esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### The Overall Construction Process of KG in the Power System Field
#### 3.1.1 Data Acquisition  
As the first step to building the mapping, the collected data directly determines the quality of KG. The data in the power domain comes from a wide variety of sources and types. The structured data stored in the knowledge engineering and expert experience knowledge base in the power domain can directly participate in the top-down ontology construction of KG without processing. However, it is the massive data generated in the actual operation and the experience of experts that are the main data sources in the power domain. These data have the characteristics of high noise and sparse data density [16]. Therefore, noise filtering and data sample expansion need to be completed before knowledge processing to improve the quality of the acquired data. These processed semi-structured or unstructured data contain a large amount of potential knowledge that needs to be transformed into structured knowledge information through a series of operations [88] and then involved in the construction of the KG.
# Transverse Movement Kinetics of a Unit for Inter-Row Crops--Case Study: Cultivator Unit
## 1 Introduction
Sometimes, row crops in inter-row zones are cultivated with units with a front-mounted cultivator [24; 25]. But, like many rear-row tilters, they have relatively small operating widths and are often used at relatively low operating speeds [26; 27]. As part of preliminary research, we developed unit variants for inter-row treatments in which row cultivators can be placed on the back, front, and sides (left and right) [28]. Such units can have an operating width of up to 16.8 m. However, there is still no sufficient justification for designing units for inter-row treatments. There are also no data on setting the most favorable technical parameters. Therefore, theoretical considerations of the kinematics of movement of inter-row cultivation units with a large operating width in the horizontal plane were undertaken. At the same time, note that the theoretical approaches used earlier to solve this problem were not inappropriate. This is because previously the lateral shift of the cultivator relative to the agricultural tractor was not considered. Moreover, analysis of the kinematics of movement of inter-row cultivation units requires taking into account the deflection angles of the tractor's front tires depending on the steering angle of the entire unit. In most cases, a row crop cultivator is attached to a tractor without the possibility of mutual rotation in the horizontal plane. However, as the operating width of the row crop units increases, the momentum generated increases. The total torque of the cultivator results from the unequal working resistances of individual sections of the cultivator over its entire operating width. As a result, its rigid connection to the tractor may negatively affect the straightness of the MTU's movement along the crop rows. The consequence may be reduced stability of the width of the protection zone and even the possibility of mechanical damage to cultivated plants. It should be emphasized once again that the scientific and information community has not found any theoretical considerations or research results that would solve this problem. Due to the above, this article aims to present the results of theoretical and experimental research on the kinematics of inter-row movement of the machine-tractor unit (MTU) to justify its scheme and design parameters, taking into account the following: (I) the operating width of the MTU; (II) the coordinates of the longitudinal position of the working devices of row cultivators; (III) the deflection angle of the tractor tires at the steering angle of its front steering wheels; (IV) the method of connecting the row cultivator to the tractor in the horizontal plane: variant (A) rigid and variant (B) articulated.
# Blind Deconvolution with Scale Ambiguity
## 3 The Proposed Approach
### Joint Prior  
Considering one representative optimization model for deblurring problem:  
\[\min_{k,u}\|u*k-f\|_{2}^{2}+\lambda\|\
abla u\|_{0.5}\qquad\text{ \emph{subject} \emph{ to}}\|k\|_{1}=1,\ k\geq 0, \tag{2}\]  
where \(f\) is the blurred image, \(u\) is the latent image, \(k\) is the blur kernel, \(*\) denotes the convolution operator, and \(\lambda\) is a positive parameter. The employed hyper-Laplacian prior \(\|\
abla u\|_{0.5}=\sum_{x}\left(|u_{h}(x)|^{0.5}+|u_{p}(x)|^{0.5}\right)\), where \(x\) denotes the pixel index. \(k\geq 0\) enforces element-wisenon-negativity. The deconvolution formulation had been solved in [41] when \(k\) is fixed. And, when \(u\) is fixed, it involves a convex problem. Nonetheless, it may fail to recover pleasing solutions because the hyper-Laplacian prior does not always favor clear images over blurred ones.  
We find that if we considering the \(L^{p}\) normalization constraint on blur kernel, the regularization can be converted into one joint version that statistically favors true solutions over trivial solutions for proper selections on \(p\). Also, we do not need to propose any novel prior on the blur kernel in (2) except for the \(L^{p}\) normalization constraint on the blur kernel. This is different from most existing blind deblurring methods formulated within the classical MAP framework. Such methods need to use additional smoothing constraint on the blur kernel (\(\|k\|_{1}\) or \(\|k\|_{2}^{2}\)) except for employing the \(L^{1}\) normalization constraint on the blur kernel. In the following, we present the transformed deconvolution formulation by introducing a scale factor and provide some discussions.
# Role of Cyclin-Dependent Kinase 1 in Translational Regulation in the M-Phase
## 1 Introduction  
### Cyclin Dependent Kinase 1 (CDK1) Is a Subunit of the M Phase-Promoting Factor (MPF)  
CDK1, a serine/threonine kinase, is a catalytic subunit of the M phase-promoting factor (MPF) complex which is essential for cell cycle control during the G1-S and G2-M phase transitions of eukaryotic cells. CDK1 is a key player in driving the M-phase in both meiosis and mitosis [1, 2]. CDK1 activity sharply increases at the beginning of the M-phase and is inactivated at the end [3, 4, 5] (Figure 1).  
CDK1 is involved in the control of events such as DNA replication and segregation, mRNA transcription, DNA repair and cell morphogenesis (reviewed in [6]). Previous studies identified several translation-associated factors as direct substrates of CDK1 in mitosis and meiosis [7, 8]. The association of CDK1 with one of several cyclins is a prerequisite of CDK1 activity (Figures 1 and 2).  
In addition to cyclin binding, CDK1 activation requires dephosphorylation on Thr14 and Tyr15 residues and phosphorylation at Thr161 [9, 10]. At the onset of the M-phase, cyclin B1 becomes translated and plays a fundamental role in cells entering the M-phase [11]. In oocytes, cyclin B1 and cyclin B2 are involved in the control of the transition from the first to the second meiotic divisions and cyclin B2 compensates for cyclin B1 in CDK1 activation during the M-phase transition in oocyte meiosis [12]. A similar compensatory ability for cyclin B2/CDK1 to interact with separase has been revealed, suggesting that cyclin B2/CDK1 and cyclin B1/CDK1 complexes likely function together in oocytes [13]. The cyclin B3/CDK1 complex is required for the M-phase to anaphase transition in oocytes [14, 15].  
Figure 1: Dynamics of cyclin dependent kinase 1 (CDK1) activity, global translation and inactivation of the translational repressor 4E-BP1 with consequent stimulation of cap-dependent translation during the M-phase. At the beginning of the M-phase, the intensity of global translation is at high levels and CDK1 activity sharply increases accompanied by nuclear envelope breakdown (NEBD). CDK1 activity peaks during assembly of the spindle and 4E-BP1 becomes exclusively hyperphosphorylated (red arrowhead) which results in its inactivation as a translational repressor. At the exit of the M-phase, CDK1 activity drops alongside the increased activity of the cap-dependent translational repressor 4E-BP1. The intensity of global translation decreases during the time course of the M-phase. Immunoblot image using pan 4E-BP1 antibody shows an exclusive phosphorylation shift in the M-phase of the mouse oocyte (red arrowhead) [7].
## 2 A Novel FPSO-GAK Clustering Method  
### The Idea of the Novel FPSO-GAK Method  
K-means is an exclusive distance-based clustered partitioning algorithm that is implemented iteratively [37]. Scholars have regarded it as an effective means to discover urban functional areas in the past decades [38; 39]. The K-means clustering problem on \(n\) points is NP-Hard for dimension \(d\geq 2\)[40], and it is a demanding but unavoidable problem to determine the optimal clustering centers accurately. The acquisition of the optimal cluster centers depends greatly on the initial cluster centers, which is also an NP-hard problem. In practice, one of the effective methods of approaching NP-hard problems is the heuristic method. Many researchers have applied heuristic algorithms to initial cluster centers [41], exposing the issues that one single heuristic algorithm is prone to fall into local optimization and premature convergence in the process of searching. These problems are caused mainly by the homogeneity of the search strategy of one single heuristic algorithm. The proposed algorithm is formed by a suitable embedded combination of PSO, GA heuristic algorithm and fuzzy system, which maintains the advantages of each algorithm and overcomes local optimization and premature convergence to a great extent. In order to solve the problems existing in the K-means clustering algorithm and make use of the advantages of the hybrid heuristic algorithm, this paper proposes a new K-means clustering algorithm which can obtain better clustering centers and capture urban hotspots under the premise of given cluster number.  
The proposed noevel FPSO-GAK algorithm consists of three parts. Given the number of clusters \(K\), perform the following steps: Firstly, the k-means++ initialization method is employed to obtain \(K\) initial cluster center points and treat them as one particle. Perform this process for the provided number of rounds to obtain the desired number of particles. Secondly, particles are put into the PSO algorithm. The PSO algorithm is employed to update the particle information and use the fuzzy system to update the parameter Settings of the PSO algorithm; the cycle is executed alternately until the iteration criteria are satisfied. Then, the optimized particles are put into the GA algorithm for optimization. In this stage, the GA, fuzzy, and PSO algorithms are executed alternately until the iterative convergence criteria is satisfied. Finally, the initial clustering center data corresponding to the optimal particle extracted in the above steps are input into the K-means clustering algorithm to cluster the given data. The excellent clustering centers, namely the urban hotspots, are captured.
## 2 Results and Discussion
### Antioxidant Activity of Scepe-A, Scepe-B and Scepe-C
#### 2.4.4 O\({}_{2}^{-}\bullet\) Scavenging Activity  
O\({}_{2}^{-}\bullet\) is the most common free radical and can produce hydrogen peroxide and hydroxyl radicals through dismutation and other reactions in vivo, which can cause damage to DNA, proteins and cell membranes. The O\({}_{2}^{-}\bullet\) scavenging activities of SCPE-A, SCPE-B and SCPE-C were studied, and the dose-effect relations were observed as the concentration gradually increased from 0.1 to 5.0 mg/mL (Figure 6D). The EC\({}_{50}\) values of SCPE-A, SCPE-B and SCPE-C were 0.08, 0.14, and 0.11 mg/mL, respectively. SCPE-A showed stronger O\({}_{2}^{-}\bullet\) scavenging activity than SCPE-B and SCPE-C and reached 91.7% \(\pm\) 2.58% scavenging activity at 5.0 mg/mL. The EC\({}_{50}\) of SCPE-A was lower than that of WDR (0.09 mg/mL), PYFNK (0.11 mg/mL), LDK(0.12 mg/mL) [19; 20], HFGDPFH (0.20 mg/mL) [32], GSQ (0.70 mg/mL) [31], Ser-Leu-Pro-Ile-Gly-Leu-Met-Ile-Ala-Met (SLPIGLIMIAM) (0.3168 mg/mL) [39], YLGAK (scavenging rate: 36.27% at 1.0 mg/mL), GGLEPINFQ (scavenging rate: 32.05% at 1.0 mg/mL) [36], His-Asp-His-Pro-Val-Cys (HDHPVC) (0.265 mg/mL) and His-Glu-Lys-Val-Cys (HEKVC) (0.235 mg/mL) [40], Tyr-Leu-Met-Arg (YLMR) (0.450 mg/mL), Val-Leu-Tyr-Glu-Glu (VLYEE) (0.693 mg/mL), Met-Ile-Leu-Met-Arg (MILMR) (0.993 mg/mL) [41], FIMGPY (1.61 mg/mL), GPAGDY (1.66 mg/mL) and IVAGPQ (1.82 mg/mL) [11] from the protein hydrolysates of scalloped hammerhead muscle, mussel sauce, Chinese leak seeds, _Mytitlus coruscus_, egg white, round scad, croceine croaker muscle and skate cartilage. O\({}_{2}^{-}\bullet\) could be catalyzed into hydrogen peroxide and oxygen by superoxide dismutase (SOD) with a reaction rate 10,000-fold higher than that of spontaneous dismutation in an organism [19]. Therefore, SCPE-A, SCPE-B and SCPE-C might have high antioxidant activity similar to SOD and could be applied as O\({}_{2}^{-}\bullet\) scavengers in biological systems.
## 4 Discussion  
The model discussed in this paper is an extension of the one proposed in [22]. Both the models (the original proposed by [PERSON] and the one proposed in this paper) are able to generate realistic ECG signals in order to simulate normal and pathological rhythms. The analysis of the synthetic ECG signal generated by the two models can be performed like a real signal of the patient, which is conducted by observing the morphology of different waves and their behavior in time, with specific interest on the QRS complex. This means that, for RBBB and LBBB, as for the other pathologies discussed in this paper and in [22], the anomalies are identified analyzing the shape of the signal and identifying the interest points in which the waves change their morphology. For example, if we want to reproduce the LBBB, we need to modify the shape of the QRS complex, as well as the polarity of the T wave [46]. As shown in Figure 6, selecting proper parameters, our model is able to reproduce the main typical characteristics of the signal related to this pathology.  
However, it is important to note that the synthetic signal may not be exactly the same as the patient's actual ECG signal. To generate a synthetic signal closer, but not equal, to the real one, it is possible to modify, inside a specific operating range, the model parameters to obtain different nuances of the same pathological behaviour. This difference is due to the uniqueness of the signal generated by the cardiac activity of each person. It means that it is possible to compare two signals belonging to two different people by observing the waveforms in time and searching the features of interest, but not comparing the shape of the tracks point by point. In addition to personal traits, there are several factors that make any real signal different from any other--for example, the noise due to the acquisition method, the constitution of patient that influenced the transmission of the electrical signal from heart to body surface, the coexistence of different pathologies, and so on. For this reason, nowadays, the interpretation of an ECG signal is mainly performed by physicians by observing the specific characteristics of tracks.
## 3 Proposed MFA-AOA Algorithm
### Proposed Method  
The proposed method works based on the characteristics of two algorithms: one for selecting the CH and the other for network routing. First, the Mayfly algorithm identifies appropriate sensors and Aquila determines the best path between the CH and the BS. Following that, the CHs send the data obtained to the BS through the path generated by the Aquila algorithm. Figure 2 illustrates the overall procedure of the proposed technique. The proposed MFA-AOA optimization approach has a powerful hybrid algorithmic framework based on the behavior of the mayfly and Aquila. The best results are attained when this technique is paired with a routing protocol.  
Cluster maintenance is one of the most critical stages in this analysis for balancing the load amongst clusters. Due to inter-cluster communication, clusters closer to the BS consume too much energy. Therefore, the cluster maintenance stage is necessary to prevent node failure. It increases the lifespan of data transmission from the SN to the BS. The MFA algorithm is re-initialized to cluster the network if the CH's residual energy exceeds the threshold level. The CHs are then chosen using the clustering technique, and the AOA is utilized to find the routing path between the CHs and the BS. The MFA algorithm is used in this proposed technique to conduct an effective CH selection. The CHs are chosen based on five criteria: node level, node density, distance from neighbors, distance from the BS, and residual energy. These characteristics are used to choose the best CH from the available nodes.
## 3 The System Performance  
As in previous work [12; 13; 28; 30], the closed-form expressions of OP and EC of this system model are derived in this section. At first, we will determine the probability density function (PDF) and the cumulative density function (CDF) of a random variable (RV) \(|h_{sr}|^{2},\big{|}h_{rd_{i}}\big{|}^{2}\). As shown in [29], the PDF of RV \(\gamma_{1}\) can be calculated by  
\[f_{|h_{sr}|^{2}}(x)=\frac{x^{m_{h_{sr}}|^{2}-1}}{(m_{|h_{sr}|^{2}}-1)!(\Omega _{|h_{sr}|^{2}})^{m_{h_{sr}}|^{2}}}\exp(-\frac{x}{\Omega_{|h_{sr}|^{2}}}). \tag{9}\]  
From Equation (9), the CDF of RV \(|h_{sr}|^{2}\) can be obtained with the help of Equation (8.353.4) in [32].  
\[F_{|h_{sr}|^{2}}(x)=1-\exp(-\frac{x}{\Omega_{|h_{sr}|^{2}}})\sum_{t=0}^{m_{h_{sr }}|^{2}-1}\frac{x^{t}}{t!(\Omega_{|h_{sr}|^{2}})^{t}} \tag{10}\]  
where \(\Omega_{|h_{sr}|^{2}}=\frac{\lambda_{sr}}{m_{h_{sr}}|^{2}}\); \(m_{|h_{sr}|^{2}}\) is the Nakagami-m parameter and note that the case of \(m_{|h_{sr}|^{2}}=1\) corresponds to Rayleigh fading; \(\lambda_{sr}=\frac{\mathrm{E}\big{[}|h_{sr}|^{2}\big{]}}{d_{sr}^{m}}\) is the mean of RV \(|h_{sr}|^{2}\).  
Furthermore, we have the PDF and CDF of RV \(\left|h_{rd_{i}}\right|^{2}\) as shown in [28] as follows:  
\[f_{h_{rd_{i}}}{}^{2}(x) =\frac{1}{\lambda_{rd_{i}}}e^{-x/\lambda_{rd_{i}}} \tag{11}\] \[F_{|h_{rd_{i}}|^{2}}(x) =1-e^{-x/\lambda_{rd_{i}}} \tag{12}\]  
where \(\lambda_{rd_{i}}=\frac{E\left[\left|h_{rd_{i}}\right|^{2}\right]}{d_{i}^{n}}\) is the mean of RV \(\left|h_{rd_{i}}\right|^{2}\).  
In this system model, the best selection user \(US_{q}\) is analyzed as follows:  
\[\left|h_{rd}\right|^{2}=\max_{i=1,2,\ldots,K}(\left|h_{rd_{i}}\right|^{2}). \tag{13}\]  
According to [19], the CDF of \(\left|h_{rd}\right|^{2}\) can be given by  
\[F_{|h_{rd}|^{2}}(y)=\sum_{p=0}^{K}{(-1)^{p}C_{K}^{p}\times e^{-py/\lambda_{rd }}}, \tag{14}\]  
where we denote \(C_{K}^{p}=\frac{K!}{p!(K-p)!}\), and \(\lambda_{rd}\) is the mean of RV \(\left|h_{rd}\right|^{2}\).  
Finally, the PDF of random variable can be determined by  
\[f_{|h_{rd}|^{2}}(y)=\frac{1}{\lambda_{rd}}\sum_{p=0}^{K-1}{(-1)^{p}C_{K-1}^{p} K\times e^{-(p+1)y/\lambda_{rd}}}. \tag{15}\]  
From Equation (19), we have  
\[SNR=\frac{\eta\rho(1-\rho)\psi|h_{sr}|^{2}|h_{rd}|^{2}}{\eta\rho|h_{rd}|^{2}d_ {sr}^{m}+(1-\rho)d_{i}^{m}}. \tag{16}\]
###### Abstract
In [20], a simplified 3D model is constructed by using an empirical equation for the heat delivered by the PCM, according to the calorimetric measurements at the material level. Another approach, proposed in [21], is the use of a RC model (resistance-capacitance), which was then compared to the 1D-3D coupled model developed in [19]. The deviation of both models from the experiments was in the order of 1 K, but the RC model had a two to three times shorter computational time, which makes it suitable for fast and reliable design. In [22], the simplification assumed is the use of lumped masses for the PCM and the metal of the HEX accuracy. The deviation of the numerical and simulation results was less than 9%; therefore, showing the applicability of such models for design purposes. A simplified model for PCM storages is presented in [23; 24], which is realized in Modelica language. The results of the simulations were used to adapt the storage geometry and PCM properties to cover a broad range of applications. In the present paper, a simplified modelling methodology of LTES is applied to an innovative configuration; i.e., a finned aluminum heat exchanger for refrigeration applications. The novelty of the configuration, whose development is presented in detail in [25; 26], is the possibility of directly charging the PCM with the refrigerant of the heat pump cycle, thus, allowing the storage unit to replace the evaporator of a heat pump, while using a separate heat transfer fluid (HTF) during discharge. The model was implemented in the Modelica language using Dymola, and was realized by considering the fluids of the storage (PCM, HTF, refrigerant) as interconnected volumes, according to an array-like fashion, which allows easy scalability of the model. The model was validated under different operating modes using experimental results and then optimization through a parametric analysis was carried out.
## 2 Materials and Methods
### Metric Analysis
Regarding the analytical aspect of this investigation, the contrast-to-noise ratio (CNR) was used as a metric to evaluate the image quality and clarity across different frequency settings in EIT, as shown in Figure 2B [37; 38]. CNR is a vital quantitative tool for measuring the object contrast within an image, distinguishing it from background noise. CNR can be calculated using the following formula:  
\[\text{CNR}=\frac{\left|\mu_{\text{ROI}}-\mu_{\text{bkg}}\right|}{\sigma_{ \text{noise}}} \tag{4}\]  
In Equation (4), \(\mu_{\text{ROI}}\) is the mean impedance value within the Region of Interest (ROI) containing the object, \(\mu_{\text{bkg}}\) is the mean impedance value of the background, and \(\sigma_{\text{noise}}\) is the standard deviation of the noise in the background. This formula and its application are illustrated in the study, providing a comprehensive analysis of the EIT image quality at varying frequencies.
###### Abstract  
The stability of bottom structures in block caving mines is significantly challenged by impact loads generated from large rockfalls and ore collapses on slopes. This study aims to investigate the mechanical response and failure characteristics of bottom structures under such dynamic and cyclic loading conditions. Discrete element methods (DEMs) were employed to simulate the impact load amplitudes caused by large rockfalls on bottom structures. Specimens with identical mechanical properties to the bottom structure were fabricated at a 1:100 scale, based on the principle of similarity ratio tests. Three distinct types of impact loads were identified and analyzed: overall impact from large-scale slope collapses, localized impact from partial rock and soil mass collapses, and continuous multiple impacts from progressive slope failures. True triaxial tests were conducted to evaluate the mechanical response of the bottom structure under these loading scenarios. The results indicate that while overall and multiple impact loads from slope collapses do not lead to catastrophic failure of the bottom structure, severe damage occurs under a 100 m thickness of ore and large block impacts. Specifically, the inner walls of ore accumulation troughs peel off, and ore pillars between troughs fracture and fail. This study highlights the need for advanced experimental and numerical approaches to accurately predict the stability and failure modes of bottom structures under complex loading conditions.  
block caving; bottom structure stability; impact loads; mechanical response; failure characteristics Article