esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Strategy Description
### Stability Analysis  
The minimum control requirement is to ensure that the system is globally stable for dynamic systems. In this paper, the traditional SMC uses a constant approximation law, which is equivalent to linearizing the system in a local range and achieving global uniformity and bounded stability. This section mainly analyzes the stability of the PMSM control system under the action of the backstepping sliding mode controller based on the nonlinear disturbance observer. The specific analysis process is as follows:  
First, explain the state parameters and variables to be used. The new sliding mode face equation of state is shown in Equation (22):  
\[\begin{array}{l}\dot{s}&=c_{1}\dot{e}+\dot{x}_{2}+\hat{d}(t)\\ &=c_{1}[ax_{1}+bx_{2}+d\left(t\right)]+cx_{1}+dx_{2}+gu+\dot{\hat{d}}(t)\end{array} \tag{22}\]Substituting Equation (21) into Equation (22):  
\[\dot{s}=-K\text{sgn}(s)+c\Big{[}d(t)-\dot{d}(t)\Big{]}+\dot{d}(t) \tag{23}\]  
where  
\[\dot{d}(t)=-l\lambda_{2}\Big{[}\dot{d}(t)-d(t)\Big{]}\]  
Equation (23) can be further simplified to Equation (24).  
\[\dot{s}=-K\text{sgn}(s)+(c+l\lambda_{2})ed(t) \tag{24}\]  
Then, construct the Lyapunov function of the system as Equation (25) and the derivative function of the _Lyapunov_ function as Equation (26).  
\[v=\frac{1}{2}s^{2}\to PD \tag{25}\]  
\[\dot{v}=s\dot{s}=\Big{[}-K\text{sgn}(s)+(c+l\lambda_{2})e_{d(t)}\Big{]}\cdot s \tag{26}\]  
According to _[PERSON]_'s second stability theory, if we want to ensure that the PMSM system is globally asymptotically stable, and we must make Equation (25) a positive definite function and Equation (26) is a negative definite function. Solving Equation (26) gives the following:  
\[\dot{v} =s\dot{s}=\Big{[}-K\text{sgn}(s)+(c+l\lambda_{2})e_{d(t)}\Big{]}\cdot s \tag{27}\] \[=-K|s|+(c+l\lambda_{2})e_{d(t)}\cdot s\]  
where \(\text{sgn}(s)=s/|s|,s\cdot\text{sgn}(s)=s/|s|=s^{2}/|s|=\big{|}s\big{|}\).  
\[\dot{v} =s\dot{s}=-K|s|+(c+l\lambda_{2})e_{d(t)}\cdot s \tag{28}\] \[\leq-K|s|+(c+l\lambda_{2})\Big{|}e_{d(t)}|\cdot|s|\] \[=-\Big{[}K-(c+l\lambda_{2})\Big{|}e_{d(t)}\Big{|}\Big{]}\cdot|s|\]  
Equation (25) can be calculated, such that \(\frac{1}{2}s^{2}=v\Rightarrow\big{|}s\big{|}=\sqrt{2}\sqrt{v}\),  
\[\dot{v}=s\dot{s}\leq=-\sqrt{2}\Big{[}K-(c+l\lambda_{2})\Big{|}e_{d(t)}\Big{|} \Big{]}\cdot\sqrt{v} \tag{29}\]  
If the value of \(K\) is greater than \((c+l\lambda_{2})\Big{|}e_{d(t)}\Big{|}\), then Equation (27) can be simplified to the following Equation:  
\[\dot{v}\leq-\eta\sqrt{v} \tag{30}\]  
As Equation (25) proves \(v(t)\) to be a positive definite function, \(v(t)^{1/2}\) is still a positive definite function. If \(K\) takes the appropriate value, then \(\eta\) is a constant greater than zero, so Equation (30) shows that \(\dot{v}(t)\) is a negative definite function. In other words, the PMSM servo speed control system based on the nonlinear disturbance observer is globally asymptotically stable.
## 3 Results and Discussion
### Performance Evaluation Result
Like the PET non-woven fabric, the aerogel particle-mixed non-woven fabric also showed an improvement in sound absorption coefficient, as predicted by the [PERSON] model. Therefore, we deduce that the sound absorption mechanism of non-woven fabrics mixed with aerogel particles aligns with [PERSON]'s theory. Table 3 presents the average sound absorption coefficient in the 500-1600 Hz range and the measured flow resistivity, which are critical in automotive noise reduction for enhancing speech clarity. In this PET non-woven fabric, the airflow resistance increases with increasing bulk density up to 100.5 kg/m\({}^{3}\), and correspondingly, the average sound absorption coefficient in the 500-1600 Hz range also increases. However, the aerogel particle-mixed non-woven fabric exhibits a higher average sound absorption coefficient in the 500-1600 Hz range than PET non-woven fabric 2, despite its lower airflow resistance. The lack of peaks at specific frequencies in the sound absorption spectrum of the aerogel particle-mixed non-woven fabric in Figure 9 suggests that inertia and viscoelastic loss of solids are unlikely to influence the results. From these observations, we infer that the inclusion of aerogel particles enhances the tortuosity, viscous characteristic length, or thermal characteristic length, thereby improving the sound absorption coefficient. The average sound absorption coefficient in the range of 500-1600 Hz was about 2.8 times higher than that of Thinsulate in the specimen made of non-woven fabric mixed with aerogel particles and laminated with a skin material with high airflow resistance. The thermal conductivities of PET non-woven fabric 1, PET non-woven fabric 2, and non-woven fabric mixed with aerogel particles as well as the thermal conductivity measured when the bulk density is altered by compressing the non-woven fabric within a steady-state thermal conductivity measurement device are presented in Figure 11. PET non-woven fabric reaches a thermal conductivity of about 0.029 W/(m-K) even when the bulk density is increased to approximately 130 kg/m\({}^{3}\). Conversely, the non-woven fabric mixed with aerogel particles exhibited a thermal conductivity of about 0.026 W/(m-K), confirming that the aerogel particles enhance the thermal insulation properties of the aerogel particle-mixed non-woven fabric as intended. Compared to Thinsulate, the thermal insulation property of the aerogel particle-mixed non-woven fabric was approximately 1.4 times higher. Figure 10: Measured normal-incidence sound absorption coefficients of particle-mixed non-woven fabric + skin material.
## 5 Mathematical Models
### Complexity and Lower Bound for the \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\)  
In this section, we present the complexity of the \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\) problem and propose a lower bound.  
**Theorem 1**.: \(1\,r_{j}\,|\,\Sigma w_{j}W_{j}\) _is strongly NP-hard._  
Proof of Theorem 1.: According to [PERSON], the \(\Sigma w_{j}W_{j}\) criterion is equivalent to \(\Sigma w_{j}\bar{C}_{j}\), because \(W_{j}=\bar{C}_{j}\,\bar{r}_{j}\,\bar{p}_{j}\) and \(r_{j}\) and \(p_{j}\) are constants whatever the sequence [38]. Thus, \(min\sum_{j=1}^{n}w_{j}(C_{j}-r_{j}-p_{j})\) is equivalent to \(min\sum_{j=1}^{n}w_{j}C_{j}+constant\). On the other hand, [PERSON] et al. showed that \(1\,1\,r_{j}\,|\,\Sigma w_{j}C_{j}\) is strongly NP-hard [76]. [PERSON] et al. proved that this problem remains strongly NP-hard, even if the preemption is authorized [77]. Hence, the problem of \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\) is strongly NP-hard.  
According to [PERSON] et al., when all release dates are equal, \(wSPT\) rule (Weighted Shortest Processing Time) in which jobs are sorted in increasing order of \(p_{i}/w_{i}\), optimally solves \(1\,|\,\Sigma w_{j}C_{j}\) problem [78]. However, \(wSPT\) rule cannot optimally solve the problem when the release dates are introduced. In the following, a lower bound for \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\) is presented. \(\square\)  
**Theorem 2**.: _The solution obtained with \(wSRPT\) is a lower bound for \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\)._  
Proof of Theorem 2.: [PERSON] obtains in 1990 a lower bound for \(1\,1\,r_{j}\,|\,\Sigma C_{j}\) problem, by authorizing the preemption and using \(SRPT\) (Shortest Remaining Processing Time) rule to solve the problem [79]. In our case, we consider the problem with preemption, \(1\,1\,r_{j}\), \(pmt_{j}W_{j}\) and we use \(wSRPT\) (Weighted Shortest Remaining Processing Time) to obtain a lower bound for \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\). Let us consider the example of Table 1, \(wSPT\) list is given by (A, C, E, D, B). Table 3 presents the sequence obtained with \(wSRPT\), denoted by \(\sigma_{WSRPT}\).  
The obtained value using \(wSRPT\) is \(\sum_{j=1}^{5}w_{j}W_{j}\,=\,30\), instead of 31 without preemption. Whatever the solution obtained by solving \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\) problem, it must be greater than or equal to the solution obtained by \(\sigma_{WSRPT}\). Thus, the solution obtained by \(\sigma_{WSRPT}\) is a lower bound for \(1\,1\,r_{j}\,|\,\Sigma w_{j}W_{j}\) problem. \(\square\)
## 2 Materials and Methods
### Hybrid Network Adjustment  
To align the hybrid network of vertical movements of the Earth's crust from leveling and GNSS data, it is necessary to establish several parameters: selection of the adjustment method, weighting of observations, identification of connective points and their spatial distribution, and establishment of reference points. During the development of VCM 2006 [37], several adjustment methods were tested and all of them gave the same results. The least squares method was adopted for strict adjustment [51]. The observations are unbalanced vertical movements between GNSS stations and unbalanced vertical movements between nodes of the double-leveling network. Uncompensated vertical movements errors were calculated as stated in the \"Materials and Methods\" section. The weighting of observations was uniform for both types of observations (vertical movements from double leveling and vertical movements from GNSS data) as the inverse of the square of the error. The tide gauge in Wladyslawowo was used as a reference point [37]. GNSS stations do not physically coincide with the nodal points of the basic height network [17]; therefore it is necessary to identify the connecting points as the so-called pseudo-nodal points [18]. These are points separated by a certain distance that represent the same movement. This assumption results from the fact that the vertical movements of the Earth's crust are not point-like, but coherent in a certain area [17; 52]. A distance criterion of 10 km was adopted, as in [18]. For the first adjustment, 50 identified common points of Figure 7 were adopted.  
The second adjustment was performed based on 11 common points (Figure 7), which were identified based on the criterion of matching the relative vertical movements at the nodal points (50) between the hybrid network adjustment and the transformation. The other criteria were not changed.
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 4 Analysis of Experimental Result
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \
\begin{tabular}{c} **Radar** \\ **Range** \\ \end{tabular} & **Background** & **BD-BR/\%** &
\begin{tabular}{c} **BD-** \\ **PSNR/dB** \\ \end{tabular} & \multicolumn{5}{c}{\(\Delta T\)**\%**} \\ \cline{3-8}  & & **QP = 22** & **QP = 27** & **QP = 32** & **QP = 37** & **Average** \\ \hline \multirow{3}{*}{1.5 NM} & Anchor & 2.92 & \(-0.21\) & \(-33.22\) & \(-36.43\) & \(-40.89\) & \(-42.69\) & \(-38.31\) \\  & Under way & 1.22 & \(-0.15\) & \(-42.56\) & \(-46.00\) & \(-49.35\) & \(-51.36\) & \(-47.32\) \\  & Port & 2.95 & \(-0.22\) & \(-36.14\) & \(-38.55\) & \(-41.36\) & \(-42.30\) & \(-39.59\) \\  & Anchor & 3.00 & \(-0.23\) & \(-25.37\) & \(-26.56\) & \(-29.98\) & \(-33.09\) & \(-28.75\) \\  & Under way & 1.36 & \(-0.16\) & \(-41.36\) & \(-42.33\) & \(-44.10\) & \(-46.85\) & \(-43.66\) \\  & Port & 2.96 & \(-0.22\) & \(-27.12\) & \(-29.88\) & \(-32.09\) & \(-33.84\) & \(-30.73\) \\  & Anchor & 2.84 & \(-0.21\) & \(-36.98\) & \(-39.76\) & \(-43.45\) & \(-44.52\) & \(-41.18\) \\
6 NM & Under way & 1.57 & \(-0.17\) & \(-33.65\) & \(-34.10\) & \(-35.87\) & \(-37.54\) & \(-35.29\) \\  & Port & 2.51 & \(-0.19\) & \(-38.01\) & \(-40.57\) & \(-43.81\) & \(-44.30\) & \(-41.67\) \\  & Anchor & 2.30 & \(-0.19\) & \(-40.29\) & \(-43.84\) & \(-46.98\) & \(-48.12\) & \(-44.81\) \\
12 NM & Under way & 1.33 & \(-0.16\) & \(-39.45\) & \(-41.28\) & \(-43.01\) & \(-45.47\) & \(-42.30\) \\  & Port & 2.21 & \(-0.18\) & \(-40.20\) & \(-42.31\) & \(-47.44\) & \(-48.13\) & \(-44.52\) \\  & Average & 2.26 & \(-0.19\) & \(-36.20\) & \(-38.47\) & \(-41.53\) & \(-43.18\) & \(-39.84\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the algorithm in different environments. The final result of the experiment shows that our method changes with the alteration of the range and the complexity of the echo region. When the range is larger and the content is smoother, the algorithm consumes less time and has better performance. In addition, the performance of the algorithm is directly related to the QP value. The higher the QP value, the lower the number of high-depth CTUs, the fewer times of traversing the CU RD-cost, and the better the performance of the algorithm.
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
## 3 Results
### Tumor Inhibition Activity  
The antitumor activity of malB-liposomes was examined in the LLC tumor model in BALB/c mice. MalB and malB-liposome had no significant change in body weight of mice at both initial time and the end time of treatment (Figure 5). As shown in Figure 6, animals treated with free malB at a concentration of 5 mg/kg body weight (b.w.) did not show a significant reduction in tumor size compared to negative controls. However, malB-nanoliposomes significantly inhibited tumor growth at the same concentration (5 mg/kg b.w.) (\(p<0.05\)). After 28 days of treatment with malB-nanoliposomes, the tumor size was decreased by 37.03% compared to the saline-treated negative control, whereas the free form reduced the tumor size by only 13.09%. When the concentration was decreased, the tumor suppression effect of malB-nanoliposomes was markedly reduced and was not significant compared with the control. Moreover, blank liposome did not affect the tumors' growth at any tested time points.  
Figure 5: Effects of malB or malB-nanoliposomes on body weight of BALB/c mice harboring a malignant tumor induced by LLC cells (\(n=6\)). Error bars represent standard error of the mean (SEM).  
Figure 6: Anti-tumor effects of malB or malB-nanoliposomes on BALB/c mice harboring a malignant tumor induced by LLC cells (\(n=6\)). Liposome-conjugated malB at the dose 5.0 mg/kg b.w. significantly inhibited tumor growth after 21 and 28 days compared with the negative control (normal saline treated) (\(\mathbf{{}^{\ast\ast}}\)\(p<0.01\) and \(\mathbf{{}^{\ast\ast\ast}}\)\(p<0.005\), respectively). \(\mathbf{\#}\)\(p<0.05\) compared to the free malB at the same dose. Error bars represent standard error of the mean (SEM).  
In addition, the administration of malB-nanoliposomes prolonged the survival of the mice but still insignificantly (\(p>0.05\)) (Table 4). The median survival of mice treated with the malB-nanoliposomal formulation was 32.20 \(\pm\) 0.97 days, which was 6.62% longer than that of control mice. Survival was also increased slightly (31.00 \(\pm\) 0.84 days) in the free malB-treated group at the treated dose.
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
## 4 Discussion  
The findings of this study provide a theoretical basis for investigating the impact of atmospheric turbulence on ship-based QKD systems at sea surface, filling a significant gap in the existing literature. While previous studies have extensively explored the performance of satellite-ground QKD systems in the atmosphere, and classical optical communication in oceanic environments, the unique challenges posed by near sea-surface turbulence in maritime QKD scenarios have remained under-explored. Unlike studies focused on vertical satellite-ground links or horizontal underwater channels, we systematically quantify the impact of turbulence on satellite-to-ship (vertical) and ship-to-ship (horizontal) QKD configurations, revealing stark performance differences. For instance, horizontal ship-to-ship communication distances collapse from 32 km (weak turbulence) to 3 km (strong turbulence). In contrast, vertical satellite links exhibit altitude-dependent degradation patterns (such as secure key thresholds dropping from 619 km to 173 km). These results suggest that the performance of horizontal links is more susceptible to ocean turbulence than vertical links.  
The results suggest several strategies for QKD deployment at sea:  
1. Time optimization: By taking advantage of diurnal and nocturnal turbulence differences, QKD experiments can prioritize low turbulence periods (such as early morning) for long-range ship-to-satellite key exchanges.
2. Adaptive optics compensation: Quantized aiming error variance provides a design specification for an adaptive beam steering system to reduce turbulence-induced aiming losses.
3. Background light suppression technology: Reducing the impact of background light on quantum light performance enables daytime QKD experiments to be performed.
4. Hybrid network architecture deployment: Combining short-range ship-to-ship QKD (under moderate turbulence conditions) with satellite relay enables secure maritime mesh networks; this is, especially important for naval operations or offshore energy infrastructure.  
While this study focuses on the BB84 protocol, extending the framework to measurement-device-independent QKD or continuous variable protocols may enhance its adaptability to turbulence. In addition, combining machine learning with real-time turbulence prediction and adaptive parameter tuning may further optimize performance in dynamic ocean environments. Overall, this work not only deepens our understanding of the impact of ocean turbulence on the performance of QKDs at sea, but also provides feasible insights for implementing robust quantum-safe communications on Earth.  
Figure 9: Photon counts versus transmission distance.
# A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexopod Robots
## 2 The Hexapod Robot
### The Hexapod Robot's Electrical Structure  
The hexapod robot's electrical structure is shown in Figure 3.  
The sensory unit includes five parts as follows.  
(1) Binocular camera for object capture and tracking.  
(2) Attitude sensor MPU-9250 for measuring the robot's posture. MPU-9250 is a System in Package (SiP) and contains a 3-axis gyroscope, a 3-axis accelerometer, and AK8963, a 3-axis digital compass. As accelerometers and magnetometers have high frequency noise while the gyroscope has low frequency noise, by utilizing their complementary characteristics in frequency and fusing the low-pass filtered accelerometer and magnetometer data with high-pass filtered gyroscope data, the attitude feedback information with high precision can be obtained.  
(3) WIFI module for remote data transmission.  
(4) Remote control receiver for instructions receiving.  
(5) Foot sensing structure for landing information.  
The control unit is STM32, and it outputs the commands to the motion unit, more specifically, the 18 servo motors through serial ports. For each leg, its three servo motors cooperate with each other in a certain time sequence and finally achieve the foot to reach the specified point in space.  
Figure 3: The hexapod robot’s electrical structure.  
Figure 2: The hexapod robot’s foot sensing structure.  
The hexapod robot's technical specifications are provided in Table 1.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 2 Familiarization with ML
### Supervised Learning  
In supervised learning, the algorithm learns from a training dataset (i.e., predefined inputs and known outputs) to make predictions about the unforeseen data. A dataset is designed using empirical data from a system in different configurations. This dataset is then divided into training and testing sub-datasets. The training dataset is used to train a model that infers a mathematical relationship between the input and output. A trained model can then be used as a standard function for the system. Figure 1 shows the methodological workflow of supervised learning.  
Different algorithms have been developed under supervised learning to solve a variety of problems. Some of the known algorithms are mentioned below:  
1. K-Nearest Neighbor (KNN): The concept behind the nearest-neighbor algorithm is to find a predefined number of training samples nearest in distance to the new value and predict the label from them. KNN depends on the surrounding limited samples, rather than relying on the method of discriminating the class domain to determine the value, and the KNN algorithm is more suitable for datasets with crossover or overlap.
2. Support Vector Machine (SVM): Support Vector Machines (SVMs) are paradigms based on statistical learning theory. They incorporate the structural risk minimization principle, which has been shown to be better than the traditional empirical risk minimization principle used by ANNs. Thus, they have a greater generalization capability. SVMs can solve classification and regression problems and perform well in a high-dimensional feature space. They handle a nonlinear classification efficiently using the kernel trick that implicitly transforms the input space into another high-dimensional feature space.
3. Gradient Boosting (GB) Tree: The GB tree is an ensemble method that builds one decision tree learner at a time by fitting the gradients of the residuals of the previously constructed tree learners. To build a tree, the method starts from a single node and iteratively adds branches to the tree until a criterion is met. For each leaf, branches are added to maximize the loss reduction after the split (gain function).
4. Random Forest (RF): The forest operates by forming a multitude of decision trees at training time, and it is mostly trained using the bagging method, which adopts randomly selected training data and then constructs the regressor. Finally, it combines the multiple decision trees to obtain more accurate and stable predictions.
5. Naive Bayes (NB): This generally works as a classifier by utilizing the clustering technique. Clustering occurs considering the conditional probability of the components. Based on the Bayes theorem, it independently predicts the probability of one feature existing over others.  
Figure 1: Workflow of supervised learning.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Detection Model
#### 4.2.1 Rock-Fall Detection-Based Computer Vision  
The computer vision algorithms detect the rock-fall events in three steps, as shown in Figure 2. They filter the image frame, perform background subtraction, and perform frame manipulation. In the first step, the blurring Gaussian filter was used to filter out the noise from the captured images. Secondly, the moving rocks were detected from the video frame sequence. Due to weather conditions and daytime, the video frame sequence suffers from background illumination variations, so the adaptive Gaussian mixture model was used to overcome this problem.  
The model treats each pixel as a composite of Gaussians before learning the image's backdrop and categorizing each pixel as background or foreground. Equation (1) represents the background model.  
\[\hat{P}(\overrightarrow{x}|\chi_{T}\,,BG)=\sum\
olimits_{m=1}^{M}\hat{\pi}_{m} \mathcal{N}(\overrightarrow{x};\overrightarrow{\hat{\mu}}_{m},\phi_{m}^{2}I) \tag{1}\]  
where \(\hat{P}\left(\overrightarrow{x}\middle|\chi_{T}\,,BG\right)\) represents the estimated background, \(\overrightarrow{x}\) is the grayscale of the pixel value at time t, \(M\) is the number of the Gaussian components, \(\chi_{T}\) represents the training set, the weight \(\hat{\pi}_{m}\) indicates how many data are part of the GMM m component of the, \(\overrightarrow{\hat{\mu}}_{m}\) is estimated means, \(I\) represents an identity matrix, and \(\delta_{m}^{2}\) is the estimated variances.  
Bayesian analysis was used to classify pixels as background or foreground from moving rock video frames [27]. Frame manipulation was used to overcome imperfections in the segmentation process.