esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of SSS Adsorbent
#### 3.1.3 BET Specific Surface Area and Other Characteristics of SSS  
The physico-chemical characteristics of SSS are summarized in Table 2. As observed, the BET surface area of the SSS was found to be 6.106 m\({}^{2}\)/g, with a total pore volume of 9.689 cm\({}^{3}\)/g, and average pore diameter of 3.17330 A; this shows that the SSS have better surface characteristics as a natural adsorbent, without any treatment.  
In addition, the SSS had a very high porosity of about 90% which is in agreement with the SEM characterization, as shown in Figure 2. On the other hand, the real and apparent density, as well as the zero load point were determined. These gave us the same resolution as the other parameters.  
[PERSON] et al. [37] showed a porosity of 79% with a bulk density of 0.026 g/cm\({}^{3}\) for orange peels treated with phosphoric acid (OP-H\({}_{3}\) PO\({}_{4}\)). [PERSON] et al. [38] determined BET surface areas of 0.8549 m\({}^{2}\)/g and 0.6140 m\({}^{2}\)/g for cucumber peel and potato peel, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Specific surface (m\({}^{2}\)/g) & 6.106 \\ \hline Average pore radius (Å) & 3.17330 \\ \hline Total pore volume (cm\({}^{3}\)/g) & 9.689 \\ \hline Porosity (\%) & 82.92 \\ \hline Porous volume (cm\({}^{3}\)/g) & 2.217 \\ \hline Apparent density (g/cm\({}^{3}\)) & 0.374 \\ \hline Real density (g/cm\({}^{3}\)) & 2.189 \\ \hline pH\({}_{\text{ZPC}}\) & 7.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and chemical characteristics of SSS.  
Figure 2: SEM micrograph of SSS.
# Energy Management Scheme for an EV Smart
## 6 Results and Discussion
### Voltage Regulation  
The EMS also drives the reactive power support of the smart charger under voltage sag or swell during reactive load onset. As shown in Figure 16, the period \(t=1\) s to \(1.5\) s is represented by a large consumption of 216 kVAr, while from \(t=2\) s to \(2.5\) s, there is a significant production of reactive power at the load end. These events are accommodated by the smart charger to lessen the stress on the grid. The EMS calculates reactive power to be provided or consumed to compensate the sag and swell, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methodology** & **Vehicle** & **V2G** & **G2V** & **Initial SoC** & **Final SoC** & **Utilization Factor** \\ \hline (RM) Battery capacity-based & \(EV_{1}\) & Yes & No & 75\% & 44\% & 75\% \\ power allocation method [24] & \(EV_{2}\) & Yes & Yes & 50\% & 57\% & 75\% \\ \hline (PM) Proposed proportional & \(EV_{1}\) & Yes & Yes & 75\% & 60\% & 100\% \\ power allocation method & \(EV_{2}\) & Yes & Yes & 50\% & 53\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: EV power allocation method comparison.  
Figure 16: Three-phase reactive powers of the grid, DG, load and smart charger.  
Figure 15: (a) Discharging and charging power of the two EVs under the reference method (RM) and the proposed method (PM). (b) \(EV_{1}\) SoC comparison RM vs. PM. (c) \(EV_{2}\) SoC comparison RM vs. PM.  
The load consuming reactive power brings about a sag at the PCC voltage, which is indicated in Figure 17 (without SC). The voltage sags below the lower limit of 0.95 pu defined for normal operation [48]. On the contrary, the reactive power produced at the load end is absorbed by the grid, thus producing a swell, and the PCC voltage surpasses the upper limit of 1.05 pu. With smart charger employment and zero active power requirements, the full capacity of the smart charger is utilized for reactive support to bring the voltage within limits, as shown in Figure 17 (with SC).  
Furthermore, the reactive power services provided by the smart charger do not engage the EV batteries at all, as can be seen from Figure 18.
# Profiling Taste and Aroma Compound Metabolism during Apricot Fruit Development and Ripening
## 2 Results
### Enzymes Involved in Flavor Compound Metabolism during Development and Ripening
Significant increase of lipoxygenenases (LOX), hydroperoxide lyase (HPL), alcohol dehydrogenase (ADH), alcohol acyl-transferases (AAT), Acyl-CoA Oxidase (ACX) activities were found throughout the whole development period of apricot fruit (Figure 7g-j). Similar remarkable increase trend was observed in peels or pulps of apricot tested during fruit development process. A rapid significant increase in carotenoid cleavage dioxygenenases (CCD) activity was found during fruit development process in both peels or pulps tested (Figure 7k), whereas the terpene synthase (TPS) activity decreased significantly during this process (Figure 7f). On the whole, the enzyme activities of aroma volatiles metabolism in peels were significantly higher than those in pulps of all cultivars tested.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 3 Attacks Against WiFi Sensing
### Active Attack
To keep the stealthiness of the attack, WiAdv [60] features a signal synthesis scheme to craft adversarial WiFi signals without affecting the communication quality. WiAdv exploits full-duplex devices (as Figure 3 shows) to tactfully forward WiFi signals to mimic dynamic multipath, which makes the artificial multipath indistinguishable from the natural ones from the perspective of WiFi Rx. Different from many methods assuming a clean wireless environment, the use of IS-WARS [54] demonstrated that there is widely existing interference from coexisting protocols such as ZigBee and Bluetooth. These signals can also be used to attack WiFi sensing systems to compromise the recognition process. Thus, IS-WARS uncovers new adversarial attacks against WiFi activity recognition systems by intentionally injecting interference using coexisting protocol signals. This adversarial attack is stealthy in terms of avoiding being detected by traffic analysis. In recent years, metasurface has been widely used to manipulate wireless signals and program wireless environments [68; 69; 70; 71; 72; 73; 74]; thus, there are some attempts being made to use metasurface for WiFi attacks. RIStealth [65] leverages a delicate metasurface to render a moving individual undetectable by WiFi-based intrusion detection systems. By using the designed metasurface to perform fine-grained manipulation of the reflected WiFi signal, an intruder can approach the protected area sneakily and lift the threshold of the alert based on the basic principle of most intrusion detection systems. Thus, the intruder can make himself invisible. Figure 3: The design of the full-duplex attacker in WiAdv [60].
## 2 Description of the Method through a 1D Example
### Inverse Processing Method Based on Bayesian Inference
Indeed, for a regular spatial discretization of size \(dz\), the intensity of a source at position \(z\) is the integration of the intensity of the sources present in the space \([z-dz/2;z+dz/2]\). Then, the intensity of a source is associated with the chosen discretization;
* In the first approximation, the discretization is chosen to be of regular thickness.
# EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration
###### Abstract  
While detecting surrounding vehicles in autonomous driving is possible with advances in object detection using deep learning, there are cases where small vehicles are not being detected accurately. Additionally, real-time processing requirements must be met for implementation in autonomous vehicles. However, detection accuracy and execution speed have an inversely proportional relationship. To improve the accuracy-speed tradeoff, this study proposes an ensemble method. An input image is downsampled first, and the vehicle detection result is acquired for the downsampled image through an object detector. Then, warping or upsampling is performed on the Region of Interest (RoI) where the small vehicles are located, and the small vehicle detection result is acquired for the transformed image through another object detector. If the input image is downsampled, the effect on the detection accuracy of large vehicles is minimal, but the effect on the detection accuracy of small vehicles is significant. Therefore, the detection accuracy of small vehicles can be improved by increasing the pixel sizes of small vehicles in the transformed image more than the given input image. To validate the proposed method's efficiency, the experiment was conducted with Argoverse vehicle data used in an autonomous vehicle contest, and the accuracy-speed tradeoff improved by up to a factor of two using the proposed ensemble method.  
autonomous vehicles; vehicle detection; image processing; deep learning; model ensemble
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 3 Method
### Learning Algorithms
\(s_{xi}\) and \(s_{yi}\) are the actual slip_x and slip_y, respectively. \(\eta_{1}\) and \(\eta_{2}\) represent coefficients. (3) Step 3: Genetic operations  
Genetic operations included three basic genetic operators: selection, crossover, and mutation. Selection was to select superior individuals from the population and eliminate inferior individuals based on fitness values. For a population of size \(N\), where the fitness of individual \(i\) was \(f_{fit\_j}\), the probability of \(i\) being selected was \(p_{i}\):  
\[p_{i}=f_{fit\_j}/\sum f_{fit\_j} \tag{8}\]  
The crossover was to recombine part of the structure of the selected parent individuals to generate new individuals. The crossover operator adopted the single-point crossover operator, an intersection was randomly selected, and two parent individuals swapped at the front or back of the point to produce new individuals. The new individuals formed after the crossover operation had a certain probability of mutation. Similar to the selection operation, this operation was based on probability. Generally speaking, the mutation probability was set very small. (4) Step 4: Iterative optimization  
When termination condition was met, the optimization process stopped, and the obtained optimized weights and thresholds were assigned to the BPNN. BPNN used the optimized result weights and thresholds to perform training, and could avoid the prediction results from falling into the local optimal solution.
# Combating Pathogens Using Carbon-Fiber Ionizers (CFIs) for Air Purification: A Narrative Review
## 4 Discussion
### Carbon-fiber Ionizers: The Pros and Cons
[63] used a carbon fiber-based ionizer with an ion-flow ionization technology, demonstrating that negative ions can effectively reduce airborne virus concentrations without generating significant levels of ozone, thus addressing the main health and safety concerns associated with the use of ionizers. Aerosol tests were performed in a 19 m\({}^{3}\) room, where ionization reduced the infectivity of aerosolized rotavirus and calicivirus by more than 97%. Additionally, the study highlighted that the ionizer completely prevented airborne transmission of influenza A virus between animals in a guinea pig model. The carbon fiber ionization process is very effective in generating significant amounts of negative ions while keeping ozone levels within safe limits. This achievement significantly increases its applicability to indoor environments [99]. The authors developed a portable cold-plasma detergent device that is effective against aerosolized pathogens and is capable of reducing total bacteria and viruses by more than 99% within a short operating time (<90 min) in indoor environments tested in sizes up to 3 \(\times\) 2.4 \(\times\) 2.4 m\({}^{3}\).
# Urban Free-Space Optical Network Optimization
## 2 Problems Statement
### Network with Minimum Combined Vertex and Edge Cost That Connects All Terminal Points  
The most general problem we consider applies costs to both vertices and edges. The motivation for this problem is clear: Costs in real-life scenarios may be applied to both vertices (placing infrastructure) and edges (varying required FSO hardware).  
**Definition 5**: _Given a red-blue visibility graph \(G_{rb}=(R,B,E)\), a vertex cost function \(w\left(v\right):R\rightarrow\mathbb{R}\), and an edge cost function \(w\left(e\right):E\rightarrow\mathbb{R}\), find a set of vertices \(R^{\prime}\in R\) and a set of edges \(E^{\prime}\in E\) that connect all the vertices in \(B\) with minimal combined vertex and edge overall cost. The solution for this problem is a \(\hat{G}_{rb}\) with \(\arg\min_{v\in R^{\prime},e\in E^{\prime}}\left(\sum_{v\in R^{\prime}}w\left( v\right)+\sum_{e\in E^{\prime}}w\left(e\right)\right)\)._  
The Steiner tree problem for graphs can be reduced to the general problem herein by settings all vertex costs to zero. Thus, following the hardness of the Steiner tree problem for graphs, the general problem is also NP-hard (on general graphs).
## 3 Results
### Detection Along the Paths
In both cases, the main source of failure is missed objects, for both paths exceeding 10% of the total number of objects. The detection of objects which are not actual supports is low, below 4% for Path A and below 1% for Path B. Apart from the summary of results, the detection quality over time can also be tracked. Figure 14 shows X locations of the objects within the region of interest, with color indicating the correctness of detection. For clearer visualization, the plots for objects on the left and right sides of the robot were separated. The track corresponding to a single idler support begins when it is first registered, at a distance of over 3 m ahead of the robot. As time progresses, this distance decreases until the X coordinate of the support is around 1 m, at which point the support is no longer visible. Figure 14: Detection of the supports in time—X coordinate of the objects. (**a**) Path A—in the first row to the left of the robot. (**b**) Path A—in the first row to the right of the robot. (**c**) Path B—in the first row to the left of the robot. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Objects** & **Correct (TP)** & **Missed (FN)** & **Wrong (FP)** \\ \hline No restrictions & & & & \\ Path A & 11,337 & 4959 (43.7\%) & 5836 (51.5\%) & 542 (4.8\%) \\ Path B & 4446 & 1388 (31.2\%) & 2106 (47.4\%) & 952 (21.4\%) \\ Region of interest 6+ & & & & \\ Path A & 3930 & 3289 (83.7\%) & 490 (12.5\%) & 151 (3.8\%) \\ Path B & 1101 & 936 (85.0\%) & 157 (14.3\%) & 8 (0.7\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection results and accuracy in a whole scan and within region of interest 6+. While traversing Path A, the total processing time does not exceed 32 ms which is significantly shorter then the 100 ms scan acquisition period of the LIDAR sensor used. The average processing time of less than 14 ms allows for real-time processing of the scans. The performance on Path B is slightly lower, with more scans requiring over 20 ms of processing and a maximum time of 75 ms. However, even at more than twice the time of Path A, the maximum processing time remains well below the point cloud acquisition period. An analysis of the duration of each stage of the process shows that the cropping and alignment stage contributes most to the total processing time. Comparing the graphs with camera images helps to identify situations where the processing time increases. Two types of such situations were observed: pillars close to the robot's path (visible in the background of Figure 4b) and mounds of material encroaching on the robot's path. Due to the smooth shapes of the material mounds, the RANSAC algorithm required more iterations to detect the ground, increasing the processing time.
## 4 Proposed Approach
### Maintenance
#### 4.3.1 Cluster Leaving  
Each node monitors its local environment through the periodically exchanged beacon messages to keep track of the neighborhood members. When a node leaves the cluster (following a disconnection factor), it stops transmitting; the surrounding neighbors detect this event and remove this node from their \(CRL\) lists. If the leaving node is a cluster head, rule (R1) will be activated and CMs re-execute this rule to elect a new CH and perform the necessary updates. Otherwise (the case where the leaving node is not a CH), rule (R3) will be activated and CMs having it as forwarder (to access the CH in multi-hop fashion) will choose another path to relay their information. The cluster leaving process is illustrated in Figure 7.
# Experimental Study on the Seismic Performance of Recycled Concrete Hollow Block Masonry Walls
## 2 Experimental Program
### Design of Specimens  
Figure 4 shows the masonry procedure of all the specimens, including masonry the bottom beams, infill walls, structural columns, and ring beams. It is important to mention that ordinary mortar with a strength of 10 MPa was selected to masonry the mortar joints of the infill walls, and the fullness degree of mortar joints should be up to 80% to ensure the propagation of shear forces according to the seismic design requirements. In addition, the horizontal joint width was required to range from 8 to 12 mm.  
The experimental parameters used to determine the seismic performance of specimens were the compressive stress, aspect ratios, and the materials of the structural columns. The test specimens were divided into two groups. The first group consisted of three walls W1, W2, and W3, and the cyclic loading tests of them were conducted to analyze the influence of different compressive stress and aspect ratio on the seismic capacity. The second group contained two specimens, W4 and W5, which were constrained by structural columns, and the tests of them were conducted to analyze the influence of structural columns of different materials on the seismic capacity. The other components of all specimens, such as bottom beams and ring beams, were consistent in the test. Additionally, the value of the compressive stress was 0.6 MPa and 0.9 MPa, respectively, corresponding to the compressive stress of the upper weight on the middle floor and the bottom floor of an ordinary 9-storey residential building, respectively. Properties and dimensions of the five masonry walls are shown in Table 3 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Aggregate** & **Grading** & **Apparent** & **Bulk Density** & **Water** & **Crushing** & **Fineness** \\  & **/mm** & **Density**/kg**:**m\({}^{-3}\) & **/kg**:**m\({}^{-3}\) & **Absorption/\%** & **Index** & **Modulus** \\ \hline NA & \(<\)5 & 2724.8 & 1448.4 & 1.02 & — & 2.18 \\ RCA & 5–10 & 2521.7 & 1096.1 & 3.50 & 24.74 & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of aggregates.  
Figure 4: Process of the construction of masonry wall specimens.  
Figure 3: The dimension of recycled concrete hollow block.  
The dimensions and the steel arrangement of the specimens should be meeting the specifications as outlined in GB/T 41112013. The section size of the structural column, ring beam, and bottom beam were 240 mm \(\times\) 120 mm, 240 mm \(\times\) 200 mm (or 240 mm \(\times\) 150 mm) and 300 mm \(\times\) 350 mm, respectively. Ordinary Portland concrete with 28 d axial strength of 42.5 MPa was used in the bottom beams during the test. The reinforcement arrangement is shown in Figure 5.
# Beam Scanning Capabilities of a 3D-Printed Perforated Dielectric Transmitarray
###### Abstract  
In this paper, the design of a beam scanning, 3D-printed dielectric Transmitarray (TA) working in Ka-band is discussed. Thanks to the use of an innovative three-layer dielectric unit-cell that exploits tapered sections to enhance the bandwidth, a 50 \(\times\) 50 elements transmitarray with improved scanning capabilities and wideband behavior has been designed and experimentally validated. The measured radiation performances over a scanning coverage of \(\pm\)27\({}^{\circ}\) shown a variation of the gain lower than 2.9 dB and a 1-dB bandwidth in any case higher than 23%. The promising results suggest that the proposed TA technology is a valid alternative to realize a passive multibeam antenna, with the additional advantage that it can be easily manufactured using 3D-printing techniques.  
Keywords:transmitarray antenna; beam scanning; planar lens; discrete lens; tapered matching; 3D-printed antenna; 3D-printing; additive manufacturing +
Footnote †: journal: Electronics Electronics
## 2 Materials and Methods
### Model Construction and Evaluation  
Multiple linear regression was used to construct rice yield estimation models. Yield was the dependent variable, and spectral information, including all types of vegetation indices, florescence difference or ratio reflectance as well as first derivative florescence reflectance were independent variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Equation** & **Index** & **Equation** \\ \hline FRIa & \(\frac{R_{r,F\left(i\right)}-R_{r,F\left(i\right)}}{R_{r,F\left(i\right)}+R_{r,F \left(i\right)}}\) & FDIa & \(\frac{R_{r,F\left(i\right)}-R_{d,F\left(i\right)}}{R_{d,F\left(i\right)}+R_{d,F \left(i\right)}}\) \\ FRIb & \(\frac{R_{r,F\left(i\right)}}{R_{r,F\left(i\right)}}\) & FDIb & \(\frac{R_{r,F\left(i\right)}}{R_{d,F\left(i\right)}}\) \\ FRIc & \(R_{r,F\left(i\right)}-R_{r,F\left(i\right)}\) & FDIc & \(R_{d,F\left(i\right)}-R_{d,F\left(i\right)}\) \\ FRId & \(\frac{R^{\prime}_{r,F\left(i\right)}-R^{\prime}_{r,F\left(i\right)}}{R^{\prime} _{r,F\left(i\right)}+R^{\prime}_{r,F\left(i\right)}}\) & FDId & \(\frac{R^{\prime}_{r,F\left(i\right)}-R^{\prime}_{r,F\left(i\right)}}{R^{\prime} _{d,F\left(i\right)}+R^{\prime}_{r,F\left(i\right)}}\) \\ FRIe & \(\frac{R^{\prime}_{r,F\left(i\right)}}{R^{\prime}_{r,F\left(i\right)}}\) & FDIe & \(\frac{R^{\prime}_{r,F\left(i\right)}}{R^{\prime}_{d,F\left(i\right)}}\) \\ FRIf & \(R^{\prime}_{r,F\left(i\right)}-R^{\prime}_{r,F\left(i\right)}\) & FDIf & \(R^{\prime}_{d,F\left(i\right)}-R^{\prime}_{d,F\left(i\right)}\) \\ \hline \hline \end{tabular} Note: \(i\) and \(j\) refers to the specific wavelength of these indices.  
\end{table}
Table 2: Florescence indices tested in this study.  
The parameters adopted to assess the performance of models were the coefficient of determination (R\({}^{2}\)), average absolute percentage error (MAPE), and root mean square error (RMSE). R\({}^{2}\) reflected the fitting degree of estimated and measured yield. The higher R\({}^{2}\) was, the higher the reliability of the yield estimation model was. MAPE and RMSE reflected the error between estimated and measured yield, with lower values indicating higher accuracy. They were defined as:  
\[\text{R}^{2}\ =\ 1-\frac{\sum\limits_{i}{(y-y^{\prime})}^{2}}{\sum\limits_{i}{(y- \overline{y})}^{2}} \tag{9}\]  
\[MAPE\ =\ \frac{\sum\limits_{i=1}^{n}{\left|\frac{y-y^{\prime}}{y}\right|}}{n} \times 100\% \tag{10}\]  
\[RMSE\ =\ \sqrt{\frac{\sum\limits_{i=1}^{n}{(y-y^{\prime})}^{2}}{n}} \tag{11}\]  
where \(y\) is the measured yield, \(y^{\prime}\) is the modeled yield, \(\overline{y}\) is the average value of the measured yield, and n is the sample size.  
The yields in 2018 ranged from 5497.27 to 9494.92 kg/ha with their average of 7404.85 kg/ha, while the yields in 2019 ranged from 4413.38 to 8803.24 kg/ha with their average of 7271.62 kg/ha. Due to the differences in rice varieties and fertilization levels in the two years, all samples of two years were ranked by yield from lowest to highest, and were evenly divided into three groups, with 2/3 as the modeling dataset and 1/3 as the validation dataset.