esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 5 Network Environment  
We conducted our simulations using the open-source Mininet (v. 2.2.0) network emulator [22] to construct a network topology consisting of Open vSwitches (OvSs), which communicate with an SDN controller (POX) via the OpenFlow protocol [20]. We utilized RouteFlow [14], an open-source project that provides virtualized IP routing services over OpenFlow-enabled routers and switches. A typical RouteFlow setup consists of an OpenFlow controller application (RFP proxy), a standalone RouteFlow server (RFServer), and a virtual network environment that mirrors the physical network's connectivity while running IP routing engines like Quagga, as illustrated in Figure 3.  
These routing engines create the forwarding information base (FIB) within the Linux routing tables, following rules defined by routing protocols such as OSPF and BGP. RouteFlow clients (RFClients) operate within the virtual network environment on the RFServer, collecting Linux IP and ARP table data and converting them into OpenFlow tuples. These tuples are then deployed to OpenFlow-compatible devices in the forwarding plane.  
Figure 3: Network environment.  
The entire setup is configured on a Linux guest system running within KVM on a Linux host. The network topology comprises eight Open vSwitches in the data plane, configured in a partial mesh topology, as shown in Figure 3. Each OvS in the data plane is paired with a virtual machine (VM) in the virtual plane, establishing a 1:1 mapping between each OvS and its corresponding VM. The VMs are equipped with the Quagga routing engine and a RouteFlow client.  
The data plane switches communicate with the controller using the OpenFlow protocol, regularly reporting their status. This setup allows the controller to maintain a global view of the network topology. The RFProxy communicates this topology to the RFServer, ensuring that the VMs in the virtual plane are connected in a way that mirrors the physical topology of the data plane switches. Consequently, the virtual plane serves as an accurate representation of the data plane.  
We configured the OSPF protocol on each VM using Quagga, populating the VMs' IP and ARP tables with OSPF routes. The RFClient, running as a daemon on each VM, collects these entries from the IP and ARP tables and forwards them to the RFProxy. The RFProxy then translates the OSPF routes into flow entries, which are installed into the corresponding switch flow tables via the OpenFlow protocol. This process repeats at regular intervals, enabling each switch in the data plane to function as an OSPF router and forward packets accordingly.
# Virtual Try-On Systems in Fashion Consumption: A Systematic Review
## 1 Introduction  
### The Role of Digital Technology in Fashion Retailing  
Digital technology has dramatically changed the apparel industry, with projections that one third of sales will happen online by 2027 [1]. In retail, digital technologies aim to enhance interactivity and assist consumers in making informed purchasing decisions [2]. These technologies encompass a range of applications, such as virtual try-on (VTO), AI-driven fashion recommendation systems, and intelligent clothing matching technologies. VTO systems are designed for scenarios where consumers have already expressed interest in trying on specific garments. Their primary objective is to provide a near-realistic try-on experience, enabling users to better evaluate products [3]. In contrast, other technologies rely on consumer preference data to recommend products of potential interest, which do not fulfill the specific need for a try-on experience [4].  
VTO systems, while innovative, still fall short of fully replicating the experience of trying on physical garments, which remains a barrier to widespread consumer adoption of online apparel shopping and contributes to high return rates [5]. Innovations in image interactivity technology (IIT) are addressing this challenge by simulating real-world interactions with products [6]. These technologies allow users to modify item features and viewing angles, providing an engaging virtual shopping experience that closely mimics physical retail environments [7]. Among the most promising developments are VTO systems leveraging web interfaces, virtual reality (VR), augmented reality (AR) and digital avatars to offer immersive simulated apparel fittings [3]. VTO gained new relevance duringthe COVID-19 pandemic by providing an entertaining and authentic method of apparel shopping safely from home [8].  
Adoption of VTO is growing in various sectors such as watches, cosmetics, footwear, apparel, eyewear, and accessories, with technological refinement continuing across these areas [9; 10; 11; 12; 13; 14; 15]. However, some retailers are skeptical about VTO's potential to significantly enhance conversions and reduce returns, highlighting the need for ongoing innovation and research on both the technical and experiential fronts.
## 4 Simulation Design of NR-ODBR-DCSK System Based on USRP
### USRP-Based NR-ODBR-DCSK Receiver  
At the receiving end of the NR-ODBR-DCSK communication system, the RF signal from the transmitting antenna is first received via the USRP receiving antenna. The received signal is processed by a filter to limit the signal to a fixed bandwidth while filtering out some of the noise components. Next, the signals are orthogonally mixed using a mixer to obtain two orthogonal signals. Then, the analog signals are converted to digital signals by an analog-to-digital converter (ADC), and the frequency of the signals is down-converted  
Figure 9: Transmitter-side oscilloscope output graph.  
to a range that can be processed by the host computer by digital down-conversion. The digital signals after these processes are transmitted over a network cable to the LabVIEW software, which stores the received data as a text file. Subsequently, the signal data in the text file are imported into MATLAB, and the original information code elements at the transmitting end can be recovered by running the demodulation program. The flowchart of the receiving end of the NR-ODBR-DCSK communication system is shown in Figure 10.  
After running the LabVIEW program, the front panel control interface is opened as shown below, which is the USRP parameter setting program. This program is used to control the transmit parameters in the front panel, which mainly include parameters such as I/Q sampling rate, carrier frequency, antenna gain, and transmit antenna channel. The front panel schematic of the receiver side is also shown below, demonstrating the configuration of each parameter and the signal reception status of the receiver side.  
After the transmission is completed, the received signal data are imported into MATLAB, and by comparing the transmitting and receiving code elements in Figures 8 and 11, it can be clearly seen that the receiving code elements are exactly the same as the original information code elements at the transmitting end, indicating that the chaotic communication experiment is successful. This experiment verifies the feasibility and effectiveness of the communication schemes in the chaotic communication system through the semi-physical simulation platform.  
Figure 11: LabVIEW front panel for NR-ODBR-DCSK receiver.  
Figure 10: Schematic of USRP-based NR-ODBR-DCSK receiver side.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Membrane Evaluation Methods
#### 5.1.3 Chemical Stability Measurements  
Three different techniques have been employed to evaluate the long term chemical stability of membranes in the VRB. In the first method, samples of pre-weighed membranes are soaked in 2 M V (V) solutions and any weight-loss is periodically determined by simple weight measurements. A more accurate approach involves soaking preweighed sample of each membrane in 0.1 M V (V) solution. Oxidation of the membrane by the V(V) ions leads to the formation of the blue V(IV) species which can be used as an indicator to measure the stability of a particular membrane [62]. The concentration of V (IV) ions in the solution is determined by ultraviolet absorption spectrometry. The absorbance of each solution is periodically determined to monitor the rate of oxidation by V (V). In order to compare the stability of each membrane when employed in the vanadium redox battery, the experiment was repeated using a constant area (10.5 cm \(\times\) 5.0 cm) of each sample exposed to 25 ml of the V (V) solution. To standardize the method, mixtures of 0.1 M V (IV) solution and 0.1 M V (V) solution are prepared with different ratios. The absorbance is determined for each mixture using a 0.1 M V (V) solution as reference for all measurements. The absorbance of each mixture was determined at a wavelength of 760 nm at which the maximum absorbance of V(IV) ions takes place [64]. In the third technique, cells incorporating the membrane under evaluation are subjected to continuous charge-discharge cycling for extended periods of time and the membrane resistivity and permeability are periodically determined to identify any changes due to membrane fouling or degradation during extended operation [62].
## 4 Experiments
### Results
The correlation for relevance of concepts is shown in bold font above the main diagonal, while the correlation for relevance of relationships is shown in italic font below the main diagonal. geographical area. The Philadelphia Smart City Roadmap document covers a total of 20 concept areas of the smart city with the most relevant ones being _Computing_, _Travel_, and _Processes_, in the order of their relevance. The US Smart City fact sheet document does not address concept areas such as _Industry_, _Devices_, or _Society_. We attribute this fact to the decentralized nature of the US administration, according to which these issues are decided at the local level. For the US Smart City fact sheet document, the most relevant areas are _Computing_, _Economy_, and _Travel_, in the order of their relevance. Lastly, the USC from UN document does not address concept areas such as _Industry_, _Devices_, _Society_, _Engineering_, or _Communications_. We attribute this to scope of UN which needs to address policies across many countries regardless of their technical implementations. Figure 5: Semantic network generated for the United Nations Smart City document. We further analyzed the strength of the relationships between concepts. For example, the USC from UN document elaborates the most on the current state of _Travel_ and emphasizes on _communication_ in this area. The _social_ and _social_ aspects of _Travel_ are also addressed but to a much lesser degree. The US Smart City fact sheet document elaborates on the current state of the _Economy_ and emphasizes on _change_ and _change_ in this area. The _consumption_ aspect is also addressed but to a much lesser degree. Finally, the Philadelphia Smart City Roadmap document elaborates on the current state of the _Computing_ and emphasizes on _possession_, _perception_, and _change_ in this area. Several other aspects of this relationship are also addressed but to a much lesser degree. Figure 6: Semantic network generated for the United States Smart City document. Finally we compared how the three documents address each smart city area comparatively. The results are shown in Figure 8. In this figure, the lower left quadrant shows the high ranked low variation smart city areas. The right bottom quadrant shows areas that are consistently ranked low according to their relevance while the two top quadrants show areas that are ranked differently across the documents. As seen from this quadrant, _Computing_ and _Travel_ are consistently ranked high in all three documents. The _Society_ area is consistently ranked low in all three documents. On the other side of the spectrum, the _Tourism_ shows the highest variation in relevance, with the highest relevance in the USC from UN document and the lowest relevance in the Philadelphia Smart City Roadmap document. Figure 7: Semantic network generated for the Philadelphia Smart City Road Map document.
## 4 Discussion
### Improvements Brought to Feature Matching Using Clustering and DENC  
The analysis in Sections 3.2-3.4 highlights the improvement in feature matching accuracy achieved through clustering. Both the proposed model and its DBSCAN-based alternative outperform the Voronoi model, relatively improving recall by 8.4% and 6.2% and precision by 2.7% and 0.6%, respectively. By dynamically segmenting the feature matching problem to spatially localized clusters, the proposed model effectively demonstrates clear advantages over exclusively point matching-based models, such as the Voronoi model.  
DENC's use of directional thresholds ensures that clusters accurately reflect the directional variability in ILI data. In contrast, while DBSCAN's reliance on isotropic thresholds captures isolated features and merging defects, as well as improving transformations, it lacks the directional precision offered by DENC. Figure 25 illustrates feature clustering  
Figure 24: Sensitivity of the proposed DBSCAN-based alternative model to the merging distance threshold \(\lambda\) using \(\varepsilon=0.250\) m, \(\delta=0.110\) m, \(\alpha=0.055\), and \(\tau=0.001\)—case study.  
for segment S5 using both DBSCAN and DENC. As shown, DENC is more effective in capturing spatially localized clusters due to its directional proximity thresholds.
## 3 Results and Discussions
### Thermo- and pH-Responsive Behavior in Aqueous Solution  
#### 3.2.1 Thermo-Responsive Behavior of SiO\({}_{2}\)-\(g\)-POEGMA  
The hydrodynamic diameter of the SiO\({}_{2}\)-\(g\)-POEGMA nanoparticle was determined by DLS over the temperature range of 55-68 \({}^{\circ}\)C. The lower critical solution temperature (LCST) of SiO\({}_{2}\)-\(g\)-POEGMA was found to be 64 \({}^{\circ}\)C (Figure 5). In this study, the LCST is defined as the temperature at which an abrupt decrease in hydrodynamic diameter is observed. There is no significant change in the hydrodynamic diameter at temperatures below 63 \({}^{\circ}\)C, while the hydrodynamic diameter of SiO\({}_{2}\)-\(g\)-POEGMA nanoparticles decreases with increasing temperature above the LCST. This is attributed to POEGMA polymers becoming less water-soluble owing to the fact that the hydrophobic interactions between the polymers are thermodynamically favored as compared to polymer-water interactions [29]. Our result indicates that SiO\({}_{2}\)-\(g\)-POEGMA is thermo-responsive, which is in line with the earlier observations [35]. On the other hand, we observed a broad transition in hydrodynamic size of SiO\({}_{2}\)-\(g\)-POEGMA nanoparticles, which has been suggested to be attributed to the interactions between neighboring POEGMA polymers [36].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Polymer** & \(M_{n}\) **(Da)** & \(M_{w}\) **(Da)** & **PDI** & \(\sigma\) **(Chains/nm2)** \\ \hline POEGMA & 12,900 & 14,300 & 1.11 & 0.21 \\ PtBA & 16,500 & 20,000 & 1.21 & 0.18 \\ P(OEGMA\({}_{30}\)-_stat_-tBA\({}_{70}\)) & 20,600 & 21,000 & 1.02 & 0.22 \\ P(OEGMA\({}_{50}\)-_stat_-tBA\({}_{50}\)) & 19,900 & 23,300 & 1.17 & 0.20 \\ P(OEGMA\({}_{70}\)-_stat_-tBA\({}_{30}\)) & 24,000 & 28,600 & 1.19 & 0.24 \\ \hline \end{tabular}
\end{table}
Table 2: Molecular weight and the polydispersity index of POEGMA, PtBA, P(OEGMA\({}_{30}\)-_stat_-tBA\({}_{70}\)), P(OEGMA\({}_{50}\)-_stat_-tBA\({}_{50}\)) and P(OEGMA\({}_{70}\)-_stat_-tBA\({}_{30}\)) polymers.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 4 Results
### Coupling Energy of Two Parallel JJs  
The Josephson energy of a single Josephson junction is defined as \(E_{\mathrm{J}}\equiv I_{0}\Phi_{0}/2\pi\), where \(I_{0}\) is the critical current. For two parallel Josephson junctions, the total energy can be considered as the algebraic sum of the individual Josephson energies. Assuming the critical currents of both junctions are the same, the energy of each junction can be expressed as \(E_{\mathrm{J1}}\equiv I_{1}\Phi_{0}/2\pi\) and \(E_{\mathrm{J2}}\equiv I_{2}\Phi_{0}/2\pi\). For two parallel Josephson junctions, assuming they have identical critical currents, when considering the phase differences across the two junctions, the total Josephson energy is influenced by these phase differences:  
\[E_{I_{\mathrm{total}}}=E_{J1}\mathrm{cos}(\delta_{1})+E_{J2}\mathrm{cos}( \delta_{2}) \tag{24}\]  
Furthermore, if there is coupling between the two Josephson junctions, such as in a SQUID loop, the coupling energy must also be considered. The coupled energy can be expressed as:  
\[E_{I_{\mathrm{coupled}}}=E_{J1}\mathrm{cos}(\delta_{1})+E_{J2}\mathrm{cos}( \delta_{2})-\frac{MI_{1}I_{2}}{2} \tag{25}\]  
where \(M\) is the mutual inductance between the two junctions.  
The geometry of the washer significantly affects the magnetic field and current distribution, which, in turn, influences the coupling energy between Josephson junctions (JJs). For instance, in our simulations, device edges lead to significant changes in the magnetic field and current distribution, and the stability of the current distribution, in turn, affects the coupling between the two Josephson junctions. We observed that the current distribution in octagonal washers is more uniform. This design minimizes regions of high current density  
Figure 9: Screening current simulations under the influence of a magnetic dipole: (**a**) Current circulation in a square washer. (**b**) Current circulation in a circular washer. (**c**) Current circulation in an octagonal washer. In the upper portion of each segment, current circulation patterns are visualized, whereas the lower sections provide a detailed view of the current intensity at specific tangential points. Different colors indicate the simulation results for different tangent positions.  
that can lead to vortex motion and reduce 1/f noise. Such a uniform current distribution results in a more stable coupling energy between the JJs.
## 1 Introduction
[1] proposed an ultrasonic inversion method using a supervised fully convolutional network (FCN) for quantitative reconstruction of high-contrast defects hidden within multilayered composite materials. This method accurately reconstructed L-wave velocity models of defects and demonstrated potential for online detection of adhesively bonded composites [1]. [PERSON] et al. [24] described ML algorithm development approaches such as kNN, naive Bayes classifier, random forest, and gradient booster for monitoring single-lap adhesive joints for weak adhesion and voids by extracting predominant features from Lamb waves [24]. In our recent work, [PERSON] et al. [28], the automated technique of the ultrasonic method combined with machine learning algorithms was presented for detection and classification of the depth of interface defects in adhesive joints. A support vector machine (SVM) was trained with prepared datasets, demonstrating high prediction accuracy and the ability to classify defect depth in adhesive joints [28]. This paper focuses on the study of ultrasonic waves for detecting interface defects in adhesive joints, with the use of post-processing algorithms to improve detectability and reliability, and the application of modern deep learning techniques, specifically convolutional neural networks. The aim of this study was to develop and optimize an ultrasonic non-destructive testing (NDT) method integrated with convolutional neural networks (CNN) to improve the detection of interface defects in bonded structures and to automate the process. Considering the challenges posed by surface curvature and other factors that cannot be fully eliminated by conventional ultrasonic NDT techniques and post-processing algorithms that limit the accuracy of interface defect detection, the proposed approach can improve the detection accuracy by exploiting the pattern recognition and non-linear relationship capabilities of CNN. In order to achieve this goal, ultrasonic pulse-echo testing was performed on aluminum adhesively bonded to composite structures to collect data for further generation of C-scan images. Datasets were prepared by classifying experimental data into defective and not defective areas. The CNN was trained with a data subset to recognize patterns and features of images. The other data subset was used for CNN validation. In addition, the generalization ability of the trained model was evaluated using data collected from another sample with interface defects where the curvature was not visible and also affected detection. The performance of the developed technique was evaluated and compared.
## 4 Security and Performance Analysis
### Security Analysis
#### 4.1.3 Security against Falsification Attacks  
To ensure secure transmission, we use the keys \(K_{si}\) and \(K_{ci}\) to encrypt data transmitted between the cluster node and between the cluster node and the base station, respectively. When the sensor node returns the data to the cluster node, \(C_{si}=E((M_{i^{{}^{\prime}}},K_{{}^{\prime\hskip-1.0 pt-\hskip-1.0 pt\mathit{msg }}}^{*},N_{i^{{}^{\prime}}}),K_{si})\) is used for encryption. When the communication between the cluster node and the base station is finished, \(K_{list}\) is obtained. The base station returns \(K_{si}\) to the cluster node, and decryption begins. If the received key cannot decrypt the received packet, the received packet will be regarded as an illegal packet and will be abandoned. This practice ensures the integrity of the data transmitted, and guarantees that the data is sent from the sensor node administered by the cluster node.
# Online Model Adaption for Energy Management in Fuel Cell Electric Vehicles (FCEVs)
## 2 Methodology
### Energy Management Strategy  
As this vehicle is not a plug-in HEV, the energy that will maintain the vehicle in the charge sustaining mode comes exclusively from the hydrogen consumed by the FC. Moreover, for a fair comparison in terms of general performance and fuel consumption between the different cases in this study, the battery state of charge (SOC) at the end of the driving cycle should be zero to guarantee charge-sustaining operation and to allow fair comparison between the scenarios. Finally, considering these constraints, the problem is defined as:  
\[\int_{t_{0}}^{t_{f}}P_{b}(u(t),E_{b}(t),t)d_{t}=0 \tag{5}\]  
So to address the constraint of the charge sustaining mode in the FCEV and the condition for which the driving cycle is not known in advance, the ECMS initially proposed by [30] is implemented in the EMS. The idea underlying the ECMS is to solve the global optimization problem (Equation (5)) by replacing it with a local one, then to reduce the problem to the minimization of the equivalent fuel consumption at each time step. Thus, it is possible to define an instantaneous cost that takes into account both hydrogen and electric energy consumption, such as:  
\[C=P_{f}-\lambda P_{b} \tag{6}\]  
Note that the \(\lambda\) parameter weights (Equation (6)) the power usage to move the vehicle at a given time step. If \(\lambda\) tends to 0, the EMS only takes into account the fuel power, tending to deplete the battery. On the other hand, if \(\lambda\) tends to \(\infty\), the variation to the SOC is minimized, and high power is provided by the fuel cell. \(P_{f}\) and \(P_{b}\) have the same units, and, consequently, the co-state \(\lambda\) is dimensionless. Due to the limitations of obtaining a \(\lambda\) that fulfils the constraint of Equation (5) in cases for which the cycle is not known in advance, a controller is implemented in the EMS to manage this task. This controller is based on what [31] proposed in his work. Basically, the controller attribute a correction factor according to the error between the actual SOC and the target SOC. A non-linear penalty function is defined heuristically in order to maintain the SOC around the target value (50%), delimited from 40% to 60%.