esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussion
As an example, we will consider the beginning of [PERSON]'s Prelude No. 7, Op. 28, in A-major. The musical notation corresponding to this fragment, along with the key detection results obtained when gradually increasing the number of analyzed notes, are presented in Figure 17. The key-finding method using the signature of fifths (SF) indicated the correct key of A-major after analysis of four notes and did not change the decision until the end of the fragment. In the case of the strictly correlational approaches based on the Temperley and Albrecht-Shanahan key-profiles the presence of E7 chords located in the second bar determined the incorrect key of E-major. Figure 11:0 The music notation and key detection results obtained for the beginning of [PERSON]’s prelude No. 4, Op. 28, written in e-minor, where: SF is the method using the Signature of Fifths and K-K, T, and A-S are the strictly correlational approaches based on the Krumhansl–Kessler, Temperley and Albrecht-Shanahan key-profiles, respectively (incorrectly detected keys are marked in red). The results of the experiments discussed in this paper can be summarized in a few points:  
* Generally, slightly better key detection efficacies are observed when representing the analyzed samples using the multiplicities of occurrences of individual pitch-classes than in the case of using the aggregate durations of the pitch-classes;
* In the scenario implementing the aggregate durations of individual pitch-classes, based on the analysis of the shortest initial fragments of music for which the key was indicated in all the considered approaches, the key detection efficacy obtained with the method using the signature of fifths was greater than the efficacies obtained with the strictly correlational approaches utilizing key-profiles (on average by 9.27 pp; Figure 15);
* In the case of the scenario implementing the multiplicities of occurrences of individual pitch-classes, based on the analysis of the shortest initial fragments of music for which the key was indicated in all the considered approaches, the average efficacy obtained with the strictly correlational approaches utilizing key-profiles was greater than the efficacy achieved with the method using the signature of fifths (by 2.7 pp; Figure 15);
* The key-finding method utilizing the signature of fifths is competitive with the strictly correlational approaches implementing the key-profiles of Krumhansl-Kessler, Temperley, and Albrecht-Shanahan, especially when one wants to detect the key by analyzing a very short fragment of music;
* The approach based on the signature of fifths usually needs a larger number of notes to determine the key, but once the decision is made, it is most often correct and less prone to further changing (as the number of analyzed notes increases) than the strictly correlational methods based on the key-profiles.
## 3 Method
### Types of Analyses
#### 3.3.4 Steady-State Heat Analysis  
The steady state heat analysis can be used to calculate the distribution of temperature when the structure is in equilibrium. This analysis can be performed linearly and nonlinearly, depending on the material properties. In the linear analysis, the temperature changes with time and the material properties do not depend on the temperature. On thecontrary, the non-linear analysis assumes the dependency between the temperature and material properties.  
The steady-state heat analysis is governed by Equation (3), where [K] is the conductivity matrix, \(\{F\}\) the applied force vector, and \(\{T\}\) the vector of nodal temperatures. The nodal temperature, flux, and temperature gradients can be calculated using this equation.  
\[[K]\{T\}=\{F\} \tag{3}\]  
The steady state heat analysis is performed by assembling the conductivity matrices and the applied vectors obtained from the nodal temperatures and the coefficients of radiation and convection. This enables finding the unknown temperature at each node and calculating the heat flux and temperature gradients. Moreover, the node flux can be used to derive the total heat flow, \(Q\), of the system (Equation (4)), which can provide insights into the energy cost and optimisation methods [51].  
\[Q=\sum_{t=0}^{n}\sum_{nodes}NodeFlux.\Delta t \tag{4}\]  
It is important to note that realistically, the temperature varies with time, and hence the transient heat analysis yields the most accurate result. However, due to various unknown parameters required by the transient solver and limited standards, this paper performed the steady-state heat analysis to get insights into the energy consumption of the structures.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 4 Experiments
### Comparison Algorithms  
The research on TTSA is relatively sparse, and GA and the various intelligent algorithms proposed are compared. The specific operation of GA refers to the specific methods in the reference literature and the reprograms based on the application problems in this paper. The specific genetic operation is described in Table 3. The other four intelligent algorithms are proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Explanation** & **Parameter Representation** & **Parameter Description** \\ \hline GA & Selection: roulette strategy, Crossover: direct crossover and then conflict processing strategy, Mutation: random mutation and then conflict processing strategy [8; 11; 12; 13] & GA (CF, MF) & New individuals are generated in the new population according to the ratio of crossover factor (CF) and mutation factor (MF) \\ \hline IGA & Selection: the elitist retention strategy, Crossover: replace the group crossover with the crossover cycle, Mutation: use the random exchange & IGA (SF, CF, MF) & In the new population, excellent individuals of the parent generation are retained according to the selection factor (SF), and new individuals are generated according to the ratio of crossover factor (CF) and mutation factor (MF) \\ \hline PSO-GA & Establish a selection mechanism based on PSO, establish a multi-agent genetic mechanism based on particle itself, particle best, and global best, and use ICO and MO of IGA & PSO-GA (MF) & After the particle update operation, some particles are changed according to the proportion of the mutation factor (MF) \\ \hline GA-PSO & Initialize the population based on PSO, according to the probability selection according to the probability selection model, replace the particle position update with ICO, and perform the MO when maintaining inertia & GA-PSO (IRF, PBF, GBF) & According to the probability model, determine the proportion of particles in the population updated according to three parameters: inertia retention factor (IRF), particle best factor (PBF), global best factor (GBF) \\ \hline DPSO & Initialize the population based on PSO, according to the probability selection model, and complete the position update operation. When the selected direction and target remain unchanged, add random disturbance & DPSO (IRF, PBF, GBF) & According to the probability model, determine the proportion of particles in the population updated according to three parameters: inertia retention factor (IRF), particle best factor (PBF), global best factor (GBF) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compare intelligent algorithm list.
## 6 Imaging Radar
### Challenges in High-Bandwidth Pulsed Radars  
For ground-based imaging and tracking radars for space observation, the combination of stability, high transmit power, and bandwidth is essential to achieve long-range detection, a high resolution, and accurate velocity measurements.  
In radar, the range resolution is directly related to the signal bandwidth, and a higher bandwidth therefore improves the spatial resolution in radar imaging while a high-power transmitter provides the SNR necessary for both accurate tracking and imaging.  
Since the relative bandwidth available from high-power microwave tube amplifiers is in the order of 5 to 15 %, it is clear that an enhancement of the range resolution requires a shift of the TIRA system imaging radar to a higher frequency band.
# Bi-SQUID Versus dc SQUID in Flux-Driven Traveling-Wave Parametric Amplifier
## 1 Introduction  
Parametric amplifiers (PAs) are known as ultimate low-noise amplifiers; however, such conventional cavity-based amplifiers are burdened with a gain-bandwidth trade-off. To overcome the drawback, traveling-wave parametric amplifiers (TWPAs) were suggested. TWPAs are based on using waveguide lines with nonlinear reactive parameters (of capacitive or inductive types) such as a kinetic inductance microwave line [1] or artificial lines composed of lumped cells with nonlinear capacitors or inductances. As the superconducting kinetic inductance of Josephson junctions \(L_{J}=L_{J0}/\cos(\varphi)\) is strongly nonlinear (here, \(L_{J0}=\Phi_{0}/(2\pi l_{c})\), \(\Phi_{0}=h/(2e)\), and \(l_{c}\) is the Josephson junction critical current and \(\varphi\) is the Josephson junction phase), the artificial lines composed of superconducting cells containing Josephson junctions are generally used in the designs of Josephson traveling-wave parametric amplifiers (JTWPAs) [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. The amplifiers capable of working at low and very low temperatures can provide the extremely high sensitivity approaching the quantum limit level. Therefore, JTWPAs are currently considered as promising readout devices for use in the field of precision quantum measurements (including single-photon detectors), quantum communications and quantum computing (see [13; 14] and review [15]).  
However, when the pump and signal waves propagate in the common artificial Josephson line of JTWPAs, the attainable gain is limited by depletion of the pump wave [8; 9; 15] because the initial amplitude of the pump wave is restricted by critical current values of the used Josephson junctions. To overcome this problem, a two-line JTWPAs design with a magnetic flux drive was suggested [16]. In the design, a separate non-Josephson (linear) transmission line is used for the pump wave propagation. This line is inductively coupled with SQUID-like cells of the signal artificial line and, therefore, the pump wave produces a traveling wave of magnetic flux applied to the signal line cells. This magnetic flux wave provides modulation of the cell inductances in the traveling-wave fashion.  
In this paper, we discuss the characteristics of the artificial waveguide lines composed of lumped element cells in connection with designing the Josephson traveling wave parametric amplifiers and consider the increase in dynamic range of the two-line flux-driven amplifier by substitution of dc SQUID cells for bi-SQUID cells.
# Evolution of Technology and Technology Governance
## 1 Introduction  
In modern society, we have become more and more dependent on technologies and their progress. In the 21 st century, life cycles of technologies have tended to shorten, and many innovations of the 1990s will be forgotten very soon (if they have not been forgotten already). This is why the concept of so-called 'technological waves' has attracted the attention of not only scientists but decision-makers at various levels--sectoral, national, and international. Mankind is turning to the sixth 'technological wave', where nanotechnology, genetic engineering, membrane and quantum technologies, photonics, micromechanics, and thermonuclear energy play more and more important roles. Still, well-known (traditional) resource- and energy-intensive technologies of the fourth 'technological wave' remain important and need to be gradually modernized to improve their efficiency, to turn to using alternative energy and to reduce negative environmental impact. Circular economy principles are set to stimulate the return of recyclable resources into production processes. Civil society begins also recognizing the natural capital value, including not just mineral resources but also natural ecosystems that provide a flow of irreplaceable services and goods.  
Starting from early 2000s, the United Nations have set different international development goals aimed at improving resource efficiency, ensuring proper waste management and overall fostering of innovation and modernization in industry and infrastructure. The current edition of these goals, known as Sustainable Development Goals (SDGs), was adopted by the United Nations General Assembly, and they are intended to be achieved by the year 2030 [1]. SDG9, 'Industry, Innovation and Infrastructure', and SDG12, 'Responsible Consumption and Production', have especially contributed to the fact thatnowadays governments pay stronger attention to the transition of technologies to more advanced and at the same time sustainable and responsible modes of production than they did in the 1980s, before the 'Renaissance' of industrial strategy in the European Union, United States and Canada. This article aims at the analysis of governance techniques that can be used to stimulate environmental and technological modernization and to provide for favorable conditions for the development and implementation of cleaner and greener technologies.
## 3 Research Method
### Model Metrics
#### 3.5.4 Coefficient of Determination, R2  
The coefficient of determination R2 is used to evaluate the goodness of fit of the model to the data and is calculated as:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{n}}(y_{\text{i}}-\hat{y_{\text{i}}})^{2}} {\sum_{i=1}^{\text{n}}(y_{\text{i}}-\overline{y})^{2}}\]  
\(\overline{y}\) is the average value of the true value. The closer R2 is to 1, the better the model fits the data, that is, the higher the degree of variation of the true value that the model can explain. In the PM2.5 concentration prediction study, R2 can help researchers determine whether the model can effectively capture the variation pattern of PM2.5 concentration. For example, if R2 = 0.8, it means that the model can explain 80% of the PM2.5 concentration changes, and the remaining 20% of the changes may be caused by factors not considered by the model (such as unincluded meteorological variables, sudden human activities, etc.). Therefore, R2 can be used as an important reference indicator for evaluating model performance and selecting the optimal model.
###### Abstract  
The research initiatives envisaged by the PNRR (Italian National Recovery and Resilience Plan) include the creation of innovation ecosystems to promote collaboration between universities, research centers, and local institutions with a focus on territorial integration and sustainability. The NODES Project (Nord-Ovest Digitale E Sostenibile) is part of this research. In this context, the Laboratory of Geomatics of the University of Pavia, in collaboration with other partners, deals with the study of the suitability maps for the renowned Pinot Noir wine. To achieve this, we considered different thematic input layers: elevation, slope, aspect, soil depth and type, Land Use Land Cover maps, NDVI, and current and forecast climatic aspects. An important thematic layer is concerned with the air temperature, which requires high spatial and temporal resolution. In the selected study area, the Lombardy Region has some accurate and reliable weather stations with high temporal resolution but low spatial resolution (7 stations in 648.5 square kilometers, i.e., one every 92 square kilometers). In addition, we considered Land Surface Temperature (LST) MODIS maps: these maps have good spatial resolution but present some voids and low temporal resolution. From the first evaluations made, the temperatures reported by MODIS are not always in excellent agreement with the ones from monitoring stations. To evaluate MODIS as a data source, we decided to use Kriging spatio-temporal interpolation. Starting from multitemporal MODIS data matrices, we interpolate them to estimate the temperature of the weather stations, in order to compare the estimation with the real weather station data, thus allowing the validation of MODIS data.  
Keywords:MODIS LST maps; weather stations; temperature data validation; NODES project +
Footnote †: journal: Applied sciences
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 5 Networking Protocol Based on Digital Twins
### Virtual and Real Interface of Twin Platform  
To meet the requirements of the first two stages of twin verification, the twin verification platform needs to have the capability to interconnect with external devices, actual satellites, testing equipment, and other physical entities to jointly build a twin verification environment, as shown in Figure 7. Based on different practical usage needs, the digital twin platform needs to investigate two connection modes: one is the direct connection mode based on multiple network interfaces, and the other is the label mode based on a single network interface. The designs of these two modes are as follows.  
1. In the direct connection mode based on multiple network interfaces, each physical device occupies one network interface of the digital twin platform and connects to the platform. The digital twin platform does not process the data transmission between  
Figure 5: Dynamic calculation logic of low-orbit constellation network topology.  
Figure 6: Two lines of reports for satellite L0101 and L0201.  
virtual and actual nodes. This mode is simple but requires a large number of physical network interfaces.
2. In the label mode based on a single network interface, VLAN tags are used to differentiate data packets on the same physical network interface of the digital twin platform. The platform adds VLAN tags to the data packets destined for external devices and removes the VLAN tags from the data packets received from external devices before sending them to the corresponding twin network nodes. This mode enables the reuse of physical network interfaces in the network testing cloud platform.
## 3 Results  
From the monitoring of the hydrogeological performance of the aquifer during the construction of the tunnel, it can be established that if precautions had not been taken to carry out prior waterproofing injections before tunnel excavation in the Autzagane aquifer, the water level in it would have dropped to the elevation of the tunnel, all the springs and other underground discharges towards the Ategorri stream would have dried up, and the excavation itself would have drained practically all the resources of this sector of the aquifer.  
The effectiveness of sequential waterproofing prior to excavation was high, as it has allowed the recovery of piezometric levels and the resurfacing of affected springs as soon as precipitation regimes returned to normal once the tunnel construction was finished. The injection system maintained the original compartmentalization of the aquifer, as demonstrated by the registered piezometry, without the tunnel unifying the piezometric levels of the different subsectors.  
In general terms, the natural hydrogeological functioning of the aquifer has been preserved. The waterproofing system used preserved not only the aquifer but also the original drainage points that constitute the springs of the Ategorri stream.  
The waterproofing solution, consisting of a combination of cement and microcement injections specifically designed for the characteristics of the study area, has therefore made it possible to comply with the main requirement imposed by the project's environmental authorization, no significant impact on the Autzagane aquifer.  
Conceptualization, J.-M.B., I.E. and M.G.; methodology, J.-M.B., I.E. and M.G.; formal analysis, J.-M.B. and I.E.; writing--original draft preparation, J.-M.B.; writing--review and editing, J.-M.B., I.E. and M.G.; supervision, J.-M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Balance Items** & **Flow in Natural Regime (L/s)** & **Flow Limit Values (L/s)** & **Variation** \\ \hline INPUT & Precipitation infiltration & 9.1 & 9.1 & - \\ \hline \multirow{6}{*}{OUTPUT} & Ategorri stream (springs and diffuse discharges) & 4.1 & 1.4 & \(-65\%\) \\  & Udrinbide tunnel & - & 4.7 & - \\  & Ekanosolo stream (springs and diffuse discharges) & 1.5 & 0.8 & \(-46\%\) \\  & Torreburn spring & 1.9 & 1.3 & \(-31\%\) \\  & Asketa spring & 0.8 & 0.3 & \(-63\%\) \\  & Sastratxu spring & 0.1 & 0 & \(-100\%\) \\  & Existing railway line tunnel & 0.7 & 0.6 & \(-14\%\) \\ \hline \multicolumn{2}{c}{Total} & 9.1 & 9.1 & - \\ \hline \end{tabular}
\end{table}
Table 4: Determination of the limit flow values ensuring the water supply in the Ategorri stream.  
**Acknowledgments:** The authors want to acknowledge the Interbiak company for the facilities provided for the development of the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSEs
## 2 Materials and Methods
### Improvements to YOLOv7
#### 2.4.2 SIoU Loss Function  
In the YOLOv7 algorithm, IoU is actually short for intersection over union, which is also known as the \"intersection and union ratio\". IoU has a crucial role in target detection and semantic segmentation. We can set the value of IoU as the ratio of the intersection and the union of two graph areas, as shown in Figure 7.  
The predictor frame regression loss uses the CIoU loss, but CIoU does not take into account the mismatch between the predictor frame and the true frame directions. An SIoU loss function is introduced in which the penalty metrics are redefined by taking into account the angle of the vectors between the desired regressions. Applied to conventional neural networks and datasets, it is shown that SIoU improves the speed of training and the accuracy of inference. SIoU further considers the vector angles between the true and predicted frames, redefining the associated loss function. SIoU consists of four components--angular loss (\(\Lambda\)) (Angle Cost), distance loss (\(\Delta\)) (Distance Cost), shape loss (\(\Omega\)) (Shape Cost), and the  
Figure 6: SE attention mechanism application.  
intersection and merger ratio loss (U) (IoU Cost)--as shown in the angular loss parameter schematic in Figure 8.  
IoU represents the intersection over union between the predicted bounding box (B) and the center of the real frame (BGT); \(\Delta\) signifies the distance loss, aiming to minimize the centroid distances between various predicted bounding boxes; and \(\Omega\) indicates the shape loss, which quantifies the deviation of the predicted bounding box's centroid from that of the center of the real frame.  
If \(\alpha\leq\pi/4\), the convergence process will first minimize \(\alpha\) and otherwise minimize \(\beta\):  
\[\beta=\frac{\Omega}{2}-\alpha \tag{6}\]  
The angular loss (\(\Lambda\)) is calculated as follows [13]:  
\[\Lambda=1-2\sin^{2}\Bigl{(}\arcsin(x)-\frac{\pi}{4}\Bigr{)} \tag{7}\]  
\[x=\frac{c_{h}}{\sigma}=\sin(\alpha) \tag{8}\]  
Figure 8: SIoU parameters.  
Figure 7: IoU interaction ratio.  
The distance loss (\(\Delta\)) is calculated as follows:  
\[\Delta=\sum_{d=x,y}\left(1-e^{-\gamma p_{t}}\right) \tag{9}\]  
Shape loss, \(\Omega\), is defined as follows:  
\[\Omega=\sum_{t=w,h}\left(1-e^{-w_{t}}\right)^{\theta}, \tag{10}\]  
\[\omega_{w}=\frac{\left|w-w^{gt}\right|}{max(w,w^{gt})}w_{h}=\frac{\left|h-\ h^{ gt}\right|}{max(h,h^{gt})} \tag{11}\]  
IoU loss is defined as follows:  
\[L_{iou}=1-U+\frac{\Delta+\Omega}{2} \tag{12}\]  
The total loss function is as follows:  
\[L=W_{box}L_{box}+W_{cls}L_{cls} \tag{13}\]