esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Pupil Shift Correction Method for Large Ground-Based Optical Telescopes
###### Abstract  
Cassegrain telescopes with larger apertures suffer significant optical pupil shifting errors caused by gravity and temperature gradients. This study constructs an optical model of a large Cassegrain telescope and a method to calculate and compensate its shifting error. First, the optical structure is simplified by incorporating only the most relevant telescope components, and the system optics is modeled using ray tracing. A computer-aided mounting-based method for calculating the misalignment error of primary and secondary mirrors is proposed, with the spatial position change of secondary mirrors as the input. Next, a compensation method based on the coma-free point theory of the Cassegrain system is proposed, with the main mirror's optical axis as the reference. Finally, using a 4 m aperture telescope as an example, the gravity- and thermal-deformation-induced shifting error is simulated. Based on this simulation, the secondary mirror position is adjusted using a secondary mirror-adjustment mechanism Hexapod platform to compensate for the misalignment error. Both gravity and temperature are major causes of shift in the pupil position, with a maximum shift of 18 mm. After compensation, this shift is controlled within 1 mm. The modeling method and the simulation process mentioned in this research can also be used in the other relevant fields.  
ground-based telescope; misalignment error; coma free compensation; optical pupil shift +
Footnote †: journal: Applied Sciences  
Article
## 5 Test Results and Interpretation
### Axial Loads on Stays  
The axial load of each stay cable during the proof load test was obtained by multiplying the axial load of the instrumented strand by the number of strands, hence assuming the load is equally distributed among each strand of the stay.  
The controlled axial loads refer to the bridge in C1 and C2 load configurations and are reported in Figure 10 in terms of the axial load increase/decrease (\(\Delta\)N) for each stay due to the load application with respect to the unloaded condition (U\({}_{0}\)). Negative and positive values in Figure 10 indicate decreases or increases, respectively, of the axial loads with respect to the actions due to the bridge self-weight in the unloaded condition U\({}_{0}\).  
As can be observed, during C1 the maximum axial load increment was reached by stays on the downstream side and, in particular, in those that were placed nearby the mid-span (with the greater value just over 900 kN) and in the two stays at the lateral sides, anchored on the abutments (with values around 1200 kN). Also during C2, the axial load increment distribution on strands was consistent with the load position on the deck; indeed, in this case the maximum axial load increment was reached by stays that were placed in the north lateral span, with maximum values on the downstream side of about 600 kN.  
Figure 10 also shows the comparison between the experimentally measured cable axial load increments/decrements with values that were obtained from the FE model. It is worth remarking that the FE model provides a very good estimation of the measured \(\Delta\)N.  
Moreover, from the measured values of the absolute forces, and the numerical applications, it was evident that all the stays remained in the elastic field. Indeed, similar values of axial loads were registered for phases U\({}_{0}\) (unloaded bridge before C1) and U\({}_{1}\) (unloaded bridge after C1), namely before and after the load test, as shown in Figure 11. Thus, it can be concluded that plastic deformations of some stays did not occur since they would have triggered a redistribution of the overall axial forces in the stays.  
Figure 10: Increase/decrease of the stay cable axial loads for C1 and C2 with respect to the U\({}_{0}\) configuration, and comparison with the numerical predictions.
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
## 5 Conclusions  
In this paper, we proposed a new method which exploited ACB scheme and SUCRe protocol to resolve the collision efficiently in crowded massive MIMO network cell. In our work, we also proposed a dynamic ACB scheme that allows BS to choose the optimal ACB factor in the each time slot. Through simulation results, it is shown that the DACB-SUCR method can enhance the access success rate and reduce pilot collisions efficiently and the DACB-SUCR method outperforms the conventional SUCRe protocol. Moreover, compared to the traditional ACB scheme and traffic-aware  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Success Access Rate (\%)**} \\  & \(M=5\) & \(M=100\) \\ \hline SUCRe & 10.13 & 14.93 \\ SUCRe with fixed ACB factor & 13.35 & 20.27 \\ SUCRe with traffic-aware ACB factor & 30.67 & 42.07 \\ DACB-SUCR & 32.49 & 43.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison among methods (\(N=10{,}000\)).  
Figure 5: Access success rate for varying number of antennas.  
ACB scheme, the proposed dynamic ACB scheme improves the access success rate of both conventional RA procedure and SUCRe protocol.  
Conceptualization, Z.D. and W.-J.H.; Data curation, T.-H.V.; Formal analysis, T.-H.V.; Funding acquisition, W.-J.H.; Investigation, T.-H.V. and W.-J.H.; Methodology, T.-H.V., Z.D., Q.-V.P. and W.-J.H.; Project administration,W.-J.H.; Software, T.-H.V.; Supervision, Z.D. and W.-J.H.; Validation, all the authors; Visualization, T.-H.V.; Writing--original draft, T.-H.V.; Writing--review and editing, all the authors.  
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2019-2016-0-00318) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.
Indeed, recent studies have successfully used machine-learning methods and satellite remote-sensing time series analysis for spatio-temporal monitoring of the water coverage and quality of lakes and aquaculture ponds [32; 33; 34; 35; 36]. However, the implementation of such techniques on small temporary ponds has been hampered by the limited extent and dynamism of these ponds due to short water detention. To ensure the effective management of temporary wetlands (both at the individual and landscape scale), spatial inventories and ecological assessments of the status of these habitats are essential, as well as information about their adjacent terrestrial matrices [37; 38; 39]. From this perspective, the application of unmanned-aerial-vehicles (UAVs)-based remote-sensing techniques [40] in studies carried out in several coastal Mediterranean regions [41] successfully helps to document both the occurrence and lifetime of temporary ponds [22]. In this context, the main aims of the present study are to (i) georeference all temporary wetlands within the Tyrrhenian central Italy coastal area; (ii) relate ponds to their proximity to stressors, land use, and lithology; (iii) calculate their water surface size variability; and (iv) evaluate their hydroperiod (i.e., water residence time, expressed in the number of days per year). The main purpose of this study is to know and describe the status of temporary ponds in central Italy, to highlight some of their hydrodynamic properties, and to provide a baseline for future comparisons.
# Polarization Sensitive Imaging with Qubits
## 2 Materials and Methods
### Experimental Setup  
The experimental setup is shown in Figure 1. A continuous wavelength, single longitudinal mode laser diode with a center wavelength at 405 nm is focused into periodically poled KTiOPO\({}_{4}\) (ppKTP), type II crystal [20,21]. Entangled photons generated via SPDC are centered at 810 nm. The crystal is placed in a temperature-controlled chamber that maintains the crystal temperature to enable quasi-phase matching and to maximize the down-conversion efficiency. Entangled photon pairs have orthogonal polarization and are emitted in a collinear configuration with the pump [20,22]. A dichroic mirror (DM) transmits SPDC photons and reflects the pump photons. No additional filtering, by fiber or otherwise, is introduced into the experimental setup to sieve for the single spatial mode. At the first polarizing beam splitter (PBS), the down-converted entangled photon pairs are separated by polarization into the two arms of a Mach Zehnder interferometer.  
The p-polarized photons are directed into an idler arm, where they are transmitted through a polarizing beam splitter (PBS), followed by a broadband quarter wave plate (QWP). The QWP converts the polarization of the photons from linear to circular. These photons are then reflected from a mirror (M3), back through the QWP, changing the photon polarization back from circular to linear, s-polarization. Polarized photons are reflected from the PBS through a QWP and an HWP that set the projection state operator of the idler beam towards the last PBS. The last PBS transmits p-polarized photons towards the left single photon counting module (SPCM) and reflects s-polarized photons towards the right SPCM module. The photons reflected by the first PBS are the sample arm photons and are directed towards a non-polarizing beam splitter (NPBS) through a broadband half wave plate (HWP), that converts vertically polarized photons into horizontally polarized. The NPBS has a 50:50 reflection/transmission ratio and transmits half of the entangled photons towards a mirror (M4). The sample is placed on a microscope slide against the mirror (M4) for transverse plane scanning (x and y directions). The QWP converts the polarization of the photons into circularly polarized photons, followed by a 20 mm focusing lens. Photons propagate through the sample and are reflected by the mirror (M4). The sample and mirror combination reflect photons towards the NPBS where they are redirected to the last PBS through QWP and HWP. The last PBS transmits p-polarized photons towards the right SPCM and reflects s-polarized photons towards the left SPCM. A combination of QWP and HWP plates before the last PBS are used to set the projection operator for sample and idler arms independently. Two single photon counting modules along with the coincidence circuit (denoted \(\otimes\)) measure the coincidence rate of entangled photons.
## 4 Major Worldwide Cyberattacks Against Energy Systems
### Ukraine Power Grid Attack  
The increased usage of energy generator SCADA systems on personal devices like smartphones or tablets, which are used for private and corporate purposes, makes the threats of cyberattacks against energy infrastructure more tangible. This increases the possibility that a third party could acquire unauthorized access to an SCADA system (a power generator unit or transformer station) and take over the device's fundamental control features. One such attack targeted the Ukrainian energy infrastructure on December 2015, more specifically hitting the electricity distribution firm Kyivoblenergo. The attackers sent emails with infected files to the company's employees. The remote access malware known as BlackEnergy3 was installed on company workstations as a result of opening the files, allowing remote access to these Internet-connected workstations. Therefore, large sections of the power grid (three major distribution companies) were isolated and shut down as a result of the attackers' access to the power company's SCADA, leaving around 230,000 consumers without power for nearly 6 h. This attack is known to be one of the first publicly recognized cyberattacks on power system automation software. The attack was launched against the Ukraine grid through the use of spear phishing techniques to implant malware, resulting in the widespread blackout. The malware got through a bad data detection system and linked remotely with workers' computers to shut down 30 substations (7 110-kV and 23 35-kV) for several hours [73].  
After months of investigating the attack, it was determined that a foreign cyberattack was involved. After breaking into the SCADA system, the hackers began to open circuit breakers throughout the distribution network. The operators were consequently obliged to manually reset the circuit breakers because they were unable to access the SCADA system. Following the 2015 breach, a second attack targeting Kiev occurred in 2016. In this attack,200 MW of the generation capacity, about 20% of the city's nighttime electrical energy consumption, was shut down by the hackers [65].
# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Detection Phase  
The initial stage in any TSDR system is locating potential sign image regions from a natural scene image input. This initial stage is called the detection stage, in which a ROI-containing traffic sign is actually localized [17; 23; 24]. Traffic signs usually have a strict color scheme (red, blue, and white) and specific shapes (round, square, and triangular). These inherent characteristics distinguish them from other outdoor objects making them suitable to be processed by a computer vision system automatically, thus, allow the TSDR system to distinguish traffic signs from the background scene [21; 25]. Therefore, traffic sign detection methods have been traditionally classified into color-based, shape-based and hybrid (color-shape-based) methods [23; 26]. Detection methods are outlined in Figure 8 and compared in the following subsections.
# Hg\({}^{2+}\) Optical Fiber Sensor Based on LSPR with PDDA-Templated AuNPs and CS/PAA Bilayers
## 3 Results  
### Characterization of the Sensor Device  
Figure 3 illustrated the evolution of the absorption spectrum when PDDA-coated optical fiber was immersed in the Au colloid solution for 90 min. In the beginning, the PDDA nanosheet had abundant positive charges, and hence easily absorbed negatively charged AuNPs on the surface. Therefore, a weak LSPR peak is immediately observed as long as the optical fiber is inserted into the Au colloid solution, and further, the absorption peak grows rapidly in the first few minutes. At 5 min, the absorption peak arrived at 0.24 at the peak wavelength of 528 nm. The location of the absorption peak is determined by the particle size and shape. At this stage, the AuNPs are isolated and devoid of interactions with neighboring particles. As time passed, the AuNPs were packed closer and began to form multilayers and aggregation of the surface. When the spacing between particles is small compared to the wavelength of light, the coupling of surface plasmons between closely spaced particles became increasingly significant. The aggregation effect generates a new red-shifted feature in the optical spectrum centered between 600 and 800 nm [31]. As shown in Figure 3, the first LPSR peak continued to grow, but a new LPSR band over 600 nm appeared at 10 minutes, and gradually became prominent. After 50 min, the intensities of both absorption peaks increased slowly and finally reached stability around 90 min.  
The aggregation of the AuNPs broadened the absorption spectrum, and furthermore, the existence of organic stabilizers and linkers made the surface unclean and limited its application. The flame brushing technique with the alcohol lamp provided a small heat range to heat only the 3-cm sensor  
Figure 2: Experimental setup of solution refractive index sensing and heavy metal detection.  
area without destroying the coating protection of the adjacent area, Existence of the fiber coating made the sensor structure robust. The organic stabilizers and linkers were burnt out in the high-temperature for cleaning the surface of AuNPs. Due to the high temperature, two or more AuNPs began to coalesce and form large particles [32]. Figure 4 illustrates the SEM images of the AuNPs before and after flame brushing, which clearly show that the AuNPs with smooth surfaces were well dispersed on the optical fiber substrate after flame brushing. Moreover, the LSPR absorption spectrum has only one peak located around 542.7 nm (Figure 3b).  
Membranes with a layer-by-layer structure were formed by sequential depositions of CS and PAA alternately. After each layer, the LSPR spectral response of the sensor was monitored, as shown in Figure 3b. It is clearly observed that there is a red shift of the LSPR absorption peak after each layer functionalization process, indicating the CS/PAA bilayers was successfully immobilized on the sensor surface, and hence changes the effective refractive index.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 2 Materials and Methods
### Multi-Task Spectral Analysis Framework
#### 2.3.1 Wavelet Transform CNN Block
For each decomposition level i, the low-frequency (W\({}_{\text{LL}}\)) and high-frequency (W\({}_{\text{LH}}\)) components are processed using small 1D convolutional kernels (W\({}_{\text{conv}}\)):  
\[\text{Y}_{\text{LL}}^{\text{(i)}},\text{Y}_{\text{LH}}^{\text{(i)}}=\text{ReLU }\Big{(}\text{Conv1d}\Big{(}\Big{[}W_{\text{LL}}^{\text{(i)}},W_{\text{LH}}^{ \text{(i)}}\Big{]},W_{\text{conv}}\Big{)}\Big{)} \tag{4}\]  
After convolution, the wavelet components are reconstructed back into the spatial domain using the inverse wavelet transform:  
\[\text{Z}=\text{IWT}(\text{Y}_{\text{LL}},\text{Y}_{\text{LH}})=\text{ConvTranspose1d }\Big{(}\text{Y},\text{f}_{\text{wavelet}}^{\top},\text{stride}=2,\text{padding}=\text{pad}\Big{)} \tag{5}\]  
Here, f\({}_{\text{wavelet}}^{\top}\) represents the reconstruction filters, and Z \(\in\)\(\mathbb{R}^{\beta\times\mathbb{C}\times L}\) is the reconstructed feature map. The final output of the Wavelet CNN module is expressed in Equation (6),  
Figure 3: (**a**) WaveConv block structure; (**b**) lightweight CNN module design; (**c**) one-dimensional Wavelet CNN module; (**d**) detailed description of module components. where \(X_{base}\) represents the base convolution operation, and \(Z_{wavelet}\) is the reconstructed wavelet output:  
\[Z=X_{base}+Z_{wavelet} \tag{6}\]  
In the WaveConv block, spectral data are passed through both the CNN-Wavelet CNN module and the Wavelet CNN module independently, with the output features combined at the end. This design enhances the feature extraction capabilities of the WaveConv block by effectively integrating local focus regions with multi-scale feature information. Additionally, the incorporation of wavelet transforms improves the model's noise resistance.
## 3 Theoretical Background  
### Sentiment Analysis  
On the Internet, there is a large amount of information, since a daily plethora of text documents is published. Very often, tweets hide information that is useful, for example information that can give us better future investments. Nevertheless, a type of information that is useful is the tone of text, which can be positive, negative or neutral. Sentiment analysis is the domain of Natural Language Processing (NLP), which aims to search and identify positive and negative opinions, attitudes and feelings expressed in a text. There are many lexical databases, resources and tools about sentiment analysis, for example WordNet-Affect [11], SenticNet 3.0 [12], SentiWordNet 3.0 [13] and AYLIEN API [14], which is a package of NLP, information retrieval and machine learning tools for extracting meaning and insight from textual and visual content with ease. The package contains many applications such as sentiment analysis.
## 2 Methodology, System Description, and Problem Formulation
### System Description
#### 2.2.2 Video Coding and FoV Prediction Model  
The tile-based 360\({}^{\circ}\) VR video encoding and FoV prediction model at the cloud server is illustrated using Figure 4.  
* Video Coding: First, a 360\({}^{\circ}\) video block \(l\) with a frame rate of \(f\) is mapped into a 2-D video plane using an equirectangular projection (ERP), covering an angular range of 180\({}^{\circ}\)\(\times\) 360\({}^{\circ}\). Then, it is divided into \(N\times M\) tiles, with each tile having a data size of \(B\) bits/tile/frame. Since encoding and compression in the cloud server rely on both spatial and temporal correlations of the images, the user or MEC server needs all the video frames of a tile to decode the data packet of that tile. Different tiles are encoded and compressed separately, and even if the FoV region only covers part of a tile, the entire tile must be transmitted. If the FoV region has a size of \(k\times k\) tiles, the typical video transmission request region size equals \((k+1)^{2}\) tiles, with the viewpoint potentially falling on any tile in the video plane.
* FoV Prediction Model: To facilitate the MEC server in providing a unified proactive caching service for 360\({}^{\circ}\) VR video to all accessed users, the designed video service system lets the cloud server predict the user's FoV based on a saliency map generated from the video. The cloud server then provides the MEC server with data caching based on the predicted probability. As shown in Figure 4, at the start of period \(l\), the cloud server can first generate the saliency map using a convolution neural network (CNN) + long short-term memory (LSTM) + Gaussian mixture model (GMM) multilayer network, as proposed in [17]. Then, using the LSTM network proposed  
Figure 3: Workflow diagram.  
in [18], it generates the probability matrix \(\mathbf{P}_{\text{F}}(n_{l},m_{l})=\left[P_{\text{F}}(n_{l},m_{l})\right]_{N\times M}\) for the tiles that will be viewed in the next period, based on the saliency map. Here, the element \(P_{\text{F}}(n_{l},m_{l})\) represents the probability of the tile at coordinate \((n_{l},m_{l})\) being viewed. Since \(P_{\text{F}}(n_{l},m_{l})\) is derived only from the saliency map, it can be pre-computed offline and is the same for any served users.