esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background Information
### Cycle of Processing Dedicated to CAS  
In irrigation, a CAS has three specific components. First, a WSN plays the role of sensing and monitoring the plot environment. Second, a DSS can (1) send notifications to farmers to support them in their decision-making process and (2) automatically make decisions and control the watering system. Third, watering devices are in charge of watering the soil in the field.  
The CAS processes can be grouped and represented as a cycle of processes [11]. This cycle is called the CAS life cycle. It is divided into four phases: (1) acquisition, (2) modeling, (3) analysis, and (4) exploitation. Please note that this section improves our previous work about CAS life cycle [12] by decreasing the number of phases. The exchange between two phases can consist of data, the low-level context or the high-level context. The CAS life cycle for smart irrigation is depicted in Figure 1. The four phases of its life cycle are described as follows.  
* **Acquisition phase** focuses on how the system retrieves and processes measurements from sensor devices and data from external data sources. The output of this phase are measurements and related data derived after raw data cleaning processes.
* **Modeling phase** focuses on the data model and the integration of input data into the system. The system is equipped with a storage service. The input data are organized with a common data model to become the low-level context. As shown in Figure 1, the input of this phase is data derived from the acquisition phase. The output of this phase is the low-level context. This paper considers ontologies as a candidate for the data modeling process.
* **Analysis phase** focuses on the transformation from the low-level context into the high-level context. The high-level context is an enrichment of the context with qualitative data. Such data summarize the situation of entities and support the decision process. The system uses a rules-based inference engine for the reasoning process. As shown in Figure 1, the input of this phase is the low-level context. The output of this phase is the high-level context. This paper considers Drools ([[https://www.drools.org/](https://www.drools.org/)]([https://www.drools.org/](https://www.drools.org/))) as a candidate for the inference engine.
* **Exploitation phase** focuses on the use of the high-level context to distribute them to corresponding agents, which can be other devices or users. The input of this phase is the high-level context. The output of this phase can be human-readable content, a message to an external system or a reaction of a watering device to the environment.  
Figure 1: Life cycle of a smart irrigation CAS.
## 2 Basic Principle of SAR Imaging Simulation
### SAR Imaging of the Ocean Surface  
In this paper, ocean surface echo signals were processed by the Range-Doppler (RD) imaging algorithm to obtain ocean SAR images. The RD algorithm consists of three key steps, namely, range compression, range migration correction, and azimuth compression, and the RD algorithm obtains high resolutions in the range and azimuth by pulse compression [31; 32].
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 6 Numerical Results
### Pure Operation of the Framework
Specifically, we observe how evacuees are distributed to evacuation routes during reinforcement-algorithm iterations (until the convergence) and what the physical characteristics of the routes that define this behavior are. In particular, we observe that most evacuees choose evacuation routes with the greatest capacity \(C_{e}\) and, among routes with the greatest capacity, they choose the one with the greatest average evacuation rate (AER) \(\lambda_{e}\).
## 3 Results and Discussion
### Real Wastewater  
The intermediate products produced during TMAH degradation [12] by the MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) process, including NH\({}_{4}^{+}\), MH, DH, and TH are presented in Figure 9. More than 80 and 73% of TMAH were removed in synthetic and real wastewaters, respectively. For example, in real wastewater, the concentrations of NH\({}_{4}^{+}\), MH, DH, TH, and TMAH were under detection limit (ND), 3.38, 1.17, ND, and 26.89 mg/L, respectively (120 min). Both NH\({}_{4}^{+}\) and TH were not observed in the aqueous solution. A small amount of MH and DH (less than 3.5 mg/L) was observed in the process. This result indicated that the MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) process could remove TMAH efficiently to meet the discharge limit of the Science Park (30 mg/L) for both synthetic and real wastewaters.  
Figure 8: Effect of t-butanol on the removal of ammonium and TMAH by the MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) process (C\({}_{\circ}\) = 100 mg/L, V = 0.5 L, solid 1 g, 0.25 mmol O\({}_{3}\)/min, 0.125 mmol H\({}_{2}\)O\({}_{2}\)/min).
# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
###### Abstract  
Unlike terrestrial Wireless Sensor Networks (WSNs), communication between buried nodes in WUSNs happens through the ground. Due to the complexity of soil, accurate estimation of the underground signal attenuation is challenging. Existing path loss models mainly rely on semi-empirical and empirical mixing models for calculating the dielectric properties of the soil. In this paper, two existing models for estimating the path loss in soil (i.e., the CRIM-Fresnel and Modified-Friis models) are compared with measurements obtained at three locations. In addition, an improved method is proposed for estimating the path loss based on a new approach for calculating the dielectric properties of soil from Time Domain Reflectometry (TDR) measurements. The proposed approach calculates the complex permittivity values from TDR waveform based on a new modified method and subsequently use them as inputs into the Modified-Friis model. The results from the field trials were compared with the proposed method and the existing models. The results of this comparison showed that the proposed estimation technique provides a better estimation of Radio Frequency (RF) attenuation than the existing models. It also eliminates the need to take samples back to the laboratory by providing in situ calculation of attenuation based on TDR.  
Wireless Underground Sensor Networks (WUSN); Time Domain Reflectometry (TDR); Path Loss; RF attenuation +
Footnote †: journal: Sensor and Actuator Networks  
Article  
A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 2 Materials and Methods
### Testing Methods
#### 2.3.8 Microstructural Analysis  
The X-ray diffraction (XRD) study was performed on powdered samples by using an XRD X'Pert 3 Powder machine with the following measurement conditions: CuK\({}_{\alpha}\) radiation (\(\lambda\) = 1.54 A\({}^{\circ}\)); scanning range: 5\({}^{\circ}\) to 80\({}^{\circ}\) 2\(\beta\); scanning speed: 0.5\({}^{\circ}\) steps/s; step size: 0.02\({}^{\circ}\)2\(\beta\). The peaks in spectra were detected by matching with the Joint Committee on Powder Diffraction Standards (JCPDS) files. Fourier transform infrared spectroscopy (FTIR) investigations were carried out on powdered samples on an AVATAR 370 device. Spectra were noted in the range of 4000-400 cm\({}^{-1}\). A field emission scanning electron microscopy (FESEM) analysis on the fracture surface of all the specimens after the compressive strength test was conducted on a FESEM SU8010 device. The elemental observations were also done by the energy-dispersive X-ray spectroscopy (EDS) technique.
# A Multi-Analytical Approach to Investigate Fresco Paintings in a Hypogeum Environment
## 3 Results  
### Characterization of Temperature and Humidity on the Fresco Walls  
Thermographic and hygrometric surveys were carried out inside the rigidarium, targeting the sections of wall that bear frescoes.  
Because the chamber lies as much as 6 m below ground level and remains perennially humid, surface temperatures proved almost uniform, varying only from 18.9 up to 19.6 \({}^{\circ}\)C (\(\Delta\)T = 0.7). This negligible gradient rendered infrared imaging uninformative, so no thermograms are presented.  
In contrast, the in situ moisture readings collected with a contact hygrometer were post-processed to create false-color maps of water content (hygrometric tomograms) across each painted surface. Figure 4 shows the hygrometric tomography of the fresco \"Madonna in trono con Bambino\".  
Figure 3: Measuring points of XRF analysis on the frescoes: (**a**) “Madonna in trono con bambino” and (**b**) “Teoria di Santi”. Red circles indicate the exact point of measurement.  
As can be seen from the image shown in Figure 4 and the relative humidity scale in false colors, the investigated fresco is affected by a widespread phenomenon of surface humidity. The phenomenon is especially visible in the central and upper areas of the fresco (highlighted by the colors turning to blue\(-\)violet), which represents a state of maximum saturation. A similar trend had been observed for the fresco \"Teoria di Santi\", with higher humidity values in the central part of the fresco.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 4 Significance of Communications in IoMT
### Wireless Connectivity Services
MBWA finds applications in mobile internet access, video streaming, online gaming, IoT connectivity, and other mobile applications. * **Optical Wireless Communications (OWC):** OWC utilizes unguided visible, infrared (IR), or ultraviolet (UV) light to transmit signals, primarily for short-range communication purposes [58]. OWC systems operating within the visible band of 390-750 nm are referred to as visible light communications (VLC). VLC systems use light-emitting diodes (LEDs), and find applications in wireless local area networks, wireless personal area networks, and vehicular networks. Terrestrial point-to-point OWC systems, known as free space optical (FSO) systems, operate in the 750-1600 nm IR spectrum and provide high data rates. OWC systems operating in the UV spectrum function at frequencies of 200-280 nm. * **Power Line Communication (PLC):** PLC uses electrical wiring for data transmission. It enables communication over power lines with data rates ranging from a few hundred bps to tens of Mbps. PLC is used for point-to-point and point-to-multipoint communication in smart grid management, home automation, remote metering, and indoor networking.
## System Model
### Problem Formulation  
This paper addresses the relays selection problem (RSP), minimizing both the delivery transmission delay and the number of the involved relays, in order to send the packet \(l\) to the whole D2D community. In formal terms, the RSP can be expressed as follows  
\[\min\max_{i\in\mathcal{D}}\Delta_{i}\;and\;\min\mathcal{R} \tag{7}\]  
where \(\mathcal{R}\) is the number of the selected relays in the considered D2D community. The problem represented by (4)-(7) aims at minimizing the delivery transmission delay and the number of the selected relays. Therefore, we propose an MT [29] based strategy to select the relays. Despite there not existing any theoretical result that strictly connects the optimal solution of problem (7)-(6) with the solution achieved by the application of MT, it is important to highlight that MT represents a powerful and low-complexity mathematical framework to reach a suboptimal solution of (7)-(6) which, as the number of EDs increases, exhibits a remarkable complexity.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Simulation Setup  
In the simulations, we have used multi-sink architecture of dimensions 10 \(\times\) 10 \(\times\) 10 km\({}^{3}\). The sensor nodes are randomly deployed in the given network field. The transmission range of a node is 2 km; the packet size is kept at 72 bytes; and the data rate is 16 kbps [4]. Each node starts with the initial energy of 100 J; where the consumption rate is 50 W during the transmission of data and 158 mW in receiving the packet. The node number varies from 100-500 during the execution of network operations. In order to handle the mobility of nodes, we have considered node speed in the horizontal direction 2 m/s. Additionally, the speed of the acoustic signal is 1500 m/s along with the bandwidth of 4 kHz. In a multi-sink architecture, we have deployed nine sinks at the surface of the water, which are housed with both acoustic and radio modems. The header size of the data packet is 11 bytes, and the ACKpacket is 50 bits. The simulations are conducted in Aqua-Sim (NS-2-based underwater sensor network simulator) to evaluate the performance of the proposed schemes against the baseline schemes. The aforementioned parameters are taken from [4] and are listed in Table 2.
## 5 Discussion
### Objective Function
#### 5.3.3 Trade-Off Method  
* Advantages: Trade-off analysis aids in identifying Pareto optimal solutions, where improving any objective would lead to the detriment of at least one other objective. It is crucial to ensure that chosen solutions are efficient from a multi-objective standpoint.
* Disadvantages: Analyzing trade-offs can become increasingly complex as the number of objectives grows. This complexity necessitates the use of advanced tools and expertise.
# Multi-GPU Acceleration for Finite Element Analysis in Structural Mechanics
## 1 Introduction
Computing using many-core architectures has also shown its benefits in aggregation AMG methods [38; 46; 47] due to the low memory requirements compared to classical AMG methods. We present our experiences, accelerating the finite element method in structural mechanics using a modern multi-GPU system. We exploit the computational capabilities using a distributed conjugate gradient solver using the memory available by each processor, including the host and device memory of each graphics card. This approach permits us to increase the problem size that can be addressed using GPU computing due to device memory constraints. We precondition the Krylov subspace iteration method using a smooth aggregation-based AMG method that increases the GPU performance since it requires less device memory than other multigrid approaches. We distribute the computational burden between the computational resources using domain decomposition techniques. We use modern high-level libraries to exploit the computational resources of many-core and multi-core architectures, in particular, ParMETIS [48] for parallel partitioning, AMGCL [49] for distributed Krylov iterative solver and multigrid preconditioning, and VEXCL [50; 51] for GPGPU development of the aggregation AMG approach, which makes extensive use of the Boost.Compute library [52] using OpenCL programming. We also present numerical experiments to evaluate the weak scalability and limitations of modern multi-GPU systems, addressing structural mechanics analysis. We also provide a quantitative comparison of the numerical results between the many-core computing approach and an efficient multi-coreimplementation, showing the benefits in performance using the multi-GPU system. Finally, we discuss and conclude with the pros and cons of using multi-GPU systems in the context of structural mechanics problems. We organize the remainder of the paper as follows. Section 2 introduces the distributed architecture and the required communications system for working the computational resources together. Section 3 reviews the basis and theoretical background of aggregation algebraic multigrid (AMG) methods. Section 4 is devoted to the parallel implementation of partitioning using domain decomposition and the solving approach using high-level libraries to exploit the computational resources of many-core and multi-core architectures. Section 5 shows the numerical experiments, evaluating the scalability and limitations of the techniques adopted to exploit multi-GPU systems in structural mechanics problems. Finally, Section 6 presents the conclusion of the evaluation of the numerical results.
## 2 Experimental System
### Data Acquisition System  
The data acquisition system was mainly composed of a high-speed data acquisition instrument and a data storage and analysis computer. A 16-channel data acquisition system was used to synchronously collect the AE signals and the load of the pressure tester. This system has the highest sampling frequency of 10 MHz, a 16-bit A/D converter, and an input signal voltage range of \(\pm\)5 V. AE data and load data is transmitted to the host control computer via a shielded cable, which can display the load-deformation curves, and load-time curves in real-time.
## 4 Identification of Recent Developments and Possible Solutions
### Summary of Existing Solutions  
Figure 6 summarizes recent developments and existing solutions for the realization of ACHP systems. The classification is based on the previously presented and discussed solutions. The fields highlighted in green indicate the currently most frequently used solutions for commercial applications of each system part. All other solutions have at least been tested on a laboratory scale and often need further investigation for commercial use.  
Four different cycle configurations were discussed for the design of the ACHP system, whereby the ACHP with single-stage solution circuit was most frequently investigated and is used commercially today. The compressor, as a major component and constraint for achieving higher sink discharge temperatures, features a wide range of solutions. Against the background of reducing occurring compressor outlet temperatures and costs, several types with different compression stages and lubrication solutions were used and studied. For commercial applications, oil-lubricated reciprocating compressors with two-stage compression and intercooling are currently employed. For the absorber and desorber, vertically aligned PHEs are used in different operating modes with the associated mixing devices. The specific design and operation remain a subject of ongoing research to further increase the efficiency while reducing the required size and costs. In addition to the general low placement of the solution pump and the design of the pipes with as little pressure loss as possible, a high static height of the liquid level upstream the pump inlet is achieved by the strategic design of the liquid-vapor separator. There have been many primarily  
Figure 6: Overview of recent developments and existing solutions for the realization of ACHP systems (fields highlighted in green indicate the currently most frequently used solutions for commercial applications of each system part).  
theoretical studies on alternative working fluid pairs, yet experimental experience for use in ACHP systems is limited to ammonia-water. The use and combination of other presented solutions are explicitly not excluded to overcome existing challenges. Depending on the circumstances, a follow-up and combination of other solutions can be a promising approach to improve the achievable operating parameter and efficiency to increase the suitable application range and competitiveness compared to VCHPs and conventional solutions for industrial high temperature applications.
## 3 Materials and Methods
### Data Analyses
#### 3.3.2 Sensitivity Index  
To determine the sensitivity index of crop yields to rainfall perturbations, a crop yield sensitivity index was calculated at both the national and sub-national scale, from the period 1991-2020 for the national scale and 1989-2014 for the sub national scale, respectively, using methods adapted from [21; 51]. Detrending was used to eliminate a linear model of the actual yield time series from the data [54; 55; 56], which was carried out by dividing the projected linear trend value by the actual observed value (Equation (2)). Detrending reduces the consequences of agricultural technological advancements and has the capacity to illustrate annual crop yield variations due to rainfall, reducing the effects of consistent reporting or bias errors resulting from estimations of expected crop yields [21; 55; 56]. For the three crops selected (wheat, barley, and maize) the expected crop yields were projected by using a trend line equation for a simple linear regression (Equations (3)-(5)). The observed and expected crop yield data were regressed against the precipitation data. Then, the sensitivity indexes for each crop were computed by dividing the mean expected crop yields by the mean observed crop yields (Equation (6)).  
\[EXP_{ynsn}=ax+b \tag{2}\]  
where \(EXP_{ynsn}\) is the projected crop yield at both the national and sub-national scales, \(a\) is the linear trend, b is the intercept when \(EXP_{ynsn}=ax\), and \(x\) is the year.  
\[EXP_{yusheatn}=-640196.62+326.62x,\ R^{2}=0.244 \tag{3}\]\[EXP_{y\_overlap}=-328425.38+168.63x,\ R^{2}=0.1075 \tag{4}\]  
\[EXP_{y\_maizen}=-213037.54+109.67x,\ R^{2}=0.1035 \tag{5}\]  
\(EXP_{y\_wheathen}\), \(EXP_{y\_sharlogn}\), \(EXP_{y\_maizen}\) represent the expected wheat, barley, and maize yields in Morocco at the national scale; the numbers in the equations are the slopes and intercepts from left to right, respectively, and \(x\) represents the years.  
\[SE_{\_synsn}=\frac{EXP_{\_synsn}}{ACT_{\_synsn}} \tag{6}\]  
where \(SE_{\_synsn}\) represents the crop yield sensitivity index at both the national and sub-national scale, \(EXP_{\_synsn}\) is the mean expected crop yield--also at the national and sub-national scale, and \(ACT_{\_synsn}\) is the mean actual crop yield at both national and sub-national scale. \(y\) and \(x\) represent the yield and the year respectively.
## 3 Results and Discussion
### Discussion
#### 3.4.2 Consideration of Soils  
Recall that the purpose of this paper is to present a model capable of assessing (the evolution of) hydrological connectivity as a function of landscape structure. In contrast to other works [54; 55], the question of incision is not addressed here. The morphology favours concentration and the slopes in small valleys are often higher than 3%, which is the minimum slope to observe incision processes [56] The soils here are thick with very good infiltration capacity (>60 mm/h), if not degraded on the surface.  
Brown leached soils from leoss cover most of the watershed. They have a silty texture with low clay content and reduced organic matter content. They are sensitive to crusting (battance phenomenon) because they have poor stability. Under the action of rainfall, the surface degrades, leading to a decrease in infiltration capacity [57; 58; 59; 60]. The soil passes from a fragmentary state to a smoothed facies by dispersion of aggregates and welding together of particles (structural crust stage) and then sedimentation in puddles (sedimentary crust). The infiltration capacity during these different phases can drop from 60 mm/h to 1 or 2 mm/h [61].  
To focus on the question we are interested in (i.e., the effect of land use on connectivity), assumptions were made, notably in assumption 4 (infiltration rate of crops = 1 mm/h, i.e., in this configuration, the agricultural soils are degraded with a completely formed surface crust). This means that the soil condition is already completely crusted during the simulation. For other uses, it is possible to apply the model with some adaptations.