esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods
### Proposed Network  
In order to improve the capability of the model, reduce the model complexity and alleviate the vanishing gradient in the training process, we proposed a model named SSENets, based on the SSE module using skip-connection strategy and the ASPP module using atrous convolutions with multi-sample rates. The structure of SSENets is shown in Figure 2, which contains the core SSE module, ASPP module and conventional convolutional layers and pooling layers. The role of the first three convolutional layers is to extract the images features. The module takes the feature maps from the second and third convolutional layers as the input of the SSE module, and uses the generated channel weights to recalibrate the feature map. The SSE module uses feature maps from different layers as input, which can improve the problem of the vanishing gradient in the training process. The structure of SSE module will be detailed in Section 2.3. So as to improve the learning capability to cracks features, the model takes the output feature map of SSE module as the input of ASPP module and extracts the multi-scale features. We structure the ASPP module with depthwise separable convolution in order to greatly reduce the parameters and model complexity. The structure of ASPP module will be detailed in Section 2.4. In addition, in order to avoid destroying the topology of the cracks after using several pooling layers to sample the feature map, we introduce atrous convolution with an atrous rate of 2 in  
Figure 1: Flowchart of crack detection.  
the last three convolutional layers. Finally, we use the Softmax function to predict whether the input images contain cracks or not.
## 5 FEM Simulations
### FEM Analysis of Fabrication Process Tolerances on Structural Stability  
In addition to temperature variations, the microfabrication process tolerances can induce a frequency mismatch between drive and sense modes, which contributes to the decrease of the proposed MEMS gyroscope mechanical sensitivity. The SOIMUMPs is a relatively mature microfabrication process with tolerances of \(\pm 1\)\(\upmu\)m for the device thickness layer. A FEM-based modal analysis is performed in ANSYS APDL module considering a change in structural layer thickness with 25 \(\upmu\)m \(\pm\) 1 \(\upmu\)m, i.e., the thickness of the mechanical spring varies between 24 \(\upmu\)m and 26 \(\upmu\)m. Figure 14 shows that the variation of the drive and sense resonant frequency values due to the thickness tolerances is nearly equal to 1.92% from the nominal values. The sense frequency remains higher than the drive one due to the initial mismatch with which the proposed MEMS gyroscope structure has been designed and, thus, the electrostatic tuning can be performed for the frequency compensation.  
Figure 14: Drive and sense resonant frequency variations due microfabrication process tolerances.  
Figure 13: Resonant frequency variations in the gyroscope operational temperature values between \(-40\)\({}^{\circ}\)C and \(100\)\({}^{\circ}\)C (**a**) Drive mode, **(b**) Sense mode, with reference temperature \(T_{F}\) of 25 \({}^{\circ}\)C.
## 4 Methodology
### Domain Clustering Based on Multi-Path Transmission
#### 4.2.4 Nash Bargaining Game Model and Algorithm Optimization  
This section introduces the MCGT-1 algorithm, which optimizes resource allocation in satellite networks through Nash bargaining games. The MCGT-1 algorithm aims to achieve a fair distribution among multiple satellite users through negotiation, while ensuring overall system utility. We derive the core mathematical model of the algorithm, presenting relevant theorems to ensure the rigor of the derivation process.  
The utility function for user \(U_{l,m}\) is defined as  
\[U_{l,m}=R_{l,m}-R_{\min,l,m} \tag{36}\]  
where \(R_{l,m}\) is the actual throughput, and \(R_{\min,l,m}\) is the minimum acceptable throughput. This difference represents the user's utility. The objective is to optimize the overall network utility by selecting the optimal resource allocation strategy for each user.  
**Theorem 8**.: _Suppose \(U\) is a non-empty, bounded, convex, and closed set. Then, there exists a unique Nash bargaining solution \(U_{\text{opt}}\) that satisfies_  
\[U_{\text{opt}}=\arg\max_{U\in U}\prod_{k=1}^{K}(U_{k}-U_{\min,k}) \tag{37}\]  
Proof.: The goal of the Nash bargaining game is to maximize the product of the utilities of all participants, ensuring both fairness and efficiency in resource allocation. Formally, the optimization problem is defined as  
\[U_{\text{opt}}=\arg\max_{U\in U}\prod_{k=1}^{K}(U_{k}-U_{\min,k}) \tag{38}\]where \(U_{k}\) is the utility of the \(k\)-th user, and \(U_{\min,k}\) is their minimum utility requirement.  
The system utility function is  
\[F(U)=\prod_{k=1}^{K}(U_{k}-U_{\min,k}) \tag{39}\]  
To find the optimal utility \(U_{\text{opt}}\), we take the natural logarithm of the utility function to simplify differentiation:  
\[\ln F(U)=\sum_{k=1}^{K}\ln(U_{k}-U_{\min,k}) \tag{40}\]  
Differentiating with respect to \(U_{k}\) and setting the derivative to zero for maximization, we obtain  
\[\frac{\partial\ln F(U)}{\partial U_{k}}=\frac{1}{U_{k}-U_{\min,k}}=0\quad \Rightarrow\quad U_{k}>U_{\min,k},\quad\forall k \tag{41}\]  
This condition ensures that each user's utility exceeds their minimum requirement, aligning with the feasibility of the solution.  
Given that \(U\) is a non-empty, bounded, convex, and closed set, and the utility functions \(U_{k}\) are concave, the optimization problem possesses a unique solution \(U_{\text{opt}}\). This uniqueness arises from the strict concavity of the objective function, which guarantees that the maximum is attained at a single point within the feasible set.  
Thus, the Nash bargaining solution \(U_{\text{opt}}\) is uniquely determined, ensuring an optimal and fair resource allocation among all participants.
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
## 4 Stability Analysis
### Zeno Phenomenon Analysis  
When a system approaches the Zeno phenomenon, this means that the system triggers an event infinitely in a finite time. The design of the event-triggering mechanism needs to ensure that there is no theoretical possibility of triggering the Zeno phenomenon.  
For the variable-payload quadrotor system with actuator faults (14), the event-triggered mechanisms are designed as (24), (28), (34), and (36). The system contains positive parameters that guarantee avoidance of the Zeno phenomenon.  
For the event-triggered mechanism of the observation channel, the triggering conditions for the velocity and position states are similar. Taking the \(z\) channel as an example, the actual output \(z_{1}(t)\) of the system is differentiable with \(|z_{1}(t)|\leq L\), where \(L\) is a positive finite constant. We define \(\Delta t_{1}=t-t_{p}^{z1}\); then, there are  
\[|z_{1}(t)-z_{1}(t_{p}^{z1})|\leq L\Delta t_{1}. \tag{62}\]  
When the event-trigger condition holds, there is  
\[|z_{1}(t)-z_{1}(t_{p}^{z1})|>\gamma_{z1}. \tag{63}\]  
Then we get  
\[\begin{split} L\Delta t_{1}&>\gamma_{z1}\\ \Delta t_{1}&>\frac{\gamma_{z1}}{L}=\iota_{1}.\end{split} \tag{64}\]  
The minimum triggering interval of the event-triggered mechanism can be represented by \(\iota_{1}\), a positive constant that guarantees the absence of the Zeno phenomenon in the observer event-triggered scheme.  
For the event-triggered mechanism of the control channel, take \(U_{2}\) as an example. The controller output \(U_{2}(t)\) is differentiable such that \(|\dot{U}_{2}(t)|\leq K\), where \(K\) is a positive finite constant. Defining \(\Delta t_{2}=t-\tau_{q}^{2}\), one gets  
\[|U_{2}(t)-U_{2}(\tau_{q}^{2})|\leq K\Delta t_{2}. \tag{65}\]  
When the event trigger condition holds, there is  
\[|U_{2}(t)-U_{2}(\tau_{q}^{2})|>M_{1}^{2}e^{-M_{2}^{2}|\hat{\lambda}_{\phi 1}-\hat{\varphi}_{d}|}. \tag{66}\]  
So we have  
\[\begin{split} K\Delta t_{2}&>M_{1}^{2}e^{-M_{2}^{ 2}|\hat{\lambda}_{\phi 1}-\hat{\varphi}_{d}|}\\ \Delta t_{2}&>\frac{M_{1}^{2}}{K}e^{-M_{2}^{2}|\hat {\lambda}_{\phi 1}-\hat{\varphi}_{d}|}=\iota_{2}.\end{split} \tag{67}\]  
According to Theorems 2.1 and 2, the term \(|\hat{\alpha}\phi 1-\phi_{d}|\) is bounded, and \(e^{-M_{2}^{2}|\hat{\lambda}\phi 1-\phi_{d}|}\) remains bounded. Thus, the minimum triggering interval, denoted by the positive constant \(\iota_{2}\), ensures the absence of the Zeno phenomenon in the controller event-triggered mechanism.  
During actual quadrotor flight operations, if the event-triggered mechanism fails and approaches Zeno behavior, the physical implementation of the system inherently prevents critical failure. All data transmission occurs through discrete sampling in suchpractical systems. The sampling frequency consequently determines the event-triggering frequency. This physical constraint guarantees finite event-triggering within any bounded time interval, thereby establishing a natural fail-safe mechanism for the event-triggered control framework.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 6 Experimental Results and Discussions
### Software Results
#### 6.1.1 2 \(\times\) 2 MIMO OFDM Model  
The BER performance values of 2 \(\times\) 2 MIMO OFDM models are shown in Figures 12-14. The performance of deep learning and conventional digital signal processing (DSP) models are compared.  
Figure 12 shows the BER performance of the conventional DSP models. For the linear and non-linear signals, the BER performances of the MLD algorithm are outstanding compared to the BER values of the ZF algorithm. The BER performances of the linear signals are almost equal to those of the non-linear 7B signals and higher than those of non-linear 5 dB signals.  
Figures 13 and 14 show the results of comparisons between the three proposed DNN models and the conventional DSP model using the ZF signal detectors. For the linear signals, non-linear 5 dB, and 7 dB signals, the BER performances of the DNN type I and type II models are not as good as those of the conventional DSP model. Meanwhile, the BER performances of the DNN type III are much higher than those of the conventional DSP model.  
From these results, we conclude that the DNN type III model is suitable for the 2 \(\times\) 2 MIMO OFDM systems. Meanwhile, two DNN type I and type II models cannot be applied to the 2 \(\times\) 2 MIMO OFDM systems.  
Figure 14: BER versus SNR for the proposed receivers with M = 2, ZF is used and the clipping level of the nonlinear PAs is 7 dB.  
Figure 13: BER versus SNR for the proposed receivers with M = 2, ZF is used and the clipping level of the nonlinear PAs is 5 dB.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 2 Materials and Methods
### Data Processing  
The GNSS movement data were downloaded using the manufacturer's software (Catapult Sprint Version 5.1.7, Melbourne, Australia) at the default 'GPS rate' of 10 Hz. Customized MATLAB codes were written to extract the relevant distance and speed time-series data using MATLAB (R2021a, MathWorks, Natick, MA, USA). The start of the sprint was identified from a sharp and continuous increase in speed above a threshold of 0.5 m/s. The duration each participant took to complete the 45.72 m distance was obtained based on the video recordings of the sprint. This sprint duration was then used to determine the end time of the sprint in the GNSS data. From the start to the end of the sprint, total distance traveled, and peak speeds were obtained from each of the 8 GNSS units. Raw GNSS data were used without further down sampling, filtering, or smoothing procedures. Due to transmission and technical errors, it was not possible to obtain complete data sets from all 8 GNSS units throughout all trials. Among the 13 participants, 7 had complete data set and 6 had missing data from either 1 or 2 GNSS units.  
For validity analysis, a reference value of the gold standard was needed. In the present study, the total distance was 45.72 m, which was the known size of half of a standard field hockey pitch. This distance was also confirmed by experimental measurement using a trundle wheel. To calculate the speed from position data, manual digitization of the player's center of the head was performed through the sprint duration using the software Kinovea (version 0.9.3, Kinovea, Bordeaux, France, available for download at: http://  
Figure 2: Experimental set-up of the sprint test over half a field hockey pitch (45.72 m) with two smartphone cameras recording the performances (Camera 1: 0 to 22.86 m, Camera 2: 22.86 m to 45.72 m).  
www.kinovea.org (accessed on 15 April 2021)). The present study used video analysis as the gold standard for kinematics, which is aligned with previous work evaluating the accuracy of 10 Hz GPS system [12]. Kinovea has been demonstrated as a reliable and accurate tool for video-based angular and linear measurements via digitization of x- and y-axis coordinates [35]. While an optimal angle of 90\({}^{\circ}\) was recommended, an accepted level accuracy was also established when the camera was placed within an angle range of 45\({}^{\circ}\) to 90\({}^{\circ}\)[35].  
Figure 3 illustrates examples of the speed-time data measured using one GNSS unit and video analysis. The raw speed data from videos were low-passed filter at 10 Hz to remove the noise associated with manual digitization. The peak value of the filtered speed data during the entire sprint duration was then identified. This video-based peak speed was used as a reference value in the subsequent validity analysis of GNSS units. The mean (SD) of the raw and filtered peak speeds were 7.82 (0.81) m/s and 7.78 (0.90) m/s, respectively.
## 3 Feature Selection
### Sequential Forward Selection (SFS)  
SFS [52; 53] begins by evaluating all samples of a dataset that consist of only one input attribute. In other words, we start from the empty set and sequentially add feature \(x_{i}\), which results in the highest-valued objective function \(J(Y_{k}+x_{i})\). Its algorithm can be broken down into the following steps:  
* Step 1: Start with empty set \(Y_{0}=\varnothing\).
* Step 2: Select the next best feature \(x^{*}=\text{argmax}[J(Y_{k}+x)]\) with \(x\
ot\in Y_{k}\). In our case, the objective function is based on the classification rate from a cross-validation test. The mean value of a 10-fold cross-validation test is used to evaluate the feature subset.
* Step 3: Update \(Y_{k+1}=Y_{k}+x^{*}\) and set \(k=k+1\).
* Step 4: Go to Step 2.  
This procedure continues until a predefined number of features are selected. According to the above process, we see that the search space is drawn such as an ellipse to emphasize the fact that there are fewer states towards the full or empty sets. To find the optimum input feature set overall, the easiest means is an exhaustive search. However, this is very expensive. Compared with an exhaustive search, forward selection is much cheaper. SFS works best when the optimal subset has a small number of features, and the main disadvantage of SFS is that it is unable to remove features that become obsolete after the addition of other features.
# Has the Toxicity of Therapeutic Deep Eutectic Systems
## 3 Animal Models
### Injection Routes: Transdermal/Subcutaneous/Intravenous Administration
The DESs were synthesized using a salt metathesis reaction at the cation/anion ratios of 1:2 and were then formulated with solubilized 100 U/mL of regular insulin. Pharmacokinetics of subcutaneous insulin administration using deep eutectics (SPADES-insulin) was compared against the equivalent dose of the clinical gold standard of the fast-acting insulin analog (Humalog) in male Wistar non-fasting rats and improved the systemic absorption of subcutaneously injected insulin faster than Humalog. The toxicity was analyzed in healthy female Balb/c mice receiving a single subcutaneous injection of DES solutions and demonstrated that SPADES is a safe and non-toxic formulation [85]. In a new paper, DESs were introduced as novel chemical penetration enhancers for transdermal iontophoresis of insulin across rat skin. The results of the in vivo studies indicated a reduction in the blood glucose level in diabetic rats using ChCl/ethylene glycol and ChCl/glycerol in the iontophoresis delivery of insulin [86]. [PERSON] et al. investigated the skin permeability of misperidone, an antipsychotic drug, using eutectic systems. The prepared THEDES mixture of misperidone and capric acid was spread on the skin of male rodents and was found to be tolerable, less irritant, and safe for application to the skin compared with the control untreated skin. A histopathological study was performed that did not show changes in the state of the skin and morphological changes [87]. [PERSON] et al. synthesized DESs, which influenced the formation of folic acid (FA)-tagged g-beta-alanine-co-Poly \(\varepsilon\)-caprolactone (PCL) polymer (DES@FA-g-\(\beta\)-alanine-co-PCL polymeric system) to encapsulate the doxorubicin drug. The enhanced anticancer potential of doxorubicin-loaded polymeric micelle was studied in an in vivo breast cancer model. The advantage of this polymeric system is that it could increase the bioavailability of hydrophobic doxorubicin drugs. The intraperitoneal injection of doxorubicin-loaded polymeric micelle significantly reduced the body weight and tumor size when compared with dimethylbenzanthracene-induced mammary carcinoma-bearing rats and polymeric micelle-treated rats [76]. Recently, [PERSON] et al. reported a transdermal delivery strategy using a silica nanoparticle matrix (MSN) encapsulated with methotrexate and impregnated in DES hydrogels for the topical management of rheumatoid arthritis. This formulated DES-MSN hydrogel system was applied to the skin of rats to test the skin irritation and the in vivo toxicity of this system. They observed a sustained penetration and accumulation of MSNs at subcutaneous inflammation sites [88].
## 2 Activity Recognition-Related Works
### Autistic Spectrum Disorder (ASD) People  
In this area, we include various works that investigate the stereotyped behaviors of autistic children. In fact, we have yet to find a vision-based deep framework for detecting autistic people's activities. [PERSON] and [PERSON] introduce an automatic method in [5] for recognizing and quantifying stereotyped behaviors in autistic children, allowing for early detection and, if necessary, behavioral intervention. Despite the strong system performance, the data used were collected from by wireless accelerometer sensors worn on children's wrists and chests, which may have made the children uncomfortable. Furthermore, this evidence was collected in laboratory and classroom settings and only captured a subset of behavior categories, such as hand flapping and body rocking. To take a quick look at the feature extraction, this study utilizes a three-layer CNN to turn the time-series of several accelerometer sensors into a new feature space that was then prepared for classification.  
On the other side, few studies propose vision-based deep solutions for stereotyped behavior identification. For example, the authors in [2] and authors in [20] identify stereotyped activities by analyzing their temporal patterns and comparing them to accessible templates of movements previously registered using Kinect camera and skeleton data. To identify stereotyped behaviors, the Dynamic Time Warping (DTW) technique is utilized in [20]. In contrast, the authors in [2] use \(P-Point\) Recognizer to match temporal patterns using the Euclidean distance function. Both works [2,20] have numerous traits that contradict our goal. (1) They aim to provide a supportive tool to help doctors and clinicians in diagnosing and therapist sessions; (2) the training data are collected from non-autistic subjects that imitate autistic behaviors; (3) the stereotypical movements considered are limited to a small set of gestures like hand flapping, and there are no realistic activities and interactions; and (4) the proposed algorithms (template-based) may be exposed to failure in the case of background noise, occlusions, or other activities.  
Moreover, [PERSON] et al. [21] suggest a new method for identifying and evaluating arm-flapping stereotypic behavior in autistic children using computer vision and machine learning approaches. The approach entails collecting frames from the input video and utilizing MediaPipe to locate and label body landmarks. The landmark data are then gathered and processed in several processes to identify stereotypic behavior. Finally, the system assesses the severity and frequency of the arm flapping activity. Using characteristics like human position estimation and skeleton modeling, the system can precisely track and evaluate the frequency and intensity of arm flapping, offering useful data for clinical assessment and intervention planning.
# Investigation and Design of the Transonic Laminar Flow Characteristics in a Laminar Aircraft
## 4 Results and Discussion of Airfoil Design  
### The Design of Airfoil  
The purpose of the design is to improve the aerodynamic characteristics of the central experimental wing without affecting the laminar flow characteristics. \(OLD\) represents the original laminar flow airfoil, while \(NEW\) represents the design airfoil based on \(OLD\).  
On the one hand, \(Ma\) and \(\alpha\) are the two most likely factors to change in a specific aircraft's high-speed flight condition. Through the sensitivity analysis in Section 3, it is known that the \(Ma\) and the \(\alpha\) directly affect the pressure gradient and the original airfoil of the central experimental wing adopts a no-shock wave design. The advantage of this design, shown in Figure 23, is that the aerodynamic characteristics at the design point are excellent. However, once the boundary conditions deviate from the design point, the aerodynamic characteristics deteriorate rapidly. In contrast, the weak shock wave design is more commonly used in aerodynamic designs, whose overall robustness displayed in Figure 23 is better than the other one. Additionally, better aerodynamic characteristics can be maintained in a wide range. This is beneficial for investigating the laminar flow characteristics under a relatively wide range of boundary conditions. If the \(C_{P}\) curve is less robust, the stability of the pressure gradient will be poor. The pressure gradient with poor stability results in highly nonlinear laminar flow characteristics, which will make it difficult to summarize the experimental law. Therefore, the weak shock wave design is more suitable for the \(C_{P}\) curve design of the airfoil in the central experimental wing. On the other hand, to increase the \(C_{L}/C_{D}\) characteristic in cruise, the airfoil trailing edge curvature is increased to increase the local rear loading of \(C_{L}\), which can improve the \(C_{L}\) aircraft and the \(C_{L}/C_{D}\) under the same \(C_{L}\) without affecting the laminar flow characteristic. (The \(C_{L}\) of the whole aircraft increases, resulting in a reduction in the cruising \(\alpha\). The decrease in cruise \(\alpha\) leads to a decrease in \(C_{D}\), so the \(C_{L}/C_{D}\) increases.) Based on the above design analysis, the following three design improvements were made to the original laminar flow airfoil shown in Figure 24:  
1. The airfoil curve from 50% chord length to 70% chord length on the upper surface of the airfoil is adjusted to slightly reduce the local airfoil thickness to promote the formation of weak shock waves;
2. The maximum thickness position of the lower surface of the airfoil is shifted forward by 10% to increase the region of aerodynamic load on the rear part of the airfoil;
3. The curvature of the trailing edge of the lower surface of the airfoil is reversed and increased to promote the formation of aerodynamic load on the rear part of the airfoil. This increases the nose-down pitching moment. Thus, this should be limited for structural and trim.  
Figure 23: Comparison with and without weak shock wave.