esa-sceva/satcom-chunk-collection huggingface dataset:
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 5 Experiment
### Results  
#### 5.3.1 Stopword Identification and Removal  
Identification and removal of stopwords are the two preprocessing tasks in many IR systems. In the past, few researches were conducted for creating Amharic stopwords [31]. The stopword lists constructed in this research are evaluated using Lemur on Amharic IR test collection. The removal of stopwords has significant impact on the retrieval effectiveness and index size. For example, the retrieval effectiveness of stem-based and root-based text representations with and without stopwords are presented in Table 8. As shown in Table 8, removal of stopwords has positive impact both in case of stem-based and root-based retrievals. For the sake of comparison between stem-based and root-based text retrieval, stem-based stopword list is created based on morpheme statistics (frequency, mean, variance, and entropy) considering the stem-based morphologically annotated corpus such as the case of root-based stopword list. In the rest of the experiments, we removed stopwords from stem-based and root-based corpora using our stem-based and root-based stopword lists.  
The overall impact of stopwords and the comparison between stem-based and root-based retrievals are indicated in Figure 2. The top two curves in red and blue represent the root-based and stem-based retrieval, respectively after stopword removal while the bottom two curves (in green and yellow) represent root-based and stem-based retrieval, respectively, with stopwords. Test results have shown that stopword removal improves the effectiveness of Amharic IR on stem-based and root-based text representations. Root-based text representation with stopword removal yields the best results.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Robustness of Methods to Exceeding the Signal Rank  
All trend extraction methods have the signal rank as a parameter. Unlike the previously studied parameters, there are no universal recommendations for selecting the signal rank. Currently, there is no justified AIC-type method for SSA, so methods such as visual determination of the rank are used. Consequently, the signal rank can be determined incorrectly. Underestimating the rank can often lead to large distortions, so it is better to overestimate the rank. The purpose of this section is to investigate the effect of overestimating the rank on the accuracy of the methods.  
Thus, we fix thresholds \(T_{0}=0.2\) and \(T_{1}=0.5\) and consider examples of signals of different structures.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Soil Name**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Porosity**} & \multicolumn{2}{c}{**Saturated**} \\  & & & ** Hydraulic Conductivity** \\ \cline{3-4}  & & & \(\mathbf{n^{\prime}}\) & \(\mathbf{k_{s}}\) **(m/s)** \\ \hline Berlin medium sand & [PERSON] et al. [44] & 0.388 & 7.30 \(\times\) 10\({}^{-5}\) \\ Boischot loamy sand & [PERSON] et al. [44] & 0.437 & 1.42 \(\times\) 10\({}^{-7}\) \\ Helecine silt loam & [PERSON] et al. [44] & 0.443 & 6.30 \(\times\) 10\({}^{-7}\) \\ Sandy loam & [PERSON] et al. [45] & 0.43 & 9.26 \(\times\) 10\({}^{-7}\) \\ Clay loam & [PERSON] et al. [45] & 0.5 & 7.52 \(\times\) 10\({}^{-8}\) \\ Gilat loam & [PERSON] [46] & 0.44 & 2.0 \(\times\) 10\({}^{-6}\) \\ Yan’an loess (silty clay) & [PERSON] et al. [7] & 0.47 & 6.43 \(\times\) 10\({}^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil porosities, saturated permeabilities and references of experimental data. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Soil Name** & \(\mathbf{a}\) & \(\mathbf{m}\) & \(\mathbf{n}\) & \(\mathbf{C_{r}}\) & \(\mathbf{\Psi_{s}}\) **(kPa)** & \(\mathbf{\Psi_{a}}\) **(kPa)** & \(\mathbf{\Psi_{r}}\) **(kPa)** & \(\mathbf{\Psi_{m,m}}\) **(kPa)** & \(S_{na}\) **(\%)** & \(S_{m,m}\) **(\%)** \\ \hline Berlin medium sand & 3.715 & 0.432 & 69.32 & 10.578 & 0.1 & 2.65 & 5.16 & 10,000 & 93.5 & 2.64 \\ Boischot loamy sand & 6.6 & 0.443 & 4.567 & 4.225 & 0.1 & 4.59 & 22.88 & 10,000 & 91.4 & 7.85 \\ Helecine silt loam & 3.416 & 0.523 & 0.828 & 6.209 & 0.1 & 0.59 & 179.76 & 10,000 & 92.4 & 14.29 \\ Sandy loam & 13.195 & 1.417 & 1.114 & \(1.6\times\) 10\({}^{5}\) & 0.1 & 3.37 & 133.48 & 10,000 & 90 & 5.7 \\ Clay loam & 40.609 & 0.843 & 0.652 & 330.225 & 0.1 & 8.2 & 1344.87 & 10,000 & 90.5 & 19.08 \\ Gilat loam & 4.832 & 0.323 & 7.888 & 2.811 & 0.1 & 3.83 & 11.84 & 10,000 & 91.6 & 9.59 \\ Yan’an loess (silty clay) & 10.82 & 1.273 & 1.386 & \(1.65\times\) 10\({}^{16}\) & 1 & 3.784 & 87.641 & 10,000 & 90.4 & 5.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fitted parameters of the SWCC for the different soils. Figure 4: Fitted SWCCs for different soils: (**a**) Berlin medium sand; (**b**) Booischot loamy sand; (**c**) Helecine silt loam; (**d**) Sandy loam; (**e**) Clay loam; (**f**) Gilat loam; (**g**) Yan’an loess (silty clay). Figure 5: Estimated hydraulic conductivity functions for the selected soils: (**a**) Berlin medium sand; (**b**) Booischot loamy sand; (**c**) Helecine silt loam; (**d**) Sandy loam; (**e**) Clay loam; (**f**) Gilat loam; (**g**) Yan’an loess (silty clay).
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 2 Methodology  
The satellite clock prediction model used in our method is introduced first. Then, we analyze the clock prediction errors and determine the weighting functions during maneuvers for different clock types. At last, the actual processing flow of the clock-constrained RPPP POD strategy for maneuvering satellites is summarized and explained.
# A Novel Connected-Components Algorithm for 2D Binarized Images
## 1 Introduction
### Modern Connected-Component Labeling Methods  
More recent advances in CCL have focused on parallel processing, GPU acceleration, and machine learning-driven optimizations. Some key methods include the following:  
Modern CCL implementations leverage parallelization and powerful architectures to perform labeling [5; 8; 11]. These methods divide the image into tiles, process labels independently, and merge results in a post-processing step. While effective for real-time applications, they require specialized hardware and are challenging to implement on CPU-based systems.  
Graph-based CCL treats the image as a connectivity graph, where pixels are nodes, and edges represent connectivity relationships [10]. These methods use graph-cut techniques for efficient segmentation but are computationally expensive for large datasets.  
An interesting extension of CCL allows for images where the foreground does not have a constant color. He et al. [18] performed complex object segmentation using a convolutional neural network (CNN) to predict component labels directly from image pixels. Methods such as this excel in handling complex textures but require large, labeled datasets for training. Region-merging approaches iteratively combine neighboring labeled regions using statistical or geometric criteria [19]. These methods are particularly useful for noisy or degraded images, where classical pixel-based approaches fail.  
In this paper, we introduce an RLE-based method that uses a combination of segment precaching, image traversal by groups of pixels, instead of individually looking at each one at a time, combined with a specialized segment traversal and merge approach, in order to obtain the final structure that can be used for labeling. The advantages of some of these steps include reduced memory overhead, efficient component merging, and scalability.  
In Section 2, we present the data structures and the steps used in the proposed algorithm, and we discuss two algorithm variants. In Section 3, we describe the evaluation methodology and compare the results of the proposed algorithm with other state-of-the-art algorithms. In Section 4, we present a discussion based on these results and draw conclusions.
# Investigation of the Effect of Capillary Barrier on Water-Oil Movement in Water Flooding
## 3 Capillary Barrier Phenomenon
### Effect of Different Contact Angles and Opening Angles on the Capillary Force
#### 3.4.1 Water-Wet Conditions
Figure 14: The influence of angles of orientation on the capillary force with different contact angles under oil-wet conditions: (**a**) \(\theta=90^{\circ}\); (**b**) \(\theta=110^{\circ}\); (**c**) \(\theta=130^{\circ}\); (**d**) \(\theta=150^{\circ}\); (**e**) \(\theta=170^{\circ}\). Figure 13: The distribution of the maximum (**a**) and minimum (**b**) capillary force varying with contact angle \(\theta\) and opening angle \(\beta\) under water-wet conditions. Figure 13 shows the distribution of the maximum and minimum capillary force varying with contact angle \(\theta\) and opening angle \(\beta\) under water-wet conditions. It can be observed from the Figure 13 that the minimum capillary pressure is obtained at the interface of liquid flowing out of the throat; while when \(90^{\circ}<\beta+\theta\), the minimum capillary pressure is negative, and the capillary barrier phenomenon occurs in the process of the liquid leaving the throat section.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 2 Related Work
Moreover, the performance validations of previous works are mainly computed comparing the estimated solution with a reference local grid which may not reflect the real accuracy of the systems. In this work a topographic total station with an angular accuracy of 1\({}^{\prime\prime}\) and a distance accuracy of 1 mm + 1.5 ppm has been used in order to provide a real accurate tracking of the user movements. Finally, an interesting aspect considered in our work is the ubiquity of the solution provided in terms of reference coordinate system of the positioning solution. Having a georeferenced UWB test-bed, the transition between GNSS-based solution and UWB-based solution does not require any intermediate module for performing coordinate conversion and can rely simply on the geo-fencing approach.
## 4 FHB Management
### Resistance in Common Wheat  
Marker-assisted selection (MAS), which is based upon the establishment of a tight linkage between a molecular marker and the chromosomal location of the gene(s) governing the trait to be selected in a particular environment, uses modern genetics to speed up the traditional process of conventional breeding. Unlike conventional breeding where the progeny must be grown and observed for the desired trait before a selection can be made, MAS can be done at any stage in the breeding programme and allows selection for and pyramiding of resistance genes at the absence of the pathogen [79]. As a result of this, the time to develop improved cultivars can be halved. Several studies have reported [111; 112] the success of the genetic tools in improving FHB resistance. The first FHB resistant cultivar Sabin a hard red spring wheat developed by MAS at the University of Minnesota Experiment Station was registered in 2009 [113]. The efficacy of MAS relies on QTLs that produce large and stable effects and the availability of makers to validate these QTLs. So far studies have reported many _Fhb_ resistance QTLs in wheat and durum wheat [94; 114; 115; 116; 113; 114; 94; 115; 116]. Of these, seven (7) with varying levels of resistance have been formally assigned with a gene name. These are _Fhb1_[105], _Fhb2_[117]_Fhb4_[118; 119], _Fhb5_[120; 121] all from common wheat cultivars and _Fhb3_, _Fhb6_, and _Fhb7_ all from wild relatives of common wheat. _Fhb1_ is the most studied QTL and confers the greatest FHB resistance as confirmed by many studies [74; 94; 105; 116]. However, there is no single QTL that confers complete FHB resistance further underscoring that FHB resistance is a complex trait that is controlled by multiple genes. Even though breeders have had success in selecting for FHB resistance with MAS, the complex genetic architecture of FHB resistance, difficulties in precision phenotyping, and delayed availability of high-density genome-wide molecular markers in wheat have hindered progress in breeding for FHB resistance. Therefore, newer methods such as genomic selection (GS) that estimates the value of all markers or QTLs in the entire genome, has been shown to be a more effective marker-based strategy when breeding for complex quantitative traits such as FHB resistance [121; 122; 123]. Moreover, GS can be combined with MAS and MARS, and for large effect QTLs.
# 2-D Seismic Response Analysis of a Slope in the Tyrrhenian Area (Italy)
## 1 Introduction  
The effects of the topography and geological conditions on seismic waves have been studied by many authors from the second half of the last century. The observation of the most important earthquakes shows that the presence of topographic irregularities can significantly modify the consequences of the seismic input motion; so much so that even the European Code [1] takes into account the modification of the seismic action due to topography through a topographical amplification factor.  
Among the most important experiences that must be remembered include the study on the Pacoima Dam in San Fernando Valley, California [2, 3], and also the study on the Pacoima Canyon, which confirmed the very important role of topographic amplification on landslide triggering [4]. In the recent past, the 1999 Athens Earthquake caused heavy damage on the eastern bank of the Kifissos River canyon and inspired the study on the topographic aggravation factor [5].  
This paper is focused on the study of the local seismic amplification for a slope located in the Tyrrhenian north-eastern part of Sicily (Italy), carried out through 2-D numerical analyses, to determine the topographical contribution on the ground acceleration [6, 7, 8, 9]. As is known, the local seismic response is significantly influenced by stratigraphic heterogeneity and by morphological irregularities. The surface morphology is relevant on the seismic amplification of the site as evidenced by the structural damages detected in correspondence of morphological elements, such as the slopes, the escarpments and the canyons. From the geotechnical point of view, the topographical amplification involves the evaluation of the seismic risk for the historical sites built on reliefs, but also for earthworksand important works as bridges, dams, natural and artificial slopes. The Italian Building Code (NTC 2018) [10] allows for the evaluation of topographic amplification using a simplified method based on the use of the topographic amplification coefficient \(\mathrm{S}_{\mathrm{T}}\), which varies from a minimum value of 1.0 to a maximum value of 1.4. However, it is important to consider that the values suggested by NTC 2018 [10] are referring to general and not particular configurations and provide numerical values that in some cases may not be completely realistic. This work concerns the determination of topographic amplification factors that could influence the slope response in the case of seismic events. The topographical amplification factors were therefore calculated with 2-D numerical models and the relative values obtained from the analyses were compared with the values provided by the Italian Building Code.  
The study area is a portion of the Caronia Municipality (Figure 1), where in 2010 a non-coseismal landslide affected the southern part of the town and caused damages to urbanized areas and infrastructures; in addition, about 120 inhabitants in an area of approximately 50 hectares were evacuated.
## 4 Experimental Study
### Classifier Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} & \multicolumn{4}{c}{**AUC (\%)**} & \multicolumn{4}{c}{**F1-Score (\%)**} \\ \cline{3-14}  & \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**(Selected Features)**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**MC**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**MC**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**MC**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**RF**} \\  & & **(165)** & & & & & & & & & & & \\ \hline \multirow{8}{*}{Baseline} & XGB & 97.02 & 97.96 & 97.83 & 97.77 & 97.51 & 98.58 & 98.55 & 98.5 & 97.04 & 98.08 & 98.03 & 97.96 \\  & KNN & 33.68 & 77.6 & 65.66 & 66.01 & 54.00 & 83.62 & 75.35 & 75.64 & 33.93 & 77.93 & 66.76 & 67.12 \\  & DT & 94.501 & 94.44 & 94.39 & 94.63 & 97.5 & 97.2 & 97.15 & 97.3 & 96.69 & 96.30 & 96.24 & 96.41 \\ model * & NB & 42.08 & 54.00 & 48.56 & 53.9 & _77.77_ & 84.66 & 79.8 & 83.78 & 69.65 & 78.54 & 72.73 & 77.35 \\  & RF & 96.08 & 97.55 & 97.178 & 97.47 & 96.68 & 98.36 & 98.21 & 98.35 & 96.72 & 97.80 & 97.63 & 97.80 \\ \hline \multirow{8}{*}{Proposed model **} & MAH\_XGB & 97.22 & 98.04 & 97.96 & 97.69 & 97.44 & 98.71 & 98.72 & 98.39 & 97.22 & 98.24 & 98.27 & 97.81 \\  & MAH\_KNN & 48.52 & 78.80 & 67.36 & 67.4 & 64.69 & 84.38 & 76.46 & 76.47 & 52.67 & 78.79 & 68.02 & 67.97 \\ \cline{1-1}  & MAH\_DT & 94.14 & 94.97 & 94.94 & 94.16 & 95.07 & 97.47 & 97.49 & 97.08 & 94.75 & 94.68 & 96.61 & 96.16 \\ \cline{1-1}  & MAH\_NB & 53.60 & 61.01 & 57.03 & 65.61 & 82.81 & 85.23 & 81.51 & 84.44 & 77.4 & 79.16 & 74.94 & 78.44 \\ \cline{1-1}  & MAH\_RF & 96.85 & 97.71 & 97.28 & 97.17 & 96.73 & 98.44 & 98.34 & 98.16 & 96.74 & 97.89 & 97.82 & 97.52 \\ \hline \hline \end{tabular}  
* The Cronbach’s alpha value was 0.641, which indicates the reliability of the scale. This can be used in our study dataset to remove outliers and predict the diabetes risk using MAH.
* The baseline model was tested using the dataset in Figure 2 and without outlier removal. *
* The proposed model was tested using the target dataset in Figure 2 with outlier removal.  
\end{table} 
Table 5: Evaluation comparison of the proposed methods on the target dataset based on training 70% and testing 30%.
## 1 Introduction
Dormant DTCs express high levels of the orphan nuclear receptor NR2F1 that acts as a master regulator of dormancy, activating the expression of the cyclin-dependent kinase (CDK) inhibitors p27, p21 and p16, as well as other genes related to stemness (SOX2 and NANOG), quiescence, and cell survival [18; 19; 20]. Tumor-associated macrophages (TAMs) have been implicated in tumor progression and in many stages during the establishment of metastasis. TAMs can induce the EMT program by secreting cytokines, such as TGF81, EGF, IL-6 and TNF\(\alpha\)[21; 22], that in turn, induce the expression of the EMT-TFs. TAMs are also implicated in the seeding and growth of metastatic cells [23; 24]. The abilities of TAMs to remodel the ECM by secreting matrix metalloproteinases (MMPs), to suppress other immune effector cells, and to enhance angiogenesis all contribute to the invasion, survival and colonization of metastatic cells [25; 26; 27]. However, the exact molecular nature of the interactions between macrophages and DTCs, which mediate the entrance or exit from dormancy, has been insufficiently studied. CD147/EMMPRIN is a multifunctional transmembrane glycoprotein that mediates interactions between tumor and stromal cells [28]. EMMPRIN is overexpressed in many types of tumors, and its expression is correlated with higher tumor grade and stage,occurrence of metastasis and poor prognosis [29]. EMMPRIN is first and foremost a proangiogenic factor, as it induces the expression of MMPs and VEGF [28; 30], but its ability to interact with different proteins as a hub protein that stabilizes multiprotein complexes implicates it in the regulation of tumor cell proliferation, lactate efflux, cell adhesion, and drug resistance, as well [29; 31; 32; 33]. EMMPRIN, which is directly regulated by Slug [34], can increase \(\beta\)-catenin phosphorylation, leading to enhanced Snail expression and suppressed E-cadherin membranal expression [35], suggesting that EMMPRIN is also involved in the induction of EMT. In this study, we hypothesized that EMMPRIN is involved in regulating the entry and exit of tumor cells from dormancy. We demonstrate that when EMMPRIN expression was knocked-down in the CT26 mouse colon carcinoma cell line (CT26-KD), the levels of the dormancy markers and the EMT-TFs were increased, whereas the proliferation and the angiogenic potential were reduced relative to the parental cells. Co-culturing these cells with RAW 264.7 macrophages reversed these effects, suggesting that EMMPRIN is involved, but is not essential, in the escape from dormancy.
# A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
## 6 Conclusions  
In this paper, we present a lightweight intrusion detection model based on single hidden layer Multilayer Perceptron (MLP) model for Fog computing. To be lightweight, our proposed feature extraction technique utilizes modified vector space representation via \(n\)-gram transformation. Sparse matrix is also applied to compress the matrix formatting. Other than that, the linear correlation coefficient (LCC) is used to compensate the zero values. We also use mutual information feature selection to reduce the number of features. The proposed method is then tested using the ADFA-LD dataset using a Raspberry Pi, which acts as the Fog device. The test is executed with 2 nodes, 3 nodes, 4 nodes, 5 nodes, and 6 nodes of the MLP single hidden layer. From the experiment, we found out that 3 nodes of the MLP single hidden layer with 80 1-gram & 120 2-gram are able to achieve 94% accuracy, 95% recall rate, and 94% F1 measure. This is comparable with the higher number of nodes (i.e., 3 nodes to 6 nodes), which gives similar results. Since the 3-node set-up has lower complexity, and there is no significant advantage when a higher number of nodes is implemented, thus the 3-node implementation is suggested for the fog device. The values from ADFA-LD, i.e., 94% accuracy, was benchmarked, while 74% was achieved using 6 nodes in ADFA-WD. ADFA-LD showed better performance than ADFA-WD.  
The power consumption of the Raspberry Pi in the experiment's configuration was calculated using the electrical current requirement and the voltage. Along with the CPU time of the experiment, the energy consumption was then calculated, which is around 0.0014 Joules.