esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Future Research Scopes
### Application Aware Adaption and Orchestration  
The remarkable rise of the IoT drives the rise of diverse applications. Edge serves as the primary storage location and computing resource for these applications and cloud is used opportunistically alongside the edge. Applications need to adapt gracefully based on the available resources in the edge as well as the priority of the applications according to the constraint and properties such as low latency, mission critical, high-volume data, etc. [19]. So, finding the appropriate adoption strategy is still an open issue for dynamically changing resources in the edge site and based on the priority of the applications. On the other hand, this end edge cloud network consists of billions of distributed connected end devices, numerous edge servers, and large-scale central servers. Nodes at different levels of networks have different abilities to compute and store, and the availability of resources dynamically change. Moreover, different deployments support different network technology such as cellular, broadband, satellite, and optical, and each technology offers different availability and performance [19; 10]. Thus, more research efforts are needed to develop network orchestration mechanisms for end edge cloud systems to optimize allocation of resources ensuring the critical needs of the adaptive applications in collaborative manner.
# An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
## 3 Kinematics of Redundant Manipulators
In this sense, finding an optimal trajectory with respect to the manipulability index can be stated as in Algorithm 1, which essentially proposes the optimization of the manipulability along the whole trajectory, considering that the manipulability can be expressed in a differential form with respect to the joint coordinates. Solutions to this problem have been proposed ([7; 19]) at significant computational expense, becoming intractable in the presence of complex obstacle settings. ```
1:Optimize: \(\sum_{i=1}^{N}\omega_{i}=\sum_{i=1}^{N}det(J_{i})_{i}^{T}\)
2:s.o. \(\mathbf{q}_{i+1}=\mathbf{q}_{i}+\mathbf{q}_{i}\star\mathbf{M}\). 3:s.o. \(\mathbf{q}_{i}=\int_{i}^{t}\mathbf{x}+k_{i}*(I-J_{i}^{\dagger}J_{i})\mathbf{q}_ {0}\)
4:RETURN \(Q=\{\mathbf{q}_{1},\mathbf{q}_{2},\ldots,\mathbf{q}_{N}\}\) the joint path
```  
**Algorithm 1** Global Manipulability Optimization. A well-known solution to invert the kinematic Equation (2) is:  
\[\dot{\mathbf{q}}=J^{\dagger}\dot{\mathbf{x}}, \tag{3}\]  
where \(J^{\dagger}\) is the Moore-Penrose pseudo-inverse [20], defined as \(J^{\dagger}=J^{T}(J)^{T})^{-1}\). This pseudo-inverse has nice properties, since it minimizes the norm \(|\dot{\mathbf{q}}^{T}\dot{\mathbf{q}}|\). Thus, given an initial joint position \(\mathbf{q}_{r}\), the length of the computed joint trajectory is, by nature, minimal. This equation allows us to compute the joint positions required to reach a set of positions and orientations \(G=\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{N}\}\) of the end effector. This minimum square solution is represented in Algorithm 2. The algorithm, for each time step, generates a joint speed that brings the solution \(\mathbf{x}\) closer to the desired goal \(\mathbf{x}_{i}\). The final solution of the algorithm depends on the initial seed used \(\mathbf{q}_{r}\), thus, infinite solutions can be obtained by initializing \(\mathbf{q}_{r}\) randomly. However, this solution may produce a path that includes kinematic singularities [3] or collides with obstacles in the environment. This simple planner makes use of Algorithm 3, computing at each time step the inverse kinematics solution from a given random joint position \(\mathbf{q}_{r}\) along the initial trajectory.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 6 Simulation
### Simulation Results  
By varying the number of nodes, message TTL value, message generation interval, and node cache size, we can analyze the performance of the five algorithms in different scenarios. In order to make the simulation results more accurate, each simulation scenario was repeated several times with different random seeds.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 3 The Proposed Scheme
### System Initialization Phase  
In the system's initialization stage, the blockchain center calculates some parameters and publishes the public parameters for patients with personal mobile devices and hospitals with medical devices.  
1. The blockchain center chooses a \(k\)-bit prime, \(p\), and determines the tuple of the elliptic curve group \((F_{p},E/F_{p},G,P)\).
2. The blockchain center then chooses \(s\) as a secret key and computes \[PK=sP,\] (1) as a public key.
3. Finally, the blockchain center chooses a hash function \((H_{1}(\cdot),H_{2}(\cdot),H_{3}(\cdot))\) and then publishes \((F_{p},E/F_{p},G,P,PK,H_{1}(\cdot),H_{2}(\cdot),H_{3}(\cdot))\) to all patients with personal mobile devices and hospitals with medical devices.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.2 TIR Radiometer  
In order to retrieve SST, Low Earth Orbit (LEO) and geostationary satellites acquire TIR data in the 7-14 \(\upmu\)m region of the spectrum [129]. The derivations of SST from TIR satellites are typically utilized to estimate the OSC velocity by tracking the motion of natural surface features (e.g., oil and algae) in cloud-free images [112; 129]. This tracking scheme is mainly carried out by analyzing the sequential TIR images in both human interactive and automatic methods [129]. In this regard, automated feature-tracking methods are more interested in retrieving OSC velocity due to the costly and time-consuming procedure of the human interactive approaches [130]. To this end, the feature tracking methods based on the MCC matching algorithm have provided promising results for NRT estimation of OSC velocity fields from consecutive TIR images [112; 129; 131; 130]. For example, [PERSON] et al. [130] used the MCC method to track features from 224 pairs of sequential TIR images acquired by Advanced Very High-Resolution Radiometer (AVHRR) between January and December 2015 in the western Mediterranean Sea. The results indicated that the satellite-based OSC resulted from tracking a small pattern with low speed and concurred well with in situ measurements, especially in summer times. Like optical satellite images, TIR radiometers are restricted by the cloud cover and undesirable viewing conditions, which reduce the spatial and temporal coverage of the resulting OSC velocity products [130].
## 4 Experiments and Discussion  
### Comparing Algorithms  
We choose four remarkable methods to perform comparative experiments with ELM-RBF-SAGA about multi-label classification. These algorithms are described as follows:  
* ML-KNN [40]: This model is a classic multi-label classification approach.
* ML-ELM-RBF [41]: This model extends ELM-RBF to deal with multi-label classification.
* ML-KELM [42]: This model makes use of an efficient projection framework to produce an optimal solution.
* ML-CK-ELM [43]: In this model, a specific module is constructed based on several predefined kernels to promote classification accuracy.
## 4 Simulations
### Evaluation of EDETA-e Protocol Underwater
#### 4.1.2 Simulation Results
This increase in the energy spent given by the node density comes with an increase of packet collisions at the initialization phase. So, different MACs, in addition to the ALOHA protocol used in this simulations, should be studied to reduce the packet collision at this phase in the underwater channel. It has to be mentioned that once the network structure was built and the normal operation began, there were no more collisions, giving 100% data packet delivery rate to the sink. So, no data packet was lost during the simulations. configuration phase. Finally, in Figure 5 we study the effect of the \(T_{SuperFrame}\) parameter on the network lifetime. To this end, we performed simulation increasing value of the \(T_{SuperFrame}\) parameter from 250 s to 800 s. As expected, network lifetime grows linearly with this parameter. We calculated a linear regression and obtained the expression noted by Equation (3) with \(R^{2}=0.999\),  
\[y=2523.5T_{SuperFrame}+88841 \tag{3}\]  
where \(R^{2}\) is the correlation coefficient and \(y\) is the estimated network lifetime. This predictable energy consumption allows us to accurately anticipate the network lifetime in absence of errors. For simplicity, only simulation results of 100 nodes and \(100\times 100\)\(m^{2}\) deployment area are shown in this graph since results for the other five scenario behave in the same way. Figure 4: Energy spent _vs._ time with 100 nodes and 200 nodes. (**a**) 100 nodes scenarios; (**b**) 200 nodes scenarios.
# Trusted Third Party Application in Durable Medium e-Service
## 3 The Proposed TTP Application
### The Main Algorithm of the Proposed e-Service
It is assumed that the first transaction related to the particular document is provided by the ISP, while the remaining blocks are related to \(n\) transactions committed on the clients' decisions. The presented algorithm assumes that there is one (the same) document for all clients. Of course, another scenario can be considered. Assume that there is a need to send annexes to the customers' agreements. In such a situation, a separate document for each client is prepared by an ISP. This means that for 10,000 clients, 10,000 documents ought to be generated. Nevertheless, the algorithm of the e-service looks similar to the one presented above. The only differences remain in the type and amount of transferred data. In the early stage of the process, the ISP sends not one document, but a set of documents with customers' identifiers assigned to them. Each document is signed by the TTP, and for each document, the TTP generates a separate symmetric AES key used for encryption. These AES keys (in an encrypted form) are sent to the ISP together with document hashes, links, and lists of customers' identifiers. The entire process is supervised by the TTP, and the blockchain is stored in the TTP infrastructure. Therefore, if there are any doubts regarding the correctness of the document processing flow or if there is a suspicion of manipulation by the ISP, then the client may gain access to the blockchain network on demand. In such a situation, it is possible to verify the document and obtain the particular decision stored in the blockchain. Such an approach greatly reduces the risk of document manipulation in the process by each party, making the proposed e-service secure and trusted. Moreover, it should be underlined that the presented e-service meets the requirements of Polish law regarding the durable medium, including the need for permanent and safe storing of electronic documents in infrastructure independent of either party, as well as the need to monitor the process of documents' storing, processing, and making them available to the customer.
# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 2 Array Stochastic Resonance Principle  
### Bistable Stochastic Resonance  
In the study of stochastic resonance theory, bistable system is one of the most commonly used models [29], which can be described by Langevin equation, namely  
\[\frac{dx}{dt}=ax-bx^{3}+s(t)+\xi(t) \tag{1}\]  
where, \(a\) and \(b\) are the structural parameters of the bistable system, and \(a>0\), \(b>0\). \(s(t)\) is the external input signal, \(\xi(t)\) is the noise signal with mean value of 0 and intensity of \(D\). When there is no input signal and noise in the system, namely \(s(t)=0\), \(\xi(t)=0\), the theoretical model expression is as follows.  
\[U(x)=-\frac{a}{2}x^{2}+\frac{b}{4}x^{4} \tag{2}\]  
where, when \(x=\pm\sqrt{a/b}\), the bistable potential function is in a stable state. When \(x=0\), it is unstable, and its barrier height is \(\Delta U=a^{2}/4b\). The potential function curve of bistable state is shown in Figure 1.  
When the external periodic signal excitation exists and the noise intensity is zero, the relative barrier of the bistable potential well will rise and fall periodically under the periodic excitation. But at this time, the energy obtained by the particle is not enough to realize the reciprocating transition between the two potential wells. When noise intensity is not zero and external periodic excitation is zero, the particle transitions between two potential wells according to Kramers transition rate [30], and the expression of transition rate \(\gamma_{k}\) is as follows:  
\[\gamma_{k}=\frac{a}{\sqrt{2\pi}}\exp(-\frac{\Delta U}{D}) \tag{3}\]  
When a certain intensity of noise is added to the bistable system, the particle can obtain enough energy to complete the transition between the double potential wells of the system under the combined action of periodic excitation and noise. At this time, the output of the system is enhanced by transferring the energy of the noise to the weak input signal.
## 5 Results
### Relative Elevation Changes of the Radar Scattering Phase Center  
We mapped the elevation change of the radar scattering phase center (PC) with reference to its position on 06/18/2011. It was mapped with a high temporal resolution during 2011-2014 but limited afterwards due to data constraints. We noted two distinct patterns of elevation change of the PC during 2011-2014. The mean elevation change of the PC increases continuously in late winters while the peaks of elevation change can be observed between spring to midsummer (black line in Figures 2e and 3b). At the main branch, this rise was most pronounced in June of 2012, but also observable in June of 2013 and March of 2014. The rise in the PC was also observed at the west branch in 2012, but the seasonal signal was accompanied by a strong drawdown in consecutive years. This change in the PC is influenced by the seasonally variable penetration depth of X-band radar signal into the glacier surface (snow, firn and ice) and thus does not depict the actual surface elevation change of the glacier throughout the year. However, we expect that the PC is located at the glacier surface when surface melt occurs. Hence, we used July for elevation change comparison. At the nearby glacier front of the main branch (M\({}_{100}\)), a positive elevation change (+7.6 m yr\({}^{-1}\)) was noticed during 2011-2012 followed by reductions in elevation in subsequent years (\(-\)16.3 m yr\({}^{-1}\) in 2012-2013, \(-\)3.5 m yr\({}^{-1}\) in 2013-2014).  
The main branch went through a significant drawdown (\(\sim\)\(-\)32.5 m yr\({}^{-1}\)) during 2014-2016. The mean observations (black line in Figure 2e) followed the similar patterns of elevation change in these years. At the west branch, despite significant retreat of its terminus, its mean surface elevation was almost balanced in 2011-2012 (black line in Figure 3b). But it also went through significant elevation changes in consecutive years (\(\sim\)\(-\)44 m yr\({}^{-1}\) in 2012-2013, \(\sim\)\(-\)40 m yr\({}^{-1}\) in 2013-2014, \(\sim\)\(-\)41 m yr\({}^{-1}\) in 2014-2016).  
Figure 2: **(a)** Daily minimum and maximum air temperatures (Cannery Creek Weather Station) and **(b)** daily snowfall (Cannery Creek Weather Station). Temporal variation of **(c)** glacier surface velocities; **(d)** mass flux; **(e)** vertical change of the scattering phase center in regard to 06/18/2011; **(f)** area change since 06/18/2011 observed at different elevations (locations indicated in Figure 1 on the main branch Columbia Glacier, Alaska. The glacier at elevation point M\({}_{100}\) calved off in 2015, as indicated in panel **(c)**. The green circles at the bottom axis indicate the time when the PC depicts actual surface elevation. Mass flux was estimated across the flux gate AB, shown in Figure 1.
# Experimental Study on Femtosecond Laser Processing Performance of Single-Crystal Silicon Carbide
## 2 Materials and Methods  
The femtosecond laser micro-processing system employed in the experiment can emit a collimated beam with a wavelength of 800 nm, a pulse width of 35 fs, maximum single pulse energy of 13 mJ and a maximum repetition rate of 1 kHz. The laser beam is horizontally linearly polarized, and the pulse energy approximately follows a Gaussian distribution. The diameter of the laser beam before focusing is 12 mm. After focusing the laser beam through a 20 \(\times\) 0.4 long-focus objective lens, the spot diameter is 36.63 \(\upmu\)m. The sample used in this experiment is N-type 4H single-crystal SiC, with a diameter of 50 mm and a thickness of 350 \(\upmu\)m. Before laser processing, the workpiece was ultrasonically cleaned with ethanol and distilled water for 15 min. After cleaning, it was installed on an X-Y workbench, and the experiment was conducted in ambient air. After the experiment, the depth, width, HAZ width and side wall inclination angle of the grooves were observed via laser microscope. The surface morphology was observed using scanning electron microscopy.  
Figure 1 shows the machined surface morphology of the groove. The groove depth, groove width, HAZ width, side wall inclination angle and cross-sectional area can be observed and measured under the laser microscope. MRR is equal to the scan speed multiplied by the cross-sectional area.  
Table 1 contains two single factor experimental schemes. Experiment 1 was used to analyze the significance of the influence of process parameters on the machined surface morphology (groove depth, groove width, HAZ width and side wall inclination angle) and  
Figure 1: Machined surface morphology of groove shape.  
MRR. Each group of experiments was repeated three times. For each group of experiments, the magnification of a long focal length objective lens is 20 and 60, respectively, and the numerical aperture NA is 0.4 and 0.6, respectively. A total of 25 experiments were conducted, with each experiment repeated three times.  
Experiment 2 shows a single factor experimental scheme, with each experiment repeated three times using a long focal length of 20 \(\times\) 0.4 objective lens processing. The morphology of the grooves after processing was observed using confocal laser microscopy and scanning electron microscopy. Use a half-wave plate to adjust the polarization (E, electric field) direction of the laser beam to be parallel to the scanning direction (polarization angle 0\({}^{\circ}\)) or perpendicular to the scanning direction (polarization angle 90\({}^{\circ}\)). As shown in Figure 2, when machining layer by layer along the z-direction (referred to as z-layer feeding), the total feed rate of the laser beam in the depth direction remains at 30 \(\upmu\)m. The purpose of this experiment is to design experimental parameters according to the previous single factor experiment, so as to obtain grooves with larger aspect ratio and smaller HAZ width.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 7 Simulations and Results
### Number of Vehicles vs. V2P Throughput  
Increasing the number of vehicles in the network improves the V2P throughput especially for the critical users the Q-learning algorithm performs well as shown in Figure 6 below.  
Figure 5: Number of Vehicular Users vs. System Throughput.  
Figure 6: Number of Vehicular Users vs. V2P Throughput.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 5 Results
### BER Performance Comparison  
The closest model to this study is that described in reference [10], so we compare the proposed VQ-LSTM-AE framework with the DAN-AE model proposed in the reference, and the results are shown in Figure 7. Figure 7a,b compare the performance curves of BER versus \(E_{b}/N_{0}\) of second-order and fourth-order LSTM-AE, BPSK, and DAN-AE, respectively. We can see that the three curves are almost identical, and the difference is too small to be considered a reasonable error, which is consistent with the consistency of the constellation distribution. In Figure 7c, VQ-LSTM-AE has almost the same performance as 8 QAM before 4 dB, which is better than DAN-AE, while DAN-AE is slightly weaker than 8 QAM before 6 dB, but surpasses it afterward; DAN-AE and VQ-LSTM-AE both surpass 8 QAM after 4 dB, and VQ-LSTM-AE drops by nearly an order of magnitude compared to DAN-AE after 10 dB. In Figure 7d, both autoencoder-based models are inferior to traditional methods, and the curves of VQ-LSTM-AE and DAN-AE overlap. In Figure 7e, at the 32 nd order, both autoencoder-based models do not surpass 32 QAM, but VQ-LSTM-AE gradually widens the gap with DAN-AE after 4 dB, and finally drops by nearly an order of magnitude. Considering that LSTM-AE reduces more than 20% of parameters compared to DAN-AE, this balance is acceptable.  
Figure 6: Constellations learned by the proposed model. (**a**) Second-order constellation diagram; (**b**) fourth-order constellation diagram; (**c**) eighth-order constellation diagram; (**d**) sixteenth-order constellation diagram; (**e**) thirty-second-order constellation diagram.
## 4 Results
### The Backscatter Coefficients of the Landfast Sea Ice  
We calibrated the backscatter calculation based on SAR images to quantify the freeze and thaw state, which provided a representative condition estimate [53] and reflected the toughness of the landfast sea ice. Additionally, the sea-ice extent adjoint to ice front has declined dramatically summer ice extent in recent days by delineating the extent of sea ice in fjord. Backscatter \(\gamma_{HV}\) (dB) value of Sentinel1 GRD data serves as an index for thaw-melt state of sea ice and ice melange. The landfast sea ice transformed into open water and break up in Figure 8 when the backscatter coefficient decreased to \(-28\) (dB) from 17 July to 26 July (calving day) accompanied by sea ice extent declining in Figure 9.  
The Google Earth Engine Platform has provided the data products so we only use it to make time series analysis of backscatter to quality the rigidity varieties of sea ice and ice melange. The results show that reduction in stiffness of the ice melange since stepped into melt season, and sea ice transformed into open water as soon as calving day (26 July 2017), which was attributed to losing the buttressing effect of sea ice and ice melange. To fully understand the spatial and temporal variability of sea-ice rigidity, we inspected 16 Sentinel-1 images acquired throughout the high melt period between 9 July and 26 July. The weakening of multiyear landfast sea ice evolved with fjord condition and glacier terminus and followed by the frontal collapse event that occurred there in 2017. Additionally, the breakup of sea ice located close to the ice front was synchronous with frontal collapse event and had an influence on glacier terminus instability. Abundant sea ice filled in fjord consolidates and fastens to the terminate in early melt season. The following time the sea-ice extent declined from 589.75 km\({}^{2}\) in 17 July to 12.81 km\({}^{2}\) in 25 July in Figure 8. Landfast sea-ice minimum extent occur nearly coincident in time with observed collapse velocity maxima. The resulting changes in buttressing of sea ice lost  
Figure 7: The damage scalar series of the Petermann Glacier on the days in July 2017, prior to the calving. (**a-d**) respectively represent mean damage scalar from the corresponding periods.  
leads to a sudden increase in effective stress that propagated the instability through the frontal calving region in only a few days. The weakening of sea ice directly accelerated ice flow immediately and the concretely calving mechanism about sea ice was found in Section 5.2.  
Figure 8: The outline of landfast sea-ice images of Landsat 8 in the Petermann glacier fjord in July of 2017. The red polygon region denotes the extent of landfast sea ice in (**a–c**). (**d**) The landfast sea ice has completely disappeared.  
Figure 9: The evolution of sea-ice extent and backscatter of the surrounding sea ice of the ice front during July 2017 prior to calving events (26 July 2017).