esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Background
### Alarm Reduction Algorithm Based on Hierarchical Clustering
#### 3.1.1 Generalization Hierarchies
: _For both abstract alarms \(A_{1}\) and \(A_{2}\), \(A_{1}\) is a generalization of \(A_{2}\) if each attribute in \(A_{1}\) is a generalization of the corresponding attribute in \(A_{2}\), and at the same time, \(A_{2}\) is a specification of \(A_{1}\)._  
**Definition 4**. : _For an alarm set, the minimum cover refers to the common generalization of all alarms in the set, and the generalization is a minimum specification._  
Based on the four definitions above, considering the four hierarchical trees shown in Figure 1, there is an alarm set that contains three alarms: \(A_{1}\) (ip1,80,h1,11), \(A_{2}\) (DMZ,80,h0, MIDDLE), and \(A_{3}\) (DMZ, PRIVATE, WEEKEND, MIDDLE). \(A_{1}\) is a basic alarm because all the attributes of the alarm are at leaf nodes in the hierarchical tree. \(A_{2}\) and \(A_{3}\) are abstract alarms because there is at least one attribute in the alarm that is the middle nodes in the hierarchy tree. \(A_{3}\) is a generalization of \(A_{1}\) and \(A_{2}\) because every attribute of \(A_{3}\) is a generalization of \(A_{1}\) and \(A_{2}\), and obviously \(A_{3}\) is a common generalization of \(A_{1}\), \(A_{2}\), and \(A_{3}\).
# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## 3 Results and Discussion
### Radial Velocity Components
#### 3.1.1 First-Order Statistics  
The inter-comparison of the mean radial components reveals an excellent agreement between the sonic and all WindScanner devices. A two-parametric linear regression between the WindScanners located around the mast and the reference sonic produces slopes (\(m\)) and intersects (\(b\)) which are very close to 1 and 0 m\(\cdot\)s\({}^{-1}\) (Table 2). The goodness of fit for the linear regression for these instruments is \(R^{2}\geq 0.998\). The linear-regression statistics for MA are slightly worse. However, this instrument is pointing vertically into the atmosphere and \(\overline{w}\) is usually small in the atmospheric boundary layer. In fact, \(\overline{v}_{r\;MA}\) only varied between approx. \(\pm 1\) m\(\cdot\)s\({}^{-1}\) and the root-mean-square deviation (RMSD) is comparable to the other WindScanners.  
The availability of all WindScanners is affected by hard targeting (mast structure, boom or guying). As a result, the number of valid measurement periods, especially for the MA location, is relatively low. A conclusion on how the measurement range affects the data availability is therefore unfortunately not possible.
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 5 Application Layer
### Energy-Aware Context Recognition Algorithm (EACRA)  
The proposed Energy-Aware Context Recognition Algorithm for data acquisition minimizes the WSN node's energy consumption by using a minimum number of sensor modules with a low sampling rate [58]. As a result, this technique minimizes the data packet size, sampling rate, and the number of packets transmitted. In addition, it increases the sleep mode time of the sensor node. This solution dynamically configures sensors to send specific data under specific conditions and at a particular time, thus avoiding redundant data transmissions. This algorithm uses SEEB declared knowledge and forces the sensor node to send data only when the context changes.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 3 Results and Discussion  
### Dataset Preparation  
The resolution of UAV imagery is 4000 \(\times\) 3000. According to the result of the ground investigation, the symptomatic sites were mainly distributed in 3 \(\times\) 3 m areas in different locations of the field. As shown in Table 1, the imagery was captured at a height of 80 m, with a resolution of 3.4 cm. To reflect the areas (3 m \(\times\) 3 m) of the symptomatic sites, smaller samples were divided from the original UAV  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formula** & **References** \\ \hline Normalized Green–Red Difference Index & \(NGRDI=\frac{Green-Red}{Green-Red}\) & [27] \\ Normalized Green–Blue Difference Index & \(NGBDI=\frac{Green-Blue}{Green-Blue}\) & [28] \\ Excess Green & ExG = 2 \(\times\) Green-Red-Blue & [29] \\ Excess Red & ExR = 1.4 \(\times\) Red-Green & [30] \\ Excess Green - Excess Red & ExGR = 3\(\times\)Green-2.4 \(\times\) Red-Blue & [30] \\ Green Leaf Index & \(GLI=\frac{2\times Green-Red-Blue}{2\times Green-Red-Blue}\) & [31] \\ Red–Green Rate Index & RGRI = Red/Green & [32] \\ Blue–Green Rate Index & BGRI = Blue/Green & [32] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of selected vegetation indices.  
imagery in the size of 100 \(\times\) 100 pixels. Two thousand and four hundred samples were generated from the collected imagery. Each sample represented a small area in the wheat field and was assigned a certain label (normal, light, medium, and heavy) according to the ground investigation results.  
At this point, the dataset was composed of a collection of samples divided from the original UAV imagery. Each sample consisted of 100 \(\times\) 100 pixels and a label (normal, light, medium, and heavy) according to the ground investigation. Since the experiment was conducted when HLB disease was in its initial stage, no heavy category was found in the field, as shown in Figure 6.  
Next, the dataset was divided into two parts: (a) training samples; (b) validation samples. At this stage of the work, 60% of samples in each category were randomly selected as the training samples, while the remaining 40% as the validation samples. In our dataset, the number of normal samples was larger than other categories. To avoid the problem of data imbalance, similar numbers of each category were selected, as shown in Table 3.  
Training samples from the dataset were used for _CNN_ training and parameter updating, while the validation samples were used for performance evaluation.
# GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
## 2 Material and Methods  
Two sites were selected to verify the quality of the GNSS signal for off-road vehicle navigation. The first measurement took place in locality 1 (Figure 2) in 2020. The second measurement took place in locality 2 (Figure 2) in 2021. Both testing areas are located in the eastern part of the Czech Republic, about 40 km northeast of Brno.
# A Parallel Monte Carlo Algorithm for the Life Cycle Asset Allocation Problem
## 3 Results  
### Glide Path  
The glide path depicted in the following graph illustrates the strategic asset allocation for a pension target-date fund, emphasizing a decreasing risk profile as participants age. The parameters we use for the model are shown in Table 1.  
Figure 2 shows the calculated glide path curve. This risk mitigation strategy is commonly employed in life cycle or target-date funds, where the equity ratio is highest during the early working years when the investor's age is below 50. This allows for a more aggressive investment stance, capitalizing on the potential for higher returns that equities can offer over the long term, while the investor can endure short-term volatility.  
As the individual nears retirement, beginning approximately at age 55, the optimal equity allocation rate begins to decrease steadily. This gradual transition, known as the \"glide path\", is designed to systematically reduce the fund's risk exposure as the investor's capacity to recover from market downturns diminishes with shorter investment horizons. By the time the investor reaches age 75, the fund has shifted most of its holdings into more conservative investments, such as bonds and cash, to preserve capital and provide income stability for the forthcoming retirement years. The graph effectively encapsulates this prudent approach to balancing growth and security through age-adjusted asset allocation.  
Figure 3 illustrates the trend of optimal consumption over time. The graph shows a consistent upward trajectory during the initial periods, reflecting a period of increasing consumption. Eventually, the consumption level stabilizes, indicating an equilibrium or a steady state.  
Figure 4 presents the evolution of pension wealth over time comparing it to the 60/40 strategy. Initially, there is a steady accumulation of pension wealth, represented by a significant upward trend, suggesting contributions or growth in value. This rise peaks  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\mu\) & 0.04 \\ \(\sigma\) & 0.2 \\ \(\gamma\) & 5.0 \\ \(\varphi\) & 0.2 \\ \(\beta\) & 0.96 \\ _start_ & 20 \\ _retirement_ & 65 \\ T & 120 \\ \(p_{t}\) & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model Parameters.  
Figure 2: Glide path.  
before a sharp decline is observed, eventually reaching a point where pension wealth drops to a stable, near-zero level. This behavior highlights the life cycle of pension wealth accumulation and decumulation, reflecting typical retirement consumption patterns.
## 3 Oscillation Analysis of FSC-VSPSU
### Excitation System and Turbine Governor Parameters  
In the event of modifying the parameters of the excitation system and the turbine governor, the corresponding single-input-single-output (SISO) impedances of the FSC-VSPSU are depicted in Figure 13. In detail, the red line represents the initial FSC-VSPSU impedance, and \"\(\bigcirc\)\", \"\(\bigcirc\)\", and \"\(\bigcirc\)\" represent the measured SISO impedances when changing \(K_{\text{pg}}\), \(K_{\text{ig}}\), and \(K\), respectively. \(K_{\text{pg}}\) and \(K_{\text{ig}}\) are the proportional and integration parameters of the turbine governor, respectively, and \(K\) is the DC gain of the series correction link of the excitation system. As it can be seen, when changing \(K_{\text{pg}}\), \(K_{\text{ig}}\), and \(K\), the SISO impedance of FSC-VSPSU also remains almost unchanged. Therefore, the corresponding excitation system and turbine governor parameters also have weak impacts on oscillation occurring in the grid-connected point.  
Figure 12: Relevant SISO impedances of FSC-VSPSU when changing the control parameters of the machine-side converter.  
Figure 13: Relevant SISO impedances of FSC-VSPSU when changing the excitation system and turbine governor parameters.
## 2 Sampling and Analysis Methods
### Geostatistical Analysis Method  
Geostatistics is considered a good tool for analyzing and interpreting spatially distributed data and a convenient method for assessing pollution and controlling environmental risks over a large area. Interpolation methods are widely used in mapping processes to estimate the accumulation of pollutants in areas where no sampling was performed. Due to various reasons (inaccessibility of places or the impossibility of sampling on the territory of industrial facilities), sampling could not be performed evenly. Therefore, empirical Bayesian kriging was chosen as a tool for mapping (ArcMap 10.2). It differs from other kriging methods since it takes into account the error associated with the estimation of the main semivariogram and provides accurate interpolation of moderately heterogeneous data, which allows visualizing data as close to the real picture as possible.
# Four-Dimensional Thermal Analysis of 888 nm Pumped Nd:YVO4 Dual-Rod Acousto-Optic Q-Switched Laser
## 4 Results and Discussion
### Discussion  
Through further numerical simulation, only about 1 s is needed for the temperature distributions to yield to convergence after the Q-switched system was turned on. The temporal evolution of maximum temperature in Xtal 1 at CW, 100 kHz Q-switched, 50 kHz Q-switched and nonlaser conditions are shown in Figure 7a.  
The \((z,t)\)-dependent temperature distributions at CW, 100 kHz Q-switched and 50 kHz Q-switched conditions are shown in Figure 7b. It can be seen that the higher the Q-switched repetition-rate, the lower the thermal effect in crystal and the closer to the CW conditions. The nonlaser condition will cause serious thermal effects and can easily damage the crystal. We also observed this kind of damage under nonlasing conditions in our previous experiment. The main reason is that the temperature under nonlasing conditions is much higher than that of the lasersing condition, which was studied in our previous work [23]. More detailed origins and principles about this are referred to in Ref. [34].  
Figure 8a shows the simulation results and corresponding experimental data of average output power versus pump powers. Figure 8b shows the simulation results and corresponding measured data of average temperature on the left end face of Xtal 1 versus pump powers. We have considered the compensation of the measurement resolution of the thermal imager in the average method. These comparisons between experimental and numerical results verify the accuracy of our model in both simulating power and thermal effect domain.  
Figure 8: The simulation results and corresponding experimental data of (**a**) average output power; (**b**) average temperature on the left end face of Xtal 1, versus pump powers at CW, 100 kHz Q-switched and 100 kHz Q-switched conditions.  
Figure 7: The temporal evolution of (**a**) maximum temperature in Xtal 1 at CW, 100 kHz Q-switched, 50 kHz Q-switched and nonlaser conditions; (**b**) temperature distribution in Xtal 1 along the \(z\)-axis at CW, 100 kHz Q-switched and 50 kHz Q-switched conditions.
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Case Study Analysis  
The worldwide distribution of study regions grouped by countries of case studies is shown in Table 4. As shown, most studies were conducted in study areas in China (35%), the USA (19%), and Canada (18%). Additionally, the literature review found the following study areas were studied at least four times: South Africa (\(n=12\)), India (\(n=9\)), Australia (\(n=9\)), France (\(n=6\)), Indonesia (\(n=6\)), Iran (\(n=5\)), Brazil (\(n=4\)), Turkiye (\(n=4\)), and Sweden (\(n=4\)).  
Based on the extent of the study sites, the publications were categorized into five groups (see Table 4). To have consistency with [28], these divisions are considered in terms of the following five groups: (1) very small (less than 100 km\({}^{2}\)), (2) local (between 100 km\({}^{2}\) and 3000 km\({}^{2}\)), (3) regional (more than 3000 km\({}^{2}\) and less than a provincial scale), (4) provincial, and (5) national (country-wide) scales. This investigation shows that most wetland RS studies call attention to very small, local, and regional scales and that very few consider provincial or country-wide scales.
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 3 Experimental Results and Analysis
### Image Processing Unit  
As shown in Figure 6, the image acquisition unit consists of a transmission mechanism, a proximity switch sensor, an industrial CCD camera, a light-emitting diode (LED) arc light source and a lens. Before the acquisition, the LED arc light source was adjusted to calibrate the brightness, and the lens was mounted on the CCD camera. Then, the aperture and focal length were adjusted to ensure the imaging quality of the acquisition unit. Under the action of the transmission mechanism, the sensor detected the workpiece and produced pulse signals when the vessel maintained a constant speed and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Convolution** & **Step** & **Input** & **Output** \\ \hline Conv0\_BN\_ReLU6(3 \(\times\) 3) & 2 & 3 & 32 \\ Conv1\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 32 & 64 \\ Conv2\_Dw\_Pw(3 \(\times\) 3) & Dw:2 & 64 & 128 \\ Conv3\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 128 & 128 \\ Conv4\_Dw\_Pw(3 \(\times\) 3) & Dw:2 & 128 & 256 \\ Conv5\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 256 & 256 \\ Conv6\_Dw\_Pw(3 \(\times\) 3) & Dw:2 & 256 & 512 \\ \(\%\)Conv7\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 512 & 512 \\ Conv8\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 512 & 512 \\ Conv9\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 512 & 512 \\ Conv10\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 512 & 512 \\ Conv11\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 512 & 512 \\ Conv12\_Dw\_Pw(3 \(\times\) 3) & Dw:2 & 512 & 1024 \\ \(\%\)Conv13\_Dw\_Pw(3 \(\times\) 3) & Dw:1 & 1024 & 1024 \\ Conv14\_1\_BN\_ReLU6(1 \(\times\) 1) & 1 & 1024 & 256 \\ \(\%\)Conv14\_2\_BN\_ReLU6(3 \(\times\) 3) & 2 & 256 & 512 \\ Conv15\_1\_BN\_ReLU6(1 \(\times\) 1) & 1 & 512 & 128 \\ \(\%\)Conv15\_2\_BN\_ReLU6(3 \(\times\) 3) & 2 & 128 & 256 \\ \(\%\)Conv16\_2\_BN\_ReLU6(1 \(\times\) 1) & 1 & 256 & 64 \\ \(\%\)Conv17\_2\_BN\_ReLU6(3 \(\times\) 3) & 2 & 64 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 2: General structure of MobileNet-SSD. \(\%\) = the feature image to be used in classification regression and bounded box regression.  
spacing. These signals triggered the CCD camera to take photos. In order to ensure that the container runs to the center of view field, sensor needs to be accurately debugged.  
The detection network was trained on the following hardware: Intel Core i7 7700K processor (Vietnam, 2017) which has a main frequency of 4.2 GHz, 32 GB memory and a GeForce TITAN X graphics processing unit (GPU). The software part used the Ubuntu 14.04.2 operating system, and the Tensorflow deep learning framework. Twenty percent of the samples in the pre-processed library were allocated to the test set and the other 80% to the training set.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 3 Results
### Variations of % of Crop and Weed Plants and Productivity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Group** & **Approach** & **Adjusted \(p\)-Value** & **Significance 1** \\ \hline \multirow{6}{*}{\% crop} & \multirow{2}{*}{Plot} & Plot I vs. Plot II & 0.095 & \multirow{6}{*}{} \\  & & Plot I vs. Plot III & 0.041 & \\  & & Plot II vs. Plot III & 1.000 & \\ \cline{2-5}  & \multirow{2}{*}{Dose applied} & 0 vs. 0.50 & 0.019 & * \\  & & 0 vs. 0.75 & 0.118 & \\  & & 0.50 vs. 0.75 & 0.418 & \\ \hline \multirow{6}{*}{\% weed} & \multirow{2}{*}{Plot} & Plot I vs. Plot II & 0.003 & * \\  & & Plot I vs. Plot III & 0.005 & * \\  & & Plot II vs. Plot III & 1 & \\ \cline{2-5}  & \multirow{2}{*}{Dose applied} & 0 vs. 0.50 & 0.001 & * \\  & & 0 vs. 0.75 & 0.008 & * \\  & & 0.50 vs. 0.75 & 0.49 & \\ \hline \multirow{6}{*}{Productivity} & \multirow{2}{*}{Plot} & Plot I vs. Plot II & 0.008 & * \\ \cline{2-5}  & & Plot I vs. Plot III & 0.000 & * \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{Dose applied} & 0 vs. 0.50 & 0.007 & * \\ \cline{1-1} \cline{2-5}  & & 0 vs. 0.75 & 0.000 & * \\ \cline{1-1} \cline{2-5}  & & 0.50 vs. 0.75 & 0.463 & \\ \hline \hline \end{tabular}  
* Significance code: Adjusted \(p\)-value \(\leq\) 0.01 “**”.  
\end{table} 
Table 5: [PERSON]’s test results for the plot and dose applied groups which presented significance in the Kruskal–Wallis test. Figure 6: (**a**) Boxplot of % crop by plot, (**b**) Boxplot of % crop by dose applied, (**c**) Boxplot of % weed by plot, (**d**) Boxplot of % weed by dose applied, (**e**) Boxplot of productivity by plot, and (**f**) Boxplot of productivity by dose applied.
## 5 Semi-Active Structural Control
### Semi-Active Controller Based on the Amplitude of Tower Vibrations
establishing the relationship between the amplitude section and the TMD gains, i.e., \(\sec_{s}-\left(k_{gTMD},\ d_{gTMD}\right)\). The GA optimization process is executed iteratively until the termination criterion is satisfied, indicated by the signal \(op_{end}=1\). At this point, the scheduler advances to the next amplitude section for optimization, updating the section indicator to \(\sec_{s}=\sec_{s}+1\). The process concludes once the optimization has been completed for all defined amplitude sections. Once the optimization process is finished, the TMD can be implemented according to the scheme presented in Figure 6.  
During each control period, the standard deviation of the TTD is calculated and used to identify the current amplitude section using the lookup table \"_amp2 sec_\". This value serves as the index in the lookup table \"_sec2 par_\" to retrieve the corresponding values of the pair \((k_{TMD},\ d_{TMD})\), which are then used as the input signals for the TMD. For the experiments, the same TMD mass and simulation settings as those used in the passive TMD cases were applied, including an initial platform pitch of \(5^{\circ}\) (free decay) and a simulation time of 100 s. The configuration of the Genetic Algorithm (GA) remains unchanged from the previous section, except that in this case, the range of the initial population is extended to [500, 500; 20,000, 20,000] in order to prevent early convergence on a local minimum. The optimal values of \(k_{TMD}\) and \(d_{TMD}\) obtained for each section are presented in Table 3 below:  
Table 3 shows that the optimal values obtained for the lower amplitude oscillations (Sections 1 and 2) are relatively similar, but they differ significantly from the values obtained for the higher amplitude oscillations (Section 3). Furthermore, the optimal parameters found for Section 3 are comparable to those determined during the optimization of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Section** & \(k_{TMD}\) **(N/m)** & \(d_{TMD}\) **(Ns/m)** \\ \hline
1 & 19,517 & 970 \\
2 & 19,477 & 503 \\
3 & 4543 & 2643 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal TMD values obtained for each section. Figure 6: Amplitude-based semi-active TMD execution scheme. passive TMD (refer to Section 4). This outcome was anticipated, as the primary focus of the optimization is to reduce larger amplitude oscillations. Based on these findings, it was decided to further subdivide Section 3 into two equal segments, as follows:  
* Section 3.1: \(\frac{2}{3}*\sigma_{TTDFA\ MAX}\leq\sigma_{TTDFA}<\frac{25}{3}*\sigma_{TTDFA\ MAX}\)
* Section 3.2: \(\frac{2.5}{3}*\sigma_{TTDFA\ MAX}\leq\sigma_{TTDFA}\)  
The results obtained in this case are as indicated in Table 4:
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network
### Price  
Table 7 shows the price of each element of the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Power Consumption (mA)** & **Time Percent Per Hour** \\ \hline Transmitting & 70 & 0.0075\% \\ \hline Receive mode & 12.2 & 0.2\% \\ \hline Deep Sleep mode & 0.0035 & 80\% \\ \hline Low Power mode & Sleep & 0.0035 & 19.0699\% \\ \cline{2-3}  & Receive & 12.2 & 0.3891\% \\ \hline Measuring & 4 & 0.3333\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total power consumption.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**END DEVICE (THERMOMETER)** & **Price (c)** \\ COMPONENTS & & \\ \hline PT100 sensor & \(5.50\) \\ \hline MAX31685 & 4.11 \\ \hline PIC16 LF1788 & 6.04 \\ \hline External structure (PVC pipe + aluminum tip) & 1.37 \\ \hline Ra-02 & 3.00 \\ \hline Antenna & 5.45 \\ \hline Lithium battery & 9.54 \\ \hline Wires, cases and components & 2.00 \\ \hline
**Total price of one end device** & **37.01** \\ \hline
**GATEWAY COMPONENTS** & \\ \hline Raspberry Pi 3B & 35.66 \\ \hline Ra-02 & 3.00 \\ \hline Antenna & 22.61 \\ \hline
**Total price of the gateway** & **61.27** \\ \hline
**REPEATER COMPONENTS** & \\ \hline PIC16 LF1788 & 6.04 \\ \hline External structure (PVC pipe + aluminum tip) & 1.37 \\ \hline Ra-02 & 3.00 \\ \hline Antenna & 5.45 \\ \hline Lithium battery & 9.54 \\ \hline Wires, cases, and components & 2.00 \\ \hline
**Total price of the repeater** & **27.40** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total price.