esa-sceva/satcom-chunk-collection huggingface dataset:
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 4 Simulation Results and Discussion
### Simulation Results
#### 4.1.1 Detection Results of Target with Smooth Motion
Figure 9 gives the EIR versus the RA, from which we can see that when the RA equals \(-0.061\), the EIR is the largest. A more accurate RA can be obtained by a smaller step length, which means a longer computational time. Figure 8: Fractional Fourier transform (FrFT) result of the 33 rd range cell. Figure 7: Doppler variation during the coherent processing interval (CPI). (**a**) Doppler variation and curve fitting result. (**b**) Curve fitting error. Figure 10 shows the coherent processing results obtained by the proposed processing technique. It can be seen from Figure 10 that the target energy is correctly accumulated over the CPI. As evident, a bright spot that is well-gathered in both range and Doppler dimensions can be noticed in the RD map, and the Doppler spreads in Figures 5 and 6 have been eliminated effectively. Figure 11 presents the Doppler cross-section of 33 rd range cell to further illustrate the effectiveness of the new proposed method. As is apparent, the new proposed method has effectively integrated the signal energy during the long dwell time. A clear peak can be individuated in the red curve. Specifically, it is seen that an improvement of more than 10 dB is obtained. Such an improvement indicates the effectiveness and robustness of the proposed processing technique.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Admission Control  
Admission control is simple mechanism with few design choices. The decision is either to accept or reject connections of a source or a receiver to a distribution tree/mesh. One design choice is the location where admission control is made: at the source, at the receiver, or at intermediate nodes.  
With admission control at an intermediate node, each node on a path checks whether it has sufficient resources to accept the multicast flow. Typically, a new source initiates a route request to establish a multicast route to its receivers. Each intermediate node checks if it has sufficient resources to meet the QoS requirements (typically available bandwidth). If so, the node forwards the route request; otherwise, the route request is discarded. The destinations therefore only receive route requests along one or more paths that have sufficient resources. The reply is sent along one of these paths back to the source. The return packets are typically used to confirm the admission control and the associated resource reservation. The actual path to select must then be confirmed in a three way handshake. Most of the protocols use this technique, for instance, protocols in [12, 15, 16, 24, 29].  
Admission control can also be done at the receivers. M-CAMP [20] performs admission control by end-to-end probing. The source initiates probes, and the receivers can then determine whether to admit the flow. Probe packets are used to measure the quality of the path from the source to the destination. When the destination receives the probe packets, it measures the quality of the probe packet and compares to the known QoS requirements. If the quality of the probe packets is satisfied, the receiver accepts the session; otherwise, the session is rejected. The receiver then sends its decision to the source.  
Finally, admission control can be performed at the source. The source calculates the QoS satisfied paths to all the destinations while building a multicast tree. Once a tree with the necessary resourceshas been identified, all nodes involved are informed of the multicast structure and the source admits itself to the single source tree. Protocols in [17, 24, 25, 26, 41, 51, 22] use this type of admission control.  
The advantage and the cost of a particular admission control must be seen in close conjunction with the resource reservation. Admission control at intermediate nodes along potential paths may result in resource reservations that will not be used. Admission control at source or a centralized node may be based on outdated information, but offer more precise resource reservation in terms of nodes involved.
# Prediction of Soil-Water Retention Curves of Road Base Aggregate with Various Clay Fine Contents
###### Abstract  
The invasion of clay fines can change the water retention properties of base layers significantly. There is no soil-water retention curve prediction equation that is capable of considering the effects of fines. In this study, the soil-water retention curves (SWRC) of road base aggregate with different fine contents (\(FC\)) were measured. The variations in inter-aggregate pores (formed by coarse grains) and intra-aggregate pores (formed by fines) with \(FC\) were deduced from the SWRC at different \(F\)s. It was found that the inclusion of fines shifted and scaled the inter-aggregate pore distribution curves and scaled the intra-aggregate pore distribution curves. Based on this finding, a prediction equation for SWRC with consideration of the effects of \(FC\) was proposed. The feasibility of this equation was verified by comparing the predicted and measured results. The proposed model can be used to predict the SWRC of soil with different \(F\)s.  
soil-water retention curve; coarse-grain soil; fine content; pore size distribution Article
# Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks
###### Abstract
These issues are addressed and resolved in [12; 10; 13; 14]. Although these methods promise good gain and robustness to the system, some loopholes exist in the functionality of these systems. The trust-based routing and selection of trustworthy nodes consumes much energy and time. This paper presents an optimal way of solving this problem by introducing a novel Trust-aware Routing Mechanism. It deals with the IoT architecture exhibiting the edge layer, which uses security-enabled designs, authorization, authentication, privacy, and intrusion detection mechanisms and protocols. It also employs an optimized way to organize the nodes and apply a trust mechanism to all nodes. Furthermore, it establishes a route path using artificial bee colony (ABC) optimization, which is specially designed for edge nodes. The motivation behind this architecture is to provide a buffer facility and enhance the system's privacy using edge nodes. Our proposed approach is efficient in conserving energy and boosting the accuracy of the system. The main and original contributions of this work are listed below:  
Figure 1: EDGE-node-based IoT application. 1. The sensor network having sensor nodes randomly deployed is clustered and the selection of cluster head is conducted using the modified gray wolf optimization approach [15]. 2. The clustered network consists of many clusters with their respective cluster heads and a mobile edge node that evaluates the trust of every node in a cluster and gathers trustworthy information from those nodes. 3. The inter- and intra-cluster routing is optimized using the artificial bee colony optimization approach, which provides the best optimal path for the data flow to the edge node. 4. The performance analysis for the proposed model is based on energy consumption, delay, detection rate, accuracy, and network lifetime. The remainder of this paper is arranged as per the sections mentioned below:  
Section 2 gives an overview of the relevant works associated with the technique used in the proposed model. Section 3 describes the problem formulation. A thorough explanation of the techniques used to build the model is discussed in Section 4. It also presents the algorithm of the proposed approach. Section 5 analyses the performance of the work on various metrics and compares it with other similar existing approaches. Section 6 gives a conclusion with a future direction to the proposed methodology.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### Performance Evaluation of Pido
#### 4.4.5 A Comparison of the Field Test and the Simulation  
To further evaluate the performance of Pido, we validate the effectiveness of Pido with and without using it in the field test as well as in the simulation, and the results are summarized in Table 4. We deployed 27 nodes in the field test, and the average under-water time at intertidal zone in Zhoushan is 458 cycles. Therefore, we set the network size and the average under-water time as 27 nodes and 458 cycles in the simulation accordingly. As shown in Table 4, the average delay decreases significantly when Pido is implemented in the field test. We also notice that the field test reveals longer average delay and higher energy consumption than the simulation results. The reason is that the environment is much uncontrollable in the field test, which leads to the link quality being even worse in the field test. On the other hand, different distributions of nodes may also lead to different routing paths in the simulation and the field test. However, the results in the field test with Pido reveal that Pido can decrease the system latency efficiently.  
Figure 9: Energy consumption with Pido, ORW and CTP in different network sizes (average under-water time: 360 cycles).
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 2 THM Coupling Model
### Mass Conservation Equation  
#### 2.2.1 Solid-Phase Skeleton Mass Conservation Equation  
According to the generalized conservation of mass equation, it is assumed that the source and sink of solid mass are 0, and the solid deformation rate is very small. Considering that the divergence of solid skeleton velocity is equal to the volumetric strain rate of the soil, the solid mass conservation equation (porosity evolution equation) is obtained as [43; 44]  
\[\frac{\partial n}{\partial t}-\left(1-n\right)\left[\frac{\chi}{K}\frac{ \partial p_{I}}{\partial t}+\frac{1-\chi}{K}\frac{\partial p_{g}}{\partial t} -\beta_{sT}\frac{\partial T}{\partial t}+\frac{\partial\epsilon_{v}}{\partial t }\right]=0 \tag{2}\]  
where \(n\) is the porosity, \(\epsilon_{v}\) is the volumetric strain of soil, \(p_{I}\) is the pore water pressure and \(p_{g}\) is the pore gas pressure, respectively.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 3 Developed System  
This section presents a detailed description of the developed system, addressing various technical and functional aspects essential for its implementation. The specifications of the LoRaWAN network are discussed in Section 3.1, providing an overview of its architecture and explaining how IoT devices connect to the Internet using modulated radio signals. The network topology, key elements, and classes of end devices are described, highlighting the flexibility and efficiency of bidirectional communication over long distances with low energy consumption. Section 3.2 details the mechanism that allows firmware updates during runtime for remote devices without interruptions or reboots. This segmented update process of the code in program memory ensures the continuity of device operations during updates.  
Section 3.3 explains how the configuration of uplink and downlink communication between IoT devices and the application server is organized and managed. The processes of data conversion and formatting, as well as the use of webhooks to automate bidirectional communication, are described in detail. Section 3.4 focuses on the description of the application server that is responsible for processing and storing data from end devices. The implementation of a machine-to-machine (M2M) server and the role of middleware in interfacing between the network server and end devices are also discussed, highlighting the importance of this component for efficient information management.  
Finally, Section 3.5 addresses the implementation of the proposed system, encompassing hardware, firmware, and the information management process. The hardware components used, the development of specific firmware for the devices, and the firmware update file management are described, offering a comprehensive view of the components and operations of the developed system.
# A Method for Evaluating the Spatial Layout of Fire Stations in Chemical Industrial Parks
## 1 Introduction
The specifics of these projects are presented in Table 1. To illustrate this proposed method, the case study of an engineering project in the chemical industrial park, referred to as Park X, will be provided. Park X covers an area of 11.33 km\({}^{2}\) and comprises primarily petrochemical and fine chemical industries.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 2 Related Works  
### Handover Schemes for User Links  
Handover strategies have been investigated widely in the literature for many years. Most of them aimed at planning user-to-satellite links (USLs). Since the user service duration may be greater than the service time provided by a satellite, the user must switch its USL to another visible satellite to prevent interruption of the ongoing communication [20]. In [21], the authors investigated three common criteria, i.e., maximum service time, minimum distance, and the maximum number of free channels. The maximum service time criterion aims to reduce handover frequency and communication interruptions. In [22], the authors propose real-time dynamic update velocity-aware handover management to reduce handover frequency. The minimum distance criterion is similar to the RSS-based and maximum elevation criteria, which aim to obtain the best channel quality and improve throughput performance [23]. With the maximum idle channels criterion, the users would switch to the satellite with abundant channels to achieve load balancing and reduce the blocking rate [24].  
The above three criteria mainly cater to conventional single-hop-based satellite access networks without considering the influence of ISLs. Some current research focuses on solving the handover issues in terms of the large-scale LEO networks equipped with ISLs. In [25], the authors proposed a handover strategy combined with multi-hop routing to reduce the end-to-end propagation latency. In [9], the authors proposed a congestion-aware handover scheme to achieve optimized end-to-end performance while limiting the handover frequency.
## 2 Materials and Methods
### Methods
#### 2.3.7 Vegetation Classification Maps  
In order to observe the spatial pattern of GPP, the model was upscaled to the surrounding areas of the eddy covariance tower. An unsupervised classification algorithm was used to identify regions with the same land cover (reflectance properties) as those identified within the flux footprint. The identified regions were assumed to have homogeneous vegetation with similar biophysical properties, photosynthesis activity and GPP seasonality of those within the footprint. The unsupervised classification was performed on a composite image created by using all the Sentinel-2 images available for the study period preprocessed as described in Section 2.2.1. The k-means clustering algorithm developed by [PERSON] and [PERSON] [70] was used to perform the unsupervised classification and implemented through the Clusterer.wekaKMeans method of the \"ee\" package in python. The algorithm minimizes the average squared distance between the points in the same cluster. The use of this robust unsupervised clustering method helps overcome the need for expert human knowledge required for supervised classification and has already been used in similar studies [71; 72; 73]. Our area of study was classified into seven classes using 5000 training pixels with a scale of 10 m retrieved from the study area
## 4 Experiments
### Anomaly Detection and Explanation
#### Accuracy  
\[\text{Accuracy}=\frac{TP+TN}{TP+FN+TN+FP} \tag{14}\]  
Accuracy is defined as the proportion of correctly predicted instances out of the total number of instances. While accuracy is an intuitive and commonly used metric, it may not be the best choice when dealing with class imbalance, as a model can achieve high accuracy by predominantly predicting the majority class.  
Recall \[=\frac{TP}{TP+FN}\] (15)  
Recall, also known as sensitivity or true positive rate, is the ratio of true positives (TP) to the total number of actual positives (TP + False Negatives, FN). In the context of ICS monitoring, a high recall indicates the effective identification of most true anomalies, which is crucial for ensuring system security.  
Precision \[=\frac{TP}{TP+FP}\] (16)  
Precision, also known as positive predictive value, is the ratio of true positives (TP) to the total number of predicted positives (TP + False Positives, FP). In ICS scenarios, high precision ensures that when the model predicts an event as anomalous, the prediction is highly reliable, thereby reducing the impact of false alarms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Avg (Fitness)** & **Avg (Support)** & **Avg (Interestingness)** & **Avg (Comprehensibility)** & **Total Rules** \\ \hline BA & 0.7879 & 0.5814 & 0.5749 & 0.5417 & 22,847 \\ DE & 0.7403 & 0.4856 & 0.5064 & 0.5410 & 20,958 \\ PSO & 0.8573 & 0.7102 & 0.7468 & 0.5866 & 21,075 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation metrics of swarm intelligence algorithms under optimal hyperparameters.  
F1-Score  
\[\text{F1-score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{17}\]  
The F1-score is the harmonic mean of precision and recall, providing a balanced measure of these two aspects. In ICS monitoring, the F1-score helps find a reasonable trade-off between the accuracy of anomaly detection and its comprehensiveness.  
By employing these metrics, we can provide a more comprehensive evaluation of the model's performance in handling ICS data, particularly in detecting rare but critical anomalies. Additionally, this multi-dimensional evaluation approach helps reveal potential biases or weaknesses in the model, guiding further improvements.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 4 Analysis of PDIV and Modelling Tool Performance for Variable Temperature
### Performance of PDIV Models for Variable Temperature
#### 4.2.6 Comparison of Models  
The analytical and FEM-based models are compared in order to provide some guidelines regarding the selection of a suitable model for PDIV estimation at variable temperatures. The [PERSON] model [74], [PERSON] model [26], FEM and [PERSON]'s law model [38; 60; 62; 69], and the proposed model are compared. Among them, the Dakin model suggested in [74] can only consider the evolution of permittivity with the temperature, whereas the [PERSON] model [26] only considers the effect of air temperature and pressure. The FEM and [PERSON]'s law model [38; 60; 62; 69] and the new proposed model consider both the evolution of relative permittivity and air properties with temperature. It is necessary to specify that only the FEM and [PERSON]'s law model, with the straight distance between wire surfaces, is represented in this comparison.  
Figure 17 shows the comparison for sample group B. On the one hand, it is shown that the Dakin model, which is unique in that it does not consider the effect of temperature on the air properties, differs considerably from the experimental results. The other models all provide similar results. The Lusuardi model proposed by [PERSON] et al. [26] returns a slightly higher error, as a constant relative permittivity is considered. The FEM-based model [38; 60; 62; 69] returns a much smaller error value. In all cases, at temperatures higher than 150 \({}^{\circ}\)C, the estimated values are not inside the measurement uncertainty range. [PERSON] et al. [73] considered a similar case. In order to understand where this difference comes from, they analysed the \(\gamma\) estimation for different temperatures. They noticed that the \(\gamma\) value calculated for high temperatures did not considerably differ from the room temperature values. Therefore, they concluded that the difference could be due to the impact of the moisture, which is not considered in the models, or the uncertainty in the sample thickness and geometry changing with temperature.  
Figure 17: Comparison of PDIV models and experimental measurements for different temperatures (sample group B): (a) PDIV and (b) error.  
Figure 18 illustrates the comparison for sample group C. Similar to sample group B, the Dakin model is not good enough to predict the PDIV at elevated temperatures. For the rest of the models, the difference between them is not appreciable. Compared to sample group B, the relative permittivity of group C is almost constant for the studied temperature range and, hence, similar results were obtained for these models.
## 3 The Proposed CA-CWA Algorithm
### Congestion Status Estimation  
To provide a backoff algorithm in the 802.11 WLAN based on the congestion status, we need to first consider how to estimate the current network congestion level based on the available observation measures. In this work, we choose the parameter called channel busyness ratio (\(\rho\)) for capturing the channel status, which refers to the literature [14; 19]. The channel busyness ratio is defined as the rate that a station finds the channel is busy during a certain time interval. Let \(T_{i}\) be the slot length of the \(i\)th slot, in a given time interval which has \(n\) time slots, and the ratio \(\rho\) can be calculated as follows:  
\[\rho=\frac{\sum_{i=1}^{n}\alpha_{i}T_{i}}{\sum_{i=1}^{n}T_{i}}. \tag{2}\]  
where \(\alpha_{i}\) is the indicator function expressed as:  
\[\alpha_{i}=\left\{\begin{array}{ll}1,&\text{if $i$th slot is busy}\\ 0,&\text{if $i$th slot is idle}\end{array}\right.. \tag{3}\]  
In the IEEE 802.11 standard, every station has the ability of carrier sensing. Thus, \(\alpha_{i}\) is easy to obtain without any additional hardware modification. However, in a real wireless scenario, both external interferences and transmission collisions can cause busy channels, which was not taken into account in the mentioned previous work. To obtain a more accurate estimation of \(\alpha_{i}\), a method should be designed to discriminate between the external interferences and transmission collisions. Let \(N\) be the number of transmissions in a given period of time for an arbitrary station, and \(S\) of them are transmitted successfully (ACK is received). On the other hand, suppose there are \(R\) slots in which the station does not transmit, and \(I\) of them are idle, we can conduct the estimation of the collision probability \(p_{c}\) and the channel error probability \(p_{e}\) by maximum likelihood estimation method [23]:  
\[p_{c}=\frac{R-I}{R}, \tag{4}\]  
\[p_{e}=1-\frac{1-(N-S)/N}{1-p_{c}}. \tag{5}\]  
With the collision probability \(p_{c}\) and the channel error probability \(p_{e}\), the modified channel busyness ratio (\(\rho^{\prime}\)) can be calculated as follows:  
\[\rho^{\prime}=\frac{p_{c}}{p_{c}+p_{e}}\frac{\sum_{i=1}^{n}\alpha_{i}T_{i}}{ \sum_{i=1}^{n}T_{i}}. \tag{6}\]