esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 4 Opportunistic Transmission Strategy for Farm WSN
### Constructing Candidate Set Based on Improved Bellman-Ford
The nodes whose transmission cost changes each time are stored in \(A\). In the next iteration, only the transmission costs of other nodes whose candidate set contains \(B\) set elements are calculated, which can ensure the quality of candidate set construction and reduce the iteration number, thus reducing the computational overhead.
## 4 Results and Discussion  
### Image Classification  
Figure 9 shows changes in land cover types in the Bumba region for a 9-year time period according to the k-means classification by R.  
Pairwise examination of the classified images allows us to examine the changes in forest structure that are caused by anthropogenic activities, including urban growth, and environmental drivers initiated by climate change. To this end, we present the results of the classification of the three target regions showing changes in the tropical moist forest along the flow of Congo River, as shown in Figures 7-9 for Bumba, Basoko and Kisangani, respectively.  
Classes of the land cover types were defined as follows: swamp forest; humid tropical forest; secondary forest; urban areas and impervious surfaces; inundated grassland; deciduous forest; shrubland and grassland; cropland and mosaic forest; savannah with sparse trees; and water bodies. The model was carried out by comparing the results of the vegetation types classified based on the Landsat imagery in 2013 and 2022. An additional image was selected for the Kisingani for 2015 due to the cloudiness of the image in 2013. As demonstrated in the resulting plots, the pixel-based models use 10 land-cover categories. The integration of the two Landsat OLI/TIRS images for land cover mapping by the R language is a challenging and promising approach that can be recommended for future similar studies on rainforest mapping. The final outputs as classified maps contained  
Figure 10: NDVI computed from 5 and 4 bands of the Landsat-8 OLI/TIRS scenes for years 2013 and 2022 for the three key areas: Bumba, Basoko, Kisangani. Mapping: RStudio. Source: authors. (**a**) Bumba region: 2013. (**b**) Bumba region: 2022. (**c**) Basoko region: 2013. (**d**) Basoko region: 2022. (**e**) Kisangani region: 2013. (**f**) Kisangani region: 2022.  
10 land cover classes recorded from the 30 m raster grids equivalent to the resolution of the initial input Landsat OLI/TIRS data.  
Land cover classification using the R approach to process remote sensing data by RStoolbox is a promising and effective approach. However, the pixel-based data processing of the satellite images results in the grid structure of the landscape with resolution corresponding to the initial data. For more detailed data, Sentinel scenes can be considered as well for a more fine-resolution classification. The continued increase in the volume of spatial Earth observation data from the satellite images and novel directions in image analysis techniques using programming tools encourage the development of object-based approaches. Therefore, technical variations in the classification process can be omitted by applying object-based image analysis to detect the landscape structure. This can be considered a further development of the R programming methods.
## 1 Introduction
The outline of this algorithm is to use the branch-and-price algorithm [11; 12] to search for the optimal combination on the current principal-assistant route set and use the proposed density-based construction to enlarge the principal-assistant route set. The density-based construction evaluates the density of a vertex based on the number of residual assistant agent-accessible vertices surrounding it and the time cost between them and then inserts the vertex with the higher density and smaller deadline as the preference. In the performance validation, the number of served vertices and the running time of algorithms are regarded as the evaluation criteria. In the 1-principal-1-assistant scenario, the hybrid combination algorithm is compared with an exact integer programming method, two adaptations based on the algorithms in [7; 13], and some combinations of inner procedures. The hybrid combination algorithm proposed in this paper is only inferior to the exact method, and its running time is far less than that of the exact method when the graph size is less than 15. When the graph size ranges from 20 to 100, the hybrid combination algorithm serves the most vertices and costs a reasonable running time among the compared algorithms. In the \(m\)-principal-\(u\)-assistant scenario, the BP-based algorithm embedded with the density-based construction method is compared with two greedy algorithms that employ the line route construction method [14] or a local iterated search method [15] as the route construction and two BP-based algorithms embedded with the line route construction and local iterated search method. The BP-based algorithm embedded with the density-based construction method outperforms the other benchmark algorithms in terms of the number of served vertices and the running time. The rest of this paper is organized as follows. In Section 2, the related work on the subject is reviewed. In Section 3, the 1-principal-1-assistant scenario is modeled and the hybrid combination algorithm is described. In Section 4, the \(m\)-principal-\(u\)-assistant scenario is modeled and the BP-based algorithm embedded with the density-based construction method is described. In Section 5, the experimental results of evaluating these algorithms are presented. In Section 6, the conclusions and future work are described.
## 1 Introduction
Numerous studies have been performed to investigate the response of group piles subjected to lateral loading based on 3D numerical analyses. [PERSON] and [PERSON] [3] performed numerical simulation of one row of piles subjected to lateral loading. It was observed that group effects are most significantly influenced by the row position and center-to-center pile spacing. [PERSON] and [PERSON] [31] performed numerical simulations of 3 \(\times\) 3 to 4 \(\times\) 3 pile groups. However, the influence of _S/D_ on \(p\)-multiplier was not reported. [PERSON] et al. [30] proposed site specific \(p\)-multipliers for vertical and battered 3 \(\times\) 4 pile groups with _S/D_ = 4.3 and 2.5 using the commercial finite element analysis code ABAQUS. The site consisted mainly of a clay deposit. The influence of number of piles, _S/D_, and soil type on \(p\)-multipliers was not accounted. [PERSON] et al. [32] performed experimental and numerical modeling of laterally loaded regular and finned pile foundation in sand. Site specific \(p\)-multipliers were calculated for the pile groups that consist of a maximum of five piles. To model the sand behavior, the Mohr-Coulomb model was used. The effect of number of piles and soil condition on \(p\)-multipliers was not evaluated. [PERSON] [14] used the procedure of [PERSON] et al. [25] to extract the \(p\)-multipliers and develop group factors for piles in sand profiles by performing a comprehensive parametric study. However, instead of using the group pile load test measurements, 3D finite difference analyses were performed. Because the 3D model and BNWF model outputs showed poor fits, \(p\)-multipliers could not be directly derived. Therefore, the shear modulus of the soil layers for the 3D model was manually adjusted. Additionally, use of the API curves, which have been reported to provide an unrealistic estimate of the soil resistance, is likely to have influenced the derived \(p\)-multipliers. These adjustments are not needed if the \(p\)-multipliers are directly extracted from a 3D continuum analysis, or more realistic \(p\)-\(y\) curves should be used in the BNWF model. Literature review reveals that uncertainties remain in estimation of \(p\)-multipliers for pile foundations in granular soils. In this study, \(p\)-multipliers and group effect parameters for piles in granular soils are developed from a parametric study utilizing 3D nonlinear finite element (FE) analyses. The \(p\)-multipliers are calculated directly from the numerical analyses. The effect of _S/D_, number of group piles, friction angle \(\varphi\), and pile fixity conditions are evaluated and quantified. Based on the simulation outcomes, a new functional form for \(p\)-multipliers of pile groups is proposed. The proposed equation is compared with available measured values. Comparisons are also made with multipliers presented in AASHTO [33] and FEMA [34] design codes.
# Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables
## 2 Methods
### Anthropometric Parameters  
Body height (BH), waist (WC), and hip (HC) circumferences were measured according to the accepted measurement procedure in anthropometry [32]. Women's body height was measured without shoes in the so-called Frankfort Horizontal Plane position, using a SECA 213 stadiometer (Hamburg, Germany) with an accuracy of 0.1 cm. Hip circumference (HC) and waist circumference (WC) were measured through the largest buttock protuberance (at the maximum circumference over the buttocks). WC was measured in the midsection between the lower edge of the last palpable rib and the top of the iliac crest. Body weight and components of body composition (body fat, fat-free body mass, total body water) were measured by bioimpedance analysis (BIA) using a TBF 300 body mass composition analyzer (Tanita Corporation, Tokyo, Japan). In addition, the following anthropometric indices were calculated using formulas: BMI = BW/BH\({}^{2}\)--body mass index, WHR = WC/HC--waist-hip ratio, and WHtR = WC/BH--waist-to-height ratio.
## 4 Discussion
Their findings were generally not common in our investigation, which may be induced by the differences of data sources, methods and time periods used. The main limitation of our study was the uncertainty of GPP and ET datasets. The ET data used in this study probably showed higher uncertainty in grasslands because few grassland flux-tower sites were applied in gridded ET estimation, which may result in slight underestimation of WUE [32]. Moreover, the interpolation of datasets with different spatial resolutions (e.g., the original spatial resolutions of ET, GPP and SPEI were 0.1\({}^{\circ}\), 0.05\({}^{\circ}\) and 0.5\({}^{\circ}\)) may also cause uncertainties. Especially, high resolution drought monitoring datasets would undoubtedly be helpful to improve the robustness of related studies. Nevertheless, our findings revealed that the non-resilient ecosystems would encounter more challenges in ecological protection, agricultural production and carbon sequestration in the future due to increasing drought disturbances [29; 72]. Policy makers could make reasonable water use strategies and efficient ecosystem management pathways to minimize the effects of drought under the warming climate.
# Sentiment Analysis Based Requirement Evolution Prediction
## 2 Related Work
### Sentiment Analysis with Deep Learning  
Deep learning has been used for sentiment analysis because of its ability to learn high-level features and to find the polarized opinion of the general public regarding a specific subject automatically [12]. RNNs have been used for sentiment analysis by explicitly modeling dependency relations within certain syntactic structures in the sentence [13]. Besides these, [PERSON] et al. attempted to improve the performance of sentiment analysis by integrating deep learning techniques with traditional surface approaches [14]. A knowledge-based recommendation system (KBRS) was presented to detect users with potential psychological disturbances based on sentiment analysis [15]. [PERSON] and [PERSON] implemented a deep recurrent neural network for opinion expression extraction [16]. The work proposed a tree-structured LSTM and conducted a comprehensive study of using LSTM in predicting the sentiment classification [17]. [PERSON] et al. proposed combining the standard recurrent neural network and word embeddings for aspect-based sentiment analysis [18]. [PERSON] et al. introduced the Long Short-Term Memory (LSTM) recurrent neural network for twitter sentiment classification [19]. The latest research shows that attention and the dynamic memory network have become an effective mechanism to obtain superior results in sentiment classification task [20; 21].  
A number of research works have been proposed for supporting requirements evolution analysis. Previous research attempted to explore a machine learning model to discover software features and their sentiment simultaneously, which captures both semantic and sentiment information about software requirements encoded in users' reviews. However, our work differs from them mainly because we consider the dependencies of features and their sentiments. Most of the existing work so far has ignored the hierarchical structure of the features and sentiments, which assumes that all features are independent of each other. Although these existing methods are simple and commonly used, they suffer from the problem of feature-dependent sentiment identification from general to the specific. Knowledge about the relations between features and differentiation of the prior and contextual polarity of each feature are crucial. In order to better address this phenomenon, we convey it to a hierarchical structure in feature requirement analysis by using the combination of deep RNN and unsupervised hierarchical topic models. The joint model is able to capture inter-features and sentiments' relations without relying on any hand-engineered features or sentiment lexica.
## 3 Results and Discussion
### Relation between Flower Development and Stolon Generation  
The number of flowers removed decreased as the number of weeks increased in the 01 to 12 week cycle (\(p<0.001\)) and after week 16 in the 12 to 21 week cycle (\(p<0.001\)) (Figure 9). Generally, strawberry flowers take 35 days from initiation to visibility at the crown ([PERSON] et al., 1997). All the flowers removed at any given week were generally initiated five weeks earlier. Therefore, in the 01 to 12 week cycle, the flowers removed from Week 1 to Week 5 were initiated before the start of treatment (under much lower temperature). A distinct reduction of flower number was noticed in Week 7 and was associated with limited flower initiation in the environmental conditions driving stolon production.  
Considering the 12 to 21 weeks cycle, after all the stolons were removed during Week 12, there was a clear increase in the number of flowers (weeks 13 to 16) (Figure 9). The flowers were formed approximately during Weeks 8 to 11, but the stolons from the 01 to 12 week cycle were serving as a stronger sink for available photoassimilates. Therefore, the dormant lateral buds were not growing to anthesis. After the stolons were removed on Week 12, the dormant lateral buds became the main sink organs and developed to anthesis. After new stolon formation (Week 16), stolons started competing for photoassimilates and, consequently, decreased the number of lateral buds reaching anthesis. When day-neutral strawberries are propagated in the field, the environmental conditions affect the development of flowers and often significant labor is used to remove developing flowers. As demonstrated in this experiment, the number of flowers can be greatly reduced by controlling environmental conditions using PIP.  
Figure 8: Total number of daughter plants per light intensity and growing cycle (01 to 12 weeks and 12 to 21 weeks). Each column is divided to represent the percent of daughter plants in each size category (crown size: XL, >11 mm; L, 8.5 mm to 11 mm; M, 6 mm to 8.5 mm; S, 3.5 mm to 6 mm; XS, <3.5 mm; and premature buds (PM).
# An Analytical Survey of WSNs Integration with Cloud and Fog Computing
## 2 Background of Study  
For this great combination, there is a need to describe the requirements for a system. From this line of research, we explain these requirements in Table 1 for cloud and FC. CC is a rising technology for the modern era that provides services to users through the Internet. CC data and applications are placed at some shared locations on the Internet and servers are placed at some remote locations which are accessed by users through the Internet. It also permits resource sharing by reducing space and cost. Considering the popularity of the cloud and its advantages, people are shifting to cloud services progressively. Many cloud service providers provide services to users. CC services give more benefits as compared to a conventional computing paradigm. These benefits include reliability, strategic edge, and manageability, and most importantly low cost. It is easier for users to access their data from anywhere by using CC irrespective of the place and machine, as the data are located in the central location [14; 15]. The main purpose that all cloud service providers seek to offer the finest cloud services is that they are competing with time to make it better with every passing day. A hybrid cloud may be an association for both people in general and private. A community cloud setup is deployed by a community to achieve its objectives. Cloud provides services in three fundamental categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) [16; 17]. IaaS is a model that combines two parties' customers and cloud providers. It plans for the virtual delivery of cloud resources at the client's doorstep. Customers can control the resources as per their needs. The cloud provider extends benefits for customers by paying for storage space, processing, and network. PaaS enables clients to use the operating system as a service.  
Figure 3: Fog computing vs cloud computing architecture.  
Figure 2: WSN to Fog integration.  
The same required operating system can be rented to numerous clients. Through SaaS, clients can use the software to rent/service [18; 19]. The rest of the paper is divided into six segments. In segment 2 of the paper, CC foundation and evolution are introduced. In segment 3, we discuss related work. In segment 4, a comparison of different proposed integration frameworks of WSN with cloud and Fog is presented [20; 21]. In segment 5, the open issues are identified with the understanding of the WSN and cloud gaps, and future work of this domain is highlighted. In segment 6, the present review is concluded briefly [22; 23; 24].
## 2 Governing Equations  
The governing PDEs are based on the proliferation-invasion (PI) model [17; 18; 19; 36] to describe glioma cell invasion throughout the brain as a reaction-diffusion process;  
\[\frac{\partial c(x,y,t)}{\partial t}=\frac{\partial}{\partial x}\left(D\frac{ \partial c(x,y,t)}{\partial x}\right)+\frac{\partial}{\partial y}\left(D\frac {\partial c(x,y,t)}{\partial y}\right)+\rho c(x,y,t)\left(1-\frac{c(x,y,t)}{c_{ \max}}\right), \tag{1}\]  
where \(c(x,y,t)\) is the tumor concentration of glioma cells at spatial locations \(x,y\) and time \(t\), \(\rho\) is the net proliferation rate in units \(day^{-1}\), and \(D(x,y)\) is the diffusion coefficient. Notice that we can solve Equation (1) as a _one_- or _two_- dimensional model by simply turning the \(y\) derivatives off and on. The birth and death rates are included in \(\rho\), assuming a Verhulst (logistic growth) law including a tissue-carrying capacity \(c_{\max}\)[37]. The diffusion coefficient can depend on the values of the concentration and may vary from patient to patient [38]. In this study, we use discontinuous diffusion coefficient profiles that contain low (gray region) and high (white region) diffusion zones [38]. In _one_- dimensional test cases, we use  
\[D(x)=\left\{\begin{array}{ll}0.13&\text{if }0\leq x\leq 7.5\text{ (gray region)}\\ 0.65&\text{if }7.5<x\leq 42.5\text{ (white region)}\\ 0.13&\text{if }42.5<x\leq 50\text{ (gray region)}\,\end{array}\right. \tag{2}\]  
where \(x\) belongs to the interval \(I=[0,50]\), and in _two_ -dimensional cases, as suggested by [PERSON] et al. [39], we set  
\[D(x,y)=\left\{\begin{array}{ll}0.65&\text{if }0\leq d\leq 17.5\text{ (white region)}\\ 0.13&\text{otherwise (gray region)}\,\end{array}\right. \tag{3}\]  
where \(d=\sqrt{(x-x_{0})^{2}+(x-y_{0})^{2}}\) and \((x_{0}\,y_{0})=(25,\,25)\) is the center of the _two_-dimensional computational domain \(D=[0,50]\times[0,50]\). The zero-flux boundary conditions and _one_ and _two_- dimensional Gaussian initial tumor profiles are defined as  
\[c_{x}(0,y,t)=c_{x}(50,y,t)=c_{y}(x,0,t)=c_{y}(x,50,t)=0, \tag{4}\]  
\[c(0,t)=g(x)=\frac{1}{\sqrt{2\pi\epsilon}}e^{-\frac{1}{2}\left(\frac{x-y}{ \epsilon}\right)^{2}}\hskip 28.452756 ptc(x,y,0)=g(x,y)=\frac{1}{\sqrt{2\pi \epsilon}}e^{-\frac{d^{2}}{2\epsilon^{2}}}, \tag{5}\]  
where we use \(\epsilon=0.01\) in our calculations. Figure 1 illustrates _one_- and _two_- dimensional Gaussian initial tumors and discontinuous diffusion coefficient profiles.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 2 Related Work
This study allows operating and comparing fuzzy numbers. Ref. [23; 24] adopted this representation, which was used to adapt the Dijkstra algorithm to fuzzy costs in a generic transportation network. In this paper, we base our proposal on the modeling of a network by assigning a triangular fuzzy number to the links costs. For the comparison of triangular fuzzy numbers, a generalized definition for the Total Integral of a fuzzy number proposed in [9] is used. This is different from the above mentioned Graded Mean Integration Representation. The different metrics discussed above (WS, SW, _Normalized Used Bandwidth_) are adapted to the definition of fuzzy number and compared with the classic versions on the simulated environment of two well known communication networks, based on the topology of the Nippon Telegraph and Telephone (NTT) and the National Science Foundation's Network (NSFNET), respectively. In general, our fuzzy versions surpass or equal the crisp versions in all cases by a small statistically significant margin. In addition, a new cost function based on the triangular fuzzy number definition is developed, and given a physical explanation of its properties. Our optimization strategy based on this latter function (named Strategy 8) surpasses the other tested algorithms, giving a result of the Delivery Bit Ratio close to 1. Parallel to this work, this Strategy 8 has already been applied to the problem of searching for the shortest pair of disjoint edge paths in a communication network [5]. Ithas proven its effectiveness in managing a network with two types of traffic. This topic is of applicability in financial or governmental services, where privacy and security against external attacks are imposed requirements. On the other hand, our methodology could be transferred to city or road traffic management by modeling road or street networks as a graph where links have a limited traffic capacity that can be measured. In these conditions, actualization of traffic density cannot be carried out in a continuous time manner, thus our fuzzy modeling of the network is justified. In [25] this problem is posed and attacked by means of methodologies based on crisp measurements.