esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### The Mechanism of Soil Moisture Controls on Carbon Fluxes  
Photosynthesis, heterotrophic respiration, C pool size, and phenology are all controlled by CO\({}_{2}\) and it makes sense that when only assimilating the atmospheric CO\({}_{2}\) concentrations, performance is better than when simultaneously assimilating CO\({}_{2}\) concentrations and soil moisture. According to [PERSON], et al. [22], the soil moisture affects the water uptake and surface energy balance and assimilating satellite-derived soil moisture constrains the model's phenology and photosynthesis. In actuality, the model's hydrological scheme's process parameters are corrected by assimilating the surface soil moisture. Since surface and root zone soil moisture share common parameters, such as soil porosity, which is used to calculate soil water properties, through these parameters, the assimilation of satellite-derived surface soil moisture also controls plant-available soil moisture. When simultaneously assimilating atmospheric CO\({}_{2}\) and soil moisture, parameters such as \(V_{cmax}\), T\(\phi\), Cw0, srdepth, Sb, emis0, and Offset (regulating the soil water and energy balance, Table A2) are additionally constrained. Here, the carbon fluxes are further constrained by the ESA-CCI soil moisture observations. Modulated by ENSO variability on inter-annual time scales, the simulated soil moisture, NEP, GPP, and CGR showed tighter correlation to the ENSO indices especially from 30\({}^{\circ}\) N to 60\({}^{\circ}\) N. That is, when assimilating both CO\({}_{2}\) and ESA-CCI data simultaneously, the optimized CCDAS can be used to better present the inter-annual variability of carbon-water couplings on large scales and the assimilation of satellite soil moisture observations helps to achieve a better representation of carbon processes in addition to soil hydrology. Our finding is consistent with [PERSON], et al. [23], who showed that remotely sensed soil moisture assimilation provided better a constraint on the global carbon cycle than assimilation of CO\({}_{2}\) alone and demonstrated the capabilities of CCDAS to assimilate soil moisture. This is because CCDAS used the strategy of optimizing model parameters and state variables (e.g., Offset) simultaneously, which constrained model parameters related to physiology, phenology, soil hydrology, as well as energy balance and carbon cycle, and by these parameters was able to influence the water uptake and surface energy balance as well as plant-available soil moisture [22; 23].
# Design of External Rotor Ferrite-Assisted Synchronous
## 3 Design of Fe-PMaSynRM for High Power Density
### Multi-Physics Analysis
#### 3.5.2 Flux Density under Demagnetizing Field  
The q-axis circuit (\(I_{q}\)) current is associated with the demagnetization. The motor is simulated for different \(Iq\) currents to observe irreversible demagnetization, and magnet-flux densities are observed. Optimizing the number of mesh and number of points in the magnet for demagnetization observation is very important for estimating and calculating time. For the proposed motor, the calculation accuracy was obtained for maximum mesh size 1 mm inside the magnet, 1.5 mm inside the core, and 1 mm on the stator's inner surface. A total of 200 uniformly distributed points were considered in the magnet to observe flux density levels and identify whether the partial demagnetization has occurred or not. The magnets' flux density levels were observed at 2.5 times rated RMS current flow through the q-axis circuit. If any PM's flux density reduces below the knee point, then the design is considered weaker against demagnetization, and the optimal model is discarded. Figure 10a shows the magnet-flux densities in two flux barrier designs, and Figure 10b shows the magnet-flux densities in the single flux barrier design. The simulation results show the magnets in both upper and lower flux barriers of the two flux barrier design will be partially demagnetized. In contrast, the single flux barrier design was demagnetization proof under the above mentioned worst demagnetizing criteria.
## 2 Materials and Methods
### Performance Evaluation Criteria  
Accuracy, sensitivity (SE), and specificity (SP) are used to evaluate the performance of all ML-based classifiers, which are computed based on true positive (TP), true negative (TN), false positive (FP), and false-negative (FN) and defined as follows:  
Accuracy is the ratio between the total number of correctly classified classes and the total number of populations and mathematically defined as:  
\[Accuracy(\%)=\frac{TP+TN}{TP+FN+FP+TN}\times 100 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier Types** & **Hyper-Parameters** & **Optimized Values** \\ \hline \multirow{6}{*}{RF} & max\_depth = (2, 3, 5), & max\_depth = 3, \\  & n\_estimators = (25, 50, 100, & n\_estimators = 200, \\  & 200, 300, 600, 1200), & min\_samples\_split = 10, \\  & min\_samples\_leaf = (2, 3, 10), & min\_samples\_leaf = 10, \\  & criterion = (gain, entropy) & criterion= entropy \\ \multirow{2}{*}{DT} & min\_samples\_leaf = (1, 2, 3, 4, & min\_samples\_leaf = 9 \\  & 5, 6, 7, 8, 9, 10, & \\  & k = (5, 6, 7, 8, 9, 10), & k = 8, leaf\_size = 1 \\  & leaf\_size = (1, 2, 3, 5) & \\  & hidden\_layer = [(50, 50, 50), & \\  & (50, 160, 50), (10, 1)], & hidden\_layer = (50, 160, 50), \\ \multirow{2}{*}{MLP} & activation = (relu, tanh, & activation = logistic, \\  & logistic), alpha = (0.0001, 0.05), & alpha = 0.05, \\  & learning\_rate = (constant, & learning\_rate = adaptive \\  & adaptive) & \\ \multirow{2}{*}{SVM} & C = (1, 10, 100, 1000), & C = 1000, and \(\gamma\) = 0.001 \\  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized hyperparameters of different classifiers using the grid search method.
# Thermodynamic Analysis of Partitioned Combined Cycle using Simple Gases
## 4 Result and Discussion
### Entropy (Second Law) and Density Profiles  
Entropy and density profiles are shown in Figures 9 and 10, respectively. As per the volumetric data, carbon dioxide offers the least volume. Hydrogen is comparable to air and nitrogen. Argon offers huge volumes due to its high flow rates (caused by low heat capacity), thus unacceptably increases the equipment sizes.  
Generally, more loads shared by PCGT increase the overall efficiency. This is because the exhaust heat is recovered (T \(<\) 171 \({}^{\circ}\)C), which compensates for the work lost due to the pressure drop and irreversibility associated with the proposed scheme. Reversible adiabatic efficiency of 100% has zero entropy change. However, if variable irreversibility is accounted, gases started to behave differently under different conditions. Second law inefficiencies, as shown in the adjacent tables to entropy profiles, clearly favor carbon dioxide as partitioned fluid. However, it does not offer much benefit at higher adiabatic efficiencies and the cycle become inefficient (the reason is stated in the previous section). Carbon dioxide starts to offer a positive efficiency increase once the cycle adiabatic efficiencies start to drop below \(\approx\)90.5%. On the other hand, argon pressure losses surpass the break, even at adiabatic efficiencies above \(\approx\)94%. Based on the discussion, hydrogen offers the best choice as partitioned fluid. It must be noted that helium properties also promise the same conclusion.  
Figure 9: Density data for different fluids shown as 3D surface plots. Black lines represent the entropy profiles of the partitioned Brayton cycle at 92.7% adiabatic efficiency. Adjacent tables provide the mass flow rates and load distribution between the main, partitioned cycle, Brayton cycle, and Rankine cycle. Green, blue, and red represent these losses at adiabatic efficiencies of 95.63%, 92.7%, and 89.9%, respectively. The main table provides the volumetric flow rates at different locations of partitioned cycle (\(\eta\) = 92.7%).
# Influence of Frost Growth and Migration in Cryogenic Heat Exchanger on Air Refrigerator
## 4 Result and Discussion
### Influence of Inlet Humidity  
The performance of the heat exchanger under different inlet relative humidity (RH) is shown in Figure 10. Simulations were conducted under the heat exchanger inlet temperature and pressure of 295.0 K and 0.510 MPa, outlet pressure of 0.100 MPa, and expansion efficiency of 0.60. As shown in Figure 10a,b, the inlet temperatures of hot fluid at the 120 th min were 104.4, 105.1, and 106.0 K, respectively. When the RH is 15%, the frosting had a great influence on the system performance with the lowest temperature rise of 3.1 K comparing the dry air condition. The higher the inlet moisture content, the faster the frost layer grows, which leads to the faster the heat transfer resistance increases. As shown in Figure 10c, the effectiveness fluctuated under different inlet RH as the cooling went on, which was due to the growth and migration of the frost layer. The effectiveness under RH of 15% was higher, which just reflected the slower cooling down trend of the refrigerator, as shown in Figure 8b. As shown in Figure 10d, when the inlet humidity was higher, it took longer for the moisture to freeze completely on the hot side. After 60 mins, the outlet moisture contents were almost zero under three conditions.  
Figure 9: Growth and migration of the frost layer: (**a**) Growth along the length; (**b**) moisture content in the hot side; (**c**) growth with time; (**d**) temperature difference between fluid and frost surface.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Histogram Analysis
#### 3.2.2 Maximum Deviation  
The maximum deviation (\(D_{max}\)) is a metric used to evaluate the uniformity of the cipher image's histogram by measuring the deviation between the histograms of a plaintext image and its cipher. The maximum deviation metric helps to determine the quality of the image encryption algorithm by testing whether the produced cipher image differs significantly from the original image. A higher value of the maximum deviation indicates a better encryption quality since the algorithm produces a cipher image that is more deviated from the plaintext image. The mathematical expression of maximum deviation can be given as follows:  
\[D_{max}=\frac{d_{0}+d_{255}}{2}+\sum_{i=1}^{254}d_{i} \tag{11}\]  
\[d=histogram(|P-C|),\]  
where \(d\) is the histogram of the absolute values of the difference between the plaintext image and its cipher, \(d_{i}\) represents the histogram \(d\) value at index \(i\), and \(d_{0}\) and \(d_{255}\) are the histogram values at index \(0\) and \(255\), respectively.  
From Equation (11), we can calculate the upper limit of \(D_{max}\). If we assume that both images are completely different from each other, \(d\) will have one element with value \(H\times W\), and the rest will be zeros. By substituting \(d\) in Equation (11), the upper limit can be calculated as follows:  
\[D_{max} =\frac{(0+H\times W)}{2}+(254\times 0) \tag{12}\] \[=(\frac{H\times W}{2}),\]  
The lower limit of \(D_{max}\) is \(0\) for any size image. Table 4 utilizes Equation (12) to compute the upper limit for different-sized images.
# Iterative Learning Control for Vibration Suppression of a Robotic Arm
###### Abstract  
This paper presents a vibration control method for the vibration suppression of a robotic arm. To understand the causes of the vibration in the six-axis manipulator and then eliminate the vibration through an effective control method, the vibration control structure can be divided into two parts. First, variational mode decomposition (VMD) and the Hilbert-Huang transform (HHT) algorithm are integrated to analyze the vibration signal and extract the vibration characteristics. Second, a smooth trajectory planning method is used to eliminate the residual vibration. Then, a feedback controller and iterative learning controller are used for controlling the vibration to stabilize the system while compensating for the torque of the elastic-joint model and repetitive errors caused by the vibration through the repeated operation of the arm. In addition, to improve the iterative learning control (ILC) performance, the integration of VMD and the HHT is used to distinguish useless information. The ILC is able to learn more quickly and compensate for the error caused by the vibration. The proposed VMD-HHT iterative learning control for the elastic joint (VH-ILC-E) method, along with a laboratory-developed six-axis degree-of-freedom (6-DOF) robotic arm, has been numerically and experimentally validated for vibration suppression.  
robotic arm; vibration control; elastic joint; iterative learning control; variational mode decomposition; Hilbert-Huang transform Article
## 2 System Model and Signal Processing Methods  
To meet the demand for precise and reliable autonomous navigation of carriers under GNSS-denied conditions, this paper focuses on the research of anti-jamming technology for BeiDou deceptive interference modes using airborne dual-antenna BeiDou/MIMU integrated navigation systems. It proposes a method for determining the direction of deceptive signal sources based on dual-antenna BeiDou/MIMU integration to endow carriers with credible navigation capabilities under BeiDou-denied conditions and counteracting abilities to determine the position of interference sources.  
This study proposes a direction-finding method for GNSS spoofing signal sources, utilising a MEMS-based dual antenna GNSS/INS integrated navigation system and a short-baseline common-clock GNSS receiver. The proposed method is usually applied to the scenario depicted in Figure 1. By carrying a dual-antenna GNSS/MIMU system and a common-clock receiver, the vehicles, which travel normally in this scenario, can process the real satellite signals as well as the spoofed signals using the proposed method, making it possible to effectively identify and locate GNSS spoofing signal sources. Additionally, the proposed method is applicable to the localisation for GNSS spoofing signal sources in airborne or shipborne scenarios.  
Figure 1: Typical application scenario for the proposed method. The dotted line represents the trajectory of the unmanned vehicle during satellite signal deception.
## 3 Results and Discussion  
### Synthesis  
The synthesis of 5-ethyl-2,2\({}^{\prime}\):5\({}^{\prime}\),2\({}^{\prime\prime}\)-terthiophene has been reported [31] as a combination of Stille and Sonogashira-Hagihara couplings. In this approach, 2,2\({}^{\prime}\):5\({}^{\prime}\),2\({}^{\prime\prime}\)-terthiophene obtained by the reaction of 2,5-dibromothiophene with 2-(tributylstannyl)thiophene reacts with NBS to give the bromo-substituted derivative. The coupling reaction between the halo-thiophene and trimethylsilylacetylene (TMS) provides the TMS-ethynyl-terthiophene that is finally deprotected, leading to the ethynyl-derivative.  
As an alternative, the synthetic route reported here can be successfully adopted. According to previous results [22], the synthesis of the ethynyl-terthiophene derivative **5-ET** (Scheme 1) involves a Sonogashira-Hagihara coupling reaction between the corresponding Br-derivative (**3**) and 2-methyl-3-butyl-n-2-ol, followed by treatment with KOH. The synthesis of **3** was first attempted using a classical Suzuki-type coupling between the 5-bromo-2,2\({}^{\prime}\)-dithiophene **1** and 5-bromo-2-thiophene acid, but it resulted in quite low yields (about 20%), ascribed to homo-coupling reactions of the bromo boronic acid. For this reason, 2-thienylylboronic acid was employed instead of 5-bromo-2-thiophene acid, and its coupling with the Br-derivative **1** allowed to obtain the unsubstituted terthiophene **2** with very good yields (95%). Then, **2** underwent a selective bromination on the C-5 by reacting with NBS [43], and **3** was obtained with a significative increase in yields (75% on the whole). The reaction of the halo-thiophene **3** with 2-methyl-3-butyln-2-ol as the ethynyl synthon gave the intermediate product **4**. Finally, the deprotection of **4** with KOH allowed to obtain the target product **5-ET** in good yields (84%).
## 1 Introduction
Aiming to solve the problems of high packet loss rate and large transmission delay in the existing LoRaWAN network downlink communication, a LoRaWAN network downlink routing control strategy is proposed based on the SDN framework and the improved ARIMA model. The SDN framework is used to establish the downlink routing model of the LoRaWAN network to realize the monitoring of the network status in the downlink routing process. In order to improve the prediction accuracy of the existing ARIMA model, an improved ARIMA model by combining the Savitzky-Golay (S-G) filtering and the sliding window method is proposed to improve the data stationarity. * According to the reconstructed data, an ARIMA-based link bandwidth occupancy rate prediction model (LBOP-ARIMA) is established, and the link bandwidth occupancy rate is predicted. * Then, according to the triangular modulus operator, parameters such as the transmission delay of the network downlink communication route, and the routing bandwidth \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Contribution** & **Exiting Problem** \\ \hline
[15] & The link delay information based on the SDN is used to & \\  & select the optimal transmission route & \\  & The resource balancing algorithm and the routing & \\
[16; 17] & reconstruction model of SDN is discussed to reduce the & \\  & delay of service data transmission & \\  & The SDN framework provides a new feasible solution & \\
[18; 19; 20; 21] & for the routing optimization of downlink & \\  & communication in LoRaWAN network & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The related work of the SDN. occupancy rate at time \(t\) and time \(t+T\) are integrated, and different routing degrees are calculated. * The downlink routing control is simulated on the Mininet platform, and the communication performance of different routing control strategies is compared. On this basis, the LoRaWAN network application platform test is then built up, and the reliability of the downlink communication is verified for the proposed scheme. The paper is organized as follows: The LoRaWAN network downlink communication is introduced in Section 2. In Section 3, the LBOP-ARIMA model is proposed. LoRaWAN downlink routing control strategy is presented. Experiment and analysis are carried out in Section 5. The conclusions are given in Section 6.
## 3 Results
### Performance of the Different Double-Exposure Combinations under Varying Shadow Proportions  
The vegetation extraction capabilities of the five double-exposure images vary depending on the shadow proportion in the vegetation images. The HUE index performs the best in extracting vegetation from shadow images. Therefore, the focus is placed on determining the most effective double-exposure image under the different shadow levels based on the FVC results using the HUE index. This approach aims to improve vegetation extraction effectiveness under shadow conditions. Based on the proportion of shadow pixels in the normal-exposure image, the experimental data are categorized into three classes: weak shadows (0-40% shadow pixels), moderate shadows (40-70% shadow pixels), and strong shadows (70-100% shadow pixels). According to Tables 7-9, in the case of weak shadow data, the nor+over2 double-exposure image demonstrates the best extraction performance. Compared to normal-exposure data, there are slight improvements in all the evaluation metrics, but the changes are not significant. For the data with moderate shadow levels, the nor+over3 double-exposure image proves optimal. It demonstrates a 10.7% increase in the mIOU metric, an 8.3% increase in accuracy, and more than a 14% increase in the other metrics. Similarly, for data with strong shadows, the nor+over3 double exposure image is the best choice, with all the metrics surpassing 0.9. Moreover, compared to those for the normal-exposure data, mIOU increases from 0.755 to 0.914, and the kappa coefficient increases from 0.645 to 0.903. These improvements greatly enhance the reliability of the vegetation extraction results and ensure high vegetation extraction accuracy under shadow conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Data ID** & **FVC Ground Truth** & **nor+over1** & **nor+over2** & **nor+over3** & **nor+over4** & **nor+over5** & **SHAR-LABFVC** \\ \hline
1 & 0.380 & 0.362 & 0.364 & 0.364 & 0.363 & 0.363 & 0.309 \\
2 & 0.427 & 0.427 & 0.427 & 0.428 & 0.429 & 0.429 & 0.402 \\
3 & 0.439 & 0.436 & 0.437 &
## 2 UAV Model and the Problem Definition
### Problem Statement and Challenges  
In Figure 2, the UAV with the flexible cable-connected payload and its pendulum effects are shown. The figure shows the different perspectives of the UAV with cable-connected payload and the variable payload in detail. A 3D-printed part is created and mounted to provide a UAV connection with a payload cable. The cable length is 35 cm and is chosen as flexible. Taking into account the pendulum effects, forces \(F_{P}\), \(F_{R}\) and the angles \(\alpha\), \(\varepsilon\), \(\beta\), and \(\lambda\) vary with the acceleration of the UAV due to [PERSON]'s second law. The maximum carrying payload is measured with empirical methods, and its maximum value is determined as 75 g for the experiments. This weight (\(m_{\text{P}}\)) is a significant value when considering the mini UAVs. Then, it is able to rewrite the equations as \(m_{\text{total}}=m_{\text{uav}}+m_{\text{P}}\). Instead of rewriting the dynamical equations and calculating the controller parameters using swinging \(m_{\text{total}}\), we propose that the compared controllers can eliminate the unknown payload effects and the variable CoM problems without redesigning or the calculation.  
Understandably, unknown payloads and disturbances directly relate to the system dynamics. Therefore, the main objective of this work is to overcome these adverse effects. At this point, instead of re-writing the equations of the UAV dynamics and redesigning the controllers by considering the whole mathematical model with the additional dynamics of the swinging payload, the controllers are designed by considering the known dynamics of the UAV itself without taking the payload and disturbance effects into account so that the robustness of the three controllers is compared with each other, then the IT2-FPID controller gives the results in terms of system stability and robustness.  
The most important question is as follows: why do we focus on uncertain parameters, such as unknown payload and flexible cable effects? This system is a simulation of a commercial agricultural UAV problem. Commercial agricultural UAVs may contain liquid or other payloads. However, the problem is that these payloads, such as seeds or pesticides, are unknown loads because of their variable weights and variable centre of masses during the flight.