esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 5 System Architecture
### System Deployment  
The secure data asset sharing and circulation system is generally deployed according to cloud-edge-end architecture. As shown in Figure 8, the cryptography service cloud center deployed in the cloud provides security protection business functions in the form of service, including identity authentication, access control, secure storage, operation management, security return, key management and other cryptography services, which are called directly by users according to the interface rules of RESTful API. The service center deployed at the edge is connected with the cryptography cloud service center and the protected data asset business system through the security gateway, and provides security services such as outsourcing storage, flow control, trusted operation, cross-domain return and so on in the process of data asset sharing and circulation. The security gateway deployed on the end side is connected with the cloud and edge control services through the built-in agent to  
Figure 6: Architecture scheme for secure data asset sharing and circulation system.  
realize the security requirement response and security service installation of the protected data asset business system.
## 1 Introduction
However, systematic errors within displacement fields significantly constrain the accuracy of deformation monitoring, yet there remains a lack of dedicated research to address this issue. To date, RapidEye has predominantly been applied to small-scale landslide deformation analysis [14], which postulated that the stacking of large sets of offset maps should compensate for this topographic bias. Errors within small-scale offset fields are typically singular and easily corrected; thus, very few systematic discussions have been conducted on them. Conversely, large-scale offset fields are characterized by complex and severe systematic errors. In conclusion, developing systematic error correction methods for large-scale offset fields derived from RapidEye is crucial for conducting quantitative surface deformation monitoring studies using RapidEye data. Significant disparities exist in error correction methods for offset fields derived from various satellites, underscoring the critical necessity for a holistic error correction framework tailored to RapidEye. Such a framework is designed to enhance the precision and efficiency of geo-disaster deformation studies utilizing RapidEye imagery, thereby maximizing the application potential of RapidEye data in geo-disaster management and monitoring. To address the aforementioned issues, we selected the Ridgecrest area in California, USA, as our study area to analyze the error characteristics of the offset fields obtained from RapidEye images and propose corresponding correction methods applied to the earthquakes that occurred in July 2019 in Ridgecrest. Additionally, the surface deformation monitoring capability of RapidEye is also discussed.
## 4 Experiments
### Face Recognition Results  
To further prove that the proposed method can recover crucial facial structures that are essential in distinguishing different faces, we also perform face recognition as a measurement. Specifically, we chose the commonly used LFW [63] dataset as the face recognition database. Then, several images are randomly picked, downsampled, and super-resolved as the reference images with different FSR methods. After that, we select face images with the same and other identities as test images for every reference. Finally, we adopt a pre-trained face recognition model, Deepface [64], to perform face recognition. Moreover, we also measure Uformer along with the proposed method for better comparison and the bicubic interpolation as the baseline. The Receiver Operator Characteristic (ROC) curve can be seen in Figure 11.  
From Figure 11, we can observe that:  
(a) The performance of Deepface [64] on the original HR images is excellent, which proves the significant improvement in the face recognition field based on the deep-learning network.  
(b) SR images with bicubic interpolation are difficult for Deepface [64] to verify. This is reasonable due to the poor SR performance of the bicubic interpolation, which can be seen from the sections mentioned above.  
(c) Both the SR images reconstructed by the Uformer and the proposed method have obtained satisfactory face recognition performance. Moreover, the proposed method has a larger AUC result, demonstrating its better performance in face recognition tasks and further proving its ability to recover crucial facial structures that are essential in distinguishing different faces.  
Figure 10: Visual comparisons for \(\times\)8 SR on face images with noises. Please zoom in for better comparison.
The modeling is accomplished by taking into consideration the dynamics of AVD. Discrete time sliding mode (DSMC) generates the control signals which are used for the suppression of chatter. DSMC is combined with type-2 fuzzy logic (DSMC-T2 fuzzy) for efficiency. The implementation of the type-2 fuzzy system ensures that the nonlinearities are compensated in an effective manner. The approach of Lyapunov stability analysis is implemented to prove that the DSMC-T2 fuzzy controller is a stable one. The chatter attenuation is achieved by the combined action of DSMC-T2 fuzzy with AVD. The wide significance of the concept and methodology is validated using numerical analysis. The results of the DSMC-T2 fuzzy are compared with discrete time sliding mode control with type-1 fuzzy (DSMC-T1 fuzzy) and discrete time PID (D-PID) to prove the effectiveness of the most suited controller. The numerical analysis results validate that the methodology of chatter control can be implemented effectively in the real time milling system. This can be achieved by designing and developing an AVD and placing it on the top of the milling spindle.
# Throughput and Packet Loss Probability Analysis of Long Range Wide Area Network
## 2 LoRaWAN System Model
In LoRaWAN, SF is determined by signal to noise ratio (SNR) given by  
\[SNR_{dB}=10 log_{10}\bigg{(}\frac{P_{signal}}{P_{noise}}\bigg{)}=P_{signal,dB}-P_{noise,dB}. \tag{7}\]  
Here, \(P_{signal,dB}\) is calculated by  
\[P_{signal,dB}=P_{TX}+G_{TX}-L_{TX}-L_{PL}-L_{M}+G_{RX}-L_{RX}, \tag{8}\]  
where \(P_{TX}\), \(G_{TX}\), \(L_{TX}\), \(L_{PL}\), \(L_{M}\), \(G_{RX}\), and \(L_{RX}\) are tx output power, tx antenna gain, transmitter losses (coax, connectors), path loss, miscellaneous losses (fading margin, body loss, polarization mismatch), rx antenna gain, and receiver losses, respectively. We simplify (8) by combining all general gains and losses as GL, which results in  
\[P_{signal,dB}=P_{TX}-L_{PL}+GL. \tag{9}\]  
LoRaWAN uses a log-distance path loss model, which is modeled for inside a building or densely populated areas. In a log-distance path loss model, the path loss, \(L_{PL}\), is defined [12] as  
\[T_{pb}=(n_{pb}+4.25)\cdot T_{S},\ \ \ T_{pl}=n_{pl}\cdot T_{S}, \tag{2}\]0  
where \(L_{PL(d_{0})}\) is the path loss at the reference distance \(d_{0}\), \(\gamma\) is the path loss exponent, and \(X_{\sigma}\) is a normal random variable with zero mean, reflecting the attenuation caused by flat fading, and \(d\) is the distance between the ED and the GW, respectively. The noise power \(P_{noise,dB}\) is defined as a function of the bandwidth (BW) and is given by [13]  
\[T_{pb}=(n_{pb}+4.25)\cdot T_{S},\ \ \ T_{pl}=n_{pl}\cdot T_{S}, \tag{2}\]1  
\(P_{TX}\) and \(GL\) are set to 14 dBm and 0, respectively, by referring to the LoRa module specification. We use \(L_{PL(d_{0})}=127.41\) dB, \(d_{0}=1\) km, and \(\gamma=2.08\) as in Reference [12]. Table 1 shows the measured distance between a GW and an ED obtained by combining (7), (9), (10), and (11), and this table is used to determine the SF of an ED for uplink transmission.
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Model Catalysts as Tips
#### 3.2.2 Region-Specific Analysis of Pt-Based Catalysts  
One phenomenon of interest is the catalytic activity of Pt-Pd alloys when exposed to C\({}_{\mathrm{x}}\)H\({}_{\mathrm{y}}\) films. Pt-20 at.% Pd specimens were prepared by the electropolishing method using an aqueous solution of 20% CaCl\({}_{2}\) and 35 V\({}_{\mathrm{A}C}\), followed by a FIB milling. A thin layer (<10 nm) of C\({}_{\mathrm{x}}\)H\({}_{\mathrm{y}}\) was deposited on the surface by e-beam deposition of phenantrene, and then heated by laser irradiation for 2 h. Untreated Pt-Pd specimens reveal a uniform concentration profile, however, on thermally treated specimens Pd segregation occurs as Pd-oxides species. An interesting point is that after heating at 873 K, the APT analysis of the phenantrene-covered specimen returns different mass spectra at the surface of the C\({}_{\mathrm{x}}\)H\({}_{\mathrm{y}}\) thin films and at the interface of the Pt-Pd/C\({}_{\mathrm{x}}\)H\({}_{\mathrm{y}}\), suggesting that a catalytic reaction occurred [115], although the details are uncertain.  
A correlative SEM, FIB, EDX (Energy-Dispersive X-ray spectroscopy), thermogravimetry and APT study was performed on Pt-Rh-Pd gauze catalyst (composition 83 at.% Pt-8 at.% Pd-9 at.% Rh) [116]. Samples for APT analysis were produced using the electropolishing method for control experiments; and using the FIB lift-out method for specific areas: near a grain boundary and at the center of the grain. After 5 h of oxidation under oxygen flow and at temperatures from 873 to 1273 K, the results highlight different segregation behaviors as a function of the region analyzed. At the grain boundary, an oxide layer is formed consisting mainly of Rh\({}_{2}\)O\({}_{3}\) species, with only traces of Pt-oxides. Below this oxide layer, the specimen is Rh depleted (with a concentration of 0.4 at.%). Further away from the grain boundary, a Pd enrichment forming a 1-2 nm layer (reaching 20-35 at.% of Pd) and a slight Rh enrichment (12-18 at.%) can be observed. This indicates that the Rh oxide is the most likely oxide to form, preferentially at the grain boundaries, and that Pd does not play a significant role in the oxidation at grain boundaries. After analyzing different regions of the same specimen by APT with site-specific lift-out, a more complete picture of the catalyst's structure and composition was obtained. This highlights one of the limitations encountered when using a tip-sample produced from a wire to mimic the behavior of a real catalyst. The next sections will focus on the investigations of real catalysts by atom probe tomography.
## 3 Results  
### Adsorbent Characterization  
Pristine graphene has a very high surface area but no functional groups to facilitate the attachment of metals or metal oxides on its surface [3]. On the other hand, GO readily disperses in water and has strong interaction with other substances, making it promising support for depositing different inorganic compounds on its surface. GO can be reduced hydrothermally [3].  
The surface topography of the rGO-Fe\({}_{2}\)O\({}_{3}\) nanocomposite was analyzed using SEM. The SEM image (Figure 1a) shows a thin platelet-like structure with lateral dimensions of a few to several \(\upmu\)m. Moreover, the HR SEM image in Figure 1b shows the 10-20-nm Fe\({}_{2}\)O\({}_{3}\) nanoparticles that are highly distributed on the surface of rGO.  
Figure 1c shows the XRD patterns of GO and rGO-Fe\({}_{2}\)O\({}_{3}\). The polar oxygen functionalities on the surface of GO and with the adsorbed water molecules expanded the interlayer spacing of GO compared to graphite, shifting the characteristic (002) peak to 20 of 10.56\({}^{\circ}\), corresponding to the d-spacing of 7.22 A. Upon reducing GO to rGO, the sharp (002) peak of GO disappeared, confirming the reduction/exfoliation of GO to rGO. The diffraction pattern of the nanocomposite had diffraction peaks of the (012), (104), (110), (113), (024), and (116) planes of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) crystal consistent with the JCPDS profile no. 33-0664.  
The N\({}_{2}\) adsorption-desorption isotherms (80 K) for the rGO-Fe\({}_{2}\)O\({}_{3}\) nanocomposite (Figure 1d) were consistent with the type IV isotherm characterized by a hysteresis loop at high \(P/P_{0}\) due to the presence of mesopores. The pore volume was calculated at lower pressure using the Barrett-Joyner-Halenda (BJH) analysis. The Kelvin equation was used to determine the pore size distribution of rGO-Fe\({}_{2}\)O\({}_{3}\). Moreover, micropore volume was calculated via density functional theory (DFT). The nanocomposite had a good surface area of 150 m\({}^{2}\)/s and a reasonable pore volume of 0.36 cm\({}^{3}\)/g.
# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
## 2 Network Architecture and System Model  
We consider an LTE-based V2X scenario, where the vehicular user equipment (VUE) is present in the network along with the cellular user equipment (CUE) and both share the uplink resources available. We assume that the VUE and CUE are distinct in terms of their requirements and applications, and hence we have a different set for CUE. The VUE is split into V2V and V2I for safety and non-safety applications, respectively. Let \(j=\{1\ldots,\)\(\ldots,\)\(\}\) be the set of CUE, k = \(\{1\ldots,\)\(\}\) be the set of VUE involved in V2I, and l = \(\{1\ldots,\)\(\}\) be the set of VUE involved in V2V communication. All the communications occur simultaneously, and we assume there is a single antenna for both transmission and reception. Let \(\chi=\{1\ldots,\)\(\}\) denote the uplink resource blocks (RBs) available.
## 3 Analysis of SCSRR-Based DGS MIMO Antennas with Different Feed Networks
### Proximity-Coupled Feed  
The simulation model with dimensions of proximity-coupled feed MIMO antennas with and without SCSRR-based DGS structures are shown in Figures 19 and 20, respectively. In order to obtain the impact of SCSRR-based DGS, MIMO antennas with and without SCSRR-based DGS have been analyzed, as illustrated Figure 21. Notable influences of the SCSRR on the antenna isolation characteristic (close to 10 dB improvement) were observed, as shown in Figure 21.  
The 3D gain patterns of the proximity coupled feed MIMO antennas with SCSRR-based DGS are depicted in Figure 22. A decrease in the main lobe was noticed (see Figure 22) due to the presence of the SCSRR-based DGS. Figure 23 shows the surface current distribution for proximity feed MIMO antennas at 5.8 GHz. It is clearly observed from the distributions that the surface current of MIMO elements without SCSRR-based DGS has been suppressed after applying the SCSRR-based DGS structure. A comparison of the antenna performance parameters with and without SCSRR-based DGS structures is provided in Table 6.  
Figure 20: Proximity-coupled feed MIMO antenna model with SCSRR-based DGS. Note: Dimensions of SCSRR are listed in Table 3.  
Figure 21: S-parameters of proximity-coupled feed MIMO antenna with and without SCSRR-based DGS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Without SCSRR** & **With SCSRR** \\ \hline \(f_{c}\) [GHz] & 5.75 & 5.75 \\ \hline Peak return loss [dB] & 18 & 17.78 \\ \hline BW [MHz] & 263 & 270 \\ \hline Peak gain [dBi] & 7.2 & 6 \\ \hline Peak isolation [dB] & 20 & 31 \\ \hline RE [\%] & 97.4 & 97.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of proximity coupled feed MIMO antenna with and without SCSRR-based DGS.  
Figure 22: The 3D gain patterns of proximity-coupled feed MIMO antennas with SCSRR-based DGS.  
Figure 23: Current distribution of proximity coupled feed MIMO antennas (**a**) without and (**b**) with SCSRR-based DGS.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 4 System Parameter Design
### Integrate Parameter Design Flow  
Based on the above discussions, the steps of system parameter design for the SSS-RSSSAR can be summarized as:  
Step 1. Set the parameters of an observation task, including the 2D swaths (\(W_{\rm a}\) and \(W_{\rm r}\)) and 2D resolutions (\(\rho_{\rm a}\) and \(\rho_{\rm r}\)); Set the referential carrier frequency \(f_{\rm c0}\).  
Step 2. Choose a proper orbit for the data acquisition with the parameters including the orbit height \(H\), platform velocity \(V_{\rm s}\) and tilt angle \(\theta\); Find the SAR position at the central data acquisition time based on the perpendicular-to-BIS CBP; Compute the central look angle \(\beta_{\rm c}\) and the central slant range \(R_{\rm c0}\).  
Step 3. Compute the sliding spotlight factor \(\eta\) based on Equation (51). Compute the DAP \(t_{\rm a}\) based on Equation (53). Compute the starting and ending SSS-RSSSAR positions, denoted as \(P_{\rm s}\) and \(P_{\rm e}\), within the DAP. Compute the along-orbit distance \(D\) between \(P_{\rm s}\) and \(P_{\rm e}\). Compute the azimuth angles \(\alpha_{\rm s}\) and \(\alpha_{\rm e}\) for \(P_{\rm s}\) and \(P_{\rm e}\), respectively.  
Step 4. Compute the azimuth bandwidth of the wavenumber spectrum \(\Delta K_{\rm X}\) based on Equation (41) by replacing \(\alpha(-N_{\rm a}/2)\) and \(\alpha(N_{\rm a}/2)\) with \(\alpha_{\rm s}\) and \(\alpha_{\rm e}\), respectively. Compute the minimum azimuth sample number \(N_{\rm Amin}\) based on (43). Set an azimuth sample number \(N_{\rm a}\) slightly higher than \(N_{\rm Amin}\). Compute the referential pulse interval \(Pl_{0}\) based on Equation (44).  
Step 5. Generate the non-uniform PI array by using the CVPI technique.  
Step 6. Compute the maximum pulse width \(T_{\rm pmax}\) based on Equation (40). Set a pulse interval \(T_{\rm p}\) slightly lower than \(T_{\rm pmax}\). Compute the required referential chirp rate \(\gamma_{0}\) based on Equations (32) and (35). Compute the sampling rate \(F_{\rm s}\) based on Equation (36). Compute the lower and upper limits for range sample number based on Equations (38) and (39), respectively. Set a range sample number \(N_{\tau}\) between these two limits.  
Step 7. Compute the azimuth-dependent carrier frequency and chirp rate based on Equation (46).  
The system parameter design flowchart for the SSS-RSSAR is shown in Figure 7.
## 4 Implementation
### ModAR Prototype  
On ModAR initialization, the users can choose from the main menu the desired pattern image, if it is already captured, and their 3D file of OBJ format from local storage. The option of capturing planar objects of their surroundings directly from the prototype's camera module and load one of the default 3D models, is also provided. In this case, the 3D model can be directly selected from a list menu that overlays the camera view. The graphics user interface of ModAR as well as a scene augmented with a pre-loaded 3D model are presented in Figure 3. ModAR comes with four 3D models and six default pattern images stored as bitmaps in the internal memory (Figure 4). Thus, each AR scene consists of one 3D model that is drawn once the corresponding pattern image is met by the camera. The 3D model is displayed in the center of the pattern object, either with a default scale (its biggest dimension being equal to the biggest dimension of the pattern image) or with a user-specified scale. If the users want to select another model to be overlaid to the same or another pattern, they have to return to the previous screen and pick again the files. The previous surface is destroyed but no cache or buffers destruction occurs. If any of the previous 3D models is selected, an instanced version is ready for visualization. Figure 5 shows screenshots from the ModAR prototype depicting the pattern recognition and AR augmentation processes.  
Figure 3: Main menu (**left**), list menu of camera interface (**middle**) and an AR scene (**right**) of the ModAR prototype.  
Figure 4: Default pattern images (depicted with correct relative size) and 3D models stored by the ModAR prototype.  
Figure 5: Screenshots from the ModAR prototype illustrating the pattern recognition and AR augmentation processes.
## 5 Numerical Results and Analysis
### The BER Performance versus K Comparison  
The BER performance curves of these detectors _versus_ the number of active users \(K\) are displayed in Figure 8, when the energy per bit of all users is set \(E_{b}/N_{0}=5\) dB. In general, as the number of users increases, the BER performances of all detectors become worse. In detail, OMD has the best capability to resist the effect of the increment of \(K\), while CD has the worst. In addition, we can see that as \(K\) increases, the gap between VL-DFC and OMD enlarges. The reason for this phenomenon is that, the more users in this system, the more severely the MAI will be affected. So as \(K\) increases, the probability that the preliminary detection results of SIC, DEC, and MMSE are all wrong is bigger, resulting in the enlargement of this gap. But most importantly, the BER performances of CA-DFC and VL-DFC are both much better than other sub-optimal MUD algorithms. The achievement by introducing decision-level fusion techniques into MUD is significant.  
Figure 7: The BER _versus_\(E_{b2\sim 10}/E_{b1}\) curves in AWGN channel when \(K=10\) and no power control is employed.
# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 2 Related Work
### Cyber Psychometrics Method  
Relying on big data, the cyber psychometrics method can help predict the users' psychological characteristics through online behavior and is extensively used by researchers. It is acknowledged that microblogging information could be used to build a picture of users and [PERSON] et al. made a prediction of psychopathy using twitter content [20]. Considering the effect of personality on real life [27], [PERSON] et al. has conducted research to predict the Big-Five personality of Chinese Weibo users based on user behaviors at social network sites using the decision tree method [28]. [PERSON] et al. used linguistic features to predict suicide probability of Weibo users through linear regression [29]. [PERSON] et al. collected information from Twitter and made a prediction of personality using the Gaussian process [30]. Researchers have made use of UGC data to predict psychological variables while the mood state is rarely studied. In addition, when dealing with multiple related tasks, studies always model every prediction task separately [31]. However, psychological variables are always interrelated and a joint prediction method such as multitask learning could improve generalization performance by training tasks together and capturing their intrinsic correlation [32]. Therefore, a multitask regression method was adopted in this study to predict the mood state of Weibo users.  
This paper obtained the mood states of social network users through the POMS, then used the cyber psychometrics method to build a model to establish the relationships between online behavior and mood state, and finally calculated the psychological variables automatically based on both the structured information and unstructured text. A lexicon of mood state was constructed in this paper and used to detect the sentiment in unstructured text. The multi-task regression model was adopted to calculate users' mood state automatically.