esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks
## 5 Simulation
(B) Impact of the number of jammers: This experiment involves altering the count of malicious attackers in an eight-channel network, as illustrated in Figure 4:0 The simulation outcomes demonstrate that as the number of attackers increases, the throughput reduces correspondingly, owing to a greater occurrence of jamming attacks. It is interesting to find that the throughput decreasing rate slows down when more jammers are continuously added to the network. We believe it is because when the number of attackers increases, the attacking ranges of the jammers are overlapped, which reduces the attacking efficiency. Again, being able to increase the number of rendezvous and preserve the randomness of channel allocation, the performance achieved by the ALL protocol is better than that of the OLAA_T and OLAA_R protocols. (C) Impact of the number of PUs: Next, we explore how the different protocols are impacted by varying numbers of PUs. As depicted in Figure 12, the performance of all three protocols remains consistent across different numbers of PUs, with ALL demonstrating noticeably superior results compared to OLAA_T and OLAA_R. The average packet delivery ratios achieved by ALL, OLAA_T, and OLAA_R under different numbers of PUs are 75%, 64%, and 65%, respectively. It is worth noting that when the number of PUs is set to four, the maximum packet delivery ratio achievable by an SU running ALL, OLAA_T,  
Figure 4:1 This figure presents the impact of the number of jammers on the performance of mesh topology in a synchronized network, as measured by two metrics: (**a**) throughput and (**b**) ATTR. and OLAA_R is 82%, 72%, and 71%, respectively. This experiment confirms that ALL is resilient and not adversely affected by an increase in PUs. (D) Impact of the mechanisms of shortening ID and allocating unfixed \(R\) frames: The performance of the two mechanisms of the ALL protocol is being observed in this experiment: shortening the ID of SUs (the resulting protocol is denoted as ALL_S) and allocating the position of unfixed \(R\) frame based on receivers' hopping matrix structures (the resulting protocol is denoted as ALL_TR). The simulation results are shown in Figure 4:2 We can see that the performance of ALL_S and ALL_TR is lower than that of ALL. In a synchronous network, the first column of each node's channel hopping matrix overlap and thus do not provide a rendezvous guarantee. ALL_S does not perform as well as ALL because a sender does not adjust its channel hopping matrix based on those of its receivers. The performance of ALL_TR lags far behind that of ALL and ALL_S since SUs running ALL_TR have similar IDs when compared to those running ALL and ALL_S. Similar IDs produce similar column structures in channel hopping matrixes and thus reduce the probability of providing a rendezvous between SUs. Figure 4:3 This figure presents the impact of the number of PUs on the performance of mesh topology in a synchronized network, as measured by the packet delivery ratio. Figure 4:4 _Cont._
## 1 Introduction
But the use of a vibration-based method in large structures such as bridges can be uncertain, just then, if damage may only cause negligible change in dynamic properties and thus may go unnoticed. Moreover, in order to find the exact location of damage, local methods are often better alternatives. AE measurements are applied for machine condition monitoring as well as SHM of large structures. In engineering materials, some common sources of AE are initiation and growth of cracks, yielding, failure of bonds, fiber failure, and pullout in composites. It should be noted that only active or growing cracks emitted elastic waves. If cracks are present but do not grow, no AE is emitted. [PERSON] [9; 10] gives in his papers a review on structural integrity evaluation and SH evaluation using AE. Another application of SHM in the broadest sense is in situ AE monitoring in mines. This monitoring involves permanent AE measurements of microcracking in parts of the mine or the entire mine. A major task in underground storage like repositories for nuclear waste and deep mines is the safety assessment. Therefore, several geotechnical monitoring methods, for instance, micro-seismic measurements are used in many mines. This study demonstrates the capability of In situ AE monitoring for SHM in mines. Section 2 deals with fundamentals about in situ AE monitoring. After some comprehensive consideration of the scale between earthquakes and AE events, a list of various applications of in situ AE monitoring found in the literature (Section 2.2) is given. Sections 3-5 show examples on in situ AE monitoring in salt mines, gold mines, and underground research laboratories.
# Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method
###### Abstract  
In this study, we propose a new agricultural data analysis method that can predict the weight during the growth stages of the field onion using a functional regression model. We have used onion weight on growth stages as the response variable and six environmental factors such as average temperature, average ground temperature, rainfall, wind speed, sunshine, and humidity as the explanatory variables in the functional regression model. We then define a least minimum integral squared residual (LMISE) measure to obtain an estimate of the function regression coefficient. In addition, a principal component regression analysis was applied to derive the estimates that minimize the defined measures. Next, to evaluate the performance of the proposed model, data were collected, and the following results were identified through analyses of the collected data. First, through graphical and correlation analysis, the ground temperature, mean temperature, and humidity have a very significant effect on the onion weights, but environmental factors such as wind speed, sunshine, and rainfall have a small negative effect on onion weights. Second, through functional regression analysis, we can determine that the ground temperature, sunshine, and precipitation have a significant effect on onion growth and are essential in the goodness-of-fit test. On the other hand, wind speed, mean temperature, and humidity did not significantly affect onion growth. In conclusion, to promote onion growth, the appropriate ground temperature and amount of sunshine are essential, the rainfall and the humidity must be low, and the appropriate wind or mean temperature must be maintained.  
agricultural data analysis method; functional regression model; onion weight; environmental factors; optimum cultivation strategy; R-packages +
Footnote †: journal: Applied Sciences  
Article
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 1 Introduction
Additionally, the strengths and weaknesses of the decision variables used in the design of these routing protocols are discussed. Finally, this survey presents possible research directions in order to optimize the energy consumption in sensor networks. We expect that this survey will help researchers and practitioners in this field to perceive the different energy-efficient and energy-balanced routing techniques in order to make decisions based on their application requirements and network formulations. Although, recent research work such as wireless rechargeable sensor networks (WRSNs) has emerged as an alternative in solving the battery problems associated with the traditional battery powered WSN, we emphasise that this survey focuses purely on battery powered SNs. For reference on WRSNs, refer to [20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. The rest of this paper is structured as follows. Section 2 presents the related survey work on routing protocols for WSNs. The terms and parameters used in this paper are defined in Section 3. In Section 4, the WSN energy model is explained. We explain, using a typical WSN scenario, the concept of energy consumption in SNs during the data transmission and reception phase in this section. Also, the section explains the objectives, principles, and challenges of designing an energy-efficient and energy-balanced routing protocol which forms the basis of our classification. We introduced a taxonomy to classify the studied energy-efficient and energy-balanced routing protocols for WSNs based on their proposed mode of communication towards the BS in Section 5. The categories included in this taxonomy are explained in this section, while also classifying the studied routing protocols under these categories. Also in this section, the surveyed energy-efficient and energy-balanced routing protocols are also classified based on the solution types or algorithms, and the input decision variables used in designing the routing algorithm. In addition, the strengths and weaknesses of the choice of the decision variables used in the studied routing protocols are discussed in this section. In Section 6, we discussed the findings of this survey and presented the possible research directions. Lastly, Section 7 concludes the paper with discernible remarks.
# Control of a Variable Blade Pitch Wind Turbine Subject to Gust Wind and Actuators Saturation
## 3 Results
### Numerical Results  
The efficacy of the fuzzy controller Equation (19) is tested by considering the wind speed input profile \(6\)\(\mathrm{m/s}\leq V\leq 30\)\(\mathrm{m/s}\) as shown in Figure 7.  
Note that the dynamic model of the wind turbine does not consider the deflection of the blades, so in the simulation, we use the static wind speed instead. However, more accurate scenarios should investigate the use of effective wind speed. The MATLAB toolbox YALMIP [47] is used to solve the LMIs in Equations (21)-(23). YALMIP is a solver used to model and solve optimization problems typically occurring in systems and control theory. It is important to mention that one way to tune the controller is by solving the LMIs in Equations (21)-(23) using different conditions. For instance, if the maximum actuators amplitude \(\delta\) are known, we can modify the condition in Equation (16) accordingly and solve the LMIs in Equations (21)-(23) to obtain the control law that satisfies the parameters of the chosen actuator. This flexibility gives the proposed fuzzy controller great advantages over a traditional controller. Another approach is to obtain a feasible solution using different initial conditions by modifying the parameter \(\sigma\) in Equation (21).  
The results of the simulation are shown in Figures 8-10. It is evident that the proposed controller is able to stabilize the wind generator subject to extreme and sudden gust wind reaching \(75\)\(\mathrm{km/h}\).  
Figure 6: Angular velocity.  
Figure 7: Wind Speed.  
As it is shown in the Figure 11, by comparing the desired blade pitch \(\beta_{d}\) with the actual blade pitch \(\beta\) we conclude that the proposed fuzzy controller performance is satisfactory. Considering for instance the absolute error \(\Delta_{\beta}=||\beta_{d}-\beta||=0.7031^{\circ}\).  
In addition, it is essential to acknowledge that the proposed (T-S) fuzzy controller has certain advantages over traditional controllers, for instance, the actuator amplitude saturation, which depends on the value of \(\delta\) in Equation (16), and the initial condition described by Equation (17) are solved simultaneously. This advantage allows the design engineer to choose the best actuator to perform the task appropriately. Finally, the (T-S) fuzzy controller has a simple form and is easy to implement (see Equation (19)).  
Figure 8: Angular displacement.  
Figure 9: Angular velocity.
# Helmet Wearing Detection Algorithm Based on YOLOv5s-FCW
## 4 Experimental Results and Analysis
### Model Indicators  
In the research field of helmet detection, the main evaluation indicators of algorithm performance include Precision, Recall, AP (Average Precision), and mAP (mean Average Precision). In addition, the evaluation of the algorithm's execution speed is quantified by Frames Per Second (FPS). This paper also follows the standard evaluation system and makes a comprehensive and detailed evaluation of the helmet target detection algorithm. The predicted results are evaluated by calculating proportion for their intersection with true values and are considered true if the resulting value exceeds a predefined threshold. This method yields four possible classification outcomes for prediction results: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN), where accuracy  
Figure 8: Changing the brightness. (**a**) Original image, (**b**) After fogging.  
Figure 7: Fogging of images. (**a**) Original image, (**b**) After fogging.  
is likelihood regarding correct detection amid all detected targets; thus, its equation is expressed as (4):  
\[\text{{Precision}}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
The concept of recall refers to the probability of accurately detecting all target instances, and its mathematical representation is given by (5):  
\[\text{{Recall}}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
AP is a measure of model precision at different recall rates, computed as the area enclosed by the Precision-Recall (PR) curve and coordinate axis. For a continuous PR curve, AP can be calculated using integration. The formula is expressed as shown in Equation (6):  
\[\text{AP}=\int_{0}^{1}\text{PR}dr \tag{6}\]  
The formula for mAP, expressed as Equation (7), represents the average of mean precision values for various target categories. In this paper, n refers to two categories, namely hat and person.  
\[\text{mAP}=\frac{\sum_{i=1}^{n}AP_{i}}{n} \tag{7}\]  
In the task of helmet wearing detection, in order to meet the requirements of real-time detection, the calculation speed of the model is also a relatively important index, so this paper also introduced FPS as an evaluation standard in the comparison experiment. FPS is the number of helmet image frames that a model can process in one second. The calculation speed of the model can be judged by comparing the number of helmet images identified per unit time and the time required to process these helmet images.
## 2 Methodology
### Phase and Code Raw-Observation Functions  
The observation functions for the raw pseudorange and carrier phase could be presented respectively as  
\[\begin{split} P^{\text{s}}_{r,j}(t)=\rho^{\text{s}}_{r}(t)+c\cdot \delta t_{r}(t)-o^{\text{s}}-c\cdot\delta t^{\text{s}}(t-\tau^{\text{s}}_{r})+T ^{\text{s}}_{r}(t)+l^{\text{s}}_{r,j}(t)+c(d_{r,j}+d^{\text{s}}_{j})\\ +dm^{\text{s}}_{r}+e^{\text{s}}_{r}\end{split} \tag{1}\]  
\[\begin{split} L^{\text{s}}_{r,j}(t)=\rho^{\text{s}}_{r}(t)+c\cdot \delta t_{r}(t)-o^{\text{s}}-c\cdot\delta t^{\text{s}}(t-\tau^{\text{s}}_{r})+ T^{\text{s}}_{r}(t)-l^{\text{s}}_{r,j}(t)+\lambda_{j}N^{\text{s}}_{r,j}\\ +\lambda_{j}(b_{r,j}-b^{\text{s}}_{j})+\delta m^{\text{s}}_{r}+e^{ \text{s}}_{r}\end{split} \tag{2}\]  
\[\rho^{\text{s}}_{r}(t)=\left\|(\overset{\rightarrow}{\tau}^{\text{s}}(t-\tau^ {\text{s}}_{r})+d\overset{\rightarrow}{\tau}^{\text{s}}(t-\tau^{\text{s}}_{r}) )-(\overset{\rightarrow}{r}_{r}(t)+d\overset{\rightarrow}{r}_{r}(t))\right\| \tag{3}\]  
\[b_{r,j}=\phi_{r,j}(t_{0})+\frac{c}{\lambda_{j}}\delta_{r,j} \tag{4}\]  
\[b^{\text{s}}_{j}=\phi^{\text{s}}_{j}(t_{0})+\frac{c}{\lambda_{j}}\delta^{\text {s}}_{j} \tag{5}\]where \(P\) and \(L\) refer to the raw observations of pseudorange and carrier phase; the indices \(s\), \(r\), and \(j\) stand for the satellite, receiver, and signal frequency, respectively; \(t-\tau_{r}^{s}\) and \(t\) are the signal transmitting and receiving time; \(c\) is the velocity of light in vacuum; \(\rho\) indicates the geometric distance between the satellite and receiver; \(\sigma^{s}\) denotes the satellite orbit bias; \(\delta t_{r}(t)\) and \(\delta t^{s}(t-\tau_{r}^{s})\) are the clock biases of the receiver and satellite; \(T_{r}^{s}(t)\) and \(I_{r,j}^{s}(t)\) are the tropospheric and ionospheric delay of the signal path at frequency \(j\), respectively; \(d_{r,j}\) and \(d_{j}^{s}\) denote the hardware time delays of receiver and satellite on the pseudorange; \(dm_{r}^{s}\) and \(\delta m_{r}^{s}\) represent the multipath biases of pseudorange and carrier phase, respectively; \(e_{r}^{s}\) and \(e_{r}^{s}\) represent the measurement noise of pseudorange and carrier phase; \(\lambda_{j}\) and \(N_{r,j}^{s}\) are the wavelength and integer ambiguity of carrier phase observations; \(\overrightarrow{r}^{s}(t-\tau_{r}^{s})\), \(\overrightarrow{r}_{r}(t)\), \(d\overrightarrow{r}^{s}(t-\tau_{r}^{s})\), and \(d\overrightarrow{r}_{r}(t)\) indicate satellite position, receiver position, antenna Phase Center Offset (PCO) [16] of satellite, and receiver PCO, respectively; \(\phi_{r,j}(t_{0})\) and \(\phi_{j}^{s}(t_{0})\) are the raw phase biases of receiver and satellite at \(t_{0}\) epoch; \(\delta_{r,j}\) and \(\delta_{j}^{s}\) refer to the hardware time delays of receiver and satellite on the carrier phase; \(b_{r,j}\) and \(b_{j}^{s}\) are the comprehensive biases of receiver and satellite.  
In addition, other errors that are not listed in the above equations, such as tidal loading, phase windup, relativistic delay, antenna Phase Center Variation (PCV) [16], etc., should also be corrected according to the conventional models [29; 30; 31].
## 4 Proposed Antenna Beam Model for RBS
### RBS Activated Period  
This section looks more closely at the handover to an RBS by considering in detail the period during which an RBS is active as a candidate BS. Figure 8a illustrates the situation where the RSS of an RBS rises steeply between time \(t=360\) and time \(t=385\). The RBS then maintains a strong RSS until time \(t=450\) after which it drops sharply.  
As shown in the figure, for the period when \(t<380\), the moving UE is connected to the orange BS2 and remains connected until the difference in signal strength between \"orange\" and \"purple\" passes the threshold (around \(t=410\)). At that point, the strongest received signal is found to be from the RBS, so the UE hands over to the RBS at that time  
Figure 8: The scenario focusing on the period in which the RBS is activated.  
Figure 7: Rogue base station established in various position against a legitimate base station to challenge the detection model.  
as shown in Figure 8b. The vertical axis scale was enlarged to observe the steep rise and the threshold.  
The period immediately before connection to the RBS is key in preserving the security of the network. The steep rise in RSS between \(t=360\) and \(t=380\) should trigger this signal to represents an RBS, so the handover logic should discount any candidate BS that exhibits this pattern from the handover calculation. Potential future work will focus on the development of techniques to analyse the RSS profile of candidate BS elements so that those that fit the profile of a rogue can be ignored before they are connected to it.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 5 Conclusions  
In the present study, the causal effect calculation and the time series forecasting model were combined to establish a multi-factor-influenced load forecasting model for power plants. In the present study, external variables that correlate with load were identified, and the causal effect of different variables on load were quantified. A robust and interpretable method for medium- and long-term load forecasting was proposed. The performance of five time series models was compared using three different error evaluation metrics, and the advancement of the proposed Causal-Informer model was demonstrated.  
Figure 11: Ablation experiments (Pred_length = 90 d). (**a**) Ablation Experiments about ATE. (**b**) Ablation Experiments about Informer_nos.  
Figure 10: Ablation experiments (Pred_length = 30 d). (**a**) Ablation Experiments about ATE. (**b**) Ablation Experiments about Informer_nos.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pred\_Length** & \multicolumn{3}{c}{**30 d**} & \multicolumn{3}{c}{**90 d**} \\
**Evaluation Metrics** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline Informer & 525.24 million kwh & 624.57 million kwh & 19.7\% & 507.09 million kwh & 625.75 million kwh & 31.2\% \\ Informer + ATE & 430.59 million kwh & 518.03 million kwh & 16.4\% & 395.81 million kwh & 560.81 million kwh & 25.3\% \\ Informer\_nos & 315.47 million kwh & 380.29 million kwh & 12.5\% & 512.03 million kwh & 593.71 million kwh & 28.1\% \\ Causal–Informer & 250.31 million kwh & 331.96 million kwh & 10.4\% & 430.59 million kwh & 535.94 million kwh & 24.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study.  
Ablation experiments were also performed to verify the improvement in model performance from all the innovations. The proposed method achieved the most accurate and stable results. The MAE, RMSE, and MAPE indicators of the proposed method reached 250.31 million kwh, 331.96 million kwh, and 10.4%, respectively, in 30 time step forecasting. In 90 time step forecasting, the MAE, RMSE, and MAPE indexes reached 430.59 million kwh, 535.94 million kwh, and 24.8%, respectively. Based on the experimental results, the following conclusions could be drawn:  
1. Causal-Informer performs well in multi-step forecasting and has strong performance potential.
2. The incorporation of causal inference within the time series prediction scenario is beneficial in improving the model's interpretability and robustness.
3. The improvements to the proposed Informer model have some positive effects on predicting non-stationary data.  
Methodology, K.Y.; data curation, K.Y.; software, K.Y.; draft writing, K.Y.; Conceptualization, F.S.; project administration, F.S.; supervision, F.S.; draft review, F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data of this study are available from the corresponding author upon request ([EMAIL_ADDRESS]).  
The authors declare no conflict of interest.
## 2 Mobility Models in FANETs
### Random Mobility Models
#### 2.1.1 Random Walk  
The Random Walk (RW) mobility model [36] was created to accommodate the unpredictability of many natural things' movements. The mobile nodes in RW imitate the erratic motion by selecting a random direction and speed each time. Each movement takes place over a fixed time interval or a fixed distance before a new speed and direction are calculated. The new direction of a node travelling to the simulation area's edge is determined by the introduced direction. RW is a memoryless mobility model since it does not save information about its prior speeds and locations. Figure 4 depicts an example of an RW motion. The RW model may be adopted in several FANETs missions and protocols, including an increase in coverage area [37] and enhancing UAV relay service [38].  
Figure 4: The trajectory of FANETs using RW models.  
Figure 3: Taxonomy of mobility models in FANETs.