esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 5 Experimental Procedures
### Assessment of Experiment Result of Other Methods
#### 5.2.5 Kr
Table 4 shows the AIC, SBIC, HQIC, and AICc values for the nine models. The AIC, SBIC, HQIC, and AICc are four commonly used model selection criteria that aim to balance the fit and complexity of models. The AIC selects the optimal model by minimizing information loss, and is suitable for small sample sizes, with smaller AIC values indicating better models. The SBIC imposes a stronger penalty on model complexity, especially for larger sample sizes, with smaller SBIC values indicating better models. The HQIC falls between the AIC and BIC, which makes it suitable for moderate sample sizes. The AICc is a corrected version of the AIC, and is designed to address small sample sizes and reduce the risk of overfitting. Overall, the AIC tends to favor models with a better fit, while the BIC and HQIC place more emphasis on avoiding overfitting, with smaller values typically indicating better models. Through careful observation of Table 3, it can be seen that the predictive performance of the LSTM and EEMD-LSTM methods is not satisfactory. However, comparing these two algorithms, it can be seen that the EEMD-LSTM method outperforms the LSTM method in terms of the MAE, RMSE, and R2 metrics, which indicates that the EEMD-LSTM outperforms the LSTM in predictive capability. Figure 13: The LR method’s prediction results for the sine time series. To facilitate comparison and analysis, we visualized the data. Figure 14 presents the visualization of the data from Table 4, providing a more intuitive way to identify the relationships between them. Through this graphical representation, readers can clearly observe the performance comparison between the proposed method and the other methods. Since the KR and LSTM methods exhibit higher metric values on the temperature sequence, which affect their comparison with other metrics, we excluded these two methods and separately presented the comparison results of the remaining seven methods, which are shown in Figure 15. However, the metric values of the NuSVR and EEMD-LSTM methods are still relatively high, which may hinder the comparison. Therefore, we excluded these two methods as well and separately provided the comparison results for the remaining five methods, as shown in Figure 16. Specifically, the line charts in the figures make it visually easier to distinguish the data, and thus facilitates comparison between the different groups. In observing Figures 14-16, it is evident that the proposed method consistently demonstrates lower values across all metrics, which indicates superior performance.
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
## 2 Date and Methodology
### Decomposition of Atmospheric Density Based on EOF  
This study refers to the Sentman model [44] for thermospheric density inversion based on accelerometer data. We also refer to the Swarm-C data provided by [[http://thermosphere.tudelft.nl/](http://thermosphere.tudelft.nl/)]([http://thermosphere.tudelft.nl/](http://thermosphere.tudelft.nl/)) (accessed on 23 March 2023).  
The eigenvector analysis method is also known as the empirical orthogonal function analysis (EOF) method [45]. The empirical orthogonal function analysis method can decompose the meteorological variable field into two parts: function with time as the variable and function with the spatial position [46]. The variance of the first few components of the principal component decomposed by the empirical orthogonal function usually accounts for a large percentage of the total variance. Hence, the variation of the principal components can describe the variation of all components with time [47]. When the empirical orthogonal analysis method is applied to analyze the temporal and spatial distribution of thermospheric density in the thermosphere, the thermospheric density extracted by the Swarm-C satellite accelerometer should be smoothed to obtain the scatter points, whose abscissa is time and ordinate is latitude. Then, the scatter set X with the same time interval and latitude interval are selected. Each datum is represented by \(x(t,\,\varphi)\). According to the basic principle of empirical orthogonal decomposition, X can be decomposed into function V with time as a variable and function Z with space as a variable, i.e., X = VZ. The decomposition is shown in Equation (3).  
\[x(t_{i},\varphi_{i})=\sum_{k=1}^{P}v^{k}(\varphi_{i})z^{k}\big{(}t_{j}\big{)} \tag{3}\]  
where \(t_{j}(j=1,2,\cdots,n)\) denotes the time corresponding to each lattice point, \(\varphi_{i}(i=1,2,\cdots,m)\) is the latitude corresponding to each grid point. \(v^{k}\) represents the \(k\)-th mode function, i.e., the variation of atmospheric density with latitude. \(z^{k}\) is the \(k\)-th time coefficient, representing the change of atmospheric density with time. The results obtained by EOF analysis are evaluated in this paper using the storm data from 8 September 2017, the latest from the selected storms.
## 3 Classification of Optical Waveguide Sensors
### Microring Structured Optical Waveguide Sensor
#### 3.1.3 Lithium Niobate Compound Microring Optical Waveguide Sensor
LiNbO\({}_{3}\):Zn is a material with potential advantages in protein detection technology owing to its electro-optic properties. To develop biochemical sensors using LiNbO\({}_{3}\) waveguides, the binding problem of biomolecules to waveguides prepared via Zn diffusion must be solved first. The calculations yielded a detection limit of around 700 molecules per pixel. The results show that the light attenuation through the LiNbO\({}_{3}\) waveguide can detect surface-attached proteins via emission or absorption measurements. The combination of antibodies and waveguides facilitates the use of antigen-antibody interactions as a model, which paves the way for biosensor design based on LiNbO\({}_{3}\):Zn waveguides.
## 3 Problem Definition and Formulations
### Problem Description
The service time windows at the customer locations for requests such as IF, ER, ED, OF, IFER, or OFED, and at the terminal for requests like IE or OE are known in advance. If a request issued by a single customer or a shipping company involves more than one container, it will be divided into separate requests, each corresponding to a single basic transport request with the same service time window. For composite requests, two trucking operation modes, i.e., Stay-with mode and Drop-and-pull mode, are allowed to facilitate more flexible and efficient truck scheduling. Regarding requests relating to empty container repositioning, both the Depot-turn and Street-turn strategies are viable, provided they minimize unnecessary detours effectively. The availability of empty containers across multiple depots, coupled with diverse types of requests, two trucking operation modes and two empty container repositioning strategies, significantly increases the following uncertainty and unpredictability of empty container allocation, truck dispatching, and route planning. First, when selecting a depot for truck dispatch to serve empty container-related requests, two key factors must be considered: the distance between the depots and the request vertex, and the dynamic number of available empty containers and trucks at each depot. A straightforward approach of selecting the nearest depot is not feasible in this context. Furthermore, when handling a composite request, the combination of Stay-with and Drop-and-pull modes may result in three potential dispatch configurations: (i) a single truck uninterruptedly completes its delivery and pickup parts; (ii) a single truck completes its delivery and pickup parts, but in between, the truck departs to handle other requests; and (iii) two trucks are assigned to separately handle the delivery part and pickup part. Thus, the sequence of trucks cannot be predetermined for composite requests. In addition, with respect to the ER/ED request, only one of the origin and destination is known, while the other needs to be determined based on the Street-turn and Depot-turn strategies during optimization. Similarly, for the IE/OE request, only one of the origin and destination is known and the other should be identified based on the optimization model. In summary, the inherent uncertainty and unpredictability associated with the decision of empty container allocation, truck dispatching and route planning must be explicitly incorporated into the model, thereby posing substantial challenges to the model process and its solution.
## 3 Materials and Methods
### Data
The metadata from Landsat images were collected by the EarthExplorer file with the.xml extension, as summarized in Table 1.  
The data used in this study consisted of nine (9) consecutive Landsat satellite scenes spanning from 2014 to 2022, with scenes taken in January on the following exact dates: (a) 1 January 2014, (b) 4 January 2015, (c) 7 January 2016, (d) 25 January 2017, (e) 28 January 2018, (f) 15 January 2019, (g) 2 January 2020, (h) 4 January 2021, (i) 15 January 2022. Through a visual inspection of the images, it is possible to see the dynamics of the salinity on the ephemeral salt lakes where bright cyan regions signify salted regions, while dark blue areas indicate water, brownish and beige colors indicate bare soil and sands with diverse landforms, and green areas indicate vegetation areas.
## 1 Introduction
Any moderating effects of denting and re-rounding of dents on material properties and fracture behavior is considered in the calibration process. The cracks in XFEM models propagate when the value of strains, stresses, and fracture energy or displacements at any point within the pipe exceed the calibrated damage parameters. Pressure required to propagate a part-wall crack to a through-wall crack is the burst pressure of the flawed pipeline, and crack growth is observed from the post processing graphics, which makes it easy to accurately determine the burst pressure of the pipeline [19]. This paper uses the XFEM procedure in Abaqus [20] to analyze propagation of longitudinal cracks inside restrained and un-restrained dent and predict burst pressure of pipeline with dent-crack defects. Pipeline specimens with longitudinal part-wall cracks inside dents are modeled and subjected to internal pressure to initiate and sustain crack propagation and burst the pipe. XFEM uses the partition of unity concept to waive the need for mesh geometry to match crack geometry by introducing local enrichment functions and additional degrees of freedom in the standard FEM formulation [21; 22; 23]. An intra-element algorithm freely lays the crack within the mesh without tying it to element boundaries, thus overcoming the requirement to match the geometry of mesh to crack geometry. The software searches for regions of crack initiation where stresses and strains exceed a critical value, after which phantom nodes and their superposed original real nodes move apart as the crack propagates, following a specified traction separation law (TSL) and damage evolution criterion. Abaqus standard [20] provides linear, exponential, and tabular TSL options and maximum principal stress (Maxps) or strain (Maxpe), and maximum nominal stress or strain for damage initiation criterion. Other damage initiation criteria options include quadratic nominal stress, quadratic nominal strain, and user-defined criterion. Damage evolution criteria include fracture energy and crack tip displacement.
## 11 Current Studies and Future Directions
### Current Research
In [125], the authors proposed a technical architecture that allows IoT architectures in military applications and demonstrates the challenges and mitigation methods. They discussed security issues when leveraging IoT for military purposes and presented mitigation methods to overcome these issues. The authors of [126] described the InterSCity platform's architecture and studied a set of experiments for their scalability. They experimentally evaluated the microservices architecture. The experimental strategy was based on an application scenario of smart parking and demonstrated the efficiency of the method employed to create artificial workloads. In [127], the authors reduced the complexity of computations in fog computing for IoT devices by integrating SDN and NFV on the edge server. They used the EstiNet simulator and MATLAB to execute the proposed algorithm and evaluated performance parameters, satisfaction, reliability, and time delay. They compared the results of ASTP and software-defined unified virtual monitoring function (SuVMF). In contrast, the proposed framework achieved high satisfaction and reliability (up to 90%) and low delay (1800 s for 200 IoT devices). The authors of [128] offered a priority-based framework for transportation systems using fog computing. They reduced latency and increased QoS using MEC and fog servers. The latency decreased by 20% and the processing time by 35% compared with centralized computing architecture. In [129], the authors introduce a conceptual framework and architectural design that effectively utilizes blockchain technology, big data analytics, and artificial intelligence (AI) to augment the cybersecurity measures used in smart cities. The suggested framework is comprehensively outlined, with a specific focus on its practical implementation. The simulation of the system was conducted using a dataset that was specifically tailored to a smart grid scenario. The results of these simulations clearly demonstrate the significant potential and effectiveness of the proposed framework in efficiently tackling the intricate cybersecurity concerns that are inherent in smart city environments. The findings not only demonstrate the theoretical robustness of the paradigm but also emphasize its practical relevance in real-life situations. The framework's efficacy, notably in the domain of smart grid cybersecurity, serves to validate its significance as a resilient solution capable of addressing cyber threats in the ever-evolving context of smart cities. Table 17 summarizes the existing proposals that consider developing a reliable smart city network. The studies are compared, and the deployed enabling technology is mentioned.
## 4 Experiments
### Performance of BCCCC and the Baselines  
Figure 2 shows performance of all systems. To verify whether differences of the performance of BCCCC and the baselines are _statistically significant_, a two-tailed and paired \(t\)-test with 99% confidence level is conducted. The results show that BCCCC performs significantly better than each baseline in all evaluation criteria MAP and Average P@X (X = 1, 3, 5, and 10). When compared with the best baseline DBC, BCCCC contributes 10.2% improvement in MAP (0.5708 vs. 0.5180), indicating that it is more capable of ranking highly related articles at higher positions. When only the top positions are considered, BCCCC yields a 7.1% improvement in Average P@1 (0.5383 vs. 0.5025), 9.8% improvement in Average P@3 (0.3620 vs. 0.3297), 9.3% improvement in Average P@5 (0.2850 vs. 0.2607), and 10.4% improvement in Average P@10 (0.1967 vs. 0.1782).  
The results justify the contribution of category-based CC to BC. The best baselines, DBC and IBS, improve BC by considering text-based similarities between reference titles. BCCCC performs significantly better than them by considering category-based CC. BCCCC is thus a further improved version of BC, which is a critical method routinely used to retrieve, cluster, and classify scientific literature. Development of BCCCC can thus significantly advance the state of the art of literature analysis.  
We further measure the percentage of the target articles that have highly related articles ranked at top-X positions (X = 1, 3, 5, and 10). A higher percentage indicates that the system performs more stably in identifying highly related articles for different target articles, making the system more helpful in practice. Figure 3 shows the results. BCCCC achieves the best performance again. When compared with the best baseline, DBC, it yields a 7.1% improvement when X = 1 (53.83% vs. 50.25%), 6.0% improvement when X = 3 (73.52% vs. 69.39%), 4.7% improvement when X = 5 (80.29% vs. 76.71%), and 4.2% improvement when X = 10 (87.59% vs. 84.08%). BCCCC contributes larger improvements when X is smaller, indicating that it is more capable in ranking highly related articles at top positions for more articles.  
Figure 3: Percentage of highly related articles that are ranked at top positions by the systems.  
Figure 2: Performance of all systems in terms of the evaluation criteria mean average precision (MAP) and Average P@X (\(\bullet\)’ on a system indicates that difference of the performance of BCCCC and the system is statistically significant with \(p<0.01\)).
###### Abstract  
With the continuous expansion of urban areas, the treatment of urban sewage is facing significant challenges. Tens of thousands of tons of municipal sludge (MS) are produced annually, which not only occupies substantial land resources but also poses potential environmental threats, thereby complicating wastewater treatment processes. Proper management of MS has thus become a critical issue requiring urgent attention. Meanwhile, water pollution continues to worsen, endangering both ecological systems and human health. MS contains a variety of organic compounds with active functional groups capable of forming strong coordination interactions with various waterborne pollutants. Building on this foundation, we successfully develop a multifunctional adsorbent using MS as the raw material through biomineralization. The synthesized adsorbent shows outstanding performance, exhibiting high adsorption capacity for fluoride (F\({}^{-}\)) and hexavalent uranium (U(VI)) in high-fluorine uranium-containing wastewater, effectively reducing the concentrations of these harmful substances. Additionally, the adsorbent shows strong affinity for the cationic dye methylene blue, making it highly suitable for the treatment of wastewater from the printing and dyeing industries. Notably, the adsorbent also possesses antibacterial properties, demonstrating significant bactericidal activity against Gram-negative _E. coli_ in wastewater. The multifunctional adsorbent not only offers a novel solution to enhancing water quality and safety, but also represents a promising strategy for sustainable wastewater treatment.  
municipal sludge; biomineralization; adsorbent; water purification; antibacterial materials 1
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model
### Overlap Avoidance for Enhanced Gauss-Markov Mobility Model  
#### 3.4.1 Enhanced Gauss-Markov Mobility Model  
Enhanced Gauss-Markov model is based on Gauss-Markov model in which a node is a mobile machine such as laptop, cellular phone, etc. On the contrary, in order to model the UAV motion for reconnaissance, Enhanced Gauss-Markov model is proposed so that a UAV node moves with smooth motion at the time of changing direction [16]. In Enhanced Gauss-Markov model, the speed \(s_{t}\) and the direction deviation \(d\_dev_{t}\) of a UAV at time \(t\) are calculated by:  
\[s_{t}\ =\ \alpha s_{t-1}+(1-\alpha)\overline{s}+\sqrt{1-\alpha^{2}}s_{x_{t-1}} \tag{4}\]  
\[d\_dev_{t}\ =\ \alpha d\_dev_{t-1}+(1-\alpha)\overline{d\_dev}+\sqrt{1- \alpha^{2}}d\_dev_{x_{t-1}} \tag{5}\]  
where \(\overline{s}\) and \(\overline{d\_dev}\) are the mean speed and the mean direction deviation, respectively. We can select a from 0 to 1 for the random degree. In Equations (4) and (5), \(s_{x_{t-1}}\) and \(d\_dev_{x_{t-1}}\) are the random Gaussian distributions represented by \(N(0,\sigma^{2})\); where, \(\sigma\) is the standard deviation, when \(t\rightarrow\infty\)[16]. Once \(d\_dev_{t}\) is calculated, the next direction, \(d_{t}\), is computed by Equation [16], where \(d_{t-1}\) is the previous direction.  
\[d_{t}\ =\ d_{t-1}+d\_dev_{t} \tag{6}\]  
Another modification of Enhanced Gauss-Markov model for UAV reconnaissance is considering boundary-avoidance scheme near the border area. When a UAV approaches to the border, it decides the turn direction either to left or to right. For a certain time period, the mean direction deviation is set to \(-22.5\) for the left turn or \(22.5\) for the right turn [16], which results in the smooth avoidance to the border, as shown in Figure 4(a).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Next Action Probability**} \\ \cline{3-5}
**Direction of Guidance Vector** & **Current Action** & **Turn Left** & **Straight Ahead** & **Turn Right** \\ \hline \multirow{3}{*}{Left} & Turn left & 90\% & 10\% & 0\% \\  & Straight ahead & 70\% & 30\% & 0\% \\  & Turn right & 0\% & 100\% & 0\% \\ \hline \multirow{3}{*}{Right} & Turn left & 0\% & 100\% & 0\% \\  & Straight ahead & 0\% & 30\% & 70\% \\ \cline{1-1}  & Turn right & 0\% & 10\% & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: UAV action table with direction of guidance vector.
## 3 Demand Response Models  
Demand response is an essential method of enhancing energy efficiency by modifying consumer behavior to align with changes in supply and demand. This study focuses on two types of demand response: incentive-based demand response and price-based demand response. The difference between these two types of demand response lies in the fact that incentive-based demand response encourages consumer participation in energy management through various incentives, such as discounts, subsidies, or other motivating measures. On the other hand, price-based demand response involves direct adjustments to electricity prices, altering user electricity consumption behavior through price fluctuations to achieve more economical electricity usage and energy conservation.
###### Abstract  
Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.  
computer vision; visual tracking; correlation filter; scale variation; occlusion; high-quality candidate object proposals +
Footnote †: journal: Medical Imaging