esa-sceva/satcom-chunk-collection huggingface dataset:
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 3 Case Study: Zhongma Landslide  
### Landslide Overview  
The Zhongma landslide locates in Pingtian Village, Guangxi Province, China (Figure 4). The volume of the whole landslide is about 212 m (vertical length) \(\times\) 111 m (horizontal length) \(\times\) 24.5 m (thickness), which is large. The main sliding direction of the landslide is 270\({}^{\circ}\). The slope failed after the rainy season in 2007. In 2013, the landslide was reactivated by the continuous heavy rainfall brought by two typhoons, \"Yut\" and \"Tammy\". A 300 m long section showed more obvious deformation displacement. On-site investigation reported that the landslide body is mainly muddy siltstone, gravel and clay containing gravel.  
The upper layer of the landslide is the Quaternary overburden, and the lower layer is bedrock. The upper soil layer of the landslide is affected by rainfall infiltration. On the one hand, the soil capacity increases and the sliding force increases. On the other hand, the soil shear strength and other parameters decrease; in particular, the friction at the  
Figure 4: A photograph of the Zhongma landslide (**left**) and the location of the study area in China (**right**).  
soil-rock interface decreases sharply, leading to a decrease in the landslide resistance. The combined effect of the two factors causes the landslide to start deformation acceleration. It can be found that rainfall is the main triggering factor of the landslide, so we must consider the rainfall factor when conducting early warning, which fully considers the landslide deformation mechanism and can improve the accuracy of early warning.
# High-Temperature Mechanical Properties of 4.5%Al \(\delta\)-TRIP Steel
## 3 Results and Discussion
### Brittleness Analysis  
Figure 6 is a comparison diagram of the high-temperature phase transformation of the sample calculated by the Thermo-Calc software and the TCFE9 database. In Figure 6, the sample did not go through the single-phase (\(\gamma\)) region, but through (\(\delta+\gamma\)) two-phase region, in the temperature range of 705-1406 \({}^{\circ}\)C. However, the ductility of the sample was excellent due to the fact that most of the RAs were higher than 40%. This is because the amount of the \(\delta\)-ferrite of the sample was high and the proportion of the film-like \(\alpha\)-ferrite was small.  
Figure 6 shows that, at the temperatures between 705 and 1406 \({}^{\circ}\)C, the \(\delta\)-ferrite was continuously transformed into the \(\gamma\) phase. The proportion of \(\gamma\) increased to 76% at 1100 \({}^{\circ}\)C and then began to decrease, and the ferrite content began to rise from 24%. It is speculated that the transformation of the \(\gamma\) phase to the \(\alpha\)-ferrite occurred at this time.  
The austenite transformed into the \(\alpha\)-ferrite at the temperature range between 705 and 1100 \({}^{\circ}\)C. This indicated that the reason why the ductility of the sample was relatively poor in the third brittle zone is that the massive transforms from the austenite to the \(\alpha\)-ferrite and the primary film-like ferrite grains generated along the austenite grain boundary produced slips. The ductility of the sample  
Figure 5: Steel tensile fracture morphologies at different temperatures: (**a**) 700 \({}^{\circ}\)C; (**b**) 925 \({}^{\circ}\)C; (**c**) 1250 \({}^{\circ}\)C; (**d**) 1300 \({}^{\circ}\)C.  
decreased first and then increased with the increase of the \(\alpha\)-ferrite. In the second ductility zone, the sample was in the two-phase regions of the ferrite phase and the \(\gamma\) phase. The ductility of the sample was excellent in the second ductility zone. Al is an element that inhibited the formation of the austenite grains, so that the austenite grains were small and the ductility was excellent.  
In the temperature range of 1110-1406 \({}^{\circ}\)C, the \(\delta\)-ferrite transformed into austenite, and the crystal structure was transformed from a body-centerd cubic to a face-centerd cubic. Moreover, within the temperature range of 705-1100 \({}^{\circ}\)C, the austenite transformed into the \(\alpha\)-ferrite, the crystal structure was transformed from a face-centerd cubic to a body-centerd cubic, and the strength of the face-centerd cubic crystal was greater than that of the body-centerd cubic crystal. With the transformation going on and the \(\alpha\)-ferrite content increasing, this will lead to lowering the substrate ductility.
# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### Two-Stage Detectors for Road Damage Analysis
Based on the RDD-2022 dataset [41], in [60], the authors introduce an ensemble approach that combines the strengths of a one-stage and a two-stage detector, specifically YOLO and Faster R-CNN. As a one-stage detector, they used YOLOv5 and YOLOv7 as baseline, respectively, Faster RCNN with Swin Transformer as the backbone for the two-stage detector part. Advancements have also been made by working with different datasets. For example, Ref. [61] presents a dataset of 323 images with a resolution of \(4128\times 2322\), captured under various shooting conditions, including different weather scenarios and illumination levels, for road crack detection. The evaluation is made with Faster R-CNN, which promises good results in terms of accuracy. A different approach is presented in [62], where the authors introduced CrdNet, an architecture that integrates a novel backbone network named LrNet [63] for enhanced feature extraction, a multi-scale and multi-aspect ratios anchor mechanism for improved sample generation, and an adaptive proposal assignment strategy for precise damage localization and classification. R-CNN and Faster R-CNN were compared in [64] with better results obtained for Faster R-CNN. Also, the paper introduced a novel dataset consisting of 1100 images from Bangladesh with three types of road damages: potholes, cracks and raveling.
## 2 Network Model
### Performance Model  
The performance model used in this paper is described as follows. It puts forward some hypothesis. Suppose that logical topology in sensor networks can keep relatively stable in data collection period \(T_{R}\) and that it makes enough data to be collected. Each sensor node in networks sends or forwards sensor data to a sink node. On a sink node it can be perceived that the data of a sensor node reach a sink node. According to sampling frequency of sensor nodes, the number of sending data packets and of loss data packets in transmission path can be known in \(T_{R}\). Suppose that packets loss among the links is independent, following the Bernoulli distribution. The data flow flowing through \(T\), can be described as a random process, \(Z=(z_{i,j})\), \(i\in\textit{d}(j),j\in\textit{V}\). In which, \(z_{i,j}\in\{0,1\}\). \(z_{i,j}=1\), represents that data sent successfully from the node \(i\) arrive the node \(j\), otherwise, data is lost on the link. Suppose \(\phi_{k}\) represents average arrival rates of packets of link \(l_{k}\), then the packets loss rate is \(1-\phi_{k}\). If path \(p_{i}\) consists of \(m\) links, that is, \(M_{i}=\{l_{1},\ \ldots,\ l_{m}\}\), suppose \(\phi_{i}\) represents average arrival rates of routing packets, then \(\phi_{i}\)=\(\hat{\Pi}\)\(\phi_{k}\).  
[PERSON] [7] describes the reason why \(\phi_{k}\) has no statistical identification, that is, the packets loss rate of each link can not be calculated using packets loss rate of a path alone, so the link is classified using the threshold value of link packets arrival rate \(t_{l}\) as: when \(\phi_{k}<t_{l}\), the link is a lossy link or bad link, otherwise it is not a lossy link. The value of \(t_{l}\) can be determined according to specific application requirements or historical data. [PERSON] [8] and [PERSON] [9] show that the link in wireless sensor networks can be clearly distinguished as a good link or bad link. Distinguishability is defined that good paths all consist of good links, and a bad path contains at least one lossy link. Under the assumption  
Figure 1: Schematic diagram of reverse tree network topology.  
that lossy links are relatively rare, the most likely understanding of using end-to-end measurement data to infer the lossy link should be the solution to the minimum number of lossy links.
## 3 Results and Discussion
### Effect of Temperature and MO Concentration on Adsorption  
To examine the dependence of adsorption efficiency on MO concentration, FeNP was used to adsorb MO from solutions with different concentrations. The adsorption capacity was determined from the following equation:  
\[q_{e}=\frac{(C_{i}-C_{e})V}{m} \tag{2}\]  
where \(q_{e}\), \(C_{i}\), and \(V\), and m are for equilibrium adsorption capacity (mg/g), initial concentration (mg/L), solution volume (L), and FeNP mass (g), respectively.  
Figure 8 shows that, for the three temperatures studied, higher MO adsorption capacity was achieved by increasing initial MO concentration. This is mainly because diffusion of MO molecules from bulk solution to the FeNP surface increases by increasing MO concentration, and thus, more MO approach active sites on FeNP surface leading to higher adsorption capacity. Moreover, Figure 8 shows that increasing temperature resulted in lower adsorption efficiency. That is, MO adsorption on FeNP is an exothermic process, with a higher possibility of physical adsorption to occur. The maximum adsorption capacities reported in this work are 9.24 mg/g, 6.23 mg/g, and 5.33 mg/g at 293 K, 303 K, and 313 K, respectively.
## 3 Methods
### Character Region Extraction  
The process of character extraction is to analyze the spatial information prediction map and determine the scope of the character area. The algorithm in this paper divides the character area into the core area and the character boundary area and can easily separate the two adjacent character areas through the core area. However, because the character core area is small, calculating the external bounding box through the character core area can not accurately locate the character area, and the boundary areas of two adjacent characters are fused, which is also not conducive to distinguishing.  
Therefore, to solve the above problems, this chapter used a boundary region separation algorithm based on a breadth-first search, as shown in Algorithm 1. First, based on the character core area in the spatial information prediction graph, the initial connected area \(C=\{c_{i}|i=1,2,\ldots,n\}\) can be extracted, where \(n\) represents the number of connected areas. Figure 5(b) shows the two initial character core areas, marked with green and blue respectively and marked with \"1\", while the boundary area is marked with orange and marked with \"2\". Next, the breadth-first search algorithm continuously searches and fuses the pixels in the boundary region. This process is only for the pixels in the boundary region. During this process, the fused pixel will change its mark to \"3\". When two of the character areas encounter the same pixel, as shown in the red box in Figure 5(b), the algorithm adopts the first come, first serve strategy. Finally, after all the boundary region pixels are searched and fused, the external bounding boxes of each connected region are calculated, and the final detection results are obtained.  
```
Input: Character core area \(C\), boundary area \(B\) Output: Character area D
1. function EXTRACTCHARACTER(\(C\), \(B\))
2. initialize character region pixel set T, boundary region pixel set \(R\), and queue \(Q\)
3. for core area pixels \(c_{i}\in C\) do
4. put \(c_{i}\) and its label into T, and put the boundary area pixels \(b_{i}\in B\) into \(R\)
5. \(Enqueue(c_{i},Q)\) queue all pixels in \(C\)
6. end for
7. while \(Q\
eq\varnothing\) do
8. \(PopqueueFront(p,Q)\) queue one pixel P from the front end of \(Q\)
9. find p adjacent pixels \(Q\)
10. if \(q\in R\) then
11. put the labels of \(q\) and \(p\) in T and delete \(q\) from \(R\)
12. \(Enqueue(c_{i},Q)\) queue \(b\)
13. end if
14. end while
15. analyze T, where the same label set outputs D
16. return D
17. end function
```  
**Algorithm 1** Character region extraction based on the breadth-first search algorithm
###### Abstract  
This study investigates the hydrodynamic and morphological effects caused by non-powered floating objects (e.g., barges, pontoons, and floating or moored platforms) that are towed by external forces (such as tugboats) across flat, shallow seabeds. This study employs an integrated approach combining advanced computational fluid dynamics (CFD) simulations with multivariate polynomial regression analysis to systematically investigate the hydrodynamic and morphological effects of non-powered floating objects on sediment resuspension. A total of 96 simulation scenarios were conducted, of which 84 significant cases (where the floating object did not touch the seabed) were analyzed. Variations included bow geometries (blunt and raked), towing speeds, and operational parameters. The results indicate that, under similar towing speeds and clearance heights, blunt bows increase the suspended sediment concentration by approximately 90-190% compared to raked bows. The regression model, attaining an R-squared value of 0.9647, identified the Froude number, squat ratio, squared towing time, and object type as critical predictors of suspended sediment concentration. Furthermore, the interaction terms between the Froude number and object type were significant, enhancing the model's predictive accuracy. These results underscore the importance of optimized design and operational strategies in minimizing the environmental impact of floating structures, especially in shallow marine environments where sediment dynamics play a crucial role in ecological balance. Careful consideration of towing speed, object geometry, and operational parameters can significantly reduce sediment resuspension, mitigating ecological consequences.  
captiontion = title]Applied sciences
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
###### Abstract  
In this paper, we present the procedure of estimating the aerodynamic coefficients for a commercial aviation aircraft from geometric parameters at low-cruise-flight conditions using US DATCOM (United States Data Compendium) and XFLR software. The purpose of this research was to compare the stability parameters from both pieces of software to determine the efficacy of software solution for a wide-body aircraft at the stated flight conditions. During the initial phase of this project, the geometric parameters were acquired from established literature. In the next phase, stability and control coefficients of the aircraft were estimated using both pieces of software in parallel. Results obtained from both pieces of software were compared for any differences and the both pieces of software were validated with analytical correlations as presented in literature. The plots of various parameters with variations of the angle of attack or control surface deflection have also been obtained and presented. The differences between the software solutions and the analytical results can be associated with approximations of techniques used in software (the vortex lattice method is the background theory used in both DATCOM and XFLR). Additionally, from the results, it can be concluded that XFLR is more reliable than DATCOM for longitudinal, directional, and lateral stability/control coefficients. Analyses of a Boeing 747-200 (a wide-body commercial airliner) in DATCOM and XFLR for complete stability/control analysis including all modes in the longitudinal and lateral directions have been presented. DATCOM already has a sample analysis of a previous version of the Boeing 737; however, the Boeing 747-200 is much larger than the former, and complete analysis was, therefore, felt necessary to study its aerodynamics characteristics. Furthermore, in this research, it was concluded that XFLR is more reliable for various categories of aircraft alike in terms of general stability and control coefficients, and hence many aircraft can be dependably modeled and analyzed in this software.  
a +
Footnote †: journal: Applied Sciences  
14 December 2020  
17 February 2021  
Submitted: 26 February 2021
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
###### Abstract  
In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user's condition. Depending on the user's verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system's reliability and scalability for real-world in-home elderly monitoring applications.  
full detection; YOLOv11; elderly care; real-time monitoring; pose estimation; voice assistant; AI for health
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 6 Model Building
### Real-Time Bid Request Filtering Process  
The bid requests are labeled in the real-time process based on the result of AudienceId_BlackList and IPUA_BlackList. Figure 11 illustrates our proposed RFBR model and how it could be integrated with the bidding process in the DSP. Our RFBR process is integrated as a plug-in program in the pre-bidding stage in real-time processes from the DSP side. The main purpose of the RFBR process is responsible for classifying bid requests into intentional and non-intentional. Specifically, a bid request on day \(d\) that contains information about audienceId, IP address, user agency, URL, etc., is checked whether it matches one of the audienceId or (IP, UA) in the blacklist of day \((d-1)\) or not. If it matches, it is labeled as non-intentional; otherwise, it is labeled as intentional. In particular, our real-time bid request labeling method goes through the following steps:  
* Step 1: First, the DSP receives a bid request with the audience and publisher information (audienceId, IP address, user agency, country, URL, publisherId, etc.) as input information of the RFBR process;
* Step 2: Second, the audienceId and (IP, UA) information of the bid request are used to check with the AudienceId_BlackList and IPUA_BlackList, respectively. If it matches, the bid request is labeled as non-intentional; otherwise, it is labeled as intentional as output information;
* Step 3: The labeling information of the bid request is written to request reply. If the bid request is non-intentional, it will be dropped; otherwise, it is sent to the auction process.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Rules** & **Threshold of AudienceId** & **Threshold of (IP, UA)** \\ \hline The percent of bid requests by day & 0.03 & 0.02 \\ The number of appeared hours & 20 & 20 \\ Bid requests within one second & 3 & 3 \\ Distinct URLs per bid request & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thresholds for detecting abnormal audiences.
## 3 Network Model
### Energy Dissipation Model of Sensor Nodes  
The energy consumed by sensor nodes is mainly used for data transmission. In this study, sensor nodes can be divided into two types: cluster members and CHs. Both of them can obtain data from the sensing region. Cluster members send the acquired data to their CH instead of directly to the sink. Each CH can aggregate data from its cluster members and send these data to the sink. The energy of cluster members is mainly used for sending data; the energy consumption of CHs includes both sending and receiving data. To calculate energy consumption, the channel model is employed based on the Euclidean distance \(d\) between the sender and the receiver. If \(d\) is smaller than a threshold \(d_{0}\), the free channel model is selected; if \(d>d_{0}\), the multipath model is adopted. The energy used by a sensor node to transmit or receive an \(L\)-bit packet is \(E_{T}\) and \(E_{R}\) respectively. \(E_{T}\) can be calculated as Equation (1):  
\[E_{T}=\left\{\begin{array}{ll}L\times E_{e}+L\times E_{f}\times d^{2},&d<d_{0 }\\ L\times E_{e}+L\times E_{m}\times d^{4},&d\geq d_{0},\end{array}\right. \tag{1}\]  
where \(E_{e}\) denotes the energy consumed to run the transmitter or receiver circuit; \(E_{f}\) and \(E_{m}\) depend on the used transmitter amplifier model (i.e., free channel or multipath model); \(d_{0}\) denotes the threshold used to determine the model to be selected and can be defined as \(\sqrt{E_{f}/E_{m}}\). \(E_{R}\) is defined as Equation (2):  
\[E_{R}=L\times E_{e}. \tag{2}\]  
It can be noticed from Equations (1) and (2) that the energy consumption of data transmission is greatly affected by the data size and distance between the sender and the receiver while the energy consumption of data reception is affected by the size of the received data. To reduce the energy consumption of sensor nodes and prolong the network lifetime, the data transmission distance and received data size should be reduced to lower the energy consumption of cluster members and CHs.
## 4 Discussion of Experimental Observations  
### Service Class and Adhesive Behaviour  
Undoubtedly, the experimental investigations revealed significant differences of the mechanical behaviour of bonded-in rod connections with different adhesives when exposed to wet climate. The test set-up and the support of the DIC system helped in obtaining empirical results of practical use. Rheological behaviour indicates that, in terms of reliability, special attention should be paid to the joints exposed to the extreme climatic conditions. Additional requirements in standards should be included or certification from the adhesive manufacturer should be sought to ensure the safe use of this type of joints.  
Tests indicated a significant effect of moisture content on the adhesive stickiness. While only small changes were observed for service class 2, for service class 3 the adhesive stickiness began to recede dramatically. For epoxy specimens, the entire adhesive matrix started to slip smoothly, what is especially characteristic for rods bonded perpendicular to the grains, while on the rods bonded parallel, any pieces of wood grains on the adhesive matrix is not visible. This is indicating that bearing capacity of the joint is defined by shear strength of the interface on exact line between the adhesive matrix and the wood. Polyurethane specimens showed enhanced behaviour, especially for rods bonded perpendicular to the grains. The reason for such behaviour can be justified in higher chemical properties of the new generation of this type of adhesives, which is recommended for use in moist environments. Indicatively, it can be stated that the use of epoxy adhesives is not recommended for service class 3, while polyurethane adhesives can be still used, but with careful consideration for the technical characteristics given by the manufacturer.