esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Empirical Mode Decomposition
In practical applications, it is typically adjusted through trial and error, and we achieved this by choosing values from 10 to 30. In the EMD algorithm, the \"maximum number IMFs\" setting determines the maximum number of IMFs the algorithm will extract from a signal. This setting is a crucial hyperparameter that directly affects how EMD works and its outcomes. The maximum number of IMFs was also set using a trial-and-error method. In this study, the maximum number of IMFs varied between 4 and 8. After extracting the IMFs and the trend component, the variance contribution rate (VCR) values of all the sub-series were calculated. This value measures the extent to which each sub-series explains the variance of the original time series. Although simple and practical, the EMD method also has some disadvantages. For example, EMD-based techniques are sensitive to the noise contained in the signal. A small change in the signal-to-noise ratio of the signal can lead to significantly different signal decomposition results [96]. The EMD method also has a mixing problem [97], which is caused by the mixing of frequency components between various IMFs. These issues should be taken into account when using the EMD algorithm.
## 4 Experiments
### Metrics  
Three evaluation metrics commonly used in previous key event detection studies [7] were used: precision, recall, and f-measure. Precision was used to assess the independence of the events detected by each cluster. Recall was used to assess the detected completeness of the key events. F-measure is the harmonic mean of precision and recall.  
Specifically, let the predicted \(l\) document clusters be \(\mathcal{K}=\{K_{1},K_{2},\ldots,K_{l}\}\); the subsequent calculation needs to consider the top-5 articles in the document cluster \(K_{i}\), so the number of articles in each cluster needs to be \(>\)5, where each \(K_{i}\) is a set of articles ranked according to the key event matching degree, and the higher the ranking indicates the higher the relevance to the key event. Assuming that there are \(N\) real key events in \(D\), the existence of \(N\) real key events is denoted as \(G=\{G_{1},G_{2},\ldots,G_{N}\}\). If more than half of the top-five articles in \(K_{j}\) belong to \(G_{i}\), then \(K_{j}\) and \(G_{i}\) are considered to match each other, noted as \(Match\big{(}G_{i},K_{j}\big{)}=1\), and \(Match\big{(}G_{i},K_{j}\big{)}=0\) if there is no match. The equations for calculating precision and recall are defined below.  
\[\begin{split} check(G_{i})\bigg{\{}\begin{array}{l}1,\;if\; \exists\;C_{i}\;let\;Match\big{(}G_{i},K_{j}\big{)}=1\\ 0,\;else\end{array}\\ \text{ }\\ precision\;=\frac{\sum_{G_{i}}check(K_{i})}{l}\\ \text{ }\\ recall\;=\frac{\sum_{G_{i}}check(K_{i})}{N}\end{split} \tag{16}\]  
F-measure is the harmonic mean of precision and recall.
## 4 Discussion
For the three load cases, CL-2, SL-1, and CSL-2 configurations were taken into account. Considering that for all combinations these configurations have shown low von Mises stress values, one could choose the minimal thicknesses levels as the parameters used for the optimal structures: t\({}_{\text{FRI}}\) = 1.5 mm and t\({}_{\text{SRI}}\) = 1 mm for CL-2 and SL-1 configurations. Meanwhile, for the CSL-2 configuration, a minimal thickness for the first region equal to t\({}_{\text{FRI}}\) = 1.5 mm combined with any of three thicknesses of the second region could be taken into account. A CSL-2 structure presenting first region thickness of 1.5 mm and a second region thickness of 1.25 mm was considered. The DOE optimization technique then proved its effectiveness in determining structures having light weight and high stiffness. Further studies using different optimization algorithms will be conducted in order to obtain optimal structures with several variations of sections and thicknesses like the Moving Least Square (MLS) and the Kriging methods and sequential quadratic programming (SQP) techniques [73; 74]. These three optimal structures were then additively manufactured by the material jetting process and more particularly with the PolyJet technique using VeroWhite material on a Stratasys Objet260 Connex3 machine (see Figure 17). Figure 17: Structures (**a**) CL-1, (**b**) SL-2, and (**c**) CSL-1 printed with the PolyJet technique on a Stratasys Objet260 Connex3 machine with VeroWhite material. All scale bars: 50 mm.
# Prestack Seismic Inversion via Nonconvex L\({}_{1-2}\) Regularization
## 3 Synthetic Examples  
To verify the effectiveness of our proposed inversion methodology (hereafter, L\({}_{1-2}\)), we compare the results of our technique against those of the conventional basis pursuit method (BP) and the prestack AVA inversion method [6]. For single-channel experimental analysis, our model test data consists of the observed logging curve for a given work area, the related anti-noise experimental analysis, and the sensitivity of those inversion results to the angle gathers. First, the logging data are resampled and segmented at a sampling interval of 2 ms to obtain the P velocity curve (red), the S velocity curve (blue), and the density curve (green) (Figure 1); the black curve represents the low-frequency models of those same three parameters. By inputting a zero-phase Ricker wavelet with a dominant frequency of 35 Hz into the Zoeppritz equation forward model, we generate a total of 15 prestack angle gathers with an angle range of 3\({}^{\circ}\) to 45\({}^{\circ}\) at an interval of 3\({}^{\circ}\) (Figure 1d) [29]. The synthetic angle gathers record has significant AVO characteristics.
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 4 Subjective Logic Trust Model
### Simulation Environment  
The trust model is implemented using Contiki OS version 3.0, and the evaluation is performed through the Cooja emulator. For a deployed architecture, we run a set of experiments that follow the same methodology to ensure their valid comparability. The phases of running experiments start by utilizing the Cooja simulator to:  
* Select a radio medium model such as distance/constant loss Unit Disk Graph Model (UDGM), Directed Graph Radio Medium (DGRM), or Multi-path Ray-tracer Medium (MRM).
* Select a note type from the supported motes such as Z1, Wismote, MicaZ, or SKY more and select the number of motes. In our study, we have used Wismote and SKY mote.
* Select a network topology such as uniformed 2D-Grid or random positioning. More detail about our selected topology comes later.
* Select the transmission range for the populated nodes.  
Then, the modules of the trust model are implemented in Cooja and follow Algorithm 1 for constructing the model by computing and updating trust-related tables. After selecting the performance evaluation metrics, Cooja simulation scripts and other tools such as Gunplot were used to collect, save, analyze, and display simulation results.  
Regarding the network topology, we consider the topology shown in Figure 4 as a physical topology for the scenarios under study. The network consists of 4 nodes as FCs and 16 nodes as members of the FCDs. The nodes are deployed in an area of size 150 m \(\times\) 150 m. The UDGM is used with a transmission range of 50 m and an interference range of 100 m. At the transport layer, UDP is used as a default protocol. The Wisemote platform is used for experimenting with Fog coordinators, while the Sky mote platform is used for experimenting with members of FCDs.  
The simulated environment consists of three FCDs. The first FCD has 10 members (i.e., IoT devices) and one FC (i.e., node id 27). There are 3 members in each of the other two FCDs having FCs 6 and 17. Various experiments have been conducted to evaluate the evolution of trust based on different scenarios. Each point in the figures is the result of five simulation runs. Table 7 shows the design parameters used in the simulation.  
Figure 4: Network Topology.
## 4 Design of an Optimization Framework for the IMS  
In this section, the methodology proposed for tackling and solving the offline truth tables estimation problem is detailed. As mentioned, a generalized approach is derived to define truth tables to be used in the Filter and Queue module for prioritization of actions. In particular, the estimation problem is formulated as an optimization task where the objective is to find a set of rules to manage information flow between vehicle and driver that minimizes both the driver workload and the queuing of the undelivered messages/notifications/indications to the driver. In this optimization task, whose overall scheme is depicted in Figure 2, the evaluation of the effects of application requests on driver cognitive load and on actions waiting queue plays a key role. Herein, two indexes \(J_{Dw}(\mathbf{x})\) and \(J_{Aq}(\mathbf{x})\), on the basis of actual DVE states and messages to be forwarded to the driver, are used to estimate the driver workload and actions queue, respectively. These two indexes are exploited to identify the functional values of truth tables by searching the \(x_{i}\in\mathbb{B}\) action-specific multipliers, with \(i\in\{1,\ \ldots,\ m\}\), belonging in a vector of decision variables \(\mathbf{x}\in\mathbb{B}^{m}\), which simultaneously minimize \(J_{Dw}(\mathbf{x})\) and \(J_{Aq}(\mathbf{x})\). These are two conflicting objectives that make the process a multi-objective optimization problem, addressed in the next sections.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
###### Abstract  
Software-defined radio (SDR) devices have made a massive contribution to communication systems by reducing the cost and development time for radio frequency (RF) designs. SDRs opened the gate to programmers and enabled them to increase the capabilities of these easily manipulated systems. The next step is to upgrade the reconfigurability into adaptability, which is the focus of this paper. This research contributes to improving SDR-based systems by designing an adaptable packet communication transmitter and receiver that can utilize the communication window of CubeSats and small satellites. According to the feedback from the receiver, the transmitter modifies the characteristics of the signal. Theoretically, the system can adopt many modes, but for simplicity and to prove the concept, here, the changes are limited to three data rates of the Gaussian minimum shift keying (GMSK) modulation scheme, i.e., 2400 bps GMSK, 4800 bps GMSK and 9600 bps GMSK, which are the most popular in amateur small satellites. The system program was developed using GNU Radio Companion (GRC) software and Python scripts. With the help of GRC software, the design was simulated and its behavior in simulated conditions observed. The transmitter packetizes the data into AX.25 packets and transmits them in patches. Between these patches, it sends signaling packets. The patch size is preselected. Alternatively, the receiver extracts the data and saves it in a dedicated file. It directly replies with a feedback message whenever it gets the signaling packets. Based on the content of the feedback message, the characteristics of the transmitted signal are altered. The packet rate and the actual useful data rate are measured and compared with the selected data rate, and the packet success rate of the system operating at a fixed data rate is also measured while simulating channel noise to achieve the desired Signal-to-Noise Ratio (SNR).  
SDR; small satellite; adaptive; GNU Radio; data rate; packet; AX.25
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 2 Literature Review
### Over-Sampling  
In practice, the class imbalanced dataset problem usually occurs since the number of data samples in one class are significantly different from those of the other one; say the imbalance ratio is 1:100. For the example of breast cancer datasets, they do not usually contain both the malignant and benign patient classes, denoted as the minority and majority classes, respectively. Without dealing with the class imbalance problem, most machine learning models aim at maximizing the accuracy of its classification rule by ignoring the minority class examples, with the classification of all testing examples being organized into the majority class [6].  
In general, there are three types of solutions to the class imbalance problem, which are algorithm level, data level, and cost-sensitivity methods. Among them, the data level methods based on data sampling techniques are usually considered first since they are used independently of the classifier [6]. Data sampling techniques focus on re-balancing the given training set. Particularly, under- and over-sampling techniques have been used, in which the former is for reducing the size of the majority class, whereas the latter is used for enlarging the size of the minority class. Among them, the synthetic minority over-sampling technique (SMOTE) is one representative method, which has been used as the baseline in many related studies [16].  
The aim of SMOTE is to produce new synthetic examples for the minority class. For example, a minority class instance \(i\) is selected as the basis to create new synthetic data. According to a specific distance metric, usually the Euclidean distance, the number of the neighbors nearest to \(i\) are chosen from the training set, e.g., \(i_{1}\), \(i_{2}\), and \(i_{3}\). Next, a randomized interpolation is conducted to obtain new synthetic data, i.e., \(s_{1}\), \(s_{2}\), and \(s_{3}\).
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
## 2 Principle of the Method  
### Two-Dimensional Digital Image Correlation (2D-DIC)  
The system configuration of a 2D-DIC is shown in Figure 1a, which consists of a camera, an imaging lens, and a group of light sources. Before a 2D-DIC measurement, the speckle pattern was first fabricated onto the specimen surface, and the imaging lens was then adjusted to make the camera focus on the surface. After acquiring the speckle images by using the 2D-DIC system, the in-plane displacement field, i.e., _U_-displacement field and _V_-displacement field, of the specimen can be calculated. As shown in Figure 1b, a region of interest (ROI) in the reference image was first specified and evenly divided into virtual grids. Then, the displacements at each point of the virtual grids can be computed to obtain the full-field deformation. Taking point \(P\) as an example, a square reference subset of (2M + 1) \(\times\) (2M + 1) pixels centered at (\(x_{0}\), \(y_{0}\)) from the reference image is chosen and used to track its corresponding target subset in the deformed image. To track the target subset, the similarity degree between the reference subset and the deformed subset was first estimatedbased on the correlation criterion. Then, the peak position (\(x_{0^{\prime}}\), \(y_{0^{\prime}}\)) of the similarity degree distribution could be identified, which is also the center of the target subset. The differences in the positions of the reference subset center and the target subset center yield the in-plane displacement vector. After acquiring the in-plane displacement fields, the in-plane strain fields on the test object surface can also be estimated using numerical differentiation.
###### Abstract
Obtained results are performed by real experiments in FIT IoT-LAB, an IoT-dedicated platform in France [10]. As demonstrated in Section 6.3, the overall E2E transmission delay performed by LC-DEX is only 0.32 s, comparing with previous Elliptic Curve Cryptography (ECC) solutions. Regarding energy efficiency, LC-DEX costs only 5.55 mJ for the hole handshake process. As a complementary extension work of our conference paper, we contribute by the following in this present work. Compared with the previous HIP-based solutions, the innovations and contributions of LC-DEX are as follows:  
1. An optimization of our previous proposed compression of HIP DEX protocol header [9] over an adaptable 6 LoWPAN compression header;
2. A minimization of the optional signaling packets (NOTIFY and CLOSE_ACK) and a proposition of a replacement technique in a novel distribution model;
3. An efficient minimization in the overall E2E transmission delays of the HIP DEX handshake;
4. An important reduction on the computational and communication energy costs of the handshake;
5. Alleviation of HIP peers from the scalar multiplication of the key generation by the integration of a trust third party;
6. Proposition of a new lightweight security association establishment scheme for LC-DEX;
7. Proposition of a HIP DEX opportunistic mode during handshake packets' exchange with the introduction of a Domain Name System service;
8. Studies of the security threats and requirements of the proposed solution. This paper presents the following sections: We present the problem statement in Section 2. In Section 3, we present the background of this work regarding the authentication in IoT networks based on HIP solutions and the improved compression model in comparison with the obtained results in the conference paper. Section 4 is dedicated to the proposed minimization in the signaling packets and the novel proposed distribution scheme. The evaluation and experimental results are presented in Section 6. We give an overview of the memory requirements of our implementation in Section 7. The security considerations are explained in Section 8. Section 9 presents the discussion of the results. Finally, we end this work with the conclusion and future work in Section 10.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 4 Experimental Setup
### Evaluation Criteria  
Anomaly detection is an unsupervised learning problem, and model evaluation in this scenario is challenging. To effectively measure and compare the performances of different models, the area under curve (AUC) was chosen as the main evaluation index, as it is the most commonly used metric for one-class problems. The AUC is defined as the area under the receiver operating characteristic (ROC) curve, which is independent of the selected threshold and can provide an assessment of the overall performance of the given classification model.  
However, when the sample proportion changes, the insensitivity of the AUC makes it difficult to observe the changes in model performance. The precision-recall curve (PRC) is a useful measure of prediction success when the classes are very imbalanced. In practical applications, the data are usually severely unbalanced, so the PRC can help to understand the actual effects of the classifier and then improve and optimize the model on this basis. Therefore, in the experiment, the ROC was used to judge the advantages and disadvantages of the given classifier, whereas the results displayed by the PRC were used to measure the classifier's ability on unbalanced data.
# Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
## 3 Waste Printed Circuit Boards Economic Feasibility Assessment (WPCB-EFA)
### Detect IC Regions  
To detect the ICs in the WPCB images, we evaluated the transfer learning strategy from the pre-trained YOLOv3 model. In order to deal with the few-shot learning problems, one approach is to use prior knowledge and perform a data augmentation procedure [14]. Therefore, we conducted data augmentation in the PCB DSLR dataset to generate smaller sub-images. A sliding window of 416 \(\times\) 416 pixels generated sub-images by passing through the original image. The training process used only sub-images containing at least a fraction of some IC. After data augmentation, the new dataset had 5347 RGB images containing ICs, each one with a size of 416 \(\times\) 416 pixels.  
Since the IC detector works with chunks of full-resolution WPCB images, the reverse process is necessary to obtain the entire WPCB image's IC bounding boxes. Given that the WPCB will be in a conveyor belt, we defined the region of interest as the conveyor belt area. Figure 2 illustrates this region of interest. In summary, we removed the region outside the conveyor belt. Since all of the PCB DSLR images were from the same perspective, the region of interest's coordinates were defined to remove the region outside the conveyor belt.  
Figure 1: Diagram of the proposed method evaluation.  
Therefore, we applied a sliding window approach in the region of interest, similarly to the process of building the training set. The YOLO model processes every image chunk gathered by the sliding window. Then, the YOLO prediction for each image chunk is combined over the original size image to obtain the bounding boxes over the entire WPCB image. Figure 3 shows the prediction result for a full-size image from the PCB DSLR Dataset.  
The sliding window stride allows multiple bounding boxes for the same IC. As we were concerned with the IC area, standard non-maximum suppression (NMS) algorithms did not apply to this problem. Therefore, all the predicted bounding boxes with a score higher than the threshold (0.8) were used to produce an image mask. Figure 3 shows the IC image masks for the YOLO predictions and ground truth bounding boxes. Based on the IC image mask, the IC pixel area was calculated.
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
## 3 Metasurface Lens Design
### Feeding-Antenna Array Design  
In order to get a better QZ performance, half the power from the feeding antenna array was designed to illuminate on the metasurface lens by adjusting the focal diameter ratio. As shown in Figure 5, a planar array of a 2 \(\times\) 2 square patch array printed on the Rogers RO4003C substrate was designed to feed the metasurface lens and to demonstrate its performance in plane-wave generator systems. The detailed dimensions of the 2 \(\times\) 2 square patch array are listed in Table 2.  
As depicted in Figure 5(a), from 24 GHz to 29 GHz, the reflection coefficient was below \(-10\) dB. The magnitude and phase distribution at the reduced distance of 1.2 m, along with the radiation directions in the E-field of the feeding-antenna array, are presented in Figure 5(b). The feeding antenna had a variable phase pattern from \(-60^{\circ}\) to \(5^{\circ}\) accompanied by a stable magnitude from \(-52.5\) to \(-53.5\) dB, which was regarded, approximately, as the spherical wave. Moreover, the phase varied sharply in the QZ edge, which would result in a non-uniform phase distribution; the metasurface lens was designed as the passive phase shifter for the plane-wave condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{L_{A}}\) & \(\mathbf{W_{A}}\) & \(\mathbf{D}\) & \(\mathbf{L_{R}}\) & \(\mathbf{H_{1}}\) & \(\mathbf{H_{2}}\) \\ \hline
405 mm & 39 mm & 8 mm & 2.4 mm & 1.575 mm & 0.254 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of feeding-antenna array.  
Figure 4: Schematic diagram of the phase distribution at position \(i\).  
Figure 5: Configuration of the 2 \(\times\) 2 square patch array: (**a**) front view and (**b**) side view.