esa-sceva/satcom-chunk-collection huggingface dataset:
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 1 Introduction
(2) Reordering and error-checking are required at the receiving end. (3) Small packet transmission based on short data has better latency. PS NoC has brought many problems too, including: (1) Packet switching itself cannot guarantee the order of arriving packets, so that the receiver should be equipped with a reorder buffer, which greatly increases the hardware overhead and transmission delay; (2) Due to the limitation of hardware and reliability, the data packet cannot be too long, so that the transmission proportion of the header is relatively large and the transmission efficiency is low; (3) The transmission time is long, redundant and uncertain because each packet must establish its own route, which is not suitable for real-time systems, which make PS NoCs are not suitable for real-time systems [5; 6]. Figure 1: Architecture for (**a**) shared bus and (**b**) fully-connected crossbar. PE means processing element. (**a**) shows that PE2 with low priority has to wait when the bus is occupied, while (**b**) shows the fully connection of all PEs. The advantages of CS NoCs are that the transmission time is low after the circuit eatablished, and there is no need to reorder the cache. But CS NoC is master-controlled and is only suitable for small-scale systems with few connections. The connection ability of CS NoC depends on the chip circuit design, which leads to little design margin for later stage design. Additionally, the circuit setup and release time are large, so the process redundancy of short data packets are large [6]. The above-mentioned PS NoCs and CS NoCs both have limitations for heterogeneous multi-core real-time SoC. Therefore, this paper proposes a novel NoC architecture that combines the routing capability feature of PS NoC and low transmission latency character of CS NoC, which is suitable for heterogeneous multi-core real-time SoC. The rest of the paper is organized as follows. Section 2 briefly describes some PS NoCs, CS NoCs and previously proposed hybrid switching mechanisms. Section 3 introduces the proposed architecture. Section 4 discusses the workflow of the proposed mechanism. Section 5 discusses simulation parameters and results of the proposed router architecture and the synthesis result. Finally, Section 6 draws the conclusion.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 3 Results and Discussion
### Evaluation of Accuracy of the 100-Hz Displacement Time Series
#### 3.2.3 Sinusoidal Motion Test
The errors in the amplitudes estimated with RTK, PPP-AR, and PPP are within 0-0.3 mm, 0-0.6 mm, and 0.1-0.7 mm, respectively. In addition, the errors in the estimated amplitudes of the first peak are slightly larger than those in the second peak, which may be attributed to the fact that the second vibration frequency is more dominant and has a larger signal-to-noise ratio with respect to the first frequency [39].
## 3 The Structure of One-Stage Detector Based on Background Prediction and Group Normalization
### Target Detection Loss Function Based on Weight Attenuation  
In the process of calculating loss, all predicted bounding boxes in the model by one-stage detector is divided into positive samples (IoU \(>\) 0.5) or negative samples (IoU \(<\) 0.4). In general, the proportion of the target is much smaller than that of the background, so the positive and negative samples are easily distinguished, and most of them are negative samples that are easy to classify. At this time, the loss  
Figure 3: Structure parameters of anchor adjustment and background prediction module. function is relatively slow and may not be optimized in the iterative process of a large number of simple samples. In this paper, loss function adds a weight attenuation to solve the problem of an unbalanced number of positive and negative samples, which is modified based on standard cross-entropy loss. The cross-entropy loss function is:  
\[CE =-ylog\ y^{\prime}-(1-y)\log\ (1-y^{\prime})=\left\{ \begin{array}{ll}-\log y ^{\prime}&y=1\\ -\log(1-y^{\prime})&y=0\end{array} \right. \tag{2}\]  
where \(y^{\prime}\) is the predicted probability, which is between 0 and 1; and \(y\) is the true label. It can be seen that in the case of cross-entropy loss, the output probability of the positive sample is higher, the loss is smaller, and the output probability of the negative sample is smaller. The modified formula is as follows:  
\[ \begin{array}{ll}\mathit{FL}=\left\{\begin{array}{ll}(1-y^{\prime})^{ \gamma}[-y\log y^{\prime}-(1-y)\log(1-y^{\prime})]\\ y^{\prime\gamma}[-y\log y^{\prime}-(1-y)\log(1-y^{\prime})]\end{array}\right. \\ \qquad\qquad=\left\{\begin{array}{ll}-(1-y^{\prime})^{\gamma}\log y^{ \prime}&y=1\\ -y^{\prime\gamma}\log(1-y^{\prime})&y=0\end{array}\right.\end{array} \right. \tag{3}\]  
In the formula, \(\gamma\) is the adjustment parameter, and the value is set as 2, which is used to adjust the rate of weight reduction of simple samples, making the model pay more attention to the difficulty in sample classification during the training process. The improved loss function is:  
\[ \begin{array}{ll}L=&\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j= 0}^{B}l_{ij}^{obj}[-t_{x_{i}}\hat{\log}t_{x_{i}}-(1-t_{x_{i}})\log(1-\hat{t}_{ x_{i}})]+\\ &\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}l_{ij}^{obj}[-t_ {y_{i}}\hat{\log}t_{y_{i}}-(1-t_{y_{i}})\log(1-\hat{t}_{y_{i}})]+\\ &\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}l_{ij}^{obj}[ (t_{w_{i}}-\hat{t}_{w_{i}})^{2}+(t_{h_{i}}-\hat{t}_{h_{i}})^{2}]+\\ &\lambda_{obj}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}l_{ij}^{obj}[-c_ {i}\hat{\log}c_{i}-(1-c_{i})\log(1-\hat{c}_{i})]+\\ &\lambda_{noobj}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}l_{ij}^{noobj}[ -c_{i}(1-\hat{c}_{i})^{\gamma}\hat{\log}c_{i}-(1-c_{i})(\hat{c}_{i})^{\gamma} \log(1-\hat{c}_{i})]+\\ &\lambda_{class}\sum\limits_{i=0}^{s^{2}}l_{i}^{obj}\sum\limits_{cclass}[-p_{ i}(c)\hat{\log}p_{i}(c)-(1-p_{i}(c))\log(1-\hat{p}_{i}(c))]\end{array}
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 15 Future Developments  
The future of photocatalytic nanomaterials for VOC removal lies in developing materials that are more efficient, durable, and tailored to specific environments and pollutants. One key area of development is the enhancement of visible-light-responsive photocatalysts, allowing them to operate effectively under ambient or indoor lighting conditions. Current research is focused on doping strategies, co-catalyst incorporation, and material hybridization to enable a wider range of nanomaterials--such as TiO\({}_{2}\), CdS, and MOFs--to be responsive to visible light, which comprises the majority of natural light. For example, advanced TiO\({}_{2}\)-based composites doped with elements like nitrogen, carbon, or sulfur are already showing promise for effective VOC degradation under visible light, which could lead to energy-efficient air purifiers suitable for residential and commercial use.  
Another promising development is the design of multifunctional photocatalysts that go beyond mere VOC degradation, enabling simultaneous antibacterial activity, odor elimination, and even CO\({}_{2}\) reduction in indoor spaces. This multifunctionality could be achieved by integrating different types of photocatalysts, such as combining TiO\({}_{2}\) with silver nanoparticles for antibacterial properties or coupling MOFs with perovskites for CO\({}_{2}\) capture and VOC degradation. These systems could be applied in \"smart\" coatings and filters for buildings, which not only remove harmful VOCs but also respond dynamically to other pollutants and pathogens, adapting to real-time air quality demands.  
Stability and environmental sustainability are additional challenges that future developments aim to address, particularly with newer materials like perovskites and TMDs, which can suffer from stability issues under long-term exposure to light and humidity. Researchers are exploring encapsulation techniques and protective coatings that maintain the photocatalytic activity of these materials while extending their operational life, making them more viable for real-world applications. This work could open the door for high performance perovskite and TMD-based photocatalytic materials to be widely adopted for outdoor air purification, targeting urban pollution sources such as vehicle emissions and industrial VOCs.  
Finally, scalability and real-time monitoring will play crucial roles in the future adoption of photocatalytic systems for air purification on a larger scale. Advanced manufacturing techniques, such as 3D printing and roll-to-roll processing, are making it possible to fabricate large-scale photocatalytic materials with precise structural control. These methods could lead to the widespread implementation of photocatalytic coatings on building facades and public infrastructure. Coupled with IoT (Internet of Things) technology, photocatalytic systems of the future could feature real-time monitoring and adaptive responses to VOC levels, automatically adjusting performance based on air quality data.
## 4 Security and Privacy in Edge Frameworks
### PUF Security Challenges  
PUF offers promising security solutions for embedded systems and IoT devices; however, their practical deployment faces several challenges. A PUF model needs to satisfy the key metrics that evaluate a PUF as a suitable and strong PUF for security purposes. Metrics like uniqueness, reliability, uniformity, bit aliasing, randomness, and high entropy are the key indicators of a strong PUF [46].  
Apart from the PUF features, they face other challenges externally, such as:  
* Environmental variations: PUFs are vulnerable to external parameters like temperature, voltage, aging, seismic activity, and so on. Ensuring a steady and reliable PUF operation under adverse conditions requires error correction systems without increasing system complexity.
* Modeling Attacks:Advanced ML techniques have been used to predict PUF responses, compromising their security. Certain PUF architectures are vulnerable to ML-based modeling attacks [14; 47]; therefore, PUF designs resilient to such attacks need to be considered for security.  
Figure 3: Application areas of PUF-based security solutions.  
* Limited CRP: Supposedly, if the PUF responses generated by a device that satisfies all the key metrics making it a strong PUF are finite in numbers, such a device cannot be employed for device authentication that requires frequent authentication.
* CRP exhaustion: There is a risk of CRP exhaustion due to repeated use over time when the PUF device cannot generate unique CRPs, and reusing the existing CRPs is not a viable solution in the context of security.
* Secure storage: In collaborative environments where distributed computing is involved, which has PUF as a security primitive, and every local device needs to store a copy of the CRP data, poses a threat to data privacy and security as the distributed environment increases the attack surface, any random vulnerable device can be targeted.
* Storage space requirements: Resource-constrained devices will find it challenging to store a large dataset of CRP securely.  
Addressing these challenges requires continuous research on robust PUF architectures with improved error detection and correction mechanisms and standardized evaluation frameworks to ensure their effective and secure deployment in the real world.
# Progress in Electrodeposition of Zinc and Zinc Nickel Alloys Using Ionic Liquids
## 6 Recent Developments
However, the anodic secondary reactions resulted in the formation of acetate-based byproducts. Though the ability of the ILs to electrodeposit zinc and zinc alloys and capability to engineer the redox chemistry are well recognized, the control of metal nucleation characteristics lacks significant research. Therefore, choosing the right combination of ionic liquid as a supporting electrolyte and additive that can tune the properties of the zinc/zinc nickel alloy deposit remains a primary challenge. In addition, systematic investigations on the electrochemical potential window to characterize the electrochemical stability and tune the zinc/zinc alloy deposit properties need a detailed study. Furthermore, the electrodeposition of zinc and zinc nickel alloys employing ionic liquids as supporting electrolytes on different types of conductive substrates such as high strength steel, aluminium, and aluminium alloys [66] and transparent conductive oxides like indium tin oxide [6] is expected to show great interest for applications in aerospace, corrosion protection, batteries, and photocatalysis. Most of the reports suggest that ILs have the potential to deliver more sustainable plating systems owing to their ability to tailor redox potentials and achieve desired alloy formation with good deposit properties. In addition, the quality of metal and metal-alloy deposits depends on the pretreatment/cleaning of the substrate before electroplating. Pretreatment of substrate involves the surface cleaning of substrate by degreasing in a solvent or a surfactant-based solution, followed by an aqueous pickle, rinse, and anodic etching to remove the oxide film, prior to deposition. It is worth noting that metal substrates such as aluminium (Al) and magnesium (Mg), having large negative electrode potentials, require larger anodic pulses for a longer period to remove the adherent oxide. In such a context, ILs can be considered as a potential alternative to remove the oxide/hydroxide layer of the substrates before deposition in ILs. The substrates pretreated using ILs will differ from aqueous pretreatments, thereby eliminating the need for drying before deposition and overcoming the problem of oxide/hydroxide layer formation, with reactive, rapidly oxidizing metals. Moreover, careful control of IL droplets (also known as droplet elimination) after deposition from the surface is very important when plating in ILs, because droplets act as weak points for the deposited layers and result in peeling off from the substrate [73]. Therefore, the surface preparation before Zn and Zn-Ni alloy plating and droplet elimination after the plating process are considered to be the key issues that require significant attention for obtaining a deposit with good adhesion in the case of fasteners, joints, bolts, and so on. In addition, recycling, regeneration, and reuse of ILs from plating solutions and rinse waters remains challenging [74].
# Practical I-Voting on Stellar Blockchain
## 1 Introduction
Stellar uniqueness comes from its consensus protocol called Federated Byzantine Agreement (FBA) [9], which is an open membership variant of Byzantine Agreement. The security of the system is achieved by nodes forming a network of trust. In contrast to proof-of-work based consensus algorithms, SCP finalizes transactions in a matter of seconds and is secure against adversaries with vast computational power. Although Stellar does not offer smart contracts in the Turing-completeness sense, it allows creating complex transactions that represent some subset of simple smart contracts. In the following sections, we present the smart contract used in the i-voting protocol. The paper is constructed as follows. The next section discusses the differences between public and private blockchains and describes current solutions that employ blockchain technology for voting implementation. As Stellar uses a unique consensus protocol, that is a key element of that type of distributed systems, we describe its approach from the perspective of other blockchain systems. Then in Section 3, we propose our method that uses the Stellar blockchain for implementation i-voting system and is composed of key elements described in subsections: ballot encoding and vote encryption, as well as the blind signature method for achieving voter anonymity. In the results (Section 4) we discuss issues related to the scalability and cost of voting on the blockchain. Finally, we propose a fully decentralized blockchain application based on a reliable voting protocol. We conclude with open issues discussion and future works.
## 2 Materials and Methods
### Isotope Measurements  
2.1 \(\delta^{13}\)C Determinations from Honey and Its Corresponding Proteins Samples Preparation  
1. Bulk Honey Samples Preparation for \(\delta^{13}\)C\({}_{\text{honey}}\) Measurements  
The honey samples were dried for 48 h in an oven at 60 \({}^{\circ}\)C. Then, 5 mg of honey were converted in CO\({}_{2}\) by dry combustion in excess of oxygen (3 h, at 555 \({}^{\circ}\)C). The resulted CO\({}_{2}\) was separated from the other combustion products by cryogenic distillation and analyzed by IRMS.  
2. Protein Extraction from Bulk Honey Samples for \(\delta^{13}\)C\({}_{\text{Protein}}\) Measurements  
An amount of 10 g of honey diluted in 10 mL of distilled water were mixed by 7 mL of tungsten acid (obtained from sodium tungstate solution, 10% Na\({}_{2}\)WO\({}_{4}\)\(\times\) 2H\({}_{2}\)O from Merck, Dermstadt, Germany, and sulfuric acid, H\({}_{2}\)SO\({}_{4}\), Merck, Dermstadt, Germany) in a thermostatic water bath at 80 \({}^{\circ}\)C for 10 min, until a flocculated suspension appeared, due to proteins precipitation (AOAC official method 998.12). Then, the samples were centrifugated for 10 min at 4000 rpm. The supernatant was decanted and the resultant protein sediment rinsed with ultrapure water, and these operations were repeated 3 times. The protein samples were dried 24 h at 60 \({}^{\circ}\)C, before the off-line combustion prior to IRMS determinations.  
3. Measurements  
For the \(\delta^{13}\)C isotope value determinations from honey (\(\delta^{13}\)C\({}_{\text{honey}}\)) and corresponding protein (\(\delta^{13}\)C\({}_{\text{protein}}\)) samples, an IRMS--isotope ratios mass spectrometer (Delta V Advantage, Thermo Fisher Scientific, Waltham, MA, USA) was used, coupled to a dual inlet system. One working standard calibrated against NBS-22 oil (IAEA--International Atomic Energy Agency) certified reference material (\(\delta^{13}\)CVPDB = \(-\)30.03%.) was measured daily prior to analysis of honey samples. All samples were measured in duplicate with an uncertainty of 0.2%. for \(\delta^{13}\)C.  
The \({}^{13}\)C/\({}^{12}\)C values were expressed in the delta scale (8%) against the international Vienna Pee Dee Belemmitte (V-PDB) standard [25], as described:  
\[\delta^{1}\text{ X}=\frac{\text{R}_{\text{sample}}}{\text{R}_{\text{standard} }}-1, \tag{1}\]  
where i is the mass number of the heavier isotope of the element X (e.g., \({}^{13}\)C), R\({}_{\text{sample}}\) is the isotope number ratio of a sample (\({}^{13}\)C/\({}^{12}\)C) and R\({}_{\text{standard}}\) is that of the international standard. The delta values were multiplied by 1000 and expressed in units \"per mil\" (%).
## 3 Application of Uncertainty Quantification Methods and Results
### Application Outline
#### 3.1.2 Quality Control  
This study on precipitation estimation based on weather radars comprised three stages (quality control, precipitation estimation, and post-processing) and quantified the corresponding uncertainties at each stage. In the quality control stage, the fuzzy and open radar product generator (ORPG) algorithms that are applied to single-polarization radar by the KMA Weather Radar Center were implemented [31]. The fuzzy algorithm controls the quality of radar variables by generating membership functions of non-meteorological echoes using statistical analysis with non-ground filtering reflectivity as input variables [31]. In contrast, the ORPG algorithm removes reflectivity above threshold in the application region and corrects reflectivity by applying ground echo filter as input variables.
# Piezoelectric Electro-Mechanical Impedance (EMI) Based Structural Crack Monitoring
## 5 Conclusions  
For small crack detection and crack propagation monitoring in structures, the method based on the peak frequency shift in the piezoelectric admittance spectrum is proposed. The feasibility of the method is analyzed theoretically. The FEA and experimental results verified the effectiveness of the method. The following conclusions are drawn:  
(1) The peak frequency in the imaginary part of the piezoelectric admittance spectrum can sensitively detect a crack in a structure, and the peak frequency decreases with the increase of the crack length.  
(2) Appropriately increasing the detection frequency band can increase the peak frequency shift at the same crack size and improve the detection sensitivity to the crack, therefore, making the detection of a small crack in a structure possible.  
(3) The experimental results are consistent with the FEM simulation results, which show the accuracy and validity of the finite element model. In future research, the resonant frequencies of complex structures can be found by a finite element model firstly, then the scanning frequency bands of piezoelectric admittance can be set to include the resonant frequencies, and finally the damage degree of structures can be judged by the peak frequency shift in the piezoelectric admittance signature.  
(4) Since the frequency is selected as a feature parameter for damage monitoring, the proposed method has a better repeatability and signal-to-noise ratio than the root mean square deviation based damage index.  
Since the method proposed in this paper is based on the change of local dynamic characteristics to monitor the small crack in a structure, there still needs to be more in-depth study on the local dynamic modeling and analysis. Further research is still needed on how to extract the feature parameter to characterize the crack propagation quantitatively, determine the optimal detection frequency band, and select the sensitive mode frequency bands, etc. Moreover, the influence of the damage size including the width and depth, the damage position, different excitation positions, the structural load and the damage identification of composite materials will be investigated in-depth in our future work.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] performed the simulation; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China under Grant No. 51375354 and 51808417, 61703215.  
The authors would like to thank for the support by National Natural Science Foundation of China under the Grant 51375354, 51808417, 61703215.  
The authors declare no conflicts of interest.