esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 2 Materials and Methods
### Statistical Analysis  
We performed the statistical analysis using the Review Manager (Revman) software version 5.4.1. To pool the results of the meta-analysis, we used mean difference (MD) with a 95% confidence interval (CI). Significance was detected at a \(p\) value less than 0.05. A random-effects model was chosen due to the anticipated clinical and methodological diversity across studies, including variations in patient populations, ML models used, features included, and study designs. Statistical heterogeneity was assessed using the Chi-square test at \(p<0.1\). Subgroup analysis based on the machine learning model tested, along with leave-one-out sensitivity analysis, was planned to explore potential sources of heterogeneity.
### PCA Method  
Principal component analysis yielded six PCs with an eigenvalue >1, which explained 96.43% of the variance. Figure 1 shows the projection plot of the samples and the original variables in the space defined by the first two PCs, which explain 70.36% of the variance.  
Soybean samples were found in the upper-right quadrant, with positive values of both PCs, due to the strong contribution of protein, ash, TPC, Ca and K to PC1, together with the correlation of C22:0 and C18:1 to the PC2. The hemp flour samples presented positive PC1 values due to the correlation of this PC1 with ash and fiber, as previously mentioned, but also with TFC and Cu contents. These samples also showed negative PC2 values, which were correlated with C18:2, Mg and Fe variables, but mainly with C18:3 levels. In the opposite quadrant were chickpea samples showing positive PC2 values, which correlated with the observed levels of C18:1 and C18:0 (as for soybean and one pea sample, which have positive PC1 values). In fact, chickpea samples had negative PC1 values due to the strong correlation with Na.  
Pea and rice samples were in the lower-left quadrant and showed negative PC1 and PC2 values. Thus, rice samples had the highest levels of C16:0 and C18:1n9t but also of carbohydrates and starch, together with the lowest values of TEC, TPC, C18:2 and most minerals. The pea samples had a high moisture content but also low values of Mg, P and Ca. However, they showed the highest values of Pb, which was associated with the negative values of PC2. As in the case of rice, the pea samples showed relatively low TPC and TEAC values. One sample showed very low amounts of C18:1, which justifies the low PC2 value observed.  
The figure also shows that the samples of the two brands of rice, hemp and chickpea were close, while the two samples of pea and especially soybean were separated due to the observed compositional differences. Indeed, the soybean, rice and hemp flours were very different from each other, while the chickpea flour and one of the pea samples were quite close.
# Development of Building CFD Model Design Process Based on BIM
## 3 Design Method of Optimized CFD Model Using BIM  
Figure 1 shows the process of the method to build an optimized CFD model from a BIM model.  
The proposed method consists of a five-step process: (1) BIM data extraction, (2) Geometry simplification, (3) Grid optimization, (4) Attribute data matching, and (5) CFD model extraction. For versatility, the research was conducted using IFC (Industry Foundation Classes), the standard format for information exchange in BIM, and OpenFOAM, an open-source CFD analysis tool. The geometry and attribute information of the subject of analysis were extracted by analyzing the IFC file. This data was utilized to design the CFD geometry model, design the grid model, and set the boundary conditions. Model optimization, which is significant for accurate CFD simulation, was performed simultaneously with model design. Finally, an OpenFOAM case containing the optimized CFD model was exported based on the input IFC file.  
Figure 1: Design process of optimized Computational Fluid Dynamics (CFD) model using Building Information Modelling (BIM). IFC, Industry Foundation Classes.
## 2 System Model of DBT-MC-FTN
### DBT-MC-FTN Transmitter
The DBT-MC-FTN signaling can be also expressed in matrix form. Let \(\mathbf{V}=\{\mathbf{v}_{0},\mathbf{v}_{1},...,\mathbf{v}_{M-1}\}\) be the output signal matrix on \(M\) subcarriers before \(Q\)-point IDFT operation, the symbol vector on the \(m\)th carrier can be given as  
\[\mathbf{v}_{m}=\sqrt{D}\mathbf{S}_{m}\mathbf{WT}_{Q,D}\mathbf{F}_{D}\mathbf{ x}_{m}, \tag{4}\]  
where \(\mathbf{x}_{m}=[x_{m}(0),x_{m}(1),\cdots,x_{m}(D-1)]^{T}\) is the data signal vector on the \(m\)th subcarrier, \(\mathbf{F}_{D}\) and \(\mathbf{F}_{D}^{H}\) are the \(D\times D\) Fourier/Inverse Fourier transform matrix, \(\mathbf{T}_{Q,D}=[\mathbf{I}_{D},\mathbf{I}_{D},\cdots,\mathbf{I}_{D}]^{T}\) indicates \(Q\times D\) cyclic extension matrix with \(D\)-dimensional identity matrix \(\mathbf{I}_{D}\). \(\mathbf{W}=diag\{W(0)W(1)\cdots W(Q-1)\}\) is defined as the frequency-domain window matrix and \(\mathbf{S}_{m}\) is the \(m\)th cyclic shift matrix. As a result, after the multi-carrier summation in Equation (3) and \(Q\)-point IDFT, the transmitted DBT-MC-FTN signal vector in Equation (2) can be shown as  
\[\mathbf{g}=\mathbf{F}_{Q}^{H}\mathbf{\Omega}\mathbf{\Lambda}\mathbf{X}= \mathbf{\Phi}\mathbf{X}, \tag{5}\]  
where \(\mathbf{\Omega}\mathbf{=}[\mathbf{S}_{0}\ \ \mathbf{S}_{1}\cdots\mathbf{S}_{M-1}]\), \(\mathbf{\Lambda}\mathbf{=diag(WT}_{Q,D}\mathbf{F}_{D})\), \(\mathbf{X}=[\mathbf{x}_{0}\ \ \mathbf{x}_{1}\cdots\mathbf{x}_{M-1}]^{T}\) and \(\mathbf{\Phi}\) is the equivalent matrix of the transmitter.
## 6 Analysis of the Algorithm
### Review of Some Classical Results in Probability  
Some well-known results in classical probability theory are listed. The readers can skip this section if they understand them well. The inclusion of these results make the paper self-contained.  
* For a list of events, \(A_{1},\cdots,A_{m}\), \(\Pr[A_{1}\cup A_{2}\cup\cdots\cup A_{m}]\leq\Pr[A_{1}]+\Pr[A_{2}]+\cdots+ \Pr[A_{m}]\).
* For two independent events, \(A\) and \(B\), \(\Pr[A\cap B]=\Pr[A]\Pr[B]\).
* For a random variable, \(Y\), \(\Pr[Y\geq t]\leq\frac{E[Y]}{t}\) for all positive real numbers, \(t\). This is called Markov inequality.  
The analysis of our algorithm employs the Chernoff bound [22] and Corollary 18 below, which can be derived from it (see [11]).  
**Theorem 15** ([22]).: _Let \(X_{1},\cdots,X_{n}\) be \(n\) independent random \(0\)\(\!\!-\!\!1\) variables, where \(X_{i}\) takes one with a probability of \(p_{i}\). Let \(X=\sum_{i=1}^{n}X_{i}\), and \(\mu=E[X]\). Then, for any \(\delta>0\):_  
1. \(\Pr(X<(1-\delta)\mu)<e^{-\frac{1}{2}\mu\delta^{2}}\) _and_
2. \(\Pr(X>(1+\delta)\mu)<\left[\frac{e^{\delta}}{(1+\delta)^{(1+\delta)}}\right]^ {\mu}\)_._  
We follow the proof of Theorem 15 to make the following version of the Chernoff bound, so that it can be used in our algorithm analysis.  
**Theorem 16**.: _Let \(X_{1},\cdots,X_{n}\) be \(n\) independent random \(0\)\(\!\!-\!\!\!1\) variables, where \(X_{i}\) takes one with a probability of at most \(p\). Let \(X=\sum_{i=1}^{n}X_{i}\). Then, for any \(\delta>0\), \(\Pr(X>(1+\delta)pn)<\left[\frac{e^{\delta}}{(1+\delta)^{(1+\delta)}}\right]^ {pn}\)._  
Define \(g(\delta)=\frac{e^{\delta}}{(1+\delta)^{(1+\delta)}}\). We note that \(g(\delta)\) is always strictly less than one for all \(\delta>0\) and \(g(\delta)\) is fixed if \(\delta\) is a constant. This can be verified by checking that the function \(f(x)=\ln\frac{e^{x}}{(1+x)^{(1+x)}}=x-(1+x)\ln(1+x)\) is decreasing and \(f(0)=0\). This is because \(f^{\prime}(x)=-\ln(1+x)\), which is less than zero for all \(x>0\).  
**Theorem 17**.: _Let \(X_{1},\cdots,X_{n}\) be \(n\) independent random \(0\)\(\!\!-\!\!\!1\) variables, where \(X_{i}\) takes one with a probability of at most \(p\). Let \(X=\sum_{i=1}^{n}X_{i}\). Then, for any \(\delta>0\), \(\Pr(X<(1-\delta)pn)<e^{-\frac{1}{2}pn\delta^{2}}\)._  
**Corollary 18** ([11]).: _Let \(X_{1},\cdots,X_{n}\) be \(n\) independent random \(0\)\(\!\!-\!\!\!1\) variables, and \(X=\sum_{i=1}^{n}X_{i}\)._  
_i) If \(X_{i}\) takes one with a probability of at most \(p\), then for any \(\frac{1}{3}>\epsilon>0\), \(\Pr(X>pn+\epsilon n)<e^{-\frac{1}{3}n\epsilon^{2}}\)._  
_ii) If \(X_{i}\) takes one with a probability of at least \(p\), then for any \(\epsilon>0\), \(\Pr(X<pn-\epsilon n)<e^{-\frac{1}{2}n\epsilon^{2}}\)._
## 2 Classification of Cholangiocarcinoma  
Primary liver cancer represents the second most important cause of worldwide mortality for malignancies. Hepatocellular carcinoma (HCC) and cholangiocarcinomaoma (CCA) are the two most common liver cancers [21]. CCA represents a heterogeneous group of malignant neoplasms originating from damaged cholangiocarcinomaytes, with a multifaceted molecular pathogenesis. It accounts, at the moment, for approximately 15% of cases with a wide geographical variability correlated to the plurality of risk factors [1]. CCA is currently classified according to the World Health Organization (WHO) and Union for International Cancer Control (UICC) in two major categories, according to their anatomical origin in the biliary tree: _Intrahepatic cholangiocarcinoma carcinoma_ (_iCCA_), which accounts for about 20% of tumors developing within the liver, from the bile duct of the second order and from the most proximal intrahepatic bile duct, and _extrahepatic bile duct carcinoma_ (_eBDC_), which represents 80% of CCAs and it can be classified into perililar (_pCCA_) (60-70%) and distal (_dCCA_) (20-30%). _pCCA_ spreads between the second-order bile ducts and the attachment of the cystic duct into the common bile duct, whereas _dCCA_ grows in areas between the cystic duct and the ampulla of Vater [22; 23] (Figure 1).  
Histologically, the majority of pCCAs and dCCAs are mucinous adenocarcinoma [24]. Conversely, iCCA is characterized by two histological subtypes: one originating from small intrahepatic bile ducts, the other from large intrahepatic bile ducts. The first type is a mixed iCCA, whereas the second type is a mucinous iCCA [25]. The previous histological division is really important and recent data pointed out a noteworthy degree of heterogeneity regarding their different cell of origin, epidemiology and risk factors, pathological and molecular profile, etiology, and pathogenesis, clinical outcome and response to treatment [22; 26].  
However, the extreme heterogeneity of CCA makes it difficult to provide a definitive classification. [PERSON] et al. had already emphasized the \"increasing burden of classifications\". They have considered a CCA classification based on: (i) anatomical location, largely used in the medical literature; (ii) macroscopic pattern of growth, (iii) microscopic features; and (iv) cells of origin. The latter is supported by emerging data regarding radiological and pathological findings in CCAs that originate from different cells inside the biliary tree. Thus, the CCAs can be reclassified as follows: CCAs derived from human hepatic progenitor/stem cell (hHpSC) lineages in canals of Hering, that include histological subtypes of iCCA; CCAs derived from human biliary tree stem/progenitor cell (hBTSC) lineages in peribiliary glands (PBGs) or from epithelium of intra- or extrahepatic large bile ducts [24; 27].
# Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation
### Computational Geometries for Blood Flow Simulation in Stenotic Carotid Artery  
Computational fluid dynamics (CFD) studies emphasize that elevated wall shear stress (WSS) is frequently associated with high-risk plaque features in coronary arteries [8; 9; 10]. Its role as a trigger for rapid plaque progression and expansive remodeling has not been clearly proven. There still remains an ongoing debate whether low or oscillatory shear parameters might be a better indirect indicator for plaque vulnerability [11]. Complex geometries with several anatomical variations [12], such as the carotid bifurcation, affect the hemodynamics to an even larger extent [13;14] than a single vessel malformation and complexity can further increase in the presence of stenoses at regions of a bifurcation. Furthermore, the geometry of the carotid artery differs from individual to individual, leading to an ensemble of different flow features. The results of CARE II study based on multicontrast magnetic resonance (MR) vessel wall imaging suggests that there is a relationship between high wall shear stress and vulnerable plaque features in smaller luminal expansion at carotid bifurcation [15]. This new finding underlines the results of previous articles [9;16], which were criticized before [8]. In a recent article [17], the authors concluded that one parameter alone is not appropriate as a surrogate marker for cardiovascular risk stratification, rather the different markers should be investigated as individual risk factors. Helical flow-based geometric descriptors [17; 18] are considered to be good indicators of disturbed flow regions (low or oscillatory WSS regions). However, as pointed out in these review articles [11; 15], further investigations are needed to understand the underlying complex mechanisms.  
The aim of our study was to reconstruct computational geometries for blood flow simulations from CTA images that are appropriate for comparison with a plaque image analysis using commercially available software. In this paper, a comparison is made between the manual and semi-automatic segmentations performed by non-medical and medical investigators in homogenous (based on main plaque morphology features) patient groups.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 2 Study Area and Data Sources  
### Overview of the Study Area  
Huangshan City is situated in the southern part of Anhui Province, China (Figure 1), within the southern Anhui mountainous region, covering 9678 km\({}^{2}\). The terrain is predominantly mountainous, with a subtropical monsoon humid climate. The area experiences distinct seasons and abundant rainfall, where the average annual temperature ranges from 16.3 \({}^{\circ}\)C to 17.4 \({}^{\circ}\)C, and the average yearly precipitation exceeds 1700 mm, primarily occurring between May and September. The topography is chiefly distinguished by low to medium mountains with well-developed structural fissures and loose rock formations. The frequent heavy rains and prolonged rainy climate, combined with human engineering activities, make the area prone to landslides and other geohazards.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 5 Results and Discussion
### Actual Wave Energy Harvesting Plant Experiment
#### 5.3.2 Output Power Estimation  
The same types of regressors as those described in the wave tank experiment were used to predict the output power in the onsite experiment. The experimental results show that the regressor combined with a classifier exhibited better performance than one without a classifier in this experiment, as well. However, the performance difference among the three machine learning algorithms in Figure 11 is smaller than that of the wave tank experiment in Figure 9. It is also noticeable that the MSE and MAE in Figure 11 are less than those in Figure 9 because the output power was measured in watts (W) in Figure 9, while kilo-watts (kW) in Figure 11. Therefore, the prediction error is higher for Figure 11. From both experiments, we can confirm that the proposed regressors are efficient and accurate regardless of the algorithms used in predicting the output power from the movement of the buoys.
## Materials and Methods
### Fabrication of the Micro-Fluxgate Sensor
Photographs of the fabricated sensor are shown in Figure 2i-k. As shown in Figure 2i,k, the fabricated microsensor contained a long rectangular magnetic core (7.3 mm \(\times\) 1.5 mm, 450 \(\upmu\)m in width for long side) as the sensitive element of the sensor, and a double-layer sensor core made of an Fe-Co-B amorphous ribbon was used to increase the cross-sectional area of the magnetic core, which increased the sensitivity of our sensor [22; 23; 24]. Three-dimensional solenoid Cu coils (one pick-up coil and four excitation coils) were applied to control the magnetic-sensitive elements of the sensor. The excitation coils (16 turns for each coil, line width of 60 \(\upmu\)m) were employed to drive the sensor core periodically into the magnetic saturation state in response to the excitation current generated in the excitation coils. The pick-up coil (59 turns, line width of 60 \(\upmu\)m) was utilized to pick up the permeability variation of the sensor core when an external magnetic field was tested. The pick-up coil was located parallel to and between the four excitation coils. The dimensions of the entire sensor measured with an outer size of 7.3 mm in length and 2.3 mm in width (not including the electrode) or 7.3 \(\times\) 2.7 mm\({}^{2}\) (including the electrode).
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
This paper establishes a security protection model of data assets throughout their life cycle, with cryptography technology as the core, and designs a security technical framework that runs through each link of data asset sharing and circulation. In addition, an architecture design scheme of data asset security sharing and a circulation system based on cryptography service technology is proposed. Figure 1: Data asset sharing and circulation in an uncontrolled environment. The main contributions of this paper are as follows. * A security protection model for the whole life cycle of data assets is established, which is closely adapted to flexible and complex business scenarios, and provides a theoretical basis for the design of data asset sharing and circulation security technology framework and system architecture. * A security technical framework covering the whole life cycle of data asset sharing and circulation is designed, which covers all the links of data asset production, storage, exchange, processing, return and destruction, and provides omni-directional technical protection for the secure sharing and circulation of data assets. * An architecture design scheme of data asset security sharing and circulation system based on cryptography service technology is proposed, based on the design ideas of refactoring, modularization, scalability and high availability. Micro-service and container technology are introduced to split the security protection function of data assets, which can quickly realize on-demand deployment, flexible access and dynamic adjustment. The remaining chapters of this paper are composed as follows. Section 2 discusses the progress of the relevant research work in detail. Section 3 focuses on the security requirements of data assets in four key scenarios: outsourcing storage, multi-party sharing, trusted operation and cross-domain return. This section also establishes the security protection model of data assets in the whole life cycle. Section 4 introduces in detail the security technology system of data asset sharing and circulation based on a password. Section 5 introduces in detail the architecture design of data asset security sharing and circulation system based on cryptography service technology. Section 6 describes the implementation results of the system and compares them with the functions proposed in other related papers. Section 7 summarizes the work of the full text.
# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
## 5 Experimental Results Validation Analysis
### Comparative Experiments with Multiple Datasets  
The utilization of diverse datasets for training and validation plays a pivotal role in the performance and robustness of models. It can significantly enhance the generalization ability, robustness, performance, and robustness of target detection models, ensuring their efficacy in a multitude of real-world applications. The original models, YOLOv8 and YOLO-RRL, were tested on two datasets: the steel surface defect dataset (NEU-DET) and the aluminum surface defect dataset (APSPC). The steel dataset comprises 1800 high-quality defect images of six defect types, including cracks, inclusions, plaques, pitting surfaces, rolled oxides, and scratched defects. The images have a high resolution and clear details, which facilitate the learning and identification of defect features by the models. The aluminum dataset comprises a total of 1885 defect images, with ten distinct types of defects, including dents, non-conductivity, efflorescence, orange peel, bottom leakage, bruising, pitting, convex powder, coating cracking, and dirty spots. The dataset is divided into three subsets, with a ratio of 7:2:1. The remaining experimental settings are identical to those described in Section 3.1. The resulting experimental outcomes are presented in Tables 3 and 4 below.  
Table 3 reveals that the YOLOv8-MPP model exhibits enhanced performance in terms of mean average precision (mAP), with an improvement of 0.8%. Additionally, the number of model parameters has been reduced by 10.3%, while the GFLOPs have decreased by 8.6%. Furthermore, the frame per second (FPS) has been enhanced by 38.2% in comparison to the YOLOv8 model. The results demonstrate that the YOLOv8-MPP model continues to exhibit superior performance in terms of detection speed and accuracy compared with the YOLOv8 model for the detection of steel surface defects.  
A comparison of Table 4 reveals that the YOLOv8-MPP model exhibits an improvement of 1.1% in mean average precision (mAP), a reduction of 10.3% in the number of model parameters, and a decrease of 8.6% in the number of floating-point operations per second (GFLOPs) compared with the YOLOv8 model. Additionally, the frame per second (FPS) has improved by 20.8%. The results demonstrate that YOLO-RRL also outperforms YOLOv8 in all aspects of the APSPC dataset.  
The experimental results on the NEU-DET and APSPC datasets demonstrate that YOLO-RRL outperforms YOLOv8 in terms of accuracy, model complexity, computation, and processing speed. The experimental results indicate that YOLO-RRL has a higher utility value in detection and is more robust than YOLOv8.