esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 6: Produce Final Design Recommendations  
Now that a list of potential nanogrid design options has been produced and ordered based on resilience, we turn our attention to producing final design recommendations that a base energy manager can use to promote upgrading existing microgrid infrastructure to include nanogrids. Two questions must be addressed to guide the final recommendations:  
Are Stakeholder Requirements Met? It is important to ensure the requirements collected in Step 1 are validated as being met. While the methodology presented above develops an understanding of energy resilience, other important requirements must be verified to still be met. For instance, a proposed design may only include diesel generatorswith no other generation sources but a requirement for a certain percentage of renewable generation may be present [9]. In such a case, this would indicate that particular nanogrid system design is inappropriate for further consideration without extreme extenuating circumstances to violate the renewable energy requirement. If no proposed designs remain after validating other requirements are met, then the base energy manager should return to Step 1 and carefully re-evaluate the requirements before verifying that no other proposed designs exist which would meet requirements.  
Is Funding Available? While a proposed nanogrid design may meet all requirements, there may not be funding available to fully implement the proposed design on the existing microgrid infrastructure. In this case, a base energy manager may choose to prioritize which critical loads receive a nanogrid first based upon available funds and cost of installing and commissioning the nanogrid(s). Depending upon the particulars of how military budgets are allocated for projects such as base energy infrastructure upgrades, it may be possible to fund a nanogrid for a critical load that is less critical than another critical load because of the specific configuration of the two different nanogrids. In other words, if there is money available to do a certain type of energy project (i.e., renewable energy projects [9], etc.) and one proposed nanogrid design meets the criteria for that funding source while another does not, then the nanogrid design that meets the funding source requirements should be pursued. When other funding becomes available, then other nanogrid designs can be pursued for other critical loads. The complexities of funding for military energy projects is largely unique to militaries and is not as pronounced in most civilian sectors.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 2 Theory and Prior Studies
### Speed Limit Estimation Method
#### 2.2.4 Injury Minimization Approach  
This approach determines the speed limit based on the vehicle crash tolerance of the human body. The basic objectives of this method are to set the speed limit to a speed that does not expose users to crash risks that could cause death or serious injuries and prevent cars from traveling above the speed. The speed limit that minimizes injuries by road type is shown in Table 3.  
Different speed limit estimation methods are compared in Table 4. In this study, we applied the engineering (operating speed) method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Data Required** & **Advantages and Disadvantages** \\ \hline Engineering & The existing speed profile as well as data on accesses, pedestrian/bicycle traffic, curbside parking, etc. & [+] Does not place an undue burden on enforcement, and provides residents and businesses with a valid indication of actual travel speeds. \\ \hline Engineering (road risk) & Functional classification of the road, setting (urban/rural), surrounding land uses, access, design features of the road. & [-] Speed limits are often set lower than the 85 th percentile speed. \\ \hline Optimal speed limits & Cost models and input data to account for air pollution, crashes, delay, etc. & [-] The speed limit and the function of the road are aligned. \\ \hline Expert system & Data needs depend on the system, but generally require the same data as used in the engineering approaches. & [+] Provides a balanced approach to setting speed limits that is considerate of many (if not all) of the impacts that speed has on society. \\ \hline Injury minimization & Crash types and patterns for different road types and survivability rates for different operating speeds. & [+] There is a sound scientific link between speed limits and serious crash prevention. Places a high priority on road safety. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Approaches to setting a speed limit.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Road Type** & **Speed Limits, mph (km/h)** \\ \hline Roads with a mix of motorized and unprotected road users & 20 (30) \\ (i.e., pedestrians and cyclists) & \\ \hline Roads with uncontrolled access where side impact crashes can occur & 30 (50) \\ \hline Undivided roads where head-on crashes can occur & 45 (70) \\ \hline Controlled access facilities with a physical median separation where at-grade access and non-motorized road users are prohibited & \textgreater{}60 (>100) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Speed limits for injury minimization.
## 4 Numerical Simulation
### Drawing Process and Characteristics  
Figure 16 demonstrates the progressive particle depletion characteristics within the Gradation II model under varying drawn ore masses. Initial displacement measurements at 24.75 kg extraction mass reveal minimal upper section movement. Progressive mass increase to 62.47 kg induces substantial vertical particle depletion, manifesting as a pronounced concave displacement recession surface. This phenomenon confirms the quasi-ellipsoidal nature of the extraction zone, where lower boundary particles maintain positional stability despite upper material discharge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Normal stiffness/shear stiffness & 1.2 \\ Solid density (kg/m\({}^{3}\)) & 2700 \\ Elastic modulus (GPa) & 7.1 \\ Friction coefficient & 0.35 \\ Normal damping coefficient, \(\beta\)n & 0.5 \\ Tangential normal damping coefficient, \(\beta\)s & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Microscopic parameters used in the numerical simulation.  
Figure 16: Evolution of the displacement field inside the model under different ore release qualities.  
The numerical framework implements unique particle identification tracking, enabling post-extraction spatial reconstruction of discharged materials for precise extraction zone profiling at multiple elevations. Figure 17 chronologically documents the extraction zone evolution across different discharge quantities. Initial 24.75 kg extraction produces limited zone dimensions, while subsequent mass increments (up to 110.75 kg) demonstrate progressive vertical expansion and lateral enlargement, with transverse dimension stabilization becoming evident beyond 110.75 kg discharge.
## 4 Interference Monitoring
### Correlator Output
#### 4.3.1 Correlator Output Power  
Correlator Output Power (\(COP\)) is defined as the result of the correlator output power when the former and later correlator are balanced divided by the expected thermal noise threshold, which is given by:  
Figure 1: The process chart of the TWSVM-based interference monitoring.  
\[COP=\frac{I^{2}+Q^{2}}{N} \tag{19}\]  
where \(I\) and \(Q\) are in-phase and quadrature signal of the correlator output, respectively. \(N\) is the expected noise level, varying from different receivers, which is a statistical expected value. Usually, the correlator output signal is:  
\[\begin{split} I=\frac{A}{\sqrt{2}}C_{w}C_{rw}X_{w}\text{cos}( \phi_{w}-\phi_{rw})\\ Q=\frac{A}{\sqrt{2}}C_{w}C_{rw}X_{w}\text{sin}(\phi_{w}-\phi_{rw}) \end{split} \tag{20}\]  
where, \(A\) is the amplitude of input signal, \(C_{w}\) is the C/A code in the sampling rate of \(w\), \(C_{rw}\) is the local pseudo-code, \(X_{w}\) is the data code in the sampling rate of \(w\), \(\phi_{w}\) is the sampling signal phase, and \(\phi_{rw}\) is the local pseudo-code phase.  
When there are interferences through receivers, distortion, delay or peak reduction will arise in the receiver correlation peak curve, which directly results in \(COP\) variation. Therefore, the quality of the received signals can be judged by \(COP\) and the preliminary analysis can determine whether there are interferences.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 3 Corrosion and Degradation of Structural Materials in Farm Environment
### Durability of Concrete and Acid-Induced Corrosion  
Acid-induced corrosion involves the leakage of lactic and acetic acids onto the lime (pH \(\sim\)13) in the concrete structures leading to the weakening as illustrated in Equations (1) and (2). The salts leached onto the concrete, leading to the weakening the binding agents and the cement paste, and consequently impacting the strength of the agricultural structure.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type** & \begin{tabular}{c} **Compressive** \\ **Strength, N/mm2** \\ \end{tabular} & \
\begin{tabular}{c} **Water Absorption** \\ **(NBN B15-215), \%** \\ \end{tabular} &
\begin{tabular}{c} **Density** \\ **kg/m3** \\ \end{tabular} \\ \hline Standard concrete slat with CEM I & 58.1 & 3.95 & 2370 \\
425 (reference) & 50.7 & 4.85 & 2360 \\ \hline Concrete with CEM III/A 42:5 & 65.7 & 3.70 & 2385 \\ \hline Concrete with CEM I 42:5 without & 69.7 & 3.30 & 2400 \\ the compound C3A & 54.8 & 4.45 & 2355 \\ \hline Fly ash & 59.3 & 3.95 & 2380 \\ \hline Silica fume & 75.3 & 3.40 & 2400 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variations in compressive strength, water absorption and density in different concretes [12]. “Reproduced with permission from [PERSON], [PERSON]. Eng. Res. published by Elsevier, 1997.”
# Understanding Microgrid Sustainability: A Systemic and Comprehensive Review
## 4 Information Field
### Information Issues
#### 4.3.1 Control Issues  
A review of the literature on microgrid control has highlighted six issues that must be addressed for a sustainable, reliable, and stable microgrid operation. These are voltage and frequency regulation, power quality, transition between two modes of operation, microgrid protection, power flow management, and optimization [24; 42; 43]. These issues are related to the layers and architectures presented previously, as shown in Table 5.  
Figure 7: Representation of the different information architectures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Control Architectures** & **V/I Regulation** & **Power Quality** & **Transitions** & **Protection** & **Power Flow** & **Optimization** \\ \hline \multirow{3}{*}{**Centralized**} & **Low** & X & X & & X & & \\  & **Intermediate** & & X & X & X & & \\  & **High** & & & X & X & X & X \\ \hline \multirow{3}{*}{**Hierarchical**} & **Low** & X & X & X & X & & \\  & **Intermediate** & & & X & X & & \\  & **High** & & & & X & X & X \\ \hline \multirow{3}{*}{**Decentralized**} & **Low** & X & X & X & X & & \\  & **Intermediate** & & & & X & X & X \\ \hline \multirow{3}{*}{**Distributed**} & **Low** & X & X & X & X & & \\  & **Intermediate** & & & X & X & & \\ \cline{1-1}  & **High** & & & & & X & X \\ \hline \hline \end{tabular}
\end{table}
Table 5: The information architectures and their relationship with control issues.  
Table 6 shows system dynamics (fast vs. slow) and geographic range (near vs. far) tendencies in the literature [43].  
Voltage and frequency regulation and power quality are focused on electric regulation through a limited time frame and location linked to the electric command, being near and fast. Transition operations and protection are focused on the process and protection through varying time frames and can operate in various geographic ranges. They can fit the not so fast and not so near middle range. Power flow management and optimization focus on energy management at a larger scale and time frame, with the purpose of correction, anticipation, and improvement of the quick command. They fit the far and slow category.  
These issues and their time frames must be thoroughly taken into account in designing and maintaining the microgrid. By deciding on satisfactory thresholds for their operation, it is possible to reach the sustainability of the information field from a control perspective.
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 2 Analysis of Pipe-Borehole Wall Contact  
During the pullback process, the pipeline contacts the borehole wall under the action of external load (including the contributions from buoyancy, gravity, and capstan effect). When the direction of the external load is upward, the pipeline may contact the upper wall of the borehole. Conversely, the pipeline may have been in contact with the bottom of the borehole. The pipe-borehole wall contact analysis under the external load is an axisymmetric mechanical problem because the pipeline and borehole have approximately circular cross-sections. Figure 2 shows the contact state between the pipeline and borehole wall by taking an upward external load (often occurring without buoyancy control) as an example. \(P\) signifies the external load per unit length of the pipeline. \(R_{1}\) and \(R_{2}\) denote the radii of the pipe and borehole, respectively. \(\beta\) represents the angle at which points on the interface depart from the central line. \(\varepsilon\) stands for the semi-angle of contact corresponding to the whole contact arc. \(l\) signifies the semi-chord length corresponding to the angle \(\varepsilon\). \(\Delta R\) represents the radial clearance of the pipe and borehole, \(\Delta R=R_{2}-R_{1}\). \(\delta\) denotes the compressive displacement.  
Figure 2: Schematic of the pipe–borehole wall contact.
## 2 Construction Aspects for Energy Performance Improvement
### Greenhouse Construction
#### 2.2.1 Greenhouse Shapes and Their Impacts
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Title** & **Purpose of the Study** & **Methodology Used** & **Main Results** \\ \hline Solar energy & Investigate six common & Steady-state analysis and experimental validation & North wall insulation can reduce greenhouse heating demand by up to 31.7\%. The correlation coefficient and the mean percentage error of the developed model were calculated at 0.79 and \(-\)2.34\%, respectively. \\ \hline Simulation of thermal & Compare six forms of greenhouses from the point of view of solar & Dynamic model and experimental validation using MATLAB & The east-west oriented regular span greenhouse captured about 8\% more solar radiation throughout the year. The use of a brick wall in the north reduced the energy loss by radiation. The mean transmission of coverage was 0.76 over the course of the study, with a good correlation between measured and predicted data. \\ \hline Effect of greenhouse & Investigate design & & The east-west orientation is optimal, reducing the annual cost of air conditioning by 9.28\% compared to the north–south orientation. The Quonset shape is the optimal one, saving 14.44\% of the annual cost compared to the Even-span shape. \\ \hline Optimal solar & Improving the energy & Application of a multi-objective genetic algorithm to optimize the design of solar & The even-span greenhouse having an ideal height of 3.8 m and a slope of 16\({}^{\circ}\). Modified arc greenhouses have an ellipse ratio of 0.8 for small and large, and 0.25 for medium. Quonset greenhouses, with ellipse ratios of 0.8 for small and 0.6 for medium and large, outperform others in annual solar performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recent investigations on the influence of greenhouse shape on energy performance.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
###### Abstract  
The problem of information security and copyright protection of video is becoming increasingly prominent. The current video watermarking algorithm does not have strong anti-compression, which has a significant impact on the visual effect of video. To solve this problem, this paper proposes a video watermarking algorithm based on H.264/AVC. The algorithm combines the non-zero quantization coefficient and the energy factor to select the appropriate chroma subblock, and then an optimized modulation is designed to embed the watermark into its DCT quantization coefficients in order to minimize the number of modifications of the subblocks. The invisibility and robustness experiments of the algorithm are conducted in the paper, and the Structural Similarity Indexes are above 0.99, and the False Bit Rates are all below 0.03. The results show that the algorithm has good invisibility, anti-compression performance and obvious advantages compared with other similar methods.  
video watermarking; H.264/AVC; recompression attack; requantization attack
## 2 Materials and Methods
### Lead-Wise Ensemble  
Ensemble learning is a powerful machine learning strategy that combines the outputs of multiple individual models to produce more accurate and reliable predictions than any single model alone. By aggregating diverse perspectives or decision boundaries, ensemble methods help reduce variance, mitigate overfitting, and improve generalization performance-particularly in settings with noisy or heterogeneous data.  
In this study, we applied an ensemble approach tailored to multi-lead ECG data, where each lead provides complementary diagnostic information for detecting LVSD in patients with LBBB. To enhance recognition performance, the final classification results were derived using a lead-wise ensemble technique. Each ECG lead captures cardiac activity from a different spatial perspective, and certain LVSD-related features may be more pronounced in specific leads. By aggregating predictions across multiple lead-specific classifiers, the ensemble approach leverages complementary information and mitigates the risk of overfitting to any single lead.  
As illustrated in Figure 10, the lead-wise ensemble operates by first obtaining predictions from individual CNN classifiers, each trained on a specific ECG lead. These lead-specific prediction results are then input into a final voting-based classifier, which determines the overall classification outcome. This structure allows the model to integrate diverse diagnostic signals across leads in a systematic and robust manner.  
In this study, we adopted hard voting, where each classifier casts a discrete vote for the predicted class, and the final prediction is determined by majority rule. We chose hard voting over soft voting to increase robustness against potential noise or abnormal signals in specific leads. In soft voting, classifiers output probability scores, which are then averaged to determine the final class. However, if one or more leads produce abnormally high or low probabilities due to artifacts or outliers, the final decision may be disproportionately influenced. Hard voting avoids this issue by treating each classifier's decision equally, thus enhancing the stability of the ensemble.  
Figure 9: Mean \(\pm\) 1 std Grad-CAM profiles for test predictions: (**top**) non-LVSD, (**bottom**) LVSD.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 10 Conclusions  
The new address sequence for the SI-OCDMA network which refers to HPC was proposed and evaluated in the communication systems in terms of BER and users-throughput product. The code construction principles and their correlation properties were presented. Furthermore, the code construction results proved that the generated number of codes in the HPC family is much greater than the number of codes generated in the other existing code families at the same code length. Moreover, the proposed code has the potential to change its length and weight while maintaining its excellent correlation properties. In addition, the good correlation characteristics of the HPC led to: (1) achieve a minimum amount of MAI; and (2) improve the network EVM and BER performance in comparison with the OOC and MPC at a higher number of users. Finally, the HPC has the potential to enhance the OCDMA network capacity.  
Conceptualization, M.A.M.; methodology, M.A.M.; software M.A.M.; validation, M.A.M. and M.H.A.; formal analysis, M.A.M.; investigation, M.A.M.; resources, M.A.M. and M.H.A.; data curation, M.A.M. and M.H.A.; writing--original draft preparation, M.A.M.; writing--review and editing, M.A.M. and M.H.A.; visualization, M.A.M.; supervision, M.H.A.; project administration, M.A.M. and M.H.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Experiments and Analysis  
### Erosion Test Setup of submerged Water Jet  
In the submerged water jet erosion test, the working water medium in the storage water tank (Figure 2a) enters the high-pressure pump (Figure 2b) for pressurization. Then working water is delivered to the nozzle (Figure 2d), which is completely immersed in the operating water tank (Figure 2c) below the free surface of the ambient water. After working water passes through the nozzle, it forms a high-speed water jet and impacts the surface of the refractory brick. The damage is shown in Figure 3. The jet pressure is controlled by a pressure regulating valve and adjusted according to different working conditions. The bottom of the refractory brick is fixed on the adjusting base, which is convenient for adjusting the jet angle and standoff distance. Experimental equipment are as follows:  
1. High-pressure pump: the rated power is 15 kW, and the rated flow is 41 L/min.
2. Pressure gauge: the measurement range is 0-40 MPa, and the accuracy level is 2.5.
3. Operating water tank: acrylic glass material, size is \(60\times 60\times 50\) cm.
4. Nozzle: stainless steel material, angle nozzle is used for better erosion performance.
5. Refractory brick: size is \(230\times 55\times 65\) mm, temperature resistance is 1350 \({}^{\circ}\)C, composition content is 40% alumina, 55% silica.  
Figure 2: Experimental equipment. (**a**) Storage water tank, (**b**) high-pressure pump, (**c**) operating water tank, (**d**) angle nozzle.