esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Water-Jet Propulsion Experiments
### Test Results  
In order to study the acceleration characteristics of the submersible, the drag coefficients at different submerged speeds were obtained from numerical calculations and flume experiments, as shown in Figure 7. During the study of the submerged drag characteristics, it was found that the simulated drag values were almost in agreement with the experimental values. The maximum error was 9.4% and the average error was 5.6%--within the allowable error range--which verifies the accuracy of CFD. This prepared us for the study of direct sailing variable acceleration motion.  
Figure 6: Experimental system of water-jet propulsion. (**1**) Three-dimensional-acoustic Doppler velocimeter, (**2**) postprocessor, (**3**) torque and rotational speed measurer, (**4**) submersible, (**5**) screw mixed-flow pump, (**6**) high-precision electronic force gauge, (**7**) self-circulating high-precision test flume.  
For the propulsion pump, we elected to study the speed-thrust and speed-torque characteristics under specified steady-state operating conditions. The propulsion characteristics of the screw mixed-flow pump were studied. We found that the thrust and torque at different rotating speeds followed a quadratic function distribution, which was in accordance with the following mathematical relationship. From the relationship functions in Equations (18) and (19), it can be seen that the coefficient of the constant term varies greatly, while the coefficients of the primary term and quadratic term vary slightly. It can be observed in Figure 8 that the calculated values were consistent with the experimental value trend, and the thrust error was within 3.5%. During the study of the torque characteristics, we found that the torque test values were consistent with the calculated value tendency, and the maximum error was 5.7%--within the allowable error range--thereby verifying the rationality of the propulsion pump simulation.  
The thrust functions:  
\[\left\{\begin{array}{l}T_{CFD}=0.05589-0.000447207n_{i}+0.00000447256{n_{i}} ^{2}\\ T_{EXP}=-0.16651-0.000989188n_{i}+0.00000452573{n_{i}}^{2}\end{array}\right., \tag{18}\]  
Figure 8: Propulsion characteristic of the water-jet pump: (**a**) thrust value with rotation speed; (**b**) torque value with rotation speed.  
Figure 7: Submerged drag with submerged speed.  
The torque functions:  
\[\left\{\begin{array}{l}M_{CFD}=-0.00208+0.00000859666n_{i}+0.000000152842{n_{i}}^ {2}\\ M_{EXP}=0.00773-0.0000345747n_{i}+0.000000154616n_{i}^{2}\end{array}\right.. \tag{19}\]
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 5 Routing Algorithm Based on Autonomous Load Regulation Mechanism
### Autonomous Load Regulation Mechanism Based on Data Relay Radius Control  
In order to enable each node to accurately analyze its relative load level and control its own load in a timely manner, we proposed an autonomous load regulation mechanism based on data relay radius control. Taking node \(v_{j}\) as an example, the relationship between the data relay radius and the relay range is as follows:  
\[RR(j)=\left\{i\big{|}d_{js}<d_{is},d_{ij}<r_{j},i\in V\right\} \tag{11}\]  
where \(RR(j)\) is the relay range of node \(v_{j}\), while \(d_{is}\) and \(d_{js}\) are the distances from node \(v_{i}\) and node \(v_{j}\) to the sink, respectively. \(d_{ij}\) is the distance between node \(v_{i}\) and node \(v_{j}\). \(r_{j}\) is the data relay radius of node \(v_{j}\) and node \(v_{j}\) only accepts relay requests of the nodes in its relay range.  
Based on the relationship above, the autonomous load regulation mechanism based on data relay radius control is described in Algorithm 1.  
```
for all\(v_{j}\in V\) if\(l_{j}-\overline{l_{j}}>\gamma\cdot\overline{l_{j}}\) and \(r_{j}>0\)then\(r_{j}--\); elseif\(\overline{l_{j}}-l_{j}>\gamma\cdot\overline{l_{j}}\) and \(r_{j}<R\)then\(r_{j}++\); else do nothing endif endfor
```  
**Algorithm 1** Autonomous load regulation mechanism based on data relay radius control.  
where \(\overline{l_{j}}\) is the average load of node \(v_{j}\) and its neighbors, \(\gamma\) is the load regulation accuracy, and \(\gamma\cdot\overline{l_{j}}\) is the threshold of load difference. If \(l_{j}\) is more than \(\gamma\cdot\overline{l_{j}}\) compared with \(\overline{l_{j}}\), node \(v_{j}\) is seriously overloaded and it should reduce its data relay radius in order to decrease its relay range and load. If \(l_{j}\) is less than \(\gamma\cdot\overline{l_{j}}\) compared with \(\overline{l_{j}}\), node \(v_{j}\) has a light load and it should increase its data relay radius and relay range in order to share load for its neighbors. If the difference between \(l_{j}\) and \(\overline{l_{j}}\) is less than \(\gamma\cdot\overline{l_{j}}\), node \(v_{j}\) is in the appropriate load state, and its relay radius does not need to be adjusted.
## 3 Methods
### Wire Spacing Detection Method  
#### 3.3.1 Pixel Size Calibration  
To measure the real adjacent distance between wires, it is necessary to know the real physical length corresponding to a single pixel on the image, that is, the conversion scale coefficient \(k\) (in mm/pixel) of the pixel coordinate system and the world coordinate system. The conversion scale factor represents the actual size represented by a single pixel [33], which can be expressed as:  
\[k=\frac{l}{p} \tag{6}\]  
In the equation, \(l\) is the real size, the unit is mm, \(p\) is the pixel size, and the unit is a pixel.  
To calibrate the camera accurately, chessboard images of different positions and heights are taken by the camera, the side length of each small square of the chessboard grid is 10 mm, and the number of corner points inside the chessboard grid is 9 \(\times\) 9, and four points are taken at the top left, bottom left, top right, bottom right, and center of each chessboard image to calculate the coefficient \(k\) once, and then the average value is used to eliminate individual deviations, and an approximate one-dimensional linear relationship between  
Figure 11: Skeleton extraction results: (**a**) skeleton image, (**b**) Hough line detection, (**c**) The effect of the skeleton on the original image.  
a set of height \(h\) and the ratio \(k\) is obtained according to Equation (6). In subsequent experiments, the conversion coefficient \(k\) at the current height can be found by simply bringing the distance from the lens to the coil group into Equation (7):  
\[k=0.0001649\times h \tag{7}\]  
In the equation, \(h\) is the shooting height and \(k\) is the pixel conversion ratio under \(h\)-height.
## 3 Materials and Methodologies
### Resampling Model
#### 3.4.2 Forecasting (Past-Present-Future Model)  
[PERSON] and [PERSON] [9] evaluate the efficiency scores of the future DMUs with their confidence intervals, and the \"future\" (\(X^{t+1},Y^{t+1}\)) is forecasted by using \"past-present\" data (\(X^{t},Y^{t}\)) (\(t=1,\ldots,T\)).The forecasting and elaboration of the efficiency score of the forecast DMUs are demonstrated as follows: (1) set up \(h^{t}\) (\(t=1,\ldots,T\)) as the observed historical data for a certain input/output of a DMU, and (2) forecast \(h^{T+1}\) from \(h^{t}\) (\(t=1,...,t\)). There are three available forecasting engines for this purpose, which is shown below:  
* Trend analysis: a simple linear least squares regression.
* Weight average: weight by Lucas number.
* Average trend and Lucas weight average.  
In this part, the \"past-present\" data (\(X^{t}\),\(Y^{t}\)) (\(t=1\),...,\(t\)) is applied to predict the \"future\" (\(X^{t+1}\),\(Y^{t+1}\)) and forecast the efficiency scores of the future DMUs along with their confidence intervals. To prevent repetition, hereafter it is necessary to discuss how the past-present time-based framework could be extended to the past-present-future context. Then, it is essential to forecast the future initially; to be more precise, given the observation of previous data (\(x^{t}_{i,j^{\prime}}\),\(x^{t}_{r,j}\)), \(t=1\),...,\(T\) for a certain input \(i\) (\(I=1\),...,\(m\)) and \(r\) (\(r=1\),...,\(s\)) of a DMU \(j\) (\(j=1\),...,\(n\)), it is expected to forecast (\(x^{T+1}_{i,j}\),\(x^{T+1}_{r,j}\)). It estimates the super-efficiency score of the \"future\" DMU (\(X^{t+1}\),\(Y^{t+1}\)), utilizing the non-oriented super slacks-based measure model once these forecasts are acquired. Finally, given the past-present-future inter-temporal dataset (\(X^{t}\),\(Y^{t}\)) (\(t=1\),..., \(t+1\)), we applied the resampling scheme proposed in the previous section and obtained confidence intervals.  
The Lucas weight model forecasted the future data by the obtained dataset (\(X^{t+1}\),\(Y^{t+1}\)). After that, the super-efficiency was used for the evaluation of the \"future\" DMU (\(X^{t+1}\),\(Y^{t+1}\)). On this basis, the application can be found on the past-present-future period dataset (\(X^{t}\),\(Y^{t}\)) (\(t=1\),..., \(t+1\)).
## 2 Materials and Methods
### Determination of Antioxidant Capacity
#### 2.4.2 DPPH Radical Scavenging Activity Assay  
The DPPH radical (2,2-diphenyl-1-picryl-hydrazyl) assay evaluates the sample's capacity to inhibit lipid oxidation by determining its free radical-scavenging capacity. The DPPH radical assay was assessed using the modified method of [PERSON] et al. [18]. The assay is based on the reaction of the antioxidants in the sample with the radical DPPH, reducing it to diphenylicrylhydrazine, leading to the discoloration of the solution [18].  
For the assay, 100 \(\mu\)L of each sample was added at different concentrations to 2 mL of 0.07 mmol/L of ethanolic solution of DPPH radical. The mixture was shaken and left to stand for 60 min at room temperature (20 \({}^{\circ}\)C) in the dark. The absorbance of samples was measured at 517 nm. The absorption of the assay using ethanol was read as a negative control. Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) was used as standard. The results were expressed as micromodes of Trolox equivalents per g of plant dry residue extract (\(\mu\)mol TE/g DW), based on the standard curve (y = 0.001 x + 0.0139, r\({}^{2}\) = 0.9992).
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 3 Proposed System Implementation
### Back-End Optimization
#### 3.3.3 Loop Closure Detection  
For loop closure detection, we adopted Bags of Words (DBoW2) [35], a state-of-the-art bag-of words place recognition approach. The loop closure detection module will be activated when the current frame is selected as a keyframe. If a loop closure is detected, the drift accumulated during the exploration will be greatly reduced [10].
## 4 Reasoner Development
### Data Generation and Feature Selection  
Data were extracted such that the faults occurred at various instances of time in the process of braking. This means that the velocity of the aircraft at the time of occurrence of fault varies throughout the dataset. The data provided are in the form of a time series. Up to nineteen such possible input parameters are available from the simulation of the model. The time interval between data points generated is 0.5 s, and the total number of data samples used in this case is 120. The mode of the length of the data series is 121, and the available data are split into training and testing datasets in a 3:1 ratio. The split is random, and care was taken to ensure that the test and train datasets did not contain the same cases.  
Figure 5: Confusion Matrix for Multiclass.  
Efforts are made to include possible extreme case scenarios so that all possible cases within the distribution are addressed. Each series of data is classified into three depending on the condition they represent, as shown in Table 3.  
Features are quantified properties that are put into a model, and up to 19 different parameters are generated from the EBS model simulation, generating 19 factorial or 1.2 \(\times\) 10\({}^{7}\) possible combinations as input features. Feeding all the features into the ML models are not a viable option due to the high number of combinations, which will translate into more processing time. In cases with a high number of data combinations, a trade-off between accuracy and processing time is considered.  
The comparative study of the previous sections shows the braking force being different in the normal braking condition simulation and the three fault modes. The wheel slip profile shows major differences for each scenario and is a parameter derived from wheel and vehicle speed. The other parameters found with major variability are the motor angular displacement and motor temperature which tends to change at the earliest sign of an anomaly.  
The braking force is used as the input feature for the univariate. For multivariate models, the number of features to be fed into the model was arbitrarily chosen as four. These four parameters are braking force, wheel slip, motor angular displacement, and motor temperature, as they display observable variation during each of the scenarios.
## 1 Introduction
All those tactics mentioned above have application in vibration attenuation of flexible appendages, but their application conditions are restrictive. They are not suitable for situations in which satellites are actuated by sustained disturbances. Motivated by the preceding discussion, finding an appropriate continuous compensating torque to attenuate the vibration of flexible appendages is the optimal option. However, direct studies about relationships between selection of compensate torques and vibration attenuation effectiveness for meteorological satellites are scarce. In this paper, based on the requirement of meteorological satellites and practical limitations, step torques given by reaction wheels are adopted to compensate for the sinusoidal periodic disturbance. Two coefficients, frequency ratio and amplitude ratio, are introduced as parameters. A typical 2-order vibration system and a flexible spacecraft system are introduced. The optimal compensate torques and corresponding effect will be discussed by analytic methods. Residual vibrations are regarded as the standard of the disturbance compensate effect. To verify the effectiveness of the proposed strategy, simulation results are presented. We aim to determine the relationship between the selection of the two ratios and disturbance compensate effectiveness. Furthermore, an experimental study is provided to verify the effectiveness of the proposed strategy. It is hoped that the work will be helpful for solving practical engineering problems. In addition, preliminary investigations in this paper have limitations, and further discussions will be summarized in our next study. The remaining part of this paper is organized as follows. Section 2 gives the mathematical modelling. Analytical relationships between ratios and residual vibration are given in Section 3. Numerical simulations, experiment results and discussions are presented in Section 3. Section 4 concludes the paper.
# Acoustic Parametric Signal Generation for Underwater Communication
## 4 Results
### Influence of Noise  
In this section, we study the influence of the noise on the communication using the parametric technique. To this end, a study is carried out that consists of adding to the received signals a white noise with different amplitude values ranging from 1 to 10 mV. Then, the correlation process described in Section 4.1 is performed and the parameters \(RA\) and \(RB\), Reconstructed bits, are analyzed.  
We can see in Figure 9 that by increasing the amplitude of the white noise the directivity is degraded (a), the \(RA\) parameter decreases (b) and, so the bit detection rate also does (c). Therefore, the signal is sensitive to changes in amplitude of noise and \(RB\) is quite dependent of the angle. For example, with 5 mV white noise \(RB\) is larger than 90% in the region \(\pm 10^{\circ}\), whereas it degrades quite fast outside this region. This could be applied for situation in which directive communication is required. The noise could either be environmental or easily produced artificially with a non-directive transducer working at low frequencies.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 5 Bit Error Rate Performance
### Bit Error Rate Performance  
According to the assumption that the chip duration \(T_{c}\) is constant regardless of the schemes, we evaluate \(P_{w}\) versus BER performance for the above-mentioned schemes using an MPSC and an inverted MPSC. It is noted that the data rates of the [PERSON]'s and modified Liu's schemes, having longer sequence lengths \(n\), are lower than those of the other schemes. We assume that the probability of transmitting the data \(I=0\) is equal to the probability of transmitting \(I=1\) for each user. We also assume perfect synchronization between the users.  
Figure 7a shows the received light power \(P_{w}\) versus the BER performance for the six schemes using the MPSC and the inverted MPSC with a length of \(16\) (\(q=4\)), \(N_{\mathrm{L}}=8\), and \(N=8\). Figure 7b shows the received light power \(P_{w}\) versus the BER performance for the six schemes using the MPSC and the inverted MPSC with a length of \(64\) (\(q=8\)), \(N_{\mathrm{L}}=32\), and \(N=32\). We found that the BERs of the EWO schemes with non-inverted and inverted MPSCs are almost the same, and that they are better than those of the other schemes. The BERs of the schemes with a non-inverted MPSC and an inverted MPSC are nearly identical for [PERSON]'s and [PERSON]'s schemes. Because the decision distance for the EWO scheme is twice that of [PERSON]'s and [PERSON]'s schemes, the difference in \(P_{w}\) is around \(3\) dBm. The computer simulation results agree well with the theoretical results.
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods  
This research proposes an automated system for generating 3D models directly from 2D images. Given an input image \(I\), which may contain objects of varying categories, shapes, and geometries, our method learns a function \(f\) to estimate a 3D model \(M\). This is achieved by intelligently fusing the features of \(I\) with an initial spherical geometry \(P\), approximating the unknown model's ground truth \(Y\). We use point clouds to represent the output (Equation (1)) and generate \(N=5000\) points from the CNN, which sufficiently captures the object's geometry and preserves its main structures. The process is formulated as follows:  
\[M=\#(P,I), \tag{1}\] \[f=loss(M,Y),\]  
where the hash symbol denotes the fusion mechanism discussed in module 3, Section 3.3. The loss function recognizes the generated and actual models with the help of Earth Mover's distance and Chamfer distance. This methodology is implemented through a four-module deep network, designed as follows:  
1. Image Encoder: Extracts high-level features from the input image.
2. 3D Encoder: Processes the initial spherical point cloud to extract high-level 3D features while aligning their distribution with the image features.
3. Dual Attention-Based Feature Fusion: Combines features from the image encoder and 3D encoder using a dual attention mechanism.
4. Decoder: Generates the final point clouds from the fused features.  
This structure ensures efficient and accurate 3D reconstruction. Below are the details of the network's modules as shown in Figure 3.
## 3 Synthesized UGW Signal Analysis  
### UGW Characteristic Analysis  
A low-carbon steel pipe with an outer diameter of 48 mm, a wall thickness of 5 mm and a length of 5.5 m was used, of which the dispersion curves of axisymmetric UGWs were drawn by PC_disp package [25] in Figure 3a,b. Since the UGW velocity is a function of frequency, the center frequency of UGW with the desired mode should be selected within the frequency band with little change in velocity. When the frequency is between 100 kHz and 150 kHz, it is easy to distinguish the L(0,2) UGW with a large and smooth phase and group velocities from other UGWs. The excitation frequency of the L(0,2) UGW was chosen as 110 kHz in this paper, and the corresponding phase and group velocity were 5460.86 m/s and 5350.47 m/s, respectively.  
It is almost impossible to not excite T(0,_m_) (\(m\geq 1\)) and L(0,_m_) (\(m\geq 3\)) UGWs when L(0,2) is the desired mode and excitation frequency is 110 kHz, because the excitation conditions of the torsional UGW are quite different from those of the longitudinal UGW, and the cutoff frequencies of L(0,_m_) (\(m\geq 3\)) UGWs are greater than 110 kHz. Therefore, the undesired L(0,1) UGW is most likely to appear with the excitation of the L(0,2) UGW. The excitation signal spectrum and the dispersion curves of the corresponding L(0,1) and L(0,2) asymmetric flexural UGWs are shown in Figure 3c,d. It can be seen that the phase velocity increases and group velocity decreases as the order \(n\) increases from 0 to 4, and velocity difference among L(0,2) and F(_n,3_) (\(1\leq n\leq 4\)) UGWs is greater than that among L(0,1) and F(_n,1_) (\(1\leq n\leq 4\)) UGWs in the excitation frequency band.  
The normalized displacement distributions of the above UGWs were plotted in Figure 4. It can be concluded that the L(0,1) and F(_n,1_) UGWs are dominated by radial displacement and share similar distribution as \(n\) varies, whereas axial displacements with  
Figure 3: Dispersion curves of the zero-order axisymmetric UGWs: (**a**) phase velocity, (**b**) group velocity and dispersion curves of the asymmetric UGWs: (**c**) phase velocity, (**d**) group velocity.  
relatively large difference as \(n\) varies are dominant in the L(0,2) and F(\(n\),3) UGWs. In addition, the dominant displacements decrease while circumferential displacements increase as \(n\) increases. The above phenomenon may be the cause of the velocity difference among UGWs, and the resolution of the L(0,2) UGW may be inevitably affected by other UGWs for the difference in displacement distributions and velocities.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### Software and Hardware  
The algorithms were developed in a Python programming environment, using SciPy libraries as needed.  
Training and tests were performed on a cluster, made available by the INCD ([[https://www.incd.pt/](https://www.incd.pt/)]([https://www.incd.pt/](https://www.incd.pt/)), accessed on 3 December 2023). The computer used was Stratus cloud computing service, which is an IaaS cloud infrastructure based on Openstack, which enables control of large pools of computing, storage, and networking resources.
# A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection
## 2 Hot-IP Finding Methods
### Comparison of Hot-IP Finding Methods  
As discussed in Section 2.2, the Count-Sketch, Count-Min and Group Testing methods are capable of finding Hot-IPs correctly in the IP packet stream. In this section, we compare these methods based on the following criteria: (1) algorithm complexity; (2) space requirements; and (3) processing time in finding Hot-IPs.
## 4 Monitoring System Framework
### Components of the Multi-Source Monitoring System
#### 4.1.6 Principle of Demodulation Technology Based on Tunable Filter of FGB  
There are many kinds of FBG demodulation technologies. The system adopts tunable filter method. It is to monitor the reflection spectrum of FBG in real time and detect the wavelength drift. Various optical filters are used in the demodulation process. The output is the convolution of FBG output spectrum and filter wave function. When the spectrum of FBG output coincides with that of attuned filter, the maximum value is 1, which makes the narrow band signal light of specified band pass through and restrains the output of other band signal wavers. By measuring the maximum point and the corresponding wavelength change of the attuned filter, the wavelength drift of FGB can be calculated.