esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The analytical methods based on NAT [17; 18; 19; 20; 21; 22; 23] are devoted to analyzing aberration field characteristics of perturbed optical systems, and deriving the complex functional relationship between perturbation parameters and aberration fields, which is of great significance for analyzing coupling between different perturbation parameters and constructing the corresponding optimal compensation correction model. In recent years, research on using NAT to quantitatively calculate perturbation parameters, to realize wave aberration correction of optical systems, has been reported. For instance, based on the idea of system restoration, [PERSON] et al. [24], formulated the corresponding perturbation parameters solution scheme for the Large Synoptic Survey Telescope (LSST) by using NAT. In addition, [PERSON] et al. [25; 26] quantitatively calculated the perturbation parameters of both an on-axis three-mirror and an unobscured off-axis two-mirror telescope by using NAT. In addition, [PERSON] et al. [27] designed and aligned an all-spherical unobscured four-mirror image relay for an ultra-broadband subpetawatt laser by using NAT. Moreover, [PERSON] [28] and [PERSON] [11] studied the solution method of perturbation parameters for unobscured off-axis three-mirror telescopes based on NAT. The main purpose of these works was to restore perturbed optical systems to nominal design to the maximum extent by analytical solution. As for analytical compensation correction of perturbed optical systems, some existing research mainly focused on the compensation methods of a single optical element. For example, [PERSON] et al. [29] discussed how to use SM lateral misalignments to compensate for primary mirror (PM) astigmatic figure errors in an off-axis two-mirror telescope. [PERSON] et al. [30] and [PERSON] et al. [31] studied how to use SM lateral misalignments to compensate for third-mirror (TM) lateral misalignments and for PM astigmatic figure errors in off-axis three-mirror telescopes, respectively. In addition, the compensation correction methods proposed in these studies did not consider the axial misalignments [32], PM coma figure errors, and PM spherical aberration figure errors. In order to compensate and correct telescopes with multiple perturbed optical elements at the same time, considering the axial misalignments, PM coma figure errors, and PM spherical aberration figure errors, some new work is conducted in this paper. The paper mainly includes the following aspects: Section 2 describes the optical design parameters of an unobscured off-axis three-mirror telescope and theoretically analyzes the coupling effects and compensation relationships of net aberration fields induced by different perturbation parameters of the system. Section 3 describes the construction process of the compensation correction models for the off-axis three-mirror telescope. In Section 4, the simulation verification experiments are carried out. We conclude in Section 5.
###### Abstract  
In seismically active areas, knowledge of the actual behavior of bridges under seismic load is extremely important, as they are crucial elements of the transport infrastructure. To assess their seismic resistance, it is necessary to know the key indicators of their seismic response. Bridges built before the adoption of standards for seismic detailing may still contain structural reserves due to the properties of the used materials and construction approach. For example, smooth reinforcement which is found in older bridges due to the material properties, detailing principles, and lower bond strength compared to ribbed reinforcement, allows for greater deformations. In bridges, columns are vital elements employed in the dissipation of seismic energy. Their cross-sections often deviate from the regular square, rectangular, or round cross-sections, which are typically found in building. Based on the behavior of the columns in the vicinity of potential plastic joints, we can determine their deformability. This paper presents an experimental study of seismic resistance indicators around a potential plastic joint for a column with an atypical cross-section, without seismic details and with smooth reinforcement. The experimental results are compared with the numerical and analytical, but also with the experimental results on samples with ribbed reinforcement. Conclusions are made about the behavior of such column elements and their seismic resistance indicators, allowing for the application of an analytical or numerical method with realistic material and element properties and derivation of correction factors due to the effect of the smooth-reinforcement slippage from the anchorage area.  
plastic hinge region; smooth reinforcement; M/\(\varphi\) characteristics; atypical cross-section Article
## 5 FSO Networks with Relay Assist
### Unmanned Aerial Vehicle
[PERSON] et al. [184] employ a cloud radio access network (C-RAN) cost-effective and upgradable network that uses NFPs, which are used as fronthaul hubs that connect the small cells to the core network via baseband unit pool; all the NFPs are connected using FSO links. However, they use MMW links to connect to the small cells. In addition, the authors use two greedy algorithms. The first algorithm, called Centralized Maximal Cells Algorithm (CMCA) is used when the small cells have limited processing power, and the second one, called Distributed Maximal Cells Algorithm used when the small cells have enough processing power because it is faster than the CMCA. Both algorithms are used for C-RAN architecture, aiming to increase the number of small cells. Moreover, performance results confirm that both algorithms can be implemented due to their low complexity compared to the branch and bound method. However, all of them have almost the same performance.
###### Abstract  
Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt-Winters, Trigonometric Seasonality Box-Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt-Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033.  
energy; demand analysis; forecasting; time series +
Footnote †: journal: Applied Sciences
# A Road Defect Detection System Using Smartphones
## 2 Related Work
### Vibration Sensor-Based Threshold Method  
The threshold-based approach utilizes inertial sensor data to detect and classify road anomalies. This method involves detecting road anomalies when certain threshold values are exceeded for signal parameters obtained from the sensors, such as amplitude, root mean square, and wavelet coefficients [24]. It presents new algorithms leveraging the hardware and software capabilities of Android devices, with a particular focus on detecting pot-holes in real-time events and using limited resources. The algorithms include Z-THRESH, Z-DIFF, STDEV(Z), and G-ZERO [25]. It introduces a system that automatically detects potholes and speed bumps using the Android operating system. This application of standard smartphones in identifying road anomalies emphasizes easier and more widespread access to road condition monitoring, addressing device reliability in critical applications such as road safety [26; 27]. Furthermore, it goes beyond previous research by incorporating crowdsourcing methods. It discusses enhancing road anomaly detection through crowd detection, which collects data from many users. This approach demonstrates how leveraging the collective detection capability of the community can improve road condition monitoring [28; 29; 30].
## 2 Beamsteering System Concept and PAA's Simulation Model  
Current 3D beamsteering systems provide sufficient bandwidth for V2I data traffic, including video transmission for computer-vision applications, by forming a narrow beam that maximizes radiation in the user's direction. To control the beam in the horizontal and vertical planes, planar multi-element PAAs are usually utilized. Changing the beam direction is performed by adjusting the amplitude-phase distribution between the PAA emitters based on a beamforming scheme (phase shifters, the Butler matrix, etc.). However,in the research, the beamsteering was reduced only to the horizontal plane, which can be implemented based on a linear PAA. Thus, the intended job of the ML algorithm is to select the required phase distribution between the emitters of the linear PAA according to the predicted azimuth of the vehicle location. Depending on the traffic intensity and road topology in the selected V2I service area, the linear PAA is replaced by a planar multi-element PAA to form a sufficient number of independent beams to connect all road users.
## 4 Design Architecture
### Distance Comparison Unit
#### 4.1.4 Result Collector  
Besides the calculation, another major challenge of the design is the collection and storage of results. After the results have been filtered by the threshold checker, they are forwarded to generic collector units that control the storage process. All relevant results from the PE are combined in a tree structure and at the end buffered in a single FIFO.  
Figure 3: Hamming distance PE.  
Since it depends on the input signatures and the applied threshold value, it is not possible to predict how many valid results will be found and need to be stored. To solve this problem a tree structure of collector units was implemented, each containing a priority arbitrator and a FIFO. In a standard configuration, each collector unit will combine the results of four units from the previous tree stage. Though, the number of combined results is easily adaptable by a generic parameter for optimizing designs. The first collector stage contains one input FIFO for every PE in the design to buffer the results from the threshold checker. Further stages with an automatically instantiated number of collecting elements are instantiated recursively until all results are merged into a single output FIFO.  
As soon as a FIFO contains data, a request is sent to the connected priority arbiter of the next tree stage. The arbiter uses the _Round Robin_[35] scheduling to grant access to one of the requesting FIFOs from the previous stage. The results are read from the FIFO and written to the FIFO in the next collector stage (see Figure 4). If a FIFO in the tree reaches the almost full state, a special priority request is sent to the arbiter. Priority requests will always be treated first from the arbiter to prevent a FIFO from running full. If more than one priority request is active, all of them will be scheduled as described above. If priority scheduling cannot prevent a FIFO from running full, the connected arbiter (same stage) that handles the input will be disabled. This way no further input can be written to the FIFO. This allows for parts of the tree to be disabled, propagating from bottom to top, until one of the FIFOs with a direct connection to the PE is running full. In this case, the entire calculation is stopped until there is enough space available to prevent data loss. The basic structure of the priority arbiter used was developed by [PERSON] [36] and has been published under the MIT License. For this design it was extended to include the mechanism of priority scheduling.
## 3 Results and Discussion
### Computational Performance of the Models  
The iteration numbers and complexities of the studied models for the load flow of the 34 bus test system are investigated in this section. Accordingly, two terms related with iteration numbers such as general and maximum machine iterations are given in Table 1. General iteration number describes  
Figure 10: Voltage angle differences (’) of each bus with models for (**a**) Phase A; (**b**) Phase B; and (**c**) Phase C.  
the number of iterations required in order to reach the solution of the load flow analysis and the machine iteration number represents the number of iterations required to meet the convergence criteria of the machine iteration within the load flow algorithm. The maximum machine iteration shows the maximum number of machine iterations that occurred during the load flow iterations. Table 1 shows that the load flow algorithms accompanied with the Developed Model and the SCP Model [10] converge in 11 and 12 iterations, respectively. In addition, the maximum number of machine iterations for the Developed Model and the SCP Model [12] are, respectively, two and six iterations.  
On the other side, numbers of operations, which are required by the models for the load flow analysis of the 34 bus test system, are shown in Figure 11. It is seen from this figure that the SCP Model [12] requires numbers of addition, subtraction, multiplication, division and square root operations as 3840, 2112, 12,672, 1272 and 744, respectively. In addition to this, the Developed model requires numbers of the respective operations as 1936, 1276, 3828, 880 and 176.  
According to the results given up to here, it can clearly be mentioned that the Developed Model is computationally more efficient than the SCP Model [12] for the load flow analysis.
## 3 Results
### Experimental Results and Analyses
#### 3.2.3 Comparison Experiment Before and After Optimization  
In complex environments with frequent sandstorms and dramatic fluctuations in lighting conditions, the model is highly susceptible to interference during critical processing steps such as feature extraction. The trend of the two accuracy curves in Figure 8 clearly demonstrates that the addition of the BN layer and the application of data augmentation techniques effectively improve the stability of license plate recognition performance under such challenging conditions. Specifically, the overall optimized model after convergence had a recognition accuracy of more than 91%, which is much higher than the original model's 80% recognition accuracy. This result highlights the importance of combining BN and data augmentation techniques during model training, further confirming the significant impact of these two strategies in enhancing model performance and generalization ability.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 3 Numerical Simulation Analysis and Verification
### Diaphragm and Support Setup  
Figure 1 illustrates the plan layout of the diaphragm wall. The wall featured a thickness of 80 cm and extended to a total depth of 32 m, with a penetration depth of 16.2 m. During construction, inclinometer and rebar strain gauges were embedded in the east, west, south, and north walls of the site for monitoring purposes.  
Figure 1: Layout plan of the diaphragm wall.  
Based on the diaphragm wall support design data, horizontal supports were installed in five stages during the six excavations. The horizontal spacing between supports was approximately 6 m, while the vertical spacing is about 3 m. The excavation design data are listed in Table 1.
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 7 Conclusions  
In this paper, we explored a dual DL and embedding-based latent factor method for recommender systems. The method combines deep learning and embedding techniques to overcome the data sparsity problem faced by latent factor models. To overcome this problem, the design first embeds users' and items' input vectors to build a dense low-dimensional representation of data using embedding techniques. Then, it projects these dense vectors separately into two well-structured deep neural network architectures to discover the abstract and non-linear representation of the data. Finally, we use the inner product to estimate the rating score in the network's output layers. By significantly improving performance, this framework effectively alleviates problems observed in latent factor-based models. We performed extensive experiments on two real-world datasets, and the resulting experimental findings revealed that our proposed model outperformed existing state-of-the-art techniques for the rating prediction task on all datasets. As we discussed in our approach, particularly effective results were obtained for the sparse dataset ML-100K rather than ML-1M. We can conclude from this result that deep learning approaches are one of the most effective technical means of improving the performance of recommendation systems.  
Figure 4: A three-hidden-layer model for the ML-100K dataset. In Part (**a**), we have the training and validation loss curve versus a number of training epochs, and in Part (**b**), we have the performance results. As observed, the method converges.  
Figure 5: A four-hidden-layer model for the ML-100K dataset. In Part (**a**), we have the training and validation loss curve versus several training epochs, and in Part (**b**), we have the performance results. As observed, the method converges.  
In the future, we plan to incorporate other deep learning architectures for recommender systems to improve the quality of their performance in advance. We also intend to look into the effect of dual embedding on dual deep learning architectures.  
[PERSON] and [PERSON] built the framework of the whole paper. [PERSON] and [PERSON] carried out preprocessing of the data. [PERSON] designed the whole experiment and method and implemented the experiment. [PERSON] provided analytical and experimental tools. [PERSON] and [PERSON] wrote the manuscript. [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. U19B2028, No. U22B2061).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
This study makes use of publicly available MovieLens datasets. This data can be found at [[https://files.grouplens.org/datasets/movielens/](https://files.grouplens.org/datasets/movielens/)]([https://files.grouplens.org/datasets/movielens/](https://files.grouplens.org/datasets/movielens/)), accessed on 26 February 2021.  
The authors declare no conflict of interest
## 3 Results and Discussion
### Uptake Experiment
Using this equation, the average SD of the estimated uptake coefficients was (5.5 \(\pm\) 1.6) \(\times\) 10\({}^{-6}\). Given a 10 \(\upmu\)m diameter for the acid water droplets, a droplet density of 10\({}^{3}\) cm\({}^{-3}\), a GEM concentration of 2 ng m\({}^{-3}\) and a temperature of 293 K, the sinking rate of GEM by the heterogeneous  
Figure 5: A schematic illustration for the oxidation of gaseous elemental mercury in the liquid phase of the acidified water with sulfuric acid. uptake of acid atmospheric droplets was approximately 4970 years. Compared to the one-year atmospheric lifetime of GEM reacting with an OH radical, this sinking rate is negligibly small. However, it should be noted that, in this calculation, the diffusivity of GEM in the gas phase was ignored, because the standard gas was always replenished. It should also be noted that the calculated atmospheric lifetime has not yet accounted for the mass accommodation of GEM on the surface of the solution, as well as variability of the droplet size and its number density. Depending on the circumstances, those parameters and variables may significantly shorten the lifetime of GEM.
## 2 Methods
### Inverse Laplace Transform Algorithm
#### 2.2.2 Laplace Inversion Algorithm  
Various numerical approaches have been applied for the Laplace inversion [27; 28; 29], and they have different influences on the results. Considering the computational load and accuracy, two typical numerical approaches, the G-S algorithm and the Talbot algorithm, will be applied to solve the inverse Laplace transform, respectively. The G-S algorithm is a real operation, which is easy to calculate and has been widely used in 2.5D land TEM. The Talbot algorithm is a complex operation. From the results yielded by numerical examples, it has the advantage of superior accuracy and weak dependence on problems under the same calculation conditions [29; 40; 41; 42].  
* G-S algorithm  
The G-S algorithm [28; 30] converts a function \(F\) in the Laplace domain into the time-domain solution according to the relation:  
\[f(t)=\frac{\ln 2}{t}\sum_{j=1}^{N}V_{j}F\bigg{(}\frac{\ln 2}{t}j\bigg{)}, \tag{7}\]  
and the weight coefficient \(V_{i}\) is calculated by the following equation:  
\[V_{j}=(-1)^{\frac{N}{2}+j}\sum_{k=INT(\frac{j+1}{2})}^{min(\frac{j}{N}/2)}\frac {k^{N/2}(2k)!}{k(k-1)!(N/2-k)!(j-k)!(2k-j)!}\, \tag{8}\]  
where \(N\) is the number of terms, the value is usually a positive even number (\(N=8\), 10, 12, 14, 16). _INT_ is the integral function, and \(min\) is the minimum value function.  
* Talbot algorithm  
The fixed version for the algorithm has been demonstrated by [PERSON] [40; 42], and the algorithm is shown below:  
\[f(t)=\frac{0.4}{t}\sum_{j=0}^{N-1}\mathrm{Re}\bigg{[}c_{j}^{T}F(\frac{\delta_ {j}}{t})\bigg{]}, \tag{9}\]  
where \(\delta_{j}\) is calculated by \(\delta_{0}=0.4N\); \(\delta_{j}=0.4\pi j[\cot{(j\pi/N)}+i]\) for \(0<j\leq N\); and the number of terms \(N\) is a positive integer. The corresponding coefficient is given by  
\[c_{0}^{T}=\frac{1}{2}\epsilon^{\delta_{0}}, \tag{10}\]  
\[c_{j}^{T}=\bigg{\{}1+i\bigg{(}\frac{j\pi}{N}\bigg{)}\bigg{[}1+\cot^{2}\! \bigg{(}\frac{j\pi}{N}\bigg{)}\bigg{]}-i\cot\!\bigg{(}\frac{j\pi}{N}\bigg{)} \bigg{\}}\epsilon^{\delta_{j}}, \tag{11}\]  
where \(i\) is the imaginary unit, \(i=\sqrt{-1}\).