esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Cooling Vests and Literature Review
### Testing and Evaluation of Comfort
#### 2.2.3 Subjective Evaluation of Cooling Vest Comfort  
Studies have indicated that firefighter uniforms reduce tolerance to the temperature of the working environment. Cooling vests can accelerate convection, conduction, and the evaporation of perspiration to maintain a normal body temperature of 35-38 \({}^{\circ}\)C. Studies have also indicated that the temperature, relative humidity, and airflow in the within-clothing microclimate must be maintained at 32 \(\pm\) 1 \({}^{\circ}\)C, 50% \(\pm\) 10%, and 0.25 \(\pm\) 0.15 m/s, respectively, for firefighters to feel comfortable. Comfort is difficult to define because it involves an individual's physiological, psychological, and physical experience of an environment [31]. Comfort can be analyzed in terms of the psychological and physical aspects of an environment or item of clothing as well as the psychological and physiological responses of the wearer [32]. An ambient temperature of 28-30 \({}^{\circ}\)C is defined as a comfortable somatosensory temperature. Studies have indicated that body temperature increases after 30 min of strength exercise because of changes in skin and ambient temperature. Feelings of discomfort are related to the rate of perspiration on the skin, and thermal discomfort is the result of the thermoregulation mechanism. Consequently, cooling vests for firefighters have a considerable effect on temperature regulation. Because thermal comfort involves subjective factors, a relationship between subjective feeling and physiological responses must be identified to quantitatively evaluate thermal comfort [33].  
According to the literature, a subjective discomfort rating could be used to measure the comfort, physical stability, and balance of an article of clothing. Vests without a cooling function can cause wearers to experience discomfort from high temperatures and affect their movement. Cooling vests reduce body temperature and thus allow users to feel comfortable.
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 3 Task Allocation
### Task Allocation Algorithm
For example, more than one UAV selects the same target as their pre-allocated target. Here, a consensus algorithm [19] is employed for conflict resolution (Line 8, Algorithm 1). Each UAV achieves a consensus on the pre-allocated target by negotiating with others. The UAV with the highest estimated reward is seen as the manager UAV \(i_{manager}\) using Equation (17), the corresponding pre-allocated target of the manager UAV is regarded as the target to be allocated in this allocation round. \[i_{manager}=\operatorname*{argmax}_{i\in N}(Reward(i,j_{i}^{*})) \tag{17}\]  
After the consensus process, every UAV knows the manager and the target to be allocated in this allocation round. Other UAVs, who have the required resource for the target are seen as the potential coalition members and will send applications to the manager UAV, the applications include their remaining resources and ETA. Then, the manager UAV uses algorithm 2 to determine the UAV coalition \(C_{j}\) to perform the target to be assigned in this allocation round (for simplicity, expressed as \(T_{j}\) in the following algorithm). Then, the UAVs in the coalition \(C_{j}\) will augment the target into their task sequence (Line 10, Algorithm 1). The manager UAV and the applicants were regarded as the potential coalition members for the target \(T_{j}\), they would form a list \(U_{j}^{p}\). The coalition formation algorithm begins with finding all the feasible coalitions with sufficient resources and the minimum size (line 1-8, Algorithm 2). Then, the manager UAV selects the most appropriate coalition \(C_{j}\) among all the feasible coalitions using Equation (18; Line 12, Algorithm 2). The utility for each coalition \(c\in C_{feasible}\) is calculated using Equation (6). \[C_{j}=\underset{c\in C_{feasible}}{\operatorname{argmax}}(utility(c)) \tag{18}\]  
Once the coalition \(C_{j}\) is determined to perform the target \(T_{j}\), the coalition members will calculate the resource consumption that satisfying the target's resource requirement (Line 11, Algorithm 1). A greedy resource management method was used in this paper. Assuming the type-\(p\) resource requirement of target \(T_{j}\) is \(R_{p}^{T_{j}}\). The type-\(p\) resource consumption \(R_{p}^{U_{k}}(\text{cost})\) of UAV \(U_{k}\) in the coalition \(C_{j}\) can be represented as:  
\[R_{p}^{U_{k}}(\text{cost})=\left\{ \begin{array}{ll}0,&if\sum\limits_{i\in U _{k,k}}R_{p}^{U_{i}}\geq R_{p}^{T_{j}}\\ \min(R_{p}^{T_{j}}-\sum\limits_{i\in U_{k,k}}R_{p}^{U_{i}}),&otherwise\end{array} \right. \tag{19}\]  
where \(U_{k,k}\) represents the UAVs before \(U_{k}\), when elements in \(C_{j}\) are arranged in descending order of reward. After the calculation of resource consumption, the UAVs in the coalition \(C_{j}\) update their remaining resources. Then, the simultaneous arrival paths for the UAVs in the coalition \(C_{j}\) are generated by the path planning process (Section 4.3).
_Appl. Sci._**2014**, \(4\), 366-379; doi:10.3390/app4030366  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 4**  
\({}^{1}\) Faculty of Engineering, University of the Ryukyus, 1 Senbaru Nishihara-cho Nakagami,  
Okinawa 903-0213, Japan; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Electrical Engineering and Computer Science, Nagoya University, Furo-cho,  
Chikusa-ku, Nagoya 464-8603, Japan; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Electrical and Computer Engineering, University of Texas at El Paso,  
500 West University Avenue, El Paso, TX 79968, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) School of Electrical and Computer Engineering, Sungkyunkwan University,  
Suwon City 440-746, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +81-98-895-8684.  
_Received: 31 January 2014; in revised form: 3 June 2014 / Accepted: 4 August 2014 / Published: 22 August 2014_  
**Abstract:** This paper proposes the re-planning operation method using Tabu Search for direct current (DC) smart house with photovoltaic (PV), solar collector (SC), battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results.  
**Keywords:** DC smart house; forecast error; re-planning operation; photovoltaic; solar collector
# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
###### Abstract  
Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called \"deep learning\" is used in machine learning. With the aid of magnetic resonance imaging (MRI), deep learning is utilized to create models for the detection and categorization of brain tumors. This allows for the quick and simple identification of brain tumors. Brain disorders are mostly the result of aberrant brain cell proliferation, which can harm the structure of the brain and ultimately result in malignant brain cancer. The early identification of brain tumors and the subsequent appropriate treatment may lower the death rate. In this study, we suggest a convolutional neural network (CNN) architecture for the efficient identification of brain tumors using MR images. This paper also discusses various models such as ResNet-50, VGG16, and Inception V3 and conducts a comparison between the proposed architecture and these models. To analyze the performance of the models, we considered different metrics such as the accuracy, recall, loss, and area under the curve (AUC). As a result of analyzing different models with our proposed model using these metrics, we concluded that the proposed model performed better than the others. Using a dataset of 3264 MR images, we found that the CNN model had an accuracy of 93.3%, an AUC of 98.43%, a recall of 91.19%, and a loss of 0.25. We may infer that the proposed model is reliable for the early detection of a variety of brain tumors after comparing it to the other models.  
brain tumor; CNN; deep learning; MR images +
Footnote †: journal:  
Article
## 3 Methodology
### Text Feature Feedback Mechanism  
#### 3.3.1 Negative Feedback Mechanism  
The PSLF-CTR model incorporates the logical rules related to temporal text representation into the machine learning model. Based on the BERT-TextCNN model, we designedthe PSL-TFR model to improve the model's accuracy in recognizing each type of temporal text feature. Figure 4 shows its structure.  
The training process of PSL-TFR adds a negative feedback adjustment mechanism to the Bert-TextCNN model. The set of probabilities \(P_{set}=\left\{P\left(l_{j}\right)\right\}\) can be obtained after the Softmax layer that the text feature satisfies for the atomic utterance \(l_{1}\)\(\sim\)\(l_{18}\). The formula is shown in (36).  
\[P\left(l_{j}\right)=\frac{e^{l_{j}}}{\sum_{i}^{m}e^{l_{j}}} \tag{36}\]  
where \(m\) is the number of atomic statements and takes the value of eighteen. The obtained \(P_{set}\) and the corresponding \(r_{c}\) to the actual type are input to the PSL calculation program, which in turn yields each atomic statement \(d_{r}\). If \(d_{r}\) > 0, the model prediction results are contradictory, and the model needs to be adjusted by negative feedback, using \(d_{r}\) as a feedback factor to expand its loss. Since the text feature recognition method incorporates logic rules for negative feedback adjustment, the loss function of the new model is based on the Bert-TextCNN model with the addition of the feedback factor \(d_{r}\). The calculation method is shown in (37):  
\[\mathcal{L}=-[y*\log\hat{y}+(1-y)*\log(1-\hat{y})]+\lambda*d_{r} \tag{37}\]  
where \(\lambda\) is the feedback coefficient, taking values in the range \((0,5]\), indicating the weight of the negative feedback factor.
## 4 Simulation Result and Discussion
### Analysis of Case Studies
#### 4.3.3 Case III: Missing PMUs Data Case  
Most of the deep learning and machine learning models fail in prediction when subjected to missing PMUs data, since they require a complete set of features input [48,49]. In a practical power system, the PMUs data become unavailable due to PMUs failure or due to communication latency [50]. In this case, the robustness of different classification models to missing PMUs data is analyzed for online application. To simulate the missing PMUs data for online STVS assessment, a certain portion of the test data set within an OTW is randomly set to NaN (not a number values). Furthermore, the missing bus voltage measurements from PMUs are imputed using k nearest neighbor (kNN) algorithms [47]. Finally, the test set with newly imputed values are used for prediction of the STVS state of the power system using pre-trained models. The STVS state is predicted using 10%, 20%, 30%, 40%, 50%, 60% and 70% of missing data in the test set. The result in Figure 13 shows that there is less influence in the prediction capability of these models with missing PMUs data. The proposed model is more tolerant of missing PMUs data, even when 50% of the data are missing, with an accuracy of more than 97% for both test systems. With merely imputed values using kNN, the missing data condition appears to have a lower impact on the overall classifier performance. Evidently, TempCNN and the LSTM classifier still provide the better accuracy up to 50% of missing data with a marginal difference, while the SVM and DT classifiers' prediction accuracy is significantly low compared to the proposed model for both the test systems.  
However, our work is confined to the assumption that the full observability of a grid is available, and the missing PMUs data can be simply imputed using kNN for online application. In an actual power system, the network topology may change due to fault, and PMUs data may not be available from some buses. In this context, the ensemble-based learning strategy presented in [50] can be applied to handle the issue of network topology changes.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Test System**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Performance Evaluation**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{IEEE 9 Bus} & Proposed & 97.93\% & 97.97\% & 97.93\% & 97.94\% \\  & TempCNN & 97.29\% & 97.48\% & 97.29\% & 97.35\% \\  & LSTM & 97.19\% & 97.49\% & 98.18\% & 97.26\% \\  & SVM & 92.75\% & 95.93\% & 92.75\% & 93.64\% \\  & DT & 88.71\% & 93.89\% & 88.71\% & 90.38\% \\ \hline \multirow{4}{*}{New England 39 Bus} & Proposed & 96.66\% & 96.65\% & 96.66\% & 96.65\% \\  & TempCNN & 95.78\% & 95.84\% & 95.78\% & 95.76\% \\ \cline{1-1}  & LSTM & 95.02\% & 95.14\% & 95.03\% & 95.06\% \\ \cline{1-1}  & SVM & 92.48\% & 92.64\% & 92.48\% & 92.44\% \\ \cline{1-1}  & DT & 87.24\% & 88.53\% & 87.24\% & 87.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance evaluation with PMUs measurement error on test systems.
# Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
## 3 Methods
### Separable Vision Transformer  
In recent years, vision transformer [9; 10] has achieved great success in various computer vision tasks, boasting a performance that exceeds that of CNNs in essential domains. However, these performances usually come at the cost of an increased computational complexity and the number of parameters.  
A separable vision transformer [11] solves this challenge by maintaining its accuracy while balancing the computational costs. In this paper, we replaced the final layer of the backbone network with the SepViT (Separable Vision Transformer) Block, which enhances the feature-extraction capability of the model and optimizes the relationship of the global information of the network. In SepViT Block, the depthwise and pointwise self-attention values reduce the computation and enable local information communication and global information interaction in windows. First, the input feature map was divided into windows, and each window was considered to be an input channel of the feature map, and each window contained different types of information. Then, depthwise self-attention (DWA) was performed on each window token and its pixel tokens. The DWA focused on fusing the spatial information of the channels, similar to a depthwise convolution in MobileNet [12; 13; 14]. The operation of DWA is as follows:  
\[\text{DWA}(f)=\text{Attention}(f\cdot W_{Q},f\cdot W_{K},f\cdot W_{V}) \tag{1}\]  
where \(f\) is the feature tokens, composed of window tokens and pixel tokens. \(W_{Q}\), \(W_{K}\), and \(W_{V}\) represent three linear layers for query, key, and value computations in a routine self-attention task. Attention means a standard self-attention operation. Pointwise self-attention (PWA) is similar to the pointwise convolution operation in MobileNet, except that a pointwise convolution is used to fuse information from different channels while PWA establishes connections between windows. After completing the DWA operation, PWA builds relationships among windows and generates the attention map by LayerNormalization (LN) and a Gelu activation function. The operation of PWA is as follows:  
\[\text{PWA}(f,wt)=\text{Attention}(\text{Gelu}(\text{LN}(\;wt))\cdot\;W_{Q}, \;\text{Gelu}(\text{LN}(\;wt))\cdot\;W_{K},f) \tag{2}\]  
where \(wt\) represents the window token. Then, SepViT Block can be expressed as  
\[\overset{\sim t}{f}=\text{Concat}(f^{n-1},wt) \tag{3}\]  
Figure 1: The architecture of the Light-YOLOv5 model.  
\[\ddot{f}^{n}=\text{DWA}(\text{LN}(\widetilde{f}^{n})) \tag{4}\] \[\dot{f}^{n},\dot{w}t=\text{Slice}(\widetilde{f}^{n})\] (5) \[\hat{f}^{n}=\text{PWA}(\hat{f}^{n},\dot{w}t)+f^{n-1}\] (6) \[f^{n}=\text{MLP}(\text{LN}(\hat{f}^{n}))+\hat{f}^{n} \tag{7}\]  
where \(f^{n}\) represents the SepViT Block. \(\hat{f}^{n}\) and \(\dot{w}t\) are the feature maps and learned window tokens. Concat denotes the concatenation operation. Slice indicates the slice operation. Figure 2 presents the structure of the SepViT Block.
# Dirac Equation-Based Formulation for the Quantum Conductivity in 2D-Nanomaterials
## 7 Conclusions  
Starting from the Dirac equation mathematical model, based on a four-component spinor description, we derive a rigorous formulation of the scalar quantum conductivity for particles with finite mass, driven by a constant d.c. field. A quantum-mechanical evaluation is then provided in the case of parabolic and linear dispersion. The graphene conductivity is then analyzed. To this aim, numerical values derived from the present method are compared with the ones from the literature (Kubo formula), in the case of \(V(x)=V_{0}\), with excellent agreement. We then analyze the case of applying a uniform electric field \(V(x)=V_{0}+\overline{E}x\). The present formulation of the conductivity can be inserted into full-wave solver for the analysis and design of graphene-based devices in the ballistic regime [37], and in the quasi-ballistic regime, e.g., in geometrical diodes, [38].  
The present method is as rigorous as it is flexible, and can be used to provide, depending on the particular problem (electric/magnetic polarization, d.c./a.c. conditions) proper formulation for charged particles wave propagation, e.g., spin waves, matter waves, electromagnetic waves.  
Conceptualization, T.R., L.P. and N.P.; software, L.P., N.P., M.O. and J.P.; validation, L.P. and N.P.; formal analysis, T.R., L.P., N.P., D.M., A.D.D.; data curation, N.P.; writing--original draft preparation, T.R., L.P. and N.P.; writing--review and editing, L.P. and N.P.; visualization, N.P.; supervision, L.P.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101006963 (GreEnergy).  
Figure 6: Grapheneconductivity for different values of \(L_{x}\), in the the presence of the longitudinal electric field \(\overline{E}=E_{x}\hat{\kappa}\), being \(V_{0}\in[-3,3]\) V.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 5 Analyzing the Selected Features
### Domestic Hot Water Equipment  
The use of DHW equipment reveals a clear trend where higher-consumption profiles are associated with an increased presence of DHW units. This correlation is presented in Figure 12.  
In the low-consumption profile, a significant proportion of dwellings, nearly 80%, have none or only one DHW unit. This figure declines to around 60% in the medium profile and drops to about 40% in the high-consumption profile, indicating a strong correlation between higher energy consumption and the number of DHW units. This pattern is consistent across the k-means, AHC, and SOM clustering methods.  
The medium-consumption profile predominantly consists of households with at least one DHW unit, while the high-consumption profile is characterized by dwellings with two or more DHW units. This delineation suggests that the presence of multiple DHW units is a significant indicator of higher energy-consumption levels.  
Consequently, households in the medium profile without any DHW unit can be considered out-profiled, deviating from the general trend of this consumption category. Similarly, in the high-consumption profile, dwellings with a maximum of one DHW unit are also deemed out-profiled, given their lower than expected DHW count for this high-energy-consumption category.
# Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China
## 2 Research Background
### BIM-Oriented Construction Cost Estimation  
BIM is one of the most promising developments in the AEC/FM industry [8]. With the development of BIM technology, a common information repository for all project participants is offered [2]. IFC is an industry-developed product data model for the whole lifecycle of buildings; supported by buildingSMART, it is a widely utilized standard for representing BIM [9]. Some widely known cost estimation software applications are Vico Estimator [10], Innovaya Visual Estimating [11], and Success Estimator [12]. Estimators still have to set conditions to comply with a specification manually, and quantity takeoff still depends on the imported design model. Due to the working complexity and compatibility, cost estimation is time-consuming and inaccurate.  
In the AEC/FM industry, there have been numerous studies regarding the application of BIM-oriented cost estimation. [PERSON] et al. [13] developed an IFC Web-based collaborative construction computer environment named WISPRER, which built an IFC-based object-oriented database to help users realize the network integration and sharing of the design, budget, schedule, and other information in construction projects. [PERSON] et al. [14] suggested an IFC-based cost estimation system that is capable of directly using the results of IFC files and then automatically applying corresponding prices to accomplish the cost estimation according to the component geometries and properties. [PERSON] et al. [15] developed a system for life-cycle cost assessment that can automatically extract cost-estimating data from the design results of IFC files and then transfer the data to a preexisting component of the life-cycle cost assessment. [PERSON] et al. [16] applied the IFC standard to the cost estimation of earthwork and accomplished the cost estimation function according to the 4D model. [PERSON] et al. [17; 18] established an information model that can be applied to the development of a construction cost estimation software and established a framework for BIM-based Construction CostEstimating (CCE) software that utilized the Chinese national mandatory specification (GB 50500-2008), which laid a solid foundation for the development of next-generation CCE software. Simultaneously, they also proposed an IFC-based discrimination model for BOQ (Bill of Quantity) items and the corresponding rules and created a semantic database by analyzing related Chinese specifications; they also formulated a mechanism to intelligently generate BOQ from IFC data [19].  
In summary, there are quite a few studies regarding cost estimation. Some mainly concentrate on cost estimation software applications based on BIM, and others propose methods of information modeling for BIM cost estimation. However, limited attention has been paid to the information integration of multiple data sources and its implementation in construction cost estimation.