esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Architecture for the TOSCA-Based Inter-Cloud M&S (ArTIC-MS)
### ArTIC-MS Rationale  
In order to properly address the inter-cloud service orchestration scenario, the design of ArTIC-MS is based on principles and assumptions illustrated in [12] and hereby summarized for the sake of completeness:  
* **Service descriptions**: The interoperable integration of services deployed in different infrastructures and implemented by the use of various technologies requires the availability of an agnostic and technology-independent service description. ArTIC-MS adopts service descriptions that specifically address:
* _Metadata_: This is used for describing services. Due to the availability of metadata, users can identify the most suitable services that have to be integrated into the MSaaS application. Metadata are stored in the _service registry_.
* _Infrastructural orchestration description_: This is used to deploy and execute a service. ArTIC-MS assumes that such a description, which is stored in a _Service repository_. This is provided as a TOSCA CSAR package. The scenario that clarifies how an ArTIC-based MSaaS implementation can be federated with other MSaaS infrastructure is discussed in Section 7.  
Figure 3: MSaaS reference architecture.  
* **Service discovery**: In federated MSaaS infrastructure, a discovery service shall be provided to enable users in identifying and retrieving services that each partner made available on its cloud platform. In this respect, it is assumed that the service registry provides an application program interface (API) for implementing inter-cloud service discovery.
* **Service availability**: The provisioning of various MSaaS services is the responsibility of the several federated infrastructure partners. In turn, each partner shall be provided with features to retrieve and integrate such services to build more complex MSaaS applications. In this respect, each service can be provided by a partner in two different configurations, according to the preferred business model:
* _Running services_: A partner might provide a running service that is deployed and configured under the responsibility of the providing partner. In this case, the service metadata includes a service _endpoint_ (e.g., a URI) and an interface description (e.g., by use of web service definition language (WSDL));
* _deployable services_: A partner might provide an instance of the service specified in terms of artifacts and an infrastructural orchestration description (e.g., a TOSCA CSAR package). In this case, the service metadata includes a reference to the service repository that stores the CSAR package.
* **Service composability**: The main objective of the ArTIC-MS platform is to support users in executing the various activities required to build, describe and make available M&S services, and to use such services for building complex MSaaS application. It should be underlined how a _MSaaS application_ can be treated as a (complex) M&S service; thus, its description and implementation will be stored in the service registry and the service repository, respectively. Moreover, it can be recursively used as a building block of other compositions.
## 3 Lightweight Cryptography for Smart Objects
### Symmetric-Key LWC Algorithms
#### 3.1.3 PRESENT Cipher  
PRESENT is an ultra-lightweight block cipher algorithm based on a Substitution-Permutation Network (SPN) [28]. PRESENT has been designed to be extremely compact and efficient in hardware. It operates on 64-bit blocks and with keys of either 80 or 128 bits. It is intended to be used in situations where low-power consumption and high chip efficiency are desired, thus making it of particular interest for constrained environments. The main design goal of PRESENT is, as for the other lightweight ciphers, simplicity. PRESENT is performed in 31 rounds, each comprising three stages:  
* key-mixing, through XOR operation and a 61-bit rotation key schedule;
* substitution layer, through 16 4-bit (input) by 4-bit (output) S-boxes;
* permutation layer.  
At the end of the 31 st round, an additional round is performed by XORing the last round subkey.  
ISO/IEC 29192-2:2012 \"Lightweight Cryptography\" specifies PRESENT as a block cipher suitable for lightweight cryptography [29].
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
###### Abstract  
This paper reports the experimental results of a test campaign performed on the radio-frequency (RF) receiver prototype operating at a 2025-2110 MHz frequency range, designed and fabricated for CubeSat applications. The prototype has been tested through a board-level test approach for the verification of the functional requirements and a component-level one for specific characterization measures. The tests have shown the following results: a \(-\)115--70 dBm sensitivity range, 390 MHz intermediate frequency, a 0 dBm output power level with \(\pm\)1 dB error, a 2.34 dB noise figure, and a 4.86 W power absorption. Such results have been largely achieved implementing an automatic gain control system by cascading two Commercial Off-The-Shelf (COTS) amplifiers. Moreover, an innovative technique based on RF test points has been successfully experimented and validated to measure the S-parameters of a custom low-pass filter integrated on the receiver, showing the possibility of even characterizing the single COTS components exposed to radiation through a unique board-level test setup. The technique may have a great impact on the cost reduction of electronic boards for space applications, since it would avoid using expensive evaluation boards for each COTS component that needs a radiation test.  
COTS; cubesat; radiation test; receiver; RF; satellite +
Footnote †: This paper is an extended version of our paper published in: [PERSON]; [PERSON]; [PERSON] Design of COTS-Based Radio-Frequency Receiver for Cubesat Applications. In Proceedings of the 2019 IEEE International Workshop on Metrology for Aerospace (MetroAeroSpace), Torino, Italy, 19-21 June 2019.
# COSMIC-2 Mission Summary at Three Years in Orbit
## 2 Mission Status and Milestones
### Ground Segment  
A network of 10 downlink ground stations located in Australia, Brazil, French Polynesia, Ghana, Guam, Hawaii, Honduras, Kuwait, Mauritius, and Taiwan receives downlink telemetry and routes the data to the National Space Organization (NSPO) and University Corporation for Atmospheric Research (UCAR) data processing centers. The sites in Taiwan, Australia, and Mauritius are capable of uplink/command. A map is given in Figure 1. The network is designed to enable frequent downlinking of COSMIC-2 data in order to meet the mission data product median latency requirements of 45 min and 30 min for neutral atmosphere and ionosphere products, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Software Version** & **Key Features** & **Date** \\ \hline v4.3.2 & \multicolumn{2}{c}{Corrected reboot issue.} & September 2019 \\ v4.3.3 & Reduced troposphere \& ionosphere data loss; corrected GLONASS pseudorange & November 2019 \\  & noise problem; connected phase for GLONASS high-rate (HR) ionosphere data. & \\  & Improved tracking channel allocation; provide 1 sec SNRs (instead of 10 sec); & \\ v4.3.4 & connected phase for GPS HR data; address connected phase problem for GLONASS & February 2020 \\  & high rate ionosphere data; improved HR scintillation trigger algorithm. & \\  & Improved GPS \& GLONASS observation scheduling; corrected GPS HR L2 \& HR \\ v4.3.5 & scintillation incomplete profile problems; fixed issue with high-rate scintillation data & August 2020 \\  & volume limiting algorithm. & \\ v4.3.6 & Improvements to GLONASS scheduling \& channel management; improved & February 2021 \\  & autonomous detection of hangup events requiring reboot. & \\  & Improved ionosphere tracking through sporadic-E layers; improved GLONASS & \\ v4.4.0 & observation scheduling; improved acquisition of rising L2P signals; correct issue & September 2021 \\  & with corrupted GLONASS HR data. & \\ v4.4.1 & Reduced time to resume tracking following restarts; improvements to GLONASS & December 2021 \\  & ionosphere scheduling and tracking through larger azimuth ranges. & \\  & Further improvements to tracking through sporadic-E layers; correct occasional & \\ v4.4.2 & issue with occultation height of straight-line computation; improved trigger to & July 2022 \\  & collect more HR ionosphere data. & \\ \hline \end{tabular}
\end{table}
Table 2: TGRS flight software updates.
## 2 Materials and Methods  
### Major Ingredients  
#### 2.1.1 Silica Fume  
Silica fume is the term normally used for the micro silica or the condensed silica byproduct produced as a result of the production of Silicon and Ferrosilicon alloys from highly pure form quartz and coal in an electric submerged-arc. The advantage of utilization of Silica fume in concrete gives special characteristics to concrete such as the reduced permeability property, improved corrosion protection for steel bars, increased resistance against the sulfate and chemicals attacks, improved and efficient mechanical strengths and performances and elevated levels of split tensile and flexural strengths [18]. The replacement of cement with different percentages of Silica Fumes, i.e., 5%, 10%, 15%, 20% and 25%, enables analyzing and interpreting clear results of the performances incurred by this valuable replacement. SF reacts with concrete in two different ways: by modifying hydration reaction in concrete and by its very well known \"Micro-filler effect\". Upon adding water to the Portland cement in the concrete, hydration process starts; thus, the two primary products are formed [19]. The first resultant product is \"Calcium silicate gel\". It is a \"glue\" of adhesive nature to bind aggregates together in concrete mix. The other product is \"Calcium hydroxide\", which comprises up to 25% volume of products formed because of hydration. Calcium hydroxide at higher levels is not useful for the concrete and induces the harmful alkali-aggregate reactions and efflorescence. The second major and efficient process through which the SF improvises concrete is the known \"Micro-filler effect\" [20]. Average particle size for the dense SF is about 0.1 micrometers, while the ordinary Portland cement has average particle size of about nearly 15 micrometers. The extremity in fineness of SF allows filling in the microscopic voids and gaps between the particles of cement [21]. Micro-filter effect greatly improves and reduces permeability and improves the important \"Paste-to-Aggregate bond\" in the SF concrete as compared to the conventional ordinary concrete used in common practice. There is no major visible difference in the moisture absorption percentage for both SF and ordinary concrete. The shade of the Silica Fumes depends mainly upon the carbon content in it and pigment inducing variables present in composition contents.
###### Abstract  
Space cameras play a pivotal role in various fields, such as astronomical exploration. When operating in orbit, these cameras encounter the relative motion between the target and the camera during the exposure, resulting in image motion, which affects the imaging quality. Therefore, it is necessary to compensate for this image motion. This paper investigates the in-orbit image motion compensation (IMC) method for space cameras with a long integration time based on a two-axis pointing platform. Firstly, the mechanical design of the camera is introduced. Secondly, the in-orbit IMC model for the camera is analyzed, and the angular motion needed for compensating for the image motion by the two-axis pointing platform are derived. Factors influencing the compensation accuracy are also analyzed. The effectiveness of the IMC model is verified through simulations. Finally, the in-orbit experimental results indicate that the energy concentration of the target star images obtained exceeded 70%, demonstrating excellent performance in space cameras and effectively enhancing imaging quality using IMC technology.  
Keywords:space camera; image motion compensation; two-axis pointing platform +
Footnote †: journal: Applied
## 5 Results, and Discussion
### Results of Training and Validation
#### 5.1.2 Analysis of Augmentation Technique Effectiveness  
As shown in Table 3, all three architectures (ViT, Swin Transformer, and PVT) benefited notably from the application of data augmentation. While ViT's AP50 rose from 85.37 to 88.33 (a gain of 2.96), Swin Transformer achieved a slightly higher boost--from 86.71 to 89.77 (a gain of 3.06)--and PVT followed closely with an AP50 increase of 2.80 (from 85.63 to 88.43). The stricter localization requirement in AP50:95 similarly showed consistent improvements: ViT's AP50:95 rose by 4.75, Swin Transformer by 4.74, and PVT by 4.02, suggesting that although all models still faced challenges aligning bounding boxes to ground truth at a higher IOU, augmentation helped mitigate these issues. Moreover, the gains in \(AP_{S}\) were particularly pronounced (6.72 for ViT, 7.17 for Swin and 4.80 for PVT), indicating that augmented training samples allowed the networks to better capture and discriminate small or partially occluded objects in cluttered construction-site scenes. Similar but smaller gains emerged for \(AP_{M}\) and \(AP_{L}\), highlighting that larger targets remained somewhat easier to detect yet still benefited from augmented features and variability. Notably, Swin Transformer showed the highest overall IOU gain of 5.51, implying that its hierarchical attention mechanism adapted well to the geometric transformations introduced by augmentation.
## 6 Conclusions  
This theoretical and experimental study assesses the normal incidence absorption performance of highly porous micro-capillary plates with micron-sized channels against those of more standard micro-perforated panel absorbers with sub-millimetric-sized holes. MCPs are modeled with pure resistances calibrated according to their channel diameter, thickness and perforation ratio. This model is verified using impedance tube measurements. It is found that unbacked 1 mm-thick MCPs with both a high porosity (greater than 60%) and channels with a diameter of 28 \(\upmu\)m are able to provide absorption values greater than 0.87 above a broad bandwidth, typically 100 Hz-2 kHz. Increasing the diameter while keeping high (resp. low) porosity drastically lowers the efficiency of the bandwidth (resp. the maximum absorption amplitude) that can be reached.  
Given their very large bandwidth-to-thickness ratio, unbacked MCPs could be used in a quiet environment as thin low-frequency anechoic terminations, complementary to existing anechoic cone terminations, that are more efficient at higher frequencies. It would be of interest to determine how robust the unbacked MCP performance is for the reactivity index of the room into which it radiates.  
This study also shows that MCP resonators backed by rigid shallow cavities have ultra-broadband absorption properties. They are found to have the highest acoustic efficiency index with respect to other resonating absorbers with either a larger aperture diameter or a lower perforation ratio (such as standard MPPs), but also with respect to engineered metamaterial absorbers. MCP resonators could potentially be used as compact lining solutions to tackle the low-mid broadband frequency content of turbofan noise in aeronautical applications, although this warrants dedicated aero-acoustic studies. A parallel MCP resonant absorber partly backed by closed and open shallow cavities is proposed to overcome the limited performance at very low frequencies of resonating absorbers while maintaining high broadband performance at mid/high frequencies. This hybrid-type MCP absorber would require further studies.  
Conceptualization, T.B. and C.M.; methodology, C.M. and T.B.; software, C.M.; validation, T.B.; formal analysis, C.M. and T.B.; investigation, T.B. and C.M.; resources, T.B. and C.M.; data curation, T.B.; writing--original draft preparation, T.B. and C.M.; writing--review and editing, C.M.; visualization, C.M.; supervision, T.B.; project administration, T.B. and C.M.; funding acquisition, T.B. and C.M. All authors have read and agreed to the published version of the manuscript.  
This work is part of the project TED2021-130103B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union \"NextGenerationEU\"/PRTR. It also received support from the French government under the France 2030 investment plan, as part of the Initiative d'Excellence d'Aix-Marseille Universite-A*MIDEX (AMX-19-IET-010).  
Not applicable.  
Data Availability Statement: Data supporting the reported results are available on request.  
The authors declare no conflict of interest.
# Accounting for Round-Off Errors When Using Gradient Minimization Methods
## 5 Improved Iterative Algorithm  
Thus, the iterative algorithm for solving system (1) with an improved stopping criterion of the iterative process will take the following form:  
1. Set \(p^{(0)}:=0\), \(s:=1\) and an arbitrary initial approximation \(x^{(1)}\).
2. Compute \(r^{(s)}:=A^{T}\big{(}A\,x^{(s)}\,-\,b\big{)}\).
3. Compute \(\sigma^{2}_{r^{(s)}_{n}}:=\sum\limits_{k=1}^{M}A^{2}_{kn}\bigg{(}\sum\limits _{l=1}^{N}\big{(}A_{kl}x^{(s)}_{l}\big{)}^{2}+b^{2}_{k}\bigg{)}\) for each \(n\in\overline{1,N}\) and go to step 6.
4. Compute \(r^{(s)}:=r^{(s-1)}\,-\,\frac{q^{(s-1)}}{\big{(}p^{(s-1)},q^{(s-1)}\big{)}}\).
5. Compute \(\sigma^{2}_{r^{(s)}_{n}}:=\sigma^{2}_{r^{(s-1)}_{n}}+\bigg{(}\frac{q^{(s-1)} _{n}}{\big{(}p^{(s-1)},q^{(s-1)}\big{)}}\bigg{)}^{2}\) for each \(n\in\overline{1,N}\).
6. Compute \(\sigma^{2}_{s}=\sum\limits_{n=1}^{N}\sigma^{2}_{r^{(s)}_{n}}\).
7. If \(\frac{\sigma^{2}_{s}\Delta^{2}}{\|r^{(s)}\|^{2}}\geqslant 1\), then stop the iterative process and set \(x^{(s)}\) as a solution of system (1).
8. Compute \(p^{(s)}:=p^{(s-1)}\,+\,\frac{r^{(s)}}{\big{(}r^{(s)},r^{(s)}\big{)}}\).
9. Compute \(q^{(s)}:=A^{T}\big{(}A\,p^{(s)}\big{)}\).  
10. Compute \(x^{(s+1)}:=x^{(s)}\,-\,\frac{p^{(s)}}{\big{(}p^{(s)},q^{(s)}\big{)}}\).
11. Redefine \(s:=s+1\) and go to step 4.
## 3 Quality Assessment
### Resolution Capability  
Spectral analysis is an effective method for studying the frequency characteristics of signals or systems. The coherence function and power spectrum reflect the degree of correlation between signals and the distribution density of power with frequency. The purpose of power spectrum estimation is to describe the frequency component distribution of signals and random processes based on limited data sequences. The coherence function can judge the similarity between two repeated periodic signals to deduce the resolution. Marks defined the standard for judging the wavelength resolution of geoid, which is the corresponding spatial wavelength when the mean square consistency reaches 0.5 [30]. We inherited this standard to analyze the resolution capability for the four HY-2 series missions uniformly. Both the HY-2 GM and ERM provided multiple cycles with repetitive ground tracks, which provided suitable data sources for analyzing the along-track resolution capabilities of geoid signals. To avoid possible data break and interruption, we selected data sequences with an extremely low data loss rate in the six open-ocean study regions. In addition, the ground tracks of selected data sequences were similar among the HY-2 missions. The spatial distribution of sample data with marked pass number is shown in Figure 4. Detailed information of the sample data sequences--date, quantity and average distance--is listed in Table 3. Sample data are basically acquired in the same season, except HY-2A, since it runs in the GM phase. A full sampling rate of SSH measurements at ~20 Hz was extracted, while the 1 Hz correction items mentioned in Section 2.1 were linearly interpolated to high-rate sampling points. In addition, the mean dynamic topography items provided in HY-2 SDR products were subtracted to obtain along-track geoid heights. Then, we executed a resample procedure in a time domain to constrain the effect of missing data and uneven spacing between adjacent measurements. Space intervals were uniformly set to 0.5 km  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Experimental** \\ **Region** \\ \end{tabular}  } & \multirow{2}{*}{**Time Limit**} & \multicolumn{2}{c}{**HY-2A**} & \multicolumn{2}{c}{**HY-2B**} & \multicolumn{2}{c}{**HY-2C**} & \multicolumn{2}{c}{**HY-2D**} \\ \cline{3-10}  & & **Num** & **SD (m)** & **Num** & **SD (m)** & **Num** & **SD (m)**  
spherical distances; moreover, 5 km low-pass filtering was adopted to weaken the influence of random noise. After the resampling in a given time domain, the along-track measurements were evenly distributed. During this procedure, interpolations were unavoidable because of missing data and data sequences had to align with others at a fixed step size. As shown in Figure 5, the along-track geoid heights smoothly changed with latitude after extraction, resampling, filtering and alignment. Red, green, yellow and blue curves, respectively, represent HY-2A, HY-2B, HY-2C and HY-2D data with repeat ground tracks. The variation
# Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network
## 4 Case Study and Experimental Results
### Quantitative Safety Assessment of the Railway System Using a Bayesian Network
#### 4.2.4 Cause Chain Analysis with the Bayesian Network  
As a result that the BN proposed in this paper is based on fault trees, i.e., the research is performed on limited text records, through the fault tree of Barrier 4, the fault tree causing B4 is identified, and by connecting the BN to the FT chain, a more consistent cause chain of the actual situation is determined. The most direct factors also can be quickly identified. For example, first, T3 is defined as a failure, and we can obtain \(P(T3=1)=0\). To acquire the cause chain, we should identify the parent nodes of T3 and choose the higher posterior probabilities among these parent nodes. Furthermore, the next parent nodes are assessed until posterior probabilities are no longer available. The following is a general example of how to determine cause chains.  
1. \(P(T3=1)\).
2. Identify the parent nodes of T3 to compare their posterior probabilities and choose the higher probability. CS1 is obtained, and we now have CS1\(\rightarrow\)T3, while B1\(\rightarrow\)CS1\(\rightarrow\)T3 is acquired.
3. Repeat step (2) to identify the corresponding parent nodes with high probabilities, i.e., B4\(\rightarrow\)CS2\(\rightarrow\)T3.  
Specifically, when a railway derailment accident occurs, the most likely cause involves the external factors during the driving process caused by B4. A more specific situation combined with the fault tree of B4 indicates that the B4 fault was caused by T110. In the fault tree of B4, when B4 is adopted as a safety barrier, the reasons for its failure include external factors, weather conditions and HFs. The external factors primarily include the rails and turnouts. \"T110 Wide\" represents gauge factors (due to defective or missing crisscrosses), so when the train is in operation, T110 is the main factor causing train derailment, and the specific cause chain is T110\(\rightarrow\) B4\(\rightarrow\)CS2\(\rightarrow\)T3.