultaneous access on three platforms for extended periods of time (1 year) was offered in order to fully exploit and test the operational capabilities of the product. To meet the increased demand for power by modern and sophisticated sensors and modules, FURO Oceanor replaced one half of the PYLOS buoy with a new hull containing fuel cells and a new water-based cooling system. The new power system was tested for a period of more than 2 years in realistic conditions.
## 3 Engineering Example
### Numerical Analysis Based on Dynamic Time-History Method
#### 3.3.1 Calculation Model  
This study models three sections of an immersed tube, each 45 m in length, totaling 135 m. The immersed tunnels are simulated using eight-node shell elements (Shell281), GINA water stops with spring elements (Combine14), and soil and bedrock with eight-node solid elements (Solid181). Additionally, the convection and impulsive effects of moving water within the tunnel are modeled using mass point elements (Mass21) and spring elements (Combine14). This creates a comprehensive three-dimensional finite element model of the tunnel-joint-soil system. A viscoelastic boundary is set at the model's bottom and sides to simulate the radiation damping effect of an infinite foundation. The equivalent linear constitutive method describes the nonlinear characteristics of the soil mass. The soil layer model extends 50 m to both sides and 50 m below the tunnel center, with the tunnel buried at a depth of 13.2 m. The soil layers from top to bottom consist of coral reef mixed sand, medium sand, gravel cushion, highly weathered biotite granite, and moderately weathered biotite granite. In the mesh generation of the model, several key considerations are necessary. First, the balance between calculation cost and accuracy must  
Figure 13: Distribution of joint opening values of immersed tunnel under different working conditions: (**a**) working condition 1, (**b**) working condition 2, (**c**) working condition 3, (**d**) working condition 4.  
be maintained. The mesh size should be controlled, adhering to the \"Standard for Seismic Design of Nuclear Power Plants\" for maximum mesh size in the foundation strata. The immersed tunnels, being central to this research, should have a mesh size between 0.2 and 2 m, as illustrated in Figure 14. For the foundation, the mesh height of soil near the tunnel should match that of the immersed pipe as closely as possible, while the mesh height of soil farther from the tunnel can be increased, with a maximum of 5 m. The overall finite element model is shown in Figure 15. The first joint of the tunnel is located in a highly weathered soil layer and a gravel sand layer, both relatively thick, resulting in weaker bearing capacity compared to the second joint.  
For convenience of expression, this paper has named the three sections of the immersed tube as follows: immersed tube No. 1, immersed tube No. 2, and immersed tube No. 3. Identically, the joints from left to right are named as follows: joint No. 1 and joint No. 2, which can be seen in Figure 16. Also, the walls of the immersed tunnel are named as follows: bottom plate 1, bottom plate 2, top plate 3, top plate 4, left side wall 5, middle partition wall 6, and right side wall 7, as shown in Figure 17.  
Figure 16: Numbers of each section of immersed tunnel.  
Figure 14: Immersed tunnel model and spring model.  
Figure 15: Immersed tunnel–joint–soil finite element model.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Simulation Settings and Dataset Used for DRFTSA  
This subsection discusses the configured simulation settings for DRFTSA as well as the dataset used in our research work. For our research work, Cloudsim [42] was used as a platform to simulate the cloud environment. After identifying the platform, we configured this simulation platform in our physical host. For simulation of Cloudsim, we need Java for backend support and the entire code for Cloudsim written in Java. Cloudsim installed in our physical host is a bear metal system with 16 GB RAM, 1 TB hard disk SSD, and i7 processor. The workload is generated in our research by giving an input of tasks fromthe real-time Google Cloud Jobs dataset captured from a supercomputing cluster which consists of various tasks with different run-time capacities. It is available from [43]. It is a real-time workload trace and available in terms of million instructions per second (MIPS) It consists of parallel worklogs which are obtained from Google cluster traces. Initially, we identified the tasks available in the dataset as small, medium, large, and very large tasks and they account for 25%, 35%, 30%, 4%, and 6% respectively. In our case, a regular dataset consists of small and medium tasks and a large dataset consists of large and very large tasks. After identifying this, we categorized datasets as regular and large datasets by running an algorithm with fixed and changing VMs. In our research, with the proposed DRFTSA we calculated makespan, energy consumption, and rate of failure. The proposed DRFTSA is compared against existing state-of-the-art approaches, i.e., PSO, ACO, and GA. The standard configuration settings are taken from [44] to carry out simulations. Table 3 presents configuration settings used in our research for simulation.
# Cardiac Nuclear Imaging Findings in Atypical Variants of Takotsubo Cardiomyopathy
## 3 Results
### Analysis of the Evidence  
The 14 selected papers were published from 2004 to 2021. Most of the studies were conducted by authors from Japan (n = 7), followed by researchers from France (n = 3) and Italy (n = 2), with the remaining two studies performed in the USA and in Germany. Only two retrospective studies and one prospective study were found according to the selection criteria. The majority of the selected studies were case reports involving one or two patients. Letters to the editor (n = 3) and interesting images (n = 2) were also included. The total number of patients was 37. It is worth specifying that this number is not the sum of the subjects involved in the selected papers but represents the result of a further selection carefully performed for each single publication to include only patients with atypical variants of TTC, since four studies also included subjects with the typical apical form. For completeness, we should also underline that the study by [PERSON] et al. [70] does not specify whether all 13 of their subjects with atypical variants were submitted to cardiac nuclear imaging, so the total number of 37 could be slightly overstated. Subjects were mostly women (30 females, 7 males). As concerning cardiac nuclear imaging, myocardial perfusion scintigraphy was performed in nine studies, specifically with technetium-99m (99 mTc) setsamibi in four papers, with thallium-201 (201 Tl) in an additional four studies and with 201 Tl or 99 mTc-tetrofosmin in the remaining one, followed by I-123-meta-iodobenzyl-guanidine (123I-mIBG) myocardial scintigraphy (n = 7), I-123-beta-metyl-iodophenyl pentadecanoic acid (123I-BMIPP) myocardial scintigraphy (n = 4), and fluorine-18-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET) (n = 3). In most cases (n = 7), a single cardiac nuclear imaging technique was performed, while in the remaining five and two studies, two and three different imaging modalities were, respectively, used. According to the literature data, the most common atypical variant of our selection of studies was the midventricular form (n = 8), followed by reverse/inverted/basal TTC (n = 5), with only a single case report of a focal pattern. The scintigraphic follow-up data concerning atypical variants are available in a minority of publications (n = 3), while in the case report by [PERSON] et al. [76], 123I-mIBG scintigraphy was not performed in the acute phase. The main characteristics of the included studies are reported in Table 1. The cardiac nuclear imaging findings of the selected papers for each type of atypical variant of TTC are described in detail in the following paragraphs.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 4 The Proposed Approach
### ACOSIMv2--Ant Colony Optimization for Sensor Selection Based on Similarity
#### 4.2.2 Solution Construction Procedure
After updating the pheromones on the edges, a new cycle is started; this continues until the termination criteria is reached. At the end of the last cycle, the best route (which is the most used) is considered as the solution (\(\Omega\)) and nodes using this route are selected to compose the virtual sensor, as illustrated in Figure 4d.
## 5 Experimental Results
### Evaluation Metrics
#### 5.2.2 Root Mean Square Error (RMSE)  
The RMSE is a measure that quantifies the discrepancy between the actual and predicted ratings. It is calculated by taking the mean value of the squared differences between the actual and predicted ratings, and finding the square root of that result, making it useful when significantly large errors are undesirable [52]. Equation (11) displays the RMSE calculation formula, in which \(d_{i}\) and \(\hat{d_{i}}\), respectively, indicate the actual and predicted ratings, while \(n\) denotes the total number of ratings.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(d_{i}-\hat{d}_{1})^{2}} \tag{11}\]
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
### Different Urban Maps
The negative impact of complex scenarios also influences the PDR of protocols, it narrows the difference of PDR between TrAD and UV-CAST, but UV-CAST consumes more transmissions. For AMD and TrAD, in Figures 7a and 8a, the first observation attracting our attention is that the difference of PDRs between AMD and TrAD is greatly narrowed from the Clermont-Ferrand scenarios to the Manhattan cases. That is, AMD fits much better a regular map (e.g., Manhattan) than a complex one (e.g., Clermont-Ferrand). Even so, the PDR and the delay of AMD are still worse than the TrAD ones. However, AMD is suitable to be used as reference, since, like TrAD, it also adopts a directional classification mechanism in the broadcast suppression technique. The difference lies in that TrAD can provide a more precise classification than AMD to handle more complex road topologies. This advantage is proved by the fact that the PDR of TrAD is always better than the one of AMD in both scenarios. Although AMD consumes the least transmissions, TrAD uses a moderate amount of transmissions (a little bit more network overhead) to achieve a better overall performance.
## 2 Literature Review
### Workplace Management
#### 2.2.2 Occupancy Sensing  
Occupancy sensing is a sensor-based methodology for enabling automatic activation of facility management services. That methodology is based on survey occupancy within the different rooms of a building. This is measured with people's presence through the interpretation of data from ultrasonic sensors. Surveying occupancy is possible with a network of sensors with lasers, image processing analysis, ultrasonic waves and temperature and pressure gradients [30]. Each of the options has limitations, depending on the monitoring scope and context. For example, infrared sensors might be unreliable because of double-counting. This can be caused by incorrect frequency settings or reflection on materials (shadows) when the camera lens is soiled. They tend to undercount people. Thermal-based sensors are more expensive and can be disrupted by the current climate conditions when surveyed. Pressure-based sensors must be positioned in a certain way to enable proper counting. Past experiences with these applications do not seem to be completely integrated and automated. The information exchange between a building and its occupants is not optimal [31]. An extensive literature review by [PERSON] [31] showed that no single method or sensor is identified that best estimates and solves the problem of occupancy information. The applications that are used are not always capable of exchanging data with the building management system and/or users. The expectation is that in the future, highly accurate methods are presented from the research community [31].
## 3 Results and Discussion
### Qualitative Properties
#### 3.5.2 Shrinkage  
Figure 8 shows the percentage of shrinkage changes in the _HAI_ dryer with ultrasonic pretreatment at different thicknesses of kiwifruit. The results show that, at high temperatures, high ultrasonic radiation time and less thickness, the amount of shrinkage is significantly reduced (\(p\) < 0.05). As kiwifruit has a high initial moisture, due to the shrinkage, which is also significant, some changes occur in the initial structure during the drying process. In this way, the fluid in the cell wall exerts pressure to the cell wall and the fluid inside the cell is put under pressure [51]. During the drying process, the removal of water from the cell reduces the stress induced by the liquid on the cell wall. This reduction in stress causes the shrinkage of the constituent texture of the substance [26]. However, air temperature, high ultrasonic radiation time and less thickness accelerate the water removal rate and do not allow the product to deform. The results of this study corroborate those obtained by [PERSON] et al. [29]. They investigated the effect of ultrasonic radiation time on blackberry shrinkage in the hot air dryer, and demonstrated that with increasing air temperature and ultrasonic radiation time, the amount of shrinkage decreased. [PERSON] et al. also achieved similar results for drying potato slices [11].