esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances of Exciplex-Based White Organic Light-Emitting Diodes
## 3 Approaches for Exciplex-Based WOLEDs
### WOLEDs Based on Exciplex Emitters
#### 3.2.1 WOLEDs Utilizing All Exciplex Emitters
Figure 3 depicts the device architecture: ITO/polyethylene dioxythiophene: polystyrene sulfonate (PEDOT: PSS, 30 nm)/1,1-bis[(di-4-tolylamino)phenyl]cyclohex (TAPC, 20 nm)/1,3-bis(N-carbazolyl)benzene (mCP, 15 nm)/mCP: ((1,3,5-triazine-2,4,6-triyl)tris(benzene-3,1-diyl))tris(diphenylphosphine oxide) (PO-T2T) (1:1, 20 nm)/PO-T2T (45 nm)/8-quinolinolatto lithium (Liq, 1 nm)/Al (1 nm)/MoO\({}_{3}\) (5 nm)/9,9-di[4-(di-p-tolyl)aminophenyl]fluorine (DTAF, 20 nm)/DTAF: PO-T2T (1:1, 20 nm)/PO-T2T (50 nm)/Liq (0.5 nm)/Al (100 nm), where the CGL is DTAF/MoO\({}_{3}\)/Al/Liq (holes and electrons are generated from the DTAF/MoO\({}_{3}\) interface), the blue and yellow exciplexes are formed from the blend of mCP: PO-T2T and DTAF: PO-T2T, respectively. The white light has been realized via the two parallel mixed layers, simultaneously producing yellow as well as blue exciplex light and hence a WOLED with a high EQE of 11.6%. One of the reasons for the high performance is the development of a very efficient blue exciplex. To realize such high-energy blue exciplexes, (i) the acceptor has low HOMO (LUMO) for effective hole-blocking (electron injection) and high T1 for confining excitons of exciplexes; (ii) the acceptor exhibits the electron mobility which can be as high as the hole mobility for common HTLs. Hence, [PERSON] et al. designed an acceptor PO-T2T with a T1 of 2.99 eV and electron mobility of 1.7 \(\times\) 3\(\cdot\)10\({}^{-3}\) to 4.4 \(\times\) 10\({}^{-3}\) cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\), meeting the above requirements [165]. As a result, the blue exciplex emission featured a low turn-on voltage of 2 V, a maximum brightness of 24,600 cd m\({}^{-2}\) and an EQE of 8.0%, which is the best blue exciplex OLED at that time. By combining this efficient blue exciplex and yellow exciplex, the tandem WOLED has been demonstrated. Remarkably, the WOLED can exhibit a good color-stability, which is attributed to the avoidance of the movement of the charge recombination zone and elimination of the energy transfer between blue and yellow EMLs. Figure 2: (**a**) Energy-level diagram of the white organic light-emitting diodes (WOLEDs). (**b**) Electroluminescence (EL) spectra of four organic light-emitting diodes (OLEDs): EL-1, EL-2, EL-3, and EL-4, where emission peaks appear at about 463, 528, 572, and 650 nm, respectively. (**c**) Photoluminescence (PL) spectra of m-MTDATA, TPD, Bphen, and Al(DBM)\({}_{3}\) films excited at 360 nm. (**d**) EL spectrum of the WOLED with x = 3 nm and R = 1:2 at 9 V (Reproduced from [159]).
# Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
## 2 Background and Literature
### State-of-the-Art
The research of building energy-saving design variables usually manifests as the research of single variable and multi-variable combination. The research of single variable is an indispensable basic part, and the combination of multi-variable constitutes the final goal of the research and a complete building energy-saving design process. 4. \"Performance coupling factor\" has been paid attention to, and the impact of indoor building environmental performance quality on the physical, mental health and comfort of residents has been paid more and more attention. The pursuit of a single environmental performance improvement often has an adverse effect on other aspects of performance, related researches on multi-factors environment and its coupling performance are getting increasing attention. 5. New tools or methods for building energy consumption simulation combined with parametric methods, BIM technology or computer programming technology are constantly emerging. On this basis, the amount of simulated data is increasing, and the reliability of the simulation results is improving. Multi-variable energy-saving design schemes and processes are more complicated than single-variable energy-saving strategies, and under the combined effect of multi-variables, the value and significance of building performance improvement are also greater. In addition, based on the perspective of architectural design, the design strategy proposed based on a single objectives (such as improving indoor lighting environment, ventilation or solar shading) during the schematic design stage does not fully conform to the architect's way of thinking and cannot effectively facilitate the building design. Therefore, it is necessary to establish the relationship between multi-variable design elements and multi-objectives, in order to building an integrated and systematic analysis framework. For building climate responsive design, energy consumption, indoor thermal environment and building life cycle cost are three conflicting basic factors. Generally speaking, to make the indoor thermal environment satisfy the human comfort as much as possible, it will lead to an increase in building energy consumption and costs. The three often contradict each other, while in the decision-making process, it is crucial to trade off these three objectives. If the building energy consumption and life cycle costs are required to be reduced while maintaining the indoor thermal environment, it is necessary to carry out quantitative prediction and multi-objective optimization of the built environment factors. Based on the meteorological parameters and design codes of typical cities in different climatic regions in the United States, a framework for optimizing the climate responsive design parameters of residential buildings in typical cities has been established. Building energy demand, thermal comfort, and life cycle costs are used as performance indicators to analyze optimal energy-saving design of residential buildings in U.S. typical cities.
# A Survey of CNN-Based Network Intrusion Detection
## 7 Other Factors of CNN-Based IDS Experiments
### Cross-Validation  
As is frequently utilized in machine learning, k-fold cross-validation (CV) is implemented to obtain large non-redundant datasets for validation and training. All samples in the dataset are divided into k groups, where k-1 groups are utilized for training while other groups are used for validation [3; 38; 51].
## 3 Results and Discussion
### Physical and Mechanical Properties
When comparing the modulus of mortars with non-modified fibers and modified fibers, the latter was characterized by a significantly lower flexural elastic modulus. After 7 days of curing, the modulus of the mortar was lower by 700 MPa (18%), and after 28 days of curing, it was 240 MPa (6%). Thus, modification of fibers enabled obtaining less stiff composite material that can deform more easily under stress, making it more flexible.
###### Abstract  
Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning accuracy, and nonmatching and mismatching are prone to occur, especially when the initial position error is large. To solve this problem, a new algorithm combining the artificial bee colony (ABC) and particle swarm optimization (PSO) was proposed according to the principle of terrain matching, to improve the matching effect. Considering that PSO easily falls into a local optimum, the acceleration factor and inertia weight of PSO were improved. The improved PSO was called WASPO. ABC was introduced based on WASPO and could help WASPO escape local optimum. The final algorithm was termed ABC search-based WASPO (F-WAPSO). During the continuous iteration of particles, F-WAPSO seeks the optimal position for the particles. Simulation tests show that F-WAPSO can effectively improve the matching accuracy. When the initial position error is 1000 m, the matching error can be reduced to 93.5 m, with a matching time of only 13.7 s.  
PSO; ABC; terrain-aided navigation Article
## 4 Experimental Results
### Walking Experiments with the Proposed UI  
The training procedure of a pilot for the WalkON Suit with the proposed UI system is shown in Figure 10. Each training session lasted about one hour, which include standing up, walking on even ground, sitting on a chair and taking a break. The human subject was asked to walk on a 20-m straight track at a walking speed he selected. After walking 20 m, he took a break while sitting down on a chair for about 5 min. After the break, he stood up from the chair and walked with crutches. For safety reasons, at least two assistants were in the vicinity of the human subject at all times; they neither held nor touched the robot and the subject. At the early stage of training, the pilot frequently looked down to check the leg movements and foot positions, as he was not accustomed to the STD glasses. Consequently, his entire body slightly leaned forward, and the neck was bent, as shown in Figure 10a; the flexion angle of the neck was larger than 10 degrees. In the middle stage of training, he started being accustomed to the STD glasses, resulting in the head positioned up, as shown in Figure 10b. The flexion angle of the neck was reduced to 5\(\sim\)7 degrees. Figure 10c shows the final stage of training when the pilot was fully accustomed to the proposed UI. The pilot did not continuously check the crutch screen or the leg movements, but looked forward while walking. Notice that the neck angle was close to zero, as shown in the figure, since the pilot could receive enough information without looking down. In spite of the proposed UI system including the HUD, however, the user occasionally looked down to check the positions of feet and to see if the ground was flat and even. Nevertheless, he did not have to look down continuously, and his head was positioned up most of the time.  
As the neck flexion angle was reduced, the walking speed was improved also. Figure 11 and Table 2 show the changes in the walking speed before and after the pilot was accustomed to the proposed UI system. As shown in the figure, the walking speed was improved from 0.12 m/s to 0.16 m/s, which is about a 33% improvement. Similarly, the average step time was reduced from 2.48 s to 1.78 s. One of the main reasons for this performance improvement is that the pilot did not spend much time checking the crutch display and his leg movements.  
Figure 10: A pilot training for wearing the WalkON Suit with the proposed UI: (**a**) the early stage of training (the first week of training), (**b**) the middle stage of training and (**c**) the final stage of training (the fourth week of training).
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 4 Design of Joint Positioning Algorithm
### Angle Estimation  
To estimate the angle, a MUSIC (Multiple Signal Classification) algorithm is adopted [17]. The algorithm model is shown in Figure 5. In Figure 5, \(\phi\) is the azimuth angle of arrival and \(\theta\) is the zenith angle of arrival. \(\mathbf{y}_{i}(t)\) is the time domain form of the received signal, corresponding to the frequency domain resource \(\mathbf{Y}_{i}\).  
The MUSIC algorithm is that the observation space of signal processing can be divided into signal subspace and noise subspace, and then the spatial-spectral function is constructed. The estimated angle of arrival direction corresponds to the angle of the peak at which the maximum of the spectral function is obtained in the spatial-spectral domain. \(\Psi(\hat{\theta}_{i},\hat{\phi}_{i})\) is expressed as  
\[\Psi(\hat{\theta}_{i},\hat{\phi}_{i})=\frac{1}{\mathbf{A}_{i}{}^{\intercal}( \hat{\theta}_{i},\hat{\phi}_{i})\mathbf{V}_{i}\mathbf{V}_{i}{}^{\intercal} \mathbf{A}_{i}(\hat{\theta}_{i},\hat{\phi}_{i})} \tag{9}\]  
where \(\mathbf{V}_{i}\) is the noise matrix; \(\mathbf{A}_{i}\) is the directional response vector with a normalized amplitude from \(\mathbf{H}_{i}\). Calculation of \(\mathbf{A}_{i}\) can be obtained by the eigenvalue decomposition of the received signal as follows  
\[\begin{split}\Re_{\mathbf{Y}}&=\text{E}\{\mathbf{ Y}_{i}\mathbf{Y}_{i}^{\intercal}\}\\ &=\mathbf{H}_{i}\Re_{\mathbf{r}}\mathbf{H}_{i}{}^{\intercal}+ \hat{\sigma}^{2}\mathbf{I}\\ &=\mathbf{U}_{i}\wedge\mathbf{U}_{i}^{\intercal}+\hat{\sigma}^{2 }\mathbf{V}_{i}\mathbf{V}_{i}^{\intercal},\end{split} \tag{10}\]  
where \(\Re_{\mathbf{Y}}\) and \(\Re_{\mathbf{r}}\) is the covariance matrix of received and transmitted SRS-Pos; \(\wedge\) is the eigenvalue matrix; \(\mathbf{U}_{i}\) is an orthogonality matrix for signal;estimation of \(\hat{\sigma}^{2}\) can be reference to (7).  
Figure 5: Signal arrival direction model.
# Methane Production Potential from Apple Pomace, Cabbage Leaves, Pumpkin Residue and Walnut Husks
## 2 Materials and Methods  
### Substrates and Inoculum  
The substrates used in biomethane potential (BMP) tests were apple pomace, pumpkin residue consisting of peels and fibrous strands, walnut husks, and cabbage leaves. Prior to the BMP, substrates were ground into a homogeneous mass with a kitchen robot with a blender function. The inoculum was collected from the digestate storage tank of a mesophilic agricultural biogas plant processing maize silage with the addition of 10-20% of food and agricultural wastes. The inoculum was degassed at a temperature of 38 \(\pm\) 1 \({}^{\circ}\)C.
###### Abstract  
Planetary gearbox vibration signals have strong modulation features due to the amplitude modulation and frequency modulation (AM-FM) effect of gear faults, as well as the amplitude modulation (AM) effect of time-varying vibration transfer paths, on gear meshing vibrations. This results in an involute sidebands structure in Fourier spectrum, possibly misleading fault diagnosis. The modulating frequency of both amplitude modulation (AM) and frequency modulation (FM) parts is closely related to the gear fault characteristic frequency. This inspires the idea of joint amplitude and frequency demodulation analysis, thus addressing the complex sidebands issue inherent in Fourier spectrum. Demodulation analysis requires mono-component signals for accurate estimation of instantaneous frequency, and proper selection of an AM-FM component sensitive to gear fault. To this end, we firstly decompose the complex signal into intrinsic mode functions (IMFs) via variational mode decomposition (VMD), by exploiting its capability in decomposing complex modulated signal into constituent AM-FM components. For effective application of VMD in complex planetary gearbox signal analysis, we propose a method to determine a key parameter in VMD, i.e., the number of IMFs to be separated. For accurate instantaneous frequency estimation, we decompose IMFs via empirical AM-FM decomposition, to remove the influence of AM on instantaneous frequency estimation. Then, we select the sensitive IMF that contains the main gear fault information for further demodulation analysis. In order to properly select the sensitive IMF, we propose a criterion based on the gear vibration characteristics and the VMD properties. Finally, we obtain the amplitude and frequency demodulated spectra by applying Fourier transform to the amplitude envelope and instantaneous frequency of the selected sensitive IMF. According to the characteristics exhibited in the demodulated spectra, we can detect planetary gearbox fault. The proposed method is illustrated via a numerical simulated planetary gearbox vibration signal, and is further validated using lab experimental vibration signals of a planetary gearbox. Faults on all the three types of gear (sun, planet and ring) are successfully identified.  
planetary gearbox; fault diagnosis; variational mode decomposition; empirical AM-FM decomposition; demodulation +
Footnote †: journal: Applied Sciences
## 3 Applications
### Oxygen Reduction Reaction (ORR)  
Energy storage and conversion are very important for the functioning of modern societies. Energy sources such as batteries and fuel cells are becoming the backbone of a conscious low-carbon economy. Vital for energy conversion, especially in the areas of fuel cells and metal-air batteries, is the oxygen reduction reaction (ORR) [62]. During the ORR process, the O\({}_{2}\) molecules are reduced by electrons. They are very difficult to break electrochemically, because the bond O = O has an exceptionally strong bond energy of 498 kJ/mol. The energy barrier can be lowered by electrocatalysts that can activate and cleave bonds. ORR in an aqueous electrolyte occurs mainly by two pathways, one is the direct 4-electron reduction pathway from O\({}_{2}\) to H\({}_{2}\)O, and the other is a 2-electron reduction pathway from O\({}_{2}\) to hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). In non-aqueous aprotic solvents and/or in alkaline solutions, the 1-electron reduction pathway from O\({}_{2}\) to superoxide (O\({}^{2-}\)) can also occur [62; 63].  
Since the introduction of the first fuel cell in 1842, platinum has been the most common catalytic material due to its properties, such as chemical inertness and properties in the field of gas adsorption and dissociation. At the same time, due to platinum's rarity and costliness, alternative catalytic materials are sought after.  
[PERSON] et al. [50] show that FeCu bimetallic nanoparticles are very active and stable electrocatalysts for energy storage batteries. ORR activity for the CuFe alloy with graphitic carbon shells was comparable to that of Pt/C electrocatalysts, CuFe showed kinetic current density much higher than that for single Cu and Fe. The reduced \"blocking effect\" of active sites in the CuFe alloy may reduce the affinity of the hydroxyl groups controlling O\({}_{2}\) adsorption for the alloy compared to single Cu or Fe catalysts. Direct pathway of oxygen reduction was deduced from the number of transferred electrons calculation (Figure 9). To demonstrate the practical application of the nanoparticles, scientists created a zinc-air battery in which the air cathode was prepared from a mixture of active carbon CuFe@graphite, and synthetic fluoropolymer binder and the anode was prepared from Zn film. They showed that the CuFe catalyzed Zn-air cell can be used in cars, and that the battery can be recharged. They tested that an air electrode made of CuFe alloy worked for over 100 h without voltage loss, which shows the high stability of the CuFe alloy catalyst.
## 2 Results and Discussion
### Reusability of MMIPs  
The reusability of the MMIPs was evaluated by determining the adsorption capacity of the MMIPs after regeneration. The MMIPs could be regenerated by treatment with 10% acetic acid-methanol (\(v/v\)) for 1 h, and then reused for the adsorption of OFL. As shown in Figure 8, the MMIPs could be reused at least six times for the extraction of \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL with a slight decrease in the adsorption capacity (2.3% and 2.7%, respectively). This result implies that the MMIPs are stable and can be recycled.
# Soft Gripper with EGaIn Soft Sensor for Detecting Grasp Status
## 2 Configuration of Variable-Stiffness Gripper  
### Design of Variable-Stiffness Structure  
Adjustable grippers require variable-stiffness structures to ensure appropriate force when a gripping action is completed. In this study, the variable-stiffness structure was fabricated based on the tendon-drive principle introduced in previous research [17]. Furthermore, soft gripper robots have been studied extensively [18], enabling the design of an appropriate variable-stiffness structure and selection of suitable parameters for this study. The variable-stiffness structure used in this gripper was constructed by fixing a tendon to the rigid structure corresponding to the fingertip and cross-connecting the soft structures with the rigid structure. Rigid structures were built from polylactic acid (PLA) using a Single Plus 3D Printer of Cubicon Inc. (Seongnam, Gyeonggi-do, Korea).  
In contrast, soft structures were fabricated by curing Ecoflex0030 silicone in a PLA mold made by the 3D printer. In addition, an integument was coated on the outside of the rigid structure by fixing it to a mold made by the 3D printer. The tendons were constructed using an iron wire with a diameter of 1.59 mm. The dimensions of rigid and flexible structures were set based on [19]. The diameter of each structure was 20 mm, and the lengths of rigid structures 1, 2, and 3 and the flexible structures were 35, 20, 25, and 15 mm. In contrast to the structure in previous research, the variable-stiffness structure designed in this study consists of two inner tendons, as shown in Figure 1, to reduce lateral motion when a finger holds an object. A prototype of this structure is shown in Figure 2.  
Figure 1: Illustrative schematic of the variable-stiffness structure.
## 2 Related Works
### Gated Recurrent Unit  
The literature also suggests the use of GRUs in traffic state prediction. Traditional RNNs are challenging to train, and for LSTMs, their training is even more challenging because of their complex architecture. However, GRUs exhibit their goodness in facilitating the training process [15]. GRUs require fewer parameters for training, and, hence, demonstrate faster convergence [16; 17] than LSTMs.  
In [18], a data fusion method was suggested to fuse information from two separate datasets, and a GRU was employed for travel time prediction to increase estimation accuracy. In [19], the authors looked at the computational cost and the optimisation of the network structure and suggested three recurrent neural network models, with the GRU model outperforming the others by achieving a root mean squared error (RMSE) of 9.26%.  
A macroscopic fundamental diagram (MFD) demonstrated its usefulness in capturing the macroscopic traffic features, i.e., division of neighbouring regions between expressways and highways for traffic speed prediction [20]. Instead of directly feeding traffic state data to the GRU networks, first, an MFD constructs the road network into a weighted graph based on similarities of traffic operation. Then, a spatio-temporal correlation coefficient helps measure the relationship between the subdivisions of the network and build a matrix of traffic speed data. Finally, the GRU networks are used to predict future traffic speed by processing this matrix. Using truck GPS data [21], the proposed approach enhanced performance compared to the standard GRU model, with a 43.9% improvement in MAPE.  
The literature also includes the application of attention mechanisms in combination with GRUs for capturing both the temporal and local aspects of traffic states [22]. This helped effectively acquire long-range dependencies and enabled the GRUs to predict over a longer time horizon (1 h) with excellent results (MAE of 1.26).