esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### RS Data Used in Wetland Studies
#### 3.4.3 Sensor Type  
Based on our search query in WoS, we can indicate an increase in the use of RS methods in wetland research during the last decade, which is likely a result of the greater availability of new RS data sets (e.g., Sentinel 1 and 2; Landsat 8) paired with the rapid development of ML approaches. As shown earlier, a variety of RS data have been applied to evaluate wetlands, including very high-resolution (VHR) satellite imagery, Landsat, and SAR.  
The most broadly used wetland RS data source is Landsat multispectral imagery that, as of Landsat 8, is 30 m in spatial resolution for most spectral channels, with a repeat cycle of 16 days, including 11 bands, which is publicly accessible at no cost [12]. Scholars in a wide range of studies have achieved accurate wetland identification results through the incorporation of the Landsat dataset, specifically from the Landsat 8 Operational Land Imagery (OLI) satellite (e.g., [79; 80]). The limitations of approaches using data at this spatial resolution, such as the inability to exactly identify wetland locations, are in turn balanced by the possibility of such data to rule out large-scale regions as not being likely to include wetlands. Landsat-5, which holds the Guinness World Record for the longest on-orbit time (from 1984 to 2012), was also widely applied to historical wetland mapping [7; 10; 45; 81; 82]. Because they provide more spatial and structural characteristics than spectral information, the high-resolution images obtained from the RapidEye, Quickbird, WorldView-2, and SPOT 5 satellites also have become one of the main sources for resources and environmental management and application [20; 83; 84; 85; 86; 87]. The utilization of optical and SAR data for wetland studies is growing. As shown in Tables 5 and 6, various satellite data with different resolutions are used in different studies. Among optical sensors, the Landsat Archive (including Landsat 4, 5, 7, and 8) has the greatest share by far (with \(n=201\) studies), followed by Sentinel-2 and RapidEye with a share of \(n=54\) and \(n=14\) studies, respectively. In SAR equipped satellites, Sentinel-1 (\(n=45\)), RADARSAT-2 (\(n=31\)), and ALOS PALSAR (\(n=25\)) are the mostly used SAR data sets.  
Figure 12: Frequency of single- and multi-source RS data usage in wetland studies.  
Along with varieties of RS data that have been engaged in wetland studies, employing the new generation of RS data provides promise for resolving the remaining challenges in this field. Meanwhile, it is recommended to utilize the RADARSAT Constellation Mission (RCM) and the upcoming NASA-ISRO SAR Mission (NISAR) in future wetland studies by establishing new methodologies.
# MEXN: Multi-Stage Extraction Network for Patent Document Classification
## 4 Experimental Results
### Visualization  
**Attention weight curve.** We experiment to investigate the distribution of the attention weight for a deeper understanding on MEXN. When the distribution of the weights is uniform, the classification  
Figure 4: Computational cost analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Section**} & \multicolumn{3}{c}{**Subsection**} & \multicolumn{3}{c}{**Overall**} \\ \cline{2-10}  & **EM** & **HL** & **Jacc** & **EM** & **HL** & **Jacc** & **EM** & **HL** & **Jacc** \\ \hline \hline Kim-CNN & 48.613 & 0.0813 & 66.221 & 24.899 & 0.0111 & 34.742 & 20.058 & 0.0157 & 48.500 \\ regLSTM & 53.756 & 0.0716 & 71.5755 & 35.821 & 0.0093 & 0.042 & 31.021 & 0.0133 & 58.799 \\ HAN & 51.441 & 0.0760 & 71.462 & 37.855 & 0.0090 & 56.976 & 30.863 & 0.0134 & 61.778 \\ patentBert & 64.891 & 0.0502 & 81.482 & 49.976 & 0.0066 & 70.833 & 45.435 & 0.0094 & 74.329 \\ MEXN & **66.692** & **0.0471** & **82.710** & **51.565** & **0.0062** & **72.716** & **45.813** & **0.0090** & **75.489** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparisons on USPD validation set.  
outputs are equally support by all paragraphs. On the other hand, when the attention weight distribution concentrates in one paragraph, MEXN performs the classification by focusing on that paragraph. We calculate the weight values for each paragraph of the document in the validation set and then average them to produce distribution. In Figure 5, we observe the skewed distribution of weight at the section categories. It means that the model set more weight title and abstract parts of the document. On the other hand, the sub-section category shows a more uniform distribution than the section category. This indicates that the detailed descriptions are more helpful to understand the documents more specifically.
## 5 Experiments and Results  
In order to verify the airborne area camera imaging control technique and image motion calculation algorithm, a series of flight tests were performed. The three-axis ISP (Figure 12) was developed using the mechanism structure presented in Section 2.1. The roll axis of the platform could rotate from \(-85^{\circ}\) to \(+85^{\circ}\), and large-scale sweep imaging could be performed. The iXU-RS1000 full-frame aerial camera [24] and video module (Figure 12) were integrated as a replaceable combination, which was mounted on the pitch frame. The focal length was 50 mm, the pixel size was 4.6 \(\upmu\)m, the array scale was 11,608 \(\times\) 8708, the cross-flight angle of view was 56.2\({}^{\circ}\), and the along-flight angle of view was 43.6\({}^{\circ}\). The three-axis ISP was loaded into the payload chamber of an unmanned aircraft (Figure 13). The elevating mechanism descended the camera and video out of the payload chamber before imaging. The aircraft velocity was 45 m/s, and the relative flight height was 1000 m during flight tests. The vertical imaging model and sweep imaging mode were both tested in the flight tests.
## 2 Materials and Methods
### CO\({}_{2}\) Adsorption  
CO\({}_{2}\) adsorption was carried out using an Infrared Gasboard-3000P gas analyzer (Cubic Sensor, Wuhan, China). Initially, samples were heated at 100 \({}^{\circ}\)C for 12 h to remove the moisture content. The dried ZIF-8/ZCAF samples (0.25, 0.5, 0.75, and 1 g) were then mixed with 5 molar solutions of monoethanolamide (MEA) in a magnetic stirrer for 30 min. In this experiment, an 80:20 ratio of N\({}_{2}\) and CO\({}_{2}\), respectively, was used, and the mass flow rate controller controlled the pressure of gases. The gases were passed through a funnel to the test tube containing the slurry (ZIF-8/ZCAF samples with MEA solution) in the water bubble bath. Here, MEA reacts with CO\({}_{2}\) and adsorbs; following this, the gases were passed through a silica gel chamber to remove the moisture content. After that, the gases were passed through the Infrared Gasboard-3000P gas analyzer. The experiments were stopped when the analyzer indicated the attainment of equilibrium composition. This meant that the inlet CO\({}_{2}\) concentration in the gas stream matched the outlet concentration, signifying complete saturation of the amine blend solution with CO\({}_{2}\). The equilibrium CO\({}_{2}\) loading (mol CO\({}_{2}\)/mol amine) was determined using a Chittick apparatus [28]. In this experiment, a 1 mL sample of the CO\({}_{2}\)-loaded solution was extracted and titrated using a 1 M HCl stock solution. The process flowchart for the synthesis can be observed in Figure 3.  
The endpoint of the titration was determined using methyl orange as an indicator. To ensure accuracy, each experiment was repeated three times, and the average value was  
Figure 3: Schematic representation of CO\({}_{2}\) adsorption in standard MEA solution with ZIF-8/ZCAF samples.  
reported as the final result. The equilibrium CO\({}_{2}\) loading (\(\delta\)) was calculated using the Pandey and Mondal correlation of equilibrium Equations (1) and (2) [29].  
\[\delta=12.1942\times\frac{\mathrm{V}_{\mathrm{CO_{2}}}}{\mathrm{V}_{\mathrm{CO_{ 2}}\times\mathrm{V}_{\mathrm{CO_{2}}saturated}}}\times\frac{1}{(273.15+\mathrm{T })}\mathrm{mol\ CO_{2}/mol\,amine} \tag{1}\]  
\[\mathrm{Adsorption\ capacity}=\delta\ \mathrm{C}_{\mathrm{blend}}\ \mathrm{mol\ CO_{2}/L}\ \mathrm{solution} \tag{2}\]  
where \(\delta\) denotes equilibrium CO\({}_{2}\) loading and V\({}_{\mathrm{CO_{2}}}\), Cblend, V\({}_{\mathrm{CO_{2}}saturated}\), and T are the volume of CO\({}_{2}\) gas released (in L) from the amine solution, amine blend concentration (in mole/L), volume of CO\({}_{2}\) saturated amine (in L), and room temperature (in \({}^{\circ}\)C), respectively.
## 3 The Carrier-Launcher System Performance: Preliminary Analyses and Results
### Modal Analyses and Aeroelastic Analyses of the Carrier-Launcher System
In both conditions, we inserted in the input file of NASTRAN a suitable list of speeds to be analysed in order to identify the sign variation of the overall damping parameter, and therefore the instability conditions. It is known that with the \(p\)-\(k\) method it is also possible to identify the conditions of static divergence; in this case, the imaginary part of the eigenvalue, which is related to the frequency of the unstable mode, tends to zero. In fact, this happened in one of the flight conditions examined. For the flight condition at sea level, we found a flutter instability in the range \(170\div 180\) m/s corresponding to a flutter speed of about 176.5 m/s with a Mach number of \(M=0.52\) and frequency of 1.4 Hz. A second form of instability instead highlighted a condition of divergence with a critical speed equal to about 220 m/s (\(M=0.64\)). In the cruise flight condition, we found flutter instabilities. The first instability occurred at a speed of about 281.5 m/s (Mach number \(M=0.954\)) with a frequency of about 1.9 Hz; other forms of instability occurred for unrealistic Mach number values. The forms of instabilities found are listed in Table 13. Damping and frequency, as a function of flight speed, are shown in Figures 19 and 20 for the sea level condition. The data calculated for the cruise flight condition are shown in Figures 21 and 22. Figure 21: Graph of damping for the First Unstable Mode (Cruise Condition). Figure 19: Graph of damping for the First Unstable Modes (Sea Level). Figure 20: Graph of frequency for the First Unstable Modes (Sea Level). In the cruise condition, divergence was found in the supersonic regime, but this result was also considered unreliable. Figure 23 shows, as an example, the sketch of the unstable Mode 6 (the flutter mode) calculated for the half-wing clamped model at the sea level flight condition (figure shows only the wing-box structure deflections).
# IPv6 Convergence for IoT Cyber-Physical Systems
## 4 Proposed Approach
### IPv6 over LoRaWAN  
If the worst-case PDU size of 81 bytes was adopted, then a protocol-packing approach can potentially support five of the eight defined data rates for both of the repeater-compatible and no-repeater configurations. We can therefore summarize as follows:  
* As a concept, protocol packing appears a feasible approach of encapsulating 6 LoWPAN PDUs in existing US and EU LoRaWAN payloads.
* Protocol packing is potentially a method to preserve standards/alliance compliance.
* For maximum usage efficiency of the underlying communications technology, we still need to develop methods to support 6 LoWPAN PDUs for the lowest LoRaWAN data rates; for example, for 19-, 59- and 61-octet payloads.  
Figure 10: Second and subsequent fragmentation header.  
Figure 9: First fragmentation header.  
In our work, however, we examined how to support IP connectivity for all LoRaWAN maximum-payload-size options and then further extended this to create an ultracompressed IP-control messaging approach. Our protocol-packing design approach is illustrated in Figure 11, and the steps are as follows:  
1. Perform command compression and additional field eliding. Copy the 6 LoWPAN PDU frame into the frame payload field of the LoRaWAN MAC payload data structure.
2. Insert this MAC payload into the LoRaWAN PHY payload frame field.
3. Map the PHY payload frame into the LoRaWAN radio frame.
4. The LoRaWAN frame is ready for dispatch.  
The key stages of our proposed ultracompression method are as follows. The normal IPv6 header, depicted in Figure 12, comprises 40 bytes.  
Figure 11: Packing 6 LoWPAN as LoRaWAN frames.  
The ICMPv6 header comprises four bytes with an optional data field (Figure 13).  
The initial ICMPv6 information request/response used for testing was an echo request, which comprises the frame format shown in Figure 14.  
Figure 14: ICMPv6 echo request packet format.  
Figure 12: IPv6 header format.  
Figure 13: ICMPv6 header format.  
We combined the 6 LoWPAN approach outlined in Section 4.1.1, Robust Header Compression (RoHC) compression [38] and field eliding, for example, and the data field was not required, as a means of producing an ultracompressed ICMPv6 packet ready for use with LPWA networks. The header format following initial RoHC compression is described in Figure 15.  
Further compression was made possible by extending a LPWA network server to maintain an active cache of endpoint addresses and states, and performing address translation at that point. In addition, for ICMPv6-like control messages, the command code length is fixed, and therefore, further field eliding was possible; for example, payload length eliding. As a result, we could compress the header to one byte. As an example, following additional field eliding, we created a frame format supporting ultracompressed IPv6 ICMPv6 commands in Figure 16.  
The sequence of transactions required to support the dispatch of 6 LoWPAN PDUs from an IPv6 node to an IPv6 node via a LoRaWAN hetnet is depicted in Figure 17.
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 2 Preliminaries
### Federated Learning  
In a federated learning scenario, there are \(N\) clients with their own local data \(\{D_{1},\,D_{2},\ldots,D_{N}\}\), and a server that wants the participants to jointly train an effective model. The initial model parameter of the server is denoted as \(W^{(0)}\). Each client has their own model, whose structure is consistent with that of the server. The client will upload the model to the server every time it trains, which will lead to a high cost of network communication and low training efficiency. In order to reduce the number of communications, the client locally trains their model for multiple rounds, and then uploads the accumulated updates of model parameters to the server. This algorithm is called _FedAvg_[4], which is one of the most widely used federated learning algorithms. In the \(r\)-th round of training, the server first randomly selects \(m\) clients from \(N\) clients, and sends current model parameter vector \(W^{(r)}\) to the selected \(m\) clients simultaneously. Then, each selected client \(i\), utilizes the model parameter sent by the server and its local data, to train a new model. We denote the new model parameter as \(G_{i}^{(r)}\). The local model update \(\Delta_{i}^{(r)}\doteq G_{i}^{(r)}-W^{(r)}\) is sent to the server. After receiving the update from each selected client, the server computes the federated model as follows:  
\[W^{(r+1)}=W^{(r)}+\frac{\sum_{i=1}^{m}(\Delta_{i}^{(r)})}{m}. \tag{2}\]  
Then the server will send \(W^{(r+1)}\) to all clients, and the \(r+1\)-th round of training begins. Generally, after several rounds of training, the federated model converges.  
In the federated learning scenario, the dataset of each client may be IID or non-IID. The FedAvg algorithm can be directly applied to IID scenarios. While for non-IID scenarios, using FedAvg will lead to a decline in the accuracy of the federated model, because the data distributions of different clients are quite different. At present, how to train a federated model on non-IID data is still an open problem in federated learning and needs to be solved urgently. To solve the non-IID problem, [PERSON] et al. [23] proposed creating a data subset that can be shared on all clients. The shared data subset can narrow the difference between the data distribution of each client and the overall data distribution. The privacy-preserving federate learning framework proposed in this paper, is obtained by incorporating an LDP perturbation mechanism and proposed aggregate methods into federated learning, and better model accuracy is obtained by modifying the model aggregation method.
## 1 Introduction
Another approach involves studying the physical relationships underlying pointing errors and developing a model about them, including the basic parameter model, mount model, and physical model with empirical terms [20; 21; 22]. Many telescopes calibrated by this method point to the arcsec level of precision [23; 24; 25]. Such models typically have fewer terms and correspond to physical errors within the telescope. Additionally, this practice is more likely to provide reliable outcomes when used to extrapolate outside of the scope of the sky that the model was initially fit [7]. However, the technique above-used for ground-based telescopes is not fully functional for MPEOT due to platform vibration and environmental disturbance. Currently, the most effective way to obtain pointing error models is by observing a group of stars in the sky region to acquire the pointing error data of the telescope at each position, including selecting stars, building mathematical models, and solving parameters [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. The model only validly works for a while. As time passes, the physical properties and environment of the telescope change, requiring a new model to be built by re-observing the stars. It is worth noting that MPEOTs require long exposures in target observations, during which residuals revised by the above models tend to fluctuate widely. This phenomenon indicates that the model parameters are time-limited, and a single correction cannot maintain the high accuracy of the MPEOT pointing over time. As a countermeasure, this paper proposes a new optimized parameter model based on the physical kinematic model and the correlation characteristics of the parameters, which can significantly mitigate the pointing error during a longer working period. The remaining part of the essay proceeds as follows. Section 2 reinterprets some operating procedures and correction guidelines. Section 3 summarizes the error sources and extracts a linear pointing correction model directly from the target localization equation. Section 4 describes the multicollinearity problem between parameters, using stepwise regression to optimize the linear model. To verify the validity of the optimized model, several experiments are conducted in Section 5. The relevant discussion and conclusions on the experimental results are presented in Sections 6 and 7, respectively.
## 4 Methodology
### Block Diagram of AES-LDPC Cryptcoding  
This method uses modified AES with LDPC coding that uses 128 bits of block length and key length. AES has a typical substitution and permutation network (SPN) architecture. Figure 4 depicts the block diagram of AES-LDPC coding (cryptcoding). The first 9 rounds are the same as the traditional AES with regard to proposed key and subkey generation. LDPC coding is embedded in the tenth round, after the substitution box (S-box). Therefore, we get the encryption control of the LDPC encoding round. The output of the tenth round is the desired ciphertext that needs to be stored in the cloud. Table 2 gives the details of parameters used for AES-LDPC cryptcoding [12; 13].
# Instability Induced by Random Background Noise in a Delay Model of Landslide Dynamics
###### Abstract  
In the present paper, we propose a new model for landslide dynamics, in the form of the spring-block mechanical model, with included delayed interaction and the effect of the background seismic noise. The introduction of the random noise in the model of landslide dynamics is confirmed by the surrogate data testing of the recorded ambient noise within the existing landslide in Serbia. The performed research classified the analyzed recordings as linear stationary stochastic processes with Gaussian inputs. The proposed mechanical model is described in the form of a nonlinear dynamical system: a set of stochastic delay-differential equations. The solution of such a system is enabled by the introduction of mean-field approximation, which resulted in a mean-field approximated model whose dynamics are qualitatively the same as the dynamics of the starting stochastic system. The dynamics of the approximated model are analyzed numerically, with rather unexpected results, implying the positive effect of background noise on landslide dynamics. Particularly, the increase of the noise intensity requires higher values of spring stiffness and displacement delay for the occurrence of bifurcation. This confirms the positive stabilizing effect of the increase in noise intensity on the dynamics of the analyzed landslide model. Present research confirms the significant role of noise in landslides near the bifurcation point (e.g., creeping landslides).  
Keywords:landslide; random noise; delayed interaction; mean-field approximation; bifurcation +
Footnote †: journal: Applied