esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Brillouin Optical Correlation Domain Reflectometry
### Generation of Brillouin Dynamic Grating in Polarization-Maintaining Fiber  
Figure 40a schematically shows the generation of the Brillouin dynamic grating in a polarization-maintaining fiber (PMF). When the pump and probe lightwave are launched at each end of the fiber, respectively, on one polarization axis, and propagate in the fiber in the opposite directions with respect to each other, the stimulated Brillouin scattering takes place. Associated with the stimulated Brillouin scattering process, the molecular thermal vibration-induced acoustic wave is enhanced, which enlarges the propagating refractive index grating. It was thought that the orthogonal polarization lightwave could also be reflected when the Bragg condition is satisfied [19; 20].  
Figure 40b shows an example of the Bragg reflection of the orthogonal polarized lightwave [20]. The two spectral lines shown on the left side of Figure 40b are the Rayleigh backscattering of the pump light launched for the +z direction on the X polarization axis, and the stimulated Brillouin scattering propagating for \(-\)z direction, also on the X polarization axis. The frequency difference between the two waves is just the BFS. On the right side of Figure 40b, two other lines are seen. One is the Rayleigh backscattering of the reading lightwave propagating for the +z direction on the Y polarization axis, and the other is the read-out wave that is the reflection of the reading lightwave that satisfies the Bragg  
Figure 39: Experimental results of distributed measurement of (**a**) the Bragg reflection spectrum and (**b**) the Bragg frequency shift [94].  
condition. Due to the birefringence of the PMF, the Bragg wavelength or the frequency is different from those of the pump lightwave. In this experiment, the Bragg frequency difference is about 45 GHz.  
After the BDG phenomenon was demonstrated, several basic studies and applications have been performed [95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105]. Examples are the discriminative distributed measurement of strain and temperature, variable optical delay lines, correlation domain-distributed sensing of the BDG spectrum, time domain-distributed sensing of the BDG spectrum, distributed temperature sensing, distributed birefringence measurement along a polarization maintaining fiber, a cm-order high spatial resolution-distributed measurement with BOTDA technologies, and so on.  
Among these application studies, the technique for the discriminative and distributed measurement of stain and temperature is described in the following section, which utilizes BOCDA and BDG technologies.
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 6 Empirical Results and Analyses
### Applicability of the Model  
The above results show that the GA-LNS algorithm proposed in this paper is effective in optimizing logistics facilities and can be widely applied to other fields. For example, in the field of cold chain logistics, the GA-LNS algorithm can optimize the distribution route of low-temperature goods to ensure the quality of these goods while reducing energy consumption; in the field of express delivery, the GA-LNS algorithm can effectively respond to fluctuations in demand and improve the efficiency of delivery; in the field of takeaways, the GA-LNS algorithm can optimize the distribution route to ensure that delivery staff can meet consumers' needs in a timely manner.  
The GA-LNS algorithm can be flexibly adjusted according to the characteristics of different geographical areas. In dense urban areas, the GA-LNS algorithm can be adjusted by combining real-time traffic data, setting the appropriate delivery time window, and introducing an intelligent delivery management system to reduce delivery times and improve delivery efficiency and adaptability. In rural areas, the GA-LNS algorithm can be adjusted by adjusting the load capacity, flexibly scheduling the delivery time, combining local characteristics, dynamically forecasting demand, and using small-scale transport to improve distribution efficiency and adaptability. All the above measures show that the GA-LNS algorithm can work effectively in different logistics environments.  
Furthermore, in order to better promote the GA-LNS algorithm in different markets, in the future, we will customize specific optimization solutions for specific regions. Through cooperation with local logistics companies and the specific logistics needs of the region, we will effectively improve the application effect and industry acceptance of the GA-LNS algorithm.
## 4 Results and Discussion
propagation detection experiment, the real FBG signals were processed by four denoising methods, and the results show that the proposed algorithm was superior than the other traditional methods, since it retained the useful damage signal information and removed environment noise. Therefore, the definition methods of the best decomposition number and balance parameter can be used in the VMD-changed wavelet methods and the proposed denoising methods can process the FBG signal for fatigue crack detection.
# Narrowband Internet of Things (NB-IoT):
###### Abstract  
Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3 GPP) for Low-Power Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers. The survey also includes an overview of Evolved Packet Core (EPC) changes to support the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets through Control Plane (CP) and User Plane (UP), the possible deployment scenarios of NB-IoT in future Heterogeneous Wireless Networks (HetNet). Finally, existing and emerging research challenges in this direction are presented to motivate future research activities.  
narrowband; IoT; PHY; NB-IoT; MAC; deployment; survey; mMTC; 5G
## 4 Simulation Results
### Results on the Benchmark Test-Suite
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Name** & **Definition** & **Dim.** & **Global Minimum** & **Bounds** \\ \hline Ackley & \(f(x_{1},\cdots,x_{n})=-20\alpha exp\left(-0.2\sqrt{\frac{1}{k}\sum_{i=1}^{n}x_{i}^ {2}}\right)-exp\left(\frac{1}{k}\sum_{i=1}^{n}cos(2\pi x_{i})\right)+20+e\) & 30 & \(f(x^{x})=0\) & \(-10\leq x_{i}\leq 10\) \\ Rosenbrock & \(f(x_{1},\cdots,x_{n})=\sum_{i=1}^{n-1}[100(x_{i+1}-x_{i}^{2})^{2}+(1-x_{i})^{2}]\) & 30 & \(f(x^{x})=0\) & \(-10\leq x_{i}\leq 10\) \\ Chung-Reynolds & \(f(x_{1},\cdots,x_{n})=(\sum_{i=1}^{n}x_{i}^{2})^{2}\) & 30 & \(f(x^{x})=0\) & \(-10\leq x_{i}\leq 10\) \\ Step & \(f(x_{1},\cdots,x_{n})=\sum_{i=1}^{n}[|x_{i}|]\) & 30 & \(f(x^{x})=0\) & \(-10\leq x_{i}\leq 100\) \\ Alpine-1 & \(f(x_{1},\cdots,x_{n})=\sum_{i=1}^{n}|x_{i}\sin(x_{i})+0.1x_{i}|\) & 30 & \(f(x^{x})=0\) & \(-10\leq x_{i}\leq 10\) \\ SumSquares & \(f(x_{1},\cdots,x_{n})=\sum_{i=1}^{n}x_{i}^{2}\) & 30 & \(f(x^{x})=0\) & \(-10\leq x_{i}\leq 10\) \\ Sphere & \(f(x_{1},\cdots,x_{n})=\sum_{i=1}^{n}x_{i}^{2}\) & 30 & \(f(x^{x})=0\) & \(-100\leq x_{i}\leq 100\) \\ Bohachevsky-3 & \(f(x_{1},x_{2})=x_{1}^{2}+2x_{2}^{2}-0.3\cos(3\pi x_{1}+4\pi x_{2})+0.3\) & 2 & \(f(x^{x})=0\) & \(-100\leq x_{i},x_{2}\leq 100\) \\ Bohachevsky-2 & \(f(x_{1},x_{2})=x_{1}^{2}+2x_{2}^{2}-0.3\cos(3\pi x_{1})\cos(4\pi x_{2})+0.3\) & 2 & \(f(x^{x})=0\) & \(-100\leq x_{1},x_{2}\leq 100\) \\ Bartels Conn & \(f(x_{1},x_{2})=|x_{1}^{2}+x_{2}^{2}+x_{1}x_{2}|+|\sin(x_{1})|+|\cos(x_{2})|\) & 2 & \(f(x^{x})=1\) & \(-500\leq x_{1},x_{2}\leq 500\) \\ Goldstein-Price & \(f(x_{1},x_{2})=\begin{array}{l}[1+(x_{1}+x_{2}+1)^{2}(19-14x_{1}+3x_{1}^{2}-1 4x_{2}+6x_{1}x_{2}+3x_{2}^{2})]\times\\ \left[30+(2x_{1}-3x_{2})^{2}(18-32x_{1}+12x_{1}^{2}+48x_{2}-36x_{1}x_{2}+27x_ {2}^{2})\right]\end{array}\times\) & 2 & \(f(x^{x})=3\) & \(f(x^{x})=3\) & \(-2\leq x_{1},x_{2}\leq 2\) \\ Matyas & \(f(x_{1},x_{2})=0.26(x_{1}^{2}+x_{2}^{2})-0.48x_{1}x_{2}\) & 2 & \(f(x^{x})=0\) & \(-10\leq x_{1},x_{2}\leq 10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benchmark functions.
## 6 Results and Discussion
### Experiment II: Victims vs. Threat
As we see in the misclassified examples 5 and 6 in Table 10, speakers mention that thousands of people have arrived, or specifically the term _avalanchas_ (avalanches of people) but also refer, in the same sentence, to some words or phrases that are in the semantic field of compassion and victims such as _suffrimiento_ (suffering) and _han dejado su vida en el intento_ (have lost their lives in the attempt). We could think that fear is an emotion stronger than compassion, so humans give more weight to the part of the sentence that generates fear than to the part that generates compassion and consider that in this _frame_ migrants are presented as a _threat_. To understand better the confusion matrix shown in Tables 9 and 10, we explore the hypothesis that some parties perform a rhetorical strategy to avoid being tagged as xenophobic that consists in mentioning some expression of compassion just before or after they present immigrants as a threat, a strategy that does not convince humans' annotators but confuses the performance of transformers. As we have seen in Table 8, the presence of the n-grams _personas muertas_ (death people) and _trafican seres humanos_ (traffic human beings) in the dimension of _Threat_ could be indicative of this rhetorical strategy that we have mentioned above: \"appealing to pity and misfortune just before presenting the immigrant collective as a threat\". To explore this hypothesis, we did an analysis using the word \"party\" in the dataset, which indicates the party that uttered the sentence. We have 12 different parties, but we keep only those parties that have more than one hundred sentences in the dataset and we created a new category for the rest (_Other Parties_). We performed a chi-square test and a residual analysis to identify if the disagreement between human annotators and the model has any relation with the rhetorical strategy of a party. We found a significant relation (Pearson X2 = 36,979, df = 8, \(p\) < 0.000, see Table 10) between parties and the type of confusion, see Table 11. As it was mentioned in Section 5, for this experiment we balanced the classes to have 736 examples per class and, therefore, in this analysis we used a total of 1472 labelled examples. With the general category, _Other Parties_ and with \"Coalición Canaria\", a regional party from the Canary Islands, very concerned about immigration, we did not find any significant differences in the type (One type of confusion is when the annotators label is _Victims_ and BETO predicted it as _Threat_, and the other type is the opposite, the annotators label is _Threat_, but BETO predicted it as _Victims_.) of confusion between humans and the model (sometimes humans label _Victim_ and the model predicts _Threat_ and the opposite) but in case of the right win party Partido Popular (PP) humans label more often _Threat_ when the model labels is _Victim_, whereas with the left win party Izquierda Unida (IU) the type
# Extraction of Creation-Time for Recovered Files on Windows FAT32 File System
## 4 Evaluation
### Performance Comparison  
We compare the performance of the proposed method with that of the previous method [24; 25] (Although [PERSON]'s method [25] is designed only for Linux FAT32 file system but not for Windows FAT32 file system, it is selected because it is the most similar previous study.). In the previous method, the lower bound of the creation-time of a recovered file is the creation-time of the neighboring front file in the storage offset and its upper bound of creation-time is the creation-time of the neighboring rear file in the storage offset. We performed the same three experiments explained in Section 4.1 for the previous method. There are five image files recovered without the information of the creation-time in the first experiment, there are nine image files recovered without the information of the creation-time in the second experiment, and there are thirteen image files recovered without the information of the creation-time in the third experiment.  
Table 3 shows the accuracy of creation-time bound analyzed by the proposed method and the previous method. In the first experiment, the creation-time bounds analyzed by the proposed method are correct for all the five recovered image files. On the contrary, the creation-time bounds analyzed by the previous method are correct only for three recovered image files, but incorrect (i.e., the actual creation-time is earlier than the analyzed lower bound, or later than the analyzed upper bound) for two recovered image files. In the second experiment, the creation-time bounds analyzed by the proposed method are correct for all the nine recovered files, whereas those analyzed by the previous method are correct only for three recovered files but incorrect for six recovered files. In the third experiment, the creation-time bounds analyzed by the proposed method are correct for all the thirteen recovered files, whereas those analyzed by the previous method are correct only for two recovered files but incorrect for eleven recovered files. As shown in Table 3, the accuracy of creation-time bound analyzed by the proposed method is 100% in all the three experiments. On the contrary, the accuracy of creation-time analyzed by the previous method becomes much worse as the number of creation files and deleted files increases.
## 4 Experiment and Analysis
### Experiment Setup and Results
#### 4.2.3 Model Comparison
The CNN model requires pooling of n consecutive grams built on the entire sentence \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **F1(\%)** \\ \hline BiLSTM-Shortest-Path & 70.2 \\ GRN & 71.7 \\ CNN & 73.0 \\ BiLSTM & 73.9 \\ Tree LSTM & 75.9 \\ Graph LSTM-FULL & 76.7 \\ AGGCN & 77.4 \\ DIF-A-GCN(ours) & **77.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results of various models on the CIVIC dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **F1(\%)** \\ \hline MV-RNN & 50.0 \\ SCNN & 67.0 \\ CNN-bioWE & 69.8 \\ MCCNN & 70.2 \\ Joint AB-LSTM & 71.5 \\ RvNN & 71.7 \\ Position-aware LSTM & 73.0 \\ BERE & 73.9 \\ DIF-A-GCN(ours) & **77.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the results of various models on the DDI dataset. to obtain features of constant length, where n is the length of the convolution or filter, which may cause problems for long sentences or sentences with important cues far apart. The main feature of LSTM is that it bears some memory ability and performs better than CNN models when processing sequences (such as a sentence), especially when biomedical data have long sentences and large entity separation lengths. The model in this paper improves 3.2 percentage points over the best RNN-based BERE model. Not only because of the power of BERT encoder, but also because we perform graph convolution operations after efficiently pruning the dependency tree of the sentence, encoding the word-to-word dependency types into the corresponding feature representation of each node as well, obtaining a more complete and dense encoding representation of each word. By applying attention mechanism to dependency connections in the graph convolution layer, assigning a weight to each edge, they can be better utilized for semantic guidance in predicting the relation between given entities. The dependency structure-based model has better performance than the sequence feature-based model. It mainly because the structure-based model uses graph neural networks to capture more complex correlations between words, thus improving the ability of the feature representation to reflect relation in the text. It is also evident from the CIVIC dataset in Table 6 that AGGCN and DIF-A-GCN proposed in this paper can extract structural features better using the graph convolution module. They comprehensively utilizes the context semantic features of words and the long-distance structural features between words, and combine local and global correlations for relation extraction to obtain better results and improve F1 score.
# Effect of Thick-Hard Main Roof Fracturing on the Spatiotemporal Evolution of Overburden Fractures
## 1 Introduction
Affected by mining, many fractures are generated in the overlying soft rock after the main roof breakage; these joints and fissures are connected, forming complex water and gas conduction fracture zones. Accordingly, it is of significance to study the development and evolution of fracture zones for efficient gas extraction. [PERSON] et al. [8] used the full columnar in situ monitoring method of surface boreholes to study the evolution process of overburden movement during the mining process of the working face and inverted the development characteristics of the spatial morphology of the overburden mining fractures. On this basis, they proposed a comprehensive gas treatment scheme including the layout of the borehole, the horizontal distance of the protrusion into the working face. and the optimal time period for extraction. [PERSON] et al. [9] used similar laboratory simulations to study the evolution of the overburden fracture and compaction zones in large-dip coal seams during mining. The high-level gas drainage roadway and long-borehole extraction techniques are widely used in gas extraction [10; 11; 12]. [PERSON] et al. [13] believed that super-large diameter borehole comprehensive drainage technology can shorten the construction period and save extraction costs while ensuring gas extraction efficiency. Fractal dimension is an important concept to describe the complexity of fractal structures, which can accurately characterize the complexity of irregular figures. [PERSON] et al. [14] reconstructed the three-dimensional coal structure based on CT images so as to reveal the relationship between the porosity, permeability of coal, and the fractal dimension volume. Aiming to evaluate the seepage phenomenon in the fracture network accurately, [PERSON] et al. [15] used the fractal dimension method to study the equivalent permeability of the rock fracture network. The weakening of thick and hard main roof fracturing is an effective measure to prevent and control hurricane disasters in mines, and it is of great significance to grasp the spatiotemporal evolution law of overburden fractures for gas extraction in high-gas mines. The key research object for high-gas mines with large overhangs is the influence of different hydraulic fracturing schemes on roof weakening effects and overburden fracture development. Based on the previous research results and methods mentioned above, the permeability model of \"permeability-fissure ratio-fractal dimension\" was established in this study. The fractal dimension was used to quantitatively characterize the development degree of overburden fractures, which provided a reference for the overrun gas control in the initial mining stage. The research is of important reference significance for solving safe mining problems such as strong mine pressure appearance and gas drainage in coal mines.
## 2 Results and Discussion
### Analysis of Cinnamon Volatiles by SPME/GC-MS
#### 2.1.3 Sesquiterpene Hydrocarbons  
Likewise, in the case of alcohols, sesquiterpene hydrocarbons were reported among cinnamon bark VOCs, though at a trace level represented by \(\beta\)-caryophyllene in _C. zeyalnicum_ bark (1.3-6.9%) [7], in addition to calamenene, \(\alpha\)-muurolene, and \(\gamma\)-muurolene [34]. The present study revealed the presence of 23 peaks amounting for 3.1-16.2% in authenticated cinnamon vs. 0.4-20.7% in commercial products of the total peak areas, Table S1.  
\(\delta\)-Cadinene was the major form in CV at 3.5%. Additionally, (\(E\))-calamenene (P76) amounted to 56% of the total sesquiterpene hydrocarbons in CI detected at 9.1%, Table S1. (\(E\))-Calamenene (P76) was reported at 0.5% in cinnamon bark oil prepared by Clevenger apparatus [35]. Major sesquiterpene hydrocarbons detected at higher levels in commercial products included \(\beta\)-caryophyllene (P58) found at higher levels in commercial products, i.e., CP-2 (0.4%) and CP-4 (2.5%). Likewise, \(\alpha\)-muurolene (P71) and \(\delta\)-cadinene (P75) were major constituents, especially in CP-4 at 5.6% and 6.6%, respectively.
## 5 Conclusions  
An optical fiber sensor system with a high spatial resolution based on BOCDA was developed to monitor the structural deformation of composite bogie frames. The optical fiber was attached along the length of the lower part of the fabricated composite side beam to measure the distribution of strain in all sections during a vertical load test. The results obtained using the optical fiber were in good agreement with the results of the FE analysis, but there were sections where stiffness mismatches occurred, owing to the non-uniformity in thickness, which was the result of the fabrication process. An optical fiber was embedded in the composite bogie frame during the fabrication process, and it was used to measure the strain distribution in all sections during the vertical load test. The left and right side beams had the same strain distribution, indicating that the composite bogie frame was well balanced. Their structural stability was confirmed from this experiment. However, unpredictable strain occurred where the left side beam met the crossbeam, as confirmed by the side peak in the Brillouin spectrum for this section. This was attributed to a fabrication mismatch during the assembly of the composite bogie frame. The maximum strain was less than the limit, and residual deformation did not occur, ensuring the structural stability of the composite bogie frame. The occurrence of internal flaws in the composite bogie frame cannot be predicted; hence, a method for measuring internal deformation in all sections is required to ensure stability. The method presented here can be used to monitor the strain distribution of the composite bogie frames, and it could be used even when the composite bogie is driving on the track. If such a smart structure were applied to actual railcars, it could prevent abrupt fractures of the primary structure, which can cause severe damage, and reduce the maintenance costs of the vehicles.  
Conceived and designed the experiments, [PERSON]-[PERSON] and [PERSON]; designed the optical fiber sensor system based on the BOCDA Technique, [PERSON]; assisted with the experiments, [PERSON] and [PERSON].  
This research was supported by a grant from the R&D Program of the Korea Railroad Research Institute, Republic of Korea.  
The authors declare no conflict of interest.  
Figure 11: Brillouin spectrum of the optical fiber where the left side beam meets the crossbeam.