esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 4 Experiment Description and Result Analysis
### Temporal Stability Analysis of Kinematic Self-Calibration  
An additional experiment (Calibration V) was conducted for temporal stability analysis. About one month later after performing self-calibration (Calibration IV), we re-calibrated the same sensor under the similar condition. More specifically, the data acquisition site and the rest of the conditions were identical to the Calibration IV for comparison. Based on the findings from the previous analysis, the minimum network configuration was considered for Calibration V. Summary of Calibration V is given in Table 15.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Estimated Values**} \\ \hline  & \multicolumn{2}{c}{**Calibration IV-1**} & \multicolumn{2}{c}{**Calibration IV-2**} \\ \hline \(X_{o}\)_(m)_ & 2.876 \(\pm\) 0.002 & & 2.870 \(\pm\) 0.003 \\ \(Y_{o}\)_(m)_ & 0.689 \(\pm\) 0.001 & & 0.686 \(\pm\) 0.002 \\ \(Z_{o}\)_(m)_ & 0.105 \(\pm\) 0.001 & & 0.101 \(\pm\) 0.001 \\ \(\omega\)(\({}^{\circ}\)) & \(-\)0.356 \(\pm\) 0.031 & & \(-\)0.359 \(\pm\) 0.055 \\ \(\phi\)(\({}^{\circ}\)) & \(-\)0.131 \(\pm\) 0.011 & & \(-\)0.133 \(\pm\) 0.019 \\ \(\kappa\)(\({}^{\circ}\)) & \(-\)3.078 \(\pm\) 0.008 & & \(-\)3.076 \(\pm\) 0.017 \\ \hline \multicolumn{5}{c}{**Laser No.**} & \multicolumn{2}{c}{\(\Delta\rho\)**(m)**} & \multicolumn{2}{c}{\(\Delta\theta\)**(\({}^{\circ}\))**} & \multicolumn{2}{c}{\(\Delta\alpha\)**(\({}^{\circ}\))**} & \multicolumn{2}{c}{\(\Delta\theta\)**(\({}^{\circ}\))**} & \multicolumn{2}{c}{  
Table 16 shows the summary of planar misclosure before and after Calibration V. The planar misclosure results before and after Calibration V are, also, given in Figure 19. As can be seen, the RMSE of planar misclosure before the calibration (i.e., 0.038 m) increased compared to Calibration IV-2 (i.e., 0.015 m). In addition, estimated AP values from Calibration V can be found in Figure 20, showing different values compared to Calibration IV-2 (Figure 18b,d,f). On the other hand, RMSE of planar misclosure after Calibration V (i.e., 0.007 m in Table 16) showed a similar result to Calibration IV-2 (i.e., 0.007 m in Table 11). Through this additional experiment, we found that the APs of the MBL system were changing unstably over time, and the calibration process provided a stable level of planar misclosure RMSE.
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 3 Dataset
### Data Collection  
For our analysis, we used of all the preprocessed datasets excluding the malware and employed the Enron-Spam dataset, which has been used in [PERSON] et al. [28]. This database has 785,648 records, with each record containing an email message written by one of the six Enron employees. Every record also contains a flag showing whether it is spam or not (ham). The data distribution also has a clear show up of spam with 48.18% (378,508) tagged as spam and 51.82% (407,140) as ham. To the same end, estimates of the word count for each of the e-mails suggest a distinct right-shift with an average of 60 words of text per  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Study** & **Methodology** & **Key Findings** & **Limitations** \\ \hline [PERSON] et al. [16] & Concept drift analysis in spam filtering & Identified different types of concept drift affecting spam classification & Concept drift remains a challenging problem; adaptive models required \\ [PERSON] et al. [17] & CNN + LSTM hybrid model & Outperformed Naive Bayes and decision trees in email classification & Computationally expensive; deep learning models require large training data \\ [PERSON] et al. [18] & Comparative study of ML models & Examined hyperparameter tuning techniques for improving spam detection & Lacks practical implementation of deep-learning-based hybrid models \\ [PERSON] et al. [19] & Fast Adaptive Stacking of Ensembles (FASE) & Achieved high accuracy for real-time spam detection & Limited to non-stationary data streams \\ [PERSON] et al. [20] & Naive Bayes, SVM, decision trees & Reported 95.67\% accuracy & Struggles with complex spam patterns \\ [PERSON] et al. [21] & Stacked CNN model & Used CNN to classify spam tweets & CNN alone may not handle semantic differences well \\ [PERSON] st al. [22] & Deep learning model for spam and phishing RNN-GRU model for phishing detection & Improved feature selection and model structure & Time complexity remains a concern \\ [PERSON] et al. [23] & Ensemble model & & \\ [PERSON] et al. [24] & Large Language Models (BERT, Seq2 Seq) & Performed well in few-shot settings & LMMs require extensive training and are resource-intensive \\ [PERSON] et al. [25] & BERT + ML classifiers (Logistic Regression, SVM) & Logistic Regression yielded the best performance & Requires high-quality labeled data \\ [PERSON] et al. [26] & Random Forest + Word Frequency Patterns & Achieved 97\% accuracy using Spambase dataset & Class imbalance still requires better handling \\ [PERSON] et al. [27] & Attention-based LSTM with pre-trained embeddings & Improved resilience to adversarial attacks & May require high computational power \\ \hline \end{tabular}
\end{table}
Table 1: Review table.  
e-mail. Exploratory Data Analysis (EDA) also reveals that most of the emails are between 60 and 75 words as highlighted above.
## 6 Analytical Homogenization Study
### Effective Elastic Properties versus the Contrasts of the Phases
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Contrast** & **Phase** & \(\mathbf{E}\) & \(\mathbf{k^{\prime\prime\prime}}\) & \(\mathbf{\mu^{\prime\prime\prime}}\) \\ \hline \multirow{3}{*}{\(C_{1}=50,C_{2}=10\)} & 1 & 500 & 416.6666 & 192.3076 \\ \cline{2-5}  & 2 & 50 & 41.6666 & 19.2307 \\ \cline{2-5}  & 3 & 1 & 0.8333 & 0.384615 \\ \hline \multirow{3}{*}{\(C_{1}=80,C_{2}=10\)} & 1 & 800 & 666.6666 & 307.6923 \\ \cline{2-5}  & 2 & 80 & 66.6666 & 30.76923 \\ \cline{2-5}  & 3 & 1 & 0.8333 & 0.384615 \\ \hline \multirow{3}{*}{\(C_{1}=100,C_{2}=10\)} & 1 & 1000 & 833.3333 & 384.6153 \\ \cline{2-5}  & 2 & 100 & 83.3333 & 38.4615 \\ \cline{2-5}  & 3 & 1 & 0.8333 & 0.3846 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The properties of the different phases with contrasts \(C_{1}=50,\ 80,\ 100;\)\(C_{2}=10\). Figure 9: Effective properties of bulk and shear moduli of the microstructure obtained with the two methods, ADH and AMS, for different volume fractions \(P_{1}=0.1,\ 0.3,\ 0.4,\)\(C_{2}=10\), **(a,b)**\(C_{1}=50\); **(c,d)**\(C_{1}=80\); **(e,f)**\(C_{1}=100\). Figure 10: Effective properties of bulk and shear moduli of the microstructure obtained by the two methods, ADH and AMS, for different volume fractions \(P_{1}=0.1,\ 0.3,\ 0.4,\ C_{2}=100\), **(a,b)**\(C_{1}=20\); **(c,d)**\(C_{1}=80\); **(e,f)**\(C_{1}=50\). A detailed comparison, based on superimposing obtained results, of the effect of the different contrast variation series on the effective elastic properties is illustrated by the curves in Figure 11. The most important result obtained from this section is that whatever the combinations between all the considered parameters, the volume fraction \(P_{1}\), the contrasts \(C_{1}\), \(C_{2}\), and regardless of the calculation method used--ADH or AMS--the homogenized elastic bulk and shear moduli of the [PERSON] et al. (1993) model ([PERSON] et al., 1993) are identical since the error does not reach 1% for all the cases of the present study, as shown in Tables A1-A11 in Appendix C.
## 6 Results
The **SBERT-LGBMTC** model demonstrates superior performance over the **SBERT-XG** model, standard estimation methods, and state-of-the-art models in 10 projects. Conversely, the **SBERT-XG** model outshines the **SBERT-LGBMTC** model and other methods in five projects. Figure 4 effectively illustrates the line plots of predicted versus actual values for the five projects where the SBERT-XG excels, highlighting its strengths in these instances. Conversely, Figure 5 showcases the predictive accuracy of the **SBERT-LGBMTC** model, where it surpasses the **SBERT-XG** model with Bayesian optimization along with standard and state-of-the-art methods in 10 projects. The graphical representation of actual versus predicted values provides a clear visualization of the estimation precision. For instance, in projects like CLOV and COMPASS, the **SBERT-XG** model's predictions closely align with actual outcomes, emphasizing its effectiveness in those specific environments. Meanwhile, in projects like APSTUD and TISTUD, the **SBERT-LGBMTC** model achieves a near-perfect match between predicted and actual values, signifying remarkable estimation accuracy beneficial for project management and planning. This congruence between predicted and actual values affirms the reliability of the **SBERT-LGBMTC** model, highlighting its capability to provide precise effort forecasts in software project management. This enhanced estimation proficiency aids in more efficient resource allocation and sprint planning, contributing to improved project timelines and budget adherence. This study enriches the field of software engineering by benchmarking the estimation accuracies of advanced predictive models and illuminating the situational advantages of each model across a diverse array of Agile projects. Figure 4: Line plot of predicted vs. actual values of the SBERT-XG model in projects (APSTUD, CLOV, COMPASS, TIMOB, and TISTUD). Figure 5: Line plot of predicted vs. actual values of the SBERT-LGBMTC model in projects (DAEMON, DM, DNN, MDL, MESOS, STL, TDP, TIDOC, and XD).
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 3 Results and Discussion
### Performance Characteristics  
When assessing the combustion process in a reciprocating engine, refer to the engine's performance. One of the main indicators is engine efficiency, which determines the engine's ability to convert the chemical energy contained in the fuel to the work obtained on the engine's crankshaft. Figure 7 shows the efficiency gains of the dual-fueled engine \(\Delta\)ITE compared to the reference fuel-fed engine. It was found that the dual-fuel engine was more efficient than the conventional engine for the entire range of propanol content. The highest value of the increase in efficiency \(\Delta\)ITE was recorded for the 50% proportion of propanol (DP50) and it amounted to 1.94%. Up to a 50% propanol share, a gradual increase in efficiency was achieved; after exceeding this share, the efficiency decreased but was still higher than for the engine powered with diesel. Figure 7 also shows the influence of propanol share on the specific energy consumption SEC. The nature of the changes in this parameter of the engine performance evaluation is consistent with the changes in ITE. For an engine powered by diesel alone, the SEC was 10.27 MJ/kWh. The lowest specific energy consumption was recorded for the DP50 power supply, and it was 9.69 MJ/kWh.  
An internal combustion engine is a cyclic machine in which each of the individual work cycles is practically different. For the quality of the engine's work, it is recommended that this uniqueness be as low as possible. The basic assessment of the operating stability of a piston engine is based on the \(\text{COV}_{\text{IMEP}}\) index. According to the literature, the limit value for piston engines used to drive machines and devices should not exceed 5% [32]. Figure 8 shows the influence of propanol content on the nature of the changes in the \(\text{COV}_{\text{IMEP}}\) uniqueness index. For the engine powered by diesel alone, the value of the \(\text{COV}_{\text{IMEP}}\) uniqueness index was 3.33%. The share of propanol in the combustion process in the engine increased the \(\text{COV}_{\text{IMEP}}\) value by nearly 2%, and for a share of 15 to 50%. For the entire range of propanol shares, an increase in the uniqueness of the IMEP value was recorded. The main reason for this is propanol's properties: it has worst self-ignition ability, which is the most important in the initial phase of flame front propagation. Small differences in the pre-combustion stage then translate into differences in the main stage of the combustion process. Other authors show a similar nature of \(\text{COV}_{\text{IMEP}}\) changes with the participation of alcoholic fuels [33; 34].  
Figure 7: Thermal efficiency (ITE) and specific energy consumption (SEC) of a dual-fuel engine fueled by diesel and propanol with alcohol content from 0 to 65%.
## 4 Discussion
### Limitations of the Research  
Of course, since we only selected SM, VPD, temperature, precipitation, and radiation as influencing factors to study the trends of vegetation peak changes, we may lack a complete explanation of the reasons behind these changes. Moreover, precipitation is the main source of SM and plays a crucial role in SM. At the same time, it can also cause changes in air humidity, thereby affecting VPD. However, different precipitation patterns often have different effects, which were not considered in this article. In future research endeavors, it would be beneficial to investigate a wider range of influencing factors to arrive at a more precise understanding of the causes of vegetation peak value changes. This would enable a more comprehensive description of the major factors affecting vegetation growth, providing valuable guidance for the ecological management of the TP. Furthermore, the data used in this research may have limitations, and it is preferable to draw conclusions based on long-term research. The reliability of the final conclusions can be ensured through further in-depth analysis of the influencing mechanisms via detailed ground experiments.
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 4 Case Study
### Simulation on 10 \(\times\) 10 5G Macro BS Network
#### 4.2.1 System Description
This is because, with DS 1, after the redundant BSs switch to sleep mode, the users in the corresponding cells are transferred to adjacent BSs to receive communication services. The transfer of users increases the geographic distance between the users and BSs. According to Equation (5), an increase in distance leads to an increase in variable power. However, in general, with DS 1, the reduction in fixed power is greater than the increase in variable power, and thus the purpose of reducing the energy consumption and the electricity costs can be achieved. We then compared the dispatching scheme of AC with two strategies, and the average indoor temperature in the cabinets is shown in Figure 11. The analysis of the dispatching scheme of AC with DS 1 is consistent with that in Section 4.1.2. With DS 2, the dispatching scheme of AC only depends on whether the indoor temperature exceeds the preset temperature range. According to Figure 11, during periods in which the outdoor temperature and electricity prices are both high, the working time of AC with DS 2 is much longer than that of DS 1. In addition, the AC fails to reduce the indoor temperature in advance before the outdoor temperature and electricity price increase; in other words, the AC fails to make full use of the thermal inertia of the cabinets, thus resulting in higher electricity costs. Furthermore, the indoor temperature with DS 2 can only be maintained within the preset temperature range, whereas the proposed DS 1 allows the indoor temperature to approach the upper bound. This is an important reason for the lower energy consumption and electricity costs with DS 1 compared with DS 2. Finally, the backup batteries are allowed to charge and discharge with DS 1. The total energy storage of backup batteries in the 5G macro BS network is shown in Figure 12. The analysis of the dispatching scheme of batteries is consistent with that described in Section 4.1.2. By storing and releasing power from the grid over time, the power during the low electricity price period is transferred to the period with a high electricity price. Compared with DS 2, where the batteries are not allowed to be dispatched, the proposed DS 1 effectively reduces the electricity costs of the network. In summary, with the proposed dispatching scheme in this study, through the coordination of each 5G BS and the economic dispatch of equipment in the network, the energy consumption and electricity costs are effectively reduced in comparison with the conventional dispatching scheme. Figure 10: (**a**) Total power of communication equipment, (**b**) fixed part of the power of communication equipment, and (**c**) variable part of the power of communication equipment.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 4 System Model  
Let \(x[n]\) be the signal being transmitted with an OFDM modulation composed by a collection of properly weighted functions expressed as follows:  
\[x[n]=\sum_{i=0}^{N-1}\beta_{i}s_{i}[n] \tag{18}\]  
where coefficients \(\beta_{i}\) are composed of: \(N_{D}\) information symbols, \(N_{P}\) pilots and \(N_{G}\) guards. The set of orthogonal \(s_{i}[n]\) functions transmitted in one block are in the following form:  
\[s_{i}[n]=\frac{1}{\sqrt{N}}e^{(-\beta\pi f_{i}n/N)}\quad, \tag{19}\]  
where \(N\) indicates the size of the OFDM symbol.  
The signal received in time domain for a determined OFDM symbol \(y[n]\) after cyclic prefix (CP) removal can be expressed in its complex baseband representation as:  
\[y[n]=\sum_{l=0}^{L-1}h[n,l]x[\left<n-l\right>_{N}]+w[n]\quad, \tag{20}\]  
where \(n=(0,\ldots,N-1)\), \(l=(0,\ldots,L-1)\), \(L=[\tau_{max}/T_{s}]\) ) is the longitude of the channel impulse response (CIR) baseband, \(\tau_{max}\) is the maximum delay spread and \(T_{s}\) is the sampling period. \(h[n,l]\) is the gain variable which corresponds to the \(n\)-th symbol for an impulse introduced at \(l\) previous samples and \(w[n]\) the noise present under system operation: corona and additive white Gaussian noise (AWGN). Assuming that the PLC channel remains quasi-static over the period of an OFDM block, Equation (20) can be rewritten in its frequency-domain representation as follows:  
\[Y[k]=H[k]X[k]+W[k]\quad, \tag{21}\]  
where \(H[k]\) represents the frequency response of the PLC channel, \(X[k]\) the transmitted OFDM symbols and \(W[k]\) the noise present under system operation: corona and AWGN. In order to estimate the transmitted signal \(\hat{x}[k]\), the following processes are carried out:  
Figure 7: Power spectral density of the corona noise for the test-case line with 400 kV in the frequency range of operation \([50,550]\) kHz.  
channel estimation \(\hat{H}[k]\), data reconstruction via least squares and equalization via Wiener filtering as reported in [15].
## 3 Geometrically Exact 3D Beam Model and Variational Form for Assigned Strains
### The Beam Model in Case of Measured Strains
#### 3.4.1 From Measured Point-Wise Axial Strains to Generalized Strains  
For a given cross-section \(s\), we need to evaluate \(\bar{\rho}(s)\) starting from the values of the axial continuum strain \(\varepsilon_{11}\) measured in three points, using the hypothesis that the strains are small and the cross-section maintains rigid after the deformation.  
Let \((s,y_{1},z_{1})\), \((s,y_{2},z_{2})\), and \((s,y_{3},z_{3})\) be the three points of the cross-section, where we have measured the axial strains \(\varepsilon_{11}\). We have  
\[\begin{cases}\varepsilon_{11}(s,y_{1},z_{1})=\varepsilon_{0}+\chi_{2}z_{1}- \chi_{3}y_{1}\\ \varepsilon_{11}(s,y_{2},z_{2})=\varepsilon_{0}+\chi_{2}z_{2}-\chi_{3}y_{2}\\ \varepsilon_{11}(s,y_{3},z_{3})=\varepsilon_{0}+\chi_{2}z_{3}-\chi_{3}y_{3} \end{cases} \tag{18}\]  
From the inverse relationship of (18) we are able to find the generalized quantities \(\varepsilon_{0},\chi_{2},\chi_{3}\) and then, from the assumption of negligible tangential strains (slender beams), i.e., \(\chi_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\), we have the generalized strain vector \(\bar{\mathbf{p}}(s)=\{\varepsilon_{0}(s),0,0,0,\chi_{2}(s),\chi_{3}(s)\}^{T}\) required to obtain the body deformation and stress state.
## 2 Experimental
### Experimental Setup  
#### 2.2.1 Experimental Setup  
Figure 10a shows a schematic illustration of the ET used in the solar collector, consisting of an evaporator, an adiabatic and a condenser located at the header tank. The solar irradiance is absorbed with the absorber layer and is transferred to the evaporator section in which the internal carrying fluid of the evacuated tube is evaporated with the boiling mechanism and the heat together with the vapor are transported to the header tank in which the thermal energy is delivered to the cold water. The condensed carrying fluid is returned to the bottom of the HP under a gravity-assisted falling film of liquid. The detailed information about the HP and the evacuated tube are represented in Figure 10a. Figure 10b shows the schematic of the apparatus; accompanied with an image of the ETSC. The heat pipes inside the evacuated tubes are charged with the working fluid (CNP/acetone nanofluid) and water is pumped from a reservoir to a solar collector with a centrifugal pump (manufactured by DAB). The flow rate of the flow is controlled with an ultrasonic flow meter connected to a valve and a servo. The temperature of the tank was monitored with a PID controller and a thermostat bath to ensure a back-to-back comparison between the data obtained for each test. The inlet and the outlet temperature of the solar collector was constantly monitored with two k-type thermocouples connected to a digital data logging system with reading frequency of 1 kHz. The outlet of the collector was chilled with a water jacket connected to the thermostat bath to ensure that the temperature of the tank is constant. Notably, a bypass was also utilized to adjust the flow rate within the system. This ensures a very accurate flow rate adjustment within the system.  
The experiments were conducted in a geographical location with the coordination of 27.19 latitude and 56.28 longitude and situated at an elevation of 8.9 m above sea level. The location has a population of 352,173. Hence, the importance of using solar thermal energy in this city is pivotal for domestic and industrial sectors. Given the fact that this city can take advantage of almost 300 sunny days within a year, the experiments were conducted there to ensure that the maximum solar share can be achieved. The maximum solar irradiance obtained in the city was 920 W/m\({}^{2}\) well above the average of the other large cities all over the world (850-890 W/m\({}^{2}\)).  
Figure 9: Dependence of heat capacity of nanofluids on temperature for various concentrations of CNPs in acetone.