esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hyperspectral Image Acquisition  
In this experiment, the hyperspectral imaging system shown in Figure 1 was used to collect the spectral data of three varieties of blueberry samples, and the spectral range was 935\(\sim\)1720 nm. The major components comprising the imaging system included a hyperspectral imager (Specim FX17, Specim, Oulu, Finland) capable of capturing 224 spectral channels with a resolution of 8 nm; an illumination section utilizing six 20 W halogen lamps; a displacement stage and stepper motor for sample movement control; and a computer for system control and data processing.  
To eliminate the effect of random thermal fluctuations on image quality, the hyperspectral imaging system needs to be thermally stabilized by 30 min of preheating before the experiment. In addition, black and white correction of the acquired raw images using Equation (1) is required to eliminate the effect of camera dark current.  
\[R=\frac{I_{raw}-I_{dark}}{I_{white}-I_{dark}} \tag{1}\]  
where \(R\) is the corrected hyperspectral image, \(I_{raw}\) is the raw image, \(I_{white}\) is the all-white image and \(I_{dark}\) is the all-black image [23]. After black-and-white correction of the original image, the blueberry region was selected using the 'Region of Interest (ROI)' tool of ENVI 5.3.1 (Exelis Visual Information Solutions, America), and the 'Build Mask' plug-in was used to generate and apply a binary mask to remove background interference and retain blueberry data. Finally, the ROI of the blueberry was marked by the 'growth' module of ENVI, and the average spectrum of the pixels in the region was calculated to obtain the average spectral data representing the spectral characteristics of the blueberry.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 10 Summary  
Contemporary cellular communication networks can be subject to a variety of serious security threats. To achieve fast network attack procedures, the initial phases of connection setup are not secured by the authentication mechanisms and are thus much more vulnerable to the attacks. With the use of beamforming techniques in 5G networks, users can be tracked by stealing their location information from the initial beam access reports, with increased precision due to the use of new millimeter-wave frequency bands. Thus, the potential to compromise security and privacy in the new generations of mobile communications will probably increase in the near future. This survey paper provided an overview of 4G/5G technology from the point of view of the physical layer, and summarized the most important security threats related to the physical layer, ranging from jamming, spoofing, and message manipulation to beamforming-related attacks. We also reviewed possible countermeasures such as machine learning, location-empowered, and behavior-based techniques for the detection of false base stations--one of the key enablers of active attacks against cellular networks. The main outputs of this survey paper provide insights into the hot topic of security threats and countermeasures at the PHY layer in 4G/5G cellular networks. This allows researcher and industry experts to identify key threats such as jamming, eavesdropping, spoofing, and more. These insights can serve as a foundation for defining and developing optimal strategies to enhance the security of 4G/5G cellular networks. All authors contributed equally to this paper. All authors have read and agreed to the published version of the manuscript. This study has been supported by the project \"Robust 5G networks\" No. VJ03030044 by the Ministry of the Interior of the Czech Republic under the Impakt1 program. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l}
3 GPP & Third Generation Partnership Project \\
4G & Fourth Generation \\
5G & Fifth Generation \\
5 GC & 5G Core Network \\
6G & Sixth Generation \\ ACK & ACKnowledged \\ AKA & Authentication and Key Agreement \\ AMM & Alternating Majorization-Minimization \\ ARQ & Automatic Repeat reQuest \\ AOA & Angle of Arrival \\ AWGN & Additive White Gaussian Noise \\ BA & Beam Alignment \\ BCD & Block Coordinate Descent \\ BFR & Beam Failure Recovery \\ BPSK & Binary Phase Shift Keying \\ BS & Base Station \\ BWP & Bandwidth Part \\ CCD & Cyclic Coordinate Descent \\ CFI & Control Format Indicator \\ CFO & Carrier Frequency Offset \\ \(C/\)M-CCD & Conventional/Modified CCD \\ CORESET & CControl REsource SET \\ CNN & Convolutional Neural Network \\ \end{tabular}
# Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
## 2 Materials and Methods
### Machine Learning Methodology  
Various ML models were evaluated for their suitability in the task of predicting injuries. A brief description of these models is given below. We tested logistic regression [24] which is an extension of the linear regression model for classification problems. Logistic regression (LR) models the probabilities for classification problems with two possible outcomes. The response variables can be both categorical or continuous and an iterative maximum likelihood procedure is implemented to fit the final model. Decision Trees (DTs) [40] were also investigated for their suitability. DTs are a non-parametric supervised learning method that is simple to understand and interpret. They require little data preparation and use a hierarchical structure to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).  
Based on their capacity to deal with the overfitting problem that appears in high-dimensional spaces, K-Nearest Neighbor (KNN) [41] as well as non-linear Support Vector Machines (SVM) algorithms [42] were also employed here. In the classification setting, the decision-making mechanism of KNN is driven by a majority vote between the K most similar instances to a given \"unseen\" observation. Similarity is defined according to a distance metric (Euclidean or other) between two data points. Furthermore, SVMs are a set of supervised learning methods that are effective in high-dimensional feature spaces and have been widely used for both classification and regression. SVMs try to maximize the separation performance and at the same time keep margin between the two classes as wide as possible so that other points can still be classified correctly. Finally, the ensemble techniques Adaboost [43] and Random Forest (RF) [44] were also evaluated using DT models as weak learners. Both ensemble techniques create a set of weak learners (decision trees in our case) whose decisions are combined by aggregating the votes received to decide the final class of the test object. Adaboost proceeds sequentially in multiple rounds using the entire training sample and iteratively improves its performance using information from the classification results in the previous rounds. RF builds independent weak learners on random subsets of the training sample and the most popular class is chosen as the final classification output.  
Hyperparameter selection was implemented to optimize the performance of our models and to avoid overfitting and bias errors. Each model was optimized with respect to a number of preselected hyperparameters. (Table 2). The code for the pre-processing of the data, feature selection, implementation and validation of the ML models was implemented in Matlab 2021b (R2021b).
## 1 Introduction
The BER is used to calculate the differences between transmitted data bits in the transmitter and recovered data bits in the receiver. The PMI is the probability of identifying a certain modulation scheme from the attacker's viewpoint. We obtain the PMI from the Convolutional Neural Network (CNN) algorithm, which is trained by BPSK, QPSK, 8-64 PSK, and 8-64 QAM with 0-30 dB SNR AWGN channel I/Q constellation data. From the BER, we evaluate how well noise signaling recovery can be accomplished. From the PMI, we evaluate how well the ring-shaped signaling system can hide a modulation scheme from an eavesdropper. This paper is organized as follows. In Section 2, we provide a literature review of previous studies about covert communication techniques. A presentation of the limitations and preferences when implementing noise-shaped signaling process in real radios is found in Section 3. The proposed ring-shaped signaling system algorithm is presented in Section 4. The necessarily required functions for implementation through SDR are presented in Section 5. The performance evaluations with the BER and PMI are shown in Section 6. Lastly, the conclusion with a discussion of future work is drawn in Section 7.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 1 Introduction
### Valve Lift, Valve Timing, and Valve Duration  
The main function of an engine valve actuation system is to control the gas exchange into and out of a combustion chamber via intake and exhaust valves, respectively. The associated valve lift or travel is typically illustrated in a valve timing diagram (see Figure 2 for an example), where valve timing, valve lift, and valve duration are defined. A valve lift profile describes the valve lift as a function of camshaft angle between its opening and closing. The opening and closing points define the valve timing in the crank angle domain and, thus, the relationship between the lift profile and the rest of the engine components and events such as the piston movement and ignition. Valve lift is often optimized for minimal pumping loss. Often, intake and exhaust valve lifts are the same, but the diameter of the intake valve is larger than the exhaust one to ensure that fresh air can be easily charged into the cylinder. Note that variable valve lift has the potential of throttling the cylinder by reducing the  
Figure 1: More electronic control of inputs into gasoline engine combustion.  
intake valve lift to reduce the pumping losses associated with the conventional throttle. However, this requires very close control of lift to match changes in engine speed and load conditions, which is yet to be fully proven. The overlap between the intake valve opening and the exhaust valve closing is an important secondary parameter that has a major impact on combustion efficiency. The area under a lift profile represents the capacity for gas exchange. For the purpose of classifying valve actuation systems, the following definitions are provided:  
* Valve lift refers to the amplitude, especially the peak value, of the valve lift profile.
* Valve timing refers to the phase shift in crank angle domain of the valve lift profile, especially the valve opening and closing events, such as EO, EC, IO, and IC.
* Valve duration refers to the duration when the valve is kept open, i.e., the span between the valve opening and closing events.  
Modern engines are often equipped with multiple intake and exhaust valves, for example, two intake and two exhaust valves. The valve lift, timing, and duration can be optimized for each individual valve (for example, using VVA technology) to optimize in-cylinder mixing.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 4 Experimentation
### Comparison Experiment  
To further verify the effectiveness of the improved algorithm proposed in this paper for steel surface defect detection, the enhanced EB-YOLOv8 algorithm was compared with  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{_AP\%_} \\ \cline{2-10}  & **Cr** & **In** & **Pa** & **Ps** & **Rs** & **Sc** & **P\%** & **R\%** & \(mAP\)\%** & \(Param\) \\ \hline YOLOv8 & 39.9 & 83.2 & 92.4 & 87.9 & 66.2 & 93.3 & 69.0 & 74.6 & 77.1 & 3006818 \\ YOLOv8+E & 38.1 & 80.9 & 93.9 & 87.0 & 76.4 & 95.7 & 72.4 & 72.5 & 78.7 & 3006874 \\ YOLOv8+B & 38.7 & 81.2 & 93.9 & 86.9 & 72.3 & 94.7 & 70.7 & 74.6 & 77.9 & 3006827 \\ Ours & 42.2 & 82.5 & 92.4 & 88.5 & 80.0 & 95.7 & 75.1 & 72.7 & 80.2 & 3006880 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different experimental modeling results on the NEU-DET dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{_AP\%_} \\ \cline{2-10}  & **Class-0** & **Class-1** & **Class-2** & **Class-3** & **R\%** & **P\%** & \(mAP\)\%** & \(Param\) \\ \hline YOLOv8 & 61.7 & 53.5 & 76.2 & 71.3 & 61.4 & 64.7 & 65.7 & 3006428 \\ YOLOv8+E & 60.1 & 55.4 & 76.7 & 71.1 & 62.3 & 66.9 & 65.8 & 3006484 \\ YOLOv8+B & 61.7 & 63.4 & 76.8 & 71.7 & 60.5 & 72.8 & 68.4 & 3006437 \\ Ours & 60.9 & 56.7 & 77.4 & 69.7 & 60.5 & 69.7 & 66.2 & 3006490 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different experimental modeling results on the SSDD dataset.  
current mainstream defect detection algorithms on both the NEU-DET and SSDD datasets. The experimental results are presented in Figures 9 and 10.  
On the NEU-DET dataset, compared with YOLOv5n [29], YOLOv8, YOLOv10n [29], and YOLOv11n [29] algorithms, our improved algorithm achieves notable \(mAP\) improvements of 5.8%, 3.1%, 5.6%, and 2.1%, respectively, surpassing all mainstream object detection methods. Under the SSDD dataset, the \(mAP\) values of the four models [30] in the comparison figure also improved to varying degrees, with the maximum increase in the \(mAP\) reaching 10.6%. The above comparison tests fully prove that the improved algorithm proposed in this paper has better results in steel surface defect detection, and high-accuracy detection algorithms are more suitable for industrial material defect detection.
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 5 A Holistic Approach toward Next Generation Smart Cities Application Management  
In this section, we present an end-to-end approach for tackling the lifecycle management of the design, development, and orchestration of IoT-based applications over cloud and edge computing resources, targeted to smart city environments. We tackle issues related to the design of IoT applications based on the adoption of cloud-native principles and the development and adoption of generic IoT functions that can serve generic application requirements, and we cover needs that are present in various application domains, the orchestration of IoT applications over dynamic programmable infrastructure, and the appropriate modeling of IoT devices to tackle interoperability issues.  
The proposed approach is in accordance with the four-layer model of a smart city that is presented in Figure 2. In the Infrastructure layer, we consider the management of a programmable network, computer, and storage infrastructure in the various parts of the network, including the management of resource-constrained devices at the edge part. In the Management layer, we consider the development of intelligent orchestration mechanisms, able to tackle the lifecycle of IoT application deployment and runtime management. In the Applications layer, we consider a set of techniques and best practices for developing distributed IoT applications.  
The proposed approach aims to tackle the set of challenges that are identified in Table 1. Convergence aspects of IoT technologies is mainly tackled in the IoT Applications and the Infrastructure Management layer, where the design of generic IoT enablers and Digital Twins can be used to provide interoperable functions that can be embedded by design or on demand in the provided IoT applications. Development of efficient and scalable IoT applications is also tackled in the Applications layer based on the adoption of cloud-native principles and the exploitation of evolving containerization technologies.  
Improved intelligence and automation is supported by IoT applications management mechanisms that are provided by emerging orchestration platforms for cloud and edge computing resources. Efficient IoT data storage is partially tackled in the Infrastructure management part with the provision of storage capabilities at different parts of the infrastructure, as well as the IoT applications development part with the provision of various generic functions that support distributed data management and analysis techniques. Finally, the design of human-centric IoT solutions is also tackled in the Infrastructure management part where the IoT devices can provide advanced interfaces for human-machine interactions.
## 4 Methods
### Configuring Testbed Scenarios in OMF
Two pairs of sender and receiver nodes are communicating with each other over one shared medium. Figure 3: Some representative testbed scenarios from related literature for evaluating network estimation tools. The black arrow represents the measurement data, while the blue and red arrows indicate independent cross-traffic flows. Abbreviations: S, sender; R, receiver; RT, router; CT-S, cross-traffic sender; CT-R, cross-traffic receiver; AP, access point; IN, intermediate node. Testbed 4: Two-hop wireless ad hoc testbed: This type of network configuration is usually used to cover larger areas. The transmission range of the radio is very limited. Some devices might not be able to communicate directly to each other because of their limited radio range. These networks need other intermediate nodes to forward messages. In such cases, intermediary devices act as relays. Figure 3d illustrates communication in the worst-case scenario through two hops before reaching its final destination. The experiment descriptions provided for the testbed setups and scenarios, as illustrated in Figure 3a through Figure 3d, include (i) the node definitions for S, R, RT, CT-S, CT-R, AP, and IN as well as their network configurations including routing entries; (ii) the tool definition files of the cross-traffic generators on nodes CT-S and CT-R as well as their parametrization; and, finally, (iii) regulation of parallel experiment executions (i.e., starting the cross-traffic generator and the probing tool concurrently) and their synchronous termination. Before using the provided experiment description files, the researcher should only configure (i) the names of the research nodes to be used for the experiment; (ii) the settings of the cross-traffic configuration (i.e., rate, packet size, distribution etc. ); and (iii) the settings of their own measurement tool they want to evaluate. To be able to execute the provided experiment description files, the researchers should ensure that the proposed OMF-enabled test network includes the required number of research nodes that are involved in the experiment scenario. Furthermore, in the case of wireless experiments, research nodes should not be out of range.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Pixel Distribution
#### 4.6.2 Efficiency Analysis  
The complete generation process shown in Figure 1 is computationally demanding, with the CGH calculation being the most time-consuming part. The specifications of our computer are as follows: CPU Intel i9-9900K (Intel Corp., Portland, OR, USA) (16 cores) GPU NVIDIA GeForce RTX 2060 (NVIDIA, Taipei, Taiwan.) (1920 CUDA cores), and 32 GB of RAM. The characteristics of the scene in Figure 2a are as follows: 116,090 triangles, 4 light sources, and 1920 \(\times\) 1080 px. For this scene, the computation times (using CUDA for amplitude and phase calculation) are as follows: For 240,000 points, 295 s with double precision and 101 s with single precision. For 1.1 M points, 1600 s and 600 s, respectively. The calculations were performed using a code implemented in C++23. Table 2 summarizes the computation times for each part of the process described in this work.  
For the encryption process (to add random phase and pixel shuffling), the same computer and Matlab R2024b were used. The encryption and decryption times do not exceed 1 s in any case, so the bottleneck in performance lies in the generation of the CGH.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Figure** & **Entropy H(X)** \\ \hline Image from 3D scene 1 & Figure 2a & 7.067178 \\ CGH & Figure 2b & 7.994257 \\ Encrypted CGH & Figure 4a & 7.994281 \\ Image from recovered scene (simulation) & Figure 5a & 5.389448 \\ Image from recovered scene (lab) 1 & Figure 5b & 6.072168 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Entropy calculated for the images shown in this work (using the “entropy” function in MATLAB).  
Since the computation of the CGH is a linear process, the computational cost is proportional to the number of pixels used for its synthesis. Table 3 presents the computation times that confirm this behavior. It is important to emphasize that these times are indicative and depend on both the hardware used and the programming languages employed to implement each part of the process.  
These graphs can aid in managing the trade-off between encryption computation time and the required level of security. Additionally, other factors complicate the process of unauthorized decryption, such as the wavelength used for CGH synthesis or the geometry of the recording process (the distance between the scene and the CGH plane). In a trial-and-error process, the computation time should also account for the cost of calculating the convolution integral to reconstruct the scene from CGH.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 2 Model Description
### Baseline Models
#### 2.2.2 Basic DNNs  
The CNN-ConvLSTM model was constructed by replacing the BiconvLSTM module in the BiRNN encoder (Figure 1b) with the ConvLSTM module. The 3 DCNN model was also constructed as an encoder-decoder structure, as shown in Figure 3. The encoder consisted of 3 3 DCNNs (Conv3D), each followed by an average pooling (AvgPooling3D) layer. The decoder consisted of 3 3 DCNN transpose (DeConv3D) layers, as shown in Figure 3. Unlike RNN-based models, the 3 DCNN model extracted high-level features by using all radar images together. Meanwhile, all the output radar images were decoded from the high-level features. The leaky ReLU activity layer was used for all 3 DCNN and 3 DCNN transpose layers.  
Figure 3: Network architecture of the three-dimensional convolutional neural network (3 DCNN) model.  
Figure 2: Schematic diagram of interpolating three radar frames from two adjacent radar images using optical flow model.