esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Algorithms  
We experimented with supervised learning and the DT, RF, and multi-class AdaBoosted DTs classification models. We also apply methods for DCS (OLA and LCA) and methods for DES (KNORA-U, KNORA-E, and META-DES).  
A DT uses the divide-and-conquer technique to solve classification and regression problems. It is an acyclic graph where each node is a division node or leaf node. The rules are based on information gain, which uses the concept of entropy to measure the randomness of a discrete random variable A (with domain \(a_{1},a_{2},\ldots,a_{n}\)) [37]. Entropy is used to calculate the difficulty of predicting the target attribute, where the entropy of A can be calculated by:  
\[\text{A}=-\sum_{i=1}^{n}p_{i}log_{2}(p_{i}) \tag{1}\]  
where, \(p_{i}\) is the probability of observing each value \(a_{1},a_{2},\ldots,a_{n}\). In the literature, DT has performed well with imbalanced datasets. Different algorithms generate the DT, such as ID3, C4.5, C5.0, and CART. The Scikit-learn library uses the CART algorithm.  
The RF algorithm is used to combine DTs, generating several random trees. The algorithm assists modelers in preventing overfitting, being more robust when compared to a DT. It uses the Gini impurity criterion to conduct the feature selection, in which the following equation [38] guides the split of a node:  
\[i(w)=\sum_{l=1}^{L}p_{w}^{l}(1-p_{w}^{l}) \tag{2}\]  
where \(p_{j}\) is the relative frequency of class \(j\)[33].  
The multi-class AdaBoosted DTs algorithm creates a set of classifiers that contribute to the classification of test samples through weighted voting. With each new iteration, the weight of the training samples is changed considering the error of the set of classifiers previously implemented [37]. A multi-class AdaBoosted DTs performs the combination of predictions from all DTs in the set for multi-class problems.  
Finally, a dynamic selection technique measures the performance level of each classifier in a classifier pool. If a classifier pool is not defined, a BaggingClassifier generates a poolcontaining 10 DTs. For the DCS method, the classifier that has achieved the highest performance level when classifying the samples in the test set is selected [22]. For the DES method, a set of classifiers that provide a minimum performance level is selected.
## 3 Test Cases
### Test Case 1  
In the first test case, a relatively simple part was selected, where lower layer thickness, cost, and tardiness are favoured. The criteria weights for evaluating the alternative process configurations from all six service providers were: layer thickness (40%), density (0%), tardiness (30%), and cost (30%). The materials to be considered were Polylactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS). Once the product design is completed, a design engineer has the option to use the part geometry analysis tool, which will automatically convert the native CAD file to an STL file and will calculate the external dimensions of the part (Figure 7). Then it will launch the Design Agent (Figure 8). For all 3D printers, the Prusa Slicer CAM tool [18] was interfaced with the corresponding Machine Agents for generating the G-code, while plain text files were used for representing the information related to the machines' availability as stored in standard MES platforms. The process profiles that are available for each machine correspond to 3 different layer thicknesses, i.e., 0.05 mm, 0.15 mm, and 0.30 mm, 2 materials (PLA, ABS), 4 infill densities (0%, 20%, 50%, 70%), and 3 support options. The overall number of profiles is therefore 72 per machine. More process configuration profiles could be prepared and used but for illustration purposes, the number of profiles in this paper was limited to 72. In this case, as per the user's layer thickness, density and material requirements, 2 layer thicknesses (0.05 mm, 0.15 mm), 2 materials (ABS, PLA), 2 infill densities (50%, 70%), and 1 support option were tested. The overall number of combinations is therefore 8, which is equal to the total number of profiles tested and simulated per agent and machine. Figure 9 presents the least and most expensive process alternatives, as well as the one that was selected as the best.  
For the test case 1, agent MA6 provided the least expensive configuration. However, due to its lower degree of availability, this alternative was not the one selected. MA4 provided the best-balanced configuration, exhibiting low cost and tardiness. The most expensive process configuration was provided by agent MA2, since the AM service provider the agent represents is the most expensive one  
Figure 8: Test Case 1—Design Agent Graphical User Interface (GUI) with technical data and information from Machine Agents.  
Figure 7: Test Case 1—Part geometry analysis tool integrated with a commercial Computer-Aided Design (CAD) system.  
and this particular configuration is related to the lowest possible layer thickness and the highest infill density, leading also to very high tardiness.
## 3 Results and Discussion
### Signal Recognition Based on Deep Learning
A dropout layer with a dropout rate of 0.5 was added during training to prevent overfitting, and the dropout operation is given by  
\[O_{dropout}[i]=\left\{ \begin{matrix}0&with\ probability\ p\\ X[i]/(1-p)&with\ probability\ 1-p\end{matrix} \right. \tag{14}\]  
where \(p\) is the dropout rate, \(X[i]\) is the \(i\)-th element of the input features, and \(O_{dropout}\) is the output after dropout, which reduces the model's dependency on specific neurons. Figure 5: Application of deep learning in recognizing grasped objects. (**a**) A schematic diagram of the convolutional neural network (CNN) structure used for recognizing different grasped objects. (**b**) Loss function and accuracy curves of the CNN on the training and validation sets during the training process. (**c**) Confusion matrices showing the training and testing sets for the object recognition tasks. Figure 5b illustrates the loss and accuracy curves for both the training and validation sets. After 80 epochs, the loss decreased, and the accuracy increased to satisfactory levels. Figure 5c presents the confusion matrix for the training and test sets of the dataset comprising various grasped objects, where the horizontal axis denotes predicted labels, and the vertical axis represents true labels. Following 80 epochs, the accuracy of the training set reached 99.88%, while the accuracy of the test set reached 99.60%, indicating a high level of recognition accuracy for the model. This is consistent with recent findings on the effectiveness of machine learning-assisted sensors for motion and gait recognition [60] and is also in line with the theoretical basis related to tactile recognition [61]. Further, we expanded the range of participants. The selected participants' information is listed in Table S3 of the supplementary document, consisting of five males and three females, for a total of eight people. The participants' ages ranged from 20 to 30. Each participant tested each task 100 times with their right hand. The data obtained were used to build a dataset for different populations. Given the increased complexity of the samples, we appropriately increased the complexity of the CNN. The number of neurons in each layer was increased to 128, the stride of each convolutional layer was adjusted to 1, the kernel size of the pooling layer was adjusted to 2, and the number of training iterations was increased to 150. Finally, as can be seen in Figure S1, the classification accuracy of the training set reached 95.30%, and that of the test set reached 93.75%. It can be observed that after increasing the complexity of the dataset, the accuracy rate decreased slightly.
## 4 Weighted Sequential Unsupervised Domain Adaption Framework
### WS-UDA Step 2  
This step involves initializing the shared feature extractor with the weights obtained in the initial adversarial learning step, with the objective of extracting deeper features beneficial for the target domain. Through retraining, the aim is to leverage improved generalization to the target domain. Additionally, starting with information from both the source and target domains is expected to enhance the overall representation, providing a more robust foundation compared to scenarios without this retraining step. The process mirrors the first step, except that the previously learned weights serve as initialization for the shared-feature extractor. This is depicted in Figure 2, indicating that learning shared features is more effective with this initialization, as the shared feature extractor begins with higher proximity to the target domain. Consequently, the target domain is more consistently represented, leading to enhanced model performance. Experimental results compared to state-of-the-art methodologies demonstrate the effectiveness of WS-UDA by positively impacting performance.  
Figure 2: WS-UDA’s architecture involves two steps. In Step 1, an adversarial-based UDA is trained to acquire the weights for the shared-feature extractor. In Step 2, the shared-feature extractor is more purposefully trained, starting with the initialization from Step 1.  
```
Data:\(k\)\(\epsilon\) the Number of domains (\(N\)); unlabeled samples from all domains (\(U\));labeled samples from source domain (\(X\)) Result: Shared feature representation Initialization; whilenot convergencedo
1 Domain discriminator classifier iterations; for\(it=1\)to\(k\)do
2 Initialization of Domain discriminator classifier loss by 0; forall\(d\)\(e\) all domainsdo
3 Sample a mini-batch \(x\sim U_{d}\);
4 Shared feature = weights of shared ;
5 Accumulate the domain discriminator classifier loss;
6 Update domain discriminator classifier parameters;
7 Main iterations;
8 Initialization of total loss by 0; forall\(d\)\(e\) all source domains (labeled)do
9 Sample \((x,y)\);
10 Shared feature = weights of shared feature network;
11 Compute the label predictor loss;
12 Update total loss by the label predictor loss;
13
14 forall\(d\)\(e\) all domainsdo
15 Sample \(x\sim U_{d}\);
16 Shared feature = weights of shared extractor;
17 Update total loss by the anticorrelated of the domain discriminator classifier loss;
18 Update the weights of shared extractor, label predictor ;
19
20 return;
```  
**Algorithm 1**Adversarial-based UDA algorithm. WS-UDA's Step 1.
## 4 Accuracy Evaluation of the Double Coordinate Free-Interface Mode Synthesis Method  
First, the modal characteristics of the irregularly-shaped bridge are calculated based on the traditional finite element analysis theory, where the eigenvalue equation is solved by a MATLAB program based on the construction of stiffness and mass matrices. In order to verify the accuracy of the double coordinate free-interface mode synthesis method, the first 20 free interface main modes and the corresponding residual adhesion modes are selected as the modal matrix for the first coordinate transformation to calculate all the modal characteristics. The computational time of the double coordinate free-interface mode synthesis method decreases from 343 s to 125 s by use of the traditional finite element analysis method. The first 10 modal frequencies of the whole structure are calculated and listed in Table 2.  
As can be seen in Table 2, the modal frequencies obtained by the double coordinate free-interface mode synthesis method are close to the results of the finite element analysis, and the relative errors are negligible. These results indicate that the double coordinate free-interface mode synthesis method is effective to solve the modal characteristics of the irregularly-shaped bridge.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bearing Positions** & **Bearing Conditions** & **Bearing Positions** & **Bearing Conditions** \\ \hline C1 & Double fixed bearing & C4 & Double fixed bearing \\ C2 & Double fixed bearing & C5 & Single vertical bearing \\ C3 & Single vertical bearing & C6 & Double fixed bearing \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bearing conditions of the typical irregularly-shaped bridge.  
Figure 5: Substructure partition of the typical irregularly-shaped bridge model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode Order** & \
\begin{tabular}{c} **Traditional Finite** \\ **Element Method** \\ \end{tabular} &
\begin{tabular}{c} **Free-Interface Mode** \\ **Synthesis Method** \\ \end{tabular} & **Relative Errors (\%)** \\ \hline
1 & 4.609 & 4.609 & 2.389 \(\times\) 10\({}^{-7}\) \\
2 & 5.931 & 5.931 & 8.425 \(\times\) 10\({}^{-7}\) \\
3 & 6.043 & 6.043 & \(-1.542\times\) 10\({}^{-8}\) \\
4 & 7.837 & 7.837 & 4.734 \(\times\) 10\({}^{-6}\) \\
5 & 8.017 & 8.017 & 5.827 \(\times\) 10\({}^{-6}\) \\
6 & 11.288 & 11.289 & 1.757 \(\times\) 10\({}^{-5}\) \\
7 & 11.749 & 11.749 & \(-4.183\times\) 10\({}^{-9}\) \\
8 & 17.057 & 17.057 & \(-2.392\times\) 10\({}^{-10}\) \\
9 & 17.783 & 17.783 & 8.464 \(\times\) 10\({}^{-5}\) \\
10 & 19.524 & 19.524 & \(-4.200\times\) 10\({}^{-11}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The first 10 natural frequencies (Hz).
# Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
## 3 State-of-the-Art Approaches for MAC-Protocols Optimization
### MAC Scheduling in Cross Layer Design

Table 5: _Cont._considering spatial multiplexing zero-forcing Bell-labs layered space-time (ZF-VBLAST) via a Multiple-Input and Multiple-Output (MIMO) time-varying flat-fading channel. The authors of [61] propose a novel Full Duplex MAC protocol named priority-based multiple access (PBMA) which is based on prioritized messaging between different nodes. Researchers have stated that there are various challenges in CLD; among them is the multi-hop communication in WSN, which makes it difficult to create a simple CLD design. In the work of [62], three types of developments are made: the first one is optimizing the transmission power of nodes, the second one is selecting relay nodes, and the third one is synthesizing the cross-layer method. This approach is applied in Wireless Body Area Network (WBAN). In the work of [63], a hybrid fuzzy and ant colony optimization method is proposed. Cluster head selection is based on three criteria: residual energy, number of neighboring nodes, and link communication quality. The concept of clustering uses the criteria to reduce the number of nodes in closer clusters to avoid hotspots. This makes the protocol more energy-efficient. For the routing layer, the algorithm uses four criteria: distance from the current cluster head and main station, residual energy, the length of the queue, and the reliability of delivery. In this study, the MAC scheduling relies on information provided from the routing layer, making the design a CLD design. Table 6 presents a summary of cross-layer-based approaches that have been proposed to enhance the performance of the MAC protocol for WSN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Paper** & **Approach** & **Type** & **Contribution** & **Results** \\ \hline \multirow{8}{*}{[59]} & & & Provides a multi-layer design & \\  & & & for a for a self- & \\  & & & healing, multihop, and & \\  & & & self-formation network to & \\  & & & minimize the time of the call & \\  & & & setup. This implements & \\  & & & collision-free & \\  & & & communication and reuses the & \\  & & & empty slots of the Time & \\  & & & Division Multiple Access & \\  & & & (TDMA) protocol, which & \\  & & & results in a low throughput & \\  & & & and high delay & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Machine learning-based approaches. Figure 3: Conceptual design of cross-layer network.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Ship Detection Dataset: 52-SHIPS  
To the best of our knowledge, no ship detection Sentinel-2 datasets for both moving and static ships with pixel level annotations has yet to be published. We introduce a novel ship detection dataset made up of littoral and harbor regions images.  
This dataset includes 16 L2A images of coastline, ports, and the Suez canal. Figure 4 shows the geographical distribution of the data. The images are of size 1783 \(\times\) 938, cover 167 sq. km each, and are annotated at the pixel level with a total of 1053 distinct ships. We also provide earth/water masks for these images, rasterized from OpenStreetMap layers.  
The ships are from various size, with areas ranging from 100 m\({}^{2}\) (e.g., pleasure boats, small fishing ships) to more than 5000 m\({}^{2}\) (e.g., cargo ships). Since the Sentinel 2 mission does not provide high sea tiles, the images are taken near coasts and we annotated mooredships and ships at sea separately. Our dataset also provides images taken under different weather conditions, including turbulent seas, clouds, or sun glint. Thus, the complex environment surrounding ships in our dataset makes it challenging for ship detection. Several samples are shown in Figure 5.  
We rasterize OpenStreetMap water layers (ocean, major rivers, canals) on the 16 geog-referenced images to produce binary masks of water. These layers sometimes have the contours of peers, jetties, but the annotation of these entities as land is not insured.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 6 Results
### Impact of Reflectivity  
All previous results have considered two different ceiling materials. In this subsection, the impact of the distinct reflectivity values of wood and plaster is statistically assessed. These two materials were selected because they can be easily found in realistic scenarios. Nonetheless, this methodology is applicable to any material regardless its reflection pattern. Table 2 shows the \(p\)-values associated to each one of the scenarios.  
The presented table provides the numerical results of carrying out [PERSON]'s \(t\)-test. Only sufficiently small \(p\)-values are shown since they indicate that considering different materials in the simulation environment produces statistically distinct results. It can be observed that the outcomes of the joint MAC/PHY simulation procedure proposed in this work suggest that there are significant differences between the two considered materials in some cases. It can be also observed that this differences were very slight for Scenario 1. This occurred because the essayed reflectivities, although they are not very similar, allowed all the nodes to conserve their mutual visibility regardless of the ceiling's material taking into account the scenario's geometry. Nevertheless, Scenario 2, which is a priori the most radius-sensitive configuration, presented a significant amount of cases in which it is statistically proven that there is a difference between plaster and wooden ceilings. Finally, the analysis showed that Scenario 3 had more differences than Scenario 1, but less than Scenario 2 since there is only a single hidden node. These results demonstrate that the network performance metrics are susceptible to be affected by the physical properties of the scenario.
## 2 Problem Description
Such a function would yield a more accurate assessment of the proximity between identified and genuine target classes. In its classification loss, Sparse R-CNN employs focal loss. The introduction of focal loss was primarily aimed at mitigating the challenge of imbalanced hard and easy samples. Given the abundance of candidate boxes generated in detection, coupled with the paucity of positive samples, a class imbalance dilemma emerges. Nonetheless, incorporating focal loss in Sparse R-CNN gives rise to certain issues. Notably, focal loss tends to excessively concentrate on intricately classifiable samples. This predicament becomes pronounced when the model attains advanced training stages, resulting in a decline in performance. To tackle the abovementioned challenges, this paper presents an image object detection methodology grounded in a feature pyramid framework. This approach rectifies the deficiencies in the multi-scale feature extraction network employed by Sparse R-CNN, consequently elevating the expressive capacity of multi-scale features within Sparse R-CNN. Furthermore, the classification loss incorporated in this methodology leverages a gradient density loss function. This function is adept at providing a more accurate assessment of classification loss concerning detection boxes. In this work, we make the following key contributions and introduce novel aspects:  
* Enhanced feature pyramid network: We propose an advanced feature pyramid network that significantly enhances multi-scale feature extraction and fusion capabilities. This innovation reduces information loss during feature fusion, leading to improved feature representations. * Gradient density loss function: Our introduction of the gradient density loss function, as a replacement for the traditional focal loss, offers a more precise assessment of object detection classification losses, particularly for challenging samples. * Comprehensive evaluation: We rigorously evaluate our method on four well-established public object detection datasets, including MS COCO 2017, MS COCO 2014, Pascal VOC 2007, and Pascal VOC 2012. Substantial improvements in average precision are achieved compared to existing methods. * Enhanced small-scale target detection: The application of the enhanced feature pyramid network results in superior detection capabilities for small-scale objects, while maintaining high accuracy for larger and medium-sized targets. * Visual comparisons: We present visual comparisons to demonstrate the practical effectiveness of our enhancements.
## 4 Measurement Results and IL Prediction
### Predictions of the DM and CM IL  
Based on the IL expressions derived in Section 4.2, the DM and CM IL of two three-phase EMI filters under various operating conditions of the motor drive system are predicted.  
The first filter considered for the analysis is a single-stage filter sample. Figure 12 shows the frequency behavior of the DM ILs obtained by connecting three different pairs of ports of the filter and the CM IL. The standard modal IL evaluated with symmetrical 50 \(\Omega\) load/source impedances is also plotted for comparison (black dash curves).  
For the DM, it can be observed that the offline DM IL (black curve) significantly underestimates the actual (i.e., when the motor drive system is working) effectiveness of the filter in the frequency band below 10 MHz. Hence, the online DM impedance shall be used in order to achieve reliable estimates of the in-circuit DM IL. Conversely, the specific operation condition (power ON, motor drive system OFF, and different switching frequencies) does not significantly affect the DM IL. The IL measured according to the standards significantly overestimates the actual DM IL from 500 kHz to 10 MHz.  
On the other hand, the CM IL is not influenced by the operating conditions, confirming the possibility of using the offline CM impedance to estimate the CM IL. Likewise for DM, the CM IL measurement obtained in the test setup foreseen by the standard does not provide reliable information on the actual filter performance, since this setup can overestimate (below 3 MHz) or underestimate (above 3 MHz) the actual CM IL.  
For comparison, Figure 13 shows the results obtained for a two-stage filter. Again, for DM IL, the estimated value based on the DM impedance measured offline does not correspond to the actual DM IL. The setup foreseen by the standard leads to overestimates of the actual DM IL from 200 kHz to around 10 MHz. For the CM IL, the insensitivity to the working conditions is confirmed. Additionally, the CM IL measured according to the standard underestimates the actual CM IL of the filter from 10 kHz to around 15 MHz.  
Figure 12: One-stage filter: DM ILs between (**a**) port 4–5, (**b**) port 5–6 and (**c**) port 4–6, and (**d**) CM IL.  
Figure 13: Two-stage filter: DM IL between (**a**) port 4–5, (**b**) port 5–6 and (**c**) port 4–6, and (**d**) CM IL.