esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Result Analysis
### Experimental Setup
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Camera Specifications**} \\ \hline Model & MV-U300 \\ Spectral Response Range (nm) & \(\approx\)400–1030 \\ Resolution & 800 \(\times\) 600 \\ Frame Rate (FPS) & 46 \\ Dynamic Range (dB) & \(>\)61 \\ Signal-to-noise Ratio (dB) & 43 \\ Pixel (H \(\times\) V) & 2048 \(\times\) 1536 \\ Pixel Size (\(\mu\)m\({}^{2}\)) & 3.2 \(\times\) 3.2 \\ Time of Exposure (ms) & 0.0556–683.8 \\ Sensitivity & 1.0 V/lux–sec 550 nm \\ Optical Filter & 650 nm Low Pass Optical Filter \\ Type of Shutter & Electronic Rolling Shutter \\ Acquisition Mode & Successive and Soft Trigger \\ Working Temperatures (\({}^{\circ}\)C) & 0–50 \\ Support Multiple Visual Software & OpenCV, LabView \\ Support Multiple Systems & Vista, Win7, Win8, Win10 \\ \hline \multicolumn{2}{c}{**LED Specifications**} \\ \hline Diameter of the LED (mm) & 150 \\ Power of the LED (W) & 6 \\ The half-power angles of LED (deg(\(\varphi_{1/2}\))) & 60 \\ \hline \multicolumn{2}{c}{**ROS Robot Specifications**} \\ \hline Size (L \(\times\) W \(\times\) H) (mm\({}^{3}\)) & 138 \(\times\) 178 \(\times\) 141 \\ Weight (+ SBC + Battery + Sensors) (kg) & 1 \\ SBC (Single Board Computer) & Raspberry Pi 3 Model B \\ MCU & 32-bit ARM Cortex® \\  & -M7 with FPU (216 MHz, 462 DMIPS) \\ Power connectors & 3.3 V/800 mA \\  & 5 V/4 A \\  & 12 V/1 A \\  & Input: 100–240 V, AC \\ Power adapter (SMPS) & 50/60 Hz, 1.5 A @max \\  & Output: 12 V DC, 5 A \\ Actuator & Dynamixel XL460-W250 \\ PC connection & USB \\ \hline \multicolumn{2}{c}{**STM32 Development Specifications**} \\ \hline CPU & STM32F407 ZGT6, LQFP144 \\ FLASH/SRAM & 1024 K/192 K \\ Power supply port & 5 V/3.3 V \\ \hline \multicolumn{2}{c}{**Drive Circuit Board Specifications**} \\ \hline Drive chip & DD311 \\ Drive current (A) & 0.1 \\ Drive voltage (V) & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the experimental system. the industry camera, simulates a moving vehicle. In the video recordings, different interferences were added, including occlusion, background interference, etc., to test the performance of the proposed algorithm in the round.
## 4 Results and Discussion  
### Free Decay  
The free-decay test of surge, heave, and pitch motions was performed because it can effectively predict the natural frequency and damping ratio of the floating structure. The former is one of the main hydrodynamic properties and it is also an important parameter of this paper. In particular, the dynamic response of the surge, heave, and pitch motions are always the major components. It should be noted also that in the free-decay test, the inflow wind speed and aerodynamic load are not included. Only the calculation of the hydrodynamic load, the mooring system, and the rigid body dynamics are considered. At the beginning of the test, an initial displacement is applied to the platform. The initial displacement here is generalized and then released. The platform will return to the equilibrium position by the restoring force, then reciprocate, and will eventually become stationary.  
Through the data obtained by the free-decay test as shown in Figure 8 and using FFT (Fast Fourier Transform), it can be calculated that the natural periods of the 3, 4, and 6 offset-column platforms for the surge motion are 113.6 s, 104.8 s, and 90.1 s, respectively, while the natural periods for the heave and pitch motions do not show obvious differences.
###### Abstract  
Large span concrete bridges with a box-shaped girder are usually built from prefabricated concrete segments or by in-situ casting of the concrete on a scaffolding system. Both technologies have their advantages and drawbacks. Recently a new approach to the construction of such bridges which combines the advantages of both existing solutions was proposed at the TU Wien. This method uses the standard precast segmental erection methods with their high construction speed, but divides the segments into easily transportable pre-fabricated thin-walled elements to create new, lighter versions of the segments. Following the installation of these lightweight segments, they are strengthened with additional concrete in their final position in the superstructure. This paper focuses on the transmission of shear forces during construction stages. Firstly, on the level of individual segments, where rigid cross-frames are necessary to guarantee the stability of the segments and secondly, on the level of a bridge girder built from such segments, where new joint types must be developed to ensure the force transfer between the segments. Different options for the formation of cross-frames as well as shear tests on double walls and concrete panels with steel girders are shown. In this experimental series, different shear transmitting elements were compared to each other and to calculations with non-linear finite element analysis, showing that all the investigated solutions are suitable for use in thin-walled bridge segments. Several methods, including a new concept for joining thin-walled pre-fabricated elements, are described for the joints between the segments. Push-off tests with a constant lateral force were carried out to assess the shear strength and deformation behaviour. The main parameters were the joint type (wet joints: plain, grooved, keyed; dry joints), the grout type, and the lateral force level. The test results are presented and the structural behaviour is further analysed using non-linear finite-element simulations.  
Keywords:thin-walled pre-fabricated elements; post-tensioning; prefabrication; joints; cross-frames; bridge construction; semi-precast-segmental bridges; unfilled double walls; segmental bridges; push-off test; shear testing; shear; experiments +
Footnote †: journal: Applied Sciences
## 5 Cancer-Related Clinical Studies  
Several clinical reports suggest tanshinone-containing Traditional Chinese Medicine (TCM) formula or TIIA as promising anti-cancer drug candidates (Table 3). The clinical use of TIIA was described in two single-case reports. One case about a 30-year old man diagnosed with acute promyelocytic leukemia (APL) was published in 2006. The patient received all-trans retinoic acid ATRA (20 mg, three times per day) for 14 days, but could not achieve complete remission, then was switched to TIIA (30 mg, oral, twice per day). After 8-12 weeks of TIIA treatment, the patient's blood (8 weeks) and bone marrow (12 weeks) routines were restored to normal levels, indicating a complete remission [279]. Another case was reported about a 21-year old man with relapsed APL after one year of ATRA, arsenic trioxide, 6-mercaptoethanol/optimine and methotrexate treatments [280]. After 54 days of TIIA (80 mg, _i.v._ once per day) treatment, the patient reached completely morphological remission without obvious side effects [280]. Well-designed clinical trials with more patients and randomized placebo-controlled design will help to further validate the clinical efficacy of TIIA and other tanshinones. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Tanshinone**} & & & & **Number of** & & \\ \multicolumn{1}{c}{**tested/Tanshinone**} & **Cancers** & **Treatment(s)** & **patients** & & \\ \multicolumn{1}{c}{**containing formula**} & & & & & \\ \hline TIIA & Leukemia & TIIA (80 mg, _i.v._ once per day) & Single case report & CR & [280] \\ TIIA & Leukemia & TIIA (30 mg, _p.o._, twice per day) & Single case report & CR & [279] \\ Fufang Danshen & Leukemia & Control: chemotherapy only; & Control: 46 & Fufang Danshen slightly & [281] \\ Injection & & Treatment: chemotherapy plus Fufang & Treatment: 86 & increased CR rate, but & \\  & & Danshen Injection & & significantly attenuate the side & \\  & & (20–30 mL, _i.v._, once per day) & & effects of chemotherapy. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cancer-related clinical studies of tanshinone IIA (TIIA) and tanshinone-containing traditional Chinese medicine (TCM) formulas. Multicenter clinical trials in China showed a complete remission (CR) rate of 96.7%-98% and a 5-year overall survival rate of 86.88% were achieved in leukemia patients receiving Realgar-_Indigo naturalis_ formula (RIF), a TCM formula containing Realgar, _Indigo naturalis_, Danshen and _Radix psudostellariae_[285, 286, 287]. Tetraarsenic tetrasulfide and Indirubin were considered as the major bioactive ingredients of Realgar and _Indigo naturalis_, respectively. The therapeutic benefit of RIF for leukemia patients were also reported by others [288, 289, 290, 291]. Following up on the clinical trials, a laboratory study showed that tetraarsenic tetrasulfide and TIIA alone could induce differentiation in
## 2 Related Work
### Foundations in UAV Path Planning  
Conventional optimization algorithms, including DE and PSO, have paved the way for UAV trajectory planning. The CDE [2] algorithm maximizes risk and distance in disaster contexts, while JGLPP-UTS [3] couples PSO and APF to implement global-local planning. Better versions like RGG-PSO+ [4] update waypoints dynamically, employing random geometric graphs, and enhance flexibility. Multi-objective approaches, such as the UAV multi-objective jellyfish search (UMOJS) [5], employ rapidly exploring random trees (RRTs) for initialization, while the improved chaos sparrow search algorithm (ICSSA) [6] employs chaotic mapping to escape local optima. Classifications of traditional and AI-based strategies are presented in surveys like [7], while the navigation-variable-based NMOPSO [8] ensures kinematic feasibility. Despite these advances, static mutation mechanisms in GA variants [8; 9; 10] limit the adaptability to real-time feedback.
## 5 Experiment
### Experimental Results Analysis
#### 5.2.4 Analysis of Robustness to Noise  
Considering the surface defect image is polluted by Gaussian noise with \(SNR\), including 22 dB, 18 dB, 14 dB and 10 dB, the same experiments are conducted to verify the robustness of the proposed DLRSD model. According to Table 1, when \(SNR\) decreases gradually, the AUC and MAE can remain a high level, especially when \(SNR\) = 18 dB, AUC can remain around 0.8. It's shown that the proposed DLRSD model is robust to noise and can lead to better saliency detection result, which establishes the good foundation for segmentation. The experimental results also indicate that adding sparse constraint for background can reduce the influence from noises, which is a reasonable strategy for surface defect detection.
# Physical Properties of _Jatropha curcas_ L. Fruits and Seeds with Respect to Their Maturity Stage
## 3 Results and Discussion
### Bulk Properties as Affected by Different Maturity Stages  
Bulk density, solid density, porosity, and dynamic and static angle of repose of _J. curcas_ fruits and seeds at different maturity stages are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fraction**} & **Bulk Density** & **Solid Density** & **Porosity \(\epsilon\) (\%)** & **Dynamic Angle of** & **Static Angle of** \\  & \(p_{b}\) **(g/cm\({}^{3}\))** & \(\rho_{s}\) **(g/cm\({}^{3}\))** & **Repose \(\theta_{d}\) (\(\zeta\))** & **Repose \(\theta_{s}\) (\({}^{\circ}\))** \\ \hline _Fruits_ & & & & & \\ Green & 0.45\({}^{\rm a}\)\(\pm\) 0.006 & 0.88\({}^{\rm a}\)\(\pm\) 0.038 & 45.4\({}^{\rm a}\)\(\pm\) 1.06 & 23.8\({}^{\rm c}\)\(\pm\) 1.11 & 26.9\({}^{\rm c}\)\(\pm\) 1.16 \\ Yellow & 0.35\({}^{\rm b}\)\(\pm\) 0.003 & 0.66\({}^{\rm b}\)\(\pm\) 0.340 & 41.6\({}^{\rm a}\)\(\pm\) 0.55 & 27.2\({}^{\rm b}\)\(\pm\) 1.25 & 29.6\({}^{\rm b}\)\(\pm\) 0.44 \\ Black & 0.21\({}^{\rm c}\)\(\pm\) 0.003 & 0.30\({}^{\rm c}\)\(\pm\) 0.013 & 25.5\({}^{\rm b}\)\(\pm\) 1.81 & 29.8\({}^{\rm a}\)\(\pm\) 1.32 & 32.7\({}^{\rm a}\)\(\pm\) 0.78 \\ _Seeds_ & & & & & \\ “Yellow” & 0.32\({}^{\rm b}\)\(\pm\) 0.002 & 0.59\({}^{\rm a}\)\(\pm\) 0.076 & 49.9\({}^{\rm a}\)\(\pm\) 0.36 & 34.7\({}^{\rm a}\)\(\pm\) 1.2 & 37.5\({}^{\rm a}\)\(\pm\) 0.79 \\ “Black” & 0.42\({}^{\rm a}\)\(\pm\) 0.006 & 0.65\({}^{\rm a}\)\(\pm\) 0.021 & 45.4\({}^{\rm b}\)\(\pm\) 0.54 & 32.9\({}^{\rm a}\)\(\pm\) 1.1 & 36.7\({}^{\rm a}\)\(\pm\) 0.61 \\ \hline \hline \end{tabular}  
* Different letters within groups (fruits, seeds) in a column indicate significant differences at \(p<0.05\). Mean values \(\pm\) standard deviation.  
\end{table}
Table 2: Bulk characteristics of _J. curcas_ fruits and seeds at different maturity stages.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 2 Materials and Methods
### Data Collection and Preprocessing  
This study utilizes real-time traffic data acquired from the Vehicle Detector (VD) sensor network installed across major intersections in Taichung City, Taiwan. The dataset comprises time-stamped vehicle counts, average speeds, and lane occupancy rates, aggregated at five-minute intervals. These data are publicly available through the city government's open data platform and accurately represent real-world traffic dynamics, providing a robust foundation for empirical model training and evaluation.  
To ensure data quality, we implement a series of preprocessing procedures. Missing values are imputed using linear interpolation, while anomalous entries are filtered based on Z-score thresholds. All input features are normalized to a consistent scale to facilitate stable model convergence. In preparation for model learning, road segments are grouped spatially according to their connectivity and historical congestion propagation patterns. This spatial structuring enables the model to capture cross-road dependencies more effectively. For temporal representation, a sliding window approach is adopted: traffic conditions over the past 30 min are used as input features to forecast congestion levels 15 to 30 min ahead.  
As illustrated in Figure 1, the system architecture ensures seamless real-time operation, and the system workflow is composed of four sequential modules. First, the DataAcquisition Layer collects real-time traffic information through the VD sensor network and CCTV video streams. Second, the Preprocessing and Data Fusion Module cleans, filters, and aggregates multivariate traffic features to enhance data reliability. Third, the xLSTM-based Prediction Engine generates short-term congestion forecasts across spatial regions. Finally, the Police Duty Notification Interface translates predictive alerts into actionable recommendations for proactive patrol deployment. This integrated architecture ensures that real-time traffic insights are efficiently captured, processed, and operationalized to support dynamic urban traffic governance [2,8].
# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
###### Abstract  
Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks. Among these works, the deep learning-based detection methods get rid of the dependence on hand-designed features of recommendation attack besides having excellent detection performance. However, most of them optimize the key hyperparameters by manual analysis which relies too much on domain experts and their experience. To address this issue, in this paper we propose an approach based on the Harris Hawks Optimization (HHO) algorithm to improve the deep learning-based detection methods. Being different from the original detection methods which optimize the key hyperparameters manually, the improved deep learning-based detection methods can optimize the key hyperparameters automatically. We first convert the key hyperparameters of discrete type to continuous type according to the uniform distribution theory to expand the application scope of HHO algorithm. Then, we use the detection stability as an early stop condition to reduce the optimization iterations to improve the HHO algorithm. After that, we use the improved HHO algorithm to automatically optimize the key hyperparameters for the deep learning-based detection methods. Finally, we use the optimized key hyperparameters to train the deep learning-based detection methods to generate classifiers for detecting the recommendation attack. The experiments conducted on two benchmark datasets illustrate that the improved deep learning-based detection methods have effective performance.  
collaborative recommender systems; recommendation attack; attack detection; Harris Hawks Optimization
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 4 Digital Twin Implementation
### Decision Engine Module  
To select the optimal traffic strategy, the above KPIs are evaluated across the different available traffic strategies.  
For the congestion use case, the optimal strategy is the one that provides maximum average speed and minimum average speed deviation between cars circulating on the highway. Low values for average speed deviation ensure that all vehicles drive at similar speeds, which will make the highway more secure. For this purpose, an additional KPI called Speed-Dev-Ratio, consisting of the relationship between the average speed of vehicles and their deviation, has been considered as follows:  
\[Speed-Dev-Ratio=\frac{average\;speed}{average\;deviation\;speed} \tag{1}\]  
Optimal performance corresponds to higher speeds and less deviation, so a higher value of Speed-Dev-Ratio is expected. Higher average intensity is better, as more vehicles per hour means higher speeds. Differently, the lower the average occupancy, the better, as it means that speed can be higher. As occupancy and intensity are proportional, only intensity is considered to make the decision. The number of vehicles is directly related to the intensity, so the former is not considered. The final KPI considered is the total travel time. As expected, the optimal total travel time corresponds to the minimum value.  
On the other hand, for the pollution use case, different KPIs are monitored during the decision-making process. In this case, the most effective traffic strategy is the one that achieves the greatest reduction in the levels of the three pollutants (CO, NOx, and PM2.5). Although the objective of this use case is to mitigate emissions, traffic parameters are monitored as well to avoid significant traffic disruptions. For that reason, the total travel time KPI is also considered when selecting the best traffic strategy to minimize emissions.
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
###### Abstract  
Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.  
transmission line fault classification; machine learning; sampling rate; conformer model +
Footnote †: journal: Applied  
1
# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 2 Materials and Methods
### Integration of Spatial and Temporal Probability  
To determine the probability of a landslide being triggered, an equation for integrating the probability of SF < 1 and the temporal probability was adapted [36]. The temporal probability is represented as the average likelihood of triggering a landslide in a pixel located in a high-susceptibility area and differs for each precipitation return period. This probability is assumed to be equal for all pixels within the high susceptibility area. On the other hand, the spatial probability is the likelihood of a pixel being affected by a landslide based on its location, and it was assumed to be equal to the probability of SF < 1. The original formulation [36] used a statistical model to estimate the spatial probability. The revised approach involves using a physical model for this estimation. The applied formula is as follows:  
\[Fpx_{j}l_{i}=\frac{Ta_{i}}{Ts}\;F(\text{SF}<1) \tag{9}\]  
where \(Fpx_{j}l_{i}\) is the conditional probability that pixel \(j\) will be affected by landslide l in precipitation event \(i\); Ta\({}_{i}\) is the number of pixels affected by landslides in precipitation event \(i\) (based on historical data); \(T_{s}\) is the number of pixels susceptible to landslides, which was considered equivalent to the entire area covered by colluviums and residual soils; and \(F(\text{SF}<1)\) is the frequency at which pixel j presents SF < 1, as the result of probabilistic modeling with PISA-m. The term \(\frac{Ta_{i}}{Ts}\) represents the temporary probability that a slide will be triggered (P\({}_{t}\)), and the term \(F(\text{SF}<1)\) represents the spatial probability (P\({}_{e}\)).
# Flexural Behavior of Corroded High-Speed Railway Simply Supported Prestressed Concrete Box Grider
## 3 Analytical Model
### Calculation of Flexural Behavior  
#### 3.2.1 Cracking Moment  
Based on the code for the design of prestressed concrete structures [41], the cracking moment can be calculated if the effective prestress and section properties are known. The strand corrosion reduces its effective prestressing force, which is taken into account here with a reduction in the elastic modulus. And the volume expansion in the corrosion products degrades the strength of the concrete, causing the test beam to crack more easily. The calculation formula for the cracking moment of a corroded prestressed beam can be obtained by introducing a mass loss amendment to the code formula.  
\[M_{\text{cr},\rho}=(\sigma_{\text{pc},\rho}+\gamma f_{\text{tk},\rho})W_{0,\rho} \tag{14}\]  
where \(W_{0,\rho}\) is the flexural section resistance factor of the converted section near the midspan; \(\sigma_{\text{pc},\rho}\) is the concrete precompression stress generated by the effective prestress at the section's lower edge; and \(\gamma\) is the plasticity coefficient of the resisting moment, i.e., \(\gamma=1.0\).