esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A series of uniaxial tensile tests on the welded junctions of HDPE geocells was performed in this work, and the probable tensile force, shear force, peeling force, and splitting force of geocell junctions in practical applications were simulated. Firstly, the clamping distance of the current junction tensile test standard was explored, and the impact of  
Figure 20: Junction test results under various clamping distances: axial stress versus peak elongation.  
various clamping distances on the test was evaluated so as to provide a data basis and suggestions for establishing a new test standard. Then, their tensile strength, pre-peak, and post-peak behaviors were compared, and the failure modes under different loading types were observed. Finally, the influence of geocells lying on a slope in engineering was discussed.  
This work draws the following conclusions:  
1. The tensile strength of the weld tensile strength test decreases with the increase in the distance between clamps. The tensile strengths of the 100 mm clamping distance were decreased by 4.51% compared with the weld tensile strength test of the 10.5 mm clamping distance.
2. The shear strength of the tensile shear test decreases with the increase in the distance between clamps. The shear strengths of the 100 mm clamping distance were decreased by 14.08% compared with those of the 10.5 mm clamping distance. The pre-peak elastic and post-peak plastic area elongation both increased significantly.
3. The peel strength and split strength remain essentially unchanged with the increase in the distance between clamps. The elongation of the plastic area post-peak in the peeling test was 2.3 times that of the splitting test.
4. The peeling strength was the key determinant of whether HDPE welded junctions were damaged in practical applications. The elongation of the elastic area pre-peak in all the tests was similar at a 100 mm clamping distance.
5. Considering both strength and deformation, the construction method of geocells spreading vertically along the surface on a subgrade slope is thought to be more reliable, improving the service life of geocells in slope protection.  
Writing--review and editing, G.Y.; writing--original draft preparation, P.S.; validation, P.X., Z.W. and H.W.; methodology, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (NSFC) (grants Nos. 52079078, 52108331 and 51709175), the Key R&D Projects in Hebei Province (grant No. 20375504D), the Hebei Provincial Department of Transportation Science and Technology Project (grant No. RW-202014), and the Scientific and Technological Research Projects of Colleges and Universities in Hebei Province (grant No. BJ2020045).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 4 Experiment and Analysis
### Analysis of Experiments  
The maximum correlation coefficient (MCC) and the lead time between the input and the output parameters are calculated from the experimental data. Steering angle and steering torque are the input parameters; roll and yaw angles and their corresponding velocities are the output parameters selected for the analysis. The MCC defined herein as a cost function to determine the maximum possible correlation between the input and the output parameters by shifting the output parameter with respect to the input parameter. The time step at which the cost function becomes maximum was defined as the lead time. The MCC is a measure of the dependency of the input parameter on the output parameter, and the lead time indicated the usability of the output parameter by the rider for predicting the steering control.  
For example, Figure 6 shows time series data for roll angle and steering angle of a professional rider, normalized in [\(-\)1,1] range. The correlation between the roll angle and the steering angle was calculated while shifting the roll angle curve in the time domain at a step size of 0.01 s in the direction as shown by the arrow in the figure. Maximum correlation was observed after shifting the roll angle curve by 0.53 s. This correlation was named as the MCC and the time by which the roll angle was shifted to arrive at it is the lead time (i.e., 0.53 s). The MCC and the lead time are determined from the experimental data for all the riders.  
In this research, the experimental data were analyzed using a statistical method. All the experimental data were divided into the sample size of 300 (i.e., 3 s) to examine the instantaneous input and output parameters while balancing the motorcycle at low speeds. The average (avg) and standard deviation (SD) from the experimental data were calculated and compared to ensure the repeatability and reliability of the experiments.  
The analysis was done in four stages. Firstly, the repeatability and reliability of the experiments were determined. Secondly, the theoretical results were validated with the experimental results using the relationship between the steering angle and the roll parameters in Equations (12) and (13). Thirdly, the MCC and the lead time were calculated for output parameters with the input parameters. These were calculated to identify the output parameters important for the low-speed stability. Steering angle and steering torque were the input parameters; roll and yaw angles and their corresponding velocities were the output parameters selected for the analysis. Finally, multiple regression analysis (MRA) was performed between the identified output parameters as independent variables and the  
Figure 6: Steering angle and roll angle curve of a rider normalized in [\(-\)1,1] range © 3.1 km/h.  
input parameters as dependent variables, to estimate and identify the most important input parameter between the steering angle and the steering torque. Further, the estimated input parameter from MRA was validated by comparing the same with the measured data.
## 3 Modeling of the TENG System
### Buck Converter
During the discontinuous interval \(\hat{d}_{3}\), both the switch and the diode are off, and the output circuit consists solely of the capacitor and the load. The circuit configuration for this interval is illustrated in Figure 7. In DCM, it is necessary to consider the state shown in Figure 7 in addition to the states shown in Figure 5a,b for modeling. While it is theoretically possible to weight and superimpose these three states, accurately measuring the rapidly fluctuating inductor current within a short period is not practical. Therefore, modeling using the conventional state-averaging method is difficult. Figure 5: Equivalent circuits that vary depending on the switch state: (**a**) equivalent circuit in the ON state, (**b**) equivalent circuit in the OFF state. Figure 6: Variation in inductor current within a PWM cycle: (**a**) the definitions of the \(\hat{d}_{1}\), \(\hat{d}_{2}\), and \(\hat{d}_{3}\) intervals according to the current state, (**b**) the \(\hat{d}_{1}\), \(\hat{d}_{2}\), and \(\hat{d}_{3}\) intervals expressed in a PWM period. Figure 7: Equivalent circuit when the inductor current is zero. In DCM, the inductor current becomes completely zero, leading to the occurrence of ringing. Due to this ringing, high-frequency components are also present in the inductor current. Here, assuming that the inductor current contains both a low-frequency component \(i_{LL}\) and a high-frequency component \(i_{LH}\), the inductor current \(i_{L}\) can be expressed as follows:  
\[i_{L}=i_{LL}+i_{LH} \tag{8}\]  
The average current of \(i_{LH}\) per switching cycle can be expressed by Equation (3) as follows, where \(T_{SW}\) is the switching period:  
\[i_{LH}=\frac{\hat{d}T_{SW}(V_{in}-V_{out})}{2L} \tag{9}\]  
In the WASAM, the inductor current \(i_{L}\) is modeled by substituting it with the aforementioned low-frequency component \(i_{LL}\) and high-frequency component \(i_{LH}\)[28]. When the state variables are \(i_{LL}\) and \(V_{out}\), the model equations can be expressed as in Equation (10):  
\[ \begin{cases}\dfrac{\hat{d}i_{LL}}{dt}=-\dfrac{V_{out}}{L}+\dfrac{\hat{d}V_{in }}{L}\\ \dfrac{dV_{out}}{dt}=\dfrac{i_{LL}}{C_{out}}-\left(\dfrac{1}{R_{ load}C_{out}}+\dfrac{\hat{d}T_{SW}}{2 LC_{out}}\right)V_{out}+\dfrac{\hat{d}T_{ SW}}{2 LC_{out}}V_{in}\end{cases}  \tag{10}\]
# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 4 The Speak Correct Interactive Experiments
### Accent Ulterances and Phoneme Errors in Speak Correct  
To determine the dependability and subsequent comparison of the users' statements, the Generalized Posterior Probability (GPP) confidence metric was used. As a result, the phoneme range was evaluated with the help of the phoneme-based confusion/matching matrix. The phoneme confusion/matching matrix was created using the saved Saudi as well as Egyptian dialects information, which included 300 speech sample illustrations from 70 presenters (35 men and 35 women) for each area, totaling 21,000 illustrations. To create the confusion matrix, [PERSON] and colleagues employed a phoneme recognition system.  
Such a confused matrix C (\(\alpha\), \(\beta\)) signifies the \(\beta\) value of phonemes accepted by \(\alpha\) phonemes. The phoneme range may be calculated through the following equation:  
\[\text{D}\;(\alpha,\beta)=-\log\frac{\text{C}(\alpha,\beta)}{\sum\text{C}( \alpha\text{i},\beta)}\]  
GPP could also be used to confirm the recognition of the sub-phrase, phrase, and statement identification. It is computed by averaging the probabilities of the various entities (sub-phrase, phrase, or statement). The association among the appreciation efficiency and also the GPP aimed at voice recognition was studied, and it was matched inside the Speak Correct dataset, as illustrated in Figure 3.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 2 Comb-Based Superchannels  
A superchannel [73] is in the most broad sense a set of channels that is considered one entity, so that all subchannels are transmitted, routed and received together. Due to this, the channels can be spaced densely and guard bands, required to allow for individual routing of channels in an optical network, can be avoided. The concept was first proposed to allow increasing the channel bandwidth beyond what is possible on a single optical carrier due to limited bandwidth of electrical components. Since then, the concept has expanded to cover every aspect of multiple jointly-treated channels. The subchannels can be separated into either the spatial [74] or the spectral domain [75]. This section will discuss the challenges and benefits brought by using frequency combs to realize superchannels.  
Superchannels constructed by combining several densely-spaced wavelength carriers are required to reach target line rates approaching 1-10 Tb/s, needed to support next generation router interfaces [74]. Even looking beyond state-of-the-art electrical and optical hardware supporting bandwidths up to and beyond 100 GHz enabling demonstrations of 180-Gbaud quadrature phase-shift-keying (QPSK) transmission [76], the required electrical bandwidth is significantly beyond what can be achieved using a single wavelength carrier. Considering state-of-the-art integrated transceivers capable of transmitting symbol rates up to 100 Gbaud [77] and assuming that such a transceiver could reach the current record WDM spectral-efficiency of 14.2 bits/4D-symbol reported in [78], about seven transceivers are still required to reach the 10-Tb/s line rate. Following this, it is important to note that even though a superchannel consists of several subchannels, each subchannel needs to have as high throughput as possible to avoid loss in spectral efficiency.
# Mechanism of HCB-Modified Asphalt and Dynamic Properties of Mixtures
## 3 Test Methods
### Property Tests of HCB-Modified Asphalt  
In order to evaluate the modification effect of HCB on asphalt, basic indicators such as penetration, softening point, ductility, and Brookfield viscosity were measured according to the requirements of the Test Procedure for Highway Engineering Asphalt and Asphalt Mixture (JTG E20-2011). A dynamic shear rheometer (DSR) was used to measure the complex shear modulus (G*), phase angle (8), and rutting factor (G*/sin \(\delta\)) to evaluate the rheological properties of the modified asphalt.
## 1 Introduction
In addition, there are many differences in the results of realistic load testing among most related papers, as they are very theoretical and rely on several network simulation tools to predict the results [9]. Therefore, this paper proposes a topological sequence recognition mechanism for forming connected car-based subgroups and recognizing the relative sequence and distance according to the dynamic topology changes of the preceding or following vehicles for each vehicle in subgroups. The experimental procedure to configure subgroups of connected cars in this paper uses the Connected Mobile Virtual Fence (CMVF) system, which has been researched and developed in advance and supports vehicle-to-vehicle (V2V) communication [4; 10]. In the CMVF system, the CMVF client is installed in each smartphone, and each smartphone is mounted in a vehicle. Thus, for the purposes of this paper, it is assumed that the CMVF client is the same as the vehicle. The topological sequence recognition mechanism proposed herein calculates the driving direction of vehicles within a subgroup of connected cars, the geographical location and relative position associated with the direction, and the relative safety distance of each vehicle. The proposed mechanism to improve the CMVF system comprises three algorithms that detect the dynamic topology changes of vehicles in terms of sequence and position in the current subgroup during driving, and determine whether a vehicle is located at the front, middle, or rear. The information that the driving vehicle itself is aware of regarding the sequence is a very important criterion for disseminating driving information messages, and in some cases, it can prevent problems such as the broadcast storm, message flooding, or message contention. The reason is that if any vehicle in a subgroup has an accident and all vehicles know their position in the sequence, preceding vehicles need not receive the emergency message; the message only needs to be transmitted to the following vehicles. In addition, the proposed mechanism applies to only an arbitrary subgroup of connected cars, but the same mechanism is also applied to recognize the geographical location and relative position of all vehicles among subgroups. In the near future, it is expected that the proposed mechanism will be implemented in the technology of connected cars and autonomous vehicles to help realize more efficient message dissemination and a safer transportation environment. The remainder of the paper is structured as follows. Section 2 introduces the CMVF system briefly and proposes a topological sequence recognition mechanism. Section 3 describes and evaluates the results of actual road driving experiments to verify the three algorithms proposed in the previous section. The last sections present the discussion and conclusions.
## 2 Materials and Methods
### Eddy Covariance and Ancillary Measurements
#### 2.3.4 Light Response Curve Fitting  
Light response curves (LRC) of daytime GPP were fitted at half-hourly time resolution using the logistic sigmoid approach by [61]:  
\[\text{GPP}=2\times\text{GPP}_{\text{max}}(0.5-\frac{1}{1+exp(\frac{-2a\text{ PAR}}{\text{GPP}_{\text{max}}})}) \tag{3}\]  
where PAR is the photosynthetically active radiation, \(\alpha\) is the apparent quantum yield in mol (CO\({}_{2}\)) mol\({}^{-1}\) (phot.) that describes the effectivity of photosynthesis at low light conditions, and the GPP\({}_{\text{max}}\) is the asymptotic maximum assimilation rate at the light saturation point in \(\upmu\)mol m\({}^{2}\) s\({}^{-1}\). The fitting was done separately for half-hourly values from years with normal conditions (2014 and 2016) and the year with drought stress conditions (2015). To eliminate night-time measurements, we used a PAR threshold 10 \(\upmu\), i.e., light compensation irradiance of 10 \(\upmu\)mol m\({}^{2}\) s\({}^{-1}\), representing light compensation point of NEE.  
Since VPD and soil moisture affect GPP [49; 62], the effects of VPD and SVWC on the LRC residuals (GPP values after the removal of the strongest PAR dependence) were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**YEARS** & \multicolumn{2}{c}{**CZ-BK1**} & \multicolumn{2}{c}{**CZ-RAJ**} \\ \hline  & SPEI VALUE & CLASS & SPEI VALUE & CLASS \\
2014 & 0.94 & Near Normal & 0.56 & Near Normal \\
2015 & \(-1.75\) & Severe Drought & \(-1.55\) & Severe Drought \\
2016 & \(-0.20\) & Near Normal & \(-0.87\) & Near Normal \\ \hline \hline \end{tabular}
\end{table}
Table 4: Categorization of dryness/wetness using Standardised Precipitation-Evapotranspirationindex (SPEI) indices for CZ-BK1 and CZ-RAJ stations in years (2014–2016).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Drought/ Wet Severity** & **SPEI Value** \\ \hline Extremely Wet & \(\geq\)2.00 \\ Severely Wet & 1.50–1.99 \\ Moderately Wet & 1.00–1.49 \\ Near Normal & \(-0.99\)–(0.99) \\ Moderate Drought & \(-1.00\)–(\(-1.49\)) \\ Severe Drought & \(-1.50\)–(\(-1.99\)) \\ Extreme Drought & \(\leq-2.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Standardised Precipitation-Evapotranspiration Index (SPEI) categories based on the classification of SPEI values by [58].  
analysed for both spruce forest sites within the period under study. A piecewise regression was performed on the LRC residuals versus VPD and SVWC to determine the site-specific environmental stress thresholds at which GPP declined during the growing season periods of normal and drought affected years.
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion
### Open-Set/Weakly Supervised Methods  
The BGAD method [33] achieved strong pixel-level performance (Table 2), successfully localizing small abnormalities, such as in sample C.I, and demonstrating robust localization in samples C.III, S.II, A.I, and A.III (Figure 8). This method is comparable to CFLOW-AD, as both networks share a similar pipeline, highlighting the significant performance boost provided by the contrastive supervision employed in BGAD. This remarkable improvement in performance can also be attributed to the two-stage training framework. In the first stage, the normal distribution is learned without contamination from abnormal samples, significantly reducing the risk of overfitting. In the second stage, the model's discriminative capabilities are enhanced by incorporating examples of abnormalities.  
In turn, DRA emerged as one of the best-performing image-level classifiers in our benchmark study (Table 2), demonstrating notable effectiveness despite its simplicity. As seen in the SupIC baseline, DRA's supervised network head plays a critical role in achieving this performance. One advantage of DRA over SupIC is that the reduction of abnormal training data does not cause as sharp a decline in performance, thanks to the embedded unsupervised mechanisms. However, when only a small number of abnormal training samples are available, DRA's performance lags behind that of several unsupervised AD methods. This limitation primarily arises from the negative contribution of the supervised head to the abnormality score, which can overturn favorable decisions made by the remaining heads of the model. Additionally, a drawback of DRA is its inability to directly produce pixel-level predictions, restricting its application to identifying abnormal samples without the capability to localize anomalies within the images.  
Figure 8: Anomaly maps generated by one semi-supervised, one open-set supervised, and one fully supervised method for the samples shown in Figure 3.
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
## 3 Results  
### Demographic Characteristics  
A total of 123 patients with brain metastases included in radiosurgical treatment were collected. Of the total sample, two patients did not complete radiosurgical treatment due to clinical worsening, so they were excluded from the final study cohort. Therefore, a total of 121 patients with BMs treated with VMAT-RS (74 males and 47 females) were analyzed. Among patients with lung cancer, the prevalence of men was higher than that of women (74.4% men vs. 25.6% women, \(p\) < 0.001), while patients with breast cancer were all women. The mean age (\(\pm\)SD) of the patients was 62.7 \(\pm\) 12.0 years (Figure 1A). The mean age of treated male patients was 64.3 \(\pm\) 11.1, while that of females was 60.3 \(\pm\) 13.1 years (Figure 2B-D), with no statistically significant differences (\(p\) = 0.08). Regarding the age distribution among the different types of primary tumors, on the one hand, those with gastrointestinal origin were the oldest (median \(\pm\) IQR; 74 \(\pm\) 17), followed by those with prostate origin (70.5). On the other hand, patients with BMs from breast cancer (52.5 \(\pm\) 22) and melanoma (51) were the youngest (\(p\) = 0.006) (Figure 2E). Finally, the most frequent primary tumor was lung cancer (67.8%), followed by breast (13.2%), gastrointestinal (7.4%), genitourinary (5.0%), melanoma (2.5%), prostate (1.7%), and the remainder (2.5%) (Figure 2E, Table 1). Figure 1: (**A**) Planning example of a patient with three brain metastases treated with 18 Gy. The images (**B–D**), represent the axial, coronal, and sagittal plane rotations, respectively. Red PTV outline, green 12 Gy isodose, light blue 9 Gy isodose, and dark blue 4.5 Gy. The 18 Gy isodose is not shown since it practically coincides with the PTV outline and would otherwise hinder viewing. Figure 2: Histogram showing the age distribution of patients with brain metastases treated with VMAT-RS: (**A**) Complete cohort, (**B**) female cohort, and (**D**) male cohort. (**C**) Boxplot of the comparative  
age variables between male and female subgroups. (**E**) Boxplot of comparative age variables between different types of primary tumors. (**F**) Sector diagram showing the relative frequencies of primary tumors in patients with BMs treated with VMAT-RS. Lung (67.8%, \(n\) = 82), breast (13.2%, \(n\) = 16), gastrointestinal (7.4%, \(n\) = 9), genitourinary (5.0%, \(n\) = 6), melanoma (2.5%, \(n\) = 3), prostate (1.7%, \(n\) = 2), and the remainder (2.4%, \(n\) = 3).
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 1 Introduction
The main contribution of our work consists of three parts:  
* A dual-generator generative adversarial network (DGGAN) is proposed to improve the accuracy of video anomaly detection;
* A noise generator is designed to generate pseudo-anomaly frames to train the model, which improves the ability of the model to perceive unknown anomalies;
* A second-order channel attention module is used to learn feature interdependencies to better utilize the important feature information. Figure 1: Illustration of the reconstruction-based video anomaly detection method. The video dataset is divided into individual frames, which undergo feature extraction to produce a feature map. During the training phase, the normal video frames are used to train the model for reconstruction. In the testing phase, the test video frames are fed into the trained model for reconstruction, and the distance between the reconstructed features and the original features is calculated and normalized to obtain the reconstruction error. The reconstruction error is then compared against a predetermined threshold to classify whether the video frame is normal or anomalous. The rest of the article structure is presented as follows. Section 2 briefly describes the recent related work. Section 3 details the components of the proposed DGGAN. Section 4 shows the superiority of DGGAN through experiments and comparisons. Finally, we summarize the work and look forward to promising research directions.