esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Experiments
#### 5.2.2 Energy Consumption  
The energy consumption of master and slave node is measured by a Tektronix-2024B oscilloscope. Figure 16 illustrates the energy consumption of master and slave node. One can clearly see that master node listens to channel every 100 ms. On the other hand, the slave node sleeps most of the time and only wakes up for data packet transmission.  
Using mode 4 of B-MAC, the average listening current of a slave node is measured. As shown in Figure 17, a node listens to the channel for 10 ms with an interval of 100 ms. Figure 18 shows the  
Figure 16: (a) Power consumption of master node. (b) Power consumption of slave node.  
Figure 15: The average packet loss rate is 5.6% and nodes with more hops to BS have greater packet loss rate.  
measured current values. The average listening current is calculated as 2.327 mA. Considering the environment noise, we set the average listening current to be 2 mA, which does not have any effect on our analysis.  
Figure 17: (a) Node listens to the channel for 10 ms with an interval of 100 ms. (b) The measure current in one listening period.  
Figure 18: The detailed values of the measured listening current.
# Study on the Properties and Fatigue Characteristics of Glass Fiber Composites Due to Porosity
## 4 Results and Discussion
### The Results of Fatigue Analysis  
Fatigue characteristics were predicted based on the material properties derived according to the porosity. The failure indicator applied was the Tsai-Hill 3D Transversely Isotropic [32], and the previous results of axial field strength, in-plane tensile strength, and transverse shear strength were applied as parameters at this time. The corresponding strain state was computed by the homogenization procedure and Pseudo grain stresses were computed for this stress state, according to the Voigt model. The Tsai-Hill 3D transverse isotropy criterion was calculated for these stresses, and the number of cycles was predicted as N. Damages from the macroscopic stress applied with the Tsai-Hill 3D Transverse Isotropic to fatigue loads are applied as follows:  
\[\begin{split} f(N)&=\frac{\sigma_{11}^{2}}{X^{2}(N )}-\frac{\sigma_{11}\big{(}\sigma^{22}+\sigma^{33}\big{)}}{X^{2}(N)}\\ &+\frac{\sigma_{22}^{2}+\sigma_{33}^{2}}{Y^{2}(N)}+\bigg{(}\frac{ 1}{X^{2}(N)}-\frac{2}{Y^{2}(N)}\bigg{)}\sigma_{22}\sigma_{33}\\ &+\frac{\sigma_{12}^{2}+\sigma_{13}^{2}}{S^{2}(N)}+\bigg{(}\frac{ 4}{Y^{2}(N)}-\frac{1}{X^{2}(N)}\bigg{)}\sigma_{23}^{2}\end{split} \tag{12}\]  
Among the above equations, \(\sigma_{ij}\) represents the stress amplitude components of the local axis system associated with each pseudo particle.  
\(X(N)\), \(Y(N)\), and \(S(N)\) indicate the axial, in-plane, and shear stress fatigue strength at breakage for the number of cycle estimates considered. The average failure index of the composites level is calculated by the number of cycles considered, and this average is calculated according to the pseudo grain weights (\(w_{i}\)) that are considered as the direction tensor, as shown in the equation below:  
\[f_{composite}(N)=\sum_{i=1}^{n}w_{i}f_{i}(N) \tag{13}\]  
The critical cycle count \(Nc\) is calculated and determined through iterative calculation by changing the estimated cycle count \(N\), as shown in the equation below:  
\[f_{composite}(N_{c})=1 \tag{14}\]  
The loading ratio \(R\) was applied to 0.5 by referring to references [13; 33]. Figure 4 shows the results of \(N_{c}\) for \(S_{max}\): the largest expected value of stress. Figures 5 and 6 shows the results of \(N_{c}\) for \(S_{a}\): stress amplitude. The analysis results in the following figures show that the more pores there are, the more vulnerable they are to fatigue, especially at a porosity of 2.5%.  
Figure 4: Graph of property changes according to VF (fiber volume fraction) analysis results.
## 3 Numerical Examples and Results
### Buckling Analysis and Pre-Stressed Modal Analysis  
For the clamped HCS plates subjected to the thermal loads, the static stabilities and dynamic characteristics are evaluated by thermal buckling analysis and pre-stressed modal analysis in thermal environments. The HCS plates are made of Ti-6 Al-4V and foam-filled with Rohacell 31. The reference geometric parameters are \(n_{c}=25\), \(a/h=50\), \(b/a=1\), \(h_{1}=h_{3}=h_{f}=0.1h\), \(t=0.1h\). Figure 8 shows the critical temperature and dimensionless fundamental frequency with \(\Delta T=100\) against geometric parameters change.  
The effects of the geometric parameters on the critical temperature change are discussed firstly. The number of the unit cells \(n_{c}\) varies from 5 to 50. With more unit cells, the critical temperature change firstly increases and then decreases with a maximum value at about \(n_{c}=15\). The inclined angle \(\theta\) takes the values from \(35^{\circ}\) to \(80^{\circ}\). The maximum critical temperature change is achieved at the minimum angle, corresponding to a triangular corrugated shape. The thickness of the face sheets \(h_{f}\) and the thickness of the corrugated members \(t\) both vary from \(0.025h\) to \(0.2h\). The critical temperature change increases and then decreases as the facesheet thickness increases, while it decreases as the corrugation thickness increases.  
The effects of geometric parameters on the dimensionless fundamental frequency with temperature change \(\Delta T=100\) have a similar tendency as those on the critical temperature change.  
Figure 7: Mode shapes of the (**a**) first; (**b**) second; (**c**) third; (**d**) fourth; (**e**) fifth; (**f**) sixth order of the clamped HCS plate with temperature change \(\Delta T=100\) obtained from MSC.Nastran simulation (blue meshes) and the present method (green meshes).
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 5 Performance Evaluation
### Experiment Settings  
In this experiment, we utilize the ground truth of semantic segmentation from the BLU dataset [28] as the digital terrain model of the deployment area, which is fed into the model to support deployment decisions for emergency communication network elements. For computational convenience, the area is divided into a \(256\times 256\) pixel grid, where each pixel corresponds to a physical space of approximately 5.5 m in edge length. The fiber-optic access points and disaster epicenters are randomly generated within the deployment area, while the locations of the remote microwave nodes are set outside the deployment region, also randomly generated, to enhance the diversity and generalization capability of the training samples. The simulation involves sequentially deploying seven types of communication network elements while taking into account dynamic environmental factors such as disaster direction.  
To evaluate the algorithm's performance, we use the total reward \(R_{total}\) obtained from all deployed elements in each episode as the key evaluation metric. The definition of \(R_{total}\) is as shown in Equation (10).  
\[R_{total}=\sum_{t=1}^{T}R_{t}, \tag{10}\]  
where \(t\) denotes the index of the communication element, \(T\) is the total number of communication elements, and \(R_{t}\) represents the reward obtained when deploying the \(t\)-th communication element.  
Based on this scenario, the algorithm is trained for 1000 episodes, with 60 environment explorations conducted in each episode. All simulations are conducted on an NVIDIA RTX 3090 Ti GPU with Ubuntu 22.04.4 LTS as the operating system. The implementation is based on Python 3.10 and the PyTorch 2.3 deep learning framework.
###### Abstract  
The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN) and boosted tree (BT), for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.  
Keywords:landslide susceptibility; artificial neural network; boosted tree; landslide inventory +
Footnote †: journal: Applied Sciences
# Performance Evaluation of Track Curvature Sensor for
## 3 On-Track Performance Evaluation of the Curvature Sensor
As described above, the curvatures of the test track were measured by installing the curvature sensor prototype on the test vehicle, and it was confirmed that the curvature measurement performance of the curvature sensor was evaluated at maximum 4.0% and on average 2.8%, and the speed had little effect on the curvature measurement. Therefore, the curvature measurement method derived from the proposed kinematic motion relationship is considered to exhibit very good curvature measurement performance.
## 3 Performance Evaluation
### Experimental Details
Training data are received from the system monitor of hardware components, which is illustrated in Table 1. The Anfis Editor window of the Fuzzy Logic Toolbox is designed to train the inference engine of the Sugeno FIS. The learning algorithm uses a hybrid algorithm, which is a combination of the gradient descent method and the least squares method. It is used to identify the parameters of fuzzy inference systems such as Sugeno. The learning process is shown in Figure 8b, training is completed in 1000 epochs and accuracy is equal to 0.211. The achieved accuracy of the training process is quite applicable for our Sugeno FIS. When the three parameters were given different input values, i.e., low, medium and high, the overall result is a fuzzy value. The output is defuzzified after the output of the used rule base. Figure 8: Defining rules for CPU utilization: (**a**) by rule base for the Mamdani FIS: (**b**) by training the dataset for the Sugeno FIS. The membership function, previously defined, is used to defuzzify the output value. In Figure 9, the Mamdani FIS and Sugeno FIS input parameters are shown with RAM = 75.2 and Storage = 96.2. The CPU utilization of value 45.3 was evaluated by the Sugeno FIS. The Mamdani FIS rated CPU utilization of 41.9 in accordance with established rules. The cumulative result is shown by a red line in the graph showing the status \"Normal\". In the Rule Viewer and the Surface Viewer (Figure 10), the user can see the input changes and affect the output for the Rule Viewer and the Surface Viewer shown in three-dimensional graphics.
# Dynamic Corrosion Test Using LiNO3 Containing Molten Salt for CSP Applications
## 4 Conclusions  
A dynamic corrosion test was performed to evaluate the compatibility of a mixture of nitrate salts containing 30 wt.% LiNO\({}_{3}\) + 13 wt.% NaNO\({}_{3}\) + 57 wt.% KNO\({}_{3}\) in A516 carbon steel.  
The composition profile for A516 corrosion was analyzed in three different zones mapping the content in iron and oxygen, obtaining the presence of magnetite (Fe\({}_{3}\)O\({}_{4}\)) for the inner layers and hematite (Fe\({}_{2}\)O\({}_{3}\)) for the outer layers. The thickness variation over the time was also monitorized during the experiment obtaining a metallographic corrosion rate of 0.015 mm/year. This corrosion rate would allow the use of this material for long-term service in CSP plants.  
Due to the lack of knowledge about the compatibility of molten salts with common container materials in the literature, this work can contribute to increase the knowledge in dynamic corrosion results using molten salts as thermal energy storage material.  
Conceptualization, A.G.F. and L.F.C.; methodology, J.N.M., B.M.-S., A.G.F. and L.F.C.; slab testing, J.N.-M., B.M.-S.; software, J.N.-M., B.M.-S.; formal analysis, B.M.-S., A.G.F., and L.F.C.; investigation, J.N.-M., A.G.F. and L.F.C.; writing--original draft preparation, A.G.F.; writing--review and editing, L.F.C.; supervision, L.F.C.; project administration, A.G.F.; funding acquisition, A.G.F.. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support provided by CONICYT/FONDAP 15110019 \"Solar Energy Research Center\" SERC-Chile. This work was partially funded by the Ministerio de Ciencia, Innovacion y Universidades de Espana (RTI2018-093849-B-C31- MCIU/AEI/FEDER, UE). This work was partially funded by the Ministerio de Ciencia, Innovacion y Universidades-Agencia Estatal de Investigacion (AEI) (RED2018-102431-T). This work is partially supported by ICREA under the ICREA Academia programme.  
The authors would like to thank the Catalan Government for the quality accreditation given to their research group (GREiA 2017 SGR 1537). GREiA is certified agent TECENIO in the category of technology developers from the Government of Catalonia.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 First-Order Theory of Beams (TBT)  
### Preliminary Comments  
In this section we consider the first-order shear deformation theory (TBT), most commonly known as the _Timoshenko beam theory_. The TBT brings the transverse shear strain \(\gamma_{xz}=2\varepsilon_{xz}\) and shear stress \(\sigma_{xz}\) into the calculations. However, in the TBT the transverse shear stress through the beam thickness is only represented as a constant, whereas the elasticity equations (as discussed in mechanics of materials books) show that the variation should be quadratic. To account for the inaccuracy in predicting the transverse shear force magnitude (not the shear stress distribution itself), _shear correction factor_ (SCF) has been introduced (see [2; 36; 37], among many others). According to [PERSON], the shear correction factor is the ratio of the average shear strain on a section to the shear strain at the geometric centroid of the cross section. The SCF, in general, is a function of the cross-sectional shape, Poisson's ratio, material properties, boundary conditions, and so on. For rectangular sections, [PERSON] [2] proposed a SCF \(K_{\mathrm{s}}=\frac{5(1+v)}{(6+5v)}\), which takes the range values \((5/6)\leq K_{\mathrm{s}}\leq(15/17)\) for \(0\leq v\leq 0.5\). Following these preliminary comments, we proceed, in somewhat parallel fashion to the developments presented for the classical beam theory (CBT), to develop the equations of equilibrium and exact solutions for the linear case.
## 4 Future Research Directions
### Open Research Direction for DSM
#### Consumers' Participation in the Proposed DSM Scheme  
The participation of consumers in various DSM schemes plays a vital role. The more consumers participate in the scheduling algorithm, the les or the demand during peak hours will be, and also the more stable the system will be. In fact, as the stability of the power system is linked with the consumers' demanded load, consumers' participation in the proposed DSM can lead to a flattened demanded load curve which can help the main grid to remain stable. To motivate consumers to join the proposed schemes, multiple incentives can be given to the consumers. This area can be further elaborated, and research attention is needed to develop new incentive schemes to attract the consumers' concentration towards the proposed DSM schemes.
# Improving Named Entity Recognition for Social Media with Data Augmentation
## 4 Experimental and Analysis  
### Set  
In our experiments, we used three social media benchmark datasets including WNUT16 (WN16) [29], WNUT17 (WN17) [30], and OntoNotes 5.0 (ON5e) [31]. The OntoNotes 5.0 is an English multitask corpus of over 90,000 documents and 160,000 sentences funded by the National Science Foundation. It includes text data of various types such as news reports, movie scripts, telephone conversations, lecture recordings, blog posts, etc. The WNUT 2016 is assembled on the basis of the Twitter NER task and contains both training and development sets. The WNUT 2017 is the second dataset and is derived mainly from Twitter. Also included are Reddit, YouTube, and StackExchange. All of these datasets are used for NER tasks and have been widely used in research in the fields of social media text analysis and NLP. These datasets feature coverage of common abbreviations, misspellings, and non-standard spellings that render NER tasks more challenging. These datasets can be used to train and evaluate NER algorithms. For all datasets, we used their original segmentation, and Table 1 reports their statistics on entity types, sentences, and overall number of entities in the train/dev/test sets.  
For model implementation, we followed the BIOES tagging scheme used by [19]. For the text input, we used both ELMo and bert-case large types of embeddings by default. In the contextual encoding layer, we utilized the Bi-LSTM encoder and set the exit rate to 0.2. To extract entity-similar words, we leveraged pre-trained GloVe word embeddings to obtain the top 10 most similar words (and n = 10) for each word. For the data augmentation module, we randomly initialized the embeddings of the extracted words to represent the information carried by these words.  
All experiments were conducted with a batch size of 64 and 100 epochs for each method, with hyperparameters tuned on the development set. We used precision (P), recall (R), and F1 score as evaluation metrics, which is consistent with previous works.
## 2 Methodology
### Sequencing the Mission Priority
#### 2.2.2 Calculation Model of Mission Priority  
For the impact factors of mission priority, the above qualitative impact factors should be quantified in the calculation model. We used technique for order preference by similarity to ideal solution (TOPSIS) [29; 30; 31] to perform a synthetic calculation and obtain quantitative indicators of mission priority. The implementation of our method is as follows:  
Step 1: For mission set \(T=\left\{t_{1},t_{2},\ldots,t_{m}\right\}\), the impact factors of mission priority are calculated to form an impact factor matrix \(X=\left[x_{ij}\right]_{m\times 7}\), where \(x_{ij}\) is the quantitative value of the \(j\)-th impact factor of mission \(t_{i}\), and \(m\) is the number of missions.  
Step 2: The range transformation method is used to transform the impact factor matrix into the standard matrix \(Y=\left[y_{ij}\right]_{m\times 7}\), \(0\leq y_{ij}\leq 1\):  
\[y_{ij}=\frac{x_{ij}-x_{\min}}{x_{\max-}x_{\min}} \tag{1}\]  
where \(x_{\max}\) is the maximum value in the column that contains \(x_{ij}\) and \(x_{\min}\) is the minimum value in the column that contains \(x_{ij}\).  
Step 3: The ideal solution is set as \(I=<I^{+},I^{-}>\), where \(I^{+}=(1,1,\ldots,1)\) is the positive ideal solution and \(I^{-}=(0,0,\ldots,0)\) is the negative ideal solution.  
Step 4: Calculate the close-degree \(C_{i}\) between mission \(t_{i}\) and the ideal solution:  
\[\begin{array}{l}C_{i}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}}\\ D_{i}^{+}=\sqrt{\sum\limits_{j=1}^{n}\left(y_{ij}-1\right)^{2}}\\ D_{i}^{-}=\sqrt{\sum\limits_{j=1}^{n}\left(y_{ij}-0\right)^{2}}\end{array} \tag{2}\]  
where \(D_{i}^{+}\) and \(D_{i}^{-}\) are the positive and negative distances between mission \(t_{i}\) and the ideal solution \(I\), and \(n\) is the number of impact factors.  
Step 5: The priority level of mission \(t_{i}\) is measured by \(p_{i}\):  
\[p_{i}=\left\lfloor C_{i}\right\rfloor\times 10 \tag{3}\]  
The priority level of each mission can be obtained through the above steps, and the level is in the range of [0, 10]. The higher the value of \(p_{i}\) is, the higher the priority of the mission.
## 2 Supervisory Control and Data Acquisition Systems  
SCADA systems have been very important in industrial automation and infrastructure management for decades. Initially developed for monitoring and controlling industrial processes, they have evolved significantly due to advancements in computing, communication technologies, to deal with the increasing complexity of critical infrastructure [2,3]. This section reviews the evolution of SCADA systems, their applications in water resource management, and the emerging trend of integrating 3D and VR technologies for enhancing operational efficiency and a more intuitive user interaction.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 3 GNSS Error Analysis
### Error Sources
#### 3.3.1 Thermal Noise  
Measurement thermal noise at the receiver is one of the components of \(w_{\rho_{i}}\) in (1). The effect of thermal noise can be accurately modeled as a white Gaussian random variable with zero mean and standard deviation \(\sigma_{\mathds{T}}\). For the pseudorange measurement, \(\sigma_{\mathds{T}}\) is typically between 10-30 cm, depending on the signal carrier-to-noise ratio, signal bandwidth, and receiver tracking bandwidth [39]. Estimation of the receiver state from multiple appropriately-weighted measurements with independent thermal-noise errors, and processing such measurements over time through a filter based on the modeled dynamics of the receiver, renders negligible the position-domain effects of uncorrelated zero-mean thermal noise. As a result, thermal noise is not a major contributor to the asymptotic accuracy of a digital map.