esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 3 The Proposed Model
### Text Pre-Processing
#### 3.2.1 TF-IDF Representation  
TF-IDF (term frequency-inverse document frequency) is a numerical statistic model used in several fields related to NLP, like information retrieval and text mining. It aims to convert the text documents into vector models based on a weight measure which is used to evaluate how important a  
Figure 1: Architecture of the spam detection model.  
word is to a document in a collection or corpus. The TF-IDF measure increases proportionally to the number of times a word appears in the document and it is offset by the number of documents in the corpus that contain the word. Equation (1) is the formula of TF-IDF used for word weighting [30].  
\[W_{i,j}=tf_{ij}*log(\frac{N}{df_{i}}) \tag{1}\]  
where \(W_{i,j}\) is the weight for word \(i\) in document \(j\), \(N\) is the number of documents in the collection, \(tf_{ij}\) is the term frequency of term \(i\) in document \(j\) and \(df_{i}\) is the document frequency of term \(i\) in the collection. The purpose of this step is to use the TF-IDF method to convert the text data into numerical data. The result of this transformation is a matrix of TF-IDF features, which will then be used as input to the traditional machine learning algorithms.
## 1 Introduction
Data collection requirements differ significantly in each model, starting from historical database collection, in situ measurements, trajectory modeling, and laboratory tests. In brief, data limitation problems and topographic, geologic, and climatic conditions of a particular area provide the basis for model modifications. North Pakistan is a geologically complex area due to the active head-on collision of three blocks, i.e., the Indian plate, the Kohistan Island Arc (KIA), and the Eurasian plate [29]. The ongoing active deformation is a root cause for multiple mountain hazards such as earthquakes, landslides, debris flows, floods, and rockfalls along different sections of the KKH. Rockfall and toppling activity is acute along a 200 km stretch from Besham to Chilas. In 2016, the KKH remained blocked for a few days at more than 100 locations for traffic due to rainfall triggered mountain hazards [30]. Regional tectonics, geology, topography, and climate have remained the long-term agents for rockfall initiation along the highway but considering short term and frequent rockfall activity in the study area, one of the most active triggering agents has been neglected and has not been included in any rockfall hazard and risk assessment study along the KKH. It is the animal activity at higher altitudes that activate rockfalls daily, causing damage to vehicles and humans. Animal activity has been scored carefully based on animal tracks, grazing altitude, and subsequent rockfalls and placed at the appropriate position in the standard RHRS model. Transportation corridors, highways, cultural heritage, and historic sites are the major study areas for rockfall hazard and risk assessment throughout the world [2; 12; 14; 31; 32; 33; 34; 35; 36; 37]. In northwest Pakistan, rockfall hazards and risk assessment have been carried out at a regional scale along the KKH by [PERSON], et al. [31]. The objective of the present study is to modify the standard RHRS model [1] by incorporating animal activity that causes frequent rockfall incidents. We have also presented a case study to prepare rockfall hazard and risk maps along the Karakoram Highway (KKH) road, which traverses a narrow gorge of the Indus River for ~78 km between Dasu and Besham, using a modified RHRSP model. The task has been achieved by compiling a rockfall, toppling, and debris slide inventory at 30 stations and collecting topographic, geologic, rock failure, road specific, and influencing factors data.
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 6 Conclusions  
In this paper, we targeted our efforts on IoT oriented heterogeneous networks with coexisting femtocell and macrocell. We aimed to manage the cognitive interference caused to macrocell as well as  
Figure 4: Sum-rate comparison with different numbers of data streams and antenna configurations.  
Figure 5: Percentage of interference leakage versus the number of data streams.  
ensuring an optimal capacity of the femtocell, hence, two novel self-organized cognitive IA schemes were proposed. The interference nulling based cognitive IA scheme can perfectly eliminate both the co-tier and cross-tier interference of the femtocell by aligning them into the lower dimension, while preserving the interference-free transmission for the macrocell. As the interference nulling based IA algorithm can achieve its optimum only with negligible noise, we also presented a partial cognitive IA (P-CIA) scheme by loosening the co-tier interference constraint, and further improved the network performance under low and intermediate SNR conditions. Additionally, the feasibility condition, capacity, and convergence analyses were derived. Both the theoretical and numerical results demonstrated that the proposed cognitive IA schemes achieved a significant improvement on the network capacity while causing no performance degradation to the primary users, indicating that the cognitive IA has a better application prospect for IoT oriented heterogeneous networks.  
In addition, in this paper, we mainly focused on the physical layer interference management with a stochastic geometry model. Extending the results of this paper to the network layer are interesting future avenues for our work. One possible approach is to exploit the multi-hop relay and explore corresponding routing protocols to further improve the network performance.  
[PERSON] conceived and designed the main idea, performed the experiments and wrote the paper; [PERSON] provided extensive support in investigation and resources for this work; [PERSON] provided extensive technical support in literature survey and theoretical analysis; [PERSON] provided support in research funding. This research received no external funding. The authors declare no conflict of interest.
# A Surrogate Model Based Multi-Objective Optimization Method for Optical Imaging System
## 2 Methodology
### Kriging Surrogate Model
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Orders** & **Number \(k\)** & **Function \(f_{i}\)** \\ \hline
0 (constant) & \(k=1\) & \(f_{1}(\mathbf{x})=1\) \\
1 (linear) & \(k=n+1\) & \(f_{1}(\mathbf{x})=1\), \(f_{2}(\mathbf{x})=x_{1}\),..., \(f_{n+1}(\mathbf{x})=x_{n}\) \\
2 (quadratic) & \(k=(n+1)(n+2)/2\) & \(f_{1}(\mathbf{x})=1\), \(f_{2}(\mathbf{x})=x_{1}\),..., \(f_{n+1}(\mathbf{x})=x_{n}\) \\  & \(f_{m}(\mathbf{x})=x_{i}x_{j}\), \(i=1,\ldots,n\), \(j=1,\ldots,n\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Regression models. \[\hat{\sigma}^{2}=\frac{1}{n_{s}}(Y-F\hat{\mu})^{T}R^{-1}(Y-F\hat{\mu}) \tag{8}\]  
The uncertainty of the predicted value of the Kriging model can be expressed as:  
\[s^{2}(x)=\hat{\sigma}^{2}\Bigg{(}1-r^{T}R^{-1}r+\frac{\left(1-FR^{-1}r\right)^{2 }}{F^{T}R^{-1}F}\Bigg{)} \tag{9}\]  
Due to \(\hat{\mu}\), \(\hat{\sigma}^{2}\), \(r(x)\), and correlation matrix R being dependent on the parameter \(\theta\), the Kriging model is trained by finding a parameter \(\theta\) that maximizes the following likelihood function. Unlike the deep neural networks, the goodness-of-fit of the Kriging model is not clearly defined. For the Kriging model, the value of \(ln(Likelihood)\) has a similar effect to the goodness-of-fit. A larger value of \(ln(Likelihood)\) represents a better-fitting effect of the Kriging model. \[ln(Likelihood)=\frac{1}{2}\Big{(}ln\hat{\sigma}^{2}+ln|R|\Big{)} \tag{10}\]  
The process to find a parameter \(\theta\) that maximizes the likelihood function is to solve an unconstrained optimization problem. For the Kriging model in the present paper, optimization algorithms such as the Genetic Algorithm (GA) [141], Particle Swarm Optimization (PSO) [142], and the pattern search algorithm [143] are used for this purpose. The GA and the PSO was chosen when the dimension was lower than 10 [144]. To verify the reliability of the surrogate model, it is important to test the model using test sample points, based on different error evaluation criteria, such as the average relative error, root-mean-square error, and correlation coefficient. The definition formula of these criteria are as follows:  
\[ARE=\frac{1}{N}\sum_{i=1}^{N}\Bigg{(}\frac{f_{i}(x)-\hat{f}_{i}(x)}{f_{i}(x)} \Bigg{)} \tag{11}\]  
\[RMSE=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(f_{i}(x)-\hat{f}_{i}(x)\right) ^{2}}}{\frac{1}{N}\sum_{i=1}^{N}|f_{i}(x)|} \tag{12}\]  
\[Correlation\ Coefficient=\frac{\sum_{i=1}^{N}\left(f_{i}(x)-\overline{f_{i}(x)} \right)\left(\hat{f}_{i}(x)-\overline{\hat{f}_{i}(x)}\right)}{\sqrt{\sum_{1} ^{N}\left(f_{i}(x)-\overline{f_{i}(x)}\right)^{2}}\sqrt{\sum_{1}^{N}\left( \hat{f}_{i}(x)-\overline{\hat{f}_{i}(x)}\right)^{2}}} \tag{13}\]
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Conditionenabolic Factors** & **Obesity Classified by BMI** & \multicolumn{2}{c}{**Obesity Classified by WC**} & \multicolumn{2}{c}{**Obesity Classified by WHR**} & \multicolumn{2}{c}{**Obesity Classified by WHR**} \\ \hline  & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** \\ \hline Age groups (years) & & & & & & & & \\
30-40 & 37\% (20,57) & 63\% (48;79) & 44\% (27;64) & 56\% (46;73) & 37\% (21;57) & 63\% (43;79) & 19\% (8;38) & 81\% (62;92) \\
41-50 & 19\% (9;33) & 81\% (67;91) & 40\% (25;55) & 60\% (45;74) & 26\% (15;41) & 74\% (99;85) & 19\% (9;33) & 81\% (67;91) \\ \textgreater{}50 & 27\% (13;47) & 73\% (53;87) & 38\% (22;59) & 62\% (41;78) & 50\% (31;69) & 50\% (31;69) & 23\% (12;43) & 77\% (56;89) \\ \hline Smoking (yes) & 32\% (17;52) & 67\% (48;83) & 50\% (32;68) & 50\% (32;68) & 39\% (23;59) & 61\% (41;77) & 25\% (12;045) & 75\% (55;88) \\ \hline Alcohol use (yes) & 23\% (13;35) & 78\% (65;87) & 35\% (23;48) & 65\% (52;77) & 38\% (62;62) & 62\% (48;74) & 15\% (7;027) & 85\% (73;93) \\ \hline Physical active (no) & 22\% (14;34) & 78\% (66;86) & 38\% (27;50) & 62\% (49;73) & 37\% (26;49) & 63\% (51;74) & 19\% (12;31) & 80\% (69;88) \\ \hline Raised SBP & 19\% (10;32) & 81\% (68;89) & 26\% (16;40) & 74\% (59;84) & 30\% (19;44) & 70\% (56;81) & 10\% (4;021) & 90\% (89;86) \\ \hline Raised DBP & 21\% (11;37) & 79\% (63;89) & 29\% (17;46) & 71\% (54;83) & 29\% (17;46) & 71\% (54;83) & 11\% (4;025) & 89\% (75;96) \\ \hline Diabetes (yes) & 14\% (3;45) & 86\% (55;97) & 29\% (11;58) & 71\% (42;89) & 21\% (6;51) & 79\% (49;93) & 8\% (0;9;4) & 92\% (61;99) \\ \hline Hypertension (yes) & 19\% (9;31) & 81\% (69;90) & 25\% (15;39) & 75\% (61;84) & 29\% (18;45) & 71\% (57;82) & 10\% (4;20) & 90\% (79;96) \\ \hline n (%) & 25 (8\%) & 71 (74\%) & 39 (41\%) & 57 (59\%) & 34 (35\%) & 62 (65\%) & 19 (20\%) & 77 (80\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prevalence of cardio-metabolic factors distributed by obesity amongst logistic company truck drivers. [PERSON]'s k was estimated to see if there was an agreement between two obesity indicators: BMI-WC, BMI-WHR, BMI-WHR, WC-WHR, WC-WHR and WHR-WHR. There was moderate agreement between obesity by BMI and WC, k = 0.59 (\(p\) < 0.001), and obesity by WC and WHtR, k = 0.48 (\(p\) < 0.001), indicating that 55 and 56 truck drivers, respectively, were obese. Obesity by BMI and WHtR had a significant agreement, k = 0.65 (\(p\) < 0.001), but obesity by BMI and WC had a small agreement. However, there was a reasonable agreement between WC and WHR with a 45.8% agreement (Table 4). In Table 5, the odds of developing hypertension were over four times higher among truck drivers than the odds of non-obese truck drivers by WC (AOR = 4.68; CI = 1.92, 11.34) and WHtR (AOR = 5.49 CI = 1.74; 17.27), as compared to two times higher by BMI (AOR = 2.74; CI = 1.04, 7.21) and WHR (AOR = 2.29; CI = 0.93, 5.61). Diabetes was significantly associated with obesity by WHR (AOR = 1.19; CI = 1.19, 31.21) only.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 3 Design and Performance Analysis of Sparse SAR Imaging Mode
### Beam Position Design  
The selection of beam position plays a critical role in the system design. The choice of PRF takes into account the limitations of transmit and nadir interference. The timing diagrams depicted in Figure 3 present the beam position designs for Stripmap, Spotlight, Sliding Spotlight, Scan, TOPS, and Mosaic, where the blue line represents the sparse imaging mode, and the red one represents the corresponding traditional one. The beam position design of these sparse modes is based on PRFs that are 75%, 50%, 50%, 75%, 75%, and 75% of their corresponding conventional ones, respectively. The selection of the downsampling ratio for sparse imaging modes depends on the practical application. In the following, we take Sliding Spotlight (high-resolution mode) and Scan (wide-swath mode) as examples to analyze the designed system's performance.  
Figure 3: Timing diagram of the designed sparse SAR imaging modes. (**a**) Sparse Stripmap. (**b**) Sparse Spotlight. (**c**) Sparse Sliding Spotlight. (**d**) Sparse Scan with three subswaths. (**e**) Sparse TOPS with three subswaths. (**f**) Sparse Mosaic with three subswaths.
## 4 Results and Discussions
### Crash Hot Spot Analysis of Ethiopian Regions and Towns
Crash hot spot zones are primarily characterized as highly densified areas that are associated with the presence of numerous activities that result in a large number of pedestrians, traffic volumes, populations and intersections. Addis Ababa, the capital city of Ethiopia was revealed as the first crash hot spot. The city is the home of AU (African Union), the Economic Commission for Africa (ECA) and over one hundred (100) embassies of several countries. The financial and administrative capital city of Ethiopia, Addis Ababa, is facing persistent growth and change. The city is transforming from its  
Figure 4: RTC Hot spot map of Ethiopia regions and federal territories. Figure 3: (a) Result of spatial autocorrelation; (b) Attribute table result of spatial autocorrelation. current administrative center to the central business district and an industrial center. Due to this change and rapid growth, there is an excessive transportation demand for the mobility of goods and people and, also, there is a higher attraction of people to the city in which a population of 3.6 million in 2013 is forecasted to be nearly 10 million in 2037 [62]. Whereas the city's efficient share of roadway infrastructures in land use requires 20% to 25%, the current share is 7%, indicating that the city's road transport system is inadequate [63]. The city's modernization, combined with an increase in motor vehicle ownership, increases the number of vehicles in the city; for example, of all registered motor vehicles in Ethiopia, 77% are found in Addis Ababa, with a yearly growth rate of 5.8% [64]. Walking (pedestrians) is a substantial portion of Addis Ababa's transportation modes, accounting for even more than 60% of daily journeys [65]. This high proportion of pedestrian road users in the city, combined with limited sidewalk facilities (such as crowded sidewalks, unpaved or inaccessible sidewalks), increases the number of pedestrian-related crashes. This is demonstrated by the fact that about 89% of the city's recorded crash causalities were pedestrian-related [66]. Addis Ababa City, the first-ranked crash hot spot zone, has a radial-shaped road network with five major roadways radiating in and out of the city's central business and administrative district. All traffic flows entering and leaving the city via these five roadways pass through the Oromia region, which was prioritized as the second crash hot spot area. High traffic volumes entering and leaving Addis Ababa have an impact on the Oromia region, which shares a common periphery on all sides with Addis Ababa (Finfinnee). Oromia was prioritized as the second hot spot due to higher traffic volumes, spatial dependence with Addis Ababa, larger size (inland coverage, roadway length and population number) and unsustainable traffic management in the region.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 3 Elements of Frequency Selective Surfaces
### Class 3: Solid Interiors or Plate Type Elements  
This group includes the plate type elements, which are of two types: (i) metallic patches mainly in shapes of a circular disk, rectangle, and square; (ii) an array of slots on metallic plates in shapes such as square, circular, etc., as shown in Figure 8. The first type is used as reflecting arrays while the other implements transparent arrays. As the patch dimensions of this group are close to \(\lambda/2\), they suffer from the issue of angle dependence and the onset of grating lobes [111].  
Figure 7: Typical shapes of FSSs. (**a**) N-pole or center-connected; (**b**) Loop type; (**c**) Solid interiors; (**d**) Combination of either first three [112].
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 4 MP-RDV: The Proposed Scheme
### Designing Delay Metrics for MP-RDV
Since the delay on link \((n_{b},n_{d})\) is the sum of the waiting delay on \(n_{b}\) (i.e., \(W(n_{a},n_{b})\)), and transportation delay \(\overline{D}(n_{b},n_{c})+\overline{D}(n_{c},n_{d})\), the delay metric of link \((n_{b},n_{d})\) corresponding to the previous-hop node \(n_{a}\) is computed as  
\[M^{(dly)}(n_{a},n_{b},n_{d})=W(n_{a},n_{b})+\overline{D}(n_{b},n_{c})+ \overline{D}(n_{c},n_{d}). \tag{8}\]  
This metric value is computed at \(n_{b}\) and advertised to \(n_{x}\) through RDV messages, and then \(n_{x}\) is possible to forward packets to \(n_{d}\) via a relay node \(n_{b}\). In this way, the delay metrics of links are computed within the framework of RDV and advertised for being used in routing decision. Figure 8: Illustration on measuring waiting delay: The waiting delay at a node is computed from the arriving time of \(m\) vehicles within the time period of \(T\).
# Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis
###### Abstract
Many reviewing systems employ the popular five-star ranking scheme to evaluate the customers' opinion in a segmented manner. The Yelp's Academic Review Dataset [10] and Amazon Fine Food Review [11] both use this five-star scheme for opinion ranking. [PERSON] et al. [12] explored the effectiveness of different ML algorithms for such a five-way classification problem using the Yelp dataset. The performances of naive Bayes, perceptron, nearest neighbor, and multi-class SVM were tested. Naive Bayes achieved the best overall performance for all the categories, perceptron had the highest precision and recall for one- and five-star ratings, but the predictions for two, three, and four stars were quite poor. Their work pointed out one of the major challenges for such sentiment analysis: it is often rather difficult to distinguish the difference amongst the middle ratings (two and four stars in particular), even for humans. On the other hand, such a challenging classification scenario also makes a good test case for evaluating and comparing different ML algorithms. Indeed, while facing this challenge, researchers often tend to merge the middle ratings to form a cohort with reduced categories for better classification performance (88.91% [13] and 95% [14]). To tackle these problems pointed out in the literature, a novel re-engineering mechanism for the generation of word embeddings is proposed in this study which could also find potential applications in other areas such as biometrics and image processing. The main contributions could be summarized as follows:1. A novel mechanism is proposed for re-engineering word embeddings, which boosts the semantic classification performance for sentiment analysis across four public benchmarking datasets. 2. The proposed mechanism is shown to reduce the system training time drastically (a few seconds) compared to the mainstream DL methods (minutes to hours). 3. Good performance is achieved using a small amount of data for training, which is particularly advantageous for certain application scenarios where the amount of annotated data is limited. This work is organized as follows: the basic methods used for data preprocessing and extraction of preliminary embeddings are presented in Section 2. In Section 3, a novel embedding re-engineering mechanism, designed to boost the performance for semantic classifications, is presented in detail. The effectiveness of the proposed mechanism is further evaluated using a range of publicly available datasets in Section 4, including both the binary and five-way classification scenarios. Section 5 provides a comparative analysis with the state-of-the-art reports. A discussion of the proposed algorithm is provided in Section 6, conclusion and some suggestions for future work are included in Section 7.
## 8 Conclusions  
In this paper, we proposed an active file mode change mechanism that automatically changes the file mode to online or local mode by considering the local storage usage ratio and file access delay time according to the directory activation ratio in the file synchronization service for cloud storage. Most research works regarding file synchronization do not support file mode change functionality [1; 2; 3; 4; 5; 6; 7]. In the existing manual file mode change method [8; 9; 10], the file mode is not changed without the user's request. On the other hand, the active file mode change mechanism selects files according to the directory activation ratio and changes them to the online or local mode, thereby maintaining an appropriate local storage usage ratio and file access latency. In addition, the client can adjust the file mode change timing and local storage usage ratio by changing various thresholds according to various requirements of normal clients and IoT devices. In future research, we plan to find the optimal parameter value that maximizes synchronization performance according to different weights of the local storage usage ratio and file access delay. We also plan to further analyze the performance of the active file mode change mechanism in more diverse file access scenarios.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1F1A1047032).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Roles** \\ \hline \hline _DSLAT_ & To control how long a file or a directory keeps the fully active state \\ \hline _OMT_ & To control when the change to the online mode starts. \\ \hline _LMT_ & To control when the change to the local mode starts. \\ \hline _USRT_ & To control when the change to the online or local mode stops. \\ \hline _MADT_ & To control minimum file size to be changed to the local mode \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters and roles of active file mode transition mechanism.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.