esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, a coupled static and dynamic SHPB apparatus was replicated using the DEM numerical method. The main conclusions are listed as follows:  
1. The influence of dynamic loading on both stress and energy characteristics is remarkable with clear laws. When the pre-stress is constant, with the increase in the impact speed within the research range, both dynamic strength and maximum strain energy of the specimen increase in an approximately linear way. The maximum kinetic energy grows slowly at first and then grows rapidly. The dynamic loading also strongly affects the release characteristics of the strain energy. Higher impact speeds contribute to more strain energy stored with the specimen and more violent release of it.
2. A static pre-stress only shows a clear influence on the strain energy characteristics when the dynamic loading is weak; namely, when the impact speed is relatively low. In such a case, the value of the maximum strain energy stored in the specimen increases slowly in an approximate way with the increase in the pre-stress. However, with the increase in the impact speed, the influence of the pre-stress on the strain energy characteristics decreases with a less clear change law. As for the maximum kinetic energy stored in the specimen during the impact test, the pre-stress fails to show a remarkable and clear influence on it.
3. Pre-stress affects the mechanical behavior of specimens significantly at lower impact speeds. Even if a low-speed impact fails to break the specimens without pre-stress or at lower pre-stress levels, it may be able to trigger the instability of the specimen at higher pre-stress levels. However, for specimens at higher impact speeds which are enough to break them without pre-stress, pre-stress has limited influence on the peak stress of the specimens, but hardly affects their failure pattern, which is dominated by the high impact speed.  
Conceptualization, J.S. and L.T.; methodology, L.D.; software, L.T.; validation, G.W.; formal analysis, G.W.; investigation, J.S.; resources, L.D.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, L.T. and H.P.; visualization, L.T. and H.P.; supervision, L.D.; project administration, L.D.; funding acquisition, L.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51934007, the Department of Education of Hunan Province, grant number 19B479, and the Natural Science Foundation of Hunan Province, grant number 2022 JJ40351.  
Not applicable.  
Data Availability StatementData are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 2 LCLS Recent Representative Highlights
### Materials Physics  
Ultrafast X-ray scattering studies at LCLS resolved for the first time the mechanism responsible for the incipient ferroelectric behavior in the bulk thermoelectric PbTe (Figure 3), and demonstrate the critical importance of electron-phonon interactions [8]. In PbTe and related materials, the ferroelectric instability is associated with thermoelectricity, phase-change behavior, and superconductivity. However, the origin of the instability has long been controversial. Recent studies have focused on the role of anharmonic phonon-phonon interactions in these materials, while largely overlooking the role of electron-phonon coupling. LCLS studies, using the novel approach of Fourier-transform inelastic X-ray scattering (FT-IXS) [9; 10], show that ultrafast infrared excitation transiently stabilizes the paraelectric phase, coupling the transverse optical and acoustic phonons propagating along the bonding direction. An important conclusion is that near band-gap electrons preferentially interact with the soft-phonons to induce ferroelectric instability. These results further reconcile the band and bond pictures of ferroelectricity, which has broad implications for broken-symmetry states in materials with strong electron-phonon interactions.  
Figure 3: (**a**) Set-up for time-resolved inelastic X-ray scattering for probing nonequilibrium lattice dynamics. (**b**) Ultrafast two-phonon spectrum of PbTe obtained through Fourier transform of the femtosecond X-ray scattering signal. The results show a combination of modes (squeezed state) indicative of photo-induced stabilization of the paraelectric state [8].  
Figure 2: (**a**) Resonant inelastic X-ray scattering (RIXS) is an element-specific probe of occupied and unoccupied valence states in molecules. (**b**) Measured Fe L\({}_{3}\)-RIXS intensity maps for Fe(CO)\({}_{5}\) ground-state (top); and difference intensities for the time intervals 0–700 fs (middle) and 0.7–3.5 ps (bottom). Reprinted with permission from [7]. Copyright Nature Publishing Group, 2015.
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 3 Method of Polarization Radiometric Calibration
### Classical Radiometric Calibration
Substituting these equations into Equation (11), we can obtain  
\[C^{\theta}_{0,45^{\circ}}=\mathcal{F}\Big{\{}S_{0}(\sigma)+b_{1}c_{1}\Big{[}a _{2}D_{fore}(\sigma,\theta)S_{0}(\sigma)+a_{1}S_{2}(\sigma)\Big{]}\Big{\}}, \tag{23}\]  
\[C^{\theta}_{0,45^{\circ}}=\mathcal{F}\Big{\{}\frac{1}{4}b_{2}(1-c_{1})\Big{[} a_{1}D_{fore}(\sigma,\theta)S_{0}(\sigma)-a_{2}S_{2}(\sigma)+i\delta_{fore}( \sigma,\theta)S_{2}(\sigma)\Big{]}\cdot\exp\{-i[\varphi_{1}(\sigma,\theta)+ \varphi_{2}(\sigma)]\}\Big{\}} \tag{24}\]  
The channels \(C^{\theta}_{0,45^{\circ}}\) and \(C^{\theta}_{4,45^{\circ}}\) are extracted by the frequency filtering technique and performed Fourier transformation independently. Then, the expression of \(\delta_{fore}(\sigma,\theta)\) can be obtained  
\[\delta_{fore}(\sigma,\theta)=\frac{4\Big{\{}1+b_{1}c_{1}\Big{[}a_{2}D_{fore}( \sigma,\theta)+a_{1}\Big{]}\Big{\}}}{b_{2}(1-c_{1})}\cdot\text{Im}\Bigg{\{} \frac{C^{\theta}_{4,45^{\circ}}}{C^{\theta}_{0,45^{\circ}}\exp\{-i[\varphi_{1}( \sigma,\theta)+\varphi_{2}(\sigma)]\}}\Bigg{\}}, \tag{25}\]  
where Im means the operator to take the imaginary part, \(D_{fore}(\sigma,\theta)\) and \(\exp\{-i[\varphi_{1}(\sigma,\theta)+\varphi_{2}(\sigma)]\}\) was obtained in the previous calibration. Because \(A^{k}(\sigma,\theta)\), \(D_{fore}(\sigma,\theta)\), \(\exp\{-i[\varphi_{1}(\sigma,\theta)+\varphi_{2}(\sigma)]\}\), and \(\delta_{fore}(\sigma,\theta)\) will change with field of view, we should calibrate them at different field of view independently. To improve the efficiency of the calibration, we can calibrate them at part of all field of view, and get the calibration results across the entire FOV through curve fitting method. At this point, the accurate calibration of the parameters in the polarization radiometric calibration model is completed.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.5 Impact of Tracking Accuracy  
Figure 9 illustrates the impact of tracking accuracy, defined as the precision of the predicted positions of the sensor nodes. Tracking accuracy cannot be controlled because the IMM framework computes the locations. Thus, to quantify accuracy, we use the real positions unknown to the IMM in  
Figure 8: Impact of maximum travel distance.  
Figure 7: Impact of minimum coverage.  
addition to the accuracies specified in the figure. In IMM framework, the last estimation result will be added to each prediction cycle, and the tracking accuracy will be greatly reduced with the increase of speed and the extension of prediction time. Since the predicted positions directly affect the quality of a deployment, improved tracking accuracy results in better coverage.
## 2 Materials and Methods
### Data Analysis  
The CoM position was calculated using DhaibaWorks--our self-developed motion analysis software [20]. The full-body motion was reconstructed by fitting the marker trajectories and individual body model using a link structure. The dimensions of the body model were estimated statistically using the height and weight of the participant based on a database of Japanese body dimensions [20]. The CoM data were digitally filtered using a fourth-order Butterworth low-pass filter with a cut-off frequency of 10 Hz. The CoM movement during the stance phase of the right limb was extracted based on the ground reaction force data. The amplitudes of the CoM movement in the vertical, progression, and lateral directions during the stance phase were calculated. The walking speed was calculated from the CoM movement in the progression direction of the CoM and the time taken during the stance phase.  
The intrasubject mean and standard deviation (SD) values were calculated during the stance phase. Next, the coefficient of variation (CV = (SD/mean) \(\times\) 100) was calculated as an index of intrasubject variability [21]. The differences between the mean and CV values of the variables obtained with and without stimulation were determined as the effect of stimulation. In addition, to examine the impact of a measurement error, we calculated the minimal detectable change (MDC). The MDC indicates the minimal amount of change that is not likely to be considered a change due to an accidental error in measurement [22; 23]. First, the standard error of measurement (SEM) was calculated using the SD and intraclass correlation coefficient. Subsequently, the MDC was calculated using the relation: \(\text{SEM}\times 1.96\times\sqrt{2}\)[22]. Low-pass filtering and calculation of these variables were performed using MATLAB R2019a (MathWorks Inc., Natick, MA, USA).
###### Abstract  
To enhance the accessibility and accuracy of plant protection knowledge for agricultural practitioners, this study develops an intelligent question-answering (QA) system based on a large language model (LLM). A local knowledge base was constructed by vectorizing 7000 research papers and books in the field of plant protection, from which 568 representative papers were selected to generate QA data using an LLM. After data cleaning and filtering, a fine-tuning dataset comprising 9000 question-answer pairs was curated. To optimize the model's performance, low-rank adaptation (LoRA) was applied to the InterLM-20B model, resulting in the InterLM-20B-LoRA, which was integrated with Langchain-ChatChat and the local knowledge base to develop the QA system. Additionally, retrieval-augmented generation (RAG) technology was implemented to enhance response accuracy by enabling the model to retrieve relevant field-specific knowledge before generating answers, effectively mitigating the risk of hallucinated information. The experimental results demonstrate that the proposed system achieves an answer accuracy of 97%, highlighting its potential as an advanced solution for intelligent agricultural QA services.  
Keywords:plant protection; QA system; LLMs; low-rank adaptive; retrieval-augmented generation +
Footnote †: journal: Applied sciences
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 3 The Proposed Decoder Architecture
Here, the saturation values are within the range \([-Q,\cdots,+Q]\). Then these saturated data are sent to the Check Node Units (CNUs). For the HOMS algorithm, the output message of each CNU comprises the first minimum, its index, and the signs of the incoming VTC messages. This message is kept in a compressed format to save memory space, which is referred to as a compressed \(\beta-\) message, as seen in Figure 3.  
These compressed \(\beta-\)messages are fed in two directions: they are stored back into the Check Node Memory Blocks (CN-MBs) for the next iteration and directed to the VNUs/APBs for the next decoding layer processing. It is worth noting that the compressed \(\beta-\)messages from CN-MBs are sent back to VNUs/APBs through Decompress units (DECOMs). Two DECOMs are put into the decoder to convert compressed to uncompressed CTV messages. They create CTV messages for further processing. Similarly, the updated AP messages are rearranged using the Write Permutation Unit (Write-PU) to ensure that they can be placed in the appropriate addresses of AP-MB. The Split block creates the serial signal stream of \(Z\times\tilde{q}\) bits that is sent to the AP-MB. One decoding layer is done after writing them back to the AP-MB. To guarantee that the decoder operates properly, the controller block is required. This block controls the synchronous operation of the other ones. Additionally, it also creates control signals to manage memory access. Initially, AP-MB storage of all AP messages takes \(n_{b}=46\) clock cycles. The proposed decoder is designed to operate in two consecutive clock cycles in every layer. In the first clock cycle, the AP-reading signal is in the \"enable\" condition to provide the AP information messages that correspond to the current BG1 matrix layer. Following one another, operations for VN and CN are updated. In the second clock cycle, the decoder executes the AP processing. After AP information computations, they are written back to AP-MB by activating the AP-writing signal at a \"high\" level. The processing described above continues until the last layer (i.e., 24 th layer) of the BG1 matrix is finished, at the moment one decoding iteration is complete. These procedures are performed in 10 iterations during the decoding process. After the 10 th iteration, the LDPC decoder produces information bits, corresponding to the first 22 memory units of the AP-MB (e.g., \(22\times 192=4224\) bits). Figure 1: Block diagram of the proposed LDPC decoder architecture. Figure 2: An example of right-shifting by the two positions rule of R-SB block with 60 elements of \(\bar{q}\)-bits. The following subsections provide a detailed explanation of some main blocks in the proposed decoder architecture.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 2 Study Area and Data
### Data  
This study employs Sentinel-2 (S2) optical imagery and Sentinel-1 (S1) SAR imagery as the main data sources, augmented by DEM data, ground survey data, sample datasets, and other auxiliary sources for the cartographic delineation of Anji tea plantations. The introduction of the data sources is as follows.
# Enhancement of the Purcell Effect by the Wire Metamaterials Formed by the Hexagonal Unit Cells
## 1 Introduction  
Nanowire metamaterials, which drastically affect fundamental physics and practical applications, have attracted significant attention over the years. These nanowire metamaterials open wide avenues from which hyperbolic dispersion can be achieved [1]. Considerable scientific interest has been sparked due to the reason that conventional geometry (such as nanowire arrays [2] and metal-dielectric nanostructures [3, 4]), allows for the achievement of hyperbolic metamaterial functionality. It is worthwhile mentioning that one may tailor the mode patterns of surface plasmons in an extraordinary way, by means of a one-dimensional, metal-based, corrugated structure that represents a simple metasurface, possessing almost realistic geometry [5].  
In doing so, the nanowire composite is a unique example of a hyperbolic medium that opens wide avenues for the high density of states [6, 7, 8]. A broadband increase in the density of photonic states, in comparison with vacuum by the factor of the order \((\lambda/a)^{2}\), where \(\lambda\) is the wavelength and \(a\) is the lattice period [9], stands as a particular feature of the wire medium. This causes the remarkable enhancement of the light-matter interaction outcomes; the former includes the spontaneous emission (the Purcell effect) [10, 11, 12], Vavilov-Cherenkov radiation [13, 14], and heat transport [15].  
Our goal was to perform a comprehensive analysis that accounted for the finite dielectric constant of the wires. The work is organized in the following way: (1) our theoretical model is presented in Section 2; (2) the study of the eigenmode dispersion of the composite is presented in Section 3. Additionally, the analytical results for the Purcell factor are considered. The discussion about whether the Purcell factor is achievable in different cases is also summarized.
## 4 Simulation Studies and Performance Assessment
### Input Enhancement  
However, upon careful observation of Figure 7, there is still some latency in the data, and deviations exist between peak values. This misalignment is not ideal for frequency regulation. Before further data processing, the network training employs an input data enhancement method. This method not only improves the progress of network training but also reduces the number of required training epochs. Meanwhile, it achieves the same stability as previous SNN training.  
Table 3 presents the comparison results between the two methods, showing average performance values from five testing rounds. In addition to the performance indicators  
\begin{table}
\begin{tabular}{c c} \hline
**Description** & **Value** \\ \hline \multirow{3}{*}{Rated frequency Controller sampling time} & \multirow{3}{*}{MCG1-DG3} \\  & & 50 Hz \\  & & \(4\times 10^{-5}\) s \\  & DG1-DG3 \\  & & \(m=1\times 10^{-5}\) \\  & Local load & 20 kW \\  & Filter & 0.02 \(\Omega\); 3.6 mH; 200 μF \\  & & 1 kV \\  & Transmission Line & \\  & DG1 & 0.05 \(\Omega\); 1.2 mH \\  & DG2 & 0.10 \(\Omega\); 2.4 mH \\  & DG3 & 0.15 \(\Omega\); 3.6 mH \\  & SNN & \\ Hidden layer neurons & & 10 \\  & Input layer to hidden layer & \\  & Hidden layer to output layer & \\  & Input enhancement & \\  & Scaling value & \(\alpha=200\) \\ \hline \end{tabular}
\end{table}
Table 2: Specifications and key parameters for the microgrid system.  
Figure 7: Comparison between SNN forecasted and desired curves. (**a**) DG1, (**b**) DG2, (**c**) DG3.  
shown in Table 3, both the SNN and SNN with input enhancement scenarios yield R values of 0.99062 and R\({}^{2}\) values of 0.98133 across these five runs. The results indicate that using the input data enhancement method can reduce the number of training epochs, thereby accelerating the training speed, which provides a faster solution for future real-time online operation.  
The performance formulae involved here are as follows:  
\[R=\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}/{\left({\sqrt{\sum_{ i=1}^{n}{(x_{i}-\overline{x})^{2}}}\sqrt{\sum_{i=1}^{n}{(y_{i}-\overline{y})^{2}}}}\right)} \tag{18}\]  
\[R^{2}=1-\sum_{i=1}^{n}{(z_{i}-\underline{z}_{i})^{2}}/{\sum_{i=1}^{n}{(z_{i}- \overline{z})^{2}}} \tag{19}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(z_{i}-\underline{z}_{i})^{2}}}} \tag{20}\]  
where \(x_{i}\) and \(y_{i}\) represent the true values and predicted values, respectively, \(\overline{x}\) and \(\overline{y}\) represent the mean of true values and predicted values, respectively, \(\underline{z}_{i}\) is the actual observed value, \(\underline{z}_{i}\) is the predicted value, \(\overline{z}\) is the mean of all actual observed values, and \(n\) is the sample size.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
[PERSON] [PERSON] (2018) analyzed the de-icing dynamic response under strong wind conditions using finite element analysis, discovering that strong wind increased and decreased longitudinal bounce height. This change might reduce the risk of phase-to-ground discharges while potentially increasing the risk of inter-phase discharges. The above research provides a detailed analysis of the dynamic responses of overhead transmission lines under various operating conditions during ice shedding, including changes in jump height, lateral swing, and tension at certain typical locations on the conductor. However, there has been a lack of research on the changes in risk areas caused by ice shedding. In order to analyze single-phase discharge faults in transmission lines under icing conditions, this study established a finite element model of ice covered conductors and ground wires. It examined the variations in ground wire sag and conductor de-icing bounce height across different conditions, computed the changes in safety distance between the conductor and ground wire, and identified the most critical de-icing scenarios. This analysis provides theoretical support for understanding and mitigating phase-to-ground discharge faults in overhead lines under complex ice-covered conditions.
## 4 Discussion
Its compatibility with standard composite manufacturing processes and its activation under moderate heating conditions make it well suited for use in aerospace interiors, automotive panels, wind turbine structures, and other applications where repeated impact and limited maintenance access are of concern. Future studies should focus on evaluating long-term durability under environmental exposure, performance under higher-energy impacts, and the feasibility of industrial-scale production methods such as resin transfer molding or autoclave processing.
# Sound Localization Framework for Construction Site Monitoring
## 1 Introduction
Therefore, in this study, a framework for estimating sound-source locations at construction sites was developed and was applied to an actual site to derive test results. This study proposes a method that uses sound data to overcome the limitations of sensor- and visual-data-based technologies. Sound data complements the blind spots of a camera, and unlike high-resolution image data, the size of the data is only one-thousandth of the video data, thus, enabling the real-time data transmission at construction sites, even at low transmission speeds. Therefore, our aim is to establish a framework that serves as the basis of a system that can react to disasters proactively by identifying unstable conditions and behaviors. The objective of this study is to develop a sound localization framework for an effective monitoring method that can be applied for labor and safety management at construction sites. We believe this system can contribute to the identification of abnormal audio signals as well as the work status, progress, and machine productivity.
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
###### Abstract  
Monitoring inland waters is of critical importance for the effective and sustainable management of water resources, especially under climate change scenarios. This paper introduces a satellite-based approach for river monitoring using optical multispectral data. Time series of percentage water content, derived by the normalized difference water index (NDWI) calculated for each satellite acquisition, are aggregated at monthly timesteps to generate monthly water frequencies. Then, the river dynamics are evaluated by comparing each month with the previous one and with the average conditions of the same month in previous years. The ability of the method to investigate hydromorphological processes over time is demonstrated with the case study of the record-breaking Po River shrinking due to the severe 2022 drought in northern Italy, through the analysis of Copernicus Sentinel-2 satellite acquisitions. Earth observation data analysis is complemented with metrics generated from in situ river discharge measurements, including the coefficient of variation and the Streamflow Drought Index (SDI), to provide a more comprehensive understanding of the severity and variability of the hydrological drought throughout the year 2022. The findings demonstrate the satellite-based observation capabilities in monitoring surface waters, thereby stimulating the development of operational services like hydromorphological assessment.  
Keywords: river shrinking; hydromorphology; percentage water content; drought; hydrological extremes; warming climate; Sentinel-2 _Article_