esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Hidden Markov Model  
A Hidden Markov Model (HMM) was employed for post-processing, refining the classification by addressing unphysical transitions, reducing spatial noise, and ensuring temporal consistency. HMMs are a powerful framework for improving the accuracy of classification results [32], and have been found to be a useful tool for addressing temporally dynamic multiclass classification problems [33; 34; 35]. This application of HMMs reduces abrupt class changes which may be deemed unphysical within the context of the target classes.  
The model requires a probability transition matrix, for which values were assigned based on accepted physical phenomena. The transition probability from the bare class to another class within the observation window was assumed to be zero. Standard mangroves were equally likely to remain mangroves or transition to the bare class after a pollution event. The forest class was more likely to remain forest than to transition to the bare class.  
This temporal smoothing technique serves the additional purpose of mitigating confusion between the'mangrove' and 'forest' classes and improving the overall spatial coherence and interoperability. In Figure 3, we present a schematic diagram describing how the various datasets are processed to produce a final classification of land surface types.  
Figure 3: Visual illustration of the main components of the classification workflow, from raw remotely sensed SAR data to the refined annual classification maps, as described in Sections 3.1-3.3.
## Chain-Based Communication in Cylindrical Underwater Wireless Sensor Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2,3, [PERSON] 4,*, [PERSON] 5 and [PERSON] 6**  
\({}^{1}\) COMSATS Institute of Information Technology, Islamabad 44000, Pakistan;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Internetworking Program, FE, Dalhousie University, Halifax B3J 4R2, Canada;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) CIS, Higher Colleges of Technology, Fujairah Campus 4114, UAE  
\({}^{4}\) B.M.T, C.A.M.S, King Saud University, Riyadh 11633, Saudi Arabia  
\({}^{5}\) Deanship of E-Transactions and Communication, King Saud University, Riyadh 11692, Saudi Arabia;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{6}\) Department of Computer Science, Kuwait University, Kuwait City 13060, Kuwait;  
E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed;  
E-Mails: [EMAIL_ADDRESS] (N.J.); [EMAIL_ADDRESS] (N.A.);  
Tel.: +92-300-5792-728 (N.J.); +966-505-268-838 (N.A.).  
Academic Editor: [PERSON]  
_Received: 13 December 2014 / Accepted: 29 January 2015 / Published: 4 February 2015_  
**Abstract:** Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, _i.e._, rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate.  
Keywords:chain-based routing; cylindrical networks; routing protocols; UWSNs
# CentralBark Image Dataset and Tree Species Classification Using Deep Learning
###### Abstract  
The task of tree species classification through deep learning has been challenging for the forestry community, and the lack of standardized datasets has hindered further progress. Our work presents a solution in the form of a large bark image dataset called CentralBark, which enhances the deep learning-based tree species classification. Additionally, we have laid out an efficient and repeatable data collection protocol to assist future works in an organized manner. The dataset contains images of 25 central hardwood and Appalachian region tree species, with over 19,000 images of varying diameters, light, and moisture conditions. We tested 25 species: elm, oak, American basswood, American beech, American elm, American sycamore, bitternut hickory, black cherry, black locust, black oak, black walnut, eastern cottonwood, hackberry, honey locust, northern red oak, Ohio buckeye, Osage-orange, pignut hickory, sassafras, shagbark hickory silver map, slippery elm, sugar maple, sweeptum, white ash, white oak, and yellow poplar. Our experiment involved testing three different models to assess the feasibility of species classification using unaltered and uncropped images during the species-classification training process. We achieved an overall accuracy of 83.21% using the EfficientNet-b3 model, which was the best of the three models (EfficientNet-b3, ResNet-50, and MobileNet-V3-small), and an average accuracy of 80.23%.  
dendrology; bark; artificial intelligence; deep learning
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 4 Results and Discussions
### Evaluation of Bonding Strength with ESPI
Figure 16: Visualized vibration modes by ESPI. (**a**) First mode; (**b**) Second mode; (**c**) Third mode; (**d**) Fourth mode; (**e**) Fifth mode; (**f**) Sixth mode; (**g**) Seventh mode; (**h**) Eighth mode. Figure 15: Frequency response of bonded specimen detected by ESPI. In this fashion, the ESPI method allowed us to find several resonant patterns and frequencies. Accordingly, we compared the bonding strength with the resonant frequency. Figure 17 plots the bonding strength as a function of the resonant frequency determined by the above-mentioned method. Yellow rhombus plots show the interface fracture detected by EPSI. Red circle plots show the interface fracture detected by the capacitance displacement gauge (Figure 12). Similarly, blue triangle plots show the base material fracture detected by the capacitance displacement gauge. Notice that the results from the capacitance displacement gauge and those from the ESPI measurement show the same general dependence on the resonant frequency. The two data sets clearly indicate that the bonding strength exponentially increases with the resonant frequency. This result demonstrates that the optoacoustic method can evaluate the bonding strength in a non-destructive and non-contact fashion. It also indicates that the resonant frequency can be detected by monitoring the total intensity of speckles in the observing surface.
## 1 Introduction
[55], and [PERSON] and [PERSON] [56] used numerical simulation or combined with a wind tunnel experiment to analyze the influence of both leading-edge and trailing-edge variable camber on the aerodynamic performance of airfoil at low-speed stage. Meanwhile, [PERSON] et al. [57] used high-fidelity aero-structural modeling to investigate the optimum deformations for a transonic airfoil, and the results reveal that morphing leading and trailing edges shows significant aerodynamic-performance improvement compared to morphing leading or trailing edges individually. [PERSON] et al. [58] performed research on the influence of both leading-edge and trailing-edge variable camber on aerodynamic characteristics and optimization of drag reduction under transonic condition. These low-speed and transonic studies have obtained conclusions consistent with the VCCW research. Based on the above aerodynamic research of variable-camber wings, it can be found that the aerodynamic benefits brought by variable-camber technology are not only reflected in the high lift performance at take-off stage, but also in the high lift-drag ratio performance under cruise stage; variable-camber technology is not only suitable for low-speed flight but also for transonic flight. Under this background, a scientific question raises about how morphing airfoil improves all-stage performance of an aircraft. Therefore, a conceptual aircraft with morphing airfoil is proposed in the present paper to investigate this scientific question. This kind of aircraft is designed to cruise at transonic speed and work at low speed. For example, it can respond rapidly from an airport hundreds of kilometers away to destinations for emergency rescue during the early stage of an epidemic or earthquake as well as for emergency detection of regional floods or forest fires, which denotes important value of application. The aerodynamic characteristics of three types of morphing airfoils (variable-camber airfoil, variable-chord airfoil, and the combination of both morphing styles) in the context of these conceptual aircraft missions is numerically investigated. The structure of the present paper is as follows: in Section 2, the conceptual morphing aircraft and the morphing airfoils are introduced; then, the method for numerical simulation is briefly described in Section 3; subsequently, Section 4 focuses on the results and discussions of aerodynamic characteristics; finally, conclusions are drawn in Section 5.
## 3 Current Research and Applications
### Delivery Services and Load Transportation  
Another highly promising application of UAVs is delivering packages or parcels. UAV delivery will be the next generation of delivery services due to its advantages over the conventional delivery process [99]. UAVs are not currently affected by road infrastructure and traffic conditions. In addition to parcel delivery, UAVs are also being explored for efficient load transportation, including passenger services such as air taxis. Utilizing VTOL technology, air taxis can bypass ground traffic and offer rapid, on-demand transport between key locations [100]. However, several critical challenges remain, including trajectory tracking, navigation, and the coordination of multiple UAVs in load transportation [101].  
Moreover, safety, risk management, and air traffic regulations must be addressed before UAV-based passenger transportation can be fully realized. Despite these challenges, advancements in UAV technology hold great promise for revolutionizing the transportation sector, highlighting the potential benefits of UAV applications.
## 2 Control Algorithm
### Parameter Tuning
#### 2.5.5 Tuning Procedures  
From previous analysis and our experience, the tuning procedures applying our algorithm are concluded as follows:  
1. Select \(\Phi\). \(0.1\leq\Phi\leq 1\) is suggested to speed up the convergence.
2. Select \(\eta\). Large \(\eta\) is helpful to speed up the convergence. However, the value of \(\eta\) is proportional to \(\tilde{u}\). Too large a value of \(\eta\) may cause too large a control signal.
3. Select \(\lambda\). From our experience, \(1\leq\lambda\leq 10\) is a better choice.
4. Select \(\rho\) and \(\alpha=\rho\lambda\). If an oscillatory response is allowed, \(\rho\) can be selected to be larger than \(1\). If not, a smaller \(\rho\) is suggested.
5. Estimate the transient properties:The overshoot can be estimated from damping ratio \(\zeta=(1+2\rho)/\sqrt{2}\rho\) for \(\rho\geq 1\).  
Estimate the total time constant. Since the convergence procedure is \(s\to 0\) and then \(e\to 0\), the total time constant of convergence, \(\tau_{e}\) can be approximated by \(\tau_{e}=\tau_{\eta}+\tau_{\alpha}\).  
The settling time \(t_{s}\), according to control theory, approximated by \(t_{s}=4\tau_{e}\).  
Build the nonlinear mathematical mode and run numerical simulations with initially selected parameters. Tune the parameters according to the transient performance and the effect of parameters.  
Remember to keep \(\alpha\) as small as possible, because a large \(\alpha\) usually indicates a very large control input.
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 1 Introduction
By jointly considering the wireless channel state and the sensors' energy distribution, the coordinator formulates and adjusts the cooperation strategy and selects appropriate relay nodes to forward packets transmitted by common nodes to enhance the packet reception rate. To the best of our knowledge, this study is the first to (1) present a classification method for relay identification and (2) determine a cooperative-based data uploading scheme in RF energy harvesting-powered WBANs such that network performances can be improved. The rest of the paper is organized as follows. Section 2 presents the related works. Section 3 shows the network model. Section 4 explains the proposed work. Performance evaluations and results are discussed in Section 5. Finally, Section 6 concludes the paper.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 4 BER in the Case of MRC Combiners
### BER for MRC Combiner in the Presence of Nakagami-m Fading  
The PDF of the signal at the output of the MRC combiner with \(L\) branches where Nakagami-\(m\) fading is present at the input can be represented as [12]:  
\[f_{MRC,Naka}(\gamma)=\sum\limits_{i=1}^{L}f_{Naka}(\gamma_{i}), \tag{31}\]  
that is, as:  
\[f_{SC,Naka}(\gamma)=\left(\frac{m}{\gamma_{0}}\right)^{Lm}\frac{\gamma^{Lm-1} }{\Gamma(Lm)}e^{-\frac{m\gamma}{\gamma_{0}}}, \tag{32}\]  
The BER of the WSN transceiver based on ZigBee technology operating in an AWGN environment and with a signal under the impact of Nakagami-\(m\) fading, previously passed through the MRC combiner with \(L\) branches, is obtained when (32) and (2) are included in (4):  
\[BER_{Zigbee,MRC,Naka}=\int\limits_{0}^{\infty}BER_{AWGN}(\gamma)f_{MRC,Naka}( \gamma)d\gamma \tag{33}\] \[=\left(\frac{m}{\gamma_{0}}\right)^{Lm}\frac{1}{\Gamma(Lm)}\left[ \int\limits_{0}^{\infty}\frac{1}{2}\gamma^{Lm-1}e^{-\frac{m\gamma_{0}}{2}} erfc(\sqrt{T\gamma})d\gamma-\int\limits_{0}^{\infty}\frac{1}{8}\gamma^{Lm-1}e^{- \frac{m\gamma_{1}}{\gamma_{0}}}erfc^{2}\left(\sqrt{T\gamma}\right)d\gamma\right]\] \[=\left(\frac{m}{\gamma_{0}}\right)^{Lm}\frac{1}{\Gamma(Lm)}[I_{3 3,1}-I_{33,2}].\]  
By using the transformation for the erfc(\(\cdot\)) function [22] Eq. (06.27.26.0006.01) and the transformation for the exponential function [23] Eq. (8.4.3), as well as by using [24] Eq. (07.34.21.0011.01), integral \(I_{33,1}\) obtains the following solution:  
\[I_{33,1}=\frac{1}{2\sqrt{\pi}T^{Lm}}G_{2,2}^{1,2}\left(\frac{m}{T\gamma_{0}} \left|\begin{array}{cc}1-Lm,&\frac{1}{2}-Lm\\ 0,&-Lm\end{array}\right.\right), \tag{34}\]  
By using the transformation for the erfc(\(\cdot\)) function [22] Eq. (06.27.26.0006.01) and the transformation for the exponential function [25] Eq. (1.211.1), as well as by using [24] Eq. (07.34.21.0011.01), integral \(I_{33,2}\) obtains the following solution:  
\[I_{33,2}=\frac{1}{8\pi T^{Lm}}\sum\limits_{n=0}^{\infty}\frac{1}{n!}\left(- \frac{m}{T\gamma_{0}}\right)^{n}G_{3,3}^{2,2}\left(1\left|\begin{array}{cc }1-n-Lm,\frac{1}{2}-n-Lm,1\\ 0,&\frac{1}{2},&-n-Lm\end{array}\right.\right), \tag{35}\]  
After substituting the integral solutions (34) and (35) in (33), the final expression for the BER of the transceiver of the IEEE 802.15.4 network operating in an environment with AWGN and with a signal impacted by Nakagami-\(m\) fading, previously passed through the MRC combiner with \(L\) branches, is obtained:  
\[BER_{Zigbee,MRC,Naka}=\left(\frac{m}{\gamma_{0}}\right)^{Lm}\frac{1}{\Gamma(Lm )}\left[\frac{1}{2\sqrt{\pi}T^{Lm}}G_{2,2}^{1,2}\left(\frac{m}{T\gamma_{0}} \left|\begin{array}{cc}1-Lm,&\frac{1}{2}-Lm\\ 0,&-Lm\end{array}\right.\right)\right. \tag{36}\] \[\left.\left.-\frac{1}{8\pi T^{Lm}}\sum\limits_{n=0}^{\infty}\frac {1}{n!}\left(-\frac{m}{T\gamma_{0}}\right)^{n}G_{3,3}^{2,2}\left(1\left| \begin{array}{cc}1-n-Lm,&\frac{1}{2}-n-Lm,1\\ 0,&\frac{1}{2},&-n-Lm\end{array}\right.\right)\right].\]
# Recommender System for University Degree Selection:
## 5 Results
### Explainable Artificial Intelligence (XAI) Approach  
The Root Mean Square Error (RMSE) is a crucial metric for evaluating the accuracy of the predictive models. Thus, lower RMSE values indicate that the model's predictions  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **XGBoost** & **RF** & **GLMNET** & **KNN** \\ \hline
1 & 29.3 & 45.2 & 40.6 & 47.9 \\
2 & 31.1 & 43.7 & 41.5 & 50.6 \\
3 & 29.9 & 44.0 & 40.9 & 53.6 \\
4 & 30.0 & 45.5 & 40.1 & 49.7 \\
5 & 30.7 & 47.2 & 41.1 & 48.1 \\
6 & 31.4 & 44.1 & 41.3 & 49.9 \\
7 & 32.0 & 42.8 & 40.7 & 48.8 \\
8 & 27.5 & 44.6 & 41.1 & 51.7 \\
9 & 29.5 & 44.9 & 41.4 & 49.3 \\
10 & 30.1 & 47.8 & 42.7 & 61.5 \\ \hline mean RMSE & 30.1 & 45.0 & 41.1 & 51.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross-validation results.  
closely match the actual outcomes, leading to more reliable student recommendations. However, while RMSE is highly effective for assessing the predictive power of models, it is not necessarily well known to most people and may not serve as a clear guide for decision-making. Along with RMSE, our recommender system proposes an Explainable Artificial Intelligence (XAI) approach to ensure students understand the reasoning behind the recommendations. For instance, if a student has a specific academic and socioeconomic profile, the system might suggest pursuing a degree in Engineering. This recommendation would be justified by explaining, \"Based on your profile, the system recommends studying Engineering, as it is the field where you are projected to achieve the highest score on the Saber Pro university exam, with an estimated 350/400 points. In comparison, other options like Mathematics (315/400 points) and Biology (310/400 points) also show strong potential but are slightly lower. This recommendation is generated using data from over 900,000 students, considering your high school academic performance and socioeconomic information, to assist you in making this important life decision\". This level of explanation, enabled by XAI, not only enhances the student's comprehension of the recommendation but also fosters trust in the system. The student can see exactly how their unique profile influenced the recommendation, increasing the transparency and knowledge involved in the decision-making process.
# Pile Base and Shaft Capacity under Various Types of Loading
## 1 Introduction  
Most of the traditional methods used to estimate the bearing capacity of a single pile are based on the concept that the total pile capacity is the sum of the base and the shaft capacities [1]. The unit resistance values on the shaft surface and the pile base can be determined using various empirical correlations developed from different soil testing methods [2; 3]. However, it is assumed that there is no mutual relationship between the base and shaft capacities. There is also a general agreement that, on the basis of the estimated unit resistances, in some cases, it is impossible to determine the pile bearing capacity with an accuracy greater than 30% [4].  
Static load testing is considered the most reliable method for assessing the pile behaviour under an applied load. Nevertheless, the test only makes it possible to determine the bearing capacity of the whole pile. Determining the behaviour of the pile's base and shaft requires conducting tests on specially instrumented piles [5]. In some cases, an alternative method to the static load test may be a bidirectional static load test, originally proposed by [PERSON] [6]. In this test, the capacity of the pile base and shaft can be measured separately without the necessity of building a retaining structure. Nevertheless, the results obtained from the Osterberg test differ from the pile bearing capacity estimated in the traditional test due to different working conditions of the shaft and the base in these two tests. Appropriate determination of these differences can make it possible to verify the method and better apply the bidirectional tests in the analysis of the pile bearing capacity.  
In this paper, three methods of pile bearing capacity examination were introduced, including traditional static load tests with the load applied to both the whole pile and the pile base and the method of bidirectional static load test [7] developed by the authors. For the proposed method, tests on laboratory-scale models of pipe piles were carried out, and numerical simulations of the tests were performed. The results of the base and shaft load capacities obtained from the tests were compared, and future recommendations were proposed. This paper is part of a bigger research program concerning tests for a new bidirectional load testing method on real-scale piles [7; 8; 9; 10], model piles [11], and numerical simulations.
## 6 Intermediators-Inhibitory By-Products
### Inhibitors to Enzymatic Hydrolysis and Fermentation Derived from Hydrothermal Pre-Treatment
#### 6.2.3 Furan Aldehydes and Weak Acids  
During hydrothermal pre-treatment, degradation of sugars to furan aldehydes has been observed. The furan aldehydes include furfural and 5 HMF formed from pentose and hexose, respectively [106; 107]. Furan aldehydes degradation generates weak acids like levulinic acid and formic acids. [107; 108]. Although the furan aldehyde and acetic acid are inhibitors of fermenting microorganisms, they have little effect on the cellulase activity. [PERSON] et al. [99] observed that acetic acid (13 g/L) and furfural (4 g/L) had no effect on enzymatic hydrolysis. [PERSON] et al. [109] stated that furfurals and 5-HMF inhibit the cell growth and ethanol production of xylose-utilising _Sachharomyces cerevisiae_ (yeast). Inhibition of yeast fermentation is caused by furan aldehydes i.e., by reduction of enzymatic and biological activities. [PERSON] et al. [110] demonstrated that HMF and furfurals decreased the volume of ethanol production by yeast, during the dilute acid hydrolysis of softwood, whereby furfural inhibition was greater on the cell growth than on ethanol production.