esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Heavy Metal Analysis  
We measured eight heavy metals in surface water and sediment samples: Arsenic, Mercury, Chromium, Nickel, Copper, Zinc, Cadmium, Lead, and Manganese. We followed the pre-treatment protocol mentioned by [3; 12] to prepare the samples for heavy metal quantification. All of the chemical reagents used were of a \(\geq\)99% purity grade. Upon collection, surface water samples were immediately acidified using nitric acid to attain a pH below 2, ensuring the stability of the metal concentrations. After this, filtration through a 0.45 \(\upmu\)m membrane was performed to remove any particulates. For comprehensive metal analysis, 50 mL of the surface water sample underwent an acid digestion process, primarily using a blend of 4 mL nitric and 1 mL hydrochloric acids. After digestion, the samples were stored in a cool, dark place until the analysis time. Conversely, for sediment samples, they were oven-dried at temperatures around 35 \({}^{\circ}\)C. Post-drying, samples were sieved to segregate fine particles and then thoroughly grounded to achieve a fine, homogeneous powder consistency. The powdered sediment underwent acid digestion similar to surface water samples. Post-digestion, samples were filtered to eliminate any residual particulates, and the resultant solution was preserved until analysis. All heavy metals were quantified using Atomic Absorption Spectroscopy (ZEEnit 700P, Analytik Jena GmbH + Co. KG, Jena, Germany). The method was refined through the careful selection of optimal atomic lines for the following elements: Pb (283.3 nm), Hg (253.7 nm), Cd (228.8 nm), Zn (213.9 nm), As (193.7 nm), Cr (357.9 nm), Cu (324.7 nm), and Ni (232 nm).
## 4 Experimental Results and Analysis  
### Experimental Environment Settings  
The experimental environment of this research uses Asus Esc8000 G3 Host, Xeon E5-2600 v3 processor, 4 GB memory, NVIDIA GeForce GTX 1080 ti \(\times\) 8 GPUs with Ubuntu 18.04 LTS OS. The research target is Taiwan Semiconductor Manufacturing Co., Ltd. (TSMC, code TPE.2330), and the experimental range is selected from 4 January 2016 to 11 May 2021, with a total of 1300 trading days, and the data source is taken from public information of Taiwan Stock Exchange (TWSE). Considering that the stock trading attribute used in this study is in days, the total trading days are not many. To avoid potential underfitting, the proportion of training data is increased in the experimental settings. In this study, 70% of the data was set as the training set, 20% as the validation set, and 10% as the test set. The model is trained for 50-200 Epochs, the batch size is uniformly set to 32, and finally the weight with the highest validation accuracy is used as the prediction model. The measures used in this study are Recall, Precision, F1 and Accuracy:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}\;+\;\text{FP}} \tag{18}\]  
\[\text{Recall}\;=\frac{\text{TP}}{\text{TP}\;+\;\text{FN}} \tag{19}\]  
\[\text{F1}=2\times\frac{\text{Recall}\times\text{Precision}}{\text{Recall} +\text{Precision}} \tag{20}\]  
\[\text{Accuracy}\;=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}\;+\;\text{ FP}\;+\;\text{FN}} \tag{21}\]  
where TP, TN, FP and FN represent the True Positive, True Negative, False Positive and False Negative, respectively. F1 measure is the is the harmonic mean of Recall and Precision. In the presentation of the experimental results, Recall can show the ratio of the actual categories that are successfully predicted, and Precision shows the proportion of successful predictions in the prediction results. The accuracy can perform an overall evaluation of the correct and incorrect prediction results. In addition, to explore potential causes of mispredictions in multi-class predictions, the resulting presentation will include confusion matrices to clearly depict the mispredicted classes. The loss function uses categorical cross entropy, which is defined as follows:  
\[\text{Loss}\ =-\sum_{i=1}^{n}\hat{y}_{i1}logy_{i1}+\hat{y}_{i2}logy_{i2}+\ldots+ \hat{y}_{im}logy_{im} \tag{22}\]  
where \(n\) is the number of samples, \(m\) is the number of categories.
## 4 Materials and Methods  
### Identification and Annotation of Genes Involved in Ascorbate Biosynthesis  
Gene sequences encoding enzymes of ascorbate biosynthesis were retrieved from _Capsicum annuum_ L. genome, cultivar UCD-10X-F1, deposited in the RefSeq Representative genome database from GenBank-NCBI ([[http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov)]([http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov))). Genome searches were carried out with the tBLASTn tool considering the e-value < \(10^{-5}\)[82] using query proteins from _Arabidopsis thaliana_ retrieved from the non-redundant protein database. The following gene families associated with Asc biosynthesis process were retrieved: D-mannose/L-galactose pathway, phosphomannose isomerase (_PMI_), phosphomannose mutase (_PMM_), GDP-mannose pyrophosphylase (_GMP_), GDP-mannose 3',5'-epimerase (_GME_), GDP-L-galactose transferase (_GGP_), L-galactose-1-phosphate phosphatase (_GPP_), L-galactose dehydrogenase (_GalDH_), L-galactone-1,4-lactone dehydrogenase (_GalLDH_); L-glucose pathway (L-gallono-1,4-lactone oxidase--_GulLO_); myo-inositol pathway (myo-inositol oxygenase--_MIOX_); and D-galacturonate pathway (D-galacturonate reductase--_GalUR_).  
Gene annotation was performed manually following the strategy proposed by [83]. UTR, exon, intron, and ORF regions were determined with BLASTn searches in reference mRNA sequences deposited in (refseq_rma) and transcriptome shotgun assembly (TSA) transcripts databases from GenBank. The deduced cDNA sequences were translated into amino acid sequences using the ExPASY Translate tool ([[http://web.expasy.org/translate](http://web.expasy.org/translate)]([http://web.expasy.org/translate](http://web.expasy.org/translate))). Furthermore, the validation of deduced protein sequences was performed by comparing them with homologous sequences deposited in non-redundant protein sequences (nr) databases using the BLASTp tool. MG2C_v2.1 online tool [84] was used to visualize the distribution of genes in chromosomes, while gene structure was presented using Gene Structure Display Server--GSDS 2.0 online tool [85].
## 5 Discussion  
The present study set the intermediate principal stress coefficient \(b\) of the surrounding rock to 0, 0.25, 0.5, 0.75, and 1 to explore the influence of intermediate principal stress on the redistribution of stress, strain, plastic zone development, the ground response curve, and the shear dilatant.  
The Effect of Intermediate Principal Stress on the Distribution of Stress and Strain of Surrounding Rock  
The prediction of the range of variation of stress and strain is an important basis for studying the stability of surrounding rock after tunnel excavation.  
Figure 5 shows the variation in the stress and strain curves with r in the plastic zone for different intermediate principal stress coefficients \(b\). The stress curves showed a rising tendency with increasing depth of surrounding rock under different \(b\), whereas strain showed an opposite pattern.  
Figure 5: Effect of different intermediate principal stress on the distribution of stress and strain.  
The position of the dotted line in the stress and strain curve gradually shifted to the left as \(b\) increased from 0 to 0.75 and the peak stress gradually increased. This observation indicated that the plastic radius of the surrounding rock decreased, whereas the rate of change of stress increased. As \(b\) increased from 0.75 to 1, the position of the dotted line in the stress and strain curve shifted to the right and the peak stress showed a slight decreasing trend. The plastic radius \(R_{p}\) first decreased and then increased with increasing intermediate principal stress coefficient \(b\) from 0 to 1. Furthermore, the spacing between the dashed lines gradually decreased and then increased with increasing intermediate principal stress coefficient \(b\) from 0 to 1, indicating that intermediate principal stress inhibited the development of the plastic zone, although this effect showed an interval property.  
Within increasing \(b\) from 0 to 0.75, the intermediate principal stress inhibited the plastic radius, although this inhibitory effect weakened with increasing intermediate principal stress. Intermediate principal stress promoted the development of the plastic region as \(b\) increased from 0.75 to 1. This result could be attributed to the increase in confining pressure improving the bearing capacity of the rock mass. However, damage of the rock occurred when the confining pressure exceeded a certain threshold (\(b\) = 0.75).  
Intermediate principal stress generally showed a strong interval effect. Within a certain range, intermediate principal stress can effectively regulate the deformation of surrounding rock and restrain the expansion of the plastic zone.
## 3 Theoretical Framework
### Bridging Theoretical Concepts with Practical Application in DFDLOF  
In this section, we aim to explicitly bridge the gap between the theoretical concepts and their practical application within the context of our study on the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF). The key theories underpinning our research, such as cognitive load theory and [PERSON]'s zone of proximal development (ZPD), are not just abstract ideas but serve as fundamental guides for the development of DFDLOF.  
For instance, cognitive load theory has informed the design of our adaptive learning content, ensuring that the information presented to learners is within their cognitive capacity to process effectively. This theory guided us in creating learning materials that are neither too challenging nor too simplistic, thus maintaining optimal engagement and learning efficiency.  
Similarly, [PERSON]'s ZPD has played a crucial role in shaping the adaptive algorithms of DFDLOF. By understanding the learner's current knowledge level and potential for growth, the framework dynamically adjusts the difficulty of tasks and content, ensuring that learners are consistently challenged just enough to facilitate learning without overwhelming them.  
Figure 3: Integrating educational psychology theories with machine learning to enhance learner engagement and motivation.  
Moreover, the integration of machine learning algorithms in DFDLOF is a practical embodiment of these theories. By analyzing learner data in real time, these algorithms enable the framework to adaptively respond to each learner's unique needs, mirroring the principles of these theoretical concepts in a tangible, operational manner.  
Through this explicit linkage, we demonstrate how theoretical concepts are not only relevant but essential for the practical application of educational technologies like DFDLOF, thereby addressing the specific requirements and objectives of our research.
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 3 Experimental Results
### Results and Analysis
#### 3.3.3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **PL** & **BD** & **BR** & **GTF** & **SV** & **LV** & **SH** & **TC** & **BC** & **ST** & **SBF** & **RA** & **HA** & **SP** & **HC** & **CC** & **mAP** \\ \hline single-scale: & & & & & & & & & & & & & & & & & & & \\ RetinaNet-O [37] & 71.43 & 77.64 & 42.12 & 64.65 & 44.53 & 56.79 & 73.31 & **90.84** & 76.02 & 59.96 & 46.95 & 69.24 & 59.65 & 64.52 & 48.06 & 0.83 & 59.16 \\ FR-O [4] & 71.89 & 74.47 & 44.45 & 59.87 & 51.28 & 68.98 & 79.37 & 90.78 & 77.38 & 67.50 & 47.75 & 69.72 & 61.22 & 65.28 & 60.47 & 1.54 & 62.00 \\ Mask R-CNN [19] & 76.84 & 73.51 & 49.90 & 57.80 & 51.31 & 71.34 & 79.75 & 90.46 & 74.21 & 66.07 & 46.21 & 70.61 & 63.07 & 64.46 & 57.81 & 9.42 & 62.67 \\ HTC [54] & 77.80 & 73.67 & 51.40 & 63.99 & 51.54 & 73.31 & 80.31 & 90.48 & 75.12 & 67.34 & 48.51 & 70.63 & 64.84 & 64.48 & 55.87 & 5.15 & 63.40 \\ CMR [54] & 77.77 & 74.62 & 51.09 & 63.44 & 51.64 & 72.70 & 79.99 & 50.35 & 74.90 & 67.58 & 49.54 & 72.85 & 64.19 & 64.88 & 55.87 & 3.20 & 63.41 \\ DAFNe [55] & - & - & - & - & - & - & - & - & - & - & - & - & - & - & - & 64.76 \\ FR-O [4] + RT [23] & 79.12 & 76.07 & 51.87 & 69.24 & 52.05 & 75.18 & 80.72 & 90.53 & 78.58 & 68.26 & 49.18 & 71.74 & 67.51 & 65.53 & 62.1* indicates multi-scale training and testing. Note. The results of RetinaNet OBB (RetinaNet-O) [37], Faster R-CNN OBB (FR-O) [4], Mask R-CNN [19] and Hybrid Task Cascade (HTC) [54] are re-implemented [33] version for DOTA, and used by some scholars [21, 41]. For the convenience of reading and comparison, we bolded and marked the red, yellow and green numbers in the column as the 1 st, 2 nd, and 3 rd largest values in that column, respectively.
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Name** & **Abreviation** & **Depth** & **Volume** & **Elevation** & **Res. Time** & **Climate** & **Visits** & **Samples** \\ \hline  & & m (max.) & \(\times 10^{6}\) m\({}^{3}\) & m.a.s.l. & years & & & \\ \hline Alarcón & ALA & 71 & 1118 & 806 & 2.15 & Cas & 3 & 13 \\ Albufera & ALB & 1.5 & 360 & 1 & 0.10 & Cas & 2 & 6 \\ Bellus & BEL & 34 & 69 & 144 & 0.24 & Cas & 5 & 20 \\ Benaixeve & BEX & 90 & 221 & 450 & 0.63 & Csb & 5 & 14 \\ Beniarrés & BEN & 53 & 27 & 318 & 0.35 & Cas & 2 & 6 \\ Cazalegas & CAZ & 6 & 11 & 385 & 0.42 & Cas & 1 & 1 \\ Contreras & CON & 129 & 852 & 669 & 1.48 & Cas & 7 & 24 \\ Maria & MCR & 38 & 18 & 100 & 5.96 & Cas & 2 & 4 \\ Cristina & PED & 60 & 246 & 50 & 0.92 & Bsh & 2 & 9 \\ Petrola & PET & 1.8 & 2 & 890 & - & Cas & 1 & 5 \\ Regajo & REG & 23 & 6 & 407 & 0.14 & Cas & 3 & 10 \\ Stijar & SIT & 58 & 49 & 160 & 0.37 & Cas & 3 & 5 \\ Tous & TOU & 110 & 378 & 135 & 0.28 & Cas & 4 & 26 \\ Valdecanas & VAL & 98 & 1446 & 315 & 0.36 & Cas & 1 & 3 \\ \hline \multicolumn{8}{c}{Total} & & & & 41 & 146 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of sampled lakes and reservoirs characteristics. Symbols and abbreviations: max.: maximum; m.a.s.l.: meters above sea level; Res.: residence.  
+++ ==ERROR: No output for this page==
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Validation**} \\ \cline{2-5}  & \(n\) & \(\mathbf{R}^{2}\) & **RMSE** & **RRMSE** & **Bias** \\ \hline C2 KCC (0-318.5): & & & & & \\ \(R704/\)R665 & & 0.923 & 77.1 & 258 & 24.72 \\ \(R740/\)R665 & & 0.792 & 73.6 & 246 & 21.61 \\ \(\left(\frac{1}{R665}-\frac{0.4}{R560}-\frac{0.6}{R701}\right)\times R740\) & & 0.231 & 77.3 & 259 & 21.34 \\ [PERSON] et al. [24] & & 0.865 & 67.5 & 226 & 17.39 \\ \hline C2X (0-318.5): & & & & & \\ \(R704/\)R665 & & 0.858 & 39.8 & 84 & 4.32 \\ \(R740/\)R665 & & 0.847 & 44.7 & 94 & 0.34 \\ \(\left(\frac{1}{R665}-\frac{0.4}{R560}-\frac{0.6}{R701}\right)\times R740\) & & 0.925 & 26.3 & 55 & 0.49 \\ [PERSON] et al. [24] & & 0.953 & 43.4 & 91 & 35.68 \\ \hline C2 XC (0-751.1): & & & & & \\ \(R704/\)R665 & & 0.968 & 38.9 & 39 & 5.45 \\ \(R740/\)R665 & & 0.964 & 48.3 & 48 & 18.33 \\ \(\left(\frac{1}{R665}-\frac{0.4}{R560}-\frac{0.6}{R701}\right)\times R740\) & & 0.974 & 85.0 & 85 & 25.18 \\ [PERSON] et al. [24] & & 0.961 & 98.3 & 98 & 6.77 \\ \hline Polymer (0-751.1): & & & & & \\ \(R704/\)R665 & & 0.362 & 154 & 140 & 40.11 \\ \(R740/\)R665 & & 0.680 & 108 & 98 & 29.81 \\ \(\left(\frac{1}{R665}-\frac{0.4}{R560}-\frac{0.6}{R701}\right)\times R740\) & & 0.186 & 303 & 276 & 154.35 \\ [PERSON] et al. [24] & & 0.491 & 68.9 & 63 & 15.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PC validation results for in situ spectrometry waters quality algorithms and AC type.
## 1 Introduction
The identification of weaknesses in compromised buildings is crucial for the design and construction of more suitable structures. Thus, it is reasonable to assert that the understanding of the seismic performance of structures has been augmented due to prior field studies. This study conducts a detailed analysis of the recorded in situ damage observed in masonry buildings. The primary aim is to identify patterns of structural vulnerabilities that are consistently present, evaluate the shortcomings of existing seismic regulations, and facilitate the advancement of construction methodologies that are both resilient and tailored to the specific contexts of Turkiye's masonry building sector. These results are especially important for nations where the condition of the current masonry building stock is equally lacking as they provide transferable knowledge on seismic behavior. The paper's structure is set out as follows. To provide background for the seismicity of eastern Turkiye and its consequences for ground motion exposure, Section 2 offers a short tectonic review. Emphasizing the effects of occupant-driven expansions and inadequate construction techniques, Section 3 provides a thorough categorization of deterioration processes seen in masonry structures. With particular emphasis on ring beam criteria, wall slenderness limitations, mortar standards, and the official handling of illegal extensions, Section 4 offers a historical and technical analysis of the Turkish seismic code. This development lets the reader follow the connection between reported harm and legal loopholes. The study ends with a summary of results and focused suggestions for better seismic risk control and structural resilience in masonry housing.
## 6 Parameter Studies and Discussions
### Influences of Parameter Selection for the Interter Element on the Transmissibility  
The parameters of additional tunable inerter element are related to the mass and location of the inerter element. In this section, the influences of the parameters \(\mu\) and \(\lambda\) on the vibration isolation performance are investigated in detail. Figure 17 shows the absolute displacement transmissibility curves under different parameters \(\mu\) and \(\lambda\). As the mass ratio increases, the peak of the transmissibility curve decreases gradually, and the transmissibility curve leans to the left. This will undoubtedly lead to a lower transmissibility and a wider vibration isolation region. It can also be seen that a larger mass ratio contributes to the better vibration isolation performance. Therefore, the selection of mass ratio should be conducted according to a practical engineering application, on the premise that it is better to choose a larger mass ratio in a reasonable range. Moreover, it can be seen that the parameter \(\lambda\) has an influence on vibration isolation performance in the low-frequency and high-frequency range. With the increasing of \(\lambda\), the peak response frequency and peak transmissibility have both increased. Nevertheless, if the value of the parameter \(\lambda\) becomes much larger, it leads to an increase in the total mass on the one-side and larger static displacement. As a result, a relatively smaller value of parameter \(\lambda\) can be better for vibration isolation performance of the QZS-IE seat suspension system.  
Figure 16: Comparisons of peak transmissibility and the peak response frequency between the QZS system and QZS-IE system (\(\lambda=0.2,\varepsilon=2\)): (**a**) The variation of peak response frequency with damping ratio (\(u_{0}=1.0\)); (**b**) the variation of peak transmissibility with damping ratio (\(u_{0}=1.0\)); (**c**) the variation of peak response frequency with excitation amplitude (\(\zeta=0.05\)); (**d**) the variation of peak transmissibility with excitation amplitude (\(\zeta=0.05\)).
# Development of a Virtual Reality-Based System for Simulating Welding Processes
## 3 Results and Discussions
### Simulation Results and Discussions  
#### 3.3.1 Simulation Results  
The functionality of the developed system was tested successfully as shown in Figure 17. For simulating the SMAW, MIG, and TIG welding processes, we inputted the value range of the welding process parameters. The position change of the welding torch in the real environment updated in real time in the virtual environment.  
According to the allowed value range of the welding process parameters for SMAW, MIG, and TIG, when we moved the real welding torch, the data, including the distance from the welding torch to the plates, the welding torch position, and the welding energy, were sent to the computer. If these data were in the allowed range, the weld joint appeared; otherwise, the weld joint failed.  
The developed system enables to describe the welding process correctly and shows the welding process information when the welding process is carried out. We can simulate the welding types, such as the filled joint, butt joint, and lap joint, by changing the position of the welding sample plates in hardware, changing the 3D model in the software, and changing the welding process parameters. The simulation results include the weld joint, which was established or not; the weld size in the case of the established weld joint; and the welding process parameters.  
Figure 18 shows the results during the TIG welding process. The TIG torch (hardware) was used and shown on the virtual environment (computer and 3D glasses). For carrying out the fillet weld joint, two sample welded plates, which were circuit boards attached to the IR sensors, were placed perpendicular, and in the virtual environment, the 3D model with two plates was also placed perpendicularly like the physical environment. With the welding process parameters, the distance from the torch to the plates and the angle between the torch with the plate were within the allowed values; and the fillet weld joint was established, which is the weld line, the weld size, the welding arc light, and the welding sound. The welding process parameters include the welding position (or weld type), plate thickness, welding material, electrode diameter, welding current, torch angle, wire federate, inert shielding gas, gas flow rate, welding speed, and arc length.  
For simulating more easily, the welding process was sent to 3D glasses. With this application, we could watch the weld formation directly in real time as we moved the TIG welding torch, changed the torch angle, and changed the angle between the torch and the plates.  
Figure 17: Testing the functionality of the developed system.
# Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics
## 3 GPS Offline Rate of Engineering Vehicles  
According to a survey, the GPS offline rate of engineering vehicles is categorized into the four levels of over 20%, 10%-20%, 5%-10%, and less than 5%, which correspond to the four risk levels of serious, general, basically safe, and safe, and are scored as [0-3], [3-6], [6-8], [6-7], or (8-10) to quantify the assessment indexes, as shown in Table 3.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 2 Materials and Methods
### Data Processing
#### 2.2.1 Satellite Data Processing
[48], the most favorable polarization mode for monitoring land subsidence in an urban environment is the VV polarization mode, as was used in the  
Figure 2: Schematic diagram presenting the workflow of the methodology combining PSI and leveling techniques for the estimation of land subsidence on the Limassol coastal front from 2017 to 2021.  
dataset for this specific study since it strongly returns part of the signal to the sensor due to the artificial scatterers on the surface (e.g., buildings).