esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### PlaceNet-Based SBD Method
#### 3.1.2 Shot Detection Network
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Proposed Year**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Default Input Size**} & \multicolumn{2}{c}{**Top-5 Error \%**} & \multirow{2}{*}{**Work Contribution**} \\  & & & & **ILSVRC 12** & \\ \hline \multirow{3}{*}{2012} & \multirow{3}{*}{AlexNet} & \multirow{3}{*}{227} & \multirow{3}{*}{15.3} & Use of rectified linear units (ReLU), \\  & & & & the dropout technique, and overlapping \\  & & & & max pooling \\ \hline
2013 & ZF Net & 224 & 14.8 & Accurate tuning of the hyper-parameters \\ \hline
2014 & VGG16/VGG19 & 224 & 6.67 & Uses multiple 3 \(\times\) 3 convolutional layers to represent complex features \\ \hline
2014 & GoogleNet/Inception & 224/299 & 7.3 & Use of 1 \(\times\) 1 convolutional blocks (NiN), \\  & & & & use of a width increase \\ \hline
2015 & ResNet50/101/152 & 224 & 3.6 & Feeding the output of two successive \\  & & & & convolutional layers AND also bypassing the \\  & & & & input to the next layers \\ \hline
2016 & InceptionResNet-v2 & 299 & 4.7 & Training with residual connections \\  & & & accelerates the training of Inception networks \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The deep neural networks. Figure 4: The top-1 classified categories of the test images by using the Places365 demo. packages from the MATLAB Neural Network Toolbox were selected for training. In the experiments, we took about 540 images (500 natural images and 40 computer-generated images) from each category for the training set, used 60 images (50 natural images and 10 computer-generated images) for the validation set, and used 100 images (80 natural images and 20 computer-generated images) for the test set. For these categories, the number of images is less than the preset number of images for training, validation, and testing. The numbers of these categories' images are distributed into three parts in percentages. The results of the classification accuracy on our used Places dataset are listed in Table 7.  
Table 7 shows that the accuracies are lower than those in the Places365 dataset. This is because the training dataset has less images than Places365 and the number of training iterations is also not large enough. However, the precision for places classification in the videos is not too bad.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Spatial Coverage
#### 3.2.1 2D-Networks  
As we presented before, 2D-networks are classified into two subcategories, namely, anchor-free and anchor-based schemes. However, unfortunately, we cannot find the paper that belongs to the anchor-free 2D-network subcategories, so we just introduce some papers belonging to the anchor-based 2D-networks. In [31], the authors propose an Underwater Tracking (UT) scheme that can also be called as Drift-dependent UT (DD-UT). In addition, the effects of the ocean current and sound-speed uncertainties are considered. Acoustic communication reports the drift velocities of the anchors that are used to obtain the drift velocity of the Tracked Node (TN). Using the Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF), Two State-Space Model (SSM)-based tracking solutions can be obtained. It is proved that UKF is more robust and superior for the complex cases than EKF when a large amount of data is available.  
The authors in [32] propose two types of localization schemes in this paper, which are fixed-position and magnified-range. The fixed-position can ameliorate connectivity of the system. In this paper, the fixed-location nodes mean that the node will not move with the target, but not to say that they are anchored in a particular position. They can move in a predefined area. Thus, the design of the fixed-position scheme can reduce the power consumption. The magnified-range is based on fewer energy constraints of anchor nodes. All regular sensor nodes can connect to anchor nodes directly or indirectly. In addition, the magnified anchors' signal range will improve the likelihood of obtaining more accurate results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Computation Algorithm** & **Coverage** & **Localization Time** & **Accuracy** & **Computational Complexity** & **Energy Consumption** \\ \hline
[24] & Centralized and estimation & Large & Short & Average & Medium & Low \\
[25] & Centralized and prediction & Small & Short & Average & Low & Low \\
[6] & Distributed and estimation & Large & Medium & High & Medium & Low \\
[26] & Distributed and estimation & Large & Medium & High & Medium & Medium \\
[27] & Distributed and estimation & Small & Long & Depend & Low & Low \\
[28] & Distributed and estimation & Large & Depend & Depend & Low & High \\
[29] & Distributed and prediction & Large & Short & Depend & Medium & Low \\
[30] & Distributed and prediction & Large & Long & Low & High & Depend \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of localization algorithms for using computation algorithms.
## 2 The Study Area and the Data Source
### Data Source and Preprocessing  
The data came from the China Centre for Resources Satellite Data and Application ([[https://data.cresda.cn/#/home](https://data.cresda.cn/#/home)]([https://data.cresda.cn/#/home](https://data.cresda.cn/#/home)), accessed on 10 January 2022). The data used were multi-spectral and panchromatic ortho-corrected images obtained by the GF-2 satellite in August 2020, including multi-spectral data at 4 m resolution (four bands of red, green, blue and near-red) and panchromatic data at 1 m resolution [27]. Radiation calibration, atmospheric correction, geometric rectification, and alignment were performed on the CF-2 images using the ENVI software, while the NNDiffuse Pan-Sharpening fusion algorithm was used to generate multi-spectral remote sensing data with 1 m resolution.
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 2 Methodologies
### Overview of the Present Approach  
According to the previous analysis, the schematic of the present method to monitor the transmission condition of 3D printer is illustrated in Figure 2.  
_Step 1_.: Collect raw monitoring data from the sensor on the 3D printer;  
_Step 2_.: Feature extraction from raw monitoring data including ten time-domain statistical features and eight time-frequency features;  
_Step 3_.: Train the ESN model using the extracted features;  
Figure 2: Schematic of transmission condition monitoring of 3D printers based on ESN.  
_Step 4_.: Test the monitoring accuracy of the healthy condition of the 3D printer with the built ESN model; and  
_Step 5_.: Output the condition of the 3D printer; end.
# Approximate Triangulations of Grassmann Manifolds
## 2 Materials and Methods
### Grassmann Manifolds  
Denote by \(\mathbb{R}^{n}\) the Euclidean space of dimension \(n\). By a _\(k\)-frame_ in \(\mathbb{R}^{n}\), we mean a \(k\)-tuple of linearly independent vectors; denote by \(V_{k}(\mathbb{R}^{n})\) the collection of \(k\)-frames in \(\mathbb{R}^{n}\). This is an open subset of the \(k\)-fold Cartesian product \(\mathbb{R}^{n}\times\cdots\times\mathbb{R}^{n}\).  
**Definition 1**.: _The Grassmann manifold \(G_{k}(\mathbb{R}^{n})\) is the set of all \(k\)-dimensional planes through the origin in \(\mathbb{R}^{n}\). It is topologized via the quotient map \(V_{k}(\mathbb{R}^{n})\to G_{k}(\mathbb{R}^{n})\) which takes a \(k\)-frame to the \(k\)-plane it spans._  
When \(k=1\), we see that \(G_{1}(\mathbb{R}^{n})\) is the real projective space \(\mathbb{R}P^{n-1}\), a manifold of dimension \(n-1\). In general, we have the following result.  
**Lemma 1**.: _The Grassmannian \(G_{k}(\mathbb{R}^{n})\) is a compact manifold of dimension \(k(n-k)\). The map \(X\to X^{\perp}\), which takes a \(k\)-plane to its orthogonal complement is a diffeomorphism between \(G_{k}(\mathbb{R}^{n})\) and \(G_{n-k}(\mathbb{R}^{n})\)._
# Mining Sequential Patterns with VC-Dimension and Rademacher Complexity
## 1 Introduction
### Our Contributions  
In this work, we study two problems in sequential pattern mining--mining _frequent sequential patterns_ and mining _true frequent sequential patterns_. We propose efficient algorithms for these problems, based on the concepts of VC-dimension and Rademacher complexity. In this regard, our contributions are:  
* We define rigorous approximations of the set of frequent sequential patterns and the set of true frequent sequential patterns. In particular, for both sets we define two approximations: one with no _false negatives_, that is, containing all elements of the set; and one with no _false positives_, that is, without any element that is not in the set. Our approximations are defined in terms of a single parameter, which controls the accuracy of the approximation and is easily interpretable.
* We study the VC-dimension and the Rademacher complexity of sequential patterns, two advanced concepts from statistical learning theory that have been used in other mining contexts, and provide algorithms to efficiently compute upper bounds for both. In particular, we provide a simple, but still effective in practice, upper bound to the VC-dimension of sequential patterns by relaxing the upper bound previously defined in Reference [8]. We also provide the first efficiently computable upper bound to the Rademacher complexity of sequential patterns. We also show how to approximate the Rademacher complexity of sequential patterns.
* We introduce a new sampling-based algorithm to identify rigorous approximations of the frequent sequential patterns with probability \(1-\delta\), where \(\delta\) is a confidence parameter set by the user. Our algorithm hinges on our novel bound on the VC-dimension of sequential patterns, and it allows to obtain a rigorous approximation of the frequent sequential patterns by mining only a fraction of the whole dataset.  
* We introduce efficient algorithms to obtain rigorous approximations of the true frequent sequential patterns with probability \(1-\delta\), where \(\delta\) is a confidence parameter set by the user. Our algorithms use the novel bounds on the VC-dimension and on Rademacher complexity that we have derived, and they allow to obtain accurate approximations of the true frequent sequential patterns, where the accuracy depends on the size of the available data.
* We perform an extensive experimental evaluation analyzing several sequential datasets, showing that our algorithms provide high-quality approximations, even better than guaranteed by their theoretical analysis, for both tasks we consider.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 4 Experiments and Results
### Datasets  
The choice of dataset can lead to overfitting, in addition to optimistic results [11]. For example, a dataset with an insufficient number of samples would result in overfitting, and similar training and test datasets would lead to optimistic results. In this study, the aim of the experiments was mainly to test whether our method can improve the performance, rather than at proving that our proposed methods produce the best performance. In fact, our proposed methods are not exclusive and may be utilized in other CNNs, and even in RNNs. Therefore, the most important factor of the experiments was to ensure that the same conditions were maintained when applying our methods or not.  
The experiments were also required to be performed on different datasets to prove the effectiveness of the proposed approaches. We chose three publicly available datasets for the experiments. The first dataset was the Jamendo corpus (JMD) [19; 34], which contains 93 songs with a total length of 371 min. The second was RWC pop [35], which contains 100 songs with a total length of 407 min. The third was Mir1k [36], which is relatively smaller, having a total length of 133 min and comprising 1000 song clips with durations ranging from 4 to 13 s. The overall summary of the three datasets is shown in Table 2.  
The original division of the training, validation, and test datasets was unchanged for JMD [19], i.e., 61 songs for training, 16 for validation, and 16 for testing. We divided the RWC dataset according to the songs' ending digits, with 0-4 selected for the training dataset, 5 and 6 for the validation dataset, and 7-9 for the test dataset. We also separated the Mir1k dataset according to the songs' ending number characters: \"05\", \"07\", and \"09\" were chosen as the validation dataset; \"06\", \"08\", and \"10\" were chosen as the test dataset; and the remainder were assigned to the training dataset. Divided in this manner, the training, validation, and test datasets of RWC and Mir1k are stochastic to some extent. It is worth noting that Mir1k is not only small, but also highly unbalanced; however, this was not relevant because our aim was not to design a perfect dataset to obtain optimal results. The songs of JMD are all by different artists, whereas the 100 pop songs of RWC are performed by 34 singers, and the 1000 song clips of Mir1k are sung by 19 artists. Therefore, in terms of artists, the similarity between the training and the dataset is the most significant for Mir1k, and second-most significant for RWC; these similarities would lead to optimistic results to some extent. We expected that JMD would be the most challenging dataset for our task, whereas Mir1k would be the easiest.
## 3 Results and Discussion
### Thermal Behavior
#### 3.3.2 Ageing Tests
This confirms that an important temperature variation has the greatest effect on mechanical behavior of the material under investigation. Moreover, it is worth nothing that the flexural strength after the three considered treatments was not significantly different from that of as printed material. Therefore, the mechanical performances of Ultem 9085 can still be considered good after the different ageing treatments. This confirms the reliability of Ultem 9085 after ageing treatments at different temperature and humidity conditions.
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 3 Experimental Results and Analysis
### Comparative Experiments and Analysis of Results
#### 3.3.2 Performance Evaluation
On one hand, the nature of the YARN cluster is mainly a high-performance computer composed of multiple computers, whose data interaction has a slight gap compared with traditional high-performance computers, and the memory interaction and network bandwidth will partially affect the computational performance. In addition, frequent data splitting and merging operations can take a toll on the performance of the data processing, particularly when they are too frequent (which is almost inevitable when the number of executors increases). Finally, the size of the data volume and the relatively simple calculation process of the filtering algorithm are perhaps key factors that limit the slow growth of the overall performance. Due to the limited hardware equipment's conditions (possessing only three nodes) and the limitation of the Spark broadcast RDDs only supporting the serialization of data below 2 GB, we only filtered the data of a small area and could not fully exploit the advantages of Spark's distributed computing in the field of Big Data.
## 2 Network Model and Operation
### Time Schedule
#### 2.2.2 Space-Time Analysis
In the first step, called pattern selection, a reduced state pattern (time slots \(\times\) nodes with Tx/Rx assigned states), which fulfils Equations (2)-(4), is found, so that it includes as many Tx/Rx states as possible. Once the pattern is found, in the second step, called alignment, the pattern is repeated in the 2D space-time lattice. If collisions arise, the pattern is discarded. When the alignment provides a collision-free schedule, in the third step, called overlap, the Tx/Rx part of a pattern is moved to fill as many idle states as possible and then, again, a check for collisions is carried out, as shown in the lower right corner of Figure 6. In the steps of alignment and overlap, the found schedule must fulfill conditions Equations (2)-(6). Figure 6 shows examples of three candidate \(3\times 3\) patterns, which fulfill constraints Equations (2)-(4), but only one of them passes both alignment and overlap tests without collisions. When repeated by overlapping in the whole 2D lattice, this candidate pattern turns into the optimal schedule presented in Equation (11). For the sake of brevity, the alignment and overlap tests have only been included in Figure 6 for pattern 1, but the procedure is similar for the other two patterns (2 and the optimal case). Figure 5: (a) State diagram for the optimal schedule in a string network with \(N=5\). An equivalent but more convenient representation to apply a geometrical method is shown in (b).
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 5 Conclusions  
This work proposed an improved variogram Kriging interpolation algorithm for sparse data according to the following requirements:  
1. In view of the sparse characteristics of aeromagnetic data, the cubic relationship between the covariance function and the lag distance of aeromagnetic data is derived by using the magnetic dipole model, which is used as the basis for selecting the spherical variogram for interpolation processing so as to conform to the magnetic trend;
2. In view of the lack of smoothness near the original point of the spherical variogram graph caused by sparse data, the quadratic lag distance is introduced for processing to solve the underfitting problem near the origin;
3. In view of the imbalance of the variogram model caused by the introduction of the quadratic lag, the hyperparameter \(\lambda\) is introduced to deal with it.  
Finally, the advantages of the improved variogram interpolation algorithm are verified by testing multiple sets of simulation data and measured data. Compared with the traditional Gaussian, spherical, and exponential models, it can improve the accuracy of data interpolation and better reflect the characteristics of the dense magnetic field in regions with drastic changes in magnetic anomaly gradient. It can greatly improve the efficiency and accuracy of constructing high-precision magnetic maps.  
Conceptualization, M.P. and Q.Z.; methodology, Z.Y. and Y.X.; software, H.L. and Z.D.; validation, Z.Y. and K.W.; formal analysis, D.C and X.L.; data curation, H.L.; writing--original draft preparation, H.L. and Z.Y.; writing--review and editing, Q.Z. and D.C.; project administration, Q.Z. and W.D.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the corresponding author on request.  
The authors declare no conflicts of interest.
# Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications
## 2 Literature Review
### Collaborative Robot with Reinforcement Learning for Pick-and-Place Applications  
[PERSON] et al. (2022) [14] proposed a system for pick-and-place tasks in collaborative robotics, integrating Deep Reinforcement Learning (DRL) with pre-trained convolutional neural networks (CNNs) for object detection and grip positioning. The system is composed of a UR3e cobot, a Robotiq 2F-85 gripper, and an Intel RealSense D435i camera, working together with the ROS framework (Figure 4). DRL allows the robot to learn to identify and manipulate objects in unknown positions without supervised training. The simulation environment in Webots was used to train the model, reducing the time required for learning before being applied to real hardware. CNN models such as MobileNet and DenseNet have been used for visual feature extraction.  
Based on our research, this work presents the following limitations [14]:  
* The CNNs used depend on input images with specific dimensions and characteristics, which can limit the applicability of the system in dynamic industrial environments.
* Although the UR3e is a cobot certified for safe interaction, the system does not include detailed force control, limiting delicate tasks such as food handling.
* The article uses ROS for communication, but does not detail the integration of RTDE for real-time synchronization between components.
* The RealSense camera is effective and affordable, but the DRL approach significantly increases training time, making the solution less practical for rapid iterations in industrial applications.
* No specific image processing or advanced lighting techniques were described to deal with variable conditions such as shadows or sudden changes in lighting.
* The gripper used does not feature advanced force control, which may compromise the safe handling of fragile or delicate objects.  
Figure 4: Experimental (**a**) and simulated (**b**) setups developed by [PERSON] et al. (adapted from [14]).  
* The system is aimed at generic handling tasks and does not address specific applications in the food industry, such as hygiene requirements or handling delicate foods.
## 5 Discussion
### Analysis of the Watermark Synchronization Mechanism  
The watermark algorithm proposed in this paper establishes a many-to-one synchronization mechanism based on the coordinates of the elements, allowing each bit of watermark information to be embedded into the BIM data multiple times. This redundancy enables the extraction of the watermark to be based on the majority principle for voting. In the event that elements in the BIM data undergo deletion or addition attacks, the possibility of errors caused by changes to these elements is reduced, and the probability of extracting complete and correct watermark information is significantly improved. For example, a watermark bit has \(N\) watermark carriers, and if \(n(n<0.5\times N)\) of the elements change, resulting in the loss of the watermark information, there are still more than 50% of the elements pointing to the correct information, ensuring that the extracted watermark sequence is correct. This concept is validated by the element deletion and element addition experiments in Section 4. Given that the elements of BIM data are relatively concentrated on each  
\begin{table}
\begin{tabular}{c c c c} \hline
**BIM Data** & **Format** & **Type** & **NC Value** \\ \hline \multirow{2}{*}{Office building} & FBX & Geometry format & 1.00 \\  & JSON & Text format & 1.00 \\ \hline \multirow{2}{*}{Gymnnasium} & FBX & Geometry format & 0.99 \\  & JSON & Text format & 0.98 \\ \hline \multirow{2}{*}{Structural model} & FBX & Geometry format & 0.98 \\  & JSON & Text format & 1.00 \\ \hline \end{tabular}
\end{table}
Table 9: Results of geometry–property separation.  
floor, this paper proposes that the watermark synchronization mechanism is optimized by the multiplication principle, thereby embedding the watermark information into the data more uniformly.  
Moreover, this paper employs the center point of the bounding box as the coordinates of the figure elements, and this calculation method is applicable to elements that are positioned according to points, lines, and surfaces. This approach standardizes the spatial positioning of BIM data, thereby enhancing the stability of the watermark bit mapping.