esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchronization of Acoustic Signals for Steganographic Transmission
## 1 Introduction  
The paper is devoted to the issues of acoustic steganography, and more precisely hidden synchronization of acoustic steganographic channels. Analyzing scientific publications in this field, it can be concluded that this is a valid topic, and the algorithms of acoustic steganography are constantly being improved. However, in many cases, the authors of the published solutions in their research ignore the significant problem of signal synchronization, in which data is embedded, often assuming perfect synchronization. In the case of practical implementations of steganographic systems, this approach is too much of a simplification, because achieving synchronization is a necessary condition for the effective extraction of payload [1, 2].  
Data transmission in a steganographic system is inextricably linked with the issue of synchronization. In the absence of synchronization mechanisms, the moment of starting the steganographic data extraction procedure is difficult to determine unequivocally, which implies the random nature of the received data, which is synonymous with low efficiency of hidden transmission.  
The bit error rate (BER) was adopted as a measure of the efficiency of steganographic data transmission [3]. The use of hidden synchronization methods should therefore result in obtaining low BER values, which, in combination with detection and correction codes, will enable error-free transmission of the payload.  
The use of hidden synchronization methods may or may not be associated with a deterioration in the quality of the cover work. Therefore, it is reasonable to search for such methods of synchronization that will not cause a significant deterioration of the quality of the signal carrying the payload. We often call cover work (original signal) with a payload Stego Object or Stego Work [1, 2].  
The paper presents four unique mechanisms that allow to achieve synchronization on the receiving side. Three of the developed methods of synchronization operate directlyon the acoustic signal, while the fourth method works in the higher layer, analyzing the structure of the decoded steganographic data stream. All of new synchronization methods have been tested against the steganography paradigms: transparency, robustness, and data rate.  
The remainder of the paper is organized as follows. Section 2 present a short description of the state of the art about speech steganography. Description of one of the methods of speech steganography is contained in Section 3. Technique development, implementation and study results are shown in Sections 4 and 5, respectively. Finally, we summarize the paper.
## 5 Performance Analysis  
As discussed in Section 2.2, the performance analysis of the LoRa network is evaluated by configuring five transmission parameters, such as transmission power (PTr), carrier frequency (CF), bandwidth (BW), code rate (CR), and spreading factor (SF). The data rate, link budget, and receiver sensitivity are the evaluation metrics that are employed to analyze the performance of the LoRa network by configuring the above-mentioned five network parameters. The analysis is as follows:
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A  
To investigate the feasibility of atmospheric density inversion with LEO satellite orbit data and the accuracy of the inversion results, a LEO satellite carrying a high-precision GPS receiver and an accelerometer with stable operation, is selected for the inversion experiments, and the inversion results of the use of the orbit data and the accelerometer data are verified with the traditional atmospheric empirical model, which lays a foundation for the improvement of the accuracy of the inversion algorithm in the future.  
The low-orbiting SWARM-A star, part of the Swarm series, the world's first Earth-exploring satellite constellation, launched on 22 November 2013, flies in a polar orbit  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Launch Time** & **Satellite Program** &
\begin{tabular}{c} **Measurement Altitude** \\ **Range** \\ \end{tabular} & **Products** \\ \hline
2000–2010 & CHAMP & \textless{}450 km & AI ME OG GPS \\ \hline
2001–2010 & TIMED & \textless{}625 km &
\begin{tabular}{c} Atmospheric data \\ Solar radiation data \\ Thermosphere and ionosphere dynamic \\ process \\ \end{tabular} \\ \hline
2002–2017 & GRACE & \textless{}480 km & GSM GA GA GAB GAC GAD \\ \hline
2006- & COSMIC & \textless{}800 km &
\begin{tabular}{c} Atmospheric profile data \\ Weather forecast data \\ \end{tabular} \\ \hline
2009–2013 & GOCE & \textless{}260 km &
\begin{tabular}{c} VT TEC and ROTI SSTI ANTEX \\ Global gravity field model and grid \\ \end{tabular} \\ \hline
2014- & Swarm & \textless{}530 km &
\begin{tabular}{c} Core Lithosphere Mantle \\ Oceans Thermosphere \\ \end{tabular} \\ \hline
2018- & GRACE-FO & \textless{}490 km &
\begin{tabular}{c} Land, water, glaciers, and ocean currents \\ Grace-FO RL-06.1 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information on Low Earth Orbit satellites used for atmospheric density research and carrying high-precision payloads (high-precision accelerometers and positioning devices) since 2000, including their launch time, developers, satellite orbit altitude, and auxiliary products.  
at an orbital altitude of 470 km and an inclination of 87.4\({}^{\circ}\) and carries high-precision magnetometers, accelerometer, and GPS receivers to observe the Earth's magnetic field in detail with unprecedented precision. The GPS receiver not only provides information on position, velocity, and time for magnetic field measurements but can also be used to determine non-conservative forces, such as atmospheric reverse drag acting on the spacecraft and solar radiation pressure, to calculate atmospheric density. Its accelerometer can directly measure the acceleration due to these forces at a much higher resolution than GPS receivers and modeling and calculations can be used to extract thermospheric atmospheric density from the Swarm satellite accelerometer.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 5 Implementation
### Implementation of Key Parameters
#### 5.3.4 Forwarding Strategies  
The _forwarding strategies_ from Section 4.1 are all already implemented in ndnSIM. Each strategy corresponds to a subclass of Strategy. A Strategy decides whether and where to forward an Interest but is also responsible for sending Data packets to adjacent nodes.
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 4 Discussion
### Increasing the Time Interval between Adjacent Signal Reception Cycles  
False-bottom formation is caused by echo disorder. A false bottom is generated during a signal cycle when the second reflection of the same ping signal experiences disorder between the second echo at the bottom and the actual bottom echo signal [18]. In this scenario, the area in which the target object appears (above the seabed) does not overlap with the area in which the false bottom emerges (below the seabed); thus, it does not affect the target object detection. However, the areas in which the other two false bottoms occur overlap with the areas where the target object appears, meaning that they must be avoided during data collection. Both types of false bottom result from echo disturbance between adjacent signal cycles, in line with the principle of false-bottom formation. Specifically, these false bottoms are caused by the echo signal from the previous cycle, which is collected during the current cycle's signal acquisition stage. To prevent mutual interference between adjacent periods, the time interval between the two signal acquisition cycles (\(t_{i}\)) can be increased, further attenuating the previous cycle's echosounder signal in water [19]. However, increasing the time interval will reduce the temporal resolution of the echosounder; therefore, the accuracy must meet the required standards when implementing this approach.
# Influence of Carbon: Metal Ratio on Tribological Behavior of Mo-W-C Coating
## 3 Results
### Coating Microstructure and Composition

Table 2: C/(Mo+W) ratio as calculated from GDOES analysis. Figure 3: Elemental distribution of Mo-W-C coatings detected by GDOES analysis: (**a**) C2, (**b**) C4, (**c**) C6 and (**d**) summary of elemental composition. led to a coating growth aligned with the crystal orientations of the substrate over a small area resulting in enhanced adhesion between coating and substrate. A similar alteration of interface structure by the highly energetic metal ions during HIPIMS etching was already reported for Ti-DLC and Cr-DLC coatings deposited via combined UBMS+HIPIMS mode on the steel substrates [27]. No flaking or interfacial spallation was observed in the SEM images of the scratch track ends, indicating excellent adhesion strength irrespective of the C/(Mo+W) ratio. Figure 4: Adhesion strength of C2, C4 and C6 demonstrating (**a–c**) individual critical load (LC3) and (**d–f**) SEM images of the respective scratch track ends for 100 N applied load. Figure 5 summarizes the nano-hardness and elastic modulus of C2, C4 and C6. A high C/(Mo+W) ratio of C2 led to an average nano-hardness value of \(\sim\)1335.8 HV, whereas both C4 and C6 showed average nano-hardness values of \(\sim\)1677.5 and \(\sim\)1702.4 HV, respectively, due to low C/(Mo+W) ratios. This could be directly linked to the presence of hard carbide phases (WC, W\({}_{2}\)C and Mo\({}_{2}\)C) in these coatings [21; 28], where C4 and C6 particularly contained more metal carbide phases due to their high Mo+W content and therefore high hardness as compared to C2. Similarly, average elastic modulus values of \(\sim\)194.5, \(\sim\)236.2 and \(\sim\)268.5 GPa were observed for C2, C4 and C6, respectively. This behavior was found to be different from Mo-DLC and W-GLC coatings but similar to the W-DLC coating. For example, a combined RF and mid-frequency DC-sputtered nanocomposite Mo-DLC coating showed no change in hardness properties (\(\sim\)10-11 GPa) but an increase in elastic modulus values (\(\sim\)100-120 GPa) with an increase in Mo content [13]. Magnetron-sputtered W-GLC coatings showed a linear increase of hardness (\(\sim\)8-15 GPa), followed by a gradual decrease with an increase in W content [20]. On the other hand, W-DLC coatings deposited by using combined ion-beam and DC-magnetron-sputtering system showed an increase in both hardness and elastic modulus values (\(\sim\)15-19 GPa and \(\sim\)170-225 GPa, respectively) with an increase in W content [19]. Mo-W-C coatings showed similar average hardness but higher elastic modulus as compared to the reported W-GLC and W-DLC coatings. Overall, a decrease in C/(Mo+W) ratio led to a linear increase in both average hardness and elastic modulus values of Mo-W-C coatings, while maintaining a steady H/E ratio (\(\sim\)0.06-0.07).
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
###### Abstract  
In this paper, a non-binary low-density parity-check (NB-LDPC) coded high-order continuous phase modulation (CPM) system is designed and optimized to improve power and iterative efficiencies. Firstly, the minimum squared normalized Euclidean distance and the 99% double-sided power bandwidth are introduced to design a competitive CPM, improving its power efficiency under a given code rate and spectral efficiency. Secondly, a three-step method based on extrinsic information transfer (EXIT) and entropy theory is used to design NB-LDPC codes, which reduces the convergence threshold approximately 0.42 and 0.58 dB compared with the candidate schemes. Thirdly, an extrinsic information operation is proposed to address the positive feedback issue in iterative detection and decoding and the value of bit error rate (BER) can approximately be reduced by \(5\times 10^{-3}\). Finally, iteration optimization employing the EXIT chart and mutual information between demodulation and decoding is performed to achieve a suitable tradeoff for the communication reliability and iterative decoding delay. Simulation results show that the resulting scheme provides an approximately 3.95 dB coding gain compared to the uncoded CPM and achieves approximately 0.5 and 0.7 dB advantages compared with the candidate schemes. The resulting NB-LDPC-coded high-order CPM for a given code rate and spectral efficiency converges earlier into a turbo cliff region compared with other competitors and significantly improves power and iterative efficiencies.  
Keywords:NB-LDPC code; CPM; iterative detection and decoding; iterative efficiency; power efficiency +
Footnote †: journal:  
_Article_  
Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 5 Technical Challenges and Ethical Issues
### Key Challenges and Requirements
#### 5.1.1 Data Quality, Standardization, and Management  
The development of a DT for healthcare requires access to social and environmental determinants and access to large-scale, high-quality healthcare data repositories, encompassing genetic information, medical records, imaging, histopathology, and other sources. These datasets must be accurate, complete, and standardized to ensure reliable digital twin models [47]. Effective data management further necessitates the use of standardized metadata, ontologies, and controlled clinical terminologies to prevent disorganized or unusable datasets, sometimes referred to as a \"data swamp\".
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Mobility Modeling  
User mobility prediction is necessary to achieve accurate traffic prediction. With the rise of mobile phone usage and connected vehicles, predicting mobility becomes an important step in understanding mobile network demands. Mobility models can be developed by considering different environments, such as urban [27] or highway patterns to predict the next station a user will likely connect to [50] in order to reduce costs for operational tasks such as handover [35]. Once the mobility patterns of users in a network are understood, then DL can also be used to predict traffic patterns and create more cost-efficient network operation schemes. For example, the expected demand for a base station can be predicted according to the spatial and temporal relationship it has to nearby stations [26]. Other studies apply LTSM to predict the position of the UE along time [51].
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Single Carrier Frequency Division Multiplexing (SC-FDM)  
The Single Carrier Frequency Division Multiplexing (SC-FDM) technique combines the advantages of OFDM, frequency-domain spread multi-carrier code-division multiple access (CDMA), and the conventional single-carrier direct-sequence CDMA (DS-CDMA). Moreover, interleaved frequency division multiple access (IFDMA) scheme in SC-FDM systems does not exhibit PAPR problems, while localized frequency division multiple access (LFDMA) implementation slightly conflicts in terms of PAPR. For these cases, we can maximize as much as possible that aforementioned figure of merit to achieve the best possible performance in the system.  
In fact, SC-FDM and not OFDM is the waveform preferred in the LTE uplink due to its low PAPR and, in this respect, the energy consumption of the mobile station is hugely reduced. SC-FDM is not a pure single carrier waveform. Its features are halfway in between the features of pure single carrier waveforms and multi-carrier waveforms. Therefore, SC-FDM presents better performance than multi-carrier waveforms in terms of PAPR, but PAPR is higher than that of pure single carrier waveforms [24]. Like basic OFDM, SC-FDM has high OOB emissions. The WOLA technique is further applied in SC-FDM in order to reduce the level of secondary lobes affecting adjacent bands.
## 6 System Evaluation
### Xbee ZB PRO S2 Radio Module
#### 6.1.2 Range Test  
In this test the maximum range of the Xbee PRO S2 has been tested. Different combinations of antennas for the receiver and the transmitter were tested ranging from 2 dBi to 5 dBi. The test consists of measuring the maximum distance at which two different nodes can communicate. Table 3 shows the results obtained. The tests performed in this section assume that the two radio modules are already associated. The antennas have been placed at different heights (on the floor and at a height of 2 m).  
The results show that there is a big difference in communication range depending on where the antenna is placed. When placed on the floor, the performance of the radio module decreases significantly. For example, in the test where the 2 dBi antenna has been used, both in the transmitter and the receiver, the communication range is 60 m. This is much less than the range depicted in the datasheet (hundreds of meters).
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology  
The proposed approach begins with the collection of AE signals from the pipeline, followed by a series of preprocessing, feature extraction, and classification steps, ultimately leading to an accurate assessment of the pipeline's health condition. This comprehensive workflow ensures that critical signal characteristics are preserved and analyzed effectively. Figure 1 visually represents the step-by-step flow of the proposed methodology, providing a clear overview of the processes involved. The detailed steps are outlined below:  
**Step I:** From the pipeline, the AE signals are collected during both leak and non-leak conditions.  
**Step II:** TD AE signals are converted into images utilizing the CWT. These images use various colors to represent different energy intensities, illustrating how energy levels vary across different time and frequency ranges.  
**Step III:** CWT images are pre-processed using a Gaussian filter to smooth and reduce noise in the scalogram images. This filtering enhances the clarity of the key features, making it easier to identify potential leaks.  
**Step IV:** To extract detailed spatial and temporal features from the enhanced scalograms, a hybrid model combining CNN and LSTM was used. The CNN component effectively captures spatial features, such as changes in energy at specific levels, including variations in AE amplitude. Meanwhile, the LSTM component is adept at capturing temporal dependencies, providing information about the sequence of events in the AE signal, such as AE intensity over time and frequency distribution. These extracted characteristics are then used for further analysis to determine the distinct characteristics of pipeline leaks.  
**Step V:** A feature vector is generated by integrating spatial and temporal features extracted from enhanced scalogram images using a hybrid CNN-LSTM model. These extracted features are collected by a genetic algorithm, which then selects the most important ones. Based on these refined features, pipeline leaks are detected, and the health state of the pipeline is determined using a fully connected layer.
## 5 Discussion and Use Cases
### Crime Scene Reconstruction  
The details of results found during this study can help in investigating the cases involving live traffic monitoring and capturing based on the Signal app. The forensics examiner can deduce the results of on-going calls, messages, and typing patterns. The communicating parties can also be identified with the IP addresses during the real-time call as well. The forensic examiner needs to obtain access to the target network with the help of the specific organization. Traffic dumps of the ongoing traffic are required to be captured at a certain time of the suspect's activities. With the help of the results deduced in the section Results and Analysis, specific traffic can be filtered out among the ongoing traffic. Specific activities with the help of predefined patterns can be distinguished. Further, the information of the users with captured IP addresses can also be verified from ISPs.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 4 Depth Distribution Algorithm Based on Linear Block Codes
### Depth Distribution Algorithm Identifies Bit Rate and Generation Matrix
#### 4.3.2 The Recognition of Generator Matrix \(G\)  
The expression form of the generation matrix \(G\) of linear block codes is not unique, but the row and column values of the generation matrix are the same, which are all matrices of \(k\) rows and \(n\) columns. According to the characteristics of the linear block code matrix generation, only \(k\) independent vectors need to be found, and each vector as a row, arranged in sequence, is to generate matrix \(G\). According to Theorem 2, linear block codes have exactly \(k\) non-zero depth values. The non-zero codeword vectors with different depth values are linearly independent, and the matrix generated as row vectors is the generation matrix \(G\). The matrix is identified according to this conclusion. A typical generation matrix  
Figure 4: Diagram of depth distribution algorithm.  
can be obtained by an elementary transformation of the generated matrix. For special codes--cyclic codes, the recognition of generated polynomials can be completed.  
The key point of the algorithm is the recognition of information bit \(k\), which has been recognized when the code rate \(t\) is recognized. For \((n,k)\) linear block codes, there are and only \(k\) non-zero values in the depth spectrum. When the prior knowledge of \(e\) and \(n\) is known, the depth value of all \(M\) code words can be obtained, which is marked as \(j\), \(j\in\left\{\begin{array}{cccc}0&1&\cdots&n\\ \end{array}\right\}\). The sum of the code words with a statistical depth of \(j\) is \(D_{j}\), and the depth distribution is \(\left\{\begin{array}{cccc}D_{0}&D_{1}&\cdots&D_{n}\\ \end{array}\right\}\). The depth of the spectrum is \(j\in\left\{1<j<n,D\
eq 0\right\}\). According to Theorem 2, any code word corresponding to all non-zero depth values in the depth spectrum is selected and placed into a matrix, which is the generated matrix \(G\).  
The flow diagram is shown as Figure 5.  
Therefore, according to different parameters, the distribution characteristics of code word depth can be expressed as:  
\[S(C(n,d))/n\leq S(C(n,d_{e}))/n\leq S(C(n_{e},d_{e}))/n \tag{21}\]  
Therefore, when the depth distribution value of the receiving codeword matrix reaches a minimum value, the parameter estimation is closest to the true value (there is a code length multiple relation), that is:  
\[(n,d)=ArgMin(S(C(n_{e},d_{e}))/n \tag{22}\]  
Figure 5: Flow diagram of recognition of rate and generating matrix.