esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Air Quality Evaluation  
The ability to evaluate and compare indoor air quality based on CO2 concentration is essential for this study. For this purpose, a KPI is introduced to quantitatively represent the estimated level of discomfort attributed to imperfect air quality. The KPI for a building space between two points in time, \(t_{0}\) and \(t_{1}\), is described by Equation (9), where \(P\) is the number of occupants, \(C_{S}\) is the space CO2 concentration and \(C_{T}\) is a chosen CO2 concentration threshold.  
\[AirKPI=\int_{t_{0}}^{t_{1}}P(t)\cdot Max(0,C_{S}(t)-C_{T})dt \tag{9}\]  
Consequently, CO2 levels below the threshold are attributed no discomfort and the KPI value scales linearly with number of occupants and with CO2 concentration above the threshold.  
CO2 concentration is generally considered the most adequate indicator for indoor air quality [19] but while a number of studies have explored correlation between indoor CO2 concentrations and discomfort/cognitive function, there is no consensus regarding an ideal concentration threshold for good air quality. In the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 62-1989 [20], a limit of 1000 ppm CO\({}_{2}\) was recommended to \"satisfy comfort (odor) criteria\". This has since become a widely used baseline for acceptable air quality; however, studies suggest that further lowering the CO\({}_{2}\) concentration is advisable. According to [PERSON] [21] \"concentrations of CO\({}_{2}\) as low as 700 to 800 ppm are often associated with complaints of poor air quality by occupants\" and a study by [PERSON] et al. [22] indicated a noticeable difference in test subjects performance between average concentrations of 486-609 ppm and average concentrations of 726-761 ppm. In this study, a CO\({}_{2}\) threshold of \(C_{T}\) = 700 ppm is used for the KPI calculations.
# A Convenient Way to Determine the Optimum Angle of Incidence of Fizeau Interferometer
## 2 Analysis of Spatial Light Intensity Distribution  
The interference intensity distribution made by predecessors is based on the simple wedge structure, and the influence of the specific composition structure of the wedge cavity on the spatial interference intensity is not considered [18; 19]. Based on the specific design structure of our spectrometer, since the parallel plate close to the telescope system is a non-focal system and does not participate in the interference optical path, this paper does not analyze the contribution of this parallel plate to the optical path. The parallel plate near the focal plane array is directly involved in the interference light path, which leads us to analyze the influence of the parallel plate on the interference light intensity distribution. As shown in Figure 2, an ideal collimated beam enters the interferometer, and the angle between the incident beam and the normal on the back of the wedge cavity is the incident angle \(\theta\). Here, it is specified that the angle of clockwise rotation of the optical axis to the incident direction is negative; conversely, \(\theta\) is positive. The incident plane wave is repeatedly reflected into a series of beams in the wedge cavity. These plane waves are in phase with the beams mentioned above, but their propagation directions are twice \(\alpha\) different from each other, and the strength decreases exponentially. However, due to the influence of the parallel plate on the optical path, the light is refracted at the interface between the parallel plate and the air, and the angle between the incident light and the mth outgoing light is no longer a simple 2m\(\alpha\). When the beam exits from the parallel plate, due to the non-focus of the parallel plate, the direction of the outgoing beam is consistent with the direction of the beam incident on the parallel plate. However, since the beam passes through the parallel plate, the spatial interference intensity distribution of a point in space is related to the thickness and refractive index of the parallel plate. Here, we assume that a point in space is \(P(z_{0},y_{0})\). The amplitude distribution of point P is only related to the relative phase difference between the reflected wavefront. It is assumed that the optical path from the incident wavefront and the optical path from the mth outgoing wavefront to P is \(L_{0}\) and \(L_{m}\), respectively. Both reflectivities in the wedge cavity are R, and the thickness of the parallel plate is d. When P is inside the wedge cavity \((0\leq z_{0}\leq d)\), the optical path expression from the incoming wavefront and the mth outgoing wavefront to point P can be calculated, respectively. \[L_{0}\ =\ z_{0}\Big{(}n^{2}-sin^{2}\theta\Big{)}^{\frac{1}{2}}+y_{0}sin\theta \tag{1}\]  
\[L_{m}\ =\ z_{0}\Big{(}n^{2}-sin^{2}(\theta+2 ma)\Big{)}^{\frac{1}{2}}+y_{0}sin( \theta+2m\alpha) \tag{2}\]  
where \(n\) is the refractive index of the parallel plate, and \(\alpha\) is the apex angle of the wedge cavity.
## 2 Beamsteering System Concept and PAA's Simulation Model
### PAA Simulation Model  
The PAA operation was simulated by relative amplitudes and phases. The array coefficient for the PAA was calculated with:  
\[A(\theta)=\sum_{j=0}^{n-1}\omega_{j}e^{j\mathrm{id}\sin\theta},\omega_{j}=1\cdot e ^{-j\cdot i\Delta\phi}=\exp\left(-j\cdot ikd\sin\theta\right) \tag{1}\]  
where \(\omega_{j}\) is the complex weight of an emitter, \(k=\frac{2\pi}{\lambda}\) is the wave number, \(d\) is the antenna array pitch, \(\theta_{main}\) is the tilt angle of the main lobe, \(n\) is the number of emitters, and \(i\) is the imaginary unit. The RP was calculated as the square of the modulus of the array coefficient.  
A correspondence between the sub-sector number, tilt angle \(\theta_{main}\), the required phase shift between emitters \(\Delta\phi\), and the eight-bit control code for the digital phase shifters is given in Table 1.  
The V2X PAA developed to solve the task posed in the study and presented in [45] was used to implement the simulation model. The parameters of the test PAA applied in the simulation:  
* Length of the antenna array is 0.388 m;
* The antenna array pitch is \(d=\lambda/2\approx 26\) mm;
* Number of emitters is 14;
* Scanning sector of the PAA in the horizontal plane \(\theta_{main}\) is \(\pm 45^{\circ}\);
* The main lobe width at half maximum in the horizontal plane is \(\Delta\theta_{0.5}=9^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sub-Sector Number** & \(\mathbf{\Theta}\)**, \(\mathbf{deg}\)** & \(\mathbf{\phi}\)**, \(\mathbf{deg}\)** & **Control Code** \\ \hline
1 & \(-40.5\) & \(-116.9\) & \(10100000\) \\
2 & \(-31.5\) & \(-94.05\) & \(10000000\) \\
3 & \(-22.5\) & \(-68.88\) & \(01100000\) \\
4 & \(-13.5\) & \(-42.02\) & \(01000000\) \\
5 & \(-4.5\) & \(-14.12\) & \(00010000\) \\
6 & 4.5 & \(14.12\) & \(00000001\) \\
7 & 13.5 & \(42.02\) & \(00000100\) \\
8 & 22.5 & \(68.88\) & \(0000110\) \\
9 & 31.5 & \(94.05\) & \(00001000\) \\
10 & 40.5 & \(116.9\) & \(00001010\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Encoding the sub-sector number in the eight-bit control code for the digital phase shifter.  
Figure 2: Beamsteering scenarios: (**a**) crossroad coverage in four directions, (**b**) covering a longer section of the road.  
The width of the main lobe was determined from the required time for the counter-emergency action of the driver in the case when the vehicle moves at a speed of 90 km/h in the direction perpendicular to the PAA's normal. If we take the average radius of the service area to equal 300 m, then, taking into account the required braking time in icy conditions of 2 s [46], the braking distance will be an average of 50 m, which corresponds to a sub-sector width of 9\({}^{\circ}\). As mentioned, the 360-degree operation requires four PAAs served by a single ML algorithm. Within the single PAA scanning sector, ten sub-sectors are allocated. The highest received signal's power determines the leading PAA for each connection. Figure 3 demonstrates the RP's shape corresponding to the generated binary control sequences.
## 4 Conclusion  
Many regions in Asia have experienced vegetation greening from 1982 to 2011. Trends in vegetation greening have been reproduced using multiple terrestrial biosphere models for most regions in Asia. The analysis in this study clearly showed statistically significant increases in the NDVI for almost 40% of the regions in Asia, whereas decreasing vegetation trends were found for only a small percentage of the studied regions (less than 5%). Among the nine sub-continental regions, seven regions showed statistically significant increases in the NDVI. Simulations using the terrestrial biosphere models showed vegetation greening trends that were consistent with the results for the NDVI in boreal and temperate regions.  
Three terrestrial biosphere models were used to determine the causes of the detected increasing trend in the NDVI. The determined contributions of climate and atmospheric CO\({}_{2}\) changes varied considerably among the models. Further assessment is required to determine the effect of CO\({}_{2}\) fertilization on GPP and assess the sensitivity of the models to climate anomalies.
###### Abstract
* Given the limitations of transmit power and EH need, the combined PA and PS control is mathematically formulated to maximise the sum rate. The remainder of the article is organized as follows. A system model is described in Section 2. Problem formulation and proposed solutions related to joint optimization are presented in Section 3. Section 4 presents a comparison associated with the complexity of the suggested as well as traditional precoders. In Section 5, multiple algorithms are simulated/evaluated, and a comparison analysis is done. Section 6 highlights the related challenges and future research direction. Section 7 has the conclusions.
## Materials and Methods
### In vitro Antibacterial Activity Using Disc Diffusion Method  
The following standard bacterial strains were used in this study belonging to Gram-positive and Gram-negative species: _Bacillus subtilis_ (_B. subtilis_), _Escherichia coli_ ATCC 25,922 (_E. coli_), _Staphylococcus aureus_ ATCC 25,923 (_S. aureus_), _Pseudomonas aeruginosa_ ATCC 27,853 (_P. aeruginosa_), _Streptococcus pneumoniae_ ATCC 49,619 (_S. pneumoniae_), _Salmonella typhi_ Ty 2 (_S. typhi_), _Vibrio choleraea_ AI 4450 (_V. choleraea_), _Shigella flexneri_ NCDC 2747-71 (_S. flexneri_) and cultured on specialized media overnight. They were obtained from the laboratory of Ho Chi Minh City Pasteur Institute, Vietnam. Due to their ability to survive in harsh conditions and their multiple environmental habitats, these bacterial organisms including Gram-positive and Gram-negative are the main sources of severe infections in humans.  
The antibacterial activity of _E. hirta_ extracts was investigated using the disk diffusion method with some modifications [35; 36; 37]. The bacterial suspension phase in physiological saline reaches a concentration of McFarland (0.5%) (equivalent to 10\({}^{8}\) CFU/mL). Preparation of Mueller-Hinton (MH) medium plates were prepared containing 5% of sheep blood after being allowed to dry before inoculating bacteria. The bacterial suspension was spread evenly over the surface of MH agar. Thereafter, we conducted holes in the surface of wells, the wells were 20-30 mm apart, and had a diameter of 6 mm. The diluted samples at five different concentrations of 25, 50, 100, 200 and 400 \(\mu\)g/mL for five extracts in DMSO solvent were prepared to add to each well of every disc. All discs were incubated at 37 \({}^{\circ}\)C for 18-20 h. Each concentration was repeated 2 times, checking for bacterial growth. The negative control was the same volume of sterilized water. The diameter (in millimeters) of the inhibition zone was measured to estimate the antibacterial activity of _E. hirta_. The inhibition zone was defined as where bacteria cannot grow.
## 2 Material and Methods
### Analytical Method and Quality Control
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metal** & **Material** & **Concentration Found (mg/kg)** & **Certified Concentration (mg/kg)** & **R (\%)** \\ \hline Na & & 0.242 \(\pm\) 0.006 & 0.238 \(\pm\) 0.010 & 98.3 \\ K & & 0.994 \(\pm\) 0.002 & 0.936 \(\pm\) 0.007 & 94.2 \\ Ca & & 116 \(\pm\) 4 & 111.1 \(\pm\) 8.5 & 95.8 \\ Mg & & 601 \(\pm\) 28 & 559.5 \(\pm\) 46 & 93.1 \\ \hline Al & & 286 \(\pm\) 9 & 2851 \(\pm\) 26 & 99.7 \\ B & & 27.0 \(\pm\) 2.0 & 27.0 \(\pm\) 1.5 & 99.9 \\ Mo & & 0.09 \(\pm\) 0.01 & 0.09 \(\pm\) 0.02 & 99.4 \\ Sr & & 25.0 \(\pm\) 2.0 & 24.6 \(\pm\) 4.0 & 98.3 \\ \hline Ba & & 1.10 \(\pm\) 0.10 & 1.13 \(\pm\) 0.09 & 102.5 \\ Ni & & 0.37 \(\pm\) 0.02 & 0.38 \(\pm\) 0.04 & 102.3 \\ Pb & & 0.044 \(\pm\) 0.000 & 0.044 \(\pm\) 0.013 & 98.9 \\ \hline Cd & & 0.026 \(\pm\) 0.002 & 0.026 \(\pm\) 0.008 & 98.4 \\ Co & & 0.006 \(\pm\) 0.00 & 0.006 \(\pm\) 0.002 & 102.4 \\ Cu & & 2.1 \(\pm\) 0.2 & 2.09 \(\pm\) 0.4 & 99.7 \\ Fe & & 14.1 \(\pm\) 0.5 & 13.9 \(\pm\) 0.3 & 98.9 \\ Mn & & 9.4 \(\pm\) 0.9 & 9.6 \(\pm\) 1.5 & 102.4 \\ V & & 0.011 \(\pm\) 0.00 & 0.011 \(\pm\) 0.00 & 99.4 \\ Zn & & 11.6 \(\pm\) 0.4 & 11.9 \(\pm\) 0.2 & 102.7 \\ \hline Li & Standard Addition Method & 0.2 \(\pm\) 0.02 & 0.19 \(\pm\) 0.03 & 95.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recovery percentage found for the reference materials used. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Elements** & **Instrumental Wavelengths (nm)** & **LOQ (mg/L)** \\ \hline Al & 167.0 & 0.012 \\ B & 249.7 & 0.012 \\ Ba & 455.4 & 0.005 \\ Ca & 317.9 & 1.995 \\ Cd & 226.5 & 0.001 \\ Co & 228.6 & 0.002 \\ Cu & 327.3 & 0.012 \\ Fe & 259.9 & 0.009 \\ K & 769.9 & 1.884 \\ Li & 670.8 & 0.013 \\ Mg & 279.1 & 1.943 \\ Mn & 257.6 & 0.008 \\ Mo & 202.0 & 0.002 \\ Na & 589.6 & 3.655 \\ Ni & 231.6 & 0.003 \\ Pb & 220.3 & 0.001 \\ Sr & 407.7 & 0.003 \\ V & 310.2 & 0.005 \\ Zn & 206.2 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Instrumental parameters and quantification limits of the analysis method. Finally, when measuring each sample, two replicates were analyzed, obtaining from each of them a mean concentration and a % RSD value for the quantifiable intervals of the method. To take a measurement as valid, a %RSD \(\leq\) 10% was established.
## 3 Ground Motion Selection  
The characteristics of any ground motions are expressed by some significant engineering factors such as amplitude (i.e., peak ground acceleration, PGA), predominant frequency or duration, etc. In the case of PGA, it just reflects the peak value of acceleration data, and it is difficult to state that an earthquake with greater PGA value has more effects than the lower ones. Besides that, predominant frequency is a crucial factor, which can be used for assessing the dynamic characteristics [57], especially for the performance of the frequency-sensitive electrical components in the electric cabinet.  
According to [PERSON] et al. [15], the high-frequency ground motions can amplify the structural response of a dominant high-frequency structure as the cabinet. Thus, the frequency content of the earthquake is considered in this study. Eighty records of ground motion are chosen from the Pacific Earthquake Engineering Research (PEER) center and historical earthquakes in Korea. These data are classified into two groups based on the different frequency contents, including 40 records are in the high-frequency, and the rest 40 records are in the low-frequency group. All data are scaled to a given value of PGA (0.5 g) and used as the input ground motion for the numeric model.  
The response spectrum can be used to identify the frequency content type of seismic excitation. In this study, the predominant frequency of a ground motion can be estimated by evaluating the predominant period. This is the period at which the maximum spectral response is observed. The threshold value of 10 Hz is used to distinguish the low- and high-frequency motions. In other words, if the dominant frequency of an earthquake is approximately smaller than 10 Hz, it is considered as low-frequency excitation. In contrast, it is considered a high-frequency motion. Response spectra of two groups of earthquakes corresponding to high- and low-frequency are shown in Figure 8.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
## 4 Data Processing and Analysis
### Original Data Analysis  
As shown in Table 2, the temperature value and the atmospheric pressure value measured by the prelaunch rocket sonde are consistent with those of the balloon sonde and the surface automatic weather station.  
The temperature readings from the ground meteorological station and balloon sonde register ambient temperature, while those from the rocket sonde indicate the temperature in the fairing. As such, it is quite reasonable that there exist some differences in the measurements recorded by the rocket sonde and the meteorological station, as well as by the balloon sonde.  
Figure 2 gives the comparison between the temperature measured by rocket sonde and balloon sonde in their coincident height range. From the figure, we can find out roughly similar trend between the two groups of temperature data below 12 km. In the range of 12-15 km above the ground, the temperature measured by rocket sonde is little higher than that gauged by the conventional sounding system. According to the previous studies made by [PERSON] et al. (2017), this is mainly because the sounding balloon is released one hour before that of the rocket, and the atmospheric state may change during this period of time, resulting in a difference in the temperature data measured by both.  
Figure 3 gives the comparison between the atmospheric pressure measured by rocket sonde and by balloon sonde in the coincident stage. It is noted that the two curves basically coincide with one another.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Surface Weather Station** & **Balloon Sonde** & **Rocketsonde** \\ \hline Temperature (\({}^{\circ}\)C) & 19.7 & 19.6 & 19.3 \\ Air pressure (hPa) & 854.3 & 854.0 & 854.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prelaunch comparison (atmospheric pressure and temperature).  
Figure 3: The atmospheric pressure profiles (0–20 km) obtained from the rocket sonde (red) and the balloon sonde (blue).  
Figure 2: The temperature profiles (0–20 km) obtained from the rocket sonde (red) and the balloon sonde (blue).
## 4 Experimental Results
### Datasets and Implementation Settings  
We used the NYU Depth V2 dataset [32] and EgoGesture dataset [33] as our training datasets. NYU Depth V2 provides pairs of RGB images and the corresponding depth map for indoor scenes. The image and depth map resolution of the dataset were captured at 640 \(\times\) 480 from Microsoft Kinect, which renders a maximum sensible distance of 10 meters. We employed the official train/test split in our experiments, which comprises 12,000 training samples and 654 testing samples. The EgoGesture dataset provides RGB images and the depth map of diverse hand gestures. The dataset was gathered by Intel RealSense SR300 with a resolution of 640 \(\times\) 480. The dataset encompasses four indoor scenes and two outdoor scenes. In total, the dataset contains 2,953,224 hand gesture frames collected for the RGB and depth modality, respectively.  
Our experiments were implemented with Python 3.9, PyTorch 1.10.2 [34], CUDA 11.3 and cuDNN 8.2 on the Ubuntu 22.04 LTS operating system. We trained our network using a single NVIDIA GeForce RTX 4090 GPU with 24 GB of memory and an Intel i7-13700K CPU. During training, we utilized a batch size of 16 and an Adam [34] optimizer with (0.95, 0.99) betas and 0.1 weight decay. The proposed depth estimation network is trained over 50 epochs with the learning rate starting from 0.000357 and decaying 20% every 5 epochs.  
Figure 8: An example of the generated LRLQ depth map with \(p=8\), while the GT map captured by the ToF depth sensor on Jorjin J/EF Plus AR glasses appears very blurry.
# Super-Frequency Sampling for Thermal Transient Analysis
## 1 Introduction
To increase the temporal resolution and the signal quality, different approaches have been discussed in the literature. One potential solution is the use of multichannel sampling [12; 13]. Here, multiple sample trains of the same underlying signal are used, and the information included in each is merged. A variant of special interest is known as recurrent non-uniform sampling, block sampling, or periodic non-uniform sampling [14]. Given a sufficient amount of independent trains or channels, the signal is reconstructed very accurately. In practice, one way to implement this measurement procedure is either using multiple independent detectors and merging their signals or repeatedly measuring the signal using the same detector. In both cases, the offsets of each train or channel are, in general, unknown. In the case of repeated measurements with the same detector, the exact knowledge and stability of the period is crucial, in particular for a large number of repetitions. In this case, the technique of period estimation in undersampled signals by Rader is appropriate [15]. In this work, a periodic non-uniform sampling technique is pursued. To generate multiple trains of samples, the thermal transient measurement is repeated periodically. Subsequently, the thermographic data are recombined by taking the modulo with the signal period, determined by an approach based on [PERSON]'s algorithm [15]. Two variants are possible for the periodic repetition. The first approach is to reduce the frequency so that for each thermal transient, the thermal equilibrium is reached. Secondly, the relaxation times following each power step overlap, allowing for the true signal to be reconstructed using the reconstruction algorithm presented by [PERSON] et al. [16]. As a result, it is possible to increase the temporal resolution of transient thermographic measurements up to approximately the integration time, as presented by [PERSON] et al. [17]. For the setup described in this work, an increase in temporal resolution by approximately one order of magnitude is achieved, i.e., from 1 ms to 0.1 ms.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 5 Point-of-Care Clinical Diagnostics in HF  
In the clinical setting, the detection of individual biomarkers or a combined analysis thereof are promising tools to support the manifestation and diagnosis of cardiac diseases, such as HF. The ACC/Aha/HFSA guidelines (2017) (2017) (2017) have also recommended the measurement of additional biomarkers, such as sST-2 and galectin-3, for the risk assessment in HF. Although multiple assays are available to detect a vast number of biomarkers, they are most often rather expensive and time-consuming. For instance, as far as most enzyme-linked immunosorbent assays (ELISA) are concerned, several hours or one entire day are necessary to obtain the assay results, depending on the kit used. However, in point-of-care clinical diagnostics, it is crucial to obtain reliable results within a short time range. Multiple companies have addressed this challenge and have already successfully rolled out some rapid tests for biomarker analysis.