esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 3 Approach
### Assumptions and Intuition  
Our approach reconstructs sensor values of the replaced sensors from time \(S+1\) to \(S+T\) based on the reference sensors and the new sensors. The underlying assumptions are:  
* Sensor values from the reference sensors are correlated with those from the replaced sensors.
* Sensor values from the reference sensors are correlated with those from the new sensors.  
Such assumptions typically hold in real-world systems because sensor values of different sensors are often correlated [16].  
Our approach is based on the following intuition: New sensors may contain complementary information over reference sensors, useful for reconstructing replaced sensors. Figure 3 illustrates this intuition, where the reference sensor, replaced sensor, and the new sensor are temperature, humidity, and dew point, respectively. The left plot shows two selected samples from historical data. We can see that for the same temperature value, humidity can take different values. The middle plot shows that if we attempt to reconstruct humidity from temperature alone, via the \(g\) function, then the reconstructed humidity  
Figure 2: Settings and notations for sensor failures and changes.  
values become exactly the same, since the temperature information alone is insufficient for the reconstruction. The right plot shows that by incorporating dew point as a new signal, the reconstructed humidity values are distributed similarly to those in the left plot. This is expected because dew point contains complementary information over temperature for reconstructing humidity. The above intuition leads to the key idea of our approach: to learn a reconstruction function that preserves the sensor value distributions before and after the sensor change.
# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 6 Conclusions  
In this work we demonstrate the effects of using spacers to reduce the effects of surface quality when carrying out electromagnetic measurements.  
* The best trade-off between electromagnetic response and surface quality was found to be a 30 \(\upmu\)m nonmagnetic spacer (Figure 6).
* It is often the case with such measurements that small sets of data exist in terms of describing the different anomaly conditions. For these reasons there was a need to find missing data usually in the form of 'Not a Number' or NaNs. By using advanced imputation algorithms, it was possible to impute missing values by intelligent interpolation and the use of other NDT data, such as that provided by Magnetic Adaptive Testing.
* In addition, by using statistical measures it was possible to apply the graphical correlations before making the predictions and imputing the missing data. Such techniques increased the data amounts and were considered to give good coverage and further useable data.
* Further to these findings, it was possible to see the effects on small datasets when n-dimensionality increases due to different parameters given within an NDT measurement, thereby providing more output values. Generally, the increase in dimensionality reduces the capacity for machine learning algorithms to provide generalistic pattern trend fitting capabilities. With the consideration of adding subtle data augmentation amounts (20% augmentation), it was possible to increase trend fitting capabilities. Beyond 20% augmentation, diminishing returns were obtained.
* Furthermore, studies of the data augmentation found that more extreme varying data amounts were better than slight varying data amounts.
* On a final note, classification and regression trees gave the best account when compared with neural networks in coping with the 'curse of dimensionality' when n-dimensional data increases.
* Future work will look further into more advanced algorithms to see how increased data can facilitate trend pattern learning as opposed to suppressing it. Also, further investigations into higher levels of data augmentation give reduced accuracy.  
Conceptualization, investigation, original draft preparation J.M.G.; investigation and methodology, A.G., G.V., J.M., M.E.F., I.U. and R.C.; writing--review and editing, A.G., G.V., J.M., M.E.F., I.U. and R.C.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"NOMAD\" project. This project (Non-destructive Evaluation System for the Inspection of Operation-Induced Material Degradation in Nuclear Power Plants) has received funding from the Euratom research and training programme 2014-2018 under grant agreement No. 755330.  
Not applicable for studies not involving humans or animals.  
Not applicable for studies not involving humans.  
The data presented in this study are available on request from the corresponding author pending agreement from NOMAD project consortium.
# The MADAG Strategy for Fault Location Techniques
## 4 Empirical Study
### Well-Known Similarity Coefficients of SBFL Technique  
To measure the fault localization effectiveness of the selected tests, we use the MADAG strategy to select test cases, and then apply the selected test cases to the three similarity coefficients of the SBFL technique, including Jaccard (or Pinpoint) ([PERSON] et al. [17]), Tarantula [18], and Ochiai [1,2]. In prior work, [PERSON] et al. [1,2] used the Jaccard similarity coefficient, which is used by the Pinpoint tool ([PERSON] et al. [17]). The Jaccard coefficient can be defined as follows:  
\[suspiciousnessfac(s)=\frac{N_{cf}}{N_{cf}+N_{uf}+N_{cs}} \tag{5}\]  
where \(N_{cf}\), \(N_{uf}\), \(N_{cs}\), \(N_{cf}\), \(N_{s}\), and \(N_{f}\) are defined as the same as in Section 2.2. Given the _suspiciousnessfac_ values calculated for each statement, \(s\), the suspiciousness is computed as shown in Figure 4.  
[PERSON] et al. [33] proposed the Tarantula technique, which was used initially for the visualization of testing information. The Tarantula technique [18] assigns two metrics to each program element--_suspiciousness_ and _confidence_--according to the coverage information on the passed and failed test cases. The Tarantula coefficient can be defined as follows:  
\[suspiciousnessTar(s)=\frac{\left(N_{cf}/\ N_{f}\right)}{N_{cs}/\ N_{s}+N_{cf}/\ N_{f}} \tag{6}\]  
where \(N_{cf}\), \(N_{uf}\), \(N_{cs}\), \(N_{cf}\), \(N_{s}\), and \(N_{f}\) are the same as in Equation (5).  
The _confidence_ metric is meant to evaluate the degree of confidence for a suspiciousness value. The Tarantula technique assigns greater confidence to statements that are covered by more test cases. The _confidence_ of a statement is calculated by the formula:  
\[confidence(s)=max\left(N_{cf}/\ N_{f}\,\ N_{cs}/\ N_{s}\ \right) \tag{7}\]  
The Tarantula technique ranks all statements at the top or bottom by suspiciousness values in the subject and uses the confidence values to resolve ties. [PERSON] et al. [1,2] also proposed the Ochiai similarity coefficient (originates from the molecular biology domain) to compare Tarantula one. The Ochiai coefficient can be defined as follows:  
\[suspiciousnessOch(s)=\frac{N_{cf}}{\sqrt{N_{f}\times\left(N_{cf}+N_{cs}\right)}} \tag{8}\]  
where \(N_{cf}\), \(N_{uf}\), \(N_{cs}\), \(N_{cf}\), \(N_{s}\), and \(N_{f}\) are the same as in Equation (5). This technique also ranks the similarity of the statements to the Jaccard and Tarantula coefficients. [PERSON] et al. [1,2] indicated that the Ochiai coefficient can yield a better diagnosis than the other coefficients, including Pinpoint ([PERSON] et al. [17]) and Tarantula ([PERSON] and [PERSON] [18]).
## 3 Mapping of Land Cover and Physiological or Structural Variables  
The mapping and quantification of biomass and carbon sequestration are important in terms of large-scale LCLU changes and policy development of national and regional REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries) initiatives in tropical forest ecosystems [50]. Therefore, the role of remote sensing is important for the accurate and up-to-date measurement of land surface characteristics to estimate carbon stocks and fluxes of the forests, despite the fact that remote sensing cannot provide direct measurements of biomass [23]. However, it is a powerful tool to upscale field/plot-based inventories to the landscape/regional/continental-level understanding [45]. It provides numerous valuable inputs, such as LCLU maps, Leaf Area Index (LAI), phenology, stand age, forest structure, and an estimate of the net and gross primary productivity, to empirical and processed oriented models of forest biomass and carbon flux measurements, productivity, structure and composition [18; 45]. The above-mentioned attributes for forest biomass, carbon flux measurements and ecosystem processes are extracted by applying various methods on an array of remote sensing data recorded by instruments that differ by platform (spaceborne and airborne), electromagnetic waveband (e.g., multispectral, hyperspectral), active (e.g., SAR and LiDAR) and passive (spectrometer) systems, resolution (spatial, temporal and spectral) and area coverage (global or regional) (Figure 2) [47; 52].
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 5 Experiments
### Evaluation
#### 5.2.5 Failure Cases  
It must be noted that the estimated depth map is propagated based on the image oversegmentation result, and the segmented area is set to an identical depth. When the scenarios contain a sloping plane, the proposed method will fail and the reconstructed image along the sloping plane will be blurry and aliased. As shown in Figure 18, the sloping plane is segmented with different sizes. Although finer segmentation granularity improves depth map results and reconstructed images to some extent, slope plane depth discontinuities persist, and the reconstructed images are still aliased and blurred.  
Figure 16: The metrics. (**a**) The image definition metrics of sub-image 1. (**b**) The metrics of sub-image 2. (**c**) The metrics of sub-image 3. (**d**) The average metrics of sub-image 1-3. (**e**) The MSE of three sub-images and their average.  
Figure 17: Reconstructed 3D point cloud map. First column: EdMP; second column: G3D; third column: BP; fourth column: Cost filter.  
By comparison with another three methods, EdMP achieved the best result for eCley-captured ACE images. This is mainly because the BP and Cost Filter methods are directly modified from the stereo matching method, but the ACE image has a lower resolution, smaller FOV, and also a large offset angle. Without incorporating multiple adjacent sub-images, the depth estimation will be influenced by the noise and image quality. G3D utilized all adjacent sub-images to estimate the depth. However, G3D calculated the incidence angle of each pixel based on the theoretical design values. The depth estimation performance is influenced by the image distortion. Our method, EdMP, corrected the image distortion parameters and utilized four adjacent sub-images for depth estimation. From the failure case, we can see that, since the EdMP uses depth propagation to estimate the depth of the non-edge area, this mechanism will fail in the sloping plane, so a sub-pixel depth estimation method is needed. In addition, the depth obtained by depth propagation can also be influenced by large textureless areas, and the depth estimation on textureless areas is also a fundamental problem in stereo depth estimation.
# Changes in Elderberry (_Sambucus nigra_ L.) Juice Concentrate Polyphenols during Storage
## 2 Materials and Methods
### Elderberry Juice Concentrate Samples  
The elderberry fruits (_Sambucus nigra_ L.) used in the production of juice concentrate were harvested in August 2020 from plants cultivated in Varosa Valley in northern Portugal. The fruits were from three cultivars, \"Sabugueira\", \"Bastardeira\", and \"Sabugueiro\". After harvest, the berries were separated from the branches and crushed in a fruit-processing factory (Regifrutas Company, Tarouca, Portugal). The obtained elderberry mash was immediately transported in isothermal tank trucks at a temperature between \(-2\) and \(0\)\({}^{\circ}\)C to a fruit juice factory (Indumape Company, Pombal, Portugal) where it was processed into juice concentrate. Two samples of juice concentrate (I and II) were collected and sent to the Agrarian School of Viseu where they were stored at \(-18\)\({}^{\circ}\)C until use. The technological process to produce the juice concentrates involved the following steps: crushing, mash pressing, depectinization, ultrafiltration, and concentration. The process to obtain concentrate I, in addition to the steps described above, also involved a prior heating step of the mash at \(80\)\({}^{\circ}\)C.  
Concentrate I and II were used in the studies of chemical and microbiological stability over time at two different storage temperatures (\(5\)\({}^{\circ}\)C and room temperature). For each concentrate, two separate aliquots of 200 mL were transferred under aseptic conditions to sterilized bottles duly identified to remain in the dark, under two different conditions, at room temperature and at \(5\)\({}^{\circ}\)C. For the chemical analyses, \"stock solutions\" of the concentrates were prepared in ultrapure water (1:25, _v:v_), hereinafter referred to as Solutions A. For the preparation of these solutions, 4 mL of each sample was measured into a 100 mL flask and dissolved in ultrapure water. Solutions A were kept at \(5\)\({}^{\circ}\)C for analysis, for no more than two days.
## 6 Comparison
### Adaptability  
Adaptability reflects how well these systems integrate with existing infrastructures and adapt to future needs. Centralised systems like LDAP, SAML, OAuth, and RADIUS are inherently adaptable due to decades of use in enterprise environments. LDAP's directory-based control scales effortlessly to support millions of users with minimal overhead. OAuth and SAML provide APIs and libraries that integrate smoothly with enterprise systems like SSO and cloud platforms, making them a go-to choice for organizations seeking rapid deployment. Their compatibility with widely used infrastructure reduces the time and complexity of integration [62].  
In contrast, decentralised systems like Sovrin, uPort, and Hyperledger Indy face unique challenges in adaptability. These systems often require middleware to bridge the gap between blockchain operations and traditional IT environments. For example, integrating Hyperledger Indy with existing enterprise databases involves creating custom APIs or plugins that enable compatibility with distributed ledgers. Public blockchain-based models like uPort, built on Ethereum, face additional constraints such as reliance on gas fees and transaction throughput, which limit real-time adaptability. In contrast, private blockchain systems, such as Sovrin and Hyperledger Indy, provide more control over operations, making them somewhat more adaptable for large-scale enterprises. However, transitioning from centralised to decentralised models demands significant infrastructure upgrades and retraining of IT teams to manage distributed systems, adding complexity and cost [2; 18].
# Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU
## 2 Materials and Methods
### Q8S Design
Each machine is in two different networks: the OpenStack provided network that connects all hosts and the local network for each host, which is generated by libvirt. For the Kubernetes cluster to properly work, each of the local networks needs to be connected to the inter-node network such that requests can be routed between the QEMU VMs while supporting the Kubernetes internal networking. Besides opening the required ports in the OpenStack security groups, we configured Flannel to use the host's network interfaces instead of the QEMU VM's interfaces for VTP by annotating the nodes with the respective public-ip setting for Flannel. Moreover, we set up NAT rules in iptables to route packets received by the host to the QEMU VM IP. This required both Source NAT (SNAT) and Destination NAT (DNAT) rules.
## 4 Simulation Results and Analysis
### The Loading Condition of a Given 1 Rad Step Signal  
Figure 9 displays the position curve of a step signal subjected to a sudden load. When a sinusoidal signal at 1 rad is abruptly loaded with a 10 N force at 0.1 s, it becomes evident that conventional ADRC exhibits weak robustness. In contrast, RBF-ADRC shows rapid convergence to the target position curve, highlighting its superior robustness. Additionally, the performance of FRBF-ADRC is more effective than that of RBF-ADRC.
# Generation and Characterization of Novel Bioactive Peptides from Fish and Beef Hydrolysates
## 1 Introduction  
Meat proteins are essential food constituents with nutritional and physiological properties. They are rich in essential amino acids, which are required for development of the body as well as the chemical and sensory characteristics of protein-enclosing products. Moreover, meat proteins contain bioactive peptides, which are protein fragments containing certain amino acids. Bioactive peptides are inert in the original protein chains but can be generated by proteolytic digestion. They could pass through the intestine and into the circulation, where they can perform a variety of biological functions. Bioactive peptides have received increasing attention and have been recommended as prophylactic and therapeutic substances for a variety of illnesses [1]. Antimicrobial, angiotensin-converting enzyme (ACE) inhibitory, and antioxidant characteristics have already been reported in a variety of bioactive peptides [2].  
Food-derived antibacterial peptides are promising alternatives to harmful chemical preservatives and can be safely used in food preservation [3]. There are multiple reports on bioactive peptides with antimicrobial activity from milk proteins and lactoferrin [4,5]. Lactenin was the first antimicrobial agent isolated from the rennet hydrolysate of milk [6]. Biological activity of lactoferrin-related peptides against bacteria, fungus, and yeast has been documented [7].  
ACE participates in the renin-angiotensin mechanism and is important in adjusting blood pressure. ACE also suppresses the vasodilation job of bradykinin [8]. Various ACE inhibitors were discovered in proteins obtained from food--including meat [9], milk [10], soya bean [11], and pork muscle [12].  
Lipid oxidation of meat is a major concern in food safety. Lipid oxidation can damage food quality and liberate harmful compounds, resulting in tumor formation and adverse cardiovascular effects in consumers [13]. Malondialdehydedehyde can be formed during lipid oxidation as a result of peroxide oxidation. Butylated hydroxyanisole and butylated hydroxytoluene are synthetic preservatives that are frequently used to lower fat oxidation and microbial growth in food. However, they have an adverse influence on public health [14]. On the other hand, bioactive peptides liberated from meat can reduce the oxidation of fat in foods, including those generated by papain and actinase E digestion of pork skeletal muscle, as well as papain digestion of marine protein [15]. Various synthetic antioxidants have been developed to control fat oxidation; however, antioxidants derived from protein hydrolysates are considered safer.  
Beef and marine muscles are promising sources for bioactive peptides. The aim of this study is to identify bioactive peptides in beef and fish hydrolysates digested with popsin, as well as evaluate their antimicrobial, antioxidant, and ACE inhibitory properties.
## 3 Results
### Statistical Measure of Disparity in Average Collision Cross Sections  
To complement the qualitative estimation of the deviations (or agreement) between the four methods of predicting the average electron-neutral collision cross sections in the previous part (through visually contrasting profile curves), the current part aims to quantitatively summarize the deviations (or agreement) in the predictions for each of the six gaseous species covered herein, and for each of the eleven electron temperatures (between 2000 K and 3000 K) adopted here in the computations and visualizations of the previous part. The selected measure of such deviation is the coefficient of variation (CV or COV) [47], which is the standard deviation divided by the arithmetic mean. This statistical quantity has the advantage of being non-dimensional and also being simple to interpret. It can be safely used here without encountering a singularity because the average electron-neutral collision cross sections (and thus, their arithmetic mean values) are neither zero nor changing their sign but are instead always positive.  
The estimated coefficient of variation reported herein is based on the sample-based standard deviation, and it is computed as  
\[\text{COV}=\frac{\sqrt{\frac{1}{3}\underline{\sum_{i=1}^{4}\left(\overline{Q }_{i}-\frac{1}{4}\underline{\sum_{i=1}^{4}\overline{Q}_{i}}\right)^{2}}{ \frac{1}{4}\underline{\sum_{i=1}^{4}\overline{Q}_{i}}}}}{\overline{Q}_{i}} \times 100\% \tag{41}\]  
where \(\overline{Q}_{i}\) is the predicted average (simplified) electron-neutral collision cross section using the \(i\)th method.  
Table 8 summarizes the estimated coefficient of variation results for the six covered species. For each species, the COV value at each electron temperature is listed, as well as the mean, minimum, and maximum of the eleven COV values of that species. From the  
Figure 12: Temperature dependence of the average electron–neutral collision cross sections of molecular oxygen (O\({}_{2}\)), as obtained using four different methods.  
mean COV values, the worst deviation among the four (\(\overline{Q}\)) prediction methods corresponds to potassium (K), with a mean COV of 43.56%, which is the largest of the six species. The least disparity between the four (\(\overline{Q}\)) predictions corresponds to molecular hydrogen (H\({}_{2}\)), which has the smallest mean COV of 12.92%. Considering the minimum COV values, it is noted that they do not necessarily correspond to a boundary temperature value (either the lower limit of 2000 K or the upper limit of 3000 K). A similar finding is true for the maximum COV.