esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Accuracy Verification of CORDIC Algorithm
#### 3.2.4 FPGA Simulation and Testing  
To verify the implementation effect of the ultrasonic signal processing technology based on the high-performance CORDIC algorithm on FPGA, the FPGA development programming software Quartus II 13.1 was used for simulation. The chip was Cyclone IV series EP4 CE15F17A7, and the simulation clock frequency was 100 MHz, using a pipeline structure for hardware acceleration. The hardware resource utilization of ultrasonic signal processing technology based on the high-performance CORDIC algorithm comprehensively realized on FPGA is shown in Table 4. As can be seen from the statistics table, the key resources, such as LUT, RAM, and FF, are still relatively free, and there is plenty of space for resource expansion. Under the premise of ensuring the computational speed and accuracy to meet the application requirements, the ultrasonic signal processing technology based on the high-performance CORDIC algorithm greatly reduces the cost of storage resources, makes full use of FPGA parallel processing to improve the data processing ability, and meets the requirements of ultrasonic system resource design. In addition, at an operating temperature of 24.7 \({}^{\circ}\)C, the overall power consumption of the system logic was only 1.673 W after being implemented on the FPGA chip layout.
## 4 Simulation and Analysis of Results
### Analysis of Optimization Ability in Different Numbers of Points  
Based on four sets of data from ulysses16, city31, eil51, gr96, and bier127, the simulation diagrams are shown in Figures 5-9, and the results of data are shown in Table 4. Based on the analysis of the data, it can be obtained that:  
Firstly, under the four sets of data, compared with SMA, the optimization ability of TPSMA is improved by 24.56, 36.10, 41.88, 49.83, and 52.93%, respectively. Therefore, the result of TPSMA is obviously better than SMA in Figure 13a.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TSP Data** & **Results of SMA** & **Results of TPSMA** & **Improved Percentages** \\ \hline
**ulysses16** & 103.1746 & 77.8372 & **24.56\%** \\
**city31** & 27,073 & 17,300 & **36.10\%** \\
**eil51** & 798.9 & 464.3 & **41.88\%** \\
**gr96** & 1178 & 591 & **49.83\%** \\
**bier127** & 274,870 & 129,390 & **52.93\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental data’s results of ulysses16, city31, eil51, gr96 and bier127.  
Figure 12: Partial paths of TPSMA under ulysses16 data.  
Secondly, as per the experimental results also shown in Figure 12(b), the improved percentages of TPSMA are gradually increased with the increasing number of points. It can be seen that TPSMA designed in this paper is more powerful in solving TSP with large points and complex distribution. Thus, the experimental results fully verify the rationality and superiority of TPSMA.
###### Abstract  
The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.  
wireless sensor networks (WSN); opportunistic routing (OR); naive Bayes; relay node; energy efficiency; reliability +
Footnote †: journal: Accepted: 10 July 2020; Published: 13 July 2020
# Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem
## 3 Problem Statement  
Let \(T=\{T_{1},T_{2},\ldots T_{t}\}\) be the set of \(t\) targets on a 2D plane. Each target is associated with \(h\) candidate heading angles. For simplicity, lets assume that these heading angles are obtained by sampling the interval \([0,2\pi]\) uniformly, i.e., \(\Phi=\{0,\frac{2\pi}{h},\ldots,c\frac{2\pi}{h},\ldots,2\pi\}\), where \(c\leq h\). Let \(\{T_{0}\}\) denote the initial target (depot). Let \((x_{i},y_{i})\) denote the location of target \(T_{i}\), and \(\theta_{i}\in\Phi\) denote the arrival angle of a vehicle at target \(i\). The angle of arrival and angle of departure of a vehicle from a target is assumed to be the same. To simplify the presentation, we also refer to \((x_{i},y_{i})\) as target \(T_{i}\). The configuration of the vehicle at a target \(T_{i}\) at an angle of \(\theta_{i}\) is denoted by \((x_{i},y_{i},\theta_{i})\) or simply \((T_{i},\theta_{i})\).  
Let \(\
u=\{1,2,\ldots m\}\) be the set of \(m\) homogeneous vehicles with a minimum turning radius \(\rho\). Let \(\tau_{k}\subset T\) be the set of targets toured by vehicle \(k\) visiting each target exactly once, starting and ending at depot \(T_{0}\). Let \(d_{k}(T_{i},\theta_{i},T_{j},\theta_{j})\) denote the shortest Dubins path traversed by vehicle \(k\in\
u\), from \((T_{i},\theta_{i})\) to \((T_{j},\theta_{j})\). Since the vehicles are homogeneous, \(d_{k}(T_{i},\theta_{i},T_{j},\theta_{j})\) remains the same for all \(k\in\
u\). Let the heading angle at the depot for all the vehicles be the same and be given. The objective of MD-GmTSP is to find a tour for each vehicle and the heading angle at each target in \(T\) such that  
* Each target in \(T\) is visited once by some vehicle at a specified heading angle;
* The tour for each vehicle starts and ends at the depot;
* The length of the longest Dubins tour among the \(m\) vehicles is minimized.
# 3D Printed Microfluid Separators for Solid/Liquid Suspensions
###### Abstract  
This study investigates the fabrication of 3D-printed microfluidic devices for solid/liquid separation, focusing on additive manufacturing technologies. Stereolithography (SLA) and fused filament fabrication (FFF) were used to create microseparators with intricate designs optimized for separation efficiency. Model suspensions containing quartz sand, nano-calcium carbonate, and talc-based baby powder in water were prepared using an electric magnetic stirrer and conveyed into the microseparator via a peristaltic pump. Different flow rates were tested to evaluate their influence on the separation efficiency. The highest separation efficiency for the model systems was observed at a flow rate of 200 mL min\({}^{-1}\). This was due to the increased turbulence at higher flow rates, which hindered the secondary flow perpendicular to the primary flow direction. The particle size distribution before and after separation was analyzed using sieve and laser diffraction, and particle morphology was inspected by scanning electron microscopy. The laser diffraction analysis revealed post-separation particle size distributions, indicating that Outlet 1 (external stream) consistently captured larger particles more effectively than Outlet 2 (internal stream). This work highlights the potential of additive manufacturing to produce customized microfluidic devices, enabling rapid prototyping and fine-tuning of complex geometries, thus enhancing separation efficiency across various industrial applications.  
additive manufacturing; 3D printing; microfluidics; microseparator; solid/liquid separation Article  
3D Printed Microfluid Separators for Solid/Liquid Suspensions
# DTV Essential Hidden Area Decoder for DTV Broadcasting
## 3 The Proposed DEHA Decoder for DEHA Utilization
### Video Encoder and Decoder for DEHA Utilization
#### 3.1.2 Proposed Block Decoder  
MPEG video compression is based on lossy compression. A block image is degraded via ME and mQuant in MPEG CBR encoding, as shown in Figure 9.  
Figure 8: The block diagram and functions of proposed HDTV receiver for DEHA utilization.  
Figure 7: Proposed video encoder and decoder for DEHA Utilization.  
In MPEG CBR video compression, block average \(D_{avg}\)(n) is used to reconstruct the metadata from the degraded block image embedded in DEHA.  
\[D_{avg}(n)=\frac{1}{64}\sum_{i=0}^{7}\sum_{j=0}^{7}B_{n}(i,j) \tag{4}\]  
Here, n denotes an n-th block in DEHA, and \(B_{n}(i,j)\) is restored a pixel value by the receiver. From the reconstructed \(D_{avg}(n)\), a bit value of metadata \(D_{th}(n)\) restores with the threshold 128 as a boundary.  
\[D_{th}(n)=\begin{cases}1&if\ D_{avg}(n)>128\\ 0&if\ D_{avg}(n)<128\end{cases} \tag{5}\]  
This \(D_{th}(n)\) is the (n mod 8)-th bit value of the n/8-th metadata. The metadata reconstructed using the block mean and the threshold value can restore the original data values despite the degradation of the block image due to MPEG CBR lossy compression.
## 3 Dataset  
The NSL-KDD dataset, a widely used and benchmark dataset for the solutions mentioned above, was proposed as an updated version of KDDcup'99, aiming to solve the redundancy and unbalanced issues of the later one, which was shown to highly affect the performance of detection systems [20]. However, both of these datasets are over a decade old and lack representation of recent and more complex attacks, especially in comparison with the UNSW-NB15 dataset [21]. This dataset is a comprehensive and fairly recent dataset created by the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) [5]. The same organization has published newer datasets such as the ToN-IoT [22], and the newer versions NF-BoTiToT-v2 and NF-ToN-IoT-v2. However, the network activities recorded on those sets are specific to the domain of IoT.  
In its original format, UNSW-NB15 collects around 2.5 million network registries containing 49 features categorized into five groups: flow features, basic features, content features, time features, and additional generated features. Each registry is classified as either a common or an attack package, this being further categorized into one of nine possible attack groups: analysis, backdoor, DoS, exploits, fuzzers, generic, reconnaissance, shellcode, and worms. A reduced format collection of \(175,341\) training and \(82,332\) test entries is available, which reduces the recorded features to those related to IP address and source/destination ports. This is the set that has been used in this work following the instructions in [2].
## 3 Data and Methodology
### Numerical Simulation  
In this study, FLAC3D was employed for numerical simulations. This software utilizes the Lagrangian difference formulation for computations and is designed to model and analyze the mechanical response of a continuous three-dimensional medium, particularly focusing on reaching equilibrium or steady plastic flow. It is particularly well suited for addressing significant geotechnical engineering challenges, including rock excavation [41].  
In accordance with the Mohr-Coulomb damage criterion, this simulation in FLAC3D requires the determination of two key material parameters: the deformation parameter and the strength parameter. The mechanical characteristics of the rock mass were ascertained using a combination of laboratory and field-based mechanical tests guided by the Hoek-Brown criterion. The deformation properties are described in terms of the bulk modulus (\(K\)) and shear modulus (\(G\)). The calculation equations are written as follows:  
\[K=\frac{E}{3(1+v)} \tag{10}\]  
\[G=\frac{E}{2(1+v)} \tag{11}\]  
where \(E\) is the modulus of elasticity (MPa), and \(v\) is Poisson's ratio.  
The 3D model used in the numerical simulation was constructed based on comprehensive data and then appropriately simplified to align with real-world conditions during the modeling process. This study employs Rhino software (6 SR10) for interactive modeling via the following steps (Figure 11): (1) Use UAV modeling software CC to generate 3D models and DEM data. Process this data in conjunction with ArcGIS software to create contour data. (2) Combine field knowledge to create a simplified 3D geological model using Rhino software and generate a grid using the Griddle plug-in. (3) Import the 3D model into FLAC3D, and perform a series of actions such as grouping, setting initial geostress, simulating excavation, and sequentially analyzing stress-strain.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Sibling Contact Regime**} & \multirow{2}{*}{
\begin{tabular}{c} **Fertile Eggs** \\ **(n)** \\ \end{tabular} } & \multicolumn{5}{c}{**Development Stage at Embryonic Mortality \({}^{1}\)**} \\  & & & \multicolumn{5}{c}{**(\% of Fertile Eggs)**} \\ \cline{4-6}  & & **FND** & **PD** & **EEM** & **LEM** & **P** & **Total** \\ \hline Without contact & 43 & 2.3 & 0.0 & 2.3 & 9.3 & 7.0 & 20.9 \\ Contact during incubation phase (1–20 days) & 43 & 0.0 & 9.3 & 4.7 & 4.7 & 4.7 & 23.4 \\ Contact during hatching phase (21 days-hatching) & 45 & 0.0 & 2.2 & 2.2 & 8.9 & 6.7 & 20.0 \\ Permanent contact (1 day-hatching) & 45 & 0.0 & 4.4 & 6.7 & 4.4 & 6.7 & 22.2 \\ Total & 176 & 0.6 & 4.0 & 4.0 & 6.8 & 6.3 & 21.7 \\ SEM & 0.6 & 1.5 & 1.5 & 1.9 & 1.8 & 3.1 \\ \(p\)-Value & 0.378 & 0.149 & 0.667 & 0.698 & 0.969 & 0.984 \\ \hline \hline \end{tabular} \({}^{1}\) FND: fertile, no development; PD: positive development; EEM: early embryonic mortality; LEM: late embryonic mortality; P: pipped but not out of shell; SEM: standard error of the mean.  
\end{table} 
Table 2: Embryonic mortality of fertile red-legged partridge eggs according to the sibling eggs contact regime during the incubation process. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sibling Contact Regime**} & \multirow{2}{*}{\begin{tabular}{c} **Fertile Eggs** \\ **(n)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Initial Weight** \\ **(g)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Weight at 20 Days of Incubation** \\ **(g)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Weight Loss at 20 Days** \\ **(\%)** \\ \end{tabular} } \\  & & & & \\ \hline Without contact & 43 & 19.5 & 17.5 & 10.5 \\ Contact during incubation phase (1–20 days) & 43 & 18.8 & 16.8 & 10.3 \\ Contact during hatching phase (21 days-hatching) & 45 & 19.1 & 17.5 & 7.3 \\ Permanent contact (1 day-hatching) & 45 & 19.3 & 17.3 & 10.1 \\ Total & 176 & 19.2 & 17.3 & 9.5 \\ SEM & 0.13 & 0.13 & 0.61 \\ \(p\)-Value & 0.206 & 0.197 & 0.187 \\ \hline \hline \end{tabular} SEM: standard error of the mean.  
\end{table} 
Table 3: Initial weight, weight at 20 days of incubation, and weight loss after 20 days of incubation of red-legged partridge fertile eggs according to the sibling eggs contact regime during incubation (mean). batch management, so that chicks that hatch earlier would not have to wait for a long period of time for extraction, thus minimizing their dehydration risk. Previous reports have shown that incubation length and hatching synchrony are affected by several factors in the artificial incubation of red-legged partridge eggs, such as the length of the storage period before incubation [9], the egg turning regime during storage before incubation [12], the time to shift from first phase of incubation to hatching phase temperature [10], or the temperature set during the hatching period [11]. However, to our knowledge, the current study is the first to demonstrate in _A. ru
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Auxiliary Data
#### 3.2.3 Coastline Data  
For the separation of land and ocean areas, we generated a land mask and a sea mask for the entire coastal zone of Asia on the basis of a detailed coastline dataset that represents the boundary between land and ocean areas. Accurate determination of the coastline is critical to the separation of water and land, as it determines the zone to be masked as containing potential aquaculture areas. For comparison, various global shoreline datasets from multiple sources were searched, collected, and processed to cover the entire coastal zone of Asia. The positional accuracy of all coastlines was examined for several coastal samples along the investigation areas. The Open Street Map (OSM) dataset turned out to be more detailed and up-to-date than other coastline vector datasets (see Figure 5).
## 3 Epidemiology and Diagnosis  
### Incidence Associated with Age and Sex  
The relationship between sex and the incidence of MRONJ is multifaceted and subject to ongoing investigation, as highlighted by recent studies. A study found that using  
Figure 1: Flowchart.  
Figure 2: Selected articles for review. Articles were categorized by their topic [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29].  
exposed necrotic bone as a diagnostic criterion did not reveal significant differences in MRONJ incidence between sexes [1]. However, it did report subdued pain responses in male mice compared to female mice in cases of stage 0 MRONJ diagnosed histologically in murine models, suggesting that males might experience under-diagnosis or delayed diagnosis due to less severe pain symptoms, potentially leading to later stage identification of MRONJ in this group [11].  
Contrastingly, another study indicated a different trend, showing that MRONJ related to dentoalveolar surgical procedures disproportionately affected males, particularly in the posterior mandibular sectors [12]. This suggests a possible higher susceptibility of males to MRONJ post-surgical procedures in specific jaw locations, though the study also emphasized the need for further research to confirm this trend [12].  
These divergent findings underscore the complexity of the sex-MRONJ incidence relationship. The first study [11] highlights the risk of under-diagnosis in males due to less pronounced pain symptoms, whereas the second study [12] points to a potentially higher incidence of MRONJ in males, especially in particular areas of the jaw after surgical interventions. Both studies stress the necessity of acknowledging sex-based differences in the presentation, diagnosis, and progression of MRONJ.  
Moreover, antiresorptive drugs like BPs and denosumab (DS), primarily used in adults for managing cancer and osteoporosis, have recently seen increased usage among children and young individuals [30; 31]. These medications are used for conditions such as osteogenesis imperfects (OI), glucocorticoid-induced osteoporosis, McCune-Albright syndrome (MAS), and malignant hypercalcemia [32; 33; 34]. A systematic review encompassing 29 articles published between 2007 and 2022 included 1192 patients, predominantly treated for OI [13]. This study revealed a relatively low presence of MRONJ in the child and youth population undergoing antiresorptive drug therapy [13]. However, it also highlighted significant challenges in data collection, with unclear therapy details and deficiencies in protocols and pharmacological characterizations in many reviewed articles.
## 4 Simulation and Fpga Implementation
### MATLAB Simulation  
The constellation diagrams are shown in Figure 6, where Figure 6a,b are the constellation diagrams before and after timing synchronization respectively. Here, SNR is set to 20 dB. The converged constellation diagram proves that the timing module works correctly.  
BER performance for 100 frames (with 16,384 bits each) transmission is carried out in Figure 7. The blue curve is the theoretical BER, \({}^{\prime}\)*\({}^{\prime}\) and \({}^{\prime}\)\(\Delta^{\prime}\) represent MATLAB fix and float point simulation respectively. The BER performance indicates that the algorithm can work efficiently with deterioration less than 0.5 dB and 1 dB in float and fix point simulation.  
Figure 6: Constellation diagrams before and after timing synchronization.  
Figure 7: Bit Error Rate (BER) performance of the improved architecture.
## 2 Related Works  
### Design Framework of Private Set Intersection Protocol  
#### 2.1.1 Design Framework Based on Public Key Encryption  
The basic idea behind early private-set intersection protocols is to encrypt data elements and then perform comparison operations on the encrypted data. The most widely used technique in this method is homomorphic encryption: The sender encrypts their dataset and sends it to the receiver. The receiver processes these ciphertexts using the properties of homomorphic encryption and returns the results to the sender. The sender then decrypts these results using their own private key to obtain the intersection of the datasets. This public-key-based method generally relies on three main security assumptions [4]:  
1. Based on Diffie-Hellman (DH) theory: [PERSON] [5] used the DH key-exchange mechanism, which is based on the discrete logarithm problem, to implement a PSI protocol. In contrast, [PERSON] et al. [6] explored the use of elliptic curve cryptography in PSI, noting its significant advantages in security and efficiency compared to traditional discrete logarithm-based PSI methods.
2. Based on the RSA assumption: [PERSON] et al. [7] developed a semi-honest PSI protocol using RSA blind signature technology based on the integer factorization problem. Another study [8] showed that PSI schemes based on discrete logarithm cryptography demonstrated higher efficiency compared to those based on integer factorization cryptography.
3. Based on homomorphic encryption: [PERSON] et al. [9] innovatively represented elements as roots of polynomials and encrypted the coefficients of these polynomials using Paillier homomorphic encryption technology, combined with zero-knowledge proofs, to implement a two-party PSI protocol resistant to malicious attacks. In 2016, [PERSON] et al. [10] further improved computational efficiency through the ElGamal encryption mechanism and reduced the protocol's computational complexity using Cuckoo Hash technology [4]. [PERSON] et al. [11] introduced a set representation method based on point-value pairs of d-degree polynomials, implemented through the Paillier encryption scheme, reducing the multiplication complexity from O(\(d^{2}\)) to O(\(d\)) [4]. [PERSON] et al. [12] adopted different polynomial representation methods, significantly reducing computational costs to be linearly proportional to the number of participants. [PERSON] et al. [13] used additive homomorphic encryption and zero-knowledge proofs to implement pseudorandom functions (PRF). [PERSON] et al. [14] developed an additive homomorphic encryption scheme that supports threshold decryption for implementing multi-party semi-honest PSI protocols. [PERSON] et al. [15] combined Paillier encryption to propose a PSI protocol based on the formula for calculating the area of triangles and rational number encoding.  
Public key encryption-based Private Set Intersection (PSI) schemes typically feature fewer communication rounds and are suitable for environments with strong computational capabilities. However, in practice, communication bandwidth and time complexity often pose significant constraints [4].