esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
## 3 Dataset  
The dataset used to train, validate and test the algorithms was distributed by MediaEval 2018 for the Multimedia Satellite Challenge [49; 50]. It consists of 7387 tweet ids for the development set and 3683 tweet ids for the test set. By the time the images were downloaded for this competition, a significant number of tweets were no longer available, which resulted in a development set of 5818 tweets and a test set of 3017 tweets. However, since the work done in this paper corresponds to an extension to the work done for the Multimedia Satellite Challenge and we do not have the ground truth for the test set data, we will divide the training set into training (4074 images), validation (872 images) and test (872 images). The images for the test set will only be used to report the final results to make the setup as close as the original challenge.  
The tweets have been collected by retrieving all the tweets with images containing the tags _flooding_, _flood_ and _floods_ during the hurricanes _Harvey_, _Irma_, and _Maria_. Since the image information is crucial to this work and a lot of images were duplicate in the tweets, a process to remove duplicate images was carried out by the dataset distributors. This process is described in detail in [49].  
The provided ground truth for the tweets was manually generated through a crowdsourcing task and consists of a binary class label for the evidence of road presence and only for those images classified as containing a road, a second binary class label for the actual passability of the road. Positive road passability is considered when the road is practicable by conventional means (no boats, off-the-road vehicles, monster trucks, Hummer, Landrover, farm equipment) and it is, therefore, related to the water level and the surrounding context.  
The annotators made the decisions based only on the content of the analyzed images. The dataset is significantly imbalanced towards the non-evidence of road, having only \(\sim\)36% of the tweet images containing roads. In \(\sim\)45% of the tweets labeled as containing roads, there is evidence of positive road passability. In Table 1, the absolute number of images pertaining to each class is displayed.
# Comprehensive Optimization of Air Quality in Kitchen Based on Auxiliary Evaluation Indicators
## 3 Experimental and Numerical Validation
### Quantitative Analysis of Kitchen Air Quality  
Several indicators were analyzed in the tested kitchen cooking space to evaluate the impact of airflow temperature and velocity on overall thermal comfort. To quantitatively assess the comfort indicator (ADPI), 36 measurement points were strategically positioned near the subject's body within the monitoring area. Using ANSYS Fluent 2021R1, temperature and air velocity values were computed at each location, and the ADPI was subsequently calculated through Equations (5) and (6). The locations of the monitoring points are shown in Figure 7. Additionally, this study developed an implementation of the PMV (predicted mean vote) equation in the C programming language. Through the User Defined Functions (UDF) interface in ANSYS Fluent, the custom C program was executed to compute PMV values near the human forehead and mouth. The averaged value of these measurements was derived as the PMV indicator.  
To comprehensively analyze CO\({}_{2}\) and COFP concentrations, measurements were taken both in the breathing area and across the entire kitchen. The breathing area refers to the zone where occupants directly inhale air during cooking activities, making its air quality a critical factor for human health. In contrast, the overall concentration reflects the air quality of the entire kitchen space. Focusing solely on the breathing area may overlook pollution in other regions; therefore, this study used the average concentrations from both areas to evaluate CO\({}_{2}\) and COFP levels.  
At this stage, the Q value can be calculated by using Equation (12). By incorporating the four key air quality indicators, i.e., ADPI, PMV, CO\({}_{2}\), and COFP, measured at the forehead, breathing point, and across the entire space, this method provides a more precise and comprehensive assessment of indoor air quality. It not only enhances the specificity of air quality evaluation but also offers a more accurate reflection of overall health and comfort in the kitchen environment. The results are presented in Table 6.  
Figure 6: Time-domain comparison curves of simulation and experimental values at different monitoring points.
## 5 Hardware-in-the-Loop Validation  
In order to demonstrate the optimization effect of the DA-ECMS in a real vehicle system, a hardware-in-the-loop (HIL) test was used to validate the optimization effect under the same WLTP conditions as in the above simulation validation. A dSPACE experimental bench was constructed to simulate the dynamic conditions and signal interactions in a real vehicle environment, which is provided by dSPACE GmbH from Paderborn, Germany. The overall frame diagram of the experimental bench and the diagram of the experimental bench are shown in Figures 21 and 22, respectively. The test results of the fuel cell stack and battery output power from the simulation and HIL tests are shown in Figures 23 and 24, respectively. The overall trends of the fuel cell stack and battery output power were basically the same, and the errors and delays mostly appeared at their power peaks.  
As shown in Figure 25, the trends and fluctuations of the battery SOC trajectory in the simulation and HIL test were similar, with very small errors. The final SOC was close to about 0.6, which confirmed that the adopted strategy could effectively maintain the  
Figure 23: Fuel cell stack output power in simulation and hardware-in-the-loop (HIL) test.  
Figure 22: Diagram of the experimental bench.  
battery charge stability and adapt to the real vehicle environment. The equivalent hydrogen consumption of the DA-ECMS in the HIL test was 361.9 g, as shown in Figure 26, which was only 2.4 g higher than the simulation results. Its trajectory was almost the same as the simulation trajectory, which indicated that the strategy could also maintain good economy in a real vehicle environment.
# Statistical Analysis of Cutting Force and Vibration in Turning X5 CrNi18-10 Steel
## 3 Results
### Force Analysis  
The force data were collected through a three-component piezoelectric dynamometer from the Kistler Corporation electronic setup; as mentioned in the experimental setup, the data were saved in \"[dot] txt\" format with the order time (s); the force in the X direction F(x), (N), is a tangential force, which is also the called the cutting force (\(\overset{\rightarrow}{\text{F}_{\text{c}}}\)), the force in Y direction F(y), (N), is the radial force, which is also called the passive force (\(\overset{\rightarrow}{\text{F}_{\text{r}}}\)), and the force in the Z direction is axial F(z), which is also called the feed force (\(\overset{\rightarrow}{\text{F}_{\text{r}}}\)), in N. The active cutting force is the sum of the tangential and axial force; so, the force in the Y direction was kept at zero intentionally, because the tool is stationary in Y, and the force value is very low in comparison with the other forces and can be ignored.  
Figure 7 represents the active force magnitude with the machining parameters for each experiment; it can be visualized from Figure 7, that the active cutting force RMS value is smaller in EX7 with DOC (a\({}_{\text{p}}\))--0.5 mm, V\({}_{\text{c}}\)--200 m/min, and _f_--0.08 mm/rev, and it can be concluded from the vibration analysis that EX7 also has one of the least active  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Exp.** & **DOC (a\({}_{\text{p}}\))** & **V\({}_{\text{c}}\)** & **_f_** & **AV\({}_{\text{b}}\) RMS** & **AF RMS** \\
**No.** & **mm** & **m/min** & **mm/rev** & **m/s\({}^{2}\)** & **N** \\ \hline
1 & 0.5 & 150 & 0.08 & 10.96 & 134.32 \\
2 & 0.5 & 150 & 0.16 & 17.83 & 212.53 \\
3 & 0.5 & 150 & 0.24 & 19.56 & 291.63 \\
4 & 0.5 & 2  
vibration RMS values. With DOC (a\({}_{\text{p}}\))--0.5 mm and _f_--0.08 mm/rev active force RMS having lower value with all cutting speeds, it can be concluded that these parameters are favorable to achieving a low cutting force value and preventing the machined product from deformation. Further, it can be also visualized that the value of the active cutting force decreases as the cutting speed increases.  
In EX12, the active cutting forces reached their maximum value with DOC (a\({}_{\text{p}}\)) 1 mm, V\({}_{\text{c}}\)--150 m/min, and _f_--0.24 mm/rev. Similarly, the vibration value was high in EX12. It can also be analyzed from Figure 7 that, with an increase in the feed rate, the active cutting force value also increases. With DOC (a\({}_{\text{p}}\))--1 mm, the value of the active cutting force RMS significantly increased, which can be understood by MRR. During the turning of X5 CrNi18-10 steel, a deep cut is not recommended, as it can impact the quality of the machined product.  
The other active force magnitude values can be visualized in Table 4 and Figure 7. Figures 6 and 7 can be compared, and the best machining parameters can be optimized with the comparison, where the vibration is low and the cutting force value is low, which will reduce chatter during the turning operation, and a good quality product can be produced.
## 2 Materials and Methods
### Heavy Metal Solutions Preparation  
Stock solutions of five heavy metals were prepared using analytical grade metal salts: Cd(NO\({}_{3}\))\({}_{2}\cdot\)4H\({}_{2}\)O, Pb(NO\({}_{3}\))\({}_{2}\), Cr(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O, Cu(NO\({}_{3}\))\({}_{2}\cdot\)3H\({}_{2}\)O, and Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O (all from Sigma-Aldrich, MI, USA, \(\geq\)99.0% purity). Solution pH adjustments were performed using analytical grade HCl (37%, Merck, Lebanon, NJ, USA) and NaOH (\(\geq\)98%, Aladdin Chemical Co., Ltd., Shanghai, China). The concentration gradients for each metal were specifically designed to span from environmentally relevant levels to severely contaminated conditions based on three criteria: (1) alignment with China's Surface Water Environmental Quality Standards concentration thresholds for Class I through V waters (GB 3838-2002) [2]; (2) representation of typical contamination levels observed in industrial discharge zones as reported in recent monitoring studies [17; 18]; and (3) inclusion of extreme scenarios (10-50\(\times\) standard limits) to evaluate system resilience under severe pollution events. This approach allowed us to assess performance across the full spectrum of potential field applications, from routine water quality improvement to emergency remediation scenarios. Solution pH was adjusted to 7.0 \(\pm\) 0.2 using 0.1 M HCl or NaOH.  
Two different experimental configurations were employed to evaluate scale-dependent performance characteristics: (1) the module-scale system used 30 g biochar in a 25 L volume (833:1 mL-g ratio) to simulate practical field deployment conditions where larger water volumes interact with modular units; and (2) the kinetic studies used 10 g biochar in 100 mL (10:1 mL-g ratio) to provide detailed insights into reaction mechanisms and kinetics under controlled laboratory conditions. This dual-scale approach allowed us to assess both practical efficiency in field-relevant configurations and fundamental removal mechanisms.  
Our experimental approach consisted of two complementary phases: (1) a long-term removal study conducted over a 10-day period to evaluate sustained performance, and (2) a short-term kinetic study spanning 24 h to characterize the initial removal mechanisms and rates. The long-term study utilized four concentration gradients (T1-T4) ranging from 0.05 to 2500 mg/L for different metals. Sampling was conducted at regular intervals (Days 0, 2, 4, 6, 8, and 10). The short-term kinetic study employed five concentration levels (C1-C5) with sampling points at 2, 4, 8, 12, and 24 h. All experiments were conducted in triplicate at 25 \(\pm\) 2 \({}^{\circ}\)C using 1000 mL sample volumes.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 2 The Improved Model
### The Improved Model  
Since the stochastic characteristic of BDS-2 satellite clock is sophisticated and non-stationary, which cannot be described well using the existing models, an improved model combing the SA model and GRNN is proposed for short term clock prediction. It is known that GRNN has a high non-linear mapping ability and strong robust properties, which is suited for capturing the remaining clock residues  
Figure 1: Basic architecture for generalized regression neural network (GRNN) [29].  
after the removal of trend and periodic terms. Because there is no explicit expression for GRNN, the part of GRNN is depicted as a function of \(f_{GRNN}\). Hence, the improved model is proposed as,  
\[\begin{array}{l}clk(t_{i})=a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2}\\ +\sum\limits_{m=1}^{2}\left[b_{m}\sin(2\pi f_{m}(t_{i}-t_{0}))+c_{m}\cos(2\pi f _{m}(t_{i}-t_{0}))\right]+f_{GRNN}+\varepsilon_{i}\end{array} \tag{3}\]  
where \(m\) is the number of periodic terms obtained from SA model, and \(b_{m}\), \(c_{m}\) are the coefficients for the period \(1\)/\(f_{m}\). Other parameters are the same as Equation (2).  
Further, the data preprocessing diagram is given in Figure 2. The input data set of the improved model are multi-days clock offset series. After preprocessing to get rid of outliers, a preliminary processed clock offset series is obtained. Then, the SA model is employed to weaken periodic characteristics, and the residuals are considered as input data set for GRNN. In the GRNN processing, different designs for GRNN parameters affect the prediction results very much. Therefore, as shown in Section 3, GRNN related parameters including length and interval of sample data as well as a smooth factor are optimized satellite by satellite to consider satellite-specific characteristics for all BDS-2 satellites.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions  
### Data Integration and Interoperability  
#### 5.1.1 Challenges  
In the smart healthcare field, data heterogeneity is a common challenge that hinders the application of AI, complicating the integration and interoperability of various data sources. Data heterogeneity refers to differences in data structures, formats, or semantics across different sources or systems, such as different departments within the same medical institution, different institutions within the same medical system, or even different medical systems. Additionally, data heterogeneity includes diverse data types, structures, and standards within the same data sources or systems, such as differences between structured, semi-structured, and unstructured data, as well as variations between text, images, audio, and video data [24] Figure 3.  
To explore this issue in detail, several aspects can be considered: within the same medical institution, different departments may use varying data formats and standards, increasing the complexity of data integration; within the same medical system, different institutions may have significant differences in data standards and formats, requiring complex mapping and conversion processes for interoperability; the difficulty of data integration between different medical systems is even greater, with differing standards and regulations between public and private medical systems creating more barriers; within the same system, the diversity of data types can also lead to heterogeneity issues, such as patient records containing text, lab test results, medical imaging, and audio recordings, each requiring different processing and analysis methods; even within the same type of data, different systems may use different standards, such as different coding systems for the same disease, complicating data comparison and integration. A detailed examination of these aspects can help better understand the impact of data heterogeneity on AI applications and propose solutions such as adopting unified standards, developing cross-system data conversion tools, and using advanced machine learning algorithms [24].
## 6 Application Scenario Based on Measured Temperature Aata
### Bayesian RUL Prediction  
Following the thermal impedance monitoring model in [PERSON] et al. [52], a data-driven approach is used to model the increase in the junction temperature change \(\Delta T_{j}\) over the PC runtime for RUL prediction of an LED system. There, the change of temperature and the change in thermal resistance over time are modelled by the use of a power law, which is typically applied for cyclic loading, and an Arrhenius model used for temperature-driven  
Figure 11: Reliability workflow of the dataset [52] using thermo–electrical loading conditions as input parameters to age the LED system. Thermal impedance (TI) analysis is further used for intake and a sequential analytical method to evaluate the status of the device under test (DuT).  
Figure 10: Visualization of Table 2: Distribution of posterior means for (**left**) \(\mu_{\text{R}_{\text{th}}}\) and (**right**) \(\sigma_{\text{R}_{\text{th}}}\) over 100 datasets (E3.a–E3.c) under different strategies, true values (red circle) and (uniform) prior distributions (yellow line).  
ageing behaviour. The combination of both enables the description of solder fatigue over the PC runtime.  
Bayesian regression modelling is performed as proposed in [PERSON] et al. [43] to model the increase in \(\Delta T_{j}\) depending on a (fixed) temperature swing at the initial state \(\Delta T_{0}\) and the number of cycles performed (\(\mathrm{N_{PC}}\)) using a linearisation of the approximation provided in [52] (Equation (2)). The approach in [43] handles the epistemic uncertainty in unknown model parameters, and thus allows more accurate RUL prediction of the system. However, potential variability stemming from production or operation is not considered due to the unavailability of relevant data. The resulting model predictions with 95% credible intervals for an LED system with fixed \(\Delta T_{0}=110.92^{\circ}\)C are shown in Figure 12.  
Taking a probabilistic approach allows quantification of the degradation level at any specific cycle number \(\mathrm{N_{PC}}\) via the probability density function (pdf) of \(\Delta T_{j}\) and the corresponding credible region of the predicted junction temperature change. This pdf can further be used to determine the pdf of failure, \(\mathrm{N_{f}}\), and the RUL, which is defined as the remaining lifetime (time to failure) until the expected temperature swing \(\Delta T_{j}\) reaches a failure threshold \(\omega\); cf. [37]. An LED system is considered failed when the LED junction temperature swing exceeds \(\omega=80^{\circ}\)C; cf. [52]. Additionally, the boundaries of the 95% credible interval for the number of cycles \(\mathrm{N_{PC}}=\mathrm{N_{f}}\) enable more accurate quantification of the predicted time to failure.
# Integrity Monitoring of PPP-RTK Positioning: Part II: LEO Augmentation
## 4 LEO Augmentation
### User Positioning  
In this section, the focus is being put on the influences of the LEO augmentation on the user positioning results. The L1/L5 LEO signals are added to single-, dual- and triple-GNSS scenarios assuming that the user might not track all the three GNSS constellations. The user processing begins 60 s after the network processing to allow for the convergence of  
Figure 9: Fix rate of the partial ambiguity resolution (PAR) for four-constellation processing using simulated data from Network 1. The different colors denote the constellation-wise PAR fix rate.  
the network solutions. The settings of the user processing are introduced at the beginning of Section 4.
## 4 Proposed Scheme
### Network Architecture  
The network architecture of EESEVBF protocol is composed of anchored nodes, relay nodes and sink nodes, as depicted in Figure 4. The terminus nodes/sink nodes are centralized stations consisting  
Figure 3: Holding time difference relationship with propagation delay.  
of acoustic and radio modems. They communicate with each other and with the external network through the radio links. Sink nodes are fixed at the water surface. The data received by any sink node is considered a successful delivery to its destination. On the other hand, relay nodes are movable with the water current while anchored nodes are fixed at the bottom. The sensor nodes communicate with each other through an acoustic link. The speed of acoustic signals (1500 m/s) is much smaller than that of electromagnetic signals (3.8 \(\times\) 10\({}^{8}\) m/s). Environmental monitoring and underwater tectonic plates monitoring are the typical applications of the network [33].
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 2 Methods
### Loss Function  
Within LCAS-DetNet, the loss function comprises two components: a category classification loss (\(\mathcal{L}_{Cls}\)) and a bounding-box regression loss (\(\mathcal{L}_{Bbox}\)).  
The category classification loss is depicted in Equation (4), which is used to determine whether the predicted category can correspond to the real label category and output the confidence score of each category.  
Figure 5: The MLCA module forms a double pyramid-like structure in the multi-scale cascade feature extractor. It reflects the changes in scale of the feature maps and the expansion of their attention receptive fields.  
\[\mathcal{L}_{Cls}=-\sum_{i=1}^{n}p_{i}logq_{i}, \tag{4}\]  
where \(p_{i}\) and \(q_{i}\) represent the true distribution and predicted distribution, respectively.  
The bounding-box regression loss is depicted in Equation (5), comprising both the DFL loss and Wise-IoUv3 loss [30]. We integrate the focusing mechanism into the bounding-box regression loss calculation by adopting the degree of deviation as a weight factor. This design helps minimize the influence of dataset quality differences on gradient updates.  
\[\begin{split}\mathcal{L}_{\text{Bbox}}=&\underbrace{ -((l_{i+1}-l)\log(S_{i})+(l-l_{i})\log(S_{i+1}))}_{\mathcal{L}_{DFL}}\\ &+\underbrace{\frac{\beta}{\delta\alpha^{\beta-\delta}}\exp \left(\frac{\left(x-x_{\text{gt}}\right)^{2}+\left(y-y_{\text{gt}}\right)^{2} }{\left(W_{\delta}^{2}+H_{\delta}^{2}\right)}\right)\left(1-IoU\right)}_{ \mathcal{L}_{Wise-IoU\leq 3}}.\end{split} \tag{5}\]  
where \(l\) is the true distribution, while \(l_{i}\) and \(l_{i+1}\) are the nearest integer to the left and right of \(l\). \(S_{i}\) is the difference between \(l_{i}\) and the predicted distribution. We use \(W_{\delta}\) and \(H_{\delta}\) to represent the size of the minimum enclosing box. \(x\),\(y\) and \(x_{gt}\),\(y_{gt}\) are the horizontal and vertical coordinates of the predict box and real box. The outlier degree \(\beta\) is an index used to assess the quality of anchor boxes. \(\alpha\) and \(\delta\) are two definable parameters used to construct the non-monotone focusing coefficient \(\frac{\beta}{\delta\alpha^{\beta-\delta}}\).