esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study and Discussion
### Simulation Setup
#### 4.1.2 Wind Farm Generation Profiles  
One-minute time step data for yearly wind power generation profiles were obtained from the Korea Power Exchange (KPX). The time step of these profiles was changed from one-minute to five-minute in order to match the optimization model time step. Additionally, characteristic days in these profiles were selected to decrease the computational burden. The selection procedure was as follows:  
* The yearly data were sorted by month;
* Power exceeding the upper and lower variation criteria for daily wind power generation in the monthly profiles was calculated;
* The day with the highest number of occurrences beyond the variation criteria was chosen as a typical day in the monthly profile; and
* The above procedures were repeated for all months.  
The selected dates, the number of violations of the variation criteria, the seasons, and months are listed in Table 2. The season was required to determine the REC multiplier during peak periods. In addition, the peak period for the REC multiplier is listed in the table.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wind Farm Capacity [kW] & 10,790 \\ ESS Capacity [kWh] & 2450 \\ \(SOC^{\text{e}nti}/SOC^{\text{e}max}\) [\%] & 20/80 \\ Turnaround efficiency [\%] & 90 \\ Charging/Discharging efficiency [\%] & 90/90 \\ Variable O&M cost of ESS [Won/kW-5 min] & 0.0275 \\ REC multiplier for discharging power in the peak period & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of a wind farm and ESS operation.  
As shown in Table 2, the number of violations of the variation criteria in winter tended to be greater than in other seasons. It was expected that the wind in winter on Jeju Island was stronger than the winds in other seasons. As expected, the variation in wind power generation at each five-minute time step in winter tended to be greater than that in other seasons, as shown in Figure 3.  
Figure 3 shows the variation profiles in rows from left to right in monthly order. The red dotted lines represent the variation criteria value. The gap between the yellow dotted lines signifies the peak period. Unlike other seasons, the wind power generation variation was unstable in winter from January to March and from November to December. The variation profiles in other seasons were mostly within the criteria. There were no noticeable trends in the variation profiles except in terms of seasons.
## 5 Conclusions  
In this paper, the IR and AE temporal and spatial distributions of different coal impact types were analyzed, the relationship between IR, AE, and coal damage and fracture mode  
Figure 9: Energy evolution characteristics about different coal impact types.  
was revealed, and the combined IR and AE instability precursor and damage model for different coal impact types were established. The following conclusions are summarized:  
1. There is a good correspondence between the IR and AE time series and spatial distribution of different coal impact types. As the impact tendency increases, the intensity of the IR and AE signals released by the coal failure gradually increases, and the distribution of the AE localization points and infrared high-temperature area are gradually concentrated.
2. During the loading process, the coal body gradually transitions from tensile failure to shear failure. As the impact tendency increases, the proportion of the shear failure AE signals gradually increases during the failure stage, and the shear cracks of no-, weak-, and strong-impact coal are 39.9%, 50.9%, and 53.7%, respectively.
3. The combined IRT and AE sensitivity index \(K\) for different coal impact types is established. As the impact tendency increases, the IRT and AE sensitivity gradually increases, but the occurrence time of the failure precursors becomes later and later. The IRT and AE sensitive instability precursor of no-, weak-, and strong-impact coal appear as 55.2%, 66.3%, and 93.4%, respectively, of the coal instability and failure.
4. The combined damage variable \(D_{\text{IA}}\) based on IR and AE is established. Before the IR and AE combined precursor point, the dissipated energy released by coal is less, and the \(D_{\text{IA}}\) slowly increases. After the IR and AE combined precursor point, the dissipated energy and \(D_{\text{IA}}\) increase rapidly, and the coal body will undergo instability and failure.  
Conceptualization, S.Y.; methodology, S.Y.; validation, Z.L. and E.W.; formal analysis, Z.L. and Y.L.; investigation, Y.N. and Y.L.; resources, Y.N. and H.Y.; data curation, S.Y. and Y.L.; writing--original draft preparation, S.Y. and Y.L.; writing--review and editing, Z.L. and E.W.; project administration, H.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant number: 52304268, 52074280), the China Postdoctoral Science Foundation (Grant number: 2023M733771), the Jiangsu Funding Program for Excellent Postdoctoral Talent (Grant number: 2022 ZB505), and the Fundamental Research Funds for the Central Universities (Grant number: 2023 QN1030).  
Not applicable.  
The data used to support the finding of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
## 1 Introduction
Compared with optical sensors, impact AE has much less data to be processed which leads to a very high speed both of recognition and sorting process, Reference [23] mentioned that the capacity of acoustic nuts separation could achieve about 40 pieces per second with a classification accuracy of around 97%. Damaged nuts have significantly different characteristics on impact AE, since their inner structures are changed but compared with normal nut pieces, they still keep the same external forms and shapes. A similar situation was found in crushed ELV plastics scraps. In order to reduce the weight of vehicles and reduce the costs, most plastic parts of vehicles are molded to have a hollow structure, hence most of the scrap shapes are found to be homogenous after crushing: generally they are flexure or non-flexure flakes, other shapes are rare, less than 5% in mass [21,22]. Therefore in principle, impact AE recognition could also be used for the sorting of crushed ELV plastic materials--the mechanical and geometrical structures of crushed plastic scraps are much simpler than nuts. Correlative researches [27,28] have shown that, characteristics of impact AE signals depend only on the mechanical and geometrical properties of the specific materials, theoretically crushed plastic scraps could be also recognized by using impact AE characterization. However, so far, related research has not been carried out. The aim of this work was preliminary research on the feasibility of impact AE sorting of crushed plastic scraps as a further development and optimization of existing traditional and sensor based sorting processes. Three kinds of commonly utilized vehicle plastic materials were used for this research.
## 6 Simulation and Results
### Link Budget Estimation for Intra-Chip Communication
Since Ansys HFSS only provides the electromagnetic wave propagation behavior in our design simulation, proper testing of the signal integrity of the WNoC transceiver requires hardware manufacturing. Some of the tests and measurements we can analyze include impedance matching, noise interference testing, channel characterization using a Vector Network Analyzer (VNA) or a Time Domain Analyzer (TDA), cross talk, reflec  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Dipole**} & \multicolumn{4}{c}{**Zigzag**} \\ \cline{2-10}
**Receiving** & **BW** & **IL** & **Bit Rate** & \multicolumn{2}{c}{**Energy per Bit (J/bit)**} & **BW** & **IL** & **Bit Rate** & \multicolumn{2}{c}{**Energy per Bit (J/bit)**} \\ \cline{3-10}
**Antennas** & **(GHz)** & **(dB)** & **(Gbps)** & **Tx (_Etx_)** & **Rx (_Erx_)** & **(GHz)** & **(dB)** & **(Gbps)** & **Tx (_Etx_)** & **Rx (_Erx_)** \\ \hline B & 9 & \(-26.64\) & 71.81 & \(5.78\times\,10^{-16}\) & \(1.25\times\,10^{-18}\) & 2 & \(-28.14\) & 15.96 & 8.17 \(\times\,10^{-16}\) & 1.25  
tions, conducting tests in various substrate environment conditions, and RF performance (transmitter power, responsivity, noise equivalent power (NEP)) [32].
# Mapping the Most Susceptible Regions to Fire in Portugal
## 2 Materials and Methods
### Applied Methodology  
#### 2.2.1 Pre-Processing of Data  
Different variables, with different temporal and spatial characteristics, were used in this study (see Table 1). Therefore, we worked with a common regular grid of 0.005\({}^{\circ}\) latitude \(\times\) 0.005\({}^{\circ}\) longitude. Elevation and FRM data were resampled using a nearest neighbour interpolation technique, whilst FRE and FWI were resampled usinga bilinear interpolation method. Regarding the land cover map, DGT 2021, data were firstly reprojected to the geographic projection (WGS 84) and then resampled to the coarser resolution (0.005\({}^{\circ}\)), using a majority rule interpolation technique. After this process, land cover classes were re-aggregated into ten main categories, as described in Table S1.  
The fire disturbance signature on vegetation is clearly detected on satellite products, leaving a clear signature in GPP datasets [48]. Moreover, such signature prevails during several months until vegetation recovers the normal productivity values. In order to mitigate the effect of extreme post-fire values, and considering the relatively short period of data, we excluded the value of the fire event month, as well as the 12 subsequent months, from GPP data. This procedure is performed for all burned pixels, including those that burned more than once, allowing the estimation of reference (fire-free) conditions against which we can assess the impact of fire.
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 3 Comparison and Discussion
### Influence of the Parameters of PZT Discs  
Figure 5 shows the influence of the number of the PZT discs on the first resonance frequency and the first electromechanical coupling factor, and Figure 6 plots the influence of the number of the PZT discs on the displacement of SAPS at \(z=h_{I1}\) with the frequency \(f=20\) kHz.  
Figure 5 reveals that the first resonance frequency decreases from 303.25 kHz to 57.96 kHz when the number of PZT discs changes from 2 to 20. Figure 5 also shows that the first electromechanical coupling factor increases and reaches the peak value of 0.421 when the number is 6, and then it decreases. The changing trend of the coupling factor demonstrates that there exists an optimal number that makes the coupling factor reaches the maximum value. Figure 6 indicates that the displacement at \(z=h_{I1}\) increases 11.95% as the number of the PZT discs increases from 2 to 20, which demonstrates that the displacement output of SAPSs can be increased by increasing the number of the connected PZT discs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **PZT-5** & **Copper** & **Epoxy** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & 7450 & 8800 & 1650 \\ Elastic modulus \(C_{33}\) (GPa) & 90 & 90 & 3.2 \\ Piezoelectric coefficient \(d_{33}\) (pC/N) & 420 & / & / \\ Permittivity coefficient \(\kappa_{33}^{*}\) (F/m) & \(1.947\times 10^{-8}\) & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters.  
Figure 5: The influence of the number of the PZT discs on the first resonance frequency and electromechanical factor of SAPSs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters (mm)** \\ \hline Diameter of PZT \(d_{p}\) & 10 \\ Thickness of PZT \(h_{p}\) & 1 \\ Thickness of lid I and II \(h_{l}\) & 1 \\ Outer diameter of protective shell \(d_{o}\) & 14 \\ Inner diameter of protective shell \(d_{in}\) & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters.  
Figure 7 describes the influence of the thickness of the PZT disc on the first resonance frequency and the first electromechanical coupling factor, and Figure 8 illustrates the relationship between the displacement at \(z=h_{I1}\) and the thickness of the PZT \(h_{p}\) at the frequency \(f=20\) kHz. It is clear from Figure 7 that the first resonance frequency decreases from 576.31 kHz to 70.34 kHz as the thickness of the PZT increases from 0.2 mm to 2 mm. Figure 7 also shows that when the thickness is 0.6 mm, the first electromechanical coupling factor saturates regardless of the number of the PZT disc. Figure 8 indicates that the displacement of SAPS with the number of PZT discs 2, 4, 6, and 8 at \(z=h_{I1}\) increases 0.879%, 2.42%, 4.70%, and 7.82% as the thickness increases from 0.2 mm to 2 mm.  
Figure 6: The influence of the connected number of the PZT discs on the displacement at \(z=h_{I1}\) with the frequency \(f=20\) kHz.  
Figure 7: The influence of the thickness of the PZT \(h_{p}\) on the first resonance frequency and electromechanical factor.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 2 Materials and Methods
### Data Handling
#### 2.3.2 CO\({}_{2}\) Flux Gap-Filling and Partitioning  
Gaps in the time series of measured CO\({}_{2}\) fluxes due to instrument malfunctioning or filtering strategies were filled using the R [47] package REddyProc [48] where a MDS (marginal distribution sampling) gap-filling algorithm is applied [30].  
The CO\({}_{2}\) flux partitioning in GPP and R\({}_{\mathrm{eco}}\) was also carried out with REddyProc. We followed the \"daytime\" approach as proposed by [16]. This approach uses nighttime CO\({}_{2}\) fluxes to infer the temperature sensitivity of R\({}_{\mathrm{eco}}\), but uses daytime data only to derive the light- and temperature-driven models for GPP and R\({}_{\mathrm{eco}}\), respectively [49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Site Name:**Lanzhot & **Sensor** & **Height/Depth (m)** \\ \hline Eddy covariance & LI-7200 RS, LI-COR, Inc. (NE, USA), HS-50, (Gill Instruments, UK) & 44 m until 22 October 2018, \\ Global incoming radiation (G\({}_{\mathrm{R}}\)) & CNR4 (Kipp \& Zonen, Delft, The Netherlands) & 42 m until 22 October 2018, \\ Air temperature (T\({}_{\mathrm{air}}\)) and relative humidity (RH) & EMS 33 (EMS Brno, Czech Republic) until 21 June & \\ Soil temperature (T\({}_{\mathrm{soil}}\)) & HMP 155 (Vaisala, Vantaa, Finland), & 35 m \\ Precipitation (P) & P100 (Sensit, CZ) & 0.02 m \\ Precipitation (P) & Laser Precipitation monitor, Thies Clima & 44 m until 22 October 2018, \\  & (Göttingen, Germany) & later 48 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview over the used instrumentation in the course of this study.
## 5 Results and Discussion
### Polystyrene (PS)
\begin{tabular}{c c c c} \hline Wave Mode & Deviation (dB/m) & Percent Deviation (\%) & Data Count \\ \hline Symmetric case: & 4.47 & 14.7 & 89 \\ \hline Asymmetric case: & 4.99 & 10.7 & 61 \\ \hline \end{tabular}   
For the Lamb-wave attenuation of PS sheets, the average deviation was approximately 5 dB/m and the average percent deviation was 11 to 15%. Because the deviation was computed using the absolute value of a difference, a measure of error is taken as twice the average deviation, or 2 \(\times\) dev. This is marked in Figure 5a,b by vertical bars. Subsequent figures are similarly marked when deviation is obtained. Although there is no comparable accuracy analysis of Lamb-wave attenuation, the average deviation of \(\alpha_{\rm L}\) values of PMMA reported in reference [44] was 3.72 dB/m/MHz for nine different plate samples, where the deviation was calculated from the average value. This is similar to the above deviation of 4.47-4.99 dB/m for PS Lamb waves. However, the percent deviation of \(\alpha_{\rm L}\) values of PMMA was about three times smaller at 4.07%. Increased percent deviation for Lamb-wave attenuation is expected because this study used point-contact sensing, which introduces uncertainties to recorded displacement signals. The use of optical interferometric methods may reduce sensing errors. Estimating the precision of Lamb-wave attenuation values requires many more repeat tests than the few series conducted in the present study. Within this limitation, two estimates of measurement precision were obtained. The first one compared observed \(\alpha\)-S\({}_{0}\) values at the same or similar (within a few kHz) frequencies. The other fitted \(\alpha\)-S\({}_{0}\) values to fourth order polynomials in frequency and determined differences at tested frequency values. Percent deviation was obtained using the average of the two \(\alpha\)-S\({}_{0}\) values. Results are given below. \begin{tabular}{c c c c} \hline Method & Deviation (dB/m) & Percent Deviation (\%) & Data Count \\ \hline
1 & 4.98 & 15.7 & 24 \\ \hline
2 & 3.94 & 14.7 & 35 \\ \hline \end{tabular}   
Although the results can be viewed only as gross approximation of precision, the data point to: (1) The precision values are comparable to those of accuracy estimates. (2) The two precision estimation methods give comparable results. (3) The observed percent deviation values are within a qualitative estimate of \(\pm\)10% precision. Because of the lack of a definitive error estimation method and limited data points at any given frequency, the average deviation is taken as an error estimate. Note that data counts and the number of symbols sometimes differ due to symbol overlapping. Despite the above deficiencies in accuracy and precision with room for improvement, it is shown that Lamb-wave attenuation of PS sheets is found to match the theoretical prediction for both the symmetric and asymmetric modes.
## 1 Introduction
Concurrently, to enhance the model's generalization capability across different central banks and time periods, the framework integrates domain-adversarial neural networks (DANN) [21], learning domain-invariant feature representations through adversarial training. Furthermore, a multi-task learning (MTL) mechanism [22] utilizes auxiliary signals, such as hawkish/dovish tendencies, to further refine representations. The uniqueness and core innovation of this paper lie in the integration of Hyena, Delta-LoRA, and DANN for the first time, which are applied to solve the complex problem of interest rate nowcasting from long central bank texts, overcoming multiple bottlenecks through technological synergy to achieve dual improvements in performance and efficiency. In summary, the main contributions of this paper are manifested in several aspects:  
* We propose and implement a novel framework integrating Hyena Hierarchy, Delta-LoRA, and DANN, offering a new and powerful solution for NLP tasks involving ultra-long sequences, high-performance parameter-efficient fine-tuning, and cross-domain generalization, particularly for central bank interest rate prediction. * On a self-constructed large-scale cross-institutional dataset (FOMC and ECB, 1977-2024) for interest rate prediction, we demonstrate that the proposed framework achieves a significant improvement of over 5 percentage points on key metrics compared to strong baseline models, while substantially reducing the inference latency for long-text processing (>60%). * Experimental validation confirms the effectiveness of the DANN component, showing that the framework generalizes better from a single source domain (e.g., FOMC) to a target domain (e.g., ECB), enhancing the model's reliability in practical applications. * Preliminary analysis of the model's internal mechanisms reveals connections between model decisions and key financial policy terminology, providing perspectives for understanding model behavior. The remainder of this paper is organized as follows: Section 2 reviews related work. Section 3 details the dataset construction and preprocessing. Section 4 elaborates on the proposed methodology. Section 5 presents comprehensive experimental results and in-depth analysis. Section 6 provides a discussion covering research implications, limitations, and the future outlook. Section 7 concludes the paper.
## 3 Experimental Results and Analysis
### Experimental Parameter Settings
#### 3.2.2 Mobile Video QoE Assessment Model Partial Parameter Setting  
In the experiments, 80% of the sample data are selected randomly for training to obtain the QoE prediction model. The remaining 20% is used for testing. In this paper, Pearson Linear Correlation Coefficient (PLCC) and Spearman's Rank Order Correlation Coefficient (SROCC) are used as the metrics to evaluate the accuracy of the obtained QoE model. PLCC is to measure the linear correlation between two variables X and Y, while SROCC to measure the monotonic relationship between two variables, which can be expressed as:  
\[PLCC=\frac{\sum\limits_{i=1}^{S}{{{{\left({y_{pi}-\overline{y_{p}}}\right)}}{ \left({y_{i}-\overline{y}}\right)}}}}{\sqrt{\sum\limits_{i=1}^{S}{{{{\left({y_ {pi}-\overline{y_{p}}}\right)}^{2}}}{\left({y_{i}-\overline{y}}\right)}^{2}}}}} \tag{7}\]  
\[SROCC=1-6\frac{\sum\limits_{i=1}^{S}{{d_{i}^{2}}}}{S(S^{2}-1)} \tag{8}\]  
where \(y_{pi}\) represents the predicted score for \(i\)th video, \(\overline{y_{p}}\) represents the average of predicted objective scores, \(y_{i}\) is the subjective score for \(i\)th video and \(\overline{y}\) is the mean value of all subjective scores; \(d_{i}\) represents the difference between ranks of subjective and objective scores for \(i\)th video, \(S\) is the number of scores.
## 2 OBS Networks
### Functional Mechanisms of OBS Nodes
#### 2.2.4 Wavelength Assignment  
This mechanism is used to determine on which particular wavelength to transmit the burst. Along the selected path, each link must also be assigned a wavelength on which the bursts are carried. In OBS networks wavelength assignment with and without wavelength conversion at the intermediate nodes is possible, whereby wavelength conversion may be fixed, limited-range, full-range, or sparse [20]. Most studies assume full wavelength conversion capabilities throughout the network, and this is also here the case. It should be noted, however, that economic and technical considerations are likely to dictate a more limited and sparse deployment of wavelength converters in the optical network. Therefore, wavelength assignment policies are also expected to be an important component of OBS networks [9].