esa-sceva/satcom-chunk-collection huggingface dataset:
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 2 Setup Design and Realization
### Setup for TC-016: SBAS Switch  
The aim of this test is to check the correct switch between SBAS satellites providing corrections. This test is split into two sub-tests depending on whether the two SBAS satellites correspond with the same service provider ID or different ones. The DUT should be set up under simulated conditions, where two SBAS signals (different PRN codes), from the same or different service providers, are available and provide valid corrections. Then, after the nominal SBAS positioning solution is obtained by the DUT, the SBAS signal that is being used by the receiver is removed. The pass/fail criteria of this test include the verification of the solution mode to check that the SBAS mode is provided and the verification of the age of corrections to assure that the signals are not lost. Figure 2 shows the timeline used for a case where two different service provider IDs are tested.
## 4 Simulation  
### Simulation Settings  
The discrete events were programmed using Python to simulate the calls of the terminal users. The call arrival was assumed to follow a Poisson distribution with a mean  
Figure 2: Reallocation for a handoff call event. (**a**) The handoff call occurs; (**b**) option 1: no reallocation; (**c**) option 2: reallocate to the call on \(ch3\); (**d**) option 3: reallocate to the call on \(ch4\).  
rate \(\lambda\). The call duration was assumed to follow an exponential distribution with a mean value \(1/\mu\). According to [15], several parameters were set as shown in Table 1. The adaptive dynamic channel allocation algorithm based on the temporal-spatial correlation analysis (TSCA) was compared with SARSA [40], a random DCA algorithm (RDCA) [37] and an FCA [19] under a uniform traffic distribution and a nonuniform traffic distribution. The performance of the channel allocation algorithm was measured by the rejection probabilities of the new call, the handoff call and the total call [41]. The rejection probability of the total call was the ratio of the sum of the rejected new call and the handoff call to the total call. It could evaluate the total performance of the LEO satellite network. Without considering the temporal-spatial correlation, SARSA dynamically allocated channels according to the environment. The RDCA randomly selected a channel from the eligible channels. The FCA allocated 10 fixed channels to each cell.
## 3 Results and Discussion
### Comparison with Existing Designs
The SLL of 5G microstrip antennas depends on various design factors, such as architecture, size, and intended application. An analysis of Table 3 shows that previously reported SLL values range from \(-2\) dB to \(-15\) dB. The proposed antenna achieves acceptable SLL performance at 38 GHz, matching well with values reported in the literature. However, the SLL of \(-2.2\) dB at 28 GHz is higher than desired, indicating poor sidelobe suppression. This issue needs improvement since effective sidelobe suppression is essential for high directivity, reduced interference, and better signal quality in 5G systems. In conclusion, the proposed antenna design offers an exceptional combination of low return loss, substantial gain, and high efficiency, outperforming several existing designs in the literature. This comparison not only validates the effectiveness of the employed design strategies but also underscores the potential for future advancements in antenna technology for the 28/38 GHz frequency bands.
## 6 Conclusions  
In this paper, we examined the use of DL to solve the coexistence problem between various communication technologies, achieving dynamic spectrum sharing and avoiding performance degradation. We studied our proposed DL method under various propagation channel models and very low SNR values. Specifically, we investigated using Convolutional Denoising Autoencoders (CDAEs) for reconstructing corrupted LTE and Wi-Fi spectrograms with the same carrier frequency under various channel scenarios and SNR values. We tested DL models to perform protocol identification for various IEEE 802.11 WLAN protocols and unlicensed LTE using CDAE weights. Our results show the benefit of performing CDAE before classifying the spectrograms under light to strong noise and different channel propagation conditions. Our proposed methodology for CDAE can reconstruct 77% of the corrupted signals sharing the same spectrum, while showing stable performance under severe noise conditions and propagation models. The achieved accuracy is sufficient to restore and preserve the preamble of the corrupted Wi-Fi 802.11 signals or the sub-frames of the transmitted unlicensed LTE signal. Furthermore, our methodology for protocol identification based on CDAE reduces the training parameters, learning time, and the number of FLOs compared to other DL models. Finally, our methodology significantly improves the average accuracy for protocol classification to 91% in identifying radio access technologies in the unlicensed bands compared to other well-known DL models such as VGG16, ResNet-v2, and Inception-V3.  
Conceptualization, E.A., N.A., E.D., and G.G.; methodology, E.A.; software, E.A.; validation, E.A.; formal analysis, E.A.; investigation, E.A.; resources, E.A.; data curation, E.A.; writing--original draft preparation, E.A.; writing--review and editing, E.A.; visualization, E.A.; supervision, N.A., E.D., and G.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 1 Introduction
### Vector Processor and Parallel Processing  
A Vector Processor (VP) differs from a Scalar Processor (SP) by its ability to process a single instruction on multiple data. That is, a single operation is performed simultaneously on N independent elements (N being the given length of the vector). The VP can fetch N elements using a single load operation, thus saving time in both fetching and decoding the data and instructions [15; 16]. In the VP architecture, it is essential to redefine operations while indicating whether the operation is performed on a scalar or a vector of a certain size. The vectors could be of constant/dynamic length. Vector Processors significantly accelerate data-parallel tasks such as multimedia encoding, digital signal filtering, and cryptographic computations [17; 18; 19]. In Turbo Encoders, the interleaver must permute entire packets of bits--often millions of elements--in one operation, creating a processing bottleneck ideally addressed by SIMD-style vector units [17].  
Our simulations revealed that 78% of processing cycles were of this stage; therefore, improving the performance of the permutation algorithm will result with speedup of the whole application. Using a VP allows to efficiently manipulate the input data by adequate vector and parallel operations, thus enhancing the performance of the runtime application both in memory access (R/W) and in total cycles. The algorithmic solution presented in this paper is general and can be reconfigured by the basic DVB-RCS2 parameters.
# Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections
## 3 Results
### Optimal Conditions for Implementing the Median U-Turn Method at Intersections  
According to Figure 9, by fitting the relationships between critical left-turn ratios \(\mathrm{R_{dcri(1)}}\), \(\mathrm{R_{dcri(2)}}\), \(\mathrm{R_{dcri(3)}}\), \(\mathrm{R_{COcri}}\), and through traffic volume \(\mathrm{Q_{s}}\), the areas suitable for using a median U-turn method (green areas) and those not suitable for using a median U-turn method (red areas) at an intersection can be obtained, as shown in Figure 9. The fitting formulas of \(\mathrm{R_{dcri(1)}}\), \(\mathrm{R_{dcri(2)}}\), \(\mathrm{R_{dcri(3)}}\) and \(\mathrm{R_{COcri}}\) are as follows:  
\[\mathrm{R_{dcri(1)}}=8.30\times\exp(-\mathrm{Q_{s}}/95.51)+0.67 \tag{4}\]  
\[\mathrm{R_{dcri(2)}}=(8.98\mathrm{E}-5)\times\exp((\mathrm{Q_{s}}-57.12)/48.23 )+0.69 \tag{5}\]  
\[\mathrm{R_{dcri(3)}}=25.43\times\exp(-\mathrm{Q_{s}}/209.41)+0.52 \tag{6}\]  
\[\mathrm{R_{COcri}}=-360035.03\times\exp(-\mathrm{Q_{s}}/67.66)+0.44 \tag{7}\]  
Figure 11: Average delay and CO emission reduction rates for \(\mathrm{Q_{s}}\geq 500\) veh/h. (**a**) Average delay; (**b**) CO emission.  
Therefore, the real left-turn ratio \(\rm R_{ls}\) suitable for using the median U-turn method under different through traffic conditions is shown in Table 2. After measuring the through traffic volume and the left-turn traffic volume and calculating the left-turn ratio of each entrance at an intersection, the median U-turn method can be used to achieve a smaller average delay and CO emissions at the intersection if the following requirements for through traffic volume and left-turn ratio are met.  
* When the through traffic volume \(\rm Q_{s}\leq 87\) veh/h, the left-turn ratio \(\rm R_{ls}\) suitable for using the median U-turn method is in the range of 0-\(\rm R_{dcri(2)}\).
* When the through traffic volume is in the range of 87-400 veh/h, the left-turn ratio \(\rm R_{ls}\) suitable for using the median U-turn method is in the range of \(\rm R_{dcri(1)}\)-4 or 0-\(\rm R_{dcri(2)}\).
* When the through traffic volume is in the range of 400-416 veh/h, the left-turn ratio \(\rm R_{ls}\) suitable for using the median U-turn method is in the range of 0-4.
* When the through traffic volume is in the range of 416-934 veh/h, the left-turn ratio \(\rm R_{ls}\) suitable for using the median U-turn method is in the range of 0-\(\rm R_{dcri(3)}\).
* When the through traffic volume \(\rm Q_{s}\geq 934\) veh/h, and the left-turn ratio \(\rm R_{ls}\) suitable for using the median U-turn method is in the range of \(\rm R_{COcri}\)-\(\rm R_{dcri(3)}\).
## 5 Calculation Method for Torsional Fatigue Life of Titanium Alloy Turning Surface
### Torsional Strength and Fatigue Life Analysis Program for Titanium Alloys  
As evident from Figures 15 and 16, the stress and strain distributions demonstrate pronounced non-uniformity during torsional plastic deformation of the titanium alloy workpiece. This heterogeneity stems from variations in both the initial characteristic surface parameters and their spatial distribution. Such non-uniform deformation induces localized stress concentrations, which may precipitate premature damage initiation or fatigue failure, ultimately compromising the material's mechanical performance and fatigue life. This study systematically analyzes how machined surface topography parameters and surface/subsurface performance characteristics influence the torsional strength, maximum torque, fatigue life, and fracture location of titanium alloy workpieces. These effects are evaluated through different parametric definitions, with the specific performance parameter configurations detailed in Table 7.
## 2 Software-Defined Networking
### Definition  
We discuss SDN along the definition given by the ONF [4]. It is the most accepted SDN definition worldwide, because most global players in the networking industry and many IT corporations participate in the ONF.  
Figure 1: A three-layer software-defined networking (SDN) architecture.  
Figure 1 illustrates the SDN framework, which consists of three layers. The lowest layer is the infrastructure layer, also called the data plane. It comprises the forwarding network elements. The responsibilities of the forwarding plane are mainly data forwarding, as well as monitoring local information and gathering statistics.  
One layer above, we find the control layer, also called the control plane. It is responsible for programming and managing the forwarding plane. To that end, it makes use of the information provided by the forwarding plane and defines network operation and routing. It comprises one or more software controllers that communicate with the forwarding network elements through standardized interfaces, which are referred to as southbound interfaces. We review protocol options for the southbound interface in Section 2.2. OpenFlow, which is one of the mostly used southbound interfaces, mainly considers switches, whereas other SDN approaches consider other network elements, such as routers. A detailed description of the OpenFlow protocol is given in Section 3.  
The application layer contains network applications that can introduce new network features, such as security and manageability, forwarding schemes or assist the control layer in the network configuration. Examples of network applications will be discussed in Section 4. The application layer can receive an abstracted and global view of the network from the controllers and use that information to provide appropriate guidance to the control layer. The interface between the application layer and the control layer is referred to as the northbound interface. For the latter, no standardized API exists today, and in practice, the control software provides its own API to applications. We briefly discuss the northbound interface in Section 2.3.
## 4 Conclusions  
In this study, an algorithm was developed to suggest the soil moisture criteria by providing appropriate soil moisture values based on the conditions. By applying this algorithm to the system, an automatic irrigation system capable of controlling the soil moisture was developed and its field applicability was confirmed.  
The existing automatic irrigation system irrigated a predetermined amount when the soil moisture value fell below a set value. Therefore, it had the disadvantage that it could not respond to many variables such as the crop type, growth stage, soil texture, and soil bulk density. To compensate for these shortcomings, an algorithm for determining the soil moisture criteria based on the crop type, growth stage, soil texture, and soil bulk density was developed and applied to the automatic irrigation system. Intermittent irrigation with reference to the soil moisture movement modeling was performed on an experimental field using the automatic irrigation system. Moreover, the soil moisture control, crop yield, and water consumption were verified.  
As a result of the empirical field experiments, it was confirmed that it could be effectively used in the field as the soil moisture control values fell within each set range and it showed a higher crop yield and water productivity compared with the empirical manual irrigation plot. In the future, we plan to conduct research on the application of automatic irrigation systems in consideration of various influences such as evapotranspiration and climatic conditions by weighing lysimeters and agroclimatic stations [28,29].  
Conceptualization, Y.K., S.L. and M.K.; methodology, Y.K.; software, S.-H.K.; validation, S.-H.K., Y.K., S.L., H.K. and M.K.; formal analysis, S.-H.K. and Y.K.; investigation, S.-H.K.; resources, S.L. and H.K.; data curation, S.-H.K. and Y.K.; writing--original draft preparation, S.-H.K.; writing--review and editing, S.-H.K., Y.K., S.L., H.K. and M.K.; visualization, S.-H.K. and Y.K.; supervision, S.L.; project administration, S.L.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This work was carried out with the support of the Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ013537032021) Rural Development Administration, Korea.  
Not applicable.  
The authors declare no conflict of interest.
# Birotons and \"Dark\" Quantum Hall Hierarchies
## 3 Results  
In our study, we considered a hypothetical two-dimensional electron system with physical parameters of GaAs/AlGaAs quantum wells to provide a direct comparison with experimental data. The calculations were carried out for a system of several electrons in toric geometry, using the electron wavefunctions introduced in [17], invariant with respect to magnetic translations. To check the correctness of the numerical results, we calculated the dispersion dependencies of the five lowest-energy neutral excitations for the main fractional states of the Laughlin-Jain hierarchy, \(1/3\) and \(2/3\), related by the electron-hole symmetry (Figure 1). The spectrum of neutral excitations consists of a magneto-roton branch (MR) [19; 20] and a continuum of multi-roton states (MMR). At zero momentum, the roton branch merges with the continuum and cannot be detected as a distinct excitation branch. The principal result of these calculations is that at zero momentum, the lowest-energy excitation is a biroton (magneto-graviton (BMR(0)) [21]) with twice the energy of the roton minimum, which is consistent with the previously reported findings [22]. The application range of our computational scheme was specified based on the condition of building the roton minimum with the smallest number of electrons (Figure 1).  
The next step in the numerical calculations involved constructing the dispersion dependency of five neutral excitations with the lowest energy for the second representative of the Laughlin-Jain hierarchy, the fractional QHE state of \(2/5\). Once again, it can be claimed, though with less certainty than in the case of the fractional state of \(1/3\), that at zero momentum, the lowest energy of neutral excitations corresponds to the doubled energy of the absolute minimum of the roton branch [23]. Numerical calculations for the other fractional states lead to the conclusion that at \(\
u<1\), the spectrum of the lowest-energy neutral excitations of any FQHE state is of the same type. It consists of a multi-roton continuum and a roton branch (with the number of minima depending on the fractional state) damped at zero momentum.  
Based on the calculations, it is natural to assume that the lowest-energy excitation with zero momentum is going to be a biproton with twice the energy of the absolute minimum of the roton branch. It corresponds to the appearance of two rotons with minimal possible energies and opposite momenta. In our analysis, we could also consider the question of the binding energy between the rotons in a biproton [13]. However, even for the main FQHE states \(1/3\) and \(2/3\), such energy turns out to be smaller than the numerical error of our simulations. Therefore, since calculating only the lowest energy of neutral excitations at zero momentum is far less complicated than finding the absolute minimum of a roton branch in the corresponding dispersion dependencies, it opens broad prospects for the comparative analysis of the excitation energies of different fractional states.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 4 Experiment and Result Analysis
### Predictive Model Training and Verification  
After the space mean speed and proportional time series data at intersections are preprocessed, 726 samples are obtained for the training model. In the time series reconstruction, the value of n is 14. That is, the space mean speed and vehicle steering ratio at the next moment of the intersection entrance section are predicted by the data of the previous 15 time periods from itself and the three downstream entrance sections.  
The Keras framework is used to construct and adjust the model structure. After the model structure is roughly built, 70% of the samples are randomly selected into the model for training, and the remaining 30% of samples are used as the test sets. In order to improve the prediction accuracy of the model, its parameters need to be adjusted. For example, the space mean speed prediction model of the south road entrance section shows the adjustment process of the neuron number and the dropout ratio in Figure 14. With the same number of iterations, the MSE of the test set sample is the lowest when the proportion of disconnected neurons in the dropout layer is 0.2. When the number of neurons in each  
Figure 11: Space mean speed change of each road entrance section.  
Figure 12: Space mean speed change of each road exit section.  
Figure 13: Change of vehicle left steering ratio at each road entrance section.  
layer is (256,256,32), the MSE of the test set sample is sufficiently low, and the number of neurons is moderate. Subsequently, the model parameters are determined.  
After the parameters are adjusted, the model is trained using the set, and the test set is fed into the model for prediction. Taking the traffic data of the south road entrance as an example, Figures 15 and 16 show the single-step prediction results of the test set model. The model can predict the traffic data of space mean speed and vehicle steering ratio well. The predicted value is close to the real value and can better follow the real data change trend.  
Tables 4 and 5 show the error evaluation of the space mean speed and vehicle left steering ratio prediction models of the entrance sections in each direction. The prediction  
Figure 16: Result of single-step prediction of vehicle left steering ratio on south road entrance.  
Figure 14: Parameter adjustment. (**a**) MSE tendency chart 1. (**b**) MSE tendency chart 2.  
Figure 15: Space mean speed single-step prediction result of the south road entrance section.  
error evaluation was low for different data sets. Additionally, the error evaluation of the model-trained prediction results by using the reconstructed sample, only considering the data of the entrance road section, and the reconstructed sample in this study is compared. The MAPE and RMSE of the model prediction results for the reconstructed samples in this study were generally smaller. The experimental results show that the model has a better prediction effect, and the validity of the proposed reconstruction method is demonstrated.
## 2 Materials and Methods
### Research Methods  
The following four subsections provide information on how the researchers collected and organized the information needed to characterize the impact variables of the 33 potential debris-flow streams.  
Data Collection  
Digital terrain model. A digital terrain model (DTM) of the focal geographical area was provided by Taiwan's Ministry of the Interior (2016) via its open-government data platform. It has a data resolution of 20 \(\times\) 20 m.  
Figure 4: Distribution map of potential debris streams and their catchment areas. Agency of Rural Development and Soil and Water Conservation, MOA. Available online: [[https://www.ardswc.gov.tw/Home/eng/](https://www.ardswc.gov.tw/Home/eng/)]([https://www.ardswc.gov.tw/Home/eng/](https://www.ardswc.gov.tw/Home/eng/)) (accessed on 5 January 2024).  
Geological map. Taiwan's open-data platform also includes a 1:250,000 regional geological digital map of Taiwan prepared by the Central Geological Survey Institute of the Ministry of Economic Affairs (2013). The geological data for this study was derived from that map.  
Satellite imagery. National Central University's Center for Space and Remote Sensing Research, which has a 12.5 m \(\times\) 12.5 m analytical imaging capacity, was the source for this paper's satellite photographs. The photos analyzed included ones taken before and after the typhoons Mindulli (15 January-18 October 2004), Sinlaku (9 March-12 November 2008), and Morakot (12 April-1 November 2009) and before and after the torrential rains of 3 May 2012 (i.e., 16 February-26 October 2012) and 1 June 2017 (i.e., 28 January-17 November 2017).  
Rainfall data. The Department of Atmospheric Sciences at Chinese Culture University maintains an Atmospheric Hydrology Research Database (2004-2017), which was the source of this paper's rainfall information. After collection, the data required for this research were trimmed and screened using a rain field.