esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
###### Abstract  
Recent years have seen unprecedentedly fast-growing prosperity in the commercial space industry. Several privately funded aerospace manufacturers, such as Space Exploration Technologies Corporation (SpaceX) and Blue Origin have transformed what we used to know about this capital-intense industry and gradually reshaped the future of human civilization. As private spaceflight and multi-planetary immigration gradually become realities from science fiction (sci-fi) and theory, both opportunities and challenges will be presented. In this article, we first review the progress in space exploration and the underlying space technologies. Next, we revisit the K-Pg extinction event and the Chelyabinsk event and predict extra-terrestrialization, terraformation, and planetary defense, including the emerging near-Earth object (NEO) observation and NEO impact avoidance technologies and strategies. Furthermore, a framework for the Solar Communication and Defense Networks (SCADN) with advanced algorithms and high efficacy is proposed to enable an Internet of distributed deep-space sensing, communications, and defense to cope with disastrous incidents such as asteroid/comet impacts. Furthermore, perspectives on the legislation, management, and supervision of founding the proposed SCADN are also discussed in depth.  
space exploration; Internet of spacecraft; extra-terrestrialization; multi-planetary civilization; near-Earth object (NEO); Internet of distributed deep-space sensing; solar communication and defense network (SCADN); multi-planetary defense; space edge computing; space edge artificial intelligence (AI); legislation +
Footnote †: journal: Physics Letters B  
10 May 2022  
Accepted: 14 June 2022  
Published: 22 June 2022
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 1 Introduction  
Under the influence of global warming, a rapid decline in sea ice coverage and an increase in the depth of Arctic waters have been observed since 2000 [PERSON] et al., 2017 [1]. The potential commercial and political importance of arctic shipping routes has been the focus of various countries, shipping companies, and international organizations. Among the three shipping routes in Arctic waters, the arctic northeast route, also known as the northern sea route (NSR), is the most critical route because it greatly reduces the sailing distance between Asia, Europe, and North America compared with rational routes via the Suez [2]. According to [3], the reduced distance can be as much as 40%. As a result, shipping companies from Germany, China, Korea, Russia, Norway, and other countries have expressed interest in investing this route to obtain a competitive edge over competitors [4]. Although the opening and commercial operation of the NSR provide vast potential opportunities, Arctic waters still present major challenges for the shipping industry [5]. Harsh natural environments and fragile ecosystems make it difficult to maintain a satisfactory safety level for shipping operations along the NSR, which greatly increases the concerns of national and international parties. For example, the International Maritime Organization (IMO) issued the Polar Code in 2010 to ensure the safety level of shipping operations in Arctic waters [6]. In addition, the International Hydrographic Organization (IHO) has initiated a program to provide an accurate navigation chart for ships traveling along the NSR. Overall, the safety level of shipping operations within Arctic waters, particularly along the NSR, has garnered attention from various levels of companies atthe national and international scales. However, the safety level of the NSR is still far from satisfactory, and accidents associated with shipping operations still occur at times. Therefore, a great deal of work has been done to better understand how to improve the security capacity and mitigate the various risks along the NSR. Therefore, in the present study, the concept of system resilience is introduced to attempt to obtain knowledge about the security capacity, and an investigation is conducted to improve the system resilience of the NSR.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Heart Rate Extraction  
Heart rate (HR) is the rate at which the heart muscle veins perform contraction and expansion to pump and absorb the blood from various parts of the body. It is an important indicator that reflects the level of exertion during exercise [90; 91]. There are several non-contact techniques to measure HR at rest or during physical exercise while avoiding the motion artifact.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA)  
A thermogravimetric analysis was performed to evaluate the thermal stability of salicylic acid samples subjected to various electromagnetic field exposures. The results are summarized in Table 1, presenting key thermal parameters, including the melting temperature (T\({}_{\rm m}\)), the onset of decomposition (T\({}_{\rm onset}\)), temperatures at 5% and 50% weight  
Figure 2: XRD patterns of salicylic acid samples: control and those exposed to various types of electromagnetic fields.  
loss (T\({}_{\rm d}\)5\({}^{\rm\%}\), T\({}_{\rm d}\)5\({}^{\rm\%}\)), and the maximum decomposition temperature (T\({}_{\rm max}\)), along with the corresponding decomposition rate. Individual TG curves for each sample are available in the Supplementary Material (Figure S3a-p).  
Small differences in thermal stability were observed among the salicylic acid samples. The lowest melting point (159.5 \({}^{\circ}\)C) was found in the sample exposed to an oscillating magnetic field at 65 Hz (OMF 65 Hz), while the highest value (160.4 \({}^{\circ}\)C) occurred in the sample subjected to a rotating magnetic field at 10 Hz (RMF 10 Hz).  
The onset of decomposition ranged between 162.5 \({}^{\circ}\)C (RMF 50 Hz) and 166.4 \({}^{\circ}\)C (PMF 10/10). Temperatures corresponding to a 5% weight loss varied from 140.9 \({}^{\circ}\)C (SMF +25 V) to 142.9 \({}^{\circ}\)C (SMF \(-\)5 V and RMF 50 Hz), and a 50% weight loss was recorded between 177.5 \({}^{\circ}\)C and 178.7 \({}^{\circ}\)C. The maximum decomposition temperature varied from 187.6 \({}^{\circ}\)C to 191.4 \({}^{\circ}\)C, with associated decomposition rates ranging from \(-\)34.97%/min to \(-\)33.19%/min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\(T_{\rm m}\)—melting temperature, T\({}_{\rm onset}\)—onset of decomposition, T\({}_{\rm d}\)5\({}_{\rm s}\)—temperature at 5% weight loss, T\({}_{\rm d}\)5\({}_{\rm s}\)—temperature at 50% weight loss, T\({}_{\rm max}\)—the maximum decomposition temperature.  
\end{table}
Table 1: Thermal stability parameters of salicylic acid samples exposed to electromagnetic fields compared to the control. Measurement error for temperature values: \(\pm\)0.5–1.0 \({}^{\circ}\)C; decomposition rate: \(\pm\)1.2%/min.  
Despite minor differences observed in onset and degradation temperatures across various EMF configurations, the overall thermal stability of salicylic acid remained comparable to the control. Specifically, most values of T\({}_{\text{onset}}\), T\({}_{\text{d}}\)5\({}^{\text{\%}}\), T\({}_{\text{max}}\) varied within a narrow range of approximately 1-2 \({}^{\circ}\)C. Given the known measurement uncertainty for thermogravimetric analysis (estimated at \(\pm\)0.5-1.0 \({}^{\circ}\)C for temperature and \(\pm\)1.2%/min for decomposition rate), these variations are considered to fall within the range of experimental error. As such, we interpret them as non-significant and not indicative of any measurable destabilization or degradation effect caused by electromagnetic field exposure.
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Floor Concrete Strength on Seismic Performance of Nodes
#### 4.5.3 Energy Consumption and Stiffness Degradation  
Analyzing the impact of different concrete strengths on the node energy dissipation performance (Table 9) reveals that the energy dissipation capability in the elastic stage slightly increases with the increase in the slab concrete strength. In the elastoplastic stage, Node FJD-3 (ordinary concrete C30) shows a significant improvement in energy dissipation, increasing by 2.95% compared to Node FJD-0; in contrast, Node FJD-4 (high-strength concrete) experiences a rapid decrease in energy dissipation. This is because the higher stiffness of high-strength concrete may lead to stiffness mismatch between the slab and beam-column nodes, causing stress concentration and reducing energy dissipation capabilities. Therefore, it is essential to ensure that the slab strength grade matches the node during design.  
Compared to the stiffness degradation (Figure 29), the stiffness trends of nodes FJD-3 and FJD-0 are similar, while node FJD-4 initially has higher ring stiffness, but as the load increases, the stiffness degradation accelerates. Ultimately, the stiffness degradation trends of all joints converge, primarily due to the brittle nature of the concrete material. This brittleness leads to the gradual fatigue failure of the concrete under cyclic loading, causing it to eventually withdraw from its effective bearing capacity.  
Figure 28: (**a**) Comparison of hysteretic curves at different concrete strength nodes; (**b**) Comparison of skeleton curves of different concrete strength nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node Number** & **Capacity Dissipation** & **Equivalent Viscosity** \\  & **Coefficient \(E\)** & **Coefficient \(h_{e}\)** \\ \hline FJD-0 & 3.05 & 0.485 \\ FJD-3 & 3.14 & 0.499 \\ FJD-4 & 3.04 & 0.483 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of energy consumption capacity of different concrete strength floor nodes.  
Figure 29: (**a**) Comparison of dissipation coefficients of different concrete strength floor nodes; (**b**) Comparison of stiffness degradation of different concrete strength floor nodes.
## 2 Fundamental Theory  
### Convolutional Neural Network  
A CNN (convolutional neural network) is a feedforward neural network with convolution operation as the core [22]. This method is widely used in image tasks because of its good feature extraction ability. In recent years, it has also achieved excellent results in the field of fault diagnosis.  
A CNN includes convolutional layers, pooling layers, and fully connected layers. The expression of the convolution operation is as follows.  
\[x_{j}^{l}=f(\sum_{i=1}^{v}W_{j}^{l}\times x_{i}^{l-1}+b_{j}^{l}) \tag{1}\]  
In Formula (1), \(x_{j}^{l}\) is the \(j\)th feature map output for the \(l\)th layer, \(v\) is the number of input feature maps, \(W_{j}^{l}\) is the convolution kernel weight matrix of the \(l\)th layer, \(x_{i}^{l-1}\) is the \(i\)th feature map of the output of the \(l-1\) layer, \(b_{j}^{l}\) is the convolution kernel bias matrix of the \(l\)th layer, and \(f(x)\) is the activation function.  
Figure 1 shows the convolution kernel calculation process. Usually, the pooling layer is added to the convolution layer after reducing the data size to prevent overfitting. The common pooling operations are max pooling and average pooling.
## 4 Validation of General ESS-Based VSM Control
### Simulation Test of a Single ESS-Based VSM under Step Load Change
#### 4.1.1 Case 1: Synchronous Generator Only  
First, no VSM is present in the system and the SG supplies the total loads. At t = 10 s, there is a 0.375 p.u. active-power load increase, the resulting frequency nadir (i.e., minimum frequency value) is 57.87 Hz at t = 10.47 s, as Figure 14 shows. The large frequency deviation and swing arise from the small inertia and low damping of SG, respectively.
## 7 Conclusions
### Task Allocation Problem  
Concerning the tasks-to-robots distribution algorithm, all previous proposals explicitly avoid the combinatorial blow-up of allocation complexity using different heuristics. Nevertheless,  
Figure 28: Over-sensing ratio (OSR) under non-ideal communication conditions. The _Yamauchi_ and _MinPos_ approach results (coloured in purple and green, respectively) obtained under ideal communication conditions are placed together to make the comparison easier.  
heuristic-based approaches make assumptions that cannot be verified at all times. In consequence, when the heuristic fails the robots choose suboptimal distributions.  
Taking into account this limitation and since the number of possible distributions depends on both the number of available tasks and the number of robots making a decision, the proposal presented here computes optimal distributions based on more general assumptions such as: (i) Robots can implicitly coordinate their actions; (ii) Asynchronous may keep the number of simultaneous decision making at small values; (iii) Pruning the furthest tasks (out of the scope defined by the human operator -_HO-Threshold_) does not prevent the computation of optimal tasks-to-robots distribution.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AESAdvanced Encryption StandardAES-CTRAdvanced Encryption Standard Counter Mode ARQAutomatic Repeat Request AVCAdvanced Video Coding CABACContext-Adaptive Binary Arithmetic Coding CAVLCContext-Adaptive Variable-Length Coding CIFCommon Intermediate Format CFBCipher Feedback Mode CUCCoding Unit DCTDiscrete Cosine Transform ECError Concealment ECCError Correcting Codes ERError Resilience FECForward Error Correction FHDFull High Definition FRFull Reference GEGilbert-Elliott GOPGroup of Pictures HDHigh Definition HEVCHigh Efficiency Video Coding InterPMTInter-Prediction Mode IPMIntra-Prediction Mode JECCJoint Encryption and Channel Coding JENCJoint Encryption and Network Coding JESCJoint Encryption and Source Coding JSVMJoint Scalable Video Model LSTMLong-Short Term Memory MSEMean Square Error MVMotion Vector MVMotion Vector MVMotion Vector MVMotion Vector MKNNo Reference OSIOpen System Interconnection PSNRPeak Signal-to-Noise Ratio QC-LDPCQuasi-Cyclic Low-Density Parity Check QPQuality Perception QoEQuality of Experience QoSQuality of Service RCResidual Coefficient RL-AFECReinforcement Learning--Adaptive Forward Error Correction RRReduced Reference
## 2 Miniaturized X-ray/Gamma-ray Imaging Payload
### Payload Operation, Data Handling and Storage  
The payload imaging detector (see Section 2.2) is controlled and readout via USB interface. Data handling and storage are done by payload computer or the spacecraft OBC which can be implemented in the form of a high-level computer running in standard operating system (MS Windows, Linux, Mac OS) or low-level CPU e.g., ARM or Raspberry platform. The necessary software (SW) for control and readout of the pixel detector, provided also by Advacam, have been implemented accordingly for high-level or low-level space deployment platforms. Additional functionality implemented include autonomous adjustment of frame acquisition of the pixel detector based on modeled orbit and satellite path. This function allows to optimize and maximize the data rate, payload operation uptime and radiation measurement duty cycle. Radiation data is acquired by the pixel detector readout by the satellite computer where they are also navigation stamped (position, altitude, attitude, time). The payload has been developed also with limited data processing capability on board the Cubesat platform. This option enables specific functions such as basic event filtering (see Section 3.1) and radiation storm alarm output.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 2 Materials and Methods
### Assessing Response of Terrestrial Carbon Fluxes to Drought  
The response of terrestrial carbon fluxes to drought was assessed using Pearson's correlation coefficients of monthly GPP, NPP, ER, and NEP with SPEI. Terrestrial carbon fluxes might be enhanced by CO\({}_{2}\) fertilization and nitrogen deposition. To isolate these effects, monthly GPP, NPP, ER, and NEP were linearly detrended prior to the correlation analysis. The linear detrending was conducted by subtracting the linear trend from the original time series using the least squares method. For monthly analysis, individual 1- to 24-month-timescale SPEI series were correlated to monthly detrended GPP, NPP, ER, and NEP. For each month, 24 correlation coefficients were calculated for each carbon flux component. For each grid cell and each carbon flux, 288 (= 24 \(\times\) 12) correlation coefficients were calculated.  
For each carbon flux, we then calculated a response measure defined as the maximum value from the 288 correlation coefficients. We also calculated a sensitivity measure defined as the change in monthly carbon flux per unit change in monthly SPEI for each pixel, where monthly carbon flux and monthly SPEI time scale were corresponded to the month and time scale when the maximum correlation coefficient was identified. The sensitivity of carbon flux to drought was quantified as the slope of the linear regression between carbon flux and SPEI. Before computing the sensitivity, monthly carbon fluxes were also linearly detrended to avoid spurious correlations.
## 4 A Synopsis and Some Remarks  
On account of their periodic array of channels and cavities, which helps experimental study and tailoring of gas adsorption properties, zeolites (and several other periodic porous solids) are widely studied with a view to optimize their performance in such technical fields as gas purification, storage and transport. To that endeavour, availability of an experimental technique that enables one to determine the structure and stability of the gas adsorption complex is of the utmost importance.  
By concisely reviewing a few illustrating case studies, it was demonstrated herein that the main advantage of VTIR spectroscopy over adsorption calorimetry is the fact that, (i) VTIR can be site specific, because the IR spectrum gives the finger print of the gas-adsorption complex, (ii) van't Hoff analysis of a series of IR spectra recorded over a temperature range renders the value of the standard enthalpy change, \(\Delta H^{0}\), involved in the adsorption process, which monitors the stability of the adsorption complex. Moreover, zeolites often contain a small amount of extraframework aluminium species [106; 107], which, by interacting with an adsorbed gas, could interfere with calorimetric measurements. Again, by being site specific, VTIR spectroscopy is likely to avoid that problem.  
In addition, some of the case studies reviewed herein show how synergy between VTIR experimental results and DFT calculations leads to a very detailed knowledge about the structure of both, the zeolite adsorption sites and the corresponding gas adsorption complex; and the same strategy can be used to investigate gas adsorption in other periodic porous solids, _inter alia_, MOFs and several kinds of related reticular solids [108; 109; 110; 45; 108; 111].  
To the endeavour of increasing current knowledge about the structure and stability of gas adsorption complexes in zeolites, the foregoing remarks should suffice to highlight the role played by VTIR spectroscopy. Nevertheless, crystallographic methods using X-ray (or neutron) diffraction should also be mentioned [112; 113; 114]. Noteworthy is that the same van't Hoff analysis used in the VTIR method has sometimes been applied to diffraction data of gas adsorption complexes, and mutually consistent results were reported. Finally, it is also relevant to mention that variable temperature IR  
Figure 12: Van’t Hoff plots derived from the IR spectra of (**a**) carbon dioxide, and (**b**) dinitrogen adsorbed on K-L. Spectra shown in Figure 11a,b, respectively (adapted from [102]).  
spectroscopy, combined with quantum-chemical computations, was reported to be a convenient means to analyse spin crossover transitions in transition metal ion complexes [115; 116; 117].  
This research received no external funding.  
The author declares no conflict of interest.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 3 DSLMS Adaptive Filter  
In the field of weak signal processing, although the traditional fixed-step-size LMS algorithm is widely used due to its simplicity, its fixed and unchanging weight-vector step factor makes it difficult to simultaneously achieve the improvement of convergence speed and the reduction in steady-state error when dealing with signals and noises in a variable environment [30].  
To solve the above problems, this paper proposes a variable-step-size adaptive filter based on the improved micro tangent trace function, which adopts the DSLMS algorithm, and by optimizing the step-size adjustment mechanism, it speeds up the convergence speed, reduces the steady-state error, and effectively improves the processing performance of weak signals. By combining the DSLMS algorithm with the pair cancelation system, the signal can be separated from the noise more effectively, the anti-interference ability of the system can be improved, and the accuracy and efficiency of signal processing can be improved through more accurate signal reconstruction. The principle of the DSLMS adaptive filter is shown in Figure 3.
## 5 Discussion
### Practical Implications for Crisis Response  
#### 5.3.1 Real-Time Responsiveness and Robustness in Crisis Scenarios  
The model's real-time inference efficiency (0.083s/instance) meets urgent needs for rapid crisis categorization in emergency response systems. Enabled by mixed-precision training and dynamic graph optimization, this performance allows high-throughput screening of disaster-related tweets, supporting tasks like resource allocation and situation awareness. The ablation-proven robustness to noisy data further ensures reliability in real-world scenarios, where crisis tweets often include low-quality media or misinformation.