esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In a single-switch forward converter, which is a type of single-input multiple-output (SIMO) DC-DC converter, voltage changes are observed at the outputs due to the current passing through the diodes, the difficulty of winding the transformer in full and half windings, differences in resistance in the windings, the presence of leakage inductance and the existence of common inductance between the windings. These voltage changes are undesirable, and cross-regulation techniques have been developed to keep the outputs at the desired value. One of these techniques, the weighted control method, in which voltage information is received from many outputs, is addressed in this article. The reason we apply this technique is to avoid additional components and their control problems. In addition, this technique is simpler and cheaper than other cross-regulation techniques. In this way, we will contribute to the literature on cross-regulation, showing that cross-regulation remains below 3.33% and circuit efficiency reaches 86.83% by using the weighted control method.  
DC-DC converter; cross-regulation; forward converter; multi-outputs; circuit modelling +
Footnote †: journal: Applied sciences  
Article
## 2 Integrated Navigation System Model
### Combined Navigation Model  
#### 2.2.1 System State Vector  
In general, the INS provides gyroscopes and accelerometers, which need to be modeled in the error model of the INS and GNSS. Meanwhile, two or even more kinds of navigation sensor systems will be used in the actual carrier, each of which uses a different phase center as a reference datum for navigation, and these sensors will have mounting errors during installation. In order to facilitate the fusion of the navigation information from the different systems, in this paper, the lever-arm errors between the inertial group and the satellite receiver, as well as the time synchronization errors, are also estimated as states. The random errors of the gyroscope and accelerometer in the three axes of the INS, the errors of the inertial guidance system, and the receiver clock error are taken together as the state space of the Kalman filter of the integrated navigation model. The state vector can be expressed as  
\[\mathbf{X}=\begin{bmatrix}\phi^{\mathsf{T}}&(\delta v)^{\mathsf{T}}&(\delta p )^{\mathsf{T}}&\left(\varepsilon^{b}\right)^{\mathsf{T}}&\left(\
abla^{b} \right)^{\mathsf{T}}&\left(\delta l^{b}\right)^{\mathsf{T}}&\delta t&\delta t _{u}&\delta t_{ru}\end{bmatrix}^{\mathsf{T}} \tag{1}\]  
where there are a total of 21 state quantities, with the superscript b representing the errors in the body frame; \(\phi\) denotes the platform attitude angle error; \(\delta v\) is the velocity error along the east-north-up direction; \(\delta p\) is the position error; \(\varepsilon^{b}\) is the random drift of the gyroscope in the body frame; \(\
abla^{b}\) is the random drift of the accelerometer in the body frame; \(\delta l^{b}\) is the lever-arm errors between inertial guide and guardian guide; \(\delta t\) is the inertial guide and guardian guide data acquisition time synchronization errors; \(\delta t_{u}\) and \(\delta t_{ru}\) are, respectively, the GNSS receiver's equivalent clock error corresponding to the distance and the equivalent clock frequency error corresponding to the distance change rate [23].
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
## 2 Data and Methodology
### Experimental Data
#### 2.1.2 Ionosonde Data  
The ionosonde can measure the IED profile from the ground to the peak height of the F2 layer (hmF2) with high accuracy, but it must be at a specific station to obtain the observation data, the spatial coverage is limited, the observation time is short, and sometimes there are data missing. In order to investigate the performance differences of the IRI-2016 model before and after the improvement at different solar activity levels and at different latitudes, we selected two ionosonde stations at different latitudes, Beijing (BP440, 40.3\({}^{\circ}\) N/116.2\({}^{\circ}\) E) and Wuhan (WU430, 30.5\({}^{\circ}\) N/114.4\({}^{\circ}\) E), in 2015 (high solar activity) and 2019 (low solar activity), respectively. Since the temporal resolution of the ionosonde observations is different from that of the IRI-2016 model, i.e., the temporal resolution of the ionosonde observations is 15 min, we calculated the IRI-2016 model values for the time and location of these two stations by bilinear interpolation.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Radio Stripes
#### 2.3.2 Simple Implementation  
While cellular APs are large, RS enable discreet placement in existing structural elements. Sensors for vibration, temperature, microphones, and speakers may also be included in an RS deployment, as well as for other functions such as fire alarms, burglar alarms, earthquake alerts, interior navigation, and climate monitoring and control.  
The RS installation necessitates a small and compact antenna. The use of millimeter wave (mmWave) (\(f_{\text{C}}>30\) GHz) in radio stripe networks allows for very compact antenna elements, making the mmWave band ideal for the purpose of providing exceptionally fast data rates (1-5 Gb/s) and ubiquitous connection. RS can be used in virtually any place. RS can be placed inside metros, buses, and populated streets [14].
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 5 Privacy Implications of Intrusion Detection Systems
### System Architecture  
An intrusion detection system for IoT can operate in two modes i.e., (1) as the standalone system, or (2) as a collaborative system. The stand-alone detection systems rely on traffic patterns observed locally within the network domain or Internet service provider. These systems work independently within a service provider network. The stand-alone systems do not have any information about the behavior of its users in other domains and, therefore, can be easily circumvented by stealth techniques and smart attacker, such as by controlling the attack traffic to one domain but target large number of domains simultaneously. An effective intrusion detection system is envisaged to consider the collective behaviour of nodes across different domains to facilitate developing a collaborative network.  
The collaborative solutions can be grouped in two types: (1) centralized--where alert information from the domain collaborators is reported to the the centralized system which classifies the behavior of traffic sender by analyzing traffic patterns from multiple domains, or (2) The distributed or decentralized settings--where alert information from each service provider is shared and processed in a completely distributed fashion without a centralized coordinator.  
The major challenge towards the design of a collaborative IDS is regarding privacy protections for the data used for detection. The domain or Internet service provider are reluctant to share operational data of their users with each other as it risks privacy of their customers. A centralized trusted aggregation can overcome the problem of privacy if the centralized repository assures cooperating domain that their provided information would not be misused and disclosed to any one. Furthermore, use of cryptographic methods or addition of noise data can be considered to anonymize user data, however, this is expected to substantially increase the network load and computation time.
# Wall Structure Geometry Verification Using TLS Data and BIM Model
## 2 Building Structures' Geometry Verification
### Plane Segmentation from Point Clouds
#### 2.2.2 Curve Segmentation
The negative gradient of \(E\) points towards the lower values (i.e., edges) and therefore is suitable to attract the segmentation curve towards the edges. The time-dependent parameter \(\lambda(\mathbf{t})\in\) [0,1] serves as a weight between expansion and edge attraction term. The standard approach is to set \(\lambda(\mathbf{t})\) at the beginning of the evolution (\(\mathbf{t}=0\)) to a number 0 \(<\lambda_{0}\ll 1\) (i.e., the edge attraction does not dominate), keep it unchanged until the curve is close to the border (moving very slowly) and then switch \(\lambda(\mathbf{t})\) to 1, which turns off the expansion and attracts the curve towards the edges. The last term \(k\mathbf{N}\) is called curvature regularization and has a smoothing effect. We use it to deal with the noise and to smooth sharp edges of the segmentation curve, mainly during the expansion phase. The parameter \(\delta(\mathbf{t})\) weighs the influence of the term. In practice, we can place many initial curves in the regression plane as well as create new initial curves during the segmentation. As the curves evolve, they can merge (if  
Figure 9: The edge detector function \(E(x)\). they have similar average properties inside) and split, too. Figure 10 shows the final segmentation curves. Postprocessing  
After the evolution process is stopped, the subsets (segments) of the point cloud are created. For each final curve, the corresponding segment of the point cloud by selecting all points that lie in pixels inside the curve is created (in Figure 10 these pixels are colored by a specific color). As the last step, the segments that correspond to the segmented wall are chosen to obtain the result--the subset of the point cloud containing the points lying on the surface of the verified wall (Figure 11). The parameters of the regression plane for wall flatness inspection are estimated from the subset of points after the above-described segmentation procedure. Orthogonal distances are calculated for each point of the segmented subset of points using (12). From the distances, a standard deviation is calculated which reflects the precision of the plane surface determination.
###### Abstract  
The quantum technology absolute gravimeters, gradiometers, and clocks are at the forefront of the instrumentation to be exploited in a future gravity mission (the QSG mission concept). Apart from the quantum payload, the mission design defines the choice of the number of satellites and the satellite orbit constellation, with the goal of optimizing the observation of the earth's gravity field and reducing aliasing phenomena. Our goal is to define the realistic gravity field changes generated by glaciers and lakes and define the sensitivity of the quantum gravity mission for the detection of hydrologic and cryospheric mass changes. The analysis focuses on mass changes in the high mountains of Asia and the South American continent. The mass changes are based on terrestrial and satellite observations and are of a climatic origin. We show that compared to the existing GRACE-FO mission, a quantum gravity mission significantly improves the detection of the climatic mass gain of lakes and mass loss of glaciers, allowing for smaller mass features to be distinguished, and smaller mass losses to be detected. The greater signal is the seasonal signal with a yearly period, which would be detected at the 10 Gt level for areas \(>\) 8000 km\({}^{2}\). The yearly mass loss of the Patagonian glaciers can be detected at the 5 Gt/yr level, an improvement from the 10 Gt/yr detectable by GRACE-FO. Spatial resolution would also be improved, with an increase of about 50% in spatial frequency for the detection of the mass change rate of lakes and glaciers in Tibet. The improved spatial resolution enables an improved localization of the lakes and glaciers affected by climatic mass change. The results will contribute to defining the user requirements of the future QSG missions.  
Keywords:quantum gravity; gravity satellite mission; GRACE-FO; glaciers mass loss; lakes mass loss; lakes in Tibet; high mountains of Asia glaciers; Patagonian glacier; Amazon basin; Parana' basin; Sobradinho water reservoir 2020
# Addressing ZSM Security Issues with Blockchain Technology
In the last few years, blockchain has been successfully adopted in several sectors beyond cryptocurrency, such as supply chain management, maritime and gaming with several distributed applications (Dapps) [16; 17; 18; 19; 20]. The current work proposes to adopt blockchain technology to address the security concerns mentioned by the ZSM standardization team in [21] and describes how blockchain technology can be used, i.e., presents the architecture of such an implementation and provides the lifecycle of a management service in a ZSM-supported scenario. Next, it qualitatively assesses this proposition to prove that it inherently addresses the security issues identified by the ZSM group. Furthermore, we identify the criteria against which an implemented solution should be evaluated. Considering that any prospective developer will use a basis for the solution of an existing blockchain or DLT platform, we evaluate indicative platforms (adopting Blockchains or Directed Acyclic Graphs (DAGs) structures), against the criteria mentioned earlier. To the best of our knowledge, this investigation and solution proposition has not been yet presented in other related works; they may mention that the use of a blockchain-based approach could be beneficial but the details of its design and implementation and the suitability of currently available blockchain approaches are not discussed. This paper is organized as follows: Section 2 presents research works related to the use of AI/ML and blockchain in modern networks focusing on the topic of ZSM networks. Section 3 highlights the main requirements and elements of the ZSM framework and examines the case of cross-domain procedures placing emphasis on the security-related open issues involved in this scenario. In Section 4, the architecture of the blockchain-enabled ZSM framework is described in detail followed by an analysis of the main components and entities of this architecture. In Section 5, the criteria against which such a solution should be evaluated in real life are presented so as to guide the prospective developers to select appropriate baseline blockchain/DLT frameworks. Section 6 provides the assessment of the proposed architecture while Section 7 is devoted to the examination of baseline Blockchain/DLT platforms that are candidates for building a ZSM-oriented solution on top of them. Finally, Section 8 concludes the paper and additionally provides the potential and the next steps of this research direction.
###### Abstract  
The structural complexity and variability of dissolved organic matter (DOM) significantly affect its binding capacity with heavy metals (HMs). This study evaluated the remediation efficacy of biochar (BC) and humic acid (HA) on Mn- and Cu-contaminated soils using four maize pot treatments: 3% BC (YB3), 6% BC (YB6), 3% BC + 1% HA (YB3H), and 6% BC + 1% HA (YB6H). The results showed that compared to the control (\(\gamma\)), Results showed Mn and Cu concentrations in rhizosphere soil decreased by 11.08-17.76%, while DOM content increased by 44.2-103.83%. BC enhanced DOM aromaticity and humification, further intensified by HA, leading to a more complex and stable DOM structure. PARAFAC identified four DOM components in BC (BC-DOM); C1 (fulvic-like), C2/C3 (humic-like), and C4 (protein-like), and in BC + HA (BC + H-DOM), an enhanced structural complexity with additional aromatic C-H groups was observed. 2D-COS analysis revealed that in BC-DOM, polysaccharides primarily interacted with Mn and Cu, followed by carboxylic acids and phenolic hydroxyl groups, but in BC + H-DOM, aromatic C-H groups preferentially bound Cu before polysaccharides, showing weaker affinity for Mn. These results elucidate the DOM-mediated immobilization mechanisms of BC and HA for HMs, offering insights for soil remediation and carbon sequestration strategies.  
metal contamination; biochar; humic acid; dissolved organic carbon; spectroscopy +
Footnote †: journal: Applied sciences
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
#### Machine Learning Tasks for Optical Networks  
A very popular aspect of optical network service where machine learning is applied is the network's survivability, which describes the network's ability to provide uninterrupted data flow [9]. Machine learning can optimize survivability rates in optical networks by recognizing issues, making self-directed decisions, and predicting traffic flows and potential failures. These different goals can be classified into standard ML tasks, which are briefly described below [1]:  
* Classification: Process of assigning threat categories;
* Regression: Predicting a value for items;
* Ranking: Ordering based on some criteria.  
While the majority of the work focuses on the above-mentioned aspect, another very important task is traffic prediction. Quite a few existing papers provide ML-based solutions for traffic prediction in communication networks. This survey will try to classify ML applications into their use of short-term versus long-term traffic prediction. Furthermore, short-term and long-term prediction differences will be examined in Local Area Networks (LAN) versus Wide Area Networks (WAN). In the following sections, this paper will discuss the motivations for applying ML to networks, followed by a general survey of techniques used in short-term predictions and long-term traffic predictions in Local and Wide Area Networks. The relevant papers explored in the survey will be categorized into the following techniques:  
1. Neural Networks;
2. Support Vector Machines;
3. Linear Regression;
4. Principal Component Analysis;
5. Statistical Models;
6. Linear Time Series Models.  
Figure 1 shows a high-level overview of the papers discussed in this survey, categorized into techniques that are applied to achieve the traffic prediction.  
The remainder of this paper is divided as follows. Section 2 motivates our survey. We then discuss definitions of short- and long-term traffic prediction, as well as the differences between the Local Area Network and Wide Area Network, in Section 3. The following sections discuss concepts of various machine learning techniques and their application towards traffic prediction. Finally, we summarize the paper in Section 10, followed by future directions. To the best of our knowledge, this is the first survey that summarizes recent advancements related to traffic prediction based on Artificial Intelligence.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 5 SMT-Based Scheduling Optimization for Fault-Aware Job Assignments
### Integration with Fault Prediction  
The SMT solver dynamically incorporates the fault probabilities predicted by the deep learning model. This integration allows the scheduler to adapt to real-time machine conditions, ensuring proactive and fault-aware scheduling. Key aspects of integration are:  
* Real-Time Data Utilization: Continuous updates of fault probabilities inform the scheduler, enabling prompt responses to changing machine states.
* Dynamic Schedule Adjustment: The SMT solver re-optimizes job assignments and timings as fault probabilities change, maintaining minimal fault risks.
* Resource Constraints Consideration: The scheduler simultaneously accounts for fault probabilities and operational constraints, ensuring feasible and optimized job assignments.
# Study of the Precipitation Process in Aging Steel Pipeline Weldments by Thermoelectric Power Means
## 4 Results and Discussion
The obtained values of hardness are inside of the limits that are recommended for avoiding fractures and cracking in the weldment. Figure 7 shows the time evolution of the TEP signal for the two micro-alloyed pipeline steels with three different microstructure welding zones at several aging temperatures, which were regularly measured by the contact TEP technique. For all aging temperatures, there is a drop in the TEP values up to around 10 h, before the TEP increases. This is observed in both micro-alloyed artificially aged X60 (Figure 7a) and X65 (Figure 7b) steels, respectively. Therefore, the relationship between the microstructure and TEP will be addressed in more detail in the following sections. At this point, it can be concluded that in the first stage of the aging process, the depletion or enrichment of the matrix from the precipitating elements and the noticeable microstructural changes in the three main welded zones are the main factors that influenced the decrease in the TEP values. An equiaxed grain was present in the base metal (BM). In the weld bead (WB), an elongated grain was observed. Finally, at the heat affected zone (HAZ), a mixture of equiaxed and elongated grains developed. In our case, the initial TEP decrease seems to be related to the inhomogeneity of the grain size/shape, which induces anisotropy in the medium. This anisotropy has an opposite negative  
Figure 6: Hardness measurements in the weldments of (**a**) API X60 and (**b**) API X65. Figure 7: Thermoelectric power measurements of the micro-alloyed steel of (**a**) API X60 and (**b**) API X65 weldments that were artificially aged. effect on the TEP and overlaps the precipitation effect due to the aging process induced by the heat treatments. In the second stage of the aging process, the precipitation of the particles (Fe\({}_{3}\)C) and carbide-\(\varepsilon\) (Fe\({}_{2}\)C) is very pronounced, which is shown in Figures 4 and 5. The volume fraction increases, which has a positive effect on the TEP. In addition, the precipitates are likely to have an intrinsic effect on the TEP [8]. In the case of metallic alloys [6; 9; 10; 11; 12; 13], it was clearly shown that the coarse incoherent precipitates have no effect on the TEP, while the coherent and semi-coherent precipitates have a strong influence. These results can be easily explained by the fact that the incoherent precipitates do not induce internal stresses in the matrix that are likely to disturb the lattice and thus, act like voids in the matrix with little influence on the TEP. In contrast, the coherent and semi-coherent precipitates induce elastic stresses in the lattice and thus, modify the TEP. It was observed that this modification depends on the size, nature, morphology and volume fraction of the precipitates. The microstructural changes due to the aging process in the micro-alloyed steel as a function of the TEP measurements should be further studied.
###### Abstract  
It is expected that the problem of the azimuth cutoff wavelength in single-satellite synthetic aperture radar (SAR) observations can be solved by means of the cooperative observation of networked SAR satellites. Multiview SAR wave synchronization data are required in the process. However, most of the current orbiting satellites are geosynchronous orbit satellites; the simultaneous observation by multiple SARs in the same sea area cannot be achieved, and multiview synchronization data cannot be obtained. Therefore, this paper studies the simulation of the multiview SAR wave synchronization data. Ocean wave spectra were simulated by using the Pierson Moskowitz (PM) spectrum. The Monte Carlo method was used to simulate two-dimensional (2D) ocean surfaces at different wind speeds. The two-scale electromagnetic scattering model was used to calculate the ocean surface backscattering coefficient, and the time-domain echo algorithm was used to generate echo signals. The echo signals were processed by the Range-Doppler (RD) imaging algorithm to obtain ocean SAR data. Based on the obtained single-SAR wave data, networked satellites consisting of three SARs were simulated, and the SAR wave data were synchronized. The results show that when SARs are used to observe the same sea area from different observation directions, the clarity of the wave fringes in the SAR images are different. For different azimuth angles, the degrees of azimuth cutoff are different. These results reflect the influences of different degrees of azimuth cutoff on SAR images. The simulated wave synchronization data can be used as the basic data source for subsequent azimuth cutoff wavelength compensation.  
networked SAR satellites; multiview; SAR wave synchronization data simulation; azimuth cutoff +
Footnote †: journal: Marine Science and Engineering