esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
External stone cladding is made use of a thin stone panel affixed to building surfaces enclosing the building structures as architectural decoration added to the external building facades [1]. It is usually installed on the building structures through mechanical anchorage systems or direct application of cement-based materials on its rear surface to a structural substrate. As a stone is a natural substance with inherent variability, its properties differ considerably between different type, colors, grades, and cutting direction, as well as even the stones of the same type, color, and bed processed from the same quarry [2]. Therefore, stone cladding needs due consideration in its unique characteristics and performance [3]. The previous studies revealed the defects and deficiencies in the completed external stone cladding systems which required the building owners to pay huge amount of money for the remedial works [4; 5; 6].  
Many studies have focused on the solutions in response to the encountered problems at different stages of the external stone cladding system. In the design stage, the knowledge gap between architects and engineers was one of the leading causes of cladding failures. Developing design standards and engineering guidelines was recommended to narrow the knowledge gap [7]. Ref. [8] also suggested the ten steps of design procedures to minimize unnecessary failures due to poor design and methods. In the maintenance stage, ref. [9] studied causes of stone decay and suggested preventive treatments through an overview of the previous researches on stone conservation. Additionally, a study by [10] developed service life prediction models for external stone cladding which allows more rational management of the maintenance of a building. However, relatively little research has been reported for the production stage and installation stage, particularly lacking the current status and important factors related to production and installation quality of the external stone cladding system in Hong Kong.  
This study concerns the definition of the production stage as a process of transformation of material into the final product after its design stage. It is the last stage until the factory is ready to deliver the tailor-made stone panels for site installation. The installation stage in this study is defined as the last phase of external stone cladding work before its completion and proceeding towards its maintenance stage. The case studies described in Section 2.2. showed the partial problems (e.g., substandard stone materials; surface defects and misalignment; and cracking and damage during transportation) occurred on the external stone cladding system in the two stages in Hong Kong. In this regard, the research objectives of this study are:  
* To identify the current status of the external stone cladding system in the two stages.
* To analyze the major factors at the stages affecting the performance of the stone cladding system.
* To suggest recommendations to alleviate the quality control of the system in the two stages.
## 2 Dynamical Model for the Robotic Drilling End-Effector
### Motion Equations of the Robotic Drilling End-Effector
\begin{array}{ll}h_{1}&=\big{[}\big{(}-I_{xx2}+I_{yy2}-I_{yy3}+I_{zz3}-m_{3} d_{3}^{2}+2m_{3}d_{3}c_{z3}\big{)}\sin(2\theta_{2})\ +2\big{(}I_{xy2}+I_{yz3}-m_{3}d_{3}c_{y3}\big{)}\cos(2\theta_{2})\big{]}\dot{ \theta}_{1}\dot{\theta}_{2}\\ &+\big{[}\big{(}-I_{xz2}+I_{xy3}\big{)}\sin(\theta_{2})+\big{(}-I_{yz2}+I_{xz3 }-m_{3}d_{3}c_{x3}\big{)}\cos(\theta_{2})\big{]}\dot{\theta}_{2}^{2}\\ \\ h_{2}=\bigg{[}\frac{1}{2}\big{(}I_{xx2}-I_{yy2}+I_{yy3}-I_{zz3}-2m_{3}d_{3}c_{ z3}+m_{3}d_{3}^{2}\big{)}\sin(2\theta_{2})\ +\big{(}-I_{xy2}-I_{yz3}+m_{3}d_{3}c_{y3}\big{)}\cos(2\theta_{2})\big{]}\dot{ \theta}_{1}^{2}\\ \\ h_{3}=m_{3}\cos(\theta_{2})\big{[}c_{y3}\sin(\theta_{2})+(c_{z3}-d_{3})\cos( \theta_{2})\big{]}\dot{\theta}_{1}^{2}+m_{3}(c_{z3}-d_{3})\dot{\theta}_{2}^{2}. \end{array}  \tag{16}\]  
A 3 \(\times\) 1-order gravity vector is calculated as:  
\[\mathbf{G}(\mathbf{q})=\left[ \begin{array}{cc}G_{1}&G_{2}&G_{3}\end{array} \right] \tag{19}\]  
with  
\[ \begin{array}{ll}G_{1}&=\big{[}m_{1}c_{x1}\sin(\theta_{1})+\big{(}m_{1}c_{y 1}+m_{2}c_{z2}+m_{3}c_{x3}\big{)}\cos(\theta_{1})\ +\big{(}-m_{2}c_{x2}+m_{3}c_{y3}\big{)}\sin(\theta_{1})\sin(\theta_{2})\\ &+\big{(}-m_{2}c_{y2}+m_{3}c_{z3}-m_{3}d_{3}\big{)}\sin(\theta_{1})\cos( \theta_{2})\big{]}g\\ \\ &G_{2}=\big{[}\big{(}-m_{2}c_{y2}+m_{3}c_{z3}-m_{3}d_{3}\big{)}\sin(\theta_{2} )\ +\big{(}m_{2}c_{x2}-m_{3}c_{y3}\big{)}\cos(\theta_{2})\big{]}g\cos(\theta_{1}) \\ \\ &G_{3}=m_{3}g\cos(\theta_{1})\cos(\theta_{2}).\end{array}  \tag{20}\]  
According to [PERSON]'s equation, the dynamical equation of the end-effector is derived as:  
\[\mathbf{D}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{H}\big{(}\mathbf{q},\dot{ \mathbf{q}}\big{)}+\mathbf{G}(\mathbf{q})=\boldsymbol{\tau} \tag{23}\]  
or  
\[\left[ \begin{array}{ccc}D_{11}&D_{12}&D_{13}\\ D_{21}&D_{22}&D_{23}\\ D_{31}&D_{32}&D_{33}\end{array} \right]\left[ \begin{array}{c}\ddot{\theta}_ {1}\\ \ddot{\theta}_{2}\\ \ddagger_{3}\end{array} \right]+\left[ \begin{array}{c}h_{1}\\ h_{2}\\ h_{3}\end{array} \right]+\left[ \begin{array}{c}G_{1}\\ G_{2}\\ G_{3}\end{array} \right]=\left[ \begin{array}{c}\tau_{1}\\ \tau_{2}\\ f_{3}\end{array} \right] \tag{24}\]  
where \(\tau_{1}\) and \(\tau_{2}\) denote the torque acting on joints 1 and 2, respectively,\(f_{3}\) denotes the force acting on prismatic Joint 3. The joint parameters of the end-effector calculated by the model are listed in Table 1.
# Experimental and Modeling of Residual Deformation of Soil-Rock Mixture under Freeze-Thaw Cycles
## 6 Conclusions  
Through the experimental exploration of the residual deformation of soil-rock mixtures with different rock contents under the action of freeze-thaw cycles, and the simulation prediction and verification of the LSTM model, the following conclusions can be drawn:  
1. Under the action of freeze-thaw cycles, the soil-rock mixture will repeatedly produce frost heave andthaw subsidence, and the initial frost heave andthaw subsidence change greatly and become steady.
2. Under the condition that the content of boulders in the soil-rock mixture is not more than 80%, as the content of boulders increases, the residual deformation caused by the freeze-thaw cycle will gradually decrease due to the skeleton effect of boulders, while the increase in the content of the boulders will increase the residual deformation.
3. The LSTM model based on the artificial neural network can effectively predict the freezing and thawing change law of the soil-rock mixture in the short term, thereby reducing the period of the freezing and thawing test and improving the fault tolerance rate.  
Conceptualization, C.W.; Data curation, Y.S.; Formal analysis, J.C.; Methodology, C.W. and A.L.; Project administration, A.L.; Resources, L.C. and Z.X.; Software, J.C.; Supervision, G.Y., M.L. and A.L.; Validation, G.Y. and M.L.; Visualization, J.C.; Writing--original draft, C.W.; Writing--review and editing, A.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Zhejiang Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards (No. PCMGH-2017-Y03), the National Key Research and development of China (Grant No. 2017 YFA0603101), the funding of the State Key Laboratory of Frozen Soil Engineering (Grant No. SKLFSE201712), the Program of the State Key Laboratory of Frozen Soil Engineering (Grant No. SKLFSE-ZT-202110).  
The authors declare no conflict of interest.
## 5 Evaluation and Discussion
### Scalability  
Scalability is a vital metric in chatbot solutions. It determines the chatbot's ability to handle many simultaneous user queries without performance degradation or increased response time. Scalability tests involved subjecting the chatbot to a high volume of user queries in a short timeframe and observing its performance. Testing up to six concurrent users showed minor increases in the average response times from 981 to 1023 ms, as shown in Figure 17. This linear scalability indicates the potential for maintaining performance with higher request volumes.
## 3 Results
### Tortuosity Measurements  
The Spearman's rank correlation coeffent between automated and subjective tortuosity indexes was 0.49 (95%CI: \(0.20,0.72\); \(p=0.0008\)) (see Figure 4a). Table 2 shows the tortuosity outcomes of the three post-LASIK groups and the total length estimated by the automated procedure. The automated tortuosity index, ranging from 0 to 30, was significantly different among groups (\(p=0.0019\)). Post hoc analysis showed that this new tortuosity index was significantly increased in the post-LASIK DED-pain group compared with the control group (\(p=0.0014\)). In contrast, there were no significant differences among groups in the subjective tortuosity grading (\(p=0.2871\)). Regarding the total length of the automatically detected nerves, the control group showed a trend (\(p=0.0582\)) for longer values than both case groups.  
Regarding the correlations between the automated tortuosity index and ocular parameters (Figure 4b-f), the [PERSON]'s rank correlation coefficients indicated that tortuosity was positively correlated with the OSDI and NRS pain scores (Figure 4b,c); \(p=0.0012\) and \(p=0.0051\), respectively). However, the tortuosity index did not correlate with CFS, TBUT and CDVA (Figure 4d-f); \(p=0.8299\), \(p=0.3468\) and \(p=0.6411\)).  
The execution time for the proposed automated procedure was \(8.74\pm 0.67\) seconds per image, \(8.66\pm 0.67\) and \(0.09\pm 0.03\) seconds for nerve tracing and splitting stages, respectively. The estimation was carried out using R coding on a machine equipped with an Intel i7 3770 CPU at 3.4 GHz.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Control (C)** & **Post-LASIK DED** & **Post-LASIK** & **ANOVA** & **Post-Hoc Comparisons** \\
**Parameter** & & **(D)** & **DED-Pain (P)** & & & & \\  & **(\(n=20\))** & **(\(n=7\))** & **(\(n=16\))** & **Global** & & & \\  & & & & **\(p\)-Value** & & & \\ \hline Total length & \(1603.68\pm 385.29\) & \(1297.07\pm 239.87\) & \(1344.61\pm 271.67\) & 0.0309 & 0.0582 & 0.0582 & 0.7499 \\ Automated tortuosity (\(0-30\)) & \(7.34\pm 4.17\) & \(9.51\pm 3.88\) & \(13.93\pm 6.53\) & 0.0019 & 0.3431 & 0.0014 & 0.0977 \\ Subjective tortuosity (\(0-4\)) & \(2.00\pm 1.00\) & \(3.00\pm 1.00\) & \(3.00\pm 0.00\) & 0.2871 & 0.4040 & 0.4040 & 0.9415 \\ \hline \end{tabular} Results are expressed as mean \(\pm\) SD, except the subjective tortuosity grading summarized by median \(\pm\) IQR.  
\end{table}
Table 2: Tortuosity indexes obtained from IVCM images for control and post-LASIK DED patients.  
Figure 4: Correlations between the automated tortuosity index and ocular parameters. [PERSON]’s rank correlation coefficients and 95% CI are shown on the scatterplots for the automated tortuosity index against (**a**) subjective tortuosity index, (**b**) OSDI score, (**c**) NRS pain score, (**d**) CFS, (**e**) TBUT and (**f**) CDVA. Values of the ordinal variables are jittered to prevent overplotting.
###### Abstract  
Monitoring and diagnosis of rotating machines has become an effective and indispensable tool for the efficient and timely detection of defects, avoiding then incidents that can have serious economic and human consequences. Bearings are the most sensitive parts of these machines; this is why special attention must be paid to its monitoring. This paper presents a methodology for diagnosing an aircraft air compressor bearing using neural networks that have been optimized by genetic algorithms. We used in our study a database of vibratory signals that were recorded on a test bench from bearings with different defects. The faults features are extracted from these noisy signals using the estimate of the spectral density. The diagnostic capacity of obtained model has been demonstrated by a comparative study with two other automatic classifiers, which are discriminant analysis and neural networks whose training has been done with the Back-Propagation algorithm. This approach has the advantage of simultaneously ensuring the optimal structure of the neural network and accomplishing its learning. The importance of this study is the construction of a diagnostic tool that is characterized by efficiency, speed of decision making and ease of implementation not only on the computers on the ground, but also on the mounted calculators on aircraft.  
neural networks; genetic algorithms; discriminant analysis; fault diagnosis; modeling; signal processing; vibration +
Footnote †: journal: Applied Soft
## 3 Results  
### Effectiveness of the Compensation Waveforms  
We can measure the vertical displacement of any point within tens of seconds before the beginning of cyclic stretching and establish the corresponding waveforms for real-time imaging.  
In this experiment, first, we used a marker imitating a single cell which is in the center of the chamber to test the effectiveness of the demonstration system. The compensation waveform for each point was obtained according to Formula (1). The chamber was stretched (lengthened) at a constant speed of 2 mm/s for a stretching distance \(\mathrm{S}d=2\) mm (Figure 2b), and then shortened to the original length. Images were taken by a 40\(\times\) objective lens during the experiments. As shown in Figure 9, using only stage 1, the marker was clearly moved out of the microscope's FOV. With the combination of stage 1 and 2, the system was able to maintain the target in the microscope's FOV, but the marker was completely out of focus. Finally, in the case of a combination of all three stages, the images and movies with higher resolution were obtained in real time. Movies of the results are shown in Supplementary Materials Video S1.  
In the purpose of verifying the system performance, the contrast of images was calculated using the following equations [48]:  
\[\mathrm{C}=\frac{1}{H\cdot W}\sum_{H}\sum_{W}(I(i,j)-\mu)^{2} \tag{2}\]  
\[\mu=\frac{1}{H\cdot W}\sum_{H}\sum_{W}I(i,j) \tag{3}\]  
where \(I\) represents the pixel at the coordinates \((i,j)\) in grey level intensity. \(H\) is the height of the image, and \(W\) is the width of the image. \(\mu\)is the mean intensity equal to the averaged gray level intensity of the image. Based on the results of the image analysis shown in Figure 10, the contrast value of the system with only stage 1 was found to be very low when the chamber was stretched, because the marker was completely out of the FOV of the microscope. The marker re-appeared in the FOV once the relaxation phase of the cycle ended, thus, the image at this point can be obtained with a high contrast value. By using both stages 1 and 2, the marker still remains in the FOV of the microscope; however, the images were blurry  
Figure 8: A diagrammatic sketch of the method to determine the maximum displacement values in the horizontal and vertical directions. The C program was used to control the three stages and record the movements of stages 2 and 3 automatically before cyclic stretching. The linear compensation waveforms were formed automatically with the C program for the lengthening, holding, and relaxation phases based on the record. Cyclic stretching was started only after the recognition of the target by human eyes and by pressing the ENTER key.  
during the lengthening and relaxation processes. This is because the marker was not able to be focused. Therefore, the contrast value decreases linearly in both processes. By using all three stages, the contract values maintain in high quality during the whole lengthening and relaxation processes since the aberration in the \(z\)-direction was well compensated compared to the other systems with only stage 1 and stage 2.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 4 The Proposed Scheme
### Digital Watermarking Algorithm of Texture Coordinates
#### 4.2.2 Watermark Extraction  
As illustrated in Figure 5, the extraction procedure for watermarks follows the same methodology as the embedding process of watermarks. The process of watermark extraction can be perceived as the inverse operation of watermark embedding.  
Figure 4: Flow chart of the texture coordinate watermark embedding.  
Intiate the data preprocessing by extracting texture coordinate data from the 3 DMOP, represented as \(T=\{T_{1},\ T_{2},\ T_{3},\ \ldots\ T_{n}\}\), where \(T_{i}(U_{i},\ V_{i})\) denotes the texture coordinates, with \(i\in[1,\ N]\), and \(N\) indicating the number of texture coordinates. Texture coordinates typically manifest as values within the range of [0, 1], signifying positions on the texture image.  
Calculate the distance of each texture coordinate from the texture coordinate origin [0, 0], resulting in \(D_{T}=\{D_{T1},\ D_{T2},\ C_{T3},\ \ldots\ D_{T1}\}\), where \(i\) represents the number of texture coordinates. The watermark data, denoted as w, is a binary matrix of size \(h*h\). Establish a mapping relationship between the watermark data \(w^{\prime}=\left\{w^{\prime}_{(i,j)},\ 0\leq i\leq h-1,\ 0\leq j\leq h-1\right\}\) and the distance \(D\) from the feature points to the virtual centroid. For the \(m\)-th texture coordinate \(T_{m}(U_{m},\ V_{m})\), compute its corresponding watermark value \(w^{\prime}_{(i,j)}\) using Equations (10) and (11):  
\[\left\{\begin{array}{c}D_{Tm}=\sqrt{\left(U^{\prime}_{m}\right)^{2}+\left(V ^{\prime}_{m}\right)^{2}}\\ d_{Tm}=trunc\left(D_{Tm}*10^{2},\ \ p\right)\\ r,l=divmod\left(Hash(d_{m}),\ \ h^{2}\right)\\ d^{\prime}_{Tm}=Int\left(D_{Tm}*10^{6}\right)\end{array}\right. \tag{10}\]  
\[w^{\prime}_{(r,l)}=\left\{\begin{array}{c}1,\ \ d^{\prime}_{Tm}mod2\ \
eq 0\\ 0,\ \ d^{\prime}_{Tm}\ mod2=0\end{array}\right. \tag{11}\]  
In Equation (10), \(trunc(a,b)\) signifies the retention of \(b\) decimal places for \(a\). \(Int(*)\) denotes the extraction of the integer part of a decimal value. \(Hash(*)\) is a function that transforms arbitrary length input into fixed length output. \(divmod(a,b)\) signifies the division with a remainder, where \(a\) is the dividend, and \(b\) is the divisor. Here, \(d_{m}\) represents the distance \(D_{Tm}\) from the texture coordinate to the texture coordinate origin, with a specified number of decimal places retained, and \(mod\) signifies the modulo calculation.  
Repeat the aforementioned procedure, iterating through all texture coordinates. There exists a many-to-one mapping relationship between texture coordinates and watermark positions, with one watermark position having multiple watermark values. Calculate the proportion of identical values in \(r\) and \(l\), and assign the watermark values of the corresponding row or column based on the result with the highest count, thereby accomplishing the extraction of watermark information.
###### Abstract  
This study explores the physical, radiation shielding, optical, and photoluminescent properties of PbO\({}_{2}\)-SiO\({}_{2}\)-based glass systems. Traditional radiation shielding materials, like lead and concrete, face challenges due to toxicity and weight. Glass materials provide an alternative, offering transparency and efficiency. Four glass systems were analyzed: PbO\({}_{2}\)-SiO\({}_{2}\) (PS), PbO\({}_{2}\)-SiO\({}_{2}\)-CeO\({}_{2}\) (PSC), PbO\({}_{2}\)-SiO\({}_{2}\)-Eu\({}_{2}\)O\({}_{3}\) (PSE), and PbO\({}_{2}\)-SiO\({}_{2}\)-Yb\({}_{2}\)O\({}_{3}\) (PSY). The results show that rare earth elements easily the glass network, thereby enhancing radiation attenuation properties, quantified through parameters like the linear attenuation coefficient (\(\mu\)), the half-value layer (HVL), and the mean free path (MFP). The PSY system exhibited the best shielding properties, demonstrating its potential for use in gamma ray shielding. Samples PS0-PS3 revealed semiconducting behavior and may be considered a promising host matrix for solar cells and w-LED applications.  
Keywords:lead-silicate glass; rare earth ions; radiation shielding; semiconductor; luminescence properties; color parameters +
Footnote †: journal: Applied sciences
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 3 Results
### Plant Response
#### 3.3.2 Plant Biomass  
The total biomass of the plants that grew in RA soil was significantly lower than the biomass in NC soil (Figure 4A).  
The addition of biochar to contaminated soil significantly increased total plant biomass compared to unamended contaminated soil (Figure 4A). The plant biomass increased with the biochar dosage. Thus, the addition of 4 and 8% of biochar increased the total biomass 16 and 22-fold, respectively, both with respect to the unamended contaminated soil (Figure 4A), although this parameter remained below the values measured in NC (58.76% and 43.64% for RA4B and RA8B, respectively).  
When comparing the aerial and root biomass of the amended soils, it can be observed that the increase in the amendment doses did not significantly modify the aerial biomass but it did increase the root biomass more than two times in the 8% treatment in relation to the 4% treatment (Figure 4B). However, shoot and root biomass in RA8B remains below (45% and 40%, respectively) the values in NC.  
The values of the calculated growth indicators confirm the higher efficacy of the 8% biochar treatment compared to the 4% treatment (Table 3). Thus, the growth indicator values (Pt, Rt, and PR) for RA4B did not differ significantly from those for RA, unlike RA8B, which showed significant differences for the three coefficients analyzed in relation to RA and similar values to NC soil.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **RA** & **RA4B** & **RA8B** & **NC** \\ \hline
**Pt** & 0.79 \(\pm\) 0.07 b & 0.81 \(\pm\) 0.01 b & 0.65 \(\pm\) 0.01 a & 0.67 \(\pm\) 0.01 a \\
**Rt** & 0.18 \(\pm\) 0.02 a & 0.19 \(\pm\) 0.01 a & 0.35 \(\pm\) 0.01 b & 0.33 \(\pm\) 0.01 b \\
**PR** & 3.86 \(\pm\) 0.49 b & 4.30 \(\pm\) 0.25 b & 1.86 \(\pm\) 0.06 a & 2.04 \(\pm\) 0.08 a \\ \hline \end{tabular}
\end{table}
Table 3: Growth indicators (Pt, aerial/total biomass; Rt, root/total biomass; and PR, aerial/root ratio) of _Trifolium patense_ in the non-contaminated soil (NC), in the unamended contaminated soil (RA) and in RA soil amended with biochar at 4% (RA4B) and 8% (RA8B). Growth indicators were calculated two months after planting. Different letters indicate significant differences (\(p\) < 0.05) between treatments according to the post-hoc [PERSON] test.  
Figure 4: Values of total biomass (**A**) and biomass of the aerial part and roots (**B**) of _Trifolium patense_ in the tested treatments (RA, unamended contaminated soil; RA4B and RA8B, contaminated soil amended with 4 and 8% of biochar, respectively; and NC, non-contaminated soil). Error bars represent standard deviation. Different letters indicate significant differences between treatments (\(p\) < 0.05) according to [PERSON]’s post-hoc test.
## 2 Height Factor Model for GNSS Tomography  
The HFM, explicitly described in this section, provides the possibility to use side signals in the tomography model. The dynamic characteristic of the top boundary of the tomography area is studied for the first time in the HFM. Moreover, two height factors are defined to show the isotropic and anisotropic peculiarities of tropospheric delay, and the SWD of inside signals is estimated by calculating the two components.