esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SEM and FTIR Analysis
In order to confirm the successful deposition of PANI/TiO\({}_{2}\) nanocomposites on the surface of CO and PET fabrics, FTIR analysis was performed in ATR mode on both control and impregnated samples. The results of the FTIR analysis are shown in Figure 3. In comparison with the FTIR spectrum of control CO fabric, a survey FTIR spectrum of the CO-PANI/TiO\({}_{2}\)-1 sample (Figure 3a) revealed a significant decrease in intensity of a broad band at 3300 cm\({}^{-1}\) assigned to stretching vibration of the cellulose OH group [31]. At the same time, the absorption band at 1645 cm\({}^{-1}\) originating from HOH banding vibrations of adsorbed water disappeared [32; 33; 34; 35]. In addition, a decrease in intensity of the band at 2880 cm\({}^{-1}\) related to stretching vibration of the C-H group in the cellulose structure was observed. The detected changes in the FTIR spectrum of the CO-PANI/TiO\({}_{2}\)-1 sample associated with characteristic vibrations of CO could be an indicator of efficient deposition of the PANI/TiO\({}_{2}\) nanocomposite layer. On the other hand, a new band appeared at 1580 cm\({}^{-1}\) in the FTIR spectrum of the CO-PANI/TiO\({}_{2}\)-1 sample, which is assigned to stretching vibration of the quinonoid (Q) ring in the PANI emeraldine salt form. Stretching vibration of the Benzenoid (B) ring in the PANI emeraldine salt form and in-plane bending vibration of the C-H group of cellulose [31; 36] contributed to the formation of a broad peak in the range between 1490 and 1420 cm\({}^{-1}\). A peak positioned at 810 cm\({}^{-1}\) corresponds to the out-of-plane deformation vibration of the aromatic C-H group in the 1_A_-disubstituted benzene ring, \(\gamma\)(C-H), in linear N-C4 coupled PANI chains [37; 38; 39; 40; 41]  
The results of FTIR analysis of the control PET and PET-PANI/TiO\({}_{2}\)-1 samples are presented in Figure 3b. The observed changes in the FTIR spectrum of the PET-PANI/TiO\({}_{2}\) sample compared to the spectrum of the PET sample also indicated the successful deposition of PANI/TiO\({}_{2}\) nanocomposite on the PET fiber surface. In particular, a new band appearing at \(\sim\)1169 cm\({}^{-1}\) in the FTIR spectrum of PET-PANI/TiO\({}_{2}\)-1 sample could be assigned to shifted stretching vibration of the -NH\({}^{+}\)= group in the B-NH\({}^{+}\)=Q segment in the bipolaron form of PANI emeraldine salt, while a low-intensity band positioned at 1039 cm\({}^{-1}\) corresponds to vibrations of the hydrogen sulfate counter-ion [36; 40]. Broadening of the strong peak at 1234 cm\({}^{-1}\) associated with stretching vibration of the ester C=O group of PET in the FTIR spectrum of PET, i.e., the appearance of a shoulder in the long wavelength region (\(\sim\)1281 cm\({}^{-1}\)), is due to the contribution of stretching vibration of the C-N\({}^{+\bullet}\) group in the polaron form of PANI emeraldine salt.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Moderate-Power Thrusters
#### 4.2.3 Pulsed Inductive Thrusters  
Pulsed inductive thrusters in the 1-20 kW range have been developed over the past few decades, although the pace of progress has lagged behind other technologies, placing PITs fairly low on their s-curve in Figure 8. In particular, planar and conical theta-pinch (CTP) pulsed inductive thrusters have been studied extensively starting with the groundbreaking work by [PERSON] and [PERSON] [91; 92] in the early 1980s. This research has culminated in the development and testing of the PIT MkV [93], which can be operated with 8 kJ pulses, and the low-energy Faraday Accelerator with Radio-frequency Assisted Discharge (FARAD) [94], demonstrating 50% thrust efficiency. Most recently, there has also been the operation of several CTPs and inductive pulsed plasma thrusters (IPPTs) and the characterization of the efficiency and impulse bit of some of these devices [95]. During this time, there have also been many numerical and theoretical investigations of these devices.  
Figure 11: A laboratory model of the NASA Evolutionary Xenon Thruster (NEXT) gridded ion thruster (**left**), tested at the University of Michigan [84]. The T6 ion thruster used on the BepiColombo mission (**right**); reproduced with permission of QinetiQ.  
Likewise, there have been significant strides in the development of field-reversed configuration devices, which inductively generate and magnetically eject isolated plasmoids in a pulsed fashion. Originating in the fusion community [96], this technology is now applied on smaller scales for propulsion purposes. Although the production and acceleration of plasmoids with these devices has been confirmed [97; 98; 99], the understanding of their practical performance and numerical simulation remain limited [100; 101]. Figure 12 shows a related example of an experimental rotating magnetic field thruster currently under test at the University of Michigan.  
Perhaps the largest challenge in the development of these thrusters is that they are expected to perform well at high power, perhaps far in excess of 20 kW, and so the construction and testing of prototype PITs requires significant investment in niche power electronics [102] and testing facilities. As a result, the research to date at almost exclusively \(<\)20 kW has made it difficult to fully explore the theoretical operation of these devices and assess their practicality.
# Seismic Vulnerability Assessment of Historical Masonry Buildings in Croatian Coastal Area
## 2 Seismic Vulnerability Assessment of the Test Site
### Collection of the Geometrical, Material, and Structural Data  
The seismic assessment of buildings in the test area requires the knowledge of their geometrical, material, and structural characteristics. The methodology for data collection was organized as follows:  
* Investigation of the buildings using historical documentation [40] and archival documentation of the town of Kastela;
* Detailed survey of geometrical characteristics, architectural measurements, and creation of architectural drawings (floor plans and cross sections);
* Identification of structural systems and materials through visual inspection, using archive documentation, literature, and thermographic imaging in the several specific cases where, due to non-documented reconstructions, it was not possible to recognize the material and structural characteristics of the buildings;
* Characterization of the soil type by means of a geophysical survey.  
Additional assistance came from high-resolution geodetic maps of the test site with precise plan dimensions, from Google Maps with Street View options, and also a map of the area made in 1968 that allowed the identification of reconstructions.  
Investigations of archival documentation and visual inspection were used to detect the main structural features crucial to the seismic vulnerability assessment, such as the type and configuration of the structural system, the texture and quality of masonry walls based on the distribution of blocks and mortar joints, as well as their thickness, the mortar quality, the type of floors, and the floor-wall connections. Furthermore, other important aspects that were investigated were the resistance along two main horizontal directions based on estimates of the maximum resistant shear of the structure, the position and foundations of the building, its horizontal and vertical configurations, the maximum distance among the walls, the typology and weight of the roof, the presence of non-structural elements, and the state of conservation. The mechanical properties of the materials (stone walls, mortar) were taken from the literature [41; 42]. A valid seismic regulation in the past was also used to identify the material properties in the case of reconstructions.
## 1 Introduction
### Proposed Model
#### 2.2.1 Embedding Layer  
Embedding layers are extensively utilized in natural language processing (NLP), where each word is represented by an array of floating point values [29; 30]. These layers are trainable, enabling the network to learn the representation of each word. In this study, character-level embeddings are employed. Consequently, each character of a domain name, after being converted to its ASCII code, is processed by the embedding layer, where it is represented by a vector of 60 values. This feature vector, constructed by the embedding layer, is subsequently passed to the CNN component of the proposed model.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 2 Methodology
### Inclusion and Exclusion Criteria  
The search results were imported into the literature management software Endnote. Following automated de-duplication, the remaining documents underwent manual filtering to select the definitive literature for review. Therefore, a set of inclusion and exclusion criteria based on the research questions was used to perform literature screening, mainly by reading titles and abstracts. The full text was read when the title and abstract could not be clearly determined.  
Peer-reviewed articles on the following topics that had full texts written in English were included:  
1. Articles that discussed real-time object detection tasks or algorithms that are applied to UAV remote sensing;
2. Articles that specifically mentioned onboard real-time processing and used optic sensors. The exclusion criteria are shown as follows in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Terms** \\ \hline Platform & UAV, UAS, RPAS, drone, unmanned aerial vehicle, remotely piloted aircraft \\ \hline Task & real-time detection, real-time identification, real-time monitoring, real-time segmentation, real-time classification \\ \hline Processing method & cloud computing, edge computing, edge intelligence, embedded \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of search terms based on three aspects.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Database** & **Search string** \\ \hline Web of Science & ((TS=(UAV OR UAS OR RPAS OR drone OR “unmanned aerial vehicle” OR “remotely piloted aircraft”)) AND TS=(real-time detect* OR real-time identify* OR real-time segment* OR real-time classité* OR real-time monitor*)) AND TS=(cloud-computing OR edge-computing OR edge-intelligen* OR embedded) \\ \hline Scopus & TITLE-ABS-KEY(UAV OR UAS OR RPAS OR drone OR “unmanned aerial vehicle” OR “remotely piloted aircraft”) AND TITLE-ABS-KEY(real-time detect* OR real-time identify* OR real-time segment* OR real-time monitor*) AND TITLE-ABS-KEY(cloud-computing OR edge-computing OR edge-intelligen* OR embedded) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The search strings for two databases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ID** & **Criterion** \\ \hline EC1 & Papers in which the full text was unavailable \\ EC2 & Papers that were not in English \\ EC3 & Review paper/conference series/book chapter \\ EC4 & Papers that did not relate to the proposed study \\ EC5 & Papers that did not conduct real-time detection \\ EC6 & Papers that did not aim for UAV RS detection \\ EC7 & Papers that only conducted simulations on a high-performance computer \\ EC8 & Papers that only provided a general description without specific methods on real-time detection based on UAVs \\ \hline \hline \end{tabular}
\end{table}
Table 3: The table of exclusion criteria during literature selection.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 3 Methods
### Formulation of the Calibration Procedure
Equation (13) tells us that, to achieve an accurate spatial alignment between 3D real-world points and computer generated elements rendered by an OST display and observed by a specific viewpoint (p\({}_{0}\)), each virtual 3D vertex \(\mathbf{v}^{\text{W}}\) must be observed by an off-axis rendering camera whose projection matrix \({}^{\text{E}}\mathbf{P}\) is modeled as follows:  
* The extrinsic parameters of the camera are: \(\left[ \begin{smallmatrix}\xi_{\text{R}}&\xi_{\text{W}}\\ \end{smallmatrix} \right]\). Unsurprisingly, the center of projection of the virtual rendering camera is the user's eye (in our procedure, the viewpoint camera), whereas its orientation matches the display virtual screen's orientation. * The intrinsic parameters of the off-axis camera are obtained by applying a homographic transformation to the ideal on-axis intrinsic matrix: \[\begin{smallmatrix}\text{off-E}\text{H}_{\text{p}}=\text{H}_{\text{p}}= \begin{bmatrix} \frac{d^{\text{C}-\pi}}{d^{\text{E}-\pi}}&0&\frac{x_{\text{C}}^{\text{E}}}{d^{ \text{E}-\pi}}\\ 0&\frac{d^{\text{C}-\pi}}{d^{\text{E}-\pi}}&\frac{y_{\text{C}}^{\text{E}}}{d^{ \text{E}-\pi}}\\ 0&0&1\\ \end{bmatrix} \] (14)  
This homography allows computing the intrinsic matrix of the real off-axis eye-display system, and it encapsulates the shift and scaling effect (i.e., the parallax contribution) due to a particular position of the user's eye.
## 2 Literature Review
### Action Definitions  
As shown in Table 1, the actions taken by an agent were as follows: (1) to choose a vehicle phase to display green with a specific duration; (2) to choose the green time for current vehicle phase; (3) to determine whether or not to end current vehicle phase; and (4) to adjust the green time for all vehicle phases in next cycle. The time intervals between two successive actions were usually not constant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Option** & **Literature Count** & **Literature Number** \\ \hline \hline \multirow{8}{*}{Action taken by an agent} & To choose a vehicle phase to display green with a specific duration & 18 & [9; 11; 12; 13; 15; 21; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 36; 37; 39; 40] \\ \cline{2-4}  & To choose the green time & To choose the green time & \multirow{2}{*}{4} & [10; 17; 23; 41] \\  & for current vehicle phase & & & \\ \cline{2-4}  & To determine whether or not & & & \\ \cline{2-4}  & to end current vehicle phase & & & \\ \cline{2-4}  & To adjust the green time for & & & \\ \cline{2-4}  & all vehicle phases in next cycle & & & \\ \hline \multirow{8}{*}{Vehicle-specific performance measure used to construct rewards} & Number of already served vehicles & 14 & [12; 13; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 33; 34; 38; 39] \\ \cline{2-4}  & Wait time of already served vehicles & 2 & [35; 38] \\ \cline{2-4}  & Number of vehicles to be served & 23 & [7; 8; 11; 13; 15; 17; 18; 20; 21; 24; 25; 27; 28; 29; 31; 32; 34; 36; 37; 39; 41] \\ \cline{2-4}  & Wait time of vehicles to be served & 12 & [8; 10; 14; 15; 16; 17; 22; 26; 30; 38; 40] \\ \cline{2-4}  & Speed of vehicles to be served & 2 & [19; 29] \\ \cline{2-4}  & Point-based mode & 3 & [10; 15; 25] \\ \cline{2-4}  & Grid-based mode & 8 & [7; 15; 25; 26; 28; 29; 30; 38] \\ \cline{2-4}  & Block-based mode & 27 & [8; 9; 11; 14; 16; 17; 24; 27; 31; 32; 33; 34; 35; 36; 37; 38; 41] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature groups with regard to RL elements.  
In fact, option (1) was unacceptable to traffic engineers, because it led to variable phase sequence and gave road users an uncomfortable perception of signal control. Option (4) adjusted signal timing too infrequently and could hardly meet the short-term fluctuations in traffic demand.
## 2 Theoretical Method Introduction
### Convolutional Neural Network (CNN)  
The Convolutional Neural Network is a typical deep neural network. It reduces the complexity of the network model through the weight sharing method and improves the efficiency of model training. The advantage of this network is that the network model can be trained with input images or signals, eliminating the need for feature extraction. The Convolutional Neural Network consists of an input layer, convolution layer, pooling layer, fully connected layer, and output layer. The input layer realizes the input of the original dataset, the feature is obtained through convolution calculation of the convolution layer, and the pooling layer performs down-sampling operation on the feature image to reduce the data dimension. The number of convolutional layers and down-sampling layers is often determined according to actual conditions. The fully connected layer, just as the neural network, mainly implements data mapping [43; 44].  
The idea of the convolutional layer is to use convolution kernels to extract local features, which is similar to an automatic feature extraction, which has been widely used in feature extraction, fault diagnosis, and so on. For time series vibration signals, the one dimension Deep Convolutional Neural Network with a Wide First-Layer Kernel (WDCNN) has strong processing capabilities, which can eliminate complex feature extraction and directly perform operations on the sequence. Its main feature is that it has the first layer of a large convolution core and a multi-layer small convolution kernel with the size \(3\times 1\). It can provide a faster processing speed and it is suitable for real-time fault diagnosis. In the proposed method, the one-dimensional Deep Convolutional Neural Network with a Wide First-Layer Kernel is used for vibration signals' feature extraction, and it is used as the coding part of the new generator.  
The detailed settings of the proposed network structure are shown in Table 1. Vibration signals are used in the input layer. Different kernel sizes, numbers, and strides have been selected to perform the one-dimension convolution operation with an appropriate number. For Conv 1, the kernel size, number, and stride are 32, 20, and 8, respectively. The BN and ReLU activation function are adopted after the Conv layer, and the pooling layers are used to perform the pooling operation. The dropout operation is used after FC1 and FC2, and four different classifications can be recognized.  
Figure 2: Structure of DCGAN.
## 3 Results and Discussion
### Heat Transfer Coefficient  
Heat transfer coefficient is considered to be a quantitative characteristic of convective heat transfer between a fluid medium (water) and the surface (wall) flowed over by the fluid [28]. It is defined as:  
\[h=\frac{qA_{h}}{A_{c}(T_{i,av}-T_{f,av})} \tag{4}\]  
in which, \(q\) is the heat flux, \(A_{h}\) is the area of heater surface, \(A_{c}\) is contact area of fluid and solid, \(T_{i,av}\) is average temperature of the interface of fluid and solid, \(T_{f,av}\) is average temperature of fluid and \(D_{h}\) is hydraulic diameter which is defined as:  
\[D_{h}=\frac{4A_{s}}{P} \tag{5}\]  
where \(A_{s}\) and \(P\) are cross-section area of channel and wetted perimeter of channel, respectively.  
Results show that the new offset serpentine design brings 65-72% growth in heat transfer coefficient (Figure 12).  
These enhancements are caused by the combinative effects of a longer coolant passage in the heatsink associated with back-and-forth route, generation of disturbance and recirculation areas along the curvy fins, creation of centrifugal forces in the U-turn (Section 3.1), and periodic breaking up boundary layers.  
Figure 11: Maximum chip temperature (\(T_{max}\)) in different volumetric flow rates for conventional offset strip fin design and new offset design.  
The advantage of having U-turns becomes apparent when studying the local heat transfer coefficient of the new offset design shown in Figure 13. Heat transfer coefficient enhancement is much larger in the region of U-turns; and the highest heat transfer coefficient is happening in the second U-turn. Also it can be seen, the line of the local heat transfer coefficients have non-linear distribution. It should be noted that the local heat transfer coefficient is calculated using constant heat flux under the heat sink.
###### Abstract  
Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.  
data fusion; underwater wireless sensor network; back propagation neural network; clustering routing protocol; genetic algorithm; network lifespan Article  
Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms.  
[EMAIL_ADDRESS] (X.X.); [EMAIL_ADDRESS] (W.W.)  
2
## 4 Centralized and Distributed Algorithms
### Description of HAS\({}^{4}\)  
Since the path planning problem is well studied as the traditional travelling salesman problem (TSP) in previous works [28], in this paper, we focus on a sink sensor selection problem. In HAS\({}^{4}\), we define \(S_{C}\) to represent the sensor set that needs to be confirmed as to whether it will be visited by AUV. At first, all sensors are in \(S_{C}\) and then HAS\({}^{4}\) determines which sensor falls into \(S_{A}\) and which sensor falls into \(S_{U}\). During this process, HAS\({}^{4}\) greedily chooses sensors with less energy to \(S_{A}\). This is because acoustic transmission consumes more energy than acoustic reception and MI communication. By selecting these sensors to \(S_{A}\), in this round of data gathering, these sensors could spend less energy to balance the network energy consumption and prolong the network lifetime.  
For the objective of minimizing the AUV cost, the mobile AUV should visit as few sensors as possible. For the purpose of minimizing the network energy consumption, the network should reduce acoustic communications, which means that AUV should visit as many sensors as it can. It is easy to observe that these two objectives are opposite to each other. In HAS\({}^{4}\), we define a variable to make a trade-off between these objectives. We provide both centralized and distributed version of HAS\({}^{4}\). Centralized HAS\({}^{4}\) can leverage all sensors' information about remainder energy, data sensing rate, channel capacity, etc. to make a sub-optimal solution for the problem in Section 3.2. Although the surface station or AUV could be the centralized decision maker, with the long propagation delay of acoustic communications and slow speed of AUV, centralized HAS\({}^{4}\) may not scalable for large underwater networks. Distributed HAS\({}^{4}\) utilizes the information from sensor-self and its one-hop neighbours to decide which set this sensor belongs to. Thus, it is very practical with both small and large size networks.