esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this work, a new family of triphenylamine-based dyes equipped with ferrocene (Fc)-containing donors have been synthesized, characterized, and studied within dye-sensitized solar cells (DSSC). With the objective of designing a redox-robust iron-containing dye that effectively sensitizes titania, a family of five structurally related dyes containing ferrocene were prepared. The physicochemical properties of all dyestuffs were studied using UV-Vis and electrochemical measurements. Different cross-coupling strategies resulted in the ability to modestly tune the Fc/Fc+ redox potentials of the dye. Despite the Fc-containing dyes having optoelectronic properties consistent with the non-ferrocene parent dye, the performance of the dyes in devices was dismal and decreased with the number of appended ferrocenes. While this finding was consistent with previous attempts to explore Fc-TPA DSSC dyes, our spectroelectrochemical data supports the hypothesis that the ferrocene component of the dye is oxidized by the electrolyte, ultimately decreasing the dye's ability to be a suitable sensitizer. While these dyes are not suitable for DSSC applications, they might find applications in other photo-induced integrated devices where charge recombination is minimal.  
triphenylamine; ferrocene; organometallic dyes; spectroelectrochemistry; cyclic voltammetry; UV-Vis absorption and fluorescence; device fabrication; dye synthesis; TDDFT
## 7 Experimental Results
### Algorithms Comparison  
The performance of the different lookup algorithms has been studied by measuring the number of lookup messages and the lookup latency. Figure 12 presents the total number of lookup messages that have been sent from a source node (which initiates the search) and retransmitted by the intermediate nodes. It may be seen that the number of messages that are required to perform the query process increases as the number of nodes does. However the rate of increase with the flooding algorithms is more abrupt than with LRA. The reason is that flooding algorithms do not use a method to know if the queried peer has been visited before. The proposed Locality-Routing Algorithm uses a routing algorithm that allows knowing what target peers have been previously visited and avoid to send redundant messages, so that the number of messages required is significantly reduced.  
Figure 13 shows the evolution of the lookup latency as the number of peers increases. It has been verified that flooding algorithms need much more time to perform lookups than the proposed algorithm. Two reasons justify this result; on one hand, only the target's peers are visited with the proposed algorithm, so that the number of peers to perform the lookup process is lower. On the other hand, each peer is visited only one time, avoiding redundant visits.  
Figure 12: Comparison of the number of lookup messages sent by the different algorithms.  
Figure 13: Comparison of lookup latency with the different algorithms.
## 3 Fault Diagnosis Experiment and Analysis
### Fault Diagnosis Process  
Figure 4 illustrates the fault diagnosis process by using the TLViT + IFI model. It involves converting the one-dimensional vibration signal into an IFI, partitioning the data into training, validation, and testing sets as required, training the ViT model by using transfer learning, and inputting the data samples from the test set into the trained TLViT model to obtain diagnosis results.
## 5 Experimental Results  
This section presents the experimental results when the hybrid PI-SMC (Section 5.1) and the proposed SMC (Section 5.2) controllers are implemented in a SBBC. Cascade PI control was not considered since it is presented in numerous previous works and the simulations show that this controller has the worst performance. Section 5.3 shows a zero crossing comparison between PI-SMC and SMC.  
The SBBC implementation is presented in Figure 17. This prototype is mainly composed of: (1) the AC supply terminals and protection system; (2) Microcontroller Printed Circuit Board (PCB) used for signal processing and control (TMS 329F28335 ZJZA); (3) measurement PCBs (ACS714 and ADUM5010) for measuring AC voltage, AC current and DC voltage; (4) EMI filter for improving electromagnetic compatibility; (5) inductors \(L_{1}\) and \(L_{2}\) for coupling \(\Delta\)C and DC systems; (6) power switching PCB with power switches (IGBTs) and diodes (G4 PC5 UD); and (7) DC bus for regulating DC voltage. SBBC Requirements and characteristics are the same as those used in the simulation results section (Table 2). A Digital scope GW Instek GDS-2204A was used (200 MHz Bandwidth, 4 Input Channel, 2 GSa/s Real-time Sampling Rate, 2 Mp Record Length) for collecting the experimental results.  
The project was completely developed in C and its coding developed in Code Composer Studio (CCS) software. The control algorithm runs in two main stages: (1) \(i\), \(v_{s}\), \(i_{0}\) and \(v_{0}\) are sampled with 12 bits of resolution, using the Analog-to-Digital Conversion (ADC) peripheral, the computation of the sliding surface according to Equation (3) and the generation of the trigger signal u. The execution time of these control routines is regulated based on an interruption of the CPU timer of the DSP F28335 every 1 us. (2) The  
Figure 16: Frequency spectrum of the AC current using the adaptive hysteresis band.  
Figure 15: Sliding surface response for load increase.  
PLL algorithm is executed, the hysteresis band computation according to Equation (22) is completed, the output voltage filtering is executed to reduce the 120 Hz ripple using a notch filter and \(i_{ref}\) is computed based on Equation (4). When the hybrid controller is run, the PI control is also computed in this routine.
## 4 Discussion
### Determination Method of \(T_{2c}\) Based on Fractal Analysis
The relationship between \(T_{2c}\) and fractal dimensions of M-Q mixtures and K-Q mixtures can be expressed by Equations (25) and (26), respectively:  
\[T_{2c}=9.511D-22.10 \tag{25}\]  
\[T_{2c}=5.089D-6.339 \tag{26}\]  
The \(R^{2}\) values of Equations (25) and (26) are 0.912 and 0.982, respectively. The predicted \(T_{2c}\) versus measured \(T_{2c}\) is displayed in Figure 15. The points are close to the line y = x, so the Equations (25) and (26) can be used to predict the \(T_{2c}\) of mixtures. Figure 14: The relationships between \(T_{2c}\) and fractal dimensions (\(D\)). The lines at the top and bottom indicate the relationships between \(T_{2c}\) and \(D\) of kaolinite-quartz (K-Q) and montmorillonite-quartz (M-Q) mixtures, respectively. The Equations in the two boxes are the Equations of the two lines. \(R^{2}\) indicates the correlation coefficient of the Equation. In order to verify the accuracy of predicting the permeability coefficient by using \(T_{2c}\) which was predicted from fractal analysis, the \(T_{2c}\) values predicted from fractal analysis were used to calculate the \(A_{FF}\) and \(A_{BF}\) in Equation (24). The predicted permeability coefficients could also be obtained from Equation (24). The predicted permeability coefficients from fractal analysis versus the measured permeability coefficients are presented in Figure 16. According to Figure 16, all the points are close to the line y = x, so the accuracy of predicting the permeability coefficient by using \(T_{2c}\) which was predicted from fractal analysis was acceptable. In order to simplify the prediction process of the permeability coefficient based on NMR technology, fractal analysis was used in this study. The following steps can be followed to predict the permeability coefficients of clay-quartz mixtures: (1) After sample saturation, the NMR test can be carried out on a sample to get the \(T_{2}\) spectrum; (2) the fractal dimension can be determined by fractal analysis based on Equation (10); (3) according to the \(T_{2}(T_{2p})\) corresponding to the peak signal amplitude, the type of clay mineral can be determined. The \(T_{2p}\) values of M-Q mixtures and K-Q mixtures are around 1 and 10 ms, respectively; (4) the \(T_{2c}\) of M-Q mixtures or K-Q mixtures can be determined by Equation (25) or (26). \(A_{FF}\) and \(A_{BF}\) can be calculated based on the \(T_{2}\) spectrum; (5) the permeability coefficient can be calculated by Equation (24). Figure 16: The predicted permeability coefficients from fractal analysis versus the measured permeability coefficients. Red solid line indicates a slope of 1:1. The closer the black point is to the line, the smaller the difference between the predicted and measured permeability coefficient is. Figure 12:0 The predicted \(T_{2c}\) versus the measured \(T_{2c}\). Red solid line indicates a slope of 1:1. The closer the black point is to the line, the smaller the difference between the predicted and measured \(T_{2c}\) is.
# The Effect of Isomorphic Pitch Layouts on the Transfer of Musical Learning +
###### Abstract  
The physical arrangement of pitches in most traditional musical instruments--including the piano and guitar--is _non-isomorphic_, which means that a given spatial relationship between two keys, buttons, or fretted strings can produce differing musical pitch intervals. Recently, a number of new musical interfaces have been developed with _isomorphic_ pitch layouts where these relationships are consistent. Since the nineteenth century, it has been widely considered that isomorphic pitch layouts facilitate the learnability and playability of instruments, particularly when a piece is transposed into a different key; however, prior to this paper, this has not been experimentally tested. To address this, we investigated four different pitch layouts to examine whether _isomorphism_ facilitates retention and transfer of musical learning within and across keys. Both non-musicians and musicians were tested on two training tasks: two immediate retention tasks and a transfer task. Each participant played every task on two distinct layouts--one being an isomorphic layout (Wicki or Bosanquet), the other being a minimally adjusted non-isomorphic version. For musicians, isomorphism was found to facilitate transfer of learning to a novel task; for non-musicians, the results were mixed. This study provides insight into features that are important to music instrument design.  
pitch layouts; isomorphic layouts; new musical instruments; motor learning; perception and action; sound and music computing +
Footnote †: This paper reports the results of a study that was preregistered on OSF at [[https://osf.io/xt3n8/register/565](https://osf.io/xt3n8/register/565) fb3678c5e4a66b5582f67]([https://osf.io/xt3n8/register/565](https://osf.io/xt3n8/register/565) fb3678c5e4a66b5582f67).
## 2 Wood-Based Panels
### Emission Issues and Legislation
In Japan, the Japanese industrial standard JIS A 5908 [40] classifies particleboards according to their formaldehyde emissions by the desiccator method, described in JIS A 1460 [41], into F******* (\(\leq\)0.3 mg\(\cdot\)L\({}^{-1}\)), F******* (\(\leq\)0.5 mg\(\cdot\)L\({}^{-1}\)) and F******* (\(\leq\)1.5 mg\(\cdot\)L\({}^{-1}\)). While formaldehydehyde is a compound with mandatory regulation in panel production, it is not the only harmful VOC emitted from WBPs. Other VOCs are also emitted, although only a few countries have set legal limits concerning VOC emissions from construction products. The legal limits on Total VOC (TVOCs) and specific VOC emissions from construction products in different countries are summarized in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Substance** & **Limits (mg\(\cdot\)m\({}^{-3}\))** & **References** \\ \hline \multirow{4}{*}{Germany} & \multicolumn{2}{c}{Carcinogenic compounds (3 days)} & 0.01 & \\ \cline{2-4}  & TVOCs (3 days) & 10 & \\ \cline{2-4}  & Carcinogenic compounds (28 days) & 0.001 & \\ \cline{2-4}  & TVOCs (28 days) & 1 & \\ \hline \multirow{4}{*}{Belgium} & Formaldehyde (28 days) & 0.1 & \\ \cline{2-4}  & Acetaldehyde (28 days) & 0.2 & \\ \cline{2-4}  & Toluene (28 days) & 0.3 & \\ \cline{2-4}  & TVOCs (28 days) & 1 & \\ \cline{2-4}  & Carcinogenic compounds (28 days) & 0.001 & \\ \hline \multirow{4}{*}{Italy} & Formaldehyde (28 days) & 0.06 & \\ \cline{2-4}  & Acetaldehyde, Xylene (28 days) & 0.3 & \\ \cline{2-4}  & Benzene (28 days) & 0.001 & \\ \cline{2-4}  & Toluene (28 days) & 0.45 & \\ \cline{2-4}  & TVOCs (28 days) & 1.5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of legal limits on VOC emissions from construction, decoration, and furniture products from different European countries, in accordance with the chamber method described in EN 16516 [42]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Substance**} & \multicolumn{2}{c}{**Limits (mg\(\cdot\)m\({}^{-3}\)) After 28 Days of Emission Testing**} \\ \cline{2-5}  & **Class C** & **Class B** & **Class A** & **Class A+** \\ \hline Formaldehyde & \textgreater{}0.12 & 0.06–0.12 & 0.01–0.06 & \textless{}0.01 \\ \hline Acetaldehyde, Xylene & \textgreater{}0.4 & 0.3–0.4 & 0.2–0.3 & \textless{}0.2 \\ \hline Toluene & \textgreater{}0.6 & 0.45–0.6 & 0.3–0.45 & \textless{}0.3 \\ \hline TVOCs & \textgreater{}2 & 1.5–2 & 1–1.5 & \textless{}1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of the French limits on VOC emissions from construction products, floorings, wall coverings, paints and varnishes, in accordance with the chamber method described in ISO 16000 series [43; 44].
# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 3 Application of the Proposed Approach to Insulator Detection
### Applied Convolutional Neural Networks  
For insulator detection, five different convolutional neural networks were selected. These CNNs are described in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name of the CNN** & **Abbreviation** & **CNN Model** & **Main Features** \\ \hline Faster region & & \(\bullet\) & The R-CNN is a special type of CNN that is able to locate and detect objects in images & It consists of only one CNN \\ convolutional neural network & Faster R-CNN & \(\bullet\) & A separate network is used to predict the region proposals (region proposal network & Much faster than its predecessors (Fast R-CNN (RPN)) [12] & predecessors (Fast R-CNN and R-CNN) [13; 14] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of selected convolution neural networks (CNNs).  
Figure 2: Examples of tagged images recorded during the flight of an unmanned aerial vehicle (UAV) along the high-voltage line.
# Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics
## 3 Supply Chain Visibility  
The supply chain's visibility is part of the larger supply chain management area. A philosophy focused on developing visibility across supply chains to facilitate internal decision-making and operational efficiency. In recent years, supply chain visibility research has been squarely established in the academic limelight. Global megatrends towards globalization have raised several challenges in handling organizations' current supply chains as they grow in line with consumer demands [6; 42]. At its core, the supply chain visibility depends on ensuring that the organization provides access to reliable and current knowledge concerning the supply chains in terms of internal and external processes [43; 44]. This is done by determining which supply chain operations are most significantly impacted by a lack of clarity and developing methods of gathering and exchanging appropriate data between all parties involved [6; 45]. Ensuring adequate visibility in a company's supply chain will have several beneficial consequences by optimizing predicting, preparation, arranging, and order implementation, to name a few [46; 47]. With the latest technologies in the industry, such as Digital Twins, Artificial intelligence (AI), Internet of things (IoT), and Robotic Process Automation (RPA), more automation is now possible for managing end-to-end supply chain operations. Figure 5 shows supply chain visibility and control tower.  
Visibility necessitates a cross-divisional platform that helps in proactively managing end-to-end supply chains in real-time. A high degree of visibility is only possible when the core elements--people, technology, and process--work in tandem. Although visibility is strongly related to information sharing, it is crucial to distinguish the two since information sharing is an operation. In contrast, visibility is a likely outcome of information sharing practices [49; 50; 51]. The key advantages of exposure in the supply chain are the general increase in market efficiency, offering a foundation for better decision-making, both at strategic and organizational levels [52; 53]. Organizations benefit from proper visibility of the supply chain at a functional level by having the capacity to reconfigure the supply chain rapidly and effectively [54]. An ability that is becoming extremely relevant in quickly changing market conditions creates a competitive advantage. In their 2010 paper, [PERSON] and [PERSON] suggest that four main visibility processes enable an enterprise to reorganize its supply chain in compliance with both its own requirements and external demands [46; 47]. These are visibility for learning, visibility for sensing, visibility for integrating, and visibility for coordinating [44].
## 7 Conclusions  
We designed and validated our harmonic interference resilient backscatter system, which effectively suppresses harmonic interference while performing frequency shifting. Furthermore, to enhance the system's compatibility, the communication process between the reader and the tags within this system is designed based on a commonly used communication protocol. The experimental results demonstrate that, compared to the backscatter system utilizing standard square waves for frequency shifting, our system achieves a 70% reduction in the BER. We believe that as the system scale increases, it is possible to meet the more stringent requirements for harmonic interference suppression by appropriately adjusting the frequency-shifting performance of different tags and configuring various adaptive pulse-width modulation techniques.  
**Author Contributions:** Conceptualization, X.L.; methodology, X.L. and W.D.; validation, B.Y. and X.H.; resources, L.P. and D.C.; writing--original draft preparation, X.C.; writing--review and editing, D.C. and W.W.; project administration, W.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Science and Technology Project of Guizhou Power Grid Co., Ltd. under Grant No. 060000 KC24100003.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Power Dispatch Control Center Guizhou Power Grid Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 7 Control Strategy and State Space Equations
### Losses in All Power Diodes  
\[I_{D1 arev}=\frac{2 Po(D+1)D}{RL(1-D)^{2}} \tag{70}\] \[I_{D2 arev}=\frac{2 Po(D+1)D}{RL(1-D)^{2}}\] \[I_{D3 arev}=I_{D4 arev}=\frac{2 Po(D+1)}{RL(1-D)}\] \[I_{D5 arev}=\frac{Po(1+D)D}{RL(1-D)}\] \[I_{D6 arev}=\frac{Po(1+D)}{RL(1-D)}\] \[I_{D7 arev}=\frac{Po(1+D)}{RL(1-D)}\]\[\begin{array}{l}P_{Dr}=IDrms^{2}rd\\ P_{Dr1}=\frac{P_{0}(1+D)^{2}D}{RL(1-D)^{4}}rd1\\ P_{Dr2}=\frac{P_{0}(1+D)^{2}D}{RL(1-D)^{4}}rd2\\ P_{Dr3}=\frac{2P_{0}(1+D)^{2}}{RL(1-D)^{3}}rd3\\ P_{Dr4}=\frac{2P_{0}(1+D)^{2}}{RL(1-D)^{2}}rd4\\ P_{Dr5}=\frac{P_{0}(1+D)^{2}D}{RL(1-D)^{2}}rd5\\ P_{Dr6}=\frac{P_{0}(1+D)^{2}D}{RL(1-D)^{4}}rd6\\ P_{Dr7}=\frac{P_{0}(1+D)^{2}}{RL(1-D)}rd7\end{array} \tag{71}\]  
\[\begin{array}{l}P_{Vf}=I_{Duv}Vf\\ P_{Vf1}=Vf_{1}\frac{2P_{0}(D+1)D}{RL(1-D)^{2}}\\ P_{Vf2}=Vf_{2}\frac{2P_{0}(D+1)D}{RL(1-D)^{2}}\\ P_{Vf3}=Vf_{3}\frac{2P_{0}(D+1)}{RL(1-D)}\\ P_{Vf4}=Vf_{2}\frac{P_{0}(D+1)}{RL(1-D)}\\ P_{Vf5}=Vf_{5}\frac{P_{0}(1+D)D}{RL(1-D)}\\ P_{Vf6}=Vf_{6}\frac{P_{0}(1+D)D}{RL(1-D)}\\ P_{Vf7}=Vf_{7}\frac{P_{0}(1+D)}{RL(1-D)}\end{array} \tag{72}\]  
\[\begin{array}{l}D_{TL}=P_{Dr1,2,3,4,5,6}+P_{Vf1,2,3,4,5,6}\end{array} \tag{73}\]  
Internal resistance losses (rd) and losses from the forward diode voltage (Vf) are considered the two main types of power loss in diodes. It is crucial to take into account all the power losses in the seven diodes of the proposed converter. To find the average current flowing through the diodes, Equation (70) is used. Using Equation (72) helps to identify losses from Vf, resulting in the power losses from the forward voltage (Pvf). On the other hand, Equation (71) sheds light on the diode power losses due to rd. Now, by adding up all these losses, we arrive at the total power losses (DTL) across the seven diodes. Equation (73) lays it all out, giving us a comprehensive view of the power losses in the diodes of the proposed converter.
# Stability Analysis of Box Tunnel Working Face under Non-Uniform Support Pressure
## 3 Results
### Parametric Analysis
When \(L/D\) = 2.0, the safety factors of the two models are very close, while when \(L/D\)_is smaller_, the difference between the two model solutions is larger, indicating that both are equally adaptable for larger width-to-height ratios. In the case of \(C/D\) = 2.0, the coefficient of safety of the paper solution decreases by 20%, while [PERSON]'s solution decreases by about 47% as \(L/D\) decreases from 1.0 to 1.5, indicating that [PERSON]'s solution is more sensitive to \(L/D\) compared to the paper's model. Observing Figure 6a,b, it can be seen that the safety coefficient pattern is the same, also verifying the reliability of the change pattern. The results obtained from [PERSON]'s equation are closer to the solution of this paper at a larger \(L/D\), also illustrating the reliability of the solution of this paper at high \(L/D\). As shown in Figure 7, when \(C/D=1.0\) and \(c^{\prime}/\gamma^{\prime}D=0.15\), \(f_{\rm s}\) shows a non-linear and significant decrease as \(B/L\) increases, indicating that \(f_{\rm s}\) is strongly influenced by \(B/L\). Considering \(\varphi^{\prime}=30^{\circ}\) and \(L/D=2\) as examples, \(B/L\) increases from 0.2 to 0.8, and \(f_{\rm s}\) decreases by 45.7% because the larger that the empty bin area is, the more soil that loses support, and the more likely that it is to slide out of stability. Comparing Figure 7a,b, it can be seen that \(f_{\rm s}\) decreases as \(L/D\) increases for a given \(B/L\) because an increase in working face width indirectly results in a larger void area and diminishes the soil arch effect, thereby increasing the risk of instability. The most unfavorable conditions shown at \(B/L=0.8\) and \(L/D=2\) have a safety factor of less than 1 when the soil quality is poor. Since [PERSON]'s equation does not account for open positions, the results do not correspond to variations in \(B/L\). Figure 6: Variations in \(f_{\rm s}\) depending on \(L/D\) ([PERSON] et al. [37]). Figure 7: Variations in \(f_{\rm s}\) depending on \(B/L\) ([PERSON] et al. [37]). As shown in Figure 8, it can be seen that, as \(\varphi^{\prime}\) increases, \(f_{\rm s}\) shows a near linear increase, and [PERSON]'s solution is more sensitive with respect to the parameters. In addition, it can be observed that the slope of the solution to [PERSON]'s equation is identical to the slope of the solution presented in this paper; both are linearly increasing. Given that [PERSON]'s equation is B/L-independent, a line is sufficient here. Figure 8a,b demonstrate that an increase in the soil's self-stabilization capacity (increase in cohesion and increase in friction angle) leads to a decrease in the volume of the damaged area and an increase in the safety factor. In Figure 8a, when \(c/\gamma^{\prime}D=0.15\), \(\varphi^{\prime}=20^{\circ}\), the solution of this paper and [PERSON]'s solution give almost the same safety factor, but the difference gradually increases with the increase in the friction angle.