esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Spatial Heterodyne Terahertz Raman Spectrometer with Echelle Gratings
## 3 Experiment
### Organic Acids Measurement Results  
We measured the terahertz Raman bands of four organic acid samples. Figure 3a,b compare the spectral curves for salicylic acid and benzoic acid samples when using the 36 gr/mm echelle gratings (red curve) and 150 gr/mm gratings (green curve). When the 36 gr/mm echelle gratings are used, the characteristic peaks are steeper, the full width at half maximum (FWHM) is narrower, and the resolution is significantly better than the corresponding properties of the spatial heterodyne terahertz Raman spectrometer using the 150 gr/mm gratings.  
Figure 2: (a) Interferogram of the mercury lamp; (b) mercury lamp level chart; (c) spectral spatial frequency of the mercury lamp.  
Gallic acid, which is also known as 3,4,5-trihydroxybenzoic acid and has the chemical formula C\({}_{7}\)H\({}_{6}\)O\({}_{5}\), has anti-inflammatory, anti-mutation, and anti-oxidation biological activities [11; 12], and is often used in anti-cancer, antibacterial, and other drug types. Figure 3c shows the terahertz Raman spectrum of gallic acid, and the characteristic peaks observed at 97 cm\({}^{-1}\) (2.91 THz) and 105.6 cm\({}^{-1}\) (3.168 THz) are characterized as the conjugated cycles of the molecules. Figure 3d shows the terahertz Raman spectrum of p-aminobenzoic acid, which is a cyclic amino acid that becomes a compound after position 4 of the benzene ring of benzoic acid is replaced with an amino group [13], and it can be used in dyes, sunscreens [14], and medicines. When compared with benzoic acid, it can be seen that it has a similar structure, but the characteristic peaks of the terahertz Raman spectra of the two materials differ significantly.
## 3 Results and Discussion
### The Role of Ag\({}_{3}\)Sn on High-Temperature Reliability Enhancement  
It should be noted that the significant change of Ag\({}_{3}\)Sn IMCs morphology also played an important role in enhancing the high-temperature reliability of solder joint. According to the particle strengthening proposed by [PERSON]: when the dislocations bypass the hard deformed particles, the repulsion force of the particles to dislocations is large enough, and the dislocation lines are blocked and bent. With the increase of applied loading, the dislocations are forced to move forward in a bending manner. The schematic diagram of dislocations bypassing the strengthened particles is shown in Figure 8.  
The critical shear stress required to bypass the particles according to Orwan mechanism is called Orwan stress, as expressed by the formula below:  
\[\tau_{0}=\frac{Gb}{\lambda}\approx\alpha f^{0.5}r^{-1} \tag{5}\]  
where the constant for edge dislocation is 0.093 and for screw dislocation is 0.14, \(f\) is the volume fraction of particles, and \(r\) is the average radius of particles (\(\mu\)m). As can be obtained, the higher density of particles and smaller radius, the effect of strengthening mechanism could perform more obviously. In SAC387-0.05 Nd/Cu solder, the size of Ag\({}_{3}\)Sn IMCs was refined obviously from micron scale to nano scale as can be seen in Figure 9 and Table 2. These nano-scaled Ag\({}_{3}\)Sn IMCs can serve as the particles above and hindering the motion of dislocations, delaying the decrease of shear force during high temperature aging. In addition, it can be obtained from Figure 9 that nano-scaled Ag\({}_{3}\)Sn  
Figure 8: Schematic diagram of dislocation bypassing Ag\({}_{3}\)Sn IMC particles.  
IMCs adsorbed on the surface of interfacial Cu\({}_{6}\)Sn\({}_{5}\)IMCs can hinder the diffusion of Sn, Cu atoms and finally hindering the growth of interfacial Cu\({}_{6}\)Sn\({}_{5}\)IMCs.
## 3 Methodology
### Model Transformation Process
#### 3.1.1 Metamodeling  
The first step of the model transformation is to depict the metamodel of each domain-specific language to describe the different concepts that integrate each domain.  
* **SysML Internal Block Diagram (IBD)** metamodel  
We select the IBD diagram in our study since an IBD expresses the internal structure of a system and the interaction of its parts.  
We describe the different concepts of an IBD diagram according to the OMG (Object Management Group) specifications [27]. An IBD model (Figure 2a) is made up of parts, connectors, and ports. One part can be connected to other parts via connectors. A port defines an interaction point for a part and can be a standard port or a flow port. A connector ensures the connection between two parts via a binding connector or an item flow.  
Figure 1: Model synchronization approach.  
* **Modelica metamodel** We express the concepts integrated into a Modelica model through the metamodel shown in Figure 2b. The metamodel is defined with regards to the specifications of Modelica language described in Reference [28].  
A Modelica metamodel consists of a fundamental structuring unit called a class, which provides the structure of objects. A class can be connected to zero or more classes. A connection between two classes is introduced by a connection equation. These connections can be presented in two types: causal connection and acausal connection.  
* **Altarica metamodel** Altarica uses a System Modeling Language (S2 ML) [29] as a structuring paradigm to provide a sufficient set of constructs to build hierarchical models. We defined the metamodel of an Altarica model based on the S2 ML language. The Altarica metamodel is shown in Figure 2c.  
Altarica has two concepts to structure models: block and class. Classes and blocks are the basic units of Altarica language since Altarica applies a prototype-oriented approach. A class or a block has interaction points defined by flow variables. These variables are used to model the propagation of the information between classes or blocks. The connections are represented by assertions. The assertion can be causal or acausal assertions.  
* **Digraph metamodel** A digraph is composed of a set of nodes (i.e., vertices) and edges (Figure 2d). Each node defines a set of leaving and entering edges. The graph is called directed because, for each edge, we define the source and the target nodes.  
In Reference [26], we define the different mapping between the entities of modeling languages (i.e., SysML, Modelica, and Altarica) and the common representational formalism.  
Figure 2: Metamodeling of different domain-specific models: (**a**) Partial meta-model of an Internal Block Diagram (IBD) model, (**b**) partial meta-model of a Modelica model, (**c**) partial meta-model of an Altarica model, and (**d**) partial meta-model of a digraph model.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 3 Delay-Sensitive NOMA-HARQ
### Packet Delivery Delay Profile of S-NOMA-HARQ  
Authors in [22] evaluated S-NOMA-HARQ with single retransmission and derived the PER ([22], Equation (14)) and throughput ([22], Equation (17)) for a given power allocation ratio. In particular, packet success probability of user \(i\) without retransmission denoted by \(p_{s_{i}}\) is derived in ([22], Equation (16)). Since each retransmission delays the transmission of the new packet by one time slot, the delay profile of S-NOMA-HARQ for delivering \(N\) packets can be calculated as follows  
\[D_{i}^{\mathsf{S}}[d]=\sum_{j=0}^{N}\binom{N}{j}p_{s_{i}}^{j}(1-p_{s_{i}})^{N -j}\delta[d-2N+j]. \tag{8}\]  
Because each packet of user \(i\) will be successful with single transmission with probability \(p_{s_{i}}\), and each retransmission causes delay of a time slot with probability \(1-p_{s_{i}}\). In S-NOMA-HARQ, since packets are orthogonal to each other, the packet delivery delay follows a binomial distribution.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## The Implementation of Chip Results and Measurement
### Data Comparison with FPGA Approach
This study wrote \(\overset{\alpha}{\mathbf{x}}_{k}\) to the SRAM of DE2-115 with its control panel. SRAM_ADDR represents the number of times of seven-segment display. \(\overset{\alpha}{\mathbf{x}}_{k}\) stored in SRAM_DQ [15:0], and then put SRAM_DQ into HEX0[6:0], HEX1[6:0], HEX2[6:0] and HEX3[6:0] with hexadecimal values and displays them on the seven-segment display. For example, in this article, the seven-segment display shows 16 bits twice, 0000 and 14 db, respectively, which is a single value, and the number of composition is 000014 db with hexadecimal. Therefore, eight times is one cycle, which is the \(X\), \(Y\), \(V_{x}\), and \(V_{y}\) of the coordinate of an algorithm-restored point. Figure 10: Software simulation results for the tracking location and speed of an MD based on \(\alpha\)-\(\beta\) tracking approach as the MD moves the clockwise direction of the path in a plane of indoor environments (**a,b**) original trajectory and speed; (**c,d**) observed trajectory and speed (positioning approach); (**e,f**) estimated trajectory and speed. Figure 11: Cumulative distribution function of the error distances, KF software-based, \(\alpha\)-\(\beta\) software-based, KF VLSI-based, and proposed approach with \(\alpha\)-\(\beta\) chip-based tracking approaches. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CDF**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**KF Approach**} & \(\alpha\)-\(\beta\) Approach & \(\alpha\)-\(\beta\) Approach & \(\alpha\)-\(\beta\) Approach & \(\alpha\)-\(\beta\) Approach \\  & & **Based on** & \(\alpha\)= **1** & \(\alpha\)= **0.75** & \(\alpha\)= **0.5** & \(\alpha\)= **0.25** \\  & & **KF Coeff.** & \(\beta\) = **1** & \(\beta\) = **0.75** & \(\beta\) = **0.5** & \(\beta\) = **0.25** \\ \hline
90\% & 3.65 m & 3.66 m & 6.82 m & 6.02 m & 5.42 m & 5.21 m \\ \hline
50\% & 2.00 m & 2.01 m & 3.71 m & 3.19 m & 2.92 m & 2.81 m \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The Cumulative distribution function of the error distances, KF software-based, \(\alpha\)-\(\beta\) software-based, KF VLSI-based, and proposed approach with \(\alpha\)-\(\beta\) chip-based tracking approaches. Figure 12: Software simulation and implement on hardware for the tracking location and speed of an MD based on \(\alpha\)-\(\beta\) tracking approach as the MD moves the counterclockwise direction of the non-linear path in a plane of indoor environments (**a**,**b**) original trajectory and speed; (**c**,**d**) observed trajectory and speed (positioning approach); (**e**,**f**) estimated trajectory and speed; (**g**,**h**) FPGA-based estimated trajectory and speed.
## 3 Results
### Definition of Different Scenarios in the Drive Test  
In general, three different scenarios test the network condition in a drive test. Each of these three scenarios examines the network status from particular aspects, and various faults are identified in these scenarios. In the following, we describe each of the three scenarios.  
**Scenario number 1:** Idle mode: In this situation, the responsible person for the test puts the mobile phone in idle mode. This means that phone calls cannot be made. This is to test the network in serving cell reselection when moving the mobile phone. In addition, the  
Figure 12: Drive test route map according to the division of urban areas and the border between base controller stations. The blue lines illustrate the 8 urban areas, the yellow lines illustrate the border between the base control stations, and the red lines illustrate the drive test route.  
received signal level is measured at different points of the network, and the locations with suitable signal levels are provided to the optimizer for further investigations.  
**Scenario number 2**: Short calls: The short call scenario means evaluating the network in terms of call establishment parameters. That means assessing whether the mobile phone subscriber can easily make a call, and what the problems in the process of making a call are, including access to the signaling and traffic channels. Call blocking parameters and thriving network access rate are investigated in this scenario.  
**Scenario number 3**: Long calls: The long call, unlike the short call, may be maintained continuously during the drive test. This scenario aims to test the network in terms of call interruption rate, conversation quality, received signal quality, and check reassignments.  
This article examines the problems affecting network service quality by reviewing all three scenarios. In the following, we first introduce the measured parameters in the drive test and then describe the process of identifying network problems in these three scenarios.
## 4 Experiments
### Comparison with the SOTA Methods
#### 4.2.2 Qualitative Comparisons  
We conducted comparison experiments on the VisDrone and DOTA datasets to demonstrate the effectiveness of AFE-YOLOv8, and we visualize the detection results in Figures 9 and 10. From the visualization of the detection results, it is evident that our AFE-YOLOv8 model outperforms other models in detecting small objects and multi-scale objects. For instance, in Figures 9 and 10, the objects enclosed within the red rectangles  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mAP** & **AP50** \\ \hline RRNet & 29.13 & 55.82 \\ DPNet-ensemble & 31.22 & 57.36 \\ SMPNet & 34.63 & \(\backslash\) \\ YOLOv8 & 35.24 & 60.92 \\ DPNetV3 & 36.37 & 61.15 \\ YOLOv9 & 37.08 & 62.36 \\ PP-YOLOE & 37.14 & 62.83 \\ YOLOv10 & 38.42 & 63.69 \\ RT-DETR & 38.84 & 64.76 \\ TPH-YOLOv5 & 39.18 & \(\backslash\) \\ CZ Det & 39.36 & 65.83 \\ AFE-YOLOv8 (ours) & **39.75** & **66.37** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparisons with the SOTA methods on the VisDrone dataset.  
represent false negatives obtained by other models, while our model successfully detected these missed objects.  
Figure 10: Visualization of the detection results of the three models on the DOTA dataset, where the red rectangular boxes show enlarged displays of the small-target dense areas.  
Figure 9: Visualization of the detection results of the three models on the VisDrone dataset, where the red rectangular boxes show enlarged displays of the target dense areas.  
In Figure 9, we visualize the detection results of YOLOv8, TPH-YOLOv5, and AFE-YOLOv8 (our method) on the VisDrone dataset. In sharp contrast to YOLOv8 and TPH-YOLOv5 object detection methods, our method exhibits a superior detection performance for dense small objects and multi-scale objects.  
Compared to the VisDrone dataset, the DOTA dataset exhibits significant variation in object scales and higher scene complexity. To evaluate the robustness and generalization of our method, we conducted an additional qualitative comparison experiment on the DOTA dataset and visualized the detection results, as shown in Figure 10. The comparison demonstrates that thanks to the inclusion of the RFEM in our method, our approach effectively detects multi-scale objects and maintains reliable robustness in complex scenes.
# Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion
## 5 Experimental Evaluation
### Comparing to the Literature
The standard Snowball provides a network with a lower density, which may depend on the graph and on the starting point of the collection. Metropolis-Hastings gives always the smallest density by far, confirming the experiments on the degree distribution where it collects less highly connected nodes than the other methods. **IVIP and degree distribution:** As displayed in Table 7, the snowball-sampled graphs achieve poor IVIP scores as they contain less communities than the original graphs. Snowball sampling collects every node around its starting location, preventing it to explore a larger region of the graph. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Facebook**} & \multicolumn{2}{c}{**Github**} & \multicolumn{2}{c}{**Google +**} & \multicolumn{1}{c}{**Youtube**} \\ \cline{2-7}  & **10\%** & **20\%** & **10\%** & **20\%** & **10\%** & **10\%** \\ \hline Metropolis-Hastings & 0.200 (0.022) & 0.378 (0.028) & 0.299 (0.020) & 0.538 (0.019) & 0.291 (0.019) & 0.367 (0.009) \\ CNRW & 0.322 (0.009) & 0.531 (0.013) & 0.513 (0.004) & 0.704 (0.002) & 0.644 (0.004) & 0.573 (0.001) \\ CNARW & 0.325 (0.011) & 0.523 (0.010) & 0.517 (0.004) & 0.709 (0.003) & 0.613 (0.004) & 0.574 (0.001) \\ Forest Fire & 0.272 (0.026) & 0.451 (0.055) & 0.502 (0.009) & 0.698 (0.021) & 0.549 (0.034) & 0.540 (0.033) \\ \hline
**Fireball** & 0.278 (0.030) & 0.482 (0.025) & 0.476 (0.028) & 0.685 (0.019) & 0.580 (0.024) & 0.547 (0.022) \\
**Edgeball** & 0.353 (0.010) & 0.552 (0.009) & 0.430 (0.027) & 0.653 (0.011) & 0.658 (0.004) & 0.549 (0.006) \\
**Hubball** & 0.354 (0.010) & 0.557 (0.005) & 0.333 (0.049) & 0.555 (0.031) & 0.630 (0.008) & 0.450 (0.009) \\
**Coreball** & 0.413 (0.013) & 0.613 (0.013) & 0.584 (0.017) & 0.801 (0.005) & 0.698 (0.003) & 0.643 (0.002) \\ \hline Snowball & 0.274 (0.053) & 0.471 (0.035) & 0.387 (0.066) & 0.632 (0.069) & 0.546 (0.052) & 0.449 (0.037) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: IVIP score (higher is better) for the different datasets, averaged over 10 sampling runs for each dataset (standard deviation is shown between parentheses). Metropolis-Hastings, with it tendency to sample more weakly connected nodes, misses a part of the high degree nodes. The Coreball gives the highest scores, showing that it focuses on collecting high degree nodes while at the same time exploring most of the communities. The other members of the Spikyball family obtain intermediate scores. Although degree distribution is better approximated by non-Spikyball sampling schemes, as shown in Table 2, this is done at the expense of having a lower fraction of the influencer nodes in the sampled graph.
## 8 The Optimized Distance between the VGs and the Sail Hull
The blue dot line denotes the radiated sound power from the model with triangular VGs at the leading edge of the sail hull. The green line denotes the radiated sound power from the model with triangular VGs at a distance of 0.2c from the leading edge of the sail hull. The pink line denotes the radiated sound power from the model with triangular VGs at a distance of 0.15c from the leading edge of the sail hull. The yellow line denotes the radiated sound power from the model with triangular VGs at a distance of 0.1c from the leading edge of the sail hull. The noise reduction comparison of the triangular VGs at different distances with the original model showed that the placement of the VGs was according to the following rule: the higher corner must be in the origin of the horseshoe vortex. The vortices from the higher corner can inhibit the formation of the horseshoe vortex. The VGs can also inject the outside energy into the boundary layer and reduce the change in the pressure gradient. Then, the separation of the boundary layer is inhibited. Since the origin of the horseshoe vortex was formed at a distance of 0.1c from the leading edge of the sail hull, the triangular VGs at this distance achieved the optimum suppression of the flow-induced noise. Through numerical calculations, the resistance of the model was changed from 35.698 N to 38.299 N, when the triangular VGs were installed on the model with the optimum hydrodynamic noise reduction effect. The resistance was increased by 7.29%. Figure 23 shows the horizontal directivity of the sound field from the two models. In the low-frequency range, the horizontal directivity of the sound field from the model with the triangular VGs was less intensive than that of the original model. This phenomenon showed that the horseshoe vortex had been successfully been suppressed. However, the sound pressure in the leading edge and the trailing edge was still intensive. In the high-frequency range, the directivity of the sound field of the model with the triangular VGs became milder than that of the original model, even if the wake flow field had been affected by the mechanical VGs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Total Level of Radiated Sound Power/dB 1** & **Noise Reduction/dB 1** \\ \hline Original & 113.51 & 0 \\ VGs at the leading edge & 108.23 & 5.28 \\ VGs at the distance of 0.1c & 104.58 & 8.93 \\ VGs at the distance of 0.15c & 106.98 & 6.53 \\ VGs at the distance of 0.2c & 104.63 & 8.88 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 3: The total level of radiated sound power from the different models. Figure 23: The directivity of the sound field of the two models at different frequencies. (**a**) \(f\) = 45 Hz; (**b**) \(f\) = 500 Hz; (**c**) \(f\) = 1200 Hz; (**d**) \(f\) = 1800 Hz.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 9 Efficient Signature Verification in Post-Quantum VerITAS
### Input Representation and Hash Verification Using Lattice and Poseidon Hashes
To prove the validity of the edit, the editor constructs a zero-knowledge proof for the relation \(R_{\text{simple}}\), defined as:  
\[R_{\text{simple}}\ :=\{((pp,f,x,com);(w,r)):f(w)=x\wedge com=PCS.commit(pp,poly(w),r) \tag{12}\]  
This relation ensures that the edited image \(x\) is derived from the original image \(w\) through the function \(f\), and that the commitment com is a valid commitment to the polynomial representation of \(w\). A key insight in this approach is the avoidance of proving com directly in the SNARK circuit by leveraging an extension of PLONK's permutation argument, which significantly reduces computational overhead. Polynomial commitments are employed for their efficiency, as they eliminate the need to hash \(w\) within the SNARK circuit, and for their binding property, which ensures that com is a valid and secure commitment to the polynomial \(W(X)\). To ensure consistency between the computation trace \(T(X)\) and the witness polynomial \(W(X)\), HyperPlonk introduces additional copy constraints. These constraints enforce that the values of \(T(\Omega)\) match the corresponding values of \(W(\Omega)\) for all witness elements. This is achieved by defining a new permutation \(\tau^{\prime}\) that captures the constraints between \(T(X)\) and \(W(X)\). A visualization of this process is provided in Figure 4, where thick green edges represent the extended copy constraints. The proof construction begins with the circuit \(C(x,w)\), which outputs 0 if \(f(w)=x\). The editor constructs the computation trace \(T(X)\) for the circuit \(C(x,w)\). The permutation \(\tau^{\prime}\) is extended to include constraints between \(T(X)\) and \(W(X)\), ensuring consistency across the computation. Finally, the editor generates a proof \(\pi\) for the relation \(R_{\text{simple}}\), using HyperPlonk's proof system, which efficiently handles large-scale computations. It is defined as follows:  
\[R_{\text{simple}}\ :=\{((pp,f,x,com);(w,r)):f(w)=x\wedge com=PCS.commit(pp,poly(w),r) \tag{13}\]  
The verifier performs two checks, as follows:  
Figure 4: HyperPlonk. 1. Signature Verification: The verifier checks that \(Vf(vk,\ com,\ \sigma)\) accepts, ensuring the authenticity of the commitment. 2. Proof Verification: The verifier confirms that the proof \(\pi\) is valid for the relation \(R_{\text{simple}}\). This approach offers several advantages:
* Efficiency: Proving \(R_{\text{simple}}\) requires no additional work beyond proving \(f(w)=x\). * Scalability: The system efficiently handles large images, such as 30 MP, due to the optimized proof construction. * Privacy: The commitment com hides the original image \(w\), revealing only the edited image \(x\).