esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results
### Sensitivity and Specificity of ML Models  
The sensitivity and specificity of different ML models were reported in four studies [30; 32; 34; 35] and are summarized in Table 4. The highest sensitivity was reported for the linear discriminant analysis model at one-year follow-up. It achieved a sensitivity of 82.81%. However, it demonstrated a very low specificity (37.76%). The lowest sensitivity was reported for the Random Forest model (0%) at one-year follow-up despite its high specificity (100%). The sensitivity of the Random Forest model was improved at two-year follow-up, reaching 9.6% with a specificity of 99.2%.  
Figure 5: Summary Receiver Operating Characteristic (SROC) curves for logistic regression (black), Random Forest (red), and XGBoost (green) in predicting mortality among hemodialysis patients. Each symbol (circle, square, or diamond) represents the sensitivity and specificity reported by an individual study, with horizontal and vertical bars indicating 95% confidence intervals. The diagonal line from the lower-left to the upper-right corner marks chance-level performance (AUC = 0.5). Curves that track closer to the top-left corner reflect higher overall discriminative power (i.e., better sensitivity and specificity). In this analysis, Random Forest shows the highest AUC, followed by XGBoost, while Logistic Regression demonstrates slightly lower—but still clinically meaningful—performance.
## 5 Conclusions  
1. After dry polishing SiC with all five solid-phase oxidants, oxygen was detected on the surface, but the percentage of oxygen atoms on the surface after polishing varied. The highest percentage of oxygen atoms was observed after dry polishing SiC with Na\({}_{2}\)CO\({}_{3}\)--1.5 H\({}_{2}\)O\({}_{2}\) and the lowest percentage of oxygen atoms was observed on the surface after dry polishing with KClO\({}_{3}\).
2. From the XRD results, it couldbe seen that the appearance of surface oxygen was due to the tribochemical reaction between the five solid-phase oxidants and the SiC in the polishing process. The reaction product was known to be silicon oxides, and the main substance was SiO\({}_{2}\). In addition, under the action of friction, due to the high flash point temperature at the polishing interface, SiC reacted with H\({}_{2}\)O and generated a SiO\({}_{2}\) oxide layer on the SiC surface.
3. The material removal rate was calculated by measuring the mass before and after polishing, and the highest material removal rate couldbe obtained after dry polishing of SiC with NaOH and the lowest material removal rate could be obtained after dry polishing with KClO\({}_{3}\).
4. After polishing SiC with oxidant NaOH, soluble Na\({}_{2}\)SiO\({}_{3}\) was generated. Therefore, more obvious scratches and pits appeared on the surface of SiC, and the roughness hada substantial increase.The surface roughness of the remaining four solid-phase oxidants didnot change significantly after polishing.  
Conceptualization, J.S. and W.Q.; methodology, W.Q.; software, W.Q.; validation, W.Q., W.X. and X.C.; formal analysis, Z.W.; investigation, W.Q.; resources, W.Q.; data curation, W.Q.; writing--original draft preparation, W.Q.; writing--review and editing, W.Q., J.S. and Z.W.; visualization, W.Q.; supervision, J.S., Z.W.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. U1804142) and the Science and Technology Research Project of Henan Province (No. 192102210058).  
The authors declare no conflict of interest.
## 4 Modeling of the System Components
### Refrigerant Pump
#### 4.2.2 Isentropic Efficiency  
Like the volumetric efficiency, the isentropic efficiency is also modeled empirically. It is defined using Equation (25), in which the actual work of the pump is considered the sum of the reversible work and the shaft loss work, which in turn is correlated to the loss torque:  
\[\eta_{is}=\frac{\dot{W}_{is}}{\dot{W}_{act}}\times 100=\frac{\dot{W}_{is}}{\dot{W}_{ is}+\dot{W}_{loss}}\times 100 \tag{25}\]  
\[\dot{W}_{loss}=\frac{2\pi}{60}\times N\cdot\tau_{loss} \tag{26}\]  
where \(N\) is the pump speed (rpm) and \(\tau_{loss}\) is the loss torque that is obtained empirically. The sensitivity analysis showed that the experimentally calculated isentropic efficiency can be correlated to the pump suction Prandtl number; thus, its non-dimensional form was adopted:  
\[\frac{\tau_{loss}}{\Delta P\cdot A_{bore}^{1.5}}=a\cdot Pr_{su} \tag{27}\]  
where \"\(a\)\" is the empirical coefficient that is correlated to the suction Prandtl number (\(Pr_{su}\)) and the reduced pump speed (\(N_{r}\)) using the minimization of the difference between the simulated isentropic efficiency and the experimentally measured values like Equation (21). The calculated isentropic efficiency using the model is depicted in Figure 14, showing good accuracy for most data points. Moreover, the predicted pump outlet temperature is plotted, showing excellent accuracy.  
Figure 14: Measured and calculated pump isentropic efficiency (**left**) and discharge temperatures (**right**).  
Figure 13: Measured and calculated pump volumetric efficiency (**left**) and pump mass flow rate (**right**).
## 1 Introduction
For example, a recent experimental and numerical study addressed the influences of concrete compressive strength, embedment depth and fire duration on the concrete pyout capacity of anchors [20]. Regarding the concrete cone failure, the resistance of cast-in-place and post-installed fasteners exposed to ISO 834-1 fire conditions can be determined with the method proposed in European Standard EN 1992-4 Anne D [21]. This method consists of decreasing the characteristic concrete cone capacity N\({}_{\text{Rk,c}}\) of a single anchor in cracked concrete at ambient temperature by a factor k(t,h\({}_{\text{ref}}\)) depending on the exposure time t and the embedment depth h\({}_{\text{ref}}\), but independent of the type of anchor (Equation (1)):  
\[ \begin{array}{l}\text{N}_{\text{Rk,c,fi,(t)}}=\text{k(t,h}_{\text{ref}}) \cdot\text{N}_{\text{Rk,c}}\\ \text{k(t,h}_{\text{ref}})=\text{h}_{\text{ref}}/200\text{ for fire exposure up to }90\text{ min}\\ \text{k(t,h}_{\text{ref}})=0.8\,\text{h}_{\text{ref}}/200\text{ for }90\text{ min }\leq\text{ t }\leq\text{ }120\text{ min}\end{array}  \tag{1}\]  
Even though Equation (1) applies for the design of bonded anchors, according to EN 1992-4 Anne D [21], no experimental evidence regarding the concrete cone capacity of bonded anchors during fire exposure has been reported in the literature. It is also worth noting that Equation (1) is supported by only a limited number of test results [22] and that, up to now, no additional experimental investigation on the concrete cone capacity of anchors during fire has been reported. This is because such tests require quite a complex setup and the results are difficult to predict. Alternatively, some recent studies investigated the residual concrete cone capacities of different post-installed anchors, i.e., the tests were performed after cooling down the concrete to ambient temperature [23, 24, 25]. The present contribution is therefore intended to address this need by analyzing the behavior of bonded anchors at high temperature without a cooling phase, with a focus on the concrete cone failure. Two types of heating scenarios are considered in this study: the standard ISO 834-1 fire and a less severe thermal load obtained with slow-rate heating. The detailed test program, setup and procedure are described, then the main test results are discussed.
## System Model
### Energy Model  
In Figure 2, \(d\) is the distance between the transmitting node and the receiving node, while \(k\) is the bytes of transmitting data. \(E_{dec}\) is the dissipated energy of the transmitting electric circle and the receiving electric circle. These electric circles are equal in this energy model. \(\varepsilon_{amp}\) is the amplifying power.  
In the simulation process, we adopted the channel model used in [41]. This channel model includes a free-space transmitting model. Therefore, if we transmit \(k\)-bit data over a distance \(d\), the energy dissipation in the transmitting parts is:  
\[E_{tx}(k,d)=kE_{elec}+k\varepsilon_{amp}d^{2} \tag{2}\]
# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 1 Introduction
The proposed method is organized into eight main sections. Section 1 describes a summary of the research and its contribution. Subsequently, the second part presents an overview of the historical background related to LOS and readmission time frame predictions, as well as the most recent solutions. Next, Section 3 provides an outline of the dataset's specifications, along with its associated statistical characteristics. The proposed method is explained in this section as well. Section 4 describes the evaluation process employed to assess the proposed method's effectiveness and the verification of the results. In Section 5, the presented methodology is summarized, and a comparison is drawn between the proposed method and similar research. Section 6 specifies the limitations and future directions of the work. Section 7 delivers a concise conclusion that encompasses the research method, results, contributions, and future prospects of the study. The final section of the study defines the abbreviations. The contributions of this research are as follows:  
* A novel cost function for the genetic optimization process to leverage the feature extraction process toward a better performance is presented;
* The proposed approach outperformed both ML and DL methods for short- and long-term LOS predictions;
* The proposed approach outperformed the surveyed ML methods in related works with respect to diabetic readmission time predictions;
* The most important features for both the LOS and readmission time frame predictions are provided.
## 7 Numerical Results and Discussion
### Simulation Setup
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Common parameters} & Underlying modulation & QPSK/16-QAM \\ \cline{2-3}  & Sub-carriers number, \(N\) & 256 \\ \cline{2-3}  & Oversampling factor, \(\eta\) & 4 \\ \cline{2-3}  & Reference signal RCM, \(\Gamma_{ref}\) & 1.52 dB \\ \cline{2-3}  & Cubic metric empirical factor, \(\kappa\) & 1.56 \\ \cline{2-3}  & Cubic metric threshold, \(\Omega_{0}\) & [2.5,6] dB \\ \cline{2-3}  & \(E_{b}/N_{0}\) & [0.9] dB \\ \hline ICF, SCF & Clipping ratio, \(C\) & 6 dB \\ \hline ICF & Number of iterations, \(L\) & 3 \\ \hline \multirow{2}{*}{SCF} & Equivalency factor, \(\lambda\) & 3 \\ \cline{2-3}  & Standard deviation, \(\sigma\) & 1/2 \\ \hline SLM & Block size, \(U\) & 16 \\ \hline \multirow{2}{*}{ANN, SNN-CF} & Network Architecture & FFMLP \\ \cline{2-3}  & Output layer activation type & Linear \\ \cline{2-3}  & Normalization & Enabled \\ \hline \multirow{6}{*}{ANN} & Number of NN modules & 2 \\ \cline{2-3}  & Number of hidden layers, \(V\) & 2 \\ \cline{2-3}  & Number of neurons in 1 st layer, \(h_{1}\) & 2 \\ \cline{2-3}  & Number of neurons in 2 nd layer, \(h_{2}\) & 1 \\ \cline{2-3}  & Hidden layers activation type & Triangular \\ \cline{2-3}  & Training algorithm & Levenberg–Marquardt \\ \cline{2-3}  & Training epochs & 100 \\ \hline \multirow{6}{*}{SNN-CF} & Number of NN modules & 1 \\ \cline{2-3}  & Number of hidden layers, \(V\) & 2 \\ \cline{2-3}  & Number of neurons in 1 st layer, \(h_{1}\) & 2 \\ \cline{2-3}  & Number of neurons in 2 nd layer, \(h_{2}\) & 2 \\ \cline{2-3}  & Hidden layers activation type & Hyperbolic tangent \\ \cline{2-3}  & Training algorithm & Proposed algorithm 2 \\ \cline{2-3}  & Training epochs & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. performed for each input-output sample in the training dataset is configured to the value of 100 iterations for ANN, whereas for the proposed SNN-CF method, we only use 50 updating iterations which is facilitated by virtue of its potentially faster convergence capability. The dataset batch used for learning the SCF scheme behavioral patterns is complied from a 100 input-output samples. The dataset is split into three parts: (a) the training set, containing 70% of the dataset samples which are only used during the training stage in which the weights and biases of all layers are found and updated to minimize the MSE over the training set; (b) the testing set, consisting of 15% of the dataset samples which are used to test the network found by applying the training set and possibly fine-tuning the weights to account for the testing set; and (c) the validation set containing the remaining 15% of the dataset and is used to validate the trained network's ability to generalize to new data it has never seen before.
## 4 Results and Analysis
### Analysis of Wave Field

Table 5: Angles of the transmitted SV- and P-waves at the soil–rock interface for a left-side-incident SV-wave. soil-rock interface in comparison to the initial incidence, which suggests that total reflection indeed occurs. Thirdly, the propagation direction in the rock mountain and that in the soil site behind the mountain are noticeably different due to refraction. Based on [PERSON]'s law, the shear wave velocity in soil and rock, and the incidence angle, the transmission angle is equal to 13\({}^{\circ}\) for the SV-wave transmitting from the rock to the soil. Considering that the slope angle of the right side of the mountain is 80\({}^{\circ}\), the angle between the transmitted wave coming from the rock mountain to the soil site and the horizon line should be 23\({}^{\circ}\). Then, the propagation direction of the transmitted wave (solid line with a single arrow) derived from the theoretical calculation is given in Figure 7b and compared with the wave's front in the displacement contour obtained by numerical simulation. It can be seen that the wave's front of the transmitted wave in the numerical results is just perpendicular to the theoretical propagation direction. In the meantime, the predefined direction of the incident wave (solid line with double arrows) is also presented in Figure 7, and it can be found that the numerical results are in good agreement with the theoretical propagation direction. Taking all the above-mentioned information into account, the analysis of the wave field shown in Figure 6 can be verified, and the validity of this study can also be verified to some extent. Figure 7: \(Cont\).
###### Abstract
Moreover, to mitigate the detrimental impact of data sparsity, we first leverage the auto-regressive user preferences to train the disentangled multi-head self-attention encoder and then employ the mutual information between user representations and group representations, ensuring congruence within groups while maintaining distinctiveness from other groups, unfolding contrastive learning to fine-tune our framework and assisting with the sparse supervised signal of group-item historical interactions. To the best of our knowledge, this is the first work that has designed a disentangled multi-head self-attention module to encode users and groups with finer granularity coupled with auto-regressive and contrastive mutual information optimization that is specifically tailored for ephemeral group recommendations. The principal contributions of this work are delineated as follows:  
* We combine disentangled neural networks with a multi-head self-attention mechanism and propose a disentangled multi-head self-attention module to generate precise group and user representations, dissecting user intents for preference encoding from multiple dimensions;
* We introduce optimization objectives based on auto-regressive user preference information and group-related mutual information maximization to counteract information scarcity in group data, thus facilitating network training and enhancing the fidelity of representations;
* Comprehensive experiments conducted across three public benchmark datasets for group recommendations corroborate the efficacy of our framework, showcasing its superiority over state-of-the-art baselines. The structure of this paper is as follows: Section 2 delves into the pertinent literature. Section 3 elucidates the foundational concepts of ephemeral group recommendations. The architecture, components and theoretical underpinnings of our DAGA framework are explicated in Section 4. Section 5 presents an empirical evaluation of the performance of our framework on real-world datasets. Section 6 concludes with a summation and an outline of prospective research directions.
## 4 Performance Evaluation
### Ping-Pong Effect Analysis  
The frequent handoff of an MN back and forth between two APs is called the ping-pong effect. The ping-pong effect causes many unnecessary handoffs, increases the handoff overhead and reduces the quality of communication services. In this subsection, the impact of the ping-pong effect was evaluated with the trajectory as shown in Figure 14(a). The moving pattern is detailed in Table 5. In this trajectory, the MN does not need to process any handoff procedure. However, when many traditional threshold-based handoff algorithms are used, they may perform various numbers of handoff.  
Figures 14(b) and 14(c) illustrate the motion detection delay and motion detection success rate of MRSS, DMRSS and MACD. The results show that the MRSS method and the DMRSS method can accurately detect the user motion and MRSS performs better than the MACD method.  
According to the user motion state, the proposed motion detection services can terminate network discovery process to save power and will not trigger any handoff when MN is in a stationary state. Figure 14(c) depicts that the traditional threshold-based handoff algorithms trigger many unnecessary handoffs. Figure 14(d) also indicates that the proposed MRSS and DMRSS motion detection algorithms can reduce unnecessary power consumption.
## 4 Results
### Spatial Variations in Crop Yield at the Sub-National Scale in Morocco
Additionally, wheat generally showed the best rates for vulnerability, adaptive capacity, and normalized growing season precipitation among all the crops. At each latitude, wheat showed the lowest vulnerability index, the highest adaptive capacity index, and the highest levels of normalized growing season precipitation; barley was second and maize showed the least. The results obtained at the sub-national scale were consistent with those at the national scale but went a step further to improve our understanding of the spatial and sub-national variations in vulnerability, adaptive capacity, and normalized growing season precipitation. Again, the correlation between the adaptive capacity index and the vulnerability index at the sub-national scale/latitudes was negative for all three crops and was the highest when compared to the other correlation outcomes (Table 2). This further shows that just as with the national scale correlations, adaptive capacity is a stronger driver of vulnerability relative to the other indexes. The spatial variations described above can be confirmed by the actual spatial variations in wheat and barley yield for various stations across Morocco. These observations showed increasing yields for both wheat and barley from stations in the south to the north (Figures 9 and 10)--a phenomenon that was closely knit to variations in rainfall and, consequently, vulnerability and adaptive capacity. This is seen as the areas of low yield in the south are more exposed to the Sahara and are host to some of the poorest and least educated populations in Morocco.
## 5 User Study
### Result
#### 5.5.5 MVF Is Easier to Understand Than EBC  
The results are summarized in Table 4 and Figure 7(a).  
From Q3, \"You find it's easier to understand the relationship between the author and attributes if the attribute appears only/more than once\" and Q6, \"You find the position mapping of the attributes is easier for you to understand the relationship between the author and attributes\", we found that the MVF and the EBC have significant differences in incomprehensibility. The MVF is easier to understand for common tasks. The scores of the MVF were significantly higher than those of the EBC for both quantitative factors (Table 4-9) (10) and location factors (Table 4-11) (12). Moreover, the difference between the quantitative factors MVF and EBC was larger (Table 4-11) (12), with an average difference of 1.7 points, indicating that the occurrence times of the MVF attributes were helpful to the understanding of the model. At the same time, the EBC was relatively unfavorable to the understanding. Hence, H5 was accepted.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Size** & **Type** & **M** & **SD** & **t** & \(p\) & **Line No.** \\ \hline \multirow{4}{*}{**Easy to Understand**} & \multirow{2}{*}{Q3 (NoO)} & MVF & 4.3 & 0.82 & & & & 9 \\ \cline{3-8}  & & EBC & 2.6 & 0.84 & 5.075 & 0.001 ** & & & 10 \\ \cline{2-8}  & & \multirow{2}{*}{Q6 (PM)} & MVF & 4.4 & 0.7 & & & & 11 \\ \cline{3-8}  & & & EBC & 3 & 1.05 & 3.096 & 0.013 ** & & & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 8: Results of task completion time in seconds. Error bars indicate the standard deviation of the measured mean.
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 6 Discussion of Results
### Comparison of FSDS, IBFS and UFSSOD with the Best 25% Features
To find an explanation for the worse performance of IBFS and UFSSOD compared to FSDS in terms of \(F1/avg\_t\) for some datasets, we have examined two exemplary ones:  
Figure 7: Exemplary \(F1\) plots for well-performing IBFS and UFSSOD using top 25% feature subsets on datasets _cardio_ (**a**), _pima_ (**b**), _satellite_ (**c**) and \(wbc\) (**d**). _must_ (Figure 6b) as a representative for the badly-performing and _satellite_ (Figure 7c) for the well-performing with decent \(F1\) scores. We then examined the plotted feature scores of both, which are depicted in Figure 8. It can clearly be seen in Figure 8a and especially in Figure 8b that the majority of scores lies densely within a certain range and is homogeneously distributed, which is an indicator that outliers tend to occur in any feature. Referring to Figure 8c and especially Figure 8d, a more heterogeneous distribution of the feature scores for the _satellite_ dataset can be seen, reasoning that outliers might tend to occur in the same features, which generally contribute more to the outlier score.
## 4 Methodology  
Once network parameters were calculated, a statistical analysis of them was performed. The methodology used in this work consists of three parts (see Figure 3). First, the associations between the centrality measures were searched and analyzed. For that, we used the HJ-Biplot technique which allows one to interpret simultaneously the position of rows and columns of a data matrix in a low-dimensional subspace. After that, a cluster analysis was performed to classify metro stations into homogeneous groups. The Biplot coordinates were used to calculate clusters (K-means method, Euclidean distance). Lastly, the relationship between these groups and passenger flow, was identified. For that, an analysis of variance (ANOVA) was used.