esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Experiments
### Experiments on Small- and Medium-Scale Instances
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**No. of Requests**} & \multirow{2}{*}{**No. of Empty**} & \multicolumn{2}{c}{**Cost**} & \multicolumn{2}{c}{**Time (s)**} & \multirow{2}{*}{**Gap**} \\ \cline{3-3} \cline{6-7}  & & **Containers** & & & & & & \\ \hline
1 & 5 (1, 0, 0, 1, 1, 0, 1) & (1, 0, 1, 0, 0, 0) & 89.8 & 89.8 & 26 & 1.21 & 0.00 \\
2 & 6 (1, 1, 0, 1, 0, 1, 1, 1) & (1, 0, 1, 0, 0, 0) & 113.6 & 113.6 & 33 & 1.30 & 0.00 \\
3 & 7 (0, 1, 1, 2, 1, 0, 0, 2) & (1, 0, 1, 0, 0, 0) & 145.3 & 145.3 & 45 & 1.27 & 0.00 \\
4 & 8 (1, 1, 0, 1, 0, 0, 4) & (1, 0, 1, 0, 0) & 198.9 & 66 & 2.25 & 0.00 \\
5 & 10 (2, 0, 1, 0, 2, 3, 2) & (1, 0, 1, 0, 0, 0) & 211.5 & 211.5 & 78 & 7.66 & 0.00 \\
6 & 12 (1, 1, 2, 0, 1, 2, 2, 3) & (1, 0, 0, 2, 1, 0) & 244.5 & 246.3 & 195 & 12.39 & 0.74 \\
7 & 14 (1, 1, 3, 3, 1, 1, 2) & (0, 1, 1, 0, 0, 0) & 245.1 & 248.8 & 213 & 19.54 & 1.51 \\
8 & 16 (3, 0, 1, 3, 1, 1, 2, 5) & (0, 1, 2, 1, 0) & 352.7 & 356.1 & 581 & 25.01 & 0.96 \\
9 & 18 (2, 3, 3, 2, 0, 2, 2, 3) & (1, 0, 0, 0, 2, 1) & 343.6 & 348.0 & 634 & 16.23 & 1.28 \\
10 & 20 (5, 3, 2, 3, 1, 1, 3, 2) & (4, 1, 0, 0, 3, 0) & 361.4 & 373.5 & 729 & 22.09 & 3.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of Gurobi and LNS for small-scale instances. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**No. of Requests**} & \multirow{2}{*}{**No. of Empty**} & \multicolumn{2}{c}{**Cost**} & \multicolumn{2}{c}{**Time (s)**} & \multirow{2}{*}{**Gap**} \\ \cline{3-3} \cline{6-7}  & & **Containers** & & & & \\ \hline
11 & 20 (0, 4, 0, 0, 0, 1, 4, 11) & (5, 0, 0, 3, 0, 0) & 569.7 & 591.4 & 3600 & 30.25 & 3.81 \\
12 & 23 (5, 2, 3, 2, 2, 1, 5, 3) & (0, 3, 5, 0, 0) & 461.4 & 476.2 & 3600 & 34.66 & 3.21 \\
13 & 26 (5, 2, 1, 5, 2, 3, 4, 4) & (3, 0, 0, 1, 0, 2) & 514.5 & 544.1 & 3600 & 15.11 & 5.75 \\
14 & 26 (2, 0, 1, 2, 0, 1, 10, 10) & (0, 5, 2, 0, 1) & 759.0 & 752.1 & 3600 & 22.54 & \(-\)0.91 \\
15 & 29 (3, 1, 3, 6, 2, 3, 5, 6) & (0, 1, 0, 0, 3, 1) & 573.9 & 543.5 & 3600 & 45.32 & \(-\)5.30 \\
16 & 29 (2, 0, 1, 2, 0, 2, 8, 14) & (4, 0, 4, 4, 0, 1) & 795.2 & 814.8 & 3600 & 52.27 & 2.46 \\
17 & 29 (2, 0, 1, 1, 1, 1, 10, 13) & (0, 0, 3, 0, 1, 0) & 771.4 & 722.6 & 3600 & 26.95 & \(-\)6.33 \\
18 & 31 (3, 5, 4, 6, 1, 2, 4, 6) & (1, 0, 4, 3, 0, 0) & 592.8 & 579.0 & 3600 & 53.26 & \(-\)2.33 \\
19 & 33 (6, 0, 1, 8, 2, 4, 5, 7) & (0, 3, 2, 0, 0, 0) & 724.6 & 765.9 & 3600 & 64.55 & 5.70 \\
20 & 35 (2, 1, 0, 2, 0, 3, 11, 16) & (0, 5, 0, 1, 1, 0) & 926.5 & 845.7 & 3600 & 72.28 & \(-\)8.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Gurobi and LNS for medium-scale instances.
# Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
## 1 Introduction
The existing method treats the existence of related diseases, like diabetes or hypertension, as binary variables, 0/1, to predict CKD risk. Details about diagnostic tests like blood sugar, blood pressure, hemoglobin are usually included in items of a regular health check. Other common physical measures (waist, BMI, vision, etc.) may possibly be related to the risk of CKD. Therefore, we got an idea to try whether we can predict the risk of CKD using these possibly related and commonly available data. In this work, we proposed a two-stage method to evaluate the risk of CKD under the condition that creatinine data is not available. In the first stage, a machine learning regression model is used to predict the value of creatinine using a supervised data. As the values of creatinine in the data, which is the target variable, are extremely unbalanced, we used an undersampling method and proposed a cost-sensitive mean squared error (MSE) loss function to deal with the problem. With respect to model selection, in this work we used three machine learning models: a bagging tree model known as Random Forest [15], a boosting tree model called XGBoost [16], a neural network based model known as ResNet [17]. To improve the result of creatinine prediction, we averaged results from eight predictors as our ensembling learning. Finally, the predicted creatinine and the original 23 features are used to predict the risk of CKD in a binary way. The paper is organized as follows. In Section 2, we describe the dataset and elaborate highlights of the experiment. Section 3 describes the proposed method. The experimental details and results are discussed in Section 4. The paper is concluded in Section 5 with some ideas about the future direction of the work.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 3 Node-Density-Based Routing Method
### Aodv
When the node density is high, the frequent broadcasting of RREQ messages results in excessive network overhead. To address this issue, ND-AODV takes advantage of the observation that in high-density environments, the performance difference between the best route and alternative routes is not significantly large. Therefore, it is possible to sacrifice the exhaustive search for the absolute best route to reduce the overall network overhead. In this way, ND-AODV lowers the cost associated with finding the optimal route and achieves improved performance in dense node environments. In Figure 3, routes 1, 2, and 3 denote three possible paths to reach the destination node, S represents the source node and D represents the destination node. The optimal route (route 2) and alternative routes (routes 1 and 3) do not exhibit significant differences. Even if routes 1 or 3 are selected, they can still effectively complete data transmission. Figure 1: The route discovery procedure of the AODV protocol. Figure 3: Similar paths between source and destination node. Figure 2: The route maintenance of the AODV protocol.
## 4 Simulation Experiments
### The Comparison and Analyzing of Different Methods  
For comparison, the simulation signals are diagnosed through current outliers eliminating methods and FDI methods, and the results are shown in Table 8. The detection methods used for comparison include 3\(\sigma\) criterion-based method [36], [PERSON] criterion-based method [37], GLT-method [27], OPT-based method [26], and Chi-square-based method [23]. Comparing Tables 7 and 8, the PCD of the proposed method remains slightly higher than previous methods.  
Running outlier eliminating methods and FDI methods at the same time will decrease the PCD, but there is no such problem in the new method. To verify this, two approaches are conducted to diagnose transient faults, noise faults, constant drift faults, and multiplicative faults: the first one is that outlier points are marked at first through Grubbs criterion and replaced by the points calculated through cubic spline interpolation method [35], then the processed signal will be diagnosed by several current FDI methods; the second way is to use the new method directly. The FDI methods used in the first strategy include the GLT-based method [27], OPT-based method [26], and Chi-square based-method [23].  
The experiment results are shown in Table 9. As the table shows, the outlier eliminating does reduce the PCD of FDI method, because the outlier eliminating methods regard every error point as the outlier, which will cause the deviation degree of error points greatly decrease, so that some faults may be missed.
# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
## 2 Methods
### Extraction of Block Feature by CNN  
CNN has been widely applied in pavement detection for the efficient image recognition algorithm with feature extraction and classification [26]. In practice, filters in CNN perform convolutional and pooling calculations on the input image data, layer by layer, to extract data features [27]. In this work, the input data of our CNN model are image blocks around the feature points. These image blocks, namely the neighborhood of the feature points, are very small (32 \(\times\) 32 pixels), which significantly reduces the complexity of the CNN model. According to this fact, we construct a CNN model for pavement damage detection, whose detailed information is displayed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Filter Size** & **Stride** & **Output Shape** & **Parameters** \\ \hline in & N/A & N/A & (32,32,1) & N/A \\ \hline conv & 3 \(\times\) 3(64) & (1,1) & (30,30,64) & 640 \\ \hline conv & 3 \(\times\) 3(64) & (1,1) & (28,28,64) & 36,928 \\ pool & 2 \(\times\) 2 & (2,2) & (14,14,64) & \\ \hline conv & 3 \(\times\) 3(128) & (1,1) & (12,12,128) & 73,856 \\ pool & 2 \(\times\) 2 & (2,2) & (6,6,128) & \\ \hline conv & 3 \(\times\) 3(128) & (1,1) & (4,4,128) & 147,584 \\ \hline flat & N/A & N/A & 2048 & N/A \\ \hline fc & (128) & N/A & 128 & 262,272 \\ \hline fc & (64) & N/A & 64 & 8256 \\ \hline out & (2) & N/A & 2 & 130 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{Total parameters: 529,666.} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed information of CNN model in this work.  
Figure 2: (**a,c**) Two typical pavement image windows of different brightness and cracks, and the local extremum points. (**b,d**) The feature points picked up from the local extremum points.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 1 Introduction
Section 3 categorizes LiDAR system types based  
Figure 1: An artistic visualization of the evolution of LiDAR systems based on a deployment platform constructed for Earth exploration, with important milestones depicted inspired by [7]: 1964—the first ground-based LiDAR, atmospheric backscattering LiDAR located in at Lexington, Massachusetts [19]; 1979—the first high attitude airborne LiDAR mission on Wb-57 aircraft [7]; 1994—LITE experiment, the first spaceborne LiDAR (atmospheric backscattering), deployed on Space Shuttle Discovery [20]; 2003—GLAS, the first standalone spaceborne LiDAR [21]. on measurement techniques, such as backscatter LiDAR, differential absorption LiDAR (DIAL), Doppler wind LiDAR, and laser altimetry. Section 4 describes the challenges and limitations of spaceborne LiDAR missions, such as spatial resolution trade-offs. Section 5 provides an overview of past and current space missions that have used LiDAR technology, such as LITE, ICESat, CALIPSO, and GEDI. Section 6 discusses the applications and studies based on the data derived from spaceborne LiDARs in various fields, such as atmospheric aerosol profiling, vegetation structure mapping, cryosphere monitoring, and urban studies. Finally, Section 7 explores the future directions of spaceborne LiDARs, including planned missions and next-generation concepts.
# Versatile High-Throughput Platform for Focused Ultrasound In Vitro Application
## 3 Results
### Influence of Filling Volume
#### 3.3.1 LIFU Platform  
In the course of the investigation into the influence of the filling volumes on the sound pressure in the individual wells, the 1.5 MHz version of the LIFU platforms was used. The measurements were conducted with varying filling volumes per well (200 \(\upmu\)L, 250 \(\upmu\)L, 300 \(\upmu\)L, 350 \(\upmu\)L) and at distinct positions within 48 individual wells. A z-line was measured in each of the wells in the direction of radiation, starting at a distance of 1 mm from the bottom of the wells and continuing up to a height of 10 mm. The step size of the measurements was 0.1 mm. Figure 12 illustrates the results of such a measurement in one representative well.  
The results of the study indicated a considerable influence of both the filling volume on the maximum sound pressure and on the axial position of the sound pressure maximum in the well (Figure 13). The results of the maximum sound pressures (p\({}_{\text{max}}\)) for the individual filling volumes are presented in Table 2. These results demonstrated a variation in maximum sound pressure between 244 kPa (standard deviation 106 kPa) for a 200 \(\upmu\)L filling volume and 294 kPa (standard deviation 107 kPa) for a 350 \(\upmu\)L filling volume when driven with a voltage of 15 V\({}_{\text{peak}}\). Additionally, the data illustrate a strong variability in the position of maximum sound pressure within the well, with values ranging between 6 and 9 mm, highlighting the impact of the filling volume and of the transducer sound field characteristics.  
Figure 12: Results of hydrophone measurements with the 1.5 MHz LIFU platform along the z-axis in a representative well (no. 21) of a 96-well plate from the bottom of the well (z = 1 mm) to the maximum filling level at the different filling volumes (200 \(\upmu\)L, 250 \(\upmu\)L, 300 \(\upmu\)L, 350 \(\upmu\)L). The step size of the measuring positions along the z-axis was 0.1 mm.
# Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
## 1 Introduction
The margin proxy is an embedding vector that determines the decision boundary and learns a high-dimensional semantic distance by adaptively adjusting the distance to the centroid proxy as well as the distance to the positive sample. Margin proxies are created in an one-to-one correspondence to the centroid proxies representing each IND intent (like the moon orbiting the earth). Moreover, to learn the discriminative features between IND intents and the OOD intent, we introduce a shared proxy that represents the class to which all IND samples  
Figure 1: An example of a banking system. If a user utterance containing a pre-defined intent (blue texts) is entered, the task proceeds normally. However, if the OOD intent (red text) is entered, it outputs that banking cannot proceed. belong. Existing methods learn using single-label samples, but our method utilizes a simple multi-label method in which each sample belongs to both the positive class and the shared class. IND samples are learned to reduce the distance from the positive IND centroid proxy and shared proxy, and OOD samples can be clearly classified in the embedding space, as only the distance from the shared proxy is reduced. The proposed method does not require the use of any additional classifiers or any changes to the model structure. In this paper, through experiments using three intent classification benchmark datasets, we confirmed that the proposed method achieves an improved performance compared to previous works. The contributions of our study can be summarized as follows:  
* We propose an Advanced Proxy-Anchor loss to conduct the OOD intent classification task. * Our method, which introduces new concepts of the margin proxy and shared proxy, effectively captures high-dimensional features to determine decision boundaries and learns discriminative features between IND intents and the OOD intent. * The results of experiments performed on three benchmark datasets demonstrate that our method achieves significant improvements compared to baseline outcomes.
## 2 Literature Review and Research Question
The study by [PERSON] and [PERSON] combined the CBR technique with process slicing to identify the process elements that are to be tailored. It consists of the steps used for generating a preliminary process slice, retrieving the most similar case applicable to the preliminary process slice and obtaining the final process slice using the retrieved case. However, the proposed approach is exclusively made for reducing the effort and errors during process tailoring for a large-sized software process; hence, it is focused on software development projects. There are also approaches that deal with modeling or describing the context of specific development projects by identifying influencing factors, tailoring criteria, or determining project characteristics, e.g., the study by [PERSON] et al. (2019). All approaches mentioned above were designed to configure project-specific development processes. Regarding MMS, there are also adapted EDP such as EDP for MMS ([PERSON] et al., 2019) or an integrated process modeling and planning of mechatronic products ([PERSON] et al., 2020). However, a generally applicable and rule-based approach to the adaptation of an EDP has not been given sufficient consideration and the individualization aspect has not been taken into account so far. All this leads to the following research question: How can a methodology be developed to tailor a reference process for engineering design that takes into account case studies that are methodologically abstracted and contextual factors that are empirically analyzed, whereby the described methodology can be illustrated by an example of an iMMS? A further question is: What are specifically the critical points within the EDP for iMMS?
# Numerical and Experimental Investigations of Laser Metal Deposition (LMD) Using STS 316L
## 2 Experimental Procedure  
Figure 1 shows a schematic diagram of the experimental setup for the LMD process. A high-power fiber laser (IPG Photonics, Oxford, USA) was used as the heat source. The laser head was moved in a zig-zag pattern, and deposition was performed layer-by-layer with no interlayer time. The powder was supplied in real time onto the substrate coaxially with the laser. An STS 316L substrate (POSCO, Pohang, Republic of Korea) with dimensions of \(200\times 80\times 20\) mm and gas-atomized STS 316L powder with a particle diameter of 53-106 \(\upmu\)m (Titd metal materials, Changsha, China) were used for deposition. Table 1 shows the chemical compositions of the substrate and powder, and Table 2 shows the optimized process parameters based on prior experiments. During the deposition process, the temperature was measured in real time by using a pyrometer (LumaSense Technologies GmbH, Frankfurt, Germany) that can measure temperatures between 580 and 3300 \({}^{\circ}\)C. The pyrometer was fixed at point 1 in Figure 1 (located at 50 mm in the +x direction and 0.8 mm in the +z direction). The emissivity of the pyrometer was suitably set for solid-state STS 316L.
## 2 Model Description
### Renewable Energy Resources  
Output power of WT and PV plants are inherently intermittent. In order to model the stochastic wind speed (and the WT behavior accordingly), the divided Weibull probability density function (PDF) is usually employed. The general Weibull PDF of wind speed can be formulated as follows [29]:  
\[PDF(v)=\frac{k}{c}(\frac{v}{c})^{k-1}\cdot e^{-(\frac{v}{c})^{k}} \tag{14}\]  
where \(v\), \(k\) and \(c\) are wind speed, shape factor (dimensionless) and scale factor, respectively.  
Besides, the output power of WT can be described by Equation (15) [30]:  
\[P_{w}(v)=P_{w}^{r}\cdot\left\{\begin{array}{lcl}0&;0\leq v\leq v_{in}\;\;and \;\;v\geq v_{out}\\ \frac{v_{in}^{3}}{v_{in}^{3}-v_{in}^{3}}+\frac{bv^{3}}{v_{in}^{3}-v_{in}^{3}} &;v_{in}\leq v\leq v_{r}\\ 1&;v_{r}\leq v\leq v_{out}\end{array}\right. \tag{15}\]  
where \(v_{r}\), \(v_{in}\) and \(v_{out}\) indicate the rated speed, cut-in speed and cut-out speed of the WT, respectively, and \(P_{w}^{r}\) represents the total rated power of WT.  
The distribution of hourly irradiance usually follows a bimodal distribution, which can be seen as a linear combination of two unimodal distribution functions [31; 32]. A Beta PDF is utilized for each unimodal, as stated in the following [31]:  
\[f_{b}(\varphi)=\left\{\begin{array}{lcl}\frac{\Gamma(\alpha+\beta)}{\Gamma( \alpha)\cdot\Gamma(\beta)}\cdot\varphi^{(\alpha-1)}\times(1-\varphi)^{\beta-1}& for&0\leq\varphi\leq 1,\;\alpha\geq 0,\;\beta\geq 0\\ 0&\text{
# A LeViT-EfficientNet-Based Feature Fusion Alzheimer's Disease Diagnosis
## 3 Results
The proposed model has achieved excellent AUROC and AUPRC, indicating high discrimination in distinguishing the multiple classes of AD. High AUROC and AUPRC highlight the reliability of the proposed AD detection model. The proposed model achieved an exceptional SD and CI, indicating a reliable and consistent outcome. In addition, a smaller SD shows that the model's performance is consistent across diverse data points. Clinicians can benefit from the model and reduce unnecessary medical interventions. The recommended feature extraction approach has produced highly discriminative features by capturing subtle patterns associated with AD. The suggested BOHB-based hyperparameter tuning has selected appropriate DXB parameters to prevent overfitting and enhance the model's robustness. Table 6 presents the performance of the AD detection models. The utilization of improved LeViT enhances the proposed model's ability to detect long-range dependencies and spatial relationships associated with AD. The scaling coefficient of the EfficientNet B7 model enables the model to handle inherent complexities and variations in the MRI image resolutions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **AUROC** & **AUPRC** \\ \hline Proposed Model & 99.8 & 99.8 & 99.4 & 0.99 & 0.97 \\ \hline [PERSON] \& [PERSON] (2021) [35] & 90.1 & 88.7 & 87.6 & 0.84 & 0.81 \\ \hline [PERSON] et al. (2022) [36] & 97.8 & 95.8 & 96.1 & 0.91 & 0.90 \\ \hline [PERSON] et al. (2022) [37] & 97.1 & 92.4 & 91.5 & 0.90 & 0.91 \\ \hline [PERSON] et al. (2023) [39] & 95.9 & 91.5 & 92.3 & 0.91 & 0.88 \\ \hline [PERSON] et al. (2022) [38] & 86.1 & 78.1 & 80.2 & 0.85 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Findings of Comparative Analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters** & **FLOPs** & **Testing Time** \\  & **(in Millions (m))** & **(in Millions (m))** & **(Seconds)** \\ \hline Proposed Model & 27 & 42 & 1.02 \\ \hline EfficientNet B7 & 39 & 53 & 2.15 \\ \hline SqueezeNet V1.1 & 46 & 59 & 1.23 \\ \hline MobileNet V3 & 47 & 61 & 2.08 \\ \hline SWIN Transformer & 52 & 59 & 1.36 \\ \hline LeViT & 37 & 45 & 1.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computational Configurations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AUROC** & **AUPRC** & **SD** & **CI** \\ \hline Proposed Model & 0.99 & 0.97 & 0.0004 & [95.8–96.8] \\ \hline EfficientNet B7 & 0.91 & 0.93 & 0.0005 & [95.1–97.5] \\ \hline SqueezeNet V1.1 & 0.89 & 0.91 & 0.0007 & [94.8–95.9] \\ \hline MobileNet V3 & 0.85 & 0.86 & 0.0011 & [96.1–97.7] \\ \hline SWIN Transformer & 0.91 & 0.90 & 0.0006 & [95.7–96.9] \\ \hline LeViT & 0.92 & 0.91 & 0.0007 & [96.1–96.9] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Reliability and Consistency Analysis.