esa-sceva/satcom-chunk-collection huggingface dataset:
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 2 Related Work
### Energy-Efficient Sensing in a Distributed MCS Environment  
[PERSON] et al. [24] proposed to leverage emerging deep reinforcement learning techniques for directing multiple mobile devices to collect data in a target area for MCS while ensuring geographical fairness and minimizing the energy consumption in a fully distributed manner. In [25], the authors proposed a new computing paradigm, named Edge Mesh, which distributes the decision-making tasks among edge devices within the network instead of sending all data to a centralized server, while in [26], the authors used game theory principles to select tasks and manage their scheduling on the network edge. [PERSON] et al. [27] considered a fog-based architecture for MCS and analyzed the trade-off between accuracy and consumed energy for collecting data from IoT sensors and performing distributed data analytics on mobile nodes passing by IoT devices, in addition to fog gateways at the network edge. They have shown that it is possible to significantly reduce the system's energy consumption by using some short-range communication technologies while maintaining a satisfactory accuracy compared to a centralized cloud solution. Similarly, [PERSON] et al. [28] aimed to minimize the total power consumption of the network by using a distributed machine learning approach in which the processing can take place in intermediary devices such as IoT nodes and fog servers in addition to the cloud. Another interesting approach is presented in [29], where authors proposed a distributed algorithm for adaptive scheduling of the video sensor node's activity that enables each node to decide when to enter a sleep mode based on the neighbors' activity, without compromising the coverage of its monitored region. A work presented in [30] introduced a distributed data collection framework for opportunistic MCS systems intending to minimize the sensing cost and data delivery to the participants while at the same time maximizing data collection utility. Similar to ours, the proposed mechanism prevents users from contributing too much data, and each device can decide the timing and duration of the sensing process.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain
#### 2.3.4 Delineating Flood Extent by Change Detection  
The change-detection method is based on the normalised difference, first used by [PERSON] and [PERSON] [50] and subsequently developed by [PERSON] and [PERSON] [51]. It has already been used to establish flood extents [52].  
For two images, Y1 and Y2, from the same scene but acquired on different dates, the normalised difference between them is given by:  
\[\text{D}=(\text{Y2}-\text{Y1})/(\text{Y2}+\text{Y1})\]  
D is between \(-1\) and \(+1\). A value close to 0 indicates no change, while a value near to \(+/-1\) corresponds to a large decrease or increase in the pixel value between the two dates.  
As the reflected signal from areas under water is much weaker, the normalised difference will be negative. We can establish a normalised difference threshold value to delineate flooded areas.
###### Abstract  
Shear induced diffusion (SID) based flow segregation is a technique that can be used for concentration and fractionation purposes, and it has the potential to become an economical and sustainable alternative for e.g., membrane separation. When compared to conventional microfiltration, problems related to fouling and cleaning are expected to be minimal. To make the best use of the opportunities that this technique holds, detailed insights in flow and particle behavior are needed. Modelling this process allows for us to chart particle segregation in flow, as well as the effect of suspension removal through a pore and the restoration of the flow profile after the pore. As a starting point, we take the computation fluid dynamics (CFD) model that is presented in a previous study. A difference in channel height to particle diameter ratio influences the entrance length of the SID profile as well as its fully developed profile. When extracting liquid through one pore, particles are systematically transmitted at a lower concentration (59-78%) than is present in the bulk. The recovery lengths of the SID profile after the pore were short, and thus pores can be placed at realistic distances, which forms a good foundation for further design of this novel separation technology that will ultimately be applied for fractionation of particles taking relatively small differences in diffusive behavior as a starting point.  
shear induced diffusion; computational fluid dynamics; particle separation; concentrated suspensions; parameter study; effect of pores +
Footnote †: journal: Applied Physics Letters B
# An INS-UWB Based Collision Avoidance System for AGV
## 1 Introduction  
Due to the development of industrial technology, AGV has been widely used in many applications [1; 2]. Positioning technology is an important technology of AGV. However, traditional positioning technologies have some drawbacks, such as hardware requirements, poor flexibility and difficulties with complex working environments [3].  
Despite many navigation methods for AGV having been proposed, accurate positioning is still a very important task [4]. The electromagnetic-based guidance system for AGV [5] presents some inconvenience such as maintenance and modifications. As an effective autonomous navigation technology, INS provides complete navigation information but requires external information to provide high positioning accuracy [6]. Laser-based navigation systems provide not only the position of the AGV but detect the presence of obstacles in the path [7]. However, they cannot be used in non-line-of-sight (NLOS) scenarios. Vision-based navigation systems obtain information of the AGV surroundings by means of image processing techniques [8], providing high positioning accuracy of a larger sensing area. However, the expensive computational cost demanded in the image analysis make them ill-suited for real-time applications.  
On the other hand, AGV positioning and obstacle detection have also been addressed by wireless networks. According to different RF protocols, indoor positioning based on WSN can be classified into Radio Frequency Identification (RFID) [9], \(WIF1\)[10], \(ZigBee\)[11], \(Bluetooth\)[12] and \(UWB\)[13].  
UWB technology is based on sending and receiving carrier-less radio impulses using extremely accurate timing and it is particularly suitable for distance estimation and positioning applications. However, the noise reduction cannot be conducted effectively, and the results are always noisy [14]. Therefore, an estimator is used to estimate state variables from noisy measurement [15] and numerous filtering. Thus, numerous filtering methods are applied with various localization techniques [16; 17]. The most popular techniques are based on Kalman filters, especially on the Extended Kalman Filter [18].  
In order to avoid collisions, an important issue in the operation and control of AGV is the obstacle detection where a possible solution could be the use of predetermining routes of the AGV. In this paper, we present a collision avoidance system for AGV. The method integrates both INS and UWB to get the position of both the AGV and the obstacle as well as the pose of the former. Compared with previous works, the proposed system improves the system flexibility, reducing costs and complexity, as well as expanding the use in different scenarios. The paper is organized as follows:  
In Section 2, the system scenario is described and the environment that AGV works in is presented. The system overview is presented in Section 3 that details the research points in this paper. The experiments and results of the research are shown in Section 4. The conclusions are given in Section 5.
# Digital Denture Fabrication: A Technical Note
## 3 Digital Denture Fabrication Procedure
### Step 1. Trial Denture Fabrication  
The procedure for fabricating a trial denture is as follows:  
1. Trial denture design After the images of the dedentulous ridge and opposite teeth with the interarch relationship are obtained by recording the digital impression and all the jaw relationships are recorded using an intraoral scanner, trial denture designing begins using the 3 Shape Dental software (3 Shape, Copenhagen, Denmark). Anatomic structures (incisal papilla, retro-molar pad, and tuberosities) are identified on the dedentulous image to correctly place the teeth (Figure 1A). Thereafter, the limit of the future denture base is drawn (Figure 1B). The occlusal plane is determined by considering the jaw relationships, the curve of Spee, and the curve of Wilson. Subsequently, denture teeth are arranged using the denture designing software (Figure 1C). The software contains a library of teeth of different brands and shapes and a function with an automatic setup of tooth position, axis, and height that are matched with the arches and the occlusal plane (Figure 1D);
2. Fabricating trial denture After the design is finalized, the digital STL files are sent to the three-dimensional (3D) printing machine. The trial denture is 3D printed as a monolithic denture, teeth, and base in one unit (Figure 1E,F);
3. Verifying jaw relation with trial denture At the trial placement, the horizontal and vertical jaw relationships are evaluated with the trial denture. **Verifying vertical relationship:** The vertical jaw relationship is evaluated by considering the following aspects: - Midline regarding the maxillary midline being aligned with the center of the nose; - Resting interoclusal distance; - Phonetics and esthetic appearance; - Vertical dimension at the rest position; - Facial measurements; - Lip length in relation to the teeth; - Lip support; - Smile line; - Oclusal plane parallel to the interpupillary line. **VerifyingCentric Relation (CR):** After verifying the vertical relationship, the patient is guided into CR. Any error in CR will be apparent when the teeth slide over each other; (4) Oclusal adjustment After confirming the CR, occlusal adjustment is carried out to achieve a bilateral balanced occlusion.  
Figure 1: _Cont_.  
Figure 1: _Cont._  
Figure 1: Trial denture fabrication. (**A**): Anatomic structures are identified on the edentulous ridge image. (**B**): Limit of the future denture base is drawn. (**C**): Denture teeth are arranged. (**D**): Denture base is designed. (**E**): Printed trial denture. (**F**): Trial denture is printed as a monolithic denture, teeth, and base in one unit.
## 2 Materials and Methods
### Evaluation of Combat Helmets under Blast Overpressure  
The headform was mounted on the Hybrid III neck (Humanetics, Plymouth, MI, USA) [33], in a rigid configuration. The headform-neck assembly was attached to the adapter plate and bolted to the bottom of the shock tube in the test section. The experiments evaluating the performance of various helmets were divided into two groups. The first set of experiments was designed to quantify the effect of orientation on the surface pressure distribution of the headform. The medium size ACH fitted with standard-issue Zorbium\({}^{\text{\textregistered}}\) Action Pads (ZAP\({}^{\text{\textregistered}}\)) 6+1 padding system (Team Wendy, Cleveland, OH, USA) was used for this purpose (Figure 1A). The ACH was mounted onto the headform and secured using a chin strap. The whole assembly was exposed to a single shock wave at two intensity levels, 5 psi and 10 psi nominal shock wave intensity. Four different helmet-headform orientations (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\); see Figure 1B for the details) were used, and exposures at each combination of orientation-shock wave intensity were repeated six times.  
The second set of experiments was designed to evaluate the performance of three modern helmets under blast loading. The following helmets were used: (1) Ops-Core FAST ST high-cut helmet (Gentex, Zeeland, MI, USA; Figure 1C), (2); Airframe\({}^{\text{\textregistered}}\) high-cut (Crye Precision, Brooklyn, NY, USA; Figure 1D); and (3) ECH (Ceradyne, Inc., Costa Mesa, CA, USA; Figure 1E). The helmets were attached to the headform using the respective chin straps included in the kit, and the same applies to the padding. The headform with the helmet was exposed six times to a shock wave with two nominal intensities: 35 kPa and 70 kPa (approx. 5 psi and 10 psi, respectively) at a zero degrees orientation. All helmets used in the tests were brand new with a set of new, unused pads.  
Figure 1: The schematic representation of the headform used in the ballistic helmet performance under blast loading experiments: (**A**) the headform with the outline of the Advanced Combat Helmet (ACH); (**B**) the top-view of the headform with the outline of the ACH illustrating the pressure sensor locations (denoted as H1 to H10) and the direction of the incident shock wave. Three types of helmets used in blast performance tests: (**C**) Ops-Core, (**D**) Airframe, and (**E**) Enhanced Combat Helmet (ECH).
## 2 Results
### Chloroplast Genome Organization of Phytolaca Species  
The sequencing of _P. acinosa_ generated 208,993,788 input reads, which were subsequently trimmed to 204,511,620 reads after quality filtering (Table S1). The total number of raw bases was 31.56 Gb, which was reduced to 29.97 Gb after trimming. A total of 913,560 reads were aligned with the reference cp genome, resulting in an average coverage depth of 878\(\times\) (Table S2). The final assembled cp genome was 156,433 bp in length. Sequencing was performed on a NovaSeq 6000 platform to ensure high-quality and comprehensive data for downstream genomic analyses (Table S3). A genome coverage map illustrating the depth of sequencing across the cp genome is shown in Figure S1. The cp genome of _P. acinosa_ exhibited a circular structure consisting of four distinct regions (Figure 2): an LSC region of 86,120 bp, an SSC region of 18,335 bp, and two identical IR regions, each measuring 25,989 bp. The genome contains 113 unique genes, 79 of which are protein-coding genes, 30 are tRNA genes, and 4 are rRNA genes (Table 1). Among them, 17 genes included introns; 4 of them (_ndhB, rps12, trn-UGC, trnL-GAU_) were located in the  
Figure 1: Habitat and morphology of _Phylolaca_ species in Korean natural population. (**A–C**) _Phylolaca acinosa_, (**D–F**) _P. americana_, (**A,D**) habit, (**B,F**) inflorescences, and (**C,E**) infrutcescence.  
IR region, and the copies overlapped (Table S4). Additionally, 14 genes contained a single intron, whereas _paff_ and _clpP1_ contained two introns (Table S5). The overall GC content of the cp genome was 36.8%, and the GC content of each region was calculated as follows: 34.6% in the LSC region, 42.6% in the IR region, and 30.3% in the SSC region, with the IR region exhibiting the highest GC content, and the SSC region the lowest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Species** & _Phylolaca acinosa_ \\ \hline Accession number & PQ584746 \\ \hline Total cp genome size (bp) & 156,433 \\ Large single-copy (LSC) region (bp) & 86,120 \\ Inverted repeat (IR) region (bp) & 25,989 \\ Small single-copy (SSC) region (bp) & 18,335 \\ \hline Total number of genes (unique) & 113 \\ Protein-coding gene (unique) & 79 \\ rRNA (unique) & 4 \\ tRNA (unique) & 30 \\ \hline GC content (\%) & 36.8 \\ LSC (\%) & 34.6 \\ IR (\%) & 42.6 \\ SSC (\%) & 30.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of _Phylolaca acinosa_ chloroplast genome.  
Figure 2: Circular chloroplast genome map of _Phylolaca acinosa_. Genes inside the circle are transcribed clockwise, while genes outside the circle are transcribed counterclockwise. The small circle around the map indicates the large single-copy (LSC) and small single-copy (SSC) regions, as well as the two inverted repeat (IR) regions. The dark gray graph inside represents the GC content of the chloroplast genome.
# Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
## 4 Simulations and Analysis
### Reliability Analysis of Optimization Results  
Under the optimization settings in Section 3, 163 design combinations of the Pareto front are finally obtained. So, this paper only takes representative combinations of the Pareto front for discussion in the analysis stage. Table 2 shows partial Pareto front points and their corresponding frequencies and coils parameters.  
From Table 2, it is evident that even in the case of random initial data, the increase of coils turns still shows a strong correlation with the improvement of efficiency and the decrease of voltage modulus gain, which is consistent with [12,13]. The main reason is that the mutual inductance \(M\) is closely related to the number of turns, and \(M\) has a significant influence on \(\eta\) and \(K\). The inner radius of the coils shows a significant fluctuation without apparent regularity. The value of the turn pitch is basically maintained near the given maximum value because the smaller the turn spacing, the bigger the AC resistance of the coils, and the AC resistance is detrimental to both efficiency and voltage modulus gain. Furthermore, it is easier to observe better voltage gain and efficiency at higher frequencies simultaneously.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Points from Pareto Front** & \
\begin{tabular}{c} **Inverter** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Inner Diameter** \\ **of Coils** \\ \end{tabular} & **Turn Pitch** & **Turns** \\ \hline \(\eta=98.8\%\), \(K=0.74\) & 772,052 & 90.0 & 11.7 & 9 \\ \hline \(\eta=96.1\%\), \(K=4.36\) & 542,260 & 133.4 & 9.7 & 4 \\ \hline \(\eta=91.6\%\), \(K=7.45\) & 386,879 & 107  
In order to verify the reliability of the optimization results, the third group of parameters was taken as an example to model and calculate the planar coils coupler values in COMSOL. After that, build the S-S compensation in Multisim to calculate the voltage modulus gain and efficiency. The model and the corresponding results are shown in Figure 10.  
The Multisim simulation data shows that the system's \(\eta=91.59\%\) and \(K=7.39\), which is very close to the value of the co-simulation. According to the same simulation method, the simulation values corresponding to the other groups of Pareto front points are obtained as shown in the following Figure 11.  
Figure 11 shows that the difference between Pareto front data and Multisim modeling is small, so the results obtained by the optimization process have high reliability and certain application value.
## 3 Case-Study Building and Retrofit  
The building analyzed as a case study is an eight-story apartment type residential building located in Bangkok, Thailand. The case study building located tropical climate, which requires no heating system and has a high solar radiation for renewable energy systems. However, this climate has a high temperature in the environment, which results in a significant portion of energy consumption coming from air conditioning unit. Thus, the retrofitted study may provide a benefit to the building owner in terms of significant energy usage reduction.  
The overall structure in the Sketchup diagram is illustrated in Figure 1. The floor plan of the building is shown in Figure 2. Note that it consists of a commercial area on the first floor with an area for parking lots, as shown in Figure 2a. From the 2 nd to the 8 th floor, the floor plan is similar, with residential units separated by the central hallway and small sections on the north side for the elevator and stairs, as shown in Figure 2b. The rooftop is a flat concrete with a metal-sheet open roof and a small section for the elevator mechanical room, as shown in Figure 2c. This residential building includes individual electric heaters instead of a central boiler as a hot-water heating system; thus, the case study does not address the evaluation of hot-water heating.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of the Building** & \(\mathbf{C_{l}}\) & \(\mathbf{C_{e}}\) & \(\mathbf{C_{o}}\) & \(\mathbf{C_{p}}\) & \(\mathbf{\eta_{h}}\) \\ \hline Group 1 (Office, Education building) & 0.84 & 0.85 & 0.90 & 0.90 & 2340 \\ \hline Group 2 (Department store, Conventional Hall, Auditorium) & 0.84 & 0.85 & 0.90 & 0.90 & 4380 \\ \hline Group 3 (Hotel, Hospital, Residential building) & 1.0 & 1.0 & 1.0 & 1.0 & 8760 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Efficiency coefficient of cooling load in building energy code.  
Figure 1: SketchUp diagram of an eight-story residential building analyzed as a case study.  
Figure 2: Floor plan of the case-study building: (**a**) First floor; (**b**) 2 nd to 8 th floor; (**c**) Rooftop.
## 5 Actual Tracking Test and Verification of AUV  
The experimental simulation data. According to the RBF function method proposed in this paper, use Algorithm 1 to test the tracking performance of the underwater robot on the horizontal plane. After several experiments and simulation tests, the more rational RBF system parameters are provided, including the node number of the input layer \(n=1\), the node number of the hidden layer \(h=30\), the learning rate \(\eta=0.08\), and the momentum factor \(\alpha=0.002\). In addition, the AUV experiment is designed to verify the AUV trajectory tracking ability. The control algorithm designed in this paper is downloaded to the AUV electronic bin shown in Figure 3, and the underwater vehicle designed in Figure 4 is used to conduct the underwater search cable experiment.  
```
Known nonlinear system structure parameters, such as \(n_{y}\), \(n_{u}\), \(d\), etc. 1. Input the initial data of the system, set the initial value of RBF network parameters \(b_{j}(0)\), \(c_{ji}(0)\), and \(w_{j}(0)\), and other parameters such as the number of hidden layer neurons \(h\), learning rate \(\eta\), momentum factor \(\alpha\), etc.; 2. Sample the actual system output \(y(k)\), and calculate the current network output \(y_{m}(k)\) using Formulas (16) and (17); 3. Use Formulas (22) and (26) to calculate the network parameter increment \(\
abla w_{j}(k)\), \(\
abla b_{j}(k)\), and \(\
abla c_{ji}(k)\); 4. Use Formulas (23) and (25) to calculate the network parameters \(w_{j}(k)\), \(b_{j}(k)\), and \(c_{ji}(k)\); 5. Use Step 2\((k\to k+1)\) to continue the cycle.
```  
**Algorithm 1** RBF-based MRAC
# PV Tracking Design Methodology Based on an Orientation Efficiency Chart
## 2 Materials and Methods
### Points and Trajectory for Solar Tracking  
This section establishes the minimum number of tracking points that can be managed each day according to the EFO range established in the previous step, as well as the verified azimuth and elevation angle ranges based on the solar trajectory at the installation site throughout the year, according to the flow diagram shown in Figure 1. Assuming that the sun advances at 15\({}^{\circ}\) per hour, according to the EFO chart, the azimuth tracking can have a range of \(\pm 30^{\circ}\) of the defacement. In hourly solar tracking from 6:00 to 18:00, the tracking can be divided into three fixed positions with evenly spaced angular points and time locations, as listed in Table 2. Regarding the adjustment of the solar tiles, a follow-up via daily adjustments based on the declination is proposed; this method considers that the declination value is 16\({}^{\circ}\), that it can be maintained within 25\({}^{\circ}\) (per the solar chart for a latitude of 27.5\({}^{\circ}\) north), and that the mechanism has a low cost.