esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of High-Strength Concrete-Steel Plate Composite Shear Walls
###### Abstract  
Shear walls are effective lateral load resisting elements in high-rise buildings. This paper presents an experimental study of the seismic performance of a composite shear wall system that consists of high-strength concrete walls with the embedded steel plate. Two sets of wall specimens with different aspect ratios (height/width, 1.5 and 2.7) were constructed and tested under quasi-static reversed cyclic loading, including five reinforced concrete shear walls (RCSW) and six reinforced concrete-steel plate shear walls (RCSPSW). The progression of damage, failure modes, and load-displacement responses of test specimens were studied and compared based on experimental observations. The test results indicated that high-strength (HS) RCSPSW system showed superior lateral load strength and acceptable deformation capability. The axial compressive load was found to have an indispensable effect on the ductility of both RCSW and RCSPSW, and an upper limit of axial compression ratio (0.5) is recommended for the application of HS RCSPSW in engineering practices. In addition, the design strength models were suggested for predicting the shear and flexure peak strength values of RCSPSW systems, and their applicability and reliability were verified by comparing with test results.  
composite shear wall; seismic behavior; quasi-static test; design strength model +
Footnote †: journal: Applied Sciences
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 4 Numerical Results
### ACF Peaks Ambiguity  
Due to the similar modulation principle to BOC, ChPN also suffers ACF peaks ambiguity, which is caused by the ACF side-peaks. That phenomenon makes it difficult for receivers to acquire and track the signal correctly, and sometimes false locking occurs in the presence of thermal noise or dynamic stress. Therefore, it is necessary for us to present the MSPRs for ChPN signals with different \(p\) and \(q\).  
The time delay corresponding to the maximum-side-peak can be computed by the following implicit equation:  
\[\tau_{\rm s}=\arg\min\biggl{\{}\frac{dR(\tau)}{d\tau}=0,\tau>0\biggr{\}} \tag{22}\]  
then, substituting \(\tau_{\rm s}\) into (9), we obtain auto-correlation value \(R_{\rm m}\), the MSPR obtained by computing the following equation:  
\[MSPR=20\log_{10}(R_{m}) \tag{23}\]  
Figure 9 is drawn based on (23), which presents the MSPRs of various ChPN modulations. As shown in the figure, the MSPRs reaches the minimum amplitude when the code rate equals to the sweep bandwidth and reaches the maximum amplitude when the sweep bandwidth is double of the code rate. The figure gives us a reference when we choose parameters for ChPN signals.
# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 2 Materials and Methods
### Model Training and Hyper-Parameter Tuning Process  
To accurately estimate injury recovery time in days, a comparative analysis of three distinct algorithms was employed, namely: linear regression (LR), decision trees (DT), and extreme gradient boosting (XGB). LR was selected as the baseline due to its proven effectiveness across a range of tasks [19]. DT was preferred for processing a large number of features and providing understandable explanations for decision-making [20], which is beneficial to medical staff. XGB [21] was chosen for its superior flexibility and performance in comparison to simpler machine learning (ML) algorithms across various datasets [22; 23]. The hyperparameters of each algorithm were fine-tuned to optimize their performance.  
A five-fold Bayesian search cross-validation (CV) was employed to determine optimal hyperparameter values for each model. Mean squared error (MSE) was used as the cost function. Subsequently, the best hyperparameter values were utilized for training and evaluating the models. Due to a very scarce dataset, and previous research on a similar topic [14], the performance was assessed using the leave-one-out (LOO) method. The nature of Bayesian search and data variability in CV folds might influence model performance. For this reason, the experiment was run 10 times. This provided insight into the model stability concerning the different distribution of data in CV folds. The metrics of \(R^{2}\), mean absolute percentage error (MAPE), mean absolute error (MAE), MSE, and root mean squared error (RMSE) were calculated for each iteration, both for Bayesian search CV and LOO performance. Ultimately, the model with the lowest average MSE value on the five-fold Bayesian search CV was selected as the representative model. This approach ensured the best possible performance for our models and provided a robust and reliable estimation of injury recovery time. The visualization of the whole process is shown in Figure 3.  
To test our hypothesis of whether incorporating human expertise into ML models could improve their performance, the process was executed twice. In the first run, the feature containing expert-estimated recovery (in days) was excluded. In the second run, this feature was included in our model. By comparing the performance of the two runs, we aimed to evaluate the added value of incorporating expert knowledge into our approach.  
Figure 2: Distribution of muscle injuries by type, as classified by the BAMIC system. The x-axis denotes different injury types, including classes 1A, 1B, 2B, 3A, and 3B, as well as contusions denoted by C. The y-axis displays the occurrence count for each type of injury.
## 1 Introduction
However, ARIA also does not currently plan to mass produce products over large regions for long time series analysis. Here, we present activities carried out for the design, development, integration, and deployment of a fully automated state-of-the-art InSAR processing chain for Sentinel-1 data developed within the Looking Inside the Continents from Space (LiCS) project by the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). The LiCS project primarily aims at understanding how the continents deform at all spatial and temporal scales, with a focus on using observations of the earthquake deformation cycle to understand how seismic hazard is distributed in space and time. For this purpose, an automated InSAR processing system, LiCSAR, has been developed. LiCSAR is capable of processing Sentinel-1 data acquired globally, with the resulting products freely accessible and downloadable through an online portal, including both wrapped and unwrapped interferograms, coherence estimates, time series and other products. Innovative algorithmic, processing and storage solutions have been implemented to allow us to reduce the computing time and the required disk space. This article is organised as follows. Section 2 presents the architecture and the main features of the LiCSAR processing system, providing basic information about facilities, data and metadata storage systems and processing chain strategies. Section 3 describes LiCSAR interferometric data products, their formats, structure and their update strategy. In Section 4, we will review how various tectonic, earthquake and volcanic applications benefit from the LiCSAR system, and illustrate this using recent results obtained in the Alpine-Himalayan belt. Conclusions and general plans for further developments of the system are given in the Section 5. As Appendix A, we include a list of abbreviations and brief explanation of external commands mentioned throughout the article.
# Bio-Based Polymers with Potential for Biodegradability
## 4 Other Bio-Based Polymers with Potential for Biodegradability  
Over the past decades there has been an increased social and economic demand for alternative energy, materials, and resources to replace current nonrenewable, fossil fuel-based products [170]. With the constant fluctuation in petroleum prices, it is imperative to find alternative resources in order to leverage the uncertainty of the oil market. Bio-based polymers derived from renewable feedstocks are a strategic area of sustainable development [171]. One key element of bio-based polymer research is biodegradability [172], although not all bio-based polymers are inherently biodegradable. As explained previously, biodegradation takes place through a reaction of enzymatic and/or chemical deterioration associated with living organisms [19]. Therefore, some petroleum-based products can be biodegradable, such as poly(caprolactone) (PCL) [19]. In this section, only polymers that are concomitantly biodegradable and bio-based will be discussed.  
Several factors make bio-based polymers and composites attractive for environmentally friendly research, including their potential for biodegradability, conservation of petroleum demands, accessibility, low toxicity, economic efficiency, and low carbon footprint [173]. Bio-based polymers are macromolecules derived from plants, trees, bacteria, algae, or other bio-renewable resources. They are often degradable through microbial processes such as composting. Some of the most common natural biopolymers include cellulose, proteins, starches, and polyesters. Their widespread use is limited due to an intrinsic difficulty in ensuring reproducibility of the main properties [174]. For example, although several distinct microorganisms are able to produce polyhydroxyalkanoates (PHAs), their polymer composition differs depending on the microorganism's nourishment intake.  
In order to obtain better control over polymer property reproducibility, bio-based monomers or resins have been isolated, modified, and/or synthesized. Polyurethanes, polyester amides, polyolefins [175], and alkyled resins [176] are some of the bio-based resins currently available at a commercial scale. In the past, in order to improve their mechanical properties, these polymers have been reinforced with inorganic and organic materials [175], while improvement of their processability has been accomplished, in some cases, by the use of volatile organic solvents [176]. For the sake of text conciseness, this section will be limited to bio-based and biodegradable polymers currently used in the areas of medical applications, plastics, elastomers, adhesives, bio-based compatibilizers and additives.
## 3 Results
### Diagnostic Test Performances
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Correct**} & \multicolumn{2}{c}{**Incorrect**} & \multicolumn{2}{c}{**FP**} & \multicolumn{2}{c}{**FN**} \\  & & **N (\%)** & **N (\%)** & **N** & **N** & **\(p\)** \\ \hline All scenes (N = 330) & 286 (86.7\%) & 44 (13.3\%) & 37 & 7 & 0.010 \\  & \(\bullet\) & in women (N = 198) & 183 (92.4\%) & 15 (7.6\%) & 26 & 3 & \\  & \(\bullet\) & in men (N = 132) & 103 (78.0\%) & 29 (22.0\%) & 11 & 4 & \\ \hline All “Motion” scenes (N = 180) & 173 (96.1\%) & 7 (3.9\%) & 0 & 7 & 1.00 \\  & \(\bullet\) & in women (N = 108) & 104 (96.3\%) & 4 (3.7\%) & 0 & 4 & \\  & \(\bullet\) & in men (N = 72) & 69 (95.8\%) & 3 (4.2\%) & 0 & 3 & \\ \hline All “No motion” scenes (N = 150) & 113 (75.3\%) & 37 (24.7) & 37 & 0 & \textless{}0.001 \\  & \(\bullet\) & in women (N = 90) & 79 (87.8\%) & 11 (12.2\%) & 11 & 0 & \\  & \(\bullet\) & in men (N = 60) & 34 (56.7\%) & 26 (43.3\%) & 26 & 0 & \\ \hline \multicolumn{7}{c}{“No motion” scenes (without phone scenes; N = 120)} & 99 (82.5\%) & 21 (17.5\%) & 21 & 0 & \textless{}0.001 \\  & \(\bullet\) & in women (N = 72) & 68 (94.4\%) & 4 (5.6\%) & 4 & 0 & \\  & \(\bullet\) & in men (N = 48) & 31 (64.6\%) & 17 (35.4\%) & 17 & 0 & \\ \hline Phone scenes (N = 30) & 14 (46.7\%) & 16 (53.3\%) & 16 & 0 & 0.052 \\  & \(\bullet\) & in women (N = 18) & 11 (61.1\%) & 7 (38.9\%) & 7 & 0 & \\  & \(\bullet\) & in men (N = 12) & 3 (25\%) & 9 (75\%) & 9 & 0 & \\ \hline \multicolumn{7}{c}{Emergency scenes (N = 75)} & 64 (85.3\%) & 11 (14.7\%) & 11 & 0 & \textless{}0.001 \\  & \(\bullet\) & in women (N = 45) & 44 (97.8\%) & 1 (2.2\%) & 1 & 0 & \\  & \(\bullet\) & in men (N = 30) & 20 (66.7\%) & 10 (33.3\%) & 10 & 0 & \\ \hline \multicolumn{7}{c}{Correct and incorrect panel results are shown for all scenes and different subgroups within the total cohort and separately for women and men (motion assignments as referenced in Table 1). Sex differences were evaluated with the [PERSON]’s exact or chi-square test (\(p\)-value women vs. men); abbreviations: FP—false positive; FN—false negative.} \\ \end{tabular}
\end{table} 
Table 4: Sex differences in diagnostic test performance. _Appl. Sci._**2003**, 13, 9319
## 4 Methods and Materials
### Library Construction, Transcriptome Sequencing, and De Novo Assembly  
Three RNA-Seq libraries corresponding to the leaf, stem, and root tissues were prepared using the Illumina TruSeq RNA sample prep kit v2 (Illumina Inc., San Diego, CA, USA), following the manufacturer's instructions, targeting 200-300 bp insert-sized libraries. The prepared libraries were quantified using a Qubit 2.0 Fluorimeter (Invitrogen, Waltham, MA, USA), followed by a quality assessment using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The paired-end sequencing (2 \(\times\) 100 bp) was performed using the NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA). The raw sequencing data obtained from all three tissues were deposited to the SRA (Sequence Read Archive) in the NCBI database, with the accession number SRP359017, under Bio Project PRJNA 804685. The high-quality clean reads having Phred scores (Q value) > 30 were extracted using the NGS Toolkit [78], and they were subsequently assembled (de novo) using Trinity RNA-Seq version 2.4.0 with default parameters [79]. Furthermore, the assembled sequences were clustered based on a 90% sequence homology using CD-HIT-EST version 4.6 [80].
## 2 Materials and Methods
### The Revised Universal Soil Loss Equation
#### 2.4.1 Rainfall Erosivity (R) Factor  
The rainfall erosivity factor, which captures the erosive impact of specific rainfall events [12], is influenced by factors such as rainfall amount, intensity, and distribution. Among these, rainfall intensity holds particular significance in determining erosion levels. The original and revised forms of the Universal Soil Loss Equation (USLE and RUSLE) traditionally utilized rainfall intensity data to represent the R factor.  
In the present case, there is limited availability of rainfall intensity data. Consequently, an alternative regression empirical equation established and refined by [PERSON] [30] was employed to compute the R factor.  
The Arnoldus Formula (3) incorporates both monthly and annual precipitation data:  
\[R=1.735\times 10^{1.5\times log\sum{(p^{n}/p)}-0.8188} \tag{3}\]  
_Pi:_ monthly precipitation,  
_P:_ annual precipitation.  
To achieve this, daily rainfall records spanning from 1980 to 2020 were obtained from twenty-one meteorological stations (Figure 1) affiliated with the General Directorate of Water Resources of Tunisia. Subsequently, the calculated R factor underwent interpolation using the inverse distance weight (IDW) method, with the resultant map being transformed into a grid format with a cell size of 30 m.
## 3 Positional Inverted Self-Index
The function \(\mathtt{bcl}_{I}(i)\) returns the length of the byte code used to encode elements of the vector \(I\) up to the position \(i\). Now we can formally define the most important parts of PISI data structure. **Definition 1**. : _The PISI inverted list \(I\) is defined as a vector of length \(N\), where \(N\) is a number of stems in \(\hat{T}\). The element \(I_{i}\) is composed of the following components encoded using the byte code:_  
* _mark byte_ \(\langle 1\rangle\) _when_ \(\hat{T}_{i}\) _is the last occurrence of the stem_ \(\hat{T}_{i}\)_._
* _mark byte_ \(\langle 0\rangle\) _when_ \(\mathtt{rank}_{\hat{T}}(\hat{T}_{i},i)=k\times\alpha\)_, where_ \(k\in\mathbb{Z}_{>0}\)_, i.e.,_ \(\mathtt{rank}_{\hat{T}}(\hat{T}_{i},i)\) _is divisible by_ \(\alpha\) _and, at the same time,_ \(\hat{T}_{i}\) _is not the last occurrence of the stem_ \(\hat{T}_{i}\)_._
* \(\mathtt{bcl}_{I}(\mathtt{select}_{\hat{T}}(\hat{T}_{i},\mathtt{rank}_{\hat{T} }(\hat{T}_{i},i)+1)-1)-\mathtt{bcl}_{I}(i)+1\) _representing the pointer (in bytes) to the next occurrence of the stem_ \(\hat{T}_{i}\) _in terms of the vector_ \(I\) _when_ \(\hat{T}_{i}\) _is not the last occurrence of the stem_ \(\hat{T}_{i}\)_._
* _the pointer_ \(S_{\hat{T}_{i}}\) _to the vocabulary representing the stem_ \(\hat{T}_{i}\) _when_ \(\mathtt{rank}_{\hat{T}}(\hat{T}_{i},i)=k\times\alpha\)_, where_ \(k\in\mathbb{Z}_{>0}\)_, or_ \(\hat{T}_{i}\) _is the last occurrence of the stem_ \(\hat{T}_{i}\)_._  
**Definition 2**. : _The PISI presentation layer \(F\) is defined as a vector of length \(N\), where \(N\) is a number of stems in \(\hat{T}\). The element \(F_{i}\) is composed of the following components encoded using the Huffman code:_  
* _zero or more Huffman codewords representing the sequence of stop words and separators (except the single space) preceding the stem_ \(\hat{T}_{i}\)_._
* _the reserved word_ \(\langle STOP\rangle\)_. This component is required._
* _the Huffman codeword representing a variant of the stem_ \(\hat{T}_{i}\)_. This component is included only if the stem_ \(\hat{T}_{i}\) _has more than one variant._  
Algorithm 1 describes the main functions performed by PISI. The function getStem returns a stem position in the vocabulary of a word at the position \(i\). It basically traverses the inverted list using  
Figure 2: PISI data structure: Detail of inverted list and presentation layer. the pointers (see rows 22 and 26). When it reaches the reserved byte \(\langle 1\rangle\) (see line 19) or the reserved byte \(\langle 0\rangle\) (see line 24) it decodes and returns the following pointer to the vocabulary.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods  
As shown in Figure 1, the proposed Flauformer is an encoding-decoding structure that is different from many Transformer-based encoding and decoding structures, as most of them are pure Transformer structures or the encoder is a Transformer and the decoder is CNN. On the contrary, our encoder consists of simple CNNs to minimize the model complexity, and the decoder is composed of the Transformer, which constitutes a structure based on CNN encoding and Transformer decoding to accomplish the real-time segmentation tasks of high-resolution urban scene imagery. In the following, we describe the details of the proposed method.
## 3 Innovative SHM Techniques
### Image and Computer-Vision Based Approach
#### 3.2.1 Photogrammetry
[156] described the application of a remote monitoring system, based on an Internet of things architecture (see Section 4.2) and a virtual reality paradigm, to three historical structures. The system consisted of a network of fixed sensors and a UAV. The latter was used to fulfil two main tasks. First, the UAV was used to obtain a 3D reconstruction of the CHS with the exact position of the sensor nodes, thus allowing an operator to interact dynamically with the real-time readings collected by the IoT network. On the other hand, the UAV was also used as a mobile sensor in the network to monitor crack patterns and anomalies. The authors highlighted several valuable advantages of using a UAV for monitoring CHS, such as versatility, robustness, time-effectiveness, safety for operators, and low cost. However, the authors also identified some limitations regarding  
Figure 11: Orthophoto from close-range photogrammetry acquisition (a), from [155]; UAV image acquisition for marker-based structural defects monitoring (b), from [156]. the image-based method for crack monitoring, such as the risk of blur effects due to the unstable hovering of the UAV during image acquisition and the inappropriate configuration of the markers installed along a crack for the camera calibration, which may greatly affect the accuracy of the analysis. The combined application of 3D photogrammetry with FBG sensors was investigated by [PERSON] et al. [162] to evaluate the long-term crack propagation and damage evolution in a tower of the Aurenelian Walls in Rome. The 3D photogrammetric reconstruction using SfM was adopted to build a 3D geometrical model, which was the source for creating an FE model to perform modal analysis and identify the dynamic properties of the structure. The FBG could be correctly positioned thanks to the modal analysis results and the map of the cracks reconstructed from the photogrammetric survey. The sensors allowed for monitoring the main cracks, eventually calibrating the FE model and correlating with long-term scheduled 3D photogrammetric surveys. [PERSON] et al. [163] compared the re-processing of analogue archival photogrammetric images of Estonian Padise Abbey captured by a metric camera in 1991 and the results of the new photogrammetric survey using a digital camera in 2017. Both images were processed using the SfM method. In this way, it was possible to evaluate the state of conservation of the abbey walls after 26 years. It was found that the overlay of the two generated orthophotos was the most accessible and understandable way for users to compare the different states of the building. The amount of information acquired through the use of photogrammetry can also be adopted for creating mixed reality experiences [164] or heritage building information modelling (HBIM) [164; 165; 166] (see Section 4.4), which can contain information about monitoring and structural assessment.
## 4 Discussion
### Biodegradation Risks and Matrix Influence in Guar Gum-Stabilised Soils  
Though the guar gum is proven to be a viable alternative for soil stabilisation under cyclic environmental loading, concerns remain about its long-term stability under subsurface conditions, particularly regarding biodegradation by indigenous soil microorganisms. Microbial degradation poses a potential risk to the durability and performance of guar gum-treated soils over extended periods [52]. Recent studies indicate that the risk of biodegradation can be substantially reduced when guar gum is incorporated within low-permeability clay matrices. These dense clay environments act as physical and biochemical barriers that limit microbial access to guar gum molecules, thereby reducing microbial activity and slowing degradation [53]. For example, compacted clay soils create microenvironments with limited oxygen diffusion and nutrient availability, which suppress microbes' abundance and metabolic function capable of degrading biopolymers like guar gum. In particular, saturated or anaerobic conditions common in guar gum-stabilised clay soils further restrict microbial degradation processes, potentially extending the functional lifespan of the stabilisation treatment [54]. However, the extent of this protective effect depends heavily on site-specific factors, including clay mineralogy, pore water chemistry, and redox potential, all of which influence microbial populations and activity. Therefore, while guar gum stabilisation shows promise for durable soil improvement, further research, especially long-term field studies, is necessary to better understand how biodegradation progresses under varying geochemical and hydrological scenarios. Such investigations will help validate laboratory results and clarify guar gum's practical longevity and effectiveness as a sustainable soil stabiliser [52].