esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 2 General Principles of the Two-Phase Volumetric Expanders
### Pressure-Volume Diagram of Two-Phase Volumetric Expanders
The main factor affecting the configuration of the pressure-volume diagram concerns the way of the admission and discharge of the working fluid in the machine. In particular, ports in the stator drive the inlet and discharge processes in rotary machines. Conversely, valves drive these processes in piston machines. As a result, the correct operation of the valves in piston machines requires the dead volume (identified as V\({}_{0}\) in Figure 1), while the latter is not mandatory in rotary machines due to the presence of the ports. Another factor affecting the configuration of the pressure-volume diagram is the timing of the suction and discharge processes. In rotary machines, the timing of the ports does not allow for the cut-off and recompression grades to be adapted the working conditions. Conversely, in pistons machines, these two grades can be adapted to the working conditions thanks to the timing of the valves [13; 14; 15]. Figure 1: Pressure–volume diagram of a piston expander. Finally, the configuration of the pressure-volume diagram also depends on the matching between the built-in volume ratio and specific fluid volumetric ratio. In particular, when these quantities match, the expansion is isentropic, i.e., the expansion losses are absent. Conversely, a mismatch causes under- or over-expansion losses. The first ones occur when the specific fluid volumetric ratio is higher than the built-in volume ratio, while the others appear in the opposite situation. In particular, the pressure of the working fluid (p\({}_{\text{discharge}}\) in Figure 2) at the end of the expansion process is higher (under-expansion process) or lower (over-expansion process) than the pressure in the exhaust port (p\({}_{\text{ exhaust}}\) in Figure 2). Some working fluid has to leave (under-expansion process) or enter (over-expansion process) the machine so that the pressure of the working fluid in the discharging chambers is equal to the pressure in the exhaust port. The related irreversibility implies the reduction in the isentropic effectiveness of the volumetric expander [10].
## 3 Results and Discussion
### Electromagnetic Field on Different European Roads
#### 3.1.5 Italy  
In Italy, electric field measurements were carried out in the car cabin during driving on urban and main roads as well as motorways (A1, A13, A14, A34, and A57), as shown in Figures 12-16.  
Figure 11: Hungary: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figure 12: Italy: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figure 14: Italy: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{xy}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
Figure 13: Italy: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
close to that reported for shopping malls in [20], being lower than the values measured in road sections between points P2 IT-P3 IT, P4 IT-P5 IT, and P5 IT-P1 Sl.  
From Figures 13, 15 and 16 and Table 3, it can be seen that when the road sections cover the ring roads of large cities such as Bologne, Padoue, etc., the measured maximum value of the electric field is high. The highest value for road sections P2 IT-P3 IT, P4 IT-P5 IT, and P5 IT-P1 Sl is 6.1 V/m, 7.2 V/m, and 9.4 V/m, respectively. From the results, we also see that the highest value for the road section P3 IT-P4 IT (see Figure 14) is 1.76 V/m. We suppose that obtained results are due to the fact that there are no EMF sources near the road, unlike the rest of the road sections, which cover ring roads of large cities and are near base station antennas of telecommunication operators.  
The median of \(E_{total}\) is between 0.23 V/m and 0.27 V/m. In almost all road sections, the lowest \(E_{total}\) value is 0.11 V/m. The mean value in different road sections varies between 0.25 V/m and 0.32 V/m.
## 3 Results  
### Thermal Behavior  
In order to study the effects of methylation at the macroscopic level, the thermal behavior was analyzed using TGA and DSC measurements. The resulting thermograms and heat flux curves are presented in Figure 2. The diagrams on the left display the protonated ILs and those on the right show the behavior of the methylated counterparts. Qualitatively, they appear similar, but the temperatures where phase transitions and thermal decomposition take place vary significantly. The derived numbers are summarized in Table 2. It can be seen that generally, methylation results in an increased melting point. The protonated iodide and tetrafluoroborate ILs are liquid at room temperature, while their methylated counterparts are solid. In the hexafluorophosphate case, both ILs are solid at room temperature, but again, methylation elevates the melting point by 50 K.  
Interestingly, methylation at the C(2) position also has an impact on the thermal decomposition. For all three anions, an increased decomposition temperature is observed. This means that the methylation has an impact not only on the thermophysical properties and molecular arrangements, which has previously been shown for other anions [4], but also on the molecular stability. In the following, we make an attempt to understand the molecular phenomena and mechanisms leading to the observed thermal behavior by analyzing the vibrational spectra at room temperature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ionic Liquid** & **Molecular Weight (g/mol)** & **Physical State @25 \({}^{\circ}\)C** & **T\({}_{\mathbf{m}}\) (\({}^{\circ}\)C) 1** & **T\({}_{\mathbf{d}}\) (\({}^{\circ}\)C) 2** \\ \hline \([\)MPrIm\({}^{+}][\)I\({}^{-}]\) & 252.1 & Liquid & – 3 & 340 \\ \([\)MMPrIm\({}^{+}][\)I\({}^{-}]\) & 266.12 & Solid & 82 & 364 \\ \([\)MPrIm\({}^{+}][\)BF\({}_{4}\)\({}^{-}]\) & 212.11 & Liquid & – 3 & 338 \\ \([\)MMPrIm\({}^{+}][\)BF\({}_{4}\)\({}^{-}]\) & 226.02 & Solid & 62 & 345 \\ \([\)MPrIm\({}^{+}][\)PP\({}_{6}\)\({}^{-}]\) & 270.16 & Solid & 31 & 486 \\ \([\)MMPrIm\({}^{+}][\)PP\({}_{6}\)\({}^{-}]\) & 284.18 & Solid & 81 & 499 \\ \hline \hline \end{tabular}  
* Melting point (onset of the endothermic peak); 2 Decomposition temperature (from thermal gravimetric analysis (TGA) decomposition peak); 3 Not detected in temperature range under study.  
\end{table}
Table 2: Thermal properties of C2-methylated and C2-protonated imidazolium-based ionic liquids.
## 3 System Model and Problem Description
### Communication Model  
For complex communication environments with obstacles, we use the Rayleigh fading channel model to characterize the wireless channel characteristics between UAVs and user devices, while considering the large-scale path loss determined by the propagation distance and the small-scale signal fading caused by multipath effects (including reflection, refraction, and scattering phenomena caused by obstacles). The channel gain between user devices and UAVs is expressed as  
\[h_{u}(t)=\sqrt{\beta_{u(t)}}g_{u}(t)=\sqrt{\beta_{0}d_{u(t)}{}^{-\alpha}}g_{u}( t)=\sqrt{\beta_{0}\,1\,w_{u}-c_{(t)}\,^{1-\alpha}}g_{u}(t) \tag{8}\]  
where \(\beta_{u(t)}\) is the large-scale average channel power gain, \(\beta_{0}\) is the reference channel gain when the distance between the sender and receiver is 1 m, and \(\alpha\) is the path loss index. The small-scale fading coefficient \(g_{u}(t)\) is represented as follows:  
\[g_{u}(t)=\sqrt{\frac{k_{u}(t)}{k_{u}(t)+1}g}+\sqrt{\frac{1}{k_{u}(t)+1}}\widetilde {g} \tag{9}\]  
where \(g\) is the line-of-sight component and \(\,|\,g\,|=1\), \(\widetilde{g}\) is a Gaussian random variable, and \(k_{u}(t)\) is the Rayleigh factor:  
\[k_{u}(t)=K_{1}e^{K_{2}\theta_{u}(t)} \tag{10}\]  
In (10), \(K_{1}\) and \(K_{2}\) are environmental coefficients, and \(\theta_{u}(t)\) are the elevation angles between the user equipment \(u\) and the UAV in time slot \(t\), expressed as  
\[\theta_{u}(t)=arcsin[(h(t)-z_{u})/d_{u}(t)] \tag{11}\]  
As can be seen from the above, the achievable rate between user equipment \(u\) and the UAV in time slot \(t\) can be expressed as  
\[R_{u}(t)=B_{u}(t)\text{log}_{2}\Bigg{[}1+\frac{|h_{u}(t)\,|\,^{2}p_{u}(t)}{ \sigma^{2}\Gamma}\Bigg{]} \tag{12}\]  
where \(B_{u}(t)\) and \(p_{u}(t)\), respectively, represent the bandwidth between the \(u\)-th user device and the UAV in time slot \(t\), as well as the transmission power when the user device offloads tasks to the UAV. \(\sigma^{2}\) is the noise power, and \(\Gamma>1\) represents the signal-to-noise ratio (SNR) difference between the theoretical Gaussian signal and the actual modulation and coding scheme.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
###### Abstract
* Alternative nodes are selected to transmit when failed nodes or links occurs, without using routing. * Reduction of computation in the node thanks to the elimination of routing tasks. * Automatic assignation of addresses to nodes using link level processes. * Reduction of the energy consumption during the end-to-end reliability transmission process. * Improvement of the scalability with a large number of sensor nodes in LWSN, allowing for the insertion of new nodes automatically. * Maximization of the entire system's lifetime due to the energy efficiency in each node. The paper is organized as follows: Section 2 discusses the related research; Section 3 presents the proposed algorithm; Section 4 analyzes the energy and the transmission time; and Section 5 describes a real implementation. We conclude our paper in Section 6.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 5 Learning with Errors (LWE)
### Certain Instances of the Variant
#### 5.4.1 The Sum of Two Entries  
In the first version of the LWE cryptographic protocol, the secret vector \(s\) is an element of \(Z_{q}^{n}\). So, the main idea was to choose a secret vector \(s\in Z_{q}^{n\times 2}\), i.e., to add an additional column in the matrix \(S\) and choose a linear transformation \(f\) that maps the elements of \(f:\mathbb{Z}_{p}^{2}\) to \(\mathbb{Z}_{p}\) without interfering with the remaining structure of the system.  
For \(l=2\), we choose the map \(f:\mathbb{Z}_{p}^{2}\rightarrow\mathbb{Z}_{p}\) defined by \(d(c_{i1},c_{i2})=c_{i1}+c_{i2}\) for each pair \((c_{i1},c_{i2})\), \(i=1,\ldots,m\) of each row of \(C\), and the result is vector \(d=(d_{1},\ldots d_{m})\), where \(d_{i}=c_{i1}+c_{i2},i=1,\ldots m\). Graphically, the sum of two vectors is the vector representing the diagonal of the parallelogram starting from the intersection of the tails.  
In \(Z_{q}^{2}\), Algorithm 2 is executed.  
```
1:\(q,n,m,l,\chi\leftarrow\ params(\lambda)\)
2:\(S\leftarrow\mathbb{Z}_{q}^{n\times 2}\)
3:for\(i\in 1\to m\)do
4:\(x_{i}\leftarrow\chi\)
5:\(a_{i}\leftarrow\mathbb{Z}_{q}^{n}\)
6:\(c_{i}\leftarrow\mathbb{Z}_{q}^{m\times 2}\)
7:\(d_{i}\gets c_{i1}+c_{i2}\in\mathbb{Z}_{q}^{m}\)
8:\(p_{i}\gets d_{i}+x_{i}\)
9:endfor
10:return\((pk=a_{i},p_{i})\)\(\{pk\) stands for public key\}
```  
**Algorithm 2**KeyGen(1): Key Generation 1  
Discrete Implementation: Let the chosen parameters for this particular implementation be \(n=4\), \(m=3\) and \(q=13\).  
* The private key is chosen uniformly at random and is such that \[S=\begin{pmatrix}2&3\\ 5&0\\ 0&4\\ 6&2\end{pmatrix}\]
* It is selected \[A=\begin{pmatrix}1&8&6&2\\ 0&5&1&3\\ 7&1&6&3\end{pmatrix}\]Compute the product \(C=AS\), such that \[C=\begin{pmatrix}1&8&6&2\\ 0&5&1&3\\ 7&1&6&3\end{pmatrix}\begin{pmatrix}2&3\\ 5&0\\ 0&4\\ 6&2\end{pmatrix}=\begin{pmatrix}2&5\\ 4&10\\ 11&12\end{pmatrix}\]
* With the aid of the linear map \(f\in\mathbb{Z}_{q}^{2}\to\in\mathbb{Z}_{q}\), \(f:(x,y)=x+y\) and the matrix transformation \[F=\begin{pmatrix}1\\ 1\end{pmatrix}\] compute the matrix product \(D=CF\), such that \[D=\begin{pmatrix}7\\ 1\\ 10\end{pmatrix}\]
* Select matrix \[E\in\mathbb{Z}_{q}^{3}=\begin{pmatrix}1\\ 0\\ -1\end{pmatrix}\] The public key is \[(A,P=D+E)=\left(\begin{pmatrix}1&8&6&2\\ 0&5&1&3\\ 7&1&6&3\end{pmatrix},\begin{pmatrix}8\\ 1\\ 9\end{pmatrix}\right).\]
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 3 Path Planning Design
### Search Path for UAV
#### 3.1.1 Snake Curve Path  
According to the distribution of cells in the space region, the region can be divided into odd rows and even rows. When an even number of rows appears, the UAV enters the nearest cell from the starting position \(S\). It moves the shape of a square wave signal to the leftmost cell of the target area. When the UAV reaches the position opposite the starting point \(S\), it moves the shape of a square wave signal to the side close to the starting point \(S\), and, finally, returns to the starting point \(S\) from the cell to the right of the cell closest to the starting point.  
In the case of an odd number of rows, the UAV enters the leftmost cell near the starting point \(S\) in the target area from the starting position \(S\) and moves to the side of the cell closest to the starting point \(S\). When the UAV reaches the position opposite the starting point, it will move to the side close to the starting point \(S\) in the shape of the square wave signal. Finally, the start point \(S\) is returned from the cell to the right of the target region and close to the start point \(S\), as shown in Figure 5. The pseudo-code for generating the snake curve path is in Algorithm 1.
# Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
## 2 Related Work
### Video and Sentence Embedding  
Video and sentence embedding is to learn a joint embedding space of visual and textual features. The embedding is essential for video retrieval from query sentences.  
In image retrieval, we need to learn a connection between query texts and target images, which focus on building a joint visual-semantic space [11]. Recent works in image-to-text retrieval embed image and sentence into a joint embedding trained with ranking loss. A penalty is applied when an incorrect sentence is ranked higher than the correct sentence [20; 21; 22; 23; 24]. Another popular loss function is triplet ranking [1; 25; 26; 27]. The VSE++model improves the triplet ranking loss by focusing on the hardest negative samples (the most similar yet incorrect sample in a mini-batch) [28].  
Like image-text retrieval approaches, most video-to-text retrieval methods learn a joint embedding space [29; 30; 31]. The method [3] incorporates web images searched with a sentence into an embedding process to take into account fine-grained visual concepts. However, the method treats video frames as a set of unrelated images and averages them out to get a final video embedding vector. Thus, it may lead to inefficiency in learning an embedding space since temporal information of the video is lost.  
[PERSON] et al. tackle this issue [32] by learning two different embedding spaces to consider temporal and appearance. Furthermore, they extract audio features from the video for learning space. This approach achieved accurate performance for the sentence-to-video retrieval task. However, this approach puts equal importance on both embedding spaces. Practically, equal importance does not work well. There are cases that one embedding space is more important than the others in capturing semantic similarity between video and sentence. Therefore, we propose a novel mechanism emphasizing a space so that we can know the critical visual cues.  
The existing works suffer from extracting visual dynamics in a video, such as object movements, actions, scene changes, etc. [3; 4; 5; 6]. The main obstacle is due to the limited number of embedding spaces, i.e., only a single space. An embedding space aligns visual and textual features globally. Thus, it is hard to align textual and local visual features in videos, such as temporal information. Therefore, we propose to use multiple embedding spaces. Specifically, we align global and local features in separate embedding spaces. Additionally, we measure the similarity between video and sentences in each space and merge the similarities using dynamical weights. Consequently, we can align video and sentence globally and locally.
# Multi-Hazard WebGIS Platform for Coastal Regions
## 2 Materials and Methods  
### Multi-Hazard Framework for Flood and Erosion Risk in Coastal Zones  
#### 2.1.1 Methodology Description  
The risk framework within the MOSAIC platform aims at predicting coastal flooding to support the emergency actions, accounting for all the relevant hazards and considering the specificities of each exposed territory [18]. This methodology addresses inundation and overtopping of dunes and coastal defense structures associated with energetic events that lead to extreme water levels and beach erosion.  
The framework is anchored in the concept of observatories, adapted here to integrate both monitoring data, from in-situ and remote sources, and numerical models that address all relevant physical processes at stake. Unlike previous applications of the concept of observatories in the coastal analysis [15], the MOSAIC framework integrates both hydrodynamics and morphodynamic monitoring and predictions as the evolution of the topo-bathymetry during energetic events is fundamental for an accurate evaluation of flooding and overtopping in coastal regions.  
The proposed methodology also integrates a downscaling approach both for model forcings and characterization of flooding impacts associated with the diversity of the exposed territory. The former is described in detail in the next section. Flooding impacts on coastal zones depend heavily on the territory occupation and its morphological characteristics. Taking the Portuguese coast as a demonstrator, a detailed analysis of past events was done at the regional level to provide the information for the classification of the coast in distinct coastal typologies [19]. Based on this regional-scale analysis, each coastal observatory in MOSAIC was defined and classified, which in turn defined both the monitoring and modeling approaches to be implemented for the risk assessment. The application of this framework is then materialized for the end-users through the WebGIS multi-hazard platform presented herein. This platform provides the relevant information, organized from regional to local observatory scales, allowing the user to examine regional analysis outputs or to drill down to the level of individual sensor data or the model prediction maps at a specific time step and for any location.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Read Access Time  
Read access time (RAT) is crucial in SRAM design as it directly affects the speed at which data can be retrieved from memory. A faster RAT leads to improved overall system performance, particularly in high-speed applications where timely data access is essential. RAT is measured as the time interval from when the word line voltage reaches 50% of VDD to when a 50 mV voltage difference is established between BL and BLB. This occurs during the read operation due to the voltage drop on the bit line through the access transistor and the pull-down transistor. Therefore, an SRAM cell with a high CR generally has a lower RAT. However, the absolute number of access transistors does not necessarily reduce the RAT. As the number of access transistors connected to the bit line increases, parasitic capacitance also increases, which can lead to a higher RAT [15]. Thus, although the RHSCC-16T uses three pairs of access transistors, it has a longer RAT due to increased parasitic capacitance. The proposed SRAM uses only two access transistors but achieves the best RAT among the compared cells due to an optimal CR (node Q = 1.5) and a moderate number of access transistors. These results demonstrate the efficiency of the proposed design, particularly in applications where fast read times are critical. The RAT values of all comparison cells are shown in Figure 8.  
Figure 7: Comparison results of the read static noise margin of the proposed RHRSE-20T with previous works.
# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 4 Results and Discussion
### Computational Complexity and Memory Usage  
As already discussed in Section 3.1, ODNMI is a one-dimensional version of NMI, and requires exactly one half of the computational operations required by NMI. This is because it uses only one-dimensional interpolation, either in the horizontal direction or the vertical direction. Thus, it is quite intuitive that the proposed scheme has reduced computational complexity compared to various two-dimensional state-of-the-art interpolation schemes, such as NMI, INP, PRM, OPR, and PTB. Further, we present the time taken by the proposed algorithm for data embedding in Table 3. It is evident from the table that the proposed scheme takes an average time of 0.4459 s for the embedding phase.  
Memory usage is another parameter of huge importance when discussing the transmission of data over channels with limited bandwidth. Memory usage of the proposed algorithm is reduced by 50%, as can be seen in Table 4, which presents data pertaining  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Image** & **Embedding time (s)** \\ \hline Image: 1 & 0.4804 \\ Image: 2 & 0.4316 \\ Image: 3 & 0.4336 \\ Image: 7 & 0.4472 \\ Image: 8 & 0.4550 \\ Image: 9 & 0.4277 \\ Average & 0.4459 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Embedding time of the proposed scheme (in seconds).  
Figure 6: Subjective analysis for reversibility.  
to the memory requirements of a conventional NMI technique and the novel ODNMI technique. The transmission of a 512 \(\times\) 512 image requires a memory of 1024 KB, using conventional NMI, while it requires only 512 KB using the proposed ODNMI technique, making the proposed technique a superior candidate in terms of both memory usage and time consumption.