esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Formalization of the Integration Approach
### Common Access to Value-Added Information  
The algebraic approach provides a straightforward solution to the integration problem. Given the DUL and SSN alignment just introduced above, we operate on different observation-based systems using their shared behavior. Above, we already illustrated how this is reflected by the common \"tell\", \"perceive\" and \"observe\" operators.  
Additionally, information from two separate sources, as for example illustrated for earth observation satellites and VGI in Figure 2 (Section 2.3), may be merged as soon as the derived information can be provided via a shared ADT (overlapping layers in the figure). Considering ourexample, this would be the abstract data type representing forest fires. Co-occurrences of forest fires can now be calculated without required knowledge about the sources that lead to the information about a particular fire. An according function could be directly implemented based on the commonly available ADT (ForestFire in this case). The signature of a co-occurrence function for forest fires--which returns a list that contains all collections of forest fires overlapping in space and time--may be defined as:  
\[\text{ffCoOccurrence}::\text{[ForestFire]}\rightarrow\text{[[ForestFire]]}\]  
For real-time applications this means that we become able to process (and in particular compare) information about events, such as forest fires, seamlessly, _i.e._, without any artificial barriers that might have been imposed by diverse use of sensors or user-generated content. In this sense, we solved a semantic integration issue in the Observation Web.  
It is worth noticing that we remain able to trace back the generation process of a data set. Having co-occurring forest fires identified, we can reveal if a particular instance has been created from a satellite image or from VGI by uncovering the used constructor function. Among other information, we may get to know if statements of two separate sources confirm or contradict each other, and thereby increase the quality of derived statements.
## 3 Initial Results
#### 3.1.1 A Systematic Literature Review (SLR) of the Analyzed Concepts
For this, we extended the Boolean logic by using OR operators instead of AND operations to include studies that addressed AI-blockchain integration with explicit emphasis on ethics, privacy, transparency, and sextortion-related contexts. This expansion led to additional papers that offered insights into adjacent topics, hence were reviewed thematically and also included in the narrative exploration in Section 3.1.2. We use Semantic Scholar and Rayyan as the main screening tools. It is to be noted here that this extra systematic review is meant to offer quantitative and thematic insights complementary to the SLR, not as a substitute. We screened 40 articles, and the findings of this supplementary SLR are structured in Figure 4.  
This supplementary SLR confirms the rarity of full-stack frameworks but points to building blocks (e.g., federated learning, explainable AI, smart contracts) that inform the conceptual model developed further on. On the thematic analysis presented by this supplementary SLR, we identify the following four areas (as shown in Table 1), with blockchain-AI integration being the most developed. Figure 4: Supplementary SLR findings. While several studies propose concrete architectures combining federated learning, secure multiparty computation, and smart contracts, ethical frameworks appear less consistently and are concentrated around privacy, consent, and bias, with fewer addressing explainability or transparency. In governance terms, centralized architectures dominate, although several studies point towards decentralization through smart contracts or identity management. Most articles address AI functionality in contexts of moderation, mitigation, or detection, while only a minority directly engage with sextortion as a phenomenon. Sextortion mitigation per se is rare, but societal resilience is growing in interest, especially related to trust and stakeholder coordination, though largely extrapolated from other domains. Finally, the alignment with frameworks like AI4 SG, or digital trust, or societal resilience is often implicit, thus confirming the gap that CF-BIAI-SXT seeks to mitigate. Building on the list of articles analyzed in this stage (excluded and included) and adding articles to connect to the regulatory aspects, as well as AI for Good, AI governance, digital trust frameworks, and the role of blockchain in ethical infrastructures, we create the narrative exploration that explains in detail the concepts linking them to the conceptual framework proposed further on.
## 3 Analysis of Experimental Data and Results
### Test of Homogeneity  
To determine if there is any significant statistical difference in the regression lines derived from the different test data, an Analysis of Covariance (ANCOVA) was used [25]. The topic of interest is whether the regression lines have similar slopes. This method allows for testing the null hypothesis that the regression model parameters were derived from samples estimating populations which all had equal slopes.  
For this hypothesis analysis, the confidence limit level was selected to be 90% (i.e., \(P_{critical}\) = 0.05). The \(F\) statistic was calculated as 0.82 and the corresponding probability (\(p\)-value) was estimated to be 0.618. Since \(F\) statistic is less than unity and \(p\)-value is greater than \(P_{critical}\), the null hypothesis could not be rejected. It was concluded that there is no statistically significant difference between the slopes of regression lines and the test of ANCOVA confirmed that the model is not influenced by the loading ratio and loading frequencies. For more information about ANCOVA, refer to References [26; 27].  
Figure 5: Crack growth rate versus AE count rate for AI7075-T6 tests at (**a**) different loading ratios; (**b**) different loading frequencies—log scale. The base scale of \(da/dN\) is in/cycle.
## 5 Verification of Collision Finite Element Method (CFEM)
### Experimental Verification of Collision Numerical Simulation Method  
The J-C constitutive model and damage failure model parameters of PMMA are established through the previous section. The finite element modeling is carried out for the test block, with \(h=4\) mm. The entire impact process and the residual velocity of the bullet, \(v_{r}\), can be calculated by applying the boundary constraints of the plate (see Figure 1 and Table 2) and the initial condition, \(v_{0}\), of the bullet.  
It is noted that the element mesh density would affect numerical results in finite element analysis. Figure 8 shows that the residual velocity of the bullet varies from element numbers in thickness at the center of the block, which means that the presented numerical method has a good convergence when the element numbers in thickness are larger than 16. So, in the following impact analysis, the model has been meshed densely to guarantee its convergence.  
Figure 9 shows a comparison between the numerical simulation calculation of the damage caused by the bullet, \(v_{0}=70\) m/s, impacting and penetrating the PMMA plate (\(h=10\) mm) and the test [14]. From Figure 9, it can be seen that the finite element simulation calculation and test damage show a cross cracking with four major fractures, although test results had one more fracture than those in simulation.  
Figure 10 shows the relationship between the initial velocity and residual velocity of bullets penetrating PMMA plates of different thicknesses, and compares them with the numerical solutions and experimental values in the literature [14]. From Figure 10, it can be seen that the calculation results of the finite element method based on the J-C constitutive and damage models constructed in this article are in good agreement with the experimental results. For the block with \(h=4\) mm, the presented numerical results are almost the same as test and reference [14] data, seen as Figure 10a, while, for the block with \(h=10\) mm, the presented numerical results are little bigger than the test data and the reference [14] data are smaller. The major reason is material property uniformity, which would affect the parameter deviation of the J-C model.  
Figure 9: _Cont._Figure 10: _Cont._  
Figure 9: Fracture patterns: experiment and numerical simulation. (**a**) Numerical simulation fracture pattern; and (**b**) experiment fracture pattern [14].  
Therefore, the analysis results indicate the rationality and accuracy of the numerical simulation calculation method constructed in this paper.
## 3 Results  
### Coastal Vulnerability in the Ria of Arosa  
After obtaining all the parameters, the coastal vulnerability of the Ria of Arosa was calculated by modifying the vulnerability index developed by the USGS. To calculate the vulnerability index using the ArcGIS program, map algebra (raster calculator) was used to combine different parameters and generate the vulnerability map along the coast of the estuary (Figure 7). Therefore, a higher index indicates greater vulnerability to rising water levels.  
Figure 7: Vulnerability index map of the Ria of Arosa. The higher the index obtained, the greater the vulnerability to rising water.  
In general, a higher vulnerability can be observed near the coast, with vulnerability decreasing as one moves away from the coastline. Along the coastline, the highest vulnerability is often found in areas dominated by beaches and wetlands, while lower vulnerability is typically observed in cliff areas. When it comes to the most vulnerable localities, Sangenjo is considered the municipality with the highest vulnerability to sea-level rise, while El Grove or the Isla de Arosa exhibit the lowest vulnerability. Vulnerability can also be observed around the mouth of the Umia River and the tombolo of El Grove, where much of the area exhibits very high vulnerability.  
In Villanueva de Arosa (Figure 7), the highest vulnerability is mainly concentrated along the coast, but it is worth noting that most of its pixels show a medium vulnerability. The most vulnerable areas in this locality are where there is no coastal protection, allowing water infiltration through the beaches in this area. However, urbanized areas protected by barriers or walls, such as the area near the port and the main urban core of the locality, show lower vulnerability values. In Cambados (Figure 7), high vulnerability values stand out in the floodplain of the Umia River, decreasing to medium and low values as one moves away from the flood-prone area. On the other hand, in the municipality of El Grove, predominantly low vulnerability values are found, increasing towards the coast but without significantly high vulnerability. However, it is observed that in the tombolo of El Grove (Figure 7), very high vulnerability prevails, making it the most vulnerable area in the entire study area. Finally, in the locality of Sangenjo (Figure 7), low vulnerability values predominate, with some high values in beach areas. Nevertheless, between Sangenjo and the locality of Portonovo, there is an area with very high vulnerability following the course of two streams that converge at the beach.
# Practical Inner Product Encryption with Constant Private Key +
## 2 Preliminaries
### Bilinear Maps
Next, we present a modified version of the DDH\({}_{\mathbb{G}_{T}}\) problem, which will be used in the security proof. **Definition 3** (The modified decisional Diffie-Hellman problem over \(\mathbb{G}_{T}\) (M-DDH\({}_{\mathbb{G}_{T}}\) problem)). : _Let \(g_{T}=e(P,P)\) be a generator of \(\mathbb{G}_{T}\). Given \((P,A^{\prime}=aP,g_{T},A=g_{T}^{a},B=g_{T}^{b},C)\in\mathbb{G}^{2}\times \mathbb{G}_{T}^{4}\), where \(a,b\xleftarrow{5}\mathbb{Z}_{p}\), decide whether \(C=g_{T}^{ab}\) or a random element from \(\mathbb{G}_{T}\)._  
**Theorem 1** (The modified decisional Diffie-Hellman assumption over \(\mathbb{G}_{T}\) (M-DDH\({}_{\mathbb{G}_{T}}\) assumption)). : _We say that the M-DDH\({}_{\mathbb{G}_{T}}\) assumption holds if there is no algorithm \(\mathcal{D}\) for solving the M-DDH\({}_{\mathbb{G}_{T}}\) problem with a nonnegligible advantage._  
Proof. : Compared with the DDH\({}_{\mathbb{G}_{T}}\) problem, the instance of the M-DDH\({}_{\mathbb{G}_{T}}\) problem contains an additional element \(A^{\prime}=aP\). The M-DDH\({}_{\mathbb{G}_{T}}\) problem is equivalent to the \((Q,R,f)\)-GDDHE problem with  
\[Q=(1,a),R=(1,a,b),f=ab.\]  
No constants exist such that the linear combination of the monomials \((1\cdot a),1,a,b\) equals the polynomial \(ab\). Therefore, considering the the results of [PERSON] et al., we conclude that the M-DDH\({}_{\mathbb{G}_{T}}\) problem is hard. Moreover, we define the advantage for an algorithm \(\mathcal{D}\) in solving the M-DDH\({}_{\mathbb{G}_{T}}\) problem as  
\[Adv^{\text{M-DDH}_{\mathbb{G}_{T}}}(\mathcal{D})=\left|\Pr[\mathcal{D}(P,A^{ \prime},g_{T},A,B,C=g_{T}^{ab})=1]\right.-\left.\Pr[\mathcal{D}(P,A^{\prime},g_{T},A,B,C\xleftarrow{5}\mathbb{G}_{T})=1]\right|.\]
## 3 Test Result and Analysis
### Strain Analysis  
Figure 19 shows the \(N/f_{c}bh\)--SSR strain relationship curves (taking SCGC2, SCGC5, and SCGC6 for examples). In these curves, \"CR\" represents the compressive SSR, \"TR\" represents the tensile SSR, and \(\varepsilon_{\text{sy}}\) represents the tensile yield strain of the SSR. It can be observed that under large eccentricity (\(e_{0}/h\geq 0.7\)), the SSR on the side farther from the axial force reaches its tensile yield strength, indicating full utilization. Additionally, the tensile and compressive curves of the SSR show different rising trends, exhibiting distinct tension-compression behavior, which is consistent with the findings of [PERSON] [19].  
Figure 20 shows the concrete strain distribution at the midspan section during various loading stages (taking SCGC1, SCGC4, and SCGC6 for examples). Here, \(\varepsilon_{\text{sc}}\) represents the SSR strain on the side closer to the axial force and \(\varepsilon_{\text{st}}\) represents the SSR strain on the side farther from the axial force. The average strain across the transverse section of the SCGC is almost linearly distributed. As the external load increases, the neutral axis gradually shifts toward the edge of the compressive zone of the SCG. During loading, the concrete strain of the midspan section varies roughly linearly along the height of the section, consistent with the assumption of a planar section.  
Figure 19: \(N/f_{c}bh\)—SSR strain relationship curves.
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
## 3 Illustrative Example
### Preliminary Models and Analysis of the Controller Settings  
In model-based control, the disturbance can be estimated by comparing the actual system variables with the values calculated from the model. The evaluation of control transients should be performed in a similar way. When using scheduled controllers, the simulated responses should correspond to the selected value of the closed-loop time constant \(T_{c}\) (denoted as \(T_{0}\) for ultralocal models and as \(T_{1}\) for local models). Similarly, the fulfillment of expectations regarding the influence of the DOB time constant \(T_{f}\) can be evaluated. The first test considers controllers with one degree of freedom (1 DoF) without control signal limitation. With \(Y=1\), the operating range of the process output is defined by the setpoint interval \(w\in[0,1]\) and by the output value \(y_{0}=0\). The system model (63) is linearized by the expressions (65)-(66). The resulting linearized process parameters \(K_{s}\) and \(T=1/a\) are shown in Figure 6. Closed-loop responses are analyzed with the scheduling parameter \(\alpha=w\) for two sets of tuning parameters given by the combination of two values of \(T_{c}\) with two values of \(T_{f}\).  
Figure 6: Parameters of the linearized system model of process (63) with \(Y=1\) according to (65)–(66) for \(w\in[0,1]\).  
\[\begin{split} T_{c}&=T_{00}=T_{01}=T_{10}=T_{11}=\{0.1,0.5 \};\\ T_{f}&=\{T_{c},ST_{c}\},\end{split} \tag{67}\]  
The corresponding four sets of equivalent PI controller parameters can be calculated using Table 2. Under the gain-scheduling control with \(\alpha=w\), the parameters \(K_{p}\) and \(K_{i}\) are shown in Figure 7. The values of \(K_{i}\) for the four controllers and the two selections \(T_{f}\): \(T_{f}=T_{c}\) and \(T_{f}=5T_{c}\) depend only on the product \(T_{c}T_{f}\). For \(T_{f}=T_{c}\), controllers with automatic reset (simplified DOB) correspond to ARC0-1 and ARC1-1 and for the full DOB are denoted as DOB0-1 and DOB1-1. For \(T_{f}=5T_{c}\) as ARC0-5, ARC1-5, DOB0-5, and DOB1-5.  
As far as the robustness of the closed loop is concerned, the values of \(K_{P}\) seem to be more interesting. For larger values of \(w\), the values of \(K_{P}\) almost overlap for controllers designed with local and ultralocal models. In contrast, for \(w\approx 0\), the local and ultralocal models yield significantly different settings for \(K_{P}\). The differences may indicate a lower robustness of the ultralocal models, as already mentioned in Remark 2. The difference between the two values of \(K_{p}\) becomes significantly larger for \(T_{c}\) increased to \(T_{c}=0.5\) (with motivation to reduce the output noise of the controller), as shown on the right side of Figure 7. It is therefore clear that the requirements for measurement noise attenuation and the robustness of the closed-loop are contradictory.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Camelina sativa L  
False flax, gold of pleasure, or amelina are commonly used names to refer to _Camelina sativa_ L., an oily, annual plant from the _Brassicaceae_ family [54,55]. This plant originates from the areas of Southeast Europe and Southwest Asia. Archeological discoveries suggest that amelina was grown in central Europe as early as 4000 BCE [56]. Camelina leaves are lance-shaped, typically 5 to 8 cm long. A single stem may be smooth or hairy and branched above. The small flowers may be white-yellow or greenish-yellow. The pear-shaped silicles contain very small yellow-to-brown seeds [56]. Camelina seeds contain up to 47% fat. The increasing interest in amelina is related to the high nourishing value of the oil obtained from its seeds [54,55]. This is mainly related to the polyunsaturated fatty acid (PUFA) content, which reaches up to 60% in amelina oil. Worth attention is the information about the content of \(\alpha\)-linolenic acid (ALA) (18:3), an n-3 essential fatty acid, whose amounts are estimated to be up to 40 percent. What is more, amelina oil contains large amounts of oleic, (18:1), linoleic, (18:2), and eicosenoic (20:1) acids, whose values, respectively, range from 14 to 16%, 15 to 23%, and 12 to 15%. Saturated fatty acids constitute only about 10% of all fatty acids and are formed mainly of palmitic (16:0) and stearic (18:0) acid [54,55,56,57]. Oil derived from camelina also contains a high content of tocopherols, which may have a positive effect on its durability [56].
## 4 Assimilation and Impact of GNSS-R Data
### Method of Assimilating GNSS-R Data  
Methods of assimilating GNSS-R ocean surface wind data are generally similar to those of assimilating conventional wind observations. Due to the characteristics of GNSS-R signals, they usually have no wind direction information. A Variational Analysis Method (VAM) can combine GNSS-R wind speeds with wind vectors offered by a priori background and completes a near-surface vector wind field. Studies have shown that using assimilation systems and the VAM to assimilate either simulated or actual CYGNSS wind speed data is effective [86, 87]. Except the VAM, we can also just assimilate the retrieved ocean surface wind speeds without directions. In addition, a popular assimilation method to improve forecasts is data thinning. GNSS-R ocean surface wind speed observations are crowded, with a distance of 6 km between each point, which is much smaller than the model resolution. Therefore, data thinning is usually implemented to avoid overfitting. Typically, we either select the nearest observation to the grid center or use the average of all GNSS-R data points within the grid for assimilation. The difference between these methods will be described next.
# Efficient Transcoding and Encryption for Live 360 CCTV System
###### Abstract  
In recent years, the rapid development of surveillance information in closed-circuit television (CCTV) has become an indispensable element in security systems. Several CCTV systems designed for video compression and encryption need to improve for the best performance and different security levels. Specially, the advent of 360 video makes the CCTV promising for surveillance without any blind areas. Compared to current systems, 360 CCTV requires the large bandwidth with low latency to run smoothly. Therefore, to improve the system performance, it needs to be more robust to run smoothly. Video transmission and transcoding is an essential process in converting codecs, changing bitrates or resizing the resolution for 360 videos. High-performance transcoding is one of the key factors of real time CCTV stream. Additionally, the security of video streams from cameras to endpoints is also an important priority in CCTV research. In this paper, a real-time transcoding system designed with the ARIA block cipher encryption algorithm is presented. Experimental results show that the proposed method achieved approximately 200% speedup compared to libx265 FFmpeg in transcoding task, and it could handle multiple transcoding sessions simultaneously at high performance for both live 360 CCTV system and existing 2D/3D CCTV system.  
C 2019 9 760 (); 18 February 2019 21 February 2019