esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ordinary mixers can hardly meet the requirements in terahertz (THz) communications due to the low-power and expensive THz sources. Sensitive harmonic mixers have been widely studied to avoid this problem, owing to the fact that the higher the number of harmonics, the lower the local oscillator (LO) frequency, and the lower the cost. High-\(T_{\rm c}\) superconducting (HTS) Josephson junction (JJ) mixers are performing candidates for THz receiver frontends because of the advantages of excellent sensitivity, wide bandwidth, high harmonic number and low LO power requirement. However, the normal-state resistance of HTS JJ is so low that traditional antennas are difficult to match it. In other words, it is quite a challenge to match the input impedance to a low input impedance for traditional antennas, especially for antennas fed by coplanar striplines (CPSs). In this work, based on the structure of bowtie, two types of stub tuners were integrated to decrease the impedance of the bowtie antenna so as to improve the coupling efficiency between the traditional bowtie antenna and the JJ. Furthermore, HTS YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7-\delta}\) (YBCO) JJ harmonic mixers coupled with the proposed structures and fed by CPSs are fabricated and measured. The measurements show that the JJ mixer coupled with a pair of open-end stubs of the bowtie antenna achieves up to 88 harmonics, with a conversion efficiency of \(-69.6\) dB. In contrast, the JJ mixer coupled with a pair of lumped-element stubs of the bowtie antenna only attains to 30 harmonics, with a conversion efficiency of \(-73.4\) dB. Additional numerical simulations indicate that the coupling efficiency is enhanced when the complex impedance of the antenna is explicitly considered. Compared with other coupled traditional antennas, the JJ mixer with bowtie antenna has the largest harmonic number. This work paves the way for the future application of low-frequency and low-cost LO for THz communications.  
Josepbson junction; harmonic mixer; terahertz; bowtie antenna; stub tuners +
Footnote †: journal: Applied Sciences
## 6 Security Analysis
### Cryptographic Key Management  
Protecting private keys in vehicle environments is indeed a crucial aspect of ensuring the overall security of the system. To prevent unauthorized access or theft of private keys, several well-established security mechanisms can be employed [17]. Hardware Security Modules (HSMs): HSMs are dedicated physical devices used to securely generate, store, and manage cryptographic keys. These modules ensure that keys never leave the secure hardware, preventing key extraction even if the system is compromised [18]. Trusted Platform Modules (TPMs): TPMs provide hardware-based security for key storage. They offer tamper-resistant storage of private keys and can provide secure cryptographic operations, making them a suitable choice for vehicle systems where physical security is paramount [19]. Secure Enclaves (e.g., Intel SGX, ARM TrustZone): These provide isolated, trusted execution environments within the vehicle's processor, safeguarding keys and sensitive data from both software and physical attacks. They are designed to protect against memory dumps, side-channel attacks, and other vulnerabilities. Key Rotation and Management: To enhance the security of private keys, periodic key rotation can be implemented. This ensures that, even if keys are compromised, they are only usable for a limited time. Additionally, key management systems can provide centralized control over key distribution and revocation [20]. Physical Tamper Resistance: In environments such as vehicles, physical tampering with devices can be mitigated by incorporating tamper-resistant hardware. For instance, systems can detect unauthorized attempts to access cryptographic modules and trigger self-destruct or zeroization processes to wipe keys [21].
## 3 Research Methodology and Procedures
### Building the LoRa Research Environment: GNU Radio Software and Hardware Setup (SDR)
#### 3.2.2 Hardware Setup (SDR)  
Nevertheless, researchers have conducted performance and reliability studies using readily available off-the-shelf LoRa transceivers. A drawback of this approach is that it limits the ability to evaluate modifications to the LoRa physical layer or the implementation of more robust receive algorithms, as these aspects are typically fixed and predefined in commercial transceivers. A comprehensive implementation of the LoRa physical layer can be effectively addressed using software-defined radios. The LoRa PHY has been extensively reverse engineered over the last few years, and the associated SDR implementations have revealed many important aspects. Wireless communication infrastructure can benefit from flexible, scalable, and durable radio equipment thanks to SDR [34].  
The SDR can also provide end users with more adaptable and potentially cheaper multistandard interfaces. This is a fundamental, practical technology for the next class of cognitive radios, i.e., context-aware, adaptive, and learning radios [35]. In this paper, we have used the USRP N321 [36]. It is a distributed, networked, software-defined radio that provides fault tolerance and reliability for use in large-scale wireless networks. This high-performance SDR offers two receiver (RX) and two transmitter (TX) channels in a half-width RU form factor, thanks to an original RF design. Each channel has an extended frequency range from 3 MHz to 6 GHz and can instantly deliver up to 200 MHz of bandwidth. Phase-coherent MIMO testbeds can be implemented thanks to a versatile synchronization architecture that supports a 10 MHz clock reference, a Pulse-per-Second (PPS) time reference, external TX and RX Local Oscillator (LO) inputs, and a GPS Disciplined Oscillator (GPSDO). With the unique ability to remotely manage activities such as debugging, software upgrades, reboots, factory resets, and system health monitoring, the USRP N321 utilizes today's USRP Hardware Driver (UHD) software advances to simplify the administration and management of multiple devices over the network.  
Figure 4: GNU Radio interface for the receiver.  
In the following section, \"Experimental and Real-World Implementation\", we delve into the practical aspects of our study, where we put our methodology to work and demonstrate the real-world application of LoRa technology.
## 3 Results and Discussion
### Material Characterization
#### 3.1.3 Thermal Stability of N/S-Carbon QDs  
Figure 9 depicts the thermogravimetric (TG) and differential scanning calorimetry (DSC) analyses of the obtained N/S-Carbon QDs. The TG curve displays two mass loss steps. The first is an endothermic process that starts at 50 \({}^{\circ}\)C and ends at 150 \({}^{\circ}\)C, corresponding to the evaporation/desorption of water molecules. The second starts at 200 \({}^{\circ}\)C and ends at 500 \({}^{\circ}\)C and consists of several exothermic processes that are related to the thermal degradation of the different moieties of the material [41]. Based on the differential TG (DTG) curve, discrete peaks were recorded at about 216, 238, 250, 264 and 355 \({}^{\circ}\)C. As a result, the prepared N/S-Carbon QDs show good thermal stability regarding their applicability under common environmental and storage conditions.
## 3 Proposed Architecture
### Architecture Description  
For us to get a better understanding of how the concepts of Identity and VIS can be instantiated in the existing Internet architecture, we propose the following separation of functions into three levels: Application, VIS and connectivity. Figure 2 describes the proposed alignment for these interfaces or APIs, which builds on ideas developed for MANA [20].  
The first step for session establishment is the discovery of the identity or identities we want to communicate with. Much like the Internet today, one can assume that discovering the domain name of a service endpoint can be done through the use of search engines or through assumed prior knowledge (e.g., google.com). The domain can then be used to identify a specific identity in the domain using a lookup mechanism such as a directory. We believe that for the purposes of the architecture, such mechanisms can be varied and heterogeneous, which is also why we do not offer further details about its instantiation in this paper. It is important at this stage that the result obtained from the lookup be compatible with the Identity Management (IDM) system. This is usually not a problem since most IDM systems are identifier agnostic. Once the identifier is obtained, a second discovery mechanism, at the connectivity layer can be used to find a path or route to the endpoint based on the context of the communication.
## 4 Electromagnetic Performance Analysis
### No-Load Back EMF  
The no-load back EMF is the main factor affecting the electromagnetic performance of the AFPMBLDCM. Therefore, at the rated voltage of the AFPMBLDCM, the no-load back EMF amplitude of the three-phase winding was analyzed at a speed of 4800 rpm. Next, phase \(B\) was selected for harmonic analysis.  
Figure 7 shows that the amplitude of no-load back EMF is about 11.85 V. The waveform is similar to a sine wave and has fine sinusoidal properties.  
Figure 8 shows that when the no-load motor runs at rated speed, the no-load back EMF mainly contains third harmonics. The harmonic of no-load back EMF mainly comes from the air-gap flux density distortion caused by stator slotting.  
Figure 6: Air-gap magnetic densities at different radii.  
Figure 7: Three-phase no-load back EMF.
## 4 Results
### Results on the Private Dataset  
The experimental results obtained on the public dataset (see Table 5) show that the RoBERTa+ViT hybrid model outperformed other hybrid deep learning models. Based  
Figure 19: Confusion matrix for RoBERTa+ ViT model on the public test dataset.  
on this, the hybrid model was chosen to classify multi-modal data on the private dataset. As above, the hybrid model's performance was evaluated by integrating the individual performances of RoBERTa and ViT and averaging their results using a late fusion module. The RoBERTa+ViT model was evaluated using various performance metrics, and the results are presented in Table 6. In the table, it can be seen that the model performed exceptionally well on the private dataset as well, achieving an accuracy of 96.10%, indicating that the model correctly predicted most of the test data. The F1-score of 95.90% highlights the model's balance in correctly identifying both positive and negative classes. The precision of 96.00% shows that the model's positive predictions were highly reliable, while the recall of 95.90% indicates that the model successfully classified nearly all positive cases. Finally, the impressive ROC-AUC value of 0.999 reflects the model's outstanding ability to distinguish between different classes, making it highly effective for classification tasks.  
Figure 20 illustrates the computed confusion matrix using the RoBERTa+ViT model, highlighting its classification performance across various classes. The model successfully classified 398 instances of class 0 as class 0. For class 1, the model correctly classified 386 instances Similarly, 408 instances were correctly classified as class 2, and 410 instances were correctly classified as class 3. However, the model misclassified 14 instances of class 0 as class 1 and 2 instances of class 0 as class 2.  
Furthermore, six instances of class 1 were misclassified as class 0, two instances of class 1 were misclassified as class 2, and four instances of class 1 were misclassified as class 3. For class 2, three instances were misclassified as class 0, and three instances were misclassified as class 1. Finally, one instance of class 3 was misclassified as class 2. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hybrid Deep Learning Model** & **Test Accuracy** & **Recall** & **F1-Score** & **Precision** & **ROC-AUC** \\ \hline RoBERTa+ViT & 0.961 & 0.959 & 0.959 & 0.960 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results obtained using the RoBERTa+ViT model on the private dataset.  
Figure 20: Confusion matrix using RoBERTa+ViT model on the private test dataset.  
confusion matrix analysis shows that the diagonal elements are large, indicating that the model performed very well in classifying most of the samples correctly, particularly for class 3, which had only one misclassification. Moreover, there were only a few misclassifications between adjacent classes (e.g., class 0 was misclassified as class 1, and class 1 was misclassified as class 0), suggesting that the model has high discriminative power between different classes.
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 4 Proposed 5G-NR Decoder Architecture  
Within this section, we introduce our innovative decoder micro-architecture that redefines the landscape of the IMT-2020 (5G-NR) wireless standards. Our design not only offers unparalleled flexibility, reduced latency, and low hardware requirements, but also achieves an unprecedented level of throughput, surpassing all prior endeavors in the field. The proposed design exhibits unparalleled versatility, seamlessly accommodating a wide spectrum of 5G applications ranging from immersing VR (virtual reality) experiences and cutting-edge AR (augmented reality) technologies to the integration of smart homes and cities, as well as the automation needs of industrial sectors.
## 6 Conclusions and Future Work  
This paper proposes a method for identifying archaeological structures in satellite images with extensive vegetation. We show that using image processing techniques and machine learning provides an effective solution for detecting archaeological sites, due to its low cost and how difficult it would be for archaeologists to perform this task manually. The proposed method significantly reduces the time used for manually labeling pixels. It is only necessary to label a small portion of the pixels to label the whole image employing a classifier. Using different color spaces and filters helped to produce adequate results in archaeological sites with a lot of vegetation. The proposed method had an acceptable performance detecting archaeological structures, although, according to our experiments, the proposed method is undoubtedly affected by the shadows in the images obtained through SASPlanet. However, using images with a good resolution and optimal environmental conditions could help to achieve even better performance. The results obtained with our proposal are promising for detecting archaeological structures in areas of difficult access.  
In future work, we will evaluate other color spaces and filters for detecting archaeological structures in images containing shadows. In addition, it is important to develop a method for detecting archaeological structures in semi-desert areas or with less vegetation, because the soil type and the structure's material degrade the detection with the method proposed in this paper.  
All authors equally contributed to the work. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the National Council of Humanities, Sciences, Technologies and Innovation of Mexico (CONAHCyT).  
Not applicable.  
This research was partially supported by the National Council of Humanities, Sciences, Technologies and Innovation of Mexico (CONAHCyT) through its graduate study scholarship program.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### The Chemical Synthesis and Structural Characterization  
2,3-Dichloro-5,6-dicyano-\(p\)-benzoquinone (DDQ), tetrahydrofuran (THF), 4-(dimethylamino) pyridine (DMAP), potassium peroxymonosulfate (OxoneTM), ammonium cerium(IV) nitrate (CAN), tetraethylammonium chloride (TEAC), E2, 5-fluorouracil (5-FU), and dimethyl sulfoxide (DMSO) were acquired from Sigma-Aldrich (St. Louis, MO, USA). E1 was purchased from Cayman Chemical (Ann Arbor, MI, USA), acetic anhydride (Ac\({}_{2}\)O) from Fluka, methanol (MeOH) and acetic acid from Fisher Chemical (Waltham, MA, USA), acetone 99% from Jose Manuel Gomes dos Santos, Lda (Odivelas, Portugal), ethanol (EtOH) 99.9% from Manuel Vieira & C\({}^{\ast}\) (Torres Novas, Portugal), and deuterated DMSO (DMSO-\(d_{6}\)) and deuterated chloroform (CDCl\({}_{3}\)) were acquired from Armar Chemicals (Leipzig, Germany). Thin-layer chromatography (TLC) with an al-backed aluminum/silica gel plate 0.20 mm (Macherey-Nagel 60 F254, Duren, Germany) was used to control all reactions. The CN-15.LC UV chamber (254 nm) was used to visualize TLCs before chemical revelation, which was performed using EtOH/concentrated sulfuric acid (95:5, v:v) and then heating at 120 \({}^{\circ}\)C. A rotary vacuum drier from Buchi (R-215) was used to evaporate solvents. The Infrared (IR) spectra were acquired on a Thermoscientific Nicolet iS10 at room temperature in the 4000-400 cm\({}^{-1}\) range by averaging 16 scans (spectral resolution of 2 cm\({}^{-1}\)). \({}^{1}\)H and \({}^{13}\)C nuclear magnetic resonance (NMR) spectra were acquired in Bruker Avance 400 MHz, and the TOPSPIN 4.07 software (Bruker, Fitchburg, WI, USA) was used. Chemical shifts are reported in parts per million (ppm) relative to tetramethylsilane (TMS) or solvent as an internal standard. Coupling constants (\(J\) values) are reported in hertz (Hz) and splitting multiplicities are described as s = singlet; brs = broad singlet; d = doublet; dd = double doublet and t = triplet.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 4 Simulation Results
### Capture and Tracking Module Simulation  
This scheme uses Matlab 2017 software to simulate the module of capture and tracking. The simulation parameters of the capture and tracking module are shown in Table 3. The simulation result of the capture module is shown in Figure 21.  
Figure 20: (a) Schematic diagram of the interference-to-signal ratio of the received signal in anti-single-band narrowband interference test; (b) schematic diagram of the interference-to-signal ratio of the received signal with frequency in three-band narrowband interference test.  
The simulation results of the carrier loop and code loop are shown in Figure 22.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Simulation Parameters**} & **Value** \\ \hline  & RF carrier frequency & 2.29745 GHz \\  & IF carrier frequency & 286.75 MHz \\  & Spreading code rate & 10.23 Mcps \\ Signal & Code length & 1023 \\ Parameters & Symbol rate & 8 kpsps \\  & Modulation & BPSK \\  & Carrier-to-noise ratio & 44 dBm \\  & Simulation time & 100 s \\ \hline  & Initial distance & 2400 km \\ Dynamic & Initial velocity & 5 km/s \\ Parameters & Initial acceleration & 1.34 km/s\({}^{2}\) \\  & Initial jerk & 98 m/s\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Capture and tracking module simulation parameters.  
Figure 21: Ratio of maximum and average values of coherence results.  
Figure 22: _Cont._  
The simulation results of the bit synchronization loop are shown in Figures 23 and 24.  
The simulation results show: (1) In a large dynamic scenario, the bit synchronization loop can complete bit synchronization within 60 ms, and the sampling point can be adjusted in real-time during operation. The loop works normally, the mean estimation error is <8.7, and the standard deviation of the estimation error is <7.5; (2) A signal with a carrier-to-noise ratio of 44 dBHz runs at a rate of 8 ksps for 100 s, and the bit error rate is 0.  
Figure 23: _Cont_.  
Figure 22: (**a**) PLL phase detector phase difference and PLL Doppler; (**b**) DLL phase detector phase difference and Doppler; (**c**) 1 ms accumulator output; (**d**) tracking carrier-to-noise ratio.  
The above simulation results verify the effectiveness and correctness of the algorithm. The algorithm consumes fewer resources compared to the traditional FFT frequency domain  
Figure 23: Bit synchronization ring operation results: (**a**) estimated data code position offset; (**b**) actual data code position offset; (**c**) data code position estimation error.  
Figure 24: Bit error rate changes over time.  
acquisition method and is easy to engineer by implementing software engineering through the JFM7K325T chip.
## 7 Simulation Results
### Distance Comparison Between CHs  
A distance comparison for both the original and modified protocols was performed using MATLAB and is shown in Table 3. As shown in Figure 9, the proposed approach will divide the network into more professional clusters. Therefore, the distance summation between nodes in the cluster is less than in the original protocol. The compression of the average distance per node in the original and modified protocols is shown in Figure 13.
## 3 Phenolic Compounds of Sweet Cherry By-Products
### Flavonoids
#### 3.2.2 Flavonols  
Flavonols are very common in the human diet, especially in fruits, vegetables, and tea. Their chemical structure is similar to that of flavones, having a 3-hydroxy pyran-4-one group on the C-ring and a 2,3-double bond. Quercetin, kaempfer, myricetin, and isorhamnetin are the compounds most found in fruits and vegetables [23; 24]. Many studies have showed that sweet cherry by-products contain several flavonols [7; 10; 12; 28].  
Kaempfer 3-O-rutinoside, kaempfer 3-O-glucose, kaempfer 0-rutinoside-O-hexoside, quercetin derivative, quercetin 3-O-rutinoside, quercetin 3-O-hexoside, quercetin 3-O-glucose, quercetin 3-O-galactoside, quercetin 0-rutinoside-O-hexoside, and methyl quercetin 0-rutinoside were detected in extracts of _P. avium_ stems (Table 3) [10; 12]. Among these flavonoids, kaempfer 3-O-rutinoside, quercetin 3-O-rutinoside, and quercetin 3-O-hexoside were the compounds found in greater amounts (Table 3) [7; 10]. Similar results were reported in sweet cherry leaves [7; 56; 57], with contents ranging from 6728.0 \(\pm\) 328.8 to 594.2 \(\pm\) 76 \(\upmu\)g/g dw (Table 3) [7]. According to [PERSON] et al. [10], the aqueous infusion of _P. avium_ leaves is rich in quercetin 3-O-rutinoside, comprising about 17.08% of total phenolic compounds.  
_P. avium_ flowers possess the same flavonoids described in stems and leaves, with quercetin 3-O-rutinoside and kaempfer \(O\)-rutinoside-O-hexoside being the major compounds identified, which represent about 6-15% of total phenolics in cherry flower extracts [7; 10].  
When compared with _P. avium_ fruits, the major flavonoid glycosides found in the fruits of four different cultivars were quercetin 3-O-rutinoside, quercetin 3-O-glucoside, kaempfer 3-O-rutinoside, and isorhamnetin 3-O-rutinoside [58]. The amount of these compounds varied between 11.4 and 46.9 \(\upmu\)g/g fresh weight (fw) for quercetin 3-O-rutinoside, 1.6 and 7.9 \(\upmu\)g/g fw for quercetin 3-O-glucose, 3.0 and 13.9 \(\upmu\)g/g fw for kaempfer 3-O-rutinoside, and 0.8 and 1.3 \(\upmu\)g/g fw for isorhamnetin 3-O-rutinoside [58]. In another study, [PERSON] et al. [12] described the presence of quercetin-O-rutinoside-O-hexoside (420 \(\pm\) 10 \(\upmu\)g/g fw) in sweet cherry fruits. Similar results were recently obtained by [PERSON] et al. [18], which found thirteen flavonoids in sweet cherry fruits: four quercetin, eight kaempfer derivatives, and one isorhamnetin 3-O-rutinoside, comprising about 2.04% of total non-colored phenolic compounds. The authors reported the kaempfer derivatives 1-3 and kaempfer hexoside derivatives 1 and 2 in this study for the first time [18].