esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 1 Introduction
Different countermeasures will typically be needed as a result of these adjustments to increase safety requirements and to repair problematic areas of the road [30]. The countermeasures can be divided into capital and maintenance work categories. However, road maintenance and new construction investments (in EUR/km) were found to result in both positive and negative effects on the severity of road accidents in Spain [31]. The work suggested further study on this observed anomaly. In an earlier study on data from the same country, the maintenance investment was found to achieve consistently lower accident fatality results [32], while investments in new construction were believed to only increase the risk and fatality of road accidents. Contrarily, a study conducted in the United States showed highway capital investment to have a negative effect on road fatalities [33]. The effects of infrastructural and demographic changes on accident frequency and fatality were studied from 8-year accident data from the State of Illinois [34]. Increasing the number and width of lanes were found to result in more crashes, while changes in demography were found to have an insignificant effect on the observed results. Reports from these studies on the effect of road investment on accidents cannot be generalized due to inconsistent findings. These contradictions could be due to several different factors related to the study area, such as demography, economy, road transport system characteristics, etc. However, it can be concluded that there has been no clear general explanation as to why road investment, whether for maintenance or new construction, can result in either a decrease or increase in the crash rate or in the number of fatalities. In this study, the inconsistent effect of road infrastructure investment on traffic accidents was shown to be due to a concave nonlinear relationship between the parameters. A simple method for estimating safe limits for both maintenance and new road infrastructure investments based on minimum Annual Road Accident (ARA) using the developed ANN-ARA model was also illustrated.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 6 Hardware Model Using Finite State Machines
### FSM Optimizations
#### 6.2.2 Second Optimization
Furthermore, the latency of a transitions system is obtained by counting all the _tick_ transitions and the latency of the FSM equals the number of all transitions. For example, the number of _ticks_ of a transition system in Figure 4 is 13, likewise, the transition count for FSM in Figure 7 is 13. While the access patterns may arguably move the SDF-AP model closer to a hardware by describing at which clock cycles the tokens are produced and consumed, the behaviour of an SDF-AP model is asynchronous making it difficult to directly compare with a synchronous FSM. On the other hand, it is not easy to observe where token productions and consumptions take place by merely looking at the state transitions. One common approach to defining conformance is using containment of set of behaviours of the two disparate models. We would simply apply this principle in our setting as in [18] by proposing FSM model \(N\) conforms to dataflow model \(\omega_{M}\) if the set of behaviours of \(N\) is a subset of the set of behaviours of \(M\). However, due to the deterministic behaviour of the SDF-AP model (under a 1-periodic schedule and throughput constraints), there is exactly one observable behaviour for FSM \(M\) (i.e., \(\omega_{M}\)) and exactly one observable behaviour for a dataflow model \(N\) (i.e., \(\omega_{N}\)). This leads to our conformance formulation \(\Omega\) which maps an observable behaviour of FSM \(\omega_{M}\) to an observable behaviour of a dataflow model \(\omega_{N}\) as  
\[\Omega:\omega_{M}\mapsto\omega_{N}=\rho_{0}\cdot\rho_{1}\cdot\rho_{1}\cdot\cdots\]  
where \(\rho_{i}=\text{tick}\cdot\alpha_{i}\) and \(\alpha_{i}:=\{\ell\in\{\tt put,get\}\}\) and the mapping \(\psi:\{\tt put,get\}\mapsto\{w,v\}\mid w=1,v=1\) maps the dataflow actions \(\ell=\{\tt put,get\}\) to respective FSM output signals output signals \(O=\{\tt w,v\}\) where their values are set to 1 (i.e., therefore resulting in \(\psi=\{\tt put\mapsto w,get\mapsto v\}\)). For example, consider the FSM observable behaviour as defined in Section 6.1  
\[\omega_{M}=(\bar{\tt w}\bar{\tt v})\Big{(}(\bar{\tt w}\bar{\tt v})(\bar{\tt w} \bar{\tt v})(\bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{ \tt v})(\bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{\tt v })(\bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{\tt v})( \bar{\tt w}\bar{\tt v})(\bar{\tt w}\bar{\tt v})\Big{)}^{\infty}.\]  
This \(\omega_{M}\) is mapped using \(\Omega\) to  
\[\Omega(\omega_{M})=\text{tick}\cdot\Big{(}\text{tick}\cdot\{\tt put\}\cdot \text{tick}\cdot\{\tt put\}\cdot\text{tick}\cdot\{\tt get\}\cdot\text{tick} \cdot\{\tt tick}\cdot\{\tt put,get\}\ \ldots\ \text{tick}\cdot\{\tt get\}\Big{)}^{\infty}\]  
which is equivalent to dataflow network observable behaviour \(\omega_{N}\) that is defined in Section 4.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 3 ICI Unaware Cost Function Based Soft Feedback Iterative Receiver
### Cost Function Controlled Soft Information Feedback  
Threshold controlled or uncontrolled hard and soft decision feedback methods are normally used in iterative receivers. We propose a new feedback method that uses cost function as a decision condition threshold. Based on the cost function threshold, it decides whether to select the current iterative channel estimation result or to use the pilot-aided initial channel estimation value. This design is based on the idea given in reference [22], where a cost function was used for the decision feedback in a SISO coded OFDM wireless communication system. However, the cost function of the channel estimation for the current iteration was always compared with the initial channel cost function \(\psi\Big{(}H_{m}^{(0)}\Big{)}\), whereas we compared the cost function of the current iteration with the cost function of the previous iteration of channel estimation \(\psi\Big{(}\hat{H}_{m}^{(j-1)}\Big{)}\). The advantage of this change is that the cost function keeps updating in comparison to the previous iteration, therefore, the performance of the channel estimation improves. Furthermore, in reference [22], pilot-assisted channel estimation was performed based on the hard feedback method. We used soft feedback and OMP channel estimation, which obviously improves the channel estimation performance [23]. This improved decision feedback method based on the cost function is given by:  
\[H_{m}^{(j)}=\left\{\begin{array}{ll}\frac{\gamma_{m}}{\hat{X}_{m}^{(j)}}& \text{if}\psi\Big{(}\frac{Y_{m}}{\hat{X}_{m}^{(j)}}\Big{)}\leq\psi\Big{(}\hat {H}_{m}^{(j-1)}\Big{)}\\ \hat{H}_{m}^{(j-1)}&\text{otherwise}\end{array}\right\}, \tag{15}\]  
where \(j\) indicates the number of iterations, \(\psi\Big{(}\frac{Y_{m}}{\hat{X}_{m}^{(j)}}\Big{)}\) and \(\psi\Big{(}\hat{H}_{m}^{(j-1)}\Big{)}\) represent the signal detection cost function and channel estimation cost function for \(m\)th subcarrier, respectively. To derive the cost function \(\psi\Big{(}\frac{Y_{m}}{\hat{X}_{m}^{(j)}}\Big{)}\), the iterative receiver input is taken from the demodulated received symbols that serves as auxiliary pilots. The modified signal model at the iterative receiver is given by:  
\[Y_{m}=H_{m}\hat{X}_{m}+\eta_{m}, \tag{16}\]  
where \(\eta_{m}\) is given by:  
\[\eta_{m}=\big{(}X_{m}-\hat{X}_{m}\big{)}\cdot H_{m}+\omega_{m}, \tag{17}\]  
The decision feedback induces this additional noise component, that can be approximated as additive white Gaussian noise (AWGN) with a zero mean and its variance is given by:  
\[\sigma_{\eta_{m}}^{2}=E\Big{\{}\big{|}\eta_{m}\big{|}^{2}\Big{\}}=N_{0}+\sigma _{\hat{X}_{m}}^{2}. \tag{18}\]  
where \(N_{0}\) is the variance of the AWGN signal and \(\sigma_{\hat{X}_{m}}^{2}\) can be generated from the LLR of the decoder output [22].
# Behaviour of the Steel Welded Grid during a Simplified Pullout Test in Fine Sand
## 2 Materials and Methods
### Welded Steel Grid  
Welded meshes are manufactured of steel wire with a tensile strength of 500-700 MPa in accordance with the PN-EN 10223-8 standard [32]. Wires with a diameter of 2.2 mm to 5.0 mm are used, and the edge wire may be the same or thicker than the filling wire. The opening is rectangular and its most common sizes are 50 \(\times\) 50 mm, 50 \(\times\) 100 mm, 75 \(\times\) 75 mm or 100 \(\times\) 100 mm, as well as 76.2 \(\times\) 76.2 mm (3 \(\times\) 3 inches).  
A commonly used anticorrosion protection of welded meshes is used to cover the wire, with one of three types of coating according to the PN-EN 10244-2 [33] and 10245-2 [34] standards:  
- type 1: thick zinc coating,  
- type 2: zinc-aluminium alloy layer up to 350 g/m\({}^{2}\),  
- type 3: a coating consisting of a pure zinc layer and an additional polymer layer, usually PVC.  
Laboratory tests were conducted on four types of welded meshes, which differed with respect to wire diameter as well as the type of anticorrosion coating used during production. They had been chosen as they are widely used for the production of gabion baskets available on the European market. It is worth mentioning here that soil reinforcement with gratings made of thick steel rods (6.3-12.8 mm in diameter) is used in practice in the North American market [35]. However, soil reinforcement with both gratings made of steel rods and welded steel meshes made of thinner wire (such as those used in the production of gabions) is an innovative method on a European scale.  
In the series of tests, sheets with dimensions of 762 \(\times\) 1676 mm, consisting of 11 tensile longitudinal wires and 23 transverse wires, are used, and in the case of the largest diameter wire mesh, also sheets with dimensions of 305 \(\times\) 1676 mm, consisting of 5 and 23 wires, respectively. The smaller number of wires was the result of the limitation of the maximum force that could be applied during the test. The choice of meshes with type 2 and type 3 coatings was based on the manufacturer's guaranteed long-term protection against corrosion under the conditions prevailing in the soil or in contact with the soil. This period is 80-100 years, and even more than 120 years respectively. The types of meshes are presented in Table 1.
## 2 Materials and Methods
### Methodology
#### 2.4.9 Sensitivity Analysis and Parameter Tuning of ACO  
To assess the influence of key parameters in ACO on path optimization performance, a systematic sensitivity analysis was carried out. The parameters considered included the selection probability associated with pheromone levels (a), ranging from 1.0 to 2.0; the heuristic influence based on estimated distance (\(\beta\)), also ranging from 1.0 to 2.0; the pheromone evaporation rate (\(\rho\)), evaluated between 0.1 and 0.5; the initial pheromone quantity (\(Q\)), with values between 1.0 and 2.0; the number of ants (\(m\)), set equal to the number of workstations (\(m=N\)); and the maximum number of iterations (\(T\)), limited to 100 cycles.  
Representative combinations of these parameters were defined, while maintaining the geometric conditions of the RC fixed, based on the layout previously optimized by GA. Each configuration was evaluated based on the total time required for the robot to visit all stations, considering kinematic constraints such as maximum speed and acceleration.  
The results revealed configurations that support convergence toward more efficient paths. Specifically, higher values of \(\alpha\) (between 1.5 and 2.0) and moderate evaporation rates (\(\rho\) between 0.2 and 0.4) led to more robust solutions by balancing the exploitation of promising paths and the exploration of new alternatives. Additionally, it was confirmed that assigning one ant per station yields good results without significantly increasing computation time. A summary of the evaluated scenarios and the best results obtained is presented in Table 2.  
The results presented in the ACO Sensitivity Table clearly demonstrate how different parameter combinations significantly affect the algorithm's performance, particularly in terms of the robot's total travel time. The most efficient configurations were observed for values of \(\alpha=1.5\), \(\beta=2.0\), \(\rho=0.3\), and an initial pheromone level of \(\tau_{0}\approx 1.5\). With this parameter setting, the algorithm was able to minimize the total path time to below 11.28 s, representing highly favorable performance in terms of temporal efficiency.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 4 Preventive and Corrective Lost Circulation Measures
### Preventive Measures to Avoid Lost Circulation during Drilling  
The preventive approach involves managing the loss of circulation by planning in advance and possibly stopping its occurrence. Preventive measures must be applied when the mud loss exceeds 0.16 m\({}^{3}\)/h which is the maximum allowable loss of fluid [41]. This loss can be measured in the pits at the surface. In all situations where a loss zone is expected, preventive measures should be taken by adjusting the mud properties and adding a circulating material (LCM) to the mud. In addition, it is extremely important to perform the leak off test (LOT) and Formation Integrity Test (FIT) to limit the possibility of lost circulation.  
The complete prevention of lost circulation is impossible during drilling through naturally fractured, cavernous or high permeability zones, but it is possible to limit the loss of circulation by taking certain precautions, especially those that avoid the occurrence of induced fractures. The most efficient approach to prevent losses is keeping the wellbore pressure equal to or very close to the formation pressure. Preventive measures are shown in Table 4 and include controlling the Bottomhole Pressure (BHP), Wellbore Pressure Containment Improvement (WPCI), running intermediate casing in the transition zones, setting expandable tubulars, and wellbore strengthening [5; 55; 56; 57; 53].  
Drilling technologies that can prevent circulation loss are briefly described below.
# Accelerating High-Resolution Seismic Imaging by Using Deep Learning
## 4 Conclusions  
The encoder-decoder network can learn the high-resolution patterns hidden in the computationally intensive QPSTM migration images. The proposed high-resolution end-to-end seismic imaging method can predict images as well as those calculated using the QPSTM. The use of small patches instead of  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Deep Learning Method (hour)** & **QPSTM Method (hour)** \\ \hline
**Computing Resource** & One TITAN XP GPU & One TITAN XP GPU \\ \hline
**Computing Process** & Data Generation (96 h) & \\  & Training Process (4 h) & 32 h \({}^{*}\) Profile Number (437) \\  & Prediction Process (0.4 h) & \\ \hline
**Total Time** & 100.4 h & 13,984 h \\ \hline
**Speedup Ratio** & 139 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the deep learning method and QPSTM method.  
Figure 15: Comparing the spectra of the ground truth QPSTM and predicted QPSTM. Using the learning method, the frequency improved by approximately 16 Hz at the high-frequency end at \(-\)20 dB compared with that of the conventional PSTM. The learned method and QPSTM exhibited comparable performances in terms of the frequency improvement.  
the full image in the training and prediction helps improve the efficiency and the generalization ability of the network considerably. A real data set and synthetic data are used to validate the effectiveness of the proposed method. End-to-end learning can hide the inherent transformation of information. Deep learning can not only perform high-dimensional data feature extraction but can also be used to accelerate scientific calculations with complex logic. It may be valuable to extend this idea to other related scientific domains.  
Conceptualization, W.L. and J.Z.; methodology, L.L. and Y.W.; software, Q.C.; validation, W.L. and L.L.; formal analysis, Y.W.; investigation, W.L.; resources, L.L.; data curation, Q.C.; writing--original draft preparation, W.L.; writing--review and editing, J.Z.; visualization, Q.C.; supervision, J.Z.; project administration, J.Z.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Oil and Gas Major Project of China (grant no. 2017 ZX05008-007), the Open Research Found from Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences (grant no. KLOR2018-2) and the National Natural Science Foundation of China (grant no. 41804129).  
The authors declare no conflict of interest.
## 4 Dependence of Results on Boundary Conditions
### Internal Width  
Another factor is the cross-sectional area, which is directly proportional to the dimensions (depending on the figure: diameter, width, height). As noted in Figure 4, near the building, there are local increases in wind speed caused by low tunnel capacity. To solve this problem, the cross-sections were increased. During the tests, it was noticed that there were still stress concentrations between the building and the tunnel walls, so another increase was applied. Figure 7 shows subsequent cases of previously selected cross-sectional shapes, increasing their dimensions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Shape** & **Width/Diameter [m]** & **Area [m\({}^{2}\)]** & **Free Space around [m\({}^{2}\)]** \\ \hline C1 & Circle & e18 & 254.5 & 149.6 \\ \hline C2 & Rectangle & \(17\times 13.5\) & 229.5 & 124.6 \\ \hline C3 & Square & \(16\times 16\) & 256 & 151.1 \\ \hline C4 & Building & \(17\times 13.9\) & 203.5 & 98.6 \\ \hline C5 & Circle & e24 & 452.4 & 347.5 \\ \hline C6 & Rectangle & \(23\times 19.5\) & 448.5 & 343.6 \\ \hline C7 & Square & \(22\) & 484 & 379.1 \\ \hline C8 & Building & \(23\times 20.4\) & 409.9 & 305.0 \\ \hline C9 & Circle & e36 & 1017.9 & 913.0 \\ \hline C10 & Rectangle & \(35\times 31.5\) & 1102.5 & 997.6 \\ \hline C11 & Square & \(34\) & 1156 & 1051.1 \\ \hline C12 & Building & \(35\times 33.5\) & 1032.9 & 928.0 \\ \hline C13 & Square & \(50\times 50\) & 2500 & 2395.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of basic parameters for cases C1–C13.  
Figure 6: Summary of the considered cases for the wind tunnel cross-section: (**a**) circle, (**b**) rectangle, (**c**) square, (**d**) polygon corresponding to the building cross-section. Each case was given a name (C1–C4), used in the following to distinguish the cases.  
Table 2 shows the basic dimensions of each case, C1-C13. Each cross-section shape and dimensions were assigned, respectively: diameter for a circle, width and height for a rectangle and square, and width and height at the highest point for a building. The area formed by the cross-section of the considered object is 104.9 m\({}^{2}\). The last column of the table corresponds to the difference between the cross-sectional area of each case and the windage area of the building.  
To ensure that further increase of the tunnel dimensions does not significantly affect the obtained results, one additional case (C13) was created with an extreme cross-sectional area--almost 10 times larger than the first case.  
Based on preliminary observations, case C13 was selected for further analysis and was considered free from the influence of the shape and dimensions of the cross-section; therefore, it was used for further analysis of the influence of other factors.
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 2 Materials and Methods
### Proposed Software Infrastructure
#### 2.2.1 AFCI  
The first proposed infrastructure studied the Azure platform services to ensure that all the required hydroponic tasks are fully accomplished in the cloud. The IoT-Based Azure Full Cloud Infrastructure (AFCI) solution includes the following services:  
* Azure IoT Hub for managing the M5 Stack devices and collecting data from sensors;
* Azure Stream Analytics for processing events in real-time and triggering the activation of the pump based on decisions made using the ML model;
* Azure SQL Database stores data that allows the creation of a history. Analyzing the variability of the data enables the development of trends for ML models;
* Azure Machine Learning is used to develop predictive models that optimize the parameters of the nutrient solution.  
The software infrastructure, schematically presented in Figure 2, is entirely cloud-based. The study aims to highlight the possibility of building such an infrastructure in the context of technological evolution, which, from a software perspective, seeks to completely change the programming paradigm by software modularity at the cloud level. In Figure 2, the hardware elements are marked with blue, the software elements for analysis and decision-making are green, and the software elements for data are red.  
The proposed architectural workflow includes sending data from the sensors to Azure IoT Hub via the M5 Stack. Azure IoT Hub directs the data to Azure Stream Analytics, which preprocesses it and then redirects it to Azure Machine Learning. This Azure component makes predictions and identifies whether activating the nutrient supply pump is necessary. This action is reported back to the Azure SQL Database through a flag. If the flag value is set to 1, the pump is activated. A cron job running on the process computer verifies the state of the flag each hour and activates the pump when necessary. After the pump starts, the flag is reset to the zero value.
## 5 Numerical Simulations
### Les
#### 5.2.1 Mesh Description  
The new mesh has the same domain and cell size as the one used for RANS, only with added refinement regions around the turbine and wake. The reason being that, whilst RANS is designed for coarser meshes, LES needs a finer mesh in order to resolve eddies at smaller length scales. Running an LES simulation with the RANS solutions interpolated onto the finer mesh makes for a more accurate result. The mesh was also changed to be uniform in the \(x\), \(y\)-, and \(z\)-directions. The refinement regions were created using the openFOAM utility snappyHexMesh, and the resulting cubic cells are described in Table 7. Pictures of the mesh are presented in Figure 11. For the simulations of yawed turbines, both wake refinement regions were moved 30 m in the negative \(y\)-direction, and the turbine refinement region was expanded 13 m in front of and behind the turbine. This ensures that both the turbine and the wake are still well captured by the finer cells. Due to the angle of the turbine, the wind velocity experienced by the rotor is decomposed by the cosine of the  
Figure 10: Velocity deficit curves of the (**a**) V80 and (**b**) Samsung turbines at different downstream locations plotted against validation data [7].  
Figure 9: Simulated (**a**) power and (**b**) thrust for the Samsung turbine plotted against curves found in literature [34].  
yaw angle. With the new wind speed assumed to be the component normal to the plane of the turbine, a new rotational speed was calculated keeping the tip speed ratio constant.