esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion
### The Range of the Non-Detection Zone
According to the theoretical \(S0\) wave group velocity (\(Cg_{S0}\)) in the dispersion curve [27] and the width of the first terminal-reflected pulse (\(\Delta h\)) in the experiment, the curves of the non-detection zone range are calculated and are shown in Figure 7. For the ferrous material used in this study, for which the frequency-thickness product (\(f\cdot d\)) is 30 \(\times\) 30 kHz\(\cdot\)mm, the range of the corresponding non-detection zone (\(\Delta L\)) is 0.6390 m in the 6000 mm long steel square bar; This is basically coincident with the simulations. During the entire process, below a value of 2.5 MHz\(\cdot\)mm, the range of the non-detection zone decreases with the increase in the frequency-thickness product; when the frequency-thickness product is about 2.5 MHz\(\cdot\)mm, the minimum length of the non-detection zone is 0.2 m. In the range of 2.5 MHz\(\cdot\)mm to 5.0 MHz\(\cdot\)mm, the range of the non-detection zone increases with the increase in the frequency-thickness product. When the ultrasonic guided wave has a frequency-thickness product of 5.0 MHz\(\cdot\)mm, the range of the non-detection zone varies slightly. In an actual long-distance ultrasonic guided wave propagation process, the range of the non-detection zone expands with the widening of the first terminal-reflected pulse wave, which is a new reference factor to be considered. Similarly, the simulations are conducted using different materials [1] and the other conditions remain unchanged, as the simulated non-detection zone shows in Figure 7. The materials have different properties, resulting in the increase in the minimum length of the non-detection zone. Because the minimization of the non-detection zone is desirable, alloying may present a solution in the low-frequency range. Although the flaw's pulse signal is lost in monitoring Interval I, the reflected pulses carrying the flaw information can still be detected in other monitoring intervals, thus allowing the determination of the flaw's location. By analyzing the simulation results that satisfy the conditions for the appearance of the non-detection zone, it can be found that the overall pulse waveform exhibits apparent variations in monitoring Interval II.
###### Abstract  
Terahertz (THz) communication is regarded as a key technology for achieving high-speed data transmission and wireless communication due to its ultra-high frequency and large bandwidth characteristics. In this study, we focus on a single-carrier THz communication system and propose a two-stage deep learning-based time-domain equalization method, specifically designed to mitigate the nonlinear distortions in such systems, thereby enhancing communication reliability and performance. The method adopts a progressive learning strategy, whereby global characteristics are initially captured before progressing to local levels. This enables the effective identification and equalization of channel characteristics, particularly in the mitigation of nonlinear distortion and random interference, which can otherwise negatively impact communication quality. In an experimental setting at a frequency of 230 GHz and a channel distance of 2.1 m, this method demonstrated a substantial reduction in the system's bit error rate (BER), exhibiting particularly noteworthy performance enhancements in comparison to before equalization. To validate the model's generalization capability, data collection and testing were also conducted at a frequency of 310 GHz and a channel distance of 1.5 m. Experimental results show that the proposed time-domain equalizer, trained using the two-stage DL framework, achieved significant BER reductions of approximately 92.15% at 230 GHz (2.1 m) and 83.33% at 310 GHz (1.5 m), compared to the system's performance prior to equalization. The method exhibits stable performance under varying conditions, supporting its use in future THz communication studies.  
a Article
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Strehl Ratio  
The SR of BDCOA is analyzed with different population sizes as shown in Figure 10. Moreover, the evaluation of SR with different optimization methods is shown in Figure 11. From the figures, it is confirmed that the SR of BDCOA is more improved than the SR of MOA, POA, and COA. Through the minimization of phase distortions by achieving optimum control signals from BDCOA for DM, the peak intensity and sharpness of focused light are enhanced in FSO. Accordingly, this leads to high SR, representing the FSO's performance nearer to the ideal case.  
Figure 10: SR for different population sizes.  
Figure 9: Coupling efficiency for different optimization approaches.
## 3 Case I: Passive Eavesdropper
### Blind Feedback Scheme  
The conventional scheme without feedback with the RLS algorithm and the SPA decoding algorithm is an optimal estimation [40] and decoding algorithm, but with a high computational complexity [41], because it requires an updated LLR sequence and decoding for each iteration. It is not suitable for short-packet FD transmission due to the high estimation error of the SI channel [10] and power consumption in IoT applications and green communications due to the high latency of the 5G QC-LDPC decoder [42; 43]. To overcome these drawbacks, the authors in [27] proposed a joint iterative algorithm for blind channel estimation and decoding, named the blind feedback scheme. The fundamental process of the blind feedback scheme is that the SI cancellation, intended channel estimation, and decoding processes of the desired signal can benefit from each other through the temporary decoding and feedback loop. Hence, the proposed scheme will only consider one iteration of SPA decoding (\(j_{max}=1\)) for each joint iteration \(i\), called temporary decoding, and it will then perform the re-encoding, re-interleaving, and re-modulating to form a feedback loop of the intended signal in order to improve the SI cancellation process in the next joint iterations. This is continue until the system reaches the maximum number of joint iterations \(i_{max}\). The proposed algorithm can not only decrease the processing time and computational complexity, but it can also improve the overall performance, which is illustrated in [27]. The flow chart of the proposed blind algorithm is described in Figure 6, which has four main steps and can be summarized as follows:  
**Step 1:** The mixed signal \(\mathbf{y_{A}}\) at the receiver side is firstly used to estimate the SI channel \(\hat{\mathbf{h}}_{AA}\) and cancel the SI component based on the reference transmitted signal \(\mathbf{x}_{A}\);  
**Step 2:** The residual signal \(\bar{\mathbf{y}}_{A}\) received from Step 1 is continuously used to estimate the intended channel \(\hat{\mathbf{h}}_{BA}\) and obtain the equalized signal by an equalizer. Here, the _blind_ channel estimation method is applied with no knowledge of the transmitting signal from the transmitter. Then, this equalized signal goes to the demodulator and de-interleaver to obtain the LLR belief information sequence.  
**Step 3:** In this step, the estimation of the binary intended signal is achieved by using 5G QC-LDPC decoding with the SPA algorithm.  
**Step 4:** When the maximum number of joint iterations (\(i_{max}\)) is not reached, the temporary message obtained from the previous Step is re-encoded, re-interleaved, and re-modulated. Then, this signal is filtered with the estimation version of the intended channel \(\hat{\mathbf{h}}_{BA}\) achieved in Step 2 to form the intended feedback signal \(\hat{\mathbf{y}}_{BA}\). Consequently, the intended feedback signal is used to temporarily remove the intended component from the received signal in order to optimize the SI channel estimation process for the next joint iteration.  
Figure 6: Joint iterative blind algorithm flowchart.
# Feasibility of Utilizing Biodegradable Plastic Film to Cover Corn Silage under Farm Conditions
###### Abstract  
The degree of anaerbiosis and its maintenance over the conservation period are key factors in obtaining high quality silage. There is currently a demand to replace petroleum-based plastic films with biodegradable materials with suitable mechanical properties. This work has evaluated, under outdoor conditions, the shelf life of a Mater-Bi(r) biodegradable plastic (MB) film and its effects on the fermentative characteristics, microbial counts and aerobic stability of corn silage, and compared it with commercially available polyethylene (PE) and high oxygen barrier (OB) films. Corn (409 g DM/kg) was ensiled in 30 drive-over piles covered with MB, PE or OB films. The piles were opened after 21, 85, 133, 195 and 230 d of conservation. The effect of the film was assessed in silage sample close to (CF) and far (FF) from the film. The OB film allowed high quality corn silages to be obtained with similar pH, lactic acid, yeast and mold counts for CF and FF during the entire 230 d of conservation. The PE film showed similar values for the FF and CF areas for the first conservation period (until 133 d). The MB film showed a similar silage quality to OB until day 85, after which it underwent biodegradation and lost its ability to preserve silage in a good state.  
biodegradable film; farm conditions; Mater-Bi(r); oxygen barrier film; polyethylene film; silage quality +
Footnote †: journal: Applied Physics Letters
## 2 Proposed Method
### Generation of New Images from a Query Image  
The new image (\(\mathbf{I}_{new}\)) can be generated from a query image (\(\mathbf{J}\)) as follows:  
\[I_{new}(u,v)=(1-\mathcal{B}(u,v))\hat{J}(u,v)+\mathcal{B}(u,v)\hat{J}(u,v). \tag{6}\]  
Unlike during the phase of B-WIM extraction, a variational image (\(\mathbf{j}\)) derived from \(\mathbf{J}\) cannot be obtained in the phase of image generation. Therefore, \(\mathbf{j}\) must be replaced with another image in a separate auxiliary set. In WIM, the mean image for each variation in the auxiliary set is used as \(\mathbf{j}\). Although it can be applied equally to all query images, morphological elements may be lost if the variation's own changes are large. For example, muffers can be worn in various ways depending on a person's personality. Moreover, the designs of muffers are also very diverse. Therefore, the mean image cannot preserve the form of all muffers.  
In this study, we select the neutral image (i.e., nearest neighbor) of the auxiliary set with a minimum Euclidean distance (L2-norm) from the query image based on the whole pixel [28].  
\[d(\mathbf{I},\mathbf{J})=||\mathbf{I}-\mathbf{J}||_{2}^{2} \tag{7}\]  
Then, \(\mathbf{j}\) is replaced by \(\mathbf{\hat{I}}_{id}\), derived from \(\mathbf{I}_{id}\), where \(id\) is the index with a minimum distance from \(\mathbf{J}(\min(d(\mathbf{I},\mathbf{J})))\). Equation (6) is redefined as  
\[I_{new}(u,v)\approx(1-\mathcal{B}(u,v))\hat{J}(u,v)+\mathcal{B}(u,v)\hat{I}_{ id}(u,v). \tag{8}\]  
Finally, \(\mathbf{I}_{new}\) is generated from Equation (8).  
The overall procedure of the proposed method is summarized as follows:  
* **Step 1**: Extraction of the normalized WIM using log-scaled standard deviation of the absolute difference between \(\mathbf{I}\) and \(\mathbf{I}\) in the auxiliary set;
* **Step 2**: Binarization of WIM from threshold (\(\theta\)) ;
* **Step 3**: Selection of the index (\(id\)) of the nearest neighbor in the auxiliary set based on Euclidean distance with the query image; (\(\min(||\mathbf{I}-\mathbf{J}||_{2}^{2})\))
* **Step 4**: Replacement of \(\mathbf{j}\) with \(\mathbf{\hat{I}}_{id}\), derived from \(\mathbf{I}_{id}\);
* **Step 5**: Generation of the new image (\(\mathbf{I}_{new}\)).  
Figure 5: Generated images from \(\mathbf{I}\) and \(\mathbf{I}\) for each threshold (\(\theta\)).
## 3 Application and Results  
### Study Area and Data  
In this study, data on the statistics of waterworks between 2005 and 2018 were used to initially select the study areas. Among the 163 local small and medium cities across South Korea, 22 study areas were selected to ensure there were no missing data or errors in terms of collecting data and that the leakage reduction effect was clear according to the activity of leakage management. To be defined as small and medium cities, cities had to be of typical type, not too big or too small. In Korea, city size is generally defined by population. The population of local small and medium cities is at least 50,000, and the maximum range of population is 1 million. The selected cities' data were accumulated for up to 14 years. For leakage management factors, PR, LR, and LD were divided by the total pipe length, and MR was used as the value divided by the water supply population in order to offset the difference depending on the size of WDS. In addition, only leakage management factors with either a revenue water ratio less than 80% or a leakage ratio higher than 10% were applied. Examples of data are shown in Table 1.
# Research on an Evaluation Method of Snowdrift Hazard for Railway Subgrades
## 3 Project Overview and Results
### Wind Speed and Direction Characteristics  
Wind is one of the critical contributing factors to the occurrence of snowdrift. Remote monitoring data indicate significant variations in wind speed and direction along the railway line. By selecting the measured wind speed values greater than the threshold wind speed of 3.3 m/s at a height of 3 m during the winter months (November to March) [19], and analyzing their frequency of occurrence as shown in Figure 6, it is determined that sections of the railway line with strong wind forces prone to snowdrift are in the region DK38 + 800 to DK67 + 600.  
The monitoring equipment within this region reveals that, based on the monitored wind speed and direction data, except at DK42 + 800, where the prevailing winds are mainly from the east (E) and east-southeast (ESE), the wind at the other five meteorological stations is primarily from the north-northwest (NNW), north (N), and north-northeast (NNE), as  
Figure 5: Monitoring sites and test section areas of weather stations.  
shown in Figure 7. Given that the Afu Railway generally runs in a northwest-southeast direction, the prevailing winds form a significant angle with the railway route, which exacerbates the impact of snowdrifts.
# Characteristics of the Damping Ratio of Undisturbed Offshore Silty Clay in Eastern Guangdong, China
## 3 Results and Analysis
### The Influence of Initial Deviatoric Stress and Cyclic Stress History  
Figures 8 and 9 present the stress-strain curves and damping ratio-cyclic shear strain curves for overconsolidated silt clay under symmetrical and asymmetrical loading, respectively. The influence of cyclic stress history on the damping ratio is significant; when the initial deviatoric stress is zero, the development of the damping ratio generally exhibits three stages: an initial decrease, followed by an increase, and subsequently another decrease. It is important to emphasize that, for each individual test, the damping ratio of the soil is primarily influenced by two factors: cyclic shear strain and the number of cycles. Assuming a constant number of cycles (N), the soil damping ratio increases with increasing cyclic shear strain. Conversely, assuming a constant cyclic shear strain, the soil damping ratio decreases with increasing the number of cycles [15]. Therefore, in each individual test, both cyclic shear strain and the number of cycles jointly influence the evolution of the damping ratio. However, the dominant factor governing the damping ratio varies at different stages, leading to the aforementioned observations. Specifically, a cyclic threshold exists, but due to limitations in the number of tests conducted, this study was unable to quantitatively investigate this threshold. This phenomenon has also been observed in the literature [27], and similarly, the underlying mechanism remains insufficiently explained. In comparing Figures 8 and 9, it is evident that the presence of initial deviatoric stress  
Figure 7: Illustration of Hysteresis Loop Processing under Asymmetric Cyclic Loading.  
significantly influences the damping ratio. This initial stress leads to a larger damping ratio at the same cyclic shear strain.  
Figure 8: _Cont._  
Figure 8: Stress–strain and damping curve under symmetric cyclic loading under \(\tau_{\alpha}\)/\(\sigma_{\alpha}\)\({}^{\prime}\) = 0.18, \(\tau_{\alpha}\)/\(\sigma_{\alpha}\)\({}^{\prime}\) = 0.15  
Figure 9: _Cont._  
Figure 10 shows the stress-strain curves and damping ratio-cyclic shear strain curves for normally consolidated silt clay under symmetrical loading. Unlike the behavior observed in overconsolidated clay, the development of the damping ratio for normally consolidated silt clay generally exhibits two stages: an initial decrease followed by an increase. It is important to note that, due to the limited number of undisturbed samples, the impact of cyclic stress history on the damping ratio of silt clay can only be qualitatively described in this study.  
Figure 10: _Cont._  
Figure 9: Stress–strain and damping curve under asymmetric cyclic loading for overconsolidated silt clay.
## 6 Clinical Presentation  
Athletes typically present with unilateral upper limb weakness and a dermatomal or non-dermatomal burning sensation (hence the terms \"burner\" and \"stinger\") that begins at the supraclavicular area and radiates down the upper limb after forceful contact between the shoulder and neck [6, 33, 34, 35]. Other sensory symptoms like numbness and a sensation of \"dead arm\" are also common [35]. The symptoms widely vary, ranging from only sensory abnormality in a dermatomal fashion to complete loss of strength and sensation in the affected limb [36]. A Canadian study of football players by [PERSON] et al. reported tingling in 77%, numbness in 61%, weakness in 44%, and neck pain in 17% of athletes [37].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Grade** & Nerve Injury & Mode of Treatment \\ \hline \multirow{2}{*}{**I**} & Neurapraxia & Usually transient with no need for any intervention. Pain control, rest, and physiotherapy as needed if symptoms persist. Consider MRI and other imaging with serial examinations if symptoms persist for \textgreater{}5 min. \\ \hline \multirow{2}{*}{**II**} & Axonotmesis & Pain control, rest and physiotherapy, serial monitoring with electrodiagnostic studies, and imaging (MRI, radiographs). \\ \hline \multirow{2}{*}{**IIIA**} & Neurotmesis & Imaging, electrodiagnostic studies, nerve repair \(\pm\) nerve transfer [28, 29, 30, 31], pain control, and physiotherapy. \\ \hline \multirow{2}{*}{**IIIB**} & \multirow{2}{*}{Nerve root avulsion} & Imaging, electrodiagnostic studies, nerve transfer [28, 29, 30, 31], pain control, and physiotherapy. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grades of stinger injury and the mode of treatment.  
Depending on the severity of the injury, symptom duration can range from seconds to days [7]. Knowing the mechanism of injury is crucial in evaluating an athlete with stingers since it can inform other possible injuries. The mechanism of injury is usually witnessed by the sideline medical staff or from video recordings of the incident. An alternative diagnosis of vertebral fracture, spinal cord, brain, or vascular injury can be indicated by other clinical symptoms and findings, such as persistent neck pain with limited range of motion, lower extremity involvement, bilateral upper extremity involvement, headache, dizziness, nausea, vomiting, changes in mentation, speech, and vision, and changes in vital signs like blood pressure, brachial-brachai index, heart rate, and temperature [7; 38; 39; 40; 41]. The presence of these symptoms warrants further evaluation.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### Elliptic Curve Criptography (ECC)
#### 3.3.2 Integrity and Authentication in ECC and ECDSA  
Digital signatures [37; 38] generated using ECDSA guarantee authentication and integrity. The procedure done to generate and verify the digital signature is the same of that used for the RSA algorithm. So, the node will generate the digital signature using its own private key and then the server, upon receipt of the tag, in order to verify that the messages are not modified illegally during the transmission, will check the validity of the tag using the public key of the sensor. The whole security mechanism, ensuring confidentiality, integrity and authentication is depicted in Figure 7.  
Figure 6: ECC sent packet.  
Figure 7: Confidentiality, Integrity and Authentication in ECC.
## 3 Experiment
### Preliminary Experiment
#### 3.1.4 Confidence Adjustment  
An example of adjusting detection target confidence:  
1. The format of the \(i\)-th target output detected after the image passes through the YOLO detection network is category, confidence C, and location coordinates (left, top, right, and bottom).
2. Obtain the coordinates of the target center point according to Formulas (3) and (4): \((\frac{left+right}{2},\frac{top+bottom}{2})\).
3. The value in the distribution matrix of Figure 3 corresponds to the target center point: \(C_{\frac{left+right}{2}}\)  
Figure 5: Three-dimensional distribution diagram: (**a**) label calculation distribution; (**b**) unsupervised learning acquisition distribution.  
Figure 6: Adjustment Matrix.  
* The value in the adjustment matrix of Figure 6 corresponds to the target center point: \(f(C_{\frac{left+rightright}}{2},\frac{left+bottom}{2})\).
* According to Formula (2), if \(C_{\frac{left+right}+right}{2},\frac{left+bottom}{2}\) > \(r_{i-1}\) and \(C_{\frac{left+right}}{2},\frac{left+bottom}{2}\) < \(r_{i}\), then \(f(C_{\frac{left+right}+right},\frac{left+bottom}{2},\frac{left+bottom}{2})=a_{i}\).
* The new confidence value is \(C_{new}=C\times a_{i}\). The confidence adjustment code is shown in Figure 7. Line 2 and line 3 obtain the center point coordinates of the prediction box, and line 4 obtains the position of the center point corresponding to the adjustment matrix and is to read the adjustment matrix in the form of a one-dimensional array. Line 5 adjusts the confidence, and line 8 records the category and adjusts the confidence and box coordinates in the file.