esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results of the Simulations
### Baseline Reference Performance  
The positioning error, in terms of the error distance, is calculated as a baseline reference for two trivial solutions. Any pattern-matching method must perform better than the trivial solutions. First, a trivial pattern-matching method is considered that returns as the position estimate the center coordinates of the positioning area (\(x=7.5\) m, \(y=3.5\) m), for any input vector of fingerprints. It is assumed here that the positioning area is equal to the area intended for positioning. Second, a trivial pattern-matching method that returns as the position estimate a random position drawn from a uniform distribution in the interval of each coordinate in a positioning area equal to the area intended for positioning is considered. The positioning error results for these baseline reference solutions are shown in Figure 16. This figure also includes the positioning error for the case with no side information. The results for this case were obtained by fingerprinting using pointwise samples, applying the pattern-matching method 1-SingFingIn-PosOut (discussed below). It is observed that fingerprinting with pointwise samples performs better than the trivial baseline cases, with a smaller error distance at each percentile. Fingerprinting using pointwise samples (with no side information) with the pattern-matching method 1-SingFingIn-PosOut is the performance to beat. Next, we focus on studying whether the pattern-matching methods with side information can perform better than fingerprinting using pointwise samples.
# Recent Advances in Flexible Resistive Random Access Memory
## 3 Material System of Flexible Resistive Memory
### Flexible Electrode Materials
#### 3.2.2 Conductive Oxide Electrode and Carbon/Nitrogen Electrode  
The metal electrode is widely used because of its good electrical conductivity, but its transmittance is relatively poor. Therefore, with the development of flexible transparent electronic technology, conductive oxide electrodes (ZnO [100], indium zinc oxide (IZO) [26, 101], ITO [10, 18, 21, 22, 27, 44, 54, 68, 69, 74, 76, 92, 102, 103], GO [30, 39, 103, 104, 105], etc.) are gradually increasing because of their high transmittance. Using ITO as the top and  
Figure 10: Finite element analysis of poly(N-vinylcarbazole)-TiO2/Cu/PET sample under tension stress [98].  
bottom, the flexible RRAM device reported by [PERSON] [68], shows an excellent transmittance of more than 85% in the whole visible region (Figure 7c). The carbon/nitrogen electrodes are characterized by high flexibility, including TaN [34], TiN [17; 40; 102], carbon powder [106], carbon nanotubes (CNTs) [107; 108] and so on. The carbide electrode and nitride electrode have similar characteristics (high conductivity, good stability, excellent plasticity, good mechanical properties and hydrophilicity). The resistance transition mechanism of these electrodes is based on the formation and fracture of conductive filaments in the dielectric layer due to carbon or nitrogen vacancies.  
Compared with metal electrodes, the conductivity of conductive oxide electrodes and carbon/nitrogen electrodes is still not ideal. Under long hours of work, the calorific value is much bigger than the metal electrode, causing serious device heating and affecting the stability of the device working. Thus, it is hard to meet the development trend of low power consumption, and They are not widely used.
# Distributed Dynamic Load Identification of Beam Structures Using a Bayesian Method
## 2 The Bayesian Formulation of the Distributed Dynamic Load Identification
### Bayesian Basis Framework  
The core of the Bayesian framework is the Bayesian formula. Based on the conditional probability and the prior probability of the assumed unknown variable, the posterior prob ability is obtained and estimated through the Bayesian formula. Therefore, the Bayesian framework is written as:  
\[p(\theta|x)=\frac{p(\theta)p(x|\theta)}{p(x)}=\frac{p(\theta)p(x|\theta)}{\int p( \theta)p(x|\theta)d\theta}\propto p(\theta)p(x|\theta) \tag{10}\]  
Notice that \(p(x)\) can be regarded as a constant. Additionally, \(p(\theta)\), which means the prior knowledge, represents the prior probability distribution of the unknown variable; \(p(x|\theta)\) represents the likelihood function, i.e., the conditional probability that the measurement is \(x\) with the unknown variables \(\theta\). Similarly, \(p(\theta|x)\) is the posterior probability of the unknown variables, representing the probability that the unknown variables are \(\theta\) when the measured response is \(x\). Based on Equation (10) and the maximum a posteriori (MAP) estimation, the estimated value \(\theta\) is obtained as Equation (11).  
We assume the measurement \(x\) is contaminated by a noise \(\mu\), which is written as in Equation (12). The noise \(\mu\) is considered to be independent identically distributed Gaussian noise with the mean value 0 and the standard deviation \(\sigma\). Consequently, the likelihood function \(p(x|\theta)\) is given by Equation (13).  
\[\hat{\theta}_{map}=\underset{\theta}{\text{argmax}}p(\theta|x) \tag{11}\]  
\[\mathbf{x}=\mathbf{\hat{x}}(\mathbf{\theta})+\mu \tag{12}\]  
\[p(x|\theta)\propto(\sigma^{2})^{-\frac{\mu}{2}}\exp(-\frac{\|\mathbf{\hat{x}}(\bm {\theta})-\mathbf{x}\|_{2}^{2}}{2\sigma^{2}}) \tag{13}\]  
where \(\|\|_{2}\) is the Euclidean norm, \(\infty\) is the positive proportion symbol and \(n\) is the dimension of the discrete signal \(x\).
## 3 Results and Discussion
### Optimization Results and Analysis of Mulberry Drying Technology
#### 3.2.3 Effects of the Microwave Power on the Quality of Mulberry  
The effects of microwave power (200 W, 250 W, 300 W, 350 W, 400 W, 450 W, and 500 W) on the mulberry quality were investigated at a fixed material surface temperature (52.0 \({}^{\circ}\)C) and vacuum degree (0.07 MPa). The test results are shown in Table 7. With an increase in the microwave power, the chromatic aberration of the mulberry increased and then stabilized. In contrast, the chewiness, soluble solid content, and hardness evidently increased, and the Vc content presented an inverted V-shaped variation trend.
# Improving Engineering Design Using Smart Evaluation of Indoor Acoustical Climate of Dining Places
## 2 Design of Dining Places
There is a need to create a simple-to-use method for architects and designers that can be used in the initial part of the design process. The desired use of an enclosure is a crucial aspect of room acoustics. Restaurants and catering facilities have different acoustic needs, and people behave differently. The room's dimensions and shape affect sound perception because of the reverberation time, a parameter that appears when surfaces reflect sound in an enclosure. People in the room hear the sound originating from its source, and all surrounding surfaces reflect this sound. The shape of the room influences the propagation of sound waves and determines the rate and level at which they reach the listener's ears. Acoustical problems in designing must be considered at the initial stage of the drawings and re-analysed throughout the entire design process. There is no universal solution for acoustic design. The results are closely related to the integration and installation of several materials or systems. Some methods can be used in the early stages of designing dining places. [PERSON] proposed prediction models for ambient noise [26; 27]. They apply the hypothesis of sound radiation and a diffuse sound field in the room. The proposed model is expressed by equations that consider the equivalent absorption area and the number of simultaneously speaking persons or group size. Many difficult methods focus on the Lombard or cocktail effect. [PERSON] et al. presented an iterative model for predicting speech and noise levels in eating establishments using optimisation techniques [4]. An analytical model to evaluate the cocktail party effect was also proposed by [PERSON] et al. [28]. The formula for the signal-to-noise ratio is presented based on analytical considerations. Although the model is simplified, it may not be easy for architects and designers to use. Another method that considers the effect of rising voices was proposed by [PERSON] et al. [29], but it is also based on formulas that designers might not be able to adapt easily. Other authors have used virtual methods to assess the acoustical climate of eating establishments. [PERSON] and [PERSON] used Catt-Acoustic software to predict the optimal verbal communication quality conditions in eating establishments [30]. The model is based on STI. [PERSON] also used virtual methods to assess speech intelligibility in restaurants [31]. Owing to special software requirements, both methods may not be simple for designers to use. Other methods used to evaluate the acoustical climate of eating establishments include objective measurements of physical aspects such as the reverberation time or noise levels [24; 32; 33], subjective methods such as questionnaires [32; 33], and qualitative and quantitative analyses [34]. Some methods are too difficult for designers and architects to use, such as those based on clustering techniques [35].
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 2 Topology of the LCC-CCC HVDC  
The LCC-CCC HVDC transmission system studied in this paper is constructed by modifying the inverter of a conventional two-terminal LCC-HVDC transmission system. The modified system adopts the conventional LCC at the rectifier side and the CCC at the inverter side, which is shown in Figure 1. Series capacitors are inserted between the converter transformer and the thyristor valves in each phase at the inverter side.  
The rated voltage and power transmission capacity of the LCC-CCC HVDC transmission system in this paper are chosen as \(\pm\)500 kV/3000 MW. Both converters adopt the operation of the bipolar twelve-pulse valve group. The length of DC power transmission line is 1150 km.
## 4 Notification-Based Querying Method (NOP Query) Proposal
### Dynamic NOP Query Chain (DQC)  
DQC receives filtered Window and Time Manager data and checks each object according to the SQC structure. It is dynamic because the NOP chain connections between the elements are assembled as the data are ingested, and when a new ID/object appears and matches, even partially, the query. When an object matches all or part of the first or second predicate query, the DQC must be updated. The object ID is needed in the current version to match the elements and create the DQC connections.  
For example, if an object/\(FBE\) with label or color matches the SQC structure (e.g., atLabel = \"car\" or atColor = \"red\"), the DQC adds that \(FBE\) to the DQC and connects it to all other existing \(FBE\) Trucks. In this way, each object relevant to the SQC belongs to a \"dynamic network\" with other related objects defined by the query. The following procedure is performed for each new \(FBE\) Car in the system:  
* Check every \(FBE\)s \"Truck\" in the DQC;
* For each \(FBE\) Truck present, then connects it to the current FBE Car;
* If this connection exists, update the corresponding _Attributes_ and start the notification mechanism.  
Figure 6: Spatial position coordinates perspective. Source: Original image of the UTFPR-HSD public dataset available at [[http://labic.utfpred.edu.br/datasets/UTFPR-HSD.html](http://labic.utfpred.edu.br/datasets/UTFPR-HSD.html)]([http://labic.utfpred.edu.br/datasets/UTFPR-HSD.html](http://labic.utfpred.edu.br/datasets/UTFPR-HSD.html)), accessed on 16 December 2023.  
Figure 7: Result of the static NOP query chain. The position values are suggested according to the query needs.  
A similar process occurs when the object belongs to the second predicate of the query, but the match is made in all the _FBEs_ of the first predicate. When two _FBEs_ are combined to form DQC once, the corresponding _Premises_ P3 and P5 (color) and _Premises_ P4 and P6 (label) are created (see Figure 7). In the next step, the corresponding _Condition_, _Rule_, _Intigation_, _Action_, and _Method_ will be created. SQC occurs once and DQC occurs when each new relevant object arrives from the stream.
## 3 Finite Element Method Simulation and Results
### Simulation Results  
Two displacement cloud images of the acoustic field generated by the laser at 1.33 \(\upmu\)s and 3.34 \(\upmu\)s are shown in Figure 4. The entire acoustic field at 1.33 \(\upmu\)s generated by the laser, transverse wave (S wave), longitudinal wave (L wave), and surface Rayleigh wave (R wave) is shown in Figure 4a. Then, at 3.34 \(\upmu\)s, the R wave continues to propagate along the surface of the material and encounters the oblique crack. Transmission and reflection acoustic fields at the crack are recorded in Figure 4b.  
Figure 4: _Cont._  
Figure 3: (**a**) Function in the time domain; (**b**) function in the spatial domain.  
In Figure 4b, the surface wave interacts with the oblique crack. A part of the energy is reflected at the left edge of the oblique crack to form a reflected echo (Rr Wave), and the other part propagates along the oblique crack wall to the other side to form a transmission surface wave (TR Wave). The reflected wave and transmitted wave in the time domain are shown by the red line in Figure 5. Two reference signals are also acquired in the model without cracks and shown by the black line in Figure 5.  
At 4.5 \(\upmu\)s, a reflection wave is generated by the crack in (a), and the amplitude reduction of the transmitted wave is shown in (b). Therefore, the FEM method is helpful for simulating acoustic fields at cracks to acquire reflected and transmitted surface waves. The results in Figure 5 represent reflected and transmitted waves that carry crack information. However, the angles and depths of cracks cannot be directly detected in Figure 5.
## 4 Performance Analysis
### Complexity Analysis  
For clusterhead selection process, each sensor initiates 2 rounds of local flooding to its 1-hop neighboring sensors, one for broadcasting sensor ID and the other for broadcasting cluster ID, to select clusterheads and form 2-hop clusters. Hence, the time complexity is \(\mathcal{O}(2)\) rounds. For gateway selection process, the clusterhead initiates the selection operation and 2 rounds of local flooding are applied in the 2-hop cluster structure. Next, the border sensors and gateway sensors generate another 2 rounds of local flooding for broadcasting the weighting values and gateway information, respectively. Thus, the time complexity is \(\mathcal{O}(4)\) rounds. Given the energy consumption analysis above, both the clusterhead selection process and the gateway selection process have a communication complexity of \(\mathcal{O}(n)\). Therefore, the communication complexity due to cluster formation is \(\mathcal{O}(n)+\mathcal{O}(n)=\mathcal{O}(n)\).
###### Abstract  
Search and rescue missions using rotorcrafts need to be reliable all year long, even in winter conditions. In some cases of deployment prior to take off, the crew may need to manually remove accumulated contaminant from the critical surfaces using tools at their disposal. However, city contaminant may be hard to remove since the rotorcrafts critical surfaces could be cooler than the environment, thus promoting adhesion. Currently, there exists several passive ice protection materials that could reduce the ice adhesion strength and assist the manual de-icing. The aim of this paper is to propose a detailed comparative procedure to assess the ability of materials to assist the manual de-icing of rotorcrafts. The proposed procedure consists of the characterization of materials using several laboratory tests in order to determine their characteristics pertaining to wettability, their icepbobic behavior, and finally their assessment under a multi-tool analysis to evaluate if they can assist. The multi-tool analysis uses different mechanical tools, which are currently used during normal operation, to execute a gradual de-icing procedure, which begins with the softest to the hardest tool using a constant number of passes or strokes, under different types of simulated precipitation. Five different materials were used to evaluate the proposed procedure: Aluminum (used as a reference), two silicone-based coatings (Nusil and SurfEllent), an epoxy-based coating (Wearlon), and finally a commercial ski wax (Swix). All of the tested materials could assist the manual de-icing, within a certain limit, when compared to the bare aluminum. However, SurfEllent was the material that obtained the best overall results. This procedure could be easily adapted to different fields of application and could be used as a development tool for the optimization and the assessment of new materials aimed to reduce ice adhesion.  
icepbobic; de-icing; anti-icing; multi-tool; rotorcraft; coating; polymers; passive ice protection +
Footnote †: journal: Applied Sciences  
1
## 2 Experiments and Methods  
### Sample Preparation  
Laser processing was carried out to produce nano/micro structures on the surface of OFHC by a line hatched pattern, with a laser spot of 15 \(\upmu\)m, a pitch spacing of 15 \(\upmu\)m, a laser power of 16 W, and a scanning speed of 20 mm s\({}^{-1}\). After laser processing, treated OFHC samples with a size of 10 mm \(\times\) 10 mm \(\times\) 0.5 mm were cleaned in absolute ethyl alcohol and acetone for 15 min, respectively. Then Ti and Ti-Zr-V-Hf films were deposited on these untreated OFHC and laser processed OFHC substrates by the direct current (DC) magnetron sputtering method. The details of the samples in this work are shown in Table 1. Specifically, after laser processing sample #1 was taken for secondary electron yields (SEY) measurement without ultrasonic cleaning.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Film Coatings** & **Substrates** & \(\delta_{\text{max}}\) & \(E_{\text{max}}\)/eV** & **Average Roughness/\(\upmu\)m** \\ \hline \#1 & Without film & laser processed OFHC & 0.99 & 1700 & 7.143 \\ \#2 & Ti & OFHC & 1.49 & 200 & 0.110 \\ \#3 & Ti–Zr–V–Hf & OFHC & 1.37 & 300 & 0.131 \\ \#4 & Ti & laser processed OFHC & 1.34 & 400 & 6.842 \\ \#5 & Ti–Zr–V–Hf & laser processed OFHC & 1.18 & 400 & 6.902 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample information in this study.
## 3 Method or Methodology
### Loss Function  
We designed a unity feedback method that down-samples the SR result \(SR\) to LR images \(LR^{\prime}\), as the inverse process of SR. Then, we compared it with the input \(LR\) to obtain the unity feedback loss, which can be used to narrow the search domain of the SR function. We chose the L1 loss function and used \(w\) to represent the weight of the unity feedback loss. Accordingly, our loss function is as follows:  
\[Loss=\left\|SR,HR\right\|_{1}+w\left\|LR^{\prime},LR\right\|_{1}, \tag{20}\]
## 2 Case Study
### Izarbel Pilot Project Description  
As previously mentioned, this pilot project has been carried out at the Izarbel technology park in Bidart, France. The PV energy CSC demonstrator is composed of three buildings, which are managed by the ESTIA Institute of Technology. Figure 1 shows the buildings participating in the CSC.  
Within the framework of the EKATE project, a total of 286 kWp of PV panels will be installed in these three buildings. Initially, the circular part of the ESTIA1 building will  
Figure 1: Buildings participating in the CSC of the Izarbel pilot project. of ESTIA Institute of Technology.  
be equipped with an installation of 117.17 kWp. The rest of the PV installation will be carried out at a later stage in the ESTIA2 and ESTIA4 buildings. It should be noted that the 117.17 kWp installation has not yet been completed. However, data from a 2004 PV installation of 5.6 kWp capacity have been used to develop the PV generation, prediction models. The 5.6 kWp installation is not part of the CSC and is located on the circular part of the roof of the ESTIA1 building, with a southeast orientation and a slope of 20%.
## 6 Conclusions  
CH selection is essential to guarantee the energy efficiency in WSN protocols based on clustering. In many previous studies, the nearest nodes have been selected as CHs after finding the ideal CH location. We considered that the difference between the ideal CH location and actual CH node location can undermine energy efficiency. Therefore, we devised SSMOECHS, a CH selection method that considers actual node locations through sampling. The optimal CHs are obtained from sampling and optimized using a modified SMO algorithm, thus preventing the divergence between ideal CH location and actual CH node location and improving energy efficiency. To evaluate the proposed method, the experiment is divided into two setups: homogeneous setup and heterogeneous setup. In the homogeneous setup, SSMOECHS improved the network lifetime and stability by averages of 13.4% and 7.1%, respectively, compared to other similar protocols (LEACH-C, PSO-C, and SMOTECP). Likewise, in the heterogeneous setup, SSMOECHS improved the network lifetime and stability by averages of 34.6% and 1.8%, respectively. The superior performance of the proposed SSMOECHS was confirmed through experimental results, as it improved network lifetime and stability through efficient energy consumption. Consequently, the existing problems can be solved by changing the location-based CH selection method to the node-based CH selection method via SSMOECHS, and the network performance can be improved.  
The work presented here was completed in collaboration among all authors. Research conceptualization, J.-G.L.; methodology, J.-G.L. and S.C.; simulations, S.C.; formal analysis, J.-G.L.; experiment result analysis, J.-G.L. and S.C.; writing--original draft preparation, J.-G.L.; writing--review and editing, J.-G.L. and H.-H.P.; visualization, J.-G.L.; supervision, H.-H.P.; funding acquisition, H.-H.P.  
This research was funded and conducted under the Chung-Ang University Graduate Research Scholarship in 2018, and the Competency Development Program for Industry Specialists of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by Korea Institute for Advancement of Technology (KIAT) (Project number: P0002397, Project name: HRD Program for Industrial Convergence of Wearable Smart Devices).  
The authors declare no conflicts of interest.
## 2 Context of the Experiments
### Control Algorithm
#### 2.7.6 Lead-Time  
As explained above, the lead-time is the difference between the time before departure and the time needed to fully charge the battery without a charging control. This lead-time has to be estimated at each time step \(\mathrm{t_{n}}\). The set points from \(\mathrm{t_{0}}\) and \(\mathrm{t_{n-1}}\) have been computed by the previous iteration of the planning algorithm. We note \(\mathrm{SetPoint(t_{p})}\) the power set point computed for the period \(\mathrm{[t_{p};t_{p}+\Delta t]}\). During this period, we consider that the car charges at the constant value \(\mathrm{SetPoint(t_{p})}\). Thus, at \(\mathrm{t_{0}}\), the value of the \(\mathrm{SOC_{future}}\), in a future point in time \(\mathrm{t_{n}}\), is calculated according to the following formula:  
\[\mathrm{SOC_{future}(t_{n})}=\mathrm{max}\Bigg{(}100,\;\mathrm{SOC_{estimate}(t_{0})}+\frac{100\times\mathrm{Y}}{ \mathrm{E_{Max}}}\times\sum_{p=0}^{p=n-1}\mathrm{SetPoint}\big{(}\mathrm{t_{p} }\big{)}\times\Delta t\Bigg{)} \tag{4}\]  
Then, from time \(\mathrm{t_{n}}\), we consider that the EV charges at \(\mathrm{P_{Max}}\) until its battery is fully charged. Given this \(\mathrm{SOC_{future}}\) value, we are able to compute the point in time \(\mathrm{t_{end}}\) such the battery is full when charged with the following formula:  
\[\mathrm{t_{end}(t_{n})}=\mathrm{t_{init}}+(100-\;\mathrm{SOC_{future}(t_{n})}) \times\frac{\mathrm{E_{Max}}}{100\times\mathrm{P_{Max}}} \tag{5}\]  
The lead-time is the difference between the time before departure \(\mathrm{t_{dep}}\) and the time \(\mathrm{t_{end}}\) to charge the battery at \(\mathrm{P_{Max}}\).  
\[\mathrm{LeadTime(t_{n})}=\mathrm{t_{dep}(t_{n})}-\mathrm{t_{end}} \tag{6}\]