esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Experimental (\(n=22\))**} & \multicolumn{2}{c}{**Control (\(n=16\))**} \\ \cline{2-5}  & **Pre-Assessment** & **Post-Assessment** & **Pre-Assessment** & **Post-Assessment** \\  & **M (SD)** & **M (SD)** & **M (SD)** & **M (SD)** \\ \hline ERQ-CA Emotional & 11.95 (3.62) & 10.59 (3.66) & 10.56 (3.67) & 10.81 (4.34) \\ suppression & 19.59 (4.40) & 18.86 (5.10) & 17.19 (3.64) & 16.94 (7.61) \\ ERQ-EA Cognitive reappraisal & 10.86 (3.24) & 9.23 (2.54) & 11.69 (3.44) & 11.50 (3.48) \\ BRIEF-2 Emotional control & 24.27 (6.12) & 20.55 (5.95) & 25.75 (6.18) & 25.19 (8.86) \\ RCADS total anxiety & 41.68 (20.27) & 32.86 (13.57) & 44.69 (18.36) & 42.06 (20.77) \\ RCADS total depression & 11.09 (6.46) & 8.00 (4.59) & 12.75 (7.29) & 12.69 (8.20) \\ RCADS total internalizing & 52.77 (23.98) & 40.86 (16.99) & 57.44 (23.89) & 54.75 (27.60) \\ GSE-self-efficacy total & 62.27 (17.78) & 66.68 (15.58) & 54.75 (18.63) & 58.75 (20.58) \\ SCWT-Words & 40.90 (5.82) & 43.71 (6.27) & 43.50 (11.28) & 47.50 (12.19) \\ SCWT-Colors & 36.05 (7.28) & 38.33 (6.12) & 38.13 (8.02) & 40.06 (9.53) \\ SCWT-Colors and words & 41.14 (7.06) & 45.48 (16.69) & 38.81 (19.16) & 44.19 (15.98) \\ TMT-A & 46.04 (3.93) & 48.95 (3.93) & 47.31 (4.59) & 50.09 (3.40) \\ TMT-B & 43.12 (5.14) & 47.12 (5.24) & 40.92 (7.26) & 44.67 (5.78) \\ Digits\_Total & 44.44 (8.77) & 44.58 (6.97) & 46.34 (9.76) & 47.70 (7.57) \\ WCST Total Errors & 55.64 (9.70) & 65.68 (8.91) & 52.81 (12.52) & 60.75 (8.94) \\ WCST Perseverative Responses & 56.41 (13.35) & 71.27 (12.53) & 52.19 (15.57) & 65.38 (14.34) \\ WCST Perseverative Errors & 55.36 (11.53) & 69.45 (11.25) & 51.75 (14.03) & 65.56 (12.64) \\ WCST Perseverative No Errors & 56.14 (11.50) & 63.64 (9.92) & 53.44 (11.53) & 56.75 (8.90) \\ \hline \hline \end{tabular} Notes: ERQ-CA = Emotion Regulation Questionnaire for Children and Adolescents; BRIEF-2 = Behavior Rating Inventory of Executive Functions, Second Edition; RRS = Ruminative Responses Scale; RCADS = Revised Child Anxiety and Depression Scale; GSE = General Self-Efficacy Scale; SCWT = Stroop Color and Word Test; TMT A-B = Trail Making Test; Digits\_Total = Digit Forward and Backward Span; WCST = Wisconsin Card Sorting Test. All neuropsychological measures report T-scores. Higher raw scores indicate poorer mental health and emotion regulation skills, except for cognitive reappraisal and general self-efficacy. Higher T-scores on neuropsychological measures indicate better cognitive performance.  
\end{table} 
Table 1: Mean and standard deviations of ER, mental health and cognitive measures of the study.
## 4 Results  
### Collocations of HY-2B/C/D and SMAP  
#### 4.1.1 Geographical Distribution  
In this paper, the HY-2B/C/D series scatterometer data of high wind speed vectors are compared and evaluated by SMAP measurements within tropical cyclones during 2019-2021. The list of tropical cyclones captured by SMAP is sorted by year and provided by Remote Sensing Systems. Then, according to the center and radius of maximum wind (RMW) of each tropical cyclone, SMAP radiometer and HY-2 scatterometer winds are selected within four times the RMW and with a time difference of no more than 1 h. The storm tracking data are provided by the Joint Typhoon Warning Center (JTWC, [[https://www.usno.navy.mil/JTWC/](https://www.usno.navy.mil/JTWC/)]([https://www.usno.navy.mil/JTWC/](https://www.usno.navy.mil/JTWC/)), accessed on 1 January 2022) in this study. Since the sea surface state of tropical cyclones varies with the spatial distribution, it is necessary to ensure that the location of the HY-2B/C/D series scatterometer wind speed measurements are as close as possible to SMAP. In this study, collocations from SMAP are collocated by less than 12.5 km.  
Figure 2 shows the density map of the distribution of all data pairs after collocation, which suggests that most of the captured tropical cyclone data from both HY-2 and SMAP are distributed in the Northwest Pacific in the northern hemisphere. This is consistent with the high frequency of tropical cyclone occurrence in the Northwest Pacific. In addition, most of the matchups are distributed in the Western Pacific Ocean and Eastern Pacific Ocean (along the coast of America). In the southern hemisphere, it is noted that no tropical cyclone has been observed in South America. In addition, there are some tropical cyclones captured by both HY-2 and SMAP in the open sea in the southern Indian Ocean. From Figure 2a,b, it can be seen that the number of matchups has a similar distribution, and compared to Figure 2c,d, this means that HY-2B captured the main tropical cyclones, and HY-2C/D accounted for a small part.  
Figure 1: Methodology for revising the high wind speed of HY-2B/C/D scatterometers.
## 3 Results and Discussions
### Fourier Transform Infrared Spectrometry (FT-IR)
In the GO-ZnO spectrum, the absorbance peak from 1508 cm\({}^{-1}\) indicates the skeletal vibration of the graphene sheets, while the peak at 1384 cm\({}^{-1}\) corresponds to the vibration of the C=O bond [15; 41]. The peaks at 1361 cm\({}^{-1}\) (C=O bond) and 1222 cm\({}^{-1}\) (C-O bond) from the GO spectrum was not observed in the other spectra (i.e., GO-ZnO and GO-ZnO-APTES). The peaks at 1049 cm\({}^{-1}\) and 981 cm\({}^{-1}\) from the GO spectrum correspond to the epoxy group and to the alkoxy group vibrations, respectively [42]. In the GO-ZnO spectrum the peak from 1047 cm\({}^{-1}\) is attributed to the C-O stretching vibration [43] and the peak from 989 cm\({}^{-1}\) corresponds to the vibration of Si=O--Zn bond. Both peaks from 1036 cm\({}^{-1}\) and 1006 cm\({}^{-1}\) in the GO-ZnO-APTS spectrum can be assigned to the Si-O-Si bond [31], while the one at 1378 cm\({}^{-1}\) is ascribed to the O-H deformations of the C-OH groups [44]. The appearance of a new band with lower peaks at 688 cm\({}^{-1}\) in the GO-ZnO spectrum and and at 692 cm\({}^{-1}\) in the GO-ZnO-APTES spectrum could be ascribed to the presence of ZnO on the graphene sheets [44; 45]. The peak at 692 cm\({}^{-1}\) in the GO-ZnO-APTES spectrum may be assigned to the Si-phenyl compounds bonds [31]. The peak at 831 cm\({}^{-1}\) can be ascribed to the C-H group [44]. The appearance of several new peaks and the decrease of the intensity of the peaks in the GO-ZnO spectrum as compared to those in the GO spectrum indicates that the ZnO is linked to the functional groups on the surface of the graphene sheets. Moreover, certain maxima found in the GO-ZnO-APTES spectrum have a lower intensity than those found in the GO-ZnO spectrum. In addition, the appearance of the maxims attributed to the silane groups confirms the presence of the APTES on the GO-ZnO sheets.
## 2 Materials and Methods
### Experimental Design
Furthermore, the hyperparameters of the ML algorithms were explored using a random grid search in a heuristic manner until the best results were achieved. For models with a small set of hyperparameters, every possible option was systematically tested whenever feasible, such as LR, ANN, and SVM kernel models. For the fusion models, the same methodology was applied; however, the exploration process was shorter, as it was guided by the results obtained from the PR and CD-based models. Additionally, it is important to note that the text representation parameters and the ML hyperparameters were optimized together, ensuring an effective combination for classification performance. The metrics used to evaluate the results of SKCV were sensitivity, specificity, and area under the curve (AUC), as they are particularly relevant in healthcare systems [48]. These metrics were computed for each testing set and then averaged. To ensure robust evaluation, the SKCV process was repeated ten times, and the mean and standard deviation of the three metrics were calculated across all repetitions, providing insight into the variability of the models. Additionally, given the imbalanced dataset, the training was weighted according to the number of samples in each class, ensuring that the metric accurately reflects the dataset distribution. Finally, the same training and testing sets were consistently used across all models to enable fair comparisons.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 2 Related Work
Our proposed NFEM uses vehicle state attribute value as packets encryption keys. The security cloud is used to store the vehicular state values. As a new generation of infrastructure-based wireless technologies, we use Long Term Evolution (LTE) to transfer request packets and response keys packets between mobile vehiclesand clouds in our NFEM architecture. There are already some standards and application, indicating that LTE is suitable for vehicular communicaition or vehicle-to-cloud communication. Thanks to its simplified flat all-IP architecture, LTE can provide a round-trip time theoretically lower than 10 ms, and transfer latency in the radio access up to 100 ms. LTE is very beneficial for delay-sensitive vehicular applications, and there is already a standard that is considered as the preliminary version of 4G mobile communications [23]. The scenarios are suitable for operating LTE-based V2X services and address the main challenges of high mobility and densely populated vehicle environments in designing technical solutions to fulfill the requirements of V2X services. By leveraging the spectral-efficient air interface, the cost-effective network deployment, and the versatile nature of supporting different communication types, LTE systems along with proper enhancements can be the key enabler of V2X services [24]. The long-term evolution-vehicle (LTE-V) standard for sidelink or V2V communications based on the PC5 interface includes two radio interfaces. The cellular interface (named Uu) supports vehicle-to- infrastructure communications, while the PC5 interface supports V2V communications based on direct LTE sidelink [25]. The European AutoMat project defines an open Common Vehicle Information Model (CIVM) in combination with a cross-industry, cloud-based big data marketplace. A simulative analysis of car-to-cloud data traffic is presented by setting up a car-to-cloud communication model, leveraging LTE uplink channels, which is founded on a measurement-based empirical channel model. CIVM Data Packages are sent to the cloud leveraging LTE sidelink. The results quantify the available data rate for car- to-cloud communication and a vehicle traffic state aware data aggregation is proposed [26]. The main contributions of the literature can be summarized as follows. 1. A Negotiation-free encryption way is proposed, which is customized for VANETs. 2. The vehicle state attribute value is used as a packet encryption key to replace the traditional IPSec key negotiation and management stage which is unique and difficult to forge. 3. The secure cloud mechanism and the unchangeable timestamps are used to ensure the data security and privacy. The proposed scheme can considerably reduce the negotiation and validation overhead. The scheme performs well even under the condition of a high density of vehicles and a high frequency of beacon conditions.
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 5 Use Cases
### Camouflage Pattern People Detection  
Camouflage Pattern People Detection is a key technology in the field of military reconnaissance, as it plays a crucial role in detecting and identifying enemy special forces, agents, or spies with advanced camouflage and concealment capabilities. This technology aids in enhancing situational awareness and improving security measures by enabling the identification of hidden threats in various operational environments. In order to evaluate the effectiveness of CISNet Camouflage Pattern People Detection, we retrained CISNet on CamouflageData [7], where 80% of the samples are used for training and 20% for testing. And the results are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Channels in MSCSub**} & \multicolumn{4}{c}{**COD10K-Test**} & \multicolumn{4}{c}{**NC4K**} \\ \cline{3-10}  & & \(S_{\mathbf{\mathrm{a}}}\uparrow\) & \(E_{\mathbf{\mathrm{\phi}}}\uparrow\) & \(F_{\mathbf{\mathrm{\beta}}}^{\mathbf{\mathrm{a}}}\uparrow\) & \(\mathbf{M}\downarrow\) & \(S_{\mathbf{\mathrm{a}}}\uparrow\) & \(E_{\mathbf{\mathrm{\phi}}}\uparrow\) & \(F_{\mathbf{\mathrm{\beta}}}^{\mathbf{\mathrm{a}}}\uparrow\) & \(\mathbf{M}\downarrow\) \\ \hline \#9 & 32 & 0.878 & 0.934 & 0.811 & 0.022 & 0.880 & 0.927 & 0.834 & 0.036 \\ \#10 & 128 & 0.879 & 0.935 & 0.813 & 0.022 & 0.881 & 0.931 & 0.838 & 0.034 \\ \#11 & 256 & 0.883 & 0.939 & 0.820 & 0.020 & 0.884 & 0.932 & 0.840 & 0.033 \\ \#OUR & 64 & 0.879 & 0.938 & 0.812 & 0.021 & 0.880 & 0.931 & 0.837 & 0.034 \\ \hline \end{tabular}  
* ”\(\uparrow\)” and ”\(\downarrow\)” indicate that larger or smaller is better.  
\end{table}
Table 4: Ablation study on the influence of channels in MSCSub on COD10K and NC4K.  
Figure 8: Visualization of features generated in the process of MSCSub applied on the backbone features. (**a**), (**b**), (**c**), (**d**), and (**e**), respectively, represent the results of \(E_{\mathbf{\mathrm{4}}}\odot E_{j}\), \(Conv_{1\times 3}(E_{\mathbf{\mathrm{4}}})\odot Conv_{1\times 3}(E_{j})\), \(Conv_{1\times 5}(E_{\mathbf{\mathrm{4}}})\odot Conv_{1\times 5}(E_{j})\), \(Conv_{3\times 1}(E_{\mathbf{\mathrm{4}}})\odot Conv_{3\times 1}(E_{j})\), and \(Conv_{5\times 1}(E_{\mathbf{\mathrm{4}}})\odot Conv_{5\times 1}(E_{j})\), where j denotes 1, 2, and 3.
###### Abstract
In addition, it approaches the optimal BER performance of the ML detector while maintaining lower complexity and computational time. Moreover, compared to conventional DNN, the B-DNN shows improvements in both training time and accuracy. Furthermore, when compared to other well-known neural networks, such as convolutional neural network (CNN), the B-DNN exhibits better BER performance, with specific numerical results provided in [15]. Building on these findings, we apply this B-DNN to ESM to demonstrate whether this neural network architecture continues to perform well in another form of SM. In the data pre-processing phase, we utilize a feature vector generator (FVG) to transform complex in-phase and quadrature (IQ) data into a pure format, enhancing the performance in the symbol classification phase. During the training phase, we design a feed-forward neural network, which is meticulously trained on input vectors from the data pre-processing phase. The output of the neural network is classified using the softmax function, renowned for its efficacy in differentiating between multiple classes. The simulation results from our experiments clearly demonstrate that the B-DNN-based detector not only accurately approximates the ML detector but also significantly outperforms the traditional MMSE and ZF detectors in terms of BER. Moreover, B-DNN based detector can also reduce the classification time compared to ML detector, which greatly enhances the efficiency of the detection process, highlighting its potential for practical applications in ESM systems.
# Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
## 3 Methodology
### D-KPW Algorithm  
Figure 2 presents a schematic diagram of the D-KPW algorithm framework. For each set of observed clock error series, the Kalman filter is applied to remove frequency white noise and link noise, extracting relevant information. The matrix parameters of the signal and observation models are configured to carry out iterative filtering.  
The state transition matrix \(\Phi\) is as follows:  
\[\Phi=\begin{bmatrix}1&T_{s}\\ 0&1\end{bmatrix} \tag{14}\]  
The observation matrix \(H\) is as follows:  
\[H=[1\;0] \tag{15}\]  
Figure 2: D-KPW algorithm framework.  
Carrying on the Kalman filter processing, the formula is as follows:  
\[\begin{array}{c}\hat{x}[k/k-1]=\Phi\hat{x}[k-1/k-1]\\ P_{\tilde{x}}[k/k-1]=\Phi P_{\tilde{x}}[k/k-1]\Phi^{T}+Q\\ K[k]=P_{\tilde{x}}[k/k-1]H^{T}(HP_{\tilde{x}}[k/k-1]H^{T}+R)^{-1}\\ \hat{x}[k/k]=\hat{x}[k/k-1]+K[k](2[k]-H\hat{x}[k/k-1])\\ P_{\tilde{x}}[k/k]=(I-K[k]H)P_{\tilde{x}}[k/k-1]\end{array} \tag{16}\]  
where \(P_{\tilde{x}}[k/k-1]\) is the prediction error covariance matrix, \(K[k]\) is the Kalman gain, \(z[k]\) is the observation data, \(\hat{x}[k/k-1]\) is the predicted filter value, \(\hat{x}[k/k]\) is the filter estimate, and \(P_{\tilde{x}}[k/k]\) is the filter error covariance.  
To make the filter converge faster, proper initialization is required as follows:  
\[\begin{array}{c}\hat{x}[0/0]=\left[\begin{array}{cc}z[2]&\frac{z[2]-z[1]}{ T_{z}}\end{array}\right]\\ P_{\tilde{x}}[0/0]=Q\end{array} \tag{17}\]  
It is better to use the weight calculated by the Allan variance for weighting, but it needs to accumulate some data, which leads to a lag. To meet the actual demand, the sliding window method is used to calculate and update the dynamic weight. A weight calculation cycle accumulates certain data, and the data are used to calculate the Allan variance \(\sigma_{i}^{2}\).  
Further calculation of the weight is carried out as follows:  
\[\left\{\begin{array}{c}P_{i}=\frac{1}{\sigma_{i}^{2}}\\ w_{i}=\frac{1}{\sum\limits_{i=1}^{N}P_{i}}\end{array}\right. \tag{18}\]  
where \(P_{i}\) is the reciprocal of the Allan variance of satellite \(i\), used to determine weights, and \(w_{i}\) is the weight of satellite \(i\).
## 3 Experiment and Results
### Dataset  
The dataset used in this study is the CHB-MIT dataset [41], which is a collaborative project between Boston Children's Hospital and MIT. It consists of 23 pediatric patients with 163 seizures and 844 h of continuous scalp electroencephalogram (sEEG) data. The data was sampled at a frequency of 256 Hz and recorded from 22 electrodes.  
In order to meet the research objectives of this study, specific selection criteria were applied to the patients:  
1. Patients who experienced less than ten seizures per day were chosen for the prediction task. Patients with a high frequency of seizures require real-time monitoring and surgical intervention, making them less suitable for seizure prediction.
2. At least 30 min of available data before the seizure event were required. In some cases, the time interval between seizures was too short, making it difficult to gather enough pre-seizure training data. Therefore, for seizures with intervals less than 30 min, they were considered a single seizure, and the time of the first seizure was used. Additionally, reserving an appropriate interval between seizures allows for sufficient time for clinical intervention.  
Based on these criteria, 13 patient datasets were selected from the CHB-MIT dataset for model validation (Table 1).  
Figure 9: Illustration of the definitions of SPH and SOP.
## 3 Scaling Laws  
In the inviscid gas dynamics formulation described above, variables such as density, velocity, pressure, and internal energy are related in ways controlled by their dimensional units. [PERSON] [3] and [PERSON] [9] show that chemical explosion fields are controlled by the non-dimensional independent variables of space, \(x\), and time, \(\tau\), according to  
\[x=r/r_{0} \tag{13}\]  
\[\tau=t/t_{0} \tag{14}\]  
where the explosion length scale is  
\[\tau_{0}=(m\cdot\Delta H_{d}/p_{a})^{1/(j+1)} \tag{15}\]  
and the explosion time scale is  
\[t_{0}=r_{0}/a_{a} \tag{16}\]  
In the above, \(m\) is the mass of the charge, \(\Delta H_{d}\) represents the heat of detonation of the explosive material, and \(a_{a}\) denotes the sound speed of the ambient atmosphere. The variable \(j\) equals 0, 1, or 2 for planarly, cylindrically, or spherically symmetric flows. Similitude theory states [3] that the non-dimensional dependent variables of \(U=\{\varrho,\;u,e,\;p,\;T\}\) of gas dynamics are functions of \(x\) and \(\tau\):  
\[\mathrm{R}=\varrho/\varrho_{a}=f_{1}(x,\tau) \tag{17}\]  
\[\mathrm{R}=\varrho/\varrho_{a}=f_{1}(x,\tau) \tag{18}\]  
\[\varepsilon=e/e_{a}=f_{3}(x,\tau) \tag{19}\]  
Figure 2: Initial conditions based on the similarity solutions for a Chapman–Jouguet (CJ) detonation wave in the gas phase by [PERSON] [14], and the linear velocity profile of an inertial flow expansion into a vacuum by [PERSON] [15] for the droplet phase.  
\[P =p/p_{a}=f_{4}(x,\tau) \tag{20}\] \[\Theta =T/T_{a}=f_{5}(x,\tau) \tag{21}\]  
For spherical explosions in three-dimensional Cartesian co-ordinates, Equation (13) becomes  
\[x=x/r_{0}\text{ and }y=y/r_{0}\text{ and }z=z/r_{0} \tag{22}\]  
and the spherical (\(j=2\)) explosion length scale becomes  
\[r_{0}=\left(m\cdot\Delta H_{d}/p_{a}\right)^{1/3} \tag{23}\]  
The dependent variables then become  
\[R =\varrho/\varrho_{a}=f_{1}(x,y,\ z,\ \tau) \tag{24}\] \[U =u/a_{a}=f_{2}(x,y,\ z,\ \tau)\] (25) \[E =e/e_{a}=f_{3}(x,y,\ z,\ \tau)\] (26) \[P =p/p_{a}=f_{4}(x,y,\ z,\ \tau)\] (27) \[\Theta =T/T_{a}=f_{6}(x,y,\ z,\ \tau) \tag{28}\]  
The above is the form of the solution of the inviscid conservation laws of gas dynamics for spherical explosions. Everything scales with the _explosion length scale_, \(r_{0}\).
## 3 Short-Reach Systems Classification and Enabling Technologies
### Mach-Zehnder Modulator
It should be noted that a simple IM-DD-based implementation with a low form factor and low-cost transmitters such as DMLs or EMLs is a promising technology for effective support of the future inter-DCI applications compared with an external modulation in which an MZM is employed. Nevertheless, the DMLs' or EMLs' system performances are limited by factors such as the modulation bandwidth and the electro-optical components' nonlinear impairments for the modulation and detection of the signal. As previously mentioned, there are different approaches that are based on advanced DSP that can be exploited to mitigate the limitations [103]. Semiconductor Optical Amplifier-PIN/Transimpedance Amplifier Receiver  
The achievement of a target BER in higher bit rate systems demands a higher SNR. This brings about a reduction in receiver sensitivity. To realize a high-power budget or receiver sensitivity, the employment of optical pre-amplification is essential. This can be achieved by employing erbium-doped fiber amplifiers (EDFAs). However, the EDFA implementation results in relatively higher costs and larger footprints, making it inappropriate for short reach applications. To address this, a monolithic integrated SOA-PIN/transimpedance amplifier (TIA) receiver can be employed. Nonetheless, it operates only in the TE polarization. Furthermore, a receiver-based integrated semiconductor optical amplifier (SOA)-PIN chip that has low-polarization-dependent gain (PDG) can also be employed. The SOA-PIN chip exploits a buried heterostructure and a deep ridge evanescent PIN PD supported on a semi-insulating InP substrate [35].