esa-sceva/satcom-chunk-collection huggingface dataset:
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
## 2 Building a Realistic UAV Simulator
### Dynamic Simulation  
The complete dynamic model of the quadrotor in (25), i.e., the equations of motion that describe the translation and rotation of this vehicle, can be utilized to perform a dynamic simulation. This simulation consists of determining the future states of a system from an initial configuration [45]. A state is the minimum set of variables that fully describe the behavior of a dynamical system at any instant of time [46]. For any mechanical system like the quadrotor, the state variables are typically the generalized coordinates and their first time derivatives. Then, the state vector \(\chi\) for the quadrotor is composed of \(\xi\) and \(\eta\), and their first time derivatives \(\dot{\xi}\) and \(\dot{\eta}\) as follows:  
\[\chi=\left[\xi,\eta,\dot{\xi},\dot{\eta}\right]^{T} \tag{28}\]  
Then, a state equation can be formulated based on \(\chi\). The state equation is a differential equation that establishes a relationship between the current and future states of a system when it is subjected to a given input [47]:  
\[\dot{\chi}=f(\chi,\vec{u},t) \tag{29}\]  
with \(\vec{u}\) as the vector of the system inputs.  
Using (25), the state equation of the quadrotor is in the form:  
\[\dot{\mathbf{\chi}}=\left[\begin{array}{c}\dot{\mathbf{\xi}}\\ \dot{\mathbf{\eta}}\\ \dot{\mathbf{\xi}}\\ \dot{\mathbf{\eta}}\end{array}\right]=\left[\begin{array}{c}\dot{\mathbf{\xi}}\\ \dot{\mathbf{\eta}}\\ M_{\rm T}^{-1}(\mathbf{\xi})[\mathbf{f}-\mathbf{g}_{T}(\mathbf{\xi})-\mathbf{f}_{f}(\dot{\mathbf{\xi}})] \\ M_{R}^{-1}(\mathbf{\eta})[\mathbf{\tau}-C_{R}(\mathbf{\eta},\dot{\mathbf{\eta}})]\dot{\mathbf{\eta}}- \mathbf{\tau}_{f}(\dot{\mathbf{\eta}})]\end{array}\right] \tag{30}\]  
If an initial condition \(\mathbf{\chi}_{0}\) is imposed on the previous equation, it becomes an Initial Value Problem (IVP) like the one shown in (31). The IVP can be solved by some numerical integration method to simulate the quadrotor behavior. Although there is a vast diversity of sophisticated numerical integration methods, this framework adopts [PERSON]'s method [48] (also known as _ode1_) due to its simplicity and low computational cost. Using [PERSON]'s method, the future states of the quadrotor are calculated after a short time window \(dt\) from a current time instant.  
\[IVP=\left\{\begin{array}{c}\dot{\mathbf{\chi}}=f(\mathbf{\chi},\mathbf{u},t)\\ \mathbf{\chi}(0)=\mathbf{\chi}_{0}\end{array}\right. \tag{31}\]  
Next, it is explained how this numerical simulation of the quadrotor integrates with the virtual environment.
# Parametric Analysis of Rotational Effects in Seismic Design of Tall Structures
###### Abstract  
This paper uses a parametric study to evaluate the significance of the rotational components of Earth's motion in a seismic design. The parametric study is based on the procedures included in Eurocode 8, Part 6. Although the answer to the question of when the effects of rotational components are important is quite a complex one and requires a more in-depth study, our aim was to try to assess this question in a relatively quick manner and with acceptable accuracy. The first part of the paper is devoted to derivation of a simple formula that can be used for expressing the importance of rotational components in comparison with the classic seismic design without their usage. The quasi-static analysis, assuming inertial forces, is used. A crucial role plays the shape of the fundamental mode of the vibration. Due to simplicity reasons, well-known expression for estimation of the first eigenmode as an exponential function with different power coefficients that vary for different types of buildings is used. The possibility of changing the soil parameters is subsequently included into the formula for estimation of the fundamental frequency of tall buildings. In the next part, the overall seismic analyses of complex FEM models of 3D buildings and chimneys are performed. The results from those analyses are then compared with those from simplified calculations. The importance of the soil characteristics for determination of whether it is necessary to take into account the rotational effects is further discussed.  
rotational components; ground motion; seismic response; fundamental mode shape; fundamental period; soil stiffness Article
## 5 Discussion
### Model Stability Analysis  
This study performed a statistical analysis of the accuracy of different years for each category in Section 4.2. In addition, we found that the accuracy is good in each year. Moreover, the annual accuracy has good stability. The results show that the accuracy of the different years for each category is roughly the same. The standard deviation of interannual precision is no more than 6%. However, the accuracy of the wetland has a higher dispersion than the other categories, with a difference of 13.3% between the maximum and the minimum. Although the accuracy is still high (85.4% for the year of the lowest precision), it has awful stability of the different years for wetland.  
We hold it is the following two reasons that cause the problem. Firstly, there are many types of wetlands such as coastal wetland, inland wetland and artificial wetland [86]. Wetlands occupy an intermediate position between truly terrestrial and aquatic ecosystems and therefore encompass a diverse array of habitats. This array of habitats is difficult to define [87]. In addition, there are many different types of organisms in the wetlands. Therefore, the quantitative remote sensing characteristics of wetlands are complex and easily confused with water bodies and other vegetation types. At the same time, the sample size of the wetland is too small, so the model is easy to misclassify the wetland into other categories. Secondly, in terms of methods, we use time series quantitative remote sensing features to classify land cover. However, the similarity in time series between wetlands and other categories can cause certain difficulties for our classification task. We will improve in the follow-up work in this part of the study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Truth Value** & **SMOTE-Predicted** & **S-SMOTE-Predicted** & **SMOTE** & **S-SMOTE** & \\ \hline CR & 75,749 & 63,996 & 65,220 & 84.5\% & 86.1\% & +1.6\% \\ FR & 83,285 & 69,539 & 76,206 & 83.5\% & 91.5\% & +8.0\% \\ SR & 13,893 & 9505 & 11,948 & 68.4\% & 86.0\% & +17.6\% \\ GR & 117,764 & 94,265 & 104,928 & 80.0\% & 89.1\% & +9.1\% \\ WT & 3911 & 2619 & 3555 & 67.0\% & 90.9\% & +23.9\% \\ GL & 1336 & 925 & 1416 & 69.2\% & 93.3\% & +24.1\% \\ WL & 2539 & 882 & 1978 & 34.7\% & 77.9\% & +43.2\% \\ CT & 2238 & 803 & 2035 & 35.9\% & 91.0\% & +55.1\% \\ DS & 81,293 & 64,517 & 75,829 & 79.4\% & 93.3\% & +13.9\% \\ TD & 2201 & 1217 & 1983 & 55.1\% & 90.1\% & +35.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: SMOTE algorithm and stratified SMOTE algorithm precision comparison.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 4 ADRC Design for Attitude Tracking  
An attitude trajectory tracking for a UAV-VTOL is discussed in this section. To address this problem, let us consider first the attitude dynamics error equation  
\[\dot{\mathbf{\omega}}_{e}=\mathbf{J}^{-1}[\mathbf{\Gamma}+\xi] \tag{11}\]  
where  
\[\xi=-[(\mathbf{\omega}_{e}+\mathbf{\omega}_{r})^{\times}]J(\mathbf{\omega}_{e}+\mathbf{\omega} _{r})+[\mathbf{\omega}_{e}^{\times}]\mathbf{\omega}_{r}-\mathbf{R}^{\mathsf{T}}(\bar{ \mathbf{q}})\dot{\mathbf{\omega}}_{d}+\mathbf{\Gamma}_{d} \tag{12}\]  
i.e., \(\xi(\cdot)\) is constituted by the sum of the endogenous perturbation and the exogenous one. Then, a control strategy based on the active disturbance rejection control (ADRC) technique is proposed. The function, \(\xi\), will be estimated through the extended state observer (ESO), which is based on the system's dynamics (11). For this purpose, one makes the following assumptions:  
* \(\mathbf{\omega}\) and \(\mathbf{q}\) are measured, such that \(\mathbf{\omega}_{e}\) is always available;
* The inertia matrix is diagonal, i.e., \(\mathbf{J}=\text{diag}(J_{1},J_{2},J_{3})\) with \(J_{1}=J_{2}<J_{3}\), and its nominal value is known;
* The perturbation function \(\xi(\cdot)\) is a uniformly absolutely bounded disturbance, i.e., \(\sup_{t}\|\xi(\cdot)\|=\|\xi(\cdot)\|_{\infty}\leq K_{0}\).
## 3 Methodology
### Feature Extraction Methods
#### 3.4.2 Gray Level Co-Occurrence Matrix (GLCM)  
In GLCM, a matrix \(C(i,j)\) known as the co-occurrence matrix is computed by counting the co-occurrences in gray pixels that occur within a distance, \(d\), and orientation, \(\theta\), which takes any value between 0 and 315\({}^{\circ}\) at 45\({}^{\circ}\) intervals. Features can be extracted from the co-occurrence matrix as a form of dimensionality reduction, with Energy, Inertia, Correlation, Difference Moment, Entropy [45], Contrast, and Homogeneity [46], as the most commonly computed features. Many other features can also be extracted from the co-occurrence matrix [47]. A \(d\) value of 3 and a \(\theta\) value of 45\({}^{\circ}\) were used, with Contrast, Dissimilarity, Homogeneity, Angular Second Moment (ASM), Energy, and Correlation used as features.
# A Survey on Blockchain-Based Federated Learning
## 2 Preliminary  
### Overview of Federated Learning  
Conventional ML algorithms necessitate the centralization of raw data on high-computational-capacity cloud servers for model training, thereby engendering uncontrollable data flow and vulnerability to sensitive data leakage. [PERSON] et al. (2017) introduced the concept of FL in 2017, allowing for the preservation of user privacy during the ML process without the aggregation of source data into a shared training dataset. Essentially, FL is a form of distributed machine learning technology, the workflow of which is depicted in Figure 1.  
Client devices, such as mobile phones, computers, and IoT devices, work together to train ML models under the supervision of a central server. In this configuration, the client devices are in charge of training local data to build local models. The central server performs weighted aggregation of these local models to produce a global model. Through iterative cycles of this process, a model \(w\) is ultimately obtained that closely approximates the outcomes of centralized ML algorithms, thereby effectively mitigating numerous privacy risks associated with the aggregation of source data in traditional ML paradigms.  
The iterative process of FL is outlined below:  
1. Client devices retrieve the global parameter \(w_{t-1}\)  from the server;
2. Each client \(k\) trains its local data to derive its local model \(w_{t,k}\) (signifying the local model update for the \(k^{th}\) client in the \(t^{th}\) communication round);
3. All participating clients transmit their local model updates to the central server;
4. Upon receiving updates from diverse clients, the central server executes weighted aggregation operations to formulate the global model \(w_{t}\) (indicating the global model update in the \(t^{th}\) communication round).  
Foremost, FL technology exhibits the following distinctive attributes: (1) The raw data engaged in FL are retained locally on client devices, with only model updates being exchanged with the central server. (2) The jointly trained model is shared collectively among all participating entities. (3) The ultimate model accuracy of FL approximates that of centralized machine learning methodologies. (4) The quality of the training data contributed by participants in FL is directly correlated with the precision of the resultant global model.  
Figure 1: The workflow of \(FL\).
###### Abstract  
Spaceborne Synthetic Aperture Radar (SAR) is extensively used in maritime surveillance due to its ability to monitor vast oceanic regions regardless of weather conditions and sun illumination. Over the years, numerous automatic ship detection algorithms have been developed, utilizing either single-polarimetric data (i.e., intensity) or leveraging additional information provided by polarimetric sensors. One of the main challenges in automatic ship detection using SAR is that sea clutter, influenced primarily by sea conditions and image acquisition angles, can exhibit strong backscatter, reducing the signal-to-clutter ratio (that is, the contrast) between ships and their surroundings. This leads inevitably to detection errors, which can be either false alarms or miss-detections. A potential solution to this issue is to develop methodologies that suppress backscattered signals from the sea while preserving the radar returns from ships. In this work, we analyse a contrast enhancement method which is designed to suppress unwanted sea clutter while preserving signals from potential ships. A key advantage of this method is that it is fully analytical, eliminating the need for numerical optimization and enabling the rapid generation of an enhanced image better suited for automatic detection. This technique, based on polarimetric orthogonality, was originally formulated for quad-polarimetric data, and here the adaptation for dual-polarimetric SAR images is also detailed. To demonstrate its effectiveness, a comprehensive set of results using both quad- and dual-polarimetric images acquired by various sensors operating at L-, C-, and X-band is presented.  
SAR image processing; polarimetry; ship detection; contrast enhancement; target detection; rank-1; orthogonality Article
# Efficient Enhancement of Second Harmonic Generation via Noninvasive Modulation
## 4 Conclusions  
In this paper, a scattering medium to construct a speckle light field with a specific spatial distribution that can affect the intensity of SHG and increase the light intensity of SHG based on the wavefront shaping method was applied. Our experiment verified that modulation of phase and phase gradient of scattering field via genetic algorithm can achieve an effective enhancement of SHG. A higher enhancement can be realized when placing an appropriate scattering medium, compared to the case of no scattering medium. The 25-fold enhancement factor \(\eta\) of SHG can be obtained when placing 220-grit ground glass in front of the BBO. The results have the potential to be applied in the fields of optical frequency conversion, second harmonic detection, and sensing. We envision that this experiment can be implemented in enhancement of higher harmonics.  
Figure 4: The enhancement of SHG with different scattering mediums using wavefront shaping technique (results from the statistical average values of multiple experimental data). (**a**) The second harmonic light field before and after optimization of different scattering mediums. (**b**) Statistical histogram of the second harmonic intensity enhancement factor corresponding to different scattering mediums with different resolutions. Note: the optimized light field is collected after three times attenuation. Scale bar: 1.468 mm.  
**Author Contributions:** Conceptualization, L.W. and W.F.; methodology, L.W. and J.P.; software, L.W. and W.F.; validation, L.W.; formal analysis, L.W. and J.P.; writing--original draft preparation, L.W.; writing--review and editing, L.W., W.F., Z.C. and J.P.; project administration, Z.C. and J.P.; funding acquisition, Z.C. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) under grant number 62005086, Graduate Student Research and Innovation Ability Training Program of Huaqiao University under grant number 18013082015.  
Not applicable.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Effect of Fermentation Humidity on Quality of Congou Black Tea
## 3 Results and Discussion
### Effects of Fermentation Humidity on Congou Black Tea Sensory Quality
This is probably due to the proper retention of polyphenols and their harmonization with other water-soluble substances, thereby enhancing the sensory quality [17]. In terms of tea infusion color, the impact of different fermentation humidity conditions on black tea's liquid color was relatively small, which was consistent with the chromatic aberration analysis of the tea infusion in Table S1. Additionally, the high fermentation humidity samples showed better performance in the redness, brightness and blending of infused leaves. The above analysis indicated that 75% fermentation humidity was an important turning point for the sensory quality of congou black tea and a high humidity fermentation environment favored the enhancement of the sensory quality of black tea. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Appearance** & **Infusion Color** & **Aroma** & **Taste** & **Infused Leaf** & **Total Scores** \\  & **(25\%)** & **(10\%)** & **(25\%)** & **(30\%)** & **(10\%)** & **Total Scores** \\ \hline RH55 & 78.2 \(\pm\) 0.2 e & 84.8 \(\pm\) 0.3 a & 73.8 \(\pm\) 0.3 e & 72.1 \(\pm\) 0.1 e & 80.1 \(\pm\) 0.1 bc & 76.1 \(\pm\) 0.1 e \\ RH65 & 79.9 \(\pm\) 0.1 d & 84.2 \(\pm\) 0.3 a & 76 \(\pm\) 0.2 d & 77 \(\pm\) 0.2 d & 80 \(\pm\) 0.2 c & 78.5 \(\pm\) 0 d \\ RH75 & 83.8 \(\pm\) 0.2 c & 85 \(\pm\) 0.9 a & 81.2 \(\pm\) 0.3 c & 80.9 \(\pm\) 0.1 c & 80.2 \(\pm\) 0.1 ab & 82.1 \(\pm\) 0.1 c \\ RH85 & 85.1 \(\pm\) 0.1 b & 84.5 \(\pm\) 0.9 a & 83.9 \(\pm\) 0.1 b & 83.9 \(\pm\) 0.1 b & 80.2 \(\pm\) 0.1 ab & 83.9 \(\pm\) 0.1 b \\ RH95 & 86 \(\pm\) 0.2 a & 84.5 \(\pm\) 0.5 a & 87 \(\pm\) 0.2 a & 88 \(\pm\) 0.2 a & 80.4 \(\pm\) 0.1 a & 86.2 \(\pm\) 0.1 a \\ \hline \hline \end{tabular} Note: Significant differences at \(p\) + 0.05 are indicated by different lowercase letters in the same column.  
\end{table} 
Table 1: Effect of fermentation humidity on sensory quality of congou black tea. Figure 1: Effect of fermentation humidity on the quality of fermented leaves, dried tea, tea infusion and infused leaf of congou black tea.
## 3 Results
### HO-1 Inhibition Reduces the Antioxidant Activity of FBCC-EP858, FBCC-EP920, and FBCC-EP1014  
To confirm whether the reduction of ROS production by FBCC-EP858, FBCC-EP920, and FBCC-EP1014 is related to the activation of the Nrf2/HO-1 pathway, we investigated the effect of an HO-1 inhibitor, ZnPP, on ROS production. As shown in Figure 7A,B, H2O2 treatment increased the population of ROS\({}^{+}\) cells, and treatment with FBCC-EP858, FBCC-EP920, and FBCC-EP1014 reduced the population (Figure 7A,B). However, treatment with ZnPP reversed the extract-induced reduction in the ROS\({}^{+}\) cell population, causing an increase in the population. Additionally, DCFDA fluorescence intensity induced by H2O2 was significantly elevated in zebrafish larvae, and treatment with FBCC-EP858, FBCC-EP920, and FBCC-EP1014 attenuated the intensity (Figure 7C,D). Consistent with the data in Figure 7A,B, treatment with ZnPP reversed the three extract-induced reductions in DCFDA fluorescence intensity, causing a reversal increase. These results suggest that the antioxidant activity induced by FBCC-EP858, FBCC-EP920, and FBCC-EP1014 is related to the activation of the Nrf2/HO-1 pathway.  
Figure 7: HO-1-mediated ROS reduction. (**A,B**) RAW 264.7 macrophages were seeded at a density of 5 \(\times\) 10\({}^{4}\) cells/mL and treated with FBCC-EP858 (100 \(\upmu\)g/mL), FBCC-EP920 (50 \(\upmu\)g/mL), and FBCC-EP1014 (100 \(\upmu\)g/mL) for 2 h followed by addition of 10 \(\upmu\)M ZnPP for 22 h. Then, 200 \(\upmu\)M hydrogen peroxide (H2O2) was treated for the last 4 h. (**A**) The cells were stained using a Muse Oxidative Stress Kit, and (**B**) ROS\({}^{+}\) cell population was analyzed using a Muse Cell Analyzer. (**C,D**) The indicated concentration (50 \(\upmu\)g/mL) of FBCC-EP858, FBCC-EP920, and FBCC-EP1014 were treated to 3 dpf zebrafish larvae for 1 h followed by treatment with 10 \(\upmu\)M ZnPP for 1 h. Then, H2O2 (1 mM) was added for 22 h. (**C**) The larvae were stained using 20 \(\upmu\)M DCFDA for 30 min and visualized using aCELENA S Digital Imaging System. (**D**) The DCFDA fluorescence intensity was calculated using ImageJ. Each value indicates the mean \(\pm\) SEM from three independent experiments. Significant difference: ***\(p<0.001\) vs. untreated group (Student's \(t\)-test), ***\(p<0.001\) vs. H\({}_{2}\)O\({}_{2}\)-treated group (one-way ANOVA), and ***\(p<0.001\) vs. FBCCs + H\({}_{2}\)O\({}_{2}\)-treated group (one-way ANOVA).
# Mode-Matching-Based Sound Field Recording and Synthesis with Circular Double-Layer Arrays
## 1 Introduction
### Least-Squares Approach  
In the recording stage, a primary sound field is first recorded by using a double-layer array of \(I_{\text{int}}\) microphones (Figure 2a).  
In the synthesis stage, two double-layer arrays of \(I_{\text{int}}\) and \(I_{\text{ext}}\) control points are arranged for simultaneously controlling both the interior and exterior sound fields. In pressure matching approaches, the arrangement of the interior control points is just the same as that of the microphone array in the primary sound field. A secondary sound field is then controlled by a double-layer array of \(L\) loudspeakers located between the interior and exterior control points with driving signals of \(L\times 1\) vector \(\mathbf{D}(k)\) (Figure 2b). The secondary sound pressures at the control points are defined as \(I(=I_{\text{int}}+I_{\text{ext}})\times 1\) vectors \(\mathbf{P}(k)\) and given:  
\[\mathbf{P}(k)=\mathbf{G}(k)\mathbf{D}(k), \tag{3}\]  
where \(\mathbf{G}(k)\) is the \(I\times L\) transfer function matrix between the receivers and the sources whose coefficients are given:  
\[G_{il}(k)=\frac{j}{4}H_{0}(k|\mathbf{r}_{i}-\mathbf{r}_{l}|). \tag{4}\]To achieve interior sound field synthesis without exterior sound propagation, the target secondary sound pressures are set:  
\[\mathbf{P}(k)=\left\{\begin{array}{ll}0,&\text{outside the loudspeaker array}\\ \mathbf{P}_{\text{org}}(k),&\text{inside the loudspeaker array}\end{array}\right., \tag{5}\]  
where \(\mathbf{P}_{\text{org}}(k)\) is the \(I_{\text{int}}\times 1\) vector of the primary sound pressures captured by the microphone array. The optimal driving signals are then derived based on the least-squares solution:  
\[\mathbf{D}_{\text{LS}}(k)=(\mathbf{G}^{H}(k)\mathbf{G}(k))^{-1}\mathbf{G}^{H}(k)\mathbf{P}(k), \tag{6}\]  
where \(\mathbf{D}_{\text{LS}}(k)\) is the driving signal for the LS method and \(\mathbf{G}^{H}(k)\) is the Hermitian transpose of \(\mathbf{G}(k)\). With the derived driving signals, both the interior and exterior sound fields are simultaneously controlled.