esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Environmental Adaptability and Fault-Tolerant Performance  
Testing across diverse aquaculture environments revealed environment-specific optimization patterns and system resilience. Near-shore environments, such as the Gongliao Aquatic Center and Xuejia fish ponds, demonstrated optimal performance with stable 4G coverage, enabling seamless multi-platform operation. Offshore deployments, particularly in Pingtung, presented moderate challenges due to signal fluctuations, yet underscored the system's adaptive capabilities. The modular architecture facilitates automatic platform switching in response to varying network conditions, with smartphones emerging as the preferred choice for daily monitoring, tablets offering superior visual experiences for detailed inspections, and centralized control centers being essential for long-term analysis. Regarding fault redundancy and system reliability, the multiple remote control system features built-in fault redundancy capabilities. When smartphone control is unavailable due to network issues or battery depletion, users can effortlessly switch to alternative devices, ensuring continuous monitoring operations. This redundancy greatly enhances overall system reliability while maintaining operational flexibility across a range of environments, from freshwater to seawater and from near-shore to deep-sea applications.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
# Two Medoid-Based Algorithms for Clustering Sets
## 3 Medoid-Based Approaches
### Medoids Initialization and Seeding Methods  
It has been demonstrated [3; 4] that the seeding method for centroids initialization can significantly affect the K-means clustering results. This is likely why the K-sets variant was inferior to the K-swaps algorithm in [8]. It was shown in [4] that significantly better results can be obtained for difficult datasets by using better initialization algorithms such as maximin and K-means++. We, therefore, consider different seeding strategies. We consider the uniform random method, maximin [16], K-means++ [14], and greedy K-means++ [15; 17; 18].  
_Uniform_: The \(K\) medoids are defined by \(K\) uniform random selections from the dataset.  
Figure 1: The matrix of all the pairwise distances for the cluster of Section 2.1.  
Let \(X_{i}\) be a data object and \(D(X_{i})\) the minimal distance of \(X_{i}\) from the currently defined centroids.  
_Maximin_: The first medoid is chosen in the dataset by a uniform random selection. Then, maximin chooses the next medoid as a data object with maximal \(D(X_{i})\) value. The procedure is continued until all the \(K\) medoids have been selected.  
_K-Means++_: This is a randomized variant of the same idea as in maximin; it selects the next medoid by a _random switch_ among the data objects of the dataset, after associating to each data object the probability of being chosen as:  
\[\pi(X_{i})=\frac{D(X_{i})^{2}}{\sum_{j=1}^{N}{\left(X_{j}\right)^{2}}}.\]  
_Greedy K-Means++_: This refines the K-means++ procedure with a sampling step (see Algorithm 3). Except for the first medoid, any new medoid is selected by sampling \(S\) candidates from the dataset, and by keeping the one which minimizes the \(SDM\) cost function, evaluated according to the currently defined medoids.  
```
\(m_{1}\gets X_{j}\), \(j\gets unif\_rand(1..N)\), \(M\gets 1\)do\(\mathrm{costBest}\leftarrow\infty\) \(\mathrm{candBest}\leftarrow?\) repeat\(S\) times {  select a data object \(X^{*}\in X\) with the K-Means++ procedure  partition \(X\) according to \(\{m_{1},\ m_{2},\dots,\ m_{M},X^{*}\}\) \(\mathrm{cost}\) = \(SDM()\) \(if(\mathrm{cost}\) \(<\)\(\mathrm{costBest}\) {  candBest \(\gets X^{*}\)  costBest \(\leftarrow\)\(\mathrm{cost}\) } } \(M\gets M+1\) \(m_{M}\leftarrow\mathrm{candBest}\) \(while(M<K)\)
```  
**Algorithm 3**The Greedy_K-Means++ seeding method  
The value \(S\) is a trade-off between the improved seeding and the extra computational due to the \(S*(K-1)\) attempts. We fix \(S=2+\log K\), as suggested in [17].  
The K-medoids Algorithm 1 is used in this work in a repeated way, where a different initialization of the medoids feeds each independent run. As for repeated K-means, the higher the number of repetitions, the higher the chance to achieve a solution \"near\" to the optimal one.
# Generalization of ALMM Based Learning Method for Planning and Scheduling
## 5 Generalized ALMM Based Learning Method
### Weakening the Assumptions  
Although the learning concepts using ALMM technology, in combination with a separable additive steering quality criterion, have been discussed by the authors in previous works, we will show below that the method can also be defined for a weaker assumption, namely for a merely separable criterion.  
**Theorem 1**: _Assumption: let the ALMM Based Learning method, defined for a problem \((\mathbf{P},\mathbf{Q})\), be additively separable._  
_Thesis: The ALMM Based Learning method can be defined for the problem \((\mathbf{P},\mathbf{Q}^{\prime})\), where \(\mathbf{Q}^{\prime}\) is any separable criterion._  
Let us analyze the local criterion \(q^{\prime}(u,s)\) for a problem \((\mathbf{P},\mathbf{Q}^{\prime})\).  
As restrictions represented by the process \(\mathbf{P}\) remain unchanged, (and, as a consequence, the restrictions of all the instances \((P,Q^{\prime})\) of the problem \((\mathbf{P},\mathbf{Q}^{\prime})\) remain unchanged), one has to consider the first two addends in formula (7) or the first addend in formula (12) only. It results from Definition 2 that:  
\[\Delta^{v}Q=Q_{i+1}-Q_{i}=f_{Q}(Q_{i},u_{i},s_{i})-Q_{i}\quad for\quad i=0,1, \ldots,d(\tilde{s})-1. \tag{13}\]  
As the value of \(Q_{i}\) is known at the state \(s_{i}\), one can compute \(f_{Q}(Q_{i},u_{i},s_{i})\) and the component \(\Delta Q_{i}\) can be used in formula (7) or formula (12). If sufficiently accurate, estimation criterion \(\tilde{Q}(u,s)\) may be difficult or not even possible and formulae (12) instead of (7) should be applied for the criterion \(q^{\prime}(u,s)\). Q.E.D.  
**Theorem 2**: _Assumption: let the ALMM Based Learning method be defined for a problem \((\mathbf{P},\mathbf{Q})\). Thesis: The ALMM Based Learning method can be defined for the problem \((\mathbf{P},\mathbf{Q}^{\prime})\), where \(\mathbf{Q}^{\prime}\) is any multiplicative criterion._  
It results from Definition 3 that the multiplicative criterion is a separable one. Thus, the thesis resulting from Theorem 1 is true, so:  
\[\Delta Q_{i}^{v}=Q_{i+1}-Q_{i}=Q_{i}\cdot v(u_{i},s_{i})-Q_{i}=Q_{i}\cdot(v(u_ {i},s_{i})-1)\quad for\quad i=0,1,\ldots,d(\tilde{s})-1\quad Q.E.D. \tag{14}\]  
These types of criteria are common in term securities investment problems that may not be redeemed early.
# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
###### Abstract  
Simultaneous Localization and Mapping (SLAM) is an effective method for estimating and correcting the pose of the mobile robot. However, a large amount of external noise and observed outliers in complex unknown environments often lead to a decrease in the estimation accuracy and robustness of the SLAM algorithm. To improve the performance of the Square Root Unscented Kalman Filter SLAM (SRUKF-SLAM), this paper proposes the Maximum Correntropy Square Root Unscented Kalman Filter SLAM (MCSRUKF-SLAM) algorithm. The method first generates an estimate of the predicted state and covariance matrix through the Unscented Transform (UT), and then obtains the square root matrix of the covariance through Cholesky and QR decomposition to replace the original covariance, effectively preserving the positive definiteness of the covariance and improving the accuracy of the algorithm. Moreover, the proposed MCSRUKF-SLAM recharacterizes measurement information at the cost of the Maximum Correntropy (MC) instead of the Minimum Mean Square Error (MMSE), effectively improving the SLAM algorithm's ability to suppress non-Gaussian noise. The simulation results show that compared with EKF-SLAM, UKF-SLAM, SRUKF-SLAM, and MCUKF-SLAM, the accuracy of the proposed MCSRUKF-SLAM in Gaussian mixture noise improves by 81.8%, 80.9%, 78.7%, and 63.6%, and the accuracy of the proposed MCSRUKF-SLAM in colored noise improves by 50.3%, 39.9%, 38.2%, and 36.3%.  
SLAM; mobile robot; non-Gaussian noise; maximum correntropy +
Footnote †: journal: Applied sciences  
1
## 4 Experimental Results and Discussion
### Analysis of Pore Size Distribution  
T\({}_{2}\) is directly proportional to the pore size, according to Formula (3). The pore of the rock is assumed to be a tube bundle in this experiment; therefore, Formula (3) can be expressed as follows:  
\[r=\rho\times T_{2}\times a \tag{5}\]  
where \(r\) is the pore radius of the specimen; \(\rho\) is the empirical coefficient; \(\rho=10\) ms/m; and a is the coefficient of the tube bundle, \(a=2\).  
Combining the NMR data and Equation (5), the pore radius of a rock can be determined, as shown in Figure 8. Moreover, 70% of the pore diameter of saturated sandstone is less than 1 \(\upmu\)m, and there are numerous micropores throughout the sample. More than 85% of the pore diameters are less than 100 \(\upmu\)m. When the axial pressure ratio is more than 80%, only 50% of the pore diameters of the saturated sandstone are less than 1 \(\upmu\)m, and the proportion of internal micropores decreases. Over 15% of the pores have diameters larger than 100 \(\upmu\)m. This indicates that crack initiation, expansion and penetration occur at this level of external loading, which leads to an increase in the size and number of pores and cracks.
## 4 AI-Driven Concepts in MCNs
### Multi-Client AI/ML Methods  
Multi-client methods can be used to combine knowledge across multiple models. Key methods include Ensemble Learning (EL), FL and Transfer Learning [57, 69]. EL allows the inference of multiple models for the same input to generate an \"average opinion\". For instance, the predictions made by different models (e.g., Decision Tree, Linear Regression and Neural Network) for a ship's fuel consumption forecasting can be averaged to obtain a more stable global prediction. FL enables distributed devices (e.g., ships, sea BSs, UAVs, control centers) to collaboratively train a shared AI model while keeping their raw data local [69]. Only model updates are exchanged, preserving data privacy and reducing bandwidth. This is a critical advantage of FL to ensure privacy, bandwidth efficiency and regulatory compliance. TL allows a model trained in one domain to be reused in another with limited retraining (e.g., a model trained by one ship for route optimization can be used  
Figure 5: DRL-based optimization of UAV trajectory to maximize sea users’ QoS.  
by other ships) [57]. This is useful when training from scratch is not feasible due to cost or limited data. Figure 6 depicts a multi-vessel FL cycle in a single sea area, where different ship-specific local models collaborate to build a global model. In the same example, the global FL model can be transferred through a UAV to a distant sea area, becoming available for inference from other vessels. These techniques enable many MCN-related optimization cases, such as:  
* **Collaborative diagnostics:** FL can securely enable each vessel to train a local model for fault prediction and share updates to build a robust global model [70].
* **Swarm intelligence-based threat detection:** AUVs and UAVs share model updates through FL to detect environmental hazards or intrusions [5].
* **Decentralized predictive routing:** Ships in a cluster learn shared FL models to predict optimal next-hop links based on their individual experiences [71].
* **Environmental transfer:** Models trained in the Mediterranean on vessel routing can be transferred and adapted to Arctic shipping lanes with a few localized updates.
* **Port-to-ship adaptation:** A model trained on container flow data in a smart port can be transferred and fine-tuned to predict cargo handling efficiency aboard a vessel [72].
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 3 Results
### Example: Make a Composite Landsat Image of China Mainland  
In this section, we will provide a comprehensive example to show the basic usage of RSIMS. In this example, we will use Landsat 8 images from 1 May 2019 to 1 October 2019 to make an RGB image covering the mainland of China. The illustrative program is written with Python3, as shown below.  
The first part of the program is used to find the matching images based on the interfaces provided by RSIAPI. These images are retrieved from RSIMeta and then represented by Python3 objects (denoted by Image) whose attributes are constructed from the corresponding item stored in the Image table. Other distinct attributes are dynamically loaded from the corresponding Metadata table. In this example, the search results include 3869 Landsat 8 images intersected with the mainland of China from 1 May 2019 to 1 October 2019, and the total size of these image files is about 460 GB.  
Bands of the remote sensing image are stored as individual files in the RSIData, and the relative access paths of these image band files are stored as the band attributes of Image objects. As described in above sections, RSIData provides two types of GDAL compatible access methods: /visrados/ and /visis3/. The absolute path is composed of the type identi  
Figure 27: Performance comparison of RSIData and HDFS.  
fication and the relative access path. Take band4 of the image LC08_L1 TP_021246_20180413_20180417_01_T1 as the example, its relative path is \"/landsat/LC08_L1 TP_021246_20180413_20180417_01_T1_B4.TIF\". The corresponding standard S3(Simple Storage Service) path is \"/vsis3/landsat/LC08_L1 TP_021246_20180413_20180417_01_T1_B4.TIF\" and the other type of path is \"/vsirados/landsat/LC08_L1 TP_021246_20180413_20180417_01_T1_B4.TIF\". In order to speed up the mosaic, we read and process the 3869 images simultaneously with 20 processes. The composite image is shown as Figure 28.  
Figure 28: The composite Landsat image of China mainland.
## 4 Experiment and Analysis  
As described in this section, we validated the performance of the proposed system through simulation experiments and tests based on a real-world sports car. For the integrated navigation algorithm, this paper will present two sets of comparative experimental results. In the first set of experiments, we compared our system with a Kalman filtering system using a loosely coupled algorithm. In the second set of comparative experiments, we demonstrated how our proposed algorithm enhances the robustness of the system in an environment where GNSS signals are limited. For the satellite selection algorithm, this paper will present the algorithm evaluation results and compare its computational efficiency with that of the optimal algorithm. Through these experiments, we can comprehensively assess the performance and efficiency of our proposed system in different application scenarios.
# A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
## 3 Digital Twin Architecture
### Physical-Cyber Interface  
After the design of the optimization workflow, the signals to be exchanged among the different actors within the DT architecture are identified. In this phase, the order in which signals are exchanged is also established. This information is depicted in Figure 6 through a sequence diagram. Sensor data are assessed from the controller and continuously uploaded to the cloud through the gateway. When an optimization is performed, historical data are sent to the local device and the optimization workflow illustrated in Section 3.3 is implemented. Once the optimal \(CCS^{*}\) and \(TRT^{*}\) have been identified, these are transmitted to the gateway. Finally, the gateway sends them to: (i) Cloud: To trace the implemented climate control strategies and crop treatments; (ii) Controller: To implement the optimal climate control strategy and crop treatment.  
Figure 5: Optimization workflow: Communication between the intelligent layer and the DTs for the optimization of the productivity. Production and energy and resource consumption are indicated in bold since they are utilized for the calculation of the productivity. In the figure, climate data indicate the set of microclimate and environmental data.
# Characterization and Reactivity of Natural Pozzolans from Guatema
## 4 Conclusions  
After carrying out studies on both the physical-chemical and pozzolanic reactivity of the natural pozzolans of volcanic origin from Guatem, it can be concluded that:  
* Chemically speaking, both VPs (T and R) were aluminosilicate-based in nature, with contents of potentially reactive acid oxides (SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\)) exceeding 85%, and their resulting LOI was acceptable (<5%). However, low reactivity was determined because a large part of the pozzolan was insoluble in acid and basic media (>40%);
* X-ray diffraction analysis confirmed the presence of not only the amorphous phase, but also crystalline phases, such as quartz, tremolite, or albite;
* The electrical conductivity measurements showed low pozzolanic reactivity, which, in this case, resulted in sample T having slightly higher reactivity than sample R;
* The Frattini test showed that the developed systems containing pozzolan at the 25-55% substitution levels could be labeled as CEM IV cements;
* The TGA confirmed the pozzolanic activity of VP: in the cement pastes, VP acted as a filler and yielded negative fixed portlandite values at early curing time. At the long-term curing time (90 days), the fixed portlandite values were positive and came close to 5-6%. The pozzolanic reactivity was enhanced in the hydrated lime pastes, mainly for the 40 \({}^{\circ}\)C curing temperature;
* For mortars, the workability decreased for the VP-containing systems. This behavior was due to the irregular-shaped pozzolan particles;
* The compressive strengths of the mortars containing 15-35% VP do not reach those in the control mortars. However, at 90 days, the pozzolanic effect is evidenced compared to LF. No significant differences appear between samples R and T.  
It can be generally concluded that VP showed limited reactivity, but a manifest pozzolanic character. This allows it to be used in lime-pozzolan mortars or PC/VP concrete with satisfactory results.  
Conceptualization, O.M.S., LS. and J.P.; methodology, O.M.S., J.P. and M.V.B.; formal analysis, J.M., M.V.B. and L.S.; investigation, All the authors; writing--original draft preparation, O.M.S. and L.S.; writing--review and editing, All the authors; visualization, J.P. and J.Q.; supervision, O.M.S., J.P., L.S. and J.Q.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Adsideo Cooperacion 2012 (Centro de Cooperacion al Desarrollo UPV).  
It is not applicable.  
We wish to thank the Adsideo Cooperacion 2012 program (Centro de Cooperacion al Desarrollo de la Universitat Politecnica de Valencia) for funding the Project \"Characterization of pozzolans of volcanic origin from Guatem: possibilities of use in prefabricated materials for the construction and rehabilitation of social housing\". We would also to thank the Mayanet project (\"Erasmus Mundus Programme Action 2- Lot 8 Latin America\"), especially [PERSON] from the OAI of the Universitat Politecnica de Valencia.  
The authors declare no conflict of interest.
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 3 Publicly Available Datasets
### Real-World Datasets
#### 3.1.3 Traffic  
Most datasets that involve traffic consist of dashcam videos or surveillance videos to support the development of systems capable of anticipating traffic accidents. However,  
Figure 4: Comparison between normal and abnormal frames extracted from real-world anomalies datasets. **(a)** Frame from a normal activity extracted from UCF-Crime [40]. **(b)** Abnormal frame from UCF-Crime [40], showing a shooting. **(c)** Frame from a normal activity in XD-Violence [59]. **(d)** Abnormal frame from XD-Violence [59], representing an explosion.  
the scope of this paper concerns anomaly detection of human-related behaviours. The Street Scene dataset [62] consists of 46 training video sequences and 35 testing video sequences taken from a static camera looking down on a scene of a two-lane street with bike lanes and pedestrian sidewalks. All of the footage is composed of daytime sequences, and it does not contain staged anomalies. The testing sequences have a total of 205 anomalous events consisting of 17 different anomaly types, such as jaywalking, cars outside their lane, and loitering. Although weather conditions are similar in every sequence, the dataset is challenging due to the variety of simultaneous activities occurring, moving background (e.g., trees moving with the wind) and changing shadows.
## 2 Materials and Methods
### Data Collection  
Images of Geomunoreum were obtained from the Sentinel hub EO browser, where Sentinel-2 satellite images are maintained and publicly available. Sentinel hub provides an Application Programming Interface (API) which enables a program to automatically download images that Sentinel-2 satellite has obtained, being operated as an Earth observation task from the Copernicus Program that collects optical images at 10 m to 60 m resolution over land and coastal seas. It revisits the same place every 10 days. Images from the Sentinel-2 satellite are processed in level-0, level-1, and level-2, and each level is classified further into specific categories such as A, B, and C. Different image corrections are applied to these levels. Only L1C (level-1C) and L2A (level-2A) images are publicly available. When a satellite captures images over land, it is not easy to handle these images in the presence of clouds. Thus, we used L2A type images because they were processed based on L1C images by applying atmospheric corrections. We excluded the days of cloudy weather where the satellite was unable to capture the land. The images were obtained from 2017 to 2022. Specific dates are as follow; 12 April 2017, 18 April 2018, 24 March 2019, 23 March 2020, and 24 March 2021.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Methods
#### 2.2.5 Step Length Estimation  
In the PDR navigation, the step length estimation presents a high number of variables: in particular, the measurements of the inertial sensors are influenced by the location where the device is worn and the stride length strongly depends on the physical characteristics of the user and the type of motion that is performed. In literature, it is possible to find mathematical models that relate the length of the steps with frequency and height of the user [16].  
The length of the \(k\)-th step \(Sl_{k}\) can therefore be expressed as a function of the frequency \(f_{k}\) of the step as follows:  
\[Sl_{k}=a\cdot f_{k}-b \tag{23}\]  
where \(a\) and \(b\) are parameters that depend on the physical characteristics of the user. Furthermore, it must be considered that during a walk some steps can be irregular and in some situations it is necessary to avoid obstacles, stopping or changing direction. All these elements increase the uncertainty on the estimation of the step length. For these reasons, it seems reasonable to determine the step length as a mean of multiple step lengths. By dividing the steps performed over the distance traveled between two successive readings of position from the satellite navigation system, it is possible to obtain the average length of the step. By means of the average step length value given by the method that uses the satellite readings, it is possible to correct any offset of the mathematical model previously described. To avoid that the estimation of the step length strongly depends on the physical characteristics of the user, a strategy that dynamically modifies the parameters of the model (23) can be implemented, or additionally can provide compensation of systematic errors from the information of the satellite data [17].