esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Damage Detection of IASC-ASCE Structural Health Monitoring Benchmarks
### Third PhaseInfluence of Various Noise Levels on the Identification Results
Based on the various combinations shown in Table 6, it can be concluded that when the noise (i.e., uncertainty) in the training and test data is similar, the highest accuracy is obtained in DMI and PoDE indices. The reliability of the proposed method decreases with increasing the noise percentage difference and ratio between training and testing data. This is due to the fact that when the level of uncertainty in the training and testing data varies from each other, noise contaminates the true information, and thus, the damage cannot be reliably detected. These results indicate the importance of determining the percentage of noise, which is used as a measure of uncertainty, in training data, which in itself requires engineering judgment and prior experience on similar vibration experiments, and therefore,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Train**} & \multirow{3}{*}{**Element**} & \multicolumn{6}{c}{**Test Uncertainties**} \\ \cline{3-11}  & & & \multicolumn{2}{c}{**0\%**} & \multicolumn{2}{c}{**2\%**} & \multicolumn{2}{c}{**5\%**} & \multicolumn{2}{c}{**15\%**} & \multicolumn{2}{c}{**20\%**} \\ \cline{3-11}  & & & **PoDE** & **DMI** & **PoDE** & **DMI** & **PoDE** & **DMI** & **PoDE** & **DMI** & **PoDE** & **DMI** \\ \hline \multirow{6}{*}{0\%} & & S1 & \(\theta_{x,y}\) & 100 & 0.43  
it can be considered a limitation of the method proposed in this study. However, as shown in Table 3 to Table 6, even when the percentage of noise in the training data is different from the percentage of noise in the test data, the proposed method is more accurate than the probabilistic surrogate method presented in previous studies [12; 49]. However, there are still some important details that need to be studied more thoroughly. The best performance of SVM is achieved in supervised mode when training data about health and damage state of structure is available. While in practical cases, it is highly unlikely to have data corresponding to different damage scenarios. Among the four levels of damage assessment identified by [PERSON] [52], the lowest level (i.e., establishing the presence of damage) has been achieved in the past through unsupervised learning by what is referred to as novelty (outlier) detection [1]. Higher levels of damage assessment in real-world structures require either a mechanism for augmenting the insufficient or incomplete training data by incorporating some form of prior knowledge into the learning and training process [53]. Therefore in real-world application the proposed method can be used as surrogate model of structure in comprehensive SHM system which will be further studied.
## 3 Methodology
### Image-Level Distance  
ResNet-50 [24] pre-trained on ImageNet [25] is used to extract image features \(\sigma\in\mathbb{R}^{F}\) from the cropped scenes and items; \(\sigma\) can describe the basic visual information of the given image, such as color, texture, fabric, etc. The extracted feature vectors are of high dimension (e.g., \(F=2048\)). If we directly use \(\sigma\) to compute the visual distance, the computation cost is very large, and the results are not accurate since \(\sigma\) contains redundant information. Hence, we learn an embedding function \(g(\Theta,\cdot)\) with parameter set \(\Theta\) to reduce the dimensions of image features \(\sigma\) from \(F\) to \(K\) (\(K<<F\)), which converts \(\sigma\) to the most representative and important visual features \(v\). \(g(\Theta,\cdot)\) is a linear layer, and it is implemented by multiplying \(\sigma\) with an embedding matrix \(E\). The process can be described as follows:  
\[v_{s}=g(\Theta,\sigma_{s})=E\sigma_{s} \tag{1}\]  
\[v_{i}=g(\Theta,\sigma_{i})=E\sigma_{i} \tag{2}\]  
where \(E\in\mathbb{R}^{K\times F}\) is a learned embedding matrix randomly initialized. Please note that \(v_{s}\) and \(v_{i}\) represent the downscaled visual feature vectors for scene \(s\) and item \(i\), respectively.  
We then calculate the \(l_{2}\) (Euclidean) distance between \(v_{s}\) and \(v_{i}\), and use it as the image-level distance \(L_{I}(s,i)\):  
\[L_{I}(s,i)=\left\|v_{s}-v_{i}\right\|_{2} \tag{3}\]
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 4 Solution Approach
### Shift Setting Stage  
Each departure interval corresponds to a unique passenger allocation and flex-route transit shift configuration. The system cost is determined by analyzing the specific connection conditions and shift arrangements following passenger allocation and scheduling. Therefore, corresponding algorithms must be developed to address this aspect. Passenger assignment and shift scheduling determine which passengers are served in each shift, a problem that can be addressed using a genetic algorithm. However, due to constraints related to passenger time windows and the operational cycle of feeder flex-route transit, the genetic algorithm generates a large number of invalid chromosomes. To enhance the overall efficiency of the algorithm, appropriate methods must be developed to convert invalid chromosomes into valid ones. If the design method is inadequate, the genetic algorithm may converge prematurely and become trapped in a local optimum. To address this issue, this section introduces a feedback mechanism that attempts to reassign rejected passengers to flex-route transit shifts during passenger allocation and vehicle scheduling. This improves solution quality and enhances algorithmic efficiency in exploring the target search space. The specific pseudo-code of the algorithm is shown in Algorithm 2.
# Data Science in the Management of Healthcare Organizations
## 3 Methodology
### Study Design  
This study aimed to evaluate the perspectives of HCWs in a HO regarding leadership, teamwork, and work engagement. It sought to answer the following research questions:  
* What are healthcare workers' perceptions of the performance of their organization in terms of leadership, teamwork, and work engagement?
* What are the levels of work satisfaction among healthcare workers in the organization, and what insights can be drawn to understand the factors influencing their satisfaction?  
The study was conducted in a hospital in Northern Portugal, chosen for its accessibility and relevance to the research objectives. Like most healthcare organizations, the hospital operates as a complex and interconnected structure, providing a suitable context for assessing the interplay between leadership, teamwork, and work engagement.  
The research followed a longitudinal design as the same participants completed questionnaires over a five-month period. A Likert scale was employed to collect data and to facilitate the transformation of non-numeric information into numeric data, following the methodology proposed in [28].  
The instrument was a comprehensive questionnaire consisting of multiple sections, each informed by a thorough literature review. These sections included the following:  
* Sociodemographic Data: Attaining age, gender, profession, academic qualifications, and years of professional experience;
* Leadership: Assessing HCWs' perceptions of their leaders' effectiveness in fostering trust, collaboration, and motivation;
* Teamwork: Evaluating the confidence and cohesion within workgroups in addressing challenges and achieving organizational goals;
* Work Engagement: Measuring HCWs' enthusiasm, dedication, and absorption in their role dedication, and absorption in work.  
The questionnaire was designed to facilitate the transformation of qualitative perceptions into quantifiable data, enabling an evaluation of organizational dynamics. This structure provided a clear framework for capturing and analyzing HCWs' insights while ensuring data validity and reliability. In focusing on HCWs' perspectives, this study aimed to provide actionable insights directly from those most engaged with the day-to-day operations of the HO. These findings contribute to a deeper understanding of the key factors influencing leadership, teamwork, and engagement within HOs.
## 1 Introduction  
### Background and Motivation  
Coal is an extremely complex heterogeneous material formed from ancient wetlands over geological processes. It consists of various organic components called macerals and a lesser amount of inorganic minerals [1; 2]. Different from the minerals with homogeneous internal composition and structures, the macroals derived from coalified plant tissues have distinct physical and chemical properties, and are related to the degree of coalification. In addition, the maceral composition is an important factor in evaluating the coal seam quality. Precise identification of the maceral components has a multitude of uses across various industry sectors, including hydrogenation, combustion, carbonization and gasification [2; 3; 4].  
Macerals can be categorized into three basic groups through petrographic analysis, including vitrinite from coalified woody tissue, liptinite from more decay-resistant parts of plants and inertinitefrom hydrogen-rich plant and decomposition products. These maceral groups are subdivided into maceral subgroups and macerals [5]. Most laboratories associated with the coke-making industry Standard Test Method for Microscopical Determination of the Maceral Composition follow the standard test methods, such as International Commission for Coal Petrology (ICCP) standard [6] and American Society for Testing and Materials (ASTM) standard D2799-13 [7], and the microscopic analysis is always performed manually for the identification of maceral components. Despite being the most widely used method, it is costly and labor-intensive to identify the maceral composition due to the complicated nature and substantial diversification of the petrographical properties of coal. Even for specialists in petrography, they may arrive at different judgements in the analysis of the same microscopic image as a consequence of the subjective factors. To address these concerns, an intelligent analytical technique which is able to automatically provide objective identification of maceral components is highly desired for the growing industrial demand.
## 3 Application of the New Charging Structure
### Results and Discussion
(**a**) the actual and theoretical excavation profile of conventional blasting, (**b**) the actual and theoretical excavation profile of the first test, (**c**) the actual and theoretical excavation profile of the second test, (**d**) the actual and theoretical excavation profile of the third test. excavation cyclical footage is 3.44 m, but most blastholes are approximately 4.0 m in length and a few can be 4.5 m long. This reflects low blasthole and explosive energy utilization rates. Every construction situation is different. The number of blastholes may be higher or lower and the blastholes may be long or short. These parameters affect the economics of construction. Therefore, this study calculates only the economic benefits of one excavation round under ideal conditions. The simulation is performed strictly according to the design drawings. Upon comparing the new and traditional charging structures, we see that each peripheral hole in the new charging structure requires 0.5 rolls of explosives more than the traditional structure. However, the traditional charging structure uses an additional 200 m of detonating cord. Under ideal conditions, the number of peripheral holes is 41. Table 3 compares the economic benefits of the new and traditional charging structures during an excavation round. Approximately 200 m of detonation cord is saved in one excavation round. The new charging structure reduces the blasting cost by 53.6%. Detonating cord accounts for a certain proportion of the cost of smooth blasting. Therefore, reducing the use of detonating cord and avoiding the transmission of detonating cord reduces production costs, simplifies processes; reduces cycle time; and improves production efficiency. This approach has broad blasting excavation application prospects and plays a decisive role in blasting technology improvement.
## 4 Discussion
### Implementation of the Proposed Method  
It is desirable to calculate the reflex gain in real time (maybe in 1-10 ms). As sEMG and kinematic signals are very sensitive to noise, the gains calculated from short-range data are useless. Figure 15 shows the prediction results with gains calculated from different lengths of data. The gains calculated using a data set 400-700 ms long are feasible for representing the reflex. However, a 400 ms time interval is too long for controlling a robotic device. Fortunately, the gains are within a limited range. An average value is beneficial to fitting muscle activation with a 1-2% decrease in VAF, which provides a feasible iterative approach to problems in controlling robotic device. The reflex gains can be calculated beforehand and used as initial control gains. As demonstrated in Figure 16, by comparing the current reflex gains and the initial gains, a modification can be applied based on a step-by-step manner using some intelligent methods.  
Figure 14: Ratio of \(d_{a}\) part contribution to \(p_{a}\) part contribution. Column (a) to (e) represents the results of subject A to E, respectively. The blue area the contribution of the \(d_{a}\) part. The gray area represents the contribution of the \(p_{a}\) part. Each column represents one subject. The x-axis is the time.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 4 Problem Formulation and Analysis
### Energy Cost
#### 4.1.2 Computation Energy Cost  
The computation energy consumption denoted as \(E_{n,m}^{c}\) refers to the amount of energy that service satellites \(m\) consume to perform a satellite terminal's computing task \(n\). The computational requirement for tasks \(W_{n}\) assigned to satellite \(m\) is \(x_{n,m}C_{n}\), and the computation data volume assigned to satellite \(m\) is \(x_{n,m}D_{n}\). According to the realistic measurements [13], the computation energy consumption is proportional to the square of the CPU frequency, and specifically \(E_{n,m}^{c}\) is described as  
\[E_{n,m}^{c}=\varepsilon(f_{m})^{2}x_{n,m}C_{n} \tag{12}\]  
where \(\varepsilon\) is the energy coefficient which depends on the chip architecture. Without loss of generality, we think the CPU frequency \(f_{m}\) is different on the satellites due to the heterogeneity of the onboard MEC server.  
Therefore, the total energy consumption of service satellite \(m\) for the computing task of ground terminal \(n\) can be expressed as  
\[E_{n,m}= E_{n,m}^{p}+E_{n,m}^{c}\] \[= P_{n}\frac{x_{n,m}D_{n}}{R_{n}}+\sum\limits_{i=1}^{hop}P_{i} \frac{x_{n,m}D_{n}}{R_{i}} \tag{13}\] \[+\varepsilon(f_{n})^{2}x_{n,m}C_{n}\]  
The total energy consumption of the computation task \(n\) offloading to the LEO constellation edge cloud can be expressed as  
\[\sum\limits_{m\in M}E_{n,m} \tag{14}\]
## 4 Results
However, because the lightweight facial expression recognition model of our proposed method enables real-time recognition even in a CPU environment, it can be applied to various applications that require edge-device or continuous expression recognition, such as mobile environments and driver monitoring. Figure 6 below shows an example of a video capture of a demo program using MobileNetV2 with Emonet as the teacher model and MobileNetV2 as the student model. In Figure 6b, the FPS is much higher than in Figure 6a, and it is easy to use in real environments. Figure 5: Confusion matrices for expression estimation; (**a**,**c**,**e**,**g**) are the results for MobileNetV2 and (**b**,**d**,**f**,**h**) are the results for EfficientFormer. (**a**,**b**): No use of knowledge distillation; (**c**,**d**): only knowledge distillation; (**e**,**f**): knowledge distillation and mixed output; (**g**,**h**) every proposed method, including mixed output and teacher bound. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Expression (\%)** & **Valence (RMSE)** & **Arousal (RMSE)** & **MAC (\(\times\)10\({}^{9}\))** \\ \hline AffectNet baseline [21] & 58 & 0.37 & 0.41 & 0.72 \\ Face-SSD [24] & - & 0.44 & 0.39 & - \\ VGG-FACE+2M images [25] & 60 & 0.37 & 0.39 & 15.51 \\ ResNet-18 [3] & - & 0.39 & 0.34 & 1.82 \\ Distract Your Attention [26] & 62.09 & - & - & - \\ EfficientNet-B [27] & 63.03 & - & - & - \\ Face2 exp [28] & 64.22 & & - & - \\ Emonet [11] & 75 & 0.29 & 0.27 & 16.99 \\ Ours (EfficientFormer) & 73.62 & 0.31 & 0.28 & 3.94 \\ Ours (MobileNetV2) & 73.36 & 0.37 & 0.30 & **0.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of proposed method. It can be seen that the same video shows very similar predictions, especially for the expression classification problem.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 5 Results Analysis
### Separation Calculation  
#### Overall Collision Risk Trend Analysis  
When the separation is small, the conflict frequency dominates, leading to a low overall collision risk initially. As the separation increases, the conflict frequency rapidly increases, raising the overall collision risk. When the separation value is large, the collision probability dominates, resulting in a rapid decline in the overall collision risk, as shown in Figure 12.  
When the separation value reaches a certain point, the overall collision risk will meet the TLS. By taking the logarithm of the overall collision risk on the vertical axis, a refined display can be achieved. The intersection point of the overall collision risk logarithmic curve and the TLS line is the required separation, 48.5 m, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**K-Means and Adam–LSTM Two-Stage Method**} \\  & **RMSE/m** & **MAE/m** & **MAPE/\%** \\ \hline Longitude & 6.12 & 4.58 & 5.65 \\ Latitude & 37.33 & 34.49 & 10.85 \\ Altitude & 1.64 & 1.28 & 5.68 \\ Total & 15.03 & 13.45 & 7.39 \\ \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Single LSTM**} \\  & **RMSE/m** & **MAE/m** & **MAPE/\%** \\ \hline Longitude & 9.58 & 8.76 & 8.52 \\ Latitude & 44.73 & 40.39 & 13.56 \\ Altitude & 2.41 & 1.95 & 5.85 \\ Total & 18.9 & 17.0 & 9.31 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of track prediction model effects.
## 4 Results
### Estimation of Power Consumption by Sensor Node  
The estimated total power consumption of the sensor network with ten nodes is presented in Table 2 below. In light of the numerous communication channels, its duty cycles have been brought up for discussion. This particular operating regime has a duty cycle of 10 min intervals.  
Node 1 is closest to the base station, and node 10 is farthest from it in a star topology when there are ten sensor nodes. Nodes that retransmit are known as retranslators. There are two types of nodes in a cluster: \"detached nodes\" and \"gateway nodes\" (node 1). Node 1 connects with other nodes for acknowledgement as a cluster head and acts as a gateway node for sensor data transfer to the base station. On the other hand, detached nodes are exclusively responsible for collecting and transmitting data to the cluster head.  
Based on column 4, the sensor network of ten nodes consumes 65.31 mW/day in total if the nodes send one piece of information in an hour for the selected operation regime. When this fact is considered, the lifespan of various types of batteries can be estimated. The RF energy harvester, which can serve as a primary charging battery source for a wireless sensor network, could be used to operate under multiple operating regimes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Nodes** & **If One Duty Cycle is 10 min, mW** & **If One Duty Cycle is 1 h, mW** & **Overall Consumption per Day, mW** \\ \hline
1 (Cluster head) & 8.450 & 1.408 & 33.792 \\ \hline
2 & 0.845 & 0.142 & 3.408 \\ \hline
3 & 0.845 & 0.142 & 3.408 \\ \hline
4 & 0.845 & 0.142 & 3.408 \\ \hline
5 & 0.845 & 0.142 & 3.408 \\ \hline
6 & 0.845 & 0.142 & 3.408 \\ \hline
7 & 0.845 & 0.142 & 3.408 \\ \hline
8 & 0.845 & 0.142 & 3.408 \\ \hline
9 & 0.845 & 0.142 & 3.408 \\ \hline
10 & 0.845 & 0.142 & 3.408 \\ \hline Time synchronisation & - & - & 0.845 \\ \hline \multicolumn{3}{c}{Overall power consumption} & 65.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power consumption of sensor network.  
Figure 12: Dependence of antenna resonance frequencies on the size of the edge of the largest triangle (**a**) and dependence of antenna resonance frequencies on the ratio between edges of the fractal triangles (**b**).
## 2 Materials and Methods
### Construction Method of Combined Model
#### 2.2.2 Basic Model Training  
In this step, the basic model \(M_{basic}\) will be trained using all data from the training set. Assume the power prediction model is defined as Equation (2).  
\[\hat{P}_{w}=M_{basic}(X,para) \tag{2}\]  
where \(X\) is the vector of input variables, \(\hat{P}_{w}\) is the power prediction, and para are the model parameters learned by the machine learning algorithm. These parameters can be obtained by minimizing the loss function on the training dataset. Specifically, this involves solving the optimization problem as follows:  
\[\begin{array}{ll}\min_{para}\mathcal{L}\big{(}P_{w},\hat{P}_{w}\big{)}=\parallel P _{w}-\hat{P}_{w}\parallel^{2}\\ s.t.&P_{w}\leftarrow\mathcal{D}(X,P_{w})\\ &\hat{P}_{w}=M_{basic}(X,para)\end{array} \tag{3}\]  
where \(\mathcal{D}(X,P_{w})\) represents the actual operational dataset acquired from the power plant, and \(\mathcal{L}\big{(}P_{w},\hat{P}_{w}\big{)}\) denotes the loss function, which is universally expressed as the error between the actual power and the predicted power. In practice, different forms of loss functions can be selected based on the specific problem. This paper employs the support vector regression (SVR) method for the training process. SVR is a popular machine learning method and has been widely applied in the energy field. Assume a set of training points, \(\{(x_{1},y_{1}),\ldots,(x_{n},y_{n})\}\), where \(x_{i}\in R^{d}\) and \(y_{i}\) is the target output. Under given parameters \(C>0\) and \(\epsilon>0\), the generalized Equation (3) transforms into the following Equation (4) in the context of SVR problem:  
\[\begin{array}{ll}\min_{w,b,\xi^{+},\xi^{-}}\frac{1}{2}w^{T}w+C\sum\limits_{ i=1}^{n}\xi_{i}^{-}+C\sum\limits_{i=1}^{n}\xi_{i}^{+}\\ s.t.&w^{T}\Phi(x_{i})+b-y_{i}\leq\epsilon+\xi_{i}^{-}\\ &y_{i}-w^{T}\Phi(x_{i})-b\leq\epsilon+\xi_{i}^{+}\\ &\xi_{i}^{-},\xi_{i}^{+}\geq 0,\ i=1,\ldots,n\end{array} \tag{4}\]  
where \(\Phi(x_{i})\) is kernel function that maps \(x_{i}\) into a higher-dimensional space to make a linear relationship between \(\Phi(x_{i})\) and \(y_{i}\). \(C>0\) is the regulation parameter. By solving the optimization problem in Equation (4), the model parameters can be obtained, and the prediction model can be derived. Upon obtaining the basic model \(M_{basic}\), subsequent establishment of condition-specific modification models will be conducted based on this framework.