esa-sceva/satcom-chunk-collection huggingface dataset:
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## 2 Preliminaries and Related Works
### Winternitz One-Time Signature Scheme (W-OTS)
#### 2.2.1 W-Ots  
W-OTS produces much shorter signatures than Lamport-Diffie one-time signature scheme [23] by iteratively applying a function on a secret key, whereas the number of iterations depends on the signed message [17]. W-OTS uses a one-way function  
\[f:\{0,1\}^{n}\rightarrow\{0,1\}^{n}. \tag{13}\]  
**Key generation:** A Winternitz parameter \(w\), which is the number of bits to be signed simultaneously is chosen. In the following, we restrict the length of the message to be signed to \(m\) bits. It is straightforward to generalize to arbitrary sized messages by using a collision resistant hash function.  
The signature key \(X\) consists of \(l\) bit strings of length \(n\) chosen uniformly at random,  
\[X=(x_{1},x_{2},\ldots,x_{l})\xleftarrow{\$}\{0,1\}^{ln}, \tag{14}\]  
where \(l\) is computed as follows.  
\[l_{1}=\lceil\frac{m}{w}\rceil,l_{2}=\lceil\frac{\lfloor\log_{2}l_{1}\rfloor+1 +w}{w}\rceil,l=l_{1}+l_{2}. \tag{15}\]  
The chaining function \(c^{s}(x)\) for W-OTS is defined as follows.  
\[c^{s}(x)=\begin{cases}x,&\text{if }s=0\\ f(c^{s-1}(x))&\text{if }1\leq s\leq 2^{w}-1\end{cases} \tag{16}\]  
The verification key \(Y\) is calculated by applying the chaining function to each \(x_{i}\) in the signature key \(2^{w}-1\) times. Thus we have  
\[Y=(y_{1},y_{2},\ldots,y_{l})=(c^{2^{w}-1}(x_{1}),c^{2^{w}-1}(x_{2}),\ldots,c^ {2^{w}-1}(x_{l})). \tag{17}\]  
**Signature generation:** A message \(M\) is split into \(l_{1}\) bit strings of length \(w\) and each bit string is converted to an integer in base-\(w\). So we have  
\[M=(m_{1},m_{2},\ldots,m_{l_{1}}) \tag{18}\]  
where  
\[m_{i}\in\{0,1,\ldots,2^{w}-1\},1\leq i\leq l_{1}. \tag{19}\]  
Then the checksum \(C\) is calculated as follows.  
\[C=\sum_{i=1}^{l_{1}}(2^{w}-m_{i}) \tag{20}\]  
The checksum \(C\) is converted to base \(w\). The base \(w\) representation of the checksum \(C\) is \(C^{\prime}=(c_{1},c_{2},\ldots,c_{l_{2}})\). The signature of \(M\) is computed as  
\[\begin{split}\sigma&=(\sigma_{1},\sigma_{2},\ldots,\sigma_{l_{1}},\sigma_{l_{1}+1},\sigma_{l_{1}+2},\ldots,\sigma_{l})\\ &=(c^{m_{1}}(x_{1}),c^{m_{2}}(x_{2}),\ldots,c^{m_{l_{1}}}(x_{l_{ 1}}),c^{c_{1}}(x_{l_{1}+1}),c^{c_{2}}(x_{l_{1}+2}),\ldots,c^{c_{l_{2}}}(x_{l_{ 1}}))\end{split} \tag{21}\]  
**Signature verification:** For the verification of the signature \(\sigma=(\sigma_{1},\sigma_{2},\ldots,\sigma_{l})\), the base-\(w\) strings \(M=(m_{1},m_{2},\ldots,m_{l_{1}})\) and \(C^{\prime}=(c_{1},c_{2},\ldots,c_{l_{2}})\) are calculated as described above. Then we check if  
\[\begin{split}(c^{2^{w}-1-m_{1}}(\sigma_{1}),\ldots,c^{2^{w}-1-m_ {l_{1}}}(\sigma_{l_{1}}),c^{2^{w}-1-c_{1}}(\sigma_{l_{1}+1}),\ldots,c^{2^{w}-1- c_{l_{2}}}(\sigma_{l}))\\ &=(y_{1},\ldots,y_{l_{1}},y_{l_{1}+1},\ldots,y_{l})\end{split} \tag{22}\]It is proved that W-OTS is strongly unforgeable under chosen message attacks if \(\mathcal{F}_{n}\) is a collision resistant family of undetectable one-way functions [20].
## 3 Data and Method
### Estimation of the Models  
In the estimation of SLM model with fixed effects, the Maximum Likelihood (ML) method was initially considered. However, this approach was subsequently dismissed due to signs of simultaneity in the data as shown in the Granger test results. To address these concerns, the Two-Stage Least Squares (2 SLS) method was employed as an alternative to ML ([PERSON] et al., 2007). The 2 SLS approach is particularly adept at mitigating simultaneity issues, making it a more suitable choice for the data at hand ([PERSON], 2015).  
In the implementation of the 2 SLS method for the panel data model with fixed effects, a specific strategy was adopted for dealing with the endogenous variables, which in this case were the 19 economic sectors. The independent variables were treated as endogenous, and their one-period temporally lagged values were utilised as instrumental variables in the 2 SLS method (see [PERSON], 2015). The Two-Stage Least Squares method for panel data with fixed effects was applied to fit all the models in this study, utilising the \"splm\" package in R.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Tracking Accuracy Analysis in Different Conditions  
Several factors affect tracking accuracy during path tracking, and they include vehicle speed, look-ahead distance, starting position and angle, and forward or backward driving modes. They are all analyzed in the following sections.  
Figure 11: Stop position accuracy measurement.  
Figure 10: Test field and start position.
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 2 Materials and Methods  
### Materials  
In this work, magnesium turnings with 99.9% purity (ACROS Organics, Morris Plains, NJ, USA) were used as the base material. Hollow glass microballoon (GMB) particles with a diameter of 11 \(\upmu\)m having a density of \(\sim\)1.05 g/cc were acquired from Sigma Aldrich, Singapore. The compositions of materials used in the present study are: (i) pure Mg and (ii) Mg/Mg-20 GMB hybrid composite. Powder metallurgy with hot extrusion followed by disintegrated melt deposition techniques were used to produce the Mg/Mg-20 GMB hybrid composite.
# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 4 Trustworthiness Model Specification
### Social Trustworthiness Layer  
This layer is responsible for leveraging the capability to autonomously establish social inter-object relationships to improve the trust between them and the correctness of the collected data. We measure this layer's trustworthiness with the successful transaction rate (\(STR\)), calculated as the proportion between the number of successful transactions (\(ST\)) and the total number of transactions (\(TT\)) in a defined time slot, as stated in Equation (4). A transaction \(l\) is considered successful when a node \(j\) expects to obtain some information or data (\(v\)) from node \(i\) before a defined maximum reception time (\(Trx_{max}\)) and receives it as expected, thus providing good feedback (\(f_{ij}^{l}=1\)) for that transaction to node \(i\). The higher the \(STR\) is, the better the social trustworthiness.  
\[STR=\frac{ST}{TT}. \tag{4}\]  
Most solutions tend to use reputational mechanisms to determine which nodes to trust when exchanging information. This reputation is commonly based on the feedback of previous transactions to build an opinion of the node's trustworthiness [59; 60; 62].
## 1 Introduction
### Contributions of Paper  
The aforementioned challenges highlight the need for improving the robustness and the computational efficiency of TF-based emitter classification. This work proposes a novel emitter classification approach that uses a hybrid DNN consisting of a convolutional denoising autoencoder (CDAE), followed by a CNN classifier. By construction, in this approach, the representation learning phase devoted to denoising (performed by the CDAE) and the classification phase (performed by the CNN) are performed separately.  
The theoretical motivation behind this approach relies on the following considerations: (1) the decoupling allows to perform a more efficient training and (2) a standalone representation learning phase focused on obtaining a reconstructed TF image before classification can more effectively support the CNN. The classifier can operate based on more clear-cut visual features of the TF representation after the representation learning. In a sense, the use of the CDAE allows incorporating explicitly into the process the priors about the protocol spectrogram original visual features that have been degraded during the signal propagation.  
In practice, we demonstrate by simulation that, compared to the state-of-the-art standalone CNNs, our hybrid DNN approach achieves high accuracy under harsh propagation conditions. Our proposed DNN requires significantly less computational resources compared to standalone CNNs. The main contributions of the work are summarized as follows:  
* A CDAE is trained to reconstruct the original visual patterns of pre-channel emissions out of spectrograms corrupted by harsh channel conditions. We conducted a comprehensive study in reconstruction performance using simulation results for several standards, including LTE and versions of Wi-Fi, under harsh propagation conditions that include SNR, multipath, NLOS, and Doppler.
* A CNN performs emitter classification using the denoised representation generated by the CDAE, leading to high classification accuracy under harsh channel conditions. Moreover, the resulting CNN requires significantly less computational resources as it operates on a compressed representation with lower dimensionality than spectrogram-fed CNN classifiers. We demonstrate, using simulations, that our proposed hybrid CDAE-CNN approach outperforms in classification accuracy and computational cost a wide range of DNN and ML-based schemes while requiring significantly less computing resources.  
The rest of the paper is structured as follows. Section 1.1 presents the related work about DL for wireless communication. Section 2 describes our system model and the problem statement. In Section 2, simulation setup and data generation for unlicensed LTE and Wi-Fi standards are explained. Section 3 details our DL-based approach to classifywireless signals operating in the same unlicensed band. In Section 4, we report results under various noise scenarios, while the comparison between our approach and other ML and DL algorithms is presented in Section 5. Conclusions are drawn in Section 6.
# Analysis of Coating Loss from Coated Stainless Steel Orthodontic Wire
## 3 Results
### Analysis of Coating Loss by Wire Bending  
Figure 3 shows stereomicrographs of four types of coated wire with various bend angles from 30\({}^{\circ}\) to 120\({}^{\circ}\). The TiN-coated wire showed cracks and detachment of the coating from the surface after bending. No surface deterioration was seen for the other wires. Figure 4 shows scanning electron micrographs of four types of coated wire with various bend angles from 30\({}^{\circ}\) to 120\({}^{\circ}\) (\(\times\)200, \(\times\)500, \(\times\)1000). The Au-coated, TiN-coated, and Rh-coated wires showed cracks and detachment of the coating on SEM analysis (Figure 4). At a bend angle of 30\({}^{\circ}\), the Au-coated wire showed only minor cracking, while clear cracks were observed in multiple locations on the TiN-coated and Rh-coated wires, and both cracking  
Figure 2: Metal ions derived from substrate metal, SS, released from four types of coated wire immersed in HCl. After immersion in HCl for 30 min, the amounts of metal ions eluted from the wire were determined by ICP–MS: (**a**) TiN-coated wire; (**b**) Au-coated wire; (**c**) Rh-coated wire; (**d**) PEN-coated wire. **\({}^{**}p<0.01\)**.  
and peeling of the coating increased with increasing bend angle. On the other hand, the PEN-coated wire showed no surface deterioration on bending. Figure 5 shows metal ions derived from the substrate metal, SS, released from four types of coated wire with various bend angles from 30\({}^{\circ}\) to 120\({}^{\circ}\) immersed in HCl. Statistical analysis showed that all data followed parametric behavior. Examination of metal ion release showed that the amount of metal ion release increased with increasing wire bend angle, exception for PEN-coated wire (Figure 5). No change in elution of metal ions with increasing wire bending angle was observed for PEN-coated wire.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 3 Load-Sharing Based Voltage Redistribution Mechanism for Modules in Each Level
### Equal Load-Sharing Rule  
Equal load-sharing rule is the simplest one, which implies that the load on the failure module will be equally assigned to the remaining normal modules. Thus, the voltage variation \(\Delta V_{p}(x)\) can be obtained by:  
\[\Delta V_{p}(x)=\begin{cases}0&p\in S_{1}(x)\\ \frac{\delta\cdot V_{q}(x-1)}{Size(S_{0}(x))}&p\in S_{0}(x)\\ x\geq 1\end{cases} \tag{12}\]
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Disturbance of Unbalanced Current on Relay  
Although there is a protective box on the track coil side of the relay, which can block most of the interference current, it is difficult to eliminate the interference of unbalanced impulse current due to the DC component.  
Figure 10: Loss of choke transformer core under different DC components.  
Figure 9: Magnetic flux density nephogram under different DC components.  
The JRJC-70/240 binary two-bit relay model and its external circuit simulation model are built. The magnetic flux density and core loss of the relay coil are simulated to verify the influence of unbalanced impulse current on the relay. In order to fit the actual situation, the LC circuit analog protection box is added to the track coil side, and a 50 Hz and exponential decay power supply is introduced into the external circuit to simulate the interference source.  
The relay local coil side voltage is 110 V, and the frequency is 25 Hz. The working voltage of the track coil side is 15 V, the frequency is 25 Hz, and the phase angle difference with the local coil side power supply is set to 90. In addition, the protective box is set, and the inductance is 0.845 H. The capacitance parameter is 12 \(\mu\)F. The added interference source waveform is shown in Figure 12.  
By setting five unbalanced impulse current interference sources of 0 A, 0.2 A, 0.4 A, 0.6 A, 0.8 A and 1 A, the magnetic flux density and core loss of the relay are simulated, as shown in Figures 13 and 14. When the unbalanced impulse current is 0 A, the magnetic flux density and core loss are small. As the invasion current of the relay increases, the magnetic flux density and core loss also increase. When the unbalanced impulse current reaches 1 A, the core loss changes more obviously with the increase of current. This will increase the heating degree of the relay and cause the vibration of the wing plate, which may eventually lead to the appearance of an abnormal red ribbon.  
Figure 11: Secondary voltage waveform under different DC components.  
Figure 12: Impulse current interference source waveform.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Linewidth Testing Method Based on Noise Analysis
#### 3.5.3 Optical Coherent Reception Method Based on a 120\({}^{\circ}\) Interferometer  
In 2015, [PERSON] et al. developed a narrow-linewidth laser noise measurement technique based on a 3 \(\times\) 3 coupler and an unbalanced MI delayed self-heterodyne method [113], as shown in Figure 10c. The system involves injecting the laser under test (LUT) into a 3 \(\times\) 3 fiber coupler (OC) through a ring resonator. The coupler divides the laser into three paths. The beam passing through ports 1 and 2 is reflected back to the OC through the FRM, while port 3 has no reflection. Three beams of light interfere with the OC, and interference fringes are obtained from ports 1, 2, and 3 on the left. The laser linewidth is obtained by calculating the noise power spectral density.  
In 2019, [PERSON] et al. used a three-step parallel phase shift measurement method based on a 120\({}^{\circ}\) phase difference MI [114] to directly demodulate the accumulated laser differential phase within the delay time, derived the relationship between the differential phase and frequency noise and phase noise, and proposed a dynamic noise characterization method for narrow-linewidth sweep lasers based on phase reconstruction [79].  
In 2022, [PERSON] et al. used a 120\({}^{\circ}\) phase difference interferometer to characterize the dynamic noise and differential phase information of a 1550 nm waveguide Bragg grating (WBG) ECDL via phase reconstruction [115]. The phase/frequency noise power spectral density of the laser can be obtained by the \(\beta\) isolation line algorithm, with a minimum integrated linewidth of 4.36 kHz and a minimum Lorentz linewidth of 3.58 kHz, as shown in Figure 10d. In 2023, this team utilized a nonequilibrium MI system to measure the linewidth of an ECDL in 1 \(\upmu\)m band [116] and used the \(\beta\)-isolation line integration algorithm to derive and evaluate linewidth in different Fourier frequency domain ranges, resulting  
Figure 10: **(a)** Laser frequency noise measurement using a frequency discriminator, which is an unbalanced Mach–Zehnder interferometer [105]; **(b)** COSH setup: A laser is split by a three-port AOM into frequency-shifted (1 st-order output) and unshifted (0 th-order output) portions [109]; **(c)**\(3\times 3\) fiber coupler unbalanced Michelson interferometer self-heterodyne technique [113]; **(d)** Measured FN-PSD spectrum with narrowest linewidth [115].  
in a minimum integrated linewidth of 25.60 kHz and a minimum Lorentz linewidth of 12.00 kHz.
### Network Lifetime  
In this section, the lifetime of the network is shown in Figures 10 and 11. We measure how long a network will sustain until any node exhausts. In Figure 10, with the increment of the initial energy of nodes, each method presents an increasing tendency in lifetime. Only a slow growth appears in DMRF as well as DMRF + LEACH. In DMRF, a small amount of nodes always exist in others' forwarding candidate sets. These nodes will firstly run out of power. In DMRF + LEACH, compared with DMRF, although the network doubles in lifetime, issue of unequal size of clusters energy is still unsolved. Consequently, the lifetime of the network is directly relevant to cluster with the smallest size. Whereas in GTRF, by implementing FATP protocol [26], VA generates clusters with nearly equal sizes and guarantees the balance of energy cost, therefore, it can prolong the lifetime a lot.  
Compared with Figure 11, distinctions appear in the lifetime of each approach, which results from the effect of the selfish nodes. Since in DMRF and DMRF + LEACH, the selfish nodes are regarded as faulty nodes which cast a burden on other nodes, therefore, the selfish nodes can shorten the lifetime of the network indirectly. From Figures 10 and 11, we can conclude that GTRF can prolong the lifetime of the network.  
Figure 10: Lifetime of the network with selfish nodes.
## 4 Proposed Efficient Monitoring System (EMS)
### Recommendation Component  
The recommendation component considers the information provided by the categorization component \(CVM\), the neighbor's assessment system \(RVN\), and the reputation value obtained by the node of its own to change the reputation value \(\gamma\) of a network node only at the end of a communication period \(RVI\). The \(RVI\) is obtained through an interface that interacts with each network node's VDTN reputation mechanism. The nodes may generate a view of their outcomes using this reputation method. The truly new node's reputation score (\(\gamma i\)) is determined by adding all three values together, as shown in Equation (9).  
\[\gamma i=\psi RVI_{i}+(1-\psi)RVN_{i}+CVMi \tag{9}\]  
where \(\psi\) is a number within a range [0, 1] that indicates how much the EMS believes the node's findings. The recommendation component sends all nodes' reputation values to the categorization component that updates its categorization table until recalculating nodes \(\gamma\). Nodes are also notified of their new reputation values by the EMS. Table 4 shows the comparison of the proposed system with other schemes.  
Figure 4: Flow Chart of the Punishment Component.
## 7 Ground-Segment Optimization Aspects  
The main optimization problem with respect to the ground segment refers to the optimization of the number and placement of ground stations all over the Earth in such a way that they are able to offer good monitoring, management, and control of all LEO satellites in the sky. However, currently, there are very few studies in open access describing optimization methods for LEO ground segments. In contrast, many such studies exist for MEO GNSS systems, and it is to be expected that some of the optimization mechanisms previously proposed for GNSS are also applicable in the LEO context. As a result, in this section, we will present the metrics and methods that have seen successful in either case.
## 4 Quantizer Design
### Design Criterion of One-Bit Quantizers  
We can see from (10) that given a specific false alarm probability, the larger the non-centrality parameter \(\lambda\), the higher the detection probability. According to (11), the non-centrality parameter \(\lambda\) is proportional to FI, which is a function of the quantization threshold \(\tau_{n}\). Therefore, in order to achieve the best asymptotic detection performance of the GLRT detector, we optimize the quantization threshold \(\tau_{n}\) by maximizing the non-centrality parameter \(\lambda\). Since \(\{y_{n}\}\) are independent of each other, the above optimization problem decouples into a collection of \(N\) independent quantization threshold design problems. Therefore, the optimum quantization threshold is identical for all sensors. Specifically, the optimization problem is given by:  
\[\max_{\tau_{n}}\Psi_{n}(\sigma_{0};\tau_{n}),\quad\forall n \tag{16}\]  
Figure 1 illustrates the non-centrality parameter of the proposed quantization scheme _versus_\(\tau_{n}/\sigma_{0}\), which denotes the ratio of the threshold to the standard deviation of the observation under hypothesis \(H_{1}\). As shown in Figure 1, in both the error-free case (_i.e._, \(\tilde{p}=0\)) and BSC scenarios, the noncentrality parameter of the one-bit proposed quantization scheme is unimodal, which implies a unique maximum. Therefore, the optimum thresholds \(\tau^{\star}\) of the proposed quantizer in perfect/imperfect channels can be easily found by a conventional numerical method, and the results are reported in Table 1. The performance of the noncentrality parameter degrades rapidly as the threshold \(\tau\) deviates from its maximum value (e.g., optimum value \(\tau^{\star}\)) and as the crossover probability \(\tilde{p}\) increases. It is noted that in the BSC scenarios (_i.e._, \(\tilde{p}\
eq 0\)), the optimum threshold \(\tau^{\star}\) is different from that in the error-free case.  
Figure 1: Non-centrality \(\lambda\) of the proposed quantization scheme over perfect and binary symmetric channels (BSCs) _versus_ the normalized threshold \(\frac{\tau_{n}}{\sigma_{0}}\), when the number of sensors \(N=100\).
## 6 Illustrative Example
### Illustration of Premature Feedback  
We now illustrate premature feedback in the execution of a PFC algorithm, after the PFC algorithm starts in \"arbitrary initial system configuration\" (Figure 2). In the figure, processes are in states \(C\), \(B\), and \(F\) when they are in the cleaning, broadcast, and feedback phases, respectively, in order. In Figure 2 Part 1, we present an initial system configuration of a distributed system in a network on six processes, where Processes 1 through 5 areenabled while Process 0 is disabled. Notice that Processes 2, 4, and 5 are part of an abnormal tree which extends (tree shown in red) to a number of disabled processes such that the elimination of the abnormal tree takes eleven rounds. It is easy to see that since their states are not consistent with those of their parents, Processes 1 and 2 are enabled to enter state \(C\) and assign 0 to their \(L\)-variables. Furthermore, Process 5 is shown to be abnormal since its level value is 25, whereas its parent (Process 5) is 10. Therefore, in Part 2, in one round, Processes 1, 3, and 5 assign \(C\) to their states and assign 0 to their \(L\)-variables.  
Observe that, in the next round, Processes 0, 1, 3, and 5 enter the broadcast phase (state \(B\)). After processes 0, 1, 3, and 5 enter the broadcast phase, they cannot locally decide whether Processes 2 and 4 are part of the normal tree or not. If they enter the feedback phase without checking if Processes 2 and 4 are included in an abnormal tree, the broadcast never reaches Processes 2 and 4, causing premature feedback, which in turn violates propagation reliability.