esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### 3D Modeling of Rowing Forces
#### 2.3.2 Simulator Coordinates for the Rowing Models  
To define the motion of the oar and resulting rowing forces, four different coordinate systems were defined. A boat-fixed coordinate system, i.e., \(\chi_{Boat}=\left\{B_{x},\,B_{y},\,B_{z}\right\}\), was defined on the boat's center of gravity such that \(B_{y}\) was directed towards the stern and \(B_{z}\) upwards (Figure 2). An oar-fixed coordinate, i.e., \(\chi_{Oar}=\left\{O_{x},\,O_{y},\,O_{z}\right\}\), was defined on the tip of the oar blade and was rotated with the blade movement. A special configuration of \(\chi_{Oar}\) was defined as the initial oar coordinate system, i.e., \(\chi_{Oar}^{initial}=\left\{O_{x}^{i},\,O_{y}^{i},\,O_{z}^{i}\right\}\), when the portside oar was in the initial configuration (\(\theta=0^{\circ},\delta=0^{\circ},\,\varphi=0^{\circ}\)) and perpendicular to the boat. Vertical (\(\delta\)), longitudinal (\(\varphi\)), and horizontal (\(\theta\)) rotations of the oar were the mathematically positive angles, i.e., defined in a counterclockwise (CCW) direction around \(O_{x}^{i},\,O_{y}^{i},\,O_{z}^{i}\) axes, respectively. To calculate the forces acting against the oar blade resulting from the relative blade velocity, a stream coordinate system, i.e., \(\chi_{Stream}=\left\{S_{x},\,S_{y},\,S_{z}\right\}\), was defined on the blade such that \(S_{y}=-B_{y}\) and \(S_{z}=B_{z}\).  
Calculated rowing forces at the portside (right) oar blade were mirrored onto the bow side (left) blade, simulating identical rowers for a pair boat. As a result, the rotation of the simulated boat around the \(B_{z}\)-axis was assumed to be \(0^{\circ}\), and the linear displacement of the boat was constrained to be along the \(B_{y}\)-axis for simplicity.
## 2 Materials and Methods
### The Properties of Packed Beds
#### 2.4.3 Specify the Kinetic Model Assumption and Mass-Transfer Coefficients  
In addition to the determination of the parameters of the adsorption isotherm, the correct description of the kinetics is of fundamental importance for an adsorbent material such as the CMS. In this case, it was decided to adopt a lumped resistance model (concentrated resistance) with a linear shape, which has the capability of simplifying the treatment as much as possible when taking into account a single resistance per component. Various authors [17] affirm that the diffusion of CH\({}_{4}\) and CO\({}_{2}\) in carbon molecular sieves can be separated into three different contributions: diffusion within the macropores, resistance to the entrance of the microporous and diffusion inside the micropores. However, [PERSON] et al. [8] reported that the contribution of the resistance to the entrance of the micro-pore is negligible in the case of CO\({}_{2}\) at the typical working pressures of the PSA process. Furthermore, on the basis of what is reported in the Aspen Adsorption Guide, it is clear that for the CMS materials, the limiting resistance is constituted by the diffusion contribution in the micropores. For this reason, as already mentioned, it was decided to simplify the treatment by taking a single  
Figure 2: Scheme of the PSA process simulated in Aspen Adsorption.  
kinetic parameter for CH\({}_{4}\) and CO\({}_{2}\). The kinetics is fitted with the linear driving force models that are typically used in kinetic studies [18]. The linear driving force model can be written as follows [19]:  
\[\frac{\partial q_{i}}{\partial t}=\text{K}_{i}\Big{(}q_{i}^{*}-q_{i}\Big{)} \tag{3}\]  
where, \(i=1\), \(2\) for CO\({}_{2}\) and CH\({}_{4}\), respectively, K\({}_{i}\) is the mass transfer coefficient (s\({}^{-1}\)), \(q_{i}\) is the adsorbed amount of component \(i\) (kmol/kg), and \(q_{i}^{*}\) is the equilibrium concentration of the adsorbed phase (kmol/kg).
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods  
This section is devoted to describing the analysis methodology followed to compare different GIF methods in a common dataset. Moreover, the state-of-the-art GIF techniques considered in this work are briefly introduced, highlighting the parameters that are tuned for the speech signal's GS and VT decomposition.
## 2 Green Scheduling Problem Model of Double Flexible Job Shop  
### Problem Description  
The DFJSP can be described as follows: \(n\) workpieces are processed on \(m\) parallel machines of different types, which can be operated by \(l\) workers with different skill levels. The workpiece \(i\) includes \(r_{i}\) processes, and the processing sequence of each process is given in advance. The \(j\)-th process \(O_{ij}\) of the workpiece \(i\) can be processed on any machine in the set of optional processing machines. At the same time, each machine can be operated by any worker in the set of optional workers. The solution of the DFJSP includes three sub-problems: machine selection, worker selection, and process scheduling. Its scheduling goal is to select the most suitable machine and matching operators for each process and determine the optimal processing sequence and start-up time of each process on each machine so that some performance indexes of the production system can be optimized.  
The double-flexible job shop green scheduling problem constructed in this paper has three optimization objectives: minimizing the maximum completion time, total labor cost, and total green index. Among them, the maximum completion time represents the final completion time of all jobs and all processes, which reflects the production efficiency. The total labor cost is an important part of the production cost of an enterprise. By reducing the total labor cost, the operating cost of an enterprise can be reduced. The total green index in this paper includes energy consumption, noise, workpiece chip recycling, and safety, making it a comprehensive green production index. Minimizing the total green index can help enterprises save energy, reduce emission, and reduce the impact on the environment.  
The assumptions and constraints to be met are as follows: (1) Each machine can handle only one process at a time. (2) Each process can be processed only once on one machine. (3) Each worker can operate only one machine at a time. (4) Each process can be processed only once by one worker. (5) There are sequential constraints between the same workpiece processes, and there are no sequential constraints between different workpiece processes. (6) Interruption is not allowed after a process starts. (7) All machines and workpieces are ready at zero hour. (8) The installation and adjustment time of the workpiece is not considered. According to the skill levels, workers are divided into three types. In addition, the processing time, the labor cost, and the energy consumption corresponding to each process, as well as the attribute values of various green indexes of the machine, are known and fixed.
## 2 Results and Discussion  
### Sequencing and Assembly  
To determine the transcriptomes of the leaves and roots of _G. rigescens_, two sequencing libraries were prepared and sequenced with the Illumina paired-end technique. As a result, over 50 million clean reads per library were obtained after cleaning and quality checks were performed. The sequencing data quality assessments are shown in Table 1. The error rate of both root and leaf is 0.03% (Q20 and Q30 are over 96% and 90%, separately), indicating a high quality of sequence. The sequencing raw data has been deposited into the Short Reads Archive (SRA) database under the accession number SRP027253.  
The clean reads were combined and assembled by using the Trinity program, which has been shown to be an excellent assembler for _de novo_ transcriptome assembly from short-read RNA-Seq data [20]. Assembled sequences were subjected to cluster using the Trinity algorithm. As a result, 191,541 contigs clustered into 78,433 Trinity components (mean size = 743 bp, N50 = 1365 bp). Each Trinity component defines a collection of transcripts that are most likely to be derived from the same locus (except a portion from very closely related paralogs) [20, 21]. This component was defined as a unigene and the longest transcript in each component was used to represent the corresponding unigene in this study. After removal of 1716 (2.2% of total) contaminant unigene sequences from non-plant species (see Materials and Methods), a transcriptome of 76,717 unigenes with a total size \(\sim\)57.7 Mb was established for _G. rigescens_. The sequences of the unigenes (longer than 200 bp) were deposited in the NCBI (National Center for Biotechnology Information) Transcriptome Shotgun Assembly Sequence Database (TSA) according to its standard criteria (downloaded from the BioProject: PRJNA211794, Accession Number: GDAB0000000). The full list of transcript sequences is also available upon request.  
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Sample**} & **Raw** & **Clean** & **Clean Bases** & **Error** & **Q20** & **Q30** & **GC** \\  & & **Reads** & **Reads** & **(G)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{2}{*}{Leaf} & Leaf\_1 & 57,802,913 & 56,289,486 & 5.63 & 0.03 & 97.97 & 93.09 & 43.00 \\  & Leaf\_2 & 57,802,913 & 56,289,486 & 5.63 & 0.03 & 97.48 & 92.61 & 43.04 \\ \hline \multirow{2}{*}{Root} & Root\_1 & 53,933,882 & 50,596,096 & 5.06 & 0.03 & 97.02 & 90.79 & 43.23 \\  & Root\_2 & 53,933,882 & 50,596,096 & 5.06 & 0.03 & 96.59 & 90.34 & 43.31 \\ \hline \hline \end{tabular}  
* Leaf\_1, Root\_1, Leaf\_2, Root\_2: The left and the right reads, separately; Raw reads: Statistical raw sequence data with four lines as a unit, to sum the sequence number of each file; The Clean Reads of the Leaf or Root were the sum of the left and right reads. Error rate: Bases error rate; Q20 and Q30: The percentages of the bases whose Phred values were more than 20 and 30, separately.  
\end{table}
Table 1: The quality assessment of the sequencing data.
## 7 Conclusions  
A sub-annual change detection (SCD) method was proposed in this study in order to detect dates of abrupt land-cover change on sub-annual scale (_i.e._, 16-day step) from very dense time series of satellite images. The proposed method divides time series into annual segments and statistically detects changes in each segment. It estimates change dates by analyzing differences between two consecutive annual segments at yearly and time-point scales coherently. Additionally, it does not require prior information about land-cover types or change trajectory, which is often necessary for existing methods. Detected change dates can be used to determine appropriate segments of a time series for land-cover trajectory mapping and also facilitate studies of land surface processes, which require specific information about land surface changes.  
The simulated time series with various land-cover classes and change types confirm that the proposed method can be applied to a range of time series representing a wide variety of land surface changes and disturbances. The method was applied to MODIS 16-day NDVI time series (2000-2010) for a study site in southeastern Ohio, USA. The results show that the proposed method is capable of detecting change dates with various types of change trajectories representing the transitions among different vegetated land-cover classes. The results were also compared with the BFAST [15] method, which offers estimation of breakpoints in a time series for trend and seasonal changes about vegetated land covers. Although the proposed method does not differentiate trend and seasonal changes, the comparison suggests that both methods can detect change dates from NDVI time series with similar accuracy (e.g., with RMSE of 4.05 and 6.02 steps for the detected change date using the proposed method and BFAST, respectively) and the proposed method is more computationally efficient (e.g., spent 16 seconds, _vs._ 635 seconds for BFAST, for 244 samples). The high efficiency can improve the processing of large data sets on both regional and global scales.  
The change detection analysis at sub-annual basis ought not to be limited to MODIS data only but other data source providing frequent and regular observations with higher spatial resolution. For instance, the coarse spatial resolution of MODIS data may be inadequate to detect land-cover changes, which occurred in fine spatial resolution (_i.e._, sub-pixel level). Future research is expected to test the method in other areas and with data sources of finer spatial resolution. It is also important to explore application with irregular observations with the development of blending techniques between high spatial resolution sensor (e.g., Landsat TM) and high temporal resolution sensor (e.g., MODIS), as well as advancement of technology in satellite constellation operation (e.g., RapidEye).
## 3 Calculation Results and Influence Factors Analysis
### Analysis of Ground Horizontal Deformation  
The excavation of the shield tunnel can result in significant horizontal deformation of the surrounding terrain, potentially leading to tensile damage to adjacent structures. Consequently, a comprehensive analysis of the horizontal deformation of the ground surrounding the double-line tunnel becomes imperative. As illustrated in Figure 8, the horizontal deformation curves of three tunnel types under various tunnel spaces are extracted, and the maximum deformation and its influence range are analyzed.  
As shown in Figure 8, the ground horizontal deformation curves of the single-line tunnel and the double-line tunnel excavated at the same time are centrosymmetric around the zero point of the symmetry axis. The zero point of the horizontal deformation of the surface is located at the surface above the central axis of the tunnel. In contrast, the horizontal deformation curve of the surface of the continuous excavation double-line tunnel is non-centrally symmetrical, and the zero point of the horizontal deformation of the surface is located on the left side of the central axis. The maximum horizontal ground deformation caused by continuous excavation of the double-line tunnel is larger than that caused by simultaneous excavation of the double-line tunnel. Due to the secondary disturbance of the soil body by the later excavated tunnel, the horizontal deformation of the first excavated tunnel is larger, which also has a greater impact on the safety of the neighboring buildings. In Figure 8a-c, the maximum horizontal ground deformation of the double-line tunnels decreases with increasing tunnel spacing. When the tunnel spacing is small, the ground level deformation of the double-line tunnel is larger than that of the single-line tunnel, but,as the tunnel spacing increases, the maximum ground level deformation of the double-line tunnel is smaller than that of the single-line tunnel. This suggests that there may be a specific value of tunnel spacing from 1.5D to 2.0D such that the maximum horizontal ground deformations of the double-line tunnels and the single-line tunnels are equal.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 4 Establishment of LTE-R Time Synchronization SPN and MC Models
### Isomorphic MC Based on SPN  
When the SPN isomorphism is a continuous-time Markov chain, the isomorphism transformation is carried out according to the following steps:  
1. Firstly, the firing rule between states in the SPN model is analyzed, and the marked reachable sets of all states are obtained;
2. Each marking of the SPN is converted into a node corresponding to the continuous-time Markov chain reachability graph;
3. The transition events between different markings in the SPN model are mapped into arcs between nodes of the CTMC reachability graph, showing the logical relationship between the different states of the system;
4. Then, the transition firing rate in the SPN model is marked on each arc in the reachability graph of CTMC, and the CTMC distribution probability function is obtained.  
Firstly, the SPN model reachable sets are constructed.In the SPN model displayed in Figure 5,when the LTE-R time synchronization process does not undergo an ARP attack and is in the normal operation state, there is a token in \(P_{0}\), \(P_{1}\), and \(P_{2}\) in the model, and the initial state \(M_{1}\) can be marked as \(M_{1}\) = (0, 1, 2). When the attacker launches an ARP attack, the tokens at \(P_{0}\), \(P_{1}\), and \(P_{2}\) begin to move. According to the relationship between the different transition events of the SPN model in Figure 5, the following state reachable sets can be obtained: \(M_{2}\) = (1, 2, 3), \(M_{3}\) = (1, 2, 5), \(M_{4}\) = (1, 2, 6), \(M_{5}\) = (1, 2, 9), \(M_{6}\) = (1, 2, 10), \(M_{7}\) = (1, 2, 4), \(M_{8}\) = (1, 2, 7), \(M_{9}\) = (1, 2, 8), \(M_{10}\) = (1, 2, 11), \(M_{11}\) = (1, 2, 13), \(M_{12}\) = (1, 2, 12), \(M_{13}\) = (1, 2, 15), and \(M_{14}\) = (1, 2, 14). \(M_{1}\)-\(M_{14}\) are 14 states of the LTE-R time synchronization SPN model, including the eNodeB abnormal end state \(M_{5}\), eNodeB normal end state \(M_{6}\), OBC abnormal end state \(M_{13}\), and OBC normal end state \(M_{14}\). According to the SPN model, they are transformed into the nodes of the CTMC reachability graph.  
Then, the transition events \(t_{i}\) converted between the different markers in the SPN model are mapped to the arcs between the nodes of the CTMC reachable graph, and the firing rates \(\lambda_{i}\) are correspondingly marked on each arc in the CTMC reachable graph. \(\lambda_{1}\)-\(\lambda_{19}\) are the average firing rates between the different states in the SPN model. The average firing rate of the different states \(\lambda_{i}\) (\(i\) = 1, 2,..., 19) depends on the occurrence of different changes. If a directed arc is used to represent the transformation of different marks or states, an MC equivalent to the SPN model can be obtained, as shown in Figure 6.
## 5 Analysis of Influence Factors
### Influence of Pile Parameters
#### 5.2.2 Influence of Pile Spacing  
As can be seen from Figure 24, the horizontal displacement of the piles is essentially the same for different pile spacings. When the pile diameter is reduced by every 0.2 m, the curve of horizontal displacement, settlement, and axial force of the pile at each pile distance are compact. Under the condition of the stratum, the pile spacing changing within the scope of 1.2 m to 2.0 m does not have a significant influence on retaining structure and settlement. Therefore, the pile spacing is suggested to be increased appropriately to 1.4-2.0 m.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 3 LP-WAN Solutions for IIoT Services
### Other Alternatives  
Besides the three solutions mentioned so far, there are other alternatives that, up to the date of preparing this article, either are in a less advanced deployment state or their technical insights are not yet available. For example, Ingenu (formerly known as On-Ramp) is a LP-WAN platform currently beginning its deployment in the USA. It is based on its proprietary RPMA (Random Phase Multiple Access) technology, which has the particularity of working in the 2.4 GHz band. In addition, it permits both star and tree topologies by using different network hardware. Although Ingenu has raised high expectations regarding the range, edge-device's battery lifetime, and available bandwidth [24],these promising figures should be confirmed in real deployments as they have been extracted so far only from simulation studies.  
Mostly focused in the Smart Cities market, Telensa [25] has also developed its own bi-directional ultra-narrow-band technology. Telensa's PLANet (Public Lighting Active Network) and PARKet are focused on street lighting control and smart parking enhancement, respectively. Both of them are defined as end-to-end systems, from edge-nodes (telecells) to the end-user interface, including base stations. By using their proprietary technology, Telensa claims to reach 2-3 km (urban) and 5-8 km (rural) real ranges. They have already deployed their solutions in different big cities worldwide.  
In turn, Dash7 is an open standard promoted by the Dash7 Alliance [26], which has its origin in the ISO/IEC 18000-7. Unlike the afore-reviewed solutions, Dash7 proposes a two-hops tree topology composed by hierarchized devices, namely endpoints, sub-controllers, and gateways. Notice that this topology is similar to the traditional WSN architecture instead of the long-range systems described in this article. The main advantages provided by the Dash7 protocol are the extended range in comparison with other pure-WSN solutions due to the use of sub-GHz bands (433 MHz and 868/915 MHz), the possibility of direct device-to-device communication, which is not currently available in any of the LP-WAN platforms described above, and its compatibility with Near Field Communication (NFC) radio devices. However, this proposal has not been widely adopted yet, and only some pilot projects have been carried out so far [27].  
Finally, it is worth mentioning other solutions such as those proposed by Helium [28], M2M Spectrum Networks [29] (recently joined the LoRa Alliance), or Amber Wireless [30] which, although less expanded, could bring more competence to this growing market in the future.
## 3 Our Approach
### Graph-STNN
#### 3.3.2 Temporal Encoder  
The temporal encoder is designed to encode the temporal information of the exploration process from the position change of the robot in the map. Thus, we only consider the historical trajectory nodes in incremental graphs.  
For the temporal encoder, we use a gated temporal convolutional network (gated TCN) [29]. Different from the original temporal convolutional network (TCN) [30], gated TCN uses a gating mechanism to improve the ability of information extraction. Compared with recurrent neural networks (RNNs) [21], gated TCN deals with parallel input, which reduces memory expense and amounts of parameters. Therefore, our temporal encoder consists of two same TCNs with different gates, i.e., Tanh and Sigmod function. We concatenate the initial features with the spatial features as:  
\[\mathbf{p}^{\prime}=\text{concat}(\mathbf{F},\mathbf{F}_{\text{Spatial}}). \tag{11}\]  
The input of our temporal encoder is \(\mathbf{P}\), which consists of the trajectory nodes' features in \(\mathbf{P}^{{}^{\prime}}\). We denote the \(t\)-th node in \(\mathbf{P}\) as \(\mathbf{p}_{t}\). The update of \(\mathbf{p}_{t}^{(l)}\) at layer \(l\) in TCN is represented as:  
\[\mathbf{p}_{t}^{(l+1)}=\mathcal{F}_{d_{l}}(\mathbf{P}^{(l)})|_{\mathbf{p}_{t}^ {(l)}}=\sum_{k=1}^{K}f_{k}\mathbf{p}_{t-(K-k)d_{l}}^{(l)}, \tag{12}\]  
where \(K\) is the one-dimensional convolution kernel size, \(d_{l}\) the expansion factor controlling the jump connection of layer \(l\), \(f_{1}\sim f_{K}\) the learnable parameters of convolution kernel. Denote the output of these two TCNs as \(\mathbf{P}_{1}\) and \(\mathbf{P}_{2}\), respectively. The final temporal feature is calculated as:  
\[\mathbf{F}_{\text{Temporal}}=\sigma_{h}(\mathbf{P}_{1})\odot\sigma_{g}( \mathbf{P}_{2}), \tag{13}\]  
where \(\sigma_{h}\) is Tanh function, \(\sigma_{g}\) Sigmod function, and \(\odot\) the element-wise product.