esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 2 Methods of Slope Stability Analysis  
Many methods exist to assess the stability of rock slopes, and each has its merits and shortcomings. These methods are limit equilibrium methods (LEMs); analytical and finite-elements; finite differences and/or discrete element methods (FEMs/FDMs/DEMs).  
LEMs (e.g., method of slices) are popular methods of analysis, where the factor of safety is obtained by dividing soil/rock mass above the hypothetical surface of failure into several vertical slices, as introduced in Equation (1). LEMs assume the location, shape of failure surface and horizontal forces acting on the sides of the slides and their directions. Despite their inherent weakness, these methods were developed and tested based on actual case histories [14; 15; 16; 17; 18].  
\[\mathbf{FOS}=\frac{\mathbf{\tau_{f}}}{\mathbf{\tau_{m}}} \tag{1}\]  
where:  
\(\mathbf{\tau_{f}}\) is the actual shear strength of rock mass,  
\(\mathbf{\tau_{m}}\) is the mean shear stress on the assumed surface of failure mobilized to maintain body in equilibrium.  
According to the aforementioned Equation (1), the factor of safety (**FOS**) is defined as the ratio of soil/rock shear strength at the failure to the mobilized shear stress on the surface of failure. Due to the simplicity of LEMs, they do not consider the stress-strain behavior of the soil/rock mass when calculating the **FOS**. Also, the pattern of critical slip surface is determined by trial and error [19; 20]. For more details about LEMs, the readers are referred to the work of [PERSON] and [PERSON] [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Numerical methods (e.g., FEMs, FDMs and DEMs) are powerful tools (e.g., they handle efficiently complex geometry) that provide approximate solutions to boundary value problems for partial differential equations [32; 33; 34; 35; 36]. They satisfy all requirements that have to be met for a complete solution to slope stability problems. The behavior of the material can be modeled with various constitutive equations and numerical simulation techniques, e.g., perfect elastoplastic analysis with Mohr-Coulomb failure criterion, creep deformation with [PERSON]'s model and jointed rock mass with interface elements [37; 38]. Hybrid techniques have be- come widely acceptable in the assessment of rock slopes. These tools combined LEMs and FEMs groundwater flow and stress analysis [27; 39; 40]. In this study, RS2D (e.g., formerly known as Phase2D) [41; 42; 43; 44; 45; 46] is used to evaluate the stability of an open pit at various general slope angles and to determine the most stable slope angle (s) employing the shear strength reduction technique (SSRT), as introduced in the next section.
## 3 Materials and Methods
### Explored Hyperparameters and Best Configurations  
In Table 1, the hyperparameters explored for each architecture and the corresponding best values identified can be observed.  
As can be observed, in all four configurations, the time window size is set to 5. This highlights the necessity of having a sufficiently large time window to correctly identify the highest number of anomalies, allowing the models to capture complex patterns and long-term relationships within the data. This choice is particularly relevant for forecasting not only sudden anomalies but also those that emerge gradually over time. Additionally, it can be noted that the loss function yielding the best results in neural network architectures is the one that combines Binary Cross-Entropy with the F1-Score. This combination has allowed the focus to shift toward configurations that not only minimize the Binary Cross-Entropy Loss but also achieve a higher F1-Score. This approach favors configurations where the model is more effective at detecting anomalies. Specifically, the values assigned to the \(\alpha\) and \(\beta\) parameters within the composite loss are 0.7 and 0.3, respectively. Finally, in all four architectures, the Oversampling technique with the replication of anomalous examples produced the best results compared to the use of alternative balancing techniques. This approach allowed for an increase in the representativeness of the anomalous class, significantly improving the model's ability to identify anomalies and minimizing the risk of overlooking critical events.
# The Local Integrity Approach for Urban Contexts:
## 4 Proof-of-Concept and Demonstration Results
### Proof-of-Concept Implementation for Live Demonstration  
The local integrity algorithm has been implemented in MATLAB(r) environment. Ad hoc software modules have been designed and optimized by means of vehicular tests and GNSS data collections on the selected urban scenario. Such modules are capable of parsing and processing in real-time the conventional NMEA output (including $GPGRS sentence) obtained from a consumer-grade GPS/EGNOS receiver (_i.e._, an uBlox-6 [16]), in order to compute the LPL values.  
It must be noticed that these software modules are suitable for processing live data from a local receiver, connected via serial port (USB cable), or from a remote receiver connected via TPC/IP. NMEA data logs can also be processed from file, for offline analysis and performance comparison purposes. In addition, the positioning information, including the LPL values, can be sent via TCP/IP connection to other components in the prototype architecture.  
Apart the LPL computation algorithm, a remarkable effort has also been devoted to the development of a proper Graphical User Interface (GUI) for real-time data display. Such GUI, shown in Figure 10, can also be used in order to control the status of the GNSS receiver, log NMEA data from it, and interactively display the local integrity results (_i.e._, LPL values).  
Figure 10: Screenshots of the local integrity software developed in MATLAB® environment for demonstration purposes: (**a**) Graphical User Interface; (**b**) Some results displayed in real-time.  
The GUI can also display the receiver outputs (e.g., standalone or SBAS fix status, UTC time, speed, pseudorange residuals, satellite skylplot, C/N\({}_{0}\) values, _etc._). In detail, the upper left plot in Figure 10b shows the estimated position of the vehicle on a map, including the latest positions during previous 30 s (blue dots) and the Local Protection Levels, displayed as a horizontal green ellipse around the current position. Such green ellipse provides immediate qualitative and quantitative information about horizontal position confidence. An instantaneous \"skylplot\" is reported on the upper right plot in Figure 10b, representing the geometry of the satellite constellation as seen by the GNSS receiver. The satellites directly used by the receiver for position computation are denoted with a full circular marker, whereas empty circles represent the satellites in view but not used in the PVT solution.  
Finally, the lower left plot in Figure 10b reports the pseudorange residuals for all the satellite signals used by the receiver. It is known that degraded satellite signals usually lead to larger residual values and rapid variations in time. These effects are more likely to occur in case of satellites at low elevation, as for example the satellite with PRN 17, shown in red in Figure 10b.
# Research on Selection and Matching of Truck-Shovel in
## 3 Selection and Matching of Trucks
### Efficiency Model of Truck-Shovel and Determination of Bucket-To-Capacity Ratio
Considering the mainstream shovel types of 35 m\({}^{3}\), 45 m\({}^{3}\) and 55 m\({}^{3}\) in oversized open-pit mines, the relationship curves between bucket-to-capacity ratio and comprehensive efficiency of the truck-shovel under different transport distances (2 km, 3 km, 4 km and 5 km) are established, as shown in Figures 1-4. Figure 1: Relationship curves at 2 km transport distance. Figure 3: Relationship curves at 4 km transport distance. Figure 2: Relationship curves at 3 km transport distance. According to the analysis in Figures 1-4, the best bucket-to-capacity ratio is related to the bucket size and transport distance. With the increase of bucket size, the best bucket-to-capacity ratio decreases, while the transport distance increases, the best bucket-to-capacity ratio gradually increases, and the best bucket-to-capacity ratio is mainly distributed in the range of 4-5:1. Therefore, for the oversize open-pit mine, the overall economy is better when the 35 m\({}^{3}\), 45 m\({}^{3}\) and 55 m\({}^{3}\) shovels are used and the bucket-to-capacity ratio is 4-5:1. However, the best bucket-to-capacity ratio should be determined comprehensively according to the truck-shovel efficiency model curve based on the transport distance of the mine. Accordingly, truck specifications suitable for matching 35 m\({}^{3}\), 45 m\({}^{3}\) and 55 m\({}^{3}\) shovels can be calculated, and the results are shown in Table 4.  
It can be seen from Table 4 that the 35 m\({}^{3}\) shovel is suitable for matching 220-270 t trucks, the 45 m\({}^{3}\) shovel is suitable for matching 270-330 t trucks and the 55 m\({}^{3}\) shovel is suitable for matching 330-400 t trucks. Considering the differences in the load tonnage and unloading height of trucks produced by different equipment manufacturers, it is necessary to further show whether the loading use coefficient of trucks and the unloading height difference meet the needs during the selection of truck-shovel equipment.
## 3 Basics for Implementation of the Machine-Learning Algorithm
### Damage-Identification Results  
The procedure for ANN design and testing results are presented in this section. As shown in Figure 17, the damage identification inverse problem was subdivided into a three-step hierarchical structure, calling three separate ANNs to perform damage detection, localization, and quantification. The first ANN was used to classify whether damage was present or not; if damage was detected, two additional ANN regressors assessed the radial position and extent of the damage.  
During training, the ANNs used the FFT of the vibration signal simulated by the FEM as an input. The training set was composed of signals affected by pit damages that varied in terms of their radial position and extent, with the latter expressed in terms of a normalized equivalent pitted area, as reported in Table 3. Specifically, the input was limited  
Figure 16: Example of noise-free signal and noise-added signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Parameter Value** & **Units** \\ \hline \(\mu_{log}\) & \(-14.5345\) & \(\left[\log\left(\frac{\pi}{\sigma}\right)\right]\) \\ \hline \(\sigma_{log}^{2}\) & \(0.6171\) & \(\left[\log\left(\frac{\pi}{\sigma}\right)\right]^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lognormal distribution parameters.  
Figure 15: (a) FFT of vibration signal measured on the test bench and (b) PDF of the peaks in the range of 10–13 kHz.  
by the FFT in the frequency range of 235 Hz to 1440 Hz. A PCA routine was implemented before the classification and regression ANNs to project their input space in order to reduce the input-space dimensionality and, consequently, the number of ANN parameters to be optimized.
## 2 Materials and Methods
### Honeybee Contamination Index (HCI)  
The Honeybee Contamination Index [28] is a valuable tool for evaluating the concentration of specific metals in the tissues of _Apis mellifera ligustica_, providing a measure of environmental contamination. This index can also be applied to honey, the hive's primary product. The HCI is applicable only to certain metals, for which the high (threshold\({}_{\text{max}}\)) and low (threshold\({}_{\text{min}}\)) threshold levels of contamination in the whole body (B) of honeybees are known [23; 25; 42]. These metals are Cd, Pb, Cr, and Ni, with values of 0.1, 0.7, 0.12, and 0.3 mg kg\({}^{-1}\) (wet weight) for the threshold\({}_{\text{max}}\), and 0.05, 0.3, 0.04, and 0.1 mg kg\({}^{-1}\) (wet weight) for the threshold\({}_{\text{min}}\), respectively.  
The same thresholds or very similar values were used in Spain [25] and the Netherlands [23].  
The HCI was also applied to honey for the same elements, i.e., Cd, Pb, Cr, and Ni [28], with values of 0.014, 0.05, 0.02, and 0.2 mg kg\({}^{-1}\) (wet weight) for the threshold\({}_{\text{max}}\), and 0.004, 0.01, 0.005, and 0.02 mg kg\({}^{-1}\) (wet weight) for the threshold\({}_{\text{min}}\), respectively. The threshold values for honeybees and honey are based on a comprehensive environmental biomonitoring campaign of metals in both matrices, with data collected and statistically analysed by the DiSTAL research group at the University of Bologna [42]. The conversion from wet to dry weight values was made considering weight loss values of 68% and 17% due to the drying process for honeybee tissues and honey, respectively [43; 44; 45].  
The equation for metals in honeybees and honey is as follows:  
\[\text{HCI}_{1}=\text{Log}_{10}\ (\text{[metal]}/\text{threshold}_{\text{max}}) ;\text{HCI}_{2}=\text{Log}_{10}\ (\text{[metal]}/\text{threshold}_{\text{min}});\]  
where  
* A positive \(\text{HCI}_{1}\) indicates a high contamination level;
* A negative \(\text{HCI}_{2}\) indicates a low contamination level;
* A negative \(\text{HCI}_{1}\) and a positive \(\text{HCI}_{2}\) indicates an intermediate contamination level.
# Integrity Analysis for GPS-Based Navigation of UAVs in Urban Environment
## 5 Conclusions  
Due to the dependence of GNSS positioning integrity on satellite visibility and environmental constraints, joint use of DOP coefficients, number of visible satellites and cut-off elevation in urban environments was investigated in this study for integrity monitoring purposes. An integrity monitoring approach utilizing these features and characterized by low computational requirements was proposed for UAV navigation using a standalone GPS receiver. The approach employs DOP-based protection levels and defines zones where integrity is maintained at the desired level utilizing information on visible satellite number and cut-off elevation as an environmental parameter. The proposed integrity analysis approach will be beneficial for UAV path planning and guidance tasks for urban unmanned traffic management (UTM) applications.  
The results and specified limits in this study are proposed based on single constellation--GPS. Performance of other constellations and their combinations should be analyzed as a part of future work to understand to what extent multi-constellation navigation can enhance positioning integrity in urban environments. Inclusion of multipath effects into consideration is another topic for future research.  
Conceptualization, O.K.I., I.P. and J.H.; Methodology, O.K.I. and I.P.; Software, O.K.I.; Formal analysis, O.K.I.; Writing--original draft preparation, O.K.I.; Writing--review and editing, I.P. and J.H.; Supervision, I.P., J.H. and A.T.; Resources-I.P. and A.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Republic of Turkey, Ministry of National Education.  
Authors would like to thank Positioning Division of Spirent Communications and personally [PERSON] for their enabling help and support in technical aspects of this study.  
The authors declare no conflict of interest.
## 2 Implementation of GPU-MOC Method
### Implementation of MOC on a GPU
#### 2.3.1 MOC Kernel on GPU  
The MOC kernel, executed on GPU, mainly calculates value of all MOC sections. The execution configuration and parameters for MOC kernel are outlined in Figure 1, in which the thread blocks and the thread grid are organized as 1D linear form that fits the line structure of pipeline well. The thread grid includes Y+1 thread blocks, and each thread block contains Z+1 threads. Each thread is allocated to a MOC section, thus, the total number of threads is equal to the total number of MOC sections. During the calculation, threads are located by thread index tx, which is accessible within the kernel through the built-in threadIdx.x and blockIdx.x variables. Threads that indexed in consecutive can be accessed by global memory at the same time to calculate the corresponding MOC sections. The problem of non-aligned access is unnoticeable because the information of the adjacent MOC sections like L  
Figure 1: Schematic of parallel strategy of the GPU-MOC method: CPU codes dispose logical operations, the Cartesian mesh in the x-t plane for MOC, and the thread organization structure of the GPU.  
and R are in linear order. Thus, shared memory may not make obvious improvement in computing efficiency. In Appendix A, an example of the MOC kernel for direct water hammer is given.  
When deciding the threading arrangement, great care should be taken in terms of the block size (number of threads per block), which may affect computing efficiency in some degree. Since resources of shared memory and registers for each block are limited, and number of threads per block and number of blocks per SM are limited. Too large or too small of a block size may reduce the number of threads in each SM. Therefore, reasonable allocation of the block size is important to achieve a high computing efficiency.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 6 Discussion and Conclusions  
In this work the architecture of a high-performance Manchester receiver, specifically designed for FPGA implementation, was described and tested. The receiver has been implanted in a research VLC system and the expected results have been verified through simulations and experiments. Thanks to the 12-bit AD in the front-end and the IQ demodulation the receiver achieves a high noise immunity, boosting the performance of the application it is embedded in.  
This approach is relatively calculation-demanding: just the demodulation for the IQ calculation requires 200 multiplications and as many additions in every \(T_{b}\), i.e., 4G operations per s. A real-life deployment of the algorithm, for example in the automotive context, requests addition security features for the implementation of the Automotive Safety Integrity Level (ASIL), which require even further calculations and hardware complication. Even if the required calculation intensity is hardly achievable through a processor and far from the reach of a \(\upmu\)C, it can be easily supported by only two DSP blocks of the FPGA (see Table 7), leaving much room for the implementation of security protocols or other algorithms.  
On the other hand, the calculation efforts of the proposed approach are similar to other complex algorithms successfully employed in widespread commercial applications, where the use of FPGA can be problematic. A notable example is the Global Position System (GPS) receiver [43]. The challenge is solved by the design of Application-Specific Integrated Circuits (ASICs) which include all the dedicated processing and, in high volume applications, are produced for few cents (see Table 1). In other high-end fields of application, FPGAs are already present. For example, where Manchester coded avionics buses like the MIL-STD-1553 [44] or the ARINC 659 [45] are used. These buses are already managed by FPGAs, and the transition to VLC is being considered [46].  
Conceptualization and methodology, S.R. and S.C.; firmware, S.R.; validation, S.C.; formal analysis, L.M.; investigation, S.R. and S.C.; resources, L.M.; writing--original draft preparation, S.R.; writing--review and editing, L.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Italian Ministry of University and Research (MIUR) and by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE00000001--program \"RESTART\").  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 3 System Model  
Our network model consists of \(N_{U}\) UAVs (\(U_{i}\)) and one ground base station (BS). UAVs are mounted with wireless Access Points (APs) having multiple Wi-Fi interfaces. One of the interfaces for each AP provides Wi-Fi service to terrestrial vehicles while the rest form mesh networks with nearby UAVs' APs. One of the UAVs is connected to the BS which has a backhaul link to the Internet and relevant cloud services. A computational unit that houses an SDN controller and serves as the network controller is also mounted on one of the UAVs. This controller is managed by a service provider remotely. UAVs are primarily used for communication, which reduces energy consumption, rather than to offload tasks. We do not take energy constraints into account for the sake of simplicity. The notations used in system modeling are listed in Table 1. The network model is shown in Figure 1.  
Figure 1: Multi-UAV edge network model investigated in our work. All the task offloading decisions are centrally made by a controller application, namely the offloading orchestrator. Two main types of terrestrial vehicles are present in the system. The first type corresponds to rescue and emergency vehicles, which are task owners and signal their task information including a deadline, priority factor, and size to the task pool. This pool is located at the offloading orchestrator on the controller. The orchestrator application assigns these tasks to the second type (compute station) of vehicles, also called task processors, and informs both parties about this assignment. Only task identifier data are essentially stored in the task pool, not the task data itself. Therefore, the data transfers are carried out directly from the task owner to the processor units. In Figure 2, this basic task assignment flow is given. The processor vehicles have their own limited-size queues to store unprocessed tasks. A processor may simultaneously download the data of multiple tasks if the policy of the active task offloading algorithm allows that. The processing speed and queue size depend on the processor group (i.e., we assume class-based processor capabilities). Similarly, the priority of a task is determined by the owner vehicle's group (i.e., class-based task priority). If a task is not completed by the deadline, the system receives a penalty proportional to the amount of time that has passed between the deadline and the task completion time. The mobility of vehicles is not linked to the task processing operation, i.e., a vehicle may leave the area, regardless of its tasks still being processed. The tasks are preserved for vehicle departures. In other words, a task is also sent back to the pool if the assigned processor vehicle leaves during packet transmission for that task. Depending on the deadline, two cases may occur for the situation in which a task owner leaves the coverage area: The system receives an extra penalty score \(z\) if the deadline is already missed; otherwise, it receives no penalty. To identify and monitor the network