esa-sceva/satcom-chunk-collection huggingface dataset:
# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
### Related Works Using One-Dimensional Signals  
According to the review carried out, three main types of sensors have been used in pothole and/or bump detection: accelerometers, cameras, and/or lidar. One of the advantages of using an accelerometer is that we only have to analyze three one-dimensional time signals from the three x, y, and z axes. For example, [11] propose automatic road anomaly detection using smart mobile devices, in which they collected triaxial acceleration data while riding a motorcycle. Their data record covers about 3 h and 60 km. In reference [12], the authors present real-time pothole detection using Android smartphones with accelerometers, achieving a 90% true positive rate. In reference [13], an approach for pavement segmentation using genetic algorithms is documented. Captured pavement images are used to define a cost function, which is then maximized by information theory to choose the optimal threshold for segmentation. Additionally, in [15], accelerometers are used to detect street conditions, specifically potholes and bumps. Experimental tests were carried out on urban roads of Calabria, Italy, and the algorithm that was developed to detect road bumps and potholes analyzes the acceleration signal from high-energy events. In another article [16], [PERSON] et al. introduce a road pothole detection Android application. This system uses the smartphone's accelerometer for pothole detection and GPS to pinpoint its location on Google Maps. Through an email, this information is sent and stored in an internet database to notify future drivers that there is a pothole. The accuracy achieved by the local system reached 90-95%.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Accuracy \%** & **Type of Detection** & **Sensor** & **Method** \\ \hline
[11] & 78.5 & Road anomaly & Accelerometer & Support vector machine \\
[12] & 90 & Pothole & Accelerometer & Z-DIFF \\
[13] & 97 & Pavement distress & Image & Neural network thresholding \\
[14] & 85 & Pedestrian crossing and speed bump & Image and LIDAR & height-difference-based algorithm \\
[15] & 93 & Potholes and bumps & Accelerometer & Energy peak acceleration value \\
[16] & 90–95 & Pothole & Accelerometer & Neural network \\
[17] & 85 & Speed bump & Image & Color image thresholding \\
[18] & 92 & Speed bump & Image & Connected component analysis. \\
[19] & 94.7 & Speed bump & Image & Gaussian mixture model \\
[8] & 97.4 & Speed hump/bump & Image (ZED) & Mobilenet-SSD CNN model \\
[4] & 94–96 & Potholes and bumps & Accelerometer & Wavelet \\
[20] & 80 & Speed bump & Image & Gray-level co-occurrence matrix \\
[5] & 97.14 & Speed bump & Accelerometer & GALGO \\
[9] & 77 & Pothole & Image & Inception V2 \\
[7] & 88.9 & Potholes and bumps & Image & YOLO \\
[21] & 90 & Speed bump & Image & Otsu thresholding \\
[22] & 90 & Pothole & Image & Tiny-YOLOv4 \\ \hline \end{tabular}
\end{table}
Table 1: Review of approaches to detect speed bumps and potholes.
## 3 Application  
### Geological Setting  
This research selected the L oil field in the north of Gurbantunggut Desert in the abdomen of Junggar Basin as the test area (Figure 8). The oil reservoir in the research area belongs to the Hutubi section of the Hutubi River Formation of the Cretaceous System. Based on the principle of \"rotational comparison and hierarchical control\", and combined with the stratigraphic thickness, the development of sands, and oil and water distribution laws, we divided the target layers of this oil group into seven sand groups and eighteen single sand layers. The whole is a set of brown and grey-green mudstone and fine sandstone interbedded in the delta-front facies, with several sets of reservoir-cover combinations developed. The source of material mainly comes from the north-northwest direction, and the sedimentary microphase is mainly dominated by the underwater distributary channel, part of which is sheet sand and estuarine dam. Reservoir porosity is mainly concentrated in 25-29%, permeability is generally less than 1000 mD, and the permeability correlativity of the reservoirs is relatively good, for high pore medium-high permeability reservoirs. At present, the development of the L oilfield is facing a difficult problem, namely, the unclear recognition of the distribution laws of the residual oil in the reservoir. One part of the residual reserves is distributed in some thin unused and lenticular reservoirs, and the other part is distributed in the edge and the interior of the developed reservoirs, so it is necessary to re-recognize the oil-bearing single sand body and analyze the scale of the sand body. The petroleum group reservoir permeability varies greatly, and the reservoir non-homogeneity is strong. The key to study the non-homogeneity of this type of reservoir is to be able to accurately describe the size of the development of the underwater distributary channel. The channel sand body width is one of the key factors for designing the well placement scheme, and it is of great significance to deduce the width and characteristics of the distribution of the channel sand body in the actual work area according to the existing conditioned data. Therefore, it is necessary to determine the width of the underwater distributary channel, and then describe the reservoir heterogeneity on the basis of it.  
The candidate model's data source is a sand layer in the Hu1 oil group of the L field. The reservoir in this area is of the braided river delta front deposition, the source is mainly from the N-NW direction, and the sedimentary microfacies are mainly distributary channels. Multi-stage channel deposits are developed on the plane, and the sand bodies between channels are in tangential and overlapping contact, with different reservoir properties in different channels. The development of sand body in this study area is relatively  
Figure 7: Proportion of conditional data identified as corresponding candidate models.  
posed in this paper can be well applied to the identification of the width of the underwater distributary channel in this area.
## 4 Tracking
### Models for Tracking
However, the noise characteristics of the actual maneuvering target will change, and the model that exhibits good tracking performance in the previous period may no longer maintain good performance. The IMM models the change of the system dynamics as a Markovian parameter having a transition probability [16]. According to the filter results, the consistency between each model and the current actual maneuvering target is estimated, and then the estimated results are generated by weighting the estimation of each model, so that the tracking performance is better than that of any single model. However, when the maneuvering target exhibits complex motion, the IMM model suffers heavy computational burden. On the other hand, the limited number of the model can not still catch all the system dynamics feature. Another model, called the adaptive model, assumes that no a prior information about the system noise is known, which must therefore be estimated based on the estimation of state [17]. Based on the basic idea that the model will effect the measurement data, and the measurement data contain the information of the model, the adaptive model describes system dynamics based on the measurement data on the condition that the system equation structure is given. Most researchers agree that the disadvantage of the \"state\" model is the requirement of the given system parameter, which can not be known exactly. Although IMM and the adaptive model have tried the best to catch the maneuvering characteristics of a target, the model still seems to be insufficiently accurate in the practical system, especially when the system is complex. The authors believed that the reasons are as follows: Firstly, the matrix form of the'state' model can not contain enough parameters to describe the complex dynamics. For example, the process matrix of adaptive model [17] only has nine parameters, although they are adjusted each step online, the amount of information is insufficient. Secondly, the structure of the model is too simple to describe the complex movement. The CV, CA, Singer model, the current statistical model as well as the Jerk model are all with a basic linear structure, and only the IMM and adaptive model have switched nonlinear structure between different models, but they still can not catch the complexity of the practical system. More complex network of non-linear structure is very necessary. In Section 5, we will explain why network-structured models can capture complex information and even make tremendous progress in pattern recognition.
## 6 Results and Discussions  
In the network topology shown in Figure 6, Node 2 is the optimal path between Node 1 and Node 4 since it shortens the route. Nevertheless, this route is not always a viable alternative since it probably leaves Node 2 exhausted. The SDN-based energy-aware routing approach proposed in this paper considers the energy consumption in each node to overcome this issue. It works as follows. At the beginning of the transmission, the shortest path is used to send the UDP packets from Access Point 1 to Access Point 4 through Access Point 2. This holds until the SDN controller decides to change the route path to the longest--but more efficient--path based on the estimation of the energy consumption of Access Point 2. The longest and more efficient path includes Access Point 3 and Access Point 5, as shown in Figure 6. The transmission from Access Point 1 to Access Point 4 through Access Point 3 and Access Point 5 holds until the battery level of Access Point 2 enables the use of the shortest path again. Then, the SDN controller shifts the routing path to the shortest path, and Access Point 1 transmits to Access Point 4 through Access Point 2 again. This procedure is repeated until the transmission ends. In this way, whenever the battery level of Access Point 2 decreases, it becomes inactive, thus receiving and transmitting data for less time and preventing its battery from becoming exhausted.  
The performance of the proposed SDN-based energy-aware routing protocol is evaluated in terms of the data traffic and the energy consumed to transmit the UDP packets from Node 1 to Node 4 in the network depicted in Figure 6. For comparison purposes, the performance of the baseline shortest-path routing method is also evaluated on the same network.  
Figures 7 and 8 show the transmitted and received bytes when using the proposed and the shortest-path routing methods. In the proposed SDN-based energy-aware routing protocol, the energy level of each of the access points in the network is the primary factor in the selection path by the SDN controller. At the same time, the data traffic impacts the total energy consumption of each of the access points. Figures 9 and 10 show the transmission and receiving energies consumed when using the proposed routing approach as well as the shortest-path routing method. In Sections 6.1-6.5, we discuss each one of the access points in the network (see Figure 6) separately in terms of data traffic and energy consumption. Finally, in Section 6.6, the packets transmitted and received by Access Point 2 as well as its power consumption are evaluated for each of the studied routing protocols.  
Figure 6: Network Topology.  
Figure 7: Bytes transmitted by access points.  
Figure 8: Bytes received by access points.  
Figure 9: Transmission energy.
## 1 Introduction
Numerous studies have explored the potential reuse of WA in diverse applications, including composting, catalysis, and filtration [30, 31]. The effectiveness of WA for the removal of various contaminants, including heavy metals [32], organic compounds [33], and anions [34; 31], is well-documented, underscoring its efficacy as an adsorbent for environmental remediation. However, mechanistic studies exploring the application of WA for fluoride removal in aqueous solutions are limited. The primary objective of this study was to assess the viability of WA as a sustainable adsorbent for treating fluoride-contaminated wastewater. In parallel, the study aimed to clarify the mechanisms of fluoride removal by analyzing the physicochemical properties of WA. Additionally, comprehensive adsorption model analyses, including kinetics, isotherms, and thermodynamics, were performed under varying reaction times, initial fluoride con centrations, and temperatures. The study further investigated fluoride adsorption behavior under different operational conditions, such as solution pH, adsorbent dosage, and the presence of competing anions.
## 5 Structural Testing and Results  
Experimental investigations included also a test of the structural behaviour of the honeycomb core structure. Compression tests were carried out at a constant cross-head speed of 0.5 mm/min by using a universal testing machine Zwick 1475 with a standard 100 kN load cell and computer-based data acquisition. Force was introduced into the specimen using one fixed flat platen and one spherical seat (self-aligning) platen (Figure 11). The only acceptable failure mode was uniform compressive failure of the sandwich core. The determination of the honeycomb sandwich panels' critical buckling loads and crushing behaviour was performed according to ASTM C365 (flatwise compressive properties of sandwich cores). Samples of square honeycombs (\(n\leq 5\)) with a number of 4 by 4 unit cells with different cell sizes and a cell wall thickness \(t_{W}\) of 0.5 mm were tested (Table 4). In the following \(\overline{x}\) is the mean or average (estimate of mean) and \(S\) is the standard deviation of a sample population for the given property. The walls were made of aluminium sheets with a density of 2.68 g/cm\({}^{3}\), a Poisson's ratio of 0.33 and Young modulus of 70.5 MPa. For all specimen the cell height was adapted to ensure an equal volume of the enclosed cavity \(V_{c}\) = 10.8 cm\({}^{3}\) to maintain a constant Helmholtz resonance.  
Figure 11: (**a**) Specimen (\(B/H\) = 0.63) for investigation of deformation behaviour; (**b**) buckling failure of honeycomb core structure.  
The compression tests showed a linear increase of the applied force up to the ultimate force prior to failure \(P_{max}\). After exceeding a critical deflection \(\delta_{max}\) (Figure 12), an instable buckling failure follows (Figure 11b) which leads to a rapid decrease of compression force and plastic deformation of the damaged structure. For all specimens there was no compressive failure confined to one corner or edge of the specimen. The ultimate flatwise compressive strength is defined as \(F_{z}^{\mathit{c}t}=P_{max}/A_{Samp}\), where \(A_{Samp}\) is the total cross-sectional area of the honeycomb sample. The resulting \(\delta_{max}\) did not show a constant trend, whereas the compression strength increases with increasing cell height. The critical deflection shows much higher deviations than the compression strength. After Buckling, starting from a relative traverse path of approximately 80%, an increase of the applied force can be seen due to a compacting of the honeycomb.
## 2 Bilinear and Weibull/IPL General Background
### Bilinear Model  
The bilinear model extends the analysis of the linear model used in fatigue analysis. In bilinear behavior, the S-N curve of a material is divided into two regions because of slope changes. For example, Region I can be governed by the plastic region, while Region II can be governed by the elastic zone. The point where the two regions intersect is called the knee-point [4]. The S-N curve, shown in Figure 1, is characterized by a high slope in the first phase of the bilinear model, and a low slope in the second phase.  
Understanding the characteristics of the S-N curve, particularly the knee-point, is crucial for fatigue analysis. The high slope in the first phase indicates a higher rate of damage accumulation, whereas the second phase, with its lower slope, suggests a slower progression of fatigue. Moreover, because fatigue is random, then a probability function is necessary, to consider the spread of fatigue data. Thus, here, we use the Weibull distribution. Its generalities are as follows.
# Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
## 2 Materials and Methods
### The Construction of Situation Type System and Its Dataset
#### 2.1.1 Classification Criteria for Situation Types  
The classification of situation types serves as the foundation to explore the interaction between lexical aspects, as well as aspects in other levels, and aspectual markers, which stand for different viewpoint aspects.  
We have selected whether a verb can co-occur with aspectual markers as the basic criterion for classification, and the main reasons are as follows:  
Firstly, constructing a situation type system according to semantic features is unreliable. Previous studies, especially linguistics studies, usually classify verbs into the four or five situation types which have been developed in [PERSON] [4] and other studies based on semantic features including [stative], [dynamic], [durative] and [telic]. These semantic features are extremely abstract and complex, and even scholars who specialize in researching this issue have significant disputes among themselves. For example, there has been an ongoing debate among scholars about what constitutes telic and what the distinction is between telic and boundary. Therefore, it is extremely difficult to determine which situation type a verb belongs to based on these features, and it is also highly subjective.  
Secondly, it has widely been accepted that the interaction between verbs and aspectual markers always plays the most important role in depicting verbs' situation types in linguistics studies [3; 6; 8; 9; 10; 11; 12; 13]. Furthermore, it is extremely easy for native speakers to judge whether two words can be collocated. This does not require them to understand and judge complex semantic features; instead, they can make the judgment merely based on their own linguistic intuition. At the same time, it becomes much simpler and more feasible to conduct cross-validation among multiple people.  
As a result, we have no intention to annotate the situation type of verbs directly. Only verbs' abilities to co-occur with different aspectual markers are annotated, which will be used to cluster verbs into different types through hierarchical clustering according to their semantic similarity calculated on the matrix of co-occurrence.
## 4 Results  
### Initial Analysis and Design Considerations  
From the data analysis, we determined that inaccuracies mainly arose from the misinterpretation of instructions, air bubbles while drawing medication, inaccuracies in measuring the weight of the patient, and knowledge gaps. This study identified underdose and  
Figure 2: RPN scatter plot for top 5 failure modes based on S, O, and D values. overdose errors, focusing on critical areas to improve medication administration protocols. Keeping all the results in one place, the objective of this study is to automate the traditional medication administration process in prehospital emergency settings to minimize the occurrence of these errors by eliminating the need for traditional questionnaires and MI-MEDIC cards. After categorizing errors and prioritizing the analysis of root causes, Table 1 outlines the classification of active and latent errors and the design considerations to address them. The RPN scatter plot (Figure 2) shows that incorrect weight and incorrect dose are the primary failure modes with the highest RPN values. Following these, in descending order of impact, are issues such as the choice of the appropriate drug, the administration of the wrong drug, and missing a dose.
## 3 The Proposed Protocol
### Three Proposed Authentication Methods  
In this study, we propose a lightweight multi-zone authentication protocol that utilizes the bilinear pairing cryptography in message signing and verification. The zoning concept allows vehicles to have security parameters for every zone, without the need for high-cost re-authentication, every time the vehicle moves from one cell to another. The proposed protocol reduces the frequency of key request messages to BSs, by allowing vehicles to request all destination zone parameters in advance.  
We propose three authentication methods that utilize ECC pairing-friendly curves to support broadcast multi-zone authentication. The proposed authentication methods are Transmitter Centric Authentication (TCA), Signature Concatenation-Authentication (SCA), and Receiver Centric Authentication (RCA).
## 3 Results and Discussion
### Penetration Depth and Distribution of the Polymer in the Pore Network
These findings permit us to describe the distribution of the polymers inside the pore structure of Cusa's stone, and in particular led to a deep understanding of the macroscopic properties observed after the application of the two fluorinated polymers. Generally, any protective coating must satisfy several requirements, such as good adhesion, water repellence and a natural water vapour permeability [27; 28]. Consequently, it is known that one of the crucial aspects for good performance is that, after the application of a protective product, the stone surface must not be sealed and pores must not be completely blocked, to allow the release of moisture which is often trapped inside [29; 30]. As can be seen from the chemical maps (see Figure 3), Fluo AQ and Fluoline HY are distributed in the porous system in a different way. In detail, Fluo AQ shows a continuous and homogeneous distribution along the surface of the pores which is ascribable to a good adhesion of the polymer to the stone support. Since a good anchoring of the polymer in the pore structure assures performance implementation especially in terms of hydrophobicity [25], this justifies the high water repellence conferred to the stone substrate and a vapour permeability similar to untreated stone. On the contrary, Fluoline HY clogs the pores due to the small accumulations of the polymer inside the porous system. The pore-clogging inhibits the vapour transport from inside to the external environment [5]. Furthermore, the non-adhesion of the polymer along the pore's surface does not reduce the interaction  
Figure 4: High-energy elemental XRF maps (acquired at 2 keV with 2 \(\upmu\)m diameter beam size, over an area of 240 \(\times\) 480 \(\upmu\)m\({}^{2}\)) of the sample treated with Fluoline HY. The third map shows RGB correlation images for 3 different chemical elements, highlighting their possible co-localization. The scale bar is 50 \(\upmu\)m.  
with water, which is retained and/or absorbed, representing a drawback for the stone protection [23]. This justifies why Fluoline HY significantly reduces the water vapour permeability of the stone, while also conferring poor water repellence. The results obtained so far show how X-ray synchrotron microscopy coupled with X-ray fluorescence is a powerful tool to overcome some drawbacks of classical imaging and to carry out an in-depth investigation on the spatial distribution of polymers in the stone, allowing one to correlate micro and macro behaviour for the description of the protective action.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space
### Path Search Algorithms
#### 5.1.2 Sample-Based Algorithms  
Two famous algorithms should be mentioned here, namely the Probabilistic RoadMap (PRM) and Rapidly-exploring Random Trees (RRT). RRT is fast, but unfortunately kinematic constraints avoidance and re-planning is not available. The path identification is fast, but it is not necessarily optimal. This method has improvements such as RRT*, DDRRT, and Anytime-RRT. PRM works with randomly scattered graph nodes, which are connected to each other through some rules, and then processes a graph search in space. Summary: environmental pre-information is required, the optimal path is found randomly.