esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Compared with traditional single-point or small-scale landslide deformation monitoring technology, it has the advantages of high monitoring accuracy, wide monitoring range, high spatial resolution, all-day and all-weather operability, etc., and is widely used in landslide research [15; 16; 17; 18; 19; 20; 21]. For example, [PERSON] et al. [15] processed ENVSTAT data covering the town of Lubietova in central Slovakia using the Persistent Scatterer (PS) InSAR technique, analyzed the catastrophic landslides that occurred in the area in 1997, determined the precise boundary of activity, and updated the landslide inventory of the area. [PERSON] et al. [16] identified two landslides in Badong County using time-series InSAR technology and found an apparent correlation between the seasonal movement of landslides and water level variation. [PERSON] et al. [17] used small baseline subset (SBAS) InSAR and PS-InSAR techniques to identify 14 landslides in the Jinsha River basin of Gongjue County, and introduced rainfall data to further analyze the spatial-temporal deformation characteristics of two large landslides. [PERSON] et al. [19] used PS-InSAR technology to delineate and monitor landslide information along the Baglihar Dam reservoir area, located in the Jammu and Kashmir, India. The SAR data consisted of 46 ascending and 70 descending track data acquired from Sentinel-1, and the qualitative analysis of the combined monitoring results was carried out in conjunction with the local precipitation data. All of the studies mentioned above have proven the effectiveness of using InSAR technology in landslide identification and landslide monitoring, and introduced external data (such as earthquake, reservoir water level, rainfall data, etc.) combined with InSAR time series to further analyze the mechanisms of landslide deformation. However, most of them have only qualitatively demonstrated the influence of external factors on landslides, and InSAR monitoring data were not sufficiently utilized. Since wavelet analysis can extend one-dimensional time series to the time and frequency domains, allowing studies to be conducted at multiple scales, this has been widely applied in geoscience [22; 23], meteorology [24], praxecology [25], and economics [26] in recent years. In the first step of this paper, we processed 103 Sentinel-1 images obtained from ESA through the SBAS-InSAR technique to extract information on surface displacements, from September 2015 to December 2019, on the right bank slope, known as the Guobu slope, upstream of the Laxiwa hydropower station. Subsequently, we analyzed the temporal and spatial features of Guobu slope deformation. In the second step, we integrated the InSAR time series results with wavelet analysis to quantitatively investigate the uncertain relationships between rainfall, reservoir levels, and deformed time series data, including the periodicity and seasonality of each time series, along with their correlations and lags. This study elucidates the association between landslide displacement and external factors from a fresh perspective, and its findings can play a vital role in disaster prevention and control.
## 2 Theory and Methodology
### Leja Interpolation Nodes
#### 2.3.2 Weighted Leja Nodes  
In more general UQ settings, e.g., for the arbitrary input distributions considered in this work, the definition of Leja sequences must be adjusted according to the input PDF \(\varrho\left(y\right)\), which acts as a weight function. To that end, we employ the definition of weighted Leja sequences given in [30]. Using this formulation, weighted Leja sequences are constructed by incorporating the PDF \(\varrho\left(y\right)\) into the optimization problem of Equation (9), which is transformed into  
\[y_{j}=\operatorname*{argmax}_{y\in\Xi}\sqrt{\varrho\left(y\right)}\prod_{k=0 }^{j-1}\left|y-y_{k}\right|. \tag{10}\]  
We note that other formulations exist as well [43; 44], however, the form given in Equation (10) is preferred due to the fact that the resulting Leja nodes are asymptotically distributed as weighted Gauss quadrature nodes [30]. While this is a promising property, it is not necessarily sufficient to produce an accurate interpolation rule. In some cases, e.g., for weight functions resembling the Gaussian PDF [44], it can be shown that weighted Leja nodes are also associated with a subexponentially growing Lebesgue constant. While more general theoretical results are currently not available, weighted Leja interpolation has been found to perform very well in practice [33; 37], as also demonstrated in this paper by the results of the numerical experiments presented in Section 3. The other beneficial properties of Lejasequences, i.e., granularity and nestedness, are preserved in the weighted case. Moreover, since the interpolation rule is now tailored to the specific weight function that is the PDF, the corresponding quadrature rules will be tailored to that PDF as well. For a more detailed discussion on the properties of weighted Leja sequences, see [30].
## 4 Evaluation Scope
### Target Group
#### 4.2.4 Professionals  
Professionals are individuals who possess specialized knowledge or expertise in a particular field or subject, often gained through formal education, training, and experience. In our sample, professionals are typically targeted with regard to the healthcare sector (such as doctors, nurses, paramedics, and emergency services providers) [44; 77; 85] or the engineering sector, including data professionals, data scientists, AI engineers, data annotators, and data categorization specialists [30; 40; 59; 72]. Here, a notable study is the one by [PERSON] et al. [30], which explicitly addresses data scientists as the target group. In most cases, the target group is only implicitly defined by the context of the studies, such as the one by [PERSON] et al. [40] focusing on XAI frameworks like LIME; [PERSON] et al. [72] on the comprehensibility of deep neural networks; [PERSON] [34] on AI-based navigation planning; [PERSON] & [PERSON] [59] on labeling handwritten digits; and [PERSON] et al. [61] on classification tasks.  
In many cases, it is not clear from the context whether the target group of a study is laypersons or professionals. For instance, a series of studies have investigated the effects of explanations about data classification, such as sentiment analysis of online reviews [53; 60], online reviews [52], toxic posts, and hate speech [46; 69]. From the context of these studies, however, it is unclear whether they are addressing professional content mediators/data analysts or lay users affected by online reviews and social media posts.  
The same ambiguity is also present in studies concerning the use of explainable artificial intelligence (XAI) in automated decision-making [31; 43; 65; 73; 86; 87]. Since the target group is not explicitly defined, it is uncertain whether the explanations are intended for individuals affected by these decisions; professionals such as judges, psychiatrists, or jurors who are making them; or other stakeholders such as the general public. This uncertainty also holds true for studies in our sample, which focused on application areas such as processing loan applications [15], making real estate transactions [50], making income predictions [68], interpreting graphs or charts [61], or making university admissions decisions [43]. In these cases, too, it remains unclear from the context whether the explanations are aimed at the decision-makers or at the individuals who are affected by these decisions.  
This ambiguity is particularly prevalent in concept-driven studies, where a specific usage scenario is either absent, only briefly described, or very generally defined [5; 16; 35; 36; 38; 51; 64; 66; 75]. In such cases, it is not possible for us to define the target group more precisely. As a result, the ecological validity of the effects measured in these studies remains uncertain.
## 2 Numerical Investigations
### Quasi-Static Bending Fatigue Simulation Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Number** & **Number of Grid Cells (Units)** & **Simulation Limit Load (N)** \\ \hline
1 & 4320 & 23,024 \\
2 & 6624 & 22,418 \\
3 & 10,752 & 20,321 \\
4 & 20,792 & 20,568 \\
5 & 31,320 & 20,564 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ultimate loads for different mesh number. Figure 2: Different densities of axial tube grids. It can be seen from Figure 4 that the loading displacement is 0-1.44 mm, and the load and displacement have a linear relationship, indicating that the CFRP shaft tube is in the linear elastic stage at this stage, and the shaft tube has not yet been damaged. The loading displacement is 1.14-12.59 mm, and the load first decreases and then rises to a peak value with the increase of the loading displacement, indicating that there is damage inside the CFRP shaft tube at this stage. This is mainly matrix damage, and the fiber damage is less significant. The displacement-load curve continued to rise and reached a peak value. After the loading displacement reached 12.59 mm, the displacement-load curve of the shaft tube showed a significant downward trend, indicating that the fiber components inside the shaft tube also began to be damaged. The maximum load value of the shaft tube in the linear elastic stage is 9231 N, which can be used to determine the three-point bending fatigue loading load of the shaft tube. The coordinate of the peak point of the displacement-load curve of the axle tube is (12.59 mm, 20,568 N), which indicates that the limit displacement that the axle tube can bear during the quasi-static three-point bending loading process is 12.59 mm, and the corresponding limit load is 20,568 N.
# Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation
## 1 Introduction
Therefore, it is reasonable to believe that independently training the encoder and decoder in the Transformer helps mitigate the mismatch problem between the big model capacity of the Transformer and the small parallel training dataset and thus contribute to a better-trained Transformer than that trained end-to-end. Third, training the decoder of the Transformer uses the monolingual corpus of the target language. Provided the scenario of low parallel corpus but high monolingual target language corpus, a byproduct of BERT supervision is that we can further improve the translation performance by training the decoder with the large-scale monolingual corpus of the target language. Although [PERSON] et al. [16] used the BERT in machine translation, there are two critical differences between our approach and the BERT-fused model. First, our approach does not change the structure of the Transformer. The parameter of the Transformer trained with our algorithm is far less than that of the BERT-fused model. Second, the BERT-fused model exploits the representation from the BERT by feeding it into all layers, while we use the BERT as the learning target of the encoder. The Transformer trained with our algorithm infers faster than the BERT-fused model. Our approach also differs from the previous  
Figure 1: The principle of our approach: aligning the source language and the target language in latent space. machine translation approaches, which used a pre-trained language model to improve the encoder and decoder of NMT [17; 18; 19]. The contributions of this paper are as follows:  
* We boost the Transformer by adding the BERT constraint on the latent representation in low-resource machine translation. As such, we design a training algorithm to optimize the Transformer in a multi-step way, including encoder training, decoder training, and joint optimization. It alleviates the mismatch problem between the capacity of the Transformer and the training data size in low-resource machine translation. * We provide a new way to incorporate the pre-trained language models in machine translation. Compared to the BERT-fused methods, a significant advantage of our approach is that it improves the performance of the Transformer in low-resource translation tasks without changing its structure and increasing the number of parameters. * Adding BERT supervision enables us to further improve the Transformer with a large-scale monolingual target language dataset in the sense of a low parallel corpus but a high monolingual target language corpus. The remainder of this paper is structured as follows. Section 2 describes related work. Section 3 introduces our approach. Section 4 describes the experiment setting. Section 5 reports the results and the analysis. Finally, Section 6 presents our conclusions.
## 3 Results and Discussion
### Effect of Growth Regulator  
Plant growth regulators (PGRs) are incorporated into the basal growth medium of cell cultures to stimulate and regulate plant development [71; 72; 73]. These PGRs govern cell division in undifferentiated cells [74] and prompt callogenesis, leading to subsequent callus proliferation. Callus induction in meristem and stipe was observed in PESI solid medium with different growth regulators: indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NANA), 6-benzylaminopurine (BAP), 2,4-dichlorophenoxyacyacetic acid (2,4-D), and kinetin (KIN) at concentrations of 1 or 5 mg L\({}^{-1}\) at 12 \({}^{\circ}\)C (Figure 7). The selected calluses developed from meristem and stipe tissues are shown in Figure 8. The highest callus induction (50.37 \(\pm\) 5.17%) in stipe occurred in standard PESI solid medium without growth regulators. Meanwhile, the highest callus induction rate (40.93 \(\pm\) 8.65%) in meristem tissue was observed in PESI medium containing 1 mg L\({}^{-1}\) IAA. Further experiments for meristem calluses were performed in PESI agar medium containing IAA, while stipe explants were cultured on standard PESI solid medium.  
Figure 8: Callus induction in meristem (**a**) on PESI medium supplemented with 1 mg L\({}^{-1}\) of IAA, and stipe (**b**,**c**) on standard PESI solid medium without containing growth regulators.  
Figure 7: Effect of growth regulators on callus induction. Values are means \(\pm\) standard errors (S.E.) of 10 individual replicates per experiment (\(n\) = 10). IAA: indole-3-acetic acid; IBA: indole-3-butyric acid; NAA: 1-naphthaleneacetic acid; BAP: 6-benzylaminopurine; 2,4-D: 2,4-dichlorophenoxyacyacetic acid; KIN: Kinetin. *: Indicates significant differences using [PERSON]’s HSD test using a multiple-way ANOVA test at \(p\) = 0.0312 for stipe and \(p\) = 0.047 for meristem.
# Evaluation and Comparison of Pear Flower Aroma Characteristics of Seven Cultivars
## 3 Results
### Identification of the Volatile Compounds  
The volatile compounds identified in pear flowers are shown in Table 2. A total of ninety-three volatile compounds were identified and quantified in the flowers of seven pear cultivars, including alcohols (15), arenes (6), phenols (3), aldehydes (13), terpenoids (13), ketones (4), heterocyclic (1), alkanes (10), and esters (28). Lyubaoshi flower contained the highest number of 52 volatiles, while Golden flower exhibited the least (39 compounds); the other flowers of [PERSON], Xizilu, Anli, Brown peel, and KorlaXiangli contained 51, 47, 46, 42, and 42 volatile chemicals, respectively. The chemical classes of ester, alcohol, aldehyde, and terpene showed the largest numbers in most cultivars. Naphthalene, 1-decanal, 2-pentyl-furan, 2-ethyl hexanol, 1-nonanal, 1-nonanol, methyl 2-ethyl hexanoate, (\(E\))-geranyl acetone, 6-methyl-5-hepten-2-one, (\(Z\))-3-hexenyl acetate, methyl 2-hydroxy-3-methyl pentanoate, benzaldehyde, hydroquinone, 2-phenylethanol, 2-nitropenol, 1-octanol, and all alkanes were the common volatiles existed in each cultivar.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 1 Introduction  
Millimeter-wave (mmWave) frequencies for 5G and 6G have been adopted as the main resource for obtaining wireless data transmission beyond Gbps [1]. The World Radio Conference 19 (WRC-19) [2] established 5G frequency bands for mmWave transmissions at 26 GHz (n258), 28 GHz (n257-261), 39 GHz (n260), 41 GHz (n259), and 47 GHz (n262). The available bandwidths were 50, 100, 200, and 400 MHz. In study [3], the 65 GHz band was presented as a band for the next generation of mobile standards. Many measurement campaigns have been carried out to analyze the channel characteristics in the mmWave bands for both indoor and outdoor environments; see [3] (Table II) for a summary. The 5G Infrastructure Public Private Partnership (5 GPPP) has published a white paper about the delivery of 5G services indoors [4]. It notes that research and innovation activities have focused primarily on outdoor use cases, as well as supporting the user and their applications while on the move. However, many use cases in indoor environments are not always adequately reflected by the eminent requirements for outdoor applications. Massive multiple-input multiple-output (mMIMO) is necessary in mmWave systems to compensate for the path loss [5]; however, traditional beamforming systems require a dedicated radio frequency (RF) chain for each antenna, which is expensive in terms ofhardware cost and power consumption [6]. Hybrid analog and digital beamforming (HBF) architecture was selected to reduce the number of RFs' chains [7,8], and achieved high spectral efficiency. There have been many recent studies about the best HBF architectures for mmWave communications. The authors in [9] presented a manifold optimization-based algorithm, and in [10], a closed-form solution was presented that showed the optimum condition between the number of RF chains (\(N_{RF}\)) and the number of streams (\(N_{S}\)). Then, in study [11], this was extended to a multicarrier. Maximizing spectral efficiency using the minimum mean square error (MMSE) based on the principle of alternating optimization was presented in [12]. Furthermore, there were many other studies that ed mmWave mMIMO transmissions, presented in Table 1.  
According to the authors' best knowledge, there is a lack of research studies about experimental indoor 5G transmissions that apply HBF algorithms in mmW. In this paper, in an indoor scenario (a lab of the university), wideband channel measurements at the 38 and 65 GHz bands are used to deduce the achievable throughput when mMIMO-HBF techniques are used. Apart from 38 GHz, the 65 GHz band is also analyzed since it is considered as a feasible band for 6G [3]. In summary, the main contribution of this work is the implementation of mMIMO algorithms into real indoor measurements in two different 5G/6G frequency bands. The algorithm described in [11] was chosen since the computational complexity is much lower than others, as claimed in [22], resulting in a spectral efficiency very similar to the algorithms defined in [9,12]. Transmission diversity is applied by combining the space
###### Abstract  
Grains number is one of the most important agronomic traits in the determination of rice productivity. To explore the underlying genetic basis of grain number in rice, quantitative trait locus (QTL) analysis was performed using three recombinant inbred line populations derived from _indica_ rice crosses of Teqing/IRBB lines, Zhenshan 97/Milyang 46, and Xieqingzao/Milyang 46, respectively. A total of 58 QTLs distributed on all 12 rice chromosomes were identified, including 22 for number of grains per panicle (NGP), 17 for number of spikelets per panicle, and 19 for spikelet fertility. The individual QTL counted for 1.5 to 22.1% of phenotypic variation. Among them, 15 QTLs shared by two or three populations and eight QTLs showed large effects with \(R^{2}\) larger than 10%. Furthermore, three QTLs with minor effects for NGP, _qNGP5.5, qNGP9.1,_ and _qNGP12.1,_ were detected and validated by eliminating the segregation of major-effect QTL using four residual heterozygote-derived populations. These results not only enrich our understanding of the mechanism of grain number, but also provide a foundation for cloning and selecting candidate for marker-assisted selection breeding in rice.  
rice; grain number; QTL; minor effect; residual heterozygote +
Footnote †: journal:
## 3 Results  
### Drip Loss  
In this study, we used frozen abalone as a raw material to develop a test HMR product. First, we analyzed the drip loss of frozen abalone to ensure the quality of raw materials by comparing HFD with conventional thawing methods (air and water temperature). The results showed that the drip loss of frozen abalone was significantly (\(p\) < 0.05) reduced when using HFD compared to conventional methods (Figure 2). HFD also reduced the thawing time (15 min) compared to thawing at water temperature (55 min) and air temperature (85 min). [PERSON] et al. [6] and [PERSON] et al. [7] also reported that the application of HFD significantly (\(p\) < 0.05) reduced drip loss and thawing time.  
The results of this study showed that the shorter thawing time resulted in less drip loss, which affects the quality of abalone and prevents cell membrane damage caused by drip loss [15,16]. Moreover, rapid thawing reduces recrystallization effects and prevents mechanical damage [16]. The texture of thawed abalone was also maintained owing to the prevention of water position changes during rapid thawing. In addition to the texture, the nutritional characteristics of thawed abalone were maintained during rapid thawing (see Section 3.5). According to [PERSON] et al. [17], some nutrients leach out with drip loss during thawing; therefore, less drip loss corresponds to less nutritional loss. Considering the low drip loss resulting from HFD, we selected this thawing method for use in further steps.
## 3 Materials and Methods
### OCR-Based Document Image Retrieval  
In OCR-based DIR, we employ Tesseract OCR [35] to extract textual information from the document image. Typically, OCRs may not be perfect and can misclassify visually similar characters. To mitigate the impact of OCR imperfections, we adopt a technique where we create d-length shingles of the text. These shingles represent the set of all sequences comprising \"d\" characters within the text [36]. Once we have extracted shingled text from both the query and the document image dataset, we apply TF-IDF-based weighting and utilize cosine similarity to rank the documents. This approach helps achieve more accurate textual information retrieval while accommodating OCR-related challenges.