esa-sceva/satcom-chunk-collection huggingface dataset:
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 2 Related Works and Contributions
However, both of these approaches focus on asynchronous networks, and do not consider the intermittent characteristics of link connection. Delay-oriented routing protocols in intermittent networks like CREST [11] and CSPR [12] are designed for intermittent situations. CREST [11] uses conditional residual time to opportunistically forward message and CSPR [12] routes message over conditional shortest path. These two protocols working in intermittent networks require the stable patterns, while, in IT-WSNs, the intermittent pattern is not stable enough and may lead to estimation error with CREST and CSPR. On the other hand, CREST and CSPR do not consider buffer condition, and may not work well in IT-WSN. Different from the above mechanisms, Pido is designed for intertidal WSNs and provides delay optimization routing paths. We summarize our contributions in this paper as follows:  
* We deploy a wireless sensor network in the intertidal area in Zhoushan and systematically investigate the components of delays based on real traces collected from the IT-WSN. Due to the harsh deployment environment, delays in IT-WSNs can be dramatically longer than WSNs more commonly used in terrestrial environments. To the best of our knowledge, this is the first work on delay optimization in real IT-WSNs. * We analyze the feasibility of utilizing implicit indicators to predict nodes future status for IT-WSNs. We specially select indicators and compare the prediction accuracy among different classification algorithms. * We introduce Pido, a delay optimization framework for IT-WSNs. Pido jointly considers delays caused by link quality and node conditions. * We evaluate the performance of Pido with real-world system data and implement it in a large-scale simulation with various network sizes. We compare the results with CTP (Collection Tree Protocol) and ORW (Opportunistic Routing in WSNs), and exhaustive results demonstrate that Pido can optimize the system delay with limited overhead.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.1 Visible Satellite Geometry Topology
In the open outdoors, the visual satellite is dispersed in the sky with a strong signal. Figure 6a demonstrates an example of sky plot and availability of visible satellites under complex scenarios using a Huawei Mate 9 smartphone. Figure 6b is a sketch that demonstrates the distribution of the maximum number of satellites within the range of \(90^{\circ}\) and \(180^{\circ}\) in the indoor environment with French windows. Based on the above analysis, we extracted the geometric topological features of the visible satellite in set \(\mathbf{G}\). We divided the range of azimuth from 0 to \(360^{\circ}\) into 36 sectors at an interval of \(10^{\circ}\). Then, we constructed a 36-dimensional feature vector \(\mathbf{d}\) to represent the distribution of satellites in these sectors. The vector elements include 0 and 1. 0 indicates no visible satellites in this sector. 1 indicates visible satellites in this sector. The proportion of sector occupied by visible satellites was considered as one feature as follows:  
\[\text{A}_{\text{z}}\_\text{dtb}\_\text{proportion}=\frac{\sum\limits_{i=1}^{36} \mathbf{d}_{i}}{36} \tag{6}\]  
Figure 5: The cumulative probability of the number of satellites in the distribution of satellite azimuth using different smartphones in the range of different angles in a week. Figure 6: Visible satellite geometry topology (**a**) An example of sky plot and availability of visible satellites under complex scenarios using Huawei Mate 9; (**b**) A sketch demonstrates the distribution of the maximum number of satellites within the range of \(90^{\circ}\) and \(180^{\circ}\) in the office window environment. To further explore the topological relationship of satellites in spatial, the maximum proportion of the number of satellites within the range of \(90^{\circ}\) and \(180^{\circ}\) of the azimuth variation of visible satellites in \(\mathbf{G}\) is extracted as features. We defined a function \(f(i,az_{j})\) that calculated whether a satellite is within the range of satellite azimuth. The formula for calculating the GS_num_proportion_range is as follows:  
\[f(i,az_{j})=\left\{ \begin{array}{l}1,\;\text{if}\;i\leq az_{j}\leq\min(i+range,360)\;\text{or}\;az_{j}\leq i+range-360;\\ 0,\;\text{otherwise}.\end{array} \right. \tag{7}\]  
where _range_ represents the range of satellite azimuth. \[\text{GS\_num\_proportion\_range}=\max_{i}\frac{\sum\limits_{j=1}^{n}f(i,az_{j} )}{n},\;0\leq i\leq 360 \tag{8}\]
## 4 Results
### Impact of Socio-Economic Factors  
GPS-enabled devices, Smartphones and computers with internet access have become omnipresent in many countries worldwide. The existence of these devices in each country does not automatically imply however that all citizens have access to them or have the financial resources to purchase them. Figure 3 has shown that the OSM community of an urban area does not necessarily relate to the population density. Thus, one question that remains is if other socio-economic factors, such as income, have an impact on the development of an internet community for portals such as OSM. The Gross National Income (GNI) is defined by the World Bank [40] as: \"The value of all final goods and services produced in a country in one year (gross domestic product) plus income that residents have received from abroad, minus income claimed by nonresidents.\" The corresponding GNI per capita is defined as: \"A country's gross national product (GNP) divided by its population.\" For the following analysis it is important to apply the GNP value, which represents each individual urban area and not the entire country. The values for 2012 utilized in this analysis are provided by The Brookings Institution [41]. Figure 9 show the results gathered for the tested urban areas when comparing the OSM contributor density with the GNP per capita.  
The statistical analysis of the results shown in Figure 9 revealed a distinct correlation between the number of members in OSM and the GNP with an R\({}_{\mathrm{S}}\) value of 0.664. Furthermore, a number of outliers such as Berlin with a slightly lower GNP but larger OSM community could be determined.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Min** & **Max** & **Mean value** & **Standard deviation** \\ \hline Active days & 3 & 15.5 & 9.3 & 3.4 \\ Nodes & 292.8 & 3133.0 & 1466.2 & 848.3 \\ Ways & 48.7 & 451.5 & 229.1 & 118.8 \\ Relations & 0.0 & 12.3 & 4.3 & 3.8 \\ \hline \end{tabular}
\end{table}
Table 2: Activity timeframe and contributions of a senior mapper (Aug.–Oct. 2012).  
Figure 9: Contributor density (Oct. 2012) and GNP per capita (2012).  
The opposite situation can be seen for Los Angeles and Sydney; both countries have a higher GNP in comparison to other tested areas but only show small OSM communities. Usually, it would be expected that these cities would show a higher concentration of OSM members based on their GNP values, if income is considered as an influential factor. However, the outliers identified in this analysis also showed that other factors next to population density or income must have an influence on the development of an OSM community in the selected areas.
# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 2 Related Work
### Multi-View 3D Object Detection  
Multi-view 3D object detection methods utilize multiple camera views to achieve accurate 3D object localization and recognition. BEV space construction methods [2; 22; 23] project multi-view images into a BEV space--a top-down representation of the scene--and employ a BEV-based detector to identify 3D objects. BEV space projection is inherently ambiguous because projecting a pixel from a camera image to its correct position in BEV re quires resolving depth information. Traditionally, creating a BEV map from camera images involves first projecting 3D space onto 2D image space using camera intrinsics, then associating 3D features with corresponding 2D image features, and finally attempting to correctly place the pixel in the BEV map [2]. However, recent transformer-based methods [23; 24; 25; 26] can implicitly predict the BEV feature map without explicit projection. BEVDet [27] introduced a view transformation network that uses a transformer to convert camera features to BEV features. BEVDet4D [28] extended this by incorporating temporal information, recognizing the importance of sequences of images for capturing dynamic scenes. StreamPETR [26] is object-centric, focusing on individual objects' temporal behavior, unlike BEVDet4D's broader BEV space approach. BEVformer [29] utilizes a transformer to learn the BEV representation, focusing on spatial cross-attention to map camera features to BEV, addressing multi-view feature fusion challenges. Transformer-based methods generate 3D object queries from the BEV and use transformers to perform cross-view attention between these queries and the features extracted from multi-view images. These methods implicitly form the BEV space without constructing a dense BEV feature [30; 31]. DETR3D [32] projects 3D object queries onto 2D image planes to gather relevant features. The integration of LiDAR with camera data has further revolutionized BEV 3D object detection, enhancing accuracy and robustness. It leverages the complementary strengths of LiDAR (precise 3D spatial data) and cameras (rich semantic information), particularly in the BEV space. MV2 DFusion-e [25] integrates camera and LiDAR data using object queries specific to each modality. SparseLIF [33] presents a high-performance, fully sparse detector for end-to-end multi-modality 3D object detection. BEVFusion [34] proposes a generic framework for multi-task, multi-sensor fusion in BEV space, unifying multi-modal features in a shared BEV representation. BEVFusion4D [35] further extends BEVFusion by integrating LiDAR and camera information in BEV space with cross-modality guidance and temporal aggregation. Misalignment affects feature alignment in the BEV feature map, leading to blurred or incorrect object representations, which can result in false positives, missed detections, or inaccurate bounding box predictions, especially for occluded or partially visible objects.
## 4 Training Datasets and Object Recognition Task Performance
### Training Datasets
#### 4.1.3 Marine Deposited Waste Dataset  
The special and very irregular view in parts of the ocean where light does not enter is often narrow and blurry due to various environmental factors (wind, region, seabed composition, tide, season, ecological environment) [59]. In addition, since most of the long-deposited waste is assimilated by nature, it is difficult to find and predict the size in many cases. This presents difficulties for object detection and segmentation tasks. As one goes deeper, the incoming light decreases and the pressure becomes stronger, so unmanned vehicles such as underwater drones play a role in areas where people cannot go due to increased risk. Since it is very important to measure the exact size of waste in the process of pre-exploration for waste collection, the marine deposited waste dataset was designated as a learning dataset [60]. There are various types of marine deposited waste, but waste fishing gear such as tires, fish traps, rope, and fish nets, which causes great damage to the fishery industry, was selected as a class. The dataset is as follows: object detection: 8781 pieces of the original dataset; instance segmentation: 2689 pieces of the original dataset. The images to which the preprocessing process was applied were 1920 \(\times\) 1080 in size.
###### Abstract  
The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review of the critical role and future potential of integrating unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance Internet of Vehicles (IoV) systems within beyond-fifth-generation (B5G) and sixth-generation (6G) networks. We explore the combination of quasi-optical millimeter-wave (mmWave) signals with UAV-enabled, RIS-assisted networks and their applications in urban environments. This review covers essential areas such as channel modeling and position-aware beamforming in dynamic networks, including UAVs and IoVs. Moreover, we investigate UAV navigation and control, emphasizing the development of obstacle-free trajectory designs in dense urban areas while meeting kinodynamic and motion constraints. The emerging potential of RIS-equipped UAVs (RISeUAVs) is highlighted, along with their role in supporting IoVs and in mobile edge computing. Optimization techniques, including convex programming methods and machine learning, are explored to tackle complex challenges, with an emphasis on studying computational complexity and feasibility for real-time operations. Additionally, this review highlights the integrated localization and communication strategies to enhance UAV and autonomous ground vehicle operations. This tutorial-style overview offers insights into the technical challenges and innovative solutions of the next-generation wireless networks in smart cities, with a focus on vehicular communications. Finally, future research directions are outlined.  
autonomous navigation; intelligent vehicles; Internet of Things (IoTs); Internet of Vehicles (IoVs); mmWave communication; reconfigurable intelligent surface (RIS); trajectory optimization; vehicular communication; wireless communication; unmanned aerial vehicles (UAVs) +
Footnote †: journal:  
16, 433. [[https://doi.org/10.3390/f16120433](https://doi.org/10.3390/f16120433)]([https://doi.org/10.3390/f16120433](https://doi.org/10.3390/f16120433))  
1][PERSON]  
[1]andrey.Savkin  
[1]School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney 2033, Australia  
[1]andrey.Savkin  
[1]Correspondence: [EMAIL_ADDRESS] (M.E.): [EMAIL_ADDRESS] (A.V.S.)
## 3 Methodology  
### Workflow for Generating High-Resolution ET, GPP, and WUE  
The high-resolution ET, GPP, and WUE processing flow in this study is diagrammed schematically in Figure 2. Here, land surface variables (AWLST, NDVI, and albedo) have been fused with periodic Landsat images at 100 m resolution and MODIS images at 1000 m resolution retrievals using a spatial and temporal fusion of multi-source remote sensing data to create ET, GPP, and WUE with both high temporal (daily) and spatial (100 m) resolution using remote sensing models.
## 6 Experimental Evaluation
### Methodology
#### 6.1.4 Statistical Analysis and Metrics  
The effectiveness of the proposed EA is assessed via proper statistical analysis. For every problem instance and experiment, 30 independent executions (i.e., using a different seed for the random numbers generator) were performed. Then, a three-step analysis is applied to study the fitness results distributions:  
1. _Normality check._ The Shapiro-Wilk statistical test is applied to determine if the results distribution is modeled by a normal distribution, i.e., computing the likeliness of the underlying randomness to be normally distributed.
2. _Mean rank comparison_ (for parametric configuration analysis). The [PERSON] rank statistical test is applied to detect/analyze the differences in the distributions of fitness values across multiple executions.
3. _Pairwise comparison of distributions._ The Kruskal-Wallis non-parametric test is applied to determine if the differences between two parameter configurations have statistic significance.
4. _Boxplots_ are used for results visualization and graphical comparison. Relevant order statistics are computed and reported: first quartile (Q1), third quartile (Q3), median, minimum, and maximum values, since the Shapiro-Wilk test confirmed that the results do not follow a normal distribution. The interquartile range (IQR) is used as a measure of statistical dispersion, as usual for non-normal distributions.  
Several metrics are used for evaluating the exact and the evolutionary approach: (i) the number of successfully synchronized trips for passengers (i.e., the objective function defined in Equations (1) and (6)), (ii) the improvements over the real solution, and (iii) the average waiting for transfers in a transfer zone.
## 5 Discussion  
When the OFDR technology is used to measure the strain of PCC pile, the optical fiber length and spatial resolution need to be carefully considered. The strain of the optical fiber is calculated as an average change of the reflective spectrum along the fiber. Regarding heterogeneous and tough concrete materials, the length and spacing of the optical fiber were chosen during post processing; as such, many peaks appear intensively on the strain curves, which make it difficult to show the variation trend. The spatial resolution and strain resolution of the OBR 4600 are 1 cm and 1\(\mu\)respectively; this satisfies the requirement of strain measurement for engineering construction. It can detect the strain with distributed measurement along the entire optical fiber. It is an ideal method to study the mechanical behavior and engineering properties of PCC pile using the optical fiber sensor. Real-time measurement can be carried out. Once the load is applied, the curve of strain changes immediately. Thedeformation characteristics of PCC pile can be obtained.  
In engineering construction, the PCC pile is casted in place by vibrating and pulling out of the mould, with a maximum length of 25 m, diameter of 1.0-1.5 m, and tube thickness of 0.12-0.2 m. When OFDR technology is used, the optical fiber can be bonded to the reinforced rebar on strain measurement, through the test demonstrated by [PERSON] [25]. This provides a new method for strain measurement and integrity evaluation of PCC pile in situ. The optical fiber used is a commercially-available product manufactured for the telecommunications industry, and without any specific protective layer. With the development of optical fiber technology, the price of equipment  
Figure 10: Comparison between the results of the test and FE.  
and software for OFDR will get lower and lower, which is beneficial for the wide application of OFDR technology in monitoring the strain of pile. OFDR technology is very sensitive to the outer environment; the special protection should be used for application in the engineering field.  
Since Rayleigh effects are both temperature and strain sensitive, the PCC energy pile was developed based on the PCC pile. It can make full use of inner space by installing equipment for heat exchange inside the PCC pile. The PCC energy pile provides support for the upper structure, and keeps a stable temperature for the structure. The measurement for temperature and strain of the PCC energy pile is difficult, because it involves the multi-field coupling problem, including the temperature field, strain field, seepage field, and so on. Considering the advantages of OFDR technology, the bearing mechanism and engineering characteristics of PCC pile and PCC energy pile can be studied using OFDR technology.
## 2 Materials and Methods
### Experimental UTM Environment  
The radio interface of the MMT enables the UAS to connect to a UTM system via cellular mobile networks. The European ATM Master Plan [29] describes future U-space services in Annex 1, which are to be used in different expansion stages (U1-U4). Since no established UTM system was available at the time of the work, a prototypical UTM system was developed and implemented to evaluate the MMT in a mixed ATM/UTM context. This system includes a server that provides the following UTM services with basic functionality:  
E-Registration (U1). This service offers the UAS operator the possibility to register himself as well as one or more UAS in the UTM network. Each operator and each UAS is assigned a unique identification number.  
Pre-tactical Geofencing (U1). The UAS operator is provided with information on airspaces with special flight restrictions (e.g., control zones). The provision of daily updated aeronautical information (e.g., NOTAMs) is not part of the UTM system in this context.  
Flight planning management (U2). Based on the requested flight route and possible airspace restrictions, the UTM server receives flight plans and grants or denies them. In case of approval, a unique flight number is generated and transmitted to the UAS operator.  
Tracking (U2). The UTM server receives position reports from UAVs and merges them into an up-to-date air picture.  
Traffic Information (U2). On request, the UTM server periodically provides a list of other aircraft located in the immediate vicinity of the UAV.  
Procedural Interface with ATC (U2). This service ensures the flow of information between the UTM server and an ATC for those cases where a flight through its control zone is intended. In this case, the decision to approve the flight plan is made with the appropriate ATC.  
Collaborative Interface with ATC (U3). ATC has the option of contacting the UAS via the UTM system and transmitting instructions (to leave the control zone, land the aircraft, etc.) in order to be able to act promptly in special situations. These are forwarded to the UAV as well as to the Ground Control Station (GCS) of the operator.
## 3 Results
### Plant Biomass and Its Spatial Distribution in Snow Drought Years  
GPP was selected to detect the variation of plant biomass in the snow drought and snow-abundant years. Based on the MOD17A2 data, we obtained the average GPP values in the Sanjiang Plain from March to October in each year (Figure 9) because of the length of the growing season. In the entire period, compared to the snow-abundant years, on average, the GPP value in the snow drought years was less than 0.61%. The spatial distribution of GPP was higher in the middle and south parts of the area, lower in the north, and decreasing from southwest to northeast. The GPP variation trend from snow-abundant year to drought year was different among the four selected periods.  
One part (including phenological periods 2003-2005, 2012-2010, and 2019-2016) showed a decreased GPP trend in the drought year. The GPP value declined 0.96%, 0.41%, and 0.48%, and the snowfall decreased 119.5 mm, 223.0 mm, and 253.8 mm, respectively, with the corresponding periods. In the 2003-2005 period, the GPP value in 2003 showed less in the west and middle of the region in cultivated land, while in the 2019-2016 period, the forest performed at a higher value in the drought year 2019. The other part (period: 2014-2015) had an increased GPP value in the drought year, and it added 1.0% more than that in the snow-abundant year. This result may be due to the temperature increase of 1.4 \({}^{\circ}\)C from 2014 to 2015. Additionally, the year 2014 had the maximum GPP value of 124.97 kg\(\cdot\)C\(\cdot\)m\({}^{2}\) over the past two decades. Higher temperature promoted plant biomass accumulation and active vegetation growth in mid-high latitude areas.  
Figure 9: The spatial distribution of the gross primary productivity (GPP) in the Sanjiang Plain. Each picture is the average GPP value from each year’s March to October. Ave GPP: average GPP value (kg\(\cdot\)C\(\cdot\)m\({}^{2}\)) in each year.