esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
## 2 eXtreme Gradient Boosting (XGBoost)
### XGBoost-Based Relationship between Displacement and Time  
We used XGBoost to map the nonlinear relationship between time \(\mathbf{T}\) and monitoring displacement \(\mathbf{Y}\). The mathematical model of XGBoost, \(XGBoost(\mathbf{T})\), is defined as:  
\[\left\{\begin{array}{l}XGBoost(\mathbf{T})\colon\mathbb{R}^{N}\to\mathbb{R}^{M} \\ \mathbf{Y}=XGBoost(\mathbf{T})\end{array}\right. \tag{10}\]  
where \(\mathbf{T}=(t_{1},t_{2},\cdots,t_{N})\), \(t_{i}\)\((i=1,2,\cdots,N)\) is a vector of time; \(N\) is the number of monitoring time; \(\mathbf{Y}=(\mathbf{y}_{1},\mathbf{y}_{2},\cdots,\mathbf{y}_{M})\) is the \(M\) dimensional vector of monitoring data, such as displacement.  
In this paper, the genetic algorithm is used to tune its hyperparameters of the XGBoost model to improve the prediction accuracy of landslide displacement.
## 3 Results and Discussion
### Effect of Salinity Discontinuity Next to the borehole
#### 3.2.2 Interface Position Estimation
The time difference is multiplied by the inverted SH wave speed, and then added to the borehole radius a = 0.12 m to obtain the position of the salinity interface d = 1.491 m. The inversion results are in good agreement with the interface position parameters used in the simulation. To further illustrate the feasibility of the method, we use the interface position or the shear moduli as variables, and carry out inversion simulations. The results are shown in Table 2. \(V_{ST}\) is the theoretical  
Figure 10: Acoustic field velocity-coherence coefficient fitting curve. Figure 9: Time-velocity coherence diagram of the acoustic field array signal. wave velocity of the SH wave, \(V_{SR}\) is the inverted SH wave velocity, \(\delta t_{T}\) is the theoretical arrival time difference between the interface responses, and \(\delta t_{R}\) is the arrival time of the two sets of interface responses obtained by the electric field. \(d_{T}=V_{ST}\times\delta t_{T}+a\) is the theoretical salinity interface position, \(d_{R}=V_{SR}\times\delta t_{R}+a\) is the inverted salinity interface position, both are defined as the radial distance of the interface from the borehole axis. The error given in the last column is the absolute value of the relative error of the interface position inversion result. The upper table is the inversion result when the formation's shear modulus \(G^{o}\) is fixed while the interface position is changed. The following table shows the inversion result when the interface position is fixed while the formation's shear modulus \(G^{o}\) is changed. From Table 2 we can state that irrespective of whether the interface position or the formation's shear modulus (or SH wave velocity) is changed, the inversion results of the interface position are in good agreement with the theoretical values. The inversion errors are within 3%, and very little trend of change is observed. It also shows that this method is applicable to different formation conditions. In summary, it is feasible to obtain the location of the borehole-side electrochemical interface by the SH-TE seismoelectric coupling wave fields. At the same time, borehole SH-TE acoustic-electric coupling effect may also become a potential method for detecting groundwater pollution and the electrical continuity of the formation. However, the disadvantage of this method is that when the interface position is very close to the borehole wall, the two sets of interface responses may have waveform aliasing, resulting in inaccurate readings of the arrival time. Therefore, when the salinity interface is close to the borehole wall, how to accurately extract the arrival time of the interface response is one of the problems that needs to be solved to further improve the method.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 5 Conclusions  
Our study investigated the Delay Tolerant Networking (DTN) routing protocols for wildlife tracking applications within two different scenarios (flying fox and pigeon scenarios). Animal motion traces from the flying foxes tracking application are used for both scenarios with different activity patterns. Three energy-aware strategies are proposed based on the available energy resources to make forwarding decisions. The three proposed strategies have been evaluated against the performance of DTN routing protocols in terms of appropriate utilization of residual energy.  
Our results show that the proposed energy-aware strategies can improve data yields of the Spray and Wait, Epidemic and Prophet routing protocols by up to 13% in both scenarios. Furthermore, the Optimized Remaining Required Energy (ORRE) strategy can automatically set the best threshold for current conditions, while offering similar performance as the predetermined Threshold strategy.  
Figure 10: Variability of results across different simulation runs for (**a**) pigeon and (**b**) flying fox scenarios.  
A limitation of our work is that it has tested one particular mobility pattern for evaluating the protocols. An interesting direction for future work is to test our approach on different mobility patterns to observe the effect of the mobility patterns on the protocol performance.  
Many thanks go to [PERSON] for his valuable input to the flying foxes monitoring system. The authors also thank [PERSON] and our other colleagues and staff members at CSIRO for their support.  
[PERSON] conceived, designed, and executed the experiments and analyzed the data, and wrote the first draft of the paper; [PERSON], [PERSON], [PERSON] and [PERSON] provided supervision, advice and critical review of the experimental design and analysis, and undertook significant review and editing of the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Performance Evaluation and Training Results of Object Detection Model  
We took the scatter plot of the key cross-section as the training set. The models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x, were used for training in the same batch. Figure 8 shows the changes in the box_loss, cls_loss, dfl_loss, and mAP values of the five models during the training process. The gradual decrease in box_loss, cls_loss, and dfl_loss indicated that the models' prediction of the object location was gradually accurate. cls_loss and dfl_loss were used to measure the accuracy of the models in terms of object classification and prediction boxes, respectively, where smaller values are more accurate. The mAP is a performance metric that evaluates a model by considering accuracy and recall, where higher values indicate better detection.  
As shown in Figure 9, the box_loss, cls_loss, and dfl_loss of each object detection model tended to balance after 100 iterations, and the mAP gradually increased during training and finally tended to stabilize. The object detection models could reach convergence after completing all the iterative training, which verified the rationality of the models parameters.  
It could be observed that the loss values of each model remained low when analyzing the loss curves of the five models on the training dataset and validation dataset. Among them, the cls_loss and dfl_loss of YOLOv8l were slightly smaller than those of the other object detection models. Moreover, the YOLOv8l model was better than the other four models in terms of accuracy and recall, and the convergence curve was smoother and less volatile. Overall, the YOLOv8l model had the best convergence performance.
## 4 Spectrum of Heterogenous Services for Transport Means and the Communication of Information
### The Information Flows and Modelling for the Context of Tracking  
The status of cargo delivery is especially important for the receiver. It also shows efficient use of resources. There are several possibilities: (a) tracking the road vehicle and (b) tracking the cargo.  
The investigations in literature are dedicated to various types of tracking means. For vehicle tracking, most of the authors discuss aspects linked with GPS tracking and less of the present options of radio frequency identification (RFID) identification. These authors present RFID data cleaning, storedRFID data compression, RFID event detection, cluster analysis of RFID data, and model of frequent data extraction. By using RFID and sensing technologies, the status of road and bridge infrastructure is identified by traffic characteristics and can predict the state of the road in real-time information. The most excellent value of tracking is the ability to extract useful vehicle information and information processed efficiently to resolve the road congestion problem [34].
## 2 Data and Processing Method
The second-order derivative of the signal carrier phase was used as the noise magnitude (Please, see the methodology description in [14]). Table 2 provides root-mean-square (RMS) estimates for the noise of phase measurements for signals from different systems and components. According to Table 2, the level of the carrier phase noise for the similar types of components and signal frequencies of GPS, GLONASS and Galileo does not differ much. At the same time, RMS of the phase measurements for SBAS signals is 1.5-3 times higher,  
Figure 2: The identification of the deviation frequency under quiet (panel (a)) and geomagnetically disturbed (panel (b)) conditions. which is in accordance with the known results by [23]. The lowest noise level among the considered signal frequencies and components was detected for L5 frequency.
## Materials and Methods
### Aroma Profile Evaluation  
Sensory evaluations were performed in a well-ventilated sensory laboratory with single testing booths under a constant temperature (25 \(\pm\) 1 \({}^{\circ}\)C). Douchi samples (5 g of each) were put in Teflon vessels and displayed to a trained sensory panel of 10 subjects (2 males and 8 females, aged 22-48 years) recruited from Beijing Key Laboratory of Flavor Chemistry in Beijing Technology and Business University. All panelists had taken part in the training sessions for months to recognize the aroma profile of douchi. Six descriptors contributed the flavor of yongchuan douchi were selected by sensory evaluations and discussions among panelists: sauce-like, sour, nutty, smoky, caramel and fruity. The intensities of the aroma contributions of douchi were ranked from 0 to 3 (0, not perceivable,and 3, strongly perceivable) with steps of 0.5 units. Each sample was estimated three times by each panelist.
# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 3 Proposed Electric Power Load Forecasting Methodology
### Proposed Multivariate Model  
Figure 6 shows the seasonal consumption patterns of the B and T companies over three years (2017-2019). The Korean weather has four distinct seasons with large differences in temperature over the course of the year, and features a large amount of rain in the summer. Considering the seasonal characteristics of Korea, we chose April in spring, July in summer, October in autumn, and January in winter. Figure 6a-d show the power loads of company B in spring, summer, autumn, and winter, respectively. Figure 6e-h illustrate the power loads of company T in spring, summer, autumn, and winter, respectively.  
Figure 6: Power consumption patterns of companies B and T for three years (2017–2019).  
Figure 6a-d show that company B has similar power consumption patterns for three years because it is not significantly affected by season and time. However, in Figure 6e-h, the power consumption pattern is irregular because company T's power consumption is independent of season and time.  
A multivariate model was proposed, as shown in Figure 7, and was designed from the input to the output layer. The input layer has three layers for each year. An artificial neural network is defined as the first input layer (input_1: InputLayer), the second (input_2: InputLayer), and the third (input_3: InputLayer) which denote the power load in 2017, 2018, and 2019, respectively. The three input layers are each composed of one dense layer (dense_1: dense, dense_2: dense, dense_3: dense). The three dense layers concatenate to form one concatenate layer (concatenate_1: Concatenate). Finally, the dense layer (dense_4: Dense) is an input layer for use in the DNN.  
Figure 8 shows a schematic of the proposed model, including the shape of the inputs and outputs of each layer. Keras' sequential API (Application Programming Interface) was adopted to implement the DNN model [57]. Keras' sequential API is a function that simply defines and uses complex models (e.g., sharing multiple layers or using various types of input and output). In Figure 8, the step size of the input and output of the InputLayer is set to three, Dense is set to three inputs, and output is set to 100 to have connectivity with the InputLayer. Three InputLayers are concatenated into one concatenate. Finally, the dense data are forecast as the final DNN structure. In future work, we will study whether forecasting performance varies depending on the step size.  
Figure 8: Schematic of the multivariate model for load forecasting.  
Figure 7: Proposed multivariate model.
# EmbeddedPigDet--Fast and Accurate Pig Detection for Embedded Board Implementations
## 3 Proposed Method
### Image Preprocessing (IP) Module  
The objective of this module is to generate attention information to allow EmbeddedPigYOLO to focus on individual pigs and reduce the effect of illumination variation. Figure 3 shows the infrared input images at different illumination conditions (i.e., at 2 AM and 8 AM). Even with the infrared input images, the gray values at 2 AM are generally darker than those at 8 AM. Furthermore, the gray values of a pig at 2 AM are too dark to detect foreground (i.e., pig) from background, whereas strong sunlight through a window at 8 AM generates many illumination noises and deletes the texture information of some pigs. By reducing the effect of this illumination variation, we want to separate pigs from background (i.e., foreground detection) and separate individual pigs from a pig group (i.e., outline detection).  
In this study, we consider two basic image preprocessing steps to help EmbeddedPigYOLO detect individual pigs. That is, we apply the contrast-limited adaptive histogram equalization (CLAHE) technique [73] in order to focus on the possible pig regions in a pig room. From the infrared input images having illumination variation, the objective of this technique is to maximize the \"inter-class\" variation (i.e., the gray values between pigs and background should be different) and minimize the \"intra-class\" variation (i.e., the gray values of pigs should be similar, regardless of its observed location and time) simultaneously. Therefore, we apply CLAHE twice with different parameter values in order to maximize the inter-class variation (block size = 2 \(\times\) 2, clip limit = 160 and denoted as CLAHE1) and minimize the intra-class variation (block size = 16 \(\times\) 16, clip limit = 12 and denoted as CLAHE2).  
Figure 2: Overview of the proposed method EmbeddedPigDet consisting of image preprocessing (IP), filter clustering (FC) and bottleneck structuring (BS).  
Finally, these preprocessed images are concatenated with the infrared input image in order to generate a three-channel composite image, which is used as an input image for EmbeddedPigYOLO. As shown in Figure 3, the composite image is less affected by the illumination conditions, compared to the infrared input image. Although the qualities of these image preprocessing steps are not ideal, we can utilize this complementary and attention information to improve the accuracy of EmbeddedPigYOLO. Furthermore, with a pipeline execution between CPU and GPU, the additional CPU time for this module can be hidden by the GPU time for EmbeddedPigYOLO (see Section 4.3).
# Aerothermoelastic Analysis of Conical Shell in Supersonic Flow
## 5 Conclusions  
In the paper, a study of the aerothermoelastic characteristics of a conical shell in supersonic airflow has been carried out. According to the Love's first approximation shell theory, a linear piston theory with a curvature correction term and linear temperature assumption, the dynamics model of the conical shell is established by a Lagrange equation. It is worth noting that the constrained modes of the structure are obtained using the Rayleigh-Ritz method by taking the characteristic orthogonal polynomial series as the admissible functions. According to the influence of temperature change and free-stream static pressure on the natural frequency of the functionally graded conical shell, the influence of temperature change and free-stream static pressure on the fundamental frequencies of the functionally graded conical shell is studied. The conclusions can be summarized as follows:  
1. For thin-walled conical shells, the linear temperature assumption is reasonable.
2. With an increase in temperature change, the fundamental frequency of the conical shell decreases gradually, and finally drops to zero. As the static pressure of the free-stream increases, two adjacent natural frequencies converge, which can cause flutter.
3. The natural frequencies of the conical shell are proportional to the ratio of thickness to radius, and inversely proportional to the ratio of length to radius and semi-vertex angle.
4. With an increase in the length-to-radius ratio, the critical buckling temperature change increases slightly, and the critical free-stream static pressure decreases obviously; the critical buckling temperature change and the critical free-stream static pressure are enhanced by raising the thickness-to-radius ratio; the critical buckling temperature change and the critical free-stream static pressure decrease when the semi-vertex angle is enhanced.
5. The critical flutter pressure of conical shell is intensified with a decrease in the Mach number and temperature.  
Conceptualization, M.W., C.Z. and S.S.; methodology, C.Z. and Y.Y.; software, M.W., L.Z. and Y.Y.; validation, C.Z. and Y.Y.; writing--original draft preparation, M.W. and L.Z.; writing--review and editing, C.Z. and S.S.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant Nos. 11802129 and 12172307), Shandong Provincial Natural Science Foundation of China (Grant No. ZR2020 QA039), and the Opening Project of Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province (Grant No. SZDKF-202103).
## Appendix A Testing Model Simulations with Field Data  
For model calibration, we also analyzed tree stem size distributions in late-successful phase (number of trees for different stem diameter classes). The distributions are found to be similar for all three parameterization versions (Figure A6) independent of its number of PFTs. Results match the stem size distribution observed in the field. Smaller differences between simulation and field data occur in the diameter class 20-30 cm (+19 trees/ha) and 70-80 cm (+9 trees/ha).
###### Abstract  
Information-centric networking (ICN) changes the way data are accessed by focusing on the content rather than the location of devices. In this model, each piece of data has a unique name, making it accessible directly by name. This approach suits the Internet of Things (IoT), where data generation and real-time processing are fundamental. Traditional host-based communication methods are less efficient for the IoT, making ICN a better fit. A key advantage of ICN is in-network caching, which temporarily stores data across various points in the network. This caching improves data access speed, minimizes retrieval time, and reduces overall network traffic by making frequently accessed data readily available. However, IoT systems involve constantly updating data, which requires managing data freshness while also ensuring their validity and processing accuracy. The interactions with cached data, such as updates, validations, and replacements, are crucial in optimizing system performance. This research introduces an ICN-IoT method to manage and process data freshness in ICN for the IoT. It optimizes network traffic by sharing only the most current and valid data, reducing unnecessary transfers. Routers in this model calculate data freshness, assess its validity, and perform cache updates based on these metrics. Simulation results across four models show that this method enhances cache hit ratios, reduces traffic load, and improves retrieval delays, outperforming similar methods. The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. This precision highlights its effectiveness in maintaining data currentness and validity while reducing network overhead.  
os 1
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 7 Discussion  
Our framework adopts the Cosmos Hub, which provides BC parallelization for faster tx processing. Figure 9d illustrates the range of measured and recorded tx relay latencies, with an overall relay latency of 13.77 s, ranging from 11.76 s to 16.30 s. We clarify that the calculated 13.77 s average latency time includes the framework's instant block finality time (6-7 s), which ensures a higher security and immutability compared to other blockchains. Ethereum L1 finality, for example, is reached in 12 min. It should also be noted that the overall latency of this framework can vary in relation to the computational resources used in generating a positive access decision (the second type of tx), which requires additional ontology parsing, resulting in a longer AC processing time. In addition, the size of the ontology file used and the number of evaluated attributes influence the AC processing times for this specific type of access transaction. Semantically evaluating the attributes makes this framework more flexible and adaptive, allowing advanced and fine-grained attribute-based access control in a cloud environment. Denying a cloud user access transaction also requires semantic reasoning without significantly acquiring knowledge about the forged attributes. In contrast to the second type of tx, the denied tx has lower AC processing times, as the cloud resource was not interrogated by the AC control node to generate a signed URL to the requested resource. Those results indicate that our framework can handle both quick access processing and additional complex processes for granting or denying an upcoming cloud client access request.  
We compared the relay performance scores for our framework with other cross-chain solutions and found that our results align with theirs. For example, in [22], the authors used the Zkbridge protocol to maintain inter-blockchain communication, resulting in a relay latency of 18 s. In [14], the latency varies from 5.38 to 23.34 s, while the authors in [23] used an asynchronous cross-chain model based on conditional transactions, achieving a relay latency that varies from 7 to 17 s.