esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution Methodology
### Summary of the Proposed Solution Methodology  
With the purpose of illustrating the application of the SSA to general optimization problems, the Algorithm 1 is employed.  
```
1 Define the distribution system under study Define the size \(n\) of the initial population;
2 Define the maximum number of iterations \(L\);
3 Generate the salp initial population \(M_{S}\);
4 Make \(l=0\); for\(l=1:L\)do
5 Evaluate the initial population by means of the slave stage;
6 Organize the population \(M_{S}\);
7 Select the leader in the population \(S_{\text{best}}\);
8 Select the constant \(C_{1}\);
9 for\(i=1:\frac{n}{2}\)do
10 Define the values of the constants \(C_{2}\) and \(C_{3}\);
11 for\(j=1:d\)do
12 if\(C_{3}\leq 0.5\)then
13 \(S_{(i,j)}=S_{(i,j)}+C_{1}\Big{(}(X_{j}^{\max}-X_{j}^{\min})C_{2}+X_{j}^{\min} \Big{)}\);
14
15 else
16 \(S_{(i,j)}=S_{(i,j)}-C_{1}\Big{(}(X_{j}^{\max}-X_{j}^{\min})C_{2}+X_{j}^{\min} \Big{)}\);
17
18 Check the feasibility of each individual;
19 for\(k=\frac{n}{2}+1:n\)do
20 for\(j=1:d\)do
21\(S_{i,j}=\frac{(S_{i,j}-S_{i-1,j})}{2}\);
22
23 Check the feasibility of each individual;
24 Report the best solution \(S_{\text{best}}\)
```  
**Algorithm 1**General application of the salp swarm algorithm to optimization problems.  
Additionally, in order to clarify the application of the SSA approach to locate and size D-STATCOMs in radial and meshed distribution networks, the flow chart of the proposed methodology is presented in Figure 1.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 3 Material and Methods
### Data Acquisition Part
#### 3.7.4 Scenario4: RRSI Acquisition  
Received Signal Strength Indicator (RSSI) indicates the strength of the signal power received by a receiving sensor node. RSSI has much application in wireless networks including localization as the distance between the transmitting and receiving devices depends on RSSI. In general, when the distance between those two devices increases, the value of RSSI will reduce [22; 23; 24; 25]. The RSSI value depends on a lot of parameters, including the distance between transmitter and receiver, the geometric orientation of sensors, and the environment characteristics such as rain, temperature, and humidity [25; 26; 27]; it can also vary with interference from the neighbor network or your own network [28]. Apart from this, the RSSI varies with the presence of a human being [29; 30]. The quality of a network depends both on the transmitter and the receiver [31]. On the receiving node, RSSI will depend on the power from  
Figure 6: The picture for the experiment.  
the transmitter, the sensibility, the orientation of the antenna toward the transmitter. The information about the received signal power can be calculated by the formulae below [32; 33]:  
\[P_{r}=P_{t}.G_{t}.G_{r}(\frac{\lambda}{4\pi d})^{2} \tag{1}\]  
And RSSI in dBm is generally delivered from the above equation and is given by [32; 34]:  
\[RSSI=-10 nlog(d)+A \tag{2}\]  
where \(P_{t}\), \(G_{t}\) are taken as the power from the transmitter and the antenna gain respectively in dBm, \(P_{r}\), \(G_{r}\): The power for the receiver and its antenna gain respectively \(\lambda\) and \(d\): The signal wavelength and the distance between transmitter and receiver's antennas respectively in meters. \(n\): the path-loss constant: This value will vary based on obstacles between the transmitter and receiver. \(A\): the value for RSSI when the distance between the transmitter and receiver is 1 m. If an AT command requesting for the signal quality is sent to a GSM SIM 800 Module, the module will respond will TA (Terminal Adaptor) value. The following Table 3 indicates the relationship between the TA value and the RSSI value [35].  
In our experiment, the GSM module will get a signal from the nearby base station (BTS) of the GSM network. For getting information about the RSSI value at a particular location from BTS, through the microcontroller we sent some AT commands.  
When AT+CSQ AT command is sent to GSM module, it will respond with the location's signal quality in the form of TA value. Data information responded by the module is received by the controller  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SN** & **TA Value** & **RSSI [dBm]** & **Condition** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Mapping between TA value and RSS in dBm.  
and then sent to Microsoft Excel through National Instrument LabVIEW. The acquisition process is shown in Figure 7 and its corresponding LabVIEW dashboard in Figure 8.
## 3 Literature Review and Comparison of Interferometric Coherence and Intensity Correlation
### Other Applications for SAR Interferometric Coherence  
The interferometric coherence and its temporal decrease (de-correlation) are also used for other applications than damage assessment of urban area after a natural disaster such as an earthquake, tsunami, storm, _etc._ This section reports a few examples.  
The interferometric coherence and the backscattering intensity of a series of ERS SAR data were used by [PERSON] _et al._[79] to distinguish between different land cover classes. [PERSON] _et al._[83] developed an automated procedure to map change over time within urban area (e.g., due to the construction of buildings) based on ERS interferometric coherence data.  
Flood detection by the difference calculation of two coherence images is reported by [127].  
SAR coherence is also used for damage assessment caused by forest fires [128]. [PERSON] _et al._[129] reported a similar approach where the coherence is related to the burn severity. Coherence is also useful for the monitoring of post-fire forest regrowth [130].  
Furthermore, [PERSON] _et al._[131] proposed the use of the interferometric coherence for battle damage assessment.  
[PERSON] _et al._[21] used a time-series of L-band ALOS PALSAR imagery for interferometric coherence based landslide detection. A similar approach is reported by [PERSON] _et al._[119] who detected landslides after the 2009 Sumatra Earthquake, Indonesia, based on post-event TerraSAR-X imagery. The interferometric coherence was used to distinguish between forest areas (low coherence values) and possible landslides characterized by bare soil areas (high coherence values).  
[PERSON] _et al._[132] used ALOS PALSAR backscattering intensity changes to estimate the area of a catastrophic rockslide debris flow event in the Jiweishange range, Chongqing, China. In addition, the pre-failure acceleration of the landslide was investigated using DInSAR. Moreover, a post-slide DEM was generated by the InSAR technique (Section 1.3) to assess the height surface changes.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 1 Introduction  
Building information modelings (BIMs) of as-built constructions which contain both geometric and semantic information are capable of assisting the facility management, reducing the operation cost, inspecting the construction quality etc. Therefore, it is highly required in communities such as Architecture, Engineering, Construction, and Facility Management (AEC/FM) [1]. Compared with BIMs generated from outdoors, the indoor perspective provides structural elements (e.g., floors, ceilings, windows, doors) as well as indoor belongings (e.g., furniture, appliances). This richer semantic information from the inner building perspective extends to indoor BIMs applied in fields such as indoor navigation, virtual reality, and interior design [1, 2].  
In recent years, the development of 3D spatial data acquisition technology experienced a large progress, which makes affordable scanning systems available on the market. Various kinds of integrating solutions emerged; these solutions can be categorized from the aspects of data acquisition methods: Terrestrial mapping and mobile mapping [3, 4]. In particular, indoor mobile mapping systems (IMMSs) can be classified into three categories on the basis of sensor carriers: (1) The trolley-based system, (2) backpack-based system, and (3) handheld-based system [5, 6]. The point clouds acquired from the state-of-the-art indoor mapping systems possess high precision, while providing a complete coverage of indoor scenes with low costs. In light of this, scanning point clouds are gaining increasing attention as the major metadata of indoor building modeling.  
In this paper, we propose a new method for automatic indoor as-built modeling for both mobile and terrestrial point clouds. The contributions of this method are three-fold: (1) An effectivelayer-wise strategy based on 3D planar primitives, (2) a novel contour refinement method achieved by a shrink-wrapping method, and (3) a generic method fitting to different kinds of point clouds.  
The remainder of this paper is organized as follows: We first review the current solutions with a conclusion of the existing limitations in Section 2. The proposed layer-wise-based method, with four different parts, is described in detail in Section 3. We evaluate the effectiveness of the proposed method on different types of point clouds, and the assessment is conducted with comparisons to other state-of-the-art methods in Section 4. Finally, Section 5 gives the conclusion of this work.
# Automatic Evaluation of Piano Performances for STEAM Education
## 5 Experiments and Results
### Experimental Results
#### 5.5.1 Legato Part Experiment  
Experiment results for the Legato part are presented in Table 4 with respect to SVM, NB, CNN, and LSTM. The system of CNN yielded an accuracy of 93.65%, which is higher than SVM's 83.80%, LSTM's 64.12%, and NB's 60.87%. In this experiment, we tuned \"nu, kernel, and decision function shape\", which is a hyperparameter for classification NuSVC (SVM). Then, we used the default hyperparameter for Gaussian Naive Bayes (NB). In addition, for CNN, we used 3-layers CNN, and for LSTM, we used 2-layers LSTM as hyperparameters.  
Figures 9-12 show the Confusion Matrices of SVM, NB, CNN, and LSTM, respectively. The correct classification and misclassification across three sound classes can be observed. We can see from Figure 10 that normal and good classes are highly misclassified in using NB, and from Figure 12 that most of the samples are classified as normal in using LSTM. By contrast, most of the samples can be correctly classified by using CNN. Moreover, CNN performs better than SVM, especially in distinguishing good and normal classes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifiers** & **Accuracy (\%)** \\ \hline SVM & 83.80\% \\ NB & 60.87\% \\ CNN & 93.65\% \\ LSTM & 64.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment results for Legato part.  
Figure 9: Confusion Matrix obtained with SVM for Legato performance.  
Figure 11: Confusion Matrix obtained with CNN for Legato performance.  
Figure 12: Confusion Matrix obtained with LSTM for Legato performance.  
Figure 10: Confusion Matrix obtained with NB for Legato performance.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 4 Experiment Results and Discussion
### Result of the Discrete HMM on the UCI Repository Dataset  
To evaluate the performance of the proposed HMM method, we split the UCI repository dataset into two datasets. One is the training dataset containing 66% (about 2/3) spam and 66% ham SMS, and the other one is the testing dataset containing the rest 34% SMS, as shown in Table 4.  
The reason we divide the dataset in this way is that we want to compare the performance of the proposed HMM method with the best performance achieved by the CNN method in [11]. [PERSON] et al. [11] used the same UCI dataset and divided the database as 2/3 for training and 1/3 for testing in their experiment.  
Following the procedures described in Section 3.7, the confusion matrix is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Dataset** & **Testing Dataset** & **Total Number of SMS** \\ \hline Spam & 493 & 254 & 747 \\ Ham & 3186 & 1641 & 4827 \\ Percentage & 66\% & 34\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and testing dataset statistics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual** & \multicolumn{2}{c}{**Predicted**} \\ \hline Negative & Negative & Positive \\ Negative & true negative (TN) & false negative (FN) \\ Positive & false positive (FP) & true positive (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics parameters.  
The evaluation results are shown in Table 6.  
To illustrate the performance of the proposed HMM method, we compare the results with those obtained by other machine learning models, including naive Bayes (NB), support vector machine (SVM), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), long short-term memory (LSTM), and convolutional neural network (CNN). The result comparison is shown in Table 7. As we pointed out before, all methods listed in Table 7 are evaluated by the same UCI dataset. Results of NB, SVM, NMF, and LDA methods were presented by [PERSON] and [PERSON] [56]. The results of LSTM and CNN were presented by [PERSON] et al. [11]. The highest accuracy is achieved by the CNN method. The proposed HMM method has slightly better accuracy than LSTM. It is compatible with the CNN method.
# Discovery and Flavor Characterization of High-Grade Markers in Baked Green Tea
## 3 Materials and Methods
### Odor Components Detection
#### 3.5.2 GC\(\times\)GC-O-MS Detection of Odor Components  
Qualitative analysis of odor compounds was performed according to the literature with modifications [35]. The extracted odor compounds were introduced in splitless mode into the Gas chromatographic instrument(Agilent 8890, Agilent Technologies, Santa Clara, CA, USA) and were simultaneously detected by Mass Spectrometer (Agilent 5975B, Agilent Technologies, Santa Clara, CA, USA) and a sniffing port (Sniffer 9100, Brechbuhler, Schlieren, Switzerland). A primary polar DB-wax capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness; Agilent Technologies) was connected to a mid-polar DB-17 ms column (1.85 m \(\times\) 0.18 mm, 0.18 \(\upmu\)m film thickness; Agilent Technologies). The initial column temperature was set at 50 \({}^{\circ}\)C for a 5 min holding period; then, it was increased to 220 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min and maintained for a 4 min final holding period. Ultrahigh-purity helium was used as the carrier gas at a flow rate of 1 mL/min.  
MS parameters were as follows: ion source temperature, 230 \({}^{\circ}\)C; transmission line temperature, 250 \({}^{\circ}\)C; quadrupole temperature, 150 \({}^{\circ}\)C; EI electron energy, 70 eV; mass range, 50-500; solvent delay, 4 min. The temperature of the sniffing port was 150 \({}^{\circ}\)C. Humid air was delivered into the pipeline of the sniffing port to maintain the sensitivity of the participants' noses at all times. The samples were sniffed by three trained panelists and the time of the appearance of the smelled odor, its specific description, and the intensity of the odor were recorded.  
The odor compounds were qualitatively analyzed by a combination of retention index (RI) from GC, ion peak from MS based on NIST14 library, and olfactory result (O) for comparative analysis. The RI values of the target compounds were calculated by referring to a previous work [35]. The relative concentrations of all odor compounds were obtained by semi-quantification method with 2-methyl-3-heptanone used as the internal standard.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 2 Materials and Methods
### Methods
#### 2.2.4 Mood Median Test
A time-series object was labeled as a planted forest if its statistic \(\chi^{2}\) exceeded a threshold determined using the significance level \((\alpha)\) and degree of freedom (df), illustrating that the NDVI median of the shapelet was distinctly lower than that of the non-shapelet. To determine the classification, the statistics \(\chi^{2}\) of all the forest objects were plotted in a frequency histogram, as shown in Figure 6. A demarcation is generated automatically in the interval of the lowest frequency (\(\chi^{2}\in[7,8)\)). In accordance with the five typical cases, we set \(\chi^{2}_{(\alpha=0.005,\text{df}=1)}=7.88\) as the final threshold. Therefore, a time-series object whose \(\chi^{2}\) exceeds 7.88 is defined as a planted forest; otherwise, it is defined as a natural forest.
## 2 Project and Geology Overview  
The layout of the water resources allocation project in Pearl River Delta is shown in Figure 1. The Nansha Branch line, which has a flow range of 5-13 m\({}^{3}\)/s, uses gravity self-flow water transmission and is designed to supply 13 m\({}^{3}\)/s. The shield tunnel length is 7.4 km and uses a single-line shield water transmission tunnel scheme. The concrete shield segment with inner and outer linings withstand internal and exterior pressure, respectively. The shield tunnel diameter is 4.1 m. The pipe piece thickness is 0.3 m and the inner diameter is 3.5 m. The lining C35 reinforced concrete thickness is 0.35 m. The tunnel water transmission inner diameter is 2.8 m. Moreover, there is a 1.2 mm waterproofing membrane put in between the pipe piece and the reinforced concrete. The composite liner must withstand a maximum internal pressure of 0.76 MPa under the conditions of the water transmission pressure of 0.7 MPa and surge pressure of 0.06 MPa.  
The Nansha Branch is dominated by the delta plain landform, with the development of rivers and waterways, sporadic hill remnants, and generally flat topography. The bedrock from top to bottom is composed of artificial fill, silt, silty clay, medium and coarse gravelly sand, and weakly weathered peitic siltstone. The peitic siltstones are mainly distributed in the depositional Tertiary Lower Xinzhuangcun Formation (E1x) and the Cretaceous Lower Baizhishan Formation (K1b). Due to the influence of composition and water on the weathering rate, peitic siltstones with different degrees of weathering are produced. The object of this test study is the weakly weathered peitic siltstone of the Lower Tertiary Xinzhuang Village Formation (E1x) and the Tertiary rock production EW/N10\({}^{\circ}\)-20\({}^{\circ}\).  
During the wet season, groundwater recharge primarily occurs through atmospheric precipitation, rivers, and channels. During the dry season, groundwater recharge to rivers is controlled by tides. Surface water and groundwater complement each other's drainage systems. The 50-year exceedance probability of 10% in the area is 0.10 g, and the corresponding essential earthquake intensity is VII. The characteristic period of the basic ground motion response spectrum is 0.35 s. The classification of regional tectonic stability is favorable.
# Confocal Laser Endomicroscopy for Bladder Cancer Detection: Where Do We Stand?
## 2 Materials and Methods
### Confocal Microscopy Instrumentation, Technique, and Image Interpretation  
The in vivo tool for CLE of bladder lesions is the Cellvizio clinical system (Cellvizio 100 series; Mauna Kea Technologies, Paris, France). The device is formed by a computer with dedicated software for image processing, a fiber-optic imaging probe for image acquisition (with variable diameter from 1.4 mm to 2.6 mm), and a laser scanning unit. The probe is delivered into the bladder through the working channel of the rigid or flexible cystoscope. Fluorescein, a contrast agent already adopted for other applications in human tissues [12], is used to stain the extracellular matrix, allowing the visualization of suspicious lesions. The contrast can be delivered intravenously or through topical bladder instillation. The confocal image is acquired in real time during the contact of the probe with urothel and delivered to the screen through video sequences at 12 frames per second and stored in the system.  
[PERSON] et al. first depicted CLE imaging in a dedicated paper [13] and then implemented it in their subsequent study. The appearance of urothelial mucosa suddenly appeared easy to compare to the histological one. Normal bladder urothel showed a uniform layer  
Figure 1: PRISMA study flowchart.  
of polygonal umbrella cell layer, then a smaller intermediate cell layer with a capillary network with erythrocytes in the lamina propria. Due to the small penetration depth of the confocal probe, the muscular layer cannot be appreciated from the surface and can only be visible in case of resection of epithelium (i.e., by contact of the probe with the tumor resection bed).  
Low-grade superficial tumors appear as densely packed urothelial cells that are homogenous and monomorphic. Other characteristics include an increased amount of cellular papillary structures and fibrovascular stalks from tumor neoangiogenesis, not observed in normal or inflamed areas.  
High-grade tumors show densely packed cells with pleomorphic population and irregular shape and loss of cellular cohesiveness, indistinct cell borders, and disorganized vasculature.  
The most challenging lesion to recognize is represented by CIS. In fact, its appearance of larger cells with indistinct cell borders and extensive acellular areas are like inflammatory conditions. However, CIS cells appear larger and more pleomorphic even if this is not always clear. Moreover, inflammation presents loosely arranged aggregations of monomorphic cells in the lamina propria consistent with local recruitment of immunity cells.
## 5 Outcomes and Valuation
### Suggestion of Optimization Procedure  
For the algorithm, the structure-1 is furnished with dissimilar algorithms distinctly via TIDN-FOID subordinate controllers. The optimization procedure adopted here are WOA, PSO [50; 51], CS [52], FA, and SHO. For WOA, the number of search individuals equals 51 and the number of iterations equals 100. In case of PSO, the adjusted constraints standards remain at \(\omega\) = 1.1, \(\omega_{\text{dampe}}\) = 0.98, \(c_{1}\) = 1.38, \(c_{2}\) = 1.97, population size equal 54, and the extreme maximum number of iterations equals 100. In case of CS, nests amount = 51, rate of detection = 0.51, exponent of levy = 1.49, supreme generation = 100, and sum of extents = 11. The tuned values for FA are \(\beta_{0}\) = 0.29, \(\alpha\) = 0.51, \(\gamma\) = 0.42, sum of fireflies = 51, and supreme figure of iterations = 100. For every algorithm, the superlative values for the TIDN-FOID subordinate controller are attained. The standards are not provided here. Through these standards, the outcomes of the diverse algorithms are distinguished in Figure 6a-c. The conforming outcomes C_O, C_U, and S_D standards are specified in Table 4, where it can be realized that C_O, C_U, S_D, and computational values obtained by the SHO-augmented TIDN-FOID subordinate controller are significantly improved compared to the added algorithmic procedure. Additionally, the dominance is arbitrated by the conjunction curvature shown in Figure 6d, where it is detected that outcomes with the SHO-augmented TIDN-FOID subordinate controller converges quicker and have a minimum value of \(PIx_{ISE}\). Consequently, supplementary analysis is supported via the SHO algorithmic procedure.  
Figure 6: Valuation for the reference of the algorithm within WOA/PSO/CS/FA/SHO procedures for the procurement of premium values of the TIDN-FOID subordinate controller for structure-1: (**a**) arena-2 frequency anomaly, (**b**) anomaly in power interrelating arena-1 and -2, (**c**) anomaly in power interrelating arena-1 and -3, and (**d**) convergence curve.
## 5 Security Analysis and Comparison
### Comparison Analysis  
In the comparison analysis, PSNR with various compression ratios, entropies, and correlation coefficients are performed. Table 8 compares the proposed method's PSNR with other literature studies for the Lena image on 0.25 and 0.5 compression ratios. Tables 9 and 10 compare the proposed method's information entropy and correlation coefficient, respectively, with other literature studies for different test images.  
Figure 16: PSNR of the cameraman image on different singular values.  
Figure 17: SSIM of the cameraman image on different singular values.  
Figure 18: PSNR and SSIM of the different test images on 150 singular values.