esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Semi-Supervised Learning Model to Coal Sample Classification
## 4 LIBS Spectral Pretreatment
### Standardization  
Spectral normalization is a data-processing technique designed to eliminate or reduce variations in spectral data to make them easier to compare and analyze. Through methods such as normalization, detrending or correction, spectral normalization ensures that spectral data obtained under different experimental conditions can be compared on the same scale. Normalization scales spectra to a standard range, detrending removes trends or drifts in the spectra, and calibration corrects errors caused by instrumental, environmental, and other factors. These processing tools help to improve the comparability of spectral data, making it more suitable for a variety of scientific research and application areas, such as quantitative analysis and modeling. Spectral standardization provides researchers with effective tools to interpret and compare spectral data more accurately and reliably. The equation is shown below:  
\[x_{Z-scores}=\frac{x-\mu}{\sigma} \tag{2}\]  
where \(x_{Z-scores}\) is the spectral intensity after standardization; \(x\) is the spectral intensity; \(\mu\) is the average spectral intensity of all spectra at the current wavelength; and \(\sigma\) is the standard deviation of the spectral intensity of all spectra at the current wavelength.
## 2 Materials and Methods
### The Design of the Tilt-Angle Subsystem  
The system discussed in the previous section can achieve complete tracking of \(\zeta_{1}\) and \(\zeta_{2}\) within a finite time, which enables us to transform it into the following form:  
\[\tilde{\zeta}_{3}=\frac{1}{\Omega}\Big{(}m_{11}c_{32}\dot{\zeta}_{2d}-m_{31}c_ {13}\dot{\zeta}_{3}+m_{11}g_{3}\Big{)}-\frac{m_{31}}{\Omega}\tau_{3}+D_{3}, \tag{37}\]  
As WIPR aims to maintain a vertical and stable direction of the pendulum during its motion, all relevant parameters \((\alpha_{d},\dot{u}_{d},\ddot{\alpha}_{d})\) can be set to zero. As such, the following definitions can be employed:  
\[\left\{\begin{array}{l}e_{\alpha}=\alpha_{d}-\alpha=-\alpha\\ e_{\dot{\alpha}}=\dot{\alpha}_{d}-\dot{\alpha}=-\dot{\alpha}\end{array} \right., \tag{38}\]  
Let the sliding mode surface be defined as Equation (39), with its derivative expressed as Equation (40).  
\[s_{4}=\gamma_{5}e_{\alpha}+e_{\dot{\alpha}}, \tag{39}\]  
\[\dot{s}_{4}=\gamma_{5}\dot{e}_{\alpha}+\dot{e}_{\dot{\alpha}}=-\gamma_{5}\dot{ \alpha}-\ddot{\alpha}=-\eta_{2}o(s_{4})\text{sgn}(s_{4})-\mu_{2}s_{4}, \tag{40}\]After substituting Equation (37), the control law for the tilt angle subsystem can be derived as presented in Equation (41).  
\[\tau_{\alpha}=-\frac{\Phi}{m_{31}}\big{(}\gamma_{5}\dot{\alpha}+\hat{D}_{3}\big{)} -\frac{1}{m_{31}}(m_{11}c_{12}\dot{\zeta}_{2}-m_{31}c_{13}\dot{\zeta}_{3}+m_{11} g_{3}-\eta_{2}o(s_{4})\text{sgn}(s_{4}))-\mu_{2}s_{4} \tag{41}\]  
To prove the stability of the designed system, the Lyapunov function is chosen as follows.  
\[V_{4}=\frac{1}{2}s_{4}^{2} \tag{42}\]  
The derivative of \(V_{4}\) for time is given by the following expression.  
\[\begin{split}\dot{V}_{4}=s_{4}\dot{s}_{4}=s_{4}\big{(}-\gamma_{5 }\dot{\alpha}-\ddot{\alpha}\big{)}=s_{4}\Big{(}-\gamma_{5}\dot{\alpha}-f_{3}- \frac{1}{m_{22}}\tau_{\alpha}-D_{3}\Big{)}\\ =s_{4}\big{[}-\eta_{2}o(s_{4})\text{sgn}(s_{4})-\mu_{2}s_{4}^{2}+ \gamma_{5}\big{(}\hat{D}_{3}-D_{3}\big{)}\big{]}\leq-\eta_{2}o(s_{4})|s_{4}|- \mu_{2}s_{4}^{2}+\gamma_{5}\big{|}\widetilde{D}_{3}\big{|}\end{split} \tag{43}\]  
By choosing \(\eta_{2}o(s_{4})>\gamma_{5}\big{|}\widetilde{D}_{3}\big{|}\), it enables \(\dot{V}_{4}<0\) to hold, indicating that the system achieves asymptotic stability.  
Figure 3 displays the schematic block diagram of the control system.
## 1 Introduction
#### Novelty and Significance  
A comprehensive review of the existing literature reveals that recent research on residential buildings in Spain primarily focuses on assessing the carbon footprint of the residential stock [65,66] and enhancing energy efficiency in the envelopes and systems of multi-family residential buildings [67, 68, 69]. Furthermore, several case studies advocate for the adoption of waste reduction principles and off-site approaches during the design phase [70,71], as well as their implementation within the BIM environment [72,73]. Regarding circularity, significant efforts have been made to develop strategies that integrate the Level(s) framework into the local context [74], and to analyse key circularity indicators within the Spanish construction sector [8]. Likewise, contributions from the Spanish guide on the use of RA derived from CDW [75] and the structural design criteria for RAC proposed by [PERSON] et al. [76] are particularly relevant, providing a solid foundation for future RAC research and application in Spain. However, despite these advancements, the literature review reveals that the circularity potential of RAC in Spanish residential building structures remains unexplored. Therefore, the study presented in this work is inherently innovative.  
This research is of vital importance to the current residential construction sector, as it addresses social barriers to the transition towards a circular economy stemming from uncertainties regarding the effectiveness of RAC as a circularity strategy at the material level in newly constructed residential structures [77, 78, 79]. This contribution is even more significant when considering the current housing shortage, which has made the construction of sustainable residential buildings a primary solution, resulting in this type of building accounting for approximately 70% of all buildings constructed annually [80].  
The paper is structured as follows: Section 1 introduces fundamental concepts, the background, and the objective of the study. Section 2 outlines the research methodology. Section 3 presents the characterisation of the most commonly constructed type of building structure. Section 4 exhibits the circular performance of the representative models in each scenario. Section 5 discusses the circularity potential of each strategy and future lines of research. Lastly, Section 6 concludes the study by providing the final observations.
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**MLP**} \\ \cline{2-7}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline All Features (92) & 63.5\% & 0.628 & 67.3\% & 0.617 & 62.8\% & 0.609 \\ Proposed Method (7) & 64.6\% & 0.603 & 68.9\% & 0.650 & 69.4\% & 0.681 \\ Information Gain (7) & 69.1\% & 0.653 & 71.6\% & 0.661 & 68.3\% & 0.650 \\ Correlation FFS (7) & 69.3\% & 0.653 & 70.9\% & 0.648 & 68.8\% & 0.665 \\ Classifier DT FFS (7) & 67.5\% & 0.662 & 67.5\% & 0.625 & 66.3\% & 0.641 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of prosodic quality classification using different sets of features and classifiers. The accuracy (Acc) and F1 score (F1) are reported for decision trees (DT), support vector machines (SVM), and multilayer perceptron (MLP). The number of features in each set is shown in brackets. Figure 2: Radar charts of the reference (RI) and confidence (CI) intervals of the TD speakers and confidence interval of the five speakers with DS for each type of evaluation (right or wrong). The polygons in blue represent the upper and lower limits of the reference intervals, the polygons in black correspond to the confidence intervals of the features extracted from the audio recordings of TD speakers, and the polygon in red corresponds to values of the confidence intervals obtained from the speaker with DS. Finally, speaker 026 has higher values in e1 and f1 than TD speakers in right recordings and higher values in f1, d2, d3, d4, and d5 features than TD speakers in wrong recordings. As well as speakers 022 and 024, the number of inner pauses and the pauses' length are higher in wrong recordings than in right recordings.
# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
## 3 Case Studies  
### Study Area and Study Data  
Changsha is a megacity in southern China. As of the end of 2018, the city's resident population was 8.15 million, of which the urban population was 6.45 million, and its urbanization rate was 79.12% [39]. The area of Changsha is 11,816.0 km\({}^{2}\). The oval areas in Figure 2 roughly show the extent of the urban areas.  
As of 2017, there were 37 fire stations in Changsha, mainly distributed in urban areas (See Figure 2). There were 61 water fire trucks deployed in 34 fire stations. There were 56 aerial fire trucks (including water tower fire trucks, aerial platform fire trucks, and aerial ladder fire trucks) deployed in 31 fire stations. Among them, 28 fire stations were equipped with both water fire trucks and aerial fire trucks. All 37 fire stations were equipped with other fire apparatuses.  
Figure 1: Flow chart of data extraction and processing.  
A dataset containing names and addresses of 6749 high-rise buildings in Changsha was gathered from the fire department. Unfortunately, the addresses in the dataset were only approximate and difficult to locate accurately. Therefore, 94,726 POIs of Changsha City were extracted from the Baidu Map by calling its web service API. Then, 5146 high-rise buildings were identified by matching names and addresses of POIs and those of 6749 high-rise buildings in the dataset. In this study, these 5146 high-rise buildings are used to represent all high-rise buildings in Changsha. They are mainly distributed in urban areas, as shown in Figure 2.
## 4 Conclusions  
The density, porosity, permeability, compressive strength, flexural strength, and freeze-thaw cycle tests of PC were conducted based on an orthogonal experiment with different coarse aggregate sizes, water-binder ratios, and designed porosities. Range analysis and variance analysis were used to evaluate the experimental results. The influences of coarse aggregate size, water-binder ratio, and porosity on PC porosity were analyzed. From the test results, the following conclusions can be drawn.  
* The density of PC mixtures changes from 2061 kg/m\({}^{3}\) to 1804 kg/m\({}^{3}\) as total porosity increases from 16.4% to 27.1%. Density decreases with an increase of total porosity. Linearity can be used to describe the relationship between them, and the correlation coefficient reaches 0.94.
* The permeability of PC is about 3.70 mm/s to 8.82 mm/s when effective porosity is 13.7% to 23.8%. The variance analysis indicates that effective porosity is the determining factor influencing permeability. Coarse aggregate size and water-binder ratio have little contribution to permeability.
* Compressive strength of PC is 15.4 MPa to 24.4 MPa. Compressive strength decreases with the increase of total porosity. Coarse aggregate size also has an influence on compressive strength. Small coarse aggregate size is superior to large coarse aggregate size in improving compressive strength when porosity is the same. Flexural strength changes from 3.03 MPa to 4.83 MPa. The influences of coarse aggregate size and porosity on flexural strength are the same as on compressive strength.  
Figure 13: Relationship between freeze-thaw cycles, designed porosity and aggregate size.  
* The freeze-thaw damage pattern of PC is different from that of ordinary concrete. It is not recommended to use the loss of mass as the evaluation index. Freeze-thaw cycles test indicates that coarse aggregate size is the main factor affecting PC freeze-thaw property. Small coarse aggregate size results in better freeze-thaw durability. In addition, porosity is another important factor that contributes to freeze-thaw durability. Higher porosity generates lower freeze-thaw durability.
* Porosity is the most important factor determining PC mixture properties (permeability, compressive strength, flexural strength, and freeze-thaw property). Higher porosity improves permeability but decreases the mechanical properties of PC. When establishing the optimum mix design, it is necessary to make a trade-off between permeability, mechanical strength, and freeze-thaw durability according to the practical engineering requirement.  
[PERSON] and [PERSON] designed the experiments and wrote the paper; [PERSON] and [PERSON] performed the experiments and analyzed the experimental results; [PERSON] wrote a part of the paper.  
This research was funded by Technology Development Program of Jilin Province 3D518E022417\".  
The authors express their appreciation for the financial support of Technology Development Program of Jilin Province (3D518E022417).  
The authors declare no conflict of interest.
## 3 Design of the Improved NMPC Controller
### Analysis of Stability
 \tag{50}\]  
According to Equations (48) and (50), to ensure the stability of the system, the following equation needs to be satisfied:  
\[\sum_{i=1}^{9}(g_{i}(s_{i})\!\cdot\!s_{i}\!\cdot\!\mathrm{dsgn}(s_{i}))\geq \overline{m}\!\cdot\!\sum_{i=1}^{9}\!\left(\left(\frac{\rho}{\underline{m}}+ \eta_{i}\right)\!\cdot\!|s_{i}|-\eta_{i}\!\cdot\!\phi_{i}\right) \tag{51}\]  
The above Equation (51) can be re-expressed as  
\[g_{i}(s_{i})>\overline{m}\!\cdot\!\left(\frac{\rho}{\underline{m}}+\eta_{i} \right)-\frac{\overline{m}\!\cdot\!\eta_{i}\!\cdot\!\phi_{i}}{|s_{i}|} \tag{52}\]  
The control law of the sliding mode variable structure control unit in Equation (47) is equivalent to  
\[u_{c}^{[i]}=-g_{i}(s_{i})\!\cdot\!\frac{1}{|s_{i}|}\!\cdot\!s_{i}=-g_{i}(s_{i })\!\cdot\!\mathrm{dsgn}(s_{i}) \tag{53}\]  
Since the slope of the sign function \(\mathrm{dsgn}()\) in Equation (53) is infinite, the control signal of the system has a serious jitter problem in the switching process. In this paper, the hyperbolic tangent function \(\mathrm{tanh}()\) with a gentler slope is introduced to replace the sign function \(\mathrm{dsgn}()\) to improve it. According to Equations (52) and (53), the improved \(u_{c}^{[i]}\) can be expressed as  
\[u_{c}^{[i]}=-g_{i}(s_{i})\!\cdot\!\mathrm{tanh}(s_{i})=-\overline{m}\!\cdot\! \left(\frac{\rho}{\underline{m}}+\eta_{i}\right)\!\cdot\!\mathrm{tanh}(s_{i}) \tag{54}\]  
According to Equation (54), we can obtain the control law \(\mathbf{u}_{c}\) of the sliding mode variable structure control unit of the system as  
\[\mathbf{u}_{c}=-\mathbf{G}\!\cdot\!\mathrm{tanh}(\mathbf{s}) \tag{55}\]
## 3 Results
### Ablation Experimental Design and Results
#### 3.1.2 Results of the Experimental Scheme  
Similar to the training process, the non-urban raster in the base year (2010) is used as a reference to crop the driving factor layer. The cropped data are then fed into the SNSCNN model, which produces a high-dimensional feature vector. This feature vector is subsequently input into a classifier for classification. The resulting classification probability represents the neighborhood probability of the raster and forms a neighborhood constraint layer. For development suitability extraction, the RF model is employed as the development suitability extractor to determine the development suitability of the current raster. This process generates the development suitability layer. By combining land development suitability, neighborhood effects, constraints, and random factors, the total non-urban to urban transition probability layer is obtained. Finally, the total number of non-urban rasters that have converted to urban rasters from 2010 to 2017 serves as a quantitative constraint for dynamic simulation. This constraint is used to generate the final predicted results for the year 2017. The actual situation in 2017 and the experimental prediction results can be observed in Figure 8.
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
###### Abstract  
Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constraints related to robot kinematics, optimization objectives, and other pertinent factors. Sample-based strategies have successfully tackled this problem, particularly the rapidly exploring random tree star (RRT*) algorithm. However, conventional path-searching algorithms may face challenges in the marine environment due to unique terrain undulations, sparse and unpredictable obstacles, and inconsistent results across multiple planning iterations. To address these issues, we propose a new approach specifically tailored to the distinct features of the marine environment for navigation path planning of underwater vehicles, named bidirectional cached rapidly exploring random tree star (BCRRT*). By incorporating bidirectional path planning and caching algorithms on top of the RRT*, the search process can be expedited, and an efficient path connection can be achieved. When encountering new obstacles, ineffective portions of the cached path can be efficiently modified and severed, thus minimizing the computational workload while enhancing the algorithm's adaptability. A certain number of simulation experiments were conducted, demonstrating that our proposed method outperformed cutting-edge techniques like the RRT* in several critical metrics such as the density of path nodes, planning time, and dynamic adaptability.  
autonomous path planning; rapidly exploring random tree star; path cache; two-way extended random tree; underwater vehicle
# Malware Detection Using Memory Analysis Data in Big Data Environment
## 5 Experiments and Evaluation
### Results and Comparison
When deep learning algorithms are evaluated in themselves, it is seen that the DNN algorithm makes less misclassification than LSTM and MLP algorithms. It is also noteworthy that the algorithms that fail the most in malware classification are deep learning algorithms. One of the deep learning approaches used in the study, LSTM networks are generally used in solving and classifying problems specific to serialized data. However, since LSTM networks are used in many intrusion detection or malware detection applications in the literature, they are used in this study to compare their performance in non-serial datasets. From the results obtained, the CIC-MalMem-2022 dataset produced relatively successful results, although it did not have serialized data. In general, when the FN and FP values are examined, it is seen that the Logistic regression, Gradient Boosted Tree, and Decision Tree algorithms have low FP values. These algorithms are more successful in benign classification. However, the remaining six algorithms have lower FN values and higher FP values. In other words, these algorithms are more successful in malware classification, but less successful in benign classification. Figure 11: Confusion Matrix of (**a**) Decision Tree; (**b**) Random Forest; (**c**) Naive Bayes; (**d**) Logistic Regression; (**e**) Gradient Boosted Tree; (**f**) Linear SVC; (**g**) MLP; (**h**) DNN; (**i**) LSTM. DNN and LSTM algorithms were run for 10 epochs. When the training loss-validation loss graphs of DNN and LSTM given in Figure 12 are examined, the inconsistency in the DNN model is remarkable. However, it tended to recover quickly after inconsistency. According to the given graphs, the generalizability ability of LSTM is higher than the DNN algorithm. However, in both graphs, the training loss rate is higher than the validation loss rate. Evaluations can be repeated with a larger epoch.
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 3 Experimental Measurements and Results
### Mechanical Compression Test Results  
Like other cementitious materials, the overall picture of ECC quality is predominantly reflected by the compressive strength. It is known that the strength evolution process, hardening with time, depends on the chemical reaction of binder (cement plus fly-ash) with free water and hence the development of a microstructure inside the composites. Three cylindrical specimens were prepared for ECC with different \(w/b\) ratios for testing of compressive strength on Day-3, Day-7, Day-14 and Day-28, respectively, using the conventional compression test method. Figure 6 shows the average values with corresponding standard deviations of compressive strength of ECC specimens. It is observed, merely from the measurement data, that the strength growing rate of PE2 is comparatively higher than other counterparts. Nevertheless, the 28-day strength values show the ECC specimens have reasonable final strength gain at a mature age. Namely, PE1 specimens having the lowest \(w/b\) result in the highest 28-day strength whereas the PE2 and PE3 specimens have their lower strengths.  
Figure 4: Measured normalized conductance versus frequency: (**a**) on Day-3; (**b**) on Day-4.  
Figure 5: Measured temporal response of electrical conductance at 2 GHz.  
Figure 6: Measured compressive strength results of three types of ECC specimens with age.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 2 Logistics Space
### The Influencing Factors of the Evolution of Logistics Space
#### 2.2.3 Market Demand  
Logistics as a service industry, near the market can get a lot of logistics demand. Changes in the logistics space follow those in the market, and the expansion of logistics has always followed that of the population and other commercial institutions ([PERSON], 2016; [PERSON], 2016). In addition, the large demand for logistics in ports, airports, and industrial parks is an important factor in attracting logistics accumulation. Manufacturing industry is the basis for logistics development. In 2016, China's industrial product logistics accounted for 93.16% of total social logistics. At the same time, the development of manufacturing industry should also be supported by logistics industry, and the logistics link accounts for more than 90% of all production processes ([PERSON], 2016). Manufacturing companies provide a lot of market demand, while logistics companies provide efficient logistics services. The coordinated development of the logistics industry and manufacturing industry is an objective requirement to reduce the cost of production enterprises and promote the development of logistics enterprises. The balance between logistics supply and logistics demand drives the evolution of logistics space. Therefore, it is necessary for the government to use the demand forecasting model to forecast and evaluate logistics requirements when planning logistics nodes and corporate decision makers in deciding company site selection ([PERSON], 2016). Somelogistics parks do not take into account the role of the market when planning, leaving these logistics parks idle. Land cost and transportation accessibility affect the operating costs of logistics facilities or logistics enterprises, and the market determines the future development and economic benefits of logistics enterprises.