esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Faults in WSNs  
As discussed previously, the faults are the data-centric approach. The calibration error and battery failure are system-centric approaches in WSN. Since WSNs are used in hazardous environments, e.g., flood level of a river, forest fire detection, rain, etc., the probability of failures in sensor nodes is high due to the nature of the environments. The faults of sensor nodes in the normal application do not have any severe impact. In tough situations such as calamities, forest fires, tsunami, etc., loss of human lives, environmental conditions and economic losses happen frequently due to node failures. Faults have to be prevented from these difficult situations to protect from losses. From the fault-finding mechanisms, faults are classified into different categories through various algorithms. The faults are categorized quickly. The corresponding actions have been taken to solve the problems by providing appropriate solutions [14]. A brief representation of fault classification is noted below with data collected from the sensor nodes.  
The collected data are represented by the triplet function \(d(n,t,f(t))\);  
\[f(t)=\alpha+\beta x+\eta \tag{1}\]  
where \(f(t)\) is the sensed data at time \(t\) of node \(n\).  
\(\alpha\)--Offset value of sensed dataset,  
\(\beta\)--Gain value,  
\(\eta\)--The noise affected in the sensed data.
###### Abstract  
The Nakdong River delta, located in southeastern Korea, preserves thick and wide sediments, which are suitable for the high-resolution study of the evolution of depositional environments in the lower delta plain area. This study traces the Holocene evolution of the Nakdong River delta using deep drill core (ND-3; 46.60 m thick) sediments from the present delta plain. Sedimentary units of the sediments were classified based on grain size compositions and sedimentary structures: (A) alluvial zone, (B) estuarine zone, (C) shallow marine, (D) prodelta, (E) delta front, and (F) delta plain. The weathered sediment, paleosol, was observed at 43.16 m below the surface. There is an uncomortality (43.10 m) to separate a Pleistocene sediment layer in the lowermost part differentiating from a Holocene sediment layer in the upper part of the core. The shallow marine sedimentary unit (32.20-23.50 m), in which grain size decreases upward is overlain by the prodelta unit (23.50-15.10 m), which consists of fine-grained sediments and relatively homogeneous sedimentary facies. The boundary between the delta front unit (15.10-8.00 m) and the delta plain unit (8.00-0.00 m) appears to lie at 8.0 m, and the variation in grain size is different; coarsening upward in the delta front unit and fining upward in the delta front unit, respectively. These sediments are characterized by a lot of sand-mud couplets and mica flakes aligned along with cross-stratification, which may be deposited in relatively high-energy environments. Until 13 cal ka BP, the sea level was 70 m below the present level and the drilling site might be located onshore. At 10 cal ka BP, the sea level was located 50 m below the present level and the drilling site might be moved to an estuarine environment. From 8 to 6 cal ka BP, a tansgression phase occurred as a result of coastline invasion by the rapid rise of the sea level. Thus, the drilling site was drowned in a shallow marine environment. After 6 cal ka BP, the sea level reached the present level, and, since then, progradation might begin to form, primarily by more sediment input. After this period, the progradation phase continues as the sediments have advanced and the delta grows.  
s +
Footnote †: journal: Appl. Sci.  
**Keywords:** sedimentary facies; depositional environment; delta; sea-level change; Holocene
# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 4 Data Ingestion  
A multi-source, multi-temporal, and multi-scale dataset, comprising Earth observation imagery, soil data, yield maps, and historic records, was collected for the needs of the current research (Table 3). Specifically, RapidEye satellite scenes were acquired on three different dates during the cultivation season of 2015, while ancillary UAS image was acquired exactly before the beginning of the first cultivation season (April 2016). Fourteen Sentinel-1 and four Sentinel-2 image scenes were acquired throughout the period 2016-2019.  
In parallel, a set of soil samples at 146 locations was collected from all over the rice farm and in different years (2016-2018, in wintertime); on average, 1 sample corresponded to an area of 0.76 ha, or 1.3 samples were collected per hectare. Yield maps were recorded by a harvester equipped with a yield monitoring system for three consecutive years (2017-2019). Finally, historic data were provided in tables since 1998, including detailed calendars on all the farming practices followed and annual yield figures per field.  
Given that datasets are never perfect, however, data preparation for predictive modelling purposes means to bring the dataset as closely as possible to model specifications or to adapt a model to the properties of the dataset [20]. In the following paragraphs, preparation of the collected data and their conversion into input parameters to the tested models is presented.
## 1 Introduction
### Network Parts  
Our proposed scheme consists of the following three main parts:  
**(a)**: **Sensor node.** As mentioned above, we need to acquire accurate and regular information about sensing field monitoring for a certain geographic area. Thus, we randomly deploy a large number of sensor nodes with particular specifications, as we will mention in the next section.
**(b)**: **Base Station (\(BS\)).** This part exploits \(BS\) characteristics, such that it has no energy constraints. The main tasks of the \(BS\) are:  
* It fully controls and manages the sensors network (sensing field).
* It divides the R-WSN field into sectors, as in [24].
* It performs the clustering process and selects the cluster head (\(CH\)) periodically (according to scheduled times and residual energy).
* It continuously and periodically (as agreed beforehand) informs nodes in the sector when electing a new \(CH\).
* It receives recharging requests from nodes with a low energy threshold and arranges the recharging trip of the MC to recharge nodes [25].  
All previous services of \(BS\) can be provided. This study also utilizes \(BS\) characteristics to offer the following additional services:  
* Processing the information collected from nodes to study the activities of different nodes.
* Evaluating recharging requests to determine whether the coming requests from nodes are normal (based on KB study nodes).
* Sending and adopting Air-R for trips to replace failed or damaged nodes in the R-WSN sensing field (the properties of Air-R are elucidated in the next sections).
* **Air-R.** Air-R is used in this proposal because it can cover a large area, and has a fast moving speed and freeway movement. The replacement process is easy because Air-R is provided with location information for failed nodes, and no complicated mechanics is required to operate the failed node. Therefore, the Air-R can detect or replace failed nodes at locations that are ordinarily unreachable.
## 2 Literature Review
According to their findings, GAAP consistently outperforms other schemes in terms of transmitting V-o-I while also attaining greater power efficiency [22; 23; 24; 25; 26]. The authors discussed a variety of WSN techniques, including energy-aware routing, as well as the parameters influencing energy-conscious routing in WSNs. In addition to demonstrating algebraic and graphical modeling of these factors, this knowledge enables students to design such algorithms more efficiently, evaluate the practicality of existing algorithms, and extend them. This section explores the various techniques employed using available routing algorithms to resolve these issues and become more energy-aware. In addition, the researchers' suggestions for development are taken into account. The authors of [27; 28; 29; 30; 31; 32] introduced a novel communication methodology known as context-aware communication (CACA-UAN). This mechanism is a scheme for addressing underwater networks, with the goal of improving the overall efficacy of objects in underwater networks through the application of artificial intelligence. After concluding their research, they determined that the CACA-UAN system increases the dependability and effectiveness of underwater communication schemes. The authors proposed an energy-aware and avoidable invalid routing protocol (EAVARP) as a novel routing approach based on hierarchical and data acquisition phases that complements existing routing methods [33; 34]. They argued that designing and planning routing protocols with extended network lifetimes and higher packet delivery rates in complex underwater environments presents significant challenges for UWSNs [35; 36]. As mentioned previously, the proposed protocol is hierarchical, with several underwater concentric shells formed around the sink nodes and the sensor network's nodes separated into different shells during the hierarchical portion of the protocol [37; 38]. Sink nodes are assigned tasks based on time intervals to ensure the network's real-time and temporal validity, thereby allowing the network's physical shape to be measured [39; 40; 41; 42; 43]. A detail evaluation of the literature review has been given in Table 1.
## 4 Simulations and Experiments
### Experimental Verifications  
Simulations demonstrate the advantages of G-PSO algorithm in terms of convergence speed and global optimization, and that G-PSO NLPID can speed up response time and suppress overshoot. In order to verify the effectiveness of G-PSO NLPID in practical applications, the X-axis servo system shown in Figure 5 is used in this paper for relevant experiments.  
The response curves of different controllers are shown in Figure 9 and the detailed performance comparison parameters are shown in Table 6. When the system is not optimized, there is overshoot, and the overshoot will introduce the backlash error in the ball screw into the system, which will reduce the precision of the CNC system. As can be seen from Table 6, the rise time and settling time of G-PSO NLPID are 0.21 s and 0.237 s, respectively, which are 22.22% and 24.52% faster than those of G-PSO PID. Furthermore, the overshoot of the system is successfully suppressed after optimization.  
Figure 8: Simulation response curves of different controllers: (**a**) Comparison of PID and NLPID controllers based on G-PSO algorithm, (**b**) Comparison of G-PSO NLPID, Fuzzy PID, and MRAC PID.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicators** & **INIT PID** & **G-PSO PID** & **MRAC PID** & **Fuzzy PID** &
\begin{tabular}{c} **G-PSO** \\ **NLPID** \\ \end{tabular} \\ \hline Rise time/s & 0.249 & 0.241 & 0.169 & 0.099 & 0.138 \\ Settling time/s & 0.414 & 0.384 & 0.278 & 0.111 & 0.182 \\ Overshoot/\% & 3.518 & 0 & 0 & 0.903 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed performance indicators of different controllers.  
As shown in Figures 8 and 9, the trends in the system response curves in simulations and experiments are the same, but the system response in the experiment is not as fast as in a simulation, mainly due to the input signal of the actual system (which is not the ideal step signal), the delay between the motion control card and servo drive, and the limitation of servo motor acceleration and deceleration. Although the simulation results and the experimental results are not the same, the dynamic performance is consistent, and the simulation is still crucial.  
The simulation and experimental results show that the NLPID controller optimized by G-PSO algorithm has better control performance.
## 2 Materials and Methods
### Impacts of Smart Roof Solutions and Technologies
#### 2.4.1 Energy Efficiency Solutions
\[\frac{\Delta Q_{H}}{A}=U_{1}\big{(}T_{ai,H}-T_{sa,1}\big{)}-U_{2}\big{(}T_{ai,H}-T_ {sa,2}\big{)}\qquad\frac{\Delta Q_{C}}{A}=U_{1}\big{(}T_{sa,1}-T_{ai,C}\big{)}-U_ {2}\big{(}T_{sa,2}-T_{ai,C}\big{)} \tag{3}\]  
with:  
\[T_{si}=T_{ai,H}-R_{si}\cdot U\Big{(}T_{ai,H}-T_{sa}+\alpha\cdot\frac{I}{h_{e}} \Big{)}\qquad\qquad T_{si}=T_{ai,C}+R_{si}\cdot U\Big{(}T_{sa}+\alpha\cdot \frac{I}{h_{e}}-T_{ai,C}\Big{)} \tag{4}\]  
where:  
\(\Delta Q_{H}\) is the energy savings during the heating season (Wh);  
\(\Delta Q_{C}\) is the energy savings during the cooling season (Wh);  
\(A\) is the roof area (m\({}^{2}\));  
\(U\) is the thermal transmittance of the roof (W/m\({}^{2}\)/K);  
\(R_{si}\) is the thermal resistance of the roof (m\({}^{2}\)K/W);  
\(T_{ai,H}\) is the internal air temperature during the heating season equal to 20 \({}^{\circ}\)C;  
\(T_{ai,C}\) is the internal air temperature during the cooling season equal to 26 \({}^{\circ}\)C;  
\(T_{sa}\) is the sol-air temperature, which was introduced to take into account not only the external air temperature but also the incident solar irradiation absorbed by the roof (\({}^{\circ}\)C);  
\(T_{si}\) is the internal surface temperature of the roof (\({}^{\circ}\)C);  
\(T_{ae}\) is the external air temperature (\({}^{\circ}\)C);  
\(\alpha\) is the solar absorption of the roof (-);  
\(I_{i}\) is the incident solar irradiance (W/m\({}^{2}\)), which with green roof was equal to \(I_{n}\) (see Equation (2)); and \(h_{e}\) is the external thermal adductance (W/m\({}^{2}\)/K). The primary energy savings for space heating and cooling were quantified as the sum of the hourly energy savings during, respectively, the heating and cooling seasons divided by the efficiency of the systems:  
\[\frac{\Delta Q_{P,H}}{A}=\frac{\sum\Delta Q_{H}}{A}\cdot n_{HS}^{-1}\qquad \quad\frac{\Delta Q_{PC}}{A}=\frac{\sum\Delta Q_{C}}{A}\cdot EER^{-1} \tag{5}\]  
where:  
\(\Delta Q_{P,H}\) is the primary energy savings during the heating season (Wh);  
\(\Delta Q_{P,C}\) is the primary energy savings during the cooling season (Wh);  
\(n_{H}\) is the average seasonal efficiency of the heating system (in Italy, for residential buildings, this value varies between 0.65 and 0.75 (-)) [17]; and  
\(EER\) is the average seasonal energy efficiency ratio, which depends on the efficiency of air conditioners (in Italy, for a typical heat pump (air/air) this value is about 3). Following the energy savings obtained from the retrofit of the rooftop, the GHG emissions' reduction was quantified.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 5 What Is New?  
The following are contributions of this paper:  
* The Bayesian grid update (BGU) method of localization using FTM RTT ranging is extended for use outdoors;
* An \"inverse\" Bayesian grid method is used to determine the locations of the APs;
* A simplified observation model for the outdoor application of the Bayesian grid method is presented;
* A strategy is introduced for selecting the set of APs to range to;
* A way of avoiding Wi-Fi scans, using instead range probes to determine what frequencies APs operate on;
* Localization results are shown for a large complex (210 \(\times\) 120 m) containing multiple buildings with over a hundred APs;
* Results are compared with coordinates provided by the smartphone's code-based GNSS.
# Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks +
## 2 Related Works
Since Wi-Fi Direct operates in unlicensed frequency bands, it does not consume any spectrum resources of cellular networks, compared with the D2D underlaying cellular networks [16; 30]. Additionally, since Wi-Fi Direct has a much higher transmission rate, compared with Bluetooth (Table 1), it is considered in our previously proposed device discovery mechanism and D2D communications [24]. However, in the find phase of Wi-Fi Direct protocol, a UE searches or listens to all three channels in a round-robin manner until the search channel hits the listen one. To reduce the discovery delay of Wi-Fi Direct protocol and thus improve the performance of our previous proposal [24], this paper proposes a \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Bluetooth 5.0** & **Wi-Fi Direct** \\ \hline Frequency & 2.4 GHz & 2.4 GHz/5 GHz \\ Range & \(\sim\)200 m & \(\sim\)200 m \\ Data Rate & 2 Mbps & 250 Mbps \\ Power Consumption & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Bluetooth 5.0 and Wi-Fi Direct. Figure 1: Device discovery in the find phase of Wi-Fi Direct protocol. modified mechanism that the BS directly assigns a specific search and listen channel to a D2D link. Moreover, the binary exponential backoff algorithm without frozen backoff time is implemented in the proposed D2D discovery mechanism. The IEEE 802.11 WLAN uses the binary exponential backoff algorithm to resolve the transmission collision or error problem [31]. Under the binary exponential backoff algorithm, when a frame transmission fails, the frame retransmission must be deferred for a backoff time which is randomly selected from the contention window \([0,2^{m}-1]\), where \(m\) is the stage parameter of the contention window. To effectively avoid successive transmission failures, the contention window is doubled after each transmission failure. However, the maximum contention window is \([0,2^{10}-1]\) in order to avoid an unacceptable transmission delay. Additionally, in the IEEE standards the backoff countdown timer must be stopped or frozen when the channel becomes busy, leading to an extra delay. Thus, in this paper we adopt the binary exponential backoff algorithm without frozen backoff time to reduce the collision probability and discovery delay. Evaluation results in Section 5 will show the superiority of the hybrid D2D discovery mechanism proposed in this paper.
# Enhancement of CO2 Removal Efficacy of Fluidized Bed Using Particle Mixing
###### Abstract  
The present study proposes a cost-effective assisted fluidization technique of particle mixing to improve the carbon capture effectiveness of a fluidized bed containing fine adsorbent powder. Using activated carbon as the adsorbent, we mixed external particle of Geldart group B classification in different fractions to examine the effectiveness of the proposed strategy of particle mixing. Four different particle-mixing cases were considered by varying the amount of added particle--0, 5, 10, and 30 wt %--on external particle-free basis. The inlet flow of the nitrogen was fixed, while two different flows of carbon dioxide were used. The adsorption experiment consisted of a three step procedure comprising purging using pure nitrogen, followed by adsorption with fixed inlet CO2 concentration, and finally the desorption step. Inlet flows of both nitrogen and CO2 were separately controlled using electronic mass flow controllers with the help of data acquisition system (DAQ). The CO2 breakthrough was carefully monitored using the CO2/O2 analyzer, whose analog output was recorded using the DAQ. Best results were obtained with 10% external particles. This is in conformity with the results of our previous study of bed hydrodynamics, which pointed to clear improvement in the fluidization behavior with particle mixing.  
CO2 removal; assisted fluidization; particle mixing; fluidized bed; Geldart group B; activated carbon +
Footnote †: journal: Applied Physics Letters
# Trajectory Planning of Robotic Arm Based on Particle Swarm Optimization Algorithm
## 1 Introduction
Therefore, the global search ability of the PSO algorithm is enhanced by adding a population exchange term, and the last elimination principle is applied to avoid falling into the local optimum. Moreover, [PERSON] et al. [17] adopted PSO as the optimizer algorithm to solve the vibration suppression problem of a flexible linkage manipulator and enhanced the efficiency and accuracy of trajectory planning. In addition, [PERSON] et al. [18] introduced chaotic sequence as a local search strategy in the PSO algorithm and employed a piecewise polynomial interpolation function to approximate the optimal trajectory. These improvements enhanced the search efficiency and optimization performance of the algorithm and were able to effectively shorten the running time of the manipulator. Moreover, they ensured the stability of the movement. Added to that, [PERSON] et al. [19] introduced an innovative time optimization strategy, known as a Second-Order Continuous Polynomial Interpolation Function (SCPIF). This approach, coupled with the cosine subweighted PSO, addresses the challenge of time-optimal trajectory optimization. Although the above research has made some achievements, there are still many shortcomings. Firstly, the existing particle swarm optimization algorithms [20; 21] are often only applicable to specific types of problems; however, they lack universality in other categories. In addition, the performance of existing algorithms still depends on the choice of parameters, such as weight factors and inertia weights. Therefore, the optimal value needs to consider the actual situation of the industry. In the practical utilization process, in dealing with nonlinear optimization problems, the existing algorithms will fall into local optimal solutions, resulting in performance degradation. As a result, considering the industrial requirements of the manipulator in practical application processes, as well as the limiting factors including the position, speed, and acceleration of each task point, a new control strategy with high speed and high stability is proposed to achieve the manipulator's functional requirements. Moreover, the algorithm will be applied for transporting liquid reagents. It is worth noting that the proposed velocity optimal trajectory optimization strategy has a high stability level. Compared to other improved PSO algorithms, it is easily implemented and applicable to a variety of scenarios. To sum up, the contributions of this paper are as follows:  
1. A new Robot Trajectory Planning Particle Swarm Optimization (RTTPPSO) algorithm is proposed in this paper;
2. The PSO algorithm is applied to the trajectory planning and parameter optimization for each robot manipulator joint.
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 2 UAV Routing with an Emphasis on the Problem Type, Transportation Mode, and Degree of Automation
### Based on Modes of Transport  
When studying routing literature, it is also apparent that it can be partitioned according to the transportation mode such as land-based, maritime, and air transport. However, the application of VRP is mainly visible in land-based and maritime-based transportation modes as described below, and the UAV routing falls under the domain of air transport along with typical airplanes used in airline industry.