esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### Evaluation Indicators  
The main objective of this paper was to provide a complex background gesture detection and recognition algorithm that could effectively weigh the accuracy and speed of the model. Therefore, this paper used three metrics, namely precision, recall, and mean average precision (mAP), to evaluate the model's accuracy. Frames per second (FPS), model size, and one billion floating point operations (GFLOPs) were used to evaluate the model's speed.  
Figure 3: Network structure of the improved YOLOv5l algorithm.  
For the calculation process of the above three metrics, the prediction frame and the real label frame needed to be compared to calculate their intersection over union (IOU) ratio, so as to determine the matching degree between the prediction frame and the real label frame. According to the matching degree, the prediction results were classified into true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The values of the required indicators were calculated accordingly.  
The probability that a positive sample would be detected in all the detected targets was given by precision, which reflected the false detection rate of the model, i.e., False Detection Rate = 1-Precision, and could be obtained by calculating the proportion of positive samples in all the detected target frames, as:  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
Recall is a measure of the probability of detecting all positive samples. In this study, it reflected the rate at which the model missed positive samples, which was equal to 1 minus the recall. This measure could be obtained by calculating the ratio of the number of actual positive samples detected to the total number of positive samples. The calculation formula was:  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
The mean average precision (mAP) takes into account both precision and recall, and provides a more comprehensive reflection of the model's performance. This measure was obtained by calculating the average precision (AP) for each gesture category at a fixed IOU threshold, and then taking the average of all AP values for all categories. The AP values were obtained by calculating the area under the precision-recall (P-R) curve for each category, where precision was plotted on the \(x\)-axis and recall on the \(y\)-axis. The formulas for calculating AP and mAP were:  
\[AP=\int_{0}^{1}p(r)dr \tag{3}\]  
\[mAP=\frac{\sum_{i=1}^{C}AP_{i}}{C} \tag{4}\]  
Here, \(p(r)\) represents the precision-recall curve and \(C\) represents the number of gesture categories.  
Figure 4: Datasets; (**a**) EgoHands dataset; (**b**) TinyHGR dataset.  
To provide a more comprehensive and rigorous evaluation of the proposed algorithm, this paper used [EMAIL_ADDRESS]:0.95 as the evaluation metric. This metric represents the average mAP at different IOU thresholds, ranging from 0.5 to 0.95, with a step size of 0.05 (0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, and 0.95). The mAP values used in this paper's experiments were all [EMAIL_ADDRESS]:0.95.
## 1 Introduction
Currently, the CORDIC algorithm is mainly applied to beam-forming technology and coordinate scanning transformation technology in ultrasound. For example, [PERSON] et al. proposed an efficient and non-iterative radix-8 CORDIC algorithm to solve the problem of low-delay and high-efficiency calculation of sine-cosine or phase-shift function in digital beam-forming [27]. [PERSON] et al. adopted a high-precision modified coordinate correction pipeline CORDIC algorithm to realize coordinate transformation and difference at the same time, improving the accuracy and image quality of coordinate transformation of the ultrasonic endoscope [28]. Few studies have used the CORDIC algorithm in the overall medical ultrasound signal processing. This paper proposes a method for processing radial artery ultrasound signals based entirely on the CORDIC algorithm. However, due to the large number of iterations and resource consumption of the traditional CORDIC algorithm [29; 30], it is not applicable when the hardware and software resources of the ultrasound system are limited. Therefore, this paper introduces various high-performance CORDIC algorithms and designs a dynamic filtering module based on the radix-4 CORDIC algorithm, a quadrature demodulation module based on the partitioned-hybrid CORDIC algorithm, and a dynamic range transformation module based on the improved scale-free CORDIC algorithm, according to the characteristics of the three stages of radial artery ultrasound signal processing. Finally, the paper demonstrates the accuracy and effectiveness of the high-performance CORDIC ultrasound signal processing algorithm based on FPGA. The radial artery diameters of 20 subjects were measured to verify the practical application of the algorithm. To sum up, this paper not only introduces the CORDIC algorithm through the whole radial artery signal processing process but also uses various high-performance CORDIC algorithms to optimize the data processing algorithm, thus ensuring the accuracy of the calculation. This paper proposes a method for processing radial artery ultrasound signal processing. method provides a new idea for ultrasonic signal processing of radial artery, which can further measure the diameter of blood vessels, analyze the physiological and pathological information such as blood flow and blood pressure of radial artery, and has practical application value for clinical diagnosis.
## 3 Controller Synthesis
### Overview and Synthesis of Kalman Filtering  
#### 3.3.1 Kalman Filtering in Drone Piloting  
Noise and uncertainty make controlling the quadcopter a hard task to accomplish; one tool that is known to be powerful enough to overcome these obstacles is Kalman filtering. Ref. [15] highlights the importance of integrating KF, where the experimental results demonstrated that the Kalman-based UAV tracking model significantly outperformed existing particle filters. It iteratively combined dynamic model prediction with noisy sensor data to refine and correct the quadcopter's state vector, as described in Figure 3. This  
Figure 2: Sensor fusion along with Kalman strategy architecture.  
resulted in robust as well as accurate estimation despite disturbances during the flight. Kalman filtering's strength lies in finding a good equilibrium between the prediction of the system's future state, on one hand, and corrections to it based on sensor measurement input, on the other. Thus, guaranteeing unmatchable efficiency when estimating the position and velocity of the UAV. This is why the Kalman algorithm dominates modern-day technology, ranging from GPS receivers to spacecraft and unmanned aerial vehicles.  
The Kalman gain is used to adaptively weight the two sources of information model dynamics and sensor data from onboard sensors. These sensors provide essential data about the UAV's velocity, orientation, and surrounding environment. Achieving stable flight depends on using well-chosen covariance matrices and optimal Kalman gain, enabling robust and adaptive state estimation.
## 2 Material and Methods
### Experimental System and Equipments  
The experimental system of WJD is mainly composed of a high-pressure pump, water tank, pressure control system, winch, flexible high-pressure hose, collection box for coal fragments and a water-jet bit (Figure 1). The high-pressure pump used in this water jet drilling experiment is a five-plunger high-pressure pump with a rated pressure of 56 MPa and a rated flow of 200 L/min. A pressure control system is used to adjust the system pressure conveniently, which includes a relief valve and a pressure gauge. The flexible high-pressure hose is used as the working pipe to convey high-pressure working fluid to break coal, and it has an ID of 10 mm, an OD of 17 mm, a minimum bending radius of 90 mm, a working pressure of 60 MPa and a bursting pressure of 135 MPa. To wine the flexible high-pressure hose in an orderly fashion, a winch is adopted to realize the high-pressure water transmission between the linear motion of high-pressure water pipe and the circumference roll of winch through the high-pressure sealing rotator.  
As the most critical component of WJD system, the water-jet bit generates high-velocity water jets to break coal and form a CBM extraction borehole or microwell. Owing to the advantages of a simple structure, compact body, low energy consumption, and high rock-breaking efficiency, the multi-nozzle bit is most commonly utilized in WJD operation [10; 34]. Thus, the self-propelled multi-nozzles bit is adopted in this WJD experiment. The multi-nozzle bit has one center nozzle, four forward lateral nozzles with an axial diffusion angle of 25\({}^{\circ}\), and eight backward nozzles with an axial diffusion angle of 30\({}^{\circ}\)[35; 36]. The nozzle diameter is 0.8 mm, as shown in Figure 2. The working principle of breaking coal to form a borehole for the multi-nozzle bit is as follows: the forward nozzles (including center nozzle and lateral nozzles) generate high-velocity jets to break up the coal in front of it and form a hole, whereas the backward nozzles form backward jets to generate the self-propelled force to pull the hose moving forward and have the functions of further breaking coal to expand the borehole diameter and discharging slags. The coal fragments formed in WJD are collected in a box through a filter net of 0.075 mm.  
Figure 1: Schematic of WJD system in underground coal mine.
## 3. SOH Prediction Method Based on the PSO-GWO-LSSVM Model
### The PSO-GWO-LSSVM Prediction Model  
Appropriate parameter settings can significantly improve the model's predictive accuracy and generalization capabilities. This paper proposes a PSO-GWO hybrid algorithm to optimize the width coefficient \(\sigma\) of the LSSVM kernel function and the regularization parameter \(\gamma\), in order to select the optimal parameters and achieve the best prediction results [39]. The integration of these two methods effectively mitigates the issue of local optima, thereby enhancing the accuracy of parameter optimization. The PSO-GWO algorithm integrates the particle position update formula from PSO to enhance the memory ability of the wolf pack during the optimization process. Additionally, an inertia constant is introduced to enhance the balance between global search ability and local exploitation in the PSO-GWO hybrid algorithm. The updated formulas for the velocity and position of the wolf pack in the hybrid algorithm are as follows:  
\[\left\{\begin{array}{l}u_{i}^{k+1}=r^{*}\left[u_{i}^{k}+f_{1}n_{1}\left(x_{1}-x_ {i}^{k}\right)\right.\\ \left.+f_{2}n_{2}\left(x_{2}-x_{1}^{k}\right)+f_{3}n_{3}\left(x_{3}-x_{i}^{k} \right)\right]\\ x_{i}^{k+1}=x_{i}^{k}+u_{i}^{k+1}\\ \left.\overset{\rightarrow}{D}_{j}=\left|\overset{\rightarrow}{C}_{m}\cdot \overset{\rightarrow}{X}_{j}-w^{*}\overset{\rightarrow}{X}\right|\end{array}\right. \tag{23}\]  
where j represents \(\alpha\), \(\beta\), and \(\delta\); m takes values 1, 2, and 3; k denotes the current iteration number; f\({}_{1}\), f\({}_{2}\), and f\({}_{3}\) represent the learning factors; n\({}_{1}\), n\({}_{2}\), and n\({}_{3}\) are random numbers in the range of (0, 1); and \(r\) denotes the inertia weight coefficient.  
The core concept of the PSO-GWO algorithm is to rank particles in each iteration based on their fitness values. The three particles with the highest fitness values are referred to as \(\alpha\), \(\beta\), and \(\delta\), and these particles help estimate the approximate range and potential location of the optimal solution. The remaining particles explore the optimal solution within the predicted range. This strategy allows the particles to find the optimal solution more quickly and effectively while also enhancing the convergence rate and path-planning capability of the PSO algorithm. As a result, this method enables a more efficient search for the width coefficient \(\sigma\) and regularization parameter \(\gamma\) of the LSSVM kernel function. The basic process of PSO-GWO-LSSVM is shown in Figure 7.  
Figure 7: Algorithm framework based on PSO-GWO-LSSVM prediction model.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Planning Phase
#### 4.2.1 Directional Antennas  
From previous works, it has been demonstrated that the localization performance based on RSS patterns increases with the number of SRs and their strategic position [56]. Then, it is intuitive to think that the more robust the RSS fingerprints are with respect to interference noise, the easier the learning process and estimation of the tag location will be. Thereby, previous criterion \(d\) has been laid down on this basis. To this aim, directional antennas are proposed to help this process by virtue of their inherent spatial filtering properties. These antennas can be pointed towards a determined spatial region. Therefore, the RSS level of beacons received from non-pointed directions will be much lower than signals from the pointed ones, obtaining stronger RSS measurements from the pointed sectors where the antenna's main beam is steered. This fact let us think about directional antennas to enhance RSS patterns associated with a particular region (area/room) of interest. This can be qualitatively understood by observing Figure 6, where a comparison between omnidirectional (Figure 6a) and directional (Figure 6b) antenna coverage inside a fictitious scenario with three defined areas (A1, A2 and A3) is shown. From this picture, it can be understood that the RSS captured by the omnidirectional antenna from a tag located in each area will be similar if compared to the RSS captured by the directional antenna mainly pointed at A1. Thus, it can be seen that the directional antenna introduces a new variable, the orientation, which allows one to increase the robustness of the RSS features in the signal space and, in theory, should enhance the robustness of the RSS patterns associated with that area, increasing the success probabilities of detecting the tag when staying at A1.  
Figure 6: Scenario defined with three areas: A1, A2 and A3. Coverage comparison between (**a**) the omnidirectional antenna and (**b**) the directional antenna oriented toward A1.
## 4 Experimental Application of the Model
### Input Data Preparation  
The SPDIAM spatial data preparation routine depends on the method that is selected for SP identification. Space syntax method requires a vector data model, as the spatial network is formed of the roads network layer. The spatial data preparation routine was previously described in Reference [2], but, for spatial meta-pattern identification, this routine is a little different as we do not need data of actual spatial entities (cities or regions). Instead, the 6 spatial entities for 6 meta-pattern values were drawn in Autodesk AutoCAD 2017 and saved as *.DXF files as ideal forms for testing spatial measures. The first spatial entity (Circle) consisted of 778 squares, and, then, the other entities had to fit in the same area of the drawing. The following geometrical structures of spatial entities were prepared: Line, Star4, Star8, Sectoral, Labyrinth, Circle. These primary spatial structures in *.DXF format were transformed to Visuality Graph using DepthmapX (0.5.0.), and each square of the spatial structure became a cell in the Visuality Graph (Figures 9 and 10). The vertices of Visuality Graph were connected to each other if they could be seen from each other without obstruction. As a result, 6 Visuality Graphs were prepared as input data for the experiment.  
Such ideal spatial structures present an idealized situation and help to analyze simple geometric forms of SP. Such structures have to be essential (most common) and elementary (the simplest transformation of the selected form). If the need arises, the library of spatial meta-patterns and their values can always be supplemented with the new members; we just have to use the same area of the drawing so that the calculated measure values will be in the same range.
## 4 Three-Phase Inverters
### Grid Feeding Inverters  
Grid feeding inverters are operated as power sources, and are mainly designed to deliver power to an energized grid. They are also called Grid Following Inverters, or inverters with P-Q control. This mode of operation is used often, since the tightly regulated output power enables robust and economical designs. Grid feeding inverters are also used with renewable power sources, in which the active power must by equal to the power produced by the source. In many applications the reactive  
Figure 23: By eliminating the cross terms, the current loop is modeled by two single-input single-output systems.  
Figure 22: The inner current loop: a typical control scheme that eliminates the cross terms \(\omega^{*}i_{q}\) and \(-\omega^{*}i_{d}\).  
power is set to zero, and the inverter operates with a power factor of unity. In this case the output current is minimal, but the inverter does not provide the reactive power that may be needed to support the grid. A basic control scheme for grid feeding inverters is shown in Figure 24.  
The design consists of an inner control loop, and an additional \"current calculations\" block that generates the target currents. These are given by  
\[\begin{split} i_{d}^{*}&=\frac{2}{v_{d}^{2}+v_{q}^{2 }}\left(P^{*}v_{d}+Q^{*}v_{q}\right),\\ i_{q}^{*}&=\frac{2}{v_{d}^{2}+v_{q}^{2}}\left(P^{* }v_{q}-Q^{*}v_{d}\right),\end{split} \tag{134}\]  
where \(P^{*}\), \(Q^{*}\) are the target active and reactive powers (for a single phase). As a result, the powers at steady state are given by  
\[\begin{split} P&=\frac{1}{2}\left(v_{d}i_{d}+v_{q} i_{q}\right)=P^{*},\\ Q&=\frac{1}{2}\left(v_{q}i_{d}-v_{d}i_{q}\right)=Q^ {*}.\end{split} \tag{135}\]  
This calculation is based on the assumption that \(i_{d}=i_{d}^{*}\) and \(i_{q}=i_{q}^{*}\) at steady state.  
The reference frame for the \(dq0\) transformation is defined by the grid voltages. Since these voltages are not directly controlled, there is a need to extract the reference angle \(\theta\) and the frequency \(\omega\) from the voltage measurements. This is usually accomplished by a Phase Locked Loop (PLL), as illustrated in Figure 25. The PLL operates by controlling the reference angle \(\theta\) in closed loop, such that the quadrature-axis component \(v_{q}\) is zeroed. If the loop is stable, then \(\theta\) must be matched to the angle of the AC voltages \(v_{a}\), \(v_{b}\), \(v_{c}\).  
Figure 24: A basic control scheme for grid feeding inverters.  
A grid feeding inverter connected to a photovoltaic (PV) source is shown in Figure 26. The challenge here is that the active power is determined by the renewable source, and is not known in advance. This design is similar to the one in Figure 24, expect that there is an additional loop that regulates the active power, in order to match it to the source. The feedback is provided by the bus-capacitor voltage. If \(v_{dc}(t)>V_{dc}^{set}\) then \(P^{*}\) increases to discharge the bus capacitor, and if \(v_{dc}(t)<V_{dc}^{set}\) then \(P^{*}\) is decreased to charge the bus capacitor. At steady state, \(P_{pp}\approx 3P^{*}\) and \(v_{dc}(t)\approx V_{dc}^{set}\).
From the early exploration of underwater imaging technology [5; 6] to the latest research, few researchers have made breakthroughs regarding the fundamental problems of optical lenses [7; 8]; specifically, extensive progress has been made in waterproofing technology for lenses from the perspective of optical design and aberration [9]. From the perspective of modern optical engineering, technologies with IP67 (industrial optics) or higher waterproof ratings and the technique of inserting inert gas into barrels are relatively mature. The novel optical design for underwater cameras in the present study could be a major breakthrough. On the basis of the requirements of the research vessel Polaris, we developed an optical design for prime and zoom lenses applicable in the underwater spectrum. First, we removed the conventional outer waterproof box included in the IP67 specification because the glass surface of the box reduces the resolving power of the camera and the penetration of light, and it increases the volume and weight of the camera. Moreover, chromatic aberration and focus-point displacement caused by the shift from the shallow-water spectrum to the blue, deep-water spectrum makes universal deep-water photography even more difficult. These two optical aberrations also reduce the resolving power. In this research, we wrote a program using the genetic algorithm by Matlab language connected with Zemax via its macro language to optimize optics and selected appropriate glass to greatly reduce the influence of chromatic aberration. Using the optimization method via CODEV and Zemax we developed a novel optical zoom lens applicable in all waters to reduce the volume and weight of the camera by more than 50% and 60%, respectively, and increase its resolving power by 30-40% relative to conventional underwater cameras. Figure 1: Underwater camera [1]: Photo from 1. [PERSON], _Metrical Fundamentals of Underwater Lens System_, Optical Engineering, vol. 16, no. 2, pp. 128–139 (1977).
## 3 Results and Discussion
### Mode-I crack growth resistance of the substrate-repair interfaces (bi-material systems)  
The bi-material specimens failed at the interface as expected, with the failure plane passing through and nearby the bond plane. Given the interfacial tortuosity achieved through the substrate roughness treatment (sandblasting), some slight deviation of the failure plane from the bond plane was expected and, in fact, desired, as this is a known toughening mechanism in interfacial cracking 1215. Same as for the material result discussion, the experimental response curves (SL-CMOD) obtained for only one representative type of fibers (8 mm PVA) are shown in Figure 8a, while the bond response curves obtained with other fibers are available in the Appendix A. Crack growth resistance curves obtained using the MLEFM approach described in Section 2.2.3 are shown in Figure 8b for 8 mm PVA fibers and in the Appendix A for the other fiber types. Similar to the FRC materials discussed above, though for smaller values of Splitting Load and Crack Mouth Opening Displacement, concrete substrate-FRC repair interfaces still exhibited a stable, subcritical crack growth (Figure 8).  
Crack propagation after the peak SL, however was quite different. Two main types of post-peak crack propagation were identified, namely: (i) Quick failure along the interface followed by sample split up in two parts along the concrete - concrete interfaces (Figure 9a), and (ii) micro-crack deviations from the interface followed by slower failure and slight residual splitting load (Figure 9b).  
Figure 8: (a) Splitting load (SL) versus crack mouth opening displacement (CMOD) curves of concrete-FRC interfaces and (b) crack growth resistance curves in terms of stress intensity factor, KI, versus effective crack length, aeff.  
Figure 10a. reports average Mode-I strength values and corresponding fiber volume fractions for three different types of fiber reinforcement. Mode-I bond strength of repair mortars reinforced with 8 mm long PVA fibers ranges between 1.1 and 1.5 kN. It ranges between 0.8 and 1.1 kN for 12 mm long PVA fibers and from 1.2 to 1.4 kN for steel fibers. Although most of the samples exhibited a bond failure, in some specimens, partial material and bond failure was observed.