esa-sceva/satcom-chunk-collection huggingface dataset:
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
## 3 GNSS Single-Epoch Ambiguity Resolution
### GNSS Ambiguity Resolution under the Constraint of BDS Triple-Frequency Observation  
Now, we classify the ambiguities of GNSS observation into two categories. One is the ambiguity (\(\
abla\Delta\)N\({}_{e}\)) of the BDS three-frequency signal, which was resolved in the last section. The other is the ambiguity (\(\
abla\Delta\)N\({}_{h}\)) of the other GNSS satellites' double-frequency observation, which is relatively difficult to resolve. The error equation of the GNSS observation can be expressed as follows:  
\[\left(\begin{array}{c}V_{1}\\ V_{2}\end{array}\right)=\left(\begin{array}{cc}A_{1}&O\\ A_{2}&C_{2}\end{array}\right)\left(\begin{array}{c}X^{\prime}\\ \
abla\Delta N_{h}\end{array}\right)-\left(\begin{array}{c}L_{1}-C_{1} \
abla\Delta N_{e}\\ L_{2}\end{array}\right) \tag{28}\]  
\(V_{1}\), \(V_{2}\) are the residual of BDS and the GNSS observations, respectively. \(X^{\prime}\) is the vector of unknown parameters of the coordinate. \(\
abla\Delta N_{e}\) and \(\
abla\Delta N_{h}\) are the ambiguities of the BDS B1 observation and the other GNSS L1 observations. \(L_{1}\), \(L_{2}\) are the carrier phase observation of BDS (\(B_{1}\)) and the GNSS observations (\(L_{1}\)), respectively. \(A_{1}\), \(A_{2}\) and \(C_{1}\), \(C_{2}\) are the corresponding coefficient matrices. Generally, Equation (28) can be simplified to  
\[V_{k}=A_{k}X_{k}-L_{k} \tag{29}\]  
where \(V_{k}\) is the matrix of observation residuals and \(A_{k}\) is the coefficient matrix of the parameters. Then, the least-squares solution is  
\[\widehat{X_{k}}=\left(A_{k}^{T}PA_{K}\right)^{-1}A_{k}^{T}PL_{k} \tag{30}\]  
In Equation (28), we involve the ambiguity of BDS, which can be fixed more easily than that of GNSS dual-frequency signals. Then, the first part of this equation is free from the effect of ambiguity, which is a restriction of this model and will increase the efficiency of the AR of the GNSS carrier phase observation.
## 2 Literature Review
### Functions of SCADA  
SCADA systems provide numerous operational benefits for industrial organizations. Centralized monitoring through SCADA allows operators to have visibility into the status of dispersed field sites and equipment from a single management portal, helping to improve oversight and control [14]. SCADA also enables centralized control where operators can send commands and adjust remote assets directly from the control room, streamlining management and coordination. Historical trend data collected by SCADA can be analyzed to identify inefficiencies, bottlenecks, and anomalies. Operators can then make targeted adjustments to optimize performance and throughput. The flexibility of SCADA also makes it easier to scale operations or reconfigure systems when changes are needed. Unexpected issues can also be addressed rapidly through alarms and real-time data available via SCADA interfaces. Predictive maintenance is another key benefit, as sensor readings and usage patterns collected over time through SCADA allow maintenance teams to proactively address potential equipment failures before downtime occurs.
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 4 Methodology
### Pre-processing of the Satellite Images and Extraction of Spectral Signatures
The next step was to suggest a strategy to improve the overall LCU classification results, using the multi-index data composed of three spectral indices, which were sensitive to built-up area/bare land, vegetation cover, and waterbodies, respectively.
## 3 Results and Discussion  
### Effect of HPH on Protein Concentrate Recovery from A. pyrenoidosa  
The protein content of APPCs from untreated and HPH-treated _A. pyrenoidosa_ biomass is presented in Figure 1a. Since in all cases the samples contained less than 80% \(w/w\) protein content, they meet the criteria for \"protein concentrates\" [32]. The protein content of APPC from untreated biomass was 58.7 \(\pm\) 1.3% \(w/w\), which is consistent with the literature for protein concentrates. [PERSON] et al. (2015) reported a protein content of chickpea protein concentrate between 61.20 and 63.12% \(w/w\)[32], while [PERSON] et al. (2019) reported a protein content of fava bean protein concentrate equal to 65% \(w/w\)[33]. Regarding microalgae, [PERSON] et al. (2021) and [PERSON] et al. (2013) reported a protein content from _Scenedesmus obliquus_ microcalga protein concentrate equal to 63.14 \(\pm\) 0.30% \(w/w\)[34] and from _Tetraselmis_ sp. equal to 64% \(w/w\), respectively [35]. According to these results, protein concentrates with protein content close to 60% \(w/w\) are suitable for various applications in the food industry. As can be observed in Figure 1a, the HPH treatment up to 800 bar for 1 pass did not significantly affect the APPC protein content. However, 4 passes at 600 bar led to a notable reduction in protein content from 58.7 (untreated) to 53.3% \(w/w\) (800 bar, 4 passes). This decrease is likely due to the higher degree of cell disruption after 4 passes compared to 1 pass. As reported in our previous work, extracts from HPH-treated biomass for 4 passes contain a higher overall protein amount than after 1 pass but also include intracellular compounds such as carbohydrates and chlorophylls, which lower the protein-to-total extract ratio [13]. Additionally, more intense HPH treatment results in a significant reduction in the particle size of cellular fragments, causing difficulties in their removal from the extract and, consequently, from the protein concentrate [16].  
As presented in Figure 1b, the protein recovery yield (\(y_{\text{p}}\)) from the _A. pyrenoidosa_ biomass significantly increased with HPH pressure and the number of passes from 17.1 g protein/100 g dry biomass (untreated) up to 26.9 g protein/100 g dry biomass (800 bar, 4 passes). This behavior was expected, due to the higher degree of cell disruption that enhances the recovery of intracellular compounds from microalgae such as proteins, carbohydrates, chlorophylls, and carotenoids [13; 23; 36].
## 2 Results and Discussion
### Volatile Composition in Wine Made of Dried and Fresh Goji Berry
#### 2.2.2 Aldehydes and Ketones  
Aldehydes and ketones are considered the important aromatic compounds in fruits, and the drying process has been reported to enhance their accumulation in dried fruits [22]. Furfural has been reported to be produced by the dehydration of sugar under acidic conditions, and its content increases as the drying period increases [25]. 2-Acetylpyrrole was synthesized from proline, hydroxyproline, and/or sugar under Strecker degradation conditions, and this volatile exhibits a burnt scent [26]. Decanal is derived from the oxidation of oleic acid in fruits during the drying process [27]. This volatile contributes soapy and green lemon odors to the overall aroma [28]. The dried goji berry fermented wine contained significantly higher concentrations of furfural, safranal, 2-butyl-2-octenal, isobutyl ketone, 6-methyl-5-heptene-2-one, 2-undecanone, \(\beta\)-cyclodextrin, dihydropyseudo-ionone, 2-acetylpyrrole, and (2,6,6-trimethyl-2-hydroxycyclohexylidene)acetic acid lactone (Tables 3 and 4). Particularly, 6-methyl-5-heptene-2-one exists in a trace amounts in the fresh goji berry fermented wine, whereas the dried goji berry fermented wine contained this compound with much higher concentration. 6-Methyl-5-heptene-2-one is a product of the hydrolysis of carotenoids in fruits. We speculate that the drying process might result in the degradation of carotenoids, which leads to a higher concentration of 6-methyl-5-heptene-2-one in the dried goji berry fruit fermented wine.
## 2 Results
### Attitude and Perception Assessment

Table 3: Multivariate logistic regression (adjusted odds ratio, aOR) of good and fair knowledge and demographic variables. health emergency issue, and this perception was more prevalent among LMIC participants (10.5%; 7/67 of LMIC participants shared this view). This difference in proportion was not statistically significant between LMIC and HIC participants. Regarding the actors that should be held responsible for tackling AMR, 30.9% (21/68) of LMIC participants reported that hospitals, veterinary clinics, and pharmaceutical industries are responsible for AMR and should solve the problem on their own. From the HIC perspective, only 10.9% (29/266) shared this view, and the difference was statistically significant (\(p<0.001\)) between the two subsets. Lastly, the majority (75.2%; 254/338) reported that the current COVID-19 pandemic increased their awareness of public health and the role of governments in outbreak prevention and preparedness (see Figure 3). A detailed overview of the proportional distribution of all answer options per statement can be found in Supplementary File 3. Of all participants, 41.6% (146/351) and 83.8% (294/351) had a good and fair personal attitude and perception, respectively. Upon univariate and multivariate regression analysis, several factors were found to be associated with a good and fair attitude and perception (see Table 4 and Supplementary File 5). Similarly, as described for good and fair knowledge scores, higher levels of education and scientific field of expertise remained associated with  
Figure 3: Bar plot (proportion (%) with 95% confidence interval) of personal attitude and perception statements from all participants, stratified for high-income country (HIC) and low- and middle-income country (LMIC) participants. The proportion represents participants who answered the statement with agree or strongly agree. Significance: ***\(p<0.001\). better attitude and perception after adjustment for all variables (see Table 4). Based on this multivariable regression model, LMIC participants were less likely to have a good or fair perception and attitude toward antimicrobial consumption and resistance, with an adjusted OR of 0.33 (0.14-0.75) for a good score compared to HIC participants.
# Cross-Platform UAV Swarm Key Management in Denied Environments
## 3 Cross-Platform Key Management of UAV Swarms in Denied Environments  
### Key Parameter Presetting in the Transport and Delivery Stage  
The network model in this study consists of the transport and delivery platform and UAV nodes. The delivery platform can be a mother ship, a large-scale transport plane, or a land-based launcher truck with strong capacities for computing, communication, and storage, which presets the key parameters and initializes the system as a top-level key management center (KMC), as shown in Figure 2.  
The UAV swarms are initialized before being launched to the sky. Suppose there are \(m\) delivery platforms engaged in a given task; the key management center of the \(j\)-th platform is \(KMC_{j}(1\leq j\leq m)\), and the identification label of \(KMC_{j}\) is \(ID_{j}\). In the delivery stage, \(KMC_{j}\) presets a private key \(UAV_{-}SK_{j_{i}}(1\leq i\leq n)\) to the \(n\) UAVs \(UAV_{-}ID_{j_{i}}\) delivered from the same delivery platform. Security channels between platforms are created through shared keys before execution of a task; \(KMC_{1}\) selects the node update key seed \(seed\), the global key \(K_{0}\), and the set of prime numbers \(B\), which are transmitted to the delivery platform \(KMC_{j}\) engaged in the task through the security channel, where \(b_{j_{i}}\in B\), and for any \(b_{j_{i}},b^{\prime}_{j_{i}},gcd\big{(}b_{j_{i}},b^{\prime}_{j_{i}}\big{)}=1\) is satisfied; then, the \(KMC_{j}\) configures the key parameters for its UAV swarm. The specific step of the algorithm is as follows (Algorithm 1):  
```
1. Initialization : \((m:number\ of\ delivery\ plat\ forms,n:number\ of\ UAVs delivered\ from thej,t:Task\ stage)\)
2. Procedure preset key parameter
3. For \((j=1;\ j<=m;j++)\)
4. { \(KMC_{1}\) Generate : \((seed,K_{0},M)\to KMC_{j}\)
6. \(KMC_{j}\ compute:S_{1D}=h\big{(}ID_{j}\big{)}\)
7. For \((i=1;\ i<=n;i++)\)
8. { \(KMC_{j}\) Generate : \(UAV_{-}SK_{j}//Generate\ private\ key\ for\ UAV\ node\)
10. \(KMC_{j}\ Generate:UAV_{-}ID_{j_{i}}=h\big{(}UAV_{-}ID_{j_{i}}\big{)}\)
11. \(KMC_{j}\to UAV_{j_{i}}:p_{i}=\Big{(}K_{0},seed,UAV_{-}SK_{j_{i}},ID_{j},\)\(S_{1D},\)\(UAV_{-}ID_{j_{i}},\)\(b_{j_{i}},P_{m},M\Big{)}\)
12. \(UAV_{j_{i}}\ compute:S^{\prime}_{1D}=h\big{(}ID_{j}\big{)},S^{\prime}_{UAV_{-}ID_{j_{i}}}=h \big{(}UAV_{-}ID_{j_{i}}\big{)}\)
13. \(If\ Compare,(S_{1D}^{\prime},S_{1D})\big{(}S^{\prime}_{1 AV_{-}ID_{j_{i}}},S_{UAV _{-}ID_{j_{i}}}\big{)}\) Then
14. \(UAV_{j_{i}}\ compute:S^{NK}_{1k}=h(seed)\)
15. Return \"Presetcorrect, ready forlaunch\"; //Presetting succeeded; ready to launch UAVs
16. Else \(If\)
17. \"Presetfailed, Exit\". //Presetting failed;exit and requestere - presetting
18. }
19. End//i
20. } End//j
21. End Procedure
```  
**Algorithm 1** System Initialization
## 3 Trajectory Optimization Using an Indirect Method  
The circle-to-circle, minimum-time continuous-thrust orbit transfer optimization is studied using an indirect method, that is, a method based on the classical calculus of variations. In this context, and keeping in mind the mathematical model described in references [2; 5], the dimensionless performance index \(J\) to be maximized is written as  
\[J\triangleq-\Delta t\equiv-\tilde{I}_{f} \tag{9}\]  
while the (dimensionless) Hamiltonian function \(\mathcal{H}\) is given, according to Equation (6), by  
\[\mathcal{H}=\lambda_{F}\,\tilde{u}+\lambda_{\theta}\,\frac{\bar{\vartheta}}{ \bar{r}}+\lambda_{\theta}\left(\frac{\bar{\vartheta}^{2}}{\bar{r}}-\frac{1}{ \bar{r}^{2}}\right)-\lambda_{\theta}\,\frac{\tilde{u}\,\bar{\vartheta}}{\bar{r }}+\mathcal{H}_{c} \tag{10}\]  
where \(\{\lambda_{F},\,\lambda_{\theta},\,\lambda_{\tilde{u}},\,\lambda_{\theta}\}\) are the dimensionless costates, and \(\mathcal{H}_{c}\) is the part of the Hamiltonian function that explicitly depends on the two control terms \(\{\tilde{a}_{p},\,\alpha\}\), viz. \[\mathcal{H}_{c}\triangleq\tilde{a}_{p}\left(\lambda_{\tilde{a}}\,\cos\alpha+ \lambda_{\theta}\,\sin\alpha\right) \tag{11}\]  
The optimal value of the controls \(\{\tilde{a}_{p},\,\alpha\}\) is obtained by using [PERSON]'s maximum principle [18], i.e., by maximizing (at any time instant) the function \(\mathcal{H}_{c}\) defined in the previous equation. More precisely, bearing in mind that \(\mathcal{H}_{c}\) is linear in \(\tilde{a}_{p}\) and that the thrust vector direction (i.e., the value of \(\alpha\)) is unconstrained, the optimal control law is simply given by  
\[\tilde{a}_{p}=\tilde{a}_{m}\triangleq\frac{a_{m}}{\mu/r_{0}^{2}}\quad,\quad \cos\alpha=\frac{\lambda_{\tilde{a}}}{\sqrt{\lambda_{\tilde{a}}^{2}+\lambda_{ \theta}^{2}}}\quad,\quad\sin\alpha=\frac{\lambda_{\theta}}{\sqrt{\lambda_{ \tilde{a}}^{2}+\lambda_{\theta}^{2}}} \tag{12}\]  
where \(\tilde{a}_{m}\) represents the dimensionless form of the maximum propulsive acceleration magnitude \(a_{m}\). Note that, as expected, the control law reassumed in Equation (12) is consistent with the result presented in reference [2]. In particular, the first of Equation (12) indicates that the rapid transfer is obtained by selecting the maximum value of \(a_{p}\) (i.e., no coasting arcs appear in the optimal transfer trajectory), while the last two equations indicate that the optimal thrust direction coincides with the classical Lawden's primer vector direction [19].
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
## 3 Power Losses Analysis  
For the proposed 13-level topology, three kinds of power loss are calculated, namely the switching losses (\(P_{\rm sw}\)), conduction losses (\(P_{\rm con}\)), and ripple losses of capacitors (\(P_{\rm rip}\)).
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 4 Receiver Architecture
### Radiation Dose and Countermeasures  
Concerning ionizing radiation, cislunar environment is quite challenging, therefore, with respect to radiation tolerance, the following methodology will be adopted:  
* The receiver PCB will be shielded by an Aluminum case of adequate thickness;
* Thanks to the miniaturization, allowed by extensive usage of ICs, a circuit redundancy can be effectively applied to the whole receiver or to a part of it (the K/Ka-band front-end, for example);
* High reliability components (automotive grade, military grade, radiation hardened and space grade) can be provided by the chip makers, if strictly needed by the considered application.  
The lunar radiation environment, indeed, is characterized by its position, which is located beyond the Earth's protective atmosphere and magnetosphere. The overall computation of the absorbed total ionizing dose should consider the dominant contribution due to the crossing of Van Allen Belts during the path from Earth towards Moon. Beyond Van Allen Belts, in transit and on the lunar surface, the main sources of ionizing radiation are galactic cosmic rays (GCR) and solar particle events (SPE). Since the latter source is widely variable according to solar behavior, Moon surface radiation cannot be determined once and for all.  
Approximately, given that radiation tolerance for components off the shelf (COTS) starts from 5 krad upwards, an adequate aluminum shielding is in the range of few millimeters for a lifespan of several months. To this purpose one could also exploits the experience gained in the past about radiation shielding in manned missions, since this is the only strategy that can be used to protect human beings from ionizing radiation effects.
## 4 Experimental Results
### Influence of Arc Behavior on Breaking Performance  
When the contact material is CuW80, an intensive arc mode occurs during the arc's stable burning process. When the arc develops into an intensive arc, the current density increases, and more metal vapor is generated between the contacts. This can be observed directly in the arc images shown in Figure 8. The experimental waveforms and arc appearances of the interruption failure at the first current zero and success at the second current zero with a 2 ms arcing time for the type A contact are shown in Figures 10 and 11, respectively. In the DC-arcing stage, the arc voltage is maintained at approximately 25 V. During the current-commutation stage, the capacitor is continuously discharged, and the voltage is continuously attenuated. When the arc current passes through the first current zero, re-ignition occurs. When the contact current passes through the second current zero, the voltage between the contacts increases until it reaches the source voltage. From Figure 11, it can be seen that the intensive arc mode develops at the center left position of the cathode contact. When the arc current is zero, more arc plasma occurs at the region of the intensive arc where arc re-ignition occurs. At the same time, a new cathode spot can be observed on the surface of the original anode during re-ignition. The position of re-ignition is the region of the intensive arc mode, as is the case in almost all re-ignition experiments. This phenomenon can be explained based on the high melt vapor density and temperature at the position of the intensive arc. Therefore, it is much easier for re-striking at the region of intensive arc. Moreover, in this re-striking process, the arc is still bright, which is observed for all re-ignition conditions. Therefore, the brightness of plasma and metal vapor after current zero is another re-ignition feature. This region is the position of the intensive burning arc before the current zero.  
Figure 9: Diameters of the diffuse columns at different current ratings and the start time of occurrence of the intensive burning arc.
## 3 Settlement Calculation with Error Corrections and Resampling  
### MDD Settlement Calculation over Time  
In this study, the determination of MDD deformation at individual laser sensors was carried out through a systematic calculation procedure. To track the deformation over time, we conducted periodic measurements of the sensor displacement and subsequently compared them to the sensor's initial position. The computation of the actual MDD settlement was performed using the following equation:  
\[S_{MDD}(t)=D_{t}-D_{0}, \tag{4}\]  
in which \(S_{MDD}\) is the computed deformation for each MDD laser sensor position; \(D_{0}\) is the initial distance from the anchored head position, and \(D_{t}\) is the current distance from the anchored head position.
## 3 Extension to 3D Structures  
The practical application of retrofitting is always on an actual 3D structure. This section will provide a way to extend the 2D results to 3D structures, which will significantly reduce the computational cost in the optimum retrofitting procedure. The 3D frame shown in Figure 1 is with 3-bay in \(X\) direction and 5-bay in \(Y\) direction. The dampers will only be installed at a few places in a 3D structure in order to minimally disturb the structural functions. Figure 10 shows the bays at which viscous dampers are placed in \(X\) direction and \(Y\) direction.  
Since the distributions of the story lateral stiffness and story strength of the 3D frame are similar to those of the planar frame used in Section 2, the distribution patterns of additional story damping coefficients can be the same as those in the 2D case, and the values can be proportional to the results obtained by using the 2D case. As stated in Section 2.2.3, scheme 5 in Table 6 is the optimum scheme in the 2D case. Table 7 provides the damping coefficients used in the retrofitting in \(X\) direction and \(Y\) direction, respectively. Note that these values are for one of the planar frames in the 3D frame; i.e., 1/4 of the total damping coefficient used in \(X\) direction and 1/2 of the total damping coefficient used in \(Y\) direction. The damping coefficients in \(X\) direction are calculated as 1.5 times the values obtained in the 2D case. For the damping coefficients in \(Y\) direction, the distribution pattern is fixed but the values need to be determined. By using a trial-and-error method, the total damping coefficient is determined to be 8,875,000 N-(s/m). The ground motions are applied in both \(X\) direction and \(Y\) direction. Figure 11 shows the average inter-story drift ratios under the frequent earthquake and the rare earthquake in \(X\) direction and \(Y\) direction, which indicate that the structural performance satisfies the code-specific requirements (1/550 under frequent earthquake and 1/50 under rare earthquake). Collapse fragility analyses are also conducted on the initial retrofitted frame and optimum retrofitted frame in \(X\) direction and \(Y\) direction which reveal that, with the same retrofitting cost, the optimum retrofitted frame has a larger collapse-resistant capacity with about 15% increases compared with the initial retrofitted frame.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**3D Frame** & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) & \(C_{6}\) & \(\sum\limits_{i=1}^{6}C_{i}\) \\ \hline \(X\) direction & 2,415,000 & 2,010,000 & 825,000 & 75,000 & 0 & 0 & 5,325,000 \\ \(Y\) direction & 4,025,000 & 3,350,000 & 1,375,000 & 125,000 & 0 & 0 & 8,875,000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimum damping coefficients of 3D frame (unit: N-(s/m)).  
Figure 10: Placement of viscous dampers in \(X\) and \(Y\) directions (placed at shadow bays).