esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Analysis
### Data Balancing  
In the dataset, we observed a significant class imbalance, which presented a major challenge for our machine learning models. Specifically, the performance of the models for the fifth class, \"Web Attacks\", was severely impacted, resulting in an F1 score of 0. This imbalance caused the models to be biased toward the majority classes, leading to difficulty in recognizing patterns in the underrepresented classes. To address this issue, we utilizeda data balancing technique known as the synthetic minority over-sampling technique (SMOTE). SMOTE creates synthetic samples for the minority class to balance the distribution, ultimately enhancing the models' performance on the underrepresented classes.  
To mitigate the common issue of class imbalance frequently found in cybersecurity datasets, we applied SMOTE to our dataset. Class imbalance can notably impair the performance of machine learning models, especially in detecting underrepresented classes like \"Web Attacks\", which are often overshadowed by more frequent classes. SMOTE works by generating synthetic samples for the minority class, resulting in a more balanced data distribution. After applying SMOTE, we retrained our machine learning models with the newly balanced dataset. The results revealed a significant improvement in the detection performance of the underrepresented classes, leading to better overall model performance. This improvement in accuracy is essential for developing a more reliable and effective intrusion detection system capable of identifying a broader range of cyber threats, including those that might otherwise be missed due to class imbalance. In Figure 1, we present a visualization of the data distribution before any data balancing technique was applied, illustrating the initial class imbalance. The plot highlights a significant disparity in the number of instances across different classes, with some classes being heavily overrepresented while others are notably sparse. Figure 2 provides a comparison by showing the data distribution after the SMOTE technique was applied, demonstrating how the dataset became more balanced, which ultimately contributed to improving the model's ability to detect minority class instances more effectively.  
Figure 1: Data distribution before applying any data balancing mechanism.  
Figure 2: Data distribution after applying SMOTE balancing mechanism.
## 4 Results and Discussions
### Data Collection and Analysis
#### 4.1.2 Simulation Output  
The output data of the simulation is a file in comma-separated value format, wherein the first column is the item name; the second column is the timestamp; and the third column is a flag that checks if the pick-up is valid or not. The two important parts that need to be extracted from this output file are the total number of items and the last timestamp recorded for a particular shopper. This timestamp is also known as the \"dwell time\" of the shopper. The dwell time over the total number of items gives us the performance metric for this validation study: the average time per item.
## 3 Experimental Setup
### Resampling  
In order to improve the sampling accuracy, improve the channel BER obtained by Alice calculation and improve the consistency of the key. Resampling is a commonly used statistical digital processing technique that makes the data in the sample more suitable for digital processing. In the experiment, the rate of the transmitter is 10 Gbps, and the sampling frequency of the oscilloscope is 40 Gbps. Therefore, it is necessary to use down-sampling technology to down-sample the four times oversampled data of the oscilloscope to make it more suitable for further data processing requirements.  
Down-sampling technology is a way to reduce the sampling rate of a signal, which is usually applied in the scenario of down-speeding data signal transmission or reducing the data size. Down sampling and interpolation are complementary relationships, and interpolation is applied to scenarios where the sampling frequency is increased. Since signal aliasing will cause a certain degree of distortion, a filter is required to reduce signal aliasing during down sampling [24; 25].  
There are two types of down sampling techniques: integer down sampling and fractional down sampling. Integer down sampling is widely used, and its process can be decomposed into two parts. First, in order to prevent signal aliasing, a low-pass filter is used to filter out the high-frequency components of the signal. Afterwards, the sampling points with an interval of M in the middle of the original signal are retained, thus down-sampling M times. If only the second step is performed, the low-frequency signal in the process of reducing the sampling rate will be mixed with some high-frequency components. This distortion is called the aliasing effect. Therefore, in order to reduce the influence of the aliasing effect, it is necessary to use a low-pass filter to filter out the high-frequency components of the signal. Figure 7 shows the data spectrum before and after down sampling, respectively.
## 1 Introduction
The image source method (ISM) [55] is used to generate the room impulse response (RIR) between the source speaker and receiver microphone array. Adaptive momentum estimation (ADAM) [56] is selected as the optimization strategy to train the proposed neural architecture. Based on computer simulation results, it can be inferred that the proposed methodology improves the intelligibility of the speech corresponding to target source in motion, under noisy reverberant conditions involving multiple moving speech sources. The proposed approach is also tested with recorded RIRs obtained from Aachen impulse response (AIR) [57] database, where it shows good results. This is largely because of the signal discriminative capability of the proposed neural approach, which it acquires due to the training procedure outlined in Section 3. The proposed approach can be applicable in many practical scenarios such as conferencing [58], assistive listening devices [59] and speech transcription systems [60]. The remainder of the article is arranged as follows. Section 2 describes the system model of the overall methodology; Section 3 describes the proposed neural architecture-based speech enhancement module; Section 4 provides a brief discussion about the proposed approach; Section 5 presents some future research directions in this area; and, finally, Section 6 concludes this article.
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
###### Abstract  
This paper presents a time-synchronization solution for operations performed by a heterogeneous set of robotic manipulators grouped into a production cell. The cell control is realized using master-slave architecture without an external control element. Information transmission in a cell is provided by a TCP/IP channel in which communication is ensured via sockets. The proposed problem solution includes an algorithm, which is verified and validated by simulation and tested in real environment. This algorithm requires minimal computational power thanks to an empirically oriented approach, which enables its processing directly by the control unit of each participating element of the robotic cell. The algorithm works on the basis of monitoring and evaluating time differences among sub-operations of master and slave devices. This ensures defined production cycle milestones of each robotic manipulator in the cell at the same time are attained. Dynamic speed adaptation of slave manipulators utilizing standard instructions of their native language is used. The proposed algorithm also includes a feedforward form of operations synchronization which responds to changes in the operating cycle of the master manipulator. The application of the solution proposal is supplemented with a visualization part. This part represents a complementary form of designed solution implementation.  
Keywords:time-based synchronization; heterogeneous multi-robotic cell; socket communication; low computational power; native language application +
Footnote †: journal: Applied
## 4 Discussion
### The Feasibility of the Proposed ATDC-SM in Two-Link Robot Manipulator
Since the reference trajectory (Figure 13) has large amplitude, its slope changes rapidly at appropriately 7 s. However, the proposed one provides fast convergence rate with less fluctuations and hence has a similar level compared with the reference trajectory in transient region. The proposed ATDC-SM was based on the conventional ATDC that fully confirmed its applicability to the real robot manipulator in [21]. For this reason, although the effectiveness of the proposed ATDC-SM was verified quantitatively through simulation based on robot manipulator model, we believe that the proposed ATDC-SM can be applied to real robot manipulators. \begin{table}
\begin{tabular}{c c} \hline \hline
**Control Strategies** & **Joint Angle (deg)** \\ \hline MNTSMC [16] & \(1.41\times 10^{-2}\) \\ ASMC [18] & \(1.01\times 10^{-2}\) \\ Conventional ATDC [21] & \(1.18\times 10^{-2}\) \\ Proposed ATDC-SM (9) & \(0.65\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMS Tracking errors for reference trajectory with non-differentiable point. Figure 14: Comparison of the tracking errors of the model-free nonsingular terminal sliding-mode control (MNTSMC) [16] (dashed-line), the adaptive sliding-mode control (ASMC) [18] (dotted-dashed-line), the conventional adaptive time-delayed control (ATDC) [21] (solid-line), and the proposed ATDC-SM (dotted-line). Figure 15: Comparison of the tracking of reference trajectory (solid-line) and the proposed ATDC-SM (dotted-line).
## 2 Materials and Methods
### ISO 13790 standard  
At European level, the prEN ISO 13790:1999 was approved in 1999, which gave final form to the ISO 13790:2004 standard, later revised in 2008. In Spain, it was transposed into the UNE-EN ISO 13790:2011 standard [11] as \"Energy efficiency of buildings. Calculation of energy consumption for space heating and cooling. (ISO 13790:2008)\". Currently, many of the used programs, both nationally and internationally, use this ISO standard, which generally operates with the inputs shown in Figure 2.  
In this sense, ISO13790 employs three calculation procedures to define the Energy needs for heating and cooling, but all of them are centered into heat and mass transfer in the building system as a multiple or just only one zone.  
1. Monthly and seasonal method;
2. Simplified time method;
3. Detailed simulation method.  
It is interesting to highlight that the second procedure is based on the model of five resistors and one capacitance being a simplification of the dynamic simulation [9] and that the third procedure, dynamic method, must pass the validation tests (Annex A of ISO 13790). In consequence, based on the main objective of stochastic approach, for our research work the monthly and seasonal method was selected. This method was established in three sections to define the heating and cooling energy needs of a building. First, for the heating energy consumption, the ISO 13790:2011 defines the Equations (1) and (2):  
\[\mathrm{Q}_{\mathrm{H,nd}}=\mathrm{Q}_{\mathrm{H,nd,cont}}=\mathrm{Q}_{ \mathrm{H,ht}}-\mathrm{\eta}_{H,gn}\mathrm{Q}_{\mathrm{H,gn}} \tag{1}\]  
Figure 1: Calculation procedures of CE3X software.  
where \(\mathrm{Q_{H,nd,cont}}\) is the energy needed as continuous heating (MJ)), \(\mathrm{Q_{H,ht}}\) is the total heat transfer (MJ), \(\mathrm{Q_{H,gn}}\) are the total heating gains (MJ) and \(\mathrm{\eta_{H,gn}}\) represents a dimensionless factor of use of heat gains (MJ). For cooling energy consumption, the ISO proposes the Equation (2):  
\[\mathrm{Q_{C,nd}}=\mathrm{Q_{C,nd,cont}}=\mathrm{Q_{C,gn}}-\mathrm{\eta_{C,js}} \mathrm{Q_{C,ht}} \tag{2}\]  
where \(\mathrm{Q_{C,nd,cont}}\) is the continuous cooling energy needed (MJ), \(\mathrm{Q_{C,ht}}\) is the heat transfer for the cooling mode (MJ), \(\mathrm{Q_{C,gn}}\) are the total heat gains (MJ) and \(\mathrm{\eta_{C,js}}\) represents a dimensionless factor of heat losses (MJ). Finally, for each building zone and calculation step (month in our case study) the heat transfer is calculated in accordance with the Equation (3):  
\[\mathrm{Q_{ht}}=\mathrm{Q_{tr+}}\mathrm{Q_{ve}} \tag{3}\]  
where \(\mathrm{Q_{tr+}}\) is the total heat transfer (MJ) and \(\mathrm{Q_{ve}}\) is the total heat transfer by ventilation (MJ). Finally, total heat gains are defined by the Equation (4):  
\[\mathrm{Q_{gn}}=\mathrm{Q_{int+}}\mathrm{Q_{sol}} \tag{4}\]  
where \(\mathrm{Q_{int+}}\) are the total internal heat gains in the calculation period (MJ) and \(\mathrm{Q_{sol}}\) are the total solar heat gains during the calculation period (MJ).
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 2 Related Work
### Related Paper  
NASA (2018) proposed the use of public blockchain technology to enhance the security of space communications, emphasizing its potential to guarantee data immutability and traceability in space exploration missions [13]. Public blockchain-based frameworks can significantly improve inter-organizational trust by ensuring tamper-proof data storage and auditability. However, this approach presents notable limitations when dealing with sensitive space mission data due to privacy concerns and the computational overhead associated with consensus mechanisms, which makes real-time processing of large datasets inefficient. Similar challenges have been observed in the healthcare sector, where blockchain-basedmodels have been applied to enable the secure sharing of electronic health records (EHRs). In these cases, maintaining both data confidentiality and reliability is considered a critical concern [14]. In response to these limitations, our study adopts a hybrid blockchain architecture that integrates the transparency of public blockchains with the access control and confidentiality of private blockchains, achieving a balanced solution between data integrity, privacy, and operational efficiency.  
The study titled \"Private Blockchain Solutions for Aerospace Data Integrity\" (2022) investigated the application of private blockchains to ensure the integrity of aerospace data [15]. The authors emphasized that private blockchains offer advantages such as controlled access and low-latency processing, making them suitable for handling sensitive and high-frequency data. However, the research also pointed out inherent limitations, particularly the lack of decentralization and external auditability, which may undermine inter-organizational trust and data reliability. In response to these limitations, our proposed hybrid blockchain model aims to preserve the benefits of private blockchains while incorporating public chain elements to enhance transparency and cross-institutional verifiability.  
Recent advancements in zero-knowledge proofs have extended their applicability to resource-constrained environments such as data streams, allowing verification of computational integrity without revealing the actual content. For instance, research by [PERSON] et al. (2023) [16] introduced streaming zero-knowledge interactive proofs that enable low-memory verification of large-scale data computations in a privacy-preserving manner. While the study focuses primarily on generic streaming data settings, it highlights the potential for scalable and efficient ZKP implementations in domains where memory and communication costs are critical--such as space exploration missions. Building upon this foundation, our research embeds ZKPs within a hybrid blockchain framework and validates their practicality by applying them to real extravehicular activity datasets. The results demonstrate that ZKP-based verification not only preserves data confidentiality but also achieves feasible performance in realistic mission scenarios.
# Fake Base Station Detection and Link Routing Defense +
## 4 Fake Base Station Threat  
### Threat Model  
An attacker has the knowledge of the cellular 5G networking protocol by Kerckhoff's principle and can launch a fake base station using software-defined radios and can exploit radio communication between the user equipment and the legitimate base station, e.g., there are even development tools and tutorials for setting up such a fake base station, e.g., Refs. [1; 2]. The attacker exploits the deterministic base station selection procedure and the lack of integrity protection of system information messages, as described in Section 3.2, to launch a fake base station attack. In our threat model, we consider such an attacker with a fake base station that can broadcast fabricated system information messages with the highest signal power so that benign user equipment selects the cell of the fake base station over a legitimate base station that meets cell selection criteria. The adversary can adaptively choose signal transmission power gain to take control of the radio channel between the base station and the user equipment. Afterward, the fake base station can establish a radio resource control (RRC) connection with the user equipment and drop, modify, or inject upper-layer communication (e.g., NAS layer) from the user equipment to the core of the network later on. We also consider that the attacker's fake base station is Sybil-capable, meaning it can take multiple false identities and system parameters. The attacker can use this Sybil capability, for example, to avoid being blacklisted.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
### Contributions  
The BSS approach separation gain depends on the selection of an appropriate source prior function for extracting the desired speech signals [31; 32]. For example, [33] proposes a mixed source prior model comprised of super-Gaussian and Student's T to enhance the performance of the BSS. Consequently, in [34], the performance is improved by using a hybrid model, consisting of multivariate super-Gaussian and generalized Gaussian source priors. This approach models the higher amplitudes of the observed convolutive mixture by a multivariate generalized Gaussian source prior, and the low amplitudes are exploited by a multivariate Gaussian source prior. Unlike these existing works, we propose an efficient multistage BSS method. In this method, multivariate generalized Gaussian and super-Gaussian source priors are combined as a hybrid source prior model. The generalized Gaussian one exploits higher-order statistical properties while other related information is modeled by the multivariate super-Gaussian one. The contributions of this research work are as follows:We propose a novel efficient multistage approach for BSS applications. This method concatenates the hybrid approach. Our proposed hybrid models combine multivariate generalized Gaussian and super-Gaussian source priors.
* Based on the hybrid model, two different schemes are introduced, i.e., first BSS followed by de-noising and second de-noising in the first stage followed by BSS.
* The performance of the proposed multistage hybrid model is evaluated with other multistage BSS methods having single source priors.
* The performance of the proposed models are investigated via extensive simulations in a noisy reverberant environment.
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 4 Data Collection  
For data acquisition, the cylindrical rolling elements of length 13 mm and diameter 10 mm are rotated on two co-rotating roles. A light emitting diode (LED) array emits pulses of light in synchronization with an E2V UNIIQA+ Essential line scan camera's trigger. The camera is connected to a workstation computer via a high bandwidth camera-link cable and a Matrox Solius eCL/XCL-B framegrabber. The camera is capturing a 1395 px line. To reduce motion blur, the exposure time of the camera is set to 50 us. Accordingly, the LED light is of high luminance to enable the camera to capture a bright image. This renders the influence of ambient light insignificant by comparison.  
A rotary encoder is attached to the co-rotating roles on which the steel parts lie. It continuously triggers the camera and lighting. During the element's 360 deg rotation, the camera captures 3335 lines of pixels, resulting in a 1395 px by 3335 px image. The resolution in both dimensions is ~107 ppcm, creating an undistorted scan. The whole scanning process takes about half a second. This sets the upper time limit on the processing time per image classification. Exceeding this limit results in additional latency in the inspection pipeline.  
The steel surface of an ideal rolling element is of specular nature. As is known in physics, the angle of incidence of light is equal to the angle of reflection. Based on this, the camera is aligned to capture the light emitted by the LEDs and reflected by the rolling element, as visualized in Figure 1. Surface defects in the form of unevenness result in the light being reflected away from the camera. Similarly, corrosion and other diffuse surface defects also result in less light reaching the camera. The scanned images thus exhibit dark spots where the steel surface is less specular or not perfectly even. The camera captures a Red-Green-Blue (RGB) image with 8-bit depth per color channel. Since no relevant color information is present, the images were stored as grayscale images. This lowers their storage requirement and their loading time during training.  
The network is trained using only defect free parts as source material. However, for evaluation and validation purposes, multiple elements with a variety of defects were scanned. Some of these defects are visualized in Figure 2.  
For the training of the neural network, 20 high-resolution scans of defect-free elements were captured. These were sliced into 4.000 windows of size \(128\times 128\) px. This allows the network to be trained on large batches of small windows instead of small batches of whole scans. The latter approach suffers from noisy gradients and thus longer convergence times.  
Figure 1: (**a**) A schematic side view of the hardware setup used for scanning the rolling elements. (**b**) and (**c**) indicate how the resulting surface scan images of size 1395 px by 3335 px correspond to the rolling element parts they originate from.  
Figure 2: Various defects on the steel surface scans.