esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research on Critical Technologies
### Capture, Alignment, and Tracking Technologies  
Optical communication capture, tracking, and alignment technologies are essential for visual communication. The main goal of this technology is to achieve stable, reliable, and highly efficient optical signal transmission over long distances while overcoming optical signal loss or instability due to various factors. Capture technology is primarily concerned with discovering and roughly localizing a target. This is usually accomplished by establishing a relatively large search range, scanning using a coarse tracking system, and then using an exemplary tracking system for more precise aiming and tracking once the transmitted signal is detected. Tracking techniques, however, involve continuously tracking a target once it has been captured. This involves the real-time measurement and prediction of information such as the target's position, velocity, and acceleration to ensure that the optical signal is transmitted continuously and stably.  
Airborne laser communication platforms place higher demands on APT (acquire, point, and track) systems due to their strong maneuverability and high vibration characteristics. These factors directly affect the continuous communication capability of these systems. A wireless laser communication system must be optimized to reconstruct efficiently and capture signals quickly and stably when a link is interrupted. To address the above problems, studying atmospheric turbulence characteristics is the key to solving atmospheric-related tracking problems. Meanwhile, fast-response mirrors can be used to improve the tracking accuracy of an airborne platform, and the interference of stray light can be weakened by using ultra-narrow band filters.  
In the exploration of capture, tracking, and alignment technology, [PERSON] of Changchun University of Science and Technology proposed an improved beaconless capture algorithm that uses beaconless capture process, that continuously adjusts the visual axis of the two communication terminals so that it can detect the optical signal from another communication terminal and finally realize an optical closed loop. The scanning method adopts a composite spiral, and the use of such a sub-area scanning method can make the capture uncertainty domain expand [24]. The scanning method assumes a composite spiral, and this sub-area scanning method can make the capture uncertainty domain grow. [PERSON]. designed a capture process at the Overall Design Department of Beijing Space Vehicle. They proposed the effect of visual axis jitter on the capture time and capture probability [25]. [PERSON] of Changchun University of Science and Technology simulated and experimentally verified the fiducial-free spiral capture scanning technique, improving the stability of follow-on aiming [26]. Comprehensive studies have found that stable and reliable tracking effects can be achieved on high-speed maneuvering platforms through in-depth research to understand the characteristics of atmospheric platforms and study and formulate tracking schemes with high adaptability and robust targeting.
# Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis
## 3 Hierarchical Time-Dependent FMEA Model
### Occurrence of a Subsystem Failure (SSF) of a Multi-Layered Structure  
In this paper, we deal with the \((n+2)\)-layered system, and the loss function is analyzed from the point of the time that the bottom-level subsystem, i.e., the \((n+1)^{\text{th}}\) layer subsystem or FC, failure occurs. We assume that failures occur sequentially from the bottom level to the top level and follow the homogeneous Poisson process (HPP). Thus, the probability density function of the \(n^{\text{th}}\) level SSF has a simple exponential function. Then, the probability density function of \((n+1)^{\text{th}}\) level SSF is a conditioned exponential function with the \(n^{\text{th}}\) level failure time \(t_{n}\). This process is continued to the \(0^{\text{th}}\) level SF. Thus, each \(k^{\text{th}}\) level probability density function, \(h^{k}_{T_{k}|T_{k+1}=t_{k+1}}(t_{k})\), is  
\[\begin{split}& h^{n}_{T_{n}}(t_{n})=\lambda_{n}e^{-\lambda_{n}t_{n}} ;\;t_{n}\geq 0\\ & h^{n-1}_{T_{n-1}|T_{n}=t_{n}}(t_{n-1})=\lambda_{n-1}e^{-\lambda _{n-1}(t_{n-1}-t_{n})};\;t_{n-1}\geq t_{n}\\ &\cdots\\ & h^{0}_{T_{0}|T_{1}=t_{1}}(t_{0})=\lambda_{0}e^{-\lambda_{0}(t 0-t1)};\;t_{0}\geq t_{1}.\end{split} \tag{8}\]  
Since we assume the \((n+1)^{\text{th}}\) subsystem (i.e., FC) has already failed at time \(t_{n+1}=0\), we do not consider the probability density function of the \((n+1)^{\text{th}}\) SSF. Then, the probability density function of the system failure is  
\[\begin{split} h_{T_{0}}(t_{0})&=h^{0}_{T_{0}|T_{1} =t_{1}}(t_{0})\cdots h^{n-1}_{T_{n-1}|T_{n}=t_{n}}(t_{n-1})\cdot h^{n}_{T_{n}} (t_{n})\\ &=\lambda_{0}e^{-\lambda_{0}(t_{0}-t_{1})}\cdots\lambda_{n-1}e^{ -\lambda_{n-1}(t_{n-1}-t_{n})}\cdot\lambda_{n}e^{-\lambda_{n}(t_{n})}\;\;. \end{split} \tag{9}\]
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
## 3 Mode Excitation Based on Mode Matrix
### Determine Excitation Direction  
Here Euclidean distance is used to describe the true distance between the vibration vectors of modes in the rail. The distances of displacement vector between mode \(m\) and other modes in \(x\), \(y\) and \(z\) directions are calculated, and the direction with the largest Euclidean distance is chosen as the optimum excitation direction of mode \(m\).  
Figure 4: Normalized strain energy amplitude of modes. (**a**) mode 7; (**b**) mode 20; (**c**) mode 3; (**d**) mode 1; (**e**) mode 2; (**f**) mode 10.  
Figure 5: Group velocity dispersion curves.  
where \(t_{s}\) is the vibration displacement of node \(t\) in mode \(m\). \(\bar{t}\) is the average value of the vibration displacement of node \(t\). \(e_{s}\) is the vibration displacement of node \(e\) of mode \(m\). \(\bar{e}\) is the average value of the vibration displacement of node \(e\).  
Similarly, Equations (8) and (9) respectively describes the covariance of node \(t\) and node \(e\) of mode \(m\) in \(x\), \(y\), and \(z\) directions at the rail web and rail head.  
\[RW\_COV_{m}(t,e)=\frac{\sum_{s=1}^{3}\bigl{(}t_{s}-\bar{t}\bigr{)}\left(e_{s} -\bar{e}\right)}{3{-}1} \tag{8}\]  
\[RH\_COV_{m}(t,e)=\frac{\sum_{s=1}^{3}\bigl{(}t_{s}-\bar{t}\bigr{)}\left(e_{s} -\bar{e}\right)}{3{-}1} \tag{9}\]  
The mode shape data of mode 1, mode 3, and mode 7 is substituted into Equations (7)-(9), respectively. The optimal excitation nodes of each mode are calculated and the excitation method is selected according to mode shape characteristics. Figure 6 shows the optimal excitation nodes.  
In summary, mode 1 selects the two-side symmetric excitation, which is excited on node 49 and node 33 in \(z\) positive direction. Node 33 receives the signal in \(z\) direction. Mode 3 selects the one-side symmetric excitation on node 73 and node 74 in \(y\) positive direction. Node 74 is selected to receive the signal in \(y\) direction. Mode 7 selects the symmetric excitation on nodes 43, node 39, node 10 and  
Figure 6: Excitation nodes of modes. (**a**) Excitation node of mode 1; (**b**) excitation nodes of mode 3; (**c**) excitation nodes of mode 7.  
node 23 in \(z\) direction. Then node 43 receives the signal in \(z\) direction. The detailed selection process of excitation node can be referred to our previous work [20].
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 1 Introduction
Finally, the structural similarity (SSIMs) and Pearson correlation coefficient (CC) between maps are calculated to evaluate the similarity between the parameters. Variograms and cross-variograms are also used to evaluate the spatial correlation in the maps.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## Appendix A Queries
### Yelp: Ranking Las Vegas by Friends' Sentiment  
#### a.3.1 Sql  
Listing A10: A SQL query that returns all the review text from reviews written by friends and mutual friends for businesses within 30 km of the Las Vegas center.
```
SELECTDISTINCTR.text,R.starsFROMreviewR JOINbusinessBONR.business_Id=B.id INNERJOINfriendsF2 ONR.user_id=F2.friend_id INNERJOINfriendsF1 ONF2.user_id=F1.friend_id WHEREF1.user_id=\"7 ueuSPSSqTLUfga6 TYP4 pg\" ANDF2.user_id<>\"7 ueuSPSSqTLUfga6 TYP4 pg\" AND(R.user_id=F1.user_idORR.user_id=F1.friend_id) ANDST_DWithin(B.location,ST_MakePoint(-115.14,36.16)::geography,30000) AND(date_part(\"month\",R.date)>=11 ANDdate_part(\"month\",R.date)<=12)
```
## 5 Ficin Use as an Exfoliant Agent in Cosmetic Applications
### Chemical Properties and Structure of Ficin
#### 5.3.2 Stability and Activity Under Different pH and Temperature Conditions  
Ficin as a cysteine protease, is strongly dependent on pH and temperature values for its activity and stability [68; 75; 91]. The optimal temperature for ficin activity is generally around 60 \({}^{\circ}\)C, with a functional range between 40 and 70 \({}^{\circ}\)C, where it retains more than 50% of its initial activity [68; 75; 91]. Temperatures exceeding 70 \({}^{\circ}\)C, however, lead to its thermal denaturation and decreased activity [75; 90]. The optimal pH for ficin activity falls within a neutral range, typically between 6.0 and 7.5, with the highest activity observed at pH 6.5 [68; 75; 90; 91]. Ficin is in fact significantly less active under acidic conditions, displaying instability at low pH [75; 90]. Ficin's stability is also maximized around pH 7.0, with deviations towards both acidic and alkaline conditions reducing its thermal stability and causing a decrease in its apparent thermal denaturation temperature [75; 90]. Therefore, ficin's activity is influenced by its environment with extreme pH values causing denaturation, while the optimal pH varies depending on the specific substrate [75; 90].
## 3 Results and Discussion
### Burger Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Main** \\ **Member Arm** \\ \end{tabular} } & \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{\(E_{e}\)} & \multicolumn{2}{c}{\(\eta_{k}\)} & \multicolumn{2}{c}{**Adj R2**} \\ \cline{3-8}  & & **Current** & **Braced** & **Current** & **Braced** & **Current** & **Braced** \\  & & **Cross-Arm** & **Cross-Arm** & **Cross-Arm** & **Cross-Arm** & **Cross-Arm** & **Cross-Arm** \\ \hline \multirow{8}{*}{Right} & 1 & 4.62 \(\times\) 10\({}^{10}\) & 4.53 \(\times\) 10\({}^{10}\) & 4.11 \(\times\) 10\({}^{14}\) & 3.72 \(\times\) 10\({}^{14}\) & 0.970 & 0.991 \\  & 2 & 5.52 \(\times\) 10\({}^{10}\) & 6.22 \(\times\) 10\({}^{10}\) & 4.53 \(\times\) 10\({}^{14}\) & 4.07 \(\times\) 10\({}^{14}\) & 0.961 & 0.987 \\  & 3 & 6.54 \(\times\) 10\({}^{10}\) & 8.20 \(\times\) 10\({}^{10}\) & 6.15 \(\times\) 10\({}^{14}\) & 5.37 \(\times\) 10\({}^{14}\) & 0.938 & 0.983 \\  & 4 & 9.91 \(\times\) 10\({}^{10}\) & 12.2 \(\times\) 10\({}^{10}\) & 9.84 \(\times\) 10\({}^{14}\) & 7.60 \(\times\) 10\({}^{14}\) & 0.888 & 0.973 \\  & 5 & 18.5 \(\times\) 10\({}^{10}\) & 24.4 \(\times\) 10\({}^{10}\) & 19.2 \(\times\) 10\({}^{14}\) & 20.9 \(\times\) 10\({}^{14}\) & 0.824 & 0.969 \\ \hline \multirow{8}{*}{Left} & 1 & 5.11 \(\times\) 10\({}^{10}\) & 5.59 \(\times\) 10\({}^{10}\) & 3.41 \(\times\) 10\({}^{14}\) & 2.38 \(\times\) 10\({}^{14}\) & 0.973 & 0.851 \\  & 2 & 5.33 \(\times\) 10\({}^{10}\) & 6.35 \(\times\) 10\({}^{10}\) & 3.67 \(\times\) 10\({}^{14}\) & 3.43 \(\times\) 10\({}^{14}\) & 0.941 & 0.695 \\ \cline{1-1}  & 3 & 6.70 \(\times\) 10\({}^{10}\) & 8.74 \(\times\) 10\({}^{10}\) & 3.86 \(\times\) 10\({}^{14}\) & 3.50 \(\times\) 10\({}^{14}\) & 0.922 & 0.802 \\ \cline{1-1}  & 4 & 8.53 \(\times\) 10\({}^{10}\) & 13.4 \(\times\) 10\({}^{10}\) & 5.51 \(\times\) 10\({}^{14}\) & 8.74 \(\times\) 10\({}^{14}\) & 0.901 & 0.559 \\ \cline{1-1}  & 5 & 14.5 \(\times\) 10\({}^{10}\) & 24.9 \(\times\) 10\({}^{10}\) & 7.26 \(\times\) 10\({}^{14}\) & 19.9 \(\times\) 10\({}^{14}\) & 0.910 & 0.518 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average parameters obtained from Burger model for both current and braced wooden cross-arms. Figure 11: \(E_{e}\) parameter for current and braced wooden cross-arms: (**a**) right; (**b**) left main members.
## 3 Data and Method
### Model Calibration and Validation  
We used the field measured crop yield data for the RF model calibration. In previous studies, the calibration of yield estimation models was mostly based on the regional level [7; 19]. Here we attempted to calibrate the RF model using yield data at pixel scale to improve the model accuracy. We used the root-mean-square error (RMSE), the relative error (RE), the coefficient of determination (\(R^{2}\)) and the adjusted \(R^{2}\) (\(\overline{R}^{2}\)) to evaluate the performance of RF model driven by different predictors, which can be calculated from  
\[\text{RMSE}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{{(S_{i}-P_{i})}^{2}}}} \tag{5}\]  
\[\text{RE}=\frac{1}{N}{\sum\limits_{i=1}^{N}{\frac{{{\left|{{P_{i}}-{S_{i}}} \right|}}}{{{P_{i}}}}}}\times 100\% \tag{6}\]  
\[R^{2}=\frac{{{\left|{\sum\limits_{i=1}^{N}{{(S_{i}-\overline{S})}{(P_{i}- \overline{P})}}}\right|}^{2}}}{{\sum\limits_{i=1}^{N}{{(S_{i}-\overline{S})}^ {2}{\sum\limits_{i=1}^{N}{{(P_{i}-\overline{P})}^{2}}}}}} \tag{7}\]  
\[\overline{R}^{2}=1-(1-R^{2})\frac{N-1}{N-p-1} \tag{8}\]  
where \(S_{i}\) and \(P_{i}\) (\(i=1,2,\ldots,N\)) are the \(i\) th field measured or official statistical crop production and model estimated crop yields, respectively, \(\overline{S}\) and \(\overline{P}\) are the corresponding mean values, and \(N\) is the total number of data used for the RF model calibration or validation, and \(p\) is the number of predictors of each model.  
Among these indexes, \(\overline{R}^{2}\) was used to adjust \(R^{2}\) in model calibration to avoid over fitting of the model by considering the influence of predictor numbers. In model validation using statistical crop yield or production, \(R^{2}\) and \(\overline{R}^{2}\) was used to indicate the linear correlation of estimated and statistical production, where \(p=1\) is used to adjust \(R^{2}\) the for the univariate regression analysis. The closer the RMSE and RE to zero and the \(R^{2}\) and \(\overline{R}^{2}\) to one, the higher the accuracy of the model.
## 3 Experiments
### Setting of Model Hyperparameters  
The creation of a novel or unknown type of attack in our proposed LVAE method is divided into two stages: the encoder and the decoder. The ReLU activation function is used in the hidden layers of both the encoder and decoder, while the Sigmoid activation function is used in the final layer. The loss function is optimized using the Adam optimizer after training the model for 100 epochs with a batch size of 10.  
In the integrated classifier section, our method combines eight distinct approaches to detect new or unknown types of attacks. The key parameters for each method are as follows:  
1. Multilayer Perceptron (MLP): The hidden layer consists of 80 nodes with ReLU activation. The model is trained for five epochs with a batch size of five, and the Adam optimizer is used for loss optimization.
2. Gaussian-Based Naive Bayes (Gaussian NB): No specific priorities are set.
3. Decision Tree (DT): The criterion is set to entropy, and the maximum number of tree layers is set to 4. The remaining parameters use default values.
4. Random Forest (RF): The number of estimators is set to 100, while the other parameters use default values.
5. Support Vector Machine (SVM): The gamma parameter is set to scale, and C is set to 1.
6. Logistic Regression (LR): The penalty is set to L2, C is set to 1, and the maximum number of iterations is set to 1,200,000.
7. Gradient Boost (GB): The random state is set to 0.
8. Gated Recurrent Unit (GRU): The hidden layer consists of 80 nodes with a sigmoid activation function. The dropout is set to 0.2. The output layer utilizes the softmax activation function. The model is trained for five epochs with a batch size of 10, and the Adam optimizer is used for loss optimization.  
Our LVAE approach accurately detects unknown types of attacks by carefully selecting and fine-tuning the model parameters, as mentioned earlier. The choice of optimal hyper-parameters is crucial for the overall performance of the model, and we have conducted numerous tests to identify these optimal values. This ensures that our approach remains effective in identifying unknown attacks in the face of new threats.
# Smart Hospitals and IoT Sensors: Why Is QoS Essential Here?
###### Abstract
Sensors close to the patient and medical staff generate numerous information collected and transmitted for remote processing. Several applications can profit from this information for further analysis and real-time feedback to actuators. * It demonstrates several lessons learned in the actual deployment of the system in a real hybrid operating room. The self-adaptative strategies of the HealthStack are agnostic to the applications' point of view. HealthStack performs reconfigurations to accommodate all applications transparently. Its leading role is to access data from several sensors and provide these data to applications with QoS, not requiring from them any adaptions to profit from the QoS strategy. In addition to the technical contributions, it is also essential to mention contributions outside the scientific scope. By employing our strategy, hospitals can offer even more reliable and secure services that directly impact the population. Therefore, citizens can benefit from the solution when seeking healthcare services.
###### Abstract  
GPS campaign measurements are still in use in the monitoring of ground deformation. Campaign measurements are frequently referred to because installing permanent stations are costly, and they cannot be installed at the desired density. Using the data from the International Global Navigation Satellite Systems (GNSS) Service (IGS) permanent GPS stations, the duration, sampling interval, etc. of the campaign measurements can be simulated. Thus, the contribution of the campaign data to the monitoring of the ground deformation can be evaluated. In this study, we carried out an experiment with the aim of determining the deformation of tectonic plates at the selected IGS stations more accurately considering by extending the observation duration to a full 24 h length. We also made an attempt to take into consideration the antenna set up errors developing a scenario referring to the information available in the literature. We have decimated the continuous data of 40 globally scattered IGS stations into monthly intervals between 2012 and 2016 and estimated the deformation rates at the IGS stations from a continuous time series of four years. The continuous time series solutions for those stations were produced by the Jet Propulsion Laboratory (JPL), NASA. We compare velocities (i.e., the deformation rates) determined from GPS campaigns (in which the sampling was performed monthly and four-monthly) with those of the continuous data. The major conclusion of this study is that the vertical velocity estimation accuracy of the GPS campaign measurements had been improved by about 85% by extending the session duration to 24 h. The repetition interval of GPS campaign measurements as per one observation every four months produced only slightly coarser accuracy (i.e., on the average 8% poorer) than those of the measurements repeated once every month.  
GPS time series; IGS network; Velocity estimation; Fourier analysis; GPS campaigns; ground deformation; tectonic motion 2019  
11
## 3 Remote-Sensing System Framework and Problem Defined
### Problem Defined  
Drone-based remote sensing is an alternative method for satellite remote-sensing. Drones capture images in sequence, which are then merged into a large-scale map in the ground control station. However, the drone is easily influenced in the high-altitude flying environment, thereby capturing unclear SWIR images. Thus, merging several unclear SWIR images would result in a blurred large-scale map, requiring performing the tasks again. This study deals with the sequence of unclear SWIR images in a drone-based remote-sensing system using automatic drone flying control. When the blurred SWIR image or the shadow area of the SWIR image has been detected, the drone must replan a new trajectory or maintain its altitude. We propose an LMFM that uses a lightweight end-to-end deep learning architecture with multimodel data input and multitask output to solve the multitask problem and for implementation in low-cost low-power devices. The proposed LMFM is presented in depth in the following section.
## 2 Methodology
### PSO-Optimized k-Means Algorithm
The optimal position searched by the \(i-th\) particle is denoted as:  
\[P_{i}\ =\ \big{(}p_{i1},\ \ \ p_{i2},\ \ \cdots,\ \ \ p_{iD}\big{)} \tag{4}\]  
The optimal position searched by the particle swarm is denoted as:  
\[g_{best}\ =\ \big{(}g_{1},\ \ \ g_{2},\ \ \cdots,\ \ \ g_{D}\big{)} \tag{5}\]Step 3: Position update. Update the position and velocity of each particle with the following equation:  
\[V_{i}^{k+1}\ =\ \omega V_{i}^{k}\ +\ c_{1}r_{1}\Big{(}P_{i}^{k}-X_{i}^{k}\Big{)}\ +\ c_{2}r_{2}\Big{(}g_{best}^{k}-X_{i}^{k}\Big{)} \tag{6}\]  
\[X_{i}^{k+1}\ =\ X_{i}^{k}\ +\ V_{i}^{k+1} \tag{7}\]  
where \(V_{i}^{k}\) denotes the velocity of the \(i-th\) particle at the \(k-th\) iteration. \(X_{i}^{k}\) denotes the position of the \(i-th\) particle at the \(k-th\) iteration. \(P_{i}^{k}\) denotes the individual optimal solution of the \(i-th\) particle up to the \(k-th\) iteration. \(g_{best}^{k}\) denotes the population optimal solution of the particle swarm as of the \(k-th\) iteration. \(c_{1}\) and \(c_{2}\) denote the acceleration constants to adjust the step size. \(r_{1}\) and \(r_{2}\) denote the random numbers between 0 and 1, respectively, to enhance the randomness of the search process. Step 4: After the velocity and position of each particle are updated, the particles that are out of the solution range are initialized randomly again. If the current fitness function value is better than the historical optimal \(P_{i}\), then update \(P_{i}\). Similarly, if the population fitness function value of the updated particle population is better than the historical optimal \(g_{best}\), then update \(g_{best}\). Step 5: Repeat Step 2 to 4, and constantly update and iterate for all particles until the maximum number of solutions is reached or the aggregation degree \(\sigma^{2}\) of the group optimal solution \(g_{best}\) is less than the given threshold. \[\sigma^{2}\ =\ \frac{1}{n}\sum_{i=1}^{n}\big{[}F(i)-\overline{F}\big{]}^{2} \tag{8}\]  
where \(\overline{F}\) is the average fitness of the particle swarm, and \(\sigma^{2}\) represents the aggregation degree of the particles in the particle swarm. The smaller its value, the higher the convergence degree of the PSO algorithm. When \(\sigma^{2}\) is less than the given threshold, this means that the particles are all clustered near the global solution. At this time, the particle with the best fitness is the initial center of the global optimal clustering, and the clustering result can be obtained using k-means for clustering.