esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 5 Conclusions  
This paper has proposed a new collaborative caching method for fetching content efficiently in vehicular networks by extending our previous method [9] with popularity inference. By organizing vehicles in clusters, the proposed method has the following merits: (i) a CH learns all content cached in its cluster to reply to content requests, which improves the cache hit rate, (ii) a CH efficiently uses all cache buffer space in its cluster by avoiding duplicate caches and storing more diverse content, which increases the probability of locally responding to a content request. Further, we let vehicles infer content popularity and store popular content in their clusters, which increases the number of requests that could be completed within a cluster. In this way, the proposed method reduces the fetching latency of data packets by 33% and improves the packet reception rate by 17% compared to C-BCCN-Pop in an environment where the number of vehicles is 200. The proposed method is expected to enable rapid acquisition of road conditions.  
In the future, we will further refine cache management to improve cache usage to get better results in both straight roads and urban models and will evaluate our method using actual V2V communications.  
Conceptualization, S.T.; methodology, H.T.; software, H.T.; validation, H.T.; writing--original draft preparation, H.T.; writing--review and editing, S.T.; visualization, H.T., supervision, S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry
## 2 Materials and Methods
### Framework Overview and Motivation  
To model the complex nonlinear relationship between process parameters and melt pool geometry, a machine learning (ML) framework is developed and trained on the augmented dataset comprising both experimental and synthetic samples. While the physics-based explicit model provides interpretable predictions of melt pool width and depth, its applicability is limited in high-dimensional spaces and in capturing multivariate couplings under varying processing conditions. Therefore, ML models are employed not to replace physical modeling, but to enhance its generalizability and flexibility by learning from both measured and simulated data. The melt pool dimensions--depth (D) and width (W)--are predicted using three representative ML algorithms: Multilayer Perceptron (MLP), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). These algorithms were selected based on their ability to handle nonlinear, multivariable regression problems with small-to-moderate training datasets, and have been previously validated in metal additive manufacturing scenarios. Each model accepts laser power (P) and scan speed (V) as inputs and outputs two independent regression targets: melt pool width and depth. The dataset used for training consists of 36 experimental samples and 15 synthetic samples generated from the calibrated explicit model, yielding a total of 51 data points. To ensure the robustness of the models and reduce overfitting in the small-sample context, a fivefold cross-validation (5-CV) strategy is applied. Model performance is quantitatively evaluated using three widely adopted regression metrics: the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and root mean square error (RMSE). These metrics are used to compare model accuracy on both the original dataset and augmented datasets. Hyperparameter tuning is performed using Bayesian optimization via the Tree-structured Parzen Estimator (TPE) algorithm, implemented in the HyperOpt package. Search spaces for each model's key parameters (e.g., learning rate, number of estimators, max depth for XGBoost; number of layers and neurons for MLP) are defined based on preliminary grid tests, and the optimization objective is to maximize R\({}^{2}\) while minimizing MAE.  
As shown in Figure 2, the proposed framework integrates physics-informed data generation with ML-based regression to achieve interpretable, accurate, and computationally efficient melt pool prediction. The enhanced model not only reduces dependency on expensive experimental data, but also enables reliable prediction over a wider process window, thereby offering practical value for real-time process design and control in L-PBF.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Drought Stress Monitoring
Recent studies have increasingly combined these indices for drought monitoring and detection in agriculture and meteorology. For instance, [PERSON] et al. [162] proposed the Synthesized Drought Index (SDI) based on MODIS and TRMM data, combining VCI, PCI, and TCI for vegetation growth monitoring. Similarly, [PERSON] et al. [172] introduced composite drought indices (MCDIs) combining VCI, TCI, PCI, and SMCI to detect drought in Shandong Province on the east coast of China. The same combination of indices from MODIS, TRMM, and AMSR-E was used by [174] to establish the Optimized Meteorological Drought Index (OMDI) and Optimized Vegetation Drought Index (OVDI). Additionally, several composite drought indices have been proposed, including the Scaled Drought Condition Index (SDCI) [166], Combined Drought Index (CDI) [157], Combined Drought Monitoring Index (CDMI) [175], Microwave Integrated Drought Index (MIDI) [163], Multivariate Drought Index (MDI) [176], and Process-based Accumulated Drought Index (PADI) [177], yielding significant findings for researchers. Table 2 provides a classification of drought indices discussed by [157]. Researchers should prioritize the development of integrated platforms that combine remote sensing indices with machine learning algorithms to enhance real-time drought monitoring capabilities. This approach, combined with the integration of multiple indices like VCI, TCI, PCI, SMCI, and ETCI, provides nuanced insights into drought severity and impacts across diverse landscapes. In addition, it will empower stakeholders with actionable insights for implementing adaptive agricultural practices and optimizing water \begin{table}
\begin{tabular}{c c c} \hline \hline
**Drought Severity** & **TCI, VCI, PCI, SMCI, ETCI \& VHI Values** & **SPI Values** \\ \hline Exceptional drought & VCI \(\leq\) 10 & SPI \(\leq-2\) \\ Critical drought & 10 \(<\) VCI \(\leq\) 20 & \(-2\) \(<\) SPI \(\leq-1.5\) \\ Moderate drought & 20 \(<\) VCI \(\leq\) 30 & \(-1.5\) \(<\) SPI \(\leq-1\) \\ Slight drought & 30 \(<\) VCI \(\leq\) 40 & \(-1\) \(<\) SPI \(\leq\) 0 \\ No drought & VCI \(\geq\) 40 & SPI \(\geq\) 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of drought indices. resource management strategies in response to increasing climate variability. Such integrated systems are crucial for fostering resilience in agriculture and ensuring sustainable water use in the face of evolving environmental challenges.
## 3 Results
### Heating Effects throughout the Winter
#### 3.3.2 Energy-Saving Efficiency  
In order to accurately evaluate the energy-saving efficiency of the attached sunspace throughout the heating season under the zero-state response control strategy and according to China's rural residential building energy efficiency design standards [26], the indoor heating temperature of the farmhouse was set to \(14\)\({}^{\circ}\)C, and the measured weather data from 1 November 2020 to 31 March 2021 were used. With the help of TRNSYS simulation software, the three scenes, including the heating energy consumption of a farmhouse with attached sunspace under the zero-state response control strategy, the heating energy consumption of farmhouse with attached sunspace but not under the zero-state response control strategy, and the heating energy of farmhouse without attached sunspace, were analyzed respectively. The simulated energy consumption result is shown in Figure 15.  
Figure 14: The simulated heating effect under the zero-state response control strategy in the winter of 2020–2021.  
As demonstrated in Equation (4), attached sunspace energy-saving efficiency (ESF) can accurately measure the operation effect of passive solar farm homes [27].  
\[\text{ESF}=1-\frac{Q_{1}}{Q} \tag{4}\]  
where \(Q_{1}\) is the farmhouse with attached sunspace's heating energy consumption, kW h; and \(Q\) is the original heating room's heating energy consumption, kW h.  
Figure 15 shows that in the heating season 2020-2021, the total energy consumption required to maintain the overall indoor temperature of 14 \({}^{\circ}\)C is 1924.39 kW h if an attached sunspace is installed and under the zero-state response control strategy in the farmhouse. The total heating energy consumption of a farmhouse with an attached sunspace under the zero-state response control strategy is 2432.43 kW h. A farmhouse's total heating energy consumption required without attached sunspace is 3203.26 kW h. In comparison to a farmhouse without an attached sunspace, the heating energy-saving efficiency of the farmhouse with attached sunspace but not under the zero-state response control strategy is 24.06%, and the heating energy-saving efficiency of the farmhouse with attached sunspace under the zero-state response control strategy is 39.92%; in comparison to the heating energy consumption of the farmhouse with attached sunspace but not under the zero-state response control strategy, the heating energy-saving rate of the farmhouse with attached sunspace under the zero-state response control strategy is 20.88%. As can be observed, the attached sunspace plays a significant role in the heating energy saving of a farmhouse and the zero-state response control strategy may further enhance the heating energy-saving efficiency in the attached sunspace while also lowering the auxiliary heat source's energy consumption and heating operation costs.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 8 Grouting Model Test Results and Discussion
### Discussion on Slurry Diffusion  
#### 8.3.1 Relationship between Slurry Diffusion Radius and Grouting Hole Distance  
The diffusion distance of slurry veins along the laying direction of the grouting pipe was studied after excavation.  
Figure 17: Cement slurry veins.  
The following conclusion can be drawn from Figures 18 and 19:  
(1) The relationship between the slurry diffusion distance and the distance from the grouting hole is similar in four directions. The diffusion radius first increases and then decreases with increasing distance from the grouting hole in all directions. This shows that the grouting pressure and the amount of grouting absorption decrease gradually with the increasing distance of the grouting hole. It also reflects the attenuation law of grouting pressure along the slurry diffusion.  
(2) The diffusion radius of the slurry has a good agreement in the horizontal direction, while the diffusion radius of slurry varies greatly in the vertical direction. One possible reason leading to this phenomenon might be that the specimen used in the test is taken from an alluvial stratum with a layered structure (i.e., the horizontal direction), and also that the specimen is layered in a model test container, which results in a homogeneous specimen in the horizontal direction, so the diffusion radius of the slurry has good agreement in the horizontal direction. During the upward diffusion of the slurry, part of the energy is dissipated due to the power consumption overcoming gravity. Therefore, the diffusion radius of the slurry varied in two vertical directions, which was not similar to what was observed in the horizontal direction. For a certain cross section, the diffusion radius in the horizontal direction is the largest, the diffusion radius in the vertically downward direction is the second, and the diffusion radius is the smallest in the vertically upward direction. The reason why the diffusion radius in the horizontal direction is larger than that in the vertical direction is that the layered filling makes it easier to crack the weak surface in the horizontal direction.
## Abbreviations  
AC CW Air Conditioning condensate water  
AD Abu Dhabi Municipality  
AED Emirati Dirham  
D.Eng Doctorate in Engineering  
EAD Abu Dhabi- Environment Agency  
EMCS Energy Monitoring and Control System  
GHG Greenhouse gas  
IPCC Intergovernmental Panel on Climate Change  
LI Landscape Irrigation  
MED Multi Effect Distillers  
MFCS Medical Facility Case Study  
MSF Multistage flash  
RO Reverse Osmosis  
S2 Baseline case scenario 2  
PRP S3 Proposed research project Scenario 3  
TCR The Climate Registry  
TSE Treated Sewage Effluent  
UAE United Arab Emirates  
WF Water feature  
WFs Water features  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Input Summary**} & \multicolumn{2}{c}{**Mean Average**} \\ \hline \multicolumn{2}{c}{**1. Physical characteristics**} \\ \hline Outdoor elements & WFs & LI \\ \hline Area (m\({}^{2}\)) & 3289 & 36,310 \\ \hline \multicolumn{2}{c}{**2. Operational parameters**} \\ \hline Pumps (h/day) & 11.7 & 13 \\ \hline Backwash period (No per month)—assumption & 4 & n/a \\ \hline Backwash duration & 15 & n/a \\ (mins)—assumption & 0.5 & n/a \\ \hline Refill period (years) & 0.5 & n/a \\ \hline \multicolumn{2}{c}{**3. Environmental Conditions**} \\ \hline Season (length) in days & \multicolumn{2}{c}{351 (14 days for maintenance)} & 365 (2017); 366 \\ \hline Rainfall (mm) average per month & \multicolumn{2}{c}{3.80 mm (November to April); 0 mm (May to October); (SCAD, 2017)} \\ \hline Evaporation (mm) average per month & \multicolumn{2}{c}{215 mm (November to April); 0 mm (December to March); (SCAD, 2017)} \\ \hline \multicolumn{2}{c}{**4. Existing Water System Components**} \\ \hline Pump Description & Pump Power (kW) & Pump Flow (m\({}^{3}\)/h) & Pump Usage per day (\%) & WFs & LI \\ \hline \multicolumn{2}{c}{Number of pumps} \\ \hline (P1) Pumps Set A & 4 & 17 & 30 & 1 \\ \hline (P1) Pumps Set B & 11 & 64 & 10 & 1 \\ \hline (P1) Pumps Set C & 4 & 17 & 30 & 1 \\ \hline (P1) Pumps Set D & 11 & 64 & 10 & 1 \\ \hline (P2) Pumps Set power & 18.5 & 64 & 100 & - & 1 \\ \hline (P2) Pumps Set power & Not known & 10 & 100 & - & 1 \\ \hline (P3) WE Pump Set & 7.5 & 45.5 & 100 & 1 & - \\ \hline (P5) Pumps Set power & 11 & 63.3 & 100 & - & - \\ \hline (P4) Ozone Filter Pump & 5.5 & Not known & 100 & 5 & - \\ \hline (P4) Ozone Display Pump & 30 & Not known & 100 & 4 & - \\ \hline (P4) Ozone Filter Pump & 2.2 & Not known & 100 & 2 & - \\ \hline (P4) Ozone display Pump & 37.5 & Not known & 100 & 1 & - \\ \hline (P4) Ozone display Pump & 3.7 & Not known & 100 & 1 & - \\ \hline (P4) Ozone display Pump & 11.2 & Not known & 100 & 1 & - \\ \hline Ozone Disinfection & \multicolumn{2}{c}{0.31 kWh/m\({}^{3}\)[55]} & \multicolumn{2}{c}{40.15 kWh/day} & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Updated MFCS main operation input summary [6].
## 2 Critical Infrastructures
### Critical Infrastructure Definition
#### 2.1.2 The United States Perspective  
In the United States, Presidential Policy Directive 21 (PPD-21): Critical Infrastructure Security and Resilience outlines the national policy for strengthening and maintaining secure, functioning, and resilient critical infrastructure. The directive identifies 16 sectors that are crucial to national security, economic well-being, and public health and safety. Thesesectors range from energy, transportation, and communication to healthcare, government facilities, and information technology. Through the Cybersecurity and Infrastructure Security Agency (CISA), the US government leads efforts to enhance the security and resilience of critical infrastructure sectors. CISA works collaboratively with public and private sector stakeholders, providing strategic direction, developing risk management frameworks, and offering tools and resources to improve sector-specific security measures [8].
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 2 Architecture  
As shown in Figure 1, the simulator contains the world, model, controller, ISMN, formation controller and relative navigation modules. In the world module, various environments, such as deserts, grasslands and canyons, can be simulated, which can be used to verify the robustness of the relative navigation. On the other hand, Google Earth containing GPS information is used for the SMN. Sensor models are strongly related to state estimation. In the simulator, a gyroscope, accelerometer, magnetometer, barometer, camera, UWB, etc., can be imported as plugins to supply accurate sensor models for the simulation platform. The controller and formation controller serve for control and swarm motion. Inertia, altitude and images are used for absolute navigation in GNSS-denied environments in the ISMN module. The relative navigation module receives images from cameras and distances from UWB and calculates the relative position for the formation controller.  
In the simulation, the flight environment is set through the world module, and the UAV flies in the environment with sensors such as accelerometers, gyroscopes, magnetometers, UWB and cameras and so on. The ISMN module collects the information of the accelerometer, gyroscope, magnetometer and camera and calculates the absolute pose of the UAV. The pose information is sent to the controller module, which can stabilize the UAV through closed-loop control. The relative navigation based on UWB and vision module collects the images of adjacent drones from the camera and the distances of adjacent drones from UWB and calculates the relative position of the UAV. The relative position is sent to the formation controller module, which can  
Figure 1: Architecture of the simulator.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 2 Sensor Integration into Observation Platforms
### Protocols and Standards
#### 2.2.3 Sensor Observation Service  
The SOS standard provides the set of operations required to provide access sensor observation data/metadata as well as to register and archive sensor data and metadata within a data repository [31]. Due to its role as a data and metadata archive, this standard is a key piece of any Sensor Web infrastructure, providing support for requirement 5, sensor registration.  
Figure 4: Requirements for a plug and play mechanism (rows) and protocols/standards that can fulfil these requirements (columns).
## 1 Introduction
This method can achieve higher simulation fidelity with less convergence. The effectiveness of the compensation method was verified by the simulation of the undamped elastic contact process. [PERSON] et al. [14] proposed an optimal wide-area damping controller considering delay, using the Smith predictor to provide delay compensation and using particle swarm optimization to further improve the controller. The simulation results showed that the method improved the delay tolerance of the closed-loop system and improved the dynamic stability of the power system. In this paper, according to the characteristics of pH value regulation of liquid fertilizers, the prediction compensation controllers based on Smith, PID-Smith and BP-PID-Smith were designed respectively. They were simulated and analyzed under the conditions of model matching and mismatching, and step response curves were obtained respectively. The performance of the three controllers was evaluated from four aspects: rise time, peak time, maximum overshoot, and adjustment time [15]. The results showed that the control effect of the BP-PID-Smith controller was the best. On this basis, an experimental platform was built to verify the practicability of the algorithm. The results showed that the BP-PID-Smith predictor compensator can effectively solve the adverse effects of the time delay and nonlinearity of the system in the fertilization process, and meet the control requirements for precise regulation of the pH value of liquid fertilizers. The purpose of this paper is to design a BP-PID-Smith predictive compensation control algorithm, which can quickly adjust the pH value of water and fertilizer to the set value, and effectively solve the problems caused by factors such as time lag and nonlinearity in the pH adjustment process. The contents of this paper are as follows: The first part introduces the research status of precise pH control and Smith prediction compensation. The second part explains the working principle of the pH regulation system, establishes the dynamic and static model of the pH regulation process, and reveals the nonlinear and time-delay characteristics of the pH regulation process. In the third part, the formulas of the Smith predictor compensator algorithm and BP neural network algorithm are derived, and the simulation models based on Smith, PID-Smith, and BP-PID-Smith predictor compensation are established by using the Simulink module in MATLAB. In the fourth part, the above three models are simulated and analyzed, respectively, and the models are evaluated according to the results. In the fifth part, experiments are carried out to verify the practicability of the controller. The sixth part gives the conclusion.
## 3 Mathematical Modeling
### Adsorption Isotherm Model of Type I
#### 3.1.3 D-R Model  
The D-R (Dubinin-Radushkevich) model is typically applied to express the adsorption mechanism with a Gaussian energy distribution onto a heterogeneous surface [40,41]. In contrast with the Langmuir surface adsorption, the D-R model also accounts for the porous structure for the adsorbent and includes the microporous filling. [PERSON] et al. [43] presented studies of Langmuir, Freundlich, Temkin, and D-R isotherm models of equilibrium sorption of Zn\({}^{2+}\) onto phosphoric acid-modified rice husk. They mentioned that the D-R model often offers successful fitted values for high-solute activities and the intermediate range of concentration data. The D-R model is given as:  
\[w=w_{0}\exp\left(-K\bigg{[}RT\ln\frac{P_{0}}{P}\bigg{]}^{2}\right) \tag{14}\]  
where \(w\) and \(w_{0}\) is the specific equilibrium uptake per unit of the adsorbent and maximum specific uptake, respectively. \(K\) is the constant parameter for each adsorbent and adsorbate system. \(P/P_{0}\) is the standard relative pressure of adsorbate, and \(T\) and \(R\) are the thermodynamic temperature and gas constant, respectively.
## 4 Analysis and Discussion  
This study analyzed transportation network vulnerability to critical-link attacks from two analytical aspects: I. identifying network performance changes under critical-link attacks; and II. capturing traffic flow changes under a critical-link attack. A detailed discussion of each analytical aspect is provided below. Notably, although the study employed 323 critical-link attacks sequentially to disrupt the transportation network, three significant phases of critical-link attacks were selected based on the notable functional changes observed in each topological parameter to assess the vulnerability. To illustrate these changes, the CCDF of the topological parameters was calculated for the first 10%, 20%, and 30% of the disruption phases.