esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Autogenous-Shrinkage Alkali-Activated Slag and Fly Ash Concrete
## 3 Results and Discussion
### Cracking Potential of Paste  
It should be noted that low autogenous shrinkage does not necessarily mean low cracking potential. If the mitigating effect on the autogenous shrinkage is at the cost of dramatic loss in strength loss, the material may be subject to higher cracking risk [31]. To investigate the effect of SAPs and MK on the cracking potential of the paste, the ring test was used to measure the stress in the paste mixtures under a restrained condition. The results are shown in Figure 4. The sudden drop in the stress to around zero indicated the occurrence of cracking.  
Figure 3: Autogenous shrinkage of AASF paste with SAPs and/or MK.  
Figure 4 shows that the plain AASF paste cracked on the third day after casting when the internal stress reached around 2.7 MPa. Substituting 10 wt. % slag by MK prolonged the cracking time by about 1 day and the paste broke at a stress of 3.7 MPa. The cracking potentials of AASF and AASFMK pastes were both \"high\" according to ASTM C1581 [22]. With internal curing by SAPs, the paste did not crack until 29 days of curing when the stress reached 6 MPa. Since the cracking time of AASFIC was close to 28 days, and the stress rate at the cracking time was 0.14 MPa/day, the cracking potential of AASFIC could be classified as \"medium-low\" according to ASTM C1581 [22].  
The results in Figure 4 indicate that both SAPs and MK were helpful in reducing the cracking potential of the paste. Meanwhile, the addition of SAPs or MK did not lead to low strength of the matrix, as indicated by the high failure stress of the pastes. When SAPs and MK were applied together into AASF, the paste showed no cracking within 3 months of curing, which could not be realized by using only SAPs or MK. According to the low stress rate (<0.1 MPa/day), the cracking risk of AASFICMK paste was rather low [22].  
Since the combined incorporation of SAPs and MK led to the lowest autogenous shrinkage and the lowest cracking potential, the mixture AASFICMK was further studied at the concrete level to develop low-shrinkage and low-cracking-potential AAMs concrete. The plain AASF concrete was studied as a reference mixture.
## 2 Materials and Methods  
Our multivariate spatial-statistical-prediction framework involves four major steps: (1) Obtain multivariate, monthly remote sensing datasets that are spatially indexed (Sections 2.1 and 2.2). (2) Use the multivariate spatial data to estimate covariance and cross-covariance functions of a multivariate spatial-statistical model (Section 2.3). (3) Use the multivariate spatial (cross-) covariance functions to predict the process of interest along with uncertainties (root-mean-squared prediction errors) on a fine-resolution (\(0.05^{\circ}\times 0.05^{\circ}\)) regular grid (Section 2.4). (4) Evaluate the predictions and their uncertainties using a variety of statistical validation metrics (Section 2.5). When applied to SIF and XCO\({}_{2}\), we obtain an estimated, de-noised, gap-filled SIF data product, coSIF, which includes uncertainty quantification. These methods and the specific datasets used to create coSIF are detailed in the following subsections.
# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 5 Lightweight Identification Model for Shoveling Position
### Network Model and Improvements
#### 5.2.4 EIoU Loss Function  
YOLOv5s uses the CIoU loss function to penalize aspect ratio differences by calculating the diagonal distance of the smallest enclosing box, thereby measuring the similarity of the rectangular boxes [26]. The formula for the CIoU loss function is as follows:  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{6}\]  
\[v=\frac{4(\arctan\frac{\omega^{gt}}{h^{gt}}-\arctan\frac{\omega}{h})^{2}}{\pi^{ 2}} \tag{7}\]  
\[L_{CIoU}=1-IoU(A,B)+\frac{\rho^{2}(A_{ctr},B_{ctr})}{c^{2}}+\alpha v \tag{8}\]  
Here, \(L_{CIoU}\) represents the similarity between the predicted box \(A\) and the ground truth box \(B\). \(\rho\) denotes the distance between the center points of the predicted box \(A\) and the ground truth box \(B\), with \(A_{ctr}\) and \(B_{ctr}\) representing these centers. \(c\) is the diagonal length of the smallest enclosing box that contains both \(A\) and \(B\). \(\omega^{gt}\) and \(h^{gt}\) are the width and height of the ground truth box, while \(\omega\) and \(h\) are the width and height of the predicted box. \(v\) is a correction factor used to further adjust the loss function.  
CIoU can effectively avoid divergence issues during training. However, when the aspect ratios of the predicted box and the ground truth box are the same, the aspect ratio penalty term becomes zero, which clearly leads to a lack of regression optimization for the predicted box. Therefore, replacing the original loss function with the EIoU loss function addresses this issue. EIoU builds upon the penalty term of CIoU by separating the aspect ratio influence factor of the predicted and ground truth boxes, calculating their lengths and widths individually [27]. This approach addresses the issues present in CIoU. The formula for the EIoU loss function is as follows:  
\[L_{EIoU}=1-IoU+\frac{\rho^{2}(A_{ctr},B_{ctr})}{c^{2}}+\frac{\rho^{2}(\omega, \omega^{gt})}{c^{2}_{\omega}}+\frac{\rho^{2}(h,h^{gt})}{c^{2}_{h}} \tag{9}\]  
Here, \(c_{\omega}\) and \(c_{h}\) represent the width and height of the smallest enclosing box that contains both boxes.
## 4 Inventory and Analysis  
### Inventory of Roof Space in the Shibei District  
#### 4.1.1 Inventory Methods  
Firstly, we selected Google Satellite Maps with a pixel resolution of 0.96 m for analysis. Next, we imported satellite images into GIS, visually interpreted and analyzed the properties of building roofs in the images, and determined their area by drawing and quantifying the surface of the building roof. Some roof outlines were not clear enough and were compared with Google's satellite map with a pixel resolution of 0.24 m and the Panorama in Baidu Maps, and some ambiguous spaces were investigated on the spot. According to the aforementioned inventory standards, using satellite remote sensing images, the roofs of buildings with sloping roofs and heights exceeding 40 m as well as the roofs of buildings with pollution and those unsuitable for agricultural cultivation in the rubber industry and industrial production and those used for parking lots were removed and not considered, neglecting those whose available space on the roof was too fragmented or too small in area. We only inspected flat roofs that were conducive to agricultural development and assigned residential and other attributes to the inspected buildings. During the inventory process, in order to avoid individual roof omissions and confusion in the inventory, it was carried out block by block, and the data were promptly checked and saved at any time.
## 4 Results
### Risk Type Mapping and Counting  
As discussed in the previous section, the risk factors and their corresponding risk types were extracted for each accident sample. Then, a binary mapping of risk types was applied to each sample in different ACs, with duplicate risk types removed. This involved assigning values of 0 or 1 to \(h\), \(v\), \(r\), \(w\), and \(m\), resulting in a five-bit encoding. For example, given an accident sample in PA, if the accident involved only human factors, then \(h=1\), \(v=0\), \(r=0\), \(w=0\), and \(m=0\). The status encoding for H, V, R, W, and M was \"1000\". By performing binary mapping on all accident samples in PA and SA, the risk type status encoding for each accident sample was obtained, and the quantity and frequency of the status encodings were counted. Figures 6 and 7 show the frequencies of different risk type status encodings in PA and SA, respectively.  
Figure 5: Entity alignment result.  
Figure 6: Frequencies of different risk type status encodings in primary accidents (PA).  
As shown in Figure 6, the risk types in PA were generally balanced. Specifically, in accident samples involving a single risk type, accidents with the status encoding \"10000\" occurred most frequently, while accidents with status encodings \"00010\" and \"00001\" had frequencies of zero. This indicates that isolated weather and management factors do not lead to accidents. An interesting phenomenon was that, for accidents involving four risk factors, the combination of human, vehicle, road, and management factors (status encoding \"11101\") occurred most frequently, while the combination without human factors (status encoding \"01110\") occurred at a much lower frequency. This suggests that human factors may be the primary driving force behind the occurrence of accidents. The same phenomenon was also observed in SA, as shown in Figure 7. In SA involving three risk factors, the combination of human, vehicle, and management factors (status encoding \"11001\") occurred most frequently, while the combination of only vehicle and management factors (status encoding \"01001\") occurred with a frequency of only 0.0192. In addition, nearly half of the accident samples involved three risk types in SA. This was also a key difference between PA and SA.
# Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride
## 3 Results
### Dislocation Motion
#### 3.2.2 Screw Dislocations
However, the transition velocity to regime II, i.e., \(v_{t}=22.1\) m/s, is larger than that of the edge dislocation (10.1 m/s) while the transition stress is smaller (700 MPa for the screw dislocation versus 1150 MPa for the edge dislocation). For the stress range 700-1125 MPa, the velocity varies linearly with the stress (regime II) and values of \(M=4546\) Pa\({}^{-1}\cdot\)s\({}^{-1}\) and \(C=-1134\) m/s fit the data points in this regime to Equation (4) with \(R^{2}=94.8\%\). For stresses larger than 1125 MPa, debris production was observed in the wake of the dislocation, which reduced the velocity. The fitting parameters of the screw dislocation mobility function are shown in Table 2. The linear mobility of the screw dislocation is larger than that of the edge dislocation by more than a factor of 5. Despite the usual caveats expected in comparing the qualitative results of two different interatomic potentials, we can expect, based on the results, that the screw dislocations most likely dominate the deformation process in UN. This picture agrees with the experimental observation that dislocations in UN are nearly pure screws [9]. Figure 7: Movement of the \(\frac{1}{2}\langle 110\rangle\{110\}\) screw dislocation at 1 K under an applied strain rate of \(10^{-4}\) s\({}^{-1}\) as predicted by the Kocevski potential. The average of the developed \(\sigma_{yz}\) stress on the dislocation is also shown.
## 2 Experimental Principle
Considering the two-dimensional sampling theorem, the parameter \(\alpha\) in the above formula needs to satisfy the below condition:  
\[\alpha\leq\alpha_{\text{max}}=\arccos(\lambda/2\Delta x) \tag{7}\]  
where \(\Delta x\) is the pixel size of the camera. Its light intensity distribution is expressed as:  
\[ \begin{array}{ll}I(x,y)&=\left|\widetilde{R}(x,y)+\widetilde{O}(x,y)\right| ^{2}\\ &=\left|\widetilde{R}\right|^{2}+\left|\widetilde{O}\right|^{2}+\widetilde{R }^{*}\widetilde{O}+\widetilde{R}\widetilde{O}^{*}\\ &=\widetilde{U}_{1}(x,y)+\widetilde{U}_{2}(x,y)+\widetilde{U}_{3}(x,y)+ \widetilde{U}_{4}(x,y)\end{array}  \tag{8}\]  
After two-dimensional Fourier analysis of the light intensity distribution image, it is easy to obtain:  
\[ \begin{array}{ll}F\left|\widetilde{U}_{1}(x,y)\right|&=\delta(u,v)\\ F\left|\widetilde{U}_{2}(x,y)\right|&=\widetilde{G}_{0}(u,v)\widetilde{G}_{0}( u,v)\\ F\left|\widetilde{U}_{3}(x,y)\right|&=\widetilde{G}_{0}(u-\alpha,v)\\ F\left|\widetilde{U}_{4}(x,y)\right|&=\widetilde{G}_{0}(-u-\alpha,-v)\end{array}  \tag{9}\]  
where \(\widetilde{G}_{0}(u\),\(v)=F(\widetilde{O}(x\),\(y))\) is the spectrum of the speckle light field, \(F\) stands for two-dimensional Fourier transform, \(\star\) represents a correlation operation, and \(\delta(u\),\(v)\) is an impulse function. \[F[I(x,y)]=\delta(u,v)+\widetilde{G}_{0}(u,v)\widetilde{G}_{0}(u,v)+\widetilde {G}_{0}(u-\alpha,v)+\widetilde{G}_{0}(-u-\alpha,-v) \tag{10}\]  
It can be seen from the two-dimensional Fourier analysis that \(\tilde{U}_{1}\), \(\tilde{U}_{2}\), \(\tilde{U}_{3}\), and \(\tilde{U}_{4}\) appear on different positions of the frequency spectrum, respectively. The appropriate two-dimensional band pass filter is used to extract the spectrum which contains the speckle light field \(\widetilde{G}_{0}(-u\)-\(\alpha\),\(-v)\) separately, and then the amplitude and phase of the speckle light field are obtained through performing two-dimensional Fourier inverse transformation. That is, we use \(F^{-1}(\widetilde{G}_{0}(-u\)-\(\alpha\),\(-v))=\tilde{U}_{4}(x\),\(y)\) to obtain \(\widetilde{R}\widetilde{O}^{*}\), which makes the phase distribution apply to the SLM, and also makes the reference light inject to the SLM at a conjugate angle to obtain a conjugate phase light field of the speckle light field. When the reference light is perpendicular to the observation surface, the conjugate light is also perpendicular to the observation surface, so it is only necessary to keep the reference light to inject perpendicularly during the phase conjugate reduction process.
## 3 Field Application  
### Building Outline  
The proposed system was applied to the construction site of actual high-rise buildings to be used for residential purposes as shown in Figure 5. The buildings are composed of two towers (tower 1: 66 stories; tower 2: 72 stories). A typical floor plan for the towers is shown in Figure 5b. Reinforced concrete shear walls, outriggers, and belt trusses were used as the lateral load resisting system for the towers (Figure 5c), and the exterior columns were designed to be steel reinforced concrete columns.  
Figure 5: 72-story high-rise building (Tower 2). (**a**) Perspective view; (**b**) Typical floor plan; (**c**) Vertical section.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Real-Time Level Selection of Negotiation Granularity
#### 2.3.1 Neural Network Description  
The negotiation information transmission is related to network status and size of information which is selected as input parameters of the neural network. In this paper, the network status is described by two parameters: remaining communication bandwidth and hop number to the _Leader_. The size of information is described by the number of targets \(n\). The outputs of the neural network are the success probabilities of negotiation in each level of granularity recorded as \(P_{L}\), \(P_{M}\)m and \(P_{H}\).  
Suppose there are \(N_{L}\) layers. All layers are fully-connected. In the \(k\)th layer, there are \(N_{n}^{k}\) nodes. They are all connected to \((k-1)\)th layer's nodes with parameters \(w_{k}\) and \(b_{k}\). The activation function is optimized during training. The mean squared error \(MSE\) was chosen to evaluate network performance:  
Figure 4: High-level granularity negotiation process.  
\[MSE=\frac{1}{M}\sum_{i=1}^{M}\left\|\mathbf{y}_{i}-\hat{\mathbf{y}}_{i}\right\|_{2 }^{2}, \tag{17}\]  
where \(\mathbf{y}_{i}\) is the vector of real value form sample; \(\hat{\mathbf{y}}_{i}\) is the vector of predicted value output by the neural network.
## 3 Data and Data Processing
### InSAR Time Series Analysis  
We used the SBAS algorithm to calculate surface velocity with 25 COSMO-SkyMed images, which were acquired during a 2-month period (16 February 2016-29 November 2017). Our criteria for interferogram selection included (1) a maximum perpendicular baseline of 500 m and (2) a maximum temporal baseline of 400 days, which resulted in a total of 122 interferograms (Figure 3). The final displacement velocity map covering the entire Karapinar area had dimensions of approximately 40 \(\times\) 40 km\({}^{2}\).  
Interferogram selection for the Sentinel-1 dataset, which includes 87 acquisitions, was based on a different strategy due to the orbital stability of Sentinel-1 along with small orbital baselines [48]. The interferogram network was constructed by connecting each SAR image with its three consecutive SAR images, which resulted in a total of 254 interferograms. The final displacement velocity map covering the Karapinar area had the dimensions of approximately 90 km (width) \(\times\) 70 km (length).  
One of the essential steps of this study is to investigate the accuracy of the time series obtained from InSAR data with the help of GNSS data located. In order to compare the deformation information collected from InSAR with GNSS, the deformations in the LOS direction that represent movements away from the satellite must first be converted into vertical deformation information. The time series of the vertical deformation can be obtained from the LOS measurements [49; 50] as follows:  
\[disp_{vert}\cong\frac{disp_{LOS}}{\cos\vartheta} \tag{1}\]  
Figure 3: (**a**): Interferogram network configuration of COSMO SkyMED images processed by Small BAseline Subset (SBAS). (**b**): Interferogram network configuration of the Sentinel-1 images processed by SBAS.  
where \(disp\)\(vert\), \(disp\)\(LOS\) and \(\vartheta\) are vertical deformations, LOS deformation, and the sensor's incidence angle, respectively. In this study, we used mean incidence angles of 37.30\({}^{\circ}\) and 33.80\({}^{\circ}\) for COSMO-SkyMed and Sentinel-1 data, respectively. This equation is based on the assumption that horizontal deformation in the study area is negligible.
# Nutritional Profile and Antioxidant Properties of Hemp (_Camanabis sativa_ L.) Seed from Romania
## 4 Discussion  
### Proximate Composition of Hemp Seeds  
According to [PERSON] et al. and [PERSON] et al., hemp seeds have a proximate composition of 18-23% protein, 25-30% oil, 30-40% fiber, and 6-7% moisture [5; 28]. The moisture content obtained aligned with findings from other studies. [PERSON] et al. [29] reported moisture values ranging between 4.53 and 7.06 g/100 g for eight hemp varieties, while [PERSON] et al. found a moisture content of 7.3% in Fedora cultivar seeds [6]. [PERSON] et al. noted moisture content from 6.54% to 7.9% in three different hemp varieties cultivated in Central Italy [30]. These variations could be attributed to environmental and storage conditions, particularly temperature.  
The ash content is consistent with the reports by [PERSON] et al. [7], who observed ash values ranging from 5.1 to 5.8 g per 100 g in ten different hemp cultivars, and [PERSON] et al., who reported 5.3 g per 100 g in Fedora seeds [6]. Similar results were obtained by [PERSON] et al. (2024), with ash values between 4.40% and 7.49% in 29 hemp varieties [31].  
Hemp seed proteins are mainly storage proteins, with albumin accounting for 25-37% and the legumin protein edestin for 67-75% [32]. These proteins make hemp seeds particularly beneficial for vegetarian diets. Our results regarding the protein content of hemp seeds are consistent with other findings. [PERSON] et al. [33] reported protein content of 22.5% in hemp seeds, while [PERSON] and colleagues found 24.8% [6]. [PERSON] et al. observed protein content ranging from 21.3 to 27.5 mg/100 g across 30 hemp seed samples [8]. Hemp proteins are primarily located in the internal layer of the seed, with minimal quantities in the hull [9].  
The lipid composition of the analyzed hemp seed varieties was in close alignment with the existing research. [PERSON] et al. reported a lipid content of 24.5%, [PERSON] et al. found 25.4-33.0 mg per 100 g, and [PERSON] et al. observed 26.9 to 30.6 mg per 100 g [6; 7; 8]. [PERSON] et al. found crude fiber content varying between 20.98 and 25.58 g/100 g [30].  
Reduced carbohydrate content in the improved samples suggests potential hypoglycemia benefits and suitability for diabetic-friendly diets. [PERSON] et al. found a carbohydrate prevalence of 38.1% [6]. [PERSON] et al. [4] reported 27.6 g per 100 g in Finola hemp seeds, while [PERSON] et al. found carbohydrate content comparable to flaxseed (34.4 \(\pm\) 1.5 g/100 g vs. 29.2 \(\pm\) 2.5 g/100 g) [34]. [PERSON] et al. reported an energy value of 2301 kJ/100 g dw for hemp seeds [34].  
Observed differences in chemical composition can be attributed to environmental conditions, soil characteristics, agricultural practices, and natural botanical variation [6; 7; 30; 31].
# Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
## 4 Experiments
### Comparison Methods  
We compared our method with the following baselines:  
* **MTransE**[7]: MTransE is one representative work of multilingual knowledge graph embedding for entity matching. MTransE combines monolingual models with a jointly trained alignment model and achieves good results on the single relation view.
* **JAPE**[10]: JAPE is a joint embedding model on relation triples and attribute triples for entity matching between knowledge graphs.
* **KDCoE**[11]: KDCoE is a semi-supervised entity matching method based on collaborative training which enhances multilingual knowledge graph embedding. It iteratively trains the two parts of the embedding model on the relation triples and entity descriptions in different languages, respectively.
* **MultiKE**[24]: Multi-KE is an entity matching framework based on multi-view knowledge graph embedding. The underlying idea is to divide the various features of knowledge graphs into multiple subsets, which are complementary to each other.
* **EVA**[13]: This research proposes the idea of entity alignment using visual information, which incorporates images along with relations and attributes to align entities in different knowledge graphs.
* **DAEA**[25]: DAEA utilizes graph convolutional networks (GCNs) to integrate the information of entities, relations, attributes and entity name embeddings to learn a unified latent representation to perform cross-lingual entity alignment.  
Moreover, we performed the ablation experiment and the multi-view integration experiment to evaluate the effectiveness of each view and our MIST method. For the ablation experiment, we eliminated one of the four views to generate the CLEM variant each time and denoted the CLEM variants without the image view, relation view, the attribute view and the description view by CLEM-RAD, CLEM-ADI, CLEM-RDI and CLEM-RAI, respectively. For the the multi-view integration experiment, we followed three multi-view integration methods of the MultiKE model (i.e., weighted view averaging, shared space learning, and in-training combination) to generate the CLEM variants which are denoted by CLEM-WVA, CLEM-SSL and CLEM-ITC, respectively. In addition, the the multi-view integration experiment included the CLEM variant which employs the vector concatenation strategy to combine the view-specific embeddings. This CLEM variant is denoted by CLEM-CAT.