esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study investigated the strengthening efficiency and flexural behavior of reinforced concrete (RC) beams strengthened by textile-reinforced mortar (TRM) with non-impregnated textile. For this purpose, the control specimen, an RC beam, was compared with nine TRM specimens.  
1. There were multiple cracks in the pure bending of the TRM specimen compared with control specimen. However, in the case of using textile lamination, the ratio of bonding area with textile and mortar decreased compared to the total cross-sectional area of the textile, and in the case of  
Figure 12: Strain distribution of different load levels: (**a**) T1M; (**b**) T3; (**c**) T2; (**d**) T2 AP; (**e**) T2E.  
Figure 13: Comparison of yield loads of TRM specimen.  
applying mechanical end anchorage (T2 AP, T2A), the initial crack strength increased. In both cases, the cracking ratio of the pure bending section was lowered. Nevertheless, TRM strengthening was beneficial for uniform distribution of cracks.
2. Non-impregnated textile is easily damaged and slipped. Experimental results show that the case of lamination of textile can reduce the damage of the textile, but it does not prevent slippage. In the case of applying mechanical end anchorage, the slippage of the textile was expected to be completely prevented.
3. Considering the low ductility of all TRM specimens compared with control specimens and that no ultimate load was observed in some specimens, the behavior of TRM specimens after yield load was considered to be unstable. Therefore, the TRM strengthening effect of non-impregnated textiles could be compared with the behavior before the yield load considering structural safety and serviceability.
4. TRM strengthening using non-impregnated textiles increased the efficiency as the textile reinforcement ratio and textile lamination increased and the mesh size of the textile decreased and mechanical end anchorage applied. In this study, it was possible to have a load-bearing capacity similar to T3 with the largest textile reinforcement ratio by applying the above method.  
Note that the conclusion is based on the experimental results. To generalize experimental results, a more experimental and analytical study is needed.  
Investigation, methodology, and writing--original draft, J.P.; writing--review and editing. S.H.; project administration and supervision, S.-K.P.  
This research was funded by National Research Foundation of Korea, grant number NRF-2017R1A2B2012535.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017R1A2B2012535) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) and funded by the Ministry of Education (2016R1A6A3A11931804).  
The authors declare no conflict of interest.
# Value of Information Based Data Retrieval in UWSNs
## 3 Value of Information in Underwater Sensor Networks
### Problem Definition
#### 3.4.1 Vol Maximization  
We use the problem definition from our previous work in [50]. It is the same problem definition that has been used in [12; 49] implicitly and [50] explicitly. The subtle difference, however, lies in the way the final time-stamp \(\tau_{f}\) is determined. The Vol for a data report that is ready to be transmitted to the sink node is given as  
\[v_{ij}=A_{ij}e^{-B_{ij}(\tau_{f_{ij}}-\tau_{v_{ij}})} \tag{18}\]  
The aggregated Vol as retrieved from the \(i_{th}\) sensor node is the combined Vol of all data reports residing on the sensor node and is given as  
\[\mathrm{Y}_{i}(t)=\sum_{j=1}^{k}A_{ij}e^{-B_{ij}(t-\tau_{v_{ij}})} \tag{19}\]  
Similarly, the total Vol accumulated from the UWSN by the AUV, after it has visited all the sensor nodes based on a planned tour, can be calculated as  
\[\mathrm{Y}_{Acc}(t)=\sum_{i=1}^{n}\mathrm{Y}_{i}(t)=\sum_{i=1}^{n}\sum_{j=1}^ {k}A_{ij}e^{-B_{ij}(t-\tau_{v_{ij}})} \tag{20}\]  
Therefore, the goal of maximizing Vol is defined as  
\[\max\sum_{i=1}^{n}\sum_{j=1}^{k}A_{ij}e^{-B_{ij}(t-\tau_{v_{ij}})} \tag{21}\]
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 4 Discussion  
### Analysis of Seed Parameter Calibration Differences Before and After Soaking  
Currently, most researchers primarily focus on dry _C. esculentus_ seeds for parameter calibration. In contrast, this study targets _C. esculentus_ seeds after soaking treatment [17; 18; 19]. By measuring the intrinsic parameters of soaked _C. esculentus_ seeds and their contact parameters with a seed metering device, we employed the cylindrical lifting method to simulate the bulk angle of repose. Additionally, we utilized free-fall impact tests, static friction tests, and rolling friction tests to calibrate the contact parameters between soaked _C. esculentus_ seeds and between _C. esculentus_ seeds and steel materials.  
In the discrete element method (DEM) parameter calibration for _C. esculentus_ seeds after the soaking treatment, it was observed that the soaking treatment significantly affected the physical properties of the seeds. These impact parameters showed consistency compared to dry seeds. Specifically, the rolling friction coefficient, static friction coefficient, and the rolling friction coefficient between the _C. esculentus_ seeds and steel plates had a significant impact on the bulk angle of repose. These impact indicators generally increased after soaking. First, the change in seed shape contour was significant; the seed surface became more regular, and the shape contour became closer to a circular form. This change in shape, combined with the increase in static and rolling friction coefficients, collectively led to an increase in the angle of repose.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 1 Introduction
The detection method [9] only measures this difference in one dimension. The similarity of this quantitative metric does not imply that our adversarial examples have the same logits distribution as normal speech. If alternative evaluation metrics [11] can detect our novel adversarial examples, the adversarial example detection method based on the dispersion of logits is still valid when the attacker is unaware of the specific quantitative measure. To further investigate whether an attack method generates adversarial examples with the same logit distribution as normal speech, this paper proposes logits-based detection methods that focus on three different features, including the intra-frame mean difference, decision frame variance, and the number of delineation statistics of logits. The main contributions of this paper are summarized as follows:  
This paper analyzed three high-precision speech adversarial examples detection methods. Among them, the Logits-based detection method is the most promising because it does not need prior knowledge when detecting adversarial examples. To evade such detection, this paper also studies two representative logits in different ASR models, and further analyzes the manifestations and causes of logits distribution differences. To quantify this difference, this paper defined logits dispersion to describe the quality of logits. A smaller dispersion of logits implies that one speech is more likely to be an adversarial example. This paper proposes a Logits-Traction attack to improve the logits  
Figure 1: Logits-Traction attack. dispersion. It constructs a new loss function to generate adversarial examples, and this attack is compatible with the logit types of different ASR models. This paper extends the logit features exploited by the detection method to evaluate the effectiveness of the above attack method. It focuses on three different features, which can detect [PERSON] and [PERSON] (C&W) adversarial samples with high accuracy, proving the effectiveness of these features. These features are then used to detect the Logits-Traction adversarial examples. The experimental results show that the detection method based on these features has different degrees of accuracy decline when detecting the Logits-Traction adversarial examples, which leads to the failure of the detection method. Logit-Traction attacks can effectively avoid detection based on logits.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
###### Abstract  
This research presents a novel binary Edwards curve (BEC) accelerator designed specifically for resource-constrained embedded systems. The proposed accelerator incorporates the fixed window algorithm, a two-stage pipelined architecture, and the Montgomery radix-4 multiplier. As a result, it achieves remarkable performance improvements in throughput and resource utilization. Experimental results, conducted on various Xilinx Field Programmable Gate Arrays (FPGAs), demonstrate impressive throughput/area ratios observed for \(GF(2^{233})\). The achieved ratios for Virtex-4, Virtex-5, Virtex-6, and Virtex-7 are 12.2, 19.07, 36.01, and 38.39, respectively. Furthermore, the processing time for one-point multiplication on a Virtex-7 platform is 15.87 us. These findings highlight the effectiveness of the proposed accelerator for improved throughput and optimal resource utilization.  
elliptic curve cryptography; binary Edward curve; scalar multiplication; Montgomery radix-4 multiplier; FPGA 2023
Furthermore, high-energy X-ray radiation from solar flares not only directly impacts signal propagation but also induces precursory disturbances in propagation paths and phases through the ionosphere's anticipatory response. In mid- and low-latitude regions, the ionospheric response exhibits unique dynamic characteristics that differ significantly from those observed in polar regions [5; 6; 7; 8]. Additionally, the variations in low-frequency time-code signals induced by solar flares offer a novel perspective for ionospheric research. By monitoring and analyzing changes in signal strength, it is possible to indirectly reflect the dynamic processes of electron density in the ionosphere, thereby providing valuable data support for understanding the characteristics of ionospheric disturbances during solar activity.
## 3 Materials and Methods
### Framework Overview
#### 3.2.3 Deep Learning Model
\[\text{MultiHead }(Q,K,V)=[H_{1},\dots,H_{m_{H}}]W_{H}\] \[H_{h}=\text{ Attention }\left(QW_{Q}^{(h)},KW_{K}^{(h)},VW_{V}^{(h)}\right)\] \[\text{TTFMultiHead }(Q,K,V)=\overline{H}W_{H}\] \[\overline{H}=\overline{A}(Q,K)VW_{V}=1/H\sum\limits_{h=1}^{m_{H}} \text{ Attention }\left(QW_{Q}^{(h)},KW_{K}^{(h)},VW_{V}^{(h)}\right)\]  
where \(Q,\ K,\ V\) are the standard attention query, key, and value matrices, \(W_{V}\) values are value weights shared across all heads, and \(W_{H}\) is used for the final linear mapping. This helps each head to learn different temporal patterns while attending a common set of input features. In this setting, the model was trained to generate 24-h-ahead forecasts of observed discharge, using a lookback window of 24 h of bias-corrected synthetic discharge along with relevant meteorological covariates. These include air temperature, precipitation, relative humidity, wind speed, and potential evapotranspiration--variables that reflect both short-term weather conditions and broader water balance dynamics. To evaluate longer forecast horizons, the TFT was retrained using 48-h and 72-h multi-output configurations. This allowed direct comparison across multiple time horizons without the compounding error typical in autoregressive rollouts. The implementation and configuration of the model followed those detailed in [PERSON] and [PERSON] (2024) [34], which served as the primary reference for both the architectural setup and training procedure used in this work. For completeness, we note that the Temporal Fusion Transformer (TFT) model employed here used 100 hidden units for a single recurrent layer and 4 attention heads. A dropout rate of 0.01 was applied to improve generalization. The learning rate was optimized using the Optuna hyperparameter tuning framework [53]. In terms of input features, the model leveraged not only meteorological and discharge variables but also lagged water level values at 12 and 24 h and accumulated precipitation from multiple stations across the watershed. Temporal features, including hour, month, and season (encoded as dummy variables), were treated as known future covariates to support real-world applicability and enhance the model's temporal context.
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Current Distribution in the Antenna  
In a microstrip antenna, the value of the current should be minimal at the end of the radiating element (the edge of the patch). The voltage at the edge of the patch is phase shifted in relation to the current. Therefore, peak voltage is present at the end of the patch, with current values close to zero [33; 37]. A similar situation occurs in the middle of the wave, further down the line, i.e., at the beginning of the patch. The phase-shifted voltage in relation to the current phase produces fields on the edges of a microstrip antenna. The current distribution of the developed antenna at frequencies of 27.51 GHz (a), 28.0 GHz (b), and 28.35 GHz (c) is presented in Figure 9.  
Figure 8: The chart of antenna gain as a function of frequency for the proposed antenna working in the 5G system.  
Figure 9: _Cont._
## 3 Equipment for Measuring Shape Imperfections  
We measured the shapes of the CFRP thin-walled cylinders with the measurement equipment shown in Figure 2, which consisted of a rotary table, a laser displacement sensor (LDS), and two stepping motors. The measurements and movements of the equipment were controlled with a laptop computer--the major specifications are shown in Table 4. The specimens were placed on the rotary table, and data were measured at ten heights (along the length direction of the cylinder) at 6\({}^{\circ}\) increments. After 61 data points were measured at a particular height (one full rotation), the laser displacement sensor was raised, and the measurements were taken at the new height.  
load is at the center of the stainless plate. Figure 4 shows the location of the four laser displacement sensors used to measure the tilt of the stainless plate. A crosshead displacement was applied until it reached 1.1 times the buckling displacement in the buckling test. Each CFRP thin-walled cylinder underwent nine tests; the order of tests is shown in Table 5. The first had no offset, the second had an offset of 0\({}^{\circ}\), the third had an offset of 90\({}^{\circ}\), the fourth had an offset of 180\({}^{\circ}\), the fifth had an offset of 270\({}^{\circ}\), the sixth and seventh tests had offsets at the maximum-minimum amplitude of the local shape imperfections, and the eighth and ninth tests had no offsets. Figure 5 is a sketch of the buckling load test. The experimental specimens of CFRP cylinders showed elastic deformation during the buckling test without material failure because of very thin walls. Also, the eighth and ninth buckling tests were consecutively conducted without any disturbance to the cylinder between them. Previous experimental results also indicate that repeated buckling has no effect on the strength of the CFRP cylinder [24].  
In the no-offset testing, the alignment fixture was used; in the offset testing, an alignment fixture was also used. The fixture is shown in Figure 6. There were two different offset fixtures, one with \(\delta/t\) = 14.6 and the other with \(\delta/t\) = 7.3, where \(\delta\) is the offset distance and \(t\) is 0.112 mm (cylinders with the thickness of HSX350C075S test specimens (Table 3)) [24]. Figure 7 shows a photograph of the equipment used in the buckling test. Figure 8 shows a buckling mode of cylindrical specimens No. 191.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Direction of Offset** \\ \hline
1 & No offset \\
2 & 0\({}^{\circ}\) \\
3 & 90\({}^{\circ}\) \\
4 & 180\({}^{\circ}\) \\
5 & 270\({}^{\circ}\) \\
6 & maximum amplitude of the local shape imperfections \\
7 & minimum amplitude of the local shape imperfections \\
8 & No offset \\
9 & No offset \\ \hline \hline \end{tabular}
\end{table}
Table 5: Offset directions in buckling test.  
Figure 6: (**a**) Test without offset; (**b**) test with 270\({}^{\circ}\) offset.
# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 4 Specific Routing and Traffic-Related Algorithms in SDN
### Dynamic Routing Algorithms  
Dynamic Routing Algorithms represent another tool for latency reduction which adapts routing paths in real-time based on current network conditions. By constantly monitoring the state of the network, these algorithms can reroute traffic away from congested or faulty links, reducing packet delays and avoiding bottlenecks. This can be especially useful in SDN environments, where the centralized control plane has a global view of the network and can make coordinated routing decisions. Another approach to latency optimization is the use of latency-aware load balancing. This involves distributing network requests across multiple servers or paths in a way that considers not only the load on each server but also the latency of each path [21]. This can help to ensure that no single path becomes a latency bottleneck and that traffic is spread out in a way that minimizes overall network delays.  
In [22], [PERSON] et al. used an SDN framework and simulated the AVRO (African Vulture Routing Optimization) algorithm having the goal to assemble the phases of the initialization, development, and exploration of the population. The AVRO method had the goal to dynamically optimize the network conditions involving an initialization that considered metrics like route optimization relative to the topology. In the phases of exploration and development, routing is changed for a better data path approach with improved results on load balancing and network usage in a secure way.
## 3 Results and Discussion
### Environmental Fluctuation Analysis for Cold Chain
#### 3.2.4 Accuracy Evaluation of Sensor Nodes  
Critical factors for the sensor nodes include packet losses rate, battery life, and the accuracy of sensor nodes [31], as can be observed in Table 2. The packet losses rate of radio transmission was below 0.2% in the peach and nectarine cold chain from Kasic to Zagreb, Croatia. The node power management circuit ensured the nodes' stable operation up until a total voltage drop to 3 V (0% battery charge). The battery charge status varied from 80% to 90% after approximately one day.
## 3 Study Area and Data Sources  
The case study area for this study is Al-Ahsa city in Saudi Arabia's Eastern Province (Figure 1). Because of the high crash rate mentioned in previous research, Al-Ahsa was chosen for this investigation. Between 2009 and 2016, in the Eastern Province, 31.9 percent of all traffic crashes occurred in Al-Ahsa, followed by Dammam, Hafr Al-Batin, Jubail, Qatif, Dhahran, and Khobar, sequentially. Other cities had crash rates of less than 5% [50]. This clearly reveals that among the cities in the Eastern Province, Al-Ahsa is the most prone to traffic crashes. Al-Ahsa has recently been recognized as one of Saudi Arabia's UNESCO-listed heritage sites and has also been given a Guinness World Record, which means that the city has tremendous potential as an international tourist attraction [51]. However, the city's high crash rate may jeopardize that possibility, necessitating immediate action. The Traffic Police Department in Dammam provided the crash data for Al-Ahsa used in this study from October 2014 to May 2018. Overall, the dataset shows that collisions between cars were the most common type of crash, accounting for about 8% and 30% of fatal and injury events, respectively. Vehicle overturning was another common form of crash, accounting for 6.5 percent of fatal and injury incidents, respectively. Furthermore, pedestrians' vulnerability was demonstrated by the fact that 12.5 percent of injury traffic crashes and 2% of deadly traffic crashes involved pedestrians. Some crash types, including collisions with road guardrails, motorcyclists, parked cars, and permanent objects, provide a risk of harm, despite fatal crash rates being low (Figure 2). Further analysis and a description of the dataset are provided in Section 4. A sample of the dataset can be seen in Appendix A.
## 4 Simulation Results and Case Study
### Case Study
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Number of Stops**} & \multicolumn{3}{c}{**Average Delay (s)**} & \multicolumn{3}{c}{**Average Speed (km/h)**} \\ \cline{3-11}  & & \multicolumn{3}{c}{**Demand (veh/h)**} & \multicolumn{3}{c}{**Demand (veh/h)**} & \multicolumn{3}{c}{**Demand (veh/h)**} \\ \cline{3-11}  & & **800** & **1200** & **1600** & **2000** & **800** & **1200** & **1600** & **2000** & **800** & **1200** & **1600** & **2000** \\ \hline \multirow{3}{*}{1.0} & Case 0 & 0 & 0 & 0 & 0 & 2.14 & 2.81 & 4.36 & 6.22 & 85.1 & 83.8 & 81.3 & 77.1 \\  & Case 1 & 0 & 0 & 0 & 0 & 2.62 & 3.00 & 3.38 & 4.92 & 88.7 & 88.2 & 88.1 & 87.9 \\  & Case 2 & 0 & 0 & 0 & 0 & 2.67 & 2.75 & 2.76 & 3.03 & 89.3 & 88.9 & 88.4 & 88.0 \\ \hline \multirow{3}{*}{1.2} & Case 0 & 0 & 0 & 0 & 8 & 4.19 & 3.08 & 4.89 & 8.50 & 84.9 & 84.0 & 80.2 & 70.6 \\  & Case 1 & 0 & 0 & 0 & 0 & 2.62 & 4.92 & 6.00 & 6.38 & 88.5 & 88.0 & 87.9 & 87.6 \\  & Case 2 & 0 & 0 & 0 & 0 & 2.68 & 2.75 & 2.76 & 3.07 & 89.2 & 88.9 & 88.2 & 87.7 \\ \hline \multirow{3}{*}{1.5} & Case 0 & 0 & 0 & 0 & 26 & 4.32 & 3.15 & 6.96 & 21.07 & 84.6 & 82.4 & 77.7 & 49.8 \\  & Case 1 & 0 & 0 & 0 & 1 & 2.62 & 6.01 & 6.39 & 4.92 & 88.3 & 87.5 & 86.8 & 85.1 \\ \cline{1-1}  & Case 2 & 0 & 0 & 0 & 0 & 3.08 & 2.76 & 2.76 & 2.69 & 89.2 & 88.9 & 88.0 & 87.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation results under the 80–20 demand.