esa-sceva/satcom-chunk-collection huggingface dataset:
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Manufacturer** & **Model** & **Technology** & **Spectral Resolution (nm)** & **Spectral Range (nm)** & **Size (mm)** & **Prize (USD)** \\ \hline Texas Instruments & NIRscan Nano & Grating–MEMS & 10 & 900–1700 & 58\(\times\)62 \(\times\) 36 & 1000 \\ VIAVI Solutions & MicroNIR 1700 & LVF–Linear & 12–20 & 950–1650 & 50 \(\times\) 45 & 14,500 \\ Si-Ware Systems & NeoSpectra & MEMS–FT & 8–16 & 1250–1700 & 178 \(\times\) 91 \(\times\) 62 & 6950 \\ Consumer Physics & SCoO & X & X & 740–1070 & 67.7 \(\times\) 40.02 \(\times\) & 3395 \\ Ocean Optics & Flame NIR & Grating & \(\sim\)10.0 & 970–1700 & 89.1 \(\times\) 63.3 \(\times\) & 9926 \\ SouthNest Technology & nanoFTIR NIR & MEMS & 6 & 1000–2600 & 143 \(\times\) 49 \(\times\) 28 & X \\ Spectral Engines & NIRONE S1.7 & Fabry–Pérot & 13–17 & 1350–1550 & 25 \(\times\) 25 \(\times\) 17.5 & 8940 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main specifications of some commercial miniaturized NIRSs. The remainder of this paper is organized as follows: Section 2 reports the materials and methods for analysing dairy farm forage quality. Section 3 presents the results and discussion. Finally, Section 4 contains conclusions and future works.
## 3 Results
### Models' Architecture Setup  
After parameter testing and optimization, two models outperformed similarly, namely ConvLSTM and FNO. The flow predictions for the best architectures of each model are presented in Figures 4 and 5. The figures shown are the specific case for the prediction of t = 0.3 s, given an input of t = 0.0-0.2 s, even though all the tests were implemented for the time ranging from 0 to 10 s, where a steady state was achieved. We chose this timestep because it represents the worst results of all tested models. The CFD simulated data are on the top row, where the first three were used as inputs to the model and the fourth was the ground truth. The predicted output lies directly below, to facilitate the comparisons.  
According to the results of Table 1, the model that performed the worst was CNN-LSTM. It could be noticed that, although it could predict the increase in the main vortex, the model was not capable of rotating it. This did not happen to the other models in the same intensity. On the other hand, the remaining models performed better and with the same error magnitude, with an improved performance of the FNO, which achieved its best results with only one layer, which had a direct impact on the computational load. This can be confirmed by the overall RMSE results for the flow velocity, presented in Table 1. Based on these results, we opted to continue with the tests using only the ConvLSTM and FNO models.  
Figure 2: Cont.  
Figure 2: Comparison of the horizontal velocity component over a vertical line obtained by the CFD model (RANS k-\(\varepsilon\)) and the reference values from [37] for the Reynolds numbers of 1000 (**a**), 10,000 (**b**) and 20,000 (**c**).  
Figure 3: Comparison of the vertical velocity component over a horizontal line obtained by the CFD model and the reference values from [37] for the Reynolds numbers of 1000 (**a**), 10,000 (**b**) and 20,000 (**c**).
## 2 Bifacial PV Module Structure and Resulted Incident Irradiance Evaluation
### Bifacial PV Structure
In fact, the wafer and cell structures significantly affect the spectrum of transmitted light, which in turn varies the light conversion efficiency of the front and back surfaces. Because of this, the characterization of \(\varphi_{\text{mod}}\) in terms of light properties is still an open question. In fact, several studies are actively investigating the variation in the bifaciality factor as a function of both the wavelength and the angle of incidence of the  
Figure 1: Monofacial, on the left, and bifacial, on the right, PV module and solar cell general structure. The yellow arrows indicate the light behavior. Image elaborated from [28]. radiation [29]. [PERSON] et al. in [30] studied the variation in the bifaciality factor in a specific model under different conditions throughout the day, including both clear sky and overcast scenarios. Their results showed that on cloudy days when the light spectrum was broadened by cloud scattering, \(\varphi_{\text{mod}}\) could vary by as much as 30%. Moreover, that variation was particularly evident when comparing midday with the beginning or end of the day, raising the question of whether the observed differences were primarily due to the wavelength or the angle of incidence of the light. Figure 3 shows the normalized spectral responsivity of three bifacial solar cells in the wavelength range of 500-1200 nm, analyzed one face at a time. The spectral responsivity is the ratio of the current generated by the solar cell to the incident power on the solar cell; thus, it is directly proportional to the conversion efficiency of the solar cell. First, it is observed that the conversion efficiency of the solar cell varies when considering the same bi-PV and across different modules when considering the same wavelength [31; 32; 33]. Second, the spectral responsivity is different between the front surface and the rear surface [34; 35; 36]. Therefore, even if this last aspect is visible, it does not correlate with the wafer structure of the module or the output power of the solar cells. Figure 2: Schematic view of the layers, front and rear efficiency, bifaciality factor, and cost of ownership for market-available photovoltaic bifacial modules. Image adapted from [37; 38].
## 6 Discussion  
In this paper, DSMs from nine different sensors were analyzed and adopted for change monitoring in a complex forest region. Height, as a fundamental feature for forest monitoring, is related to management activities and natural disasters. Sensors have a defined life time, and limited acquisition capacity. A continuous monitoring with the same sensor is in many cases impossible to obtain. Thus an extensive understanding of the characteristics, especially the quality of height information from various sensors is necessary. Earlier studies have often assessed one remote sensing data set [6; 9; 14; 19] or compared two to four types of data sets [3; 4; 16; 19; 20; 23; 27; 37]. In this paper, besides forest change detection, in the experimental part DSMs from both satellite and aerial images were compared to reference LiDAR DSMs and to forest inventory plots. Moreover, within the analysis of satellite photogrammetry results, the applicability of stereo imagery with resolutions ranging from 0.5 m to 6.5 m has been assessed.  
The evaluated datasets available for the \"Stadtwald Traunstein\" represent a variety of sensors with different spatial resolution and stereo capability. These sensors have diverse advantages and disadvantages. LiDAR is usually considered as the most suitable remote sensing technique to acquire forest height data. However, compared to photogrammetric surveys, LiDAR data are acquired with lower flying heights and slower flying speeds which results in higher costs [48]. Thus, it can be challenging to monitor large forest regions within a short time period with LiDAR.  
Nowadays, DSMs from airborne stereo sensors benefit from the new matching techniques and can achieve accuracies similar to LiDAR [37]. In previous literature [20], stereo images were matched with different window sizes, which influenced the DSM quality. However, currently available dense matching approaches have greatly improved accuracy and are not anymore influenced by this parameter. Therefore, it may be possible to adopt space-borne sensors with relatively low resolution to provide sufficient accurate DSMs [49]. Space-borne sensors exhibit larger coverage in comparison to airborne sensors, which is very helpful for monitoring larger regions. When considering the cost, lower resolution images like Cartosat-1 are considerably cheaper and more easily available for monitoring larger regions than very high resolution WordView-2 data. Among active sensors, TanDEM-X is a new active satellite constellation operation in the X-band frequency, which provides a worldwide consistent DSM. The X-band has less penetration compared to other longer wavelengths such as L- or P-band, and allows estimating the forest canopy surface. TanDEM-X can continually operate from a space platform and is not influenced by weather. In general, it is difficult to quantify the applicability of the mentioned sensors. A detailed understanding of the quality of the specific DSMs and their behavior in forest change detection can help to select some of them for practical application.
# A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow
## 4 Results
### Network Workload
Finally, Figure 3c shows the bitrate distribution for PS Now. Given the lack of application settings or debug information, we only show the overall distribution of the bitrate. The online documentation recommends a minimum bandwidth of 5 Mbit/s and states that video streaming has a 720p resolution. However, we observe the bitrate reaching up to 13 Mbit/s, with a consistent variability. Interestingly, when we impose a 10 Mbit/s or lower bandwidth limitation on the network, the bitrate adapts consequently (see the peak in the distribution at 5 Mbit/s). However, we cannot link it with a resolution lower than 720p. We now study the impact of packet losses on cloud gaming, focusing on Stadia as a case study. As described in Section 3, we run experiments in which we enforce artificial packet loss via the tc-netem tool, ranging from 1% to 10%. We then study how the application reacts to these scenarios, focusing on the sending video bitrate and quality level. In Figure 4, we show the distribution of bitrate for experiments with 1, 5 and 10% packet loss, and report the bitrate of sessions with no packet loss as a reference (solid red line). We notice that a packet loss of 1 and 5% (blue and green dashed lines, respectively) does not cause significant bitrate variations. Indeed, the bitrate reaches 30 Mbit/s, and we notice that the game is running at 1080p most of the time, with some 720p periods. We notice a constant activity on the retransmission stream, used to recover lost packets. Different is the case with 10% packet loss (yellow dashed line). In this case, Stadia keeps the video resolution at 720p most of the time, and, as such, the bitrate is limited to 10 Mbit/s. We notice a high activity again on the retransmission stream, which, however, is not deemed sufficient to go for higher video resolutions. In a few cases, 1080p is achieved, but for short 5-10 s periods. In all the cases, the games were still playable and enjoyable, as reported by the volunteers. _Take away: Stadia has three video resolutions, with a rather constant bitrate of (median of 11, 29 and 44 Mbit/s). Packet losses up to 5% do not cause a reduction in resolution. GeForce Now streams video at many different resolutions, each having a large variability in the bitrate, reaching up to 45 Mbit/s. PS Now does not exceed 13 Mbit/s, and we cannot infer the employed quality levels._  
Figure 4: Bitrate distribution with different artificial packet loss.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 3 Trusted Sensor Nodes Based on SRAM PUFs
### Physical Identities of Sensor Nodes Based on SRAM PUFs
The average probability that a start-up value in the genuine PUF responses may change (the bit error or bit flipping probability \(p\)) can be estimated by the average fractional Hdist in (1) computed for the genuine population of PUF responses. The comparison between each pair of bits in the PUF responses is modeled essentially as a Bernoulli trial, which takes value '\(1\)' (if the bit changes) with probability \(p\) and a value of \(0\)' (if the bit does not change) with probability \(1-p\). If the \(n\) bits obtained from the start-up values of \(n\) cells are assumed to be independent, the probability of finding \(t\) flipping bits (or errors) in them is given by a binomial distribution, as follows:  
\[P(t)=\left( \begin{array}{c}n\\ t\end{array} \right)\cdot p^{t}\cdot(1-p)^{n-t}. \tag{2}\]  
Hence, the probability that a PUF response of \(n\) bits contains more than \(t\) flipping bits is given by:  
\[P_{total}=1-\sum\
olimits_{i=0}^{t}\left( \begin{array}{c}n\\ i\end{array} \right)\cdot p^{i}\cdot(1-p)^{n-i}. \tag{3}\]  
In order to cope with the noise of genuine PUF responses, Error Correcting Codes (ECCs) are employed. A model using Equations (1)-(3) is used by many authors to select the most suitable ECC according to the bit error probability, \(p\), in the PUF response [9; 11]. Given \(p\) (estimated by the average fractional Hdist in (1) for the genuine population) and given an ECC (with \(n\)-bit codewords and capacity to correct up to \(t\) errors), the capability of the ECC to achieve a given \(P_{total}\) can be evaluated with Equation (3). For a given \(P_{total}\), the number of errors (flipping bits) to be corrected and, hence, the complexity of the ECC, decreases as the value of the average fractional Hdist for the genuine population gets lower. This is why using only STB cells instead of all SRAM cells is better to obfuscate secrets, as proposed in the following section.
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 3 Results
### Group Size and Order of the Experimental Condition  
With respect to the subjects' performance and the experimental condition, the effect of the number of members in the group (group size) was investigated. Results show that this variable had no effect on subjects' performance, regardless of experimental conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Effect** & **Wilks’ Lambda** & **F** & **Sig.** & \(\eta^{2}\) \\ \hline Education & 0.92 & 9.37 & \(p<0.001\) & 0.083 \\ Experimental condition & 0.95 & 4.41 & \(p<0.05\) & 0.021 \\ Type of list & 0.91 & 10.60 & \(p<0.001\) & 0.093 \\ Exp. cond. * List & 0.95 & 2.67 & \(p<0.05\) & 0.025 \\ \hline Source & Dependent V. & F & Sig. \\ \hline Education & Non-studied words & 18.80 & \(p<0.001\) \\ \hline Experimental condition & Studied words (C \(>\) N/I) & 4.12 & \(p<0.05\) \\ \hline Type of list & Non-studied words (A \(>\) B) & 14.06 & \(p<0.001\) \\ \hline Exp. cond. * List & Non-studied words & 2.59 & \(p<0.05\) \\ \hline \end{tabular}
\end{table}
Table 3: MANCOVA on the subjects’ performance. The letters “C”, “N”, “I” mean, respectively, the collaborative, nominal and individual experimental conditions. The letters “A” and “B” refer to the type of list, respectively the list of abstract words (feelings) and the list of concrete words (musical instruments). The model explains the 16% of the variance (\(r^{2}=0.159\)). * indicates the combined effects.  
Instead, a Student's \(t\)-test revealed an effect regarding the experimental conditions order. Particularly, the collaborative condition showed a better performance when the nominal condition came first. In fact, the number of studied words was significantly greater when the collaborative condition was subsequent (\(t=-2.5\), \(p<0.05\); \(M=7.64\) vs. \(M=8.44\)), while false negatives were significantly lower (\(t=2.5\), \(p<0.05\); \(M=2.36\) vs. \(M=1.56\)). On the other hand, any other experimental condition was found to be significant.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion
### Non-Stationary Signals and Motion  
The UBFC-RPPG dataset contains videos during which the participants played a time-sensitive mathematical game that supposedly raised their pulse rate. High pulse rate values can effectively be observed on the Bland-Altman plots (Figure 10). In addition to natural motion (head movements), the game tends to drastically change the PPG signal frequency over time. These signals are thus predominantly non-stationary. This has not been considered in the method we propose in this study, the synthetic generator (Section 3.2) producing only stationary signals.  
Non-stationary PPG signals have an impact on the histogram distribution computed from the prediction maps. Figure 12 presents a typical example: the more a video patch contains a non-stationary PPG signal (Figure 12b, bottom row), the more bins are present in the histogram of predictions (Figure 12c, bottom row), which thus increases entropy and reduces the pulse rate prediction accuracy.  
Figure 11: Prediction maps (subject #1, 60 first frames) for different training epochs. (**a**) Video stream excerpt (face close-up). Map for (**b**) epoch #25, (**c**) epoch #45, (**d**) epoch #100, (**e**) epoch #1000. The corresponding normalized histograms are presented in the bottom row. We can visually observe that the pixels labeled with pulse rates converge into relevant areas, while pixels that contain no PPG information are properly identified.  
Strong and wide movements were not considered in this work: except for the trends (Section 3.2.3), the network has not been trained with data that includes motion artifacts. In addition, natural variations of the trend observed in an image sequence may not follow a linear, quadratic, or cubic order. We believe that there is room for improvement and plan to investigate further in this direction. If a deeper architecture is adopted, transfer learning and/or fine-tuning approaches could be viable options for increasing performance and, presumably, handling motion [61].
## 5 Conclusions  
In this paper, we studied the CBIS and proposed the CICBIS to improve the utilization efficiency of the inerter system for shear-type MDOF structure seismic response control. A demand-based CBIS design method is developed as well. The main contributions of this paper are summarized as follows:  
* The ability of the CICBIS to reduce the total apparent mass and damping coefficient requirements of the inerter systems without increasing the control force has been confirmed. Thus, the CICBIS with a high efficiency provides a feasible solution to attenuate the in-built or existing building's seismic response.
* The demand-based CBIS design method proposed herein can exert the damping enhancement of the inerter system and the adjustability of the cable-bracing system. It allows the CBISs with the same inerter to realize an optimal vertical distribution, facilitating the manufacture and deployment of the inerter system.
* The effective inerter-mass ratio reflecting the influence of the installation location and layout of the CBIS on the inerter system's utilization efficiency is used to explain the CICBIS's high efficiency.  
Figure 13: The displacement responses of the bottom layer CBIS: (**a**) CICBIS-A11; (**b**) CICBIS-1; (**c**) IICBIS-1.  
It provides a reference for the further optimization of the installation location and layout of the inerter system.  
Although the discussion herein is limited to the shear-type structure and some existing cable-bracing systems, based on the tension-only characteristics and flexibility of the cable, it is foreseeable that entirely using these properties will make the application of the CICBIS more prominent. The discussions on the efficiency of the CICBIS equipped in the structures other than shear-type, and the experimental verifications will be carried out in subsequent studies.  
Conceptualization, S.X. and L.X.; methodology, J.K.; software, J.K. and X.B.; validation, J.K. and X.B.; formal analysis, J.K.; writing--original draft preparation, J.K.; writing--review and editing, R.Z., S.X and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No.51778490 and 51978525), Open Research Fund Program of Guangdong Key Laboratory of Earthquake Engineering and Application Technology (Grant No. 2017B030314068), the Natural Science Foundation of Shanghai (Grant No. 202R1461800).  
The authors declare no conflict of interest.
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
## 3 Results and Discussion
### Other Analysed Crops  
In addition to the \"traditional\" energy crops, also soybean, soriz, switchgrass and paulownia had been analysed according to their methane yield potential. These results are provided in Table 6. Soybean (_Glycine max (L.[PERSON]_) can be successfully grown in rotation with a large variety of other plants [11], as well as in widely diverse climates and on varied soil types [48]. Soriz (_Sorghum oryzaeidum_) is a hybrid of sorghum and rice, which was selected for this study for being nonexxacting to soil with lodging resistance, as well as with resistance to smut diseases and ice [29]. Switchgrass (_Panicum virgatum L._) is a perennial plant, which is valued for its soil stabilizing, phytoremediation and windbreaking capacities in crop fields [16; 31]. Paulownia (_Paulownia Sieb. et Zucc._, species _P. tomentosa x P. fortunei_) is a very fast-growing plant, which is extremely adaptive to a wide range of soils and climatic conditions and can also be grown on marginal lands [34; 49].  
For switchgrass, specific methane yields between 0.191 and 0.309 m\({}^{3}\) kg\({}^{-1}\)VS are reported for South European countries (Spain) [25]. Similar SMY were measured for switchgrass from Canada (between 0.210 and 0.365 m\({}^{3}\) kg\({}^{-1}\)VS) with an AMY between 1500 and 3280 m\({}^{3}\) ha\({}^{-1}\)[21]. The results of these investigations on the specific methane yields from Ukrainian switchgrass are in the middle range  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Crop**} & **Analysed** & **DMFM \({}^{*}\)**, & **VSDM \({}^{**}\)**, & **DMY \({}^{***}\)**, & **Crop N Uptake**, & **SMY,** & **AMY,** \\  & **Cultivars** & **[\%]** & **[\%]** & **[\%]** & **[\(\mathbf{\text{fpm ha}^{-1}}\)]** & **[\(\mathbf{\text{kgs ha}^{-  
of those values reported in the literature. However, the maximal results of the areal methane yields determined for Canadian switchgrass were higher than those values achieved in our study.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 2 Proposed Design of Satellite Networks
### Design of the Network Layer  
The proposed design of the network layer is shown in Figure 2. In the proposed design for the routing, the hierarchical structure is considered as the network architecture. The satellite network can be divided into the sub-autonomous system (AS) by each service provider or each beam. If all UTs share routing information among each other, a large resource is consumed to share it [32; 33; 34; 35]. Thus, in the same AS, an open shortest path first (OSPF) routing scheme is used, and a border gateway protocol (BGP) is used between ASs for the routing, as shown in Figure 2[34; 35]. In the proposed design, for source and destination terminals in the same AS, there is a one-hop delay in the data transmission between source and destination terminals. On the other hand, when source and destination terminals are located in different ASs, respectively, multi-hop delay is needed for the data transmission via hub terminals.  
Figure 2: System model of the proposed design (network layer). AS, autonomous system; OSPF, open shortest path first; BGP, border gateway protocol.