esa-sceva/satcom-chunk-collection huggingface dataset:
# Neighbor Session Solutions for Integrated Routing Protocols
## 2 Dual-Stack IP Routing Protocols
### Enhanced Interior Gateway Routing Protocol  
The Enhanced Interior Gateway Routing Protocol (EIGRP) is an internal distance vector routing protocol with advanced features. EIGRP shares with its predecessor Interior Gateway Routing Protocol (IGRP) the maximum number of hops, the use of a composite metric, and the support of various network protocols, including IPv4 and IPv6. EIGRP, like OSPFv3, creates adjacencies for each protocol separately when both network protocols are operating, but the IPv4 protocol is used as transport for IPv4 routing information, and IPv6 packets are created for IPv6 routing information. EIGRP was originally a proprietary protocol. However, in 2013, the decision was made to release the basic EIGRP specification, which was later published as RFC 7868 in 2016, allowing the creation of various open-source implementations of this protocol [37; 40].  
Compared with other distance vector routing protocols, EIGRP is designed to operate in large-scale networks, supports the exchange of routing information with a variable netmask and the authentication of forwarded messages, and excels in very fast convergence. Unlike the Routing Information Protocol (RIP), it sends differential updates to neighbors rather than periodic entire routing tables, which is possible by forming adjacencies with surrounding routers and using its own Reliable Transport Protocol (RTP), which supports both reliable unicast and multicast message delivery. Therefore, EIGRP is incorrectly referred to as a hybrid routing protocol in some works in the literature, as this behavior strongly resembles link state routing protocols. A unique feature that no other routing protocol offers is the guarantee of loop-free operation, provided that it is configured correctly [36; 40; 41; 42; 43].  
As mentioned in the introduction, the EIGRP is important to this work because of its unique properties, as well as the fact that it was chosen as a model for the creation of a third-class protocol integration solution model. Further details about the EIGRP protocol design, analysis of the published RFC, and testing of the current EIGRP implementations can be found in our previous papers [33; 34].
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Storage and Transport
#### 2.2.9. Bags, Boxes, and Smaller Containers for Independent Transport  
Commercial solutions to transport temperature sensitive goods, including also food, are common today. To keep the temperature in the required temperature range, good insulation is always the first approach. Active cooling of small boxes can be done by a Peltier element if electricity is available, e.g., in a car. For independent, passive cooling, ice or other PCM can be added to the temperature sensitive goods to stabilize their temperature to the temperature required. The temperature stabilizing effect of the PCM lasts typically from a few hours to several days. In situations where a heat source or a cooling machine cannot be used, for example in the cargo bay of a plane, using PCM or dry ice etc. is the only option. The most common example is an insulated bag or box, used together with ice packs, e.g., for a picnic. Commonly, the PCM is macroencapsulated, e.g., water in the ice packs, and charged (cooled) externally.  
Figure 10 shows some examples. The bag shown (left) can for example be used to transport ice cream. Similar commercial solutions exist for the transport including also storage of breast milk [64]. Another, a coffee mug with PCM, is to get hot coffee to a suitable drinking temperature quickly and then keep it there for a long time [65]. Robust boxes are also available, for any kind of use (Figure 10, center; details, including performance, are available at [66]), or for specific use like storage and transport of hot food  
for catering (e.g., [67]). Small containers for plane transport are commercially available with temperature stabilization by PCM (Figure 10, right), electrically driven heating or compression cooling, or cooling by dry ice, but typically not used for food.  
The decision to choose a specific solution depends largely on the value of a product, if handling of small amounts of the product is economic and feasible, and the means of transport. As mentioned above, storing fish in ice boxes, typically used on small boats, would cause excessive handling at large catch volumes on large boats, such that on large boats fish caught far away from the shore is stored in tanks until delivery for onshore processing. The same applies also to other food products, not just fish. Transport of temperature sensitive goods in smaller units with own means for temperature stabilization is only one of several options. No matter if a product is packaged in smaller amounts or not, transport of large amounts on a truck, train, or ship can be done in large compartments fixed on these, or by independent containers of a standardized size, which can be transported and easily switch between them.
## 2 Materials and Methods
### Statistical Analysis  
Data were analysed using SPSS statistical software (IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA). The normality of the data distribution was checked by using the Shapiro-Wilk test. The associations between variables were tested with the Spearman correlation coefficient and interpreted as follows: 0 to 0.20--negligible; 0.21 to 0.40--weak; 0.41 to 0.60--moderate; 0.61 to 0.80--strong; 0.81 to 1.00--very strong ([PERSON], 2018). Statistical significance was set at \(p\leq 0.05\).
# Encoding Text Information with Graph Convolutional Networks for Personality Recognition
## 2 Related Work  
In previous studies, the most widely used personality representation method was self-reporting, which meant that data were collected from users who filled out standardized questionnaires [17]. Therefore, this method is affected by user subjectivity. At present, the method of automatic personality recognition based on big data analysis has become the main research point. Social networks are the main source of people's opinions and thoughts, and analyzing them can provide valuable insights for personality recognition.  
Traditionally, the identification of users' personality based on social information has been studied as a text classification problem [21; 22; 23; 24], focusing on personality traits or textual indicators that indicate the personality of the user. Generally, features are extracted from text, like linguistic inquiry and word count (LIWC) [25], Mairesse [26], and the Medical Research Council (MRC) [27], which are then fed into standard machine learning classifiers such as decision tree (DT), support vector machine (SVM), and naive Bayes. On the other hand, learning word embeddings and representing them as vectors (using GloVe or word2 vec) is also a common method [28].  
The work in [29] introduced personality recognition tasks from multiple computational perspectives by comparing the use of written papers and speech corpora as input data. Contrary to the use of psycholinguistic-driven features in [29] and other literature, the work in [30] used the naive Bayesian model and SVM model to classify personality traits using n-gram models. Accuracy assessment based on individual blog collections could reach up to 65%. The work in [31] used charand POSn-gram models, and the work in [29] used TF-IDF counting and style features. However, to our knowledge, there is little ongoing research based on the correlation between the five personality traits in texts. In other words, the research on text personality analysis of social network users is inaccurate when the correlation between the five personality traits of users is ignored.  
Recently, a new research direction called graph neural network or graph embeddings has attracted widespread attention [32]. Graph neural networks have effectively completed tasks with rich relational structures and can retain the global structure information of graphs in graph embeddings [33]. We used a recently proposed graph embedding framework, graph convolutional networks (GCN) [34], to capture these relationships. Therefore, this paper proposes a personality GCN representation model for user personality recognition.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 4 Results
### Results
#### 4.2.1 Homogeneous Media  
Soft biological tissue is made up mostly of water and therefore can be viewed approximately as a homogeneous medium. The sound speed in water is 1480 m/s, which can be taken as the speed in tissue. The specific value of the constant is irrelevant to the performance of the algorithms, as the value can always be adjusted by choosing different units. What matters is that the speed must be uniform everywhere. Therefore, we simply take the speed as 1 in our numerical test, i.e., we set \(c(r)=1\). Ultrasound signals are generated using the forward model (1). The signals are then exploited in TR, ATR, Landweber iteration, and DL algorithms, respectively, to reconstruct the original image. The input of the neural network is the output of the first iteration of ATR, which is denoted as ATR\({}^{\star}\).  
Based on an independent testing set of 200 CT image patches, we investigated these reconstruction algorithms with 0%, 10%, 20%, 30%, 40% noise added to the ultrasound signals respectively, and reported the box plots of the image quality that was evaluated by PSNR and SSIM in reference to the ground-truth simulated initial source in Figure 3. The experimental results show the DL algorithm has at least two advantages over the others. On the one hand, it is relatively more stable as the noise level increases. Its PSNR outperforms the others at all noise levels, with the only exception of the ideal zero-noise case where ATR provides the best reconstruction. On the other hand, the DL algorithm is time-saving. Although training the neural network takes large amount of time, its output is almost immediate once the training process is accomplished. In contrast, the ATR and Landweber iteration takes several minutes to achieve an image of satisfactory quality.  
A few reconstructed images randomly selected from the testing set are displayed in Figure 4. Each column corresponds to the reconstruction using the algorithm labeled at the bottom. The ATR\({}^{\star}\) column consists of images from the first iteration of ATR, which are the inputs to the neural network. The IS column consists of the ground-truth initial source images used to generate the ultrasound signals by the forward model. Among all the algorithms, TR gives the worst result, which can be expected as it does not resolve reflected artifacts. ATR tends to lose detailed information at a high noise level (see the last row). Landweber performs better in resolving noise as it has regularization effect, which helps remove the high-frequency content of the image. The DL algorithm exhibits superior reconstruction in general.  
Figure 3: The box plots of the image quality evaluated by PSNR and SSIM on the testing set in a homogeneous medium. From top row to bottom row: noise increases from 0 to 0.4. Diamonds (\(\diamond\)) indicate outliers.
## 6 Experiment and Analysis  
### Experimental Setting  
In the simulation experiment, the topology of the ground network uses the real NTT global network topology from the Topology Zoo [26]. There are 47 hosts in the network which links North America, Asia, Europe, and Australia, but there are 15 isolated hosts in the Middle East, Africa, and some small islands.  
Referring to the network topology in the experiment of [PERSON] et al. [27], we simulated a satellite network composed of 58 satellites whose physical properties are shown in Table 2. Firstly, there is an orbit in the top layer with an angle of 0\({}^{\circ}\) from the equator, there are 3 GEO satellites in total. In addition, the mesosphere has 2 orbits with an angle of 45\({}^{\circ}\) from the equator, each with 5 MEO satellites. Furthermore, there are 5 orbits in the lowest layer, and the angle between the orbits and the equator is 90\({}^{\circ}\); there are 9 satellites in each orbit, and 3 LEO satellites are randomly selected to be introduced into the MEC. The communication delay between satellites is shown in Table 3.  
UAVs are characterized by versatility and high mobility and can provide communication services by installing a communication transceiver as an aerial communication platform [28; 29]. On the other hand, UAVs can also be used as aerial hosts to realize various applications from cargo delivery to surveillance [30; 31]. In this experiment, UAVs are used to assist ground networks and satellite networks in achieving full coverage of the network [32].  
The resource situation of the servers is shown in Table 4. A host in the ground network is selected as the cloud server. Compared with other edge hosts, it has more computing and bandwidth resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Satellites**} & \multirow{2}{*}{**Running Period (\%/h)**} & \multicolumn{2}{c}{**Orbital Inclination**} & \\  & & **(\({}^{\circ}\))** & \\ \hline GEO & 15 & \(1\times 3\) & 0 \\ MEO & 60 & \(2\times 5\) & 45 \\ LEO & 210 & \(5\times 9\) & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Running period, number and orbital inclination of different satellites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GEO** & **MEO** & **LEO** & **SGs** \\ \hline GEO & - & 86 ms & - & - \\ MEO & 86 ms & 66 ms & 50 ms & - \\ LEO & - & 50 ms & - & 3 ms \\ SGs & - & - & 3 ms & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Satellite-to-satellite and satellite-to-ground latency.
## 3 Results  
### Annual Sky Status  
Figure 2 depicts the annual hours across all 43 sites in Greece for the duration of clear skies (DCS), for intermediate skies (DIS), and for overcast ones (DOS). It is seen that the hour ranges for the three types of skies are [802 h, 1809 h], [1353 h, 1972 h], and [792 h, 1538 h] for DCS, DIS, and DOS, with average values of 1363.33 \(\pm\) 219.54 h, 1647.27 \(\pm\) 136.49 h, and 1083.76 \(\pm\) 185.59 h, respectively. These results show that the intermediate-sky conditions over Greece prevail all over the year, followed by the clear-sky ones and then by the overcast situations. As the intermediate skies correspond to days with broken or fast-moving or low-level or cirrus clouds, it is implied that most of the time the weather conditions over the country favour this dynamic situation on an annual basis. Another interesting observation is the spread of the sky-condition hours as expressed by their standard deviations; the highest spread is for clear skies (219.54 h), followed by that for overcast (185.59 h), and then for intermediate (136.49 h). This observation dictates the conclusion of a wide-spread uniformity of dynamic weather conditions (e.g., fast-moving fronts) over Greece resulting in a sky status between clear and overcast. As far as the high standard deviation for the clear conditions is concerned, this may be due to the inhomogeneity of weather over Greece at the same time (e.g., overcast conditions in northern Greece, clear skies in the south). Figure 3 is rather repetitive of Figure 2; in this case, the abscissa has been replaced with the geographical latitude of the sites in order to investigate its influence on sky conditions, as implied by the discrepancy in the standard deviations discussed above. Both linear and non-linear fits to the data points have been drawn in order to show their accuracy. Table 1 gives the expression of the fits and some statistics; all fits are at a 95% confidence level. The following observations can be induced from the Figures. Figure 2: \(Cont.\)  
Figure 3: _Cont._  
Figure 2: Variation in the total annual hours for (**a**) clear (DCS), (**b**) intermediate (DIS), and (**c**) overcast (DOS) skies across the 43 sites in Greece (see the solid red lines). The green dashed lines represent the averages over all sites. Figure 3: Variation in the total annual hours for (**a**) clear (DCS), (**b**) intermediate (DIS), and (**c**) overcast (DOS) skies across Greece as function of the geographical latitude of the sites (see the red dots). The green dashed lines represent the best non-linear fits to the sky status hours, and the blue dashed ones are linear fits. The expressions of the fits are given in Table 1.
# Compressive Sensing-Based IoT Applications: A Review
## 5 Challenges and Research Trends
### Structured Sensing Matrix  
A crucial task in CS is to select the appropriate sensing matrix, which has a crucial impact on the reconstruction performance. Moreover, selecting inappropriate sensing matrix would lead to additional system complexity and may provide unacceptable reconstruction quality.  
In CS, the sensing matrix is usually designed using a dense random matrix with entries drawn from an independent identical sub-Gaussian distribution [149; 150]. Gaussian processes are widely used as sensing matrices in CS; they offer a low coherence with most of the natural basis used in signal processing [148]. Moreover, they satisfy the RIP condition with high probability [151]. However, they suffer from several limitations in term of implementation on hardware due to their dense nature. Consequently, they can be implemented using a non-chip random seed to generate all the entries or they can be generated offline and stored in a large memory [152]. In addition, they slow down the measurement process of CS because it has to deal with all non zero entries. Thus, the random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware.  
Therefore, using structured sensing matrices would improve both the sensing and the reconstruction processes. At the sensing level, deploying sparse Bernoulli matrices with entries \(\{0,1\}\) can reduce the number of multiplication operations by replacing the inner product by just an accumulation process [153], which can be seen as an NUS approach.  
Furthermore, structured sensing matrices would accelerate the reconstruction time and minimize the onboard computation on the receiver side. Therefore, incorporating such techniques aligns perfectly with the vision of providing efficient CS-based IoT platform.  
Using Bernoulli matrices with entries \(\{1,-1\}\)[71] can be seen as good fit for FPGA reconstruction, where the multiplication process can be replaced by data accumulation only. Several works have also focused on structured sensing matrices such as Toeplitz, circular, block diagonal and permuted block diagonal (BPBD) [154] and deterministic binary bloc-diagonal (DBBD) [152]. These structured matrices have been shown to be hardware friendly by discarding the need to implement on-chip random seed for random matrices [152].
# Static Analysis of Composite Plates with Periodic Curvatures in Material Using Navier Method
## 3 Results and Discussion
### Limitations  
This study focuses on laminated rectangular plates with small-amplitude, one-dimensional periodic curvature (\(\Lambda<<h\), \(\varepsilon\leq 0.009\)) under simply-supported boundary conditions. The analysis assumes linear elasticity and small deflections, neglecting geometric nonlinearity. Moreover, the formulation is based on the classical Kirchhoff-Love plate theory, where transverse shear deformations are not considered.
# High-Speed, High-Performance DQPSK Optical Links with Reduced Complexity VDEE Equalizers
## 3 Proposed Partially-Joint Single-Ended DQPSK Equalization
In the undesired situation when \(n(t)\) is much weaker than the remaining signals, the numerical behavior of the algorithm utilized for the estimation of the equalizer parameters (also known as the linear system solver) will deteriorate, resulting in severe ill-conditioning. A remedy to this problem is to resort to the use of proper regularization, such as the diagonal loading method, requiring extra effort for the handling of this overhead. On the contrary, the proposed partially-joint VDFE and its derivatives,  
Figure 5: The differences in FF part structure for an equalizer of \(M_{f}=4\) between (**a**) VDFE and (**b**) SVDFE. do not suffer from such an effect, as three out of four electrical signals \(I_{c}(t)\), \(I_{d}(t)\), \(Q_{c}(t)\), and \(Q_{d}(t)\), are engaged only. Simulation results indicated that the calculated condition number of the matrices involved into the estimation of the parameters of the equalizers (linear system of equations), vary from \(10^{6}\) up to \(10^{10}\) in the case of fully-joint constructive/destructive equalization. This figure is reduced to \(10^{3}\) in the case of partially-joint equalization.
## 4 Results and Discussion
### Relationship between Bearing Estimation Error and Horizontal Peak Displacement
The relationship between the \(x\)- or \(y\)-direction peak displacements and 1/3 octave  
Figure 6: The bearing estimation and peak displacements for each frequency band at site S2: (**a**) the bearing estimation, (**b**) \(x\)-direction peak displacements and (**c**) \(y\)-direction peak displacements. frequencies is shown in Figure 7b,c. The threshold value is \(x\) = 5 \(\times\) 10\({}^{-7}\) m in Figure 7b and \(y\) = 1 \(\times\) 10\({}^{-6}\) m in Figure 7c. As shown in Figure 7, when the frequency is in the range of 400 Hz to 1000 Hz, the bearing estimation errors significantly increase and are almost greater than 20\({}^{\circ}\), except at 1000 Hz. Similarly, in the same frequency band, the \(x\)- or \(y\)-direction peak displacements are small and lower than the threshold. In the case of small peak displacement, the bearing estimation error at 1000 Hz is still small and remains to be studied further. Figure 7: The bearing estimation and peak displacements for each frequency band at site S3: (**a**) the bearing estimation, (**b**) \(x\)-direction peak displacements and (**c**) \(y\)–direction peak displacements. By comparing the experimental results of the peak displacement and bearing estimation error in different frequency bands at the three sites, it can be found that the bearing estimation error is related to the \(x\)- or \(y\)-direction peak displacement. This occurs partly because in the bearing estimation method mentioned in Section 3, the maximum radial power in Equation (1) is closely related to the horizontal displacement in the \(x\) and \(y\)-directions of the particle motion. The other reason is that the low values of the \(x\)- or \(y\)-direction peak displacements mean the maximum radial power is small, which increases the bearing estimation errors in this frequency band. For this reason, if the horizontal peak displacements of the received signal in some frequency bands are too low, the reliability of the bearing estimations for the corresponding frequency band will be reduced. Through these analyses, it can be determined that the peak displacement, error and the size of the corresponding frequency band are closely related. In this way, we can use these basic relationships and further experiments to analyze the causes of bearing errors at three different sites.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Design of Action Space  
\(a_{t}\) is the control action made on the congestion window at time \(t\). The paper defines \(a_{t}\) as increasing the congestion window size \(cwnd\) by \(n\) segment lengths \(s^{\prime}\), as shown in Equation (15).  
\[cwnd=cwnd+ns^{\prime} \tag{15}\]  
Based on the current QoS parameter information of the V2X network, the growth rate \(n\) of \(cwnd\) is determined. When network congestion occurs, set \(n\leq 0\), maintain or reduce the congestion window size, reduce the amount of data injected into the network, and reduce the pressure of network congestion. When in a high-bandwidth environment, set \(n>1\) so that the congestion window size grows at an exponential rate. When in a low-bandwidth environment, set \(n=1\), so that the congestion window size grows linearly.