esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion for Actual Implementations
The multiplexer output is sent directly to the clock and data recovery module [24]. In the case where the signal on the LOS detector has been blocked, the \(\upmu\)C can send a signal to the RF uplink unit requesting that the VLC transmitter increase or decrease the transmission modulation depth [24]. Unlike in a simulation, the required transmit channel knowledge for the weight vector adaptation is not perfect. A training sequence with one of the following channel estimation methods, _i.e._, the correlation method, the least square (LS) method or the minimum mean square error (MMSE) method, can be used. The performance of the correlation method is the worst, the LS method is suitable for high SNR, and the MMSE method obtains the best performance with the highest complexity. Regarding the feedback of the beamforming, a feedback can be realized in the same way as the brightness control, _i.e._, the feedback signal is sent by the \(\upmu\)C through the RF uplink for adjusting the beamforming vectors in our system [28]. The feedback bits sent from the receiver to the transmitter for beamforming, when the transmitter does not have _a priori_ channel knowledge, a limited feedback method, is employed [29]. When implementing limited feedback beamforming, the beamforming vector is restricted to lie in a finite codebook that is known to both the transmitter and receiver. The receiver uses its channel estimate to choose the vector from the codebook that maximizes the conditional receive signal-to-noise ratio (SNR). The binary index of the chosen vector is then conveyed to the transmitter over a limited rate feedback channel. Grassmannian beamforming can be implemented with a lookup table format. When the channel is slowly varying, it may be possible to reduce the necessary number of bits sent back by using some successive refinement techniques based on channel correlation [29].
## 8 A Look-Ahead, Missing Functionality and Research Challenges
### Research Challenges  
#### 8.3.1 Accurate Estimation of Compute Resources  
A first open research question is achieving both (i) cost-effective use of node resources by means of co-locating workloads and (ii) meeting tail latencies of service-level objectives (SLOs) (Tail latencies refer to 99 th percentile of the response times of a service).  
Competitive Software-as-a-Service (SaaS) markets drive application providers to offer high-quality services while reducing operating cost. Today's SaaS applications are typically developed as multi-tiered applications that can be easily deployed using container orchestration frameworks. By taking advantage of fine-grained resource control features offered by these frameworks, application managers already have the capability of maximizing their resource utilization. However, mapping SLOs into resource allocations for containers is a difficult task. Especially with the trend from monolithic business tiers to micro-service architectures, it is a difficult task for application managers to allocate optimal resources quantities for the different micro-services [393].  
Therefore, an automated resource optimization approach is needed in order to assist the application managers and reduce human errors. However, existing techniques for resource optimization often require the specification of an accurate performance model representing the application [394; 395]. Moreover, SaaS providers might offer custom features and custom SLOs to different tenants, which complicates this modelling task. Therefore, what is needed is a generic, black-box optimization solution that does not require any model of the application or another type of domain expertise.
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 4 Simulation Validation
### Adjustment Algorithm Verification  
The pointing adjustment limit of the payload is tested first. The coarse actuators traversed the joint space boundary in a step of \(2\) mm so that the boundary of the workspace could be traversed in an ergodic way. The resultant angular coordinates are all expressed in the form of roll-pitch-yaw angles. The envelope shape and limit parameters are shown in Figure 6 and Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Angular Coordinates** & **Maximum/(deg)** & **Minimum/(deg)** \\ \hline Roll angle & \(9.60083\) & \(-8.97929\) \\ Pitch angle & \(9.7894\) & \(-10.6959\) \\ Yaw angle & \(16.6285\) & \(-14.4935\) \\ \hline \end{tabular}
\end{table}
Table 1: Workspace limit parameters of the pointing and stabilizing manipulator.  
Figure 5: The virtual prototype of the simulation validation.  
Figure 6: Envelope shape of the workspace of the pointing and stabilizing manipulator.  
It is seen that the limit of roll, pitch, and yaw angle exceeds \(\pm 8.5^{\circ}\), and the envelope shape has similar sizes in all directions. Therefore, it could be accepted that the pointing and stabilizing manipulator could adjust the pointing direction of the payload on a large scale.  
The algorithm of large-scale adjustment would be next to verify. Taking the actuator displacement as the input command, the response of the virtual prototype could be obtained from ADAMS. Then, the accuracy of the algorithm is checked by comparing the simulation results with the target angular coordinates. The target angular coordinates are first chosen from three planes and formed in a closed quadrilateral trajectory to enclose as many areas as possible. Then, a path of a random walk is chosen as the target trajectory to validate the algorithm in a fuller way.  
The comparison results are shown in Figure 7. It is seen that the target trajectories and simulated trajectories are highly consistent on all planes. The error magnitudes of the angle coordinates are all kept at the level of \(10^{-9}\) degrees-\(10^{-8}\) degrees, which is close to the accuracy of the numerical calculation of ADAMS. Therefore, the effectiveness of the proposed adjustment algorithm could be accepted.  
Figure 7: _Cont._
## 3 Analysis of Datasets: Dimensional Analysis and Experimental Tests
### Experimental Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimensional** & **Minimum** & **Maximum** & **Average** & **Standard** \\
**Variables** & **Minimum** & **Maximum** & **Average** & **Default** \\ \hline \(e\)[mm] & 5.0 & 20.0 & 11.97 & 5.445 \\ \(U_{w}\)[m/s] & 0.3 & 0.5 & 0.40 & 0.068 \\ \(T\)[s] & 1.5 & 2.0 & 1.79 & 0.193 \\ \(V_{H}\)[mm/s] & 1.6 & 6.5 & 3.32 & 1.289 \\ \(V_{R}\)[mm/s] & 1.4 & 5.7 & 3.32 & 1.215 \\ \(V_{L}\)[mm/s] & 1.5 & 6.1 & 3.32 & 1.210 \\ \hline
**Dimensionless** & **Minimum** & **Maximum** & **Average** & **Standard** \\
**Variables** & **Minimum** & **Maximum** & **Average** & **Default** \\ \hline \(sin\)[-] & 0.10 & 0.71 & 0.32 & 0.263 \\ \(e/D\)[-] & 0.10 & 0.2143 & 0.0893 & 0.0893 \\ \(K\)[-] & 8.70 & 18.00 & 14.68 & 3.698 \\ \(\theta_{w}\)[-] & 0.18 & 0.30 & 0.27 & 0.051 \\ \(V_{R}^{a}\)[-] & 1.27 & 5.16 & 2.64 & 1.025 \\ \(V_{R}^{a}\)[-] & 1.19 & 4.85 & 2.64 & 0.961 \\ \(V_{L}^{a}\)[-] & 1.11 & 4.53 & 2.64 & 0.965 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic statistical properties of the variables under study for AI modeling. Figure 4: Frequency and cumulative relative frequency for the independent non-dimensional variables. (31.58%) devoted to \(\alpha=0\). As observed in Figure 4b, the frequency distribution of \(e/D\) is relatively symmetric and the tail of the distribution inclines to \(e/D=0.4\), whereas the majority of experimental observations (around 55%) were collected for \(e/D\leq 0.2\). Figure 4c also illustrates that the Kelugan-Carpenter number, \(KC\), was not exhaustively examined, with very fragmented distribution (i.e., \(KC=8.7,\ 15.8,\ \text{and}\ 18.0\)). In addition, the Shields' parameter, \(\theta_{w}\), was not systematically explored, with most of the experiments characterized by \(\theta_{w}\) around 0.3 (Figure 4d). It is important to emphasize that all the experiments were performed using only one bed sediment and in live-bed conditions. This represents a limitation for the current available datasets, and further investigations on clear-water regime and various sediment beds would be of interest. For the sake of completeness, the tests of [PERSON] et al. [9] considered in this study are summarized in Appendix A (Table A1).
# Carbon Spheres as CO\({}_{2}\) Sorbents
## 1 Introduction
[18], chemical activation with potassium oxalate resulted in a large increase in the surface area of carbon materials (from 680 m\({}^{2}\)/g to 1490 m\({}^{2}\)/g) and an increase of CO\({}_{2}\) uptake from 3.03 mmol/g to 7.67 mmol/g in 0 \({}^{\circ}\)C at 1 atm. This example showed how modification with potassium oxalate can significantly enhance specific surface area and CO\({}_{2}\) adsorption of carbon spheres. In order to improve synthesis conditions of carbon nanospheres, microwave assisted solvothermal reactor has been used [33; 34]. Performing the reaction in a common autoclave takes a significant amount of time, often several hours, while the reaction in microwave assisted solvothermal reactor is very fast, about 15 min. The temperature gradient using a microwave in the reactor volume is very low and can be negligible. The microwave's influence on the behavior of polar solvents in the reaction is significant, and volume nucleation points are created rapidly. In this work, the influence of the concentration of activator, potassium oxalate, carbonization temperature, and influence of ethylene diamine (EDA) on the physical properties and adsorption of carbon dioxide were investigated. [PERSON] and [PERSON] [19] performed a simple one-pot synthesis of carbon spheres and obtained very good CO\({}_{2}\) adsorption values. In this work, simple autoclave was replaced by microwave assisted solvothermal reactor. The use of such a reactor enabled a significantly shortened reaction time. Heating with microwaves avoids a variance in the temperature profile in the reactor volume, no local overheating, and the products obtained are of very good quality, with uniform shape and size of the produced particles. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Carbon Source** & **Activator** & \
\begin{tabular}{c} **CO\({}_{2}\) Adsorption 0 \({}^{\circ}\)C** \\ **[mmol/g] 1 bar** \\ \end{tabular} &
\begin{tabular}{c} **CO\({}_{2}\) Adsorption 25 \({}^{\circ}\)C** \\ **[mmol/g] 1 bar** \\ \end{tabular} & **Reference** \\ \hline Resorcinol–formaldehyde resin & K\({}_{2}\)C\({}_{2}\)O\({}_{4}\) & 7.67 & 4.95 & [18] \\ Resorcinol–formaldehyde resin & K\({}_{2}\)C\({}_{2}\)O\({}_{4}\) & 6.6 & - & [19] \\ Resorcinol–formaldehyde resin & KOH & 7.34 & 4.83 & [20] \\ Resorcinol–formaldehyde & EDA; CO\({}_{2}\) & 6.2 & 4.1 & [21] \\ Furfuryl alcohol & KOH & 5.8 & 3.3 & [22] \\ Polyacrylonitrile & KOH & - & 2.74 & [23] \\ Waste wool & KOH & 3.73 & 2.81 & [24] \\ Starch & KOH & 6.6 & 4.3 & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of CO\({}_{2}\) uptake on various carbon adsorbents. Previous research obtained in this research community has been promising [33; 34]. In the present work, we describe more in depth research on the influence of modificator and carbonization temperature on surface area, porosity, and carbon dioxide adsorption.
## 2 Materials and Methods  
### Research Design of Fields and Soils Samples  
Long-term fertilization studies in agroecosystems were initiated in 1967, focusing on rice monoculture paddy fields situated within the RDA (Rural Development Administration; coordinates: 36\({}^{\circ}\)36\({}^{\prime}\) N Latitude, 128\({}^{\circ}\)45\({}^{\prime}\) E Longitude) in the Republic of Korea [12]. The research station employed an arrangement of experimental plots measuring 10 m \(\times\) 10 m each, in a completely randomized design and in triplicate for every treatment. Different fertilization treatments were applied including no fertilizer application (NF), NPK fertilizer in combination with compost (NPKCom), and compost application (Com). Urea (120 kg ha\({}^{-1}\)), superphosphate (80 kg ha\({}^{-1}\)), and KCl (80 kg ha\({}^{-1}\)) were applied as the NPK fertilizers. The initial application, administered just prior to transplantation, con tributed 50% N, 100% P, and 70% K. On the other hand, the compost was produced by six months decomposition of cattle manure and rice straw mixture resulting in a compost with composition values of 19.8 g kg\({}^{-1}\) N, 5.2 g kg\({}^{-1}\) P, 29.1 g kg\({}^{-1}\) K, and 431 g kg\({}^{-1}\) total C. The compost treatment received an annual equivalent of 10 tons ha\({}^{-1}\). Before flooding and rice seedling transplantation, samples were gathered in a soil depth of approximately 20 cm (five soil subsamples, three replicates per treatment). A composite sample for each treatment replicate was made by pooling the subsamples. They were immediately sieved (<2 mm) after removing plant residues, placed in bags (sterile polypropylene), and transported on ice packs. Soils intended for standard analyses were kept at \(-20\)\({}^{\circ}\)C (molecular) and \(4\)\({}^{\circ}\)C (chemical).  
For the physical properties of the soils, paddy field soils are characterized as fine silty, mixed, nonacid, mesic, and Type Endoaquets based on the USDA soil taxonomy and classification. The bulk density of the soil in different treatments in different soil depths of 5, 10, 15, and 20 cm was 1.29, 1.32, 1.42, and 1.54 g/cm\({}^{-3}\), respectively, for NF soils; 1.07, 1.21, 1.26, and 1.49 g/cm\({}^{-3}\), respectively, for Com fertilized soils; and 1.18, 1.14, 1.24, and 1.42 g/cm\({}^{-3}\), respectively, for NPKCom treated soils.
###### Abstract  
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (\(R^{2}\) = 0.6427, \(p\) < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 \({}^{\circ}\)C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.  
keywords:multi-angle; hyperspectral; PRI; LUE; sub-tropical coniferous forest +
Footnote †: journal: Journal of the American Statistical Association
## 1 Introduction
The effectiveness of using harmonic wavelets for the classification of hydroacoustic signals was proved. A technique to reduce the feature space dimension using a'modulo N reduction' method was developed. A classification of 26 individual whales is presented for the dataset. It was shown that the deep-learning-based approach provides the best result for the dataset both for whale types and individuals. The remainder of this paper is organized as follows. In Section 2, we briefly describe hydroacoustic signal processing and review related works on it. In Section 3, we introduce details of the harmonic wavelets and their application to the processing of hydroacoustic signals. In Section 4, we review the kNN algorithm for classification based on harmonic wavelets and present experimental results to verify the proposed approach. In Section 5, experimental results are presented to verify the approach for classification based on neural networks and machine learning. In Section 6, we discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. Future research directions also are highlighted. Finally, we present the conclusions in Section 7.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 2 Analysis of Models and Limitations in VLC Systems
### PD Model  
The equivalent circuit model of the PD is presented in Figure 3, and it consisted of an intrinsic bandwidth limitation model and a diode dark current noise model. The intrinsic model comprised a Verilog code model representing carrier lifetime and photoelectric response and intrinsic parasitic capacitor \(\mathrm{C_{f}}\) with parasitic resistor \(\mathrm{R_{f}}\) for the \(-3\) dB bandwidth of the PD. For the dark current noise model, \(\mathrm{C_{T}}\) was the terminal capacitance, which included the capacitances of the PD, pad, and bonding wire. To determine the values of these components for an accurate PD model, physical manufacturing principles and photoelectric conversion were adopted to build the Verilog model, while test results and data from the datasheet were used for parameter fitting.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 4 Lifetime-Extended Transmission Power Optimization Algorithm  
### Problem Description  
In this paper, the network lifetime is defined as the duration from the beginning time of the network operation to the time when the first node exhausts its energy [36; 43]. Based on this definition and the above cooperative transmission model, the key issue to extend the network lifetime is to select the cooperative nodes with the best channel quality in order to reduce the energy consumption of each data packet transmission and improve node energy efficiency, and meanwhile, optimize the transmission powers of the sender and the cooperative nodes to guarantee that each node consumes its energy evenly and avoid parts of nodes exhausting their energy while the other nodes still have much residual energy.  
Network lifetime is a global variable, which is related to the data packet generation of each node and network traffic load. However, network traffic load distribution is determined by actual application, and each node cannot predict its future data packet arrival rate. In this case, in order to extend network lifetime, we can use the local optimization to replace the global optimization according to the greedy algorithm. That is to say, we should maximize the minimum residual energy among the sender and cooperative nodes during the current data packet transmission [36; 43]. This strategy can guarantee that the local energy of network are consumed evenly and avoid exhausting the energy of some nodes too early.  
The minimum residual energy \(e\) of the sender and cooperative nodes after the current data packet transmission can be expressed as:  
\[e=\min\big{\{}E_{\rm{s}}-P_{\rm{s}}T,E_{\rm{R_{1}}}-P_{\rm{R_{1}}}T,\cdots,E_ {\rm{R_{i}}}-P_{\rm{R_{i}}}T,\cdots,E_{\rm{R_{N}}}-P_{\rm{R_{N}}}T\big{\}},i \in[1,N] \tag{1}\]  
where \(E_{\rm{s}}\) is the residual energy of the sender before the current data packet transmission, \(P_{\rm{S}}\) is the transmission power of the sender to be used during the current data packet transmission, and \(T\) is the duration of the data packet transmission. Then, \(E_{\rm{S}}-P_{\rm{S}}T\) represents the residual energy of the sender after the current data packet transmission. Likewise, \(E_{\rm{R_{i}}}-P_{\rm{R_{i}}}T\) represents the residual energy of the cooperative node \(\rm{R_{i}}\) (\(i=1,2,3,\ldots\) ) after the current cooperative transmission using a transmission power \(P_{\rm{R_{i}}}\). In order to maximize the network lifetime, the proposed transmission power optimization algorithm should optimize the transmission power \(P_{\rm{S}}\) and \(P_{\rm{R_{i}}}\) to guarantee the value of \(e\) to be maximum after the current data packet transmission, i.e., the ultimate objective of the LETPO algorithm is:  
\[\max_{\{P_{\rm{S}},P_{\rm{R_{1}}},P_{\rm{R_{2}}},\cdots\}}e,\ i\in[1,N] \tag{2}\]
## 3 Results
### SEM Analysis of the Samples  
The SEM micrographs showed a satisfactory dispersion of the nanoparticles for both sizes, although in low quantity and small sizes, usually forming agglomerates. In Figure 10, the micrographs taken of the composite materials with 15 and 45 nm AgNPs, with and without ultrasonic treatment, can be observed.  
Figure 10: SEM micrographs of the samples with 15 nm AgNPs, (**a**) without ultrasound, (**b**) with ultrasound, and samples with 45 nm AgNPs, (**c**) without ultrasound, and (**d**) with ultrasound.  
Figure 9: Pareto Front for the set of solutions between objective 1 and objective 2 for the multi-objective optimization problem. The blue points represent the feasible solutions for the optimization problem and are intersections between the vectors (the blue lines) generated in the process according to the optimization problem.  
In micrograph from Figure 10a, an agglomerate of AgNPs formed by smaller nanoparticles (15 nm) can be observed when the ultrasound treatment has not been applied to the system. When the ultrasound treatment is applied while obtaining the composite material, it can be observed that the agglomerates considerably diminish in size, as can be appreciated in micrograph (Figure 10b). The nanoparticles, especially metallic ones like silver, strongly tend to agglomerate. The formation of agglomerates causes a reduction of the total particle surface and, therefore, could reduce the biocide properties.  
For the 45 nm AgNPs samples, agglomerates are also observed in the micrographs; however, the agglomerates are smaller in size (see Figure 10a,c); thus, a larger surface area and higher biocide activity could be expected in comparison to the composite materials containing 15 nm AgNPs.  
By applying the ultrasound treatment on the composite materials containing 45 nm AgNPs, the agglomerates reduce in size, becoming even smaller than the agglomerates found in 15 nm AgNPs containing composite materials after the ultrasound treatment. These results are consistent with the optimization analysis of the studied parameters, in which it was established that to obtain the lesser _T. rubrum_ fungal growth, 45 nm nanoparticles, high concentrations, and ultrasonic treatment should be used.  
These results are consistent with those reported in the literature, which indicate that the formation of agglomerates tends to reduce the nanotoxicity [44]. As for the effect of the concentration, it is logical that composite materials show a higher fungal growth inhibition when the presence of nanoparticles in the medium is higher.