esa-sceva/satcom-chunk-collection huggingface dataset:
# Individual Differences in Psychological Stress Associated with Data Breach Experiences
## 3 Results
### Hierarchical Multiple Regression Predicting IES-R Scores
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(R\) & \(R^{2}\) & \(\Delta R^{2}\) & \(\Delta F\) & \(p\) \\ \hline Model 1 (with DBSI) & 0.240 & 0.057 & 0.057 & 31.78 & \textless{}0.001 \\ Model 2 (with gender, sample, age, years of education) & 0.285 & 0.081 & 0.024 & 3.31 & 0.011 \\ Model 3 (with NE, PSWQ, GAD-7 scores) & 0.417 & 0.174 & 0.093 & 19.26 & \textless{}0.001 \\ Model 4 (with smartphone use, social media use, browser use) & 0.423 & 0.182 & 0.008 & 1.75 & 0.157 \\ Model 5 (with digital security expertise, post-data breach security) & 0.448 & 0.201 & 0.019 & 6.01 & 0.003 \\ \hline \hline \end{tabular} \(R\) and \(R^{2}\) are the values for each model. \(\Delta R^{2}\) is the increase in variance due to the addition of a set of predictors; the associated \(F\) statistic and \(p\)-value are a test of whether the increase in variance explained by the predictors is statistically significant (greater than zero). For Model 1, the predictor was DBSI scores. For Model 2, the additional predictors were gender, sample, age, and years of education. For Model 3, the additional predictors were negative emotionality (NE), PSWQ, and GAD-7 scores. For Model 4, the additional predictors were smartphone use (hours per day), social media use (hours per day), and browser use (hours per day). For Model 5, the additional predictors were digital security expertise and post-data breach security.  
\end{table} 
Table 8: Summary of hierarchical linear regression analysis predicting IES-R scores. \(F(3,102)=1.88\), \(p=0.138\). For Model 4, the addition of smartphone use, social media use, and browser use resulted in a significant increase in \(R^{2}\), \(\Delta R^{2}=7.4\%\), \(F(3,99)=3.70\), \(p=0.014\). Social media use was a significant predictor, with greater daily social media use associated with higher IES-R scores, \(\beta=0.266\), \(t(99)=2.57\), \(p=0.012\). For Model 5, which added digital security expertise and post-data breach security, the percentage of variance explained increased significantly, \(\Delta R^{2}=6.1\%\), \(F(2,97)=4.97\), \(p=0.009\). Digital security expertise was a significant predictor, with higher expertise associated with lower IES-R scores, \(\beta=-0.212\), \(t(97)=2.36\), \(p=0.020\). The total percentage of variance accounted for by Model 5 was 40.2% (adjusted \(R^{2}=32.2\%\)), \(F(13,97)=5.02\), \(p<0.001\), and the significant predictors were DBSI scores, social media use, and digital security expertise (note that for PSWQ scores, \(p=0.051\)). The same predictors were statistically significant when using robust standard errors to correct for possible heteroscedasticity. Excluding the DBSI score predictor from the regression analysis reduced the total R\({}^{2}\) to 32.0%, \(F(12,98)=3.85\), \(p<0.001\). Social media use and digital security expertise remained significant predictors (\(p<0.01\)).
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 2 Complex Network Models of the Satellite Swarm  
### Introduction of Slots  
The communication task of a satellite swarm, such as transmitting data and orders, are always time-sensitive, which means there is a task time interval during which the task must be fulfilled. It is unnecessary to worry about which ISLs are established in the satellite swarm every second because there is a better way to cope with the establishment of ISLs during the task time interval. That method is the finite state automata (FSA) [14; 15], which can save computing and storage resources and improve the management simplicity of link planning [2]. As Figure 1 shows, the task time interval is divided into \(\Phi\) equal parts (\(\Phi\in\mathrm{N}_{+}\)), which are called state periods; then, each state period is further divided into \(\Gamma\) equal parts (\(1\leq k\leq\Gamma\)), which are called slots. Each slot corresponds to an FSA state indicating which ISLs are established. As each slot will occur in the cycle, we only need to focus on these \(\Gamma\) FSA states. The topology of an ICNS is believed to remain unchanged in each slot.  
The length of a slot is denoted by \(\Delta T_{\text{slot}}\). All temporal concepts were counted by the slot in this paper unless there is a special explanation.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Selection of Cluster Head (CH) using Fuzzy Logic
#### 4.2.1 Link Weight (Centrality)  
In selecting the CH in a cluster, the link weight is an important parameter to be considered. During this step, the distances of all nodes/vehicles from the center point is calculated by employing the normalized distance Equation (5).  
\[dist_{vi}=\left[norm\bigg{(}\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}}\bigg{)} \right]\times 100 \tag{5}\]  
The shortest distance from the center position is given a higher weightage and is to be chosen as a CH. However, other parameters also impact the selection process.
## 3 Results
### Relationships between SiF, SiF \(L_{650}{}^{-1}\), GPP\({}_{30}\) and Lulie\({}_{30}\)
Past that afternoon peak, _SiF_\(L_{650}-^{1}\) usually dropped rapidly toward the evening. PAM \(\Delta F/F_{m}{}^{\prime}\) showed diurnal courses going in opposite direction to those of _SiF_\(L_{650}-^{1}\). Most months showed a maximum \(\Delta F/F_{m}{}^{\prime}\) in the early hours of the morning, which decreased until it reached a minimum value at a time that occurred increasingly later as the season progressed. Generally, \(\Delta F/F_{m}{}^{\prime}\) and _SiF_\(L_{650}-^{1}\) both decreased from the morning until midday to early afternoon, after which they either moved in the same direction (Figure 6B,C) or diverged (Figure 6D,E). However, the time at which each variables reached a minimum daily value and started to increase and move in opposite directions as the season progressed. For _SiF_\(L_{650}-^{1}\), this time occurred earlier during the day from April to September, while for \(\Delta F/F_{m}{}^{\prime}\) it occurred later as the months passed. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Slope** & **Intercept** & **R2** & **Std Error** & **F** & **df** & _p_**-Value** \\ \hline B & 0.0002 & 0.0003 & 0.26 & 0.001 & 39.76 & 117 & \textless{}0.0001 \\ D & 0.0251 & 0.4468 & 0.25 & 0.166 & 38.15 & 117 & \textless{}0.0001 \\ F & 0.2123 & 0.0032 & 0.24 & 0.001 & 37.18 & 115 & \textless{}0.0001 \\ H & 34 Figure 6: Diurnal course of _Sif_\(L_{650}{}^{-1}\) (black circles), quantum yield of PSII (\(\Delta F/F_{m}{}^{\prime}\), black triangles) and solar zenith angle (SZA, blue curve) for the same clear days shown in Figure 7. (**A**) April 28 th, (**B**) May 10 th, (**C**) June 5 th, (**D**) August 27 th, and (**E**) September 5 th. Note that there are no PAM data for July. Solar noon indicated by the grey line.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 4 DYNAATIC Multiple RPL Instances for Multiple IoT ApplicatIons (DYNASTI)
### Implementation Notes  
We used Contiki (version 3.0) [28] embedded operating system to implement DYNASTI and Cooja simulator [27] to perform the simulations studies. There is no standardization of multiple instances yet, and, for the development of this work, we employed an adaptation of multiple instances implementation provided by [21; 22]. Our implementation is available on GitHub [32].  
Sporadic Event Detector is responsible to detect and report events that change instance scheduling. The event can be triggered by a motion, smoke sensor, etc., so Sporadic Event Detector is related to the application domain and needs to monitor the sensors in the node. Since we are using simulation, Sporadic Event Detector is implemented based in timers (as described in details in the next section), triggering events in some random time.  
Figure 6: Sequence diagram that shows the interaction between sink, database, and sensor nodes.  
Instance Scheduler was implemented as a data structure that stores predefined information scheduling, i.e., sets of instances and their status (actived or deactivated). When an event is detected, Instance Scheduler selects a set accordingly and changes the instances' status in the Instances Database, which is another data structure just to keep instances information.  
We had to alter Contiki's network stack in order to implement Message Controller and DIO/DAO messages modification. When an instance is deactivated, all its packets need to be dropped, so we added code in RPL to query the Instances Database before sending/receiving packets. In the same way, the control messages DIO and DAO were changed in order to forward the information from the Instances Database, thus DYNASTI uses the reserved field (reserved) of the RPL messages to transmit the status of the instances in a passive way. It is worth noting that DYNASTI takes advantage of constant send interval of these RPL messages (mostly controlled by the RPL trickle algorithm). Hence, DYNASTI does not incur new additional DIO and DAO messages.  
All nodes have the described components excepting the sink, which does not detect events nor schedule instance. The role of the sink is just to disseminate the instance changes in the network, so that it does not have Sporadic Event Detector and Instance Scheduler.
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 2 Performance Comparison between Conventional and Flat Luneburg Lens
The back lobe of the FLL is evidently larger than that of the conventional LL, because the used isotropic approximation and the high permittivity (or the neglect of permeability) cause a mismatch at the surfaces between the FLL and the air, resulting in an evident reflection. It was proven that the reflection can produce \(-10\) dB backside radiation in [20], which degrades the gain and efficiency of the FLL. Figure 3: Comparison between conventional LL and FLL: simulated \(S_{11}\) and realized gain. Figure 2: Luneburg lens: (**a**) conventional spherical LL, (**b**) transformed FLL.
## 5 Experimental Results and Discussion  
We conducted comprehensive experiments to determine what happens to digital data sent through an indoor M-QAM-based SD-PLC system [31] when various types of noisy household electrical equipment are turned on and off at random intervals. As mentioned in the previous section, we examined two noise scenarios: one with weakly interrupted noise from a single household device and another with strongly interrupted noise from a combination of two household devices. We used a laptop charger and a computer monitor as noise sources in the weakly interrupted noise scenario. A CFL bulb, a hairdryer, a laptop charger, and a computer monitor were paired to generate noise in the strongly interrupted noise scenario.  
This section presents the findings of the statistical error patterns derived from the comparison of transmitted and received bits in the context of this study. Then, using the three-state FMM and BWA techniques, this paper analyzes the impulse noise errors in the SD-PLC channel. Furthermore, the procedure for the validation of the model and analysis of the findings is described in depth.
## 4 Results and Discussion
### Performance of K-NN, SVM and LDA with Different Parameter Values  
This subsection displays the experimental results obtained with the conventional supervised machine learning classifiers, K-NN and Support Vector Machines (SVM), for the different parameters described in Section 3.5.1. To train and test conventional classifiers, we used deep features extracted through AlexNet and ResNet-18 that were provided as input to the classifiers. The SVM classifier was trained using linear, Radial Basis Functions (RBF), and polynomial kernels. Figure 4a shows that the linear SVM performs better than the RBF and polynomial kernels, i.e., with the AlexNet extracted features bearing an accuracy of 94.97%, while the SVM with RBF kernels achieves a low accuracy of less than 25%. As linear SVM achieves better accuracy, this implies the data are linearly separable. On the other hand, Figure 4b shows the accuracy obtained with K-NN classifiers for different odd values of k between 1 and 15. To train and test K-NN, we used features extracted with AlexNet and ResNet-18. The results show that for k = 1, we achieved the highest accuracy of 94.66% and 94.97% with features extracted from AlexNet and Resnet-18 deep models, respectively. With a higher k value, the classification performance deteriorates further. A possible reason behind the lower accuracy for a greater value of k, i.e., number of neighbors may represent the inter-similarity among different emotion classes emojis that ultimately misguide the classifier. K-NN achieves higher performance with features extracted by the ResNet-18 model. K-NN achieves the highest accuracy of 94.97% with k = 1 on the ResNet-18 feature descriptor, while linear SVM achieves the same high accuracy of 94.97% on the AlexNet feature descriptor. The results show that K-NN and SVM achieve the same highest accuracy (i.e., 94.97%) on ResNet-18 and AlexNet features, respectively.  
LDA was trained using five different discriminants, i.e., 'linear', 'diagLinear', 'diagQuadratic', 'pseudoLinear' and 'pseudoQuadratic'. Experimental results in Figure 5 show achievement of the highest accuracy 92.62% by LDA w  
Figure 4: SVM and K-NN Performance: (**a**) Shown on the left, the performance of SVM for different kernels while, (**b**) the right side shows the performance of K-NN for different values of \(K\) on ResNet-18 and Alexnet deep features.  
pseudoLinear, and Alexnet features. Through ResNet-18 features, we achieve the highest accuracy 91.55% with the pseudoQuadratic discriminant. On the other hand, discriminant types 'diagQuadratic' and 'diagLinear' performed poorest, both on Alexnet and ResNet-18 features.
## 4 Performance Evaluation
### Discussion  
In this section, we further discuss the proposed approach in terms of computational load and its potential modifications to improve detection performance in more complicated scenarios.  
The processing time of the proposed method is evaluated offline. Our server information is as follows: MATLAB 2022, the processor is Intel(R) Xeon(R) E5-2630 [EMAIL_ADDRESS] GHz, 64 bit operating system, 256 GB memory. The CPI is 0.1678 s, which is the same as that in numerical results. We use the tic/toc command in MATLAB to obtain the average running time via 100 independent and repetitive trials. For algorithmic acceleration, parallel processing is employed in the range search since each range search is linearly separable. The running times of the MF-RFT and ME-PSO algorithm are listed in Table 5, where we can find that the most time-consuming part in the proposed method is the ME-PSO process, taking up to 59.6% of the running time for M = 3, and 53.0% for M = 5. The purpose of this part is to compare the computational load between the conventional methods and the proposed one, as shown in Table 6. The processing time of the proposed method is shown as being pretty long due to the limited computing resources on our server. The online processing time can be significantly decreased by increasing the computing resources, such as resorting to graphics processing unit (GPU) acceleration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Processing Time (M = 3)** & **Processing Time (M = 5)** \\ \hline MF-RFT & 11.5 s & 23.5 s \\ ME-PSO & 21.4 s & 26.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the processing times of the main procedures in the proposed method.  
Figure 23: The detection results using different fusion methods in the Doppler profile when \(R=36\) m, and \(P_{fa}=2\times 10^{-7}\): (**a**) MF-NCI-EL; (**b**) MF-RFT-NCI; (**c**) MF-RFT without ME-PSO; (**d**) MF-RFT-QCI(proposed).  
In the field test results, it is noted that the acceleration of the target is not obvious due to the narrow beam of our system and the driving mode of the cruise control. If a target with high maneuverability is considered, it is necessary to extend our method to the generalized MF-RFT (MF-GRFT) by searching high-order motion parameters, including acceleration, jerk, and so forth. Moreover, this paper solely focuses on the effect of MF-DFM. However, it is worth noting that RCM will occur as the CPI continues to increase. As such, this detection problem will become more complicated. Future work will focus on combining the proposed method with KT, track-before-detection (TBD), and other powerful techniques for achieving long-time integration in the presence of both RCM and MF-DFM.
# Activity Evaluation Methodology for the Disposed Medical Linear Accelerators
## 3 Results
### Gamma Spectroscopy of the Components  
The gamma spectra of the Siemens linac components were acquired using the Canberra in situ HPGe detector. As shown in Figure 5, we simulated each component with a geometry that was as reflected the real dimension and shape as possible, considering the design  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Background Dose Rate** & **0.137 μSv/h** & & & & & \\ \hline
**Component/Distance** & **0 cm** & **1 cm** & **2 cm** & **3 cm** & **4 cm** & **5 cm** \\ \hline Target & 7.049 & 4.990 & 3.722 & 2.884 & 2.167 & 1.724 * \\ Target holder & 0.570 & 0.333 & 0.269 & 0.244 & 0.221 & 0.208 \\ Scattering foil & 0.414 & 0.341 & 0.272 & 0.240 & 0.231 & 0.203 \\
15 MV primary collimator & 7.002 & 5.180 & 2.570 & 2.106 & 1.865 & 1.408 * \\
6 MV primary collimator & 0.262 & 0.131 & 0.122 & 0.112 & 0.110 & 0.113 \\
15 MV flattening filter & 3.636 & 2.416 & 2.246 & 1.570 & 1.220 & 1.030 * \\
6 MV flattening filter & 0.160 & 0.136 & 0.127 & 0.113 & 0.109 & 0.104 \\ Ion chamber & 0.118 & 0.111 & 0.116 & 0.118 & 0.118 & 0.116 \\ Mirror & 0.134 & 0.121 & 0.115 & 0.124 & 0.111 & 0.109 \\ Y jaw & 0.392 & 0.319 & 0.264 & 0.248 & 0.236 & 0.221 \\ Multileaf collimator (MLC) & 0.202 & 0.185 & 0.184 & 0.174 & 0.163 & 0.149 \\ Shielding block & 0.483 & 0.368 & 0.271 & 0.257 & 0.240 & 0.212 \\ \hline \hline \end{tabular}  
* Dose rate over 0.5 μSv/h at 5 cm distance.  
\end{table}
Table 4: Five weeks after disposal: Dose rate according to the distance of components ( μSv/h).  
Figure 4: Dose rate of the component by distance: 0 cm to 5 cm from the surface.  
drawing of the linac and the real measured dimensions of the part, and obtained the efficiency coefficient of each component.  
Table 5 lists the radionuclides generated from the radioactivity of the components. The naturally occurring radionuclides and the radionuclides that exhibited lower activity than the minimum detectable activity (MDA) were also excluded. Approximately twenty different radionuclides, including \({}^{57}\)Co, \({}^{51}\)Cr, \({}^{54}\)Mn, \({}^{57}\)Ni, \({}^{58}\)Co, \({}^{60}\)Co, \({}^{65}\)Zn, \({}^{99}\)Mo, \({}^{181}\)W, \({}^{187}\)W, \({}^{196}\)Au, and \({}^{198}\)Au, were identified following Siemens linac component gamma spectroscopy. Figure 6 is the spectra of the target and 15 MV primary collimator. For target which composed of gold, copper, and tungsten materials, the energy peaks of \({}^{196}\)Au, \({}^{198}\)Au, \({}^{57}\)Co, \({}^{60}\)Co, \({}^{54}\)Mn, \({}^{99}\)Mo, and \({}^{187}\)W were distinguished as shown in Figure 6a. The energy peaks of \({}^{57}\)Co, \({}^{58}\)Co, \({}^{60}\)Co, \({}^{54}\)Mn, \({}^{99}\)Mo, \({}^{57}\)Ni, and \({}^{187}\)W were identified for the 15 MV primary collimator, as shown in Figure 6b.  
Figure 5: Two examples of the linac component simulation with ISOCS\({}^{\text{TM}}\) software.  
Figure 6: Two examples of the linac component spectrometry with HPGe detector: (**a**) Target, (**b**) 15 MV Primary Collimator. (Blue line: double peak).
###### Abstract  
We propose a semi-empirical method--based on a filtered Markov process--to convert 10 min rain rate time series into 1 min time series, i.e., quasi-instantaneous rainfall--the latter to be used as input to the synthetic storm technique, which is a very reliable tool for calculating rain attenuation time series in satellite communication systems or for estimating runoff, erosion, pollutant transport, and other applications in hydrology. To develop the method, we used a very large data bank of 1 min rain rate time series collected in several sites with different climatic conditions. The experimental and simulated 1 min rain rate time series agree very well. Afterward, we used them to simulate rain attenuation time series at 20.7 GHz, in 35.5\({}^{\circ}\) slant paths to geostationary satellites. The two simulated annual rain attenuation probability distributions show very small differences. We conclude that the rain rate conversion method is very reliable.  
Keywords:instantaneous rainfall; filtered Markov process; meteorology; rain attenuation; satellite communication; rain rate conversion; rain rate time series +
Footnote †: journal: Applied Sciences
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 5 Results
### Tissue Interface Sharpness on Complex Phantoms  
Images in Figure 6a,b correspond to the results of DAS and RiB on the simulated phantom, respectively. The most significant difference between both is the sharpness of the spiric section. This is mainly due to the lowering influence of the tissue interface close to \(y=0\) plane. The speckle introduced by that interface is considerably reduced after RiB processing. Also, due to the overall image contrast improvement, anechoic hollows are clearly distinguished.  
As in the last results, RiB shows better performance regarding tissue interface sharpness and anechoic regions characterisation, see Figure 6d. In fact, after NLmeans filtering, RiB result poses a successful approximation to the expected spiric section. Contrarily, filtering result on DAS, see Figure 6c, flattens the spiric section and the deeper parts of the torus in the centre of the image. This is  
Figure 6: Simulated phantom analysis. Formed by interlaced tori section: (**a**) DAS, (**b**) RiB 10%, (**c**) NLmeans-DAS and (**d**) NLmeans-RiB  
clearly rejected in the case of RiB. This brings to light differences on kernel memories for convolutive noise--see (12) and (13).
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 6 Simulation Analysis  
In this paper, 12 UAVs were used for formation assembly, switching, and formation flight simulation under abnormal flight and communication environments, and 8 UAVs were used for formation obstacle avoidance simulation. In the simulation experiment, the algorithm was programmed in the C++ language, the platform tool was Microsoft Visual Studio 2016, and the hardware environment was a PC with an inter-core i5-4210 CPU, 2.60 GHz dual-core processor, and 8 GB memory.