esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Bonding Tests  
In order to demonstrate the impact of the applied plasma treatment on the stability of bonded glass components, uncoated fused silica samples were tested initially. As shown in Figure 5, the shear strength was generally increased by the plasma treatment. With respect to the untreated reference which was cleaned by hand according to the common standards of optics manufacturing and wringed, this increase can be quantified by a factor of about two in the case of storing the bonded doublet at ambient temperature. The stability is further increased by curing at higher temperatures. Here, the shear strength was 2.6 times and 5.2 times higher after curing at 100 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. The general increase in shear-strength can be attributed to a plasma-induced modification of the glass surface shown in Figure 5.  
Since the outermost layer of both investigated coatings of interest, NPBS and PBS, basically consist of silicon dioxide, the observed increase in shear strength is also to be expected in the case of coated optics as presented in the subsequently. Here, the goal was to achieve the highest possible stability. Figure 6 shows the shear strengths of bonded doublets coated with the NPBS coating described in Section 2.1. As observed for uncoated optics (Figure 5), the plasma treatment resulted in an increase in shear strength. The maximum possible curing temperature was 100 \({}^{\circ}\)C since the NPBS coating was damaged at higher temperatures due to the involved silver.  
Figure 5: Comparison of shear strengths of bonded fused silica blanks without any plasma treatment (untreated) and after plasma treatment for 30 s and curing at different annealing temperatures.  
Figure 6: Shear strength of bond surfaces; pure fused silica and fused silica coated with a non-polarising beam splitter coating, without any plasma treatment and after plasma treatment for 30 s and curing at different annealing temperatures.  
With respect to the untreated reference (cleaned and wrringed according to optics manufacturing standards), the plasma treatment caused an increase in shear strength by a factor of 2.6 after storing at ambient temperature and 4.8 after curing at 100 \({}^{\circ}\)C. A quite comparable behaviour was observed for doublets coated with the PBS coating, see Figure 7.  
Here, the shear strength was increased by a factor of 2.3 and 4.3 due to storing at ambient temperature and curing at 200 \({}^{\circ}\)C, respectively. Even though here a higher curing temperature could be applied since the coating does not contain any metallic components, the plasma-induced increase is in the same order of magnitude as for NPBS-coated optics.
## 3 Results and Discussion
### Cultivation of K. pastoris on Raw Hydrolysates  
The raw hydrolysates, after pH adjustment to pH 5, were used as media for the cultivation of _K. pastoris_ to evaluate their suitability to support cell growth and xylitol synthesis (Table 3). The results show that the highest biomass production was obtained for banana peels (15.18 \(\pm\) 0.33 g/L) and grape stalks (14.58 \(\pm\) 0.19 g/L), concomitant with the high glucose consumption (8.20 and 6.68 g/L, respectively) as observed for both assays. The obtained CDW values are slightly higher than the ones reported for the same strain (12.15 and 12.08 g/L) using glucose/xylose mixtures with similar glucose:xylose ratios (15% and 30% xylose) [33].  
Figure 4: Concentration of furfural, HMF, and acetic acid in the raw hydrolysates.  
Despite their low glucose contents, corncob and grape pomace hydrolyses resulted in a CDW of 13.70 \(\pm\) 0.15 g/L and 7.08 \(\pm\) 0.17 g/L, respectively (Table 3), which might be explained by the consumption of oligosaccharides resulting from the hydrolysis of cellulose and hemicellulose. This was not observed for sawdust hydrolysate, for which a very low glucose consumption (0.09 g/L) was noticed, possibly due to the presence of high concentrations of degradation products, especially furfural (0.32 g/L) (Figure 4), which is known to affect yeast cell growth [53].  
_K. pastoris_ was able to consume xylose from all the raw hydrolyses and converting it into xylitol, although with differing efficiencies (Table 3). Despite of the high xylose content in the sawdust hydrolysate, only traces of xylitol were detected because of the poor cell growth in the assay. The highest xylose consumption was observed in the assay performed with grape stalks (3.10 \(\pm\) 1.2 g/L), but only 0.78 \(\pm\) 0.04 g/L of xylitol were produced, with a Y\({}_{\rm P/S}\) of 0.25 g/g on a xylose basis. For banana peel and corncob hydrolyses, even though high cell growth was attained (15.18 \(\pm\) 0.33 and 13.70 \(\pm\) 0.15 g/L, respectively), xylitol synthesis was low. This might be explained by the low xylose concentration available in banana peel hydrolysate (Table 3) and by the presence of high contents of inhibitory byproducts in corncob hydrolysate (Figure 4).  
Despite its low biomass production (5.70 \(\pm\) 0.94 g/L), BSG hydrolysate resulted in the highest production of xylitol (1.51 \(\pm\) 0.07 g/L) with a xylitol yield of 0.66 g/g (Table 3). Analogous result was reported by [PERSON] et al. [54] where a Y\({}_{\rm P/S}\) of 0.70 g/g was achieved by the yeast _Candidia guilliermondii_ using BSG hydrolysate.  
Arabitol was detected at low concentrations (<1.0 g/L) in the experiments carried out with BSG, corncob, grape pomace, and grape stalk hydrolyses, which is concomitant with the consumption of arabinose [55]. The highest arabitol synthesis was noticed for BSG hydrolysate (0.90 \(\pm\) 0.09 g/L), which had the highest arabinose content (6.2 \(\pm\) 2.1 g/L). These results revealed that _K. pastoris_ can simultaneously synthesize both polyols from the same feedstock.
# Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations
## 5 Conclusions  
In this paper, the output consensus problems for heterogeneous agents were studied under the output saturations. Applying the output regulation approach to solve the consensus problem, we have proposed the observer-based consensus algorithms considering leaderless and leader-following cases. Specifically, the proposed algorithm consists of three parts: the nonlinear observer, the reference generator, and the regulator. By defining the error dynamics, we have transformed the consensus problem into the stability problem of the error dynamics. Then, based on the Lasalle's Invariance Principle and the input-to-state stability, the stability of error dynamics and the existence of the control gains have been derived under the standard assumptions for the consensus. Finally, two numerical examples have been given to demonstrate the theoretical results. Although the effect of the control gain \(\beta_{i}\) has been investigated by simulation, the performance in a group has not been addressed. Thus, the consensus control with the performance analysis would be worthwhile for a further study. Moreover, as mentioned in Remark 1, the proposed algorithm requires global information, i.e., \(\lambda_{2}(L)\) and \(\lambda_{1}(L)\). To solve this problem, the fully distributed algorithm has been widely used [14]. By using the state dependent control gain, the consensus can be solved without global information. Therefore, it would be interesting to extend the results of this paper to fully distributed consensus.  
Conceptualization, Y.-H.L. and G.-S.L.; methodology, Y.-H.L.; software, G.-S.L.; validation, Y.-H.L. and G.-S.L.; formal analysis, Y.-H.L. and G.-S.L.; investigation, Y.-H.L.; resources, Y.-H.L.; data curation, G.-S.L.; writing--original draft preparation, Y.-H.L.; writing--review and editing, G.-S.L.; visualization, G.-S.L.; supervision, G.-S.L.; project administration, G.-S.L.; funding acquisition, G.-S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Gyeongsang National University Grant in 2020-2021.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: The trajectories of the controlled outputs (solid line), the measured outputs (solid line), and the leader’s output (dashed line) using the proposed algorithm (24).
## 4 Results and Discussion  
### Optimization of ANFIS Parameters  
The optimization procedure of consequent and antecedent ANFIS parameters using the SA and BBO techniques is presented in this section. The development of ANFIS, ANFIS-SA, and ANFIS-BBO codes were done using Matlab [84]. The population size of the SA algorithm was chosen as 25, while the number of habitats of the BBO technique was set as 50. These values were the optimal choices, deduced from trial-and-error tests. Performing with normalized data in the [\(-\)1; 1] range, the optimization procedure was investigated for 100 iterations with ANFIS-SA and ANFIS-BBO models. Figure 4 presents the optimization results in terms of R, RMSE, and MAE, showing the optimal iterations giving the highest accuracy of predicted results (i.e., the iteration when errors of the testing data  
Figure 3: Methodology flow chart of this study.  
started to increase). It was found that the optimal iterations were 72 and 20 using ANFIS-SA and ANFIS-BBO, respectively.
# An SSI-Based Solution to Support Lawful Interception
## 5 Design of the SSI Stack
### Layer 2  
Layer 2 refers to the communication protocol between agents and the interfaces available to the developers. As mentioned in the previous section, at this level the peer-to-peer communications between agents are established.  
The protocol used in our solution is DIDComm [28], which is fully compliant with the ToIP stack. This protocol is based on message exchange and is independent of the underlying transport protocol. Each agent of the communication is identified through a DID and the communication is encrypted through the public key associated with the DID. Since any transport protocol is allowed, the most common choice falls on HTTP or HTTPS (as in our approach), allowing us to develop a web application based on the request-response paradigm (see Section 6). Therefore, no particular technology of the involved parties is required.  
At this level, some message-oriented interfaces for the developers are proposed. In detail, we distinguish two types of nodes: agent and validator. The agent nodes can be hosted everywhere (smartphone, laptop, cloud, and so on). Each agent maintains one or more connections, each one associated with a Peer DID. As already discussed, these connections are used to exchange cryptographic credentials and proofs. The validator nodes allow the agent nodes to interface with the blockchain selected in Layer 1. Specifically, in our approach, the agent nodes rely on the validator nodes to access the public keys associated with the public DIDs of the LEA, LEMF, and EA and/or to write the Schema and the Credential Definition before releasing the credentials (in this case the agent nodes are hosted on the issuers).
## 4 Data Classification Results
### Results of the CNN-Based Classification  
A CNN-based classification method was applied to the prepared labeled datasets. The labeled data must be converted into images for the CNN-based classification. The converted dual-channel images contain time and frequency information in the red and green channels, respectively. Examples of the dual-channel images for all seven classes are shown in Figure 15.  
The training dataset was prepared by randomly selecting data samples from the labeled dataset. _Trend-ambiguous_ and _Drift-ambiguous_ classes were not included in the training and test datasets to ensure consistency with a previous CNN-based classification study [26]. The details of both the training and test datasets are listed in Table 4. A balanced dataset with the same amount of data for each class was used to train the CNN model because an imbalanced dataset usually shows poor performance in training the network. A total of 1000 random samples from each class were selected from the database to train the CNN model. The training dataset for the CNN model was further divided into 70% training and 30% validation datasets. The remaining data samples from the database were used as datasets to test the accuracy of the trained CNN model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Number** & **Class** & **Quantity** & **Ratio (\%)** \\ \hline
1 & _Normal_ & 20,000 & 34.05 \\
2 & _Missing_ & 5105 & 8.69 \\
3 & _Minor_ & 5091 & 8.67 \\
4 & _Outlier_ & 4536 & 7.72 \\
5 & _Square_ & 5000 & 8.51 \\
6 & _Trend_ & 5883 & 10.01 \\
7 & _Drift_ & 2425 & 4.13 \\
8 & _Trend-ambiguous_ & 7216 & 12.28 \\
9 & _Drift-ambiguous_ & 3489 & 5.94 \\ \hline
**Total** & **58,745** & **100** \\ \hline \end{tabular}
\end{table}
Table 3: Total samples and percentage of each class anomaly in database.  
Figure 15: Examples of dual-channel images: (**a**) _Normal_, (**b**) _Missing_, (**c**) _Minor_, (**d**) _Outlier_, (**e**) _Square_, (**f**) _Trend_, and (**g**) _Drift_.  
The trained CNN model was then used to test for data anomalies in the remaining test dataset of the database. The results for the test dataset are shown in Figure 16. The overall accuracy of the test dataset was 93.3%. For the _Normal_ and _Minor_ classes, the recall values were 88.6% and 92.8%, respectively. Except for the _Normal_ and _Minor_ classes, all classes exhibited >95% recall values. The _Minor_ and _Drift_ classes showed 83.3% and 56.8% precision values, respectively, which were less than the precision values of the other classes. The results are similar to those of a previous study [26], proving that the CNN-based classification was appropriately implemented. Nonetheless, certain critical issues such as intrinsic ambiguity in the anomaly class and low resolution in the converted images must be investigated to determine the applicability of the data anomaly detection process. Further discussion of these issues is presented in the next section of this paper.
# Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System
## 2 Reflectarray Antenna
However, for reflectarray antennas having beams with more complicated shapes, this phase distribution is obtained using optimization techniques.
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
## 2 Methods
### Proposed Solution
#### 2.3.2 Phase 2: Trajectory Planning with Constant Velocity Profile
These dimensions are appropriately chosen such that the entire system can be placed inside without colliding with obstacles. Therefore, this approach leads to the introduction of the two inequality conditions expressed by Equation (14). These constraints represent the positional limitation of the truck's reference point in the last time step of the searched trajectories with respect to the desired outcome. Here, the vector \(\overrightarrow{x_{tf}}\) is a vector of state variables indicating the desired value at the end of the procedure. \[x_{tf_{1}}-\frac{x_{\text{tgt}}}{2}\leq z_{3N}\leq x_{tf_{1}}+\frac{x_{\text{tgt}}}{2} \tag{14}\] \[x_{tf_{2}}-\frac{y_{\text{tgt}}}{2}\leq z_{4N}\leq x_{tf_{2}}+\frac{y_{\text{tgt}}}{2}\]  
The cost function used here is defined by Equation (15). This function penalizes the distance between truck's reference point \(R_{0}\) and the corresponding path point, and also minimizes the total time. Numerical time integration is computed using the trapezoidal method. The variables \(C_{xn}\) and \(C_{yn}\) correspond to the coordinates of the found path \(\overrightarrow{C_{x}}\) and \(\overrightarrow{C_{y}}\), respectively, as described in Section 2.3.1. \[J=50\cdot\sum_{n=1}^{N}\sqrt{\left(x_{1_{n}}-C_{x_{n}}\right)^{2}+\left(x_{2_{n }}-C_{y_{n}}\right)^{2}}+\frac{1}{N}\cdot\left(\frac{1}{2}x_{T_{1}}^{2}+\sum_{ n=2}^{N-1}x_{T_{n}}^{2}+\frac{1}{2}x_{T_{N}}^{2}\right) \tag{15}\]  
If a solution is successfully found, it is necessary to extract individual control inputs and state variables from decision vector. The state variable \(\overrightarrow{x_{T}}\) becomes constant, i.e., \(\overrightarrow{x_{T}}\equiv x_{T}\), and represents the total travel time of the system. For further steps, it is necessary to determine the sampling period from these data, which is computed according to Equation (16). \[T_{s}=\frac{x_{T}}{N-1} \tag{16}\]  
Simple bounds corresponding to Equation (7) are defined by Equation (17). Note that only relevant pairs are used throughout particular phases of solution. The cost function in Equation (15), differential defect constraints in Equation (10) and related dynamics in Equation (12), equality boundary constraints in Equation (13), inequality constraints in Equation (14), and simple bounds in Equation (17) form the full optimization task for this phase of the solution. \[ \begin{split}-3\leq& u_{1}\leq 3\\ -\frac{\pi}{3}\leq& u_{2}\leq\frac{\pi}{3}\\ -1\leq& x_{1}\equiv X_{0}\leq 50\\ -1\leq& x_{2}\equiv Y_{0}\leq 40\\ -\frac{\pi}{3}\leq& x_{3}\equiv\phi_{0}\leq\frac{\pi}{3 }\\ -2\pi\leq& x_{4}\equiv\theta_{0}\leq 2\pi\\ 20\leq& x_{T}\leq 60\\ -\frac{\pi}{3}\leq& x_{5}\equiv\phi_{1}\leq\frac{\pi}{3 }\\ -\frac{\pi}{3}\leq& x_{6}\equiv\phi_{2}\leq\frac{\pi}{3 }\\ \end{split}  \tag{17}\]
## 4 Results and Discussion  
### Contruction Phase  
The aim of a life cycle impact assessment (LCIA) is to present the potential link between the product life cycle and the environmental impacts and to quantify them. The impact categories considered in this analysis are the cumulative energy demand (CED), the AD-fossil fuel and climate change.  
The results of the impact assessment of the construction phase and of the production of the materials are listed in Table 9 and Figure 5.  
If the materials used to build the e-road are examined more closely, the role of the WPT components emerges (see Table 10).  
Even though the WPT components represent less than 1% by weight, they play a significant role on the impacts (see Figure 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Amount** & **CD [M]/km]** & **AD—Fossil Fuels** & **Climate Change** \\  & **[kg/km]** & **CD [M]/km]** & **[kg CO2-Eq/km]** \\ \hline \multirow{2}{*}{Asphalt} & 992,394 & 3,149,172 & 3,002,414 & 44,484 \\  & 89\% & 66\% & 70\% & 29\% \\ \hline \multirow{2}{*}{Concrete} & 113,760 & 72,386 & 67,544 & 10,316 \\  & 10\% & 2\% & 2\% & 7\% \\ \hline \multirow{2}{*}{Bituminous emulsion} & 2190 & 61,226 & 59,177 & 480 \\  & 0\% & 1\% & 1\% & 0\% \\ \hline \multirow{2}{*}{WPT components} & 10,687 & 1,470,084 & 1,184,449 & 99,834 \\  & 1\% & 31\% & 27\% & 64\% \\ \hline Total & 1,119,031 & 4,752,868 & 4,313,584 & 155,114 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The materials used for the construction phase.  
Figure 5: The percentages of the different phases on the overall impact.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Phase**} & **CED [M]/km]** & **AD—Fossil Fuels [M]/km]** & **Climate Change [kg CO2-eq/km]** \\ \hline Material production & 4,752,868 & 4,313,584 & 155,114 \\ Transport & 144,597 & 143,051 & 9040 \\ Construction & 64,227 & 63,941 & 4162 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The impacts of the e-road construction per functional unit, i.e., per 1 km.  
Impacts of the sub-phases are reported more in detail in Table 11.
## Appendix F Data from Assets--Systems or Databases  
This appendix describes the databases and attributes identified in searching only open source data, related to the category Data from Assets.  
* Systems or Databases--RENAVAM (National Registry of Motor Vehicles--Brazil), DMV (Department of Motor Vehicles--by US state), Systeme d'Immatriculation des Vehicules (Vehicle Registration System--France), KBA (Federal Motor Transport Authority--Germany), Driver and Vehicle Licensing Agency--Vehicle Records (UK), Vahan (National Register of Motor Vehicles--India), Cadastro de Embaracoes (Brazilian Navy--Brazil), Canadian Register of Vessels (Transport Canada), USCG National Vessel Documentation Center (US Coast Guard--US), Registre International Francais (RIF--France), German International Shipping Register (GIS--Germany), UK Ship Register (Maritime and Coastguard Agency--UK), Registro Aeronautico Brasileiro (ANAC--Brazil C Civil Aviation Agency), Canadian Civil Aircraft Register Computer System (CCARCS--Canada), FAA Aircraft Registry (Federal Aviation Administration--US), Registre des Aeronefs Civils (DGAC--French Civil Aviation Authority--France), Luftfahrt-Bundesamt Database (LBA--Germany), G-INFO (Civil Aviation Authority--UK), DGCA Aircraft Register (Directorate General of Civil Aviation--India), Cadastro de Imoveis Rurais (Cafir--Rural Property Register--Brazil), o Cadastro Ambiental Rural (CAR--Environmental Rural Register--Brazil), Certificado de Cadastro de Imoveis Rurais (CCIR--Certificate of Registration of Rural Property--Brazil), Ontario Land Registry Access (Canada), HM Land Registry Database--UK, Registro (Brazil), declaration of assets of public servants (Brazil), in electoral dispute (Brazil), United States Patent and Trademark Office Database (USPTO), European Patent Office Database (EPO), European Union Intellectual Property Office Database, United States Copyright Office Public Catalog.  
* Common Attributes Present--Aircraft: registration/serial number, aircraft type, manufacturer, year of manufacture. Vessel: registration number, hull identification, owner, type of vessel, tonnage, year of construction. Motor Vehicles: license plate number, chassis number, owner, vehicle type, year of manufacture, category. Real Estate: property ID, owner name, location/address, property type (urban/rural), size, cadastral number, legal description, date of acquisition, zoning information, boundaries, liens or encumbrances, how much the property was last sold for. Assets in General: monetary value, type, description.
## 4 Results and Discussion
### Comparison between Experimental Measurements and Numerical Data
#### 4.1.2 Nanofluid as the Cooling Fluid
As shown in Figure 15, it is expected that silver structures exhibit a higher PEC when compared to the aluminum ones; however, in both cases, in the presence and the absence of the nanofluid, both water and the nanofluid exhibit similar performance evaluation criteria. The reason is that the nanofluid can achieve a better heat enhancement but at the expense of reducing the pressure drop. Figure 14: Temperature variation for different flow rates using the nanofluid. (**a**) Aluminum structure. (**b**) Silver structure. Figure 15: Performance evaluation criteria in the presence of the nanofluid. (**a**)Aluminum TPMS structure. (**b**) Silver TPMS structure.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm
### Node Wake-Up Scheme
```
1:if\(E_{n_{i}}<E_{tth}\)then
2: Find the neighbor node of \(n_{i}\), \(u_{i}\) \(u_{i}=\left\{n_{j}\in C\left|d\left(n_{i},n_{j}\right)<r_{s},i\
eq j,j\in \left[1,N\right]\right.\right\}\);
3: Find \(\rho\), which is the number of nonempty subsets Y of the neighbor node \(u_{i}\) \(\mathrm{Y}=\left\{\mathrm{Y}_{p}\left|\forall n^{*}\in\mathrm{Y}_{p},\exists n ^{*}\in u_{i}\right.\right\}\), \(\rho=2^{length(u_{i})}-1\);
4:for\(j=1,2,\cdots\rho\)do
5:for\(k=1,2,\cdots\).length(Y\({}_{j}\))do
6:\(c_{\mathrm{cov}}^{Y_{k}}=c_{n\left[\mathrm{Y}_{j}(k)\right]}\cup c_{\mathrm{ cov}}^{Y_{k}}\);
7:endfor
8:\(Value=c_{\mathrm{cov}}^{Y_{k}}\) & \(c_{un\,c}^{n_{i}}\);
9:if\(\sum\limits_{1\in Value}1>optval\)then
10:\(optval\)=Y\({}_{j}\);
11:endif
12:endfor
13:endfor
14:\(optcom\);
15:endif
```  
**Algorithm 2** Node wake-up scheme. Figure 4: Time slot diagram of a TDMA frame period. The main goal of the wake-up scheme is to generate an optimal scheduling combination to wake a node from sleep state by sending a wake-up signal to the neighbor node of the dead node. At the beginning of the next round, the sink wakes up some nodes according to the wake-up scheme. If a node dies again, then the wake-up scheme will reevaluate the coverage and decide to wake some other nodes to resume uncovered events.