esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 4 Time-Modulated Van Atta Array Testing
### On-Off Van Atta Array Test  
The designed on-off Van Atta array is shown in Figure 12b. Four microstrip switches are connected to eight antennas through coaxial lines of equal length. The microstrip switches use the same design to ensure that the four switch paths have the same phase delay.  
Figure 10: The periodic modulating signal in time domain.  
In a microwave anechoic chamber, the RCS of the designed on-off Van Atta array was tested as shown in Figure 12a. The Van Atta array was vertically fixed on the turntable, with the array plane facing the test horn antenna, and the polarization direction of the array was the same as that of the antenna. The pulse frequency modulation signal was transmitted through a horn antenna, and the equivalent RCS of the Van Atta array was obtained by the ratio of the echo signal to the transmitted signal power.  
When the switch is turned on, the Van Atta array can maintain a larger RCS in a larger angle range, and when the switch is turned off, the RCS decreases rapidly with the increase in the incident angle. Active Van Atta arrays have lost their directional backtracking properties. Due to the specular reflection, the Van Atta array is equivalent to a flat metal plate, and the RCS decreases rapidly with the increase in the incident angle.  
The test results, as shown in Figure 13, show that within the range of \(\pm\)10 \({}^{\circ}\), the RCS of the on-off state of the Van Atta array is similar. However, when the incident angle is greater than 10 \({}^{\circ}\), the RCS of the on state of the Van Atta array increases by about 5 dB compared to the off state due to its directional backtracking characteristic. This means that in the case of oblique incidence, the Van Atta array has the potential for amplitude modulation and can support time modulation.  
Figure 11: The measurement results of the SPST switch.  
Figure 12: On-off switching diagram of an active Van Atta array. (**a**) Testing environment; (**b**) Switch structure.
## 3 Materials and Methods
### Model Deployment, Fine-Tuning, and Predictive Evaluation
#### 3.3.1 GPT Model Deployment and Fine-Tuning
It is important to note that predictions and fine-tuning for the GPT models were conducted via the official OpenAI API, where we only had control over the batch size, learning rate, and the number of epochs. Furthermore, according to statements from Azure CTO [PERSON] (2016), Azure leverages low-rank adaptation (LoRA), parameter-efficient fine-tuning (PEFT), and DeepSpeed tech niques to optimize GPU usage and improve memory efficiency during the fine-tuning of its GPT models.
###### Abstract  
A mobile ad-hoc network (MANET) necessitates appropriate routing techniques to enable optimal data transfer. The selection of appropriate routing protocols while utilizing the default settings is required to solve the existing problems. To enable effective video streaming in MANETs, this study proposes a novel optimized link state routing (OLSR) protocol that incorporates a deep-learning model. Initially, the input videos are collected from the Kaggle dataset. Then, the black-hole node is detected using a novel twin-attention-based dense convolutional bidirectional gated network (SA_DCBiGNet) model. Next, the neighboring nodes are analyzed using trust values, and routing is performed using the extended espyne-aided optimized link state routing protocol (EO_OLSRP) technique. Similarly, the extended espyne optimization algorithm (EOOA) selects the optimal feature based on parameters such as node stability and link stability. Finally, blockchain storage is included to improve the security of MANET data using interplanetary file system (IPFS) technology. Additionally, the proposed blockchain system is validated utilizing a consensus technique based on delegated proof-of-stake (DPoS). The proposed method utilizes Python and it is evaluated using data acquired from various mobile simulator models accompanied by the NS3 simulator. The proposed model performs better with a packet-delivery ratio (PDR) of 91.6%, average end delay (AED) of 23.6 s, and throughput of 2110 bytes when compared with the existing methods which have a PDR of 89.1%, AED of 22 s, and throughput of 1780 bytes, respectively.  
mobile ad-hoc networks (MANETs); blockchain storage; interplanetary file system (IPFS); deep-learning method; delegated proof-of-stake (DPoS); optimized link state routing protocol (OLSRP) Article
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 5 Conclusions  
The development of 360 video technology has led to the promised development of CCTV system, which has been maintained with long-standing technologies. In this paper, we propose the transcoding and encryption scheme for live CCTV video streams in real-time. The proposed method can be used for low bandwidth and capacity for 2D/3D CCTV systems that only support H.264 and earlier codecs. Experimental results show that the combination of parallel video processing and encryption enhances the security without increasing the overall complexity of the system, while still ensuring the real-time live streams from CCTV cameras to control center. The proposed system optimized real-time transcoding for six 1080p sessions and two 4K sessions with speeds of 36.4 FPS and 33.6 FPS, respectively.  
In the future, we intend to apply cubemap projection extensively into 360 CCTV cameras to improve the quality of video input. Additionally, the application of cubemap projection also offers many advantages in optimizing the encoding of the 360 CCTV cameras. In addition, perfecting the encoding/decoding mechanism is also an important factor in improving the performance of CCTV systems in the coming years. Regarding security issue, we also plan to experiment with applying cryptographic attack and plaintext attack to check the security feature.  
Conceptualization, Data curation and Writing--original draft, T.T.L.; Data curation and Investigation, J.J.; and Project administration, Supervision and Writing--review and editing, E.-S.R. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2017-0-00307, Development of Tiled Streaming Technology for High Quality VR Contents Real Time Service). It also was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2017-0-01630) supervised by the IITP (Institute for Information & Communications Technology Promotion). The authors declare no conflict of interest. The authors declare no conflict of interest.
## 3 Experiments
### Experimental Setup  
DR detection system was implemented on the PyTorch platform, run in a single GPU (GTX 1080 Ti). The Adam optimizer, which is an algorithm for optimization technique for gradient descent, was used in most experiments, while the SGD optimizer was adopted in other experiments, including self-supervised learning and fine-tuning of the classifier. In most experiments, the initial learning rate was set to \(1\times 10^{-4}\) or \(1\times 10^{-5}\), and the weight decay rate was set to \(1\times 10^{-5}\) or \(1\times 10^{-6}\). The learning rate was automatically adjusted according to the losses monitored by the program during training using the cosine decay schedule. The training process was stopped if the accuracy of the validation dataset did not increase fivefold. Data were augmented by rotation, flipping, cropping, and were affined during the training process.  
To compare the DR detection performance before and after applying the proposed method, a baseline was set without resorting to the proposed method. Several different sets of experiments were performed at the baseline in order to choose the best experimental result for comparison with the proposed method.
## 5 Conclusions  
In this paper, we propose a novel domain adaptation technique called SF-ECG, and also devise a new domain adaptation task in ECG classification, which is source-free domain adaptation. Our framework is developed for interpatient adaptation tasks, mostly for arrhythmia classification. We design a framework that consists of three modules, namely, data adjustment, local clustering, and source-free adaptation. With a more balanced performance across multiple categories, we achieve results that are comparable to those of other contemporary arts. To efficiently improve the deep learning model, we use four loss functions to reduce the performance-degrading distribution discrepancies of various records. In the inference phase, our method does not require additional data or even sources, and does not necessitate the addition of annotations to new records. The proposed approach is adaptable to new data and has the potential to significantly enhance deep learning models' interpatient performance. Our approach is also capable of data privacy and generalizability in unseen samples. We obtain more stable results in minor categories where samples are deficient.  
Conceptualization, T.H.R.; methodology, T.H.R.; software, [PERSON] and [PERSON]-W.K.; validation, [PERSON] and [PERSON]-W.K.; investigation, [PERSON] and [PERSON]-W.K.; writing--original draft preparation, T.H.R. and [PERSON]-W.K.; visualization, T.H.R.; writing--review and editing, [PERSON] and [PERSON]-W.K.; supervision, [PERSON]-W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1F1A1064069).  
Not applicable.  
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tasks** & **w/o UDA** & **UDA with Source** & **SFDA (Ours)** \\ \hline DS1 \(\rightarrow\) DS2 & 91.7 & 94.0 & **98.4** \\ \hline DS2 \(\rightarrow\) DS1 & 89.5 & 93.2 & **96.5** \\ \hline PTB\(\rightarrow\) PTB\_2 & 61.4 & 93.2 & **95.5** \\ \hline PTB \(\rightarrow\) PTB\_XL & 59.8 & 90.3 & **93.2** \\ \hline \hline \end{tabular}
\end{table}
Table 16: The accuracy of without/with UDA-based tasks in both MITDB, PTB, and PTB\_XL, which demonstrates the generalization and impact of our SFDA method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **No. of Samples** & **w/o UDA** & **SFDA** \\ \hline S & 3712 & 92.2 & **96.2** \\ \cline{2-4}  & 2764 & 81.2 & **83.2** \\ \hline F & 1871 & 79.4 & **86.2** \\ \cline{2-4}  & 801 & 77.6 & **84.8** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Results on the effectiveness of increasing the number of samples in adaptation task.
## 4 Discussion
### GPP, Re, and NEP Empirical Models  
Re and NEP empirical models captured some basic characteristics of Re and NEP, which were in line with other studies. It should be noted that GPP, Re, and NEP empirical models describe a dynamic and multiple-direction energy balance, more specifically, quantify the relationships between energy (PAR) and atmospheric substances (CO\({}_{2}\) and other absorbing and scattering GLPs). The complicated and interactive CO\({}_{2}\) processes in the atmosphere, biosphere, and land were expressed using PAR and its related energy. It is an advantage that these PAR energy empirical models can simplify numerous CO\({}_{2}\) processes and capture the main variation properties of CO\({}_{2}\) (GPP, Re, and NEP). Thus, we do not need to pay too much attention to specific processes. In the future, to obtain more reasonable simulations of seasonal NEP, more studies are necessary. It should be emphasized that solar radiation, atmosphere, biosphere, and land should be considered as a whole system. The interactions in PAR energy usage between photons, CO\({}_{2}\) molecules, and other GLPs quantum obey the statistical law. It should be noted that there were larger RMSE values for the simulations of Re in 2016 (e.g., Tables 8-10) than in other time periods, which was also mainly caused by measurement problems and very low Re. Thus, timely instrument maintenance is significantly important, and the empirical model can provide valuable data.  
Water vapor is an objective parameter to represent air temperature and humidity (T, RH), as water vapor is a function of T and RH [85]. Water vapor is also a vital participant and reactor in chemical and photochemical reactions, associating with OH radicals and BVOCs, as well as solar radiation [86; 55].  
The simulated monthly sums of Re and NEP exhibited larger variation ranges than their observations (Section 3.2), which may reveal some problems in data processing methods, e.g., spike detection, threshold (u\({}^{\star}_{c}\)) determination of nocturnal friction velocity (u\({}^{\star}\)), and gap-filling model selection had important impacts on the estimation of annual NEE [87]. In more detail, the relative estimation deviations of annual gross ecosystem exchange and ecosystem respiration induced by the uncertainty in data processing were 3.88-11.41% and 6.45-24.91%, respectively [87]. Additionally, more than 50% of the night data are eliminated using the threshold (u\({}^{\star}_{c}\)) [87]. Large original flux data (e.g., the assumed abnormal data) removal caused by different methods may result in small variation ranges in measured Re and NEP and inaccurate understanding. The ratios of the observed annual sum Re/GPP and NEE/GPP were 0.764 and 0.236, respectively, for 2013-2016. These small ratios help us to clearly understand the different levels and ranges of GPP, Re, and NEP. Therefore, more and more strict appreciated data processing methods are required to improve flux data quality.
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
## 1 Introduction
### Research Questions  
Starting with a scenario of oceanic communications, we tested the viability of establishing a communication network with wide coverage and low cost in the context of the Azores archipelago. The network aims at real-time machine-to-machine communications between small fishing vessels (including ones without a battery bank) and the islands, primarily transmitting timestamped positioning data.  
Considering that traditional maritime communication technologies cannot comply with the energy restrictions that some systems face, as well as the final purpose of the network, the following research questions are posed:  
* RQ1: Which wireless communication solution enables real-time data extraction on small vessels with limited power?  
**Hypothesis 1 (H1).**_Employing LoRaWAN will facilitate the communication of long-range, low-bandwidth data between small vessels and gateways on an island._  
This may lead to the second question:  
* RQ2: At what maximum distance can effective LoRaWAN communications be carried out between the vessels and the gateway for tracking their location?  
**Hypothesis 2 (H2).**_By deploying a node (IoT device) on a vessel and installing a gateway on land, it is feasible to establish data communication between the two entities for a distance of approximately 100 km._
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 3 Implementation Optimization
### Fixed-Point Design
Both the numerator and denominator are represented in fixed-point format fi(0,16,0), with the actual division performed in post-processing. This design choice significantly reduces hardware complexity while maintaining the necessary precision for the power ratio estimation. The PMS averaging computation (Equations (10) and (11)) uses a wider fixed-point representation of fi(1,32,8) to accommodate the accumulation of power measurements and ensure sufficient dynamic range for both strong and weak signal conditions. The second path performs the averaging of both the mitigated and unmitigated PMS measurements. The final power estimation is obtained by normalizing the sum of non-discarded PMS samples by their corresponding gamma factors, ensuring accurate power measurements even in the presence of RFI blanking.
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
###### Abstract  
In the course of asphalt pavement usage, exposure to fuel infiltration accelerates particle detachment, leading to the occurrence of problems such as looseness and peeling. The aim of this study was to comprehensively evaluate the impact of a fuel-resistant modifier (FRM; 1%, 3%, 5%, 7%) on the performance of asphalt and asphalt mixture. Conventional physical tests and high-low temperature rheological tests were conducted on the fuel-resistant modified asphalt (FRMA). The results indicate that, with increased FRM content, the penetration and ductility of FRMA decreased, while the softening point increased. The high-temperature performance improved, but the low-temperature ductility declined. High-low temperature rheological test results demonstrate that the addition of FRM significantly enhanced the asphalt's shear deformation resistance. A moderate amount of FRM modification improved the asphalt's low-temperature crack resistance, but excessive FRM resulted in reduced flexibility. In addition, fuel-resistant modified asphalt mixture (FRMAM) specimens were prepared and evaluated for performance. In comparison to the base asphalt, FRM modification enhanced the resistance to oil erosion and peeling as well as the Marshall stability of asphalt mixtures. It reduced the scattering loss caused by fuel dissolution and improved both low-temperature indirect tensile (IDT) strength and high-temperature shear strength. A low content of FRM effectively reduced the sensitivity of asphalt mixtures to fuel infiltration, thus enhancing their road performance.  
asphalt; asphalt mixture; fuel-resistant modifier; road performance Article
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
## 3 Description of the Dataset
Phins manufactured by iXblue [34] was used as the INS for providing position, true heading, attitude, speed, and heave. The roll and pitch uncertainties of the system were 0.01\({}^{\circ}\) (similarly, the misalignment uncertainties were assumed to be 0.01\({}^{\circ}\)). Generally speaking, degradation in the quality of the MBES bathymetric measurements is not solely due to the uncertainties inherent to the MBES. Systematic error sources, categorized as static and dynamic, also contribute (see [35] for a detailed discussion and [24] for a brief explanation). These errors lead to depth errors through the systematic rise and fall of all the beams, see [35, 36]. Correcting the measurements for the systematic errors is often carried out using patch tests, correlation analysis between the motion time series, and depth derivatives or assessing the agreement between the depth measurements at the overlapping parts of the adjacent swaths [36, 37, 38]. By using these investigation tools, we concluded that the variations of the depth measurements in the surveyed areas were not caused by the systematic error sources. For the data acquisition, a Continuous Wave (CW) pulse type was used with a total pulse length, \(T\), equaling 27 us, 54 us, and 134 us (corresponding to the effective pulse length of \(T_{\text{eff}}=37\%T\)[39]). Measurements were taken at depths of around 10 m and 30 m, along tracks of nearly 760 m and 640 m in length, respectively. Shown in Figure 1 are the bathymetry maps, derived from Qimera (developed by QPS BV), for the water depths of 10 m (c) and 30 m (d). These were based on the measurements with a pulse length of 54 us. The bathymetry maps using other pulse lengths were also derived, but showed no differences, and hence are not shown here. The location of both datasets was chosen such that they represented a flat seafloor to comply with the assumptions behind the model. However, as seen from the bathymetry maps and their zoomed-in versions (Figure 1), small-scale variations of the bathymetry still existed, indicating that the assumption of the flat seafloor was not fully valid. Thus, the approach presented in Section 2.2 was used to eliminate the corresponding contribution from the measurements. It should be noted that although the approach presented accounted for the effect small-scale variations of the bottom morphology, still it was decided to consider a small area, i.e., not the full track line, see the black rectangles in Figure 1e,f. In this paper, it was assumed that the effect of potentially remaining small-scale bathymetry variations could be neglected in the modeling.
# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 3 Results and Discussion
### Cross-Comparison with TDR Sensor for Varying \(\theta\)
Additionally, observed differences could be a soil packing artefact where the lower \(\varepsilon_{\text{TDR}}\) in the middle region is due to the presence of more air pockets. Since the TDR measurements were collected as an average of data coming from all the rods along the profiler and from a much higher volume of influence, it is possible more air pockets are present. It is worth noting, the results are consistent with the response of Wet2, a similar sensor in design and operation mode, where previous studies report an overestimation of \(\varepsilon\) when compared to the TDR instrumentation [12; 32; 33].
# Characteristics of Summer Flash Drought and Its Effect on Maize Growth in Liaoning Province, China
## 4 Discussion
### Causes of Summer Flash Droughts in Liaoning Province in Typical Years  
Insufficient precipitation is a precondition for the development of drought. Similar to conventional drought, atmospheric conditions causing flash droughts are characterized by a positive geopotential height anomaly and downward atmospheric movement [15]. However, flash droughts have stronger meteorological drivers than conventional droughts [2; 38]. In addition, high temperatures in Northeast China are strongly associated with the local deep anticyclonic anomalous circulation, and concurrently, this anticyclonic circulation suppresses precipitation [39]. In the present study, we analyzed the atmospheric circulation patterns in the summers of 1989, 1997, and 2018. In the summers of 1989 and 1997, the position of the subtropical high was abnormally southward and eastward, and the intensity was weak, which was unfavorable for the northward transport of water vapor. In the summer of 2018, the position of the subtropical high was anomalously westward and northward, the intensity was weak, and the subtropical high and South Asian high were superimposed on each other. The airspace over Liaoning was controlled by an abnormal anticyclonic circulation, which was not conducive to precipitation. The abnormal enhancement and superposition of the South Asian high and the Western Pacific subtropical high in the summer of 2018 were the main causes of the extremely high temperatures and drought in Liaoning [35; 39]. The atmospheric conditions of the summer flash drought in the three typical years were characterized by a high-pressure anomaly and atmospheric subsidence, which were unfavorable for precipitation and favorable for the occurrence of flash droughts. Simultaneously, the continuous high-pressure anomaly facilitated the maintenance of a flash drought.