esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Model for Energy Purchase and Sale of the Electric-Gas Interconnection System
### Model Transformation  
In the process of solving the above robust optimization model, considering that some components in Formula (14) are independent of variables, Formula (14) can be simplified as follows:  
\[\underset{P_{E,f}^{\text{bald}},P_{G,t}^{\text{bald}}}{\text{max}}\text{E}\left( \sum_{t=1}^{T}\left(\lambda_{\text{E},t}P_{\text{E},t}^{\text{bid}}+\lambda_{ \text{G},t}P_{\text{G},t}^{\text{bid}}\right)+\underset{P_{E,f}^{\text{real}}} {\text{min}}\sum_{t=1}^{T}\left[-\lambda_{\text{R},t}(\Delta_{\text{E},t}^{+}+ \Delta_{\text{E},t}^{-})\right]\right\} \tag{37}\]  
By introducing variable \(C\), Formula (37) can be changed to:  
\[\underset{P_{E,f}^{\text{bald}},P_{G,t}^{\text{bid}}}{\text{max}}\text{E} \left\{\sum_{t=1}^{T}\left(\lambda_{\text{E},t}P_{\text{E},t}^{\text{bid}}+ \lambda_{\text{G},t}P_{\text{G},t}^{\text{bid}}\right)+C\right\} \tag{38}\]  
\[C\leq\underset{P_{E,f}^{\text{real}}}{\text{min}}\text{E}\sum_{t=1}^{T}\left[ -\lambda_{\text{R},t}(\Delta_{\text{E},t}^{+}+\Delta_{\text{E},t}^{-})\right] \tag{39}\]  
Obviously, in order to get the maximum value of the objective function in Formula (37), the middle sign of Equation (39) should hold, so Formulas (38) and (39) is equivalent to Equation (37). Similarly, the relationship between \(\Delta_{\text{E},t}^{+}\), \(\Delta_{\text{E},t}^{-}\) and \(\widetilde{P_{E,t}^{\text{real}}}\) is shown in Formulas (15) and (16). For the convenience of solution, it is simplified as follows:  
\[\underset{P_{E,f}^{\text{real}}}{\text{min}}\underset{\Delta_{\text{E},t}^{ +},\Delta_{\text{E},t}^{-}}{\text{max}}\text{E}\sum_{t=1}^{T}\left[-\lambda_{ \text{R},t}(\Delta_{\text{E},t}^{+}+\Delta_{\text{E},t}^{-})\right] \tag{40}\]  
\[\text{s.t.}\Delta_{\text{E},t}^{+}\geq\widetilde{P_{E,t}^{\text{real}}}-P_{ \text{E},t}^{\text{bid}} \tag{41}\]  
\[\Delta_{\text{E},t}^{+}\geq 0 \tag{42}\]  
\[\Delta_{\text{E},t}^{-}\geq P_{\text{E},t}^{\text{bid}}-\widetilde{P_{E,t}^{ \text{real}}} \tag{43}\]  
\[\Delta_{\text{E},t}^{-}\geq 0 \tag{44}\]  
The solution of robust optimization problem belongs to NP-hard problem mathematically, so this paper decomposes the original problem into main problem and sub problem. The main problem takes \(P_{E,t}^{\text{bid}}\) and \(C\) as decision variables, and its goal is to maximize the profit of the electric-gas interconnection system in all the worst cases of the sub problem.
## 6 COTS Hardware Components
### Transmit-Receive Switching Board  
For a related problem, we built a custom transmit-receive switching and amplifier board, which effectively combines a TR switch and power amplifiers into a nice form factor. This component is not COTS, but could be substituted with any number of standard TR switches and amplifiers without loss of generality and with only slightly worse noise figure and slightly more power consumption. The power amplifiers (PAs) provide 30 dB gain on transmit up to 30 dBm. The LNA provide 20 dB gain on receive. For testing, both of these amplifier chains can be bypassed. This board also includes an external 40 MHz OCXO clock source that can be used if necessary. This device is depicted in Figure 9.
## 2 Design of the Non-Circular Gear
### Design of the Pitch Curve
#### 2.1.2. Design of the Pitch Curve  
In non-circular gears, the rotation angle (\(\varphi\)) of the driven gear can be obtained differently from the rotation angle (\(\theta\)) of the driving gear with a constant angular velocity, and the rotation angle (\(\varphi\)) of the driven gear is expressed by Equation (2).  
\[\varnothing=f(\theta) \tag{2}\]  
Here, \(\frac{d\varnothing}{d\theta}=f^{\prime}(\theta)\) represents the angular velocity ratio for the rotation angle (\(\varnothing\)) of the driven gear to the rotation angle (\(\theta\)) of the driving gear.  
As shown in Figure 5, if the distance (\(c\)) between the rotation axes of the driving and driven gears is constant as shown in Equation (3) and the gear ratio is 1:1, since the pitch curves are in rolling contact with each other, rolling distance \(l_{1}\) of the pitch curve of the driving gear is equal to the rolling distance \(l_{2}\) of the pitch curve of the driven gear. Therefore, an infinitesimal change in each rolling distance at an arbitrary pitch point can be expressed by Equation (4).  
\[r_{1}(\theta)+r_{2}(\theta)=c \tag{3}\]  
where,  
\[r_{1}d\theta=r_{2}d\varnothing \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Section** & **\(\theta\) [Radian]** & **\(f\) (\(\theta\))** \\ \hline \⃝1 & \(\left[0,\frac{3}{16}2\pi\right]\) & \(f_{1}^{\prime}=a\) \\ \⃝2 & \(\left[\frac{3}{16}2\pi,\frac{3}{16}2\pi\right]\) & \(f_{2}^{\prime}=a+\frac{1}{2}\cdot(1+\cos(8\pi))\) \\ \⃝3 & \(\left[\frac{4}{16}2\pi,\frac{5}{16}2\pi\right]\) & \(f_{3}^{\prime}=a+h\) \\ \⃝4 & \(\left[\frac{7}{16}2\pi,\frac{9}{16}2\pi\right]\) & \(a+h-h\cdot\left(1+\cos(4x+\frac{x}{2})\right)\) \\ \⃝5 & \(\left[\frac{7}{16}2\pi,\frac{9}{16}2\pi\right]\) & \(a-h+h\cdot\left(1+\cos(4x-\frac{x}{2})\right)\) \\ \⃝6 & \(\left[\frac{9}{16}2\pi,\frac{14}{16}2\pi\right]\) & \(f_{6}^{\prime}=a+h-\) \\ \⃝7 & \(\left[\frac{14}{16}2\pi,\frac{15}{16}2\pi\right]\) & \(f_{7}^{\prime}=a-h\) \\ \⃝8 & \(\left[\frac{15}{16}2\pi,2\pi\right]\) & \(f_{8}^{\prime}=a-h+\frac{h}{2}\cdot(1+\cos(8x))\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundaries of sectional angular and each function.  
Figure 5: Pitch curves of driving gear and driven gear.  
By combining Equations (3) and (4), \(r_{1}\) and \(r_{2}\) are derived as follows:  
\[r_{1}(\theta)=c\cdot\frac{\frac{d\varnothing}{d\theta}}{1+\frac{d\varnothing}{d \theta}}=c\cdot\frac{f^{\prime}(\theta)}{1+f^{\prime}(\theta)}r_{2}(\theta)=c \cdot\frac{1}{1+\frac{d\varnothing}{d\theta}}=c\cdot\frac{1}{1+f^{\prime}( \theta)} \tag{5}\]  
The length of the pitch curve of each section is given by Equation (6).  
\[S_{i}=\int_{\theta_{i-1}}^{\theta_{i}}\sqrt{\left(r_{1}^{\prime}\right)^{2}+ \left(r_{2}^{\prime}\right)^{2}}d\theta \tag{6}\]  
The total length of the pitch curve is calculated by Equation (7).  
\[S=\sum_{i=1}^{i}S_{i} \tag{7}\]  
In other words, if the angular velocity ratio \(f^{\prime}(\theta)\) and the inter-axis distance 'c' are determined, the pitch curve can be determined from the derived \(r_{1}(\theta)\) and \(r_{2}(\theta)\), and its results were shown in Table 2.
## 4 Hydrothermal Pre-Treatment
### Effect of Pre-Treatment on Lignin  
L Lignin is an amorphous and water-insoluble heteropolymer. It is composed of phenylpropane units (coniferyl, p-counaryl and sinapyl alcohol) held together by different linkages [61]. It binds cellulose and hemicellulose together and helps in supporting of plant structure and prevents them against microbial attack. The fermentation of lignocellulosic biomass is difficult due to the high recalcitrant lignin and inadequate accessibility of enzymes [46]. Lignin derivatives having aldehyde groups are inhibitory to methanogenesis stage [62]. The precursors in lignin, i.e., p-counaryl, coniferyl, sinapyl alcohol, decide the solubility of lignin in acidic, neutral or alkaline environment [63]. When the hydrothermal pre-treatment of lignocellulosic biomass is performed, the dissolved lignin may inhibit the activity of cellulase, xylanase and glucosidase [64]. Recent researches conclude that irrespective of its solubilisation, the lignin content change is related to the solidification and re-deposition due to cooling after severe pre-treatment. Therefore, only re-allocation of lignin takes place, instead of lignin removal during pre-treatment at high temperature and pressure [65; 66]. In the hydrothermal pre-treatment of switchgrass and paper tube residuals, such a re-allocation of lignin was observed [67; 68]. Besides this, the lignin present in raw corncob has a negative yield of sugars with furans produced from the same. However, if the corncob would have undergone hydrothermal pre-treatment, the delignification can give high sugar yields, which indicates that lignin dominates in biomass conversion giving sugar yields [69].
## 4 Results and Discussion
### Accuracy  
The accuracy metric demonstrates somewhat similar tendencies for all models--longer system calls sequences tend to provide better results (Table 1).  
None of the models reached the 90% accuracy for the sequence of 10 system calls, and the 90% threshold is achieved with 20 system calls sequence by all models. The sequence of 600 system calls can be considered as a threshold that starts providing practically applicable accuracy of more than 98% for all models (Figure 8).  
All models are performing almost the same, and there is no clear winner (results vary from 98.8% to 99.3% for the sequence of 1000 system calls). None of the dual-flow FCN family models demonstrates significantly better results than single-flow models, except cases with short system call sequences (up to 10 system calls). Addition of an RNN (LSTM or GRU) layer to the single-flow models also does not result in the expected better accuracy. For longer sequences, single-flow models are demonstrating better accuracy. The reduction of batch size from 100 to 20 had no statistically significant impact on the accuracy of the AWSCTD-CNN-D model.  
\begin{table}
\begin{tabular}{c|c c c c c c c} \hline \hline \multirow{2}{*}{**Count**} & **AWSCTD-** & **AWSCTD-** & **AWSCTD-** & **AWSCTD-** & **FCN** & **LSTM-** & **GRU-** \\  & **CNN-S** & **CNN-D** & **CNN-LSTM** & **CNN-GRU** & **FCN** & **FCN** \\ \hline
10 & 88.8 & 88.0 & 88.6 & 89.0 & 89.3 & 89.2 & 88.8 \\
20 & 91.7 & 92.7 & 92.7 & 92.8 & 92.3 & 92.7 & 92.8 \\
40 & 92
## 2 Materials and Methods
### Central Data Processing System
#### 2.3.2 The Concept of an FRM4 DOAS Module  
As already indicated in Section 2.3.1, the arrival of a new file triggers the start of the process, and this is managed via trigger lists and independent entities that we call wrappers. In Figure 7, we show a conceptual diagram of a module. The module wrapper (indicated by the brown-bordered circle) runs every 15 min (every 5 min for the TROPO.profiling module) and checks the module's trigger list for new entries. If new entries are found, information about the instrument and channel number is extracted from the file name, and based on this, the correct instrument configuration file(s) are extracted from the pool of instrument-specific module configuration files. This, together with the input file in question, is passed to the module, and the module is started, loading further general module configuration files.  
During the processing, warnings and errors are reported and gathered in log files that are saved and eventually archived. If no major errors occur, an output file is produced, and the name of this file is registered in the trigger list of the following module.  
While most of the modules themselves are implemented in Python version 3.9 (except for the core MMF algorithm and QDOAS (version 3.4.7), which are implemented in Fortran90 and C/C++ standard, respectively), the module wrappers are implemented as bash scripts.  
All log files concerning a specific level-1 file are gathered in a short report, including all warnings that occurred during any step of the processing, and sent via email to the data provider either daily, weekly or monthly, as chosen by the data provider.
## 2 Survey for Outdoor Agricultural UAV Applications
The applications in Table 1, which are not explained in detail below, are path-planning, field-monitoring and artificial pollination studies. These path-planning and field-monitoring studies aim to control the UAV performing this task in a more optimized approach, rather than performing an agricultural task. For a more realistic and generalizable scenario, some problems regarding the autonomous movement of the UAV need to be resolved. These can be summarized as the movement of the UAV in a dynamic environment, prevention of collisions and determination of the shortest path between mission points. Such studies are very few in the field of agriculture. Another agricultural UAV task with a very limited number of studies is artificial pollination. The aim is to distribute pollen among plants using small-sized UAVs. This field also has a gap in terms of application and needs new studies.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases
### New Mobile Edge Applications
#### 6.2.1 Internet-of-(Every)Thing  
By 2022, the number of IoT devices is expected to increase to 18 billion [111], each requiring network connectivity. Mobile edge computing can benefit small-scale personal IoT devices to large-scale design situations, such as smart cities and new industrial applications. Small devices such as in-home IoT tools (Amazon Alexa, Nest Cam, Google Home), can use mobile edge computing to offload computational tasks that are too complex for their small memory capacity [112; 113]. Users streaming videos from their mobile devices can enjoy cached versions of their desired content from mobile edge computing base stations [114], or videos automatically delivered in a quality/bandwidth supportable by their network based on local network conditions [115]. The growing augmented reality systems, such as Pokemon Go can store locally relevant information to overlay the user's environment in local mobile edge computing base stations such users experience reduced latency in comparison to information retrieval from regional cloud data centers [116].
## 3 Results
### Characterization of PS/OMMT Nanocomposite
#### 3.2.5 SEM-EDS Analysis  
Figure 9 shows the SEM images of the PS, PS/P-MMT, PS/P-SLS-MMT, and PS/P-KHS70-MMT nanocomposites. As seen in Figure 9A, the particles of pure PS were mostly spherical and the particle size was approximately 380 nm with a broad size distribution. In particular, a certain degree of coagulation of the particles was observed due to the poor stability. Compared with the pure PS, the particle morphology of the PS/OMMT nanocomposites was changed by the presence of the OMMT. When the OMMT was introduced in Figure 9B-D, the particle size distribution became narrow, and the particle size decreased to 250-350 nm. These results were in good agreement with the results of TEM and DLS. Moreover, each PS/OMMT particle kept good dispersion stability because of the stabilizing effect of OMMT for the polymer particle.  
The results of the SEM-EDS analysis are given in Table 3. The most pronounced distinction between the pure PS and the PS/OMMT nanocomposites was the drastic increases in the amount of Al and Si content in the PS/OMMT samples. This result indicated that the OMMT was successfully introduced in the polymer matrix.
# Performance Comparisons of Broadband Power Line Communication Technologies
## 2 Structures of PLC  
### IoT PLC  
The structure of an IoT PLC transmitter is shown in Figure 1[9]. Two information subblocks \(A\) and \(B\) with \(N\) bits are provided to the CTC encoder. IoT PLC uses two values for \(N\). For the data frame (DF), which is used for data transmission, \(N\) is 1440. The control frame (CF) or Mini DF uses a shorter frame length. For these frames, \(N\) is 48.
## 1 Introduction
Initially, minimization of make-span was selected as the objective function in order to develop the algorithm. Results from the developed algorithm are compared with a commercial scheduling software solution to show its effectiveness. Subsequently, the dependency of performance of the algorithm on the tuning parameters has been investigated. In addition, an alternative for the objective function, which was minimization of tardiness, was implemented for the algorithm to cater for practical use cases. Last but not least, the ACO algorithm has been further developed for the dynamic scheduling capability in an application deployed in the Model Factory at the Advanced Remanufacturing and Technology Centre (ARTC), which is a research and development program of the Agency for Science, Technology, and Research (A*STAR), Singapore. The outcome of the research can benefit not only MRO but any industrial sector with complex and variable process routings as well as dynamic changes in the shop floor. The structure of the paper is as follows. First, we define the problem in Section 2. After that, state of art and the developed ACO algorithm for solving scheduling problem is described in Section 3. Section 4 presents and discusses numerical results that include the comparison with a commercial software, effects of tuning parameters on performance of the algorithm, the expansion to using minimization of total weighted tardiness as the objective function and the dynamic scheduling capability. The conclusion and further improvements are pointed out in Section 5.
# A New Coding Paradigm for the Primitive Relay Channel +
## 2 Existing Upper and Lower Bounds
### Decode-and-Forward Lower Bound  
In decode-and-forward, the relay completely decodes the received sequence and cooperates with the source to communicate the message to the destination.  
For the _general relay channel_, decode-and-forward allows for achieving the following rate ([17] Theorem 16.2):  
\[R_{\rm DF}=\max_{p_{X_{\rm S}},p_{\rm R}}\min\{I(X_{\rm S},X_{\rm R};Y_{\rm SD }),I(X_{\rm S};Y_{\rm SR}|X_{\rm R})\}. \tag{14}\]  
For the case of the _primitive relay channel_, the decode-and-forward lower bound specializes to ([26] Proposition 2)  
\[R_{\rm DF}=\max_{p_{X_{\rm S}}}\min\{I(X_{\rm S};Y_{\rm SD})+C_{\rm RD};I(X_{ \rm S};Y_{\rm SR})\}. \tag{15}\]  
Note that the decode-and-forward lower bound (15) meets the cut-set upper bound (2) and is equal to the capacity of the primitive relay channel when either of the following two conditions holds:1. the primitive relay channel is degraded, which implies that \(I(X_{\rm s};Y_{\rm sg})=I(X_{\rm s};Y_{\rm sg},Y_{\rm SD})\);
2. \(I(X_{\rm s};Y_{\rm sg})\geq I(X_{\rm s};Y_{\rm SD})+C_{\rm ED}\).  
For the special case of the _erasure relay channel_, the decode-and-forward lower bound can be rewritten as  
\[R_{\rm DF}=\min\{1-\varepsilon_{\rm SD}+C_{\rm ED};1-\varepsilon_{\rm SR}\}. \tag{16}\]  
The decode-and-forward lower bound (16) meets the cut-set upper bound (3), and it equals the capacity of the erasure relay channel when either \(1-\varepsilon_{\rm SR}=1-\varepsilon_{\rm SR}\varepsilon_{\rm SD}\) or \(1-\varepsilon_{\rm SD}+C_{\rm ED}\leq 1-\varepsilon_{\rm SR}\).
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 2 Model Order Selection
### Information Criteria
#### 2.1.3 Information Criteria for a Sequence of Vectors  
In [31], a model for a sequence of vectors is stated: \(X_{i}=\mathbf{A}C_{i}+\mathcal{E}_{i}\in\mathbb{R}^{L}\), \(i=1,\ldots,K\); the rank of the matrix \(\mathbf{A}\in\mathbb{R}^{L\times r}\) is equal to an unknown \(r\), and the noises are independent. Here \(C_{i}\in\mathbb{R}^{r}\) consists of \(r\) stationary processes, and \(L\) noisy linear combinations of these processes are observed. In matrix form, the model is \(\mathbf{X}=\mathbf{A}\mathbf{C}+\boldsymbol{\mathcal{E}}\), where \(\boldsymbol{\mathcal{E}}\) is a matrix composed of independent noises.  
Thus, in such a model, \(X_{i}\) is a stationary process, allowing us to discuss the model of its auto-covariance matrix. It has a form \(\mathbf{R}=\mathbf{A}\mathbf{S}\mathbf{A}^{\mathsf{T}}+\sigma^{2}I\), where \(\mathbf{S}\) represents the auto-covariance matrix of the signals.  
The same model can be valid in a more general formulation of the problem; for instance, the matrix \(\mathbf{C}\) is not necessarily random and thus \(\mathbf{R}\) is not necessarily a covariance matrix. Additionally, the stationarity condition is not a prerequisite.  
Let \(\{\mu_{i}\}\) be the singular values of the matrix \(\mathbf{R}\). Then, as shown in [31], the logarithm of the maximum value of the likelihood function has the form  
\[\ln\mathcal{L}=-K\sum_{i=r+1}^{L}\ln\mu_{i}+(L-r)\ln(\sigma^{2}), \tag{5}\]  
where  
\[\sigma^{2}=\sum_{i=r+1}^{L}\mu_{i}/(L-r).\]  
The number of parameters is equal to \(r(2L-r)+1\)[31]. After adjusting for the penalty, we obtain the following:  
\[\mathrm{MDL}(r)=-2\ln\mathcal{L}-r(2L-r)\ln K. \tag{6}\]  
In this case, we consider the variant of MDL that is the same as BIC. However, to distinguish it from BIC in time series form, we will keep the name MDL.  
**Remark 1**.: _Let us show the similarities and differences with the problem given in the time series form. The transfer from time series to trajectory matrices yields the following: \(\mathbf{X}\) is the trajectory data matrix, \(\mathbf{A}\mathbf{C}\) is a possible form for the presentation of a trajectory signal matrix of rank \(r\), and \(\boldsymbol{\mathcal{E}}\) is the trajectory noise matrix. Then, \(\mathbf{R}\), which is formally not an auto-covariance matrix here, can be estimated as \(\mathbf{X}\mathbf{X}^{\mathsf{T}}/K\). The distinction lies in the fact that, in the case of time series, the elements of the noise matrix are not independent. This is due to the specific structure of the trajectory noise matrix, which is Hankel, meaning that the noise elements on each anti-diagonal are identical._
## 3 Multi-AGV Path Planning Based on Time Window
### Multi-AGV Path Planning Algorithm Based on Time Window  
Based on the above analysis, the running steps of the time window-based multi-AGV conflict-free path optimization algorithm are as follows:  
Step 1: Convert the AGV work environment into computer-recognizable information based on raster maps and set the AGV operating parameters;  
Step 2: Given a task list, read the task number, position information of starting point S, and position information of ending point E from the AGVs' task allocation table;  
Step 3: Assign tasks based on the task list, then search for available AGVs. If there are available AGVs, assign tasks to nearby available AGVs and continue to the next step; if there is no idle AGV, search again after one second;  
Step 4: Use the improved A* algorithm to plan the shortest transportation path for the AGV and obtain the time window information of the AGV passing through each grid cell on the shortest path. Continue to the next step;  
Step 5: Determine whether the shortest path passes through the grid cell where the idle AGV is located. If so, proceed to step 10; if not, proceed to the next step;  
Step 6: Determine if there is a time window conflict with the AGV carrying out the transportation task. If one exists, proceed to step 7. If it does not exist, proceed to step 11;  
Step 7: Determine the type of AGV conflict. If there is a first or second type of grid conflict with the AGV in transportation status, proceed to step 8. If there is a third or fourth type of grid conflict with the AGV in loading and unloading status, proceed to step 9. If there is a fixed-point conflict with the AGV in loading and unloading status, proceed to step 10;  
Step 8: Determine the priority of conflicting AGVs. The AGVs with a higher priority should maintain normal operation, while the AGVs with a lower priority should set the conflict grid cell as an obstacle cell in the grid graph. Go to step 4 and replan the path;  
Step 9: Determine the priority of conflicting AGVs. The higher priority AGVs pass through the planned path, while the lower priority AGVs wait for the higher priority AGVs to pass through the conflict grid cell, update the time window information, and proceed to step 5;  
Step 10: Set the grid cell where the AGV is idle or in loading and unloading status as an obstacle cell, and proceed to step 4 to replan the path;  
Step 11: The AGV performs transportation tasks. When the AGV reaches the endpoint, set the AGV status to idle and proceed to the next step;  
Step 12: Retrieve the task list to see if there are any unfinished transportation tasks. If so, proceed to step 3. If all tasks have been completed, proceed to the next step;  
Step 13: The algorithm ends.  
The flowchart is shown in Figure 7.