esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Reliability Analysis  
This study implemented a reliability analysis to assess the structural efficacy and security. As previously mentioned, reliability analysis guarantees structural design safety under uncertain circumstances. That is why the variability in materials and loads' properties should be considered to realize precise and safe structural design. For this reason, the objective function of this study is constrained by the reliability index (\(\beta\)). This value is calculated by the generation of the FEM model through the PyNite and FORM analysis through the PyNite library to perform multiple FEM analyses for uncertain variables representing distinct design scenarios by using the Python node depicted with a part of the code in Figure 4. The distribution of design variables can be adjusted along with specified mean and standard deviations corresponding to the probabilistic nature of the variables, as depicted in Figure 3a. Certain failure conditions of the structure, defined through LSF, must be met with constraints specified by the inputs in Figure 4. The LSF can be the displacement at a particular node or entire structure, or stress values limited to a certain amount. Based on the results obtained from FORM analysis, the reliability index, which quantifies the issue of safety and gives a clue to the safety margin of the structure and the probability of failure, which was estimated from the reliability index, were determined for each design scenario. The higher value of \(\beta\) suggests that the probability of failure is lower. For better understanding, following pseudo-code, which serves as a foundation for the corresponding Python implementation shown in Figure 4, has been prepared and presented in Algorithm 2.
Model development is based on the experiences gained from a Colombian coffee farm, named \"La Sultana,\" and evaluated using data from the Costa Rican Coffee Institute (ICAFE). The dataset used in the case study contains actual information about the cost of coffee production, different production volume levels (High, Average, and Low), and the international market, mainly price and exchange rate. HMP-Coffee achieved 81.72% accuracy, 81.33% precision, 92.30% recall, and 83.46% F-Score in the evaluation results. From the obtained results, we conclude that HMP-Coffee is a promising solution for precisely estimating the level of profitability in small coffee productions in developing countries and supporting correct decision-making. The remainder of the paper is organized as follows. Section 2 introduces the background and related work. Section 3 presents material and methods; Section 4 describes ICAFE study case, discussion, and practicability. Finally, Section 5 outlines the conclusions and future work.
## 2 Materials and Methods
### Data Contraction Detection Methods
#### 2.2.2 Teager Energy Operator  
The Teager energy operator (TE) [37] is an energy estimator that has been used in the context of EHG processing for IUP calculation [35]. The TE provides a representation similar to the mechanical process of a myometrial contraction [26]. The TE operator, denoted by \(\Phi\), is a nonlinear transformation of an input signal \(x(t)\). In the continuous-time case, it is given by:  
\[\Phi[x(t)]=\left(dx(t)/dt\right)^{2}-x(t)\;d^{2}x(t)dt^{2} \tag{3}\]  
In the discrete domain, the TE operator takes the form:  
Figure 1: Representation of the Icelandic electrohysterogram (EHG) database channels disposition [31]. The blue arrow is the selected channel for this work.  
\[\Phi[x(n)]=x(n)^{2}-x(n-1)x(n+1) \tag{4}\]  
where \(n\) is the sample index in \(x(t)\). A smoothing process is required in order to reduce the transformed signal \(\Phi[x(n)]\) variance, which is done using a sliding window moving average filter:  
\[[\Phi[x(n)]]_{smooth}=\frac{1}{M+1}\sum_{m=-M/2}^{M/2}\Phi[x(m+n)] \tag{5}\]  
The success of the application of this operator in the EHG for energy estimation will depend on the \(M\) value [26].
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 3 Methodology
### Classification Algorithms
#### 3.2.4 Convolutional Neural Networks  
In our work, the CNNs are employed to apply convolutional operations to the sequence of context embeddings in order to identify local patterns or features within each input sentence. The CNNs employ convolutional layers to capture local patterns or features in the input sequence of context embeddings. The input sequence is structured as a matrix, where each row corresponds to a word embedding and each column represents a dimension of the embedding vector. The convolutional layers utilize one-dimensional convolutions to process these matrices, where each filter performs a dot product with the input at each position, generating a feature map. These feature maps are then passed through a pooling layer to reduce their dimensionality before being fed into a fully connected layer for alarm classification. By applying convolutional operations to context embeddings, CNNs can learn to recognize local patterns or features in alarm data, which are valuable for distinguishing between false positives and actionable alarms. Figure 6 illustrates the architecture of the CNN model, as well as the overall classification procedures.  
To elucidate this, we implement a customized class named _CNN_, which is a classifier inherited from the _nn.Module_ class in which context embeddings undergo convolution along the second dimension. Following the application of multiple convolutional filters, the resulting outputs are then subjected to max-pooling, which serves to reduce the dimensionality of the features while preserving the most important ones. Finally, the resulting features are passed through the fully connected layers for classification, enabling the prediction of whether the alarms are actionable.  
In general, we utilized the pre-trained CodeBERT model to generate context embeddings for the warning details and corresponding code snippets, which served as inputs to the classification algorithms mentioned above. The neural network architectures were  
Figure 5: The illustration of a gated recurrent unit (GRU).  
employed for classification to determine whether an alarm was an actionable warning or a false alarm. A detailed description of the performance of these four classifiers will be provided in the next section.
# Temperature Effect on Stability of Tunnel Face Under Unsaturated Seepage Condition
###### Abstract  
As tunnel excavation technology matures and the demand for transportation infrastructure continues to grow, several high-temperature tunnels have successively emerged in high geothermal areas. The construction of tunnels in high-temperature regions is gradually becoming a new challenge encountered in the engineering field. This study aims to conduct a stability analysis of tunnel face excavation under different temperatures. In addition, soil is often considered to be unsaturated. A framework for assessing the stability of tunnel faces in unsaturated soils under fluctuating temperature conditions is proposed, with an analytical approach. The theoretical basis of this framework is established on the influence of temperature on the shear strength of unsaturated soil. The matric suction of unsaturated soil changes with temperature, thereby inducing variations in shear strength. The temperature-induced variation in apparent cohesion is quantified utilizing a temperature-sensitive effective stress model coupled with a soil-water characteristic curve. These models are subsequently incorporated into the stability assessment of tunnel faces in unsaturated soils under steady-state flow conditions. A three-dimensional logarithmic spiral model is utilized to ascertain the unsupported pressure on tunnel faces, with the safety factor (FS) being calculated through an iterative methodology. Subsequently, a comprehensive suite of parametric studies is undertaken to explore the influence of temperature on tunnel face stability under unsaturated seepage conditions, offering valuable insights for practical engineering endeavors.  
Keywords:temperature effect; matric suction; tunnel face stability; unsaturated seepage; safety factor +
Footnote †: journal: Applied Sciences
Sensors_**2012**, _12_, 6764-6801; doi:10.3390/s120606764  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation**  
**[PERSON] \({}^{\star}\) and [PERSON] [PERSON]**  
Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924 Lodz, Poland;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +48-42-631-2685.  
_Received: 6 March 2012; in revised form: 19 April 2012 / Accepted: 29 April 2012 / Published: 25 May 2012_  
**Abstract:** The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian's steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences _etc._ This limits position inaccuracy to _ca._ 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS.  
**Keywords:** particle filtering; stereovision; digital maps; GPS; Monte Carlo; dead reckoning
## 4 Results and Analysis
### Results of Classifiers with Hyperparameter Optimization
#### 4.2.3 Hyperparameter Optimization of J48 DT  
For the J48 decision tree classifier, the confidence factor (C) used for pruning and the minimum number of instances per leaf (M) were tuned with the unpruned option as False for all the trainings. The results are provided in Table 10.  
From Table 10, J48 has been trained 9 times by changing the C values to 0.25, 0.5, and 0.75 and M values to 1, 2, and 3. The accuracy of J48 at M = 1, C = 0.25 and 0.5 is 95.1134%, and the accuracy at M = 2, C = 0.25, 0.5 and 0.75 is 93.8918%. The best accuracy yielded by J48 is 95.4625% at an M value of 1 and a C value of 0.75 at the 7 th training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training No.** & **Confidence Factor, C** &
\begin{tabular}{c} **Min. Number of Instances** \\ **per Leaf, M** \\ \end{tabular} & **Accuracy** \\ \hline
1. & 0.25 & 1 & 95.1134 \\ \hline
2. & 0.25 & 2 & 93.8918 \\ \hline
3. & 0.25 & 3 & 93.1937 \\ \hline
4. & 0.50 & 1 & 95.1134 \\ \hline
5. & 0.50 & 2 & 93.8918 \\ \hline
6. & 0.50 & 3 & 93.5428 \\ \hline
7. & 0.75 & 1 & 95.4625 \\ \hline
8. & 0.75 & 2 & 93.8918 \\ \hline
9. & 0.75 & 3 & 92.6702 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prediction accuracy of j48 decision tree hyperparameter optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training No.** & **Number of Neighbors, K** & **Distance Weighting** & **Accuracy** \\ \hline
1. & 1 & No distance weighting & 95.8115 \\ \hline
2. & 3 & No distance weighting & 89.5288 \\ \hline
3. & 5 & No distance weighting & 85.1658 \\ \hline
4. & 7 & No distance weighting & 84.8168 \\ \hline
5. & 1 & 1/distance & 95.8115 \\ \hline
6. & 3 & 1/distance & 96.6841 \\ \
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Device Layer
#### 3.3.3 Secure Booting  
Secure booting is critical to prevent attackers from compromising operational IoT programs [50], because any malicious code inserted into a device could make that device part of a botnet. For example, an attacker may implant a local device with a file containing malware. If the device is not protected by a secure boot, the malicious code will execute on the next reboot to compromise the device. With the secure boot process, security checks during reboots identify unauthorized executables, prevent any threats from running, and enable the associated protections.
## 5 Comprehensive Analysis of Pantograph Uplift Force  
In order to comprehensively analyze the factors affecting the aerodynamic lifting force of the pantograph, we derive a comprehensive relationship between the lifting force, train speed, crosswind speed, and wind direction angle based on the calculated values obtained in this section. Here, we define the ratio of crosswind speed \(V_{w}\) to train speed \(V_{\text{t}}\) as the wind speed to velocity ratio \(\lambda_{\text{net}}\), and represent the influence of wind direction angle \(\theta\) using \(\cos\theta\). Through a data-fitting process, we obtain the functional form of the comprehensive formula for aerodynamic lifting force, train speed, wind speed, and wind direction angle. Thus, we ultimately derive the comprehensive relationship between the pantograph aerodynamic lifting force \(F_{\text{e}}\) and the wind speed to velocity ratio \(\lambda_{\text{net}}\), as well as the wind direction angle \(\theta\).  
\[F_{\text{e}}=20.216\times\lambda_{\text{net}}^{0.787}\Big{(}1\ +\ 22.151\lambda_{\text{net}}^{-1.660}|\cos\theta|^{0.933} \Big{)} \tag{13}\]  
Figure 37: Pantograph lift force (crosswind).  
Figure 36: Pantograph lift force (vehicle speed).  
Without considering the effect of crosswind, the relative air density \(p_{v}\) can be obtained through \(p_{v}=0.00097V^{2}~{}+~{}70\) Taking into account the influence of train speed, crosswind speed, and wind direction angle, the aerodynamic uplift force of the pantograph is no longer a simple quadratic function of the cosine of the wind speed-to-vehicle speed ratio cos0. Therefore, using the above formula to calculate the contact force of the pantograph is inaccurate. Empirical calculations suggest that by considering the vector sum of train speed and crosswind speed (\(V=\overrightarrow{V_{t}}~{}+~{}\overrightarrow{V_{w}}=\sqrt{V_{t}^{2}~{}+~{}V_{ w}^{2}~{}+~{}2V_{t}V_{w}\cos\theta}\)), the calculated values using this formula have errors within 10% when the train speed is 350 km/h and crosswind speed is within 20 m/s. However, when the crosswind speed increases to 25 m/s and 30 m/s, the formula deviates from the real situation. In the calculation of the aerodynamic uplift force of the pantograph, the transmission coefficient is determined by establishing the balance equation based on the geometric relationship of each component of the pantograph and force analysis. Since aerodynamic forces include the influence of crosswind, under the effect of crosswind, the uplift force of the pantograph should be determined by the following equation:  
\[F_{m}=F_{e}~{}+~{}70=20.216\times\lambda_{wt}^{0.787}\Big{(}1~{}+~{}22.151 \lambda_{wt}^{-1.660}|\cos\theta|^{0.933}\Big{)}~{}+~{}70 \tag{14}\]  
Figure 38 clearly outlines the process from analyzing the main factors influencing pantograph uplift force, to detailed investigations of each sub-factor, culminating in a final synthesis and design recommendations for optimizing performance.
## 2 Method
### Geometry and Materials Properties
In this research, we utilized bars with a length of 11.7 \(\upmu\)m in order to provide a better connection between the membrane and CSK. The substrate was modeled as an elastic solid disc. The diameter of this disc was 30 \(\upmu\)m and the thickness was 5 \(\upmu\)m.  
All cell components were defined as homogenous and isotropic elastic. Their material properties and dimensions are shown in Table 1.  
The crucial factor in providing tensegrity stability is the pre-stress generated in the structure's elements. This pre-stress is generated by applying a 24% strain to the cable elements of tensegrity. [PERSON] proposed an 82 kPa initial tension value in order to define the pre-stress in the cables. It is evident that applying pre-tension on the cables results in compression in the struts, and 12.4 kPa pre-compression was defined on the struts, according to previous research [9]. Cells can be classified into three categories according to their environment and condition. 1. Suspended cells like erythrocytes. In this case, there is no tight link between the cell and environment, thus tensegrity is not an appropriate model to describe their mechanical behavior. 2. Cells in a biological position, e.g., osteocytes in the lacuna. [PERSON] [8] developed a mechanical model of the osteocyte in lacuna using tensegrity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** &
\begin{tabular}{c} **Young’s** \\ **Modulus (KPa)** \\ \end{tabular} & **Passion Ratio** & **Dimensions** \\ \hline Cytoplasm [21] & 0.1 & 0.49 & - \\ \hline Membrane [11] & 2 & 0.3 & Thickness = 0.2 \(\upmu\)m \\ \hline Nuclear Envelope [4] & 0.925 & 0.3 & Thickness = 40 nm \\ \hline Actin [5] & 2,600,000 & 0.3 & Cross-sectional Area = 57 nm\({}^{2}\) \\ \hline Microtubule [5] & 1,200,000 & 0.3 & Cross-sectional Area = 314 nm\({}^{2}\) \\ \hline Chromatin [20] & 2,440,000 & 0.3 & Cross-sectional Area = 1.14 nm\({}^{2}\) \\ \hline Lamina [20] & 1,400,000 & 0.3 & Cross-sectional Area = 7.8 nm\({}^{2}\) \\ \hline Scaffold [22] & 100 & 0.3 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mechanical properties and geometry of simulated cell components. Figure 2: The struts of a tensegrity structure located in 3 mutually perpendicular planes. Adherent cells. Most models presented use simulated adherent conditions. In this study, tensegrity was used to simulate an adherent cell. Atomic force microscopy (AFM) is a high-precision technique that is used to probe the mechanical properties of living matter like cells [27]. A microscopic probe indents the cell and the force and associated deflection are measured. The mechanical properties and adhesion force of cells on the surface of biomaterials are the two most computable characteristics of the cells that can be extracted by the AFM technique [28; 29]. [PERSON] et al. [30] concluded that AFM test results were more accurate when a microsphere with a diameter between 2 and 5 \(\upmu\)m was used. Based on these results, a rigid semi-sphere with a 4.5 \(\upmu\)m diameter shell was used to simulate the AFM bead.
## 3 Laboratory Tests
### Effects of Different Parameters on the Bending Behaviour
#### 3.3.2 Effect of the Casing Thickness  
The effect of the casing thickness on the moment capacity is shown in Figure 10. The thickness of the steel casing influenced the ultimate bending capacity and the elastic stiffness. The ultimate bending capacity of the composite beams increased from 549.1 kNm to 643.4 kNm, 588.9 kNm to 634.2 kNm, and 543.6 kNm to 679.5 kNm, as shown in Figure 11a-c, respectively, when the thickness of the steel casing increased from 5 to 10 mm. These increases in the ultimate bending capacities of the specimens were caused by the increase in the resistance when tensor failure occurred in the concrete parts of the beam. The increase in the steel casing thickness also enhanced the elastic stiffness and ductility. Therefore, the influence of casing thickness should be considered in the calculation of the section stiffness of the DSPC.  
Figure 10: The effect of the casing thickness on the moment capacity of specimens: (**a**) no slurry, (**b**) thicker slurry, and (**c**) thickest slurry conditions.  
Figure 9: The bending moment-deflection curves of beams with different slurry conditions with (**a**) \(t=5\) mm and (**b**) \(t=10\) mm.
## 3 Research Results--Analysis and Discussion
### Atterberg's Limits (LL and PL) and Plasticity Index (PI)  
For each of the considered stabilisers, the change in the Atterberg's limits (the liquid limit (LL) and the plastic limit (PL)) was registered on the samples prepared with the optimal content of the stabiliser in the mixture, and on their basis, the corresponding value of the plasticity index (PI) was calculated as the difference between LL and PL. The results are shown in Figures 6 and 7.  
Figure 6: Change in the soil Atterberg’s limits over time given the optimal content of the considered conventional, waste, and alternative materials as stabilisers.  
Figure 7: Change in the soil PI value over time given the optimal content of the considered conventional, waste, and alternative materials as stabilisers.  
Based on Figure 6, in which the lower part of the columns (solid colour) shows the measured **values of LL**, it is concluded that there were no significant changes in the LL value with the addition of conventional stabilisers, as well as waste materials, regardless of the time interval of soil testing after the treatment. In the case of each of the considered alternative chloride-based stabilisers, the LL value decreased after 28 days since the treatment. Compared to the natural state of the soil (LL = 49%), the maximum decrease in LL value was registered with the addition of 2% ferric chloride after 28 days (LL = 42%), whereas the maximum increase in LL value was achieved with the addition of 15% fly ash after 7 days (LL = 53%).  
On the other hand, an increase in **PL values** was registered in all the considered cases of stabilisers, as presented in Figure 6, in which the upper part of the columns (dotted colour) represents the measured values of PL. It is also noted that for each of the considered stabilisers, the greatest increase in PL value was achieved after 28 days since the treatment. The maximum increase in the value of PL compared to the natural state of the soil (PL = 23%) was registered with the addition of the optimal content of line in the amount of 5% (PL = 34%), i.e., cement binder in the amount of 15% (PL = 35%).  
Regarding the **PI value**, the greatest change was registered by adding the optimal content of cement binder, in which case 28 days after the chemical treatment the PI value decreased from the initial 26% to 13% (Figure 7). A significant decrease in the value of PI after 28 days since the treatment was also registered in the case of adding the optimal content of lime (PI = 18%), as well as in the case of application of the considered chlorides (PI \(<\) 16%). By adding waste materials, the value of PI did not change significantly, that is, the values of LL and values of PL are similar to the values in the natural soil state. It can be assumed that the reason for this result is the absence of chemical reactions between soil and waste materials. On the other hand, the addition of traditional stabilisers or chlorides affects the formation of compounds that absorb water from the soil and thus reduces its moisture content.