esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The effective penetration and sensitivity of the microwave signal is strongly dependent on the moisture abundance and size, and distribution of scattering elements within the sensor footprint. However, the soil FT signal is generally reduced under greater snow, surface water, and vegetation biomass levels [12; 13; 14]. The FT signal can also be degraded where the sensor footprint is unable to effectively resolve sub-grid heterogeneity; FT heterogeneity is generally larger over complex terrain and during seasonal FT transitions where the microclimate variability is enhanced [15]. The latest (R16) SMAP global and northern hemisphere FT data records encompass a larger land domain than previous product versions [16; 17], including all land areas where frozen temperaturesare a key limitation to terrestrial ecosystem processes. Grid cells dominated by permanent snow/ice cover over Greenland and Antarctica, large water bodies or areas where seasonal FT conditions have an insignificant impact on ecosystem processes are excluded from the SMAP FT classification. Previous SMAP FT records showed a general consistency with proxy FT observations from in-situ soil temperatures, land model surface temperatures, and alternative FT records derived from higher frequency satellite microwave sensors [7; 18; 19]. In this investigation, we conducted a global validation assessment of the latest (R16) SMAP FT global data records (L3_FT_P[E]). The R16 FT record incorporates updated calibration refinements to the lower order Tb measurements and includes a new dual algorithm approach, larger global domain, and longer satellite operational record (March 2015 to present) than prior assessments. Here, we examined the SMAP FT global record over two complete annual cycles from 1 January 2016 to 31 December 2017. The R16 record was validated using independent FT estimates determined from global weather station surface air temperature measurements. Spatial and seasonal variations in the apparent FT accuracy were compared between the baseline 36 km and enhanced 9 km grid L3_FT products, in relation to potential noise factors, including open water fraction, percent tree cover, and the elevation gradient within a SMAP grid cell. Potential utility of the SMAP FT products was examined for selected regional case studies involving the detection of anomalous frost events over Australia and spring onset patterns over Alaska. The following sections describe the data and methods used in this analysis (Section 2), results of the global validation and case study assessments (Section 3), and a summary and discussion of the key findings from this work (Section 4).
## 1 Introduction
### Metal Casings in borehole-to-Surface Electromagnetic Imaging  
In electromagnetic excitation environments involving wells, the presence of metal casings is a complex factor affecting electromagnetic response observations [36; 37; 38; 39]. During crosswell or borehole-to-surface electromagnetic monitoring, metal casings may cause signal attenuation [40; 41; 42; 43]. Additionally, electromagnetic responses from metal casings can generate interference signals that must be considered in numerical simulations or inversions [44; 45]. Although metal casings complicate numerical modeling and inversion, they can be regarded as \"extended electrodes\" that help excite deep targets and enhance signals, which might be unobservable without casings.  
Many scholars have discussed solutions for simulating borehole-to-surface electromagnetic imaging observation systems in models with metal casings [46; 47; 48]. [PERSON] et al. (2017) verified the effectiveness of borehole-to-surface DC resistivity observations through finite volume simulations [46]. In subsequent research, [PERSON] et al. (2023) performed finite volume simulations for time-domain and frequency-domain borehole-to-surface electromagnetic methods, demonstrating that magnetic permeability enhances the inductive components of electromagnetic responses [47]. However, the finite volume simulation methods applied to borehole-to-surface electrical exploration techniques still demand further technological advancement and systematic summarization. This is essential to enable them to effectively tackle more intricate geological scenarios and multi-source interference environments, thereby enhancing the accuracy and applicability of electrical method interpretations in complex hydrogeological, engineering geological, petroleum, and mineral exploration projects [49; 50; 51].  
For borehole-to-surface electromagnetic imaging technology, it is essential to account for the influence of metal casings and develop efficient simulation methods. The goal is to model the geometry and physical properties of metal casings without compromising computational efficiency or the accuracy of target area calculations, thereby improving the inversion accuracy in target regions.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Routing Approaches Considered for the Analysis
#### 3.5.1 Reactive Routing Approach  
The reactive routing approach aims at sending packets only when it is necessary to transmit information. Thus, reactive routing could prevent the waste of resources, especially considering the power and processing limitations of ad hoc IoT nodes.  
The \"Ad hoc On-Demand Distance Vector\" (AODV) routing protocol is the de facto standard for reactive routing [51]. AODV initiates the basic discovery process by broadcasting a \"Route Request\" (RRQ) packet to find the destination node. Each intermediate node rebroadcasts the \"RRQ\" packet, increasing the hop counter until the \"RRQ\" packet reaches the \"Time to Live\" hop limit or finds the destination. Upon \"RRQ\" packet reception, the destination node unicasts a \"Route Reply\" (RRP) packet towards the source node to establish the route through intermediate nodes.
## 3 Results and Discussion
### Spatial Distribution of Litchi Fruit Trees  
The key growth stage characteristics and combinations of different polarization characteristics were identified by the decision tree. The proposed fruit tree identification model was tested with satellite data from 2018 and 2022. The recognition results are shown in Figures 7 and A3. The identification results in 2018 were consistent with those in 2022. According to the actual survey, there has been no significant deforestation or reforestation. It confirmed that the identification results were reasonable, and it also demonstrated that the fruit tree identification model was stable. In 2022, the total planting area of litchi in Maoming was about 90,333 hectares, the total area under longan cultivation was about 52,000 hectares, and the total area under citrus cultivation was about 2560 hectares. The recognition accuracy of litchi fruit trees was 90.34%, which was consistent with the results of the field survey. It can be seen from the figure that the planting distribution of the three fruit trees was not uniform. From the north-south direction, the topography of Maoming was mainly divided into southern plains, hills, and northern mountains. In the southern plains and hills, the planting of fruit trees was concentrated and extensive, while, in the northern mountains, the planting area was small and scattered.  
Figure 6: Decision tree modeling for classification and recognition of litchi, longan, and citrus.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 5 Experimental Results and Discussion
### The Anomaly Detection System  
In order to verify the practicability of the above abnormal network traffic detection methods, our team designed a real-time dynamic monitoring system for network abnormality detection combined with a deep learning model and verified the effectiveness of the system in an ultra-large-scale high-speed network traffic environment.  
The intelligent network anomaly detection real-time dynamic monitoring system used a browser/server (browser/server, B/S) structure. The system obtained traffic information by deploying security engines at various key points of the network. The system used an encrypted secure network to exchange information with the control center to achieve network data acquisition, analysis, and detection. At the same time, according to the results of the detection of abnormal traffic, the abnormal behavior that violates the security policy was merged and recorded or automatically filtered and blocked, and relevant information was sent to the control center in real time.  
Based on the algorithm presented in this paper, the anomaly detection module in this system was constructed. The anomaly detection link was responsible for building a model to intelligently analyze the processed data and output the detection results. The network detection module implemented packet capture based on WinPcap and Tshark, and the feature engineering was implemented based on C++, and multi-threading was used to improve the processing speed of the system in the face of high-speed network traffic. The processed data contained 109-dimensional features. Based on the OC-LSTM algorithm proposed in this paper, the captured features were classified and trained, and then the classification model and prediction results were output and visualized. Among these, if the classification result was abnormal, a warning would be sent to the security center for blocking.
# Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
## 6 Conclusions  
This article proposes the MESN-based dynamic DC with OOR for EH-WSNs. The proposed MESN model is used to act as a predictor for finding the expected energy acquisition of the next slot. The proposed modified echo state network (MESN) model comprises a WOA for optimally selecting the weights of the neurons in the reservoir layer of the echo state network. The proposed scheme is the novel research in this article for stopping the unstable output of ESN due to random weight selection in the reservoir layer. It is noticeable that the proposed MESN model overcomes the shortcomings of ESN. The OOR scheme also utilized a hybrid relay selection and duty cycle optimization scheme. Relay set is optimally selected using multiple parameters such as energy consumption, congestion rate and EDW. Finally, the duty-cycle is adjusted based on energy consumption, energy acquisition, and energy threshold. The average percentage improvements of the suggested OOR scheme with existing state-of-art techniques clear that the suggested scheme performs outstands than existing schemes.  
Conceptualization, R.S.R.; Formal analysis, R.S.R.; Investigation, R.S.R.; Methodology, R.S.R.; Resources, R.S.R.; Supervision, S.S. & R.K.; Validation, K.A.; Writing, R.S.R.; Review & Editing, R.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5 GD, UK and is also supported by Department of Computer Science and Engineering, Deenbandhu Chhoturam University of Science and Technology, Murthal (Sonipat), Haryana-131039, India.  
The authors declare no conflict of interest.
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 3 Preliminaries  
### Attention Mechanism as Nadaraya-Watson Regression  
If we consider an attention mechanism as a method for enhancing the accuracy of iForest for the solution of the anomaly detection problem, this allows us to automatically distinguish the relative importance of features, instances, and isolation trees. According to [18; 57], the original idea of attention can be understood from the statistical point of view by applying the Nadaraya-Watson kernel regression model [24; 25].  
Given \(n\) instances \(\mathcal{D}=\{(\mathbf{x}_{1},y_{1}),\ldots,(\mathbf{x}_{n},y_{n})\}\), in which \(\mathbf{x}_{i}=(x_{i1},\ldots,x_{id})\in\mathbb{R}^{d}\) is a feature vector involving \(m\) features and \(y_{i}\in\mathbb{R}\) represents the regression outputs, the task of regression is to construct a regressor \(f:\mathbb{R}^{m}\rightarrow\mathbb{R}\) that can predict the output value \(\hat{y}\) of a new observation \(\mathbf{x}\) by using available data \(S\). A similar task can be formulated for classification problems.  
The original idea behind the attention mechanism is to replace the simple average of outputs \(\hat{y}=n^{-1}\sum_{i=1}^{n}y_{i}\) for estimating the regression output \(y\), corresponding to a new input feature vector \(\mathbf{x}\), with the weighted average in the form of the Nadaraya-Watson regression model [24; 25]:  
\[\tilde{y}=\sum_{i=1}^{n}\alpha(\mathbf{x},\mathbf{x}_{i})y_{i}, \tag{1}\]  
where the weight \(\alpha(\mathbf{x},\mathbf{x}_{i})\) conforms with the relevance of the \(i\)-th instance to the vector \(\mathbf{x}\), i.e., it is defined in agreement with the location of the corresponding input \(\mathbf{x}_{i}\) relative to the input variable \(\mathbf{x}\) (the closer an input \(\mathbf{x}_{i}\) is to the given variable \(\mathbf{x}\), the greater \(\alpha(\mathbf{x},\mathbf{x}_{i})\) will be).  
In terms of the attention mechanism [26], the vectors \(\mathbf{x}\), \(\mathbf{x}_{i}\) and outputs \(y_{i}\) are called the _query_, _keys_, and _values_, respectively. The weight \(\alpha(\mathbf{x},\mathbf{x}_{i})\) is called the attention weight.  
The attention weights \(\alpha(\mathbf{x},\mathbf{x}_{i})\) can be defined by a normalized kernel \(K\) as:  
\[\alpha(\mathbf{x},\mathbf{x}_{i})=\frac{K(\mathbf{x},\mathbf{x}_{i})}{\sum_{j =1}^{n}K(\mathbf{x},\mathbf{x}_{j})}. \tag{2}\]  
For a Gaussian kernel with the parameter \(\omega\), the attention weights are represented through the softmax operation as:  
\[\alpha(\mathbf{x},\mathbf{x}_{i})=\sigma\Bigg{(}-\frac{\left\|\mathbf{x}- \mathbf{x}_{i}\right\|^{2}}{\omega}\Bigg{)}. \tag{3}\]  
In order to enhance the attention capabilities, weights are added by trainable parameters. Several definitions of attention weights and attention mechanisms have been proposed. The most popular definitions are additive attention [26], multiplicative attention, and dot-product attention [30; 31].
## 3 Test Data Analysis
### Frequency-Division Vibration Level Analysis
#### 3.3.1 Track System Comparison  
The 1/3-octave frequency analysis was carried out on the vibration acceleration of the rail, track bed and tunnel wall under the four test conditions, and the acceleration level in the range of 1-8000 Hz was obtained. The 1/3-octave acceleration levels of the rail, track bed, and tunnel walls in each working condition are compared. As shown in Figure 17, it can be seen that with the vibration transmission of the rail, the track bed, and the tunnel wall, the sub-frequency vibration level of each frequency band decreases in turn.  
According to Figure 17a,c, it can be seen that the frequency-division vibration level curve of rail acceleration of the ordinary monolithic track is far away from the frequency-division vibration level curve of the track bed acceleration. The distance between the frequency-division vibration level curve of the acceleration of the track bed and that of the tunnel wall acceleration is relatively close, and the vibration level attenuation in the high- frequency band is small. In the frequency band above 50 Hz, the frequency-division vibration level curve of ordinary monolithic track is relatively close, indicating that this frequency band is the main segment where ordinary monolithic track vibration transmission occurs. It can be seen that the ordinary monolithic track is particularly poor for vibration isolation in frequency bands above 50 Hz. At the same time, the high-frequency vibration effect of rail, track bed, and tunnel walls in the curved segment is more significant, and there are multiple peak points.  
According to Figure 17b,d, it can be seen that the distance between the rail acceleration frequency division vibration level curve of the steel-spring-floating slab track and the track bed acceleration frequency-division vibration level curve is relatively close. The distance between the frequency-division vibration level curve of the acceleration of the track bed and the vibration level of the tunnel wall acceleration is far away, and the vibration level attenuation effect of each frequency band, especially the high-frequency band, is larger.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.1 Load Balancing
5:\(\varphi\leftarrow\) Mobile devices' requirements in terms of CPU cycles and task size. 6:\(\eta\leftarrow\) Calculate the computation capabilities and data rate assigned for each device at GBSs. 7:\(\theta\leftarrow\) Determine the mobile devices that can be reallocated to another GSBs. 8: Determine the optimum GBSs for each mobile user and then force it to hand over with respect to the values of\(\zeta,\eta,\theta\). 9:endfor
10:/*Phase Two - Provide the Overloaded GBSs with UAVs*/
11:for all GBSs \(j\) and at given time slot \(t\)do
12:\(\zeta\leftarrow\) Find the updated mobile devices' numbers associated with each GBS. 13:if\(\zeta>\theta\)then
14: Provide one UAV with computation and storage capabilities hovering above this GBS. 15:endif
16:endfor
```  
**Algorithm 1** GBSs Balancing Load  
As shown in Table 2, the central control manager first collects summary information about the current environment, such as the number of GBSs and their connected devices, the number of devices that can be reallocated, the data rate available, and the task requirements for each device, such as data size and CPU cycles. Then, it iterates over the reallocated devices and determines the optimal GBS based on the estimated execution time. For instance, \(D_{10}\) will be handed over to \(GBS_{3}\) where the estimated time (i.e., upload and computation) is 23.2 s at \(GBS_{1}\) and 6.8 s at \(GBS_{3}\). In addition, \(D_{11}\) and \(D_{12}\) will be handed over to \(GBS_{2}\) which reduces the time, whereas \(D_{15}\) and \(D_{16}\) remain connected to \(GBS_{3}\). In our example, after the IoT devices are allocated to the best GBSs, some GBSs may still be overloaded (e.g., \(GBS_{1}\) in our example). Therefore, we can proceed to the second phase, UAV deployment, which can be performed as follows. Based on the updated number of connected devices for each GBS, and according to the given threshold \(/\)_theta_(e.g., \(/theta=6\)), UAVs are hovering above the area of GBSs that have devices more than or equal to \(\theta\) (i.e., \(GBS_{1}\)) and providing the computation capabilities to IoT devices.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 3 Multi-UAV-Based Network Infrastructure Analysis
### Ad Hoc Network  
The UAVs in the network infrastructure should forward the data from the mobile devices to the Internet gateway (uplink) and from the gateway to the mobile devices (downlink). In the multi-hop communication, the same phenomenon introduced in Section 3.2 should be considered in chain. For example, if the UAV 0 in Figure 2 tries to transmit the data to UAV 2, the UAV should contend with the UAV 1 to use the medium. Also, the contention between the UAV 2 and the UAV 3 affects the throughput of the data received from the UAV 0 or the UAV 1. If we let \(P_{d_{j}}\) refer to the set of the UAVs included in the routing path to the GCS (e.g., \(P_{A}=\{C,E\}\) in Figure 2), the actual throughput (received data rate of the GCS) \(T^{\prime}_{d_{j}}\) becomes  
\[T^{\prime}_{d_{j}}=T_{d_{j}}\prod_{m\in P_{d_{j}}}\mathcal{D}(N_{d_{m}}). \tag{8}\]  
The analysis suggests that the expected routing path of the UAVs should be given to calculate the network throughput of the infrastructure. Our formation control algorithm estimates the expected routing path and the congestion of the wireless medium by the location information of the UAVs, collected periodically on the basis of the assumption stated in Section 3.1. Also, on the layer of ad hoc network, increasing \(N_{d_{j}}\) and the hop count decreases the network throughput of the infrastructure.  
Assuming that all UAVs are in the ad hoc network, the overall network performance of the multi-UAV-based network infrastructure \(\mathcal{N}\) is calculated to  
\[\mathcal{N}(D,M)=\sum_{d_{j}\in D}T^{\prime}_{d_{j}}. \tag{9}\]  
As shown in Equation (9), optimizing \(D\) whose object function is a non-linear \(|D|\times|M|\) dimension function is a remarkably complicated and might take high cost of time to estimate while following the changes of \(M\). To cope with the unexpected change of the \(M\), we periodically determine the flying direction of the UAVs to enhance the network throughput of the infrastructure in our formation control algorithm. The observations of the analysis are considered on the algorithm.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Alarm System  
An auditory alarm system is a critical component of the SFSS's incursion deterrent technique. When possible intruders, whether animals or unauthorized people, violate the border of the agricultural field, this device emits sound or rings a bell. The careful incorporation of this auditory deterrent mechanism aims to increase the system's overall effectiveness in protecting crops.  
* Speaker Deployment for Auditory Deterrence: The deployment of speakers strategically positioned throughout the agricultural field is crucial to this auditory deterrence method. These speakers act as aural sentinels, ready to react quickly when the system triggers them. They are outfitted with noises meant to frighten and repel intruders, successfully preventing any unwelcome presence in the region.
* Integration with ESP32 for an Instant Response: The SFSS's intellect is stored in an ESP32, a powerful microcontroller, which is used to coordinate the auditory deterrent. Signals are routed from the cloud server to the ESP32 as soon as the system senses an intruder, whether animal or human. This acts as a trigger mechanism, causing the speaker to engage. Instant activation is critical because it provides a quick and forceful reaction to possible threats.  
The SFSS's use of auditory deterrents through speakers and the ESP32 is a manifestation of its comprehensive defensive approach. This audio-visual technology detects intrusion while also deploying a high-impact reaction mechanism. It efficiently scares away animals and trespassers by producing noises or ringing bells. This loud discouragement serves a dual purpose: it protects the crops while also alerting unauthorized persons to the existence of a strong security system. Because this system integrates cutting-edge technology with auditory alarm systems, it strengthens its position as a complete farm protection system.