esa-sceva/satcom-chunk-collection huggingface dataset:
sion models [9; 10] has shown significant improvements in guiding effectiveness. These methods do not require any labeled information and solve pretext tasks at the image level. However, since the guidance information produced by this self-supervision is often disconnected from the information of the real categories, we are unable to generate images of specific real categories during image generation.  
Therefore, we propose the GDUI framework, which uses guided diffusion to generate images of specified real categories without requiring specific labeled datasets. The GDUI framework can be divided into three stages. In the first stage of GDUI, the image is encoded into a semantically meaningful latent vector using a self-supervised clustering framework, and the diffusion pseudo-label-matching algorithm is used to further map the semantic information. In the second stage, GDUI uses the label-matching refinement algorithm to filter out irrelevant semantic information. In the third stage, the conditional diffusion model uses the matched real semantic information of the image as guidance to generate the image.  
We summarize the contributions as follows. (1) We propose GDUI, which achieves the task of guided image generation based on real categories on unlabeled datasets, through the similarity of the data itself and the downstream transferability of CLIP [11]. (2) We design a pseudo-label-matching algorithm that completes the matching of the real semantic information of the image based on image similarity to help the model better accomplish the guided generation of specified real categories. (3) We design a label-matching refinement algorithm to adjust irrelevant semantic information of the data. (4) The experimental results demonstrate that, on unlabeled datasets, the fidelity and diversity of images generated by guided generation with specified categories are superior to unconditionally generated images, and even surpass those generated by using ground-truth labels.
## 2 Preliminary
### [PERSON] [28] proposed the Elliptic Curve Digital Signature Algorithm (ECDSA) in 1992, which is a derived type of the Digital Signature Algorithm (DSA) that uses Elliptic Curve Cryptography (ECC). ECC is a type of public-key cryptography based on the algebraic structure of elliptic curves over finite fields. The characteristics of ECC make the ECDSA require a significantly smaller key size with the same level of security which offers faster computations and less storage space.  
[PERSON] et al. [29] introduced the ECDSA in detail, including its acceptance in any global standards. ECDSA is accepted in the following standards:  
1. ISO 14888-3: International Standards Organization standard in 1998.
2. ANSI X9.62: American National Standards Institute standard in 1999.
3. IEEE 1363-2000: Institute of Electrical and Electronics Engineers standard in 2000.
4. FIPS 186-4: Federal Information Processing standard in 2013.
5. ANSI X9.142-2020: American National Standards Institute standard in 2020.  
Next, we briefly describe the three phases of ECDSA key generation for verification:  
1. Key generation phase: We assume that any participant must apply to our blockchain center for public and private keys, and the key generation with the ECDSA is as follows: Q\({}_{X}=d_{X}G\), where X is the participant ID, Q\({}_{X}\) is the public key, \(d_{X}\) is the private key, and \(G\) is a generating point based on the elliptic curve. The public key Q\({}_{X}\) and private key \(d_{X}\) are sent to the participant and stored. Q\({}_{X}\) is also stored in the blockchain center.
2. Signature phase: There is a message that needs to be sent by participant \(X\) to \(Y\). \(X\) choose a random number \(k\) between \(1\) and \(n-1\), and calculates a point on the curve as follows \((x,y)=k\times G\). Then, it calculates \(R_{X}=x\text{mod}n\). Next, \(X\) signs a message \(M\) as follows: \(Sig_{X}=k^{-1}(H(M)+R_{X}d_{X})\), sends \((M,R_{X},Sig_{X})\) to \(Y\).
3. Verification phase: When \(Y\) receives \((M,R_{X},Sig_{X})\), it then calculates the parameters as follows: \((x^{\prime},y^{\prime})=(H(M)Sig_{X}{}^{-1}\text{mod}n)G+(R_{X}Sig_{X}{}^{-1} \text{mod}n)Q_{X}\). Then, it verifies \(x^{\prime}\stackrel{{?}}{{=}}R_{X}\text{mod}n\) to determine if the signature is valid.  
In general, some key parameters need to be defined: in our proposed scheme, the length of bits and Secure Hash Algorithm (SHA), according to the NIST's minimum security-strength requirement [30], set the length of n with 224 bits and SHA-512/224 for digital signature generation.
# Effect of Laser Parameters on Optical Stealth Transmission System Performance
## 2 Generation of Optical Stealth Carrier
At 0.04 A, the relaxation oscillation frequency is 2.49 GHz, the generated optical pulses vary greatly in pulse position and amplitude. The modulation frequency is larger than the relaxation oscillation frequency, and the modulation current to bias current ratio is 5; the spike of relaxation oscillator is not entirely caught. Therefore, the eye diagram exhibits large time and amplitude jitters. The bias current is then switched to 0.08 A, and the relaxation oscillation frequency is 5.24 GHz. Gain-switched optical pulse with less jitters than that at 0.04 A is generated. As the bias current is increased to 0.16 A, the generated optical pulses display a tailing in the trailing edge. The relaxation oscillation frequency is 7.36 GHz and the second spike is excited with power lower than the first spike. Finally, with the bias current set to 0.24 A, the relaxation oscillation frequency is 7.98 GHz. Then, the power of the second spike is enhanced and the generated optical pulse exhibits a two-pronged shape. The spectral output from the gain-switched laser is shown in Figure 2b. At 0.04 A, the optical spectrum shows obvious broadening due to frequency chirp. The frequency chirp across optical pulses is caused by a time-varying carrier density in the laser active region during the direct modulation. Then, at 0.08 A, the optical spectrum is broadened. At 0.16 A, a peak begins to appear in the spectrum due to the tailing in the optical pulses. Finally, discernible spectral lines appear in the optical spectrum, and the offset of each tone is an integral multiple of the modulation frequency. The bandwidth, TNR, and full width at half maximum (FWHM) of the generated optical pulse with different modulation currents and frequencies are shown in Figure 3. The bias current is set to 0.08 A. In Figure 3a, the 10 dB bandwidth is proportional to the modulation current. During the tuning of modulation frequency, the modulation current is set to 0.2 A. As modulation frequency increases, bandwidth increases first and then decreases, presenting a convex function. Then, as shown in Figure 3b, the TNR increases nonlinearly as modulation frequency increases. The optical spectrum presents an optical frequency comb at a high modulation frequency. However, the changing trend of TNR versus modulation current is opposite to that of TNR versus modulation frequency, and spectral lines appear at a low modulation current. In the time domain, as shown in Figure 3c, the FWHM of optical pulse is not sensitive to modulation frequency. As the addition of modulation current, the FWHM of optical pulse first decreases and then becomes stable. and after encoding are shown in Figure 4, in which \(m\) is 8. The bias current is 0.08 A and the modulation current is 0.2 A. Being encoded, the signal is spread over time.
## 6 Case Study
### Case 2: Exploring the Moving Semantics of Road Segments
#### 6.2.2 Topics Extracted by Acceleration  
As for the acceleration, in Figure 8b, users could summarize the four topics of acceleration as **generally stable cycling, changeable cycling, dangerous cycling and relatively smooth cycling**. The acceleration responded to the change of velocity, and its distribution of topics was mainly distributed between 0 and 4 m/s\({}^{2}\). Among them, in addition to 4 m/s\({}^{2}\), the first topic mainly focused on 0-2 m/s\({}^{2}\), which meant complicated but generally stable cycling. In addition, changeable cycling conditions were distributed dispersedly, mainly concentrated around 1 m/s\({}^{2}\), 3 m/s\({}^{2}\) and 4 m/s\({}^{2}\), uncovering the instability of  
Figure 7: (**a**,**b**) Topics of recreational cycling distributed on the map. (**c**,**d**) Topics of connected cycling distributed on the map. (**e**-**i**) Topics of daily commuting cycling distributed on the map. (**j**) Topic of exercising cycling distributed on the map.  
cycling. Moreover, the dangerous cycling accelerations were mainly between 2 and 4 m/s\({}^{2}\), which meant that the cycling conditions changed rapidly and emergency acceleration and deceleration occurred. Furthermore, although the distribution of the fourth topic was relatively average, the probability of accelerations decreased from 0 m/s\({}^{2}\) to 4 m/s\({}^{2}\). When the acceleration was 4 m/s\({}^{2}\), the probability was already very small. Thus, we regarded the last topic as relatively smooth cycling.  
_Road segments colored by blue and orange support relatively stable cycling. Cyclists may enjoy smooth cycling by choosing the area with plenty of blue or orange segments._
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 2 DVB-T Based Passive Radar Signal Processing Scheme
To this end we resort to the linear approach presented in [9] which is based on the cascade of the pilot signals' equalization and a Residual Peaks Removal (RPR) filter to remove the zero-Doppler peaks. Notice that, when we are interested in slowly moving target detection, the pilots equalization stage can be neglected as it allows removal of the side-peaks appearing at quite high Doppler values. However, this stage is required when aerial targets are also present in the considered scenario. Moreover, in [9] we show that pilot equalization can be obtained via a simple subtraction of a properly delayed replica from the original reference signal, which clearly represents a cost-effective solution. However, we observe that the AF sidelobe control is an essential processing step for the considered applications because a severe masking effect is expected by the strong target returns on the weak target echoes. Therefore, a wide dynamic range should be guaranteed. In this regard, it is worth noticing that, when applied against real data, the proposed AF control technique allows one to limit the sidelobe level down to 45 dB from the peak throughout the surveillance area defined on the range/velocity map. For a SF operation, once the CAF has been evaluated at all the available surveillance channels, a conventional Cell Average Constant False Alarm Rate (CA-CFAR) threshold is separately applied to each map to detect targets with a given probability of false alarm. In this paper, a circular symmetric complex Gaussian zero mean PDF is assumed for the interference in all the considered scenarios. Recent studies [29; 30] have shown that it is a quite accurate assumption for passive radar based on DVB-T signals at least with reference to the range-velocity CAF regions appearing at non-negligible Doppler values. At very low Doppler frequencies, alternative models would be better suited and, consequently, alternative detectors optimized for such models would provide enhanced results. However this is beyond the scope of this paper and could be considered as a future step for this research. After the first detection stage, a M-out-of-N criterion can be applied to integrate the detection results obtained at the surveillance channels thus allowing a reduction in the number of false alarms. Then, assuming that the surveillance receiving channels are connected to a few horizontally aligned antenna elements, an interferometric approach can be used to estimate the direction of arrival (DoA) of the detected target echo. Finally, the bistatic range and the DoA information are converted into local Cartesian coordinates. In addition, a tracking algorithm can be applied to reduce the false tracks while yielding more accurate measurements. In order to improve the detection performance of the proposed system, in this paper we consider the joint exploitation of multiple DVB-T frequency channels according to appropriate schemes as illustrated in the following section.
## 1 Introduction
In this study, the kerf channel was prepared without an inclined substrate to represent the cutting edge. The authors in [17] studied and simulated the exit jet from a straight nozzle on an inclined substrate at various inclination angles. They claimed that the inclined substrate angle had a significant effect on the exit jet pattern within the cut kerf and had a negative effect on both the ability to remove molten materials and the cutting quality, due to the steep pressure gradient at a higher inclined angle. [PERSON] et al. [21] performed an experimental investigation with the shadow graph technique and demonstrated the effects of inlet stagnation pressure, nozzle tip to work-piece stand-off distance, cut kerf width, and thickness of the workpiece in relation to the behavior of the gas jet patterns inside a simulated kerf. Their findings suggested that the high-speed jet increases the ability to remove cross and consequently improves cutting quality. However, these studies did not provide a comprehensive numerical-experimental comparison of gas flow behavior inside the cut kerf slots. The aim of the present study was to investigate the gas flow behavior inside the simulated thermal cut kerf numerically and experimentally, in a way that would be applicable to the plasma arc and laser cutting processes. A simulated cut kerf is prepared based on the real cut kerf dimension and the Schlieren method is used to visualize the gas flow behavior through the simulated kerf. The Schlieren results are then used to compare with the numerical simulation results carried out in the 3D numerical model designed based on the cut kerf geometry. The similarities in the numerical and experimental results can be used to validate the proposed model. However, the thermal state of the gas due to the heat source added during the thermal cutting process (especially during the plasma arc cutting process) and its effects on gas behavior are not covered in this study. This paper presents an efficient numerical model to simulate the gas flow through a cut kerf that can be employed under variable conditions including nozzle exit diameter, stand-off distance, and variable pressure inlet. In this study, the numerical simulation of the gas flow was carried out using various turbulent models and the results were compared to find an optimal model which can be further used for the simulation of variable conditions as faced when thermal cutting a thick material. To the best of the authors' knowledge, no published contributions carried out numerical modeling that can predict the gas flow behavior inside a cut kerf geometry in presence of the effects due to adjacent kerf walls. The proposed model provides a fairly good prediction compared with the experimental measurements, sufficient for a costly manufacturing process. The findings of this study can be helpful in thick metal cutting industries to increase productivity with potential cost reduction during the design process.
## 3 Results and Discussion  
### Isothermal Reaction Control  
By using the PI controller of the RC1e calorimeter, quasi-isothermal reaction control could be ensured during the acrylic acid (AA) polymerisation reaction, as shown in Figure 2. The reactions were carried out at four different temperatures (60, 65, 70 and 75 \({}^{\circ}\)C) with 1.0 mol\(\%\) (regarding AA content) of AIBN.  
Figure 2: Raman spectra of the reaction of 150 g/L acrylic acid in isopropyl alcohol at 75 \({}^{\circ}\)C every 32 s up to a conversion of 95%, determined by the integration of the highlighted band at 1635 cm\({}^{-1}\) corresponding to the C=C stretching vibration.  
Figure 3 shows the temperature development of the reactor temperature and the corresponding jacket temperatures over the course of the reaction conversion. In the beginning of the polymerisation, it was observed that especially with higher starting temperatures a closed-loop temperature control is advisable. When using thermal initiators such as AIBN, cooled initiator solutions are usually used in order not to generate radicals before the solution is joined with the reaction mixture. As a result, the reactor temperature dropped briefly by up to 3.3 K at the beginning of the reaction. The heat released by the reaction then exceeded the set reaction temperature by up to only 3.5 K for the reaction at 75 \({}^{\circ}\)C. Due to the jacket counter-cooling, the temperature stroke was quickly damped so that quasi-isothermal conditions (\(T\)\(-\)\(T_{\rm set}\) < 1 K) were reached 4-7 min (corresponding to 20-45% conversion) after the peak temperature. While the temperature stroke was independently reached after 2 min, it was reached at higher conversions with an increasing setpoint temperature. During the rest of the reaction, the temperature was always 0.5 K below the respective target temperature.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Parametric Analysis
#### 2.5.5 Effect of Radius of Semi-Circular Slot (R\({}_{\text{S}}\))  
Finally, the analysis of R\({}_{\text{S}}\) at 1.6, 2.6, and 3.6 mm revealed that R\({}_{\text{S}}\) = 2.6 mm optimizes bandwidth and impedance matching. The simulated S\({}_{11}\) for various R\({}_{\text{S}}\) values is shown in Figure 7e, while other radii produce less favorable results. As a conclusion, the parametric analysis underscores the sensitivity of the DGS-based monopole patch antenna to variations in design dimensions, which are crucial for enhancing performance metrics like bandwidth and reflection coefficient. Each parameter significantly affects these metrics, emphasizing the need for careful optimization.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.3 Reduced Downtime  
In fault management, machine learning can be used to analyze traffic patterns to predict potential device or link failures even before they really occur, so that network administrators can prevent such failures by proactively attending to affected devices either by repairing or replacing them. Moreover, KDN can automatically detect network faults such as malfunctioning of devices by analyzing patterns of data to generate knowledge on device functionality so that malfunctioning devices can be isolated. Another example is detecting malicious activities of network devices, where KDN can in realtime analyze traffic patterns, security logs, etc. to detect any anomaly in a network device and can take necessary actions. For instance, if malware is detected on a device, KDN can take action to immediately isolate such devices to prevent it from spreading to other parts of the network. In case of such device/link failures or device anomaly detection, KDN can automatically reroute traffic through alternative paths or automatically switch to backup devices or links, making sure that the network operates with fewer interruptions and minimizing the impact of failures or intrusions on network operations.
## 4 Experiment Results and Discussion
### Observation of the Failure Surface  
Figure 9 shows the slip sinkage (\(z_{j}\))-slip displacement (\(j\)) relationships assessed through the vertical and longitudinal displacement measured in the model experiments. (Downward slip sinkage is denoted using positive values); here, a Savitzky-Golay filter [30] was applied for the purpose of smoothing the data.  
Figure 9: Slip sinkage and slip displacement relationships for (**a**) \(D_{r}=40\%\), (**b**) \(D_{r}=60\%\), and (**c**) \(D_{r}=80\%\).  
Excluding the slight upward slip sinkage that occurred at the beginning of shearing for T4 (relative density of 60% and vertical stress of 7.0 kPa), downward slip sinkage occurred under all test conditions. The extent of the downward slip sinkage increased with slip displacement. However, the rate of increase gradually decreased. This behavior is similar to the tendency of the increase in vertical displacement of the soil sample as shearing progressed in the direct shear test. However, while upward vertical displacement is normally observed in the direct shear test for dense soil due to the volumetric expansion [31], in this experiment, downward slip sinkage also occurred in soil with a relative density of 80%. This is because unlike the sample of the direct shear test, in which all sides are rigidly constrained, the soil block is rigidly constrained only on the side on which the grouser is located, resulting in a volumetric change in all unconstrained directions.  
The slip sinkage in the model test increased as the surcharge mass increased and the relative density of the soil decreased. As mentioned above, as the slip sinkage of the off-road tracked vehicle increases, compaction resistance, a type of motion resistance, also increases. Therefore, we expect that it will be difficult to ensure tractive performance when a heavy-weight off-road tracked vehicle is driven on loose soil due to the increasing effect of compaction resistance caused by slip sinkage.
## 2 The Nucleotide-Binding Domain-Like Receptor Protein 3 (NLRP3) Inflammatory
(**b**) Signal 2 involves activation of the primed components by a range of PAMPs/DAMPs/HAMPs that include extracellular oxidised DNA, extracellular ATP, lysosomal degradation products, calcium influx and potassium efflux, mitochondrial damage with altered membrane potential, and the release of oxidised mitochondrial DNA. BRCC-3-mediated deubiquitinination of NLRP3 allows for the activation and oligomerisation of NLRP3 with ASC and pro-caspase-1. After oligomerisation, conformational change activates caspase-1, allowing cleavage and release of IL-1\(\beta\), IL-18, and GSDMD. GSDMD translocates to the cell membrane where it allows for rapid release of IL-1\(\beta\) and IL-18 into the external environment and mediates pyroptosis. PRR—pattern-recognition receptor. PAMP—pathogen-associated molecular pattern. DAMP—danger-associated molecular pattern. HAMP—homeostasis-altering molecular process. NF-kB—nuclear factor kappa-light-chain-enhancer of activated B cells. NLRP3—nucleotide-binding domain-like receptor protein 3. IL—interleukin. GSDMD—gasdermin D. ATP—adenosine triphosphate. DNA—deoxyribonucleic acid. BRCC-3—BRCAI/BRCA2-containing complex, subunit 3. ASC—apoptosis-associated speck-like protein. Adapted from [PERSON] et al. with permission from publisher [31]. are strong activators of caspase 11 and in animal models, an absence of caspase 11 attenuates the inflammasome response to several Gram-negative bacteria such as _Escherichia coli_[21; 22; 32]. Disordered inflammation is implicated in the pathogenesis of an extraordinary range of human diseases, and as our understanding of the inflammasome continues to grow, it has become clear that the inflammasome plays a significant pathological role [21]. Mutations in NLRP3 can lead to a heterogeneous group of cyclical fever syndromes known as cryopyrin-associated periodic syndromes (CAPS) [33]. Inappropriate inflammasome activation may affect outcomes from infections including respiratory tract infections (such as the novel coronavirus, _Mycobacterium tuberculosis_, and bacterial pneumonas). Increasingly, inflammosomes are also being linked to autoimmunity conditions, where the inflammasome may be the initiator or exaggerator of inflammatory responses. These include neurodegenerative conditions (multiple sclerosis, Alzheimer's disease, and Parkinson's disease), rheumatological conditions (gout, rheumatoid arthritis, psoriatic arthritis), and metabolic conditions (obesity, atherosclerotic cardiovascular disease, and diabetes mellitus) [21; 23].