esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A low-profile high gain FPC antenna that can simultaneously realize low backscattering as well as enhanced gain deflected beam operation has been presented in this article. To construct the cavity, an absorptive FSS was designed to work in conjunction with a double-sided PRS, and mounted on top of a patch radiator. One side of PRS is a capacitive grid, while the other side is an inductive grid. A dimensional gradient was implemented in the inductive part. For an incident wave, wideband RCS reduction was achieved and also included in-band frequencies. In the transmission mode of antenna, high gain as well as off-broadside beam radiation was achieved. Antenna cavity height is \(\sim\)\(\lambda/4\). The low scattering property of the proposed antenna makes it suitable to be integrated with stealth type platforms for communication, and it can find multiple applications in the military and defense realm; examples include side looking air borne radars, surveillance UAVs, and any military application that requires above/below the horizon communication. In future, the work can be extended to incorporate linear/circularly polarized MIMO antennas.  
Conceptualization, H.U. and T.B.A.L.; methodology, H.U. and T.H.; software, H.U. and T.H.; investigation, H.U.; resources, T.B.A.L., Y.Y., and K.K.; writing--original draft preparation, H.U.; writing--review and editing, T.B.A.L.; supervision, T.B.A.L., W.N.L.B.W.M., M.O., M.I.H.; project administration, T.B.A.L., Y.Y., W.N.L.B.W.M., and K.K.; funding acquisition, T.B.A.L., Y.Y., and M.I.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by University of Malaya (UM), grant number GPF015A-2019.  
Not applicable.  
The authors declare no conflict of interest.
# Mass Processing of Sentinel-1 Images for
## 2 Study Definition and Datasets  
### Study Definition  
The area of interest (AoI) of the study is the Mediterranean Sea (including the Sea of Marmara). The study period is almost two full years, from 3 October 2014 (start of Sentinel-1A operational phase) to 30 September 2016. Figure 1 maps the AoI.
# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 4 Scan to BIM Mapping in 3D GIS  
### Mapping Process Survey  
In this section, to confirm the scope of the study describing the Scan to BIM mapping process, the existing work process is investigated through interviews with three experts with more than seven years of experience in the related field. The experts who participated in the interview carried out various projects for a long time at an aerial surveying company and had experience scanning the entire city through an ALS device and developing LoD2 and LoD3 city models as a major company in the ALS field.  
Scan to BIM mapping in 3D GIS proceeds with the steps of extracting the footprint of the building using the vectorizing tool from the PCD obtained from ALS, modeling the geometry, and mapping it on the GIS.  
In more detail, ALS data are collected from aerial LiDAR and GPS. The data obtained from each sensor is post-processed to match the acquired data based on a specific coordinate system. Post-processing includes matching data from LIDAR, IMU, GPS, etc. to timestamp units using expensive commercial tools and setting a reference coordinate system.  
Matched PCDs are often not of sufficient capacity to be handled by a single computer. Therefore, the PCD file is divided into tile units that can process data using a Global Mapper tool that can process large amounts of data. These tile data files are stripped of unnecessary parts (e.g., the surface) and converted to a Digital Terrain Model (DTM) using a tool such as ESRI ArcGIS Geoprocessing.  
The building footprint is extracted after the DTM is converted to a DEM raster model with height information. The extracted footprint is expressed as an initial polygon set composed of vertices at points where the curvature changes. Since this initial polygon set is challenging to use in practice, it undergoes a normalization process such as simplification.  
Then, the base and roof heights of each building are extracted from the DEM and ground surface. Then, the 3D building geometry is modeled using Sketchup, Autodesk Revit, etc. The modeled building geometry is mapped on the GIS according to the coordinate system.  
This whole process is not easy to achieve the desired result in the first place. Therefore, each step is repeated until a product that satisfies the requirements is obtained.  
Figure 2 and Table 1 show this process.  
For reference, if the data registration process is performed from P1 to P3 in Table 1, the entire PCD capacity can be terabytes or more. It is difficult to process this big data in limited computer memory. P4 of Table 1 divides and stores large capacity PCD files into blocks that can be operated on a computer considering user requirements or adjust the LoD level of PCD to enable data processing.  
The subject of this study is limited to stages P5-P12 related to SBMD.
## 2 Autonomous Vehicle Take over Simulation
### Driver-Vehicle Interface (DVI)  
In 2016, the National Highway Traffic Safety Administration (NHTSA) proposed overall DVI development guidelines [61] including visual, auditory, and haptic interfaces. In 2018, additional human factor design guidance was mentioned, especially for Level 2 and Level 3 automated vehicles [62].  
Studies on the use and content of text, shape and color of icons, and display position for visual notifications have been conducted [63; 64; 65]. For auditory notifications, the type of sound (voice message or simple beep), volume, period, and direction were considered [66]. For haptic notification, the part where feedback is provided (steering, pedal, seat, or seat belt), the strength of the feedback, and the provision of spatial information were researched [66; 67].  
In this study, the visual, auditory, and haptic interfaces were constructed in the simulator by referring to the NHTSA DVI design guidelines [62], UNEC ALKS proposal [5], and previous studies [63; 64; 65; 66; 67]. Visual information was provided by the vehicle cluster monitor shown in Figure 7a and the heads-up display (HUD) on the front driving screen shown in Figure 7b. The cluster monitor can be vertically divided into three areas: A, B, and C. The primary information provided by each area, respectively, was as follows:  
Figure 5: Biosignal acquisition while driving: (**a**) ECG (electrocardiography) and (**b**) respiration.  
Figure 6: Gaze tracking: (**a**) calibration targets; (**b**) gaze tracking results (manual driving heatmap).  
1. Vehicle speed, gear status, gaze recognition status LED (green = open eyes, red = closed eyes).
2. Autonomous driving system status (text message, image), emergency stoplight, driving map.
3. Steering control/forward lookup request (red = request, off = do not request), autonomous driving mode LED (green = autonomous, off = manual). The current vehicle speed and steering control request icon were displayed through the HUD.  
The auditory notification was provided as three different simple beeps: driving mode change, forward gaze request, and takeover request. The takeover request alarm gradually increased in frequency depending on the time remaining until autonomous driving was incapacitated, thereby sending an alert to the driver. Finally, the warning was delivered such that it strengthened the vibration intensity and frequency of the vibration module installed in the driver's seat of the simulator, considering the time until system failure. According to NHTSA's DVI guidelines, vibrotactile seat displays are highly likely to be detected by most drivers.
# Constant-Time Complete Visibility for Robots with Lights: The Asynchronous Case +
## 1 Introduction
The question of minimizing the number of colors and the precise bound on runtime was left open. Reference [6; 7] provided solutions minimizing number of colors with finite runtime. References [8; 9] provided solutions with provable runtime bounds keeping the number of colors constant. For example, Reference [9] showed that Complete Visibility can be solved in \(\mathcal{O}(1)\) time using \(\mathcal{O}(1)\) colors in the semi-synchronous setting. There is no existing work that provides a runtime bound for Complete Visibility in the asynchronous setting, which is the main goal of this paper. A straightforward algorithm can be designed to solve Complete Visibility in the asynchronous setting with runtime \(\mathcal{O}(N)\) using \(\mathcal{O}(1)\) colors, combining the semi-synchronous algorithm of Reference [9] for Complete Visibility with the result of [PERSON] et al. [2]. The result of [PERSON] et al. [2] is as follows: Any algorithm (for any problem) in the robots with lights model that uses \(k>1\) colors in the semi-synchronous setting can be simulated in the asynchronous setting with, at most, \(5\cdot k\) colors. Since the semi-synchronous Complete Visibility algorithm of Reference [9] uses 12 colors, the total number of colors in the new algorithm becomes \(5\cdot 12=60=\mathcal{O}(1)\). However, [PERSON] et al. [2] does not say anything about the runtime of their simulation and it can be shown that, for \(N\) robots, this combination increases the runtime by a \(\mathcal{O}(N)\)-factor, meaning that \(\mathcal{O}(1)\) runtime for Complete Visibility in the semi-synchronous setting becomes \(\mathcal{O}(N)\) time for Complete Visibility in the asynchronous setting. In this paper, we present an algorithm for Complete Visibility that runs in \(\mathcal{O}(1)\) time using \(\mathcal{O}(1)\) colors even in the asynchronous setting. Note that \(\mathcal{O}(1)\) runtime is optimal for Complete Visibility, irrespective of the number of colors.
## 2 Materials and Methods
### Description of Gas Analysers
#### 2.1.3 Picarro  
The Picarro G2103 is a dual-gas analyser for NH\({}_{3}\) and water vapour based on CRDS. During measurement a narrowband laser pulse tuned at 6548.5 to 6549.2 cm\({}^{-1}\) wave number is induced into a cavity containing the gas sample. While the laser beam is reflected back and forth between two highly reflective mirrors, the light intensity decays over time due to absorption by the cavity wall, scattering and reflectivity losses. The presence of NH\({}_{3}\) molecules will lead to a faster decay governed by the Beer-Lambert law. The NH\({}_{3}\) concentration is then determined based on the shortened decay time with respect to the NH\({}_{3}\)-free condition. In Picarro, measurements are taken under 187 mbar and 45 \({}^{\circ}\)C regulated automatically by an embedded controller. It was reported that the presence of 1.6 vol% water vapour could induce up to 3% bias in the raw NH\({}_{3}\) measurements, and to compensate for this interference a correction equation is implemented in the Picarro analyser software [18].
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 2 Materials and Methods
### Radiation of the Antennas in the Presence of Buildings
The wire-grid method used herein, together with the associated computer code, may be considered as a semi-analytical solution to the problem, since it involves the numerical handling of the final set of equations. Thus, it possesses the pros and cons of any such solution. On one hand, it is time- and memory-efficient, whereas its complexity is only related to certain analytical manipulations (carried out once). On the other hand, it may not simulate the shape or material of the buildings in detail. However, since the accurate modeling of the latter is not our main purpose (as explained in the Introduction) the method provides an acceptably accurate solution to the \"real-life\" problem. It should be noted that this method was chosen in conjunction with the frequency band considered herein and, evidently, it may not be suitable for higher frequencies.
## 3 Proposed HEMOGWO for SCOS
### Differential Evolutionary Strategies
#### 3.3.1 Mutation  
The DE algorithm, known for its distinctive vector-based mutation mechanism and greedy selection strategy, maintains a balance between population diversity and convergence efficiency. The mutation operation is a crucial component that enables global exploration in DE. It generates new search directions by applying vector differential perturbations among individuals in the population. Taking the classical DE/rand/1 strategy [26] as an example, the mutation vector \(v_{i}(g)\) for a target individual \(x_{i}(g)\) is generated according to Equation (13).  
\[v_{i}(g)=x_{r1}(g)+F\cdot(x_{r2}(g)-x_{r3}(g)) \tag{13}\]  
where \(x_{r1}\), \(x_{r2}\), and \(x_{r3}\) represent three randomly selected distinct individuals, and \(F\in[0,2]\) is the scaling factor. This mechanism guides the search towards potential regions by using the scaled differential vector (\(x_{r2}(g)-x_{r3}(g)\)) to expand the search space. Studies have shown that when \(F>1\), the algorithm tends to favor global exploration, whereas when \(F<1\), it leans towards local exploitation.
## 2 Materials and Methods
### Measure of Location, Distribution, and Normalized Volume of Osteolysis  
To determine the extent and location of osteolysis in the distal tibia and talus, nine regions were defined on each CT image, all of which included periprosthetic bone. The spikes of the metal tibial component and metal talar pegs served as landmarks. After 3D reconstruction of the ankle joints, 54 volumes of interest (VOIs; 1893.3 \(\pm\) 1343.1 mm\({}^{3}\)) were generated, 27 in the distal tibia and 27 in the talus (Figure 3). VOIs were grouped according to anatomical direction: superior-inferior (closest peripheral (C_VOI), mid-peripheral (M_VOI), distant peripheral (D_VOI)), anterior (VOI 1-3)-posterior (VOI 7-9), and medial (VOI 1, 4, 7)-lateral (VOI 3, 6, 9). Particularly in the talus, VOIs that included peg of talar component (VOI 4-6) were grouped and compared with the other VOIs (VOI 1-3, 7-9). The location, distribution, and volume of osteolysis within each VOI were measured, and the volume was normalized by reference to the size of the ankle joint of each patient.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Patient**} & \multicolumn{2}{c}{**Age at Surgery**} & \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**BMI (kg/m\({}^{2}\))**} & \multicolumn{2}{c}{**Time to**} & \multirow{2}{*}{**Reasons for the Revision Surgery**} \\  & & & & & & \\  & **(Years)** & & & & & \\ \hline
1 & 73 & F & 24.4 & 1.8 & Tibia and talus osteolysis 1  \\
2 & 68 & M & 23.6 & 2.8 & Talus osteolysis 2  \\
3 & 67 & M & 24.9 & 8.2 & Tibia and talus osteolysis 1  \\
4 & 64 & M & 25.7 & 1.7 & Tibia and talus osteolysis 1  \\
5 & 66 & F & 27.8 & 3.0 & Tibia and talus osteolysis 1  \\
6 & 64 & F & 24.5 & 5.2 & Talus osteolysis 1  \\
7 & 69 & F & 20.8 & 4.2 & Talus osteolysis 1  \\
8 & 74 & M & 28.7 & 9.2 & Talar component subsidence by talus osteolysis 1  \\
9 & 63 & M & 27.8 & 1.6 & Talus osteolysis 3  \\
10 & 73 & F & 23.8 & 4.2 & Talus osteolysis 1  \\
11 & 66 & F & 33.8 & 8.5 & Talus osteolysis 1  \\
12 & 75 & M & 21.7 & 6.4 & Talus osteolysis 1  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient descriptions.  
Figure 1: A 3D-CT reconstruction of the post-TAA ankle joint (red, gray, and light yellow indicate osteolysis, TAA metal components, and bones, respectively).  
Figure 2: Segmentation of osteolysis in the distal tibia (**a**) and talus (**b**) based on 3D-CT reconstruction (red and blue indicate osteolysis and the caloric border of osteolysis, respectively).
# Survey for Soil Sensing with IOT and Traditional Systems
###### Abstract  
Smart Agriculture has gained significant attention in recent years due to its benefits for both humans and the environment. However, the high costs associated with commercial devices have prevented some agricultural lands from reaping the advantages of technological advancements. Traditional methods, such as reflectance spectroscopy, offer reliable and repeatable solutions for soil property sensing, but the high costs and redundancy of preprocessing steps limit their on-site applications in real-world scenarios. Recently, RF-based soil sensing systems have opened a new dimension in soil property analysis using IoT-based systems. These systems are not only portable, but also significantly cheaper than traditional methods. In this paper, we carry out a comprehensive review of state-of-the-art soil property sensing, divided into four areas. First, we delve into the fundamental knowledge and studies of reflectance-spectroscopy-based soil sensing, also known as traditional methods. Secondly, we introduce some RF-based IoT soil sensing systems employing a variety of signal types. In the third segment, we introduce the details of sample pretreatment, inference methods, and evaluation metrics. Finally, after analyzing the strengths and weaknesses of the current work, we discuss potential future aspects of soil property sensing.  
soil sensing; chemical analysis; spectroscopy; Internet of Things (IoT) 2023
## 3 Results
### Kinematic GNSS Measurements
#### 3.2.6 Comparison with Other Data  
Satellite altimetry provides a real information on glacier elevation. However, these are only partially usable. There are gaps of several kilometers between the ground satellite tracks (see Figure 15). A direct comparison of our traverse data with those of the satellite altimetry is only possible at the crossing points. The Geoscience Laser Altimeter System  
Figure 14: Ice velocity from synthetic aperture radar of Sentinel-1 acquired over October 2015–September 2016 with camps of the Greenland North Expedition route. The direction of glacier flow is from the central axis of the Greenland ice sheet towards the coast [17].  
Figure 13: Accumulation (blue) and ablation (red) along the expedition route based on data from (a) 2015–2020. (b) 2015–2021.  
(GLAS) is developed for the IceSat mission and has a precision of about 3 cm for a footprint with less than 80 m diameter [37].  
The NASA IceSat satellite (Ice, Cloud, and land Elevation Satellite, 2003-2010) data were used. Comparing the profiles measured in this project with an elevation model derived from IceSat data [29], the qualitative difference between the two data sets becomes clearly visible (Figure 16). The elevation model is from 2010, based on the end of the IceSat mission and shows considerable deviations from the profile measurements due to the different spatial resolution. The IceSat provides a set of laser pulses, which have approximately 70 m spots on Earth's surface with a spacing of 170 m. The model from IceSat is interpolated and smoothed (blue line) compared to the profile measurement, which is more detailed (red line).  
C30, C31, C32 and C33 are some of the points measured multiple times since 2002. The waves in the surface profile with amplitudes of up to 20 m are clearly visible. The blue line shows the heights derived from IceSat data; the red line shows the measured profile from this project.  
Figure 16: Part of the elevation profile from the west side (2015).  
Figure 15: Coverage of the operation area by the IceSat mission (2003–2010). The diagonal line represents the profile measured by GKE on the ground.
## 3 Results and Discussion
### Dry Matter and Water Activity  
The parameters characterizing the stability of the dried material are dry matter and water activity, to which the color of the biological materials is related. Biological material is considered to be properly dried if the dry matter content is \(\geq\)88% [49]. Low water activity (Aw) of \(<\)0.6 characterizes biological materials as resistant to biochemical and physical changes and microbial proliferation [49]. In our study, Aw in the dried products ranged from 0.082 to 0.496, which ensures microbiological safety (Table 5). In other studies, an Aw value of 0.2836 was found in lyophilizing C. alata [50], and 0.184 for loquat fruit [51].  
In fresh sweet potatoes, the dry matter was 20.70%, and, after drying, it ranged from 94.93% to 98.29%, indicating that all samples were properly dried (Table 5). Other researchers have reported a dry matter value of 6.17% for fresh zucchini [13]. A significant effect of the drying method (\(p<0.05\)) on the dry matter and water activity values of the dried products was observed. Dry weight positively correlated with the impregnating solution (r = 0.60), water activity (r = 0.66), and ABTS (r = 0.60), and negatively correlated with the hardness (r = \(-\)0.62) and gumminess (r = \(-\)0.60). Higher values of dry matter and lower values of Aw were found in dried products subjected to sublimation drying. These results are consistent with previous observations of dried sweet potato slices [5]. The use of vegetable juices as impregnating solutions resulted in higher dry matter values (\(p<0.05\)) in the samples before drying. This may be due to the presence of solid particles in freshly squeezed onion and kale juice, which hinder water transport. The type of impregnating liquid had no effect on the water activity value (\(p>\)0.05). Water activity negatively correlated with the hardness (r = \(-\)0.80) and cohesiveness (r = \(-\)0.69), springiness (r = \(-\)0.65) and gumminess (r = \(-\)0.71), and VOCs (0.73). The results of dry matter and water activity were consistent with other studies in which kale leaves were dried using VI with NaCl and O+ NaCl; the dry matter values were lower for the solution containing fresh onion juice [11].
## 3 Simulation Experiment  
### Simulated Working Face Overview  
The simulated working face inclination angle was 5\({}^{\circ}\), the mining height was 3 m, the mining depth was 500 m, the average advancement speed of the working face was 6 m/day, the designed working face strike length was 1000 m, the inclined width was 200 m, and the dip azimuth was 0\({}^{\circ}\). The engineering experiment shows that the characteristics of the mining subsidence in Huainan conform to the DIB prediction model. The predicted parameters of mining subsidence in the simulated working face were: \(q=0.9\), \(P=0.4\), \(b=0.3\), \(\theta=89\), \(S_{1}=S_{2}=S_{3}=S_{4}=10\), tan\(\beta_{1}=0.6\), tan\(\beta_{2}=2.2\), and \(C_{0}=2.8\). The simulated SAR satellite revisit period was 12 days, the radar incident angle was \(\theta_{\rm i}=44.494\), the spatial resolution was 10 \(\times\) 10 m, the radar wavelength was 31 mm, and the satellite's line of sight azimuth was \(\alpha_{\rm i}=350.596^{\circ}\). This simulation involved four excavation processes at the working face, simulating the cumulative deformation values of the surface LOS when the working face advanced from 12 to 36 days, 36 to 60 days, 60 to 84 days, and 84 to 108 days, respectively. The results are shown in Figure 3.  
Figure 3: Simulated LOS deformation map.  
Figure 2: Technical principle diagram of the InSAR-DIB dynamic prediction model.