esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Four-Dimensional (Bio-)printing
Thus, being able to use the advantages of 3D (bio-)printing (e.g., customization), it is possible to improve the features of hydrogels by promoting self-weldability or self-unfoldability according to an external stimulus. As discussed in literature, both these characteristics, as 4D bio-origami hydrogels, may significantly contribute to achieve functional tissues [15], and to the fabrication of engineered tissues, such as liver [32] and heart [33], making a major breakthrough in the area of TE [24; 34]. Extensive research has been carried out on the development of smart hydrogels. From a mechanical standpoint, hydrogels can show bare elastic up to viscous-elastic behavior, because of crosslinking type and density. To characterize hydrogels, Young's modulus (E), storage modulus (G'), and loss modulus (G\"), as well as other hydrogel-specific parameters, such as mesh size, polymer volume fraction in the swollen state, and average molecular weight between crosslinks, should be evaluated. Since water does act as a plasticizer among the polymer chains, hydrogels used in their swollen state, as in TE, are well above their glass transition temperature (T\({}_{g}\)) and show viscoelasticity under loading conditions. For example, appropriate mechanical stimulation and biomechanical signals are key factors in tendon/ligament (T/L) engineering. As the native T/L is able to respond to mechanical forces by changing structure, composition, and mechanical properties [35; 36; 37], smart hydrogels obtained via 4D printing cold help to achieve such a critical goal. Typical mechanical values of hydrogels (e.g., \(\approx\)0.1 MPa Young's moduli) are usually far from satisfying T/L mechanics (i.e., 1.2-1.8 GPa tensile modulus) that can be achieved only by high modulus chemically crosslinked hydrogels [38; 39]. Moreover, transition zones of T/L into muscle or bone need a gradient of properties capable of responding to personalized tissue mechanics. As the field of 4D (bio-)printing continues to grow, in the next years it is expected to rapidly expand across fields, including regenerative medicine, robotics, and medical devices [16]. In the following sections, we focus on smart hydrogels and their applications in 4D (bio-)printing.
###### Abstract
The robustness of DSFs against noise and transient signals is evaluated. The formulation of the DSFs that are evaluated is provided in Section 2.1. First, the performance of the DSFs on the earthquake-induced response of a simplified simulated spring-mass model is evaluated in Section 3.1, while Section 3.2 presents a comparative assessment on experimental shake-table datasets obtained from the testing of a mixed reinforced concrete unreinforced masonry shear building.
## 3 Materials and Methods
### Data Processing
#### 3.4.2 GNSS Post-Processing of GCP Data  
The GNSS data of the GCPs was post-processed using the differential GNSS method [41]. The Svalbard Satellite Station (SvalSat) was used as the baseline for the Svalbard areas which were located approximately 5 km from the studied areas (78\({}^{\circ}\)13\({}^{\prime}\)45.9\({}^{\prime\prime}\)N 15\({}^{\circ}\)23\({}^{\prime}\)42.9\({}^{\prime\prime}\)E, WGS 84). For Greenland, the Sisimiut reference station, located approximately 29 km from the studied areas (66\({}^{\circ}\)56\({}^{\prime}\)06.8\({}^{\prime\prime}\)N 53\({}^{\circ}\)38\({}^{\prime}\)37.8\({}^{\prime\prime}\)W, WGS 84) was used as a baseline. The agencies in charge of the reference stations (Kongsberg Satellite Services and DTU Space) provided the precise location of their reference stations and the 24 h positioning data acquired for each of the surveyed days. The reference station data was accessed using the _Hatanaka_ compression and decompression program, providing the RINEX files (data interchange format for raw satellite navigation system data). The GNSS data downloaded from the JAVAD receivers were also transformed to RINEX using _jps2 rin_ open-source software. The corrections were computed through the Leica Geo Office (LGO) software. As provided by LGO, the static GNSS method with post-processed differential corrections achieved an average position and height better than 0.02 m depending on the area and survey region.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 6 Conclusions  
This paper took the main urban area of Hefei as the research area, used Luojia1-01 NTL imagery to estimate the UBD, and combined this with the Landsat data inversion LST to carry out small-scale correlation analysis and modeling.  
The emergence of Luojia1-01 NTL imagery opens a new door for microscale research, and it is essential to explore its potential. In this paper, we applied remote sensing image data sourced from Luojia1-01 NTL imagery to the internal research of a city, which proved that it can be used effectively for the extraction of urban information. However, the research still has the following shortcomings: (1) The team at Wuhan University only released the Luojia1-01 NTL data for 2018, so it is hard to perform a multi-time series analysis and we lack a comprehensive evaluation method. (2) Many other  
Figure 14: Schematic diagram of the local fitting results of the GWR model for UBD and LST.  
factors, such as rainfall, vegetation, and wind speed, also affect LST changes. Future research could integrate more factors into the GWR model for a more detailed analysis. We will strive to improve the reliability and comparability of the Luojia1-01 NTL data estimation results so as to facilitate a more detailed analysis of the heat island distribution pattern and its evolution, which is our future research direction.  
Conceptualization, X.W., and T.Z.; data curation, F.Z.; funding acquisition, T.Z., and F.T.; methodology, X.W., T.Z., and F.T.; project administration, T.Z., and F.T.; software, X.W.; validation, X.W., T.Z., and F.T.; visualization, X.W.; writing--original draft, X.W., T.Z., and F.Z.; writing--review & editing, T.Z., F.T., and F.Z.  
This research was funded by the National Natural Science Foundation of China under Grant 41301514 and Grant 41401456, in part by the Nantong Key Laboratory Project under Grant CP12016005.  
The authors would like to thank the editor and the anonymous reviewers who provided insightful comments on improving this article and The Hong Kong Polytechnic University for providing the opportunity of academic exchange. The authors also thank the research team at Wuhan University for freely providing the Luojia1-01 night-time light imagery.  
The authors declare no conflicts of interest.
## 4 Interactive Compression
### An interactive communication protocol
The Sender shall act accordingly. The Sender will not send more than two fingerprints to identify a word: after two unsuccessful fingerprint trials it sends the word by entropy coding. Every time a new word is acknowledged by the Receiver then a new codeword is independently, but consistently, assigned to that word by both Sender and the Receiver. If the current input word is not in the Sender's dictionary than the Sender switches to a standard lossless compressor and entropy codes the input word. This approach exploits compression by taking into account shared data and interaction. It is possible to analyze, as in [8], the possibility of a communication error, _i.e._, the possibility that the Receiver decodes a word incorrectly, and to prove that this probability is very low. As an example, if we assume that the length in bits of the dictionary words is n = 250, and that t = \(\left|\mathrm{D}\right|\) = 4,000, \(\mathrm{M}=\mathrm{nt}^{2}\) = 4 \(\times\) 109, \(\delta\) = 0.01 then by using this protocol the probability of a false match for the fingerprinting algorithm is less than 2 \(\times\) 10\({}^{-8}\), and the overall communication error probability is less than 4 \(\times\) 10\({}^{-10}\). The Sender with probability higher than 0.98 will send a word w to the Receiver by using only fingerprints (and it will spend less than 70 bits for word), or otherwise it will send the word by entropy coding (by spending more than 70 + 125 bits per word) with probability less than 0.0199. In [8] the protocol is experimentally tested for the compression of source programs in the C programming language. These experimental results show an average improvement of 5-6%  with respect to the standard, dictionary based, lossless compressors.
## 2 Materials and Methods
### Mitigation and Statistical Analyses  
NH\({}_{3}\), CO\({}_{2}\), N\({}_{2}\)O, and CH\({}_{4}\) were all measured in units of parts per million (ppm). The concentrations were converted to flux in units of (mg/m\({}^{2}\)/h) using measured environmental conditions [2]. For VOCs, all analyses of treatment effectiveness were completed in the units of peak area counts (PACs) as an MS detector response to compounds abundance in the gas sample. Although the PAC unit is arbitrary, it is sufficient for estimating thepercent reduction due to each treatment by comparing the PACs for Treatment with Control. The overall mean percent reduction was calculated with Equation (1):  
\[\%R=\frac{E_{Control}-E_{Treatment}}{E_{Control}}*100\% \tag{1}\]  
where \(\%R\) is the overall mean percent reduction, \(E_{Control}\) and \(E_{Treatment}\) are the means (\(n=3\) replicates) of flux, odor concentrations, or PACs from Control and Treatment, respectively.  
The one-way Analysis of Variance (ANOVA) and Tukey-Kramer Method were used to determine the \(p\)-values of the reduction. All statistical analyses were completed using JMP software (version Pro 15, SAS Institute, Inc., Cary, NC, USA). When a \(p\)-value was less than or equal to 0.05, the reduction was considered statistically significant. Tables S1-S10 report detailed statistical results, including the least-square means, standard errors, 95% confident intervals, and letters indicating significance.
# Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor
## 3 Simulation Results and Analysis  
The seeker servo system can be simplified as the following control system of a permanent magnet synchronous motor:  
\[\left\{\begin{array}{l}\frac{d\theta(t)}{dt}=\omega(t)\\ \frac{d\omega(t)}{dt}=\frac{\rho}{f}T(t)-\frac{\rho}{f}T_{L}(\omega,t)-\frac{ \rho}{f}\omega(t)-\frac{\rho}{f}\theta(t)\end{array}\right.\]  
where \(T_{e}\) is the electromagnetic torque, \(T_{L}\) is the load torque, \(\theta\) is the angular position of the motor, \(\omega\) is the angular speed of the motor, \(p=2\) is the number of pairs, \(B=0.0001\) Nms is the viscous friction coefficient, \(J=0.00284\) kgm\({}^{2}\) is the equivalent moment of inertia, and \(F=0.00001\) Nm is the coefficient of elasticity. The output variable is \(\omega(t)\), the control variable is \(T(t)\), and the initial conditions are \(\theta(0)=0\) and \(\omega(0)=0\). A previous study [27] noted that the influence of the torque ripple above the 6 th order on the rotation speed can be neglected; thus, we focus our analysis on the 1 st, 2 nd, and 6 th harmonic torque of the torque ripple. The load torque can be expressed as  
\[T_{L}(t,\omega)=T_{L\_0}(t)+T_{L\_1}\cos(\omega t+\varphi_{1})+T_{L\_2}\cos(2 \omega t+\varphi_{2})+T_{L\_6}\cos(6\omega t+\varphi_{6})\]  
The parameters of torque ripple are set as follows: \(T_{L\_0}=1\) is the average load torque, \(T_{L\_1}=0.5\) is the first harmonic content of the torque ripple, \(T_{L\_2}=0.1\) is the second harmonic content of the torque ripple, and \(T_{L\_6}=0.05\) is the sixth harmonic content of the torque ripple. The unit is Nm, and the phase of harmonic torque in the simulation is zero.  
Figure 2 shows the process of tracking trajectory of the system output for the motor control system under the 1 st-6 th iterative learning steps. In particular, the dotted line is the expected speed, the dashed line is the tracking curve under the traditional P-type iterative learning control (\(K_{P}=0.1\)), and the solid line is the tracking curve under described by Equation (3) (\(K_{P}=0.1\) and \(K_{E}=3\)). Figure 2a indicates that after the first iteration, the two control methods both have a relatively large error. Figure 2c shows the control effect after the third iteration. The convergence speed of the proposed method is faster, and the tracking error satisfies the system requirements. Figure 2f shows that for the traditional P-type iterative learning control, after the 6 th iteration, the control effect also essentially satisfies the requirement, but the error is larger than that for the proposed method.
## 2 Adaptability Analysis of Non-Condensate Gas Injection in Heavy-Oil Reservoir  
After half a century of research and application, heavy-oil gas-injection-assisted thermal oil-recovery technology has been recognized as one of the feasible and most effective ways [10]. At the beginning, the mechanism of the gas injection to assist thermal oil recovery was recognized as a physical effect. The chemical reaction between high temperature gas and some components of heavy oil has been noticed in recent years, especially the modification effect of non-condensate gas on heavy oil [6]. For a heavy-oil reservoir, there is a challenge to change the development method after a long period of thermal recovery, and the non-condensate gas injection stimulation technology is the most typical and widely used replacement method at present [11].  
The study of the remaining oil distribution in a reservoir is of great significance to the adaptability of the subsequent injection of non-condensate gas. Generally, the remaining oil-saturation distribution in the late period of heavy-oil thermal recovery is mainly controlled by reservoir geological conditions and development schemes [12], and the future study of the enhanced oil recovery in the late stage of thermal recovery would mainly focus on increasing the production of the remaining oil. In the process of enhanced oil recovery for the heavy-oil reservoirs, it is very important to select appropriate development methods. However, some production techniques are only suitable for the reservoir types with limited boundaries. The selection of specific development methods is influenced by reservoir properties, economic benefits, and other aspects [13]. The adaptability of a heavy-oil reservoir is generally determined by numerical simulation, among which the increase in recovery efficiency and cumulative oil-gas ratio are the most effective evaluation indexes. Compared with conventional steam development, non-condensate gas injection has a wider adaptability [14].
# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
## 2 Materials and Methods
### Classification Models  
#### 2.6.1 Support Vector Machine (SVM)  
For clean and preprocessed data that is linearly separable, SVM creates a hyperplane between the support vectors called the decision boundary, as shown in Figure 7. Data points on either side of the decision boundary belong to different classes. The hyperplane is one dimension lower than the dimension of the dataset; e.g., if the dataset is three-dimensional, the hyperplane would be two-dimensional. SVM tries to maximize the margin from the closest points of each class using supervised learning [23; 24].
# A CMOS Multiplied Input Differential Difference Amplifier: A New Active Device and Its Applications
## 5 Conclusions  
The MIDDA device presented in this paper offers the following advantageous features, simultaneously: (a) the possibility to construct lossless, non/inverting voltage- or current-mode integrators with electronically controllable parameters, when only one external and grounded capacitor is added (useful for application in linear active filters, oscillators, and other subsystems)--to the best of authors' knowledge, none of the commercially available multipliers have an accessible output response in the form of current; (b) the nonlinear operation of multiplication is available (for purposes of modulation, demodulation, mixing, shaping, etc., as was proven by this paper); (c) the direct availability of summation/subtraction operations (useful for multi-feedback systems); (d) it excludes utilization of opamp-based differential-summing amplifiers, including floating feedback resistors in the VDDB subpart, see internal structure of AD633, HA2556, for example, due to a different concept of the CMOS structure; (e) comparable or better features to commercially available solutions (GBW, voltage ranges, offsets), even under lower supply voltages (\(\pm 1.65\) V). The proposed MIDDA device can be easily applied in miniaturized IC solutions for amplitude modulation and demodulation purposes, in long-wave and medium-wave communication systems. In comparison to [5; 6], the MIDDA can be easily used for AM-DSB modulation (Figure 9), whereas MMCC suffers from the unavailability of output operation of summation of product of modulation and carrier wave with carrier wave. To the best of the authors' knowledge, this device is the very first fabricated prototype based on the differential difference principle in the output section, and allows multiplication at the input section simultaneously. We suppose that the future will see the development of many other (more complex) electronically controlled applications of MIDDA, in the field of harmonic oscillators (the useful methods for their
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 3 Platform Design and Simulation  
### Simulation Tools  
The LSCN security performance simulation platform is jointly implemented by STK, NS3, and Exata. Exata is a network simulation tool developed by Scalable Networks Technologies (SNT). Compared with QualNet, the performance of Exata has been further improved, with the advantages of parallel distributed simulation, real-time semi-physical simulation, and high-precision large-scale simulation. The packet-level simulation is carried out through NS3, and the importance of key satellite nodes in the region to the regional network is studied and analyzed. For LSCN network communication, DDoS attack deployment, and network-wide data analysis, Exata is utilized as a platform for network simulation. STK is used to generate the orbital information and data of the LEO and import them into Exata.
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 2 Materials and Methods
### Audio Representations
These studies demonstrated a significant relationship between the fundamental frequency and how CNNs learn to recognize emotions in time-frequency representations, further supporting the rationale for choosing this feature in our approach. A general list of the feature space and the number of individual groups is presented in Figure 2. The letters \(U\), \(V\) and \(G\) represent accordingly: unvoiced part, voiced segment and gradient. The symbols \(LR\), \(\Delta\), \(J\), and \(SH\) represent linear regression, derivative, jitter and shimmer. Whereas the letter \(S\) represents a set of statistical descriptors which includes mean, minimum and maximum value, standard deviation, range, and interquartile range. The numbers in the brackets on the bottom represent the number of low-level features in each group. Additionally, in the Figure 3 examples of individual representations of the speech signal are depicted.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 3 PQ-DPoL
### Block Generation and Verification  
As described in Algorithm 5, the selected nodes use the practical Byzantine fault tolerance (PBFT) [24] method to generate and verify blocks. PBFT ensures that all honest nodes agree on the sequence of blocks, maintaining the consistency and integrity of the blockchain, even in the presence of malicious nodes. The PBFT process is divided into four main phases:  
**Pre-Preparation phase:** the primary node proposes a block, including a sequence number.  
**Preparation phase:** replica nodes verify the block and send prepare messages to confirm its validity.  
**Commitment phase:** after receiving prepare messages from two-thirds of the nodes, replica nodes send commit messages.  
**Finalization phase:** once commit messages from two-thirds of the nodes are collected, the block is added to the blockchain, and a confirmation message is broadcast.  
In PQ-DPoL, PBFT ensures both security and efficiency by avoiding energy-intensive computations, making it a suitable consensus mechanism for a post-quantum environment. By integrating PBFT, PQ-DPoL is resilient against quantum threats while maintaining high throughput and scalability. To address the challenges posed by post-quantum cryptography (PQC), such as larger keys and signatures, PQ-DPoL incorporates a block compression algorithm. This ensures faster processing and storage efficiency, even with the larger cryptographic parameters introduced by PQC. Experimental results demonstrate that the combination of PBFT and PQC within PQ-DPoL enhances both network security and performance, ensuring scalability and quantum resistance while supporting a high number of transactions per second (TPSs).  
```
1:Input: ProposedBlock, SequenceNumber
2:Output: FinalizedBlock
3:Phase 1: Pre-Preparation
4: Primary node \(P\) proposes ProposedBlock with SequenceNumber
5:\(P\) sends \((\textit{ProposedBlock},\textit{SequenceNumber})\) to all replicas \(R_{i}\)
6:Phase 2: Preparation
7:for each replica node \(R_{i}\)do
8: Verify ProposedBlock
9:if ProposedBlock is valid then
10: Send\(\textit{Prepare}(\textit{SequenceNumber},H(\textit{ProposedBlock}))\) to all replicas \(R_{j}\)
11:endif
12: Collect Prepare messages from other replicas \(R_{j}\)
13:endfor
14:Phase 3: Commitment
15:for each replica node \(R_{i}\)do
16:if received Prepare messages from \(\geq\frac{2n}{3}\) replicas then
17: Enter the Commitment phase
18: Send\(\textit{Commit}(\textit{SequenceNumber},H(\textit{ProposedBlock}))\) to all replicas \(R_{j}\)
19:endif
20: Collect Commit messages from other replicas \(R_{j}\)
21:endfor
22:Phase 4: Finalization
23:for each replica node \(R_{i}\)do
24:if received \(\textit{Commit}\) messages from \(\geq\frac{2n}{3}\) replicas then
25: Add\(\textit{ProposedBlock}\) to blockchain
26: Broadcast confirmation message to network
27:endif
28:endfor
```  
**Algorithm 5** Block generation and verification