esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging User Comments for Recommendation in E-Commerce
## 4 Proposed Approach
### Step 1--Developing Item and User Vectors
Note that the input value at node \(i\) is 1, while those at the other nodes in the input layer are 0. In addition, the desired value at node \(k\) is 1, while those at the other nodes in the output layer are 0. The network output at node \(k\) is  
\[o_{k}=\frac{1}{1+e^{-\mathbf{v}_{i}^{1}\cdot\mathbf{v}_{k}^{2}}} \tag{4}\]  
Figure 1: The Word2 Vec network. where '\(\cdot\)' is the inner product operator. Let \(J\) be \(J=-\log_{e}\omega_{k}\). By steepest descent, \(\mathbf{v}_{i}^{1}\) and \(\mathbf{v}_{k}^{2}\) are adjusted as  
\[\mathbf{v}_{i}^{1}\leftarrow\mathbf{v}_{i}^{1}-\eta\
abla_{\mathbf{v}_{i}^{1}}J =\mathbf{v}_{i}^{1}-\eta(\omega_{k}-1)\mathbf{v}_{k}^{2}, \tag{5}\]  
\[\mathbf{v}_{k}^{2}\leftarrow\mathbf{v}_{k}^{2}-\eta\
abla_{\mathbf{v}_{k}^{2}}J =\mathbf{v}_{k}^{2}-\eta(\omega_{k}-1)\mathbf{v}_{i}^{1} \tag{6}\]  
where \(\eta\) is the learning rate. During the learning, if two training patterns \((w_{i},w_{k})\) and \(\left(w_{j},w_{k}\right)\) occur frequently in the training documents, \(\mathbf{v}_{i}^{1}\) and \(\mathbf{v}_{j}^{1}\) will be close to each other. When the learning is completed, the input weights of the network are taken to be item vectors. That is, \(\mathbf{v}_{1}^{1}\) is the word vector of word \(w_{1}\), \(\mathbf{v}_{2}^{1}\) is the word vector of word \(w_{2}\), etc. Note that item vectors are word vectors of the item identities. For \(M\) products, we have \(M\) item vectors in total, \(\mathbf{p}_{1}\), \(\mathbf{p}_{2},\cdots,\)\(\mathbf{p}_{M}\), as shown below:  
\[\mathbf{p}_{i}=\left[ \begin{array}{c}p_{1i}\\ p_{2i}\\ \vdots\\ p_{Hi}\end{array} \right]=\left[ \begin{array}{c}v_{1i}^{1}\\ v_{2i}^{1}\\ \vdots\\ v_{Hi}^{1}\end{array} \right] \tag{7}\]  
for \(i=1\), \(\cdots\), \(M\). Clearly, each item vector contains \(H\) components. In this work, we set \(H=100\). Note that if two items are semantically similar, then their corresponding item vectors are close to each other. Now that item vectors are available, we can derive user vectors, which can then be used for similarity computation. First, we normalize each row in the user-item rating matrix \(\mathbf{R}\) as follows:  
\[a_{ij}=\frac{r_{ij}}{\sum_{k=1}^{M}r_{ik}},\ 1\leq j\leq M \tag{8}\]  
for \(1\leq i\leq N\). Then, user vectors, \(\mathbf{u}_{k}\), \(1\leq k\leq N\), are derived as follows:  
\[\mathbf{u}_{k}=\left[ \begin{array}{c}u_{1k}\\ u_{2k}\\ \vdots\\ u_{Hk}\end{array} \right]=\sum_{j=1}^{M}a_{kj}\mathbf{p}_{j}. \tag{9}\]  
Note that each user vector also contains \(H\) components.
## 1 Introduction
### Related Work
#### 1.2.2 Methods Based on 3D Point Clouds
Despite the advancements made with these 3D point cloud-based methods, there are still notable challenges. First, the computational cost associated with processing high-resolution point clouds is significant, particularly in real-time applications. Furthermore, the reliance on dense point clouds for accurate defect detection often limits the scalabilityof these methods for large-scale industrial applications, where point cloud data may be sparse or incomplete.
## 3 The PFAC Measurements and the FRM Uncertainty Analysis
### Absolute Coordinates Constituent
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site/Time Span** & **Lat [degree]** & **Lon [degree]** & **Ellipsoid** & **N\_velocity** & **E\_velocity** & **UP\_velocity** \\  & & & **Height [m]** & **[m yr\({}^{-1}\)]** & **[m yr\({}^{-1}\)]** & **[m yr\({}^{-1}\)]** \\ \hline CDN0 & N35\({}^{\circ}\)20\({}^{\circ}\)16.024375\({}^{\prime\prime}\) & E23\({}^{\circ}\)46\({}^{\circ}\)6854809\({}^{\prime\prime}\) & 1049.5186 & \(-\)0.0126 & 0.0076 & 0.0002 \\
[2014.49-2019.99] & \(\pm\)3.1 mm & \(\pm\)2.1 mm & \(\pm\)8.0 mm & \(\pm\)0.3 mm & \(\pm\)0.5 mm & \(\pm\)1.0 mm \\ \hline CDN2 & N35\({}^{\circ}\)20\({}^{\circ}\)16.2911099\({}^{\prime\prime}\) & E23\({}^{\circ}\)46\({}^{\circ}\)68.29112\({}^{\prime\prime}\) & 1050.4068 & \(-\)0.0127 & 0.0070 & \(-\)0.0025 \\
[2016.40-2019.99] & \(\pm\)1.8 mm & \(\pm\)2.2 mm & \(\pm\)8.5 mm & \(\pm\)0.4 mm & \(\pm\)0.5 mm & \(\pm\)2.0 mm \\ \hline GVD7 & N34\({}^{\circ}\)50\({}^{\circ}\)52.744568\({}^{\prime\prime}\) & E24\({}^{\circ}\)711.205643\({}^{\prime\prime}\) & 201.685 & \(-\)0.0137 & 0.0083 & \(-\)0.0004 \\
[2009.37-2019.55] & \(\pm\)1.6 mm & \(\pm\)2.2 mm & \(\pm\)6.0 mm & \(\pm\)0.1 mm & \(\pm\)0.2 mm & \(\pm\)0.5 mm \\ \hline GVD8 & N34\({}^{\circ}\)50\({}^{\circ}\)52.612211\({}^{\prime\prime}\) & E24\({}^{\circ}\)711.399214\({}^{\prime\prime}\) & 22.2760 & \(-\)0.0143 & 0.0078 & 0.0004 \\
[2010.36-2019.99] & \(\pm\)1.8 mm & \(\pm\)2.2 mm & \(\pm\)6.4 mm & \(\pm\)0.2 mm & \(\pm\)0.2 mm & \(\pm\)0.5 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The International Terrestrial Reference Frame 2014 (ITRF2014) coordinates and velocities of two pairs of GNSS stations operating at the CDN1 transponder (CDN0 and CDN2) and Gavdos sea-surface (GVD7 and GVD8) Cal/Val sites. The time span and uncertainties in the form of a weighted root mean square error of each component are also given. Figure 5: Dispersion values for the height component of the GVD8 GNSS station in the form of coordinate time series as derived by relative differential (upper image) and precise point positioning processing (lower image). The reference epoch is 2013.5 and solutions refer to the ITRF2014 reference frame. The gap observed in the period between 2016.5 and 2017.6 corresponds to construction works carried out at the Gavdos harbor that forced temporary decommissioning of GVD8. The coordinates and velocities of each GNSS station as determined by diverse GNSS processing techniques have been found to agree at the mm-level and sub-mm level, respectively (Table 3). In the following subsection, the uncertainty for the sea-surface and transponder Cal/Val, as implemented at the PFAC, is given along with the verification that in-situ measurements are in compliance with the FRM4 ALT standards.
## 3 Results and Discussion
### Overall Performance of Each HSSF-CW in EDC Removal  
Four scenarios (quadrants) were identified (Figure 4) according to the weekly effluent removal efficiencies of BPA and NP. Zone I is the worst scenario in which both compounds were removed with less than 50% efficiency. In Zone II, BPA removal was greater than 50%, but NP was less than 50%. Zone III shows a BPA removal efficiency less than 50% and NP greater than 50%. Finally, in Zone IV, both compounds were removed with efficiencies greater than 50%. The best CW removal efficiencies were observed for the _Heliconia_-CW, in which six of seven (85%) of the data points are in zone IV, compared with the unplanted-CW with more than five of seven (71%) of the data points in zone II. Overall, the BPA and NP removal efficiencies were in the following descending order: _Heliconia_-CW > _Phragmites_-CW > unplanted-CW. This performance is likely due to sorption in the HSSF-CW onto support media. Also, the rhizosphere of planted CWs likely generates benefits such as increasing DO concentrations and releasing organic exudates that serve as nutrient sources for the growth of microorganisms [25].  
Figure 4: Quadrant chart for NP removal efficiency versus BPA removal efficiency for the _Heliconia_-CW (H), _Phragmites_-CW (P) and unplanted-CW (C).  
Figure 3: Endocrine disruptive compound(EDC) removal rate for H-CW, C-CW, and P-CW against their mass loading rates (**a**) BPA and (**b**) NP. Continuous line represents 100% removal.  
This study showed that BPA and NP can beeffectively removed by planted HSSF CW under tropical conditions, with removal being more efficient withthe native and marketable plant _Heliconia_ sp. in the CW.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 4 Modulation Technology in VLC System
### Multilevel Pulse Amplitude Modulation (M-PAM) Technique  
Multilevel pulse amplitude modulation (M-PAM) is a conventional multilevel modulation scheme and is the generalization of non-return-to-zero (NRZ) on-off keying (OOK) from a set of two symbols to a set of M symbols, as illustrated in Figure 9. For example, when the LED/Laser is turned off, the light intensity will not be completely switched off, but the optical intensity will be diminished. Where \(Q(.)\) is the Q function, the BER and SNR relation of M-PAM can be determined by Equation (13) [117]:  
\[BER_{M-PAM}=\frac{M-1}{M}\frac{2}{\log_{2}M}\mathcal{Q}\bigg{(}\frac{\sqrt{ SNR}}{M-1}\bigg{)} \tag{13}\]
## 2 Related Work
[26] made use of mathematical algorithms to optimize crane positions and material supply drop-off points for trucks in high-rise constructions, also taking into account BIM models. Here, the BIM models are mainly used for a 4D simulation to verify the plausibility of the defined points on-site. [PERSON] et al. [27] are addressing optimal crane selections by means of simulations as well. However, their simulations are based on 3D representations, whilst the metadata of BIM models are not considered. Besides crane selection and layout planning, other scientific work in the light of BIM and construction equipment must be mentioned: another related field of research deals with decision-making processes in the work preparation phase, where BIM can be considered a source of knowledge. A question that arises in this context is for example which technological solution to decide for when several solutions are possible. Practical examples for this problem are given in terms of comparison of conventional and modular construction [28], and assessment of building stocks [29]. In addition to that, [PERSON] et al. [30] presented a decision support system for the automatic generation of scaffolding plans in different scenarios, which are evaluated according to safety, cost, and installation durations based on BIM information as input. Likewise, in the field of scaffolding planning, [PERSON] and [PERSON] [31] proposed a framework for automatized planning using BIM authoring software which is based on an ontology model from the perspective of material management, taking into account BIM-based quantity take-offs. In conclusion of this literature review, to our knowledge, there have been very few approaches leveraging the actual metadata available in BIM models to automate and streamline the internal processes of a construction equipment supplying company. Most of the studies account solely for 3D visualization as an inherent part of BIM models with respect to optimization and layout problems. In particular, we have found little scientific attention to the assembly and installation of temporary construction containers themselves, which are the focus of this study.
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Guidance Scheme  
Various guidance schemes for drag-modulation aerocapture have been studied in the existing literature [13; 39; 40]. [PERSON] et al. showed that using accelerometer measurements to create a density interpolator as the vehicle descends into the atmosphere performs consistently better than the density scale factor approaches used in earlier studies [41]. Using the measured density profile enables the guidance algorithm not to be reliant on any on-board density profiles. A simple guidance scheme using on-board density interpolation is used in this work to evaluate the preliminary design. For simplicity, the study assumes that there is no error in the density from accelerometer measurements and perfect knowledge of vehicle's navigated state during entry. Once a pre-defined altitude rate threshold is exceeded, the vehicle starts predicting its apoopsis altitude at exit if the drag skirt is jettisoned in the next guidance cycle loop. Figure 9 shows the evolution of altitude rate and altitude for a guided Mars drag-modulation aerocapture trajectory. On-board density begins at entry and continues until a preset altitude rate of \(-\)200 m/s is exceeded at approximately 120 s after entry. At this point, apoopsis prediction is initiated in a 2 Hz guidance loop using the measured density model. For altitudes below the range for which measurements were available, a simple exponential scale height-based extrapolation is used. At approximately 188 s after entry, the drag-skirt jettison event is commanded.  
Figure 8: Effect of density variation on Mars aerocapture corridor bounds. The black dash-dot line indicates the selected EFPA, and the black dotted lines indicate the assumed \(\pm\)3\(\sigma\) delivery error.
## 4 Engineering Applications  
### Geological and Engineering Background  
A slope project is located in Taihe Town, Xichang City, Sichuan Province (Figure 4). The geographic coordinates of the foot of the slope are east longitude: 102\({}^{\circ}\)07\({}^{\prime}\)35.44\({}^{\prime\prime}\), north latitude: 27\({}^{\circ}\)54\({}^{\prime}\)26.27\({}^{\prime\prime}\). The slope of the quarry is surrounded by high mountains in the west, south and north, being open in the east. Under the action of artificial mining, the quarry has formed a number of steps, with the slope angle of the mining steps ranging from 45 to 54\({}^{\circ}\) and the angle of the overall slope ranging from 29 to 44\({}^{\circ}\). At present, the elevation of the bottom of the pit is 1530 m. The maximum difference in elevation of the slope is 618 m, making it a super-high slope. The slope stratum of the quarry is composed of powdery clay, moraine soil, gabbro and gabbro, from top to bottom. According to the observation data from the Xichang Meteorological Station, the average annual rainfall is around 1013.50 mm. The rainy season typically spans from May to October, during which the annual rainfall can peak at around 1471.10 mm and drop to a minimum of approximately 691.20 mm.  
The study area of this paper contains a wedge (Figure 5) and the area circled by the blue curve in Figure 6, which is developed in the middle of the slope on the west side of the northern area of the open-pit quarry, and is in the deformation zone of the wedge.  
Figure 4: Slope of the northern area of the mine.  
The slope surface of the wedge is modified by mining stripping in a micro-geomorphic style, in the form of steps, involving 11 steps, with elevations from 1660 m to 1758 m and a step height of about 12 m. The platform width ranges from 2 m to 39 m (the lower part of the slope, not yet mined). The measured slope gradient on the upper part of the steps is about 40\({}^{\circ}\), while the lower part ranges from 50\({}^{\circ}\) to 65\({}^{\circ}\), resulting in an overall gradient transition from gentle, at the upper part, to steep, at the lower part. The plane of the wedge is approximately rectangular, with a maximum longitudinal length of about 280 m and a transverse width of about 62 m. The maximum deformation depth is about 50 m, obtained from deep displacement monitoring, with a square volume of about 50 \(\times\) 10\({}^{4}\) m\({}^{3}\), a slip direction of about 175\({}^{\circ}\), and an average height difference of 98 m along the slip direction.  
Figure 5: Full view of the wedge.  
Figure 6: Schematic diagram of the wedge plane.  
The quarry will need to continue to be mined 100 m down from its current base, a process that will be carried out in two layers, each at a depth of 50 m. The rocky slope is unstable and could collapse or landslide in the event of heavy rainfall, which would reduce the mechanical strength of the soil as it is soaked by rain.
## 2 Materials and Methods  
### Study Area  
The QTP (26\({}^{\circ}\)00\({}^{\prime}\)-39\({}^{\circ}\)47\({}^{\prime}\)N, 73\({}^{\circ}\)19\({}^{\prime}\)-104\({}^{\circ}\)47\({}^{\prime}\)E), known as the Third Pole of the earth, covering about 2.5 million km\({}^{2}\), is the largest and highest plateau in the world (averageelevation \(>\)4000 m a.s.l) [6; 7; 8]. The QTP is higher in the west and lower in the east, with diverse land cover, including glaciers, permafrost, deserts, and various vegetation types, but it is mainly (more than 60%) covered by grassland (Figure 1a) [8; 15; 16]. The annual average air temperature in most areas of the plateau is below 0 \({}^{\circ}\)C, and the average monthly air temperature ranged from -11.6 \({}^{\circ}\)C in January to 9.1 \({}^{\circ}\)C in July during 2000-2020. The annual average precipitation ranges from 100 mm year\({}^{-1}\) on the northwest to over 1000 mm year\({}^{-1}\) on the southeast [35]. Regionally, the air temperature and precipitation on the QTP increase from the northwest to the southeast (Figure 1c,d) [9; 36]. From 2000 to 2020, the air temperature (TEMP) and precipitation (PPT) over the grassland increased at rates of 0.037 \({}^{\circ}\)C yr\({}^{-1}\) and 1.97 mm yr\({}^{-1}\), respectively (Figure 1b). In this study, six types of grassland on the QTP were involved (Figure 1a), among which alpine meadow and alpine steppe are the main grassland types, accounting for 45.8% and 44.9% of the total grassland area, respectively. The other four grassland types are temperate meadow, temperate steppe, alpine desert steppe, and tropical-subtropical grassland, and their area accounts for less than 10%. The grassland was divided into six types and different model parameters were assigned to reflect the differences among different grasslands and improve the accuracy of carbon budget simulations. However, in terms of spatial heterogeneity, this study focused on distinguishing the differences in carbon budget among different orientations and climate zones (Figure 1a, Arid, Semi-arid, Semi-humid, and Humid) on the QTP.  
Figure 1: Distribution of grassland types and eddy covariance observation sites (**a**), the trends (**b**) of precipitation (blue line) and temperature (red line), the spatial pattern of multi-year (2000–2020) mean annual precipitation (**c**) and multi-year mean air temperature (**d**) over the grassland on the Qinghai–Tibet Plateau (QTP) from 2000 to 2020 (reference system: WGS 84).
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 2 Model Description
### Dynamic Calculation Model of Drag and Friction  
In the borehole, the movement form of DS can be divided into two types, free movement state (Figure 2) and the contact state between DS and borehole wall (Figure 3). When the DS is not in contact with the borehole wall, the DS moves freely within the hole, and will not be subject to the constraint force given by the hole wall. At this point the DS is in free motion; when the DS surface is close to the borehole wall and there is a tendency to move out of the borehole, the movement is bound by the borehole wall. Subsequently, the DS collides with the borehole wall and produces contact force and friction. When the DS is in contact with the borehole wall, the normal contact reactionary force \(f_{n}\) is generated at the contact position. In addition, tangential friction force \(f_{\tau}\) and axial friction force \(f\) are generated at the contact point.  
The contact element between DS and borehole wall is constructed, and the thickness of the contact element is assumed to be 0.001 m [30]. Then, the interaction distance between DS and borehole wall can be obtained by Equation (3).  
\[r=\left\{\begin{array}{cc}0&r_{\rm d}-(R_{w}-0.001)<0\\ r_{\rm d}-(R_{w}-0.001)&r_{\rm d}-(R_{w}-0.001)\geq 0\end{array}\right. \tag{3}\]  
where \(r\) is the approaching distance between the DS and the borehole wall; \(r_{d}\) is the radial displacement of the DS element; \(R_{w}\) is the diameter of the borehole;  
Figure 2: Free movement state.  
The hertzian contact theory is used to calculate the contact force between DS and borehole wall at the contact point. Assuming that the resistance factor of rock and DS is \(k\) and the damping coefficient is \(c\), the interaction between DS and borehole wall can be obtained by Equation (4).  
\[F_{\rm n}=kr+cv_{r} \tag{4}\]  
where \(F_{n}\) is the contact force; \(v_{r}\) is the radial velocity at the DS element.  
Based on the Coulomb friction theorem, the friction generated at the contact point is calculated by Equations (5) and (6).  
\[f=-sign(v)uF_{\rm n} \tag{5}\]  
\[f_{\rm\tau}=-sign(v_{\rm\tau})u_{\rm\tau}F_{\rm n} \tag{6}\]  
where \(f\), \(u\), and \(v\) are the axial friction force, axial friction coefficient, and axial velocity, respectively, and \(f_{\rm\tau}\), \(u_{\rm\tau}\), and \(v_{\rm\tau}\) are the tangential friction force, tangential friction coefficient and tangential velocity, respectively.
## 2 Materials and Methods  
### Study Area  
The Qinghai-Tibetan Plateau is situated in southwestern China and covers all of Tibet and the Qinghai provinces and is also a part of the Xinjiang, Sichuan, Gansu, and Yunnan provinces (Figure 1). The Qinghai-Tibetan Plateau is considered the third pole in the world, has an average altitude of \(>\)4000 m, and is characterized by a plateau monsoon climate with low temperatures, low precipitation, and strong insolation. More than 54% of the Qinghai-Tibetan Plateau area has a total annual precipitation below 400 mm and temperatures below 0 \({}^{\circ}\)C [22]. This region is known as the Asia water tower and is home to the headstreams of the Yangtze, Yellow, Lantsang, and Indus rivers. The alpine, cold, and dry climatic conditions lead to unique vegetation types on the Qinghai-Tibetan Plateau. A climate gradient exists from warm-humid in the southeast to cold-dry in the northwest, along which the vegetation types transition from forestland, meadow, steppe, and desert. The grassland, which includes meadow, steppe, and desert steppe, and covers 51.05% of the Qinghai-Tibetan Plateau area, is the most important ecosystem and sensitive to climate change. An understanding of grassland dynamics under the climate and human disturbance conditions is crucial for regional ecological security.  
We divided the entire Qinghai-Tibetan Plateau into 12 subregions (Figure 1, Table 1) based on the bio-geographical division proposed by [PERSON] et al. [30]. The grassland distribution was extracted according to a China vegetation map (scale = 1:100,000) [31], eliminating subregions X, XI, and XII, for which the main vegetation types are desert, forestland, and forestland, respectively. Only the remaining nine subregions (I-IX) are analyzed in this study, covering meadow, steppe, and desert grassland (Table 1). Of these nine subregions, we focused in detail on subregion I, which has the highest annual accumulated temperature above 0 \({}^{\circ}\)C (AGDD\({}_{0}\)) and medium moisture index (MI). Subregions II and III had relatively high MI values that decreased from southeast to northwest. Each subregion exhibited unique climatic conditions and economic development levels, as well as different vegetation responses to climate and human activities.  
Figure 1: Study area and geographical subregions. The black circles represent the locations of 209 meteorological stations on the Qinghai-Tibetan Plateau.