esa-sceva/satcom-chunk-collection huggingface dataset:
# Escaping Printable Encoded Streams to Embed Out-of-Band Data
## 1 Introduction  
Printable string encoding of binary data is a well-known and widely spread technique used by systems designed to manage only printable characters: in other words, printable string encoding is an encapsulation method to process, in a transparent way, any possible bit string by systems able to treat only printable strings (for example, some mail servers).  
Typical examples of this are old mail servers that are not able to directly accept binary data and the QR-code representation of the European Union Digital COVID Certificate.  
To allow the storage and transmission of binary data with printable strings, many encodings have been proposed: in Section 3, some of these works will be recalled, emphasizing the use of the legal strings and computing the space of unused configurations that allow the embedding of additional information.  
In general, the approach is to define an alphabet made of symbols that are used to compose strings, each one associated with a binary configuration to be encoded. For example, Base41 [1, 2] uses three symbols from an alphabet of 41 printable characters to encode a pair of octets: in this case the possible printable strings are \(41^{3}=68\),921, while the configurations of pair of octets are \(2^{16}=65\),536, leaving \(41^{3}-2^{16}=3385\) free printable strings that, in general, are considered \"illegal\" but may be employed to store additional data such as a Cyclic Redundancy Check (CRC) value, a cryptographic hash, a Message Authentication Code (MAC), or a digital signature.  
The main contributions of this paper are as follows:  
* Showing the redundancy present in some printable encodings;
* Developing a methodology to embed extra data in a printable encoded stream;
* Showing the effectiveness of embedding these data for security purposes;
* Showing some other applications that leverage unused printable strings.  
The following Section 2 will establish a uniform nomenclature and notation to be used throughout the paper, after which Section 3 will present some printable encoding methods and applications. Section 4 will give details on some of the encodings that allow the embedding of extra (i.e., payload) data and will present some methods for performing this operation. Section 5 will present a numerical analysis for some embodiments of the case studies introduced in Sections 4 and 6 will discuss some conclusions on the proposed methodology and its applications.
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 2 Model Architecture
Conversely, the PAN down-samples features from the lower layers to the higher ones, ensuring that the upper layers retain detailed feature information. This approach lets the model make precise predictions across various image sizes. YOLOv8 adopts an improved version of this method, called FP-PAN (Feature Pyramid-Path Aggregation Network. ), which eliminates some convolution operations during up-sampling to reduce computational costs. The head of the YOLOv8 model is designed for the final object detection, including classification and localization tasks. YOLOv5 utilizes a coupled head, where classification and detection  
Figure 1: The architecture of the YOLOv8 algorithm for real-time object detection. It consists of three main parts: the backbone for feature extraction, the neck for the fusion of features, and the head for localization and classification of features in the images. are combined; on the other hand, YOLOv8 utilizes a decoupled head architecture. The separation of classification and regression heads enhances detection precision and accuracy. YOLOv8's head eliminates the objectness branch, focusing solely on the classification and regression branches. Unlike traditional anchor-based methods that rely on numerous anchors for object location, YOLOv8 employs an anchor-free approach. This method determines the object center and estimates the bounding box dimensions relative to the center, allowing for more accurate and flexible object localization. By leveraging a decoupled head and anchor-free method, YOLOv8 achieves superior accuracy and efficiency in object detection, especially when using complex datasets.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 3 Proposed Method
### Problem Statement  
Consider a multi-source behavior sequence of a user, which contains \(\ell\) consecutive behavior events, denoted as \(s=\{e_{i}\}_{i=1}^{l}\), where each event \(e_{i}\) represents a user behavior and event \(e_{i}=(x_{1},\cdots,x_{m})\) consists of \(m\) distinct entities, such as time, host ID, and operation.  
Assuming we have \(n\) normal multi-source behavior event sequences \(\left\{s_{q}\in S\right\}_{q=1}^{n}\), we first train an encoder-decoder model. The encoder takes as input a multi-source behavior sequence of length \(\ell\) and outputs a fixed-length feature vector \(z_{l}\) for each sequence. The decoder, using the fixed-length feature vector \(z_{l}\), predicts all entities of the \((\ell+1)\)-th behavior event. The deviation between the predicted value and the true value is used as the loss function to adjust the model parameters. After training, we discard the decoder and keep only the encoder.  
Given a set of multi-source behavior event sequences \(S^{\prime}\), we input these sequences into the encoder to obtain the user multi-source behavior sequence feature space \(z_{l}\). We then initialize the cluster centers \(u_{j},j=1,\ldots,k\) using k-means clustering. Next, we use stochastic gradient descent to iteratively update the cluster centers \(u_{j},j=1,\ldots,k\), and the user multi-source behavior sequence feature space \(z_{l}\) based on the objective function of deep clustering. Deep clustering outputs the class labels and class assignment probabilities of the multi-source behavior event sequences in the set \(S^{\prime}\). Finally, we identify the clusters that are relatively sparse and the user multi-source behavior sequences with low class assignment probabilities as anomalies.
## 3 Problem Formulation
### Constraint Conditions  
Constraints of ESC include energy constraints, equipment and material constraints, total energy constraints, basic performance constraints, and real-time constraints. The meaning and mathematical expression of the specific constraints are as follows.  
Energy constraint  
Any form of ES access will change the energy distribution of EI, but the overall energy conservation should be always met, which means that Equation (4) ought to be satisfied.  
\[E_{RE}\,-E_{SE}=E_{CE} \tag{4}\]  
where \(E_{RE}\) represents the energy released by various energy networks, \(E_{SE}\) is the energy stored by ESs and \(E_{CE}\) is the consumed energy.  
Due to raw material and equipment hardware, the capacity of stored energy can be limited. Suppose the maximum storage capacity of each ES is \(X_{imax}\), and the constraint is expressed in Equation (5).  
\[x_{i}\leq X_{imax} \tag{5}\]  
Total energy constraint  
In order to ensure the stable operation of EI, the stored energy should meet the overall demand, which means the capacity of total stored energy should be no less than the total amount of energy required, which is shown in Equation (6).  
\[\sum_{i=1}^{n}\,x_{i}\cdot\rho_{i}\geq E_{total} \tag{6}\]  
where, \(\rho_{i}\) represents energy conversion efficiency of the \(i\)th ES.  
Basic performance constraint  
The basic requirements of EI for ES ought to be guaranteed for system operation, as shown in Equation (7).  
\[x_{i}\cdot\rho_{i}\geq e_{i}(i\in m\,) \tag{7}\]  
Real-time constraint For some large disturbances and transient faults, high real-time ES (RTES) forms are usually in need, including superconducting magnetic energy storage (SMES), flywheel energy storage (FES), super capacitor energy storage (SCES) etc. The constraint is presented in Equation (8).  
\[\sum(x_{i}\cdot p_{i}-e_{i}\ )\geq E_{real}(x_{i}\in\text{RTES}) \tag{8}\]  
In order to solve this optimization problem, MAPSO can quickly and accurately converge to the optimal solutions proposed in this study. The source, analysis and concrete steps of MAPSO are described and analyzed in detail in Section 4.
## 3 Results
### S Factor  
The S factor is calculated from the absorbed fraction using Equation (2). In Figure 7, we present the S factor difference, \(100\frac{(\mathrm{S_{PW}}-\mathrm{S_{MBDcell}})}{\mathrm{S_{MBDcell}}}\), between the analytic calculations of the present work (PW) and the MIRDcell software for monoenergetic electrons in the energy range 1 keV-4 MeV and various sphere radii (10, 50, 100, 500, 1000, 5000 \(\upmu\)m).
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## 3 Results and Discussion
### Spectrum Analysis  
The first analysis was GFDM spectrum scrutiny, which was carried out with and without a predistorter as shown in Figure 7. The power spectral density (PSD) of complex signals is no longer symmetrical around _Fs_/2 even though it is still of real value. The \(x\) axis is the sampling frequency _Fs_ and the \(y\) axis is the normalized PSD. The PSD of GFDM after being given predistorter resulted in low OOB while the inband was better. The aim of the predistorter is to modify the signal on the transmitter, so the nonlinear distortion seen in the PA output decreases [30]. Predistorters can be implemented as inverses directly from the characteristics of the HPA.  
The signal spectrum in GFDM was analyzed with the predistorter varied by IBO as shown in Figure 8. It was evident the IBO was getting smaller, the nonlinear distortion was enlarging, the OOB was increasing, and the inband was rippling.  
Figure 6: AM/AM characteristic curve of predistorter.  
Figure 7: PSD SISO GFDM + HPA with and without predistorter.  
Furthermore, the application of predistorter on the effect of the MIMO-GFDM signal spectrum was as shown in Figure 9. The MIMO-GFDM signal spectrum with predistorter become better than the system without predistorters. This approach has several advantages, such as the reduction of the out-of-band distortion. Nonetheless, the estimated PA memory is needed at the transmitter.  
PA operating points need to be carefully selected to meet the spectral mask and should be optimized to get maximum power efficiency. These two constraints--power efficiency and adjacent channel interference (ACI)--are the key factors in a PA design.  
Figure 8: Comparison of PSD GFDM+predistorter with input back-off (IBO) variations.  
Figure 9: MIMO PSD GFDM with and without predistorter.
## 3 Point Cloud Registration Method
### ICP-Based Variants
[53] proposed an enhanced ICP method that combines a hierarchical search scheme with an octree-based ICP algorithm to achieve coarse-to-fine registration of large-scale multi-resolution data. An early warning mechanism is utilized to sense local minimum problems. A heuristic escape scheme based on sampling potential transformation vectors to avoid local extremum. The superior performance of the exploited algorithm is checked by experiments. Kdtree can be used in the process of searching for the nearest point to optimize and reduce the search time [54; 55]. [PERSON] [56] used the general nonlinear optimization (Levenberg-Marquardt algorithm) to directly minimize the registration error. [PERSON] et al. [57] designed an ICP algorithm using Anderson acceleration (AA-ICP), which accelerates ICP by modifying the iterative process and improves the convergence speed and effect of registration. [PERSON] et al. [58] proposed a new robust registration method with fast convergence. This method accelerates its convergence by using the Anderson acceleration method. In addition, the Welsch function is introduced to improve the robustness of ICP. For the problem that the ICP algorithm is sensitive to outliers and missing data, [PERSON] et al. [59] improved the ICP algorithm and used the sparsity induction norms to optimize the registration, which improved the robustness of ICP to outliers and incomplete point cloud data. [PERSON] et al. [60] elaborated an improved standard ICP data association strategy, which associates each point in the source point cloud with a set of points in the target point cloud. Experimental analysis shows that the improved method can effectively deal with the alignment problem between dense point clouds and sparse point clouds. TrimmedICP [61] introduced a solution for partially overlapped point cloud registration, using least-trimmed squares to solve the ICP problem, which improves the robustness to noise and allows two-point cloud sets to have poor initial values of transformation parameters. [PERSON] [62] proposed a symmetric objective function for ICP, which realizes the simplicity and computational efficiency of point-to-plane optimization and improves the convergence speed and wider convergence range. [PERSON] et al. [63] and [PERSON] et al. [64] introduced the maximum correntropy criterion (MCC) as a robust metric for point-set rigid registration. The ICP algorithm based on MCC (MCC-ICP) can deal with noise and outliers well and shows its superiority in accuracy and robustness. A recent work [65] introduced soft constraints as the cost of ICP optimization, allowing elastic deformation of the scan during registration to improve accuracy and robustness to high-frequency motion from discontinuities.
## 2 HSI Procedure
### Hyperspectral Wavelength Selection
#### 2.3.3 Successive Projections Algorithm (SPA)  
The SPA is a robust method for variable selection with the aim of reducing multi-collinearity in hyperspectral data. In this algorithm, the variables are iteratively projected to maximize the variance, thereby enabling SPA to choose the most informative wavelengths by reducing redundancy as much as possible. In this respect, SPA is particularly useful to simplify such a complex dataset and enhance the interpretability of models [85]. There has been a wide variety of applications using SPA in HSI-based food quality and safety assessments. We have shown the efficiency of SPA in the SSC and FI of Malus micromalus Makino. A combination of SPA with ELM and GWO-SVM can ensure a fast, non-destructive prediction of the features of fruit quality [86]. Simultaneously, the reduction in data complexity did not affect the accuracy; therefore, SPA is suitable for large-scale applications of quality control.  
In seafood analysis, the freshness of largemouth bass file under different thawing conditions was assessed using SPA. According to the values of electrical conductivity in the SPA-PLSR model, seven key wavelengths were selected for the prediction of freshness,which showed high accuracy and thus enabled the mapping of spatial distribution [87]. Spatial distribution maps were used to visually display freshness in filets as a rapid, non-invasive analytical technique to quality control in seafood. Furthermore, [PERSON] et al. (2021) applied the SPA to authenticate fresh and cooked mutton rolls with pork and duck adulteration. Herein, the SPAPLS-DA model was able to classify samples into their categories with 100% accuracy with minimum computational complexity, hence proving to be useful for investigation in food fraud detection problems [88]. This kind of application is of paramount importance in view of ensuring authenticity and safety in food items.
## 6 Discussion and Conclusions  
In this study, a new approach for intensity stereophonic recording has been investigated. Guided by the playback situation and its auditory requirements, we decided to postulate a setup consisting of two crossed end-fire microphone arrays and a fitting desired directivity pattern. The difference between the directivity pattern obtained and the one desired was minimized by a superdirective beamforming algorithm. It was based on convex numeric optimization and also contains a frequency-dependent WNG constraint to ensure a robust beamforming design.  
In addition to designing the filters of the microphones via beamforming algorithms, we found an ideal array design. This design maximizes the spatial aliasing frequency and also takes practical issues into account, which will appear in an actualization of the arrays. The extent of the microphones demands a particular spacing, also to avoid interferences between them.  
A comparison between the new stereo system and the state-of-the-art ones, which use two microphones, has shown that the former has the advantage of less recorded reverberant sound, as it is more directive in the look direction than the latter are. This matches the requirements posed by the recording method proposed in [PERSON] and [PERSON] [PERSON] (2015), which requires separate dry and reverberated representations of the audio signal. The reverberated sound field can be taken from single microphone signals.  
Future research could develop a method to optimize the directivity pattern of both arrays as one system rather than handling them separately. Furthermore, two additional beams pointing into the diffuse field could be introduced for optimization to replace the two microphones placed in that field and to use only the array system.  
A final assessment of the proposed recording and playback system needs to run listening tests and investigate the perception of the recording and playback room.  
We would like to thank the Deutsche Forschungsgemeinschaft for supporting this work as part of the Forschergruppe Individualisierte Hoerakustik (FOR-1732). We also would like to thank the reviewers for their helpful and insightful comments.  
[PERSON] and [PERSON] formulated the constraints for the true stereo microphone array. [PERSON] developed and evaluated the methods for optimizing the true stereo microphone array. [PERSON] planed and performed the localization experiment.  
The authors declare no conflict of interest
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 2 Operations Chain Model
### Standard Operation Sequence  
An assessment question corresponds to an operation task. Therefore, the operations chain model corresponding to the question can be established according to the requirements of the task and the principle of the operations chain model. The operations chain model should be developed by professionals with permission. The model serves as the basis for comparing with the actual operation sequence of a trainee.  
After compiling the standard operations chain for the task, it is necessary to transform it into one or more standard operation sequences based on the actual relationships among basic operations. The basis for transformation is the order of operation nodes and the \"AND\", \"OR\", or \"MEX\" relationships. The trainee's operation performance can be obtained by aligning the operation sequence submitted by a trainee with the standard operation sequences.  
Figure 4 shows an example of an operations chain model. There are four nodes in the chain. The operation nodes on the operations chain are composed of multiple basic operations combined with \"AND\", \"OR\", and \"MEX\" relationships. In node a, there are only two basic operations with \"AND\" relationships. That means the two basic operations 1 and 2 should both be completed. The operation sequence of node a may be \(1\to 2\) or \(2\to 1\). In node b, there are two basic operations with \"OR\" relationships. That means that any one of the two operations could be completed. Thus, the operation sequence of node b may be 3 or 4. In node c, there are also two basic operations with a \"MEX\" relationship. The operation sequence of node c may be 5 or 6. However, in the total sequence, operation 5 and operation 6 cannot occur simultaneously; therefore, either operation 5 or 6 will be set as the critical operation, and when operations 5 and 6 are in the trainee's sequence, a punishment score should be subtracted from the total score. In node d, there is only one basic operation, so the operation sequence for node d is 7. Multiple standard operation sequences will be obtained by connecting all node sequences in series, as shown in Table 1.  
Therefore, there may be one or more operation paths to accomplish the node task at each operation node. These may consist of more than one standard operation sequence in a single training session described by the operations chain model. When comparing the operation sequence formed by the actual operations of a trainee, it is necessary to compare each of these multiple paths one by one.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Standard Operation Sequence** \\ \hline
1 & 1, 2, 3, 5, 7 \\
2 & 1, 2, 3, 6, 7 \\
3 & 1, 2, 4, 5, 7 \\
4 & 1, 2, 4, 6, 7 \\
5 & 2, 1, 3, 5, 7 \\
6 & 2, 1, 3, 6, 7 \\
7 & 2, 1, 4, 5, 7 \\
8 & 2, 1, 4, 6, 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of multiple standard sequences.  
Figure 4: An example of an operations chain model.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 3 Materials and Methods
### The Space Segment: The UVSQ-SAT CubeSat Payload
#### 3.2.3 The TW Sensor  
The Teach' Wear (TW) sensor (50 mm \(\times\) 25 mm \(\times\) 8 mm) consists of an inertial measurement unit (a three axis accelerometer and a three axis gyroscope) and a three axis compass (simple type of magnetometer). TW is a new three axis accelerometer/gyroscope/compass, which will be used for determining the attitude of the UVSQ-SAT CubeSat. The main expected TW sensor technical requirements are provided in Table 7.  
In the framework of the UVSQ-SAT mission, a qualification/validation of the Teach' Wear technology to TRL 9 (\"flight proven\") will be done through successful mission operations and validation of the expected performances of the TW sensor in space. The TW sensor will be used in the future as medical devices to prevent health problems for astronauts in space. Indeed, astronauts are subjected to serious health problems due to the nature of their jobs, which involve extreme atmospheric conditions and environments, particularly during prolonged space missions.
# Global Time-Varying Path Planning Method Based on Tunable Bezier Curves
## 6 Simulation
### Comparison of Bezier Curves under Different Orders  
The high-order Bezier curve has some disadvantages, such as a large calculation amount, oscillation, complexity of the trajectory and it cannot be applied to dynamic environments. Although the low-order Bezier curve can guarantee the continuity of the path, it cannot provide the continuous curvature and any setting of the second derivative of the Bezier curve.In short, it is necessary to balance the computational load of trajectory planning with the flexibility of the trajectory. In this paper, the characteristics of Bezier curves under different orders are compared and analyzed. Finally, the GTVP method selects the 5-order Bezier curve with a global real-time obstacle avoidance function and the calculation amount of the 5-order Bezier curve is moderate.  
Taking the time-varying environment with five obstacles mentioned in this paper as the simulation environment, a set of Bezier curves is obtained every 0.1 s in the process of  
Figure 14: The computational time of each method. **(a)** RRT. **(b)** Q-RRT\({}^{*}\). **(c)** MDA+RRT. **(d)** GTVP.  
0 s to 100 s. The total simulation time \(t_{n}\) required for real-time path planning using Bezier curves of different orders is summarized in Table 2.  
The GTVP method in this paper considers the starting direction, the ending direction and there are 6 points representing the characteristics of obstacles. Therefore, the degrees of the Bezier curve is at least 5-order, while the 3-order Bezier curve cannot meet the requirements. It can be seen from the table that the simulation time increases with the rise of the order. However, the generated path is more complex when the order of Bezier curve is 9 and the trajectory mutation with oscillation will occur. Moreover, the simulation time is as long as 71.552 s, which cannot guarantee real-time planning. In summary, the degrees of the Bezier curve with the GTVP method can be 5-order or 7-order. This paper preferentially selects the 5-order Bezier curve planning method to ensure real-time requirements.