esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Methodology  
### High-Resolution Physical Model  
The latest Advanced Research WRF (ARW) model version 3.9.1, which is a limited-area mesoscale model based on a fully compressible and non-hydrostatic dynamic core, is chosen to predict day-ahead surface solar radiation (hourly interval).  
The high-resolution model is operated with the WRF Single-Moment 6-class (WSM6) microphysics parameterization [24], the Kain-Fritsch convective parameterization [25], the Noah land surface model (LSM) [26], and the planetary boundary layer scheme we selected is the YSU (Yonsei University) scheme [27]. The specific information of the short- and longwave radiation schemes we have chosen is discussed in Section 4.1. We re-initialized the WRF model using \"cold-start\" mode at 6:00 p.m. UTC each day and each re-initialization runs for 30-h. The outputs during initial 6-h are excluded because of the consideration of spin-up time. It is worthwhile to note that, in this study, UTC of WRF output is converted to the local time of the study site, in order to compare with in situ observations conveniently.
# On the Performance of Underlay Device-to-Device Communications
## 2 State-of-the-Art
It is emphasized that considering both the interference and noise together will typically lead to an intractable mathematical framework. We, however, are able to derive all metrics in the closed-form expressions. In addition, we consider different power allocation schemes rather than only the equal power allocation as in other works. The main contributions and novelties are summarized as follows:  
* To the best of authors knowledge, this is the first work that computes the outage probability, average rate, and amount of fading of the D2D networks under the impact of interference from D2D and cellular networks, as well as the background noise in closed-form expressions with three distinct power allocation schemes, namely, the equal, random, and path-loss based schemes. * We derive the coverage probability of the cellular users under the aggregate interference of D2D transmission. * The impact of the total transmit power of D2D networks on the performance of the OP is explicitly studied. * Monte Carlo simulation results are presented to verify the accuracy of the proposed mathematical framework as well as to highlight the behavior of these metrics with respect to system parameters. The remainder of this paper is organized as follows: The system model is given in Section 3. The derivation of key performance metrics, e.g., the OP, the average rate and the AoF, is provided in Section 4. Numerical results are presented in Section 5. Section 6 concludes the paper. The list of notations and acronyms are given in Table 1.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 3 Applications and Use Cases of FANETs
### Smart Farming  
In order to increase production, smart farming aims to utilize IoT connectivity technologies. UAV-based IoT connectivity should enable farmers to remotely manage their crops using a variety of networked sensors and equipment in smart farming [64; 65]. For example, a UAV can approach a passive sensor, like RFID sensors [66], on a farm and collect its data. Such integrated UAV-based data gathering of sensors is anticipated to significantly enhance smart farming in a number of ways. Data gathering in agriculture is fundamentally focused on enhancing and speeding up agricultural operations. The classification of UAV deployment in agriculture yields distinct applications of effective water management; crop monitoring; cattle and pest control; chemical level monitoring and management; etc. [67].
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 2 Motivation: The Need to Revolutionize the Water Supply System  
Managing and monitoring large-scale water supply networks has been a serious challenge facing water utilities. In the past, monitoring and control capabilities in the water supply system are provided with supervisory control and data acquisition systems (SCADA). However, due to the dynamism, uncertainties and complexity involved in water supply system operations, there is a need to revolutionize the water industry with the current paradigm shift in technology to better monitoring and control capabilities. Hence, there is a need for in situ, smart systems for on-line monitoring of the complex water distribution piping networks. Unfortunately, the existing ICT solutions do not provide the capability to handle the dynamic nature of the water distribution networks. Thus, a renewed focus is aimed at the adoption of new digital technologies with such monitoring capability. The new industrial revolution termed \"Industry 4.0\" allows the use of emerging technologies with better monitoring and control capabilities, and better computational and decision support systems to optimize the operation of water supply systems. Thus, integrating SCADA with network simulation models and the features of Industry 4.0, proactive monitoring and control and management of the complex water supply distribution networks could be achieved. Such an integrated system forms the foundation of Water 4.0 for the provision of a real-time smart water network decision support system. To have a fully functioning system, the key drivers of Water 4.0 as well as the application domain where such an integrated system could be used in water supply systems are discussed. Furthermore, technical challenges and future opportunities offered by the technology for sustainable operation of municipal water services are highlighted.
# Optimal Design of Switchable Wearable Antenna Array for Wireless Sensor Networks
## 2 Models and Methods
### Inverse Problem Formulation
#### 2.2.1 Min-Max Approach  
The first approach to solve the inverse problem formulated in Section 2.2 was based on a single-objective evolutionary algorithm of the lowest order--Estra, originally proposed in [25]. The objective function \(f_{m}\) defined by Equation (3), to be maximized, expresses the min-max norm of the ratio of the array gain towards the signal to the array gain towards the interference (SIR) that could be obtained with all possible combinations of \(n\)-\(th\) antenna phases \(\alpha(n)\)\(n=\) [1.4] for the \(\psi_{a}\) vector dictated by the optimization algorithm, namely  
\[f_{m}(\psi_{a},\ \alpha(n))=\underset{\varphi_{i}\varphi_{i}}{\min}\bigg{|} \underset{\alpha(n)}{\max}\frac{G_{s}(\psi_{a},\varphi_{s},\ \alpha(n))}{G_{i}(\psi_{a},\varphi_{i},\ \alpha(n))}\bigg{|};\]  
\[\varphi_{s}=0^{\circ},45^{\circ},\ldots\ 315^{\circ};\varphi_{i}=0^{\circ},45^{ \circ},\ldots\ 315^{\circ};\varphi_{s}\
eq\varphi_{i};\]  
\[\alpha(n)=0,\ \alpha_{const} \tag{3}\]  
Therefore, maximizing Equation (3) gives rise to a single-objective optimization. Nevertheless, the following remark can be put forward. After a few numerical experiments, it is straightforward to realize that the objective function components \(G_{s}\) and \(G_{i}\) defined in Equation (3) are competing objectives. In fact, if \(G_{s}\) and \(G_{i}\) are independently considered, it comes out that the values of antenna positions \(\psi_{a}\) and phase angle \(\alpha\) which maximize \(G_{s}\) function alone are not the same which minimize \(G_{i}\) function alone. This puts the ground for the multi-objective approach developed in the subsequent sections.
## 3 Results and Discussion
### Effect of Model Configuration on Detection Performance
#### 3.2.3 Effect of Class Weight  
By comparing Figure 6a,b with Figure 6c,d, we can evaluate the impact of class weighting on the F1 scores. Class weighting with fine-tuning decreased the model performance; however, the decreasing effect was absent or minimal in models that used SVM as the classifier. Without fine-tuning, class weighting had a significantly negative impact on the performance of the models with FC(s). Considering class weight emphasizes the effect of fewer data (positive data, in this case) and reduces false negatives at the expense of increasing false positives, which makes precision worse and recall improved (Equations (9) and (10)).  
Even when evaluated using recall, class weighting improved the model performance (Figure 7). However, for models using nonlinear SVMs with fine-tuning, the effect of class weighting was minimal.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Design of Wind Turbines
#### 3.1.2 Performance Optimization for Local Wind Conditions  
The ability of AI to analyze historical wind data and local weather conditions makes it possible to adapt the design of turbines. Traditional \"one-size-fits-all\" solutions often lead to sub-optimal results in different environments. AI models can identify optimal design parameters such as blade shape, rotor diameter and spacing that are tailored to the specific wind conditions to maximize energy production.  
The Windstar project, for example, used AI to customize the shape of turbine blades based on local wind patterns. The results of the project suggest that this could capture up to 18% more wind energy compared with conventional designs, although this has not been scientifically proven.  
EvoPhase utilized AI to develop a turbine that can operate efficiently in Birmingham's unique wind conditions. The AI-generated design included innovative features such as curved blades that rotate around a central point to optimize performance in the turbulent urban airflows. The ability to create and test over 2000 designs in just a few weeks significantly accelerated the development process compared with traditional methods [36].
## 4 Comparative Simulation Analysis  
Verification of the control strategy through simulation, as carried out in this study, is an important aspect in the development of a control strategy. In this section, the robustness of the MPC system is first discussed, and then the DLC strategy [22] is used as a comparative strategy to verify the superiority of the proposed control strategy. The main technical parameters of the electric vehicle are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Project** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Mass and inertia} & Half-body mass \(m_{b}\) (kg) & 846 \\  & Half-body moment inertia \(l_{p}\) (kgem\({}^{2}\)) & 1516 \\  & Front unsprung mass \(m_{usf}\) (kg) & 47 \\  & Rear unsprung mass \(m_{usf}\) (kg) & 47 \\ \hline \multirow{4}{*}{Stiffness and damping} & Front suspension stiffness \(K_{sf}\) (N/m) & 21,000 \\  & Rear suspension stiffness \(K_{sr}\) (N/m) & 27,300 \\ \cline{1-1}  & Front tire stiffness \(K_{tf}\) (N/m) & 238,000 \\ \cline{1-1}  & Rear tire stiffness \(K_{tr}\) (N/m) & 238,000 \\ \cline{1-1}  & Front suspension damping \(C_{sf}\) (N*s/m) & 1861 \\ \cline{1-1}  & Rear suspension damping \(C_{sr}\) (N*s/m) & 1861 \\ \hline \multirow{4}{*}{Geometric size} & Distance from the body mass center to the front axle \(a\) (m) & 1.29 \\  & Distance from the body mass center to the rear axle \(b\) (m) & 1.61 \\ \cline{1-1}  & Wheelbase \(L\) (m) & 2.90 \\ \cline{1-1}  & Height of body mass center \(h_{g}\) (m) & 0.82 \\ \cline{1-1}  & Maximum front suspension dynamic deflection (m) & 0.08 \\ \cline{1-1}  & Maximum rear suspension dynamic deflection (m) & 0.08 \\ \hline \multirow{2}{*}{Force} & Maximum front suspension control force (N) & 1500 \\ \cline{1-1}  & Maximum rear suspension control force (N) & 1500 \\ \hline \multirow{4}{*}{Control parameters} & Sampling time \(T_{s}\) (s) & 0.02 \\ \cline{1-1}  & Forecast time domain \(Np\) & 20 \\ \cline{1-1}  & Control time domain \(Nc\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main technical parameters of the electric vehicle.  
The purpose of the control in this study is to restore the vehicle's vertical direction to a stable state as soon as possible during the braking process. A gradual braking condition [38] is selected as the braking condition, as shown in Figure 3.  
The braking condition of the vehicle adopts a gradual form, and its maximum braking intensity is 0.5. The vehicle was considered to be running on a high-adhesion road to ensure that the vehicle is in a normal braking condition.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 4 Results and Discussions
### Determination of the Strength of the Signal
#### 4.2.3 Non-Uniform Population Density  
Determining the cell tower location for the area where there is non-uniform population density. The area of the RGIPT campus is taken, which consists of a hostel area, administrative building, and academic blocks 1 and 2 (AB1 and AB2), as shown in Figure 13a. The cell tower location is calculated according to the method discussed above. The signal strength map is shown in Figure 13b.  
The population in the hostel area is more compared to the rest of the area. Sometimes there is a need for better signal strength in the highly populated area as most of the population is there. Initially, the cell tower location using the proposed method is determined, which is irrespective of the population density. The signal strength in Figure 13b shows that both the high and low-populated areas have moderate signal strengths. Therefore, the area from the cell tower location in Figure 13b is divided into two parts according to the area divided (here divided in two halves as high and low population). To determine the cell tower location for the two halves, the above procedure is used. Signal strength for both halves is shown in Figure 14a,b.  
According to the proposed method, which is discussed above, the whole area in Figure 13a is divided into 6050 equal cells of 5 m \(\times\) 5 m, where 1800 cells are from a highly populated area, and 4250 cells are from a lowly populated area. Changes in signal strength concerning cells for the two halves are shown in Figure 15a. The total number of cells lies in (range \(-\)50 to \(-\)75 dB m) and in (range \(-\)75 to \(-\)95 dB m) for the main tower location, locations \"1\" and \"2\" are shown in Figure 15b. Percentage change in signal strength for locations \"1\" and \"2\" to the main location of the cell phone tower is mentioned in Table 1.  
Figure 13: (**a**) RGIPT area with non-uniform population. (**b**) Signal strength map for the main location initially determined by the proposed method.
## 2 Retaining Wall Displacement Measurement Review
### Previous Studies and Technologies Related to Retaining Wall Displacement Measurement
\begin{table}
\begin{tabular}{c c c c c} \hline
**Author/Company** & **Category** & **Object** & **Main Measurement Technologies** & **Stage** \\ \hline [PERSON] et al., 2018 [6] & Article & Excavation retaining wall & MEMS-based inclinometer, and CDMA module & Field test \\ [PERSON] et al., 2020 [7] & Article & Excavation retaining wall & Inclinometer, data integrator, and gateway & Field test \\ RST [8] & Product & Excavation retaining wall & MEMS inclinometer, data loggers, and gateway & Commercialization \\ Trimble [9] & Product & Retaining wall & Tiltmeter, laser-tilt sensors, data loggers, and gateway & Commercialization \\ Atmax [10] & Product & Retaining wall & Inclinometer and data loggers & Commercialization \\ Specto Technology [11] & Product & Retaining wall & Tiltmeter and data loggers & Commercialization \\ \hline \end{tabular}
\end{table} 
Table 1: Previous studies related to retaining wall displacement measurement using contact measurement methods. \begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Category** & **Measurement Technology** & **Object** & **Stage** \\ \hline [PERSON] et al., 2006 [4] & TLS & Retaining wall for excavation & Field test \\ [PERSON] et al., 2011 [2] & TLS & Retaining wall for excavation & Field test \\ [PERSON] et al., 2021 [11] & TLS & Retaining wall for excavation & Field test \\ [PERSON] et al., 2016 [12] & TLS & Retaining wall for highway & Laboratory experiments (simulation) \\ [PERSON] et al., 2019 [13] & TLS & Retaining wall for highway & Field test \\ [PERSON], 2021 [14] & TLS & Retaining wall for tunnel & Field test \\ [PERSON] et al., 2020 [16] & Article & MLS & Retaining wall for highway & Field test \\ [PERSON] et al., 2021 [5] & MLS & Retaining wall for highway & Field test \\ [PERSON] et al., 2017 [3] & Vision (photogrammetry) & Retaining wall & Laboratory experiments \\ [PERSON] et al., 2021 [15] & Vision & Retaining wall for excavation & Laboratory experiments \\  & & (D.L.C.) & \multirow{2}{*}{Retaining wall} & \multirow{2}{*}{Laboratory experiments} \\ [PERSON] et al., 2022 [17] & Vision & & \\ \cline{1-1} [PERSON] et al., 2022 [17] & (KAZE) & Retaining wall & Laboratory experiments \\ \hline \end{tabular}
\end{table} 
Table 2: Previous studies related to retaining wall displacement measurement using non-contact measurement methods. the retaining wall displacement measurement system developed in this study uses a non-contact measurement method that address the limitations of conventional inclinometers. Regarding the measurement technology, a 2D LiDAR sensor that is less expensive than TLS and can continuously be used outdoors at construction sites was applied.
## 3 Results
### Spatial Patterns in SMAP/CYGNSS \(\beta\) Values
#### 3.3.2 Relationship between NDVI and \(\beta\) Values  
SMAP emissivity and CYGNSS reflectivity are often poorly correlated in forested areas (Figure 6a,f) due to a lack of significant changes in both SMAP emissivity and CYGNSS reflectivity (Figure 4e). This is because the dense vegetation in forests interferes with microwave sensing of the soil. We used MODIS/Terra mean NDVI (described in Section 2.3) to compare SMAP/CYGNSS \(\beta\) values with vegetation density.  
Figure 7: **(a)** Correlations for 45-day SMAP emissivity and CYGNSS reflectivity linear regressions (\(\beta\) values). **(b)** SMAP/CYGNSS \(\beta\) values. **(c)** SMAP active-passive \(\beta\) values. Mean of daily SMAP \(\beta\) (K/dB) divided by daily Ts (K) from 15 April 2015–7 July 2015 [29]. **(d)** NSSM variance, calculated using 9 KM SMAP soil moisture data from 2020. **(e)** Mean annual precipitation, calculated using FLDAS precipitation data (described in Section 2.3). **(f)** Mean NDVI, calculated using MODIS/Terra data (described in Section 2.3). **(g)** 2020 MODIS IGBP landcover class. A landcover class key is included in the second figure in Section 3.3.1.  
The relationship between SMAP/CYGNSS \(\beta\) values and mean NDVI is shown in Figure 9c. As expected, areas with high mean NDVI values (>0.8) have near-zero SMAP/CYGNSS \(\beta\) values. Areas with low mean NDVI values (<0.1) also have near-zero SMAP/CYGNSS \(\beta\) values, because barren areas are usually very dry. In contrast, the most negative SMAP/CYGNSS \(\beta\) values, which ultimately lead to the greatest spatial heterogeneity in downscaled TBs, are found in areas with moderate mean NDVI values (-0.2-0.6).  
Figure 8: A depiction of the effect that NSSM variability has on \(\beta\) values. All displayed data falls within the 33 km box centered on the 9 km grid cell at [36.1N, 119.6W]. (**a**) Bi-monthly SMAP emissivity and CYGNSS reflectivity data, collocated using \(\pm\)half the time between successive SMAP observations, and their corresponding linear regressions. The seasonally varying \(\beta\) values are included in each plot. The SMAP/CYGNSS data points are colored to match the timeseries in (**c**). (**b**) 45-day SMAP emissivity and CYGNSS reflectivity data (as described in Section 2.2.2) and the corresponding \(\beta\) value. (**c**) SMAP NSSM timeseries with FLDAS monthly rainfall data. The NSSM data points are colored bi-monthly, to match the scatter plots in (**a**).
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 4 Simulation Verification and Analysis
The above results show that the proposed method can significantly reduce the influence of systematic errors on the track of the target observed by the radar, and verify the effectiveness of the proposed method. Figure 6a shows that there is still a small amount of systematic error in the _x_-axis direction, which is because the algorithm's estimation of the GNSS communication delay is not accurate enough. In addition, it can be seen from Figure 6b,c that, with the increase in measurement points, the error value fluctuates greatly, which is caused by the presence of azimuth measurement noise and elevation measurement noise. It also indicates the necessity of regular systematic error corrections in the radar system. Figure 5: Target real track and radar measurement comparisons before and after calibration. Figure 6: Error correction effect. (**a**) _x_-axis error; (**b**) _y_-axis error; (**c**) _z_-axis error.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
###### Abstract  
Passive radio-frequency identification (RFID) was recently used to monitor landslide displacement at a high spatio-temporal resolution but only measured 1D displacement. This study demonstrates the tracking of 2D displacements, using an array of antennas connected to an RFID interrogator. Ten tags were deployed on a landslide for 12 months and 2D relative localization was performed using a phase-of-arrival approach. A period of landslide activity was monitored through RFID and displacements were confirmed by reference measurements. The tags showed displacements of up to 1.2 m over the monitored period. The centimeter-scale accuracy of the technique was confirmed experimentally and theoretically for horizontal localization by developing a measurement model that included antenna and tag positions, as well as multipath interference. This study confirms that 2D landslide displacement tracking with RFID is feasible at relatively low instrumental and maintenance cost.  
Keywords:phase localization; landslides; RFID; remote sensing; wireless sensor network; early warning +
Footnote †: journal: remote sensing
# Post-Quantum Authentication in the MQTT Protocol
## 1 Introduction
### Public-Key Cryptography
#### 1.2.1 Key Encapsulation Mechanisms  
Public-key encryption requires the device to have two different keys: one public key to encrypt and one private (or secret) key to decrypt. These keys are used to facilitate secure communication between the devices by deciding on a shared symmetric key using a key agreement protocol. Postquantum key agreement works slightly differently, through a key encapsulation mechanism (KEM). With KEM, a key is generated and then encapsulated (similar to encryption) with the other devices's public key. The encapsulated key is then sent to the other party, which decapsulates the key (similar to decryption) [16]. This results in a shared secret key between the two devices.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 4 Distributed Maintaining Connectivity and Coverage Maximization (MCCM)
### Bridging  
In the earlier section, we introduced the maximization process which runs in a distributed manner. Every device knows about the devices that belong to the same neighborhood only. This might form a network with different disconnected neighborhoods (islands) or isolated devices. We mean by isolated device, the one that does not belong to any neighborhood, and we refer to it as an island with only one device. One of the real examples for such problems is losing the connectivity between the different troops in the battle fields. To provide connectivity between the different isolated islands. We need to build connections (bridges) between these islands. These bridges do not need to cover the whole area. It is enough to have them between these islands and the central station to maintain the flow of data. In this section, we provide two different type of bridging: A distributed and centralized bridging.