esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Irradiance Synthesizer for Optical GEO Satellite Downlinks in 5G Networks
## 2 ARTEMIS Optical Satellite Program  
The ARTEMIS optical satellite link program was a data-relay mission set up by the European Space Agency (ESA) in 2003 where a series of optical link experiments were carried out between the ARTEMIS GEO satellite and the ESA's optical ground station (OGS) in Tenerife, Spain [13]. The location parameters of ARTEMIS are shown in Table 1 and the technical features are shown in Table 2[13].  
An OGS was installed at a high altitude of 2400 m in order to achieve good atmospheric conditions for the optical channel study and contained two terminals: ESA's 1m diameter telescope [13] and the smaller 0.26 m diameter LUCE optical receiver [14]. LUCE was designed by the Japanese Aerospace Exploration Agency (JAXA) for inter-orbit optical communications and was placed initially in OGS for compatibility tests [14].  
In Figure 1 the setup of the complete optical experiment is exhibited. The downlink ARTEMIS to OGS through the turbulent atmosphere which causes irradiance scintillation is shown. The two telescopes installed allow for different reception while the weather station nearby provides information on the humidity, pressure, temperature and wind speed for the ground level. Two sets of experimental measurements are available in accordance with the two terminals and are used to compare the scintillation effects regarding the different aperture sizes. Afterwards, the downlink ARTEMIS GEO Satellite to the LUCE receiver will be specifically and thoroughly examined due to LUCE's smaller aperture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Longitude** & **Latitude** & **Altitude** & **Elevation Angle** \\ \hline
21.5\({}^{\circ}\) East & 0.0\({}^{\circ}\)\(\pm\) 2.81\({}^{\circ}\) North & 35,787 km & 37\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ARTEMIS location parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wavelength** & **Beam Diameter (1/\(e^{2}\))** & **Transmitted Power** \\ \hline
819 nm & 125 mm & 10 mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: ARTEMIS technical features.  
Figure 1: Setup of the ARTEMIS optical experiments.
###### Abstract  
The target review has been proven to be able to predict the target user's rating of the target item. However, in practice, it is difficult to obtain the target review promptly. In addition, the target review and the rating may sometimes be inconsistent (such as preference reviews and low ratings). There is currently a lack of research on the above issues. Therefore, this paper proposed a Recommendation algorithm that Simulates the generation of target review semantics and fuses the ID Information (RSII). Specifically, based on the characteristics of the target review available during the model training, this paper designed a teacher module and a review semantics learning module. The teacher module learned the semantics of the target review and guided the review semantics learning model to learn these semantics. Then, this study used the fusion module to dynamically fuse the target review semantics and the ID information, enriching the representation of predictive features, thereby, alleviating the problem of inconsistency between the target review and the rating. Finally, the RSII model was extensively tested on three public datasets. The results showed that compared with seven of the latest and most advanced models, the RSII model improved the MSE metric by 8.81% and the MAE metric by 10.29%.  
recommendation system; deep learning; review; representation learning; rate prediction +
Footnote †: journal: Applied Sciences
## 2 Related Works  
Object Detection. Early object detection methods were based on the sliding-window methods, which applied handcrafted features and classifiers on dense image grids to locate objects. However, the traditional handcrafted feature extraction method for object detection has some limitations, such as poor robustness to changing objects, high time complexity, and redundant detection windows. With the arrival of the deep convolutional neural networks, it became possible to solve the problems of traditional handcrafted feature extraction methods and improve the detection speed and accuracy of object detection. The object detection task has quickly become dominated by CNN, which can be divided into two-stage object detection [5; 10; 11] and one-stage object detection [12; 13; 14].  
Cross-domain Object Detection. The concept of domain adaptive object detection has been raised very recently for unconstrained scenes. The purpose of cross-domain object detection is to detect objects in different domains. Research in this direction was first carried out by [PERSON] et al. [1], who proposed a domain-adaptive Faster R-CNN that reduced the difference between image-level and instance-level distributions by embedding adversarial feature adaptation in a two-stage detection pipeline. [PERSON] et al. [15] proposed aligning shallow local perceptual fields with deeper image-level features, i.e., strong local alignment and weak global alignment. This proposal addressed the issue of adaptability from the perspective of domain diversity. The work of [16] utilized the classification consistency of image-level and instance-level predictions with the assistance of a multilabel classification model. The work of [6] proposed a center-aware feature alignment method that enabled the discriminator to focus on features from object regions.  
In contrast, several recent works have attempted to address the cross-domain object detection problem with one-stage detectors [6; 7; 17; 18; 19]. Ref. [6] adapted [20] to explicitly extract objectivity graphs. I3 Net [7] introduced a complementary module that was specifically designed for the single-shot multibox detector (SSD) [14] architecture.  
Google first proposed the Transformer model in 2017 [21]. It resulted in a large performance improvement for various tasks in natural language processing (NLP). Moreover, [22] proposed a Detection TRansformer (DETR) model for object detection that provided a real end-to-end deep learning solution. In [23], the authors proposed a new sequence feature alignment method specifically designed for the adaption of the Transformer detectors. The work of [24] designed three levels of source-target feature alignment strategies based on Transformer to improve the quality of the pseudo labels in the target domain.  
In this paper, we aspire to exploit the advantages of the one-stage detector and the transformer architecture to improve the performance of the cross-domain detection model.
## 3 Results and Discussion
### Thermal Stability of the Regenerated Cellulose Films  
TGA curves for the regenerated cellulose films are shown in Figure 4. Arapid decomposition in a narrow temperature range from 285 to 310 \({}^{\circ}\)C was observed for the original cellulose sample. The regenerated samples exhibited lower onset temperatures for decomposition (from 165 to 175 \({}^{\circ}\)C), but gave higher char yields (nonvolatile carbonaceous material) on pyrolysis, as indicated by the high residual masses after the decomposition step. This result illustrates that many hydrogen bonds of cellulose are destroyed after dissolution in ILs, so the ratio of the lose amorphous structure was increased, causing a reduction in the thermal stability of the regenerated cellulose. The regenerated cellulose contains two types of anhydroglucose with different backbone structures: \(\beta\)-cellulose and \(\gamma\)-cellulose [24; 25; 26]. The onset temperatures of these two structures are lower than that of the \(\alpha\)-cellulose (the original natural cellulose), but the two structures exhibit larger residual masses after thermal treatment; e.g., CQA-5 has nearly four times the residual weight of MCC. This thus indicates that  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cellulose Materials** & **LOI A\({}_{\textbf{1432}}\)/A\({}_{\textbf{998}}\)** & **TCI A\({}_{\textbf{1372}}\)/A\({}_{\textbf{2904}}\)** & **Degree of Polymerization** \\ \hline MCC & 2.525 & 0.118 & 309 \\ CW-5 & 2.448 & 0.093 & 299 \\ CA-5 & 1.815 & 0.085 & 208 \\ CQA-5 & 1.124 & 0.054 & 62 \\ CTA-5 & 0.482 & 0.036 & 146 \\ CNA-2 & 0.275 & 0.020 & 79 \\ CNA-5 & 0.382 & 0.023 & 174 \\ CNA-10 & 0.534 & 0.028 & 219 \\ CNA-12 & 0.859 & 0.055 & 269 \\ \hline \end{tabular}
\end{table}
Table 3: Crystallinity index and degree of polymerization of the cellulose samples.  
the regenerated cellulose materials can be applied in the preparation of porous carbon nanofibers, activated carbon, and biomaterials.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 11 A Deep Learning-Assisted Cognitive Information Communication System
### Traditional Communication System Architecture  
The traditional communication system mainly includes the following five parts: information source, transmitter, channel, receiver, and sink. The information source refers to original data, such as sound signal, picture, audio and video that a user needs to transmit, and corresponding preprocessing is required at the information source. The transmitter mainly completes encoding and modulation of data, such as source encoding, channel encoding, encryption, channel multiplexing and spectrum spreading, to improve the effectiveness, reliability, and security of data transmission. The channel is the physical transmission medium of user data in nature, including wired and wireless channels. The receiver completes a series of decoding processes completed by the user after receiving the data, including despreading, demultiplexing, decryption, channel decoding, source decoding, etc., and restores the transmitted original signals. Sink is the receiving end of signals. Thus, in the whole communication process, the sending end mainly completes encoding and modulation of signals, while the receiving end mainly completes the corresponding decoding and demodulation.
## 3 Signal Processing Methods  
### Gabor Filter  
In our previous works [13], we have developed an efficient filter for filtering the noise signals based on Gabor transform (Gabor filter). The Gabor transform uses a Gaussian sliding window \(\left(e^{-\pi\left(z-\tau\right)^{2}}\right)\) as a weight for the Fourier transform, as expressed in Equation (6); where \(\omega\) and \(\tau\) are the angular frequency and scan distance, respectively. The noise signal and corrosion signal were separated by selecting frequencies for each signal in the coefficients of the Gabor transform. Then, the coefficients of respect to the noise signal were set to zero values, as expressed in Equation (7). The remained coefficients of respect to the corrosion signal were then reconstructed, and the corrosion signal is obtained, as expressed in Equation (8). For applying the Gabor filter, predefined low and high cutoff frequencies (\(\omega_{L}\) and \(\omega_{H}\)) to separate the noise and corrosion signal are required. However, the cutoff frequencies are generally unknown in prior.  
\[GT(\tau,\omega)=\int_{-\infty}^{+\infty}V_{RMS}(x)e^{-\pi\left(z-\tau\right)^{ 2}}e^{-j\omega z}dz \tag{6}\]  
\[GT(\tau,-\omega_{L}<\omega<\omega_{L}\ \&\ \omega<-\omega_{H}\ \&\ \omega>\omega_{H}):=0 \tag{7}\]  
\[V_{RMS}(z)=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}G(\tau,\omega)e^{- j\omega z}d\tau d\omega \tag{8}\]
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
## 3 Main Assumptions
### Latency Constraints  
The technical specification of the Ethernet switch utilized in the flexiHaul system [17] mentions the implementation of the TSN mechanisms defined by standards IEEE 802.1 CM and IEEE 802.1 Qbu. We assume that these TSN mechanisms are applied and they assure the prioritized transmission of Ethernet frames carrying latency-sensitive data, in particular, related to 5G fronthaul traffic. Still, under heavy fronthaul traffic loads, the latencies of higher priority frames may be increased due to the need for their buffering since they compete each other for transmission resources at the switch output link. To account for this effect, we consider that the length of transmission paths between remote sites and the hub is constrained additionally by the latency requirements of latency-sensitive data flows.  
Namely, in the analysis performed in this work, we consider that the one-way latency budget of latency-sensitive flows is equal to 100 \(\upmu\)s (according to Reference [29]). Within this budget, we assume that 50 \(\upmu\)s is reserved, among others, for the store-and-forward operations in the switch (a few \(\upmu\)s, as mentioned in Annex B in Reference [9]), the transmission time of frames (not exceeding a few \(\upmu\)s in a 100 Gbps optical link [30]), and queuing latency (up to some tens of \(\upmu\)s), whereas the remaining 50 \(\upmu\)s is left for signal propagation in the optical fiber. Note that, in the transmission system studied in this work, the buffering of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MUX Loss**} & \multicolumn{4}{c}{**Number of OADMs (N)**} \\ \cline{2-5}  & **0** & **1** & **2** & **3** \\ \hline
1.80 dB & 20.00 km & 13.60 km & 4.40 km & – \\
1.62 dB & 20.00 km & 15.04 km & 6.56 km & – \\
1.44 dB & 20.00 km & 16.48 km & 8.72 km & 0.96 km \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transmission distance \(L\) as a function of MUX loss and number of installed OADMs.  
Figure 2: System configuration with denoted parameters necessary to estimate the transmission distance \(L\).  
frames in the xHau network occurs once for each transmission direction, namely during aggregation of radio data flows into wavelength connections at the remote TSN switch and at the switch located in the hub, respectively, for uplink and downlink direction. Assuming a periodic traffic model [31], in which the radio data frames are transmitted periodically and within a certain transmission window of the size of up to some tens of \(\upmu\)s, the queuing latency does not exceed the transmission window duration in the worst case, in which all HP frames arrive at the same moment, and the link capacity is fully utilized.  
The above assumed 50 \(\upmu\)s propagation delay limit corresponds to the maximum optical path length equal to 10 km. The numerical results presented in Section 5 are obtained considering this limit.
## 3 Results
### FT-IR Analysis of the Oil Samples  
Figure 1 shows the overlapped FT-IR spectra of a randomly selected authentic poppy seed oil sample, some potential adulterants such as canola oil, maize (corn) oil, soybean oil, and sunflower oil, and two adulterated poppy seed oil samples, highlighting the significant resemblance in their spectral characteristics according to the prior assignments [36, 37, 38]. The FT-IR spectra can be divided into three distinct regions: 3045-2800 cm\({}^{-1}\), 1825-1630 cm\({}^{-1}\), and 1510-650 cm\({}^{-1}\). The most prominent absorption bands in the first region include those at 3005 cm\({}^{-1}\), associated with =C-H _cis_ stretching vibration, 2920 cm\({}^{-1}\) associated with -C-H (CH\({}_{2}\)) asymmetric stretching vibration, and 2851 cm\({}^{-1}\) corresponding to -C-H (CH\({}_{2}\)) symmetric stretching vibration, while the minor shoulder at 2950 cm\({}^{-1}\) is associated with -C-H (CH\({}_{3}\)) asymmetric stretching vibration. In the second region, the absorbance band at 1743 cm\({}^{-1}\) is attributed to the -C=O ester stretching vibration of the triglycerides, and 1710 cm\({}^{-1}\) represents the -C=O stretching vibrations. The -C-H- _cis_ stretching vibration leads to the absorbance at 1649 cm\({}^{-1}\). Lastly, the third region exhibits absorbance bands at 1459 cm\({}^{-1}\) attributed to -C-H bending vibration, 1416 cm\({}^{-1}\) corresponds to =C-H _cis_ bending vibration, 1395 cm\({}^{-1}\) represents bending vibration, 1375 cm\({}^{-1}\) is related to -C-H symmetric bending vibration, 1233 and 1157 cm\({}^{-1}\) correspond to -C-O and -CH\({}_{2}\)-stretching and bending vibrations. Furthermore, 1116, 1096, and 1032 cm\({}^{-1}\) attributed to -C-O stretching vibration, 912 cm\({}^{-1}\) associated with -HC=CH- _cis_ bending out of plane vibration, and 720 cm\({}^{-1}\) corresponding to -(CH\({}_{2}\))_n_- and -HC=CH- _cis_ bending vibrations.  
Figure 1: Representative raw mid-infrared absorption spectrum of analyzed oil samples in wavelength range 4000–650 cm\({}^{-1}\) range, obtained using portable FT-IR spectrometer. * arbitrary units.  
Despite the high similarity in the FT-IR spectra of the tested oils, variations arise from distinct triglyceride substitution patterns resulting from the differences in the degree and form of saturation levels of the acyl groups and the length of their fatty acid chains [39]. The strength of the olefinic band at 3005 cm\({}^{-1}\) served as an indicator of the level of polyunsaturated in the oils [36]. In this regard, both canola and sunflower oils exhibited the lowest band intensity compared to the other oils, while authentic poppy seed oil provided the highest band intensity. This difference can be attributed to their elevated levels of polyunsaturated fatty acids, specifically linoleic and linolenic acids.
## 2 Model Description
### Momentum Conservation Equations  
The mass flow rate in a fluid branch was calculated from the balance of fluid forces on a given branch, involving the momentum conservation equation [14]:  
\[\begin{split}&\frac{(m_{ij}\,u_{ij})_{t+\Delta t}-(m_{ij}\,u_{ij})_{ t}}{\Delta t}+\dot{m}_{ij}\left(u_{ij}-u_{u}\right)=\\ &\left(p_{i}-p_{j}\right)A_{ij}+\rho gV_{ij}\cos\theta-\Delta p _{t}+\rho K_{\text{rot}}^{2}\omega^{2}A\end{split} \tag{2}\]  
Figure 2: Example of the schematization of a simple system into nodes and branches with indexing practice.  
where the velocity is determined from the mass flow rate and the branch density, \(u=\frac{\dot{m}}{\dot{p}_{i}\dot{A}_{ij}}\). The fluid's inertia is represented by the two terms on the left side of the equation. The first term is the time-dependent term required for the transient formulation. The second term is relevant only for significant variations in area or density from branch to branch. On the right side of the equation, the forces linked to the pressure gradient in the branch and the effect of gravity, friction, and the centrifugal force (present only when the branch is rotating) are considered, respectively. Regarding the test case used to validate the model, the fluid density and the differences in height are relatively low. Therefore, it is possible to assume that the gravitational force term in the momentum conservation equation can be neglected.
## 4 Overcurrent Protection Coordination Results, Analysis and Discussion
### Protection Coordination Results in Scenario 4
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**The OCRs**} & \multirow{2}{*}{**50P**} & \multirow{2}{*}{**50G**} & \multicolumn{6}{c}{**51P**} & \multicolumn{6}{c}{**51G**} \\ \cline{3-11}  & & & & **GA** & **GSA** & **PSO-GSA** & **GA** & **GSA** & **PSO-GSA** \\ \hline \multirow{4}{*}{A CB GEN OCR} & \multirow{4}{*}{\(I_{f}\)} & \multirow{4}{*}{816 A} & \multirow{4}{*}{\(I_{f}\)} & A & 4.39 & 57.46 & 0.01 & A & 0.23 & 67.95 & 0.01 \\  & & & & B & 2.41 & 2.17 & 2.50 & B & 2.45 & 2.24 & 2.25 \\  & & & & C & 1.18 & 0.59 & 0.05 & C & 0.76 & 0.80 & 0.80 \\ \cline{2-11}  & & & & & TDS & 0.09 & 0.01 & 7.16 & TDS & 0.35 & 0.01 & 0.45 \\ \cline{2-11}  & \multirow{4}{*}{\(t_{trip}^{defined}\)} & \multirow{4}{*}{0.01 s} & \multirow{4}{*}{\(t_{trip}^{defined}\)} & \multirow{4}{*}{0.01 s} & \multirow{4}{*}{0.01 s} & \multirow{4}{*}{\(\frac{\text{TDS}}{\text{The tripping time of the relay for the typical fault at Bus 3:}}\)} \\ \cline{6-11}  & & & & & \(t_{trip}\) & 0.35 s & 0.32 s & 0.35 s & \(t_{trip}\) & 0.35 s & 0.32 s & 0.35 s \\ \hline \multirow{4}{*}{The REC Can-Thanh 163 OCR} & \multirow{4}{*}{\(t_{trip}^{defined}\)} & \multirow{4}{*}{0.01 s} & A & 1.11 & 59.93 & 0.01 & A & 0.01 & 47.43 & 0.01 \\  & & & & B & 2.03 & 2.05 & 2.31 & B & 2.45 & 2.22 & 2.50 \\ \cline{1-1} \cline{2-11}  & & & & C & 0.00 & 0.81 & 0.01 & C & 1.01 & 0.66 & 0.00 \\ \cline{1-1} \cline{2-11}  & \multirow{4}{*}{\(t_{trip}^{defined}\)} & \multirow{4}{*}{0.01 s} & \multirow{4}{*}{\(t_{trip}^{defined}\)} & \multirow{4}{*}{0.01 s} & TDS & 0.01 & 0.01 & 0.69 & TDS & 0.01 & 0.01 & 1.02 \\ \cline{1-1} \cline{2-11}  & & & & & \(t_{trip}\) & 0.01 s & 0.01 s & 0.01 s & \(t_{trip}\) & 0.01 s & 0.01 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal protection coordination results of the OCRs in Scenario 3 and their tripping time when the fault is at Bus 3.  
protection functions of the REC Can-Thanh 163 OCR. By further considering the REC Can-Thanh 163 OCR, the tripping current \(I_{f}\) of its 50G function is set to 2640 A as shown in Table 7. Thus, this tripping current is considered as a terminated point of the inverse-time characteristic curve of the 51G function in the same REC Can-Thanh 163 OCR. Similarly, the tripping current \(I_{f}\) of the 50G function of the REC Can-Thanh Hao-Vo OCR which is 2980 A is considered as a finishing point of the inverse-time characteristic curve of its 51G function, as clearly illustrated by the curve of Relay 2, i.e., the red curves in Figure 13.
## 3 Results and Discussion
### Effects of RPIDLs on Vehicle Speeds at Entrance of Long Downhill Tunnel
#### 3.1.3 Effect on Average Vehicle Acceleration  
The average vehicle acceleration \(\overline{a}\) [m/s\({}^{2}\)] is calculated as:  
\[\overline{a}=\frac{v_{\text{i}+1}^{2}-v_{\text{i}}^{2}}{2s}, \tag{1}\]  
where \(v_{\text{i}}\) and \(v_{\text{i}+1}\) [m/s] are the vehicle speeds at points i and i + 1, respectively, and s [m] is the distance between those points. The average vehicle accelerations in different sections from observation point 1 to point 6 are shown in Figure 13. In Sections 2 and 4, the 120 m RPLDs gave the most obvious deceleration, with the highest average acceleration being \(-0.42\) m/s\({}^{2}\); this was followed by the 60 m RPLDs, with which the vehicle speeds decreased obviously in Sections 3 and 4, and the average accelerations in these two sections were \(-0.34\) and \(-0.36\) m/s\({}^{2}\), respectively. However, the speeds of vehicles controlled by the 30 m RPLDs were relatively unstable, exhibiting alternating deceleration and acceleration from Section 1 to Section 5.  
Figure 12: Average speeds and standard deviations of vehicles near tunnel entrance with and without RPIDLs.  
Figure 13: Average accelerations of vehicles near tunnel entrance with and without RPIDLs.  
There is an obvious positive correlation between the speed and severity of traffic accidents, and the dispersion of speed is another important factor affecting the accident rate [32]. The above analysis shows that without RPIDLs, drivers tend to decelerate slightly at the tunnel entrance because of the changed driving environment upon entering the tunnel; having become accustomed to the environment, the drivers then tend to accelerate. Note that the vehicle speed fluctuates greatly without RPIDLs, which constitutes a traffic safety hazard.  
By contrast, all the RPIDLs affect the behavior of drivers, causing them to decelerate. Comparison shows that the 60 m RPIDLs give rise to the most obvious deceleration effect: the average speed and standard deviation decrease by 7.0% and 11.1%, respectively. Therefore, the 60 m RPIDLs have the best deceleration effect on vehicles near the tunnel entrance. The reason is that the RPIDLs affect the perspective of the road, causing the drivers to underestimate the real distance and then take measures to slow down. The resulting lower and less-dispersed speeds are good for decreasing the accident rate and improving the traffic safety of the tunnel.