esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 NOAA Ocean Satellite Course
### Datasets  
The datasets used for the course are primarily off-the-shelf satellite products, which are on regular spatial grids (as opposed to swath products). Whenever possible, we make available near real-time versions of each product, which have lower quality control but are available with low latency, and delayed release (science quality) versions that are binned into daily, weekly, and monthly averaged composites. Other products included in the course are those that blend multiple sensors into a dataset, thereby providing improved spatial coverage and often extending the temporal coverage back a decade or longer [6; 25]. This mix of products supports many of the fisheries management activities carried out by NMFS. Stock assessment, habitat classification, and conservation efforts require long-term satellite  
Figure 4: Output graphic from an R script which matches up locations from a tagged fish with satellite chlorophyll data.  
records of the highest possible quality on decadal timescales, in order to accurately detect trends and anomalies that might be influencing animal populations. In contrast, some fishery management needs, such as optimizing ship surveys in real-time and driving models in the emerging field of dynamic ocean management [9; 10; 11], require recent and near-real-time satellite data.
## 4 Results and Discussion
### Discussion
As the bearing capacity is above the allowable BC at BC/BC\({}_{\rm NV}\) = 0.3, it can be expected that in this range less impact on the footing occurs. The cases of intermediate soils with high cohesion with variable \(\phi\) (Figure 9c,d) showed the BC/BC\({}_{\rm NV}\) = 0.65-0.98 with a larger influence zone when \(\phi\) is low. For this last case, as previously, these results also revealed that \(\phi\) controls the direction of the zone influence. However, within the soils with larger strengths, the influence of the cavity is mostly at shallow depths. In deep depths, soils with larger strengths exhibit reduced influence of the cavity due to the nonlinear increase of shear strength with \(\phi\) as a function of depth. The observed results align with findings by [PERSON] et al. [28] who investigated strip footing bearing capacity on \(c\)-\(\phi\) soils with square voids. Comparative results of normalized bearing capacity factors (Nc/Nc\({}_{\rm NV}\)) with studies by [PERSON] [18], [PERSON] et al. [28], and [PERSON] et al. [29] illustrate agreement with results obtained from RPFEM (Figure 12). The failure mechanisms observed in cavities with horizontal gaps from footing centerlines delineate two types of failures, emphasizing the significance of soil shear resistance in predicting failure mechanisms. Bearing failure occurs when cavities are sufficiently distant from the footing centerline and outside the slip surface. On the contrary, bearing and void failure result in rupture lines developing from the edges of the footing to cavity walls and roof, potentially inducing differential settlements over a broader area. While [PERSON] et al. [28] confirmed similar behavior, their proposed solution lacks a formula for evaluating stability levels. In contrast, based on RPFEM results, this study proposes an equation for the critical boundary, incorporating normalized parameters \(R\) and \(H\), cavity dimensions, and soil strength. This solution confidently predicts the influence zone based on normalized parameters. In summary, RPFEM demonstrates robustness in analyzing the studied issue, encompassing both bearing capacity and stability concerns. Figure 12: Comparison between Nc derived from RPFEM and that of previous studies [18; 28; 29] (\(c\) = 60 kPa and \(c\) = 300 kPa).
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 5 Results and Discussion
### The Role of Human Factors in Predicting Workload  
This study mainly focuses on low-interference detection metrics for workload assessment. However, other metrics, such as the ones related to human factors, may also contribute to the model prediction. To explore and validate this idea, we incorporated age, gender, and flight ability assessment scores from the pre-experiment for each subject into the dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors (or Model Name)** & **Year** & **Physiological Signal** & **Feature Selection** & **Classification Model** & **Number of Categories** & **Whether Device** & **Can Be** & **Can Be** & **Achieved in** & **ACC** \\ \hline D. [PERSON] et al. & [103] & 2022 & ECG, GSR & None & K-NN & 3 & Yes & No & 72\% \\ [PERSON] et al. & [104] & 2017 & ECG, GSR & SFFS, MDA & K-NN & 3 & Yes & No & 78\% \\ [PERSON] et al. & [105] & 2021 & HRV, GSR & Pearson-r & ANN & 3 & Yes & No & 73\% \\ [PERSON] et al. & [106] & 2023 & EEG, Eye tracking & LDA & LR & 2 & Yes & No & 82.7\% \\ [PERSON] et al. & [83] & 2021 & fNIRS, ECG & None & LDA & 3 & Yes & No & 68\% \\ [PERSON] et al. & [82] & 2018 & EEG & None & SVM & 3 & Yes & No & 69\% \\ FCN-DG & 2024 & Eye tracking, Seat pressure & DG-FCN & FCN & 3 & No & Yes & 65\% \\ AKE-PWD & 2024 & Eye tracking, Seat pressure & None & ensemble learning & 3 & No & Yes & 82.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the method proposed in this study and existing methods.  
Figure 11: The architecture of the FCN-DG model.  
The results showed that adding age and gender did not improve the model's performance. This may be due to the relatively concentrated age distribution of the recruited pilots and the predominantly male composition (to reflect the gender ratio in the pilot population). As a result, these variables did not provide sufficient variability to enhance the model's predictive performance; thus, they were not included in the model.  
However, incorporating the flight ability assessment scores increased the model's prediction accuracy by approximately 2%. Specifically, as shown in Table 7, for the prediction of high workload level, the inclusion of the flight ability metric resulted in improvements in precision, recall, and F1-score. This finding indicates that human factor data related to the subject's baseline abilities can indeed enhance the model's predictive performance of pilot workload. This flight ability is subjectively judged through simulated flight tests. However, given that there might be various ways to evaluate the flight ability, its reference value for broader application can be demonstrated further.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 6 Open Research Issues and Trends in 5G
### Fronthaul/Backhaul/C-RAN Issues  
The performance achieved in sum-rate can be enhanced by using the adaptive before/ after-precoding method. For this purpose, it is essential to measure and analyze precoding problems that use minimum backhaul. Similarly, the users' accurate profiling is a important breakthrough when examining suitable approaches for the development of backhaul re-configuration in CRAN. Furthermore, effective algorithms need to be developed to increase the performance of the existing system depending on traffic load and user profiles to evaluate the optimal backhaul.  
Additionally, BS performance investigation with clustering (specifically having large size clusters), while keeping in mind the reconfigurable backhaul ultra-dense BSs deployment, will likely be an auspicious research gap in the future. Furthermore, the study in this domain should emphasize examining effective resource optimization methods by keeping in mind the limitations of both backhaul and fronthaul links while considering the user-side demands.
# A Review on Methods and Systems for Remote Collaboration
###### Abstract
Figure 1 shows the main devices used in remote assistance in the last few years, and depicts a collaborative scenario for connecting an on-site worker with a remote expert. The minimal equipment needed is either a PC with conferencing capability and remote control equipped with an LCD/console or a smartphone. A robotic arm on the worksite can be controlled remotely to point or move objects and ease the worker's tasks. The head-mounted display, the web camera, and the projector are useful for presenting the visual information used as the basis for giving specific guidance. To the best of our knowledge, there has not been any comprehensive review of remote assistive technology until now. The motivation for conducting this review was the need to have an overview of the current technologies employed. There was no such type of review  
Figure 1: Worker side and remote expert side with devices enabling remote collaboration. when we researched in the IEEE Xplore, Springer Link, and ACM Digital Library online databases. It is of great interest for researchers to have a clear view of the main methods and systems that would allow them to identify the position of their research in the field. Therefore, this paper reviews the current methods and systems employed in remote assistance and collaboration to gain clear guidelines for future research. We review state-of-the-art remote assistance technologies, devices used, visual display technologies, and communication cues.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 5 Discussion  
Visible light is difficult to be quantitatively analyzed due to the variability and complexity of hydrological and atmospheric factors during their propagation. The improved band ratio algorithm, which is used to retrieve depth data from passive remote sensing, belongs to the classification of semi-analytical models. In this model classification, the detection capability of water depths is limited by the depth range of control points. Given that the control points provided by the Aquarius LiDAR data are in the range of 0-20 m, the derived bathymetry can be narrowed to a relatively small range.  
The extraction of echo signals from extremely shallow waters and the bottom of deep waters has certain limitations. Further research is needed to identify and extract LiDAR waveform signals. The incident angle is a key parameter of bathymetric inversion. This study assumes that the incident angle for laser launching into waters is equal to the launch angle of the LiDAR. However, the assumption is inappropriate in complex sea conditions, and it needs to be gradually improved in later experiments.
# Deep Learning Model for the Inspection of Coffee Bean Defects
## 1 Introduction  
After its introduction to Europe in the 17 th century, coffee has transitioned from a luxurious rarity that was only available to the nobility to a common drink that is affordable to people from all strata of society. With the development of the global economy, specialty coffee has emerged. The Specialty Coffee Association (SCA) has defined new standards for selecting specialty coffee. These increasingly rigorous standards raise the difficulty of coffee bean selection, which mostly involves manual screening that is labor-intensive and inconsistent in the application of screening standards.  
Machine learning has been used in detection tasks involving high risk, time consumption [1] and precision requirements [2]. Most agricultural products must be sorted prior to their sale. Agricultural product grading [3], defect screening [4, 5], crop health inspection [6], and pest monitoring [7] are labor-intensive tasks. Diversity in agricultural products increases the complexity of product defects. Therefore, machine vision systems with deep learning algorithms that have a detection capacity comparable or superior to that of human vision can be employed in the agricultural industry to save considerable human resources and enhance product fineness.  
The SCA stipulates strict definitions for coffee bean defects and devises scoring standards for each defect type. The quantity of defects in a batch of coffee beans is used to determine its grade. In this study, deep learning was used to learn and detect coffee bean defects according to the defect types defined in the SCA standard. Coffee bean samples with different defects were used to construct a convolutional neural network (CNN) through feature training. Experiments were performed with the constructed CNN model, and the obtained experimental results were analyzed to adjust the architecture and parameters of this model for accurately detecting coffee bean defects.
# Train Speed Estimation from Track Structure Vibration Measurements
## 4 Verification of the Train Speed Estimation Method
### Train Speed Estimation Results  
In this paper, twenty passing trains are used to identify the reliability of the method by using the displacement of the track slab, the acceleration of the track slab, and the acceleration of the rail. The relative difference \(\delta\) of the proposed method can be calculated by Equation (7):  
\[\delta=\frac{V_{v}-V_{T}}{V_{T}}\times 100\% \tag{7}\]  
where \(V_{\text{g}}\) is the train speed estimated from track structure vibration measurements and \(V_{T}\) is the true train speed. The train speeds ranged from 124.6 km/h to 227.2 km/h, which were estimated using the proposed method and wheel counter. The train speed estimated using the wheel counter is assumed as the true value because the wheel counter is very accurate [16; 22].  
As shown in Figure 11, the relative differences of the proposed method are mostly less than 2% based on slab displacement, 5% based on rail acceleration, and 10% based on slab acceleration, respectively. The absolute average differences are listed in Table 3. It is apparent from the table that the slab displacement signals are very stable regardless of the sensor position and the track structure situation. The results of the rail acceleration can be affected by the track structure situation. The accuracy of estimation results is improved lightly when a gap between the slab and the support layer exists. In contrast, the existing gap seems to amplify the differences in the slab acceleration estimation results. Furthermore, the estimation results may be not influenced by the track curve. In summary, the validation results show that this proposed method can effectively estimate the train speed.  
Figure 11: The automatic train speed estimation results of (**a**) the slab displacement, (**b**) the rail acceleration, and (**c**) the slab acceleration, respectively.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 1 Introduction
In the enriched isogeometric analysis mentioned above, point projection is used to enrich the base approximations with those on lower-dimensional geometrical features such as crack surfaces and phase boundaries, enabling simulations of fracture propagation [6; 10] and solidification [11]. A fast and robust point projection method is critical to efficiently solving these problems. The rest of the paper is organized as follows. In Section 2, a review of the literature pertaining to the point projection problem is carried out. In Section 3, the algebraic estimation of distance from a low-degree NURBS curve or surface is reviewed. In Section 4.1, a detailed algorithm for two-dimensional algebraic point projection is presented followed in Section 4.2 by its extension to three-dimensional NURBS surfaces. Several examples are provided in Section 5 to validate the developed algorithm. The paper is concluded with remarks in Section 6.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attacks on CC
#### 5.2.1 SYN Floods  
An adversary executes SYN flood attacks on CC by sending SYN requests using a spoofed source IP address, forcing the VM to respond and allocate the necessary resources to handle the requests. The VM waits for an acknowledgment from the'sender' that never arrives. Continuous attacker's requests finally exhaust the VM of all its resources, such as memory and CPU [31]. Consequently, the VM is forced to reject all subsequent user requests, including the legitimate ones, as illustrated in Figure 4.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Setup Phase (SP)
#### 3.2.3. Contention Access Period (CAP)  
CAP is designed for those non-cluster head nodes that failed to join the cluster or moved position to join the cluster again, such as \(N_{1}\) in Figure 4. The process is implemented by sending a join request and data slot declaration (JAD_REQ) message to CH in a CSMA/CA manner, and declaring the number of data slots required. JAD_REQ is similar in format to JC_REQ, except that the former has 1 extra byte to indicate the required data slots. If a non-cluster node wants to join a cluster in CAP, it needs to listen to the CS_AOC message to calculate the start time of CAP, that is, \(CAP\_ST=L\_CS\times TOT\_CS\).  
Since each cluster can hold up to 256 member nodes, the maximum duration of CAP is \((256-TOT\_CS)\times T_{cap}^{u}\), where \(T_{cap}^{u}\) is the unit slot duration of CAP.  
Before sending JAD_REQ, every non-cluster head node sets a random backoff period (\(R\_backoff\)) for collision avoidance, and \(R\_backoff=random(0,MaxBW)\times T_{delay}\), where \(MaxBW\) is defined as the maximum backoff window and is a fixed integer, and \(T_{delay}\) is the unit length of random backoff. The node performs CCA to sense the channel after completing the backoff process. If the channel is idle, the node sends JAD_REQ to its CH; otherwise, it performs backoff and CCA again until the channel is idle.  
\(T_{cap}^{u}\) is determined using \(MaxBW\) and \(T_{delay}\). Assume that the time it takes for a node to perform a CCA is \(T_{cca}\), and the time it takes to send a JAD_REQ message is \(T_{data}\), \(T_{cap}^{u}=T_{cca}+MaxBW\times T_{delay}+T_{data}\).
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 1 Introduction
What is more noteworthy is that, in literature [16], the effect of pollution non-uniformity distribution on the insulators performance under ac voltage has been studied with three types of distribution: Transversal, periodic longitudinal and non-periodic longitudinal. Transversal and periodic longitudinal distribution are similar to W/L and T/B non-uniform pollution noted above. However, the effect of non-periodic longitudinal distribution on the insulator's performance is indeed a new discovery, which showed that this category has allowed the identification of the existence of a minimum voltage of about 42% of those acquired under uniform distribution. From the above analysis, we can see that the contamination non-uniformity did have great influence on flashover voltage, but few of the reports on pollution non-uniformity in energized condition were presented. Natural exposure tests with \(-\)280 kV DC voltage energization were carried out for 5 years in reference [17]. The results show that insulators near the line and ground sides of a string collect more contaminant than the middle when DC voltage is applied. However, the specific values and influencing factors of non-uniformity are not given. In this paper, both field contamination experiments of HVDC transmission lines and wind tunnel contamination simulation tests in dc energized condition were conducted, and pollution non-uniformity coefficients K\({}_{\text{T/B}}\), K\({}_{\text{W/L}}\) and K\({}_{\text{H/M}}\) were proposed and obtained. Thus, the effects of particle diameter, wind velocity and relatively humidity on pollution non-uniformity were quantitatively computed. In order to explain the uneven distribution phenomenon along the string, the coupling-physics model of composite insulator string was established. Furthermore, basing on field charging theory, the mechanism of non-uniformity along insulator sting was then revealed. Research results are valuable for field pollution level mapping works and for DC lines outdoor insulation design.
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 5 A Smart City Testbed: The Rigers Project
### Network-Level Performance Assessment via Simulations
Finally, note that the trends of the curves reported in this figure are very similar to those of the curves reported in Figure 9 of [31]: in particular, a weak dependence of the packet delivery ratio on the number of devices is observed also in [31] in case of connectivity issues (almost flat behavior of the curve related  
Figure 8: Packet success rates vs. number of buildings in both the Navile and Saragozza districts. to \(SF=7\)). Moreover, also in [31] the choice \(SF=12\) appears the best solution when few nodes are present, but it becomes the worst case when increasing the traffic generated, because of collisions. As far as Saragozza is concerned, being the power decay index lower, there are not connectivity issues: a PSR of 100% is obtained for low values of \(N\). As a result, the PSR decreases with \(N\) for all values of \(SF\) and \(SF=7\) is the value that maximizes the success rate, since it is characterized by the lowest value of airtime. In this case, if the application requires a minimum PSR of 90%, the LoRa gateway is able to serve up to approximately 1700 buildings. In this scenario, instead, the use of a variable \(SF\) allows to maximize the performance, because less connectivity issues are present and only few nodes increase \(SF\). As a result, performance is better than in the case \(SF=7\), since more nodes can reach the gateway, but just few nodes increase \(SF\), therefore collisions are still mitigated. In the above figures, the standard deviation of the PSR is always below 2%.
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 3 Experiments and Results  
### Experimental Area and Data Classification  
Eighteen GNSS stations of the Hong Kong Satellite Positioning Reference Station Network and a radiosonde station (ID: 45004) in Hong Kong, were selected for the experiment (as shown in Figure 7). Sounding data were used to calculate the initial values and determine the maximal height for tomography. The study area ranged from 22.190\({}^{\circ}\)N to 22.575\({}^{\circ}\)N, and 113.870\({}^{\circ}\)E to 114.350\({}^{\circ}\)E, about 42.6 km from north to south and 49.4 km from east to west. The ERA5 data area ranged from 22.0\({}^{\circ}\)N to 22.75\({}^{\circ}\)N, and 113.75\({}^{\circ}\)E to 114.5\({}^{\circ}\)E, which was larger than the tomographic area. The experiment period was DoY 260 2018.  
The maximal height of tomography could influence the choice of observations. The sounding station data for 2 years were used to analyze the appropriate maximal height of tomography. The relationship between water vapor density and height, calculated from the sounding stations in Hong Kong from 2016 to 2017, is plotted in Figure 8. The maximal height was about 9600 m; therefore, the maximal altitude of the tomography voxels was set to be 9600 m. The height of each voxel layer was 800 m, which was converted to orthometric height. Therefore, the total number of voxels was 7 \(\times\) 8 \(\times\) 12 (7 latitudinal, 8 longitudinal, and 12 vertical).  
In order to study the performance of tropospheric tomography combined with the LEO constellation, observational data combinations were classified into five schemes, namely, G, L288, GREC, GRECL60, and GRECL288, where G, R, E, C, L60, and L288 denote the GPS, GLONASS, Galileo, BDS, 60, and 288 satellite LEO constellations, respectively.  
Figure 7: Distribution of GNSS and radiosonde stations located in Hong Kong. Red triangles represent GNSS stations, and blue circles represent radar stations.