esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning
### Model Evaluation Metrics  
In general, evaluation metrics such as accuracy and F1-Score are used for classification problems in machine learning models. In models where the distribution of the output data is not imbalanced, accuracy is sufficient for evaluating model performance. However, in the case of data imbalance, evaluating the model performance based on accuracy alone is extremely challenging. Therefore, it is appropriate to evaluate the model performance using the F1-score, which reflects the data imbalance [33; 34; 35]. In this study, the F1-score of the model was prioritized for evaluation, and in cases of identical F1-scores, the accuracy was compared. Furthermore, accuracy was utilized to assess the overfitting of the model. The accuracies of the training and evaluation sets were compared, and if the difference was less than 0.1, the overfitting problem was considered to be addressed.  
The confusion matrix shown in Table 5 was used to evaluate the performance metrics of the model. The accuracy, recall, precision, F1-score, and specificity were calculated using Equations (7)-(11) [36; 37]. The reliability of the models was evaluated by examining the area under the curve (AUC) for the best-performing models. AUC can be derived using the receiver operating characteristic (ROC) curve [38]. The AUC, which represents the area under the ROC curve (Figure 2), is shown in Table 6.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
\[Recall(Sensitivity)=\frac{TP}{TP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{10}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AUC** & **Evaluation** \\ \hline AUC \(\geq\) 0.9 & Excellent \\
0.8 \(\leq\) AUC \(<\) 0.9 & Good \\
0.7 \(\leq\) AUC \(<\) 0.8 & Fair \\ AUC \(<\) 0.7 & Poor \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model evaluation according to AUC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Confusion**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{2-4}
**Matrix** & \multicolumn{2}{c}{**Negative**} & **Positive** \\ \hline \multirow{4}{*}{Reference} & \multirow{2}{*}{Negative} & True negative & FALSE \\  & & (TN) & positive \\ \cline{1-1}  & & & (FP) \\ \cline{1-1}  & & & FALSE \\ \cline{1-1}  & & negative & positive \\ \cline{1-1}  & & (FN) & (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix.
## 2 Statistical Analyses of Individual Hazards
### Assessment of the Peak Flood Discharge after a Dam Breakage
#### 2.3.2 The Method of Calculating the Downstream Peak Flood Discharge  
[PERSON] [34] proposed an empirical formula called the attenuation formula of the peak outflow rate to predict the peak flood discharge somewhere downstream:  
\[Q_{L_{0}}=\frac{V_{l}}{\frac{V_{l}}{Q_{p}}+\frac{L_{0}}{V_{\max}K}} \tag{18}\]  
where \(L_{0}\) is the distance from the dam site to somewhere downstream (m), \(Q_{L_{0}}\) is the peak flood discharge at \(L_{0}\) from the dam site (m\({}^{3}\)/s), and \(V_{\max}\) is the maximum average flow velocity during the flood period (m/s). The historical maximum velocity can be used in the areas with detailed data. If there are no data, [PERSON] [34] suggests that 3.0-5.0 m/s can be used in general mountainous areas, 2.0-3.0 m/s in semi-mountainous areas, and 1.0-2.0 m/s in plains. \(K\) is an empirical coefficient. [PERSON] [34] also suggests that \(K\) equals 1.1-1.5 in mountainous areas, 1.0 in semi-mountainous areas, and 0.8-0.9 in plains.  
Figure 6: Comparison between the measured peak outflow rate and the calculated peak outflow rate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dam Material** & **Erodibility of the Dam** & \(\beta\) \\ \hline Rock & Low & \(2.2\times 10^{-4}\) \\ Rock and debris & Medium & \(8\times 10^{-4}\) \\ Debris and earth & & \\ \hline Earth & High & \(2\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Suggested values of \(\beta\).  
If we compare \(Q_{L_{0}}\) to the check flood discharge \(Q_{\max}\) and the average annual discharge \(\overline{Q}\), the safety index (\(S_{i}\)) can be obtained. The formulation of \(S_{i}\) is expressed as follows:  
\[S_{i}=\left\{\begin{array}{cc}0&Q_{L_{0}}\leq\overline{Q}\\ \frac{Q_{L_{0}}-\overline{Q}}{Q_{\max}-\overline{Q}}&\overline{Q}<Q_{L_{0}} \leq Q_{\max}\\ \frac{Q_{L_{0}}}{Q_{\max}}&Q_{\max}<Q_{L_{0}}\end{array}\right. \tag{19}\]
## 6 Experimental Verification
### Performance Test Method  
#### 6.2.1 Low-Speed Motion Performance  
In order to effectively evaluate the performance of low-speed motion with the different control methods, the angular fluctuation rate \(\varepsilon\) can be defined to quantify the low-speed-motion performance as:  
\[\varepsilon=\frac{\Delta\theta_{pp}}{\theta_{ref}} \tag{30}\]  
where \(\theta_{ref}\) represents the variation of the ramp position, and \(\Delta\theta_{pp}\) is the peak-to-peak value of the tracking error [16], as is shown in Figure 6. Obviously, for the same displacement \(\theta_{ref}\), if the speed fluctuates more obviously, \(\Delta\theta_{pp}\) will be larger, and then \(\varepsilon\) will increase. Ideally, when the component rotates in a constant speed, then \(\Delta\theta_{pp}=0\) and \(\varepsilon=0\) exactly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Device** & **Type** & **Parameter** & **Value** \\ \hline Inertial sensor & Gyroscope & STIM210 & Resolution(\({}^{\circ}\)/sec) & \(4.768\times 10^{-5}\) \\ Direct-driving & Motor & 130 LCX-2 & Rated voltage/V & 24 \\ component & Tachometer & - & Speed coefficient (r-min\({}^{-1}\)) & \(\pm 300\) \\ Motor & 12 CDT-003 & Rated voltage/V & 24 \\ Harmonic-driving & Harmonic reducer & XBS80-100 & Transmission ratio & 100 \\ component & Absolute encoder & BCE90K40-17 & Resolution(\({}^{\circ}\)) & 0.0027 \\  & Motor & 80B110550430 & Rated voltage/V & 24 \\ RV-driving & RV reducer & RV-201 & Transmission ratio & 161 \\   component & Incremental encoder & EW100409A & Lines & 4000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the experimental setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Direct-Driving** & **Harmonic-Driving** & **RV-Driving** & \\  & **Component** & **Component** & **Component** & **Unit** \\ \hline J & \(3.2\times 10^{-5}\) & \(3.44\times 10^{-5}\) & \(3.6\times 10^{-5}\) & Kg m\({}^{2}\) \\ B & \(1.0\times 10^{-1}\) & \(1.1\times 10^{-1}\) & \(1.2\times 10^{-1}\) & Nm/(rad s\({}^{-1}\)) \\ Kp & \(4.78\times 10^{-2}\) & \(5.26\times 10^{-2}\) & \(5.75\times 10^{-2}\) & Nm/(rad s\({}^{-1}\)) \\ Kl & 6.9 & 7.5864 & 8.2722 & Nm/rad \\ \(\sigma_{0}\) & 0 & \(1.85\times 10^{-14}\) & \(7.93\times 10^{-12}\) & rad\({}^{2}\) \\ \(\sigma_{v}\) & \(4.86\times 10^{-2}\) & \(1.85\times 10^{-8}\) & \(7.93\times 10^{-6}\) & (rad/s\({}^{2}\)) \\ \(\sigma_{d}\) & \(4.90\times 10^{-3}\) & \(2.04\times 10^{-9}\) & \(9.8\times 10^{-7}\) & (Nm)\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: System and controller parameters of the components.  
Figure 5: Block diagram of the experimental setup.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 5 Example Analysis
### Results and Analysis
#### 5.2.3 Pallet Loading Results  
In all carriages, pallets at each customer point are loaded in accordance with the goal of maximizing the loading rate. Due to space constraints, we show the pallets loading at all customer points in Carriage 11 in Figure 10. Figure 11 is the layout diagram of the pallets, and Figure 12 is the display diagram of all the customers' goods after loading into pallets. Pallets are loaded inside the carriage according to the targets' maximum loading rate. The transport route is 0-43-61-42-44-24-57-0.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Route Number** & **Optimal Path** & **Vehicle Loading Rate** & \
\begin{tabular}{c} **Violation Time** \\ **Window (h)** \\ \end{tabular} &
\begin{tabular}{c} **Total Cost** \\ **(CNY)** \\ \end{tabular} & **Target Value** \\ \hline  
& 82.42\% & 1.35 & 352.36 & 0.26 \\ Average & & 81.68\% & 1.43 & 260.95 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Presentation of the pallet 3D loading solution results.  
Figure 11: Carriage loading result.  
Figure 12: Cont.  
Figure 10: 3D loading of pallets in some carriages.  
The number of goods and the pallet loading rate are positively correlated, as the algorithm will look for the optimal cargo box to be retained in the iteration with a similar height to this layer to reduce the height gap when the number of goods is high in the layer theory. In the example, the cargo demand is generally high, so the loading rate is mostly above 80%. When the cargo is large enough to fill a whole pallet, the pallet loading rate can even reach more than 85%.  
Figure 12: Partial node pallet loading results.
## 1 Introduction
To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Additionally, we introduce a hierarchical POI representation learning strategy that fuses information from multiple scales to generate comprehensive POI embeddings. Our key contributions are as follows:  
Figure 1: Next POI recommendation. * We propose a novel hierarchical graph learning framework that models POI relationships at multiple scales through a three-level structure, effectively capturing both fine-grained POI transitions and coarse-grained mobility patterns. * We design a cross-layer information propagation mechanism that enables bidirectional message passing between different hierarchical levels, allowing the model to effectively integrate information across scales and address the data sparsity challenge. * Through extensive experimentation on two real-world datasets, we demonstrate that our hierarchical approach significantly outperforms existing state-of-the-art POI recommendation methods. The remainder of this paper is organized as follows. In Section 2, we review related work. Section 3 details the proposed hierarchical graph learning framework. In Section 4, we present the experimental setup and evaluation metrics. Section 5 discusses the experimental results and comparative analysis. Finally, Section 6 presents the limitations and future work, along with a summary of our contributions and future research directions.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 2 Background
### Multi-Target Bayes Filter  
The label space of the birth targets at time \(k\) is \(\mathbb{L}_{k}\), the label space of the targets (including the targets prior to \(k-1\)) at \(k-1\) is \(\mathbb{L}_{0:k-1}\) and \(\mathbb{L}_{0:k}=\mathbb{L}_{0:k-1}\bigcup\mathbb{L}_{k}\) denotes the label space at \(k\). The multi-target state at \(k\) is \(\mathbf{X}_{k}\subset\mathbb{X}\times\mathbb{L}_{0:k}\) and the multi-target state at \(k-1\) is \(\mathbf{X}_{k-1}\subset\mathbb{X}\times\mathbb{L}_{0:k-1}\). The measurement vector at time \(k\) is \(\mathrm{Y}_{k}\). The prediction and update for the multi-target Bayes filter can be written as [6]  
\[\pi_{k|k-1}\left(\mathbf{X}_{k}\right)=\int f_{k|k-1}\left(\mathbf{X}_{k}| \mathbf{X}_{k-1}\right)\pi_{k-1}\left(\mathbf{X}_{k-1}\right)\delta\mathbf{X}_ {k-1} \tag{19}\]\[\pi_{k}\left(\mathbf{X}_{k}\right)=\frac{g_{k}\left(\mathbf{Y}_{k}|\mathbf{X}_{k} \right)\pi_{k|k-1}\left(\mathbf{X}_{k}\right)}{\int g_{k}\left(\mathbf{Y}_{k}| \mathbf{X}_{k}\right)\pi_{k|k-1}\left(\mathbf{X}_{k}\right)\delta\mathbf{X}_{k}} \tag{20}\]  
where \(\pi_{k-1}\) is the multi-target prior, \(\pi_{k|k-1}\) is the predicted multi-target posterior, \(f_{k|k-1}\) is the multi-target Markov transition density, \(g_{k}\) is the multi-target likelihood function and \(\pi_{k}\) is the multi-target posterior density at \(k\). The integral is the set integral. For simplicity, we define \(\mathbb{L}\triangleq\mathbb{L}_{0:k-1}\), \(\mathbb{B}\triangleq\mathbb{L}_{k}\), \(\mathbb{L}_{+}\triangleq\mathbb{L}_{0:k}\), \(\pi\triangleq\pi_{k-1}\), \(\pi_{+}\triangleq\pi_{k|k-1}\), \(\pi\triangleq\pi_{k}\), \(g\triangleq g_{k}\) and \(f\triangleq f_{k|k-1}\).
## 2 LNA Design Methodology
### Device Selection  
#### 2.2.1 Finger Width and Number of Fingers  
In addition to its small signal gain, \(NF\) is one of the most essential design specifications of an LNA. As the finger size and layout of the transistors contribute to much of the gate resistance and other parasitics of the NMOS device, choosing the optimized finger width can substantially help reduce the minimum noise figure (\(NF_{min}\)). Figure 1 shows the pre-layout (or pre-PEX) simulations of the \(NF_{min}\) of a single NFET as a function of the frequency of various finger widths from 0.3 \(\upmu\)m to 2.4 \(\upmu\)m under a constant total width of 19.2 \(\upmu\)m (total width = finger width \(\times\) number of fingers). The NMOS transistor was biased with a gate voltage of 0.45 V and a drain voltage of 0.9 V. \(NF_{min}\) increases with frequency and is lowest in the case where a finger width of 0.3 \(\upmu\)m was simulated. Therefore, we chose 0.3 \(\upmu\)m finger width for all the NMOS devices in this design.
## 3 Methodology and Mathematical Formulations
### Onsite Measurement for Realistic Boundary Conditions  
Microclimatic conditions (air velocity, temperature, and relative humidity) were measured to use the actual climatic conditions during the reference periods. Due to the popularity of the Na Phra Meru Historical Temple, which attracts over a thousand tourists daily, it was impractical to close the entire site for an extended period to conduct experi  
Figure 4: Details of computational domain: (**a**) point cloud of Na Phra Meru Historical Temple produced using FARO focus laser scanning technology, and (**b**) 3D solid model after surface finishing.  
ments. Consequently, we received authorization to temporarily restrict access only to the installation points for the duration of the study, with a limited time frame of just 3 h. The monitoring process was conducted across four different periods, beginning on 9 August 2024, from 8:00 to 11:00 AM. Data for velocity, temperature, and relative humidity were recorded at 15 min intervals during the 3 h to establish boundary conditions and validate the results. The measurement points and equipment used in this onsite monitoring, along with their accuracy and tolerance levels, are illustrated in Figure 5.  
The measurement data for air velocity, temperature, humidity, and thermal heat flux are presented in Table 1 under Case 1 (Validation). Additional cases represent scenarios designed to explore flow behavior within the heritage building. The average values derived from the measurement data were utilized. It was assumed that all aerodynamic properties remained uniform and constant across the incoming plane, which served as the inlet boundary condition.  
1. FARO focus laser scanning technology  
Accuracy: 250,000 points/s  
Measurement tolerance: within \(\pm\)2 mm.  
2. Testo 174H  
Temperature: NTC  
Measuring range: -20 to +70 \({}^{\circ}\)C  
Accuracy: \(\pm\)0.5 \({}^{\circ}\)C (-20 to +70 \({}^{\circ}\)C)  
Resolution: 0.1 \({}^{\circ}\)C  
Humidity: Capacitive  
Measuring range: 0 to 100%RH  
Accuracy: \(\pm\)3%RH (2 to +98%RH) at +25 \({}^{\circ}\)C  
Resolution: 0.1%RH  
3. Hotwire anemometer  
Accuracy: \(\pm\)5% + 0.1 m/s  
4. Thermal imager testo 875i  
Infrared resolution: 160 \(\times\) 120 pixels  
SuperResolution technology: 320 \(\times\) 240 pixels  
Figure 5: _Cont_.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 4 FlexBFT Design
### FlexBFT Algorithm Structure (Overview)  
As shown in Figure 2, FlexBFT can be broadly divided into two parts at a high level: fast mode and back-up mode. Fast mode and back-up mode are designed to address semisynchronous and asynchronous network environments, respectively. In the initial state, each node's default entry point is fast mode. Fast mode internally implements an optimistic combination of snw-ABC and pace-sync, which will be mentioned below. In fast mode, nodes can rapidly achieve consensus in semisynchronous network conditions and handle some Byzantine behavior of nodes. Nodes continuously achieve consensus rapidly in fast mode through epochs. However, when fast mode encounters an asynchronous network where no progress can be made, it transitions to back-up mode while ensuring that honest nodes have the same progress. Back-up mode is implemented with mature asynchronous consensus protocols, ensuring liveness even in asynchronous conditions but suffering from higher latency due to the requirement for randomness. It is worth noting that after FlexBFT enters back-up mode, the consensus process will be repeated and pushed forward in back-up mode until progress can be made in fast mode before returning to fast mode. The persistence of back-up mode results in FlexBFT not wasting too many communication cycles on the fast lane rollback when facing continuous network fluctuations. In addition, FlexBFT adopts a parallelized dideterministic protocols and asynchronous protocols approach, which can not only obtain the opportunity to restart fast mode, but also effectively shorten the high latency caused by MVBA.
## 3 Methods
### Detection of Crops Fertilization Treatment at Landscape Level  
The NDVI response of a given crop type at a given date may also differ due to landscape position determining soil physical and chemical properties, agronomic management (including fertilizer application, sowing, weeding and harvesting dates) and thus plant growing conditions. Accounting for systematic variation in any of these factors can improve discrimination between fertilizer application amounts and other management practices. A hierarchical linear model was setup for each crop type to decompose the total NDVI variance to estimate the contribution of three components of variation: plot (induced by fertility treatment), field (mostly induced by other management factors) and landscape (mostly induced by environmental factors, including soil and elevation). The hierarchical model was selected, as fields are subordinate to strata while fertilizer application treatments were replicated in these fields. The average NDVI per plot was the dependent variable in the model. Therefore, a generalized linear model was fitted to the data, including all plots with treatments B-E (B-F for millet and sorghum), within the design matrix the categorical variables plot, stratum and fields. Fields factor was nested within the stratum. The GLM procedure in S.A.S. software (version 9.3) was used to fit the model with the statement \"MODEL = plot, stratum, field (stratum)\".  
The levels of the stratum effect were defined using expert knowledge on catena position (plateau, midslope, and lowlands), soil texture and drainage class. We expect that within a landscape stratum, soil types are reasonably similar and that farming practices are somewhat similar. The fields were selected in the landscape and fertility plots were repeated in each field (as described in the Section 2.3). All three factors were included as fixed terms in the model.  
The larger the fraction of variance explained at the plot scale, the better fertilization application differences could be discriminated with NDVI for the considered crop type. If the strata effect explains an important part of the variance along with the plot effect, the NDVI of all the fields have to be compared within a given stratum (and not between strata) to discriminate fertilization application difference.
## 1 Introduction
However, reported optimal inclination angles of heat pipes varies significantly such as 30\({}^{\circ}\)[29], 45\({}^{\circ}\)[30], 60\({}^{\circ}\)[31], 75\({}^{\circ}\)[23], or 90\({}^{\circ}\)[32]. No criterion has been proposed to determine the optimal inclination angle for different types of heat pipes. It is challenging to accurately predict the optimal inclination angle of heat pipes with various working fluids and heat loads. Therefore, a criterion to predict the optimal inclination angle of heat pipes is demanded. This study firstly systematically characterized the thermal performance of the MF-AHPA, of which the thermal characteristics are different from those of conventional round or flat plate heat pipes. The MF-AHPA has a higher condensation rate and extremely longer liquid return path due to high surface-volume ratio and length-diameter ratio. Three experimental setups were designed and built to characterise the heat pipe start-up features and thermal resistance, respectively. Furthermore, the liquid film thickness profiles of heat pipes with various inclination angles are modelled. This research firstly discovered and verified the potential relationship between the optimal inclination angle and liquid film distribution. A criterion is also originally proposed to quantificationally predict the optimal inclination angles of the MF-AHPAs with various working fluids and heat loads.
## 2 Materials and Methods
### Determination of the Microbial Diversity in the Samples  
Microbial DNA was extracted from all the samples using a FastDNA Spin Kit (MP Biomedicals, Santa Ana, CA, USA). A Nanodrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) was used to measure the final DNA concentration and purity. The DNA quality was checked using 0.8% agarose gel electrophoresis.  
The 16S primers were 338F (5\({}^{\prime}\)-ACTCCTACGGGGAGGCA-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\)). The ITS primers were ITS1-F-KYO2 (TAGAGGAAGTAAAAGTCGTAA) and ITS86R (TTCAAAGATTCGATGATTCAC). PCR amplification was performed under the following conditions: initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 35 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 54 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 45 s, with a final elongation at 72 \({}^{\circ}\)C for 10 min. The PCR products were extracted by 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA).  
The amplicon library was generated using a TruSeq Nano DNA LT Library Prep Kit (Illumina, San Diego, CA, USA). High through OTU sequencing was performed using an Illumina HiSeq platform (Sichuan PANNOMIX Biotech Co., Ltd., Chengdu, China).  
Low-quality sequences were removed by QIIME2 (Vision 2019.4) ([[https://view.qime2.org/](https://view.qime2.org/)]([https://view.qime2.org/](https://view.qime2.org/)), accessed on 23 December 2024). The raw 16S RNA and ITS gene sequences were denoised or clustered into amplicon sequence variants (ASVs) using QIIME2 (Vision 2019.4). The alpha diversity, such as the Chao 1 richness and Shannon diversity indices, was evaluated using QIIME2.