esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Pulse Position Modulation  
The modulation scheme selected for MOCT is PPM, since PPM encodes several bits of data with every pulse transmitted. In PPM, the time of arrival of a pulse carries the information being transmitted. When the receiver captures a pulse, the arrival time is compared with time windows called slots that each map to a symbol, \(S_{N}\). The duration of a slot is called the slot width, \(\tau\). The number of slots is denoted as \(M\), where \(M=2^{n}\) and \(n\) corresponds to the number of bits that each symbol (slot) encodes. The guard time, \(T_{g}\), is the minimum time between pulses. The guard time is often limited by the transmitter producing enough power for the link, however, it can also be limited by other systems on the transmitter or receiver. An example of PPM modulation for two consecutive symbols (\(S_{N}\) and \(S_{N+1}\)) can be seen in Figure 1. Utilizing PPM with photo-counting receiver technology (i.e., APDs) reduces the required transmitter power and decreases the required SWaP on the satellite as a result [7].  
The data rate for PPM is \(D_{PPM}\) and can be seen in Equation (2). Longer slot widths and longer guard times correspond to lower PPM data rates. The number of slots, \(M\), can be selected to maximize the data rate for a given \(\tau\) and \(T_{g}\). Both \(\tau\) and \(T_{g}\) should be kept as short as the system will allow to help maximize the data rate.  
\[D_{PPM}=\frac{\log_{2}(M)}{M\tau+T_{g}} \tag{2}\]  
Other design considerations that can conflict with maximizing the PPM data rate include efficiently generating the required transmitter power and achieving a low Bit Error Ratio (BER). The BER is the total number bit errors divided by the total number of transmitted bits. Longer slot widths are associated with a lower BER. Using a longer guard time allows more time for the transmitter to amplify the pulse, which reduces the required optical pump power and power consumption for an optical amplifier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Senario** & **Link Dist.** & **Tx Diam.** & **Rx Diam.** & **Req. Peak Power** \\ \hline Moon to Earth & 3.8 \(\times\) 10\({}^{5}\) km & 10 cm & 1 m & 1.0 kW \\ LEO Crosslink & 1000 km & 1 cm & 1 cm & 340 W \\ LEO to Earth & 500 km & 1 cm & 10 cm & 1.0 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: The required peak powers for the MOCT link scenarios for select transmitter (Tx) and receiver (Rx) diameters.  
Figure 1: Example string of pulse position modulation (PPM) where the first symbol transmitted (S\({}_{N}\)) encodes a “0” and the second symbol (S\({}_{N+1}\)) encodes a “2”.
## 4 Numerical Simulation Analysis and Results  
### Microscopic Numerical Simulation  
To study the feasibility of the car-following model for different vehicle types, the traffic environment was set as follows: Car 1 is the leading vehicle with a preset value of acceleration; Car 2, Car 3, and Car 4 are followers, corresponding to an RV, an ACC vehicle, and a CACC vehicle, respectively; Car 5 is the test vehicle designated as an RV (with each of four driver types) or an ACC or CACC vehicle, as shown in Figure 4. Through numerical simulation, the acceleration fluctuations of different vehicles in the starting and braking processes were analyzed.
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 4 Evaluation Indexes
### Tamper Detection Accuracy  
The evaluation indexes for tamper detection accuracy of semi-fragile watermarking algorithms mainly include the false positive rate \(R_{\text{FP}}\), the false negative rate \(R_{\text{FN}}\), and the true positive rate \(R_{\text{TP}}\). Among them, \(R_{\text{FP}}\) is the probability that the unmodified pixels are judged as tampered pixels; \(R_{\text{FN}}\) refers to the probability that the modified pixels are detected as real pixels; \(R_{\text{TP}}\) denotes the probability that the modified pixels are correctly defined as tampered pixels [9]. The smaller the values of \(R_{\text{FP}}\) and \(R_{\text{FN}}\), the better the detection accuracy will be, but in general, they cannot be arbitrarily small at the same time. Usually, papers will give the probability of one of them, making it under a certain value, and then make the other value as small as possible. \(R_{\text{FP}}\), \(R_{\text{FN}}\), and \(R_{\text{TP}}\) are defined as Equation (3):  
\[R_{\text{FP}}\ =\ \frac{N_{\text{TF}}}{N_{\text{T}}},R_{\text{FN}}\ =\ \frac{N_{\text{FT}}}{N_{\text{F}}},\ R_{\text{TP}}\ =\ \frac{N_{\text{F}}-\ N_{\text{FT}}}{N_{\text{F}}} \tag{3}\]where \(N_{\text{TF}}\) represents the number of real pixels that are judged to be modified pixels, \(N_{\text{T}}\) refers to the number of pixels that have not been tampered, \(N_{\text{FT}}\) denotes the number of modified pixels that are detected to be real pixels, and \(N_{\text{F}}\) indicates the number of modified pixels.
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
###### Abstract  
This paper presents the analytical investigation of a novel system for combined Dew Point Cooling and Water Recovery (DPC-WR system). The operating principle of the presented system is to utilize the dew point cooling phenomenon implemented in two stages in order to obtain both air cooling and water recovery. The system performance is described by different indicators, including the coefficient of performance (COP), gained output ratio (GOR), energy utilization factor (EUF), specific energy consumption (SEC) and specific daily water production (SDWP). The performance indicators are calculated for various climatic zones using a validated analytical model based on the convective heat transfer coefficient. By utilizing the dew point cooling phenomenon, it is possible to minimize the heat and electric energy consumption from external sources, which results in the COP and GOR values being an order of magnitude higher than for other cooling and water recovery technologies. The EUF value of the DPC-WR system ranges from 0.76 to 0.96, with an average of 0.90. The SEC value ranges from 0.5 to 2.0 kWh/m3 and the SDWP value ranges from 100 to 600 L/day/(kg/s). In addition, the DPC-WR system is modular, i.e., it can be multiplied as needed to achieve the required cooling or water recovery capacity.  
dew point cooling; indirect evaporative cooling; water recovery Article
## 1 Introduction
The main contributions of this work are as follows:  
* We propose a new RIS-equipped UAV FSO communication technique in a non-LOS scenario with atmospheric and pointing error loss and derive a closed-form expression with the laser beam incident upon the PD having non-orthogonality status. An ellipse \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Channel** & **Fog Impact** & **No Transmission** & **RIS** & **UAV Trajectory** \\  & **Medium** & **Modelling** & **Delay** & **Optimization** & **Design** \\ \hline
[5] & FSO/RF & ✗ & ✗ & N/A & N/A \\ \hline
[6] & FSO & ✗ & ✗ & N/A & N/A \\ \hline
[7] & RF & N/A & ✗ & N/A & ✗ \\ \hline
[8] & RF & N/A & ✗ & N/A & ✗ \\ \hline
[9] & FSO/RF & ✗ & ✗ & N/A & ✗ \\ \hline
[10] & FSO/RF & ✗ & ✗ & N/A & ✗ \\ \hline
[11] & FSO & ✗ & ✗ & N/A & ✗ \\ \hline
[12] & RF & N/A & ✓ & ✓ & ✗ \\ \hline
[13] & RF & N/A & ✓ & ✓ & ✗ \\ \hline
[16] & FSO & ✗ & ✓ & ✓ & N/A \\ \hline
[17] & FSO & ✗ & ✓ & ✓ & N/A \\ \hline
[18] & FSO & ✗ & ✓ & ✓ & ✗ \\ \hline This paper & FSO & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of some existing works, the ✗ stands for not included, the ✓ stands for included, and the N/A stands for not applicable. beam footprint geometrical model is considered to express the power density on the PD. * Based on the proposed framework, we derived the closed-form expression for physical impacts, a novel leading angle assisted PSO optimization method is proposed to optimize the numerical results of pointing error loss as a precondition. Then, the PPO method is introduced to solve the UAV trajectory optimization to reduce the complicated physical impacts and optimize the average capacity. * Simulation results verify that the leading angle assisted PSO and PPO methods are efficient and indicate that the UAV tends to fly to the area with the maximum value of average capacity and avoids the fog. Furthermore, the proposed RIS-assisted strategy improves the average capacity significantly compared to the conventional decode-and-forward (DF) relay-assisted UAV networks. The remainder of this paper is organized as follows. In Section 2, the system and problem formulation are described, where we clear our aim and model the FSO channel coefficients. After confirming the target of optimization, Section 3 presents the first optimization with the PSO method for optimizing the phase shifts of the RIS, which minimizes the impact of pointing error loss. Then, Section 4 operates the second optimization for the UAV trajectory, based on the PPO learning method. The details of the UAV operation under these two optimizations with analysis and discussion are shown in Section 5. Finally, the conclusion of the work is given in Section 6. _Notations:_ We use lower-case, boldface lower-case letters to represent scalars and vectors, respectively. Furthermore, \(||\cdot||\) is the norm of a vector; \(\text{erf}(\cdot)\) denotes the error function and \(\exp(\cdot)\) denotes the exponential function; \" \(\cdot\) \" and \" \(\times\) \" denote the dot and cross product between vectors, respectively.
## 3 Multiscale Numerical Model
### Sub-Modelling Finite-Element Model  
Owing to its relatively great tensile stiffness, the shortest hangers sustain more force than other hangers, inducing a greater probability of fatigue failure in the joining region of the bridge decks. To assess the actual stress distribution and evaluate the fatigue performance in such a joining region, a refined model with four faces of solid elements is introduced to simulate the mechanical performance of the joining region. To improve computational efficiency, the members of the superstructure were simulated by a sparse finite-element model, where the hanger and arch members were simulated by beam elements and bridge decks were simulated by shell elements. To coordinate the mechanical performance between the sparse and refined elements, a multipoint restriction method was applied to unify the boundary between the two-level models.  
As shown in Figure 3, the two-level finite-element model of the Dashengguan Yangtze River High-speed Railway Bridge was created and established in the commercial FEA software ANSYS 15.0. In the sparse model, Beam 181 was used to simulate truss and arch members, and Shell 11 was used to simulate the bridge deck. In the refined model, Solid 92 was used to simulate the mechanical performance of the joining region between the hangers and tie beams. The average unit size of the refined model was defined as 20 mm, and the average unit size of the sparse mode was defined as 1.0 m. The multipoint restriction method was introduced to realize rigid connections between these models of two levels. The nodes of the truss, arch and deck in the two ends in the longitudinal direction, and the bottom nodes of the arch members, were defined as fixed to simulate the action of auxiliary bridge and bridge piers. As the steel material of the bridge structure is Q345b,the elastic modulus and Poisson's ratio in the numerical model were 206 GPa and 0.31, respectively [17].  
Figure 2: Dashengguan Yangtze River High-speed Railway Bridge; (**a**) side view; (**b**) monitoring section; (**c**) eight traffic cases; (**d**) distance distribution of wheel groups.
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 4 Electric Propulsion
### PMSM Motor Control  
There are three different ways of controlling the PMSM motor, they are:  
* Field-Oriented Control (FOC)  
A PM synchronous motor can utilize control strategies employed by induction motors such as FOC and direct torque control. The FOC has been utilized to the PM synchronous motor for driving EVs, PM field excitation in the PM synchronous motor deviates PMSM from induction motor.  
* Flux-Weakening Control (FWC)  
The terminal voltage equals the rated voltage at the base speed of PMSM. As back EMF grows with the speed, the extension of speed range is possible only when the air-gap flux is reduced, the so-called flux-weakening operation. Thus, the torque decreases while the speed increases, thus operating in constant power.  
The Figure 10 shows the torque-speed capabilities of the PM synchronous motor. It can be found that the higher the _Ld Ir/\(\lambda m\)_ ratio is adopted, the better the flux-weakening capability can be achieved.  
* Position Sensorless Control (PSC)  
This position sensor in the PMSM is for its control system and is usually based on an optical encoder. A PM synchronous motor with a position sensor is seldom adopted for driving EVs.  
Application of PMSM Drive in Electric Vehicles  
PMSM motors have been the preferred choice in EV drive trains due to their higher power density and efficient control system.
## 4 Data Analysis and Discussion
### Descriptive Analysis
The distribution of keywords over time is important in understanding the evolution and trends in the field of neural processing. The latter shows that 21.6% (7761) of the keywords are used during the time period 2007 to 2011, 28.9% (10,390) in the period 2012 to 2015, and 49.5% (17,822) in the period 2016 to 2019. Table 3 provides a list of the top 50 keywords related to ANNs and their respective frequencies based on an analysis of the articles in the dataset. the later shows the rank of each keyword based on the frequency of its occurrence in the articles, along with the actual keyword and the number of times it appears in the dataset. Some of the most frequently occurring keywords are \"artificial neural network\", \"classification\", \"machine learning\", and \"deep learning\". Other highly ranked keywords include \"genetic algorithm\", \"support vector machine\", and \"particle swarm optimization\". Table 3 provides insight into the most commonly used keywords in articles related to ANNs, indicating the current trends in research in this field. The prominence of these keywords suggests that research in ANNs is focused on the development and optimization of neural network architectures, machine learning techniques, and pattern recognition algorithms. Additionally, the high frequency of certain keywords, such as \"classification\" and \"support vector machine\" highlights the significant application of ANNs in classification and prediction tasks. The analysis provided can help researchers identify current areas of interest and potential research directions in the field of ANNs. It is well known that a keyword could not exactly correspond to  
Figure 4: The distribution of research articles among the three time intervals. the name of the technology, but it could be linked to it. The considered results show that \"neural network\" and \"artificial neural network\" are at the head of the list with a higher frequency. Other keyword's frequencies are nearly equal, such as \"deep learning\", \"genetic algorithm\", \"data mining\", and \"swarm intelligence\". Figure 5: The distribution of keywords according to the three time intervals. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Rank** & **Keyword** & **Keywords Count** \\ \hline
1 & Neural network & 678 \\
2 & Artificial neural network & 392 \\
3 & Classification & 222 \\
4 & Machine learning & 177 \\
5 & Deep learning & 169 \\
6 & Genetic algorithm & 163 \\
7 & Support vector machine & 153 \\
8 & Particle swarm optimization & 145 \\
9 & Feature extraction & 128 \\
10 & Optimization & 125 \\
11 & Feature selection & 123 \\
12 & Recurrent Neural Network & 110 \\
13 & Extreme learning machine & 106 \\
14 & Clustering & 103 \\
15 & Dopamine & 101 \\
16 & Face recognition & 98 \\
17 & Pattern recognition & 90 \\
18 & Data mining & 98 \\
19 & [PERSON]’s disease & 87 \\
20 & Schizophrenia & 85 \\
21 & Support vector machines & 80 \\
22 & Synchronization & 78 \\
23 & Depression & 76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top 50 keywords related to ANN topics.
## 2 Materials and Methods  
### Materials  
The material that was studied in this research was taken from a segment of a subsea pipeline consisting of API 5L X60 pipeline steel and a girth weld made from ESAB 70S-6 weldment steel. The weld was intentionally over-matched, meaning its strength is higher than that of the base metal. Although the pipe was not used for offshore gas transportation, the girth weld was made according to industrial standards. A weld macro is shown in Figure 1, which also shows measurements of the wall thickness of 15.4 mm and weld thickness of 6 mm. The thickness of the first and last weld passes were both measured to be under 2.5 mm, meaning that these were not included in the specimen geometry which is discussed later. The weld geometry is assumed to be identical over the entire pipe circumference as the girth weld was made automatically. Start and stop weld positions were excluded from testing.  
Figure 2 shows the microstructure of the base and weld metals (BM and WM). From Figure 2a, it can be seen that the BM consists predominantly of polygonal ferrite with a small fraction of pearlite grains which can be identified as the black areas. The WM consists of large prior austenite grain boundaries that can also be observed in Figure 1, which contain a more refined microstructure that is shown in Figure 2b. Widmanstatten ferrite structures grow from the prior austenite grain boundaries but eventually transition into a fine grained acicular ferrite phase that comprises most of the bulk of the WM. The acicular ferrite phase consists of small elongated particles that form a basket weave pattern. It should be noted that the high density of grain boundaries in the WM compared to the BM will likely change its behaviour in a hydrogen environment. The hardness of the BM and WM are HV1 200 \(\pm\) 3.7 and HV1 248 \(\pm\) 9.9, respectively. The compositions of both steels were measured using X-ray fluorescence (XRF) and LECO combustion analysis and are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Element** & **C** & **Mn** & **Si** & **Cr** & **Nb** & **Al** & **P** & **S** & **Bal** \\ \hline Base Metal & 0.06 & 1.66 & 0.26 & 0.06 & 0.04 & 0.04 & \(<\)0.01 & \(<\)0.01 & 97.88 \\ Weld Metal & 0.07 & 1.45 & 0.58 & 0.05 & 0.01 & 0.01 & 0.01 & 0.01 & 97.81 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of the base metal and weld metal. Bal is Fe.  
Figure 1: Weld macro overview of the girth weld including measurements of the wall thickness, weld thickness and thicknesses of the first and last weld passes.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Localization-Free Routing Schemes  
Localization-free routing schemes do not require the position information of nodes in order to determine routes for data forwarding. Table 2 analyzes these protocols based on a number of parameters explained in Section 1. We can categorize localization-free routing schemes based on whether or not they consider node mobility.
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
## 5 Result Analysis
### SRL to HU  
In this scenario, we examined an inter-continental transmission between SRL in Norway and HU in China (see Table 1). The geographical distance between the two sites is around 8600 km, and the network communication [15; 27] can take paths from Europe westwards via North America to Asia, as well as from Europe eastwards directly to Asia. SRL is connected with consumer-grade ADSL connections from PowerTech and Telenor, the Norwegian research network fiber connection from Uninett, and a business-grade fiber connection from Kvantel. HU is connected via the research network from CERNET, and the consumer-grade fiber connection from China Unicom (CnUnicom). Therefore, according to Equation (3), the total number of subflows is 8, and the subflows are named K-C, K-CU, P-C, P-CU, T-C, T-CU, U-C, and U-CU. For simplification, we label the subflows according to the \"abbreviated ISP name at local site\"-\"abbreviated ISP name at remote site\" (see Table 1; e.g., K-C refers to Kvantel-CERNET). First, we measured the throughput utilizing Equation (4), and then calculated the \(\Omega\) of each subflow using Equation (5). The results of three scenarios are listed in Table 2, for each CC and combination of ISPs. Based on the results from Table 2, the optional subflow subset can be obtained according to Equation (6), as shown in Table 3, and the throughput is re-measured by Equation (7). Taking the CC, PM and BS as input, the prediction model of them with recalculated throughput can be established by multiple regression analysis according to Equation (8) in Section 3. Following the Step 7 in Section 3, we find the minimum BS in a certain range of maximum throughput. By using Equation (9), the relationship between BS and throughput for each CC and PM is obtained, as shown in Equation (13). The calculation results based on Equation (13) are presented in Table 4 (e.g., when PM is FullMesh, for Cubic, \(CC_{1}=1\), \(CC_{2}=CC_{3}=CC_{4}=CC_{5}=CC_{6}=0\), then \(tp=3.49+4.85 bs-0.15 bs^{2}\)). \[tp=\left\{ \begin{array}{ll}3.63+4.17 bs-0.07 CC_{1}+1.69 CC_{2}+1.14 CC_{3}+0.42 CC _{4}-0.07 CC_{5}+0.52 CC_{6}-\\ 0.15 bs^{2}+0.68 bs*CC_{1}+1.09 bs*CC_{2}+0.83 bs*CC_{3}+0.42 bs*CC_{4}+0.68 bs*CC_{5}+ \\ 0.46 bs*CC_{6}-0.07 CC_{1}^{2}+1.69 CC_{2}^{2}+1.14 CC_{3}^{2}+0.42 CC_{4}^{2}-0.07 CC _{5}^{2}+0.52 CC_{6}^{2}\\ 4.89+4.92 bs+0.71 CC_{1}-1.24 CC_{2}+9.8 CC_{3}-0.7 CC_{4}-1.45 CC_{5}-2.21 CC_{6}-\\ 0.17 bs^{2}+0.53 bs*CC_{1}+1.59 bs*CC_{2}+1.24 bs*CC_{3}+0.38 bs*CC_{4}+0.72 bs*CC_{5}+ \\ 0.46 bs*CC_{6}+0.71 CC_{1}^{2}-1.24 CC_{2}^{2}+9.8 CC_{3}^{2}-0.7 CC_{4}^{2}-1.45 CC_{5 }^{2}-2.21 CC_{6}^{2}\\ \end{array} \right\}\quad\text{if }PM=\text{FullMesh} \tag{13}\]