esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Evaluation and Numerical Results  
### Evaluation Metric, Objects, and Simulation Setup  
We evaluate our proposed protocols using an NS-2 simulator, which is integrated with the IEEE 802.15.4 based MAC module [12; 13]. In the simulation description, we call conventional on-demand approaches S2 (Sink-sensors), and our proposed approach S3 (sinl-sink_proxy-sensors).  
The following protocols are evaluated in the simulation:  
Flooding: A S2 approach with flooding based query dissemination and many-to-one data collection from reverse paths of the flooding [3].  
LBM based information retrieval: A S2 approach with location-based multicasting for query dissemination and data collection from reverse paths of multicasting [20].  
LDQD: Localised Data Query Distribution in S3 approach.  
LSDC: Localised Sensing Data Collection in S3 approach.  
LDQD and LSDC schemes are classified according to the position of the sink proxy. For instance, schemes that choose the centre node in the query area as a sink proxy are called LDQD centre and LSDC centre; schemes that use the nearest node in the query area as a sink proxy are called LDQD sink; schemes that use the random selected sink proxy in the query area are called LDQD limited random and LSDC limited random, schemes that use the network-wide random selected sink proxy are called LDQD network random and LSDC network random, _etc._  
The protocols are evaluated using the following metrics:  
_Ratio of energy consumption_: The ratio of energy consumption for the proposed protocol S3 compared with conventional approaches S2, which includes flooding-based and LBM based protocols. It is defined as: \(R_{{}_{\text{ratio}}}=\frac{E_{S1}}{E_{S2}}\); where \(E_{S2}\) is the energy consumption of conventional approaches of on-demand information retrieval (including Flooding and LBM); and \(E_{S3}\) is the energy consumption of proposed approaches of on-demand information retrieval.  
_Energy consumption at each node_: The energy consumption at each sensor node during the simulation.  
Unless otherwise stated, the simulation is set up as follows. There are 100 sensor nodes and 1 sink node in the network. The sensor nodes are distributed over a square of 100 m \(\times\) 100 m, and the detailed topology of the sensor network is shown in Figure 5. The network topology is static and all nodes are connected to each other by either single-hop or multi-hop links. Each sensor node is equipped with an IEEE 802.15.4 radio module, and has a radio range of 15 m. The maximum number of sensing data that can be aggregated in a packet is set to 10.
# Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
## 3 Theoretical Framework
### Internet Searches  
[PERSON] and [PERSON] [170] proposed to modify Equation (2) in such a way that it includes the information from Internet queries. The motivation behind this is that the weight ascribed to the model should include the information about the interest of investors in variables in this model. For instance, if there is an increasing interest in exchange rates, then models with this explanatory variable should be given somehow higher weights than the models without this variable. The data about Internet searches are available directly from, for example, Google [171]. These data are the numbers between 0 and 100. Moreover, they correspond not to the absolute volume of searches, but to the relative one - in comparison to all searches. Therefore, these numbers can reasonable represent interest in given variable (representing some economic factor). In order, to interpret these data as probabilities they just have to be divided by 100 to fit between 0 and 1. They are called _Google probabilities_ in this paper.  
Of course, it is another topic how to suitably choose search terms in the context of a particular research study. Moreover, how to include this information in model averaging is also an open topic. What [PERSON] and [PERSON] [170] proposed is, first, to compute  
\[p_{t,k}=\prod_{i\in IN}g_{i,t}\cdot\prod_{j\in OUT}(1-g_{j,t})\quad, \tag{17}\]  
where \(g_{i,t}\) denotes the Google probability of \(i\)-th variable at time \(t\), i.e., the number obtained from Google Trends [171] corresponding to Internet searches of the query associated with \(i\)-th variable, divided by 100; and \(IN\) denotes variables included in \(k\)-th model, and \(OUT\) denotes variables not included in \(k\)-th model.  
It can happen during the numerical estimations that some \(\prod_{i\in IN}g_{i,t}\) or \(\prod_{j\in OUT}(1-g_{j,t})\) will be approximated to 0. In such a case, in functions in fDMA it will be replaced by a small constant computed analogously as in Equation (3).  
Next, Equation (2) is replaced with  
\[\pi_{t|t-1,k}=\omega\cdot\frac{(\pi_{t-1|t-1,k})^{\alpha}}{\sum_{l=1}^{K}(\pi _{t-1|t-1,l})^{\alpha}}+(1-\omega)\cdot p_{t,k}\quad, \tag{18}\]  
where \(\omega\) is a fixed parameter, i.e., a number between 0 and 1. Of course, in case of \(\omega=1\) this modification replicates the basic DMA without Google data.  
Naturally, for any other kind of model averaging the above concept can be similarly mimicked. The only thing is to replace the weights used in that forecast combination scheme, \(w_{t,k}\), by \(\omega\cdot w_{t,k}+(1-\omega)\cdot p_{t,k}\).
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 3 Research Process
### Experimental Details  
To ascertain the model's efficacy, the algorithm was trained using the Pytorch framework on NVIDIA GTX 1060 Ti and Intel Core i7-7700 HQ 3.7 GHz CPU. The training samples underwent data preprocessing, which included operations such as rotation and cropping, before being fed into the model in the form of pixel blocks for training. The enhanced output images from the network were then compared with reference images, and the  
Figure 4: Structure of color restoration network.  
Figure 5: Structure of denoising network.  
corresponding loss function was computed. Subsequently, the model's weights and biases were iteratively adjusted based on the results of the loss function to enhance its performance in low-light image enhancement. Upon completion of the training, the model's performance was evaluated using a test dataset to determine whether it met the expected level of performance. In the image decomposition network module's training phase, a learning rate of \(10^{-3}\) was chosen and a batch size of 8 was employed. For the training of the light enhancement network module, a learning rate of \(10^{-3}\) and a batch size of 16 were adopted. During the training of the color restoration module, a learning rate of \(10^{-4}\) was utilized and the batch size was set to 1.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.3 Impact of Minimum Coverage  
Figure 7 shows the impact of imposing a minimum level of coverage, which in some cases can cause the optimization problem to lack a feasible solution. For instance, when the minimum coverage required is 60%, a deployment must use at least three gateways. It can also be seen that  
Figure 5: Dynamic vs. Static deployment.  
Figure 6: Impact of transmission range.  
after the minimum coverage is satisfied, the constraint will no longer be an issue and solutions to the deployment can be found without difficulty.
# Lightweight Research in Engineering: A Review
## 4 Lightweight Pathways and Research Progress
### Structure Lightweight
#### 4.2.3 Topology Lightweight
In addition, it is an important step to make the optimization result manufacturable. This also provides a design idea for general mechanical structure problems. [PERSON] et al. [PERSON] et al. (2018) carried out the topological optimization design of the rack and obtained the optimum material distribution of the rack structure. Referring to the optimized structure, the frame model is rebuilt, and the finite element analysis is carried out to verify the reliability of the results, which improves the utilization rate of materials under the condition of meeting the requirements. [PERSON] et al. [PERSON] et al. (2018) took the smallest flexibility of a harvester gearbox shell as the objective function, combined with the variable density method and Lagrange multiplier method in order to optimize the gearbox topology, removed some redundant materials, and designed reinforcing ribs. In order to realize the lightweight design of the cold-end fan blade in the case of bird impact, [PERSON] et al. [PERSON] et al. (2018) started from the point of structural topology optimization calculation, met the strength requirements of airworthiness regulations, while reducing the mass by 37.9%. It has certain practical reference value and broad application prospects in dynamic optimization of engineering structures. Level set-based optimization for two-dimensional structural configurations with thin members is presented. A structural domain with thin thickness is defined as a narrow band region on the zero-level contour [PERSON] et al. (2018) of the level set function. No additional constraints or penalty functional is required to enforce semi-uniformity in member thickness. An improved topology optimization approach named adaptive bubble method (ABM) was proposed to overcome the shortcomings of the traditional bubble method, such as the frequent remeshing operation and the tedious merge process of holes [PERSON] and [PERSON] (2018). Recently, an algorithm [PERSON] and [PERSON] (2018) combining solid isotropic material with penalty (SIMP) and bidirectional evolutionary structure optimization (BESO) was proposed, while topological optimization of lightweight cellular materials and structures. An example of a simple support beam and a cantilever beam demonstrates the effectiveness of the method, but the method assumes the uniqueness of the microstructure of the lightweight porous material, which is somewhat idealized and is not conducive to actual demand.
## 6 Simulation Results and Discussions
### Complexity Analysis  
The number of parameters and multiplications in NOMS, NSMS, TT-NOMS, TT-NSMS, TR-NOMS and TR-NSMS algorithms are compared in Table 3, where \(E\) is the number of edges in Tanner graph, \(B\) is the number of batch size, \(T\) is the number of iterations. The number of multiplications in Tables 2 and 3 include multiplications in CNs layers.  
Figure 11: BER performance of TT-NOMS TR-NOMS and NOMS [22] algorithms for LDPC(576,432) code with 5 and 15 iteration.  
Figure 10: BER performance of TR-NOMS, TR-NSMS, NOMS [22] and NSMS algorithms for LDPC(576,432) code.  
The same rank is used in TT and TR decomposition. We only use 108 parameters in the TT-NOMS and TR-NOMS algorithms and 216 parameters in TT-NSMS and TR-NSMS algorithms for Hamming(7,3) code, and the percentage reduction of parameters is 1.5%. The number of multiplication has been reduced to 0.45% in TT-NSMS and TR-NSMS algorithms. We only use 316 parameters in the TT-NOMS and TR-NOMS algorithms and 632 parameters in TT-NSMS and TR-NSMS algorithms for RCH(63,45) code, and the percentage reduction of parameters is 0.12%. The number of multiplication has been reduced to 0.0068% in TT-NSMS and TR-NSMS algorithms.We only use 430 parameters in the TT-NOMS and TR-NOMS algorithms and 860 parameters in TT-NSMS and TR-NSMS algorithms in BCH(127,106) code, and the percentage reduction of parameters is 0.70%. The number of multiplication has been reduced to 0.0093% in TT-NSMS and TR-NSMS algorithms. We use 474 parameters in the TT-NOMS and TR-NOMS algorithms and 984 parameters in TT-NSMS and TR-NSMS algorithms for LDPC(576,432) code, the percentage reduction of parameters is 0.03%. The number of multiplication has been reduced to 0.006% in TT-NSMS and TR-NSMS algorithms. TT-NOMS, TR-NOMS, TT-NSMS, TR-NSMS algorithms can reduce the storage space and computational complexity during deployment.
## 4 Results and Discussion
### Spatial Motion Prediction and Analysis  
The identified hydrodynamic coefficients were updated in the motion model, and the spatial motion of the submarine was predicted. The vessel's resulting trajectory was also obtained under the conditions, as discussed in Section 3.2. Figure 17 compares the spatial motion trajectories, showing a high level of overlap, with a maximum error of only 0.22 m. Figure 18 compares the temporal curves of the spatial motion and angular velocity components. Except for the roll angular velocity (\(p\)), the deviations within the other motion parameters are relatively small, with a maximum error of 0.089 rad/s for \(p\). Thus, it is evident that using the Bayesian ridge regression model to identify the hydrodynamic coefficients of a submarine yields good results when predicting its spatial motion.  
Figure 17: Comparison of the spatial motion trajectories.  
Figure 18: _Cont._
# Energy Consumption Model for Drilling Processes Based on Cutting Force
## 5 Conclusions  
In this paper, an energy consumption model for drilling processes was proposed. It is based on an equation consisting of the idle power, calculated based on the spindle speed, the cutting power, calculated from the cutting force, and the auxiliary power, calculated based on the cutting power. This proposed model was implemented and verified during the drilling process on a three-axis vertical machining center. Comparative experiments showed that the proposed model had higher accuracy than the existing MRR-based model, the model based on the MRR and spindle speed, and the model based only on cutting force.  
Although the new power model from this work is only specific to the particular machine tool and the specific workpiece material on which the experiments were carried out, we believe they are applicable to the majority of machine tools and workpiece materials, albeit with different coefficients in the model.  
On the potential future study of the subject, the effect of tool wear on energy consumption should be considered, especially in the drilling of difficult-to-machine materials wherein the roughing stage often has radical cutting parameters. This calls for the consideration of the impact of tool wear on energy consumption. Secondly, the proposed model only considers the energy consumption of simple holes, which may not be applicable for deep hole drilling, and further research on the energy consumption of deep hole drilling should be carried out.  
Conceptualization, Q.W., D.Z. and B.C.; Methodology, Q.W., Y.Z. and B.W.; Statistical analysis, Q.W. and Y.Z.; Formal analysis and investigation, Q.W. and B.C.; Writing--original draft preparation, Q.W.; Writing--review and editing, B.C. and Y.Z.; Supervision, B.W.; Project administration and funding acquisition, B.C., Y.Z. and B.W.  
This research was funded by [the Major National Science and Technology Projects] grant number [No.2017 ZX04011011] and [the China Scholarship Council] grant number [No. 20170629503] and [the Shaanxi Key Research and Development Program in Industrial Domain] grant number [No.2018 ZDXM-GY-063] and [the Fundamental Research Funds for the Central Universities] grant number [No.31020190505003].  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline _No._ & _P (W)_ & _P * (W)_ & _P1 (W)_ & _P2 (W)_ & _P3 (W)_ & _Error *_ & _Error 1_ & _Error 2_ & _Error 3_ \\ \hline
1 & 1851.0 & 1826.1 & 1971.6 & 1884.2 & 2.16\% & 1.34\% & 6.52\% & 1.80\% & 2.72\% \\
2 & 1925.7 & 1917.3 & 1971.6 & 1934.3 & 1.31\% & 0.43\% & 2.39\% & 0.45\% & 1.31\% \\
3 & 2010.6 & 2022.4 & 1971.6 & 1984.4 & 0.47\% & 0.59\% & 1.94\% & 1.30\% & 0.47\% \\
4 & 2105.2 & 2141.1 & 1971.6 & 2034.5 & 2.93\% & 1.70\% & 6.35\% & 3.36\% & 2.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparing the different models with the data from Experiment-II.  
Figure 9: The prediction errors of the four models (Experiment-II).
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 3 Problem Description and Model Design
### Improved Informer Modeling
#### 3.3.3 LSTM Module Extracts Temporal Long- and Short-Term Memory  
After the decoder output data are processed by the STCN module, this paper introduces the long short-term memory network (LSTM module) to extract the long- and short-term dependencies of the temporal data. The LSTM module can mitigate gradient explosion and vanishing problems when learning long-term dependency terms [47], and outputs to the fully connected layer, obtaining the predicted values through the multilayer perceptual machine. The structure of the LSTM module is shown in Figure 4, and the oblivion gate determines the discarded information from the cell state and reads the previous output and the current input to perform a nonlinear mapping. The output vector is multiplied with the cell state; the input gate determines the new information that is stored in the cell state; the old state is multiplied with the forget gate to determine the discarded information together with the candidate value to realize the cell-state update; and finally, based on the cell state, the result is outputted through the output gate. The overall principle of the work is shown below [48].  
\[\begin{array}{l}f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f})\\ i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ \widetilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C})\\ C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t}\\ o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}=o_{t}*\tanh(C_{t})\end{array} \tag{16}\]  
where \(x_{t}\) represents the input of the current moment, \(\widetilde{C}_{t}\) represents the information candidate state, \(h_{t-1}\) indicates the hidden state of the previous moment, \(f_{t}\) denotes the forget gate control signal, \(i_{t}\) denotes the input gate control signal, \(o_{t}\) denotes the output gate control signal, \(C_{t-1}\) denotes the memory cell (cell state) of the previous moment, \(C_{t}\) denotes the memory cell of the current moment, and \(h_{t}\) denotes the hidden state of the current moment.
# Influence of Soil Characteristics on Wood Biodeterioration by Brown Rot Fungi
## 3 Results
### Long Term Exposition Test of Wood Pole Segments
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sample** & **Treatment** & **Initial Cu in Wood** & **Final Cu in Wood** & **Cu Leached** & **Cu released in Water** \\  & **(HA\%)** & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** & **(\%)** & **(mg L\({}^{-1}\))** \\ \hline Control & 0.000 & 0.00 \(\pm\) 0.00 & 0.00 (aA) \(\pm\) 0.00 & - & 0.00 (aA) \(\pm\) 0.00 \\  & 0.005 & 0.00 \(\pm\) 0.00 & 0.00 (aA) \(\pm\) 0.00 & - & 0.00 (aA) \(\pm\) 0.00 \\  & 0.020 & 0.00 \(\pm\) 0.00 & 0.00 (aA) \(\pm\) 0.00 & - & 0.00 (aA) \(\pm\) 0.00 \\ \hline \hline \multirow{3}{*}{CC} & 0.000 & 5.13 \(\pm\) 0.16 & 4.60 (abB) \(\pm\) 0.11 & 10.27 & 17.36 (ab) \(\pm\) 0.83 \\  & 0.005 & 3.85 \(\pm\) 0.27 & 2.99 (cb) \(\pm\) 0.21 & 22.27 & 19.62 (bcB) \(\pm\) 0.54 \\  & 0.020 & 4.37 \(\pm\) 0.31 & 4.01 (ab) \(\pm\) 0.30 & 8.23 & 26.82 (cb) \(\pm\) 1.19 \\ \hline \hline \multirow{3}{*}{CCB} & 0.000 & 6.25 \(\pm\) 0.34 & 5.73 (abC) \(\pm\) 0.28 & 8.42 & 29.62 (aC) \(\pm\) 1.16 \\  & 0.005 & 6.21 \(\pm\) 0.22 & 5.42 (bc) \(\pm\) 0.07 & 12.67 & 33.61 (abCB) \(\pm\) 1.21 \\  & 0.020 & 5.61 \(\pm\) 0.15 & 5.47 (cAB) \(\pm\) 0.19 & 2.44 & 29.85 (ab) \(\pm\) 1.00 \\ \hline \hline \multirow{3}{*}{Cu-HDO} & 0.000 & 6.75 \(\pm\) 0.17 & 5.39 (aD) \(\pm\) 0.14 & 20.06 & 130.95 (aD) \(\pm\) 1.53 \\  & 0.005 & 6.65 \(\pm\) 0.12 & 5.57 (abBC) \(\pm\) 0.15 & 16.20 & 143.34 (abD) \(\pm\) 3.61 \\ \cline{1-1}  & 0.020 & 6.65 \(\pm\) 0.26 & 5.69 (bc) \(\pm\) 0.29 & 14.44 & 123.23 (aC) \(\pm\) 1.82 \\ \hline \hline \multirow{3}{*}{ACQ} & 0.000 & 5.39 \(\pm\) 0.26 & 4.76 (aB) \(\pm\) 0.33 & 11.70 & 53.96 (aC) \(\pm\) 1.28 \\ \cline{1-1}  & 0.005 & 5.38 \(\pm\) 0.23 & 4.23 (bcB) \(\pm\) 0.27 & 21.49 & 65.44 (abC) \(\pm\) 0.94 \\ \cline{1-1}  & 0.020 & 5.91 \(\pm\) 0.15 & 4.74 (bCD) \(\pm\) 0.47 & 19.90 & 71.88 (bD) \(\pm\) 0.89 \\ \hline \hline \end{tabular}  
* Lowercase letters denote significant differences between the Cu-leaching at the studied HA concentrations. Uppercase letters denote significant differences between Cu-leaching by the wood preservative formulations for the same humic acid (HA) concentration. Data represented as mean \(\pm\) SD of three replicates (\(p<0.05\)).  
\end{table} 
Table 2: Effect of humic acid on wood preservative leaching from treated wood specimens.
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 4 Experiments  
### Setup of Experiments  
For function verification and performance evaluation, the Xilinx Virtex-6 FPGA chip is adopted for circuit realization. The evaluation board ML605 (designed by Xilinx Inc., San Jose, CA, USA) is used. The reference clock of the board is a crystal oscillator with a frequency of 200 MHz. CLKA and CLKB are generated by the Mixed-Mode Clock Managers (MMCMs), which is realized by multiplying or fractional dividing the reference clock. CLKA and CLKB are connected to global clock buffers, which is a dedicated interconnect network specifically designed to reach all clock inputs of the various resources inside an FPGA chip. These networks feature low skew and low duty cycle distortion, low power, and improved jitter tolerance.  
The DTC evaluation board communicates with the PC via PCI (peripheral component interconnect) Express interface. The time interval signal generated is outputted through the SMA (small-motion accelerometer) connector, which has the advantage of low noise. The test system of the DTC is shown in Figure 8. The evaluation board is supplied by RIGOL DP832A (RIGOL Technologies Inc., Beijing, China), and a digital oscilloscope (Keysight DSAZ592A, Keysight Technologies, Santa Rosa, CA, USA) is employed to measure the time interval generated. Additionally, a thermal chamber (ESPEC SH642, designed by ESPEC Corp., Osaka, Japan) is utilized to evaluate the temperature stability of the DTC.
# Evaluation of the Coupling of a Hybrid Power Plant with a Water Generation System
###### Abstract  
This paper presents the design and analysis of an energy/water system that combines a 20 MW hybrid concentrated solar/biomass power plant with an advanced wastewater treatment facility. Designed to be installed in one of the most demanding areas of the Iberian Peninsula, the Spanish region of Andalusia, this plant seeks to provide the area with potable water and electricity. The solar block works with a mixture of molten salts, while the biomass backup system of the power plant uses olive pomace. The implementation of a direct potable reuse facility further enhances the sustainability of the project. Urban sewage from the region is collected and passed through a series of purification procedures in order to generate potable water ready to be directly blended into the water distribution system. A sensitivity analysis is conducted to determine the feasibility of the co-generation of electricity and water in the area. With a capacity factor of 85% and an annual operation of 7,446 hours, the hybrid solar/biomass power plant generates 148.92 GWh. Exergetic analyses have been realized for two extreme cases: exclusive use of the solar block and exclusive use of the biomass system. An overall plant exergetic efficiency of 15% is found when the solar block is used and an efficiency of 34% is calculated when the biomass support system is used. Following an economic analysis, a total investment of 211,526,000 \(\epsilon\) is required for the full implementation of the system with a resulting levelized cost of energy of 0.25 \(\epsilon\)/kWh. We find that the selling price of the generated potable water which makes the plant operation economically viable is found to be 14.61 \(\epsilon\)/m\({}^{3}\). At present, this price seems relatively high in view of current conditions; yet it is expected to become more realistic under future heightened water scarcity conditions, especially in arid regions.  
Keywords:hybrid solar power plant; solar tower; biomass combustion; exergy analysis; economic analysis; potable water reuse +
Footnote †: journal: Applied Soft. 2019, 9, 4989; doi:10.3390/appr9234989