esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multiscale Recurrence Analysis of a Model Cable-Stayed Bridge  
### Test Description  
A 1:20 scale model cable-stayed bridge subjected to dynamic tests will be taken as the case study for the validation of the proposed algorithm in this section. The model bridge, which is shown in Figure 6, may consist of five spans based on the arrangement of the location of towers and piers. The span lengths in this context are: 2.45 m + 4.05 m + 14.24 m + 4.05 m + 2.45 m, which gives the bridge a total length of 27.24 m.  
The superstructure of the model bridge is constructed of a steel \(\pi\)-shaped girder with box cross-section on both sides of the girder. The width of the girder is 1.1 m and the height of the girder is 0.081 m. The superstructure is divided into six sections, which are made separately and connected by connecting plates which are numbered inside a circle in the figure, e.g., 1, 2, etc. The superstructure is supported by 60 pairs (or 120 in total) of stay-cables, which are fabricated from wire-ropes of 10 mm diameter and are anchored to the top of the tower. The tower is constructed of two rectangular (0.21 m \(\times\) 0.14 m) hollow steel columns with wall thickness of 8 mm, which stands  
Figure 5: A data-driven RQA framework for nonstationary and nonlinear signal testing.  
Figure 6: A model cable-stayed bridge (unit: m).  
5.95 m in height above the footing. Three cross-beams connect the columns at each tower. The two upper cross-beams are at spacing of 0.34 m center-to-center at the top, and the lower cross-beam is at spacing of 1.397 m center-to-center at the columns. The end spans of the superstructure are supported by four piers. The towers and superstructure are constructed of steel plate, with a yield strength of \(f_{y}^{\prime}=235\) MPa. The piers are constructed of concrete, with a compressive strength of \(f_{c}^{\prime}=30\) MPa.  
Dynamic properties of the model cable-stayed bridge are characterized by uni-directional piezoelectric accelerometers, which have a sensitivity of 2500 mv/g and an operating frequency in the range of 0.05 to 300 Hz. The arrangement of accelerometers is shown in Figure 6, where 13 solid triangles have been used to denote the location of the vertical accelerometers mounted on the girders. Pulse excitation at the location of the middle span is used to characterize the dynamic properties of the bridge. The damped acceleration attenuation responses of the girder are measured by 13 vertical accelerometers. Different damage locations of the girder is modeled by loosening the bottom bolts of the connecting plate, as shown in Figure 7b. The cable damage is modeled by uninstalling the stay cable, as shown in Figure 7c. In this paper, the responses measured from the girder corresponding to the damage of the connecting plate 2 are used to verify the proposed algorithm.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 5 Security Analysis
### Informal Analysis
#### 5.1.8. Ephemeral Secret Leakage (ESL) Attacks  
In the authentication for delegation phase, \(DU_{A}\) and \(DU_{B}\) establish the same session key \(SK=h_{1}(V_{A}||V_{B}||AGID_{1}||T_{3})\). Based on the CK-threat model Section 3.2, the short term ephemeral secrets \(r_{A}\), \(r_{B}\) can be leaked. However, the adversary still cannot compute \(SK\) because the adversary does not have \(b_{a}\) and \(b_{b}\). Furthermore, assuming that the long-term secret keys \(b_{a}\) and \(b_{b}\) have been leaked, the adversary cannot calculate the session key because \(r_{A}\) and \(r_{B}\) cannot be known. \(SK\) can be computed only when both short term and long term are leaked, and since this is a computationally infeasible problem, our scheme can resist ESL attacks.
# Substrate-Driven Atomic Layer Deposition of High-\(\kappa\) Dielectrics on 2D Materials
###### Abstract  
Atomic layer deposition (ALD) of high-\(\kappa\) dielectrics on two-dimensional (2D) materials (including graphene and transition metal dichalcogenides) still represents a challenge due to the lack of out-of-plane bonds on the pristine surfaces of 2D materials, thus making the nucleation process highly disadvantaged. The typical methods to promote the nucleation (i.e., the predeposition of seed layers or the surface activation via chemical treatments) certainly improve the ALD growth but can affect, to some extent, the electronic properties of 2D materials and the interface with high-\(\kappa\) dielectrics. Hence, direct ALD on 2D materials without seed and functionalization layers remains highly desirable. In this context, a crucial role can be played by the interaction with the substrate supporting the 2D membrane. In particular, metallic substrates such as copper or gold have been found to enhance the ALD nucleation of Al\({}_{2}\)O\({}_{3}\) and HfO\({}_{2}\) both on monolayer (1 L) graphene and MoS\({}_{2}\). Similarly, uniform ALD growth of Al\({}_{2}\)O\({}_{3}\) on the surface of 1 L epitaxial graphene (EG) on SiC (0001) has been ascribed to the peculiar EG/SiC interface properties. This review provides a detailed discussion of the substrate-driven ALD growth of high-\(\kappa\) dielectrics on 2D materials, mainly on graphene and MoS\({}_{2}\). The nucleation mechanism and the influence of the ALD parameters (namely the ALD temperature and cycle number) on the coverage as well as the structural and electrical properties of the deposited high-\(\kappa\) thin films are described. Finally, the open challenges for applications are discussed.  
ALD; high-\(\kappa\); 2D materials; graphene; MoS\({}_{2}\); substrate effect +
Footnote †: journal: Applied Sciences
# A Diagnosability-Integrated Design Approach Based on Graph Theory
## 3 Diagnosability Integrated Design Method Details
### Measurement Point Update Mechanism  
The diagnosis result of the current fault diagnosis scheme can be evaluated by Equation (14), and when the evaluation result is not good, it means that the current set of maximal cliques \(C^{*}\), i.e., the selected measurement points, does not fully reflect the fault information of the system, and additional information needs to be obtained by adding new measurement points and using this new information to diagnose the system.  
For the existing set \(C^{*}\) of maximal cliques, it is necessary to find new maximal cliques from the set \(C-C^{*}\) to supplement the fault information of the system, and the following principles should be followed when selecting new maximal cliques:1. The new maximal clique should cover as many faults as possible in order to provide more information for the fault diagnosis of the system;
2. When there are multiple maximal cliques in \(C-C^{*}\) that can cover the same number of faults, the most economical one is selected based on the principle of economy.
## 3 The PMT Readout and Trigger System
### Boards Interwork  
The communication between the PMT&T and the other subsystems is managed by a register-based SpaceWire protocol, which is described in Figure 5.  
At each trigger event, the PMT&T board sends the trigger signal to the T-DAQ and the DPCU and starts the ADC conversions. Once the conversions finish, the FIFO is filled with data (first arrow in Figure 5).  
If the DPCU is not busy (the \(\overline{\texttt{DPCU\_BUSY}}\) signal, as shown in Figure 5, is high), the first packet in the FIFO is transferred to a series of SpaceWire registers and the total length of the data is stored in a separate register (second and third arrow of the timing diagram).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Length (bytes)** & **Description** \\ \hline Trigger counter & 4 & Number of events acquired \\ Timestamp & 4 & Time from the power-on (16 \(\upmu\)s resolution) \\ Trigger ID & 1 & Identification number for the trigger configuration \\ ADC data & 192 & ADC conversion of the two CITIROCs’ output \\ Lost trigger & 2 & Triggers counted during dead time \\ Alive time & 4 & Alive time counter (5 \(\upmu\)s resolution) \\ Dead time & 4 & Dead time counter (5 \(\upmu\)s resolution) \\ Trigger flags & 8 & Flags indicating over-the-threshold channels \\ Turret flags & 1 & Flags indicating which tracker turret has been hit \\ Turret counters & 20 & Signals counted for each turret \\ \hline Total & 240 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data packet description.  
Figure 4: Data packet finite state machines.  
Figure 5: Timing diagram describing the dataflow inside the FPGA and the signals used to communicate with DPCU and TDAQ, the arrows indicate a cause-effect relationship between signals.  
When the PMT&T board asserts the DATA_READY signal, the DPCU starts reading all the data registers.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 4 Track Condition Monitoring Results Using Lateral Vibration Acceleration  
### Effect of Centrifugal Acceleration  
The lateral vibration acceleration of vehicles is correlated significantly with track alignment irregularity. However, lateral vibration acceleration includes a steady component owing to the centrifugal acceleration when the train operates on a curved track. This steady component must be eliminated to enable evaluation based on the vibration acceleration generated solely by the track irregularity. Therefore, the centrifugal acceleration was calculated based on the onboard sensing device measurements of travelling velocity and yaw angular velocity, which were then deducted from the lateral vibration acceleration measurements.
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## Materials and Methods
### Field Experiments  
Field experiments were conducted from April to June 2016, in mixed-fruit orchards belonging to smallholder farmers of Chano-Mille, Arbaminch, Southern Ethiopia. Traps used for this experiment were Easytrap(r) (14 \(\times\) 9 \(\times\) 5 cm [PERSON], INIA, Madrid, Spain), which consists of a two-port rectangular plastic with an inbuilt hook and a capacity of holding up to 400 mL of liquid attractants. A synthetic blend in paraffin oil based on the response of female _B. dorsalis_ (dorsablend) was compared to GF-120 and Torula yeast. Three plots (\(\sim\)70 m \(\times\) 70 m) were selected for experiments. Traps containing 200 mL of the commercial baits (GF-120 and Torula yeast) and traps containing 2 mL of the dorsablend and paraffin oil were hung at every corner of each plot. The dorsablend and the paraffin oil were applied on a cotton wick in a 4 mL vial and placed inside the trap. Distances between traps were 30 m. Traps were checked and serviced every week, loaded with fresh lures, and rotated. The experiment was replicated four times, with each replicate being one week of trap-catches.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology
### Experiment 2-Multiple Point Audio Sources
#### 3.5.1 Content  
For the second experiment six audio samples have been originated from the EBU music database, one (pinkReverb) was synthetically generated, and one (babble noise) was taken from the TCD-VoIP dataset [30]. All audio clips were sampled at 48 kHz. They were converted to mono format and then encoded to FOA and 3 OA formats with a variety of localizations. Details of audio samples used in experiment 2 can be found in Table 4.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 5 Experimental Results  
This section reports the performance of the proposed method in terms of invisibility and robustness. A comparison experiment is also conducted using the method described by [PERSON] [4], which uses the same domain and insertion method as the proposed method. Unlike the proposed watermarking method, [PERSON]'s method transforms the whole image into a curvelet domain to insert a watermark. Whereas [PERSON]'s method inserts multi-bits by dividing the curvelet directions, the proposed watermarking method inserts multi-bits by dividing the image into spatial blocks.  
We also experimented with the proposed template for the new type of DIBR watermarking [31]. This DIBR watermarking method exploits the DT-CWT domain, which is robust against DIBR attacks and signal distortions, but vulnerable to geometric distortion. By comparing the performance before and after applying the template to the DIBR watermarking method, we show that the  
Figure 7: The process of inserting a template and watermark into images of various sizes.  
proposed template can compensate the vulnerability of the geometric distortion for the new watermarking method.
# Study of a Synchronization System for Distributed Inverters Conceived for FPGA Devices
## 2 Materials and Methods
### Circulating Current Phenomenon  
The circulating current has been defined as the difference between the output current of each inverter and the average current (that is the sum of the outputs current divided by the number of inverters) [24]:  
\[\overline{I_{1}}-\overline{I_{avg}}=\frac{2\cdot\overline{I_{1}}-\overline{I_{2 }}-\overline{I_{1}}}{2}=\frac{\overline{I_{1}}-\overline{I_{2}}}{2}=\overline{ I_{H1}} \tag{1}\]  
\[\overline{I_{2}}-\overline{I_{avg}}=\frac{2\cdot\overline{I_{2}}-\overline{I_ {1}}-\overline{I_{2}}}{2}=\frac{\overline{I_{2}}-\overline{I_{1}}}{2}=\overline {I_{H2}} \tag{2}\]  
where \(I_{1}\) and \(I_{2}\) are the output currents of the first and the second inverter, \(I_{avg}\) is the average output current, \(I_{H1}\) and \(I_{H2}\) are the circulating currents seen by the first inverter and by the second inverter, respectively. All the currents are expressed in Ampere.  
The discussion so far can be generalized in the case of N inverters. Similar to what has been performed previously, the current seen by each inverter can be calculated as:  
\[\overline{I_{k}}=\frac{\overline{E_{k}}-\overline{V_{0}}}{\overline{Z_{k}}} \tag{3}\]  
where \(k\) is the number of the inverter considered, \(E_{k}\) is the output voltage of the \(k\)-th inverter and \(V_{0}\) the load voltage, both expressed in Volt. \(Z_{k}\) is the load impedance in Ohm. The average output current, then, is defined as:  
\[\overline{I_{avg}}=\frac{\sum_{j=1}^{N}\overline{I_{j}}}{N} \tag{4}\]  
where \(I_{j}\) is the output current of the \(j\)-th inverter and N is the number of inverters.  
Finally, the circulating current seen by an inverter in a system with N parallel-connected inverters can be calculated as the difference of its own output current and the average current:  
\[\overline{I_{Hk}}=\frac{N\cdot\overline{I_{k}}-\sum_{j=1}^{N}\overline{I_{j}} }{N} \tag{5}\]  
where \(k\) is the inverter number, \(N\) is the total number of inverters and \(\overline{I_{Hk}}\) is the circulating current seen by the \(k\)-th inverter expressed in Ampere.
## 3 Results and Discussion
### Comparative Analysis with Existing Methods  
A comprehensive comparative analysis was conducted to evaluate the performance of the proposed hybrid method against both [PERSON]'s method and conventional approaches. The analysis focused on three critical error domains: frequency domain error, time domain error, and steady-state error in Table 5.  
The frequency domain error analysis reveals significant improvements with the proposed hybrid method. Compared to [PERSON]'s method, which exhibits an error of 1.8928, and the conventional approach with 1.1454, the hybrid method achieves a substantially lower error of 0.8234. This represents a 56.5% improvement over [PERSON]'s method and a 28.1% improvement over conventional approaches. The reduction in frequency domain error can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **Error in Frequency** & **Error in Time** & \multirow{2}{*}{**Steady-State Error**} \\  & **Domain** & **Domain** & \\ \hline [PERSON]’s method & 1.8928 & 0.8646 & 0.0003 \\ Conventional & 1.1454 & 0.0856 & 0 \\ Proposed Hybrid & 0.8234 & 0.0412 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error domain comparison.  
be attributed to the LSTM network's ability to capture complex frequency characteristics and the Genetic Algorithm's optimization of model parameters.  
In the time domain, the proposed hybrid method demonstrates even more substantial improvements. The time domain error is reduced to 0.0412, compared to 0.8646 for [PERSON]'s method and 0.0856 for conventional approaches. This represents a 95.2% improvement over [PERSON]'s method and a 51.9% improvement over conventional methods. The superior time domain performance can be expressed mathematically as follows:  
\[E_{t}=\min\left(\left|\left|\mathrm{y(t)-\hat{y}(t)}\right|\right|^{2}\right) \tag{22}\]  
where \(E_{t}\) represents the time domain error, y(t) is the actual system output, and \(\hat{y}(t)\) is the predicted output.  
The steady-state error analysis shows that both the conventional and proposed hybrid methods achieve zero steady-state error, while [PERSON]'s method exhibits a small but non-zero error of 0.0003. This can be represented as follows:  
\[\lim(\mathrm{t}\rightarrow\infty)\left|\left.\mathrm{r(t)-y(t)}\right|\right.=0 \tag{23}\]  
where r(t) is the reference input and y(t) is the system output.
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 4 Logo-Based Advertisement Detector
### Logo Locator
From the analysis of the dataset, advertisement segments account for just 26 s on average; we tested several window sizes around this value, and consider a region to be temporal consistent if it is segmented in the same location for at least 30 s.  
To keep track and validate each logo candidate, the temporal consistency of the logos is modeled as a heat map with regions where the temperature reaches a certain value being considered as good logo candidates. To account for missed detections that may belong to segmentation clutter, two different processes are employed: heating, which is responsible to increase temperature given detection candidates, and annealing, that is applied to a region where no detection was found. For every frame and for every filtered detection, we increase the temperature on the corresponding region in the heat map by appending a circular mask that is centered on the middle point of the detection with diameter corresponding to the diagonal length of the region's bounding box. As static logos may be segmented differently according to their surroundings, resulting in (slightly) different regions in subsequent frames, this process enables a steady increase of the temperature in regions where constant detections are obtained. Some logo candidates may be derived from segmentation errors or even logos of advertisements that are present for a limited period, requiring the annealing of regions where no detection can be found. As a result, the temperature value of regions with consistent detections will be considerably superior to the remaining space. As this procedure is applied to each frame, it is necessary to specify a stopping criteria. Given our definition of temporal consistency, we specify the stopping criteria for the temperature as \(T_{max}=30\times fps\), where \(fps\) represents the frame rate of the input video. To allow for multiple possible candidates, as some channels may have more than one logo, we also select as logo candidates all the regions where the temperature reaches up to 90% of \(T_{max}\). An illustration of this procedure is presented in Figure 7. In Figure 7a, we have an unprocessed input frame; in Figure 7b, we have the resulting graph segmentation of the input region. As can be seen in the upper corners of the image, the two possible logo regions are clearly identified. Finally, in Figure 7c we have the extracted region of the candidate logos that were validated with temporal consistency. Whenever a logo candidate is found, the previous detections are re-analyzed, and patches that have a center location in the vicinity of the maximum temperature points are extracted. Thus, positive samples associated to each logo candidate are selected. To provide negative samples of the logo, we also extract random patches from the images. This information is then passed to the Logo Training module.
## 4 Simulation Results and Dissections
### Residential Load  
In this case, the overall reactive and real load demand during 24 h are 34.43 MVAr and 48.57 MW, respectively. Without integration PV and BES in RDS, the total reactive and real loss during 24 h are 0.85 MVAr and 1.87 MW, respectively. Installing one PV alone in RDS at bus 61 reduces the system loss to 1.39 MW. Additionally, installing two PV alone in RDS at buses 61 and 17 reduces the system loss to 1.35 MW. The total energies of one and two PV alone in RDS during the day are illustrated in Figures 8 and 9. Table 2 illustrates the locations and sizes of PV, the total energy of PV, and the injection energy from PV to the grid. Therefore, installing three PV alone reduces the system loss to 1.34 MW at buses 61, 18, and 11. From Figure 10, the total energy of three PV alone is 15.64 MWh. From Table 3, simultaneous integration of PV and BES gives better results than integration of PV alone in RDS. Installing one BESS and PV in RDS decreases the system loss to 0.71 MW at bus 61. The energies of PV and BESS during the day by incorporating one PV with BESS in RDS are illustrated in Figures 11 and 12. Installing two and three PV with BES in RDS decrease the system loss to 0.61 MW and 0.59 MW, respectively. The energies of PV and BESS during the day by incorporating two PV with BESS in RDS are illustrated in Figures 13 and 14. Additionally, energies of PV and BESS during the day by incorporating three PV with BESS in RDS are illustrated in Figures 15 and 16. Table 3 illustrates the locations and sizes of PV and BESS, the total energy of PV, the injection energy from PV to the grid, the charging energy from PV to BESS, and the discharging energy from BESS to the grid. Figure 8: PV output during the day by installing one PV alone in residential system load. Figure 7: IEEE 69-bus RDS. Figure 10: PV output during the day by installing three PV alone in residential system load. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Position (Size (kW))** & **PV Energy (kWh)** & **Total PV Energy (kWh)** & **Ploss (kW)** \\ \hline \multirow{4}{*}{Residential Load} & Without PV & - & - & - & 1867.977 \\  & 1-PV & 61 (1489) & 61 (11,207) & 11,207 & 1389.4 \\  & 2-PV & 61 (1417.5) & 61 (10,668) & 13,823.3 & 1349.2 \\  & & 17 (419.2) & 17 (3155.3) & 13,823.3 & 1349.2 \\  & & 61 (1369) & 61 (10,304) & & \\  & 3-PV & 18 (302.8) & 18 (2279) & 15,639.6 & 1341.6 \\  & & 11 (406.1) & 11 (3056.6) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The obtained results with and without installing PV alone in residential system loads. Figure 9: PV output during the day by installing two PV alone in residential system load. Figure 11: PV output during the day by installing one PV with BES in residential system load.
## 1 Introduction
With the integration of the data already available from on-board systems such as ADIRU and Global Positioning System (GPS), an algorithm was developed. An original element of the work is also the integration of the simulation environment enabling the generation and recording of wind gust parameters (ideal) and their estimation using data provided by standard on-board instruments. The simulations were carried out using a complex non-linear model of aircraft dynamics in the X-Plane 11 environment, while the estimation algorithm uses a simplified linear model practical for efficient computations.