esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 2 Data  
### Dataset Description  
The AMGC-AT model presented in this paper was evaluated using a real-world dataset. The dataset was the Hangzhou Metro smart-card data, published by the Tianchi Big Data Competition, for 25 days from 1 January 2019 to 25 January 2019. The data covered about 70 million metro passengers across 81 metro stations from three metro lines. The raw data, as shown in Table 1, recorded tap-in and tap-out time, line ID, device ID, user ID, and pay type. The data obtained from AFC card processing has an operating time from 06:00 to 23:30 for tap-in and outgoing station passenger flow sequence data, which is integrated into specific time intervals of 10 min, 15 min, 30 min, and progress passenger flow sequence at 10 min time granularity, as shown in Table 2. The first week passenger inflow as shown in Figure 1. The distribution map of the Hangzhou metro line as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **Line ID** & **Station ID** & **Device ID** & **Status** & **User ID** & **Pay Type** \\ \hline
1 January & & & & & \\
2019 6:54 & B & 30 & 1482 & 0 & D92a70 cb & 3 \\
1 January & & & & & \\
2019 7:24 & A & 72 & 3311 & 1 & Af95c8 cc & 0 \\
1 January & & & & & \\
2019 8:03 & C & 46 & 2176 & 0 & Bf8 ed2b8 & 1 \\ \(\ldots\) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of original data record.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Index** & **06:00–06:10** & **06:10–06:20** & **06:20–06:30** & **23:20–23:30** \\ \hline
1 & 21 & 40 & 37 & 8 \\
2 & 17 & 14 & 29 & 28 \\
3 & 25 & 56 & 77 & 15 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
81 & 1
###### Abstract  
Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, \(\varepsilon\)) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on \(\varepsilon\). This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m\({}^{-2}\) d\({}^{-1}\)) over the original study in which in situ data were used (RMSE = 2.6 g C m\({}^{-2}\) d\({}^{-1}\)). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R\({}^{2}\) = 0.80 and RMSE of 3.5 g C m\({}^{-2}\) d\({}^{-1}\)). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP.  
gross primary production (GPP); light use efficiency (LUE); AmeriFlux; U.S. Corn Belt; gridded weather data 2020, 12, 3956; doi:10.3390/rs12233956
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 6 Discussion and Comparison
### Attackability  
PUFs used in secure systems for protection mechanisms are targeted by attackers in many different ways, like side-channel attacks, machine learning, or tampering.  
As introduced in Section 1, oscillation-based PUFs (e.g., loop-PUF) are prone to side-channel attacks (SCAs). Ref. [20] stated that reduced measurement time of the attacker decreases the accuracy of the attack. A TERO-PUF was attacked via a side-channel in [37], and the authors mentioned that multiple parallel oscillations increase the difficulty for the attacker to reveal the resulting bit. Both findings promise advantages of the EoA-PUF against attackability because it evaluates pairs of ROs with a very limited amount of oscillations to reach a decision (see Section 5.3).  
Further, strong PUFs are known to be attackable via machine learning. Several approaches against these attacks have been proposed in the literature, like [38; 39]. However, some approaches, such as the XOR-arbiter PUF, have been shown to be broken [40]. In the present work, Hadamard codewords have been employed, reducing the generally large number of challenges to a very limited number of codewords, thus rendering the strong PUF into a weak PUF for key extraction (see Section 4), which makes machine learning attacks obsolete as a challenge input, no longer accessible by the attacker.  
Tampering with helper data is another known method to attack PUFs [41]. Helper data for the presented EoA-PUF are the tuning vectors for the arbiter and offset compensation, as well as the stability information, which is used for bit-masking. The tuning vector for the arbiter and the stability information can be regenerated within the system by the tuning algorithm and the auto-detection mechanism and therefore need not be stored permanently or made available externally. However, the offset compensation tuning must be stored as helper data and may offer an attacking-vector. As this data would also be necessary in an RO or arbiter PUF featuring a challenge input, as discussed above, it is not regarded as a distinct disadvantage of the EoA-PUF.  
Actual attacks are not covered in this work as the focus was on the first implementation, feature description, and performance evaluation of the EoA-PUF on FPGAs. Nevertheless, future work is motivated concerning the security of the challenged EoA-PUF.
## 4 Experiments and Results
### Results of the HMT Gesture Recognition  
#### 4.2.1 Results of the Continuous Gesture Segmentation  
According to the segmentation algorithm of continuous gestures defined in Section 3, 10 consecutive gestures of all of the groups were segmented to extract the effective single gesture. In the experiment, 94 sets of 100 consecutive gestures from the five subjects were segmented into 10 corresponding gestures correctly. The start and end points of the segmentation were also in line with the expectations of the algorithm. Among the six groups of gestures with incorrect segmentation, there were three groups that were caused by the absence of start and end points because of the non-standardized gestures (including the too-small motion of the fist bobbing before the single effective gesture, and the loss of a motionless state after finishing the single effective gesture). The remaining three groups of segmentation errors were caused by the motion of fist bobbing during the process of the effective HMT gesture, which lead to an extra wrong segmentation. Finally, there were 992 gestures completely segmented out of the whole 1000 gestures correctly, which led to a segmentation accuracy rate of 99.20%. The effective gestures that were not correctly segmented were processed manually by specifying the start and end points.
# Augmented Reality in Precision Farming: Concepts and Applications
## 5 Conclusions and Future Work  
The mixed reality technology domain, of which AR resides, is a constantly evolving field [33] and as [PERSON] et al. discuss, new innovations tend to define new interaction methods. Evidence suggests AR will become vital for the success of precision farming, particularly as there is a migration towards the use of data-driven farming and autonomous agro-robotics in the endeavour to produce more with fewer resources. The autonomy that wearable-based hardware provides farmers makes AR especially suitable for precision livestock farming, both when monitoring individuals through large spaces [13] or during more static practices like the milking process [16]. In this article, related literature was discussed regarding only applied AR solutions within agriculture. In all the articles discussed, AR has demonstrated a significant impact for the end-user stakeholder, yet there may be applications for other stakeholders beyond the investigation of this study (within a wider agriculture setting), such as in the supply chain as evidenced in [23]. It is also clear in the findings that AR must be coupled with other technologies for a beneficial impact; namely sensors, networking, machine learning and location-based communication technologies. The limitations of this work include the aforementioned reduced level of existing articles on AR for agricultural application. AR is a relatively young technology for use within both livestock and crop-based farming, and this is evident in the low number of articles published on this topic between 2016 and 2021. This also meant that a systematic literature review process was not possible, instead a snowballing approach was adopted to investigate the articles referenced.  
Future directions for this research could include extending the survey outside of agriculture to related domains within the food supply chain (e.g., transport, shipping, manufacturing/processing, etc.) to reflect on the wider benefits of AR within the entire food supply network. Furthermore, this article also did not conduct a user experience (UX) or human-computer interaction-based investigation, as this was outside the scope. Rather the intention was to focus on applied agriculture projects and discuss the hardware in place and potential benefits of emerging solutions could have for precision farming. However, both considerations are possible areas of investigation for a further extension of this research.  
Conceptualization, W.H., F.R.M.; methodology, W.H., F.R.M.; formal analysis, W.H., F.R.M.; investigation, W.H., F.R.M.; resources, W.H., F.R.M.; writing--original draft preparation, W.H., F.R.M., B.T.; writing--review and editing, W.H., F.R.M., B.T.; visualization, W.H., F.R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding support.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Material Characterization
### Concrete Creep and Shrinkage  
Apart from the standard concrete characterization tests, the experimental program also included creep and shrinkage tests for the investigated concrete. For that purpose, concrete cylindrical specimens with a diameter of 150 mm and a height of 300 mm were cast. After being removed from the moulds, they were kept together with the specimens for the standard characterization in lime saturated water until the age of loading (225 days). Each specimen was instrumented equally with three 50 mm long strain gauges distributed uniformly around the perimeter of the cylinder and aligned with the axis of the cylinder at the midsection to measure the creep and shrinkage strains. All creep and shrinkage tests were performed in an environmentally controlled room together with the sustained load anchor specimens at a constant temperature \(T=~{}22~{}\pm~{}0.5~{}^{\circ}\)C and relative humidity \(RH=~{}50\%~{}\pm~{}3\%\).  
Creep tests were carried out for two sealed and two drying specimens. All specimens were loaded hydraulically in a creep frame up to 30% of the cylinder compressive strength (\(f_{c,spl}\)) obtained on companion specimens at the same day at an age of 225 days. Shrinkage tests were performed on two drying and two sealed specimens. The two specimens used for autogeneous shrinkage were instrumented just after being demoulded 24 hours after casting. All concrete cylindrical specimens  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Age [d]** & \(f_{c,150}\) [MPa] & \(f_{c,spl}\) [MPa] & \(f_{t,spl}\) [MPa] & \(E_{spl}\) [GPa] \\ \hline
28 & \(39.8\pm 6.4\%\) & - & - & - \\ \hline
225 & \(53.7\pm 6.3\%\) & \(49.83\pm 5.9\%\) & \(4.7\pm 3.2\%\) & \(49.8\pm 5.9\%\) \\ \hline \end{tabular}
\end{table}
Table 2: Experimentally obtained material properties.  
used for creep and shrinkage tests had the same geometry and were instrumented in the same way with strain-gauges. More detailed information about creep and shrinkage tests and drawings of the creep frame can be found in [28]. The creep response of a bonded anchor system under sustained load in a confined configuration originates mostly from the creep of the adhesive layer. However, previous findings [28] showed also a contribution of concrete creep to the system long-term deformations which was about 25-30% of the total deformation for the investigated confined configuration. Therefore, in this study, concrete creep and shrinkage were also experimentally characterized, as an essential input for further numerical investigations, e.g. based on coupled nonlinear analysis [39].  
Figure 2 shows the total strains obtained from (a) loaded drying specimens (creep), and (b) unloaded drying specimens (shrinkage). Each curve is the average strain time history of three strain gauges attached to a concrete specimen.
# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 5 Discussion
### Maximum Story Drift Ratio  
In initial stage, the maximum story drift ratio does not clearly decrease with the variation of objective function. On the contrary, it oscillates during the optimization process, but it tends to remain stable in the later stage. There are mainly three obvious trends: when the weight coefficient \(\alpha\) = 0.0, compared with the initial value, the maximum story drift ratio does not decrease significantly, and even increases in the case of 0.0-0.8-0.2. Because the seismic decrease coefficient is not considered during the optimization process, when \(\alpha\) = 0.0, seismic decrease coefficient and base shear force would not decrease significantly, thus the reduction of seismic action on the superstructure is not significant, as is the variation trend of maximum story drift ratio. However, the maximum story drift ratio decreases significantly from its initial value when weight coefficient \(\alpha\) > 0.0. Especially in the cases of \(\alpha\) > 0, \(\gamma\) < 0.4, \(\lambda\) = 0.8, 0.6, 0.4, and 0.2, the decrease amplitude of maximum story drift ratio is 29.9%, 20.7%, 23.8%, and 28.4%, respectively, compared to initial the value, as depicted in Figure 14. The greater the weight coefficient of maximum story drift ratio \(\lambda\) is, the larger the decrease percent of maximum story drift ratio is. Fixing weight coefficient of maximum story drift ratio \(\lambda\) at 0, when weight coefficient of seismic decrease coefficient \(\alpha\) increases from 0.2 to 0.8, maximum story drift ratio increases by 9.8%. Fixing weight coefficient of maximum story drift ratio \(\lambda\) at 0.2, when weight coefficient of seismic decrease coefficient \(\alpha\) increases from 0.2 to 0.8, maximum story drift ratio decreases by 11.8%, 12.9%, and 13.7%, respectively. If fixing the weight coefficient \(\lambda\) at 0.4, when weight coefficient \(\alpha\) increases from 0.2 to 0.4, maximum story drift ratio decreases by 19.2%.  
In a nutshell, when the weight coefficient \(\alpha\) = 0.0 or \(\lambda\) = 0, the maximum story drift ratio would not decrease significantly or even increase. When maximum story drift ratio is mainly considered, the weight coefficient of the maximum story drift ratio \(\lambda\) would better be larger than 0.2. Meanwhile, the maximum story drift ratio would acquire 1-5% decrease for every increase of 0.2 in weight coefficient \(\alpha\).
## 4 Materials and Methods
### RNA Immunoprecipitation (RNA-IP)  
Primary astrocytes were cultured in 15 cm dishes and harvested on DIV14. Cell pellets were resuspended in fractionation buffer (10 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl2, 0.5% Nonidet P-40 (NP-40), 1 mM DTT, 100 units/mL RNase inhibitor, 1x protease inhibitor) and incubated on ice for 10 min with occasional pipetting to lyse cells. Then, the samples were centrifuged at 1000\(\times\)\(g\), 5 min, at 4 \({}^{\circ}\)C, and the supernatant containing the cytoplasmic fraction was transferred into fresh tubes. The pellet was washed with TSE buffer (10 mM Tris, 300 mM sucrose, 1 mM EDTA, 0.1% NP-40, 1 mM DTT, 100 units/mL RNase inhibitor, 1\(\times\) protease inhibitor), resuspended in fresh TSE buffer, transferred to bioruptor tubes, and sonicated in a Bioruptor Plus (both Diagenode, Denville, NJ, USA) for ten cycles (30 s on, 30 s off). Afterward, the samples were incubated on ice for 20 min with occasional vortexing and centrifuged at 14,500\(\times\)\(g\) for ten minutes. The supernatant was transferred to a fresh tube, and the protein concentration of both fractions was determined, as described above. Then, all samples were flash-frozen and stored at \(-80\)\({}^{\circ}\)C until further use.  
For the RNA-IP, \(500\)\(\mathrm{\SIUnitSymbolMicro g}\) protein from the nuclear lysate per sample was pre-cleared using \(25\)\(\mathrm{\SIUnitSymbolMicro L}\) Pierce(tm) Protein A/G magnetic beads (ThermoFisher) for \(1\) h at \(4\)\({}^{\circ}\)C. Simultaneously, \(1.5\)\(\mathrm{\SIUnitSymbolMicro g}\) Npas3 antibody (rabbit, ThermoFisher) or \(1.5\)\(\mathrm{\SIUnitSymbolMicro g}\) IgG isotype control (rabbit, ThermoFisher) per sample were incubated with \(50\)\(\mathrm{\SIUnitSymbolMicro L}\) Protein A/G magnetic beads for two hours at RT, followed by a wash with RNA-IP buffer (\(50\) mM Tris-HCl, \(100\) mM NaCl, \(32\) mM NaF, \(0.5\)\(\mathrm{\SIUnitSymbolMicro w}\) NP-\(40\)). When pre-clearing of the samples was performed, samples were put on a magnetic rack to separate the beads from the lysate; \(10\)% of each sample was collected to serve as an input control, and the remaining liquid was added to the beads. The samples were incubated overnight at \(4\)\({}^{\circ}\)C with mixing. On the next day, the beads were washed five times with RNA-IP buffer and then resuspended in a proteinase K buffer containing proteinase K (Qiagen, Hilden, Germany), followed by an incubation at \(37\)\({}^{\circ}\)C for one hour. The beads were again separated using a magnetic rack, and the supernatant was transferred to fresh tubes. RNA was extracted using the RNA clean and concentrator-\(5\) kit (Zymo Research) according to the manufacturer's instructions.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 4 Constructing the Multispectral Datasets
### Landsat 9  
Together, the Landsat and Sentinel-2 programs are globally renowned as the flagship programs for medium-resolution land imaging [77]. As shown in Table 3, Landsat 9 and Sentinel-2 have 8 bands that closely overlap in terms of central wavelength and bandwidth. Thus, Landsat 9 and Sentinel-2 data were selected for their significance and also for their overlapping bands, which allowed us to assess whether aligning their characteristics as closely as possible could reduce any domain gap between the datasets. We shall see in Section 8.1 that despite this \"artificial\" alignment, a domain gap demonstrably exists. Note that the choice of Sentinel-2 and Landsat 9 distinguishes our study from the prior studies in Table 2.  
Landsat 9 data products consist of data cubes with 11 spectral bands and spatial resolutions of 15 m, 30 m and 100 m. Cloud masks are also provided. The data cubes were preprocessed in a similar manner as in [75] by first converting the quantised and calibrated scaled digital numbers (\(DN\)) to TOA reflectances using the formula  
\[R=M\cdot DN+A,\]  
where \(R\) is the TOA reflectance before sun angle correction, \(M\) is the band-specific multiplicative rescaling factor from the metadata (i.e., REFLECTANCE_MULT_BAND_X, where \(X\) is the band number), and \(A\) is the band-specific additive rescaling factor from the metadata (i.e., REFLECTANCE_ADD_BAND_X). To correct for the sun angle, the following formula was applied:  
\[R_{c}=\frac{R}{\sin(\theta_{SE})},\]  
where \(R_{c}\) is the corrected TOA reflectance, and \(\theta_{SE}\) is the local sun elevation angle from the metadata (i.e., SUN_ELEVATION, specific to each data product). After correction, all bands were resampled (if necessary) to a spatial resolution of 30 m using bilinear interpolation. Finally, the data cubes were divided into 2000 data (sub)cubes of 512 \(\times\) 512 pixels each.
## 4 Signal Processing Algorithms for RIS Channel Estimation
### Composition-Based Algorithms for RIS Channel Estimation  
The direction of algorithm evolution has been described, it is found that deep learning algorithms are being integrated with conventional algorithms and used in the channel estimation process widely. Deep learning can make up for the shortcomings of conventional algorithms, such as improving the channel estimation accuracy and joint optimization of multiple modules. The deficiencies of conventional algorithms with insufficient accuracy can be further improved by using the good denoising function of neural networks to improve the channel estimation accuracy. **SemiDefinite relaxation (SDR)-deep learning:** Elements on the RIS were usually arranged in two dimensions, treating the channel information as a single image. [PERSON] et al. [112] first used a SDR scheme and then built an end to end deep learning channel prediction model that predicted the entire CSI based on the pilot information. It outperforms existing approaches based on grouping of RIS elements in terms of achievable rate. **LS-deep learning:** [PERSON] et al. [45] modelled the channel information estimated by LS as a low-resolution image and then interpolate it into a high-resolution image linearly. **OMP-deep learning:** Using the conventional CS OMP algorithm, it is assumed that the arrival and departure angle bases fall on the discrete grid during the computation exactly, which does not match the reality causing errors. [PERSON] et al. [69] fused deep learning in the OMP algorithm to get results after the residual network to improve the performance.
# Acute Effects of Midsole Bending Stiffness on Lower Extremity Biomechanics during Layout Jumps
###### Abstract  
Purpose: This study aims to investigate the acute effects of shoe midsole stiffness on the joint biomechanics of the lower extremities during specific basketball movements. Methods: Thirty participants wearing stiff midsole shoes (SS) and control shoes (CS) performed layup jumps (LJs) while the kinematics and ground reaction forces were simultaneously collected via the Vicon motion capture system and Kistler force plates. Furthermore, the joint angles, range of motion (ROM), joint power, joint energy, and jump height were calculated. Results: No significant differences were observed between SS and CS conditions for both jump height and the metatarsophalangeal (MTP) joint biomechanics except that the minimum angular velocity of the MTP joint was significantly lower in SS the condition. However, the ROM in the ankle joint was significantly greater in the SS condition than in the CS condition (\(p<0.05\)). Additionally, the maximum plantar flexion power, energy absorption (EA), and energy generation (EG) in the ankle joint were significantly greater in the SS condition than in the CS condition (\(p<0.05\)). Compared with the CS condition, jump height in the SS condition did not increase. Conclusion: During a single LJ, the longitudinal midsole stiffness did not influence the jump height and MTP joint biomechanical patterns but significantly increased the maximum power, EA, and EG during the push-off phase of the ankle joint. These preliminary results indicate that wearing SS could change the ankle joint mechanical patterns by modulating the lower extremity kinetic chain, and may enhance muscle strength in the ankle.  
Keywords: Stiff midsole; lower extremity; layup jumping; metatarsophalangeal joint; kinematics; kinetics +
Footnote †: journal: Applied Sciences