esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 3 Results and Discussion
### Prediction Results from the ARIMA Model  
Figure 5 shows the prediction results from the ARIMA model after the model fitting shown in Figure 6. In total, there were 21 models and the best-fitting model was the ARIMA (4, 2, 1). The simulation was completed for four different sets of data, namely, 28 days, 180 days, 1 year, and 5 years. It can be confirmed from Figure 5 that the prediction results for short periods comprised single values, but after certain periods, the prediction results fell in a range of values. In other words, for the 28-day sets of input data, the prediction results comprised single values for approximately 283 days. Similarly, for the 180-day, 1-year, and 5-year sets of input data, the results comprised single values for approximately 452, 388, and 2081 days, respectively. After that, however, the obtained results fell in a range of values showing the maximum and minimum strength the concrete can gain during those periods. The predicted maximum strength was very high. When compared with the actual experimental data, the value is considered incorrect. The minimum value for each prediction was zero as it is theoretically impossible to obtain a value below zero. The predicted results, when compared with the actual experimental data, produced accurate results for the 1-year and 5-year sets of input data with the single-value output. Meanwhile, the output results were lower than the actual experimental data for the 28-day and 180-day sets of input data in the part of the single-value output. The main reason for this discrepancy might be the limited amount of input data and the working principle of this method. As for the range of values, it diverged exponentially from the actual experimental data. Thus, it cannot be used as a reliable reference for long-term prediction. However, since the immediate prediction results for all four models were accurate for a short span and the accuracy span increased proportionately for a large amount of input data, the applicability of the ARIMA model for long-term prediction might be limited.  
Figure 4: Time-series compressive strength of the granulated blast-furnace slag (GBFS).  
Figure 5: Prediction results of the ARIMA model.
## 3 Application Examples
### Calculation of Fracture Parameters
#### 3.1.3 Fracture Porosity  
Calculate fracture porosity using Equation (5) based on conventional logging interpretation of deep and shallow resistivity data. According to the statistical analysis results shown in Figure 5, the range of fracture porosity is mainly distributed between 0.1% and 0.5%.  
Figure 2: Distribution map of fracture density in the study area.  
Figure 4: Distribution of fracture hydrodynamic width in the study area.  
Figure 5: Distribution of Fracture Porosity in the Study Area.  
Figure 3: Distribution of fracture angle in the study area.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 3 Our Method
### Network Architecture
#### 3.2.1 Multi-View Convolutional Encoders  
In the proposed method, each view of the original images has its own encoder to extract low-dimensional features, which helps to obtain different features for various views. Let us assume that \(\Theta_{\epsilon}^{(v)}\) represents the parameters of multi-view encoders, and it aims to transform the input images \(Q^{(v)}\) to \(S^{(v)}\) with a non-linear mapping \(E_{v}(Q^{(v)};\Theta_{\epsilon}^{(v)})\), where \(E_{v}\) donates the encoding network parameterized by \(\Theta_{\epsilon}^{(v)}\). Given the use of low-resolution datasets in subspace clustering, employing larger convolutional kernels or deeper network often results in the loss of fine-grained details, while smaller kernels may hinder the extraction of deep-level information. Therefore, we set three-layer encoders with [10; 20; 30] channels correspondingly and adopt [\(4\times 4\), \(3\times 3\), \(4\times 4\)] kernel size to extract the representations of each view. We also employ the Mish activation function to ensure smoother gradients during the training process. Meanwhile, this implementation is the same as [55] which can ensure the fairness of the comparison experiments.
## 6 Review of Studies Related to Micromobility in the Context of Multi-Modal Mobility
### Review of Studies
The analysis was based on the integration ratio, which is the percentage of trips using e-scooter sharing as a feeder to public transit over all e-scooter sharing trips. The analysis shows that the integration ratios of e-scooter sharing vary from 5.59% to 51.40% with a mean value of 31.58% and a standard deviation of 8.47%. Analysis of the temporal patterns shows that an increase in the integration ratio for first-mile trips is associated with a decrease in the integration ratio for last mile in the time series. By applying a bottom-up hierarchical clustering method, the analysis of the study in [63] divides these cities into four clusters according to their temporal variations in the integration ratios. By applying a machine learning approach to analyze the impact of city-level factors on the integration ratio, it shows that the public transit stations density and the ratio of the young are positively associated with the integration ratio. 11. Question: How governance stakeholders might shape sustainability transitions and technology uptake of e-bike? Results: Governance stakeholders play a pivotal role in taking action toward sustainable development through promoting enterprise transformation and industrial transformation. However, existing studies on the role of governance stakeholders in shaping e-bike technology uptake/rejection is limited. Understanding how governance stakeholders might shape sustainability transitions and technology uptake is an important issue. The study [64] provides insight into how governance stakeholders shape sustainability transitions and technology uptake by exploring their perspectives in depth. This study finds that e-bikes possess niche benefits for helping disrupt auto-centric socio-technical systems through strengthening first and last mile connectivity, multi-modal integration and shared mobility innovations. To benefit from e-bikes, distinction between e-bike models, clarity on usage and transition at the city scale are required.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 5 Data Transferring in LS-WSNs
### Feature-Based Routing Protocols  
In this section, routing protocols are categorized according to the features they implement. These features are based on data routing, route selection, route discovery, and alternating routes.  
* _Routing protocols based on data routing._ In this kind of protocol, each sensor node must choose a node of its neighborhood to deliver it the task of conveying these data. This decision is made using different techniques, including table-based routing, broadcasting, and filtering by neighbors, or flooding.
* _Routing protocols based on route selection._ Route selection is the process of choosing the best route to a sink from the potential routes depending on the constraints of the network and the sensor nodes. Although most protocols only choose a better route (single-path routing), multipath protocols select and save more than one path for different purposes, such as energy, traffic balance, reliability, and robustness. The entities involved in the selection process may include the source sensor node, the intermediate nodes, or the sink node.
* _Routing protocols based on route discovery._ Each sensor node of a WSN acts as a router and participates in the routing of data to the sink. Many debates and works have discussed when and how many routes each node must maintain in its routing table. Indeed, a node must memorize the roads to reach a sink, or only search and keep track of roads when it has an interest in reaching the sink. As a result, three types of routing protocols are distinguished depending on how routes are maintained in the routing tables. Route discovery is categorized into proactive, reactive, and hybrid routing protocols.
* _Routing protocols based on alternating routes._ In a WSN, from a sensor node, a routing protocol can maintain a single path that allows it to reach a sink. It can also maintain multiple paths to reach a single destination. We distinguish two classes of protocols: single path and multiple paths.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Video Processing
#### 2.2.2 Motion Detection
Entropy is low for less or no motion and comparatively high when there is more movement. The current study in this paper employs inter-frame difference as a method for detecting motion, as it is accurate, while being less computationally complex compared to some other methods. By keeping in view the resource constraints of IoMT devices, this research aims to reduce the amount of video data handled by those devices. Data will be reduced before it is transferred to another IoT device or injected into a network of multiple IoMT devices. Thus, this study presents a compression technique, to reduce data in an IoT device so as to reduce energy consumption on a frame-by-frame basis and, in that way, prolonging the duration in time of the network, which would otherwise be quickly affected as devices fail through energy exhaustion. Each frame is checked for changes as compared to its predecessor and sensors communicate only sufficient information to indicate any changes. As a result, the communication load can be balanced in an IoMT.
## 3 Results and Discussion
### Imputation
#### 3.1.1 Mean Dissimilarity  
To measure mean dissimilarity between the imputed and original data, we used the normalized Euclidean distance. The closer the value of mean dissimilarity to zero, the better the quality of imputed data. Table 1 shows random forest imputation separately, with bagging and stacking ensembles producing values closer to zero indicating better performance.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 2 Theoretical Background
### Propagation of Ultrasound in Materials  
When ultrasound propagates through a medium, its behavior is governed by the following acoustic wave equation:  
\[\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}-\
abla^{2}p=S, \tag{2}\]  
where \(p\) is the acoustic pressure, \(c\) is the speed of sound, and \(S\) represents a source term. Reflection, refraction, and diffraction occur when ultrasound encounters material interfaces or inhomogeneities [25]. In solid materials, two primary types of waves propagate:  
* **Longitudinal Waves:** Particle motion is parallel to the wave direction.
* **Shear Waves:** Particle motion is perpendicular to the wave direction, typically slower than longitudinal waves.  
The behavior of ultrasound depends on the angle of incidence relative to the material's surface, which determines whether the wave is reflected, transmitted, refracted, or converted into other wave types. These interactions are governed by the acoustic properties of the materials involved and the geometry of the interaction. At normal (zero-degree)incidence, the reflection coefficient (\(r\)) and transmission coefficient (\(\tau\)) for an interface between two materials can be expressed as  
\[r=\frac{Z_{2}-Z_{1}}{Z_{2}+Z_{1}},\quad\tau=1-r, \tag{3}\]  
where \(Z_{1}\) and \(Z_{2}\) are the acoustic impedances of the first and second medium, respectively [24]. This interaction allows for the detection of inclusions or voids, which create distinct acoustic impedance contrasts. For oblique incidence, [PERSON]'s law governs the relationship between the angles of incidence (\(\theta_{i}\)), reflection (\(\theta_{i}\)), and refraction (\(\theta_{t}\)):  
\[\frac{\sin\theta_{i}}{c_{1}}=\frac{\sin\theta_{t}}{c_{2}} \tag{4}\]  
where \(c_{1}\) and \(c_{2}\) are the sound velocities in the two materials. Mode conversion may occur at the interface, leading to the generation of shear waves in addition to reflected and refracted longitudinal waves.
# Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments
## 4 Discussion  
This study presents a novel approach for localizing aerial assets using optical data acquired by a camera. By applying a series of rigid transformations, the world coordinates of the camera can be determined without the need for iterative processes. The method requires only two world points with known coordinates and their projections in the image, along with Pitch and Roll (Euler) angles, focal length, and detector dimensions.  
Validation of the method involved using 11 optical images acquired by a quadcopter, with two landmark points selected in each image. The world coordinates of these points were derived from a LiDAR model of a building. The results were compared against GNSS data and pose estimations from photogrammetric software. The proposed method consistently produced positional data within the GNSS uncertainty range, with divergences from the pose estimations ranging from 1 to 2 m. In previous studies [16], which are comparable with this one for approach (P2P) and type of asset used (air vehicles), the measured displacement appears to be at the sub-meter level. This confirms that even a very reduced number of reference points may lead to very accurate positional data. However, studies like [16] are conducted indoors where, on one side, highly precise motion capture systems can accurately measure the positional translation but, on the other hand, do not fully represent realistic case scenarios like the one considered in this contribution. Continuously Operating Reference Stations for Real-Time Kinematic (RTK) systems would be needed to achieve very accurate positional data of the aerial assets in realistic outdoor conditions. Unfortunately, such a technology was not available in this study to allow a comparison with the results of the method presented. Consequently, this study could not precisely verify the error of the results. It is reasonable to assume that factors such as the accuracy of the LiDAR data, which impacts the world coordinates used in the calculations, and biases in the gyro and accelerometer could introduce some errors. Further research is necessary to accurately assess the precision and limitations of this method.
## 5 Conclusions  
NIRS can be used to detect differences in spectra of the samples with known concentrations of known oral cancer biomarkers (L-fucose and L-proline) in aqueous solutions. To the authors' best knowledge, no previous study has utilized NIRS to distinguish the spectral differences of various concentrations of oral cancer biomarkers from aqueous solutions or from saliva. The next step in research would be to spike saliva samples of healthy volunteers with different concentrations of L-fucose and L-proline and measure the NIRS spectra of those spiked samples. The sensitivity of the measurements could be enhanced using more samples and adapting a multivariate analysis to the larger data set. The measurement setup could be further enhanced by utilizing a probe dedicated to fluid measurements. This study provides a basis to further develop the investigational method to achieve quantitative and reproducible results and to develop NIRS setup to a feasible screening method to be able to carry out preclinical test series.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11209662/s1](https://www.mdpi.com/article/10.3390/app11209662/s1)]([https://www.mdpi.com/article/10.3390/app11209662/s1](https://www.mdpi.com/article/10.3390/app11209662/s1)), Table S1: Data sheet.  
**Author Contributions:** Conceptualization, S.M. and R.L.; methodology, M.O.H. and J.K.S.; software M.O.H. and J.K.S.; validation, M.O.H.; formal analysis, M.O.H. and J.K.S.; investigation, M.O.H. and J.K.S.; resources, S.M., W.A.G.-A., A.K. and R.L.; data curation, M.O.H., J.K.S. and S.M.; writing--original draft preparation, M.O.H.; writing--review and editing, M.O.H., J.K.S., S.M., W.A.G.-A., A.K., R.L.; visualization, M.O.H.; supervision, R.L.; project administration, S.M. and R.L.; funding acquisition, M.O.H., S.M., A.K., R.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Business Finland (former TEKES), grant number 52/31/2014. The M.O.H. was funded by Waldemar von Frenckells stiftelse.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee for Human Studies of Piracicaba Dental School, Brazil (protocol code 142/2010).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available in this article or as Supplementary Materials.  
**Acknowledgments:** We thank [PERSON] for his help in the preparation of the L-ftocose and L-proline reference sample tablets.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Computational Fluid Dynamics Applied to
## 5 Results
### Categorization of Relevant Papers Exploiting the Rotating/Multi Reference Frame Approaches  
In this section, the results of the paper classification using RRF/MRF approaches are presented. In particular, the classified papers are listed in Table 3. The literature search highlights 23 pertinent records, published since 2007. Among the 22 total works, 11 deal with gears, eight with pumps, and three with bearings. The most studied phenomenon through this strategy is undoubtedly the windage power losses in gears. Indeed, the windage power losses were studied through RRF/MRF approaches for spur gears in [23; 24; 25; 72; 73; 74; 75], for level gears in [26; 76] and for a planetary gearbox in [28]. The study of churning losses for a spur gear exploiting these approaches can be found in [27].  
Through the RRF/MRF approaches different kind of pumps were studied. In particular, centrifugal pumps were simulated in [77; 78; 79; 80; 81], an axial-flow pump was studied in [82], and a vortex pump working with highly viscous liquid flow condition was simulated in [83]. In addition, also the study of a Francis turbine was performed through these approaches [84].  
With respect to the bearing simulation through RRF/MRF approaches, it is possible to notice two papers aimed at investigating the oil flow within roller bearings [29; 30]. However, these two studies were not experimentally validated. On the other hand, in [85] it is presented the only one experimentally validated work that simulates a bearing. It shows an accuracy between 80%-90%.  
In Table 3, it is possible to notice that three works were carried out through the open-source software OpenFOAM(r)[77; 84; 85]. Therefore, most of the work using the RRF/MRF approaches has been conducted through commercial software. More specifically, ANSYS FLUENT was used in nine works [23; 25; 26; 27; 28; 30; 76; 78; 83] and ANSYS CFX was exploited in 8 papers [24; 72; 73; 77; 80; 81]. ANSYS FLOTRAN was used in [82] while Altair HyperWorks AcuSolve in [74]. In addition, it is possible to notice that most of the works exploiting open-source software (two out of three) achieved an accuracy of 80%-90% while, exploiting commercial software it is possible to achieve an accuracy generally high, e.g., accuracy \(>\)90% for 10 works out of 11 where the accuracy is available and commercial software were used. In Table 3, it is possible to notice that each relevant paper exploiting the RRF/MRF approaches shows a level of complexity of 2, i.e., semi-transient simulations where the transitory is purely numerical without physical meaning leading to an averaged solution.  
Figure 2: Number of studies exploiting the static mesh approach shown based on the accuracy achieved and the complexity of the modelling.  
In Figure 3, it is possible to notice that when the accuracy is available (in 14 papers out of 22) it is distributed in a range from 80%-90% to >95%. More specifically, three papers show an accuracy of 80%-90%, five papers show an accuracy of 90%-95%, and five papers show an accuracy of >95%. However, in many of the pertinent papers the accuracy is not available.
## 1 Introduction
Instead of using conventional methods of providing feedback in response to detected collisions, the authors [16] used optical flow measured by a wide angle camera mounted on a moving robot for generating haptic forces in the virtual environment. The analysis of responses solicited by using NASA TLX suggested that the operator workload was reduced when haptic feedback was provided for the interaction with virtual environments [17; 18]. Haptic feedback can be used to minimize visual illusions experienced by operators while landing in a featured or featureless night environment. Operators rely on haptic cues more in a virtual landing task, as compared to visual cues which can reduce landing illusions. Moreover, haptic feedback has more impact for up/down movements (i.e., elevator control or pitch rotation) rather than left/right movement (i.e., roll and yaw rotations) [19]. Other benefits of haptic feedback were presented in terms of efficient control [17; 18], enhanced skill learning [20; 21], and improved situational awareness [22]. On the basis of the analysis of previous studies, it is observed that there exists a significant effect of incorporating haptic feedback in UAV controls for obstacle avoidance, exploratory missions, increasing situational awareness, minimizing landing illusions, and so forth. Here, we evaluate the effect of haptic feedback on navigational tasks for fixed wing UAVs. We develop a bare-bones UAV flight simulator for optimizing the navigational control of a virtual aircraft. The external forces acting on the aircraft are simulated and the operator is asked to continuously adjust controls to complete a navigation task. The operator provides motion commands to the UAV while receiving force feedback from a 6 DOF haptic device. We believe that the force feedback will provide better situational awareness and effective navigation. To this end, the following hypotheses are made:  
* While the presence of accurately modeled haptic forces provides better control of a UAV, unrealistic or exaggerated forces may produce better handling of the aircraft. * The role of haptic feedback in UAV handling varies for different phases of flight. In other words, a single force model may not produce better navigational control across all phases of flight.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 3 PQC-Based PKE Protocols for Using the PQC Algorithm  
According to the protocols described in [12], NIST recommends using the strongest algorithm parameters to ensure the effective use of PQC algorithms. The main reason for this is to avoid the cost and resources required to upgrade cryptographic implementations to a higher security level, as it is possible that upgrading may require modifications to the application/software where it will be needed. However, using stronger security parameters may have an adverse effect on application performance. Therefore, it is important to understand the impact of using PQC algorithms in various applications before carefully designing an optimization method or selecting a PQC algorithm for data encryption and decryption in various applications. To determine the impact of using PQC algorithms on applications and various networks, a protocol to encrypt and decrypt data transferred over the network is necessary. Therefore, we propose PQC standalone and PQC-AES hybrid PKE protocols that assume safe key exchange between the client and the server using the PQC KEM algorithm.