esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## Appendix A Blur Measure Operators
### Miscellaneous Operators  
\([MIS01]\) _Brenner's Operator_  
A blur measure for a pixel \((x,y)\) can be derived from Brenner [67] by taking horizontal and vertical squared second differences of gray-levels in an image neighborhood window \(\Omega(x,y)\)  
\[M_{BRE}(x,y)=\sum_{(i,j)\in\Omega(x,y)}[max\{I(i,j)-I(i-2,j),I(i,j)-I(i,j-2)\} ]^{2}. \tag{103}\]  
\([MIS02]\) _Image Contrast_  
[PERSON] et al.'s operator [72] can be used as a blur measure by calculating image contrast for a pixel \((x,y)\) in a local neighborhood window \(\Omega(x,y)\) as given by  
\[M_{CON}(x,y)=\sum_{(i,j)\in\Omega(x,y)}C(i,j), \tag{104}\]  
where \(C(i,j)=\sum_{d=i-1}^{i+1}\sum_{m=j-1}^{j+1}|I(i,j)-I(l,m)|\) is the image contrast for pixel \((x,y)\).  
\([MIS03]\) _Image Curvature_  
[PERSON] and [PERSON] [73] interpolated the gray-levels of the image through a quadratic surface. The curvature of this surface can be used as a blur measure  
\[M_{CUR}(x,y)=|c_{0}|+|c_{1}|+|c_{2}|+|c_{3}|, \tag{105}\]where \(c_{i}\)'s are the coefficients of the surface.  
\([MIS04]\) _Steerable Filters-Based Measure_  
The filtered image obtained by applying steerable filters [74] on the image can be considered for the blur map estimation  
\[M_{STF}(x,y)=\sum_{(i,j)\in\Omega(x,y)}I_{f}(i,j), \tag{10}\]  
where \(I_{f}(i,j)\) is the maximum response out of the \(N\) responses obtained by applying filters.  
\([MIS05]\) _Spatial Frequency_  
The operator of [75] can be considered as the blur measure  
\[M_{SF}(x,y)=\sqrt{\sum_{(i,j)\in\Omega(x,y)}I_{x}(i,j)^{2}+\sum_{(i,j)\in\Omega( x,y)}I_{y}(i,j)^{2}}, \tag{11}\]  
where \(I_{x}\) and \(I_{y}\) are the derivatives of the image in \(x\) and \(y\) directions respectively.  
\([MIS06]\) _Vollath's Autocorrelation_  
Image autocorrelation-based operator proposed by [67] for auto-focusing can be utilized for blur measurement  
\[M_{VOLL}(x,y)=\sum_{(i,j)\in\Omega(x,y)}I(i,j).I(i+1,j)-\sum_{(i,j)\in\Omega(x,y)}I(i,j).I(i+2,j). \tag{12}\]
## Example Analysis
### Analysis of Prediction Results
Table 6 contains the results of the \(p\)-value for the hypothesis test between EN-GMDH and the other models. The \(p\)-value shown in Table 6 indicates that the difference between the EN-GMDH and other mainstream forecasting models is significant, except for SVM, where a \(p\)-value of 0.163 was obtained. Furthermore, the difference between the models is not equal. GMDH and RR-GMDH performed well in part of the data, but in others, GMDH prediction accuracy declined because GMDH only has the function of selecting variables and does not have the ability to handle variables. When the input variables were linearly correlated and the data were excessively complex, the possible overfitting and multicollinearity will reduce the prediction accuracy of GMDH. The Lasso-GMDH prediction effect in the majority of the data of performances is poorer partially due to the way that variables are eliminated by lasso to compress the input variables. The double elimination of variables by Lasso and GMDH will excessively delete useful information and fail to solve the problem of linear correlation of input variables, resulting in poor prediction results. BP performed better than Lasso-GMDH in the majority of sample data. BP needs to preset multiple parameters in advance, and the selection of parameters affects the accuracy of load forecasting. SVM performs well in the sample data. Similarly, different parameters in SVM have different effects on the prediction results. In BP and SVM, parameter selection has a great influence on the accuracy of load forecasting results, while GMDH and its improved methods avoid this deficiency. In summary, the prediction accuracy of EN-GMDH has been greatly improved, and the evaluation metrics of EN-GMDH indicates that it has excellent performance in short-term load forecasting. EN-GMDH can cope with the complex and changeable prediction environment and meet the prediction requirements of multiple input variables. However, there are some shortcomings of EN-GMDH. Due to the principle of GMDH, the number of characteristic variables selected by EN-GMDH is limited. Although the elastic net evaluates all input variables when estimating polynomial coefficients, when there are multiple input variables that have a large influence on the prediction results, the final output of EN-GMDH will ignore the input variable of the second influence variable, which will result in a small error. By changing the order of the K-G polynomial, increasing the number of input variables can solve this problem, but the amount of computation will be slightly larger, which is where the algorithm needs further improvement. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & \(p\)**-Values** \\ \hline EN-GMDH \& GMDH & 0.035 \\ EN-GMDH \& Lasso-GMDH & 0.014 \\ EN-GMDH \& RR-GMDH & 0.047 \\ EN-GMDH \& BP & 0.025 \\ EN-GMDH \& SVM & 0.163 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(p\)-value.
# On Resolvability- and Domination-Related Parameters of Complete Multipartite Graphs
## 3 Resolvability-Related Parameters
### Metric Dimension
For \(i=1,\ldots,k\), let \(T_{i}\setminus R=\{x_{i}\}\) such that \(V(K_{t_{1},t_{2},\ldots,t_{k}})\setminus R=\{x_{1},\ldots,x_{k}\}\). Note that for every \(i=1,\ldots,k\), we have:  
\[r_{R}(x_{i}) = (c_{1},c_{2},\ldots,c_{i},\ldots,c_{k})\text{ where}\] \[c_{j} = (\underbrace{1,1,\ldots,1}_{t_{j}-1\text{ times}})\text{ for }i\
eq j=1, \ldots,k\text{ and} \tag{3}\] \[c_{i} = (\underbrace{2,2,\ldots,2}_{t_{i}-1\text{ times}}).\]  
Clearly, \(r_{R}(x_{i})\
eq r_{R}(x_{j})\), where \(1\leq i,j\leq k\) and \(i\
eq j\). Now by adding \((k+1)\)th partite set, say, \(T_{k+1}\) to \(K_{t_{1},\ldots,t_{k}}\), we obtain \(K_{t_{1},\ldots,t_{k},t_{k+1}}\). Let \(R^{\prime}\) be the union of \(R\) and any of the \(t_{k+1}-1\) vertices of \(T_{k+1}\). Assume that \(T_{k+1}\setminus R^{\prime}=\{x_{k+1}\}\). Then, we have \(r_{R^{\prime}}(x_{i})=(c_{1},c_{2},\ldots,c_{i},\ldots,c_{k},c_{k+1})\), where \(c_{j}=(\underbrace{1,1,\ldots,1}_{t_{j}-1\text{ times}})\) for \(i\
eq j=1\)\(1,\ldots,k+1\) and \(c_{i}=(\underbrace{2,2,\ldots,2}_{t_{i}-1\text{ times}})\). Then, with (3), we obtain that \(r_{R^{\prime}}(x_{i})\
eq r_{R^{\prime}}(x_{j})\), where \(1\leq i,j\leq k+1\) and \(i\
eq j\). This shows that \(R^{\prime}\) is a resolving set and its minimum since \(R\) is minimum. Thus:  
\[\beta(K_{t_{1},t_{2},\ldots,t_{k+1}})=\sum\limits_{i=1}^{k+1}t_{i}-(k+1).\]  
By the induction hypothesis, the proof is finished. The following example illustrates an application of Theorem 2. **Example 1**. : _Let \(K_{2,3,5}\) be the complete multipartite graph as depicted in Figure 3. Then, its seven black vertices form a metric generator. By Theorem 2, we obtain that \(\beta(K_{2,3,5})=10-3=7\)._  
It is important to remark the following:  
**Remark 1**. : _Note that Theorem 2 generalizes a result of [PERSON] et al. [58], who computed bounds on the metric dimension of complete multipartite graphs._
# Shear Capacity of Textile-Reinforced Concrete Slabs without Shear Reinforcement
## 2 Experimental Investigation on Shear Capacity  
### Test Setup and Instrumentation  
For the experimental study of the shear capacity, single-span slab segments with single loading in mid-span were tested. The test setup was variable and scalable, which allowed a systematic parameter study. Figure 1 shows the test setup. The load was introduced under displacement control by means of a steel roll along the width of the specimen. An elastomer strip prevented local stress concentration on the upper surfaces of the specimens. The load rate was chosen to 1 mm/min. One fixed and one free roll were used to guarantee vertical support without horizontal constraints.  
The vertical displacements were measured by two linear variable displacement transducers (LVDT) at mid-span. The longitudinal strain was tracked by a concrete strain gauge on top of the specimen in mid-span between two steel load-introduction plates and by an LVDT with a measuring length of 25 cm fixed to the bottom of the specimen. The load was measured continuously by a built-in load cell of the electric testing machine (100 kN maximum load capacity).
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 4 Combination of Optical and Thermal Remote Sensing
### Simple Integrations  
The most straightforward approach is to construct a drought index using VI and LST at the same time. [PERSON] et al. [202; 203] first used the ratio of LST to NDVI as an indicator of surface soil moisture, which was later named the Vegetation Supply Water Index (VSWI) and has been widely applied as a drought index [204; 205; 206].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Index** & **Expression** & **Notes** & **Year Introduced** & **Applications** \\ \hline Apparent Thermal Inertia (ATI) & \(\dfrac{C(1-a)}{T_{\mathrm{day}}-T_{\mathrm{night}}}\) & \(\dfrac{\mathrm{d}C}{\mathrm{is}}\) a constant coefficient, \(a\) is the surface albedo, and \(T_{\mathrm{day}}\) and \(T_{\mathrm{night}}\) are day/night LST & 1985 [171] & China [192], Thailand [165] \\ \hline Normalized Difference Temperature Index (NDTI) & \(\dfrac{T_{\infty}-T_{\mathrm{s}}}{T_{\infty}-T_{0}}\) & \(\dfrac{T_{\infty}-T_{0}}{T_{0}}\) & \(\dfrac{\mathrm{c}C}{\mathrm{is}}\) a constant coefficient, \(a\) is the surface albedo, and \(T_{\mathrm{day}}\) and \(T_{\mathrm{night}}\) are day/night LST & 1992 [188] & Australia [187] \\ \hline Temperature Condition Index (TCI) & \(100\dfrac{T_{\mathrm{max}}-T}{T_{\mathrm{max}}-T_{\mathrm{min}}}\) & \(\dfrac{T}{T}\) is the smoothed weekly temperature, and \(T_{\mathrm{max}}\) and \(T_{\mathrm{min}}\) are the multi-year maximum and minimum & 1995 [102] & U.S. [102] \\ \hline Temperature Rise Index (TRI) & \(\dfrac{(\mathrm{d}T/\mathrm{d}t)_{\mathrm{max}}-(\mathrm{d}T/\mathrm{d}t)_{i}} \dfrac{(\mathrm{d}T/\mathrm{d}t)_{\mathrm{max}}}{(\mathrm{d}T/\mathrm{d}t)_{ \mathrm{max}}-(\mathrm{d}T/\mathrm{d}t)_{\mathrm{min}}}\) & \(\dfrac{(\mathrm{d}T/\mathrm{d}t)_{\mathrm{min}}}{(\mathrm{d}T/\mathrm{d}t)_{ \mathrm{max}}-(\mathrm{d}T/\mathrm{d}t)_{\mathrm{min}}}\) & 2020 [191] & Australia [191] \\ \hline \end{tabular}
\end{table}
Table 3: Summary of thermal indices suitable for agricultural drought monitoring.  
Based on VCI and TCI, the Vegetation Health Index (VHI) was proposed by [PERSON] [102; 186; 207; 208]. It was originally named V/TCI in Kogan [102], VCI/T4 in Kogan [186], and VTI in Kogan [207]. VHI is a linear mixture of VCI and TCI:  
\[\text{VHI}=\alpha\text{VCI}+(1-\alpha)\text{TCI} \tag{15}\]  
In [PERSON] [102], \(\alpha=0.7\) was suggested, while in most later works, \(\alpha=0.5\) was used as a default value [184; 186].  
[PERSON] [102] mentioned that the weighting coefficient \(\alpha\) could be calibrated via correlation analysis on the validation dataset but did not provide a practical scheme. [PERSON] et al. [183; 185] proposed a reference-based method, in which the Standardized Precipitation-Evapotranspiration Index (SPEI) was used as a reference to estimate the value of \(\alpha\). From their findings, VCI could dominate as high as 80% for dry land pixels, which suggests that a larger \(\alpha\) should be chosen for drought monitoring.
## 3 Case Validation: Typhoon Haikui  
Typhoon Haikui not only serves as the case study in this paper, but the methods used are also applicable to situational awareness for other disasters, such as floods, earthquakes, and landslides, making them broadly representative. The effectiveness of the method was tested during Typhoon Haikui from 3 September to 10 September 2023. This paper uses the severe damage caused by Typhoon Haikui in Fuzhou, Fujian Province, as an example to explore the application of multimodal social media data in disaster situational awareness. Haikui made landfall in Dongshan County, Fujian, on 5 September, bringing extreme precipitation that broke multiple historical records and resulted in significant damage. The intensity of precipitation in Fuzhou was influenced by multiple meteorological factors, leading to prolonged heavy rainfall and exacerbating the severity of the disaster.
## 3 Operation of the Proposed Converter
### Operation Principle during the SIDO Mode
By applying the voltage balance principle on the inductors L3, the following equation can be obtained:  
\[\text{d}_{5}\text{V}_{\text{PV}}+(1-\text{d}_{5})(\text{V}_{\text{PV}}-\text{V }_{\text{O}})=0 \tag{3}\]  
Therefore  
\[\text{V}_{\text{PV}}=(1-\text{d}_{5})\text{V}_{\text{O}} \tag{4}\]  
where d\({}_{5}\) is the duty cycle of the PV stage switch S5, and for a given bus voltage V\({}_{\text{O}}\) is dependent only on PV voltage to enable extracting maximum power from the PV. Figure 3: Possible operation modes of the MPC: (**a**) SIDO mode; (**b**) SITO mode; (**c**) DISO-I mode; (**d**) TISO mode; (**e**) SISO mode and (**f**) DISO-II mode. Similarly, by applying the voltage balance principle on the inductors L2, the following equation can be obtained:  
\[\mathrm{d_{3}V_{batt}}-(1-\mathrm{d_{5}})\mathrm{V_{O}}=0 \tag{5}\]  
Therefore  
\[\mathrm{V_{O}}=\frac{\mathrm{d_{3}}}{(1-\mathrm{d_{5}})}\mathrm{V_{batt}} \tag{6}\]  
where \(\mathrm{d_{3}}\) is the duty cycle of the switch S3, which, for a particular \(\mathrm{d_{5}}\) and bus voltage, is dependent only on the battery voltage.
## 3 Application to the Resource Allocation Problem in the Mobile Harbor System
### Operation Specification and Cost  
In this section, we discuss the scenario of the Surabaya port numerical experiment by using a queuing model and simulation. Based on the expected throughput pattern, we considered that a new port system will be constructed to serve container ships with capacities between 1000 and 5999 TEU. The remaining ships will be served by the existing terminal and thus will not be considered in our study. Two types of mobile harbors are experimented on. The operation speed and cost of the mobile harbor was calculated in the 2011 mobile harbor project report provided by the KAIST mobile harbor team. We will hereafter refer to this report as MH report 2010. The cost of the mobile harbor consists of initial construction cost and operation costs such as labor, maintenance, power, and insurance. Operational speed and cost information is specified in Table 2. We developed four MH designs characterized by capacity and operational speed at land berth. Travel time between two places was calculated by using the 40 km channel distance and the travel speed of the mobile harbor. The ratio of 20 feet containers to 40 feet containers is 1:1 for the loading or unloading speed. RORO resource cost accounts for the lower part of the overall cost. Also, RORO resource cost (AGV or train) belong to the MH cost. Berth is just infrastructure to utilize these RORO resources.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 7 Applications and Contributions
### Real-World Case Study and Promising Investments
Zero Energy Cities and Energy Independence: The region can become more energy independent by generating its electricity from renewable sources, reducing its vulnerability to global energy market fluctuations. 4. Economic Benefits: Large-scale wind energy projects can create jobs, stimulate economic growth, and attract investment in the region. 5. Regional Cooperation: Developing wind energy infrastructure can foster regional cooperation and collaboration in addressing shared energy challenges.
# Effect of Injecting Epoxy Resin Adhesive into Cement Mortar on Tile Adhesion Performance
## 2 Materials and Methods  
### Materials and Mixture Proportions  
Epoxy resin comprising diglycidyl ether of bisphenol A (DGEBA, ~189 g/eq, 11,500 cps) as the primary ingredient and a curing agent comprising amino amine as the main ingredient was used. Both the epoxy resin and the curing agent were purchased from Epoxy Industrial Co. (Incheon, Korea). A xylene-type solvent was purchased from GS Caltex (Seoul, Korea).  
The epoxy resin and the curing agent were mixed in a 2:1 ratio. Various solvent mixing ratios (0, 5, 10, and 15 wt%) were used to evaluate the penetration; based on the evaluation result, 5 wt% solvent was used for subsequent studies. Dry ready-mixed cement (DM) and adhesive (AM) mortars, which are popular in South Korea, were used; their compositions are listed in Table 1. AM and DM were mixed with water in a 5:1 ratio. Commercial ceramic, porcelain, and polished tiles suitable for KS (Korean Industrial Standards) L 1001 were used.
## 2 Materials and Methods
### Land Surface Models (LSMs)  
To understand the potential of SIF data to benchmark and constrain transpiration in state-of-the-art LSMs and hydrological models, the \(\tau\) resulting from a range of offline model runs is also compared against in situ \(\tau\) estimates. These runs come from the earthH2 Observe Tier-2 database, which provides water resource reanalysis datasets at a global scale [57]. Model data comes at 0.25\({}^{\circ}\) spatial resolution forced by the WATCH Forcing Data methodology applied to ERA-Interim reanalysis (WFDEI) and  
Figure 1: The hypothesis. Conceptual diagram of the expected surface energy balance and photosynthetically active radiation (PAR) partitioning in response to phenological and physiological constraints. Variables represent the absorbed PAR (i.e., aPAR), solar-induced fluorescence (SIF), non-radiative decay and non-photochemical quenching (Q), gross primary production (GPP), net radiation (R\({}_{\text{n}}\)), latent heat (\(\lambda\)E), sensible heat (H) and ground heat flux (G).  
the Multi-Source Weighted-Ensemble Precipitation (MSWEP) [70]. Again, \(\tau\) for each model is approximated by the ratio of T to E\({}_{\text{P}}\) (Equation (2)). All eartH2 Observe models provide offline estimates of T, and while some provide estimates of E\({}_{\text{P}}\), inconsistencies in the definition of E\({}_{\text{P}}\) across models makes their inter-comparison difficult. For a fair comparison between models and in situ measurements, we calculate potential evaporation using the same approach as for the towers.  
In total, six models from the eartH2 Observe database were used: the Global Land Evaporation Amsterdam Model (GLEAM) v3a [26; 71], the WorldWide Water Resources Assessment (W3 RA) based on the hydrology component of the Australian Water Resource Assessment Landscape model (AWRA-L) version 1 [72; 73], the Hydrology Tiled European Centre for Medium-Range Weather Forecasts (ECMWF) Scheme for Surface Exchanges over Land--Catchment-based Macro-scale Floodplain model (HTESSEL-CaMa) [74; 75], the Joint United Kingdom Land Environment Simulator (JULES) [76], the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) [77], and the surface _externalisee_ with TRIP river routing (SURFEX-TRIP) [78; 79]. The latter four are LSMs, W3 RA is a simplified LSM that is often considered to be a hydrologic model, and GLEAM is a remote sensing-based evaporation retrieval model. Once again, GLOBSNOW snow water equivalent data are used to mask for snow, and MSWEP is used to mask out rain days to avoid the effects of interception.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 3 Resistance of Concrete to External Chloride in the Presence of Sulphate
### Effect of Cation Type  
Studies have shown that the resistance of concrete may vary depending on the nature of cation in the attacking compound. For instance, magnesium sulphate in seawater and other sources is more destructive to cements than sodium sulphate, due to the formation of Mg(OH)\({}_{2}\) with relatively low solubility and pH. The C-S-H phase becomes destabilized as Ca is substituted for Mg, leading to a lack of cohesion [142; 154; 155; 156]. Similarly, studies have shown that exposure to MgCl\({}_{2}\) and CaCl\({}_{2}\) tends to bind more chlorides than exposure to NaCl. This may be attributed to changes in pH of the exposure solution and the chloride binding capacity of the C-S-H phase [55; 157].
# Deep Subspace Clustering with Block Diagonal Constraint
## 3 Experiments
### Experimental Settings
#### 3.1.3 Training Strategy and Parameter Settings  
During model training, we first use the convolutional autoencoder as the pre-training model to initialize the network parameters \(\Theta\), and then the \(\Theta\) is used as the initial parameter of fine-tuning.  
During pre-training, we remove the self-expression module, and the pre-trained network degenerates into the simplest convolutional auto-encoder. The output \(X^{{}^{\prime}}\) of the decoder is only a function of \(\{\theta_{e},\theta_{d}\}\). We pre-train the network by minimizing the following reconstruction error:  
\[\mathcal{L}(\theta_{e},\theta_{d})=\frac{1}{2}\|X-X^{{}^{\prime}}\|_{F}^{2}. \tag{21}\]  
When the convolutional auto-encoder is trained, we add a self-expression module, and at the same time extract the parameters of the self-expression module, which is the coefficient matrix \(Z\in\mathbb{R}^{N\times N}\). The output \(X^{{}^{\prime}}\) of the decoder is only a function of \(\{\theta_{e},\theta_{s},\theta_{d}\}\). This is the deep subspace clustering network, which can be trained by minimizing the following loss:  
\[\mathcal{L}(\theta_{e},\theta_{s},\theta_{d})=\frac{1}{2}\|X-X^{{}^{\prime}}\| _{F}^{2}+\frac{1}{2}\|F-FZ\|_{F}^{2}+\frac{\alpha_{1}}{2}\|Z\|_{F}^{2}. \tag{22}\]  
For the convolutional layers, the stride of kernel is 2 in both horizontal and vertical directions, and nonlinear activation is the Rectified Linear Unit (ReLU). At the same time, the learning rate is set as 0.001 in all experiments.  
For all databases, the kernel size in convolutional layers is set to 3 \(\times\) 3. For Extended Yale B database, we set the channels as 20. In the fine-tuning stage, we set \(\alpha_{1}=2\), \(\alpha_{2}=40\), \(\alpha_{3}=1.25\), \(\alpha_{4}=2\) and \(\epsilon=1\times 10^{-6}\) and run 450 epochs for DSC-BDC. For ORL database, we set the channels as 3-3-5-5-3-3. In the fine-tuning stage, we set \(epoch=700\), \(\alpha_{1}=2\), \(\alpha_{2}=40\), \(\alpha_{3}=1.25\), \(\alpha_{4}=2\) and \(\epsilon=1\times 10^{-6}\). In addition, for PIE database, we set the number of channels to 20 and set \(epoch=450\), \(\alpha_{1}=1\), \(\alpha_{2}=40\), \(\alpha_{3}=1\), \(\alpha_{4}=2\) and \(\epsilon=1\times 10^{-6}\). On COIL20 database, the number of channels is 15, and the parameters are consistent with PIE databases except that \(epoch=40\).