esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution Method
### The EHOO Approach  
Figure 2 presents the flow diagram of the EHOO approach (Algorithm 3). The step-wise procedure of the proposed EHOO is explained below.  
```
Step 0: Configure the parameters of \(M\), \(I\), \(K\), \(c_{i}\), \(\alpha_{\min}\), \(\alpha_{\max}\), \(\beta_{\min}\), \(\beta_{\max}\), \(t_{\max}\), \(N\), \(L_{a}\), \(L_{0}\) and \(\Delta\). Step 1: Arbitrarily select \(M\) x's from the solution space and evaluate \(F_{a}(\mathbf{x})\), then off-line train the RMTS using these \(M\) training samples. Step 2: Arbitrarily choose \(\Phi\) x's as the initial population and apply Algorithm 1 to these individuals assisted by RMTS. After Algorithm 1 terminates, rank all the final \(\Phi\) x's based on their approximate fitness from low to high and choose the prior \(N\) x's to be the \(N\) significant solutions. Step 3: Employ Algorithm 2 to the \(N\) significant solutions and find the optimum \(\mathbf{x}^{\prime}\), and this one is the superb solution that we seek.
```  
**Algorithm 3: The EHOO**  
Figure 2: Flow diagram of the coupling elephant herding optimization with ordinal optimization approach.
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 4 Improved Aggregation Method
### Probability-Based Selection Method  
In the above method, we assign different weights to different clients, so that the client who has chosen a higher privacy budget will have a greater impact on the federated model. To further weaken the impact of clients who have chosen a small privacy budget, here we propose another strategy. The basic idea is to randomly select a subset of clients to participate in one round of model aggregation. For a client, the probability of being selected is determined by the privacy budget chosen. In short, choosing a high privacy budget means that the client does not need a high level of privacy protection, and it will not add too much noise to the local model's parameters. Correspondingly, a high probability will be assigned to the client's local model, and the client's local model is more likely to participate in this round of federated learning.  
In the \(r\)-th round of training, after collecting the perturbed local parameter \(\widetilde{G}_{i}^{(r)}\), of each client \(i\), the server first randomly selects a subset of \(m\) clients, and then performs an arithmetic average operation on the local models from the selected clients, to obtain \(W^{(r)}\). Let \(P_{i}\) denote the probability of client \(i\) being selected in this round. The probability \(P_{i}\), is computed as:  
\[P_{i}=\frac{\rho_{i}}{\sum_{i=1}^{N}\rho_{i}}, \tag{14}\]  
where \(\rho_{i}\) denotes the inverse of the standard deviation \(\sigma_{i}\), of the Gaussian distribution of the \(i\)-th client, and \(0<P_{i}\leq 1\). The larger the standard deviation \(\sigma_{i}\), the smaller the probability \(P_{i}\).  
Before the iterative training process starts, the server will utilize the privacy budget to compute the probability \(P_{i}\), for each client. Then, in each round of training, the server takes the following steps to select \(m\) clients. The server first generates a uniformly distributed random number \(\omega\in[0,1]\). For each client \(i\), if there is \(P_{i}>\omega\), then the client is selected. Otherwise, the client will not be selected.  
Specifically, for independent identically distributed data, the model aggregation rule (line 7 in Algorithm 1) is now given by  
\[W^{(r)}=f(\widetilde{G}_{1}^{(r)},\widetilde{G}_{2}^{(r)},\ldots,\widetilde{ G}_{N}^{(r)})=\frac{1}{m}\sum_{i=1}^{m}\widetilde{G}_{i}^{(r)}. \tag{15}\]  
For non-independent identically distributed data, the model aggregation rule (line 7 in Algorithm 2) is now given by  
\[W^{(r)}=sign(f(\widetilde{G}_{1}^{(r)},\widetilde{G}_{2}^{(r)},\ldots, \widetilde{G}_{N}^{(r)}))=sign(\frac{1}{m}\sum_{i=1}^{m}\widetilde{G}_{i}^{(r) }). \tag{16}\]
## 5 Experimental Results
### Quantitative Analysis--The Maximum Processed Tasks  
Figure 15 presents the hypothetical (theoretical estimation) limits of each of the tested configurations. These limitations become more evident when we are dealing with a safety system, wherein resources are replicated. In such cases, the same task may be executed concurrently by two or more identical processors. Of course, we must remember that when we decide to switch to the multitasking pipelined architecture (MTP), in order to maintain predictability, the operating frequency must be increased. Table 4 contains comparisons between the main parameters of different MTP scenarios.  
Figure 16 shows the relationship between the minimum achievable power per task and the maximum system capacity corresponding to the maximum number of tasks that the system can perform. Each point of the graph was further described by a configuration (scenario) for which this minimum could be achieved. The characteristics showed a clear upward trend, i.e., if we want to process more and different tasks, it is necessary to change the architecture to multitasking, and this involves raising the system frequency. As a result, the average dynamic power consumption increased. This should be considered while multitasking embedded systems with strong power constraints are designed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Frequency (\(F_{\text{sys}}\) [MHz])** & \begin{tabular}{c} **Maximum Number of** \\ **Tasks** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum Number of** \\ **Cores** \\ \end{tabular} &
\begin{tabular}{c} **Minimum Total** \\ **Average Power per** \\ **Core (\(AP\) [mW])** \\ \end{tabular} \\ \hline MTP RG core 30 task & 150 & 360 & 12 & 32.10 \\ MTP RG core 15 task & 75 & 345 & 23 & 20.43 \\ MTP RG core 10 task & 50 & 260 & 26 & 13.03 \\ MTP RG core 6 task & 30 & 250 & 42 & 8.10 \\ MTP RG core 5 task & 25 & 245 & 49 & 6.67 \\ STP CG & 25 & 215 & 215 & 4.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained for different systems with the same productivity: \(SP\) = 150.  
Figure 14: The comparison of the average dynamic power consumed by a single task by different structures with \(SP\) = 150.
# Control of Flow around an Oscillating Plate for Lift Enhancement by Plasma Actuators
## 2 Method
### Configurations and Control Parameters of Plasma Actuator  
Figure 3 illustrates the cross-section of the plasma actuator utilized in this study. To minimize the influence on the flow due to the protrusion of the actuator from the wall, a flush-mounted plasma actuator [24; 26; 31] was utilized. A copper foil with a thickness of \(0.006b\) was used as the upper and lower electrodes and polyimide with a thickness of \(0.2b\) was used as the dielectric layer. The alternating voltage with a peak-to-peak voltage of \(E_{\mathrm{PA}}=4.8\) kV\({}_{\mathrm{P-P}}\) was applied with the frequency of \(f_{\mathrm{PA}}=4.2\) kHz. The widths of the upper electrode and the lower electrode were \(0.5b\) and \(1.5b\), respectively. The plasma actuator was installed \(0.25b\) downstream from the upstream edge. In the previous paper [31], it has been confirmed that the actuator with these configurations suppressed the flow separation around the plate, where the control effects are shown in Section 3. It was found in the literature [24] that the flow control by the plasma actuator set near the separation point was effective for suppression of flow separation. However, the protrusion of the actuator could disturb the flow in itself by setting the actuator just at the leading edge because the shape of the leading edge of the flat plate affects the flow phenomena such as vortex shedding. Based on these considerations, the actuator was located at slightly downstream position from the upstream edge.  
Figure 3: Cross-section of plasma actuator.  
Figure 2: Schematic of the oscillating plate.  
Also, to clarify the effects of installation surface on the control, the control effects were investigated with the actuators installed on the top surface, bottom surface and both surfaces, as shown in Figure 3.  
To clarify the influence of the driving conditions of the actuator on the lift force, the control by the intermittently driven actuator with two changed parameters was investigated along with the continuous control. The first parameter is the ratio of time duration for driving the actuator to the oscillation period (driving ratio), \(R\). The second parameter is the difference between the time to start driving the actuator and that for the bottom dead point of plate oscillation (time offset), \(t_{0}/T\). These two parameters were changed as shown in Figure 4, where \(T\) is an oscillating period and \(t=0\) is the time for the bottom dead center of the oscillating motion. A driving ratio of 0% indicates that the plasma actuator is not driven, while a driving ratio of 100% indicates that the plasma actuator is continuously driven throughout the oscillation. The offset of 0.0 means that the actuator is driven from the time at which the up-stroke motion begins.
## Methods and Data  
### Study Area  
The Murray-Darling Basin (hereafter MDB) is the most productive agricultural area in Australia and a globally recognized epitome of successful water management. It covers around 14% of Australia's land, but produces about 40% of Australia's gross value of agricultural production. As one of the most important catchments in the MDB, the MRC has an area of 84,000 square kilometers and accounts for about 7.9% of the MDB (Figure 2). It encompasses all of the Australian Capital Territory. The Murrumbidgee River rises in the Snowy Mountains and flows 1690 km from east to west. The average annual runoff is about \(37.84\times 10^{8}\) m\({}^{3}\). The highest precipitation occurs in the eastern mountains of the catchment and averages 1500 mm yr\({}^{-1}\), while the average annual rainfall in the lower catchment is about a fifth of this figure. The annual potential ET ranges from 1250 mm to 1500 mm. Most of the precipitation losses are in the form of ET and account for up to 90%.  
The MRC was opened to settlement in the 1830s and soon became an important farming area, and now its irrigated area accounts for about 15% of the total in the MDB, especially the irrigated rice area which is up to about two third of the total rice area in the MDB according to the statistical data from Australian Bureau of Agricultural and Resource Economics and Sciences (ABARE). In the MRC, agricultural land, grassland, and forest are the main land cover types, which accounts for about 60%, 15%, and 20%, respectively [21]. The Murrumbidgee River is one of the most regulated rivers in Australia on which there are 26 water storage structures. Irrigation for agriculture is the greatest user of water in the MRC, and the total volume of irrigation entitlements is approximately \(28.0\times 10^{8}\) m\({}^{3}\), which accounts for 74.0% of the average annual runoff [22]. However, with the development of the farming practices, there is increasing deterioration of river health, thus, protection of the environment is on the political agenda, along with a commitment not only to return water to rivers to nurse them back to health, but also to help agricultural industries rise up to the challenge of a drier climate [23]. In such a catchment, precise and reliable water resources management is extremely important for agricultural development and ecosystem management.
## 3 Result and Discussion
### Power Capacity Measurement
#### 3.5.2 Time Consumption  
The time measurement was conducted to obtain the time consumption of each process in CO\({}_{2}\) concentration measurement. Further, the time consumption was used to estimate and analyze the energy capacity of sensor nodes in one-cycle CO\({}_{2}\) measurement. The sensor node starts to measure CO\({}_{2}\) concentration with the initialization process, the CO\({}_{2}\) reading process, and CO\({}_{2}\) data-transmitting process, followed by the time interval between two CO\({}_{2}\) measurement cycles. A program-based measurement technique was used to measure the time consumption of each process. The program will measure the start and stop time of each process in CO\({}_{2}\) concentration measurement to obtain the time consumption of each process. The time-processing measurement result is shown in Figure 14. The initialization process only needs 7 ms, while the CO\({}_{2}\) reading and CO\({}_{2}\) transmitting need 25 ms and 1319 ms, respectively. The highest time consumption is in the transmitting process, depending on the internet/Wi-Fi connection. If the connection is good, the time of the transmitting process will be faster. The time consumption measurement results revealed that the total time consumption for one cycle of CO\({}_{2}\) measurement, including the sleep stage, is 11.351 s.  
In the time interval, the sleep-mode stage can be activated, and it was set at 10 s. When this time interval scenario is applied, the sensor node runs in low-power mode operation until the time interval is achieved and returns to the initialization process to start the CO\({}_{2}\) measurement again. This strategy will reduce the total sensor-node energy consumption because enabling low-power mode consumes much less power than disabling low-power mode. Moreover, the time interval between two CO\({}_{2}\) measurement cycles (low-power mode) does not significantly affect CO\({}_{2}\) measurement results because of the relatively slow  
Figure 12: The current measurement configuration.  
Figure 13: The current consumption of sensor-node process.  
change in indoor CO\({}_{2}\) concentration. The time interval of 10 s can still provide real-time CO\({}_{2}\) measurement results.
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 5 Case Study: Spatio-Temporal Alignment of Indicators
### Experimental Results
#### 5.3.1 Disambiguation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Exp. & **Tool** & \(\mathcal{R}\)\({}^{\ddagger}\) & \(\mathcal{A}\)\({}^{\ddagger}\) & _Amb\({}^{\lx@sectionsign}\)_ & _Precision_ & _Recall_ & _F-Measure_ & _Overall_ & _Ambiguity_ \\ \hline \multirow{2}{*}{Exp. 1} & LogMap & 267 & 211 & 0 & 1.0 & 0.79 & 0.882 & 0.79 & 0\% \\  & AML & 118 & 0 & 0.559 & 0.247 & 0.342 & 0.052 & 0\% \\ \hline \multirow{2}{*}{**Exp. 2**} & LogMap & 446 & 200 & 0 & 1.0 & 0.448 & 0.619 & 0.448 & 0\% \\  & AML & 218 & 0 & 0.958 & 0.468 & 0.629 & 0.448 & 0\% \\ \hline \multirow{2}{*}{**Exp. 3**} & LogMap & 434 & 194 & 0 & 0.984 & 0.44 & 0.6 & 0.433 & 0\% \\  & AML & 210 & 0 & 0.952 & 0.46 & 0.621 & 0.437 & 0\% \\ \hline \multirow{2}{*}{**Exp. 4**} & LogMap & 55 & 61 & 0 & 0.426 & 0.472 & 0.448 & \(-\)0.163 & 0\% \\  & AML & 55 & 26 & 0 & 0.346 & 0.163 & 0.222 & \(-\)0.145 & 0\% \\ \hline \multirow{2}{*}{**Exp. 5**} & LogMap & 137 & 3 & 0 & 1.0 & 0.021 & 0.042 & 0.021 & 0\% \\  & AML & 3 & 0 & 1.0 & 0.021 & 0.042 & 0.021 & 0\% \\ \hline \multirow{2}{*}{**Exp. 6**} & LogMap & 102 & 31 & 0 & 1.0 & 0.3 & 0.466 & 0.3 & 0\% \\  & AML & 434 & 210 & 0 & 0.952 & 0.46 & 0.621 & 0.437 & 0\% \\ \hline \multirow{2}{*}{**Exp. 4**} & LogMap & 55 & 61 & 0 & 0.426 & 0.472 & 0.448 & \(-\)0.163 & 0\% \\  & AML & 55 & 26 & 0 & 0.346 & 0.163 & 0.222 & \(-\)0.145 & 0\% \\ \hline \multirow{2}{*}{**Exp. 5**} & LogMap & 137 & 3 & 0 & 1.0 & 0.021 & 0.042 & 0.021 & 0\% \\  & AML & 3 & 0 & 1.0 & 0.021 & 0.042 & 0.021 & 0\% \\ \hline \multirow{2}{*}{**Exp. 6**} & LogMap & – & 196 & 0 & 1.0 & 0.949 & 0.949 & 0.9 & 0\% \\  & AML & – & 192 & 2 & 0.973 & 0.954 & 0.963 & 0.928 & 1\% \\ \hline \multirow{2}{*}{**Exp. 10**} & LogMap & – & 179 & 0 & 1.0 & 0.91 & 0.952 & 0.91 & 0\% \\  & AML & – & 187 & 0 & 0.994 & 0.944 & 0.968 & 0.939 & 0\% \\ \hline \hline \end{tabular} \({}^{\dagger}\)\(\mathcal{R}\): expected correspondences. \({}^{\ddagger}\)\(\mathcal{A}\): detected correspondences. \({}^{\lx@sectionsign}\)_Amb_: ambiguous correspondences in \(\mathcal{A}\).  
\end{table} 
Table 3: Results after alignment _disambiguation_.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 3 Description and Design of the Photovoltaic System
### Wireless Conversion Unit (WCU)  
#### 3.2.1 DC/AC Converter  
The DC/AC converter, also known as an inverter, is the device responsible for converting the direct power (DC) from the PV system into alternating power (AC) for the electrical grid. Thus, the DC/AC converter connects the PV system to the electrical network. Moreover, the inverter has to provide the maximum active power to the grid.  
In this study, a single-phase inverter is used. It is composed of four insulated-gate bipolar transistors (IGBT), IRGB4062 DPB (International Rectifier, RS Components, Corby, UK), devices with anti-parallel diodes, as Figure 5 shows, driven by IR2106 devices. Those transistors are switched in a specific sequence to achieve the AC signals. The inverter output is connected to the grid by means of an output reactance, \(L_{1}\), (with a value of 20 mH) to control the DC/AC converter output current \(i_{0}\). Thus, the current injected into the grid, \(i_{grid}\), has to be co-linear with the network voltage. Moreover, the current injected by the inverter must be nearly sinusoidal to avoid harmonic distortion. The boost converter output voltage is the inverter input voltage. This voltage must be controlled to keep it almost constant, which is achieved by means of a three-level PWM sinusoidal modulator.  
Figure 4: Flowchart of the adaptive Lyapunov control for the DC/DC converter.  
Figure 5: DC/AC converter topology.  
The DC/AC converter has been built in order to conduct the experiments. The maximum input voltage is 120 V and the maximum input current is 4.5 A, whereas the maximum output voltage is 85 V and the maximum output power is 400 W. The efficiency of the inverter is 94%.
## 5 Security Analysis and Comparison
### Keyspace  
To make the brute-force search difficult, the keyspace of an image encryption technique should normally be rather wide. Equation (25) is the formula used for calculating an approximate keyspace size. It is of the utmost importance to determine which parameters are the original secrets. In principle, the keyspace for this approach can be as large as necessary or infinitely theoretically. The suggested method employs several different cryptographic keys, including \(a\), \(b\), \(c\), \(d\), \(x^{1}\), \(y^{1}\), and \(z^{1}\) for the 3D Gauss map (key 4), and \(p\) (key 1), \(q\) (key 2), and \(r\) (key 3) for the logistic, Arnold, and sine maps, respectively. Quantifying the complete keyspace makes use of the IEEE floating-point standard [32]. The comparison of keyspace among various recent literature is tabulated in Table 6.  
\[\text{Keyspace}=10^{15}\times 10^{15}\times 10^{15}\times 10^{15}\times 10^{15} \times 10^{15}\times 10^{15}\times 10^{15}\times 10^{15}=10^{150}\approx 2^{498} \tag{25}\]
# Fused Gromov-Wasserstein Distance for Structured Objects
## 6 Conclusions  
This paper presents the Fused Gromov-Wasserstein (\(FGW\)) distance. Inspired by both Wasserstein and Gromov-Wasserstein distances, \(FGW\) can compare structured objects by including the inherent relations that exist between the elements of the objects, constituting their structure information, and their feature information, part of a common ground space between each structured objects. We have stated mathematical results about this new distance, such as metric, interpolation, and geodesic properties. We have also provided a concentration result for the convergence of finite samples. In the discrete case, algorithms to compute \(FGW\) itself and related FrA(c)chet means are provided. The use of this new distance is illustrated on problems involving structured objects, such as time series embedding, graph classification, graph barycenter computation, and graph clustering. Several questions are raised by this work. From a practical side, the FGW method is quite expensive to compute and further works will try to lower the computational complexity of the underlying optimization problem to ensure better scalability to very large graphs. Moreover, while we mostly consider in this work structure of graphs described by the shortest path, other choices could be made, such as the distances based on the Laplacian of the graph. Finally, from a theoretical point of view, it is often valuable that the geodesic path be unique, so as to defined properly objects, such as gradient flows. One interesting result would be, for example, to see if the geodesic is unique with respect to the strong isomorphism relation.
## 7 Antenna Qualification and Acceptance Test
### Visual Inspection  
Subsequent to the execution of each environmental test, a visual inspection is conducted to ensure that there are no noticeable changes. The validity criteria for this visual inspection are as follows: The absence of color change in the dielectric, which could in  
Figure 19: QM S-band qualification test flow.  
dicate material degradation, is to be noted. Furthermore, the lateral adhesive SCOTCH WELD 2216 A + B, Batch EGE 208 [30], must not be detached. Additionally, the two SMP connectors must not be displaced or broken, and no visible marks or cracks must be present on the copper plates or the dielectric.
###### Abstract  
Dystrophic Epidermolysis bullosa (DEB) is a rare, severe subtype of epidermolysis bullosa (EB), characterized by blisters and military rashes of the skin. Dystrophic EB (DEB) includes variants inherited both in an autosomal-dominant or autosomal-recessive manner. Recessive dystrophic EB (RDEB) is divided into many subtypes and prevails as a result of biallelic genetic mutations in _COL7A1_ gene encoding type VII collagen, a major stabilizing molecule of the dermo-epidermal junction. The blister formation is mainly due to the variable structural and functional impairment of anchoring fibrils in VII collagen (COLVII), responsible for the adhesion of the epidermis to the dermis. **Method:** Three Pakistani families (A, B and C) affected with congenital dystrophic epidermolysis bullosa were recruited in the present study. The whole-exome sequencing (WES) approach was utilized for the detection of the pathogenic sequence variants in probands. The segregation of these variants in other participants was confirmed by Sanger sequencing. **Results:** This study identified a novel missense variant c.7034G>A, p. Gly2345 Asp in exon 91, a novel Frameshift mutation c.385 del (p. His129 MetfsTer18) in a homozygous form in exon no 3, and a previously known nonsense variation (c.1573 C>T; p. Arg525 Ter) in exon 12 of _COL7A1_ gene in families A, B, and C, respectively, as causative mutations responsible for dystrophic epidermolysis bullosa in these families. **Conclusion:** Our study validates the involvement of the _COL7A1_ gene in the etiology of dystrophic epidermolysis bullosa. It further expands the _COL7A1_ gene mutation database and provides an additional scientific basis for diagnosis, genetic counseling, and prognosis purposes for EB patients.  
dytrophic epidermolysis bullosa; autosomal recessive; whole-exome sequencing; _COL7A1_ gene; sequence variants +
Footnote †: journal:  
_diagnostics_