esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Result Analysis
### Materials and Methods
#### 3.1.2 Experimental Test Standard  
The evaluation measures used in this experiment were MSE, RMSE, and MAE. These indicators all share the property that a model is performing better when its values become closer to 0. On the other hand, the metric value rises as the difference between the expected and actual values becomes bigger. The following are the formulas:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(\widehat{y}_{i}-y_{i})^{2} \tag{33}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\widehat{y}_{i}-y_{i})^{2}} \tag{34}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|\widehat{y}_{i}-y_{i}| \tag{35}\]  
These three evaluation measures make a more accurate measurement of the discrepancies between model predictions and the actual situation possible. In the aforementioned equation, where \(y_{i}\) is the real value and \(\hat{y}_{i}\) is the predicted value, a smaller value for each of the three evaluation indices indicates a higher prediction accuracy, which also indicates a closer fit between the predicted and real trajectories.
## 2 Model Systems and Computational Methods
### Convoluting Charge Density Maps and Deconvoluting Tunneling Current Maps  
In a practical application, the read-out process of the charge density map (CDM) could be achieved by measuring and deconvoluting the tunneling current map (TCM). Similar to the STM working principles, where the tunneling current depends on the local density of states of the sample, here, the extraction probability depends on the particle density distribution of the many-body ground state. Taking into account the geometry particularities of the STM tip, we expect that the TCM is a convoluted image of the CDM, i.e., a blurred and potentially deformed image of the ground state density.  
Machine learning methods based on convolutional neural networks were already employed to denoise simulated STM images, and the ML approach appears to be more proficient than the conventional filters [37]. Moreover, the use of artificial intelligence with reinforcement learning was proposed for the real-time classification of the experimentally acquired images in order to distinguish between qualitative results and faulty ones without constant human supervision [38].  
One of the simplest choices for the filter corresponds to a two-dimensional Gaussian with standard deviations \(\sigma_{x}\) and \(\sigma_{y}\) along the \(x\)- and \(y\) directions, respectively:  
\[G(x,y)=\frac{1}{2\pi\sigma_{x}\sigma_{y}}\exp\left[-\left(\frac{x^{2}}{2\sigma _{x}^{2}}+\frac{y^{2}}{2\sigma_{y}^{2}}\right)\right]. \tag{10}\]  
A particular case is obtained when \(\sigma_{x}=\sigma_{y}=\sigma\), corresponding to an ideal symmetric tip. However, in realistic applications, this function is more complicated, as it depends on the tip geometry and the potential imperfections and, therefore, it is typically unknown. In the following, we shall consider convolution kernels with \(\sigma_{x}\
eq\sigma_{y}\) as well as the impact of noise on the TCM. This is added to the Gaussian kernel as a random field, with values extracted from a normal distribution with \(\sigma_{n}\) standard deviation and zero mean. We then apply the general purpose image-to-image translation method to deconvolute the TCM and investigate the conditions and limitations for achieving meaningful predictions.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## Appendix B Derivation of (15)  
We write (14) as  
\[ \begin{split}\mathcal{P}_{1}^{ipsIC}&=1-\underbrace{ \Pr\left\{\left|h_{n^{\star},1}\right|^{2}>\delta\left(\left|h_{k^{\star},2} \right|^{2}\rho+1\right),\left|h_{k^{\star},2}\right|^{2}>\delta\left(\left|f_{ 1}\right|^{2}\alpha\rho+1\right)\right\}}_{\stackrel{{\Delta}}{{=}} \xi_{3}}\\ &\quad\times\underbrace{\Pr\left\{\left|g_{1,n^{\star}}\right|^{2}> \delta\chi_{i}\left|g_{1,n^{\star}}\right|^{2}>\tilde{\phi}\left(\left|f_{2} \right|^{2}\alpha\rho+1\right)\right\}}_{\stackrel{{\Delta}}{{=}}\xi_{4 }}.\end{split}  \tag{15}\]Note that \(\varsigma_{3}\) is defined and expanded as  
\[ \begin{split}\varsigma_{3}&=\Pr\left\{\left|h_{n^{*},1} \right|^{2}>\delta\left(\left|h_{k^{*},2}\right|^{2}\rho+1\right),\left|h_{k^{ *},2}\right|^{2}>\delta\left(\left|f_{1}\right|^{2}\varpi\rho+1\right)\right\} \\ &=\int_{0}^{\infty}f_{\left|f_{1}\right|^{2}}\left(x\right)\int_{ \tilde{\delta}\left(x\varpi\rho+1\right)}^{\infty}f_{\left|h_{k^{*},2}\right|^ {2}}\left(y\right)\left[1-F_{\left|h_{n^{*},1}\right|^{2}}\left(\delta\left(y \rho+1\right)\right)\right]dx\\ &=\underbrace{\int_{0}^{\infty}\int_{\tilde{\delta}\left(x\varpi \rho+1\right)}^{\infty}f_{\left|f_{1}\right|^{2}}\left(x\right)f_{\left|h_{k^ {*},2}\right|^{2}}\left(y\right)dxdy}_{\equiv\varsigma_{3,1}}\\ &\quad-\underbrace{\int_{0}^{\infty}\int_{\tilde{\delta}\left(x \varpi\rho+1\right)}^{\infty}f_{\left|f_{1}\right|^{2}}\left(x\right)f_{ \left|h_{k^{*},2}\right|^{2}}\left(y\right)F_{\left|h_{n^{*},1}\right|^{2}} \left(\delta\left(y\rho+1\right)\right)dxdy}_{\equiv\varsigma_{3,2}},\end{split}  \tag{10}\]  
where  
\[
## 3 Results and Discussion
### Characterization of the Prepared SPME
#### 3.1.4 The Effect of SPME Fiber  
The determinant property of the fiber coating is the extraction efficacy. Different wrapping materials possess different polarities; based on this, they can selectively absorb volatile compounds according to the \"like dissolves like\" principle [26]. PDMS is a universal hydrophobic adsorbent. DVB coatings are usually applicable to relatively large molecules and a mono matrix that has several targeted analytes, while carbon centers on molecules of relatively low molecular weight. In this work, the outer layer of commercial DVB/CAR/PDMS is mainly DVB, and the inner layer is mainly CAR/PDMS. Small molecules are first adsorbed inside the DVB coating, while large molecules are adsorbed on its outer surface.
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 2 System Model
### Network Model  
In order to represent the impact of dynamic topology on execution process of remote sensing missions effectively, we modified the time-expanded graph (TEG) [18; 19] by adding virtual vertices and virtual arcs, which simplifies the model of the mission execution process. The scheduling horizon is divided into \(K\) time slots of equal length. It is assumed that the network topology is static in each time slot and only changes at the time of time slot switching [19; 20]. As shown in Figure 2, the time-expanded graph \(G_{K}(V,A)\) is a \(K\)-layered directed graph, where each layer corresponds to a time slot in the network. \(V\) and \(A\) represent the set of vertices and arcs of the graph, respectively. There are two classes of vertices, namely ordinary vertices and virtual vertices. The vertex set is denoted by \(V=V_{OD}\cup V_{VT}\), where \(V_{OD}\) and \(V_{VT}\) represent the set of ordinary vertices and virtual vertices, respectively. Similarly, the arc set \(A\) contains two types of arcs, namely ordinary arcs and virtual arcs. The arc set is denoted by \(A=A_{OD}\cup A_{VT}\). Hereinafter, we introduce the ordinary vertices, ordinary arcs, virtual vertices, and virtual arcs in TEG.
# Preparation of Cementitious Material with Wet Fly Ash by Hydrothermal Reaction and Calcination
## 3 Results and Discussion
### Calcination System Analysis  
#### 3.2.1 The Effect of Calcination Temperature on the Formation of Calcined Minerals  
Figure 6 shows the XRD patterns of the reaction specimens at calcination temperatures of 800 \({}^{\circ}\)C, 850 \({}^{\circ}\)C, and 900 \({}^{\circ}\)C for different calcination times. At calcination temperatures of 800 \({}^{\circ}\)C and 850 \({}^{\circ}\)C, the newly formed minerals include CaO, C\({}_{12}\)A\({}_{7}\), and \(\alpha^{\prime}\)L-C\({}_{2}\)S. C\({}_{12}\)A\({}_{7}\), accelerates the rate of hydration of the cementitious material, shortens the setting time, and increases its early strength and water demand. \(\alpha^{\prime}\)L-C\({}_{2}\)S disappears at a calcination temperature of 900 \({}^{\circ}\)C with the simultaneous production of \(\beta\)-C\({}_{2}\)S and C\({}_{2}\)AS. The activity of \(\beta\)-C\({}_{2}\)S is lower than that of \(\alpha^{\prime}\)L -C\({}_{2}\)S. The rate of hydration and early strength of the clinker is more adversely affected when there is more \(\beta\)-C\({}_{2}\)S. The hydrothermal activity of the newly generated C\({}_{2}\)AS is low, so it was determined that the appropriate calcination temperature should be below 900 \({}^{\circ}\)C to avoid the formation of C\({}_{2}\)AS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample No.** & \begin{tabular}{c} **Experimental** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **C\({}_{2}\)SH** \\ **/wt\%** \\ \end{tabular} & \
\begin{tabular}{c} **C\({}_{3}\)AH\({}_{6}\)** \\ **/wt\%** \\ \end{tabular} &
\begin{tabular}{c} **C\({}_{3}\)ASH\({}_{4}\)** \\ **/wt\%** \\ \end{tabular} \\ \hline
1 & 120 \({}^{\circ}\)C–4 h—22\% & 6.94–17.69 & 0–17.14 & 2.91–5.99 \\
2 & 160 \({}^{\circ}\)C–6 h—22\% & 13.87–17.46 & 0–5.74 & 15.03–16.08 \\
3 & 140 \({}^{\circ}\)C–8 h—22\% & 14.33–18.53 & 0–6.70 & 14.06–15.26 \\
4 & 160 \({}^{\circ}\)C–4 h—26\% & 14.90–25.98 & 0–17.64 & 1.34–4.46 \\
5 & 140 \({}^{\circ}\)C–6 h—26\% & 16.05–20.67 & 0–7.35 & 12.26–13.59 \\
6 & 120 \({}^{\circ}\)C–8 h—26\% & 13.91–15.53 & 0–2.59 & 17.35–17.81 \\
7 & 140 \({}^{\circ}\)C–4 h—30\% & 16.82–24.64 & 0–12.46 & 5.77–7.98 \\
8 & 120 \({}^{\circ}\)C–6 h—30\% & 18.51–24.45 & 0–9.44 & 8.95–10.61 \\
9 & 160 \({}^{\circ}\)C–8 h—30\% & 22.08–24.59 & 0–4.02 & 14.75–15.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Content of hydration products of different autoclaved samples.  
Figure 6: XRD patterns of samples calcined at 800 \({}^{\circ}\)C (**a**), 850 \({}^{\circ}\)C (**b**), and 900 \({}^{\circ}\)C (**c**).
## 4 Results
### Experimental Results on the WHU Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Methods** & **Image1** & **Image2** & **Image3** & **Image4** & **Mean** \\ \hline \multirow{4}{*}{OA} & FCN8s & 0.938 & 0.945 & 0.963 & 0.938 & 0.946 \\  & PSPNet & 0.931 & 0.899 & 0.927 & 0.906 & 0.915 \\  & SegNet & 0.951 & 0.938 & 0.973 & 0.944 & 0.951 \\  & U-Net & 0.973 & 0.953 & 0.969 & 0.959 & 0.936 \\  & ASGANN & **0.977** & **0.971** & **0.981** & **0.968** & **0.974** \\ \hline \multirow{4}{*}{Precision} & FCN8s & **0.967** & 0.953 & 0.876 & 0.925 & 0.931 \\  & PSPNet & 0.944 & 0.921 & 0.924 & 0.926 & 0.928 \\  & SegNet & 0.954 & 0.955 & **0.954** & 0.957 & **0.955** \\  & U-Net & 0.938 & **0.961** & 0.913 & **0.959** & 0.942 \\  & ASGANN & 0.947 & 0.937 & 0.936 & 0.932 & 0.938 \\ \hline \multirow{4}{*}{Recall} & FCN8s & 0.785 & 0.874 & 0.897 & 0.871 & 0.856 \\  & PSPNet & 0.779 & 0.787 & 0.703 & 0.787 & 0.764 \\  & SegNet & 0.804 & 0.828 & 0.864 & 0.836 & 0.833 \\  & U-Net & 0.939 & 0.889 & 0.899 & 0.901 & 0.907 \\  & ASGANN & **0.948** & **0.962** & **0.936** & **0.955** & **0.951** \\ \hline \multirow{4}{*}{F1-score} & FCN8s & 0.867 & 0.912 & 0.887 & 0.897 & 0.891 \\  & PSPNet & 0.854 & 0.849 & 0.703 & 0.851 & 0.814 \\  & SegNet & 0.873 & 0.887 & 0.907 & 0.893 & 0.891 \\  & U-Net & 0.939 & 0.923 & 0.906 & 0.929 & 0.924 \\  & ASGANN & **0.947** & **0.951** & **0.936** & **0.943** & **0.944** \\ \hline \multirow{4}{*}{IoU} & FCN8s & 0.765 & 0.838 & 0.797 & 0.814 & 0.803 \\  & PSPNet & 0.745 & 0.737 & 0.665 & 0.741 & 0.722 \\ \cline{1-1}  & SegNet & 0.775 & 0.797 & 0.831 & 0.806 & 0.802 \\ \cline{1-1}  & U-Net & 0.885 & 0.858 & 0.828 & 0.867 & 0.859 \\ \cline{1-1}  & ASGANN & **0.901** & **0.904** & **0.881** & **0.893** & **0.894** \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison with the state-of-the-art models on the WHU dataset. The highest value for each metric is marked as bold. Figure 11: Examples of segmentation results by different models on the WHU dataset. The green, red, blue, and black pixels of the maps represent the true positive, false positive, false negative, and true negative predictions, respectively.
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 2 Review and Background Knowledge
### Malware Family Classification  
However, malicious code has not only evolved technically, but also in terms of threat. They not only damage files or computer systems, as they used to behave in the 1970s and 1980s, but they are also used to spy, steal information, or demand monetary ransoms.  
**Viruses**: Usually incorporated into existing software, they are activated when the user executes the software. Their consequences are diverse, ranging from slowing down the system to corrupting or deleting information [4; 5].  
**Worms**: Do not need, at first, user intervention and have the ability, in contrast to computer viruses, to self-propagate through the network [6; 7].  
**Trojans**: Hidden inside apparently legitimate software for the purpose of going undetected. This type of malicious program can perform any action it has been programmed to perform on the system [8; 9].  
**Spyware**: Like worms, it does not require user intervention to install itself on the system and usually works in a hidden mode by collecting user or system information in an unauthorized manner [8; 9].  
**Adware**: Usually inserted in the system through the installation process of other software and its mission is to display unwanted advertising, usually in the form of pop-up windows without the user's authorization. Now, there is Adware in the form of browser extensions or plugins.  
**Ransomware**: It is generally executed from another malware, such as a worm, virus or trojan. Its mission is to completely sequester the system, encrypting all the files and demanding a payment from the user or organization [10; 11; 12; 13].  
**Rootkit**: It has the function to access and hide in particularly sensitive areas of the devices it infects, including areas that are not normally accessible to users. Their purpose is to take control of the system and facilitate its remote control while remaining hidden [14; 15].
# Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor
## 3 Results and Discussion
### Optimized Cleaning Model for the Entire Target Area
#### 3.1.4 Results of the Fourth Spray  
To calculate the cleaning rate of the fourth spray, the quadratic model was the most suitable and it showed high explanatory power with an R-squared value of 0.9147 and adj. R-squared value of 0.8822. The adeq. Precision of the fourth spray model (14.573) was appropriate as it was higher than 4. Variance analysis results of the fourth spray model are presented in Table 6.  
Figure 10: Three-dimensional graphs of the cleaning rate of the second spray to (**a**) washer pressure and spray time, (**b**) spray angle and spray time, (**c**) target point and spray time, (**d**) spray angle and washer pressure, (**e**) target point and washer pressure, and (**f**) target point and spray angle.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **R-Squared** & **Adj.** & **Adeq.** & **F-Value** & **_p-Value_** \\
**Response** & **R-Squared** & **R-Squared** & **Precision** & & \\ \hline Third spray & 0.9206 & 0.8655 & 14.173 & 24.33 & \textless{}0.0001 ** \\ \hline \hline \end{tabular}  
* \(p\) \(<\) 0.05.  
\end{table}
Table 5: Variance analysis results of the third spray model.  
In the fourth spray model, the primary terms of the spray time and washer pressure were significant at a significance level of 5%, and the quadratic term of the washer pressure was significant at a significance level of 5%. When the regression coefficients of the factors were estimated based on the method of least squares, the quadratic equation was expressed as  
\[\begin{split}\text{\emph{Cleaning Rate (Fourth spray)}}=-6.4554+13.9533\times\text{\emph{ spray time}}+16.7453\times\\ \text{\emph{washer pressure}}-1.1604\times\text{\emph{washer pressure}}^{2}\end{split} \tag{4}\]  
The 3D surface and 2D contour graphs drawn based on the quadratic equation of the fourth spray model are shown in Figure 12.  
The results of the fourth spray model were similar to those of the third spray model. As observed from the ANOVA results, the washer pressure had the largest impact on the cleaning rate, and the cleaning rate increased according to the spray time. The washer pressure quadratically affected the cleaning rate.
## 4 Classification of Auto-Configuration Protocols
### Hybrid Protocols
#### 4.3.2 Pacman  
_Passive Autoconfiguration for Mobile_ ad hoc _Networks_ (PACMAN) [27] is a passive auto-configuration protocol for MANET. It uses elements from stateless and stateful protocols, so it could be considered somewhat hybrid. Its operation is based on each node assigning itself an address when joining the network, and passive monitoring of communications for the duplicate address detection.  
To achieve the minimum overhead in the communications, the information is shared among different network layers. Specifically, the information handled by the routing protocol is monitored.The method used to choose the own IP address consists of a probabilistic algorithm. The probability of attempting to choose an IP address currently in use by another node is close to zero, this algorithm takes into account, among other factors, an assignment table. This table is created with information from the routing protocol on IP addresses already in use.  
PACMAN uses the PDAD process to monitor the communications in search of duplicated addresses. This is necessary because the mechanism used for address assignment does not guarantee uniqueness (even if it attempts to reduce the probability of collision), and it may cause merging of networks containing nodes with the same IP address. Broadly speaking there are two types of events that indicate duplicity of IP addresses: firstly, we have the events that never occur if the address is unique, and always occur if the address is duplicated. These events confirm that there is a problem detected. On the other hand, we have the events that occur rarely if the address is unique, and often if the address is duplicated. In this way the possibility of problems is detected, so it is probabilistic algorithms. When it detects that two nodes are using the same IP address, it reports the problem to one of them using a _unicast_ message to change its address. Moreover, it takes into account the problem of changing an address that has some communication going on. To fix this, when changing an address, a node notifies the nodes with which it has ongoing communications of its new IP address, so that they can make an encapsulation of the messages properly.
###### Abstract  
In order to solve the problem of one-dimensional code length, two-dimensional code spatial length, phase induced intensity noise PINN effect, improved system capacity, and increased the number of simultaneous users, a new three-dimensional spectral/time/spatial variable weight zero cross correlation code for non-coherent spectral amplitude coding-optical code division multiple access (3D-VWZCC-SAC-OCDMA) is proposed in this paper. Its construction is based on a one-dimensional (1D) spectral sequence and two-dimensional (2D) temporal/spatial sequences, which are characterized by the property of zero cross correlation ZCC. The simulation results demonstrate that our code proves high immunity against PINN noise and shot noise, it increases multiplexing ability when the passage is from (1D) and (2D) to (3D) up to 5.112 and 2.248 times, and it saves \(-7.04\) dBm and \(-5.9\) dBm of the receiver power due to simple detection at the receiver; furthermore, the 3D-VWZCC system capacity has outperformed the 3D-PD, 3D-PD/MD and 3D-DCS/MD codes, which reach 3686, 2908, and 3234 times, respectively. Moreover, our code offers better performance, in terms of data rates, with up to 2 Gbps compared to previous codes, which makes the system meet the requirements of optical communication networks. Further, 3D-VWZCC is also simulated in Optisystem software, where our code offers a transmission quality Q reaching 11.56 with a bit error rate BER of \(1.99\times 10^{-31}\) despite a high number of users.  
Keywords:phase-induced intensity noise (PINN); variable weight zero cross correlation (VWZCC) code; optical code division multiple access (OCDMA); bit error rate (BER) +
Footnote †: journal: _Photonics_
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 3 Results and Discussion
### Basic Statistical Indices
The early and late products outperformed the final product, especially in the places that were less affected by the storm events (low precipitation), as shown in Figure 9. The average values of the KGE show that the late product was found to be the best model for capturing the event, with an average of 0.39, followed by the Early and Final products, with KGE values of 0.36 and 0.16, respectively. Around one-third of the study area showed a KGE of more than 0.5 in the Early and Late products, whereas only one-quarter was indicated in the Final product.
## 4 Communication Architecture
### Modeling of Intra-Level Communication Architecture
#### 4.1.2 Cluster Head  
Cluster heads (CHs) are intelligent sensors with more power and energy capacity, responsible for the clusters formed by the level 1 (N1) sensors. They have a data aggregation capacity, which allows them to propose a first level of aggregation of received data. The CHs manage the connection between the level 1 network and the level 2 network. The data received by the CH is analyzed by the latter in order to propose an appropriate routing (level 2 or sink sensors). Four states are used to model the behavior of the CH as shown in the Figure 6.  
In state 1, the CHs are waiting for sensor data. If they receive a message, a transition to state 2 takes place. In this state, they analyze the received msg, add their own information and route it to the well. Several cases can be considered to select an appropriate route (destination address). For example, if a data requires a short delay and a high priority, the CH first tries to reach the well directly (sending an MSG with the well address). If the MSG-ACK is not received, they try to send the message to a level 2 sensor indicating the importance of the data in order to adapt the parameters: selection of the type of content to be transmitted, relay of the collected data, and/or transmission of the images to remove the doubt in the case of an abnormal situation. In state 4, they wait for the _msg-ok_ from the level 2 sensor. If the _msg-ok_ is not received it returns to state 2, otherwise they return to the initial state 1. We note that in states 3 and 4, a selective retransmission mechanism is required to avoid infinite cycling (defined delay and/or limitation of the number of retransmissions).