esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Extraction Results of Building Information
#### 4.1.3 Extraction Results of the Story Number  
In this study, the story numbers of buildings are classified into five categories: 1-3 (low-rise building), 4-6 (multi-rise building), 7-9 (mid-rise building), 10-33 (high-rise building), and more than 33 (super high-rise building) according to the building codes in China and some scholars' research [32; 33; 34]. It should be noted that industrial buildings are divided into single-story industrial buildings and multi-story industrial buildings in this study. For the extraction of the story number of buildings in the urban area of Tangshan City, two main steps were performed: (1) spatial matching of the story number information of the deep network data with the building footprint to obtain the story number information of buildings; (2) for other buildings with unknown story number, the relationship between the building distance \(d_{ij}\) and the story semi-variance \(\gamma_{ij}\) is established as follows:  
\[\gamma_{ij}=11.03+24.46\times\left(1-e^{\frac{d_{ij}}{\sigma_{ij}\sigma_{353}}}\right) \tag{14}\]  
The information on the story number of the building is extracted using Kriging interpolation. An example is shown in Figure 12. According to the deep network data, the distribution of story numbers in the urban area of Tangshan City was obtained by using the Kriging interpolation method, as shown in Figure 13. As seen from the figure, there are more high-rise buildings in the west and south of the urban area and more low-rise buildings in the east and north. The low-rise buildings are mainly distributed in the old urban area and the urban-rural area. Combining the satellite map and Baidu 3D street view map, it is clear that most of these buildings are old residential buildings that have not been renovated. Multi-rise buildings are common in the central old city. With the expansion of urban areas, buildings gradually develop into the air to save the land and improve land utilization, and mid-rise and high-rise buildings gradually become more numerous. The number of building stories in the urban-rural area (the edge of the urban area) gradually decreases. These characteristics reflect the development of urban areas from the center to the edge of the suburbs.
## 2 The Evolution and Trends of 6G Network Architecture
### Opportunities for Fundamental Change
#### 2.2.2 Flattened Compute-Storage-Transport  
A redesigned 6G architecture, propelled by a robust air interface and an updated core and transport network, facilitates a flattened transport-storage-compute model. Under this framework, transport is virtualized over existing fiber but segregated using contemporary software-defined networking (SDN) and virtualization techniques. Concurrently, core network functions adopt a micro-service architecture, dynamically facilitated through containers or serverless computing structures. To accommodate innovative gaming applications, a more distinct segregation of central processing unit (CPU) and graphics processing unit (GPU) possibly instruction sets enables individual virtualization. For instance, GPU instructions can be processed locally on the phone, while CPU instructions are executed on a nearby virtual mobile edge computing (MEC) platform.
# Integrated People and Freight Transportation:
## 4 Integrated People and Freight Transportation (IPFT)  
Although people and freight transportation issues are discussed separately in the literature, IPFT has started to take its place in research in recent years. In this section, the studies carried out under the title of IPFT are examined under the subheadings of strategic, tactical, and operational level decisions. Under these headings, studies dealing with uncertain situations are detailed within the scope of SDL, TDL, and ODL. Uncertainty situations include dynamic structures, stochasticity, and real-time activities. The general framework and classification of the flow in the study are shown in Figure 3.  
When comprehensive studies and literature reviews on IPFT are examined, [PERSON] et al. [8] propose performance indicators to evaluate potential improvements of IPFT flows in environmental, operational, and social contexts. In their study, [PERSON] and [PERSON] [3] conduct a literature review and a descriptive analysis that includes publication types, geographical regions, publication sources, research methods, and research design. Additionally, the study describes the main content-related aspects, including text mining analysis, semantic analysis, clustering, and regional scales. [PERSON] and [PERSON] [87] conduct a comprehensive investigation of IPFT through a systematic literature review. In the study, IPFT is analyzed by classifying it into quantitative and qualitative approaches regarding the mode of transportation, shared directions, basic network, and data used.  
Additionally, opportunities and barriers related to IPFT are identified. [PERSON] et al. [12] present the results of a Delphi method of international participants evaluating the design of service features and operational performances. According to the results, IPFT shows the necessity of an efficient service regarding information, security, environmental performance,  
Figure 3: The General Framework.  
and space sharing. The study states that policymakers and practitioners can use it to define performance requirements and limits regarding the design of the service. [PERSON] et al. [4] propose a general framework to realize the planning and operation of new forms in the future. The study focuses on route planning, facility location, and pricing under SaR, FoT, and crowdshipping headings. [PERSON] et al. [88] review the vehicle routing literature from the perspective of last-mile delivery and scope crowd shipping. The study identifies the successes and limitations of research in the areas and suggests a future research agenda. [PERSON] et al. [89] define and evaluate the key factors for developing IPFT systems and classify the benefits, barriers, challenges, and strengths.
## 2 The Proposed Approach
### Data Measurement from Bench Test  
To identify the natural frequencies of the alternator, a vibration fatigue test was conducted using a 40 kN electrodynamic shaker and a thermal chamber to simulate the environmental conditions. A vibration fatigue test was performed as sine sweep for 72 h in the frequency range of 100-500 Hz with constant 10 g amplitude. To detect the resonance frequencies before the durability test, a sine sweep test was performed in the frequency range of 100-1000 Hz at a sweep rate of 120 Hz/min with a 5 g peak acceleration.  
Throughout the whole test, the alternator was rotated at 3000 rpm, and 90% of the nominal current produced at this speed was provided by an electronic load. Simultaneously, the alternator was maintained at a constant temperature of 100 \({}^{\circ}\)C inside the thermal chamber. The test setup is shown in Figure 2. The characterization was performed separately for the Y- and Z-axes of the alternator, and the acceleration versus frequency plot for the Z-axis characterization is shown in Figure 3.  
Figure 3: Resonance frequency characterization test results of alternator at Z-axis.  
Figure 2: Alternator resonance frequency characterization test setup.  
The first five natural frequencies obtained from the characterization test are shown in Table 1.  
Following the characterization test, a vibration fatigue test was performed on the component according to the alternator life test specification, with a 10 g peak sine sweep vibration applied in the \(Z\)-axis direction for 72 h in the frequency range of 100-500 Hz. The effect of the local mode in the B+ region resulted in fracture occurring in the heatsink bolt area, as shown in Figure 4.
## 4 Method
### Local Atmospheric Light Estimation  
Using the global atmospheric light may lead to the unintended darkening of the image or color artifacts, particularly in complex scenarios. Consequently, it is essential to factor in local illumination disparities when estimating atmospheric light [34]. Drawing from this idea, we evenly divide the color-corrected sandstorm image into rectangular segments and estimate the atmospheric light in each block. Firstly, we calculate the dark channel of the image via Equation (6):  
\[I^{dark}_{cc}(x)=GuidedFilter\bigg{(}\min_{c\in\{R,G,B\}}\bigg{(}\min_{y\in \Omega(x)}(I^{c}_{cc}(y))\bigg{)},I_{cc}(x)\bigg{)}. \tag{6}\]  
Then, we select the brightest pixel in the dark channel as the local atmospheric light in each block. Upon performing patch-wise computations, a rough local airlight map is generated. However, patch-based calculations introduce noticeable block artifacts. To ensure consistency in local illumination while preserving edges, we refine the local atmospheric map using a guided filter [35]. The window radius of the guided filter is set to 60, while the regularization parameter is set to 0.001.  
\[A_{rl}(x)=GuidedFilter(A_{l}(x),I_{cc}(x)). \tag{7}\]  
Existing research [36] indicates that hazy conditions may increase the brightness components of images, leading to overestimated atmospheric light values compared to the actual atmospheric light. Consequently, images restored using the atmospheric scattering model often appear darker than the original hazy images. Sandstorm image restoration, which employs dehazing priors, faces similar challenges. In response to this challenge, we propose an illumination adjustment strategy to adjust the refined local atmospheric map, as shown in Equation (8):  
\[A_{adj}(x)=\text{max}\ (0.95\cdot A_{rl}(x),I_{cc}(x)), \tag{8}\]where \(A_{adj}(x)\) represents the adjusted local atmospheric light map. Sand-dust images often exhibit low illumination, requiring more illumination compensation than haze images. We selected 0.95 as a general compensation factor, as lower values can further enhance the image brightness but may also degrade the restoration performance due to reduced accuracy in atmospheric light. Additionally, since the dark channel prior assumes that the brightest pixel represents the atmospheric light value, the atmospheric light values should not be smaller than the pixel values within a local region. Therefore, we applied a max operation on the compensated atmospheric light map. Compared to the method in [34], our process delivers superior visual results, as depicted in Figure 3.
## 3 The Proposed Method  
When it comes to the time-varying interference, the classical time-frequency analysis based on WVD and RSPWVD provides superior performance. However, WVD suffers from cross-term interference seriously when the analytic signals have two or more components [20]. The RSPWVD can reduce the cross-term interference, but it cannot deal with the signals with crossed or overlapped frequency by the peak detection method [18]. An example of RSPWVD with two chirps whose frequencies are crossed is shown as Figure 1. Figure 1a depicts that the peaks of two chirps are clear except the overlapped frequency part. In Figure 1b, it is obvious that the outline of two chirps in the overlapped frequency is blurred so that it is difficult to distinguish which signal the frequency of the overlapped part belongs to. Therefore, the proposed method based on Hough transform of RSPWVD is introduced [21; 22; 23].  
Figure 1: The RSPWVD of two chirps with crossed frequency. (**a**) Mesh of RSPWVD. (**b**) Contour of RSPWVD.
# Characteristics of the Damping Ratio of Undisturbed Offshore Silty Clay in Eastern Guangdong, China
## 2 Experimental Method  
### Soil Sample Overview  
The silty clay samples used in this experimental study were collected from an offshore wind farm in eastern Guangdong at depths ranging from 16 to 18 m. The sampling location is situated in the southern waters of Gangkou Town, Huizhou City, with a water depth ranging from 31 to 40 m and approximately 22 km away from land. This type of soil is widely distributed in the offshore regions of Guangdong Province, making it highly representative. The soil samples used in this study were collected on-site using a vessel-mounted drilling rig with wave compensation capability, employing static pressure for sampling. The sampler has an outer diameter of 76 mm and an inner diameter of 72 mm, made of seamless steel pipe with a cutting edge. After the sampler is lifted to the deck, both ends are immediately secured and wax sealed to ensure the moisture content of the soil sample remains unchanged. During transport, the specially designed soil sample box is used for packaging to avoid disturbances to the samples. During testing, the soil sample is pushed out of the sampler using a bulldozer, and the tests are conducted promptly. A variety of basic physical property tests were conducted on the samples, providing data on parameters such as moisture content, saturated unit weight, plasticity index, and particle gradation. One-dimensional consolidation tests indicated an overconsolidation ratio (_OCR_) of 2 for the soil samples. The detailed test results are shown in Table 1
## 3 Proposed Method
### Acoustic Summary Statistics Extraction for ASC
#### 3.3.1 Unsupervised Novelty Analysis of Acoustic Scene  
Our goal is to enhance scene relevant acoustic patterns and to suppress scene-irrelevant events as well during summary statistics extraction. To this end, it is necessary to discern the two components in environmental sound clips. According to (1), two category sounds are linearly combined, we adopt (linear) subspace method to detect events in an acoustic scene in an unsupervised manner. Sound textures, which are composed of the superposition of many acoustic events with high similarity, will reside in principal acoustic subspace; on the contrary, scene-irrelevant events are anticipated to exhibit distinct distance to the subspace. The procedure is based on principal component analysis (PCA) [22] and we start the process from computing correlation matrix of input feature:  
\[C_{\mathbf{D}}=\frac{1}{N}\sum_{n=1}^{N}\mathbf{d}_{n}\mathbf{d}_{n}^{\top} \tag{4}\]  
where \(\mathbf{d}_{n},n\in[1,N]\) is acoustic feature vector extracted from one audio sample, i.e. 30 s clip, then, eigen decomposition is performed:  
\[\lambda\mathbf{v}=C_{\mathbf{D}}\mathbf{v} \tag{5}\]  
Figure 5: HOG coding scheme on spectro-temporal local regions.  
Let \(\mathbf{P}=[\mathbf{v}_{1},...,\mathbf{v}_{K^{\prime}}]\) denotes subspace accommodating predominant textures, which is composed of \(K^{\prime}\)-th eigen vectors with highest eigenvalues. \(K^{\prime}\) is determined by contribution rate which is defined as \(\eta^{\prime}_{K}=\sum_{k=1}^{K^{\prime}}\lambda_{k}/\sum_{k=1}^{K}\lambda_{k}\). The deviation distance to subspace can be computed by:  
\[h=\mathbf{d}^{\top}\mathbf{d}-\mathbf{d}^{\top}\mathbf{P}\mathbf{P}^{\top} \mathbf{d} \tag{6}\]  
By examining residual \(h\), we are able to detect outlier events in the acoustic scene. Sound textures, due to high temporal homogeneity, will generate \(h\) obeying normal distribution. In contrast, scene-irrelevant events superimposed on textures will introduce long tail to the histogram of \(h\), therefore, \(h\) can no longer be well described by Gaussian. Based on such property, we introduce Gaussianity measures of kurtosis and skewness to discern acoustic events, which are denoted as \(v_{kurt}(h)\) and \(v_{skew}(h)\), respectively. Thresholds on two measures, which are \(\varepsilon_{kurt},\varepsilon_{skew}\), are experimentally set to detect scene-irrelevant events. If there are no events detected by thresholding, uniform weights can be applied since there are mostly homogeneous textures. Otherwise, we develop weights to suppress scene-irrelevant events as follows.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 2 Materials and Methods
### Data Processing and Analysis
#### 2.4.2 ROC Curve Analysis  
The ROC curve is a comprehensive index of the specificity of reaction and the sensitivity of continuous variables [19]. This method reveals the reciprocal relationships between sensitivity and specificity by composition method (true positive rate (\(TPR\)) as vertical coordinate, false positive rate (\(FPR\)) as horizontal coordinate). For the two classification problem in this study, the classification results may appear in four cases as follows. Normal samples are correctly identified (true positive, \(TP\)), normal samples were misjudged as damage samples (false negative, \(FN\)), damaged samples were correctly identified (true negative, \(TN\)), and damaged samples were misjudged as normal samples (false positive, \(FP\)). For a given threshold, \(TPR\) and \(FPR\) can be calculated as follows:  
\[TPR=\frac{TP}{(TP+FN)} \tag{3}\]  
\[FPR=\frac{FP}{(FP+TN)} \tag{4}\]  
To a separate classifier, the change of the threshold is finally drawn into a ROC curve. The larger the area under the curve (AUC) is, the better the classification effect is. In this study, each wavelength of PCA is extracted as a classifier to draw the ROC curve, and two characteristic wavelengths were selected to establish the final PLS-DA model by comparing the AUC value of each classifier.
## 2 Thermodynamic Analysis
### Main Scope and Field of Application  
In the present paper, the achievable performance by two AdHT configurations will be comparatively analyzed; namely, one bed connected to one condenser/evaporator (Figure 3a) and two separated beds connected to one condenser and one evaporator (Figure 3b). Configuration II has the merits of:  
* Offering a quasi-continuous delivery of upgraded heat (with configuration I only during the adsorption phase);
* The possibility of implementing a heat recovery between the adsorber and the desorber beds allowing higher COP. Similarly to the case of adsorption heat pumps, this solution can be implemented without excessive technical complication to the system [22; 23].  
It has to be pointed out that the thermodynamic analysis can be conducted either considering the ideal case, in which no temperature gradient exists between external sources/sinks and the component, or considering a suitable temperature gradient. Accordingly, temperature inside each component will be evaluated as:  
\[\text{T}_{\text{A}}=\text{T}_{\text{A}}^{\prime}+\Delta\text{T} \tag{2}\]  
\[\text{T}_{0}=\text{T}_{0}^{\prime}+\Delta\text{T} \tag{3}\]  
\[\text{T}_{\text{L}}=\text{T}_{\text{L}}^{\prime}-\Delta\text{T} \tag{4}\]  
where \(\Delta\text{T}=0\) K in the ideal case and \(\Delta\text{T}>0\) K in real conditions. In the following, the thermodynamic model derived for both Configuration I and Configuration II is reported, which consists of heat and mass balance for each component. It is worth pointing out that the analysis presented here is intended as a first screening of different working pairs to identify the most suitable ones for different applications, mainly District Heating Networks (DHN) of recent construction (fourth generation) and low-grade industrial heat upgrade. Such a screening is a necessary step for the design of an AdHT, which needs to take into account also the dynamic aspects [8].
## 3 Results
### Phytochemical Analysis of Extracts  
The qualitative composition of biologically active compounds (BACs) in the soft extracts of _E. planum_ was studied using identification reactions and thin-layer chromatography (TLC). The soft extracts of the _E. planum_ herb contain amino acids, simple phenols, coumarins, flavonoids, hydroxycinnamic acids, tannins, and saponins.  
The HPLC method was used to identify and quantify phenolic compounds in the soft extracts of the _E. planum_ herb (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**N**} & \multirow{2}{*}{**Compound**} & \multicolumn{3}{c}{**Content in Dense Extract, \(\mu\)g/g**} \\  & & **EP1** & **EP2** & **EP3** \\ \hline \multicolumn{5}{c}{Hydrooxycinnamic acids *} \\ \hline
1 & Chlorogenic acid & 3945.81 \(\pm\) 174.12 & 4931.72 \(\pm\) 215.32 & 4843.06 \(\pm\) 92.07 \\
2 & Caffe acid & 1268.87 \(\pm\) 94.14 & 1492.33 \(\pm\) 87.12 & 1319.78 \(\pm\) 79.16 \\
3 & Syringic acid & 176.09 \(\pm\) 13.07 & 234.98 \(\pm\) 19.06 & 312.86 \(\pm\) 15.37 \\
4 & _p_-Coumaric acid & 418.61 \(\pm\) 21.16 & 531.85 \(\pm\) 16.43 & 591.77 \(\pm\) 28.92 \\
5 & _trans_-Ferulic acid & 729.98 \(\pm\) 31.88 & 910.11 \(\pm\) 29.19 & 937.32 \(\pm\) 51.06 \\
6 & Sinapic acid & 3011.84 \(\pm\) 156.18 & 3532.33 \(\pm\) 141.82 & 3319.96 \(\pm\) 138.46 \\
7 & _trans_-Cinnamic acid & 2358.07 \(\pm\) 93.61 & 2841.49 \(\pm\) 105.04 & 2909.52 \(\pm\) 135.47 \\ \hline \multicolumn{5}{c}{Flavonoids *} \\ \hline
8 & Rutin & 1382.762 \(\pm\) 102.04 & 2412.37 \(\pm\) 92.08 & 2631.62 \(\pm\) 108.37 \\
9 & Quercetin-3-O- & 509.71 \(\pm\) 25.07 & 917.74 \(\pm\) 37.05 & 993.52 \(\pm\) 47.08 \\
10 & Naringenin & 2944.71 \(\pm\) 172.73 & 4430.62 \(\pm\) 201.42 & 4873.07 \(\pm\) 212.92 \\
11 & Neobsperidin & 989.72 \(\pm\) 39.09 & 2091.52 \(\pm\) 87.41 & 2313.43 \(\pm\) 93.36 \\
12 & Quercetin & 697.92 \(\pm\) 29.93 & 1393.55 \(\pm\) 65.12 & 1493.72 \(\pm\) 72.17 \\
13 & Kaempfer & 919.23 \(\pm\) 58.39 & 1801.17 \(\pm\) 11.03 & 1945.91 \(\pm\) 74.32 \\ \hline \multicolumn{5}{c}{Tannin metabolites **} \\ \hline
14 & Epicatechin & 1507.72 \(\pm\) 82.06 & 1420.08 \(\pm\) 73.93 & 1274.58 \(\pm\) 59.37 \\
15 & Epicatechin gallate & 431.25 \(\pm\) 15.03 & 368.42 \(\pm\) 10.96 & 331.09 \(\pm\) 21.12 \\
16 & Gallocatechin & 1607.71 \(\pm\) 57.86 & 1525.03 \(\pm\) 65.41 & 1318.62 \(\pm\) 45.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Content of phenolic compounds in the soft extracts of the _Eryngiium planum_ herb.  
In total, 16 phenolic compounds were identified in the soft extracts of the _E. planum_ herb. The dominant hydroxycinnamic acids in the extracts were chlorogenic acid, sinapic acid, and _trans_-cinnamic acid. Six flavonoids were identified and quantified in the soft extracts, with naringenin being the most abundant. Among the tannin metabolites, the dominant compounds were epicatechin and gallocatechin.
## 4 Experimental Results
### Datasets
#### 4.1.2 DeepGlobe Road Dataset  
Similarly, we used the DeepGlobe dataset [33] as the second dataset to test the performance of our proposed method. It consists of 6226 images with their corresponding road-extracted masks, with a resolution of \(1024\times 1024\). To maintain consistency while considering computational demands, we cropped the images and masks into \(512\times 512\) tiles, resulting in 24,904 images and 24,904 masks. Due to memory constraints, we selected 9000 images and their corresponding masks and divided the dataset using an 80:20 split into training and testing sets.
## 4 Results and Discussion
### Results
#### 4.2.4 Employment-Inducing Effects  
The results of assessing the employment-inducing effects of the NG supply sector are presented in Table 5. In South Korea, the employment-inducing effect due to the investment of USD 1 billion in the NG supply sector was analyzed to be 343.5 people in other industries and 459.9 people in its own industry, for a total of 803.4 people. On the other hand, in Japan, the employment-inducing effects due to the investment of USD 1 billion in the NG supply sector were analyzed to be 624.1 people in other industries and 1350.6 people in its own industry, for a total of 1974.7 people. Specifically, in South Korea, the employment-inducing effects were large in the order of \"26. Business support services\", \"2. Mined and quarried goods\", and \"23. Finance and insurance\". In Japan, it was analyzed that the employment inducing effects were high in the order of \"2. Mined and quarried goods\", \"20. Transportation\", and \"6. Petroleum and coal products\".