esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Choice of predictor variables can now become less stringent compared to the linear model that was based on a balance equation, so we can actually make statistically more justified aggregations of particle channels, e.g., by using PCA. Since for the linear model aggregation was based on correlation matrices derived from complete dataset, this could introduce so called \"knowledge leak\" from training to test data, and is thus best to avoid it, e.g., by using PCA. Furthermore, when using PCA it is more clear what amount of variance is left out of the model predictors when reducing the number of particulate matter related features. For example, first two PCA components explain 62% of the particle channels variance. The number of neurons in the hidden layer, and the optimal number of input PCA components was determined in optimization procedure. The most optimal training/test ratio was observed for ANN that has 4 neurons with ReLU activation function in the hidden layer and uses 2 PCA components and radon concentration, a total of 3 signals, as input. The optimal ANN model achieved median absolute error of about 40 ions/cm\({}^{3}\) and explained variance of about 0.70. Some additional details about network architecture, hyperparameters and tuning procedures are given in Appendix C.  
Predictive power of the optimized ANN model is illustrated in Figure 5. It seems that the model now doesn't significantly under predict small ion concentration, and it improves on the linear model, despite having smaller number of input predictors. Furthermore, the score on test set is significantly improved. It also appears that some of the noise introduced by radon measurements is now less pronounced compared to the linear model output shown in Figure 4. However, it seems that while the overall trend in small ions concentration is well modelled, the shape of the peaks is not always preserved. Since final ANN has rather simple architecture and there are no discrepancies between training and test scores it can be concluded that the model is not overly complex to introduce overfit, and additionally, training and test datasets are sufficiently complete. Figure 4: Comparison of a linear model based on radon and particle aggregates for School 1 (solid line) and a measurement of small ions (dotted line). Unit is [#/cm\({}^{3}\)]. Training/test score 0.44/0.49.
## 3 POCD Platform and Strategy  
Since the 1990s, the Shanghai Astronomical Observatory of Chinese Academy of Sciences has carried out a series of studies on GNSS and LEO data processing, and abundant experience and knowledge has been accumulated. The Shanghai Astronomical Observatory Orbit Determination (SHAOOD) software is written in FORTRAN language with a unified code style. To improve the overall solution efficiency, the clock offset reduction algorithm [33] is added to the least square batch processing. Users only need to change the parameters of the configuration card and provide the necessary files to realize the personalized solution.  
With SHAOOD software, we model the perturbation forces to achieve the best results. The ionosphere-free pseudo range combination (PC) and carrier phase combination (LC) are formed to eliminate first-order ionospheric delay error. For GPS precise orbit and clock offset, the final orbit and clock offset products from the Center for Orbit Determination in  
Figure 3: Signal to noise ratio series with elevation for GRCC and CS01.  
Figure 2: Multipath error variations with elevation for GRCC and CS01.  
Europe (CODE) [34] are used. Additionally, we use the International GNSS Service (IGS) model from \"igs14.atx\" [35] to correct the GPS Phase Center Offset (PCO) and PCV. As for GPS observations, the cut-off elevation angle is set to 5\({}^{\circ}\) and the data sampling rate is 10 s. The LEO attitude model, PWU and PCV corrections will be discussed and analyzed in the following section. Table 1 provides more detailed information about the measurement models, dynamic models and estimated parameters.
## 2 Dataset and Methodology  
### Dataset  
For this study, we explore PAMAP2 (Physical Activity Monitoring Dataset), an open-source dataset available at the UCI Machine Learning Repository [16]. It contains a variety of physical activities and postures recorded under laboratory conditions.  
Collection  
Nine subjects performed an extensive range of physical activities, including a combination of household and sport activities. The participants wore three wireless Inertial Measurement Units (IMUs) and a heart rate monitor and were made to follow the same protocol when performing 12 key activities. These sensors were placed on the chest, the wrist of the dominant arm, and the ankle. Each IMU contains a 3D magnetic sensor, two 3D accelerometers, and a 3D gyroscope. The IMU sampling frequency is 100 Hz, and the sampling frequency of the heart rate monitor is 9 Hz, which means that data are collected at 0.01 s and 0.111 s, respectively.  
In Figure 2, the distribution of activities is represented, including activities like walking, which are more prevalent than rope-jumping.
## 3 Strong Tracking CKF Based on Multiple Fading Factors
### Multiple Fading Factor Strong Tracking CKF
Therefore, a strong tracking cubature Kalman filter (STCKF) algorithm based on multiple fading factors is introduced:In the time update process, the symmetric fading factor is introduced into the state prediction error covariance matrix, and \(\mathbf{P}_{k}^{-}\) is changed to the following formula:  
\[\mathbf{P}_{k}^{-}=\lambda_{k+1}[\frac{1}{m}{\sum_{i=1}^{m}{\left({\mathbf{\chi}_{i,k}^{- }-\hat{\mathbf{x}}_{k}^{-}}\right)\left({\mathbf{\chi}_{i,k}^{-}-\hat{\mathbf{x}}_{k}^{-}} \right)^{T}}}]{\lambda_{k+1}}^{T}+\mathbf{Q}_{k-1} \tag{21}\]  
In the measurement update process, a fading factor is introduced into the innovation covariance matrix and the measurement autocovariance matrix:  
\[\mathbf{P}_{z}=\lambda_{k+1}[\frac{1}{m}{\sum_{i=1}^{m}{\mathbf{Z}_{i,k}^{-}\left({\mathbf{ Z}_{i,k}^{-}}\right)^{T}-\hat{\mathbf{z}}_{k}^{-}\left({\hat{\mathbf{z}}_{k}^{-}}\right)^{T} }}]+\mathbf{R}_{k} \tag{22}\]  
\[\mathbf{P}_{xz}=\lambda_{k+1}[\frac{1}{m}{\sum_{i=1}^{m}{\mathbf{X}_{i,k}^{-}\left({ \mathbf{Z}_{i,k}^{-}}\right)^{T}-\hat{\mathbf{x}}_{k}^{-}\left({\hat{\mathbf{z}}_{k}^{-}} \right)^{T}}}] \tag{23}\]  
Compared with [10], the algorithm in this paper adds a fading factor in the time updating process, which can adjust the prediction covariance matrix in real time and the gain matrix more obviously. With a slight increase in the amount of calculation, the algorithm accuracy can be improved. In nonlinear systems, the system noise covariance matrix \(\mathbf{Q}_{k}\) is often time-varying and uncertain [24]. A suboptimal unbiased MAP constant noise estimator can be designed according to the maximum a posteriori estimation principle and the exponential weighting method. The fading factor is added to the filter to adjust the system noise, and the following recursive formula is given:  
\[\hat{\mathbf{Q}}_{k+1}=(1-d_{k+1})\hat{\mathbf{Q}}_{k-1}+d_{k+1}[{\mathbf{K}_{k+1}\mathbf{ \epsilon}_{k+1}\mathbf{\epsilon}_{k+1}\mathbf{\Gamma}^{T}{\mathbf{K}_{k+1}}^{T}}+\mathbf{P}_{k +1}-\sum_{i=1}^{m}\frac{1}{m}{\left({\mathbf{\chi}_{i,k}^{-}-\hat{\mathbf{x}}_{k}^{-} }\right)\left({\mathbf{\chi}_{i,k}^{-}-\hat{\mathbf{x}}_{k}^{-}}\right)^{T}}] \tag{24}\]  
where \(d_{k}=(1-b)/(1-b^{k})\), \(b\) is the noise fading factor, and the value range is 0\(<\)\(b\)\(<\)1. Through multiple simulations, it can be seen that a better tracking filtering effect can be obtained when \(b\) is 0.89. Substitute the selected \(b\) value into the formula to acquire:  
\[\mathbf{Q}_{k}=(1-d_{k})\mathbf{Q}_{k-1}+d_{k}[{\mathbf{K}_{k}\mathbf{\epsilon}_{k}\mathbf{ \epsilon}_{k}}^{T}{\mathbf{K}_{k}}^{T}]k\geq 2 \tag{25}\]  
Add \(\mathbf{Q}_{k}\) to the multi fading factor strong tracking algorithm, the filtering value of the target is closer to the real value and the filtering accuracy is improved.
## 3 Case Study and Results
### Validation of the Proposed Feature Selection  
Table 3 compares the average MAPEs for predicting all the body features with and without the proposed importance-based feature selection. As seen, without the feature selection, the XGBoost and the RF can achieve an average MAPE of 2.98% and 2.17%, respectively, for the prediction of all body features. With the proposed feature selection, the MAPE of all body feature prediction for the XGBoost and the RF are 3.85% and 2.88%, respectively, which are slightly higher than that without the feature selection for both the XGBoost and the RF. The results in Table 3 show there will be a slight performance reduction in terms of the MAPE when the feature selection is considered.  
To further examine the performance of the two ML methods with the proposed feature selection, box plots of the MAPE for predicting all the body features are shown in Figure 9. As seen, the minimum MAPE of the XGBoost and the RF is similar; however, the maximum MAPE of the XGBoost is around 14% while the maximum MAPE of the RF is 8%. Analogous to Table 3, the mean MAPE of the RF is lower than the that of the XGBoost. The results  
Figure 8: Top 20 most important body features by ranking feature importance.  
in Figure 9 demonstrate that the RF has a better performance than the XGBoost when a combination of Bayesian search and feature selection is used.  
Table 4 shows the running time of the proposed framework for all body features. The running time consists of (1) dataset pre-processing; (2) fine-tuning the hyper-parameters via Baysian search; (3) importance-based feature selection; and (4) the validation of selected features. It is seen that the XGBoost takes a much less running time than the RF does even when the XGBoost has much more possible hyperparameter combinations than the RF does (i.e., 1140 versus 380). The programming is developed in Python version 3.8.12. The RF and XGBoost are from the Python library sklearn version 1.0.1 and xgboost version 1.5.1, respectively. The computing platform is a PC with Intel Xeon CPU E5-2640 dual processors and 256 GB RAM. We speculate that the much shorter running time is because the XGBoost can employ the CPU cache to store calculated gradients for expediting all the calculations. Note that the parallel running is not enabled in this study, which might lead to quite different results. More detailed experiments of the running time is out of scope of this paper, and will be further explored in our subsequent studies.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 2 Related Work
A battery is not a good choice for charging, harvesting and storing, or discharging purposes. Solar energy harvesting is implemented to prolong the battery time. All types of energy harvesting, solar, piezoelectric vibration, and thermometric acoustic noise are manageable and also useable. The authors analyzed all types of components relating to solar energy. The existing studies have a common factor that is an area management strategy to evaluate the network performance. In [18], the overall system architecture is also discussed and considers the nodes' placements for emergency communication. In [39], the authors discussed the software-defined-networks-based (SDN) manageable topology formation to construct a UAV formation. The proposedsolution considers the set of graph theories for network evaluation. The authors also used particle swarm optimization (PSO) for the selection process and to maximize the number of interconnected nodes. Table 1 shows the technical comparison of existing solutions. Different solutions have been designed to address the coverage area issue in cellular networks. Some studies have divided the area based on user distribution data, calculating distance, allocation of DBS, cluster formation, establishing the DBS at the optimal location, and coverage area specification in 2D and 3D planes, which can still obtain the signal from the source due to strong reflections and diversions. The probability of having an LoS connection between a transmitter and a receiver is an important factor for modelling such channels. There is a need to design a solution to find the best position of the UAVs-BS in a microcell environment by using better techniques and providing optimal coverage and increased capacity throughout the services area.
## 2 Materials and Methods
### Deep Learning Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**YOLO Version** & **Parameters** & **Processing Speed (FPS)** & **mAP (COCO)** \\ \hline YOLOv3 [38] & 61.5 million & \(\sim\)10–15 FPS & 57.9\% \\ \hline YOLOv4-tiny [39] & 6 million & \(\sim\)25–30 FPS & 36.9\% \\ \hline YOLOv5-Small [40] & 7.2 million & \(\sim\)20–25 FPS & 39.4\% \\ \hline YOLOv5-medium [40] & 21.2 million & \(\sim\)15–20 FPS & 45.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between most used YOLO version in embedded system with NVIDIA Jetson TX2. Figure 2: YOLOv3 network architecture [44]. architecture state-of-the-art in many deep learning models for segmentation tasks [46], image denoising [47], and object detection. The YOLOv3 algorithm uses a multi-scale feature extraction network named DarkNet53, which originally had 53 layers pretrained on the ImageNet dataset. For the detection task, an additional 53 layers are stacked on top of it, giving a fully convolutional underlying architecture of 106 layers for YOLOv3 (Figure 3). Also, it consists of three prediction stages, each followed by an up-sampling layer to increase the size of the feature maps. This enables the model to detect objects of various sizes effectively [48]. Initially, predictions are made on smaller feature maps to detect large-scale objects. Subsequently, predictions are made on medium-sized feature maps to identify medium-sized objects, and finally, predictions on the largest feature maps focus on detecting small objects. By combining these predictions into a single output, the model generates an image with bounding boxes delineating different objects. One other characteristic of the YOLOv3 model is the employment of intersection over union (IOU) [49]. This metric is frequently used in object detection benchmarks. YOLOv3 uses this metric by determining the error between the predicted bounding box and the ground truth. These bounding boxes are determined by five predictions. The center of the bounding box coordinates is labeled \(x\) and \(y\), the dimensions of the bounding box are labeled \(h\) and \(w\), and finally, the class probability is determined. The probability value is equal to 1 if a grid cell has a portion of the ground truth bounding box, and 0 if not. Figure 4 illustrates the detection process of the YOLO models. The first step involves taking the image grids (Figure 4a) and predicting the class probabilities map, distinguishing the object from the background (Figure 4b). Subsequently, the model predicts the bounding boxes (Figure 4c), which collectively form the predicted bounding box surrounding the object. In this instance, the object refers to the plants (Figure 4d). Figure 4: Detection steps of the YOLO model. (**a**) Input image with an \(N\times N\) grid, (**b**) class probability prediction map, (**c**) bonding box prediction for each grid, and (**d**) the detection result. Figure 3: DarkNet53 Multi-scale feature extraction pipeline.
## 3 Results and Discussion
### Model Evaluation Criteria  
To effectively evaluate the performance of the object detection model, this work evaluates it in terms of several metrics: mean average precision (_mAP_), precision (_P_), recall (_R_), and frames per second (FPS). Here, \(P\) reflects how many of the samples predicted to be positive cases are true cases and \(R\) reflects how many of the samples that are positive cases are predicted to be positive cases. The calculation formulae are shown, respectively, as follows:  
\[P=\frac{TP}{TP+FP}, \tag{24}\]  
\[R=\frac{TP}{TP+FN}, \tag{25}\]  
where _TP_ (true positive) is the number of positive classes predicted as positive, _FP_ (false positive) is the number of negative classes predicted as positive, and _FN_ (false negative) is the number of positive classes predicted as negative. A _P-R_ curve can be drawn with recall as the horizontal axis \(R\) and precision as the vertical axis \(P\). The _AP_ value is the area enclosed by the _P-R_ curve in the interval 0 to 1 with the horizontal axis, i.e.,  
\[AP=\int_{0}^{1}P(R)dR, \tag{26}\]  
The average of all the category accuracies (_AP_) is calculated using _mAP_. The calculation formula is shown in Equation (27), where \(N\) represents the number of target detection categories:  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{27}\]  
FPS is a measurement of the efficiency of object detection.
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 3 Simulations and Dataset Generation
### Simulation Environment
#### 3.1.1 Phase 1: Predictors Selection  
In the first phase of simulation, multiple essential parameters, such as the number of nodes (NN), number of interests (NI), pending interest table (PIT), router bandwidth (RB), router delay (RD), content store (CS) capacity, and cache replacement strategies, were considered. A total of 90 simulation rounds were executed to analyze the behavior of the ISR thoroughly based on the aforementioned parameters. At first, the impact of the PIT timer was examined while holding all other parameters at their optimal values. A total of 20 simulation rounds were executed for the PIT timer. After analyzing the behavior of the PIT timer, we observed that it did not have much impact on the ISR. The reasons for this were: (1) the WAPs were at a one-hop distance from the edge device; (2) the link delay was a minimum of 10 ms; and (3) the RB was 20 Mbps; therefore, these values were satisfactory for satisfying the maximum number of packets. Moreover, the link was wired from the WAPs to the edge device. A wired link with no path loss or fading issues results in a higher packet delivery ratio, and hence, the network achieved a higher ISR. The reasons mentioned above led us to exclude the PIT timer parameter from our statistical analysis. In addition to the PIT timer, CS capacity (with various CS sizes) and various cache replacement policies were analyzed. A total of 40 rounds of simulations were executed to examine the impact of different cache replacement strategies and the cache capacity on the ISR. From the simulation results, we concluded that these two parameters were well suited for latency or round-trip time measurement analysis, since caching data closer to the client reduces latency and round-trip time. Therefore, we also excluded these two parameters from our current analysis and left the latency and round-trip time measurement analysis for future work.  
In the second phase, we simulated 256 rounds, which included NN, NI, RB, and RD. The simulation rounds were based on a four-way factorial design. An overview of the factorial design method along with the details of the network parameters and their variations are presented in the following section.
## 2 System Model and Problem Formulation  
### System Configuration  
In this paper, we consider the dense LEO satellite communication system composed of many LEO satellites with different orbital planes, which leads to overlapped coverage areas. Both satellites and UTs are equipped with uniform rectangular arrays (URAs). Every satellite can generate several fixed pointing circular beams to provide service for ground users, as shown in Figure 1. These satellite beams move with the motion of the satellite. The UTs are generated randomly, and each UT has one flexible circular beam to realize beam alignment. Thus, the existence time of the overlapping coverage area for fixed-position UTs is limited; moreover, the full frequency reuse is considered among all satellites. Generally, the satellite ephemeris broadcasted periodically can be utilized to obtain the satellite location. The time-varying beam center position can also be obtained via its relative position to the location of the nadir of the satellite, as shown in Figure 1.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 4 Experimental Evaluation  
### Datasets and Experimental Environment  
(1) Object detection in optical remote sensing images (DIOR) dataset [44]  
The object detection in optical remote sensing images (DIOR) dataset is a large-scale dataset designed for object detection in remote sensing images. It includes 20 categories such as airplanes, airports, vehicles, bridges, and train stations, comprising a total of 23,463 images.  
(2) Remote sensing images with scene graph (RSSG) ocean remote sensing dataset  
The remote sensing images with scene graph (RSSG) ocean remote sensing dataset is a custom dataset created by us. It covers relevant images from a coastal area between 2020 and 2021, containing five categories of sea fields, ships, aquaculture rafts, oil tanks, and houses, with total number of 929 images. A corresponding scene graph was created for each image. Annotations were performed using Labellmg to outline the regions of interest in the sample images, followed by the building of scene graphs based on these regions using NetworkX. An example of the dataset is presented in Figure 12.  
(3) Experimental environment  
The hardware and software information used in the experiment is in the Table 2:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Equipment** & **Parameters** \\ \hline Operating system & Ubuntu20.04.2 LTS \\ CPU & Intel® CoreTM i7-13700 CPU @ 2.10 GHz \(\times\)16 \\ Memory & 32G \\ GPU & NVIDIA GeForce GTX 3090 \\ Programming language and framework & Python3.8, PyTorch \\ Development tools & PyCharm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental equipment.  
Figure 11: Flowchart of the feature similarity calculation module.  
Figure 12: Sample of the RSSG dataset.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Interpretable AI  
While explainable AI (XAI) encompasses diverse methodologies, to generate human-understandable rationales for a model's behavior, interpretable AI fundamentally differs: interpretability is an intrinsic property of the model itself, not an outcome of applied techniques. Models are interpretable when their internal structure--such as parameters, logic flows, or decision pathways--is inherently transparent and directly comprehensible to humans. For example, Decision Trees or linear models are intrinsically interpretable: their architecture enables users to trace input-to-output reasoning through direct inspection, without relying on external explanatory methods. Thus, interpretability arises from model design choices (e.g., simplicity, modularity, transparency), not from external techniques that act as wrappers around a model. This distinction clarifies why interpretability lacks a \"toolbox\" akin to XAI: it is a foundational characteristic of the model architecture.