esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Researchers have proved that the inclusion of ceramic aggregate slightly increases the concentration (Na and K) and reduces the concentration of other elements (B, Si, Cl, and Mg) in water. It has been found that the levels of all elements are lower than limit values laid down in current legislation on water intended for human consumption. The authors of [31] have also confirmed the positive effect of using RCA aggregate in the form of sanitary ceramics, in which 20% and then 25% of the coarse aggregate NA was replaced with crushed sanitary ceramics. It has been found that electrical resistance increases with the use of recovered aggregate due to the intrinsic properties of this material. The new concretes have proved to be as durable as conventional materials and are effective over the whole lifetime of the concrete. The use of sanitary ceramics as concrete aggregate has also proved beneficial in relation to special concrete resistant to fire conditions. There are also attempts to use finely crushed sanitary waste in mortar. The authors of [37] have stated that 0-20% of the aggregate was replaced with crushed sanitary ceramics. It was shown that mortars with 20% content of waste aggregate have significantly higher bending strength than mortars with natural aggregate. These differences reached up to 250% with reference to the reference mortar. The results of these tests were also confirmed in studies [38] in which the change of aggregates in mortar ranged from 0-100%. In the studies conducted by [6; 39], the authors compared the use of finely ground aggregate from bricks and sanitary ceramics for concretes. Increased water absorption of mixtures with recycled aggregates was shown to occur, along with increased porosity in the case of using sanitary ceramics, concurrently demonstrating that with appropriate use, they can be an effective alternative to the sand used for concrete. The authors of [40; 41; 42] examined the material properties of aluminous cement with ceramic aggregate compared to concrete on natural aggregate. The samples were heated at 1000 \({}^{\circ}\)C. In contrast to samples of concrete with traditional aggregate, samples with ceramic aggregate have retained their shape and consistency, and there were not any cracks or defects. Despite a decrease in strength, these samples still showed high compressive and tensile strength after heating. The tests have shown that for aluminous cement with sanitary ceramics aggregate, it is possible to eliminate the thermal effect of concrete particles' chipping off using air entraining admixture. The admixture also caused an increase in the strength of the concrete in relation to those without it, as well as a decrease in the strength drop in the case of heating samples to high temperatures in accordance with the assumed temperature-time distribution. Previous research has confirmed that the recycle aggregate derived from waste sanitary fittings may be an alternative to the natural aggregate, but there is no comprehensive study on the basic characteristics of the concrete to fully determine its suitability.
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
###### Abstract  
The main objective of implementing primary ground-controlling methods, such as applying rock bolting systems, is to increase the strength of surrounding rock mass. Among all rock bolting systems, fully grouted rock bolting systems are the most popular and reliable retaining systems due to their simplicity, availability of materials, ease of installation in the field, and cost-effectiveness. While these types of rock bolts experience both axial and shear forces, understanding their response to axial loads remains complex and dependent on several factors. Extensive research has addressed the overall behaviour of the fully grouted rock bolting system, but a systematic review of the axial load transfer mechanism and its impact on overall performance is lacking. This study addresses this gap by employing a bibliometric analysis of 77 peer-reviewed publications to explore the current state of knowledge regarding the axial load transfer mechanism in fully grouted rock bolting systems. The analysis identifies influential journals, publishers, researchers, highly cited articles, and emerging keywords within this field. Furthermore, it reveals three key parameters significantly impacting the axial behaviour: (a) rock mass and boundary conditions, (b) mechanical behaviours of the grouts, and (c) the geometry and surface profile of the rock bolt. These parameters are subsequently discussed in detail, highlighting their influence on the axial performance of the system. Finally, this article concludes by suggesting promising directions for future research.  
fully grouted rock bolt; axial load transfer; systematic review; bibliometric analysis +
Footnote †: journal: Medical Imaging
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 2 Problem Formulation
### Objective Function
#### 2.2.1 Road Utilization  
We model road utilization as the degree of utilization of bus lanes by buses. The existing methods consider the utilization rate of bus lanes mainly from two aspects: the number of covered buses [10] and the bus frequency [8; 27]. Additionally, taking into account the fact that bus routes and the number of buses on each road segment are different, continuous bus lanes help passengers have a comfortable experience. We therefore emphasize the impact of bus route continuity on the utilization of bus lanes. In total, three factors are considered in terms of road utilization: (1) number of buses covered by bus lanes, (2) bus frequency, and (3) continuity of bus routes. Then, we organize this information into an objective function based on the quantitative possibility of a road being a candidate road, which is called the score in this paper. First, to distinguish the scores of road segments with different numbers of buses, the scores of each road segment are calculated by summing the score of every trajectory covered by the road segment. Then, since bus trajectory does not directly record bus frequency information, we calculate every trajectory segment score for each bus passing through a road segment to reflect the bus frequency. Finally, we split the bus trajectories according to the covered continuous planned road segments and then calculate the score separately for each bus trajectory segment.  
Inspired by the utilization rate function of bicycle lanes based on GPS trajectories from bicycles [19], the road utilization rate score of bus lanes is formulated based on the three factors in Equation (6):  
\[tr_{j}\cdot g=\sum_{s\in j}\alpha^{\frac{L_{s}}{L_{min}}}\times\frac{L_{s}}{L_ {min}} \tag{6}\]  
where \(tr_{j}\cdot g\) represents the road utilization score of trajectory \(tr_{j}\) and \(j\) is the set of continuous road segments that overlaps with trajectory \(tr_{j}\) in the planned road segment set \(E^{\prime}\cdot s\) is one continuous road segment belonging to set \(j\). \(L_{s}\) is the length of the road segments \(s\). The normalized length of the continuous segments is calculated as \(L_{s}/L_{min}\), where \(L_{min}\) is the minimum length of a road segment covered by the trajectories. By performing an exponential operation on the normalization time, when \(\alpha>1.0\), the weight of the continuity of the trajectory will be high; when \(\alpha<1.0\), the bus number and frequency will have relatively high weights; and when \(\alpha=1.0\), the weights of the above three objectives, namely, the number of buses, the frequency and the continuity, are equals.  
Finally, the maximum total road utilization score \(E^{\prime}\cdot g\) is calculated for the total candidate planned road segment, determined by the superposition of each bus trajectory score. This score is formulated as Equation (7):  
\[\max E^{\prime}\cdot g=\sum_{tr_{j}\in T\&tr_{j}\cap E^{\prime}\
eq\varnothing} tr_{j}\cdot g \tag{7}\]  
where \(Tr\) is all the bus trajectories.
## 2 Materials and Methods
### Data Pre-Processing and Land Use/Land Cover Classification  
Four Landsat imagines acquired on 26 September 1987, 4 October 1996, 31 July 2007 and 23 July 2016 were downloaded from the Chinese geospatial data cloud website ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))), as shown in Table 1. After being rectified and georeferenced to the UTM projection system (ellipsoid WGS84, datum WGS84, zone 50), the four images were interpreted by the supervised classification method in ArcGIS and ENVI software. Considering the surface characteristics, such as surface radiative, soil water content and thermal properties [22; 23; 24; 25], they were categorized into four main categories, namely farmland (arable agriculture lands but not orchards), greening land (forestland, grassland, orchards and urban green spaces), water bodies (rivers, lakes, reservoirs and pond waters) and construction land (the developed areas covered by impervious surfaces, such as commercial land, residential land and industrial land). Based on the Google earth correction and classification accuracy assessment, the overall classification accuracies and kappa coefficients were 87.3%, 92.2%, 89.0% and 93.0% and 0.81, 0.90, 0.84 and 0.85 in 1987, 1996, 2007 and 2016, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Date of Acquisition** & **Path/Row** & **Resolution (m)** & **Cloud Cover (\%)** & **Thermal Infrared Band** \\ \hline Landsat-5 TM & 1987/09/26 & 123/039 & 30/120 & 0.00 & band 6 \\ Landsat-5 TM & 1996/10/04 & 123/039 & 30/120 & 0.01 & band 6 \\ Landsat-5 TM & 2007/07/31 & 123/039 & 30/120 & 0.47 & band 6 \\ Landsat-8 OLI/TIRS & 2016/07/23 & 123/039 & 30/100 & 0.41 & band 10/band 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data details of the Landsat images used in this study.  
Figure 1: The location of the study area.
## 4 Multiple Metrics Implementation
### Link Metric Weighing
Assigning Weights to All RLMTs  
When the RLMTs are chosen for different application traffic types, assigning weights on each RLMT is used to determine the importance of the RLMT in each application. The RLMT Weight (RLMTW) should be a value between zero and one, and the weight sum of RLMTWs from each set of RLMTs must be equal to one, as proposed by [22]. The process of assigning weights is achieved through priority weightings. Priority weightings involve the calculation of the average normalised priority pair-wise comparison matrix (\(anprpcm\)) to determine the importance of each metric relative to others. The metrics are then assigned weights in the range of [0, 1], where the sum of all of the weights equals one. For example, if two RLMTs are considered and \(anprpcm\) is set as \([0.5,0.5]\) (which means both metrics have equal priorities), then the total score for each path is expressed as:  
\[Total-Normalised-score-of-pathj=\sum_{i=1}^{n}(anprpcm[i]*anppcm[i,j]),j=1,...,N \tag{8}\]  
For application traffic Type A, the weights for ETX \((RLMTW_{A}^{ETX})\) and ML \((RLMTW_{A}^{ML})\) are set as 0.5; while the weights of RLMTs for traffic Type B, which considers ETX as the most important RLMT, are set as \((RLMTW_{B}^{ETX}=0.7)\) and \((RLMTW_{B}^{ML}=0.3)\). For traffic Type C where ML is of higher priority, the weights of ETX and ML are set as \((RLMTW_{B}^{ETX}=0.3)\) and \((RLMTW_{B}^{ML}=0.7)\). The weights were decided through a case study on RLMTs to find the one best suited for each application traffic as presented in [12]. The total normalised score of path \(j\) used in the communication between Nodes 0 and 8 is presented in Table 2. Path B has the best link quality when both metrics are given no priority. When ETX is assigned priority (i.e., \(anprpcm\) = [0.7, 0.3]), Path C is the best link quality overall. While when ML is given priority (i.e., \(anprpcm\) = [0.3, 0.7]), Path B has the best quality. In order to adaptively guarantee the QoS required for each of the application traffic, Path C can be used for loss-tolerant traffic, while Path B can be used for delay-sensitive traffic. Making route decisions to select a path to destination  
Following the selection of the RLMTs and RLMVs, the AHP algorithm is applied, and the RLMTWs are used to set priorities for different application traffic types. Each application traffic type is then transmitted using the best link calculated by AHP. This is achieved by modifying existing procedures in ns-2 run-time to map each link calculated by the AHP to a traffic type on all of the smart meters in the network. A representation of the multiple metric framework is presented in Figure 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path** & **A** & **B** & **C** & **D** \\ \hline Total score no. priority & 0.2030 & 0.2961 & 0.2834 & 0.2175 \\ Total score ETX & 0.2139 & 0.2495 & 0.3035 & 0.2331 \\ Total score ML & 0.1921 & 0.3428 & 0.2633 & 0.2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Path score for different priority weights.
## 2 Materials and Methods
### Cotton-YOLO Structure
#### 2.3.2 C2f-CBAM Module  
In this study, the application of YOLOv8 for detecting cotton bolls against complex backgrounds was bolstered by introducing the CBAM, which replaced the traditional C2f module with a new C2f-CBAM structure. This improvement aims to refine the model's ability to discriminate features, thereby enhancing detection accuracy for small targets, particularly in scenarios with complex background information or small target sizes. The CBAM module improves feature representation through its unique dual attention mechanisms: the channel attention mechanism (CAM) and the spatial attention mechanism (SAM). The channel attention mechanism concentrates on assessing and emphasizing the importance of features across different channels, thus enabling the model to highlight features that are crucial for target recognition. Conversely, the spatial attention mechanism sharpens the focus on the spatial distribution of features, thereby reducing interference from background noise.  
Figure 7 distinctly displays the structure of the CBAM, which includes both channel and spatial attention components. The channel attention component highlights important features by assessing the significance of each channel. The spatial attention component further improves the spatial distribution of features, effectively minimizing the interference from complex backgrounds. The introduction of this dual attention mechanism enables the network to more effectively concentrate on key targets in the image, such as cotton bolls, thus demonstrating improved performance especially in environments with complex backgrounds or small target sizes.  
Figure 6: Data enhancement strategies: (**a**) Raw image, (**b**) Random hybrid enhancement, (**c**) Random hybrid enhancement.  
After implementing the C2/-CBAM replacement in the backbone of YOLOv8, a series of experiments was conducted to demonstrate the effectiveness of this structural improvement in the task of cotton boll detection. The experimental results indicate that the C2/-CBAM structure significantly enhances detection accuracy and stability, particularly exhibiting superior performance in counting and tracking cotton bolls in dynamic environments. Furthermore, the application of this module also demonstrates good generalizability, effectively maintaining high performance levels across different datasets and various environmental conditions.  
From both theoretical and practical perspectives, the introduction of CBAM not only optimizes the network structure but also boosts the model's capability to handle small targets in complex scenes. This approach of improving performance through internal adjustments to the deep learning model's structure opens up new directions and possibilities for further exploration in the field of visual recognition. This improvement also provides an effective strategy for researchers in related fields, aiming to optimize and enhance the reliability and efficiency of models in practical applications.
## 2 Materials and Methods
### Quantification of Gene Transcription  
The transcriptions of ten genes (_trh_, _trhr_1, _tsh_\(\beta\), _tra_, _tr_\(\beta\), _tpo_, _tg_, _nis_, _deio_1, and _deio_2) related to the HPT axis and two reference genes (_\(\beta\)-actin_ and _rpl8_) were analyzed. Whole bodies of zebrafish larvae were homogenized (ten larvae per sample with four replicates) and total RNA was isolated using the RNeasy mini kit (QIAGEN, Hilden, Germany). RNA quality (260/280 ratio \(>\)1.8) was validated using an Epoch(tm)-Take3 microplate spectrophotometer (Biotek Instruments Inc., Winooski, VT, USA). Then, complementary DNA (cDNA) was synthesized using an iScript cDNA synthesis kit (Bio-Rad, Hercules, CA, USA). Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was performed using SYBR Green(tm) PCR master mix kit (Applied Biosystems, Foster City, CA, USA) and analyzed on an ABI 7500 Fast Real-Time PCR System (Applied Biosystems). The analyzed genes and primer sequences are listed in Table 1. The thermal cycling conditions were maintained at 50 \({}^{\circ}\)C for 1 min and 95 \({}^{\circ}\)C for 10 min; this was followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 1 min. Melting curve analyses were performed at the end of the amplification phase to ensure the amplification of a single product. The threshold cycle (Ct) was determined for each reaction, and the Ct values of the genes of interest were normalized to the reference genes using the 2\({}^{-\Delta\Delta\text{Ct}}\) method [27]. The degree of induction or inhibition of the genes of interest was expressed as a fold difference, as compared to the normalized control values.
## 3 The Proposed Fault Diagnosis Method
### Learning Procedure
#### 3.2.3 Faults Diagnosis Based on Cross-Domain Few-Shot Learning  
We employed the aforementioned method to convert the 1D vibration signal into a 2D image in order to obtain additional details and achieve fault diagnosis for bearings under various operating situations. The converted image data were divided into support set \(S=\left\{\left(x_{1}^{q},y_{1}^{q}\right),\left(x_{2}^{q},y_{2}^{q}\right), \ldots,\left(x_{N*L}^{q},y_{N*L}^{q}\right),\text{ N representing the number of categories contained in the support set, K representing the number of samples contained in each category and query set \(Q=\left\{\left(x_{1}^{q},y_{1}^{q}\right),\left(x_{2}^{q},y_{2}^{q}\right), \ldots,\left(x_{N*L}^{q},y_{N*L}^{q}\right),\text{ and L representing the number of samples contained in each category. When classifying the bearing fault types in the target  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers.** & **Kernel Size** & **Kernel Number** & **Output Size** & **Activation Function** \\ \hline Input & / & / & 84 \(\times\) 84 \(\times\) 3 & / \\ Conv1 & 3 \(\times\) 3 & 16 & 84 \(\times\) 84 \(\times\) 16 & ReLU \\ Pool1 & 2 \(\times\) 2 & 16 & 42 \(\times\) 42 \(\times\) 16 & / \\ Conv2 & 3 \(\times\) 3 & 32 &  
domain, we used a different classifier from the full connection layer used in the training stage of the Conv5 model. The framework proposed in this paper uses a weight matrix \(W_{n}\in R^{(d\times c)}\) as a classifier, which is based on the idea of cosine distance. For each of the different target domains, the weight matrix \(W_{n}\) was adjusted according to the image in the support set of target domain. When adjusting the weight matrix \(W_{n}\), we first used the \(f_{\theta}(x)\) to extract the features of the \(x_{i}^{s}\) image in the support set, \(f_{\theta}(x)\) is the rest of Conv5 trained in A after removing the full connection layer. The similarity score \([s_{i,1},s_{i,2},\ldots,s_{i,c}]\) is obtained by calculating the cosine similarity between \(f_{\theta}(x_{i}^{s})\) and each weight vector \([w_{1},w_{2},\ldots,w_{c}]\) in \(W_{n}\) Cosine similarity calculation formula is as follows:  
\[s_{i,j}=\frac{f_{\theta}(x_{i})^{T}w_{j}}{\|f_{\theta}(x_{i})^{T}\|\|w_{j}\|} \tag{9}\]  
Then, we normalized these similarity scores using softmax function to obtain the prediction probability \([p_{i,1},p_{i,2},\ldots,p_{i,c}]\) of each category, and the predicted results \(p_{i}\) is taken from the maximum value in \([p_{1},p_{2},\ldots,p_{c}]\). Finally, we used Stochastic Gradient Descent (SGD) to fine tune \(W_{n}\) based on the loss value calculated from the label and predicted result of image in support set. Loss value calculation formula is as follows:  
\[Loss=-\frac{1}{N*L}\sum_{i=1}^{N*L}y_{i}^{s}*p_{i} \tag{10}\]  
After adjusting the weight matrix \(W_{n}\), we diagnosed the bearing fault data in the query set by calculating the cosine similarity between \(f_{\theta}(x_{i}^{q})\) and the adjusted weight matrix \(W_{n}\) and classified the fault types of each image in the query set based on the cosine similarity.
## 3 Benefit Components
### Maintenance Benefit Evaluation System Based on AHP Model
\begin{table}
\begin{tabular}{c c c} \hline
**Hierarchal Structure** & **Criterion Layer** & **Indicator Layer** & **Weighted Results** \\ \hline \multirow{5}{*}{\(\mathrm{w}_{\mathrm{Economic}}^{\mathrm{Criterion}}=0.500\)} & \(\mathrm{w}_{\mathrm{A}}^{\mathrm{Indicato}}=1\) & 0.500 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{B_{1}}}^{\mathrm{Indicato}}=0.3593\) & 0.090 \\ \cline{2-4}  & \(\mathrm{w}_{\mathrm{Social}}^{\mathrm{Criterion}}=0.250\) & \(\mathrm{w}_{\mathrm{B_{2}}}^{\mathrm{Indicato}}=0.3593\) & 0.090 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{B_{3}}}^{\mathrm{B_{2}}}=0.3090\) & 0.006 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{B_{3}}}^{\mathrm{B_{2}}}=0.1095\) & 0.002 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{B_{3}}}^{\mathrm{B_{2}}}=0.5816\) & 0.012 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{B_{4}}}^{\mathrm{Indicato}}=0.1999\) & 0.050 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{B_{2}}}^{\mathrm{Cl_{1}}}=0.5409\) & 0.091 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{C_{1}}}^{\mathrm{Cl_{1}}}=0.2298\) & 0.038 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{C_{1}}}^{\mathrm{Cl_{1}}}=0.0758\) & 0.013 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{C_{1}}}^{\mathrm{Cl_{1}}}=0.1535\) & 0.026 \\ \cline{2-4}  & & \(\mathrm{w}_{\mathrm{C_{2}}}^{\mathrm{Indicato}}=0.33\) & 0.082 \\ \hline \end{tabular}
\end{table} 
Table 15: Model weight coefficient. According to the model weight coefficient obtained in Table 9, the specific calculation formula of the comprehensive maintenance benefit model is obtained, as shown in Formula (35). \[ \begin{split}\text{A}=0.5\times\text{MBCR}+0.09\times\Delta\text{T }^{\text{j}}+(0.09\times\Delta\text{TTC}+0.006\times\Delta\text{FCC}+0.002\times \Delta\text{WC}+0.012\times\Delta\text{RC}+0.05\times\Delta\text{AC}\\ +0.091\times\Delta\text{EEC}_{\text{CO2}}+0.038\times\Delta\text{ EEC}_{\text{HC}}+0.013\times\Delta\text{EEC}_{\text{NOX}}+0.026\times\Delta\text{ EEC}_{\text{CO}}+0.082\times\Delta\text{NC}_{\text{oper}}\end{split}  \tag{35}\]  
Define the bracket part of the comprehensive benefit calculation Formula (35) as the cost difference (CD), that is, the difference between the cost of pavement use before maintenance and the cost of pavement use after maintenance. Then Formula (35) can be expressed as:  
\[\text{A}=0.5\times\text{MBCR}+0.09\times\Delta\text{T}^{\text{j}}+\text{CD} \tag{36}\]  
For Equation (36), different evaluation indicators have different units, and in order to make the comprehensive benefits of the final calculation comparable, it is first necessary to standardize different indicators. The purpose of maximizing is to use the maximum value as a reference standard, dividing all data by the maximum value. The calculation formula is X/Max, which means taking the maximum value as the unit and removing all data from the maximum value. \[\text{A}^{\prime}=0.5\times\text{MBCR}^{\prime}+0.09\times\Delta\text{T}^{ \text{j}^{\prime}}+\text{CD}^{\prime} \tag{37}\]  
where  
\(\text{MBCR}^{\prime}\), \(\Delta\text{T}^{\text{j}^{\prime}}\), \(\text{CD}^{\prime}\)--Original data dimensionalized value.