esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Routing Protocol
### Data Packet Format  
In the proposed system, every data packet consists of seven main elements. The packet format and its elements are represented in Table 2. Every generated data packet involves the represented elements. Every element explains one of the data packet features. Furthermore, the buffer structure of the sensor  
Figure 3: A network model to route the data packets by Static Three-Dimensional Fuzzy Routing Based on the Receiving Probability (SFRRP) in a typical Wireless Sensor Network (WSN).  
nodes consists of similar elements to facilitate transmitting the information of packets in the routing procedure. The buffer structure of the base station is also the same as the packet format of sensor nodes, because details of the transmitted data packets should be informed by the base station.  
It is assumed that in the packet format, the initiator ID indicates the sender node ID. The initiator sequence number represents the number of generated packets by that node. The partial route involves the _node ID_ of the intermediate nodes that the data packet is transferred through them to the base station. The start round represents the time of network that the data is transmitted by the sender node, and the finish round indicates the time of network that the data packet is delivered to the base station. Moreover, the delay time represents the total time required to transmit the packet from the sender node to the base station.  
When new data is sensed by a sensor node, a data packet is generated by that node based on the elements of the packet format. The sender node sets its _node ID_ in the initiator ID part of the packet. Furthermore, it puts the incremented sequence number of all sensed data into the initiator sequence number of the data packet. This is done to inform the number of sensed data at sender node to the base station for future decisions. The data element of the packet is also valued by sensed data of the sender node. Moreover, the sender node sets the start round by local time of the network. Each node that receives a data packet, first appends its _node ID_ to the partial route, then forwards that packet to the selected node from among its neighbors. In fact, the data packet is transmitted between the intermediate nodes in the transmission path until it is delivered to the base station. The delay time is finally calculated by the base station when the data packet is delivered.
# Functional Roles of Saccades for a Hand Movement
## 1 Introduction  
Complex movement is considered to have consecutive segments of movement [1; 2]. A connecting point of each segment can be called a via-point [3]. Thus, previous studies have focused on hitting or reaching movements with the hand as an elemental movement or a movement segment to reveal behavioral characteristics [4; 5] and to investigate neuronal activity patterns [6; 7]. Such studies often include a drawing movement [8]. Investigation of generation mechanism of the via-point may lead to understanding a complex movement.  
In daily life, movements depend heavily on visual information, which is used for feedforward and feedback controls. For example, goal-directed movements of the hand are usually accompanied by an eye saccade that precisely directs an individual's gaze to an observed target location in case of stational target [9; 10; 11; 12; 13] or a predicted target location in case of moving target [14; 15] before the hand starts to move. This type of saccades has been thought to provide visual information about the observed or predicted target location to guide a hand movement. Feedback control of the reaching movements also relies substantially on visual information [16; 17; 18; 19]. Additionally, another type of saccade was identified during the performance of a simple line-drawing movement in which a sequence of small eye saccades closely followed the trajectory of a pencil [20]. The author of this study suggested that this type of saccade contributed to feedback control. Therefore, at least two types of saccades may be associated with hand movement: a saccade that directs an individual's gaze to a target position, which has been observed during a reaching movement, and a saccade that directs an individual's gaze to a hand position, which has been observed during the drawing of a simple line.  
The goal of the present study was to reveal the relationship between the two types of saccades and hand movements, especially its feedforward control, and to get an insight into the role of visual information acquired by the saccades on movement control of the hand. We adopted hitting and circle-drawing movements as discrete and continuous movements, respectively. These two types of movements were selected instead of reaching and circle-tracking movements to increase the relative contribution of feedforward control to feedback control. Saccades during the movements were quantitatively classified as either a leading saccade (LS) or catching saccade (CS) depending on the relative gaze position of the saccade to a cursor position that represented the hand position: LS and CS directed the gaze to the cursor position in the direction of the cursor movement and to around the current cursor position, respectively. Precision of the hand movements was analyzed in relation to the saccades.
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 5 The Proposed Cluster-Based VANET Routing Protocol
### Routing within a Cluster  
When a cluster member needs to establish a link, it sends a request to the CH. The CH after receiving the request verifies whether the intended vehicle is a member of the cluster or not. In case both vehicles are in the same cluster, the CH finds from its storage the location information of the source and the destination vehicle, then starts the process of best route selection depending on the destination and source locations.  
For better understanding of the proposed inter-cluster routing protocol, we assume five vehicles, as follows. \(V_{1}\) is the source vehicle located at the coordinates \((x_{V_{1}},y_{V_{1}})\). Both \(V_{2}\) and \(V_{3}\) are two vehicles in between the source and the destination with \((x_{V_{2}},y_{V_{2}})\) and \((x_{V_{3}},y_{V_{3}})\) coordinates, respectively. \(V_{4}\) is the destination vehicle located at \((x_{V_{4}},y_{V_{4}})\) coordinates, as shown in Figure 6.  
Figure 6: Routing within a cluster.  
The distance between the CH to \(V_{1}\), \(V_{2}\), \(V_{3}\) and \(V_{4}\) satisfies Equation (22). The CH selects the best route according to:  
\[L\geq\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}} \tag{22}\]  
\[A\geq\sqrt{(x_{ch}-x_{V_{1}})^{2}+(y_{ch}-y_{V_{1}})^{2}} \tag{23}\]  
\[B\geq\sqrt{(x_{ch}-x_{V_{4}})^{2}+(y_{ch}-y_{V_{4}})^{2}} \tag{24}\]  
\[C\geq\sqrt{(x_{V_{1}}-x_{V_{2}})^{2}+(y_{V_{1}}-y_{V_{2}})^{2}} \tag{25}\]  
\[D\geq\sqrt{(x_{V_{2}}-x_{V_{4}})^{2}+(y_{V_{2}}-y_{V_{4}})^{2}} \tag{26}\]  
\[E\geq\sqrt{(x_{V_{1}}-x_{V_{3}})^{2}+(y_{V_{1}}-y_{V_{3}})^{2}} \tag{27}\]  
\[F\geq\sqrt{(x_{V_{3}}-x_{V_{4}})^{2}+(y_{V_{3}}-y_{V_{4}})^{2}} \tag{28}\]  
If \(C+D\leq A+B\), hence, the route \(C,D\) is the best one, and the next hop is \(V_{2}\). The CH notifies \(V_{1}\) about the best route, which decreases the risk and burden on the CH. If \(A+B<C+D\), this means that the best route will be through the CH, which will forward the packet to \(V_{4}\). That is to say, besides being responsible for the route selection, the CH may also participate in the packet forwarding process. The route \(E,F\) will be stored as a backup. The overall procedure flowchart is described in Figure 7.  
Figure 7: Routing algorithm within a cluster.
## 2 Theory
### Photothermal Heating  
This section models the dynamics of light-induced heating and how this effect can be related to the photothermal contribution to the photomechanical effect. In short, light energy is absorbed and converted to heat in the material, which increases the temperature and leads to photothermal expansion. Here, we will consider the force required to keep the length of the fiber fixed for the configuration used in our experiments.  
We are interested in two material properties: (1) the stress in the material when the sample length is kept constant as the temperature increases; (2) the response time of the stress as a function of absorbed light intensity. These two properties determine the heating contribution to the photomechanical response [23]. The model described here is based on the work of [PERSON] et al. [23].  
The rate of temperature increase due to light intensity \(I_{0}\) that is turned on at time \(t=0\) is given by the heat equation to be [23]  
\[\frac{dT}{dt}=-\frac{1}{t_{N}}(T-T_{a})+\frac{I_{0}A}{mc}, \tag{3}\]  
where \(t_{N}\) is the Newton cooling time constant, \(T\) is the elevated temperature due to heating, \(T_{a}\) is the ambient temperature, \(I_{0}\) is the absorbed intensity, \(A\) is the area of illumination, \(m\) is the mass of the illuminated part of the sample, and \(c\) is the specific heat. The last term is the heating term.  
At infinite time, the temperature increase above ambient temperature is reached when \(dT/dt=0\); so, Equation (3) gives [23]  
\[\Delta T\equiv(T_{\infty}-T_{a})=\frac{I_{0}At_{N}}{mc}. \tag{4}\]The mass of the illuminated part of the sample can be defined as the illuminated volume \(v\) and polymer density \(\rho\) as \(m=\rho v\)[41; 42]. Then, Equation (4) becomes [23]  
\[\Delta T=\frac{I_{0}t_{N}}{\rho cv}, \tag{5}\]  
where the volume is given by \(v=Aw\) with \(w\) being the sample thickness.  
Next, we need the temperature-dependent stress to relate the temperature change induced by the light to the photomechanical stress response. As we show later, dye-doped polymers obey the empirically determined function [23]  
\[\sigma=\sigma_{0}+\frac{\sigma_{1}}{(1+(\frac{T}{T_{0}})^{n})}. \tag{6}\]  
Using Equation (6), the linear photomechanical stress response, defined by \(\Delta\sigma/I_{0}\), due to photothermal heating is given by [23]  
\[\kappa_{\sigma}^{(1)}=-\frac{d\sigma}{dT}\cdot\frac{\Delta T}{I_{0}}. \tag{7}\]  
Using Equations (5) and (6), the photothermal response is given by  
\[\kappa_{\sigma}^{(1)}=-\left(-\frac{n(\sigma-\sigma_{0})}{T\Big{(}1+\left( \frac{T_{0}}{T}\right)^{n}\Big{)}}\right)\cdot\frac{t_{N}}{\rho cv}, \tag{8}\]  
where \(\sigma_{0}\) and \(T_{0}\) will be determined from a fit of Equation (6) to the data.
## 2 Study Areas and Data
### Data
#### 2.2.2 Remote Sensing Data  
A high-resolution passive microwave \(T_{b}\) dataset from the Making Earth System data records for Use in Research for Earth Science (MEASUREs) multi-platform Calibrated Brightness Temperature Earth System Data Record (ESDR) (CETB) was used in this study [39]. This dataset was released by NSIDC in 2017. It compiles global \(T_{b}\) time series generated from data records of the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Image/Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensors from 1978 to present. These data have been produced at smoothed 25 km resolution and an imaging reconstruction algorithm developed at the Brigham Young University was used to enhance the data resolution up to 12.5 km, and 3.125 km [40]. The CETB has been well evaluated in studies of water and snow surface detection with the H-polarized 36.5 or 37 GHz frequency channels showing the highest sensitivity to water surface changes [27; 41; 42]. Therefore, the high-resolution \(T_{b}\) data extracted from the SSM/I sensor 37 GHz at H-polarization with a data period of 1996-2008 at a resolution of 3.125 km, was utilized for the \(M/C\) signal to detect river runoff changes in this study.  
Figure 1: Distribution of the 79 typical stations in the 9 major basins, where dark blue dots indicate data period is 1999–2008 and light blue dots indicate data period is 1996–2005.
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 2 Related Work
### Non-Machine Learning Algorithms  
Non-machine learning algorithms include periodic detection, spectrum analysis, information metrics, correlation coefficient analysis, and so on. [PERSON] et al. [5] propose discrete wavelet (DWT) to analyze abnormal time series data. However, one of the most obvious disadvantages of time-frequency domain transformation is the high complexity of detection time, which is not suitable for low delay data center network. [PERSON] et al. [6] and [PERSON] et al. [7,8] propose the entropy theory to measure the change of source IP distribution. If the source IP entropy is less than the threshold, it indicates that there may be an abnormal attack. [PERSON] and [PERSON] [9] propose an early detection method of information entropy based on destination IP, to prevent the controller from paralyzing and reducing the controller's burden in SDN. This kind of detection is effective with relatively large flow attacks, but the effect is not obvious in a low-rate attack similar to a normal traffic [4], it may make a high false positive rate. [PERSON] and [PERSON] [10] propose the maximum entropy model for detection. [PERSON] et al. [11] propose the correlation analysis between data flows and combined with k-nearest neighbor (KNN) algorithm for detection. In [12; 13; 14], there is a linear relationship between the traffic in a normal state. No matter what the traffic obeys, Spearman and partial rank correlation coefficient are used to detect the attack. However, this detection method is not effective for L-DDoS attack with similarity, which will result in a low detection rate. [PERSON] et al. [15] propose a multivariate analysis method that makes full use of the feature with an attack attribute change at least in an attack state,and for threshold detection, but it can only detect attacks with high intensity of attack. [PERSON] et al. (2016) use congestion control characteristics to detect the packet loss rate. In all, all these methods assume that the network has complete control ability. However, traditional networks cannot control all routers, they cannot respond to traffic information in time. These detection methods tend to cause a low detection rate.
###### Abstract  
Composite resins are commonly used in dentistry for direct restorations. The color stability of these materials still represents a major concern for both the clinician and the patient. In recent years, ozone therapy has been extensively used in dentistry to manage wounds healing, dental caries, oral lichen planus, gingivitis and periodontitis, halitosis, osteheecrosis of the jaw, post-surgical pain, plaque and biofilms, root canal treatment, dentin hypersensitivity, temporomandibular joint disorders, and teeth whitening. To date, several studies have evaluated the bleaching effect exerted by ozone on natural teeth, but no studies have been conducted to determine the effect on the color of composite resins. The aim of the present study is to determine whether oxonized oils/gels could determine a color change on composite resin. A total of 40 discs of an A3 shade restorative composite were divided into two groups, respectively exposed to a pigmentation treatment consisting of 10 applications of 10 min each of a 1% chlorohexidine-based gel (trial group) and to storage into physiological solution (control group). The samples of both groups were respectively subdivided into four subgroups which underwent four different protocols, three of which were based on the exposure to different ozonized products and the latter representing the control. A colorimetric analysis with the CIELAB method was conducted with the following timing: after 24 h of storage in physiological solution (before the experimental procedures) (T0), after the subdivision into groups A and B (corresponding to the pigmentation for samples of group A and storage in physiological solution for samples of group B) (T1), and after subdivision into subgroups 1-4 (corresponding to the application of the ozonized products vs. control) (T2). No statistically significant difference was found between the samples at T0. The 20 samples of group A, exposed to the colorant agent, underwent a color change from T0 to T1, whereas the 20 samples exposed to the physiologic solution did not undergo any significant color change. A positive but moderate influence was assessed for E and L values, whereas no significant change occurred for A and B values. Therefore, dental ozonized oils/gels could be valuable in restorative dentistry as bleaching agents of resin composites exposed to discoloration, an alternative to the traditional hydrogen peroxide and carbamide peroxide, but further studies are required to confirm these findings.  
ozone; ozone therapy; resin composite; composite materials; bleaching; chlorohexidine; restoration; dentistry +
Footnote †: journal: Applied Sciences
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
###### Abstract  
It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for long-term prediction.  
PM2.5; time-series data prediction; decomposition mechanism; the long-term prediction; gated recurrent unit
# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 2 Methods
### CNN-BiLSTM-Attention Model  
As shown in Figure 4, during the training process, the CNN-BiLSTM-Attention model goes through the following steps: establishing a multi-factor correlation dataset, analyzing the correlation factors of the dataset based on Pearson correlation analysis, and obtaining the correlation coefficient matrix \(A\). Then, during the convolution feature extraction process, the correlation coefficient matrix is continuously updated. Simultaneously, the dataset is placed in a CNN layer for convolution and pooling operations to extract the feature matrix \(F\), merge \(A\) and \(F\) and flatten them, input them into BiLSTM for prediction, focus on local features through the attention layer, and reweight them. The propagation mode between \(A\) and \(F\) is  
\[A^{*}=A+I \tag{2}\]  
\[F^{i+1}=f(F^{i},A) \tag{3}\]  
where \(A\) is the correlation coefficient matrix, \(I\) is the identity matrix, and \(F^{i}\) is the feature matrix of the \(i\)-th layer. Finally, the predicted value is obtained through a fully connected layer. To achieve higher computational speed and ensure prediction accuracy, the number of convolutional and pooling layers in the convolution process is set to 2.
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 3 Test Results
### Local Buckling  
Under axial loads, square thin-walled CFST columns are easier to have local buckling occur than round columns, especially when the ratio of cross-section width of the tube to tube-wall thickness is high. To study the effect of steel-bar stiffeners on delaying or avoiding local buckling, the loads when local buckling was observed to be occurred, called local-buckling load in the paper, were compared in Table 6. In Table 6, \(N_{u}\) is ultimate load; \(N_{q}\) is the local-buckling load; \(N_{qi}\)/\(N_{q2}\) is the ratio of local-buckling load of other columns to the load of column C2.  
It can be seen from Table 6 that the \(N_{q}\) of every column is less than \(N_{u}\), which proved that all the columns had local buckling before they damaged under axial loads.  
Comparing the local-buckling loads, C2 was much higher than C1, which means that the concrete inside provided significant help in delaying local buckling. As to the columns with steel-bar stiffeners inside or outside the tube, the local-buckling loads were obviously higher than C2, especially C6, with 16 mm diameter steel bars outside the tube, which was higher than C2 by 41.9%. It can be concluded that steel-bar stiffeners are effective in delaying local buckling.
## 3 Spacecraft Dynamics
### Control Strategy
#### 3.3.3 Platform Control: Bang-Bang Maneuver  
To explore the vibration control issue in case of the innovative composite material, simulations including a simple attitude maneuver (bang-bang control) are proposed in this research. A bang-bang control, often used when the rotation of a spacecraft is controlled by thrusters, being an impulsive approach can indeed entail a significant excitation of structural modes. Nonetheless, being a time-optimal control, this approach allows the fastest re-orientation.  
By way of illustration of the mentioned issue, an attitude maneuver has been simulated here to study the performance of the active control in damping out the remaining elastic vibrations. A simple bang-bang maneuver about the nadir-pointing axis (i.e., satellite \(z\)-axis) to reach a desired angle of \(\theta_{d}=55\) deg is performed by way of illustration. By supposing the only actions on the spacecraft are those of the thrusters, the equation for the rotation for a rigid body along a prescribed axis can be written as  
\[\dot{J}\ddot{\theta}=T^{G}\Big{(}\delta(t)-\delta\Big{(}t-t_{f}\Big{)}\Big{)} \tag{29}\]  
where \(J\) is the inertia about the considered axis, \(T^{G}\) is the torque to be applied, \(t_{f}\) is the final instant of the maneuver, and \(\delta(\cdot)\) is the Heaviside function. By integrating twice with respect to time, and by considering two thruster pulses, the needed torque to reach the desired angle can be obtained as  
\[T^{G}=\frac{J\ \theta_{f}}{\tau_{p}\Big{(}t_{f}-\tau_{p}\Big{)}}\rightarrow\left\{ \begin{array}{ll}A:\ \tau_{p}=10s,\ \ t_{f}=20s&T^{G}=1.59 Nm\\ B:\ \tau_{p}=2s,\ \ t_{f}=50s&T^{G}=1.67 Nm\end{array}\right. \tag{30}\]  
where \(\tau_{p}\) is the pulse duration, and \(\theta_{f}\) is the desired angle. Two maneuvers are simulated, as described in Equation (29), by varying the applied pulse and motion duration. The applied torques are illustrated in Figure 13. The two cases were chosen to have similar value of the applied torque, so as to have comparable initial excitation of the spacecraft flexible dynamics, with the aim of analyzing the control effectiveness for a fast maneuver (rate of 2.75\({}^{\circ}\)/s, with long pulses) and an agile one (rate of 1.1\({}^{\circ}\)/s, with short pulses).