esa-sceva/satcom-chunk-collection huggingface dataset:
# NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency
## 4 Experiments and Analysis
### Visual Comparisons of Superpixel Results  
Figure 7 illustrates several subjective results for visual comparison of superpixels obtained by the above four algorithms. Benefiting from the outstanding performance of conventional SNIC, ANIC, ENIC and NICE all present relatively compact and uniform partitions. Theologically, the results of ANIC and SNIC, as well as ENIC and NICE are supposed to be the same, since AIR merely speeds up the sorting efficiency without any modification on clustering. Intuitively, it is hard to find a clear difference between them. Whereas some neighboring pixels with the same feature distance are pushed by a different order, the fluctuations in the convergence process may result in local bias on several superpixels (e.g., the worm tail). In addition, EIR alleviates the inevitably shape regulation in SNIC by color correlation. Therefore, both ENIC and NICE superpixels provide more accurate outline detection on complex object shape and weak boundaries.  
Figure 7: Visual comparison of superpixels with \(K=200\) on BSDS500. (**a**) SNIC; (**b**) Accelerated Implementation based on Recursion (AIR)-based Non-Iterative Clustering (ANIC); (**c**) EIR-based Non-Iterative Clustering (ENIC); (**d**) non-iterative clustering with efficiency (NICE). Alternating rows show each segmented image followed by local details of each image.
###### Abstract  
Anthracycllines, including epirubicin (Epi), are effective chemotherapeutics but are known for their cardiotoxic side effects, primarily inducing cardiomyocyte apoptosis. This study investigates the protective role of hesperetin (HSP) against cardiomyopathy triggered by Epi in a murine model. Male CD1 mice were divided into four groups, with the Epi group receiving a cumulative dose of 12 mg/kg intraperitoneallyally, reflecting a clinically relevant dosage. The co-treatment group received 100 mg/kg of HSP daily for 13 days. After the treatment period, mice were euthanized, and heart tissues were collected for histopathological, immunofluorescence/immunohistochemistry, and transmission electron microscopy (TEM) analyses. Histologically, Epi treatment led to cytoplasmic vacuolization, myofibril loss, and fiber disarray, while co-treatment with HSP preserved cardiac structure. Immunofluorescent analysis of Bcl-2 family proteins revealed Epi-induced upregulation of the pro-apoptotic protein Bax and a decrease in anti-apoptotic Bcl-2, which HSP treatment reversed. TEM observations confirmed the preservation of mitochondrial ultrastructure with HSP treatment. Moreover, in situ detection of DNA fragmentation highlighted a decrease in apoptotic nuclei with HSP treatment. In conclusion, HSP demonstrates a protective effect against Epi-induced cardiac injury and apoptosis, suggesting its potential as an adjunctive therapy in anthracycline-induced cardiomyopathy. Further studies, including chronic cardiotoxicity models and clinical trials, are warranted to optimize its therapeutic application in Epi-related cardiac dysfunction.  
epirubicin; hesperetin; cardiac toxicity
## 2 Methodology for Implementing the SaaS Solution-Coestim
### Fuzzy Logic Techniques  
To give a more accurate example, a schematic diagram has been outlined showing the use of fuzzy logic, which is composed of four main stages, namely, fuzzification, fuzzy rule base, fuzzy inference engine, and also defuzzification presented in the logic diagram in Figure 3. We can say that in the fuzzification stage, numerical maps are introduced in the form of fuzzy data sets associated with a recursive value.  
When a fuzzy set, \(O\), is defined on the set \(X\), the domain of the fuzzy variable is characterized by its belonging to \(\mu_{U}(x)\), being in the range [0,1], expressed as follows in Equation (3):  
\[O=\{(x,\mu_{U}(x))\backslash x\in X,\ \mu_{U}(x)\in[0,1]\} \tag{3}\]  
Therefore the membership function is defined for any unclear set, applying it also to the input parameters in order to determine the degree of truth. According to fuzzy principles, see Figure 3, the way to acquire basic information is at the beginning of every algorithm for building an expert system. We can say that the properties imposed in the form of IF-THEN rules can be assimilated from the analyzed data. The accumulation of well-defined rules  
Figure 3: Illustration of the general diagram of a fuzzy system.  
and properties builds a fuzzy system that is based on these characteristics. Any rule starts from a premise invoked by \"If\" and \"Then\", the existence of logical operators such as \"And, Or, No\", is used for the connection depending on the rules applied to the parties. We can say that the last step in such a process is Defuzzification, it has the role of converting the unclear data sets that are output to a clear value [29]. This step can be performed by several methods, and the most used is exemplified by the Equation (4):  
\[Centroid=\frac{\int_{x}\mu_{u}(x)xdx}{\int_{x}\mu_{u}(x)dx} \tag{4}\]
## 4 Geomechanics Modelling Establishment of Different Lithologies  
### Construction of Geometric Models  
As a discrete element method code, 3 DEC (7.0) software is used to solve the responses of discontinuous media under static or dynamic loading. Its built-in FISH function permits the user to develop user-defined functions and variables to cater to a wide range of simulation demands. The bonded-block model (BBM) is used in this study to simulate the rock mass, which is built by bonding a series of polygonal blocks (tetrahedral blocks are used in this study) through the contact surfaces. The BBM technique allows the computational model to be discretized into a collection of discrete blocks cut by a finite structural plane, thus capturing the macro-mechanical features of the rock mass while reflecting the micro-behavior of the friction between the smaller blocks [25].  
To study the mechanical behavior of surrounding rocks in deep-buried roadways traversing multi-stratigraphic tectonics and to assess the rockburst risk in the vicinity of the traversing stratigraphic boundary, the working conditions of the developing entry through the C\({}_{1}\)b (limestone) and D\({}_{3}\)zg (dolomite) at the same time near the 14# exploration line of the 1104 middle section (depth-buried 1276 m) of the Huize lead-zinc mine were taken as the background for the research. The location of the corresponding three-dimensional geological model is marked in Figure 8. The shape and size of the actual excavated roadway are simplified, considering Saint-Venant's Principle and the scope of excavation influence, to eliminate the boundary effect generated by the calculation. The calculation model was established with dimensions of 30 m \(\times\) 30 m \(\times\) 40 m. The shape of the roadway is a straight-walled arch with dimensions of 4 m \(\times\) 4 m, and the numerical model of the roadway excavation is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rockburst Potential Criterion**} & \multirow{2}{*}{**Lithology**} & \multicolumn{4}{c}{**Brittleness Index Criterion**} \\ \cline{3-6}  & & & **Expression** & **Value** & **Rockburst Classification** \\ \hline Brittleness index criterion [28] & D\({}_{3}\)zg & \({}_{1}\)d & \(\frac{UCS}{UTS}=\left\{\begin{array}{ll}\mathcal{<}2.2&\text{No Rockburst}\\ 7.2\sim 16.5&\text{Light Rockburst}\\ 16.5\sim 22.5&\text{Moderate Rockburst}\\ >22.5&\text{Serious Rockburst}\end{array}\right.\) & 16.55 & Moderate \\ \hline \multicolumn{6}{c}{Maximum stored elastic strain energy index} \\ \cline{3-6}  & & Expression & Value & \(\begin{array}{ll}\text{Rockburst}\\ \text{classification}\end{array}\) \\ \hline Energy index criteria [10] & D\({}_{3}\)zg & \({}_{1}\)d & \(\frac{UCS^{2}}{2\hbar}=\left\{\begin{array}{ll}<40&\text{No Rockburst}\\ 40\sim 100&\text{Light Rockburst}\\ 100\sim 200&\text{Moderate Rockburst}\\ >200&\text{Serious Rockburst}\end{array}\right.\) & 79.37 & Light \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of rockburst potential of different stratigraphic lithologies.
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
## 4 Results and Analysis
### Load Strain Curve of Composite Floor  
Except for the strain gauges at the midspan position, other strain gauges were arranged symmetrically, so only one side load strain curve was drawn, as shown in Figure 12.  
As can be seen from Figure 12a, at the quarter point of the bottom of composite floor, the cavity steel plate was compressed and the strain increased linearly under the load \(<\)126 kN, indicating that the steel plate at the quarter point was basically in the elastic deformation; when the load was greater than 126 kN, the increasing rate of the strain versus load increased, which suggests the change to plastic deformation.  
Strain gauge No. 6-10 recorded the variation of strain at the third point of the bottom of composite floor. Before the load reached 106 kN, the strain maintained linear growth, and the steel plate was basically in the elastic deformation stage; when the loading continued to increase, the strain increased rapidly, and the cavity steel plate began to develop plastic deformation. Finally, the strain stopped increasing and the slope of the curve tended to zero. At this stage, the composite floor could no longer bear the load and fails.  
The gauge No. 11-15 monitored the strain at the mid span position of the composite floor. The steel plate in the middle of the span was basically in the elastic deformation stage under a load \(<\)96 kN.  
Figure 12: Load-strain curve of composite floor: (**a**) no. 1–5 strain gauge; (**b**) no. 6–10 strain gauge; (**c**) no. 11–15 strain gauge and (**d**) no. 26–33 strain gauge.  
Beyond the critical value (96 kN), the strain increased rapidly, the plastic deformation took place. Finally, the strain stopped increasing and the composite floor failed under greater load.  
No. 26, No. 27, No. 30 and No. 31 strain gauges were attached to the upper side of the composite floor. Before the load reached 96 kN, the four strain gauges were in compression state. When the loading continued, the side of the shell steel plate buckled and the shell steel plate stretched outward, making the strain gauge change from compression state to tension state. For the strain gauges attached to the lower part of the composite floor, they are always in the tensile state. When the loading continued to increase, the plastic deformation of the steel plate began to occur, so the strain increased rapidly. Finally, the slope of the load-strain curve tended to zero, which indicated that the composite floor could no longer bear the load and the floor fails. These conclusions conform to the experimental phenomenon.
## 3 Determination of Skeletal Age  
Assessing growth potential and estimating the time of the pubertal growth spurt are of great importance for the correction of dysgnathia in children and adolescents [36]. The dynamics of growth in adolescence is highly variable from individual to individual, so the assessment of chronological age by itself is insufficient for estimating the extent of remaining growth [37; 38]. Skeletal age is far more suitable for the individual assessment of growth [39; 40; 41; 42; 43], which can be determined radiologically using the growth-related changes in the wrist bones or the vertebral bodies, also known and abbreviated as the CVM-method (cervical vertebral maturation) [44; 45]. Analyses of both methods are significantly correlated with each other [43; 46; 47]. In contrast to X-rays of the wrist, the assessment of skeletal age on the basis of vertebral body maturation comes with the advantage of not requiring any additional radiation exposure for the adolescent patient since the assessment of the vertebral bodies is performed on lateral cephalometric X-rays, which are part of the orthodontic diagnostic routine anyway [36; 48; 49; 50].  
In the CVM method, growth-related morphological changes of the vertebral bodies C2-C4 are analyzed and divided into six stages of skeletal maturity [51]. However, the correct determination of skeletal maturity using this method is difficult for inexperienced practitioners and is prone to errors due to interindividual differences [37; 52; 53; 54; 55]. In order to avoid this problem, there have been several recent approaches to automate this process using AI to objectivity the determination of skeletal age [36; 54; 57; 58; 59; 60]. However, previously published studies have shown quite heterogeneous results: While some authors found only moderate agreement of 58% to 71% between the predictions of the CVM stages of the AI and the humans' gold standard [36; 58; 59; 60], [PERSON] et al., were able to demonstrate accuracies of the predictions for different AI algorithms of more than 90% [54]. Furthermore, the different stages of skeletal maturity were predicted at different levels of accuracy, with the stages around the growth peak in particular showing generally lower agreement with the gold standard [36; 60].  
When interpreting the results, it is important to note that previous studies have usually only evaluated the precise prediction of one exact stage, whereas it is clinically irrelevant when an adjacent stage is considered for the assessment of skeletal maturity [61]. Moreover, as only one or very few experts in existing studies have defined the gold standard, differences between the gold standard and AI might also be the result of inaccuracies in the expert assessments. Based on the current state of research, it can be concluded that AI algorithms have already achieved promising results for the determination of skeletal maturity, but further studies are still required.
## 2 Preliminaries
### Elliptic Curve Algorithm
#### 2.3.2. Signature Verification Process  
1. Generate Key Pair: Key generation involves selecting a private key and calculating the corresponding public key, \(P0\), using the base point \(G0\) on the elliptic curve and the private key, \(K0\). The calculation follows the formula:  
\[P0=K0\times G0 \tag{1}\]  
2. Signature Process: The signature process entails selecting a random number, \(k1\), calculating the corresponding elliptic curve point, \(P1\), generating the x-coordinate, R, of the elliptic curve point as a component of the signature, and ultimately obtaining the signature through a series of calculations. The formula involves a hash value \(H\) obtained through a hashing algorithm applied to the message to be signed and a random number \(p\).  
\[S=k1^{-1}(H+K0\times R)\text{ mod }p \tag{2}\]  
While the signature process of the elliptic curve algorithm may be somewhat more intricate than other signature algorithms, this complexity does not impede its widespread application. Optimization and hardware support can enhance the efficiency of mathematical operations on elliptic curves, mitigating any perceived complexity.  
3. Verification Process: The verification process involves calculating and comparing points on the elliptic curve. It ensures the integrity and authenticity of information by verifying the legitimacy of the signature. The public key \(P0\) is utilized in this process,  
Figure 3: Representation of point addition diagram on a curve. Points \(P\) and \(Q\) are selected points, and Point \(R\) can be obtained by intersecting the \(PQ\) line and the curve. Then, point \(R\) is perpendicular to the x-axis to obtain point \(R^{\prime}\), which means \(P+Q=R^{\prime}\) point summation form.  
and verification is successful if the x-coordinate of \(P\) matches \(R\). Importantly, the private key is not involved in the signature verification process.  
\[P=S^{-1}\times H\times G+S^{-1}\times R\times P0 \tag{3}\]  
The elliptic curve algorithm is widely used in digital signatures because of its simple signing and verification process. This algorithm has good performance and its security has been verified in long-term use. It is one of the most widely used signature algorithms. In the BDIDA-IoT solution, we use the elliptic curve algorithm to verify the Verifiable Credential issued by the DID. During the verification process, users can choose to use different types of elliptic curve algorithms, and the overall verification process is automated.
## 4 Experimental Setup
### Preprocessing  
The consumption data of the individual devices and the total of the houses in the dataset were pre-processed before being elaborated by the neural network. The first step was to clip the measurements of the absorbed power of the appliances to filter the measurement errors, so the maximum power was limited to the values shown in the Table 2. The time series were then sub-sampled to a period of 1 min, taking the average value of the power in the time intervals.  
The activation status for individual appliances is derived from power absorption measurements in a similar way to the procedure described in [30]. A household appliance can be considered in operation when the absorbed power exceeds a certain threshold value. This criterion is sufficient to determine the state of activation of appliances operating in an ON/OFF mode, such as a fridge. When, on the other hand, the functioning of the appliance is more complex, as in the case of a dishwasher or a washing machine, the absorption can drop below the threshold value for short periods, without a washing programme having actually been completed. A minimum time is then set during which the power is maintained below the threshold value so that the appliance can be considered to be actually switched off. Finally, a minimum time is established with absorbed power above the threshold to be considered as actually switched on, in order to filter out spurious activations linked to errors in the metering.  
The Table 2 shows the values used for the above-mentioned parameters for the household appliances under consideration. The values chosen are the same as those used in [30] and in the recent literature that has reviewed the same dataset.  
Aggregate load data is normalized, dividing the load by a reference power value of 2000 W. The disaggregation is carried out on time windows of fixed length, with an extension of 510 samples as input and a valid output of 480 min or 8 h. The difference in size between input and output is due to the convolution filters for which no padding was used. The time series of the absorptions has been therefore divided in segments of 510 elements providing an overlay of 30 elements between successive segments so that a complete time series can be obtained by concatenating the output of the model applied to the single segments. As a last step of preprocessing, the average power absorbed in the range considered is subtracted from the signal so that the set of input values has zero average.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **House 1** & **House 2** & **House 3** & **House 4** & **House 5** \\ \hline Duration (days) & 612 & 134 & 33 & 199 & 64 \\ Fridge activations (-) & 16,240 & 3525 & 20 & 4682 & 1489 \\ Dishwasher activations (-) & 197 & 98 & 0 & 0 & 23 \\ Washing machine activations (-) & 513 & 54 & 0 & 0 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Duration of measurements and number of appliance activations in UK-DALE dataset houses.
## 2 Description of Experiments
### Analytical Methods  
The liquid samples were filtered through a 0.45 \(\upmu\)m filter before analytical determinations. The temperature, conductivity, and pH were estimated by means of a multiparametric probe; COD (chemical oxygen demand) and alkalinity by means of titration techniques; BOD\({}_{5}\) (biochemical oxygen demand) by respirometric procedure; ammonium nitrogen and reactive phosphorus through UV spectrophotometric methods; and atomic adsorption spectrophotometry (GBC Scientific Equipment, Braeside (Victora), Australia ) was used for determination of magnesium, calcium, and potassium [45]. The total (TS) and volatile (VS) suspended solids were measured by drying the sample, respectively, at 105 \({}^{\circ}\)C and 550 \({}^{\circ}\)C. The solid compound produced during the various processes was washed using deionized water, filtered at 0.45 \(\upmu\)m, and dried at room temperature before characterization by means of X-ray diffraction (GNR Analytical Instruments, Agrate Conturbia (Novara), Italy) and chemical analysis. The analysis of the elements was performed after an acid digestion of dried sample, which was conducted according to the procedure described in a previous work [35].  
Each measurement was conducted five times and the mean value was calculated; the relative standard deviation was always lower than 5%
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 3 Multi-Objective Robust Optimization Imaging Model
### Proposed Multi-Objective Robust Optimization Imaging Model
#### 3.4.4 New Multi-Objective Robust Optimization Imaging Model  
Drawing on the defined LLOP and ULOP, we reframe the imaging problem as a novel multi-objective bilevel optimization problem:  
\[\left\{\begin{array}{l}\min_{\mathbf{\alpha}}\{\text{mean}(R(\mathbf{\alpha})), \text{var}(R(\mathbf{\alpha}))\}\\ s.t.\ \mathbf{u}(\mathbf{\alpha})=\min_{\mathbf{u}}\left\{\frac{1}{2}\bigg{|}\bigg{|}\mathbf{A}\mathbf{u} -\mathbf{b}_{j}\bigg{|}\bigg{|}^{2}+\alpha_{1}\bigg{|}\bigg{|}\mathbf{u}-\mathbf{u}_{pj} \bigg{|}\bigg{|}_{1}+\alpha_{2}\bigg{|}\bigg{|}\mathbf{W}\mathbf{u}\bigg{|}\bigg{|}_{1} \right\}\end{array}\right. \tag{12}\]  
This new model integrates supervised learning methods, multi-objective optimization, robust optimization, and bilevel optimization principles. This is reflected in two aspects. Firstly, the LLOP uses optimization principles to achieve image reconstruction given the model parameters. The ULOP employs multi-objective learning principles to adjust the parameters of the LLOP to meet the requirements of multiple performance metrics. In the ULOP, robustness is used as an optimization goal to ensure that the model maintains good performance in uncertain environments. This integrated approach not only enhances the model's reconstruction accuracy and robustness but also provides a systematic framework that aids in parameter tuning and performance optimization for practical applications. Secondly, the MLPI is integrated into the reconstruction model, playing a dual role. On the one hand, it is incorporated as a prior constraint term within the regularization framework of the imaging model to improve reconstruction quality. On the other hand, it serves as a high-quality initial solution for iteration, improving the convergence characteristics of the optimization algorithm.  
This multi-objective robust optimization model is established based on the principles of multi-objective optimization, bilevel optimization, and robust design optimization. It integrates supervised learning methods and optimization principles, fully leveraging their complementary advantages to ensure the model can better handle complex and volatile application scenarios. The multi-objective bilevel optimization model can simultaneously consider multiple performance metrics and different levels of optimization needs, resulting in more comprehensive and diverse optimal solutions. This helps in selecting the most suitable parameter configuration based on specific needs in practical applications.  
By solving the optimization problem in Equation (12), we can obtain the optimal model parameters. Subsequently, when new capacitance data become available, these estimated parameters along with the inferred prior image can be used to solve the LLOP for image reconstruction.