esa-sceva/satcom-chunk-collection huggingface dataset:
# A Phase Correction Model for Fourier Transform Spectroscopy
## 1 Introduction  
In recent years, Fourier transform spectroscopy (FTS) has undergone significant development. It has been widely applied in various disciplines and fields such as analytical chemistry [1], hyperspectral imaging [2], frequency comb spectroscopy [3], earth sciences [4], ultrafast spectrometry [5], and so on. Traditional FTS provides interference data of spectra, requiring further processing of interferograms to obtain the desired spectral information. This includes operations such as apodization, phase correction, and fast Fourier transform, among others.  
In FTS, phase correction is crucial for eliminating phase errors in the recovered spectra. Several methods, including the double-sided interferogram method, the convolution method, and the Mertz method [6; 7; 8], can be employed. The double-sided interferogram method calculates all data points in a double-sided interferogram, completely eliminating phase errors. However, it is highly sensitive to noise. Therefore, in the recovery of spectra, single-sided interferogram methods are commonly employed. Among them, the Mertz method and the convolution method are the most commonly used. The Mertz method is easy to compute, but it may not be as effective in correcting nonlinear phase errors. On the other hand, the convolution method can correct nonlinear phase errors, but when the phase error is substantial it may require multiple convolution operations, resulting in increased computational complexity and instability [9; 10].  
Using the phase correlation method to correct phase errors in FTS was first proposed by [PERSON] in 2011 [11]. This method can reach high precision in linear phase correction. The phase correlation method involves time delay calculation of two channels of interferograms to correct linear phase errors.  
A phase correction method based on all-pass filters was initially proposed by [PERSON] in 2005 [12] and improved by [PERSON] in 2015 [13], who introduced an all-pass filter-delayer-phaser (ADP) model for phase correction. The all-pass filter has a magnitude-frequency response of 1 across the entire frequency range. Therefore, it is used as a filter to correct residual phase errors in the interferogram. In this way, the amplitude of the output spectra remains stable, and only the phase is modified [12; 13; 14].  
This paper presents a practical phase correction model called the PCM-APF model. The proposed model shows high correction accuracy and efficiency, combining the phase correlation method with all-pass filters. For filter optimization algorithm, we adopt an improved gold sine-subtraction average-based optimizer (GS-SABO) algorithm, combining the subtraction average-based optimizer (SABO) and the golden sine algorithm (Gold-SA) to optimize the parameter settings of the filters [15; 16]. The PCM-APF model uses an improved phase correlation method to correct linear phase errors, while all-pass filters are used to correct the remaining phase errors.
## 2 Results
### Reproducibility  
The reproducibility of the methods varied by technique and by compound (Table 1). To compare the reproducibility of the methods, the percent relative standard deviation (RSD's) for every compound extracted by each method was averaged to provide an average percent relative standard deviation (ARSD) for the method. Based on the average percent relative standard deviations across all compounds SPME and SBSE were the most reproducible with 14.3% and 15.4% ARSD. HSSE and SAFE had higher levels of variability with 26.7% and 49.7% ARSD. In general, the RSD's were acceptable for most compounds detected by SAFE but the terpen were less consistent and may have been, at least partially, due to contamination from the environment. For example, limonene and \(p\)-cymene had RSD's around 120% and 94% respectively while the RSD for dimethyl sulphone was only 8.3%, heptanal was 13.0%, and the internal standards were all 5.3% or less. HSSE presented similar discrepancies with a minimum RSD of 3.0% for 1-pentanol and a maximum of 99.5% for acetic acid. SPME and SBSE were more consistent overall, with a lower range of RSD values.  
On the low end, SPME delivered RSD's of 7.0% for decane and 7.6% for pentanal, while the highest RSD was dimethyl sulphone at 35.1%. SBSE was similar to SPME with a minimum RSD of 2.4% for 3,5-octadien-2-one and 42.4% for neophytadiene. Given both the overall average RSD's and the range of RSD's, SPME and SBSE were the most reproducible techniques.
## 5 Applications
### Desalination  
A total of 97.5% of the global water is saltwater or seawater on Earth [158]. The desalination process can obtain clean water from salt/seawater. This can solve the problem of normal water necessary for industry, drinking, agriculture, etc. The size of holes that are created in GR due to the double and triple bonding of carbon atoms is of the order of chains, and the water molecules can pass through the membrane made of GR. However, it is too small for sodium and chlorine ions surrounded by electrically attracted water molecules (Figure 22).  
Figure 21: (**a**,**b**) Diffusion path and energy barriers of Ca atoms on \(\gamma\)-graphyne surface for hydrogen storage. (**c**) Charge density difference upon H\({}_{2}\) adsorption. (**d**) Molecular configuration of H\({}_{2}\) adsorbed onto Ca sites. The figure is adopted with permission from reference [157] @ 2011 ACS.  
The GR can block approximately 100% of all types of ions (Figure 22), such as Ca2+, Na+, Cl-, K+, and Mg2+[159]. The calculations run with the help of the first principle modeling and MD simulations gave roughly two orders of magnitude higher than that of commercialized state-of-the-art turnabout osmosis membranes with a salt blockage of 98.5% [159]. The GR shows the blockage of ions (Figure 23), which is independent of ion concentration and operating pressure. It is suggested that these stopping barriers of GR produce energy barriers that are higher for salt ions than water [159].
## 4 Results and Discussion
### Load-Displacement Curve
#### 4.3.2 Beam S1-4  
In employing a similar method to measure crack patterns, the study obtained values for width, height, and spacing, as detailed in Table 7. These measurements were conducted at five different positions of the crack patterns under the service load on beam S1-4, as illustrated in Figure 13.  
The findings reveal that the FE model accurately predicts the mean width of the beam, demonstrating a deviation of 3.4%. However, substantial differences exist in the height and spacing of cracks, with disparities of 18.39% and 10.28%, respectively. The observed distinctions in the crack width, height, and spacing between the experimental and simulation models may stem from variations in concrete properties. Notably, the concrete's response to compression and tension in the simulation differs from that in the experiment. Furthermore, the simulation did not entirely capture the embedded constraints between the concrete and reinforcements, affecting their behavior. Additionally, the presence of voids and defects in the concrete significantly influences its behavior and the interaction between the concrete and reinforcements under bending loads.  
The results imply that the suggested FE models have the potential to be employed for predicting crack patterns, encompassing crack height, width, and spacing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Crack Position**} & \multicolumn{3}{c}{**EXP**} & \multicolumn{3}{c}{**FEA**} \\ \cline{2-7}  & **w** & **h** & **s** & **w** & **h** & **s** \\ \cline{2-7}  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
1 & 0.142 & 170 & & 0.160 & 190 & \\
2 & 0.100 & 170 & & 0.090 & 140 & \\
3 & 0.100 & 75 & & 0.070 & 110 & \\
4 & 0.100 & 180 & & 0.070 & 60 & \\
5 & 0.090 & 190 & & 0.160 & 190 & \\ Mean value (mm) & 0.106 & 157 & 97.88 & 0.110 & 138 & 110 \\ \hline Mean Error & & & & 3.38\% & 12.10\% & 11.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of crack width between the EXP and FE model of the beam S1-2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Crack Position**} & \multicolumn{3}{c}{**EXP**} & \multicolumn{3}{c}{**FEA**} \\ \cline{2-7}  & **w** & **h** & **s** & **w** & **h** & **s** \\ \cline{2-7}  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
1 & 0.140 & 190 & & 0.146 & 190 & \\
2 & 0.100 & 150 & & 0.051 & 70 & \\
# Full Life Cycle Evaluation of Stability Pile in High Slope with Multi-Layer Weak Interlayers
## 6 Discussion
### Discussion of the Pile Length (\(L_{\mathrm{p}}\))
The pile group and the arching effect can not only prevent soil slide between piles but also modify the stress state and further enhance slope stability [43; 44; 45; 52]. However, the effect of piles will be independent, and the arching effect will fade away with an increase of \(S_{\rm p}/D\)[21; 22; 52], as is directly identifiable in Figure 12 and indirectly identifiable in Figure 13 in the limit equilibrium state. Therefore, an appropriate \(S_{\rm p}/D\) is helpful for the generation of the arching effect and the pile group effect. The existing literature has reported that the effects work as \(S_{\rm p}/D<4\)[43; 51] or \(<\)5 [52]. Given the particularity of the slope with multi-layer weak interlayers and the results presented in Section 5.5, the \(S_{\rm p}/D<3\) should be considered optimal in this case, which is stricter than in previous studies. At the same time, the complex situation and the material properties require a pile diameter of \(>\)1.4 m.
# _Pipper nigrum_ Fruit Extract as an Antibiotic Resistance Reversal Agent in MDR Bacteria
## 3 Results
### Antibiotic Resistance Reversal of S. aureus by P. nigrum Extracts
#### 3.2.1. Cefoxitin  
For _S. aureus_, the zone of inhibition of cefoxitin (Cfx) was 12 mm (Figure 2), whereas the inhibition zone of _S. aureus_ is \(\geq\)25 for cefoxitin according to CLSI standards (Table 1). By using plant extracts of _Piper nigrum_, the _S. aureus_ zone of inhibition increased to a maximum of 18 mm with MPN + Cfx, whereas zones of inhibition of Cfx in combination with n-HPN, CPN, EAPN, n-BPN, and APN treatments increased in the range of about 1 to 2 mm (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Antibiotics Tested (\(\mu\)g)**} & \multirow{2}{*}{
\begin{tabular}{c} **Zone of Inhibition** \\ **for Test Strain (mm)** \\ \end{tabular} } & \multicolumn{3}{c}{**Zone of Inhibition**} \\  & & & **According to CLSI (mm) [29; 30]** & \\ \hline Cefoxitin (30) & 12 & \(\geq\)25 & - & \(\leq\)20 \\ \hline Erythromycin (15) & 15 & \(\geq\)23 & 14–22 & \(\leq\)13 \\ \hline Clindamycin (10) & 20 & \(\geq\)21 & 15–20 & \(\leq\)14 \\ \hline Levofloxacin (5) & 14 & \(\geq\)19 & 16–18 & \(\leq\)15 \\ \hline Ciprofloxacinacin (5) & 11 & \(\geq\)21 & 16–20 & \(\leq\)15 \\ \hline Amikacin (30) & 14 & \(\geq\)17 & 13–14 & \(\leq\)12 \\ \hline Gentamicin (10) & 15 & \(\geq\)15 & 13–14 & \(\leq\)12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antibiotic susceptibility of _S. aureus_.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Antibiotics Tested (\(\mu\)g)**} & \multirow{2}{*}{
\begin{tabular}{c} **Zone of Inhibition** \\ **for Test Strain (mm)** \\ \end{tabular} } & \multicolumn{3}{c}{**Zone of Inhibition**} \\  & & & **According to CLSI (mm) [29; 30]** & \\ \hline \multicolumn{3}{c}{**Sensitive**} & **Intermediate** & **Resistant** \\ \hline Ceftazidime (30) & 11 & \(\geq\)26 & 23–25 & \(\leq\)22 \\ \hline Tigecycline (10) & 11 & \(\geq\)16 & - & \(\leq\)12 \\ \hline Levofloxacin (5) & 15 & \(\geq\)19 & 14–16 & \(\leq\)13 \\ \hline Ciprofloxacinacin (5) & 14 & \(\geq\)21 & 16–20 & \(\leq\)15 \\ \hline Amikacin (30) & 17 & \(\geq\)17 & 13–14 & \(\leq\)12 \\ \hline Gentamicin (10) & 16 & \(\geq\)15 & 13–14 & \(\leq\)12 \\ \hline Imipenem (10) & 25 & \(\geq\)23 & 20–22 & \(\leq\)19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antibiotic susceptibility test results of _S. typhi_.  
Figure 2: Antibiotic reversal activity of _P. nigrum_ extracts in combination with cefoxitin (30 \(\mu\)g) for _S. aureus_.
## 5 Discussion of Results
### Segmentation Performance Assessment  
The first aspect to be evaluated is the overall capability of the model of completing the segmentation task. In particular, it is important to assess whether the newly introduced architecture provides at least the same model performance offered by the original HAFNet. The following results are presented after running the model both in the validation phase and in the test phase. After 1.5 training epochs, the model reached the same performance of the original HAFNet, probably thanks to a combination of:  
* the pre-trained encoders already providing good basic encoding power, plus
* the reduced overall model size speeding up training.  
These first training steps set a solid starting point; however, we needed to assert that specific characteristics of the previous model were preserved, as confirmed through several experiments: SegNet-like re-localization capability and re-weighting of decision-level features. As stated in [PERSON] et al. [8] regarding adaptability of the scheme to different networks, we can confirm this applies to the HAFNetE model where a U-Net network in each thread replaces the previously proposed SegNet. Moreover, the highly discriminative power granted by the attention fusion block at the decision level remains intact. To give the reader a visual sense of typical results from the proposed method, Figure 6 shows the final classification results on a set of test patches. Figure 7 shows, instead, the classification results on a larger scale, providing examples on two entire sample tiles.  
Although the biggest advancement from the previous model can be measured in terms of computational efficiency, a segmentation performance improvement can be noticed thanks to the fine-tuning procedure that further enhanced the model's segmentation capabilities, raising the F1-score to 96.68% and IoU to 93.64%. Refer to Table 3 for further details. For the reader's convenience, F1-scores for other state-of-the-art methods on the Potsdam dataset (building) are presented in Table 4.  
Performance metrics show that transfer learning is a suitable technique for achieving great segmentation results also in the Earth Observation domain and that the EfficientNet-B0 encoder is highly capable of extracting discriminative features, even from the very beginning of the training process. In the next paragraph, the benefits of the EfficientNet structure will be presented.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Mode** & **Validation IoU** & **Validation F1-Score** & **Validation Accuracy** \\ \hline Main training & 91.58\% & 95.59\% & 96.41\% \\ Fine-tuning & 93.64\% & 96.68\% & 97.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative validation results after the main training phase and after fine-tuning.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 3 Methods
### Local Perception Mechanism  
Inspired by the concept of local perception, this paper designs a local perception mechanism (LMA) tailored for long sequences. Specifically, the input tensor is mapped into query (q), key (k), and value (v) vectors through linear layers, and these vectors are reshaped into multiple attention heads. Local attention is achieved by defining a fixed window size, thereby limiting the attention computation scope of each time step to the current step and its surrounding context. When calculating the attention score for each time step, the query vector performs a matrix multiplication with the key vectors within the window, and the scores are normalized using softmax to obtain attention weights. Then, the weights of all heads are averaged to form a global attention matrix. These weights are subsequently used to perform a weighted sum of the value vectors, yielding the final output,  
Finally, through linear projection and residual connections, the model can effectively integrate local information, enhancing feature representation capabilities while maintaining computational efficiency. The principle of the local perception mechanism module is illustrated in Figure 4.
## 2 Background
### Theoretical Context
#### 2.1.1 Internet of Things (IoT)  
Thanks to IoT nodes, power grids acquire the flexibility needed to face a more electrical future. Smart grids make it possible to know consumption and demand and to adopt predictive maintenance strategies [18; 19; 20; 21; 22; 23]. IoT devices offer many connectivity options in energy infrastructure, helping to monitor all essential energy assets to achieve efficient energy use. Today, it is a technology that has become a benchmark for data collection and processing systems [24; 25; 26; 27].  
One of the core elements of the SGs is the use of IoT to increase the visibility of the entire environment, providing greater dynamism and efficiency in the collection and distribution of data, which will be used to generate optimization notes for the system [28; 29]. IoT is one of the core elements of SGs by providing greater dynamism and efficiency in data collection and distribution and more capacity for direct interaction and control over specific parts of the network [30; 31; 32; 33].  
In the research of [PERSON] and [PERSON] [34; 35], IoT integration is used to achieve reliable data transmission in communication infrastructures at different levels of SGs:  
1. Generation: Monitoring electricity generation from different types of power plants, gas emissions, energy storage, and energy consumption, and predicting the energy needed to supply consumers.
2. Transmission: Monitoring and control of transmission lines and substations, protection of transmission towers.
3. Distribution: Distribution automation, equipment management and protection, fault management.
4. Consumption: Smart home and home appliances, smart charging and discharging of electric vehicles, power load control, multi-grid management.
## 6 Conclusions  
Machine realizing machine reading comprehension and question-and-answering are hot topics in the current artificial intelligence field. Applying question-and-answering in agriculture can facilitate data management and practical query use. Therefore, this paper presented a text model based on a textual relation chain to achieve high-performance automatic question-and-answering in agricultural scenarios. In order to improve the effectiveness of the model, we modified the BERT network: the semi-supervision and contrastive techniques were introduced into BERT, and a large number of test experiments and ablation experiments were undertaken based on many datasets. The main contributions of this paper are as follows:  
1. We proposed a text model based on the chain of textual relations.
2. We modified the BERT grid based on semi-supervised and contrastive techniques.
3. We used extensive experimental validation on open-source datasets.
4. We designed a low-computing-power model for edge-computing scenarios and finally deployed it using an information aggregation method based on contrastive learning.
5. We developed applications for the model to be used in agricultural scenarios. The application is capable of providing detailed instructions for planting after being questioned. For example, it can answer the appropriate time to plant vegetables or fruit. In addition, the automatic question-and-answering system will deliver proper treatment for a specific plant disease.  
The accuracy of the proposed model can outperform the mainstream models on many datasets. Our method based on contrastive learning reached 86.5 micro-F1, 68.8 macro-F1, and 4.9 classification samples per second on the CAIL2018 dataset. Moreover, it also achieved 84.9 micro-F1, 50.1 macro-F1, and 5.1 classification samples per second on the CoLA dataset. The efficient mode is the best in terms of efficiency. The information aggregation method based on contrastive learning has achieved a state-of-the-art level in the agricultural scenario dataset. Further research will be conducted based on this method to improve the classification effect of agricultural questions.  
Y.H.Writing--original draft, Writing--review editing, Visualization, Methodology; J.L.: Writing--original draft; Validation; C.L.: Funding acquisition, Project administration, Writing--review editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Fund Project in Shandong grant number ZR202102220347.  
Not applicable.  
The authors would like to acknowledge the support from [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 1 Introduction
### Production Methods  
The production of high-performing FRPs from endless fibres could be divided into 2 main steps: the organization of the fibres and application of matrix.  
Depending on the selection of type of matrix (thermoplastic or thermoset) and the application method, the order of steps may differ. The fibres are arranged into sheets of profiles by modern textile fabrication techniques such as weaving, knitting, winding, Tailored Fibre Placement (TFP) or braiding [1,2]. These steps are followed by a molding process. In thermoset composite molding, the fibres are pre-impregnated (prepreg fibre) with the resin matrix before being molded to their final form through compression or heat, while in wet molding the fibres are laid in the resin matrix at the time of molding [1]. The molding process is resource intensive as every unique part requires a customized mold, often fabricated through subtractive methods. For these reasons typical FRP forming by molding is highly efficient for standardized parts, but not for the one-off products common in the field of architecture.  
Although novel production processes such as Coreless Filament Winding (CFW) developed at the ICD University of Stuttgart, have made significant advances in removing the mold from FRP manufacturing for architectural components, they still require a frame which sometimes becomes embedded within the final product [9,10]. Part of our research investigates a moldless forming process that allows for geometric freedom without the need to create multiple costly molds, therefore improving resource efficiency of FRP production.