esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### ADMET Profiling  
Understanding the ADMET characteristics of compounds is crucial for rational drug development and has become an important part of drug discovery in the search for new therapies. These properties include several kinds of characteristics that control the fate of ligands in biological systems, impacting their effectiveness and safety. Clarifying the ADMET profile of ligands can accelerate the drug development process, enabling the identification of candidates with optimal pharmacokinetic profiles and reduced toxicity liabilities. The predicted ADMET properties of the 15 compounds are listed in Table S2. The ADMET properties of the top five compounds are listed in Table 5.  
Absorption, the initial step in drug bioavailability, dictates the extent to which a ligand enters systemic circulation following administration. The absorption properties of the small molecules were assessed via (1) water solubility (in log mol/L), (2) Caco-2 permeability (in log Papp in \(10^{-6}\) cm/s), and (3) human intestinal absorption (in % absorbed). The water solubility shows molecule solubility in water at 25 \({}^{\circ}\)C. Caco-2 is a human intestinal mucosa method that measures the absorption range of oral drugs. Intestinal absorption measures the proportion of compounds observed in the small intestine in humans. A compound is considered poorly absorbed if it has low intestinal absorption (<30%). The predicted values of water solubility indicate that ZINC82121447, ZINC02412146, and ZINC02170552 have moderate solubility while ZINC65274016 and ZINC10763862 are soluble. ZINC02412146 shows lower Caco-2 permeability and others indicate good permeability comparatively. ZINC65274016 and ZINC10763862 show excellent values for intestinal absorption, which is 90% for oral drugs, and ZINC02170552 shows 50% intestinal adsorption. Distribution encompasses the dissemination of ligands throughout bodily tissues, influenced by factors such as (1) Fu (human fraction unbound); (2) BBB (logBB) permeability; \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & \(\mathbf{\Delta E_{vdw}}\) & \(\mathbf{\Delta E_{elec}}\) & \(\mathbf{\Delta G_{pb}}\) & \(\mathbf{\Delta G_{np}}\) & \(\mathbf{\Delta G_{bind}}\) \\ \hline ZINC82121447 & \(-92.04\pm 6.86\) & \(-872.1\pm 1.3\) & \(863.04\pm 8.2\) & \(-12.9\pm 0.2\) & \(-114.03\pm 10.8\) \\ ZINC02170552 & \(-132.7\pm 3.1\) & \(218.7\pm 11.1\) & \(-153.5\pm 16.9\) & \(-15.71\pm 0.1\) & \(-83.2\pm 20.4\) \\ ZINC65274016 & \(-165.36\pm 3.9\) & \(-59.02\pm 5.2\) & \(165.36\pm 0.62\) & \(-16.17\pm 0.08\) & \(-75.24\pm 8.4\) \\ ZINC10763862 & \(-168.37\pm 6.5\) & \(-121.9\pm 5.8\) & \(233.28\pm 4.7\) & \(-16.26\pm 0.3\) & \(-73.35\pm 8.4\) \\ ZINC02412146 & \(-158.42\pm 6.1\) & \(-50.07\pm 3.4\) & \(157.87\pm 6.3\) & \(-17.09\pm 0.2\) & \(-67.72\pm 9.4\) \\
6 MZ & \(-137.79\pm 14.1\) & \(-72.43\pm 35.3\) & \(190.8\pm 43.8\) & \(15.31\pm 1.0\) & \(-34.75\pm 16.4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MM/PBSA results for the considered putative hits. Listed energies are in kJ/mol. The uncertainty is measured with a standard deviation.
## 2 Experimental Descriptions  
### Cryostat Design  
We designed and constructed a set of LHe-free cryostats which can perform vertical and horizontal testing of superconducting cavities. Figure 1 shows the picture of the cryostat. Five G-M cryocoolers manufactured by the Sumitomo Corporation are installed on top of the cryostat, including four 4 K cryocoolers (SRDK-418D, 1.8 W at 4.2 K) and one 10 K cryocooler (SRDK-408R, 5.4 W at 10 K). To reduce heat leakage at 4 K, two thermal shields made by OFC are designed, which are cooled by two stages of the 10 K cryocooler. Two shields are respectively suspended on the top plate of the cryostat by four garolite rods, and the side and bottom of the outer thermal shield are wrapped with 10 layers of multi-layer insulation (MLI). Two magnetic shields are designed to reduce the residual magnetic field in the cavity area and are placed inside the two thermal shields, respectively. The superconducting cavity is mounted on a garolite base that hangs from the top of the inner thermal shield via four titanium rods. A total of 13 silicon diode thermometers (DT-670-CU, Lake Shore Cryotronics, Westerville, OH, USA) are installed in the cryostat to measure the temperature of the cavity and other locations. RF power is coupled into and out of the cavity via two RF cables. The cryostat is equipped with a dry scroll pump (nXDS10i, Edwards China, Shanghai, China), a turbomolecular pump (nEXT240D, Edwards China, Shanghai, China), and a wide-range vacuum gauge (WRG-S, Edwards China, Shanghai, China). In the test of the NS03 cavity (Part 4), 3 cryogenic flux-gate magnetometers were installed inside the cryostat to monitor the magnetic field variations, and four heaters were also installed to control the cooling rate of the cavity.
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
###### Abstract  
Energy efficiency is a primary design goal for future green wireless communication technologies. Multiple-input multiple-output (MIMO) schemes have been proposed in the literature to improve the throughput of communication systems, and they are expected to play a prominent role in the upcoming fifth generation (5G) standard. This paper presents a novel, high-efficiency MIMO decoder based on the K-Best algorithm with lattice reduction. We have designed a novel hardware architecture for this decoder, which was implemented using 32 nm standard CMOS technology. Our results show that the proposed decoder can achieve on average a four-fold reduction in the power costs compared to recently published designs for 5G networks. The throughput of the design is 506 Mbits/s, which is comparable to existing designs.  
Sphere Decoding (SD); Lattice Reduction; K-Best Algorithm; MIMO; Green Communication; Low Power; VLSI; Hardware Design +
Footnote †: journal: Low Power Electronics and Applications
## 4 Discussion
In this study, the observed chemical changes in zirconia, LD glass ceramic, and VITA _En_ can be attributed to a combination of confined surface reactions, the adsorption of carbon and oxygen from the ambient environment, and the intercalation of carbon from laser-induced plasma, all facilitated by the high-energy conditions created by _fs_-laser pulses. However, this study has several limitations, which may affect the accuracy, interpretation, scalability, and generalized outcomes of the findings. Some of these potential limitations arise from the fact that the study probed the uppermost laser-irradiated layer only using SEM and EDS. Other surface analytical methods, such as Fourier-transformed infrared spectroscopy, were not undertaken. Hence, the present study did not capture changes occurring deeper within the sample materials. Surface artifacts or elemental entrapments (e.g., carbon deposition from the ambient environment) may not be representative of the behavior of the bulk material. Moreover, the spatial resolution of SEM and EDS might not be sufficient to detect subtle chemical changes induced by _fs_-laser irradiation, especially at the nanoscale. This study used three representative dental ceramic materials of different types; however, it is not possible to directly extrapolate the findings to other dental ceramic materials, as these will differ in their chemical compositions, crystalline structures, phase compositions, and physical properties. It will be necessary to test individual materials to determine their ablation thresholds and response to _fs_-laser processing. The present study undertook laser processing in normal room air, so the results could be influenced by the atmospheric composition as well as by the ambient temperature and humidity. There are also issues around the intrinsic variability of experimental conditions, such as laser power stability, beam alignment, and/or sample positioning, which all could affect the outcomes. Finally, the scope of the present study excluded exploring the impacts of _fs_-laser processing on biological responses, such as how cells attach and spread on laser-processed dental ceramics. Such issues can be explored in future studies.
## 3 Results
### Spatiotemporal Characteristics of NDVI
#### 3.2.2 LULC Spatiotemporal Change  
The LULC changes in the coastal region of the Guangdong Province show a phased pattern (Figure 6). The transfer map shows that forest land remains dominant, especially in the west and north (Figure 6a-d). The second is the Pearl River Delta and the southwest plain, where the cultivated land remained relatively unchanged. The temporal periodicity is reflected in the relatively small changes in the LULC from 2000 to 2015, and the relatively drastic changes from 2015 to 2020, which did not change the LULC structure (Figure 6e).  
Figure 5: LULC spatiotemporal distribution from 2000 to 2020. (**a**) LULC distribution and area proportion in 2020, (**b**) 2005, (**c**) 2010, (**d**) 2015, and (**e**) 2020.  
The LULC transfer area from 2015 to 2020 was relatively large, with arable land transferred to forest land (7090 km\({}^{2}\)) and forest land transferred to arable land (6914 km\({}^{2}\)). In addition, the transfer of arable land to built-up land (3279 km\({}^{2}\)) and the transfer of built-up land to arable land (3040 km\({}^{2}\)) also had a relatively large area.  
Figure 6: LULC spatiotemporal change from 2000 to 2020. (**a**) Distribution of LULC changes from 2000 to 2005, (**b**) 2005–2010, (**c**) 2010–2015, and (**d**) 2015–2020. (**e**) Sankey diagram visualizes the LULC transition matrix.  
The dynamic degree of LULC changes shows that only built-up land maintained a positive value, indicating that the built-up land area continues to increase, but the increase rate gradually slows down (Table 2). The single dynamic degree of LULC changes shows that from 2000 to 2005, the dynamic degrees of forest land (\(-0.105\)), grassland (\(-0.773\)), and water bodies (\(-0.412\)) were within \(-1\), while the dynamic degrees of cultivated land (\(-1.06\)) and unused land (\(-1.25\)) exceeded \(-1\), indicating relatively significant changes in the LULC. Compared to the previous stage, the dynamic degree of LULC changes from 2005 to 2010 was relatively low, manifesting in lower dynamic changes (below \(-0.5\)) in cultivated land (\(-0.329\)), forest land (\(-0.055\)), grassland (\(-0.322\)), and water bodies (\(-0.271\)). From 2010 to 2015 and 2015 to 2020, in addition to built-up land, grassland (0.599) and water bodies (0.187) also had positive values, respectively. The dynamic degree of other LULC types in these two stages was still relatively low (below \(-0.5\)). Throughout the entire research period from 2000 to 2020, the single dynamic degree of LULC changes indicates that in the past 20 years, the LULC changes in the study area have been characterized by a sustained and rapid increase in built-up land, a rapid decrease in cultivated and unused land, and a moderate decrease in grassland and water bodies, with the slowest decrease seen in forest land. The comprehensive dynamic degree of the LULC changes from 2015 to 2020 (97.887) was significantly higher than from 2000 to 2015 (below 2.614), and the overall dynamic degree of the entire study period was relatively high (106.306), indicating that the LULC changes here are intense.
## 5 Vector Tracking Loop Architectures
#### Vector Tracking Module
The clock bias and drift are expressed as:  
\[b_{r,k+1}=b_{r,k}+\Delta T\big{(}d_{r,k+1}-d_{r,k}\big{)}, \tag{10}\]  
\[\hat{x}_{k+1}=x_{k}+\Delta T\;v_{k}, \tag{1}\]0  
The change in the clock drift \(\Delta d_{r,k}\) can be modeled as WGN because GPS oscillators are of a very high quality and usually have a stable clock drift over a short period. According to Equations (8)-(11), the state model of the system can be represented in the matrix form as [16]:  
\[\left[ \begin{array}{c}\delta x_{k+1}\\ \delta y_{k+1}\\ \delta z_{k+1}\\ b_{r,k+1}\\ \delta v_{x,k+1}\\ \delta v_{y,k+1}\\ \delta v_{z,k+1}\\ \Delta d_{r,k+1}\end{array} \right]=\left[ \begin{array}{cccccc}0&0&0&0& \Delta T&0&0&0\\ 0&0&0&0&0&\Delta T&0&0\\ 0&0&0&1&0&0&0&\Delta T\\ 0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0\end{array} \right]\left[ \begin{array}{c}\delta x_{k}\\ \delta y_{k}\\ \delta z_{k}\\ b_{r,k}\\ \delta v_{x,k}\\ \delta v_{y,k}\\ \delta v_{z,k}\\ \Delta d_{r,k}\end{array} \right]+W_{k}, \tag{12}\]  
where \(W_{k}\) is WGN. The measurements of the system are the outputs of the code and carrier discriminators which are expressed by Equations (6) and (7). Thus, the measurement model is represented in the matrix form as [16]:  
\[\left[ \begin{array}{c}E^{1}_{\text{code},k}\\ E^{2}_{\text{code},k}\\ \vdots\\ E^{M}_{\text{code},k}\\ E^{1}_{\text{carrier},k}\\ E^{2}_{\text{carrier},k}\\ \vdots\\ E^{M}_{\text{carrier},k}\\ \vdots\\ E^{M}_{\text{carrier},k}\end{array} \right]=\left[ \begin{array}{cccccc}1^{1}_{x,k }&1^{1}_{y,k}&1^{1}_{z,k}&1&0&0&0&0\\ 1^{2}_{x,k}&1^{2}_{y,k}&1^{2}_{z,k}&1&0&0&0&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1^{M}_{x,k}&1^{M}_{y,k}&1^{M}_{z,k}&1&0&0&0&0\\ 0&0&0&0&1^{1}_{x,k}&1^{1}_{y,k}&1^{1}_{z,k}&1\\ 0&0&0&0&1^{2}_{x,k}&1^{2}_{y,k}&1^{2}_{z,k}&1\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0&0&0&0&1^{M}_{x,k}&1^{M}_{y,k}&1^{M}_{z,k}&1\end{array} \right]\left[ \begin{array} []{c}\delta x_{k}\\ \delta y_{k}\\ \delta z_{k}\\ b_{r,k}\\ \delta v_{x,k}\\ \delta v_{y,k}\\ \delta v_{y,k}\\ \delta v_{z,k}\\ \Delta d_{r,k}\end{array} \right]+\varepsilon_{k}, \tag{13}\]
# Responses of Forest Carbon Cycle to Drought and Elevated CO2
## 3 Elevated CO2 Effect on Plants and Ecosystem
### How Plants Respond to Elevated CO2
#### 3.1.3 Photosynthetic Acclimation  
The photosynthetic rate is accelerated by short-term CO\({}_{2}\) increases, but plants may eventually adapt to elevated CO\({}_{2}\) atmosphere in the long-term. Plants grown in long-term elevated CO\({}_{2}\) will experience photosynthetic down-regulation, accompanied by higher concentrations of carbohydrates, lower concentrations of soluble proteins and Rubisco than those grown in the normal ambient atmosphere [4]. The stomatal acclimation to elevated CO\({}_{2}\) shows a significant downtrend in stomatal conductance, which is also associated with photosynthetic acclimation [45; 51]. Nevertheless, the decline in \(g_{s}\) is not considered the dominant reason for the down-regulation of photosynthesis because there is no obvious difference in Ci/Ca between plants grown in elevated and ambient CO\({}_{2}\)[4; 44]. The mechanism of acclimation of photosynthesis and stomata behavior is not completely understood, but there are several common possibilities. First, plants are unable to use extra carbohydrates produced by CO\({}_{2}\)-stimulated photosynthesis, which then trigger the feedback mechanism that inhibits carbohydrate production at the source [4; 45]. Second, the content and activity of Rubisco decrease under elevated CO\({}_{2}\)[4; 52]. While there is no widespread agreement that photosynthesis will acclimate to elevated CO\({}_{2}\), it is hasty to consider photosynthetic acclimation as the eventual result of the plant response to elevated CO\({}_{2}\), especially at ecosystem scale.
## 4 Experiments
### Metrics
Therefore, the energy consumed by the monitoring network for transmitting the captured packets from the target network is determined by Equation (8):  
\[Psent=Pcaptured+Predundant \tag{7}\]  
\[Et=\ 38,42\times 10^{-6}\times(Pcaptured+Predundant) \tag{8}\]  
When the aggregation of headers is used, as explained in Section 3.1.2, the headers of several packets of the target network are sent in the same monitoring message in order to reduce the energy consumption of the sniffers. In our experiments, the monitoring message carries the address of the sniffer (1 byte) and, the timestamp (2 bytes) and the header (11 bytes) of two packets of the target network. Therefore, in the experiment \"_election with aggregation_\" each packet sent by the sniffers has size (\(Plength\)) of 32 bytes, with 05 bytes of header and 27 bytes of the monitoring message (see Figure 5). In this experiment, the number of packets sent by the sniffers is determined by Equation (9). By replacing \(Psent\), determined by Equation (9), and \(Plength\) equal to 32 in Equation (6), we obtain Equation (10), which corresponds to the energy consumed by the monitoring network for transmission of captured packets when the aggregation of headers is used:  
Figure 12:0  
Figure 12:1  
The average energy consumed by each sniffer for transmission of captured packets (\(EtSniffer\)) is determined by Equation (11), where \(N\) is the number of sniffers and \(Et\) may be calculated through Equation (8) or (10) according to the type of experiment:  
Figure 12:2  
To exemplify how the variables used in Equations (1)-(11) affect the energy consumption, Table 6 shows the values of each variable for a single round with duration of 15 minutes for each type of experiment for a monitoring network with 11 sniffers (Figure 12e). It can be observed in Table 6 that the number of redundant packets captured by the monitoring network is zero when the election mechanism proposed in this paper is used. This occurs because in general only one sniffer captures packets sent by a given node of the target network, as explained in Section 3.1.1. \begin{table}
\begin{tabular}{c c c c} \hline  & & \multicolumn{3}{c}{**Type of Experiment**} \\
**Variable** & **without Election** & **Election without Aggregation** & **Election with Aggregation** \\ \hline _PsentTarget_ & 325 & 324 & 324 \\ _Pcaptured_ & 316 & 312 & 316 \\ _\%Pcaptured_ & 97.23\% & 96.30\% & 97.53\% \\ _Predundant_ & 679 & 0 & 0 \\ _Et_ (mJ) & 38.23 & 11.99 & 8.45 \\ _EtSniffer_ (mJ) & 3.48 & 1.09 & 0.77 \\ \hline \end{tabular}
\end{table} 
Table 6: Values of variables for a single round with 11 sniffers.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 2 System and Theoretical Model  
As shown in Figure 1, we consider a mixed RF/FSO UAV dual-hop communication system based on MRR and RIS, where MRR is installed at the UAV relay and RIS acts as the RF source (i.e., the user) put together with the RF signal generator. Considering scenarios where the user end is indoors, or outdoors where it is obscured by buildings, trees, etc., we use the Rayleigh distribution commonly used in multipath fading without direct line of sight in the RF channel [29]. For the FSO link, the classical Gamma-Gamma distribution was chosen because of its good fit with measurements over a wide range of turbulent conditions (weak to strong) [30]. The end-to-end uplink of the system is the transmission of data from the user to the destination (i.e., User \(\rightarrow\) Relay \(\rightarrow\) Destination), while the end-to-end downlink is the reverse (i.e., Destination \(\rightarrow\) Relay \(\rightarrow\) User). At the UAV relay, the decode-and-forward (DF) protocol is adopted. The addition of MRR allows the ATP system and laser transmitter to exist only at the destination, thus reducing the weight, size, and power consumption of the UAV relay. Moreover, the RIS in the RF link, with many passive reflective elements to enhance the signal, provides a significant improvement in system performance.  
Figure 1: A mixed RF/FSO UAV communication system based on MRR and RIS.  
In this system, taking the end-to-end uplink as an example, the signal received at the relay is expressed as \(y_{1}=h_{1}x+n_{1}\), and the signal received at the destination is denoted as \(y_{2}=h_{2}y_{1}+n_{2}=h_{2}(h_{1}x+n_{1})+n_{2}\), where \(x\) is the signal source, \(h_{1}\) and \(h_{2}\) are the channel coefficients of RF and FSO links, \(n_{1}\) and \(n_{2}\) are the additive Gaussian white noise (AWGN). Furthermore, the working principle in the end-to-end downlink is similar. In the downlink, the information received by the user is also related to the channel coefficients of the RF and FSO links.
## 4 Discussion
On the other hand, if the two probes have completely separate spectra, the possibility of unwanted excitation of the tau probe when one wants to excite only the Ab probe will be significantly reduced. This phenomenon is one of the problems we had to deal with during our experiments. The absorption spectra of Cy7 and Cy7.5 partly overlapped, causing excitation of both probes together when we needed to measure the fluorescence of each one of them separately. Despite the use of filters for fluorescence emission collection, the combined excitation increased the intensity of the measured fluorescence and possibly contributed to some of the errors. One of the main advantages of the proposed method, compared to other methods, which measure concentrations in vivo, is that it does not require a priori knowledge regarding the tissue anatomical and optical properties. It is true that measuring the concentrations of amyloid beta and \(\alpha\)- tau directly from a collected CSF sample will probably result in a better accuracy. However, this will require performing a lumbar puncture procedure. In our method, as opposed to the lumbar puncture, there is no need to penetrate the dura and collect CSF samples, and the risks and side effects are significantly reduced. These risks may be further reduced by adding a feedback system to our method, which will provide real-time information regarding the location of the needle and its distance from the dura matter and the CSF. This feedback system, as we have shown, is based on the time-resolved intensity curves, by extracting the values of t\({}_{\text{max}}\) and the slope, thus enabling accurate estimation of the absolute value of the needle distance from the CSF. The accuracy of the estimation may be increased by taking multiple time-resolved intensity measurements during the insertion of the needle into the lumbar section. The needle distance may then be estimated without a priori knowledge of the tissue's optical properties. In addition, once a fluorescence signal is detected, with a sufficient intensity that ensures a good signal to noise ratio, further insertion of the needle may be stopped. The system will evaluate the concentration ratio and the needle distance from the CSF and may indicate to the physician how to continue. If the distance is big enough and the signal to noise ratio is still poor, the system will indicate to the user to continue to push in the needle. In case that the estimated distance between the needle and the CSF is too small the system will indicate the physician to stop additional insertion in order to reduce the risk of dura penetration. We demonstrated the ability to accurately estimate the concentration ration of two fluorescent probes underlying a tissue with a thickness of up to 5 mm. Therefore, we assume that the system may alert the user of being close to the dura in an estimated distance of 3 mm, thus avoiding penetration of the dura even with distance estimation errors of several hundreds of percent but with estimating the concentration ratio with a rather high accuracy.
## 3 Link Level Capacity Expressions  
This section presents the link level capacity for different candidate MN MIMO transmission algorithms in mobile WMSNs. We use the term link level capacity to denote the ergodic mutual information assuming that equal power allocation is applied at the transmitter [35]. Essentially, we assume that the transmitter is uninformed about the transmit or receive correlation and, thus, does not attempt to perform eigenmode adaptation or water-filling according to the solution of the optimum link level capacity in correlated channels [37]. We assume the perfect knowledge of the CIR at the receiver. The link level capacity under these assumptions for several different SN MIMO transmission algorithms has already been analyzed in [13, 14, 35, 36]. We extend these results to MN MIMO for SM with zero forcing receivers and OSTBC by explicitly accounting for the structure in the MN MIMO transmit correlation matrix in Equation (6).