esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Energy Efficiency of the Modular Buildings
## 2 Research Gap, Study Objective, and Significance of This Study  
Nevertheless, all the identified and reviewed articles explored agricultural products and investigated clothes drying using tumble dryers, which investigated the potential to make the drying process more efficient; however, it is outside of the construction field context within which sits the current research that investigates better ways to optimise energy consumption in drying rooms. Since the process of drying clothes consumes a significant amount of energy [16], no report on the energy consumption of drying construction garments exists to date in the UK. Hence the objective of the present study is to achieve the following:  
* Show the inefficiency of using fan heaters to dry construction garments.
* Create a baseline computational model to assess and investigate the potential for energy savings and to ensure efficient functioning of the heating system.
* Reduce energy costs and carbon emissions in construction sites in the UK. This work is oriented (but not limited) to construction site drying. The novelty of this study lies in its focus on optimising the energy efficiency of drying rooms in the construction industry, specifically in the UK. This research contributes to the field by means of the following:
* Quantifying energy consumption: It provides a baseline measurement of energy usage in a traditional drying room, establishing a benchmark for comparison and improvement.  
* Developing a thermal model: The use of IESVE software allows for accurate prediction of energy consumption, enabling informed decision-making regarding energy-saving measures.
* Testing alternative drying systems: This study compares various drying systems (dehumidifier, air mover, heater) to identify the most energy-efficient combination, offering practical solutions for industry professionals.
* Analysing the impact of environmental factors: This research demonstrates the significant influence of humidity, air velocity, and temperature on drying time and energy consumption, providing valuable insights for future design and optimisation.  
Overall, this study advances the field of construction energy efficiency by offering a data-driven approach to optimising drying processes and reducing environmental impact. The findings can be applied to improve the sustainability and cost-effectiveness of construction operations.
## 4 Experimental Results  
### Datasets and Evaluation Metrics  
**Charades-STA [26]:** this dataset includes 5338 videos and 12,408 video-query pairs for training, and 1334 videos and 3720 pairs for testing.  
**ActivityNet-Captions [56; 57]:** The training set of this dataset contains 10,009 videos and 37,417 video-query pairs for training, 4917 videos and 17,505 pairs for validation, and 5044 and 17,031 pairs for testing. The average length of videos and ground-truth temporal moments are 29.8 s and 8 s, respectively.  
**Evaluation metrics:** We adopted \"R@K IoU@X\" to evaluate the performance. \"R@K\" means top \(K\) recall of the proposal. \(X\) is the threshold on IoU. \"IoU@X\" means the percentage of predicted temporal moments that have an IoU higher than \(X\). Since we only selected the top prediction in candidate windows, we only report the results of \"R@1\", and thresholds were 0.3, 0.5, and 0.7, respectively.
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
## 3 Data Sets and Processing Strategy
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Models** \\ \hline Observations & Pseudorange and Carrier-phase observations \\ \hline Signal selection & GPS: L1/L2; BDS-2/BDS-3: B1/B3; GLONASS: L1/L2; Galileo: E1/E5a \\ \hline Sampling rate & 30 s \\ \hline Elevation cutoff & 7.5\({}^{\circ}\) \\ \hline \multirow{7}{*}{Observation weighting} & GPS: A priori precision 0.3 m and 0.003 m for code and phase \\  & observations, respectively. BDS-2/BDS-3: A priori precision 0.6 m and 0.006 m for code and phase observations, respectively. In view that BDS \\ \cline{1-1}  & GEO satellite orbit and clock offset are at a relatively lower accuracy \\ \cline{1-1}  & [23; 24], their measurements are down-weighted with a factor of ten. \\ \cline{1-1}  & GLONASS: A priori precision 0.3 m and 0.0045 m for code and phase \\ \cline{1-1}  & observations, respectively. Galileo: A priori precision 0.3 m and 0.006 m \\ \cline{1-1}  & for code and phase observations, respectively. \\ \hline \multicolumn{2}{c}{Tropospheric dry delay} & GPT3 + VMF3 [25] \\ \hline Ionospheric delay & Eliminated by IF linear combination \\ \hline Satellite \& Receiver antenna & ANTEX PCO + PCV [26] \\ \hline Phase windup effect & IERS conventions 2010 [26] \\ \hline Earth rotation & IERS conventions 2010 [26] \\ \hline Relativistic effect & IERS conventions 2010 [26] \\ \hline Station displacement & Solid Earth, Pole and Ocean tide (IERS2010) [26] \\ \hline Receiver coordinate & Static PPP: Estimated as constant \\ \cline{1-1}  & Kinematic PPP: Estimated as white noise process \\ \hline Receiver clock offset & Estimated as white noise process \\ \hline \multicolumn{2}{c}{Tropospheric wet delay} & Estimated as random walk \\ \hline ISB & Estimated as white noise process \\ \hline Satellite DCB & BDS: Eliminated by IF linear combination [12] \\ \cline{1-1}  & GPS/GLOANSS/Galileo: MGEX DCB products [27; 28] \\ \hline Ambiguity & Estimated as constant; float values \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Precise point positioning (PPP) processing strategy.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 5 Registration Cost
The density function of \(Rm,f_{r}(t)\) is from a random observer property [26],  
\[f_{r}(t)=\lambda_{m}\int_{\tau=t}^{\infty}f_{m}(\tau)d\tau=\lambda_{m}(1-F_{m }(t)) \tag{7}\]  
The Laplace-Stieltjes transform for the distribution is shown as follows:  
\[\begin{array}{ll}f_{r}^{*}(s)&=\int_{t=0}^{\infty}e^{-st}f_{r}(t)dt=\int_{t =0}^{\infty}e^{-st}\lambda_{m}(1-F_{m}(t))dt=\frac{\lambda_{m}}{s}-\int_{t=0} ^{\infty}e^{-st}\lambda  
In this case, the staying time in state 0' is different from the staying time in other states. The staying time in state 0' is the interval from the time a call to/from a UE occurs in a cell until the time it transits (moves to a neighboring cell or generates a call again). On the other hand, the staying time in other states, except for state 0', is the interval from the time the UE enters a cell until the time it transits (moves to a neighboring cell or generates a call). To accurately evaluate the performance, the fact that the staying time in each state is different should be considered. First, the staying time in state 0' can be expressed as follows:  
\[ \begin{array}{ll}T_{c},&if\ \ T_{c}\leq R_{m}\\ R_{m},&if\ \ T_{c}>R_{m}\end{array} \]  
Its mean can be obtained as follows:  
\[ \begin{array}{ll}\tau_{0^{\prime}}&=\int_{0}^{\infty}\int_{0}^{r_{m}}t_{c}f_ {c}(t_{c})dt_{c}f_{r}(m_{m})dr_{m}+\int_{0}^{\infty}\int_{r_{m}}^{\infty}r_{m} f_{c}(t_{c})dt_{c}f_{r}(r_{m})dr_{m}\\ &=\int_{0}^{\infty}[\int_{0}^{r_{m}}t_{c}f_{c}(t_{c})dt_{c}+\int_{r_{m}}^{ \infty}m_{f}c_{c}(t_{c})dt_{c}]f_{r}(r_{m})dr_{m}=\frac{1}{\lambda_{c}}(1- \frac{\lambda_{m}}{\lambda_{c}}[1-f_{m}^{*}(\lambda_{c})])\end{array}  \tag{10}\]  
Next, the staying time in other states, except for state 0', can be expressed as follows:  
\[ \begin{array}{ll}T_{c},&if\ \ T_{c}\leq T_{m}\\ T_{m},&if\ \ T_{c}>T_{m}\end{array} \]  
Its mean \(\tau_{i}\) (\(i=0,1,2,\dots\) ) can be derived as follows:  
\[ \begin{array}{ll}\tau_{i}&=\int_{0}^{\infty}\int_{0}^{t_{m}}t_{c}f_{c}(t_{c })dt_{c}f_{m}(t_{m})dt_{m}+\int_{0}^{\infty}\int_{t_{m}}^{\infty}t_{m}f_{c}(t _{c})dt_{c}f_{m}(t_{m})dt_{m}\\ &=\int_{0}^{\infty}[\int_{0}^{t_{m}}t_{c}f_{c}(t_{c})dt_{c}+\int_{t_{m}}^{ \infty}t_{m}f_{c}(t_{c})dt_{c}]f_{m}(t_{m})=\frac{1}{\lambda_{c}}(1-f_{m}^{*} (\lambda_{c}))\end{array}  \tag{11}\]  
In order to obtain the steady-state probability \(\widetilde{\pi}\), the steady-state probability \(\pi\) for the usual Markov chain with transition probability \(P\) should be calculated first using balanced equations [26] as follows:  
\[\pi P=\pi,\ \ \ \ \sum_{j}\pi_{j}=1 \tag{12}\]  
Considering different staying time, the final steady-state probability of the semi-Markov process can be obtained as follows [26]:  
\[\widetilde{\pi}_{i}=\frac{\pi_{i}\tau_{i}}{\sum_{j}\pi_{j}\tau_{j}} \tag{13}\]
## 3 Materials and Methods
### Deep Learning-based Temperature Recommendation Method  
#### 3.1.1 Dataset Generation  
The proposed microwave captures the food image as soon as the door is closed. The image is then classified, and a target temperature is recommended. Now, if the user indicates that the food item is a new food, then a new image class is created. To train and validate the deep learning model for the new class, more images are required. This is done by image augmentation [17]. Image data augmentation is used to artificially increase the size of a dataset by creating variations of an image using different transformations such as horizontal and vertical flip, rotation, height and width shift, shear, brightness, rescaling, etc. Food will have different shapes each time when it is placed on a plate or when it is stirred. Training deep learning neural network models with augmented images can improve to generalize the model, as the food does not have any fixed shape.  
_Keras_ is a high-level Application Programming Interface (API) for neural networks, written in Python running on top of TensorFlow [18]. It provides a library to generate augmented images. Figure 2 shows the original image and some generated augmented images. For every new image class, 64 augmented images are generated for training and 32 augmented images are generated for validation from the original image. The images have a fixed size of \(32\times 24\).
# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 3 Approach of This Paper
### Recommendation Module  
The recommendation module in this paper is primarily an improvement based on the NCF [32] model. The core idea of the NCF recommendation model is to represent users and papers as vectors and to use a neural network to learn the interactions between them. The NCF recommendation model can be represented by the following formula:  
\[Res_{ui}=MLP(u_{i},v_{j})+GMF(u_{i},v_{j}) \tag{23}\]  
where \(Res_{ui}\) represents the predicted rating of user \(u\) for paper \(i\), then \(u_{i}\) and \(v_{j}\), respectively, denote the feature vectors of user \(i\) and paper \(j\).  
The original NCF model only utilized the paper's identifier information to obtain the paper vectors, while the user vectors were randomly initialized. This led to an inadequate expression of information for both users and papers. Therefore, this paper introduces a multi-feature-enhanced representation for both users and papers, which is input into the improved NCF model.  
In the preceding sections, the paper text vector \(V_{w}\) and the paper relationship vector \(V_{kg}\) were obtained separately. By concatenating these two vectors and passing them through a fully connected layer, the model derived the final paper vector \(V_{fin}\). Ultimately, the final user vector \(h_{v}\) and the final paper vector \(V_{fin}\) are used as inputs to the improved NCF model, yielding the final result \(r_{ui}\). The loss function is defined by Equation (24), and the Adam optimizer is employed to learn the model parameters:  
\[L=-\sum_{(i,j)\in\gamma\cup\gamma^{-}}y_{ij}\log(Res_{ui})+(1-y_{ij})\log(1- Res_{ui}) \tag{24}\]  
In Formula (24), \(y_{ij}\) equal to 1 indicates that user \(u_{i}\) has interacted with paper \(v_{j}\), while \(y_{ij}\) equal to 0 indicates that user \(u_{i}\) has not interacted with paper \(v_{j}\). The symbol \(\gamma\) represents the set of positive samples, and \(\gamma^{-}\) represents the set of negative samples [33].
# Study About the Influence of Diatoms on the Durability of Monumental Limestone
## 3 Results and Discussions
### Results of Spray Water Absorption Test  
Figure 20 shows the average mass of water absorbed by the control and diatom-treated samples during the spray water absorption test across the different time intervals. In the initial moments, the diatom-treated samples absorbed slightly more water than the control samples. However, this behavior was quickly reversed. The diatom-treated samples followed a trend line with a less pronounced slope, absorbing a smaller amount of water per square meter for the remainder of the test, ultimately resulting in 41% less total absorption.  
Figure 19: Average mass of water absorbed per unit area in each time interval (samples without insulation).  
The average mass of water absorbed per unit area was also analyzed for each time interval for both the control and diatom-treated samples, as shown in Figure 21. The trend in water absorption between each spraying cycle for both sample types was downward. This is a logical behavior, indicating that the greater the number of spraying cycles, the less water the samples can absorb until reaching saturation.  
Figure 21: Average mass of water absorbed per unit area in grams in each time interval.  
Figure 20: Average mass of water per unit area absorbed in grams since the beginning of the test.  
It can be observed that, except for the first three spray intervals (3 s, 5 s, and 10 s), in each subsequent 20 s spray, the diatom-treated samples absorbed a smaller and more stable amount of water per surface area compared with the control samples.  
As a result of this test, a favorable difference in behavior can be observed for the diatom-treated samples.
## 4 Multi-Physics Simulation Analyses of GMM-FBG Current Sensor
### Multi-Physics Simulation Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline GMM & Young’s Modulus & \begin{tabular}{c} **Thermal Expansion** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **Easy-Axis Conversion** \\ **Temperature** \\ \end{tabular} & **Curie Temperature** \\ \hline Symbol/unit & \(E_{\rm s}/{\rm Pa}\) & \(\alpha/{\rm K}^{-1}\) & \(T_{\rm r}/{\rm K}\) & \(T_{\rm c}/{\rm K}\) \\ value & \(6.0\times 10^{9}\) & \(1.2\times 10^{-5}\) [22] & 273 [21] & 635 \\ \hline GMM & \begin{tabular}{c} Saturated \\ magnetization \\ temperature coefficient \\ \end{tabular} & \
\begin{tabular}{c} Saturated \\ strain; \\ Temperature variation \\ coefficient \\ \end{tabular} &
\begin{tabular}{c} Saturated magnetostrictive \\ strain; \\ temperature variation \\ coefficient \\ \end{tabular} & Magnetic saturation & Initial susceptibility \\ \hline \end{tabular}
\end{table} 
Table 1: Material characteristics of GMM rod and ferrite magnetizer adopted for multi-physics simulations of solid mechanics/magnetostatics/thermal expansion. Although the temperature drift in the magnetic characteristics of the ferrite material will have no palpable interference on the magnetic flux of the GMM rod, the thermal strain in the ferrite magnetizer should cause a significant change in the air gap at the free terminal of the GMM rod, directly affecting the magnetic flux density in the GMM rod. Therefore, the magnetic characteristics of the ferrite material are considered to be constant for different temperatures only when the thermal expansion model of solid mechanics is resolved for a ferrite magnetizer in multi-physics simulations. The magnetic properties and thermal expansion coefficients of the ferrite material adopted in simulations are listed in the bottom line of Table 1.  
As implemented by the multi-physics field-coupling correlation of the COMSOL 4.4 finite-element software package, the thermal strain fields in ferrite-GMM-FBG current sensor are specifically evaluated, which are self-consistently coupling with magnetostatic fields. The free triangular mesh generations with the Delaunay triangulation algorithm are adopted to refine local mesh at the position where the magnetic or strain fields vary greatly. The minimum mesh elements are adjusted with the adaptive method until the obtuse-angle triangulation disappears, and the maximum elements in regions of the ferrite magnetizer/current cable/air computational domain, GMM rod/air gap and FBG are, respectively limited, to 0.1, 0.05 and 0.001 m. The mesh refinement amplitude (growth rate of element amount) and the relaxation degree of the narrow region are set to 30% and 1, respectively, for mesh generations. The high-density mesh operations allow the finite-element solutions to be obtained with a sufficient accuracy. The relative tolerance of the iterative solution and the nonlinear residual are controlled below \(10^{-4}\) and 0.001, respectively.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 3 Methodology
### Pca  
Suppose there are \(n\) training image samples \(\mathbf{X}_{1},\mathbf{X}_{2},...,\mathbf{X}_{n}\), where \(\mathbf{X}_{i}\in\mathbb{R}^{h\times m}\); \(i=1,2,...,n\); and \(h\) and \(w\) are the image height and image width, respectively. Let \(d=h\times w\). When applying PCA for image analysis, 2D image matrices should be reshaped into corresponding 1D image vectors prior [11], generating \(\mathbf{y}_{1},\mathbf{y}_{2},...,\mathbf{y}_{n}\), where \(\mathbf{y}_{i}\in\mathbb{R}^{d\times 1}\), \(i=1,2,...,n\). PCA [9; 10] aims at finding a projection matrix \(\mathbf{W}\in\mathbb{R}^{d\times m}\) by maximizing the total scatter of projected image samples as follows:  
\[\max_{\mathbf{W}^{T}\mathbf{W}=\mathbf{I}_{m}}tr\mathbf{W}^{T}\frac{1}{n-1} \sum_{i=1}^{n}(\mathbf{y}_{i}-\overline{\mathbf{y}})(\mathbf{y}_{i}-\overline {\mathbf{y}})^{T}\mathbf{W}, \tag{1}\]  
where \(m\) is the feature number, \(\mathbf{I}_{m}\in\mathbb{R}^{m\times m}\) denotes an identity matrix, \(tr(\cdot)\) denotes the trace of a matrix and \(\overline{\mathbf{y}}=\frac{1}{n}\sum_{i=1}^{n}\mathbf{y}_{i}\) denotes the mean image vector. By eigenvalue decomposition of the covariance matrix [9; 10]  
\[\mathbf{S}=\frac{1}{n-1}\sum_{i=1}^{n}(\mathbf{y}_{i}-\overline{\mathbf{y}})( \mathbf{y}_{i}-\overline{\mathbf{y}})^{T}\in\mathbb{R}^{d\times d}, \tag{2}\]  
we can obtain \(m\) eigenvalues \(\lambda_{1},\lambda_{2},...,\lambda_{m}\) and corresponding eigenvectors \(\mathbf{w}_{1},\mathbf{w}_{2},...,\mathbf{w}_{m}\). Without loss of generality, we assume that the eigenvalues are sorted in descending order. The eigenvectors are the principal components and they make up the projection matrix \(\mathbf{W}\). The percentage of total variance [10] explained by these principal components is  
\[\frac{\sum_{i=1}^{m}\lambda_{i}}{\sum_{i=1}^{n}\lambda_{i}}. \tag{3}\]  
For a test image sample \(\widetilde{\mathbf{y}}\in\mathbb{R}^{d\times 1}\), its feature matrix is  
\[(\widetilde{\mathbf{y}}-\overline{\mathbf{y}})^{T}\mathbf{W}\in\mathbb{R}^{1 \times m}. \tag{4}\]  
The reconstruction result of sample \(\widetilde{\mathbf{y}}\) is  
\[\mathbf{W}\mathbf{W}^{T}(\widetilde{\mathbf{y}}-\overline{\mathbf{y}})+ \overline{\mathbf{y}}\in\mathbb{R}^{d\times 1}. \tag{5}\]  
It can be further reshaped from a vector into a \(h\times w\) image matrix in order to generate the reconstruction image. Eigenvalue decomposition of the covariance matrix \(\mathbf{S}\) is difficult since the size of \(\mathbf{S}\) is huge.
## 4 Conclusions  
The physical and chemical characteristics of lignocellulosic (PSD) and herbaceous biomass (kenaf) pretreated with torrefraction and ashless process were investigated and compared. The conclusions were as follows:  
The torrefraction and ashless processes of PSD and kenaf improved the fuel ratio, with decreased volatile matter and increased fixed carbon and heating values, compared with that of untreated biomasses; further, the atomic ratios of O/C and H/C decreased. When kenaf was pretreated with both torrefraction and ashless processes, the increasing rate of HHV and atomic ratios of O/C and H/C improved compared to pretreated PSD, suggesting that herbaceous biomass was favorable to be carbonized.  
The results of BET show that the torrefraction and ashless processes for R. PSD and R. Kenaf generally led to a more porous structure. However, for R. Kenaf, macropores were reduced after pretreatment, implying that kenaf particles lost their fibrous structure more easily and became more cracked compared with R. PSD. These results were also confirmed by SEM images.  
R. Kenaf was more hydrophilic than R. PSD. However, when pretreatment was conducted, hydrophilicity improved and became hydrophobic. Both samples were excellent when the torrefraction and ashless processes were conducted simultaneously, indicating that combining the ashless process with torrefraction lead to further hydrophobic characteristics.  
The grindability of the samples was assessed by measuring the particle size distribution after grinding. The results demonstrated the R. Kenaf was easier to grind compared with R. PSD. Combining the torrefraction and ashless processes lead to increased grindability. When kenaf was pre-treated, the effect for grindability was much greater.  
In conclusion, the torrefraction and ashless processes could considerably improve the physicochemical properties in terms of HHV, hydrophobicity, and grindability. In particular, when the ashless fuel was torrefied again, the effect was confirmed to be very large. When comparing PSD and  
Figure 7: Cumulative particle mass by range of 0.6 mm of raw and pretreated biomasses.  
kenaf, the physical and chemical characteristics of kenaf for herbaceous were better than those of PSD for woody.  
[PERSON], [PERSON] and [PERSON] conceived and planned the research. [PERSON], carried out all experiments for this research. [PERSON], [PERSON] and [PERSON] contributed to the analysis of the results. [PERSON], [PERSON] and [PERSON] contributed to the review of original and revised papeR. [PERSON] and [PERSON] took the lead in writing the manuscript. All authors helped shape the research and discussed the results and contributed to the final manuscript.  
This research received no external funding.  
This work was supported by the Human Resources Development program (No. 20184030202060) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy.  
The authors declare no conflict of interest.