esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DC Arc Fault Detection Based on the Proposed Model
### Thresholds Determination for the Proposed Method  
For the AR-model-based arc fault detection method, some parameters need to be determined before application. The first one is how long the observing time window should be. For a given observing time window of 1 ms to 20 ms, variances of \(r_{i}\) can be calculated under normal and arc fault conditions of PV inverter \(\#A\), \(\#B\) and \(\#C\), respectively,  
Figure 5: The flowchart of arc fault identification based on the proposed method.  
as shown in Table 4. Under normal operation conditions, the variances of all observing time windows are small, which means few changes in the PSD of inverter noises and are in accordance with the theoretical analysis of Section 2.2. On the contrary, due to the non-stationary characteristics of the arc current, the corresponding variance will become larger, which is beneficial to arc fault detection. Therefore, the preferable observing time window should make the variance of arc faults significantly greater than normal operation. Here we choose 10 ms as the best observing time window. The other parameter to be determined is the threshold \(r_{th}\). Here plot the \(|r_{i}-r_{i-1}|\) under all conditions in Figure 6. The values of \(|r_{i}-r_{i-1}|\) under arc faults are significantly higher than that under normal operation no matter what type of inverter. So 0.1 is selected as the value of threshold \(r_{th}\) (normal value less than 0.05).
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 2 Preliminaries and Related Work
### Traffic State Estimation  
Traffic state inference is a central problem in transportation engineering and serves as the foundation for traffic operation and management. Definition 2: Traffic state estimation (TSE) _infers traffic states in single lane or multiple lanes along a stretch of highway or arterial segments over a period of time, represented by traffic density (veh/km/lane, denoted by \(\rho\)), traffic velocity (km/lane/hour, denoted by \(u\)), and traffic flux or volume (veh/lane/hour, denoted by \(q\)) using noisy observations from sparse traffic sensors [12]. The ultimate goal of TSE is traffic management and control, building on the inferred traffic states._  
Remark 1:
1. _These three traffic quantities are connected via a universal formula:_ \[q=\rho u.\] (4)  
Figure 2: Schematic of PDE solution approximation. Knowing two of these quantities automatically derives the other. Thus, in this paper, we will primarily focus on \(\rho,u\), and \(q\) can be derived using Equation (4). 2. \(q\in[0,q_{max}],\rho\in[0,\rho_{jam}],u\in[0,u_{max}]\), where \(q_{max}\) is the capacity of a road, \(\rho_{jam}\) is the jam density (i.e., the bumper-to-bumper traffic scenario), and \(u_{max}\) is the maximum speed (and usually represented by a speed limit). How to calibrate these parameters is shown in Table 1.  
TSE is essentially end-to-end learning from the ST domain to labels. Denote a mapping parameterized by \(\theta\) as \(f_{\theta}(\cdot)\), from an ST domain \((\mathbf{x},t)\in\mathcal{D}\) to traffic states \(\mathbf{s}=\{\rho,u\}\):  
\[f_{\theta}:(\mathbf{x},t)\longrightarrow\mathbf{s}=\{\rho,u\}. \tag{5}\]  
The key question is to find a set of parameters \(\theta^{*}\) and the functional form \(f_{\theta}(\cdot)\) that fit the observational data the best. **Remark 2**. : _TSE can be implemented using supervised learning as presented in this paper, where physics-based models are used to regularize the training of data-driven models. TSE can also be formulated as unsupervised learning, such as matrix/tensor completion, that estimates unknown traffic states [32; 33]. Instead of using physics-based models, matrix/tensor completion methods use prior knowledge, such as low-rank property, to regularize the estimation. We would like to pinpoint here that such prior knowledge regularization can also be integrated into our framework by including the rank of the predicted traffic state matrix in the PIDL loss function._  
Traffic sensors range from conventional ones placed on roadside infrastructure (in Eulerian coordinates), such as inductive loop detectors and roadside closed-circuit television (CCTV) or surveillance cameras, to in-vehicle sensors (in Lagrangian coordinates), including global positioning systems (GPSs), on-board cameras, LiDARs, and smart phones.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 6 Summary  
In summary, modulation format recognition technology for 6G mobile VLC applications should focus on the core goals of \"lightweight, high-precision, and low-power consumption\". By deeply integrating algorithm innovation, architectural optimization, and hardware acceleration, existing technological bottlenecks can be overcome. With the advancement of edge computing power and new learning paradigms, MFI technology is expected to achieve substantial breakthroughs in complex channel scenarios and real-time dynamic recognition, providing reliable technical support for adaptive resource allocation in dynamic wireless optical networks.  
**Author Contributions:** Conceptualization, S.Z., W.D. and C.L.; methodology, S.Z. and W.D.; formal analysis, S.Z. and W.D.; investigation, S.Z. and W.D.; resources, S.Z., C.L. and S.L.; data curation, S.L. and R.L.; writing--original draft preparation, S.Z. and W.D.; writing--review and editing, S.Z., W.D. and C.L.; visualization, S.L. and R.L.; supervision, S.Z. and C.L.; project administration, S.Z. and W.D.; funding acquisition, S.Z., C.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Innovation Project of Guangxi Graduate Education (No. YCSW2025507), Guangxi Natural Science Foundation (No. 2025 GXNSFBA069254), Guangxi Key Technologies R&D Program (No. AB241484046).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Adaptive LQG-FF Current-Voltage Controller
### Selection of the Supercapacitor  
The capacitance value is calculated to avoid high variations in the supercapacitor state of charge, which depends on the energy absorbed or delivered in the different operating conditions. The current of the supercapacitor corresponds to the high-frequency component of the DC bus current, which is represented by the following equation considering a step change on the DC bus current:  
\[i_{sc}(s)=\Delta i_{dcmax}\cdot\frac{s}{s+2\cdot\pi\cdot f_{c}}\cdot\frac{1}{s} \tag{26}\]  
In Equation (26), \(\Delta i_{dcmax}\) represents the magnitude of the DC bus current perturbation, and \(f_{c}\) corresponds to the cut frequency (in Hz) of the filter HPF used in the controller. The time representation of (26), obtained from the inverse Laplace transformation, is:  
\[i_{sc}(t)=\Delta i_{dcmax}\cdot e^{-2\pi\cdot f_{c}\cdot t} \tag{27}\]  
Equation (28) reports the voltage variation in the supercapacitor caused by the previous current perturbation, where \(t_{s}\) corresponds to the stabilization time of the supercapacitor current for a unitary step perturbation in the bus current.  
\[\Delta v_{sc}=\frac{1}{C_{sc}}\int_{0}^{t_{s}}i_{sc}(t)\cdot dt \tag{28}\]  
Then, solving Equation (28) leads to the minimum value of \(C_{sc}\) needed to ensure a maximum voltage deviation (\(\Delta v_{sc}\)):  
\[C_{scmin}=\frac{\Delta i_{dcmax}}{\Delta v_{sc}\cdot 2\pi\cdot fc}\cdot(1-e^{-4}) \tag{29}\]  
A security factor of 50% is introduced to take into account parametric tolerances in the electronic components and parasitic losses in the cables, among other unpredicted conditions, leading to the final supercapacitor selection range:  
\[C_{sc}\geq 1.5\cdot C_{scmin} \tag{30}\]
Influence of Heterointerfaces on the Kinetics of Oxygen Surface Exchange on Epitaxial La\({}_{\bf 1.85}\)Sr\({}_{\bf 0.15}\)CuO\({}_{\bf 4}\) Thin Films  
[PERSON]  
1  
[PERSON]  
2  
[PERSON]  
2  
[PERSON]  
3  
[PERSON]  
Correspondence: [PERSON]ADDRESS]; Tel.: +1-803-77-0084330  
anisotropic oxygen ion migration [6; 18; 19; 20; 21]. In addition, RP oxides show no discernible cation segregation, which is the major degradation mechanism of ABO\({}_{3}\) oxides [22; 23]. Recently, La\({}_{2-x}\)Sr\({}_{x}\)CuO\({}_{4\pm 8}\) has attracted attentions not only because of its comparable oxygen surface exchange kinetics to that of LSCF at intermediate temperatures [24], but also because of its multifunctionality including high \(T_{\rm c}\) superconductivity [25; 26; 27]. In the case of Sr-doped La\({}_{2}\)CuO\({}_{4}\), oxygen vacancies are the majority oxygen defects controlling the ORR kinetics; thus, the larger the oxygen vacancies, the faster the ORR kinetics. In this regard, the heterointerface formed by La\({}_{2-x}\)Sr\({}_{x}\)CuO\({}_{4\pm 8}\) decorated with ABO\({}_{3}\) lead to the enhancement in the ORR kinetics of La\({}_{2-x}\)Sr\({}_{x}\)CuO\({}_{4\pm 8}\), as oxygen vacancies can increase at the interface between RP phases and ABO\({}_{3}\)[28; 29]. Therefore, in order to increase oxygen vacancies at the heterointerface in this system, ABO\({}_{3}\) oxides are required to readily form oxygen vacancies. LaNiO\({}_{3}\) (LNO) can be a suitable candidate within the ABO\({}_{3}\) perovskites since LNO possesses a relatively low formation energy for oxygen vacancies [30]. In particular, the (001) surface of LNO is known to more easily form oxygen vacancies compared with the LNO lattice [31]. Accordingly, LNO has been intensively used in composite oxides for SOFC cathodes. For instance, the cathode performance was significantly enhanced by mixing LNO with La\({}_{2}\)NiO\({}_{4+8}\) (LNO214) as a composite cathode [32]. Furthermore, time-resolved X-ray diffraction showed no substantial barrier to ionic transport at the interface, which might contribute to the enhanced ORR activity of LNO-LNO214 composites [33]. Even though oxygen interstitials are the major defects in LNO214 [34], the contribution of LNO to the enhanced ORR kinetics in LNO-LNO214 composites is not very well understood.  
Here, by growing heteroepitaxial oxide thin films formed by La\({}_{1.85}\)Sr\({}_{0.15}\)CuO\({}_{4}\) (LSCO) decorated with LNO as a model system, we show that the oxygen surface exchange kinetics of LSCO can be significantly influenced by LNO decorations that enable an increase in the vacancy content in the LSCO films. We demonstrate that partial LNO decorations can increase the oxygen surface exchange coefficients (\(k_{\rm chem}\)) of the LSCO film, whereas full LNO coverage reduces the \(k_{\rm chem}\) values.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 2 Related Work
### Routing Optimization of Topic-Based Publish/Subscribe Systems  
There have been several famous topic-based publish/subscribe systems, e.g., SCRIBE [9], Bayeux [10], TERA [12], Corona [13] and NICE [14], _etc_. Nevertheless, early research work mostly focused on the realization of distributed systems; the work for the routing optimization problem is scanty.  
[PERSON] _et al._ first presented the theoretical problem for a minimum topic-connected overlay network (in short, Min-TCO) [15] in the conference of the Association for Computing Machinery (ACM) symposium on the principles of distributed computing (PODC) in 2007. The optimization objective of Min-Tco was to minimize nodes' average degree. Additionally, they proposed a greedy merge algorithm called GM to solve this problem. [PERSON] _et al._ also tried to address the issue of minimum average degree topic-connected overlay join problem (MinAvg-Ico-Join) [16] and proposed a divide and conquer algorithm aiming at this problem. The difference is that [PERSON] pursued the total optimal routing cost, while [PERSON] was committed to efficiently constructing the overlay when nodes dynamically join two or more topic-connected overlays. The latter did to some extent in subsequent work by devising algorithms for a topic-connected overlay design, which sought a balance between time efficiency and the number of edges [17].  
[PERSON] s _et al._ tended to minimize the maximum degree of topic-connected overlay (MinMax-Tco) [18], and presented the low degree topic-connected overlay problem [19]. They proved that the two problems are both NP-complete.  
[PERSON] _et al._ proposed a web services network architecture and integrated publish/subscribe system, which presents a topic as a routing entity, including the functionality for topic creation, subscription and publication in accordance with the basic modes of web services [20]. This architecture can be implemented across any suitable computer network. However, how to deploy it on SDN is not discussed in this patent.  
There were other systems that did not attempt to minimize the average degree or maximum degree of the overlay. In [9; 12], a separate overlay, such as a multicast tree, was maintained for each topic through a distributed protocol. However, the average degree would be roughly twice the average subscription size.  
Other systems, like SIENA [21], turned to reducing the number of non-interest relay nodes, while relaxing topic-connectivity, which can potentially decrease the overlay degree. They did not dispose of the tradeoff between the extra overhead incurred by forwarding unwanted events and the overlay degree.
# Two-Way Linear Probing Revisited
## Appendix A Lemmas Needed for Theorem 10
That is, if \(|S|=j\), then \(\
u(S)\geq j\). So, by the binomial tail inequality given earlier, we see that  
\[\mathbb{P}\{|S|=j\}=\mathbb{P}\{[\,\
u(S)\geq j\,]\cap[\,|S|=j\}\,]\leq\mathbb{ P}\{\text{binomial}(m,2j/n)\geq j\}\leq c^{j},\]  
where the constant \(c:=\varphi(1/(2\alpha))=2\alpha e^{1-2\alpha}<1\), because \(\alpha<1/2\). Let  
\[\ell:=\log_{c}\frac{1-c}{\xi^{2}}=O(\log\beta_{3}).\]and notice that, for \(n\) that is large enough,  
\[\xi\geq\delta\beta_{3}\geq\frac{\delta 2m(\ell+\beta_{3})}{(1+\ell/\beta_{3})2 \alpha n}\geq y\frac{2m(\ell+\beta_{3})}{n},\]  
where \(y=1/2+\delta/(4\alpha)>1\), because \(\delta\in(2\alpha,1)\). Clearly, by the same property of \(S\) stated above, \(L\leq\
u(S\cup B)\); and hence, by the binomial tail inequality again, we conclude that, for \(n\) that is sufficiently large,  
\[\mathbb{P}\{L\geq\xi\} \leq \mathbb{P}\{\left[\,\
u(S\cup B)\geq\xi\,\right]\cap\left[\,|S| \leq\ell\,\right]\}+\sum_{j=\ell}^{m}\mathbb{P}\{|S|=j\}\] \[\leq \mathbb{P}\{\text{binomial}(m,2(\ell+\beta_{3})/n)\geq\xi\}+ \frac{c^{\ell}}{1-c}\] \[\leq (\varphi(y))^{\xi}+\frac{c^{\ell}}{1-c}\;\leq\;\frac{1}{\xi^{2} }+\frac{1}{\xi^{2}}\;=\;\frac{2}{\xi^{2}}.\]  
Thence, \(\mathbb{E}[\,N\,]\leq 2n/\left(\beta_{3}\xi^{2}\right)\) which implies by [PERSON]'s inequality that  
\[\mathbb{P}\{\text{binomial}(r,p)\geq t\}\leq\left(\varphi\Big{(}\frac{t}{rp} \Big{)}\right)^{t}\leq(\varphi(\eta))^{t},\]0  
**Lemma 3**. : _In any witness tree \(W_{k}(h)\in\mathcal{W}_{k}(h,w,b)\), if \(h\geq 2\) and \(w\leq 2^{h-\eta}\), where \(\eta\geq 1\), then the number \(b\) of black nodes is \(\geq\eta\), i.e., \(\mathbb{I}_{\left[\,\left[\,b\geq\eta\right]\cup\left[w>2^{h-\eta}\right]\, \right]}\;=1\)._  
Proof. : Note that any witness tree has at least one block node at distance \(h\) from the root. If we have \(b\) black nodes, the number of block nodes is at least \(2^{h-b}\). Since \(w\leq 2^{h-\eta}\), then \(2^{h-\eta}-b+1\geq w-b+1\geq 2^{h-b}\). If \(b=0\), then we have a contradiction. So, assume \(b\geq 1\). But then \(2^{h-\eta}\geq 2^{h-b}\); that is, \(b\geq\eta\).
# Suspicious Financial Activity in the Context of In-Game Asset Exchange Marketplace
## 2 Background
### Existing Research  
Recent research showed that accurately identifying suspicious transactions is important for many reasons. Detecting suspicious transactions is crucial in the cyber-physical environment today since fraudulent activities pose huge financial losses and are increasing in rate [18]. Identifying suspicious transactions also enables detection to help enhance security by reacting to different kinds of fraud, such as credit card fraud and identity theft, which further establishes a secure financial system [18]. This also improves customer trust, as protecting customers from financial harm causes them to develop confidence in the system [18].  
The importance of reducing false positives in the detection process is also emphasized, and time-frequency analysis helps identify genuine suspicious transactions [19]. This reduces the number of false positives, which typically leads to analysts becoming accustomed to real cases [19].  
Existing research shows that there are three main activities that can lead to suspicious activities. The first is high-volume transactions [20]. Comprehensive analysis shows that users with 30 or more total transactions are considered to have a high volume [21]. As the literature states, high-volume transactions are always suspicious and need to be further analyzed. Such high-volume transactions become more suspicious if they are performed on the same day and between the same people, especially if the situations repeat themselves over a period of time. In order to identify high-volume transactions, a threshold needs to be established. According to the existing research, this threshold value is the 95 th percentile [22].  
The second suspicious activity is the fragmentation of high-volume transactions over time, that is, not buying in a single day but rather buying in small amounts with a high volume in total. Users pursue these small transactions in order not to fall within the reporting thresholds. The idea is to divide illicit money into small amounts and transfer it to a destination account, usually within a short period of time [23].  
The final suspicious activity is U-turn transactions, where buying and reselling activities happen between the same two users. [PERSON] and [PERSON] conducted a study utilizing data visualization to identify suspicious activity, which proved how these transactions led to suspicious activities [24].  
After reading the existing literature, the suspicious activities were defined. The three types of suspicious activities are high-volume transactions, fragmentation of transactions, and U-turn transactions. These emphasize the different types of techniques used by the users to hide their suspicious actions. In the following section, we will discuss the methodology used to detect and analyze suspicious activities.
# Amplification of Chirality in Photopatterned 3D Nanostructures of Chiral/Achiral Mixtures
## 2 Materials and Methods  
Preparation of materials: The PFBT was prepared based on published procedures, with palladium-catalyzed Suzuki polycondensation used as a final procedure [41]. To produce a high-molecular-weight PFBT (M\({}_{\text{n}}\) = 13,000, M\({}_{\text{w}}\) = 30,000), synthesized PFBT was purified by Soxhlet extraction and a fluorene monomer was attached by (S)-3,7-dimethyloctyl substituents for the introduction of the chirality. For the photopatterning, SU-8 2025 (Kayaku Advanced Materials, Inc., Westborough, MA, USA) was selected, which is a highly branched polymeric epoxy resin, and the photoinitiator PC-2506 (Polyset Co., Mechanicville, NY, USA) was also added. The mixture ratio of PFBT, SU-8 2025 and PC-2506 was 1:30:0.3. Before the addition of the SU-8 2025, the PFBT was first dissolved in tetrahydrofuran (THF) (Sigma-Aldrich, St. Louis, MO, USA) for 24 h using a magnetic stirrer. After dissolving the PFBT, SU-8 was added to PFBT/THF solution and mixed for 48 h with a magnetic stirrer. For comparison, several samples were prepared with different solid contents of the SU-8 photoresist, with mixtures of 10%, 20%, 30%, 40% and 50%.  
Laser writing: The two-photon lithography system used an 800 nm writing-wavelength Ti:sapphire laser (Mira 900, Coherent, Santa Clara, CA, USA), as shown in Figure 2. This system had a piezo stage, so the substrate on the stage could move in the x, y and z directions to make designable 3D structures. The prepared PFBT/SU-8 solution was spin-coated on the glass and baked for 80 min at 60 \({}^{\circ}\)C to stabilize and remove the air in the coated solution, followed by baking at 95 \({}^{\circ}\)C for 8 min and then cooling down for 3 h. To fabricate the pattern, we designed an 'L' shape using a computer and the PFBT/SU-8-coated substrate, exposed by a femtosecond laser with a cumulative dose of 0.2-0.8 W-cm\({}^{2}\). After exposure, the substrate was post-baked at 65 \({}^{\circ}\)C for 10 min, followed by heating at 95 \({}^{\circ}\)C for 10 min to accelerate the crosslinking speed of the sample, then cooling down for 2 h. For the development of the laser-exposed sample, propylene glycol methyl ether acetate (PGMEA) (Sigma-Aldrich, St. Louis, MO, USA) was used; the sample was submerged in PGMEA for 3 h to dissolve the uncrosslinked areas. Isopropyl alcohol (IPA) (Sigma-Aldrich, St. Louis, MO, USA) and deionized water were used to rinse the sample, and finally, the sample was annealed at 150 \({}^{\circ}\)C for 15 min in the glove box, then cooled down to room temperature. The PFBT/SU-8-coated samples and patterned PFBT/SU-8 samples were measured using circular dichroism (CD) spectroscopy (J-815, Jasco, Great Dunmow, UK) for a comparison of the CD intensity, and the photopatterned structures were analyzed using scanning electron microscopy (SU-4000, Hitachi, Tokyo, Japan).
## 4 Conclusions  
The present work's main goal was to characterize the influence that material and geometrical features of re-entrant hexagonal honeycomb auxetic structures have on the shear correction factor required by the first-order shear deformation factor and on the maximum static deflection of beams made solely of such materials. For this purpose, a set of case studies were considered. Firstly, one has characterized the Poisson ratios and the bending and transverse shear stiffness coefficients for different auxetic FGM beams. Following this, two other case studies focused on the shear correction factor were conducted to assess how this factor is influenced by the unit cell aspect ratios \(\eta 1\) and \(\eta 3\), the angle \(\theta\) and by the material properties of the structure. The two final case studies were meant to investigate the influence of those parameters on the beams' maximum static deflection. From the results achieved, it is possible to conclude that the mixture of constituent materials with more differentiated Poisson ratios, namely the Al-SiC and Al-Al\({}_{2}\)O\({}_{3}\) FGM, when compared with the Al-WC and Al-ZrO\({}_{2}\) FGM, which have closer Poisson ratios, significantly influence the different stiffness coefficients of the resulting structural composite. The resulting Poisson's ratios of the honeycomb auxetic material also demonstrate to be very influenced by this. These conclusions are aligned with ([PERSON], 1991) [1], namely regarding the importance that the Poisson ratio may have on a structure's behavior improvement, and thus, the need to address this aspect.