esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement, Processing, and Display Application  
For the application, developed in NI LabVIEW, the connection diagram uses a vector-type processing object, shown in Figure 2. Data are arranged in six columns. The signals received by the acquisition board are 3 voltages (u1, u2, u3) and 3 currents (i1, i2, i3) in alternate sequence, as shown in Figure 3 (current moment of sampling is mark by vertical dotted line and voltages and currents are represented grouped, not interleaved). The data sequence that is repeated is u1, i1, u2, i2, u3, i3. To select each column, for digital processing, must use index between 0 and 5. Next step is to scale data, to reach the real value. The data are subsequently processed with or without correction, resulting in a series of quantities (active power, apparent power, reactive power, power factor, cosine of the phase shift). When imposing the frequency of the samples and the number of samples, it must be taken into account that they must be a multiple of 6, to consider a whole number of samples, the same for each size. Since, as shown in Figure 3, on each channel, the acquisition takes place in different moments, the determination of the active power, as an average of the instantaneous power, can be affected by errors. In order to determine the parameters of interest, it is necessary to carry out some mathematical operations. Some parameters such as powers, phase shifts, different coefficients are determined by mathematical operations. Even if, mathematically, the system is simple to operate, there is the possibility of an impediment caused by physical phenomena [8]. The voltage sample and the current sample on each line are measured in different moments; however, for a correct evaluation of the instantaneous power, they must be measured at the same time. If a simultaneous sampling data acquisition card is used, the problem is solved, but the commonly used cards (chosen because of their lower cost) allow for sequential sampling [14; 15].  
Figure 1: Sketch of sampling error analysis by the three-phase acquisition system.  
Because a reasonable sampling frequency was used, the evolution of voltage and current on the same line was considered to proceed according to a linear gradient. This hypothesis was considered reasonable because the samples on the same line were considered to be adjacent [16].  
Of course, other sequences of the quantities of interest can be used, but in order to have close voltage and current on each phase, this arrangement was preferred [17].
## 3 Results and Discussion
### Communication Cost
The communication cost required in the proposed scheme, on the other hand, is 1696 bits (for case 1) and 3168 bits (for instance 2) when the FGWN is involved in the authentication phase. In our scheme, during the log-in phase, the log-in request message, \(M_{1}=[SN_{ID},TID_{i},CT_{i}]\) requires 704 bits, where 32 and 160 bits are required for \(SN_{ID}\) and \(TID_{i}\), respectively, and \((160+32+160+32+32)/128\)e = 512 bits for \(CT_{i}=E_{SK_{i}}[HGW_{ID},SN_{ID},W_{i},N_{i},TS_{1}]\). For the authentication and key agreement phase, consider case 1, the messages of \(M_{2}=SN_{ID},CT_{i}\) and \(M_{3}=SN_{ID},B_{i},Z_{i},TS_{3}\) 640 bits and 384 bits are required, respectively. As a result, the proposed scheme's communication cost for case 1 becomes (\(704+640+384\)) = 1696 bits. Similarly, the communication cost for case 2 in our scheme is 3168 bits due to the messages' involvement \(M_{1}[SN_{ID},TID_{i},CT_{i}],M_{5}=SN_{ID},CT_{f},M_{6}=SN_{ID},F_{i},Q_{i}, TS_{5}\), \(M_{7}=SN_{ID},CT_{if},andM_{8}=SN_{ID},D_{i},J_{i},TS_{7}\). Table 4 compares the costs of communication in terms of the number of bits required for message exchange; it illustrates that [PERSON]. [PERSON] et al. [41], [PERSON] and [PERSON] [38], [PERSON] et al. [28], and [PERSON] et al. [45] require 5792 bits, 4016 bits, 2400 bits and 2752 bits, independently. This comparison proves that the proposed scheme has less communication cost, which required 1696 bits.
# State-of-the-Art Internet of Things in Protected Agriculture
## 4 IoT Applications in Protected Agriculture
### Plant Management
The proposed system could provide quantitative information with high spatiotemporal resolution for floral farmers and contribute to updating farming strategies for phalaenopsis in the future [91]. Diseases and insect pests bring a great threat to the growth of crops as and traditional technology and chemical prevention and control has a certain lag and negative impact [92; 93]. The rise of IoT has brought more efficient and smarter solutions to crop disease and pest control. Many types of IoT sensors can collect information about location, greenhouse environment status, crop growth and pest situation anywhere in real-time, which helps farmers keep track of crop pests and diseases. Then, all raw image and data are sent to cloud centers and later processed and analyzed by various models and algorithms based on different diseases and pests [94; 95]. Finally, these cloud centers generally provide farmers with the following services: disease or pest identification, disaster prediction and warning and recommended governance measures from expert systems. [PERSON] et al. proposed a pest insect trap automatic monitoring system using ZigBee technology, which can estimate the insect density by collected data from different sites and send a warning message to farmers when it exceeded the set value [96]. [PERSON] et al. proposed a pest monitoring system which detected the presence of pests by the acquisition of ultrasound and assisted others in building up protocols for early exterminate of the pests [97]. [PERSON] et al. presented a potato late blight prevention and decision support system using cloud IoT and helped the agriculturalists take effective action to treat this disease [98]. Both Semios and Spensa Company launched their own integrated pest management system, which was able to count the number of pests by images, as well as characterize and capture the insects. At present, pest and disease warning research mainly provides medium and long-term warning based on historical data, which can provide macro guidance for crop production but low timeliness. Therefore, future research should focus on online monitoring as well as diagnosis and early warning of agricultural diseases.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 2 Theoretical Model of VBB Seismometer
### Noise Source Models of Seismometer  
The noises in seismometers primarily arise from two sources: Brownian noise from the suspension pendulum and electronic noise from the circuitry.
###### Abstract  
High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPCD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.  
fiber laser; polarization extinction ratio; active polarization control; root mean square propagation algorithm
## 4 Joint Decoding-Complexity and Rate Control
### CTU-Level Rate and Decoding-Complexity Allocation
The decoding-complexity-rate-distortion model in (6) is used to these ends, and as the model parameters therein are functions of QP and content, the MSE of the \(k\)th CTU in the \(j\)th frame of the GOP is first predicted for each QP \(q\) which can be expressed as  
\[\text{MSE}_{k,j}^{\rm CTU}(q)=a_{kj}(q)\times\text{bpp}_{\text{avg}(q)}^{b_{kj }(q)}+c_{k,j}(q)\times\text{cpp}_{\text{avg}(q)}^{d_{kj}(q)}, \tag{11}\]  
where \(a_{k,j}(q)\), \(b_{kj,j}(q)\), \(c_{k,j}(q)\), and \(d_{kj}(q)\) are the appropriate model parameters for that CTU. Here, bppavg and cppavg are the bpp and cpp calculated as the averages of the minimum and maximum of each respective parameter observed so far within the encoded sequence for the \(k\)th QP for a given frame type. Next, the actual MSE of the co-located CTU, \(\text{MSE}_{k,j,\text{co}}^{\rm CTU}\), is then compared with \(\text{MSE}_{k,j}^{\rm CTU}\) for all QPs to obtain a QP \(q_{0}\) such that  
\[\min_{q_{0}}\,\left|\text{MSE}_{k,j}^{\rm CTU}(q)-\text{MSE}_{k,j,\text{co}}^ {\rm CTU}\right|. \tag{12}\]  
The bppavg and cppavg for QP \(q_{0}\) are then used as the weights for both the bit rate and decoding-complexity to the CTU, respectively. Thus, the final target bit rate and decoding-complexity of the \(k\)th CTU in the \(j\)th frame in the GOP can be expressed as  
\[X_{\rm T}^{\rm CTU}(k,j)=\frac{\omega_{X}^{\rm CTU}(k,j)}{\sum_{k=k}^{\Phi} \omega_{X}^{\rm CTU}(\phi,j)}\times\Delta(k,j), \tag{13}\]where \(\Delta(k,j)=\left\{X_{\mathrm{T}}^{\mathrm{Frame}}(j)-\sum_{\phi=1}^{k-1}X_{ \mathrm{T}}^{\mathrm{CTU}}(\phi,j)\right\}\) is the remaining bits or decoding-complexity available to the remaining CTUs in the frame, \(\Phi\) is the total number of CTUs in the frame, and \(\omega_{X}^{\mathrm{CTU}}(k,j)\) is the bit or decoding-complexity weight for the CTU. Note that these bit rates and decoding-complexities can be expressed in terms of bpp or cpp by simply dividing \(X_{\mathrm{T}}^{\mathrm{CTU}}\) by the number of pixels in the CTU.
## 4 Conclusions  
A new application of PVC/Aliquat-336-based PIMs at different mass ratios (1/0.5, 1/0.75, and 1/1) using a multiframe flat sheet membrane module for Cr(VI) extraction has been successfully carried out. Nonrigid, dense, and transparent membranes, showing a plasticizing character due to the addition of the Aliquat-336 carrier, were produced. The presence of Aliquat-336 in the polymer matrix increases the hydrophilic character and the thickness of membranes.  
To optimize the different parameters in the feed and receiving phases, a two-level, full factorial DOE was used. The developed model was validated with experimental data. From the results, the highest extraction efficiency of 93.03% can be reached by working at the following conditions in the feed phase: 10 ppm Cr(VI), 20 \({}^{\circ}\)C, and PVC/Aliquat-336 ratio of 1/0.5.  
In the case of the receiving phase, the optimal value for the back-extraction yield of 59.34% can be obtained by working with: NaCl concentration of 2 M, 50 \({}^{\circ}\)C, and PVC/Aliquat-336 ratio of 1/1. When working at the PVC/Aliquat-336 ratio of 1/0.5, at which the highest extraction efficiency was obtained, the back-extraction efficiency decreased to 55.40%.  
The obtained results clearly indicate that the optimal yields of extraction and back-extraction cannot be reached with the same membrane. Moreover, even the highest back-extraction yield was always lower than the percentage of extraction (60% vs 93%). A possible practical solution to increase the back-extraction could be to send, at the end of the extraction experiment, fresh receiving phases (at 50 \({}^{\circ}\)C and NaCl 2 M) at both sides of the membrane.  
The multiframe module proposed in this work is an example of a compact design that could be adopted in scaling up the technique. This study focused on optimizing the parameters with synthetic solutions. Also, no effect of ionic forces was analyzed. In the future, the designed multiframe module could be a potential alternative to the classical double cell systems for continuous metal ion separation in real solutions.  
**Author Contributions:** Experiments and calculations, writing the draft paper, H.S.; Membranes characterization, discussion of results, writing, S.B.; Membranes characterization, F.R.; Writing and reviewing the draft paper, A.F. and A.C.; Supervising, B.M. and E.D.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 6 Conclusions  
In this paper, we propose two communication-efficient federated IHT methods--Fed-HT and FedIter-HT--to deal with \(\ell_{0}\)-norm regularized sparse learning with decentralized non-IID data. The Fed-HT algorithm is designed to impose a hard thresholding operator at a central server, whereas the FedIter-HT applies this operator at each update regardless of local clients or a central server. Both methods reduce communication costs--in both the communication rounds and the communication load at each round. Theoretical analyses show a linear convergence rate for both algorithms where the Fed-HT has a better convergence rate \(\theta\), but the FedIter-HT has a better statistical estimation bias. Similar to the conventional IHT methods with IID data, there is still a guarantee to recover the best sparse estimator even with decentralized non-IID data. They outperform the traditional Distributed-IHT in simulations and on benchmark datasets, according to empirical findings.  
Figure 5: Visualization of 10 K-means clusters for RCV1 using t-SNE.  
Figure 6: Comparison of the algorithms on different datasets in terms of the objective function value vs. communication rounds. \(f^{*}\) is a lower bound of \(f(x)\). FedIter-HT performs consistently better across all datasets, which confirms our theoretical result.  
**Author Contributions:** Conceptualization, Q.T. and G.L.; methodology, Q.T. and J.B.; software, G.L.; validation, G.L., J.D. and T.Z.; formal analysis, Q.T. and G.L.; investigation, Q.T.; resources, Q.T., G.L. and J.B.; writing--original draft preparation, Q.T., G.L. and J.B.; writing--review and editing, J.B. and M.P.; visualization, Q.T., G.L., J.D. and T.Z.; supervision, J.B.; project administration, J.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work of [PERSON] and [PERSON] was partially funded by the U.S. National Science Foundation (NSF) under grants IIS-1718738 to [PERSON]. [PERSON] was partially funded by a grant of National Institutes of Health (NIH) 5K02 DA043063-03 to [PERSON]. [PERSON] was also funded by an NIH R01-DA051922-01 grant. The work of [PERSON] and [PERSON] was funded in part by the NSF under grants US CNS-1801925 and CNS-2029569 to [PERSON].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
It is an effective approach to use Steel Tube Slab (STS) structure combined with the Pile-Beam-Arch (PBA) method to construct a large-space underground station. Traditional construction methods cannot meet the requirement of construction because of the complicated soil layers and high building densities in urban areas. The STS method can effectively increase the rigidity of the supporting system by using steel tubes. Firstly, the stress of bolts and steel tubes are investigated in the construction process based on the field monitoring data. Subsequently, FLAC3D is used to establish a three-dimensional model, which is verified based on the in-situ monitoring data; the effect of excavation process on ground settlement, deformation of STS structure and bridge pile are studied by numerical results. Moreover, the key parameters such as welding of flanges and the step length are studied. The results show that the stress of the steel tubes and flanges does not exceed the designed strength during the construction process. Based on the numerical simulation data, it is indicated that the STS structure can be a very effective and dependable measure in controlling and reducing the surface settlement and the existing adjacent buildings. The numerical results can be used to guide the later construction.  
large span underground spaces; Steel Tube Slab structure; excavation sequence; ground settlement; structural deformation
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 4 Forecasting Download and Upload Traffic
### Download Traffic  
For download traffic, the parameters for the SARIMA model are as follows:  
\[\text{SARIMA}(2,0,1)(0,0,2)_{24}.\]  
For the Holt-Winters model, the trend appears to be multiplicative and the seasonality additive.  
Figures 4 and 5 show three-day forecast plots for download traffic using the SARIMA and Holt-Winters models, respectively. The black line represents the training data, the black dashed line represents the test data, the forecast is shown in purple, and the red line shows the forecast for comparison with the actual data. Table 9 summarizes the evaluation metrics that demonstrate the superiority of the Holt-Winters model in forecasting download traffic.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **Formula** \\ \hline Mean squared error (MSE) & \(\dfrac{1}{n}\sum\limits_{t=1}^{n}{(x_{t}-y_{t})^{2}}\) \\ Root mean square error (RMSE) & \(\sqrt{\dfrac{1}{n}\sum\limits_{t=1}^{n}{(x_{t}-y_{t})^{2}}}\) \\ Mean absolute error (MAE) & \(\dfrac{1}{n}\sum\limits_{t=1}^{n}{|x_{t}-y_{t}|}\) \\ Mean absolute percentage error (MAPE) & \(\dfrac{1}{n}\sum\limits_{t=1}^{n}{\left|\dfrac{x_{t}-y_{t}}{x_{t}}\right|} \cdot 100\%\) \\ Mean squared logarithmic error (MSLE) & \(\dfrac{1}{n}\sum\limits_{t=1}^{n}{\left|\log(x_{t}+1)-\log(y_{t}+1)\right|^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Metrics for traffic forecast evaluation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **SARIMA Model** & **Holt-Winters Model** \\ \hline MSE & 0.0181 & **0.00021** \\ RMSE & 0.0181 & **0.0145** \\ MAE & 0.01513 & **0.01217** \\ MAPE & 15\% & **11.2\%** \\ MSLE & 0.000258 & **0.000163** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Metrics for download traffic forecast evaluation.
# Air Entrainment and Air Demand in the Spillway Tunnel at the Jinping-I Dam
## 1 Introduction
The information of air entrainment phenomenon can be obtained through directly monitoring the operating of hydraulic structures, and detailed prototype results can be used to verify and validate mathematical models and also evaluate laboratory experiments. Up to now, prototype observations on cavitation damage and the air entrainment phenomenon have been conducted on many projects. Theica Dam in Canada was the first to be used to conduct prototype observations on air entrainment and the devices used proved to be successful in cavitation damage alleviation. Similar examples include the Glen Canyon Dam in the United States [24], Foz do Areia Dam in Brazil [5], Guri 2 Dam in Venezuela [25], Tarbela Dam in Pakistan [26], and the Bratsk Dam in the former Soviet Union [27], as well as the Fengjiashan Dam, Wujiangdu Dam [28], and Xiaowan Dam in China. Jinping-I Dam, with its height of 305 m, is the highest arched dam in the world at present. Flow velocity in its spillway tunnel can reach up to 50 m/s, so a cavitation problem caused by high-velocity flow is prominent. In order to alleviate cavitation damage, aeration devices are employed in the spillway tunnel according to engineering experiences and results of model test [29]. This study aims to use data obtained from a systematic observation of the spillway tunnel to investigate the performance of the aeration system. Together with a series of prototype data from other projects, model scale effect and calculation methods of air demand are discussed, and a new formula to estimate the optimum air demand is also proposed.
## Results
### Fatty Acid Profile  
The fatty acid profile of _S. limacinum_ is shown in Table 2, where palmitic acid (16:0) and stearic acid (18:0) were the major fatty acids in the total lipid content with DHA and myristic acid (14:0) exhibiting lower levers. The use of crude glycerol and effluent as culture media significantly changed the fatty acid profile of _S. limacinum_. In particular, the levels of 16:0 were significantly reduced and those of 18:0 were significantly increased in these treatments compared to the control one. In addition, there was a trend with elevated effluent concentrations resulting in increased levels of 16:0 and decreased levels of 18:0, but this was not significant (\(p>0.05\)). With regard to the DHA content, moderate effluent  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Effluent** & **Crude** & **Crude Lipid** & **Crude** & **Moisture** & **Ash** & **Gross** \\
**Concentration** & **Protein** & **(\%)** & **Carbohydrate** & **Content** & **Content (\%)** & **Center (\%)** \\
**\% (v/v)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(M) kg\({}^{-1}\)** \\ \hline
0 & 30.8 \(\pm\) 0.9 a1 & 8.9 \(\pm\) 1.4 a & 54.3 \(\pm\) 3.1 a & 0.37 \(\pm\) 0.03 a & 5.5 \(\pm\) 0.3 a & 18.5 \(\pm\) 0.1 a \\
8 & 31.5 \(\pm\) 1.1 a & 5.3 \(\pm\) 0.6 a & 57.33 \(\pm\) 2.8 a & 0.27 \(\pm\) 0.01 a & 5.6 \(\pm\) 0.2 a & 18.7 \(\pm\) 2.8 a \\
16 & 20.7 \(\pm\) 0.1 b & 7.8 \(\pm\) 0.8 a & 39.89 \(\pm\) 1.1 b & 0.51 \(\pm\) 0.05 a & 31.1 \(\pm\) 1.0 b & 12.2 \(\pm\) 0.4 b \\
32 & 22.0 \(\pm\) 0.2 b & 17.1 \(\pm\) 3.4 b & 33.86 \(\pm\) 3.4 b & 0.44 \(\pm\) 0.08 a & 26.6 \(\pm\) 2.0 b & 13.6 \(\pm\) 0.3 b \\
48 & 25.0 \(\pm\) 0.8 b & 17.3 \(\pm\) 3.5 b & 26.96 \(\pm\) 2.2 c & 0.34 \(\pm\) 0.04 a & 30.4 \(\pm\) 8.1 b & 16.1 \(\pm\) 0.1 c \\ \hline \end{tabular}  
* Values (percentages) are represented as the mean \(\pm\) standard deviation of triplicates, whereas letters (a, b and c) indicate statistical differences analyzed at a level of \(p<0.05\).  
\end{table}
Table 1: Proximate composition of dried algal biomass of _S. limacinum_ grown on medium containing effluent digestate at varying concentrations (0–48%) at the same carbon and nitrogen concentration without the addition of micronutrients.  
Figure 4: DHA yield of _S. limacinum_ grown on medium containing effluent digestate at varying concentrations (0–48%) with the same carbon and nitrogen concentration without the addition of micronutrients. Values are represented as mean \(\pm\) standard deviation of triplicates, whereas asterisks indicate statistical differences analyzed at a level of \(p<0.05\).  
concentrations (8% and 16%) produced very limited amounts that were lower than those found in the control treatment. However, higher concentrations (32% and 48%) resulted in considerable amounts of DHA that were similar or even slightly higher to those in the control. Furthermore, DPA was non-detectable in any substitution treatments.