esa-sceva/satcom-chunk-collection huggingface dataset:
# Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion
## 4 Theoretical Properties
### Coreball Family  
The last theoretical result concerns the Coreball family and shows the influence of the parameter \(\gamma\) on degree distribution of the sampled nodes.  
Let us define a well-connected graph \(G\). Let \(S\) be a random set of nodes of \(G\). In a well connected graph \(G\), the probability of having one or more direct neighbors of \(S\) connected to two or more nodes in \(S\) is high.  
**Theorem 3**: _In a well-connected graph, the Coreball family changes the degree distribution of the collected nodes compared to the Snowball sampling with high probability. If the degree distribution changes, for \(\gamma>0\) the density of nodes with degree 1 decreases and for \(\gamma<0\) it increases._  
This result is illustrated on Figure 2(b) for the Facebook graph. Even if the theorem is limited to nodes with degree 1, the results hold for higher degrees as shown on the figure. The shape of the degree distribution is changed as \(\gamma\) evolves, with an inflexion point around nodes with degree 20. This makes the exponent \(\gamma\) of the Coreball family an effective parameter for shaping the degree distribution of the sampled nodes. The degree \(d_{j}^{(out)}\) used in Coreball is a partial view of the real degree of node \(j\). However, it is a good estimate of the real degree.  
Let \(p(i)\) denote the probability to collect node \(i\) and \(p(j|i)\) the conditional probability of collecting node \(j\) if node \(i\) has been collected. Since the collection follow the edges (except for the initial node), one can write  
\[p(j)=\sum_{i\in\mathcal{N}_{j}}p(j|i)p(i),\]  
where \(\mathcal{N}_{j}\) is the set of neighbors of node \(j\). Let us assume a subset \(\mathcal{S}_{j}\subset\mathcal{N}_{j}\) of nodes have been collected at layer \(k\). The probability to collect node \(j\) at layer \(k+1\) is:  
\[p(j)=\sum_{i\in\mathcal{S}_{j}}p(j|i).\]  
In the present work, the conditional probability \(p(j|i)=p_{k}(j|i)=p_{k}(e_{ij})\) is chosen to influence the collection of some category of nodes. For Coreball:  
\[p(j|i)=w_{ij}d_{j}^{(out)\gamma}.\]  
The probability to collect node \(j\) depends on \(d_{j}^{(out)}\), i.e., the number of connection it has with nodes at layer \(k\). For \(\gamma>0\) the neighbors with highest connections to layer \(k\) will be collected with a larger probability. Since the real degree of a neighbor is unknown, \(d_{j}^{(out)}\) is different form \(d_{j}\) except when \(d_{j}=1\). Since the graph is highly connected, there is a high probability that \(d_{j}^{(out)}>1\) for some nodes, so that \(p(j|i)\) will not be uniform.
## 1 Introduction
In this paper, an automatic field monitoring system to measure the shortening of column and walls in high-rise buildings during construction is presented. This system comprises the following components: (1) a WSN that can automatically measure vertical shortening using numerous sensors embedded in columns and walls and transmit the measured data to a server and (2) monitoring software that enables construction administrators to monitor the real-time data collected on a server via an Internet connection. To improve the applicability of long-term monitoring of the shortening of vertical members in high-rise buildings under construction, wireless sensing units (sensor nodes, master nodes, and repeater nodes) with power-saving functions and acceptable diffraction capabilities are employed in the WSN. The proposed monitoring system can contribute both to the prompt verification of collected data from any location and to the automatic sensing of the shortening of vertical members in high-rise buildings under construction. The proposed system was applied to the construction sites of actual 66-floor and 72-floor high-rise buildings to monitor shortenings during construction. 39 vibrating wire strain gauge sensors, 23 sensor nodes, and four master nodes were installed in the automatic sensing system. The measurement data collected by the proposed monitoring system during the monitoring period of 88 days are provided and discussed.
## 2 Study Area and Data  
As a key metropolitan area in the central part of China, the Wuhan urban agglomeration, which includes nine prefectures, was selected to investigate the spatiotemporal variations in GDP development. This area is one of the largest city clusters in central China. Additionally, the agglomeration is not only the core of economic development in Hubei but is also an important strategic fulcrum for the rise of central China. Notably, it is the central node of the Yangtze River Economic Zone and plays an important role in linking the east and west and connecting the south and north in terms of economic development in China.  
GDP statistical data and DMSP-OLS nighttime light data collected in the study area are used in this study. The GDP data from each prefecture in the Wuhan urban agglomeration were obtained from the statistical yearbook of the corresponding city from 1992 to 2012. Meanwhile, the two types of DMSP-OLS datasets used in this study were provided by the National Oceanic and Atmospheric Administration (NOAA)-National Geophysical Data Center (NGDC) [15]. The 'Global DMSP-OLS Nighttime Light Time Series (Version 4)' dataset, which includes non-radiance-calibrated (NRC) light intensity recorded as 6-bit DNs ranging from 0 (no light) to 63 (saturation) by averaging DN values of stable light images throughout the year, collected from 1992 to 2012 was used to create the dasymetric map of GDP. The 'Global Radiance Calibrated Product', which includes radiance-calibrated light intensity, from 2011 was used to correct saturation problems in the version 4 dataset. All these global datasets were extracted based on the boundary of the Wuhan urban agglomeration, reprojected to the Universal Transverse Mercator (UTM) North 49 projection with the WGS84 datum, and resampled to a 1 km grid. Figure 1 shows the NRC DMSP-OLS data from the Wuhan urban agglomeration in 2011.  
Additionally, Chinese land use/cover data from 1995, 2000, 2005, and 2010, all with a spatial resolution of 1 km, were provided by the Data Center for Resources and Environmental Sciences of the Chinese Academy of Science [16]. These data were produced through visual interpretation of Landsat TM images [17]. This dataset was used as ancillary data in our study.  
Figure 1: Study area.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 1 Introduction  
Today, the world is more connected than ever thanks to the machine-to-machine (M2M) communication, also called machine-type-communication (MTC). M2M describes the exchange of data between smart devices and remote servers. These intelligent machines integrate computing capabilities that enable them to capture data around them and share this with other devices without the need of human intervention. Moreover, M2M applications are used in many domains, including e-health monitoring, remote security, smart grids, smart homes, transportation systems and so on [1, 2]. Cellular networks, such as 2G, 3G and long term evolution (LTE)/long-term evolution-advanced (LTE-A), play a fundamental role in the deployment of M2M communication, thanks to their ubiquitous coverage and their long range. Besides, dedicated M2M cellular architecture, as LORA and Sigfox [3] are built to provide high coverage and very low cost connectivity. However, they support only very low throughput, on the order of few bytes per minutes. Enhanced machine type communication (eMTC) and narrow band internet of things (NB-IOT) are built from existing LTE functionalities [4]. LTE/LTE-A offers high data rate, low latency, high capacity and more flexibility. However, most ofthe M2M devices are battery driven, and sometimes it is difficult and even impossible to replace the battery. For this reason, reducing power consumption is crucial for M2M devices. A possible solution to extend a device's life is the use of power-aware resource allocation techniques in the uplink, as the main traffic of M2M devices is in the uplink side.  
In this paper, we propose two uplink resource allocation strategies which take into account the coexistence H2H/M2M and which aim to reduce energy consumption for MTC devices. Both proposed schedulers consider channel quality while allocating radio resources for active devices with two different allocation algorithms. Moreover, the second scheduler considers the delay tolerance of MTC devices, which can vary from a few milliseconds to several hours [5], when allocating the limited resource blocks. The proposed schedulers are able to reduce the total transmission power while satisfying the quality of service (QoS) requirements in term of delay for the second scheduler.  
The paper is organised as follows. Section 2 describes, briefly, the M2M communication, the packet scheduler for the LTE network and presents some works from the literature. Section 3 details system model and proposed schedulers. Section 4 discusses the results of simulation, and finally, Section 5 concludes this paper.
## 4 Coverage Path Planning  
In this paper, we propose a full-coverage path planning algorithm tailored for area division based on terrain characteristics. The flowchart of the algorithm is depicted in Figure 5. The algorithm is primarily divided into two components: planning for simple terrain and planning for special terrain. Simple terrain pertains to areas with flat or gently undulating landscapes, whereas special terrain encompasses areas with significant topographical variations.  
Figure 4: Horizontal coverage.
# An Algorithm for Finding Optimal \(k\)-Core in Attribute Networks
###### Abstract  
As a structural indicator of dense subgraphs, \(k\)-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of \(k\)-core. However, relevant algorithms often set \(k\) values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper \(k\) setting. Especially in attribute social networks, characterizing communities with only \(k\)-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal \(k\)-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the \(k\)-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute \(k\)-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively.  
attribute network; dense subgraph; community search; \(k\)-core; hierarchical forest
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 1 Introduction  
The Pavement Condition Index (PCI) is a critical metric for evaluating the condition of road surfaces and guiding maintenance strategies. In modern transportation infrastructure, accurate PCI evaluation is essential to ensure road safety and optimize resource allocation. However, the lack of a unified standard and certification mechanism in the past has raised concerns about the consistency and reliability of PCI inspection results. To address these issues, this study aims to improve the accuracy and reproducibility of manual PCI inspections by developing a robust validation framework. ASTM D6433-23 [1], a globally recognized standard for assessing flexible and rigid pavements, serves as the foundation for this research. By focusing on flexible pavements commonly found in Taiwan, this study establishes a comprehensive methodology for PCI evaluation, covering 20 distinct types of pavement distresses and their corresponding severity levels.  
Recent research has emphasized the need for consistent validation mechanisms in manual PCI inspections to serve as a benchmark for automated systems ([PERSON] et al., 2022; [PERSON] et al., 2021) [2, 3]. Studies such as those conducted by [PERSON] et al. and [PERSON] et al.  
(2023) [4, 5] have also explored AI-based inspection frameworks, but many lack rigorous ground-truth verification from human inspection, which remains essential in practice, especially for smaller municipalities.  
This study aims to fill that gap by providing a practical validation mechanism that is applicable to current manual inspections and potentially usable as a reference for future automated evaluation. The proposed validation mechanism includes statistical methods such as the coefficient of variation to assess the precision of inspection data. Furthermore, this study emphasizes the importance of inspector training in minimizing subjective errors and improving the identification of pavement distresses. By addressing these challenges, this research not only enhances the standardization of PCI inspections but also provides valuable insights for road maintenance and management practices [6].
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 4 EGR Protocol  
In this section, a robust routing protocol called EGR for WMSNs with multiple sinks is presented, which is based on EG. This clustering technique conserves a lot of energy in WMSNs as well as conventional WSNs because the network performance is significantly improved by decreasing the number of hops from the sensor nodes (SNs) to sink node and reducing the amount of data to be delivered [29; 30]. In cluster-based networks, more energy is consumed by CH nodes than the normal  
Figure 1: Example network scenarios.  
SNs (which can also be regarded as cluster members). It is obvious because more computing and communication loads are assigned to CHs [31]. The variation in the energy consumed by nodes being CHs and CMs may cause unexpected early death of some nodes. LEACH is the most famous clustering protocol that can resolve this type of energy imbalance [21].  
In EGR, nodes will organize themselves into clusters based on overlapped FoVs. One node will act as a CH and other CMs will transmit collected data to the CH. CHs will transmit data to sink nodes after some processing. Therefore, energy expenditure of the CH nodes is higher than that of the CM nodes. The operation of EGR protocol can be divided into rounds as discussed earlier (\"setup\" and \"steady-state\" phases). In the \"setup\" phase, clusters are formed, CH nodes are selected intelligently using EG, CHs advertise to the CMs within the cluster, and CMs send join requests to the CHs. In the \"steady state,\" CHs determine the sink to transmit data based on the RSSI value. Finally, a data redundancy avoidance algorithm is used before processing and transmitting the data to the sink nodes by CHs. Figure 2 shows the traditional rounding system used in our EGR routing protocol. Additionally, Table 2 presents the operations in two states of a round in EGR.  
We will describe our entire routing process in the following sections. In Section 4.1, we will describe the cluster formation technique. In Section 4.2, we will elaborate the CH election process. In Section 4.3, we will describe our data redundancy avoidance algorithm along with the proposed EGR algorithm.
## 3 Methodology
### Source Domain-Based Pre-Training  
The source domain-based pre-training process focuses on creating a pre-trained model for the building extraction by using a deep semantic segmentation model and an open access source domain dataset, such as the Inria Aerial Image Labeling [33] and the WHU dataset (Wuhan University dataset) [34]. The AD-DAF framework is not limited to a particular deep semantic segmentation model. Any of the deep semantic segmentation models such as fully convolutional networks, UNet, PSPNet, and LinkNet [35], and their improvements can be applied to this framework. This means that our framework can be used as a complement to the current approaches. In other words, this also means that our framework can be used as a post-processing method for the current mainstream building extraction methods.  
The pre-trained model can output the classification probability that each pixel is classified as a building. Based on the output probability, the input image in the target domain can be divided into building, mixed, and non-building layers through the given thresholds. Regions with a pixel classification probability greater than 0.6 are classified as the building layer, regions with a pixel classification probability less than 0.2 are classified  
Figure 1: Overall architecture of our anomaly detection-based domain adaptation framework.  
as the non-building layer, and regions with a pixel classification probability between 0.2 and 0.6 are classified as the mixed layer. Obviously, the above given thresholds and the pre-trained model cannot ensure that the building layer is completely correct; it can only ensure that most of the pixels in the building layer are buildings. This is mainly because the pre-trained model is obtained based on the labeled images in the source domain, which holds bias towards the unlabeled images in the target domain.  
To reduce the possibility of bias between the images in the source and target domains, a test time augmentation strategy [36] is applied in this paper. Specifically, each input image in the target domain is rotated by \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\), flipped up and down, and flipped horizontally to obtain six new images. The final classification probability of the input image is the average of the classification probabilities of the corresponding pixels in these six augmented images. This will subsequently improve the accuracy of the classification probability.  
In addition, it is important to note that to reduce the disturbance of roads on the building extraction, roads are roughly extracted by using the same deep semantic segmentation model and DeepGlobe road extraction dataset [37], and then these extracted roads are removed from the output of the pre-trained model.
## 7 Discussion and Future Research
### Future Research  
With the expanding network of sensor-equipped smart roadside infrastructure, cooperative perception algorithms are evolving rapidly. However, on a highway section, which can consist of many hundreds of kilometers, the choice of sensors matters significantly. As the highway has controlled access routes, our algorithm enables the significant reduction of LiDAR sensors needed. In order to test this, we plan to evaluate the effectiveness of our method on the soon-to-be launched M1-M7 sensor system in Hungary, which is a smart-road section of the M1-M7 Motorway equipped with state-of-the-art sensors.  
One of the most significant weaknesses of the proposed method is the reliance on the pretrained OpenPiffPaf model, which was trained on the Apollocar3D dataset. This contains images only from a given perspective and context, meaning that the performance of the 2D detector is degraded slightly in different contexts and on vehicles not present in the training set. However, the accurate localization and re-projection of the _Semantic Mesh Model_ vertices may enable the automatic 2D annotation of novel views. This concept will be explored in future research.  
Currently, our method reconstructs the _Semantic Mesh Model_ offline on a recorded sequence of data. Future research will include the possibility of online, on the fly reconstruction. Note that this does not inhibit its current applicability in the presented contexts.
# Evaluation Model of Rice Seeding Production Line Seeding Quality Based on Deep Learning
## 5 Conclusions  
The study proposes a sophisticated method for rice sowing quality detection, based on a modified ResNet50 model, applied to eight types of images obtained from the actual environment of rice seedling production lines. Enhancements to the original ResNet50 model include the incorporation of the SE attention mechanism, the use of the Adam optimizer, and the application of cosine annealing to adjust the learning rate, all directed towards the accurate identification of missed sowing areas in seedling trays. The main research conclusions are as follows:  
1. A significant outcome of this research is the introduction of a virtual grid-style image segmentation preprocessing method, which shifts the focus from object detection to classification in the detection of missed sowing areas. This approach substantially improves the model's precision and operational efficiency, demonstrating outstanding performance even with small-scale training datasets.
2. Upon enhancing the ResNet50 model with the SE attention mechanism and selecting an initial learning rate of 0.01 with 50 iteration cycles using the Adam optimizer, the model attained a test set accuracy of 95.82%. This marks a substantial improvement in test set accuracy compared to the AlexNet, DenseNet, and VGG16 models, with respective increases of 4.55%, 2.07%, and 2.62%.
3. The capability to detect missed sowing areas in rice seedling trays is invaluable for assisting farmers and producers in monitoring their production progress and analyzing pertinent data. This serves as a robust foundation for making informed decisions regarding reseeding operations. The model for automatic evaluation of rice sowing quality, introduced in this research, facilitates rapid assessment of sowing quality within the intelligent seedling cultivation process. It not only accurately identifies and quantifies missed seeding in individual trays but also has the potential to be integrated with image acquisition systems on production lines in the future. Such integration will make it a critical element in rice seedling production lines, thereby contributing significantly to the scientific cultivation of rice seedlings.  
Conceptualization, Y.L., P.H. and C.Z.; methodology, P.H.; software, Y.L., Y.C., C.Z. and S.D.; validation, Y.L. and Y.C.; data curation, S.D.; writing--original draft, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Sichuan Provincial Financial Independent Innovation Special Project--Application Research of Spatiotemporal Big Data Analysis in Agricultural Production Services (2022 ZZCX034), National Industrial Technology System Sichuan Meat Sheep Innovation Team (SCCXTD-2023-14).  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to our image datasets are self-built.