esa-sceva/satcom-chunk-collection huggingface dataset:
# Deepwater 3D Measurements with a Novel Sensor System
## 3 Results
### Evaluation Measurements  
The measurements to determine the measurement accuracy are used to evaluate the calibration quality and to estimate systematic and random measurement errors in the 3D reconstruction of the underwater measurement objects. They are based on the standard for evaluating area-measuring optical sensors according to VDI/VDE [36]. This particularly concerns the determination of ball distances, the shape and dimensions of the balls, and the determination of the flatness deviation of planar measurement standards in accordance with [36].  
Various experiments were carried out to determine random and systematic measurement errors for the UWS1 and UWS2 measurement systems. The studies were carried out both in clean fresh water (water basin) and in seawater (Baltic Sea). The experiments are  
Figure 7: UWS mounted on ROV (**a**) and placement of the ArUco markerboards into the water (**b**).  
described in detail in [32; 35]. The relevant overall results are presented here. The accuracy tests were based on the VDI/VDE standard for area-measuring optical systems [36]. Length measurement errors, flatness deviations, spherical size, and shape errors, as well as measurement noise on flat and spherical surfaces, were determined. The results are documented in Tables 1 and 2. Thanks to the mechanical fixation, the measurement distance to the plane normal could be achieved without fluctuations in the water basin.  
The maximal mean error of the ball-bar length measurements in the water basin was 0.53 mm, which corresponds to about 0.1%. At offshore measurements, this maximum error increased to 4.1 mm, corresponding to 0.8%.  
The reason for the strong reduction in the measurement accuracy between the measurements in the water basin and the offshore measurements is likely primarily due to the poorer environmental conditions during calibration. The accuracy of recording the selected calibration points may be lower than under optimal conditions in clear fresh water. Nevertheless, it cannot be ruled out that different calibrations may turn out differently by chance. Unfortunately, due to time and capacity constraints, this could not be investigated experimentally and should be an aspect of future investigations.  
Absolute measurement errors of less than 1% amount to an acceptable result when measuring lengths under relatively adverse conditions underwater.  
The standard deviation of the Euclidean distance of the 3D measurement points from a fitted sphere or plane is determined as a measure of noise. This measure provides an indication of the average deviation between individual 3D measurement values and the actual values. A higher point density allows for better noise reduction through averaging. Lower noise levels enable the improved detection of fine structures, ideally close to the resolution limit.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 4 Modelling the Noise
### Gaussian White Noise Model  
The simplest noise model treats measurements as statistically independent, normally distributed random variables: \(X_{t}-\mu=E_{t}\) for fixed \(\mu\). Thus, \(X_{t}\sim\mathcal{N}(\mu,\sigma^{2})\), with yet unknown parameter values \(\mu\) and \(\sigma\). Using the maximum likelihood approach, these parameters are estimated from the observations as  
\[\widehat{\mu}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\quad\text{and}\quad\widehat{ \sigma}^{2}=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\widehat{\mu})^{2}.\]
# Nano Aerial Vehicles for Tree Pollination
## 3 Proposed Solution
### Sensorial Module  
The sensor module consists of an IMU and a distance sensor. The purpose of this module is to help in the navigation and control of the NAB.  
The Crazyflie 2.1 has an IMU consisting of a gyroscope, accelerometer (BMI088), and pressure sensor (BMP388). The BMP388 enables accurate altitude tracking. Light Detection and Ranging (LiDAR) sensors can perform the pressure sensor function. We opted to use the VL53L1X ([[https://www.st.com/resource/en/datasheet/vl531](https://www.st.com/resource/en/datasheet/vl531) lx.pdf]([https://www.st.com/resource/en/datasheet/vl531](https://www.st.com/resource/en/datasheet/vl531) lx.pdf), accessed on 27 March 2023).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Features** & **RP2040** & **STM32F405 RG** \\ \hline Cores & Dual-core & Single-core \\ Core architecture & 32-bit ARM Cortex-M0+ & 32-bit ARM Cortex-M4 \\ \hline CPU clock & Flexible clock up to 133 MHz & 168 MHz \\ \hline RAM size & 264 KByte SRAM & up to 192 + 4 Kbytes SRAM \\ Flash size & up to 16 MByte external flash & up to 1 MByte external flash \\ \hline MCU power voltage & 3.3 VDC & 3.3 VDC \\ \hline GPIO & 30 & 51 \\ UART & 2 & 2 \\ IZC & 2 & 3 \\ SPI & 2 & 3 \\ Dimensions & 7.75 \(\times\) 7.75 mm & 12.7 \(\times\) 12.7 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: RP2040 and STM32F405 RG microcontroller characteristics.  
March 2023) and BMI088 ([[https://download.mikroe.com/documents/datasheets/BMI088](https://download.mikroe.com/documents/datasheets/BMI088)]([https://download.mikroe.com/documents/datasheets/BMI088](https://download.mikroe.com/documents/datasheets/BMI088)) _Datasheet.pdf, accessed on 27 March 2023).  
The BMI088 is an IMU that combines a gyroscope and an accelerometer to detect movements and rotations. The VL53L1X is equipped with a Time-of-Flight (ToF) laser-ranging sensor that can accurately measure distances of up to 4 m and has a fast-ranging frequency of up to 50 Hz, regardless of the target's colour and reflectance. This component is often used in drones to assist with flight.  
By adding the VL53L1X component, we have upgraded the IMU module to a sensory module. In addition to the features of an IMU, with the VL53L1X, it is possible to measure the relative distance to the ground.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 2 Experimental Methodology  
### Rock Specimens  
The intact marble, sandstone, and granite were selected as research materials and processed into cubic specimens with a side length of 50 mm for considerations of the end effect and the limited loading capacity of the apparatus in this study, as shown in Figure 2a.  
Before the biaxial compression testing, the mineral compositions of the three rocks were achieved by petrographic thin-section tests. The test results are shown in Figure 2b-d. The analysis results showed that the main mineral components of granite are feldspar (50-60%) and quartz (20-30%) (Figure 2b), the main mineral components of marble are magnesite (35-45%), calcite (40-50%), and interstitial material (5-25%) (Figure 2c), whereas the main mineral components of sandstone are quartz (50-70%) and feldspar (10-30%), as well as a little of biotite and muscovite (Figure 2d).
## 4 Discussion
### Limitations of the Study and Future Work  
As aforementioned, the high-speed video analysis revealed that the front vent of the helmet was detached during the impact in two of the three replicas of the +40% V\({}_{\rm T}\) case with the headform covered. This fact was the cause of the low repeatability obtained for the angular velocity and angular acceleration in the mentioned condition. In consequence, these tests were not considered in the statistical analysis.  
The rigid magnesium EN960 headforms do not have an outer layer to simulate the scalp tissue or hair because they are not designed to respond like a human head to impact. Therefore, slippage between the scalp and skull, the effect of the hair (which could reduce the head/helmet friction) and the tensioning effect of the skin were not covered in this work [33; 34]. Even if a study identified that scalp tissue affects head biomechanics in a significant way [28], it also concluded that the friction coefficient of the outer layer of the headform and the interaction with the helmet reduced the relative effect of the scalp.  
The influence of the neck and body in the kinematics of the headform was not included in this study. Several studies suggest that the neck and body play only a small role during helmeted head impacts [35; 36] whereas other studies suggest that the presence of the neck and body have a significant influence on head kinematics and that it is strongly dependent on the impact configuration [37; 38]. Isolated head surrogate was used in this study due to its simplicity and because this is the condition used in the European helmet testing regulation [26].  
Despite tissue-based metrics such as Maximum Principal Strain (MPS) are desirable for assessing brain injury risk since they are a measure of the primary injury mechanism, this study looked only into four kinematic-based metrics (PLA, HIC, BrIC and PAA). These were the parameters that could be obtained directly from test measurements. In addition, BrIC has been shown to have a good correlation with several tissue-based metrics in a bicycle helmet study [39].  
Another limitation of this work is that only one full-face helmet model and three impact directions were tested. Therefore, the results of this study may vary for other helmet types and other impact directions. Future research should investigate the friction between different materials used as comfort padding and the human scalp, considering the physiological skin condition and the effect of sweat.
## 2 Materials and Methods
### Proposed Method
#### 2.3.3 Defect Segmentation Based on Region-Growing Algorithm  
Region growing is a process of aggregating pixels or sub-regions into larger regions based on pre-defined similarity criteria. The basic method is to start by selecting one or more pixels as seed points. Then, according to some similarity criterion, neighboring pixels or regions with similar properties are grouped together to grow the region until there are no more pixels or other small regions to be grouped together [36]. In practical applications, the key issues are the manner of seed point picking and the establishment of similarity criteria [37].  
Traditional defect detection content based on region-growing algorithms generally involves segmenting the target region in the interference region, which has relatively complex features, while the target region generally has similar features. After improved SSR image enhancement, the defective areas were affected by the halo phenomenon and the degree of decay itself with a different gray scale starting points and trends. However, the normal surface area showed greater similarity. Thus, the effect of segmenting defects could be achieved by segmenting the normal surface area.  
The normal surface area features were obvious, the gray intensity was above 250, and the distinction with defects was obvious. However, in order to prevent errors caused by large spatial distances, the image needed to be partitioned by a certain size, and a seed point was selected by gray intensity within each region.  
The similarity criteria was adaptive thresholding based on the standard deviation and mean of the gray scale of the growing region. Let \(R\) be the grown area; then the mean gray value \(m\) and standard deviation \(\sigma\) within the grown area are calculated as follows:  
\[m=\frac{1}{n}\sum_{(x,y)\in R}f(x,y) \tag{16}\]  
\[\sigma=\sqrt{\frac{1}{n}\sum_{(x,y)\in R}|f(x,y)-m|^{2}} \tag{17}\]  
where \(f(x,y)\) is the gray value of the pixel at the point; \(n\) is the number of pixels in the grown area.  
Let the initial threshold be \(T\); then the growth region changes to generate a new threshold \(T^{*}\), which is calculated as  
\[T^{*}=T(1-\frac{\sigma}{m}) \tag{18}\]  
The initial threshold is determined based on the grayscale histogram of the reflected component. Let \(T=(255-gray_{m})/2\), where \(gray_{m}\) is the grayscale histogram grayscale mean value.
## 3 Materials and Methods
### Model Performance and Evaluation  
The TP, TN, FP, and FN confusion matrix metrics in Table 3 provide values for correct or incorrect classification of packets at the firewall. Recall, also known as sensitivity, measures the model's ability to identify positive cases correctly. It is computed as the ratio of accurate positive predictions to the total number of actual positive cases. Precision evaluates the proportion of relevant instances among those retrieved by the model, defined as the ratio of true positive predictions to all positive predictions made by the model. Accuracy represents the overall effectiveness of a model in making correct predictions, calculated as the proportion of true predictions (both true positives and true negatives) to the total number of cases. F-Score, or F1-Score, integrates precision and recall into a single metric by calculating their harmonic mean. This metric is handy when seeking a balance between precision and recall, providing a more holistic evaluation of model performance. These values were used to calculate precision, recall, f-measure, and accuracy metrics as follows:  
\[\text{Precision}=\frac{TP}{(TP+FP)} \tag{4}\]  
\[\text{Recall}=\frac{TP}{(TP+FN)} \tag{5}\]  
\[\text{F-measure}=\frac{(2*precision*recall)}{(precision+recall)} \tag{6}\]  
\[\text{Accuracy}=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Predict Class**} \\ \hline \multirow{2}{*}{Actual Class} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & No \\  & & & TP & FN \\ \cline{1-1} \cline{
## 2 Related Works
### HNOMA for Reduced HWD and SIC Complexity  
This section discusses HNOMA systems' significance in reducing complexity and propagation errors. In [4], the authors analyzed the outage probability of NOMA systems under iSIC conditions. The closed-form expressions are derived for the outage probabilityof the Rician fading channel model, approximating with a Gamma distribution to simplify calculations. A low-complexity PA strategy is introduced to maximize SSE, achieving improvements of about \(\sim\)4.14% to \(\sim\)6.19% in DL and uplink scenarios. The authors of [5] introduced opportunistic UE to share time slots with legacy UE, improving transmission opportunities. The study presented a power-reducing coefficient to ensure lower energy consumption than conventional OMA under HWD and iSIC conditions. Section 3.1 elaborates on the pairing strategies used in the HNOMA system.
## 4 Simulation and Experimental Results
Experimental values for the power gain are reported in Table 2, together with the corresponding values obtained by the simulations reported in Figure 10. Experimental tests confirm the simulations' results and validate the performance improvement that the double coil harvester can provide. Finally, note that an increase in the extracted power can also be obtained by increasing the number of turns of the single coil standard harvester. In particular, if the number of turns is doubled (e.g., by inserting the additional coil, equal to the first coil, in series with it) the electromechanical coupling coefficient \(\theta\) and the electrical equivalent resistance \(R_{c}\) of the resulting coil are doubled. Therefore, according to Equation (6), the extracted power \(P_{SERIES}\) with the two series-connected coils is confined between once and twice that (\(P_{OPT}\)) of the original standard harvester. In particular, for small values of \(R_{c}\), \(P_{SERIES}\) tends to \(P_{OPT}\); for large values of \(R_{c}\), \(P_{SERIES}\) tends to twice \(P_{OPT}\). On the other hand, the double coil harvester investigated here is able to extract a power greater than twice \(P_{OPT}\), as shown in Table 2. In particular, the power gain can be about five times greater than that of a standard single coil harvester. Figure 13: Measured average load power for the single coil circuit in Figure 8, in the four test cases. (**a**) Power as a function of the load resistance for the optimal load capacitance. (**b**) Power as a function of the load capacitance for the optimal load resistance.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 2 Related Work  
### Energy Harvesting Applications  
Energy harvesting technology is widely used in IoT and mobile edge computing (MEC) [1; 2; 3; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Owing to the capacity-limited battery, the lifetime of wireless devices or sensor devices is an important issue discussed in numerous studies. There are several sources of energy harvesting, namely renewable energy [3; 11; 12], radio frequency [1; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], resonant coupling [10], etc.  
[PERSON] et al. proposed an AmbiMax system to perform maximum power point tracking to manage multiple power sources simultaneously and automatically [2]. [PERSON] et al. proposed a method of fair and high throughput with solar energy [3]. [PERSON] et al. concluded that a node must collect RF energy for at least 150 s to support node activity within 100 ms by analyzing the lifetime of IoT nodes based on RF energy harvesting [13].  
[PERSON] et al. discussed the difference between the omnidirectional antenna and the directional antenna and explored the feasibility of wireless-powered wearable devices [6]. To detect the underlying devices of the charge and activity sensor, [PERSON] proposed kinetic energy harvesters as context sensors and energy sources simultaneously [11].  
[PERSON] et al. aimed to find the optimal path for a mobile charger [9]. They proposed a greedy bundle generation algorithm and a TSP-based solution by considering adjacent charging locations. [PERSON] et al. proposed a SmartCharge system to meet the requirement of a community for wireless charging solutions without interfering with device usage and movement [10]. The system also allows multiple devices to be used simultaneously. [PERSON] et al. proposed an energy-harvesting-aware routing algorithm to improve both the lifetime of sensor devices and QoS under dynamic traffic load and variable energy availability [12].  
[PERSON] et al. considered a multi-user MEC network powered by wireless power transmission [17]. Because of the combinatorial complexity of multi-user computation offloading, a coordinated descent method is proposed to optimize the decisions of computation offloading. [PERSON] et al. considered a NOMA-based WET-MEC network and proposed a Dinkelbach-based iterative algorithm to maximize computation energy efficiency [8].  
[PERSON] et al. proposed a new idea of harvesting RF energy from Wi-Fi transmission combined with beamforming [1]. They applied the idea to a prototype wearable device that can capture and transmit accelerometer data. [PERSON] et al. attempted to minimize the system energy consumption based on beamforming and computation offloading [14]. They also used relay technology to solve the degradation of transmission performance caused by long distances.
## 3 Experimental Results Analysis  
### Load-Displacement Curve  
The relationship between load and displacement of the specimens obtained from the experiments is illustrated in Figure 3. The displacement of the specimens with the development of load can be primarily divided into three stages: elastic stage, crack initiation and propagation stage, and failure stage. When the load is less than 0.27 of the failure load (225 kN), the load-deflection curve exhibits predominantly linear development (elastic stage). Subsequently, the specimen enters the crack initiation and propagation stage, where cracking occurs at the interface between the old and new segments of the bridge deck panel and the bottom of the loading slab, resulting in a sudden increase in displacement. At this point, the displacement at the loading position of Slab 1 is 1.41 mm, and for Slab 2, it is 2.12 mm. Continuation of loading until reaching 0.9 of the failure load (750 kN) leads to the specimen entering the failure stage (failure load being 822 kN). Vertical displacement at the loading positions of the bridge deck panels continues to increase, and the stiffness of the specimen decreases. Ultimately, failure occurs near the interface of the old and new segments of the bridge deck panel, with displacements at the loading positions of Slab 1 and Slab 2 reaching 13.13 mm and 21.64 mm, respectively.
# Biological Methods in Biodiesel Production and Their Environmental Impact
## 2 Production of Biodiesel by Chemical Transesterification Process
The reaction time is more varied and ranges from 1 h (for, e.g., NaOH, Na/NaOH, LiNO\({}_{3}\)/Al\({}_{2}\)O\({}_{2}\)) to even 8-9 h (for, e.g., KI/SiO, Al\({}_{2}\)O\({}_{3}\)/KI, ZnO/KF) [11]. The yield of alcoholysis depends on the kind of catalyst. Authors of [12] investigated the effects of four alkaline catalysts: NaOH, KOH, CH\({}_{3}\)Ona, and CH\({}_{3}\)OK. They noted that the process yield for the sodium or potassium methoxide (99.33 wt.% and 98.46 wt.%, respectively) was higher than for sodium and potassium hydroxides (86.71 wt.% and 91.67 wt.%, respectively). The decrease in the yield for hydroxides is due to saponification reactions of triglycerides and dissolution of methyl ester in glycerol. The second kind of catalyst used for biodiesel production is acid catalysts, where sulfuric acid is the most commonly used catalyst [13; 14], and as shown in the research conducted in [15], this catalyst is characterized by the best fatty acid methyl esters conversion rate in comparison to other acids, such as acetyl chloride, benzenesulfic acid, sulfuryl chloride, methane sulfonic acid, p-toluenesulfic acid, thionyl chloride, and dimethyl sulfate. The biodiesel yield was about 80% for the reaction conducted at 80 \({}^{\circ}\)C for two hours at a weight to volume ratio of 1:5 (\(w/v\)) for soybean oil and methanol. The yield of the acid-catalyzed reaction depends, among other things, on process temperature and its time. The higher the temperature and longer the time, the better the conversion rate. In comparison to the alkali-catalyzed process, the reaction with such type of catalyst requires 50 h to finish and a high temperature (even above 100 \({}^{\circ}\)C) because the reaction is very slow [16]. This reaction is believed to be four thousand times slower than the alkali-catalyzed reaction [17]. For example, the transesterification of soybean oil with butanol catalyzed by 1% sulfuric acid at a molar ratio of 1:30 was completed in 3 h when the process temperature was 117 \({}^{\circ}\)C, and in 20 h when it was 77 \({}^{\circ}\)C [18]. The advantage of the acid-catalyzed reaction is the possibility of carrying out the transesterification process for oils containing a significant amount of free fatty acids. Thus, the biodiesel can be produced from low-cost feedstocks (used greases and cooking oil) [19]. The process catalyzed by acids is slower and more corrosive than that catalyzed by alkalis, and therefore it is not often used on an industrial scale [7].