esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulations
### Numerical Results  
Taking three AOAs (\(0^{\circ},2^{\circ},4^{\circ}\)) as examples, the experimental data and the numerical simulation data of \(C_{d}\) and \(C_{l}\), with error ratios between them, under the conditions of six NACA0012 models, three far-field distances, and two turbulence models, are shown in Tables 5-7. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Validation Parameters**} \\ \hline Solver & Pressure-based with coupled algorithm & Pressure-based with coupled algorithm \\ \hline Turbulence Models & SST k-omega & Spalart–Allmaras \\ \hline \multirow{3}{*}{Materials} & Density (kg/m\({}^{3}\)): 1.225 & Density (kg/m\({}^{3}\)): 1.225 \\  & Viscosity (Pa \(\times\) s): 1.0474 \(\times\)\(10^{-5}\) & Viscosity (Pa \(\times\) s): 1.0474 \(\times\)\(10^{-5}\) \\ \hline \multirow{3}{*}{Initial conditions} & Mach number: 0.15 & Mach number: 0.15 \\  & Static temperature: 300 K & Static temperature: 300 K \\ \hline \multirow{3}{*}{Boundary conditions} & INLET: velocity-inlet & INLET: velocity-inlet \\  & OUTLET: pressure outlet & OUTLET: pressure outlet \\  & AIRFOIL: no-slip, adiabatic \\ \hline \multirow{3}{*}{Spatial discretization} & Gradient: least-squares cell-based & Gradient: least-squares cell-based \\  & Pressure: second-order & Pressure: second-order \\ \cline{1-1}  & Momentum: second-order upwind & Momentum: second-order upwind \\ \cline{1-1}  & Turbulent kinetic energy: second-order upwind & Modified turbulent viscosity: first-order upwind \\ \cline{1-1}  & Specific dissipation rate: second-order upwind & \\ \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters of convergence numerical results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{\multirow{2}{*}{\
\begin{tabular}{c} **Far-Field** \\ **Distance** \\ \end{tabular} }} & \multicolumn{5}{c}{**Angle of Attack (\({}^{\circ}\)) and Experiment Data of \(C_{d}\)**} \\ \cline{3-6}  & \multirow{2}{*}{
\begin{tabular}{c} **Type of** \\ **Upwind** \\ **Order** \\ \end{tabular} } & \multicolumn{2}{c}{\(0^{\circ}\)} & \multicolumn{2}{c}{\(2^{\circ}\)} & \multicolumn{2}{c}{\(4^{\circ}\)} \\ \cline{3-6}  & & **0.00809** & **0** & **0.00816** & **0.2125** & **0.00823** & **0.4316** \\ \hline \multirow{3}{*}{12 m} & First-order & 0.00809427 & Approx. 0 & 0.00832899 & 0.21984088 & 0.00900243 & 0.43858932 \\  & 0.05\% & / & 2.07\% & 3.45\% & 9.39\% & 1.62\% \\ \cline{2-6}  & Second-order & 0.00810023 & Approx. 0 & 0.00833216 & 0.21977944 & 0.00900414 & 0.43871334 \\  & Second-order & 0.13\% & / & 2.11\% & 3.43\% & 9.41\% & 1.65\% \\ \hline \multirow{3}{*}{16 m} & First-order & 0.00811742 & Approx. 0 & 0.00832344 & 0.22078817 & 0.00893481 & 0.44042730 \\  & 0.34\% & / & 2.00\% & 3.90\% & 8.56\% & 2.05\% \\ \cline{1-1} \cline{2-6}  & Second-order & 0.0081189 & Approx. 0 & 0.008322422 & 0.22087445 & 0.00892964 & 0.44054850 \\ \cline{1-1} \cline{2-6}  & 0.36\% & / & 2.01\% & 3.94\% & 8.50\% & 2.07\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparative data of the modified turbulent viscosity of Spalart–Allmaras applying first-order upwind and second-order upwind.
## 3 Path Loss Models
### UMi Path Loss Models
#### 3.1.3 METIS Model  
LOS PL of METIS model depends on break point distance (\(d_{BP1}\)) and path loss offset (\(PL_{0}\)). \(d_{BP1}\) and \(PL_{0}\) are given by [18]  
\[d_{BP}=0.87 exp\left(\frac{-log_{10}(f)}{0.65}\right)\times\frac{4(h_{11})(h_{2 1})f\times 10^{9}}{c} \tag{2}\]  
\[PL_{0}[dB] = -1.38 log_{10}(f)+3.34 \tag{3}\]  
The large-scale ABG model is used to estimate the PL in UMI-NLOS scenario.
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 4 Evaluation
### Velocity Input Feature Study  
As the velocity component is a strong feature used in classical radar processing, we evaluate its relevance to the result of our segmentation model. We retrain our KPConv model with a reduced input feature space without velocity information. The results are shown in the last row of Table 1. Without the velocity information, the F1-scores are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **All Vehicle Data Set** & **Moving Vehicle Data Set** \\ \hline PointNet++ & 59.91\% & 75.83\% \\ KPConv & 59.88\% & 74.68\% \\ KPConv-LSTM & 59.69\% & 75.81\% \\ KPConv-CLSTM & 60.05\% & 75.42\% \\ KPLSTM & 57.89\% & 75.34\% \\ \hline KPConv w/o vel & 52.36\% & 52.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation macro-averaged F1-scores on the nuScenes validation data set.  
Figure 7: Segmentation confusion matrices for different class weight configurations of the data set classes.  
significantly lower for both data sets. Especially for segmenting moving vehicles, the velocity dimension seems to be a discriminative feature. Without using the strong velocity feature, the segmentation quality of moving and static vehicles reaches a similar score.  
Figure 8 shows a qualitative comparison of the segmentation result of a moving bus with and without the velocity feature dimensions. The radar returns from within the bounding box are segmented correctly in Figure 8a. The radar return in the top of the figure, which also comprises a non-zero relative velocity, is correctly segmented as background. Figure 8b shows that the segmentation failed when not using velocity features. Points in the lower left part of the figure are segmented as a moving vehicle. Geometrically, these points show a great similarity to the points of the moving vehicle or even the wall along the road. The RCS feature is not discriminative enough to compensate for this fact.
# Optimal Prefix Free Codes with Partial Sorting +
## 3 Analysis
### Lower Bound  
A complexity within \(O(n(1+\log\alpha))\) is exactly what one could expect, by analogy with multiset sorting: there are \(\alpha\) groups of weights, so that the order within each groups does not matter much, but the order between weights from different groups does matter. We combine two results:  
1. a linear time reduction from multiset sorting to the computation of optimal prefix free codes; and
2. the lower bound within \(\Omega(n\log\alpha)\) (tight in the comparison model) suggested by information theory for the computational complexity of multiset sorting in the worst case over multisets of size \(n\) with at most \(\alpha\) distinct elements.  
This yields a lower bound within \(\Omega(n\log\alpha)\) on the computational complexity of computing optimal binary prefix free codes in the worst case over instances of size \(n\) and alternation \(\alpha\).  
**Lemma 8**: _Given the integers \(n\leq 2\) and \(\alpha\in[1..n-1]\), for any correct algorithm \(A\) computing optimal binary prefix free codes in the algebraic decision tree computational model, there is a set \(W[1..n]\) of \(n\) positive weights of alternation \(\alpha\) such that \(A\) performs within \(\Omega(n\log\alpha)\) algebraic operations._  
For any Multiset \(A[1..n]=\{x_{1},\ldots,x_{n}\}\) of \(n\) values from an alphabet of \(\alpha\) distinct values, define the instance \(W_{A}=\{2^{x_{1}},\ldots,2^{x_{n}}\}\) of size \(n\), so that computing an optimal prefix free code for \(W\), sorted by code length, provides an ordering for \(A\). \(W\) has alternation \(\alpha\): for any two distinct values \(x\) and \(y\) from \(A\), the algorithm suggested by [PERSON] [1] as well as [PERSON]'s algorithm [14] pair all the weights of value \(2^{x}\) before pairing any weight of value \(2^{y}\), so that the EI signature of \(W_{A}\) has \(\alpha\) blocks of consecutive \(E\)s. The lower bound then results from the classical lower bound on sorting mmultisets in the comparison model in the worst case over multisets of size \(n\) with \(\alpha\) distinct values [25], itself based on the number \(\alpha^{n}\) of possible such multisets.  
Having shown that the GDM algorithm takes optimally advantage of \(\alpha\), we are left to check whether values of \(\alpha\) in practice are small enough for GDM's improvements to be worth of notice. We show in the next section that, at least for one application, it does not seem to be the case.
# A Case Study on User Evaluation of Scientific Publication Summarization by Japanese Students
## 6 Discussion
### Evaluation Metric and Request
#### 6.1.2 Simplicity and Terminology  
Regarding simplicity, subjects mentioned that \"a simple summary is preferred\" and also that \"the summary is too simple to understand,\" as shown in Table 6. About 80% of subjects gave answers indicating that they preferred simple summaries, for example, \"this summary is too long to read\" (by BBL07) and \"this summary gives an overview simply and is easy to understand\" (by ABM04). In addition, in answering the related question in experiment 2, about 80% of subjects also expressed a preference for simple summaries.  
Simplifying document sequences is an active research topic in the NLP field, and two types of simplification methods have been studied: lexical and syntactic [34]. One characteristic of scientific research papers is the existence of terminology. Authors assume that readers have prior knowledge of the field, so they often leave out explanations of terminology. As a result, university students without prior knowledge, especially Bachelor's and Master's students, must deal with unfamiliar vocabulary. Indeed, some subjects mentioned, for example, \"I could not understand both summaries. I am not familiar with the terminology so I could not understand it\" (by ABH01) and \"supplementing the terminology is helpful\" (by BmL12). These comments are consistent with research on science communication [23], where specialized terminology has been shown to create access barriers for new users. For this reason, although redundant sentences are not desired, a lexical simplification or explanations of the terminology are required.
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 5 Fault Tree Analysis
### Quantitative Calculation
#### 5.6.3 Importance Analysis  
Importance analysis provides a quantitative measurement of the influence degree of each basic event on the occurrence of the top event, serving as one of the core components in fault tree analysis methodology [28,29]. A higher importance value indicates that the component has a greater likelihood of causing the top event. However, there are multiple quantification methods for importance measures in fault tree analysis [19]. Among them, the core characteristics of the F-V index are highly consistent with fault tree analysis (FTA). Specifically, this consistency is manifested as follows: the higher the frequency of a component appearing in the minimal cut sets and the greater the occurrence probability of the corresponding cut sets, the higher its F-V index. Meanwhile, this index is more targeted at \"weak link identification\"--it focuses more on \"the'structural role' of components in the system failure path\" rather than simply relying on the failure probability of the components themselves. Based on the above characteristics, this study adopts the Fussell-Vesely component importance analysis method, expressed as Formula (4):  
\[I_{FV}=\frac{P\big{(}F_{sys}\cap F_{i}\big{)}}{P\big{(}F_{sys}\big{)}} \tag{4}\]  
where \(P\big{(}F_{sys}\big{)}\) is the failure rate of the system, \(P\big{(}F_{sys}\cap F_{i}\big{)}\) is the probability of system failure associated with component \(I\). For the entire beam source system, the importance measures of each subsystem and component are as follows in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Numbering** & **Failed Component** & **Fault Bottom Event** & **Fussell-Vesely Importance** \\ \hline
1 & & The surface flatness is not up to standard & \(8.36\times 10^{-2}\) \\
2 & Plasma Electrode & Electrode Endplate Blockage Fault & \(8.36\times 10^{-2}\) \\
3 & Failure & many impurities deposited on the electrode. & \(8.36\times 10^{-2}\) \\
4 & & PG electrode water pipe is leaking & \(2.51\times 10^{-2}\) \\
5 & & Electrode Over-Deposition Polishing & \(1.68\times 10^{-2}\) \\ \hline \multirow{3}{*}{6} & Common cause & \multirow{3}{*}{PG and EG short circuit} & \multirow{3}{*}{\(1.68\times 10^{-2}\)} \\  & basic events of PG & & \\ \cline{1-1} failure & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: NNBI beam source component fault events and F-V importance. Figure 11: Probability curve of a single-shot experiment simulated by AutoFTA 3.9. The bar chart generated from the aforementioned table is shown in Figure 12, while Figure 13 displays the F-V (Fussell-Vesely) importance measure bar chart for failures of the plasma grid (PG), extraction grid (EG), acceleration grid (AG), ground grid (GG), RF driver (RF), and expansion chamber (EC). (For the common cause basic event, its relevant data will be evenly allocated to the two fault groups: plasma electrode failure and extraction electrode failure.)
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 4 IoT-Based Smart Healthcare Systems
### Home Healthcare and Healthcare for the Elderly  
IoT and reality technologies can be deployed at home to continuously monitor elderly people who move slowly and take longer to arrive at the hospital for routine or urgent health care services. _[PERSON] et al._[78] proposed a life support health monitoring system. Indeed, the authors used varying sensor types to collect medical signals, and then, the information was transferred to the cloud infrastructure for handling and big data analyses. Additionally, they also designed a prototype to prove the effectiveness of the proposed solution. This system consists of six medical sensor types: a Bluetooth-based communication module, a Raspberry Pi-based microprocessor, and a Wi-Fi-based communication module. All the medical signals are stored in cloud infrastructure.  
_[PERSON] et al._[79] proposed a system of individualized health care for people who live alone and use wheelchairs. The system combines IoT technology and WBAN technology to provide efficient medical solutions for wheelchair patients by monitoring real-time heart rate, ECGs, blood pressure, and environmental indicators. [PERSON] et al. [80] presented a new method for patient health monitoring using IoT technologies. Patients are fitted with medical sensors to monitor ECGs and survival indicators, and then, this medical signal is transferred to cloud servers that rely upon wireless links.  
The IoHT systems analyzed above have varying requirements. Some IoHT systems have no service response time. Contrarily, others have strict requirements for service response time. The computing architectures directly affect the system performance. In the next section, we will discuss several recent existing computing architectures for IoHT systems.
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 2 Reactive Power Optimization Based on Load-Curve Segmentation  
### Load-Curve Segmentation  
When conducting reactive power optimization, the adjustment of reactive power compensation devices will change with the variations of loads. It is common that on a daily basis load levels in a certain period remain relatively stable. Peak load usually occurs at noon or in the evening. Therefore, it is possible to segment the load curve and hereafter to optimize only once for this certain time period using average load level.  
In order to segment a load curve into sections with lowest inner deviation, the method of wind ramp detection [16; 17] is used. A filtered curve is first derived to represent the magnitude of change around each time stamp. Two methods of filtering are introduced in this paper. In the first filtering method, the filtered curve calculates maximum load difference in neighboring time periods and at the same time ignoring any changes within that period [16]. In detail, the original load-curve time series \(L_{t}\) can be transformed into a filtered representation \(L_{t}^{f}\) as below.  
\[L_{t}^{f}=\max(L(t:t+n))-\min(L(t:t+n)) \tag{1}\]  
In (1), the load curve is noted as \(L(t)\), the filtered curve is written as \(L_{t}^{f}\), and \(n\) represents the time span of interest when considering changes.  
The second filtering method [17], noted as method 2, refers to a different calculation of the filtered curve, as shown in (2).  
\[L_{t}^{f}=mean[L(t+h)-L(t+h-n);\quad h=1,\ldots n] \tag{2}\]  
In (2), a smaller \(n\) indicates a shorter time period in consideration when judging changing of load curves, corresponding to relatively sharp load changes.  
After deriving filtering curves, the time stamps corresponding to big load changes can be extracted using a threshold \(L_{thr}\) as in (3).  
\[t:\left|L_{t}^{f}\right|>L_{thr} \tag{3}\]  
These time stamps can then be used as separation points for load curve segmentation. In detail, if more than two neighboring points are all above the threshold, those in the middle become independent sections themselves, and the two points on the edge are grouped with their neighbors below the threshold. If the neighbors of one separation point are all below the threshold, this point can be grouped with the neighbor who has relatively similar load value.  
As for each section, the average load is calculated, and then be used for reactive power optimization for the time period of this section.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 3 Methodology
### Overview  
Figure 3 illustrates the overall structure of the cross-modal adaptive interaction network (CMANet). In the encoder part, the RGB and depth images are preprocessed, and then two sub-networks are used to perform feature extraction on the two modalities to obtain their primary features at multiple scales. Within the integrated encoder subnetwork, the CMF module is utilized to learn integrated features. Moving to the decoder section, the U-Net's [19] skip connection mechanism integrates corresponding multi-level features from the encoder to generate saliency maps. In the integrated decoder subnet, the AFFM module adaptively fuses features from the modality-specific subnetwork and integrated features from the upper layer, enhancing integrated features and enriching cross-modal information. The DeFR module combines feature concatenation and feature enhancement operations from the RFB [40] module. Finally, the saliency maps generated by the integrated decoder subnet serve as the final output.  
Figure 3: This figure illustrates the CMANet network, which adopts an encoder–decoder architecture comprising a modality-specific network and a modality integration network. The modality-specific network extracts features from RGB images and depth images, while the modality integration network integrates and fuses complementary features from both modalities, generating rich cross-modal information. The encoder and decoder are connected using U-Net’s skip connection method, and the saliency map output by the modality integration network in the decoder section serves as the final detection result.
## 3 Results
### Saucerneol Inhibits CVB3 Replication and Pancreas Inflammation In Vivo  
To assess the antiviral effects of saucerneol in vivo, BALB/c mice were intraperitoneally injected with CVB3. Initially, we monitored body weight changes in CVB3-infected mice treated with saucerneol for 8 days following infection. CVB3-infected mice experienced an approximately 20% decrease in body weight over the eight-day period, and the administration of saucerneol did not significantly prevent weight loss attributed to CVB3  
Figure 5: Saucerneol induced the STING/TBK-1/IRF3 signaling pathway in Enterovirus 71- and Coxyskeletons A16-infected cells. **(A)** Representative immunoblotol results of STING, p-STING, TBK-1, p-TBK-1, IRF3, p-IRF3, and \(\beta\)-actin to evaluate activation of their expression in (A) EV71 and (**B**) CVA16 -infected Vero cells or uninfected cells after treatment with saucerneol (10 \(\mu\)g/mL). The samples were derived from the same experiment, and gels/blots were processed in parallel.  
infection (Figure 6A). Next, considering that the pancreas is a well-known target organ of CVB3, we evaluated CVB3 gene expression and CVB3-induced pancreatitis in mice [27]. Eight days after CVB3 infection, we analyzed the viral RNA in the mouse pancreas using RT-PCR. The results indicate that there was a significant reduction in viral RNA in the group treated with saucerneol compared to that in the CVB3-infected group (Figure 6B). Furthermore, histological analysis of the mouse pancreas revealed that, in uninfected mice, the pancreatic tissue appeared to be histologically normal. However, in mice infected with CVB3 for 8 days, almost all acinar cells were depleted and inflammatory cells infiltrated the tissue. In CVB3-infected mice, a major portion of the pancreas was damaged by CVB3 infection; however, partially undamaged acinar cells could still be identified (Figure 6C).  
Figure 6: Antiviral activity of saucerneol against coxsackeivirus B3 (CVB3) in vivo. (**A**) BALB/c mice were infected intraperitoneally with a \(1\times 10^{6}\) PFU of CVB3 and then assessed by body weight. Saucerneol treatment was administered intraperitoneally at 2 mg/kg daily until the mice were euthanized 8 days post infection. (**B**) Enterovirus RNA levels in mice pancreas treated with saucerneol. RNA was isolated and viral RNA was analyzed using reverse transcription–real-time PCR. (**C**) Representative hematoxylin and eosin (H&E)-stained images of pancreas sections showing uninfected, CVB3-infected, and CVB3-infected groups treated with saucerneol (scale bar = 20 \(\upmu\)m). The data were obtained through two independent experiments for validation assessment. **+- \(p<0.01\)** for comparison with the CVB3-infected group based on one-way ANOVA with [PERSON]’s multiple comparison test.