esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodology
### Methodology
#### 2.2.4 MAPS Scanning  
The composition of minerals in rocks is dependent on the depositional environment of rock sedimentation, the geological time, and later transformation, and has a significant variance that is especially notable in clay minerals. MAPS mineralogy allows quantitative analysis of multiple mineral phases within each pixel. MAPS scanning technology uses the secondary electron (SE), BSE, and X-ray signals generated by the electron beam excitation of the sample surface to obtain information, such as sample surface morphology and mineral density, and, in turn, expresses it in grayscale. In order to study shale reservoirs, BSE backscatter was generally applied to the surface of samples for imaging. In addition, the MAPS technology can achieve multi-scale visualization of SEM scans by splicing > 10,000 images and utilizing pyramid image processing techniques. The high-precision MAPS backscattering electron scanning equipment developed by the FEI company, Apreo SEM, is equipped with multiple detectors including the T1 in-lens backscatter detector, advanced SmartAlign Technology for automatic alignments and FLASH Technology for automatic image stitching. It has the following specifications: resolution (low vacuum): 1.8 nm; resolution (high vacuum): 1.2 nm; accelerating voltage: 200 V\(\sim\)30 kV.
## Appendix A
Moreover, by noting that:  
1. for more complex nonlinear systems with parametric uncertainties the MPC controller requires a high online computational burden,
2. in general, the properties of asymptotic stability depend in a complex way on the model chosen to predict the evolution and on other parameters of the controller,  
it can be deduced that, in many cases, controllers based on other control techniques should be preferred with respect MPC. **Example A5**. : _Consider the system:_  
\[M\ddot{y}+K\dot{y}+M5\sin y=u+d,\ M\in[0.50,\ 1.50],\ K\in[1,\ 2]\] (A25)  
In the hypothesis that:  
\[d=0.5\sin(2t),\ r=\sin(t)\] (A26)  
consider the control law:  
\[u=10 sign(e+\dot{e})\] (A27)  
In Figures A6 and A7, the time histories of \(u,\ e,\ \dot{e}\) are reported in the hypothesis that \(M=1,K=1\) and that downstream of the controller there is a zero holder hold (ZOH) with sampling time \(T=0.01\), \(0.10\) respectively. From Figures A6 and A7 it can be deduced that the chattering control law with finite switching frequency does not provide good performance, especially of the error derivative \(\dot{e}\). Under the hypothesis of real actuators and real derivative actions, the control system can be even unstable. **Example A6**. : _Still, consider the system (A25) with \(d\) and \(r\) in (A26)._  
By using the simple control law:  
\[u=3(10^{2}e+10\dot{e})\] (A28)  
designed with the proposed methodology, in the hypothesis that \(M=1,\ K=1\) and that downstream of the controller there is a pulse-width modulation (PWM) modulator with sampling time \(T=0.01,\ 0.10\) the time histories of \(u,\ e,\ \dot{e}\) are reported in Figures A8 and A9, respectively. In Figure 10, the control signals in Figures 11 and 12 are reported with detail. From Figures 13 and 12 and from the corresponding Figures 14 and 11 it emerges how the performance of the control system with a controller designed by using the stated theory and implemented by using a PWM modulator are better, both regarding to the amplitude of the control signal (see Figure 12, as well) and the precision, than the performance obtained with a controller designed with the control techniques using high frequency and high-amplitude control signals (e.g., [4, 10, 14, 31, 40, 41] and related references therein) and implemented with commutation devices with evaluation of the sign at finite frequency. Moreover, it is worth noting that the PWM modulator technology is at low cost and very used in the engineering practice. Finally, take also into account:  
(1) the results already published in [34, 35, 38],that the articulated mechanical systems are very complex (see [6]) and that, hence, techniques using their online models are difficult to be implemented without delays; therefore, the delays due to the online computation times can make unstable the control system. Thus, it clearly emerges that the results proposed in this paper (see also Applications 1-4), which are a broad generalization of the technique presented in [21], are very useful from a theoretical and applicative point of view.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 3 Risk Analysis
### Semi-Quantitative Risk Assessment (Sqra)  
SQRA is to be applied whenever the studied system requires further details than the qualitative approach [49]. It covers some quantity of probability, outcomes, and risk value. It may be conducted using fault tree analysis (FTA), Dynamic fault tree (DFT), event tree analysis (ETA), bow-tie analysis (BT), or risk ranking matrix [52].
## 4 UAV Data Acquisition and Preprocessing
### Flight Control Strategy
#### 4.1.1 Flight Height  
To determine the optimal altitude of the UAV and ensure its efficient flight, the following considerations should be taken into account: (i) the UAV camera view should cover the full width of the road that needs to be inspected; (ii) it is important to avoid any interference from auxiliary facilities such as road trees and street lights during the flight; and (iii) to minimize image distortion, it is crucial to maintain a constant altitude, consistent speed, and capture vertical imagery.  
To cover the full width of the pavement, a minimum flight altitude is required. Based on the Pinhole Imaging Principle and the Triangular Similarity Geometric Relationship (Figure 5a), the minimum flight altitude should satisfy Equation (1):  
\[H\geq(f*W)/S_{w} \tag{1}\]  
where \(H\) represents flight vertical height. \(f\) represents the focal length of the camera. \(W\) represents the width of the road to be inspected. \(S_{w}\) represents the camera sensor size (\(S_{w}\times S_{h}\)).  
In our experiment, the DJI Mini 2 drone was chosen to perform the flight mission. The camera sensor format was CMOS 1/2.3 inches, with a full-frame sensor size of 17.3 mm \(\times\) 13.0 mm. The main focal length (\(f\)) was 24.0 mm. The experimental pavement consisted of a bi-directional eight-lane road. To ensure high-definition imagery quality, the experiment was conducted only on the left lane, from east to west. The width of the road (W) was measured to be 16 m. The minimum flight altitude was calculated at 22.20 m. Taking into account the tolerance for flight stability, the final flight height was chosen as 22.5 m.
## 2 System Model
### Single-Slot Static ISAL Network  
Figure 3 introduces the single-slot static ISAL network. In this network, localization and sensing are simultaneously considered, corresponding to the existence of both the localization transmission links and the sensing transmission links. Only the positions of anchors are known, while the positions of the other two kinds of nodes both need to be estimated. The signal propagation is as follows:  
* LOS transmissions between anchors and radars: the anchors and radars send ranging signals to each other to position the radars with active localization. (Localization)
* LOS transmissions between radars themselves: the radars also send ranging signals to the other radars to help the active localization process with the spatial cooperative information contained. (Spatial cooperation for localization)
* NLOS transmissions via the passive targets: the anchors and radars, called active nodes, send sensing signals, which are reflected by the targets, and then received by the other active nodes. (Sensing)
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 2 Methodology  
Five different software photovoltaic models: Photovoltaic Geographical Information System (PVGIS) [9], PVsyst [10], RESTcreen [11], PV*SOL [12] and System Advisor Model (SAM) [13], are evaluated in this work. The AC energy generation estimates of each software package were compared with the real AC energy production data of three different types of PV plants. Table 1 details a comparison of the main features of each program, whether solar radiation databases can be used, as well as product libraries (PV module and inverter), the number of parameters that are required, difficulty of using the software, number of results calculated, shadow modeling, electrical loses and license costs.  
One of the greatest benefits of these programs is that preliminary studies of PV project feasibility may be performed even before fully developing the design of the PV plant. The main objective of this type of study is to estimate the energy generated with a low number of known parameters. Although the power plant can be described in detail in some of these programs, the comparison in this study is focused on a preliminary study, where only essential parameters have been defined: location, type of solar tracking technology, installed capacity, number of modules, and efficiency and peak power of the PV module and inverter. The parameters of each station are summarized in Table 2. All other parameters, such as wire and shadow losses, as and when required, can be set as default parameters or  
with the standard recommended values of the software manual. All the models implement different climate database options, either online or integrated, so the option recommended by each program was selected and compared when different options were available.
## 3 Literature Review  
Many researchers have investigated reducing noise and improving experimental results through circuit manipulations or truncations. For instance, using imperfect Hamiltonian representations for NISQ-era adiabatic quantum optimization has been shown to yield better results when employing specific techniques [10]. Additionally, several studies have examined the variable encoding procedures, noting that different encodings and embeddings significantly affect circuit performance [11; 12; 13].  
A particularly interesting branch of research related to our approach involves specific variants of the QAOA algorithm [7]. One notable example is the ADAPT-QAOA algorithm, which iteratively selects operators to enhance the QAOA mixer Hamiltonians based on gradient descent algorithm outputs, thereby reducing the overall number of necessary entanglement gates [14]. Another algorithm, focusing on problem Hamiltonians, draws inspiration from classical neural networks by introducing a quantum dropout approach [15]. This method shows that selectively dropping clauses that define the problem Hamiltonian while maintaining the cost function can enhance QAOA performance.  
Our work diverges from these approaches by being the first, to our knowledge, to design a QAOA problem Hamiltonian from the beginning to be simpler than its ideal counterpart. This simplification involves the relaxation of constraints within the problem Hamiltonian, aiming to retain the core structure and characteristics of the original problem while making it more amenable to efficient quantum optimization. By doing so, we strive to balance the complexity of the problem representation with the capabilities of current quantum hardware, which is often limited by noise and decoherence.
## 3 Retrieval Adaptations
### Noise Model
Outliers represent poor fits, e.g., due to complicated atmospheric conditions which cannot be well described by the forward model. As they usually enhance the RSR, we have to approach the expectation value of RSR from the lowermost values. The 2.28 th and 15.9 th percentile (Figure 4, red and orange points) of the Gaussian distribution are two and one standard deviations smaller than the expectation value. We used this to estimate the expectation value (Figure 4, green points) from which we determined the free fit parameter \(\delta F\) of Equation (1) (numerical values are shown in Figure 4). Note that adding 4% instead of 2% of the continuum radiance to the measurement noise gave similar results (not shown here). Soundings with a RSR being more than two standard deviations larger than expected from Equation (1) are considered outliers. For this purpose, we fitted the second order polynomial  
\[2\,\sigma=a_{0}+a_{1}\,NSR+a_{2}\,NSR^{2} \tag{2}\]  
and use it as threshold for the maximal allowed deviation from the RSR model (Figure 4, gray lines). Figure 3: Sampling of all pre-filtered soundings analyzed in order to determine the noise model. The data set consists of 10% of all pre-filtered OCO-2 soundings (randomly selected) of 24 days in 2015 (13.01, 15.01, 14.02, 16.02, 10.03, 20.03, 03.04, 19.04, 08.05, 23.05, 08.06, 24.06, 15.07, 16.07, 15.08, 16.08, 15.09, 16.09, 15.10, 16.10, 15.11, 17.11, 12.12, 18.12). This results in a manageable but still representative data set with respect to nadir/glint observation geometry, season, and spatial distribution. We define the noise model which modifies the reported OCO-2 L1b radiance noise \(N\) analog to Equation (1):  
\[N^{\prime}=\sqrt{N^{2}+l_{\text{cont}}^{2}\,\delta F^{2}}. \tag{3}\]
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 6 Conclusions
In the second stage of the proposed method, a Multi-Stage Discharge (MSD) primary battery was developed to enable a hardware-based low battery alert before the battery is fully discharged. This design ensures the reliable operation of IoT devices, particularly those not actively managed through real-time SOC monitoring in the first stage. By providing timely low battery alerts, the MSD battery minimizes the risk of unexpected device shutdowns, thereby enhancing the overall reliability and operational continuity of IoT devices in remote metering scenarios. These contributions collectively address critical challenges in battery management and pave the way for more robust IoT applications in real-world environments.Furthermore, our proposed method demonstrates significant potential for practical deployment, as it effectively reduces the reliance on expensive SOC measurement equipment while maintaining robust performance, even in challenging environments. However, challenges remain in integrating the model into resource-constrained devices and ensuring consistent accuracy across diverse operational conditions. Future work will focus on optimizing system integration and enhancing adaptability to further improve the scalability and reliability of the proposed approach in real-world applications. [PERSON] proposed the idea and defined the concept for this study, and also prepared the data and scenario design. S.K. contributed to data collection, while [PERSON] led the design and development of the MSD battery. The first draft of the manuscript was written by [PERSON] The review process, supervision, and approval of the final manuscript was done by J.K. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Technology Innovation Program (RS-2022-00154678, Development of Intelligent Sensor Platform Technology for Connected Sensor) funded By the Ministry of Trade, Industry & Energy (MOTIE, Republic of Korea); and in part by the Technology Innovation Program (20015961, The Development of Lifetime Prediction and State of Charge Estimating Techniques for Primary Lithium Batteries in Industrial IoT Devices) funded By the Ministry of Trade, Industry & Energy (MOTIE, Republic of Korea). Not applicable. The datasets presented in this article are not readily available because they are part of an ongoing study and contain proprietary experimental data. Requests to access the datasets should be directed to the corresponding author. Author [PERSON] was employed by the company \"Vitzro Cell Co., Ltd.\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Conclusions  
### Summary of Results  
To address the practical needs of roadside RSU deployment in a cooperative vehicle infrastructure system, this paper proposes an optimal deployment method for roadside RSU in the coexistence of V2V and V2I communication. From the perspective of an intelligent expressway sensor network builder, it analyzes the changes in the income model under the influence of permeability and traffic density and determines the values of permeability under several different income models so that the spacing of roadside RSU unit deployment under each model can be inferred. In addition, the reliability of each part of the model is verified by the Warshell algorithm and the Kmeans clustering algorithm. The main results of this paper include the following aspects:  
1. Proposed income model for an intelligent expressway sensor network under the permeation of intelligent connected vehicles. The results of the income equation under different permeabilities and traffic densities are analyzed to determine the optimal spacing of roadside RSU deployment under the corresponding conditions. Taking the traffic density of 60 as an example, the following situations apply. 1. When the intelligent connected vehicles reach 29%, the effective connection between the vehicles begins to form; or, when it is located on the state curve L3, the deployment spacing is 290 m. 2. With the increase in intelligent connected vehicles, when it reaches 54%, the income model is at the maximum. At this time, the deployment spacing is 684 m and the income model is optimal on the peak curve L1. 3. When the intelligent connected vehicle reaches 82%, the income model is zero. At this time, the information transmission of cooperative vehicle infrastructure is dominated by V2V and supplemented by V2I, whose corresponding deployment spacing is 2684 m and the income model is in equilibrium. 2. The Warshell algorithm is used to simulate and verify the permeability of effective connectivity between vehicles when the traffic density is 20, 40, 60, and 80. The simulation results show that the part of positive income has a reliability of 93.2%. The Kmeans clustering algorithm is used to simulate and verify the deficit caused by information transmission when the traffic density is 60 and the permeability is 20%, 40%, 60%, and 80%. The simulation results show that the information transmission deficit part has a reliability of 94.91%.  
In summary, since both modeling and model simulation adopt uniform traffic flow, the income model has high reliability in determining layout spacing in the environment of the expressway and can be used as the optimal layout of roadside RSU.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 3 Formation of Weighted Path Aware Greedy Perimeter Stateless Routing Protocol
### Influence of Reliable Node Density on Communication Reliability  
As shown in Figure 2, when node S transmits data to destination node D, its neighbor nodes A and B are the same distance from destination node D. If the judgment is based on the density of all neighbor nodes within the communication range, at this time, the densities of neighbor nodes of node A are greater than those of node B; thus, the data packet should be transmitted to node A. However, during data packet transmission, only nodes in the reliable communication area contribute to the communication reliability, such as node Q. The reliable neighbor nodes of node A are nodes C and Q, and the reliable neighbor nodes of node B are nodes U, T, E, and Y; thus, the reliable node density of node B is higher. Node S forwards data to node B, and the probability of establishing a reliable routing link in the future is greater, thereby reducing the probability of losing data packets.
# RF-EMF Exposure near 5G NR Small Cells
## 2 Materials and Methods
### 5G NR Base Station Sites  
The measurements were performed at two NR test sites in Belgium where it was possible to measure very closely to the antennas. The parameters are listed in Table 2.  
The first was a site (Site #1, Table 2) with three base station radios at a height of about 5.5 m. Each radio consisted of a 64T64R array antenna made up of 96 antenna elements in a 12-by-8 array. This base station was thus capable of massive multiple-input-multiple-output (MIMO) and beamforming and it is further denoted as an Advanced Antenna System (AAS). The second site (Site #2, Table 2) had a smaller, lower-power base station with one 4T2R antenna and thus no beamforming.  
At both sites, UEs were provided by the operator: an Oppo Reno4 Pro smartphone at the first site and a Nokia 8.3 5G smartphone at the second. In both cases, the UE was connected to a 5G standalone network. To maximize the downlink traffic load (see previous section), 100 GB HTTP file downloads were set up during the measurements (from [[http://speedtest.tele2.net](http://speedtest.tele2.net)]([http://speedtest.tele2.net](http://speedtest.tele2.net)) (accessed on 23 November 2021)).  
It should be noted that, based on the transmit powers and EIRPs of the base stations under study, neither of them could be classified as'small cells' (compare Tables 1 and 2). However, since the exposures are directly dependent on the base station powers, the measurement results were rescaled to the respective small-cell powers, that is, to a transmit power of 30 dBm for site #1 and to an EIRP of 50 dBm for site #2 (Table 1). Moreover, a distance of 1 m to the base station was taken as boundary between workers and members of the general public (see \(D_{m}\) for E100 in Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline  & **Site \#1** & **Site \#2** \\ \hline Frequency band & n78 (FR1) & n78 (FR1) \\ Channel center frequency & 3.775 GHz & 3.430 GHz \\ Channel bandwidth & 50 MHz & 40 MHz \\ Subcarrier spacing & 30 kHz & 30 kHz \\ Maximum number of resource blocks (\(N_{RB}\)) & 133 & 106 \\ Number of antenna elements (per polarization) & 96 & 4 \\ MIMO & 64T64R & 4T2R \\ Advanced Antenna System (AAS)? & Yes & No \\ Transmit power & 49.7 dBm (92.5 W) & 43 dBm (20 W) \\ Maximum gain (array gain + antenna element gain) & 25 dBi & 10.5 dBi \\ EIRP & 74.7 dBm (29.5 kW) & 53.5 dBm (112 W) \\ Height & 5.5 m & 4.5 m \\ Size of radio unit & 795 mm \(\times\) 470 mm \(\times\) 190 mm & 295 mm \(\times\) 270 mm \(\times\) 20 mm \\ Technology duty cycle factor (\(f_{TDC}\)) & 0.743 & 0.743 \\ \hline FR1 = Frequency Range 1 (410 MHz to 7125 MHz), MIMO = multiple-input-multiple-output, EIRP = equivalent isotropic radiated power. & \\ \end{tabular}
\end{table}
Table 2: Parameters of the 5G new radio (NR) base station radios at the two considered sites in Belgium.