esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Influence of soaking Time on Inactivation Effect of Microorganisms  
The inactivation efficiency of PAW was affected by the soaking time (Figure 2). Especially, SDW treatment for 30 min did not significantly change the population of bacteria as well as that of total yeasts and molds compared to the untreated group. The inactivation efficiency was improved along with extending the soaking time. An approximately 1.26 log\({}_{10}\) CFU/g reduction of total bacteria and a 0.91 log\({}_{10}\) CFU/g reduction of yeasts and molds on the thin sheets of bean curd were achieved after PAW90 treatment for 30 min. Therefore, the inactivation efficiency of PAW treatment was influenced by the soaking time in PAW, which is consistent with the conclusion of [PERSON] et al. (2018). According to a study by [PERSON] et al. (2018), an approximately 0.5 log reduction of _Saccharomyces cerevisiae_ inoculated on grapes was achieved after PAW treatment for 30 min. The less-than-expected effects of PAW on the thin sheets of bean curd and grapes may be attributed to the nonsmooth morphology of their surfaces, which makes the microorganisms easy to stack ([PERSON] et al., 2018). A better microbial decontamination effect may be achieved when the synergistic treatment of PAW and other treatments (mild heating, nisin, etc.) is employed in the preservation of foods.  
Our previous study showed that PAW activated by dielectric barrier discharge (DBD) plasma for 30 s had a lower pH value and higher ORP, electrical conductivity, ROS, and reactive nitrogen species (RNS) compared with SDW ([PERSON] et al., 2018). An acidic pH may inactivate the microbes by the interaction of a high level of protons and macromolecules ([PERSON] et al., 2018; [PERSON] et al., 2019). ORP was perceived as contributing to the apoptosis and necrosis of microbes ([PERSON] et al., 2018). A considerable number of active ions may result in higher electrical conductivity. Further, it was reported that generated ROS and RNS in PAW could play an important role in the lethal effect on microbes ([PERSON] et al., 2018). These elements in PAW would interact with microbes more efficiently and yield better results over the soaking time.  
Figure 2: The numbers of surviving bacteria (**A**) and yeasts and molds (**B**) on thin sheets of bean curd before and after PAW treatments with different soaking times. Different letters in the bars indicate statistically significant differences (\(p<0.05\)).
## 4 Analysis of Old Hanger Test Detection
### Steel Wire Detection
#### 4.4.2 Torsional Performance  
One specimen with a length of 250 mm was selected from the hanger's upper, middle, and lower parts to conduct the steel wire torsion test. The specimen was placed in a flat  
Figure 14: Hardness test results of anchor cup and anchor ring.  
Figure 15: Steel wire tensile test.  
fxture of the testing machine, and the rotating clamp was rotated at a constant speed. The torsion speed was no more than 0.25 rad/s, and the specimen was twisted until fracture. The number of rotation cycles was recorded. The test results are shown in Table 1. As can be seen from the table, the maximum number of times the specimen was twisted was five, which could not meet the specification requirements [18], indicating that the steel wire torsional performance decay is extensive. The primary factor contributing to the degradation of torsional properties in the steel wire is the prolonged usage of the hanger over 15 years. The steel wire is susceptible to fatigue when subjected to high dynamic loads, resulting in a reduction in its elasticity. This reduction in elasticity leads to a weakening of the torsional properties exhibited by the steel wire.
# Matched-Filter Thermography
## 4 Matched Filtering in Thermography
### Thermal-Wave Radar (TWR)
#### 4.1.2 Instrumentation  
The experimental setup for matched-filter thermography can be constructed using either a single point detector or an infrared camera, Figure 4a,b, respectively. In either case, the excitation source is a continuous laser system capable of modulating the intensity of light via an analogue signal input. The sample is usually fixed on a 4 degree-of-freedom stage with its region of interest located at the focus of the infrared light collection system. An elliptic mirror can be used to feed the infrared radiation from the sample surface, located at one focal point of ellipse, to the other focal point of the mirror where the detector resides. For thermography systems, an extension tube can be used between the lens and camera's detector array to achieve a magnification around unity. Multifunctional data acquisition devices can be used to generate three signals: detector trigger signal, flag signal, and reference/modulation signal. The detector trigger signal determines when data need to be acquired from the detector either as a point reading or acquisition of a frame. Flag pulse train determines the beginning of each modulation cycle (e.g., beginning of each linearly LFM chirp) and is used for coherent averaging of responses for improving SNR. Reference/modulation signal serves as the pre-known waveform/filter and is also responsible for modulating the intensity of light source. Research grade infrared cameras often have the ability to store the flag pulse train and reference/modulation signal information in the header of each acquired frame so that the information can later be retrieved and used in the post-acquisition signal processing.  
Figure 4: Schematic presentation of TWR experimental setups using (**a**) a single detector [23] and (**b**) an infrared camera [23], (**c**) signal processing block diagram of TWR [23].  
Figure 4c depicts the block diagram of matched-filter signal processing, which can be utilized in experimental setups to carry out Equations (3) and (9) using the operators/subroutines available in common instrumentation software such as Matlab and LabView. In this diagram, \(r(t)\) represents the reference signal (i.e., pre-known waveform or filter) and \(s(t)\) represents the detector signal. For thermography systems, the signal processing algorithm is applied to the time-lapsed signal corresponding to each camera pixel.
# Design of a Broadband Transition from a Coaxial Cable to a Reduced-Height Rectangular Waveguide
## 3 Design
Figure 9a shows the structure, and Figure 9b shows the reflection and trans  
Figure 8: Higher-order rectangular waveguide modes in the proposed transition with \(b/a=0.125\). Figure 7: (**a**) Structure and (**b**) reflection and transmission coefficients of the proposed transition with \(b/a=0.125\). results show a reflection of \(<\)\(-\)20 dB across 8.03-21.9 GHz. The simulated transmission coefficient in Figure 10 reveals that the higher-order mode with the largest magnitude is the TE\({}_{30}\) mode, having a transmission coefficient of \(-\)57.3 dB at 21.9 GHz. The TE\({}_{20}\) mode is highly suppressed and is not plotted in Figure 10. With \(a\) = 19.05 mm and \(b\) = 0.25\(a\) = 4.76 mm, the cutoff frequency of the first eight higher-order modes in the output waveguide is 15.74, 23.61, 31.47, 31.49, 32.46, 35.20, 39.36, and 44.52 GHz for TE\({}_{mn}\)/TM\({}_{mn}\) modes with \(mn\) = 20, 30, 40, 01, 11, 21, 31, and 41, respectively. Figures 11 and 12 show the structure and the performance of the proposed transition with \(b/a\) = 0.375. Figure 11a shows that the reflection coefficient is \(<\)\(-\)20 dB at 8.05-19.8 GHz. In Figure 12, one can see that the TM\({}_{11}\) mode is the strongest, with a transmission coefficient of \(-\)80.7 dB at 19.8 GHz. Again, one can see that the TE\({}_{20}\) mode is greatly suppressed, and it is not included in Figure 12. With \(a\) = 19.05 mm and \(b\) = 0.375\(a\) = 7.14 mm, the cutoff frequency of the first eight higher-order modes in the output waveguide is 15.74, 20.99, 22.42, 23.61, 26.24, 31.47, 31.59, and 37.83 GHz for TE\({}_{mn}\)/TM\({}_{mn}\) modes with \(mn\) = 20, 01, 11, 30, 21, 40, 31, and 41, respectively. The results achieved are summarized in Table 3 below with a comparison to previous works [5; 6; 7; 8; 9; 10; 11; 12]. We included the performance of a commercial half-height waveguide adapter by Tei Teng Wireless Co., shown in Figure 13. The specifications of this product, as in other products, are minimum guaranteed by the manufacturer. Actual performance would be better: the bandwidth can be wider, and the VSWR smaller than the manufacturer's specifications. Figure 10: Higher-order rectangular waveguide modes in the proposed transition with \(b/a\) = 0.25. Figure 6:0 (**a**) Structure and (**b**) reflection and transmission coefficients of the proposed transition with \(b/a\) = 0.25. Figure 6:1 (a) Structure and (b) reflection and transmission coefficients of the proposed transition with \(b/a=0.375\).
## 3 Congestion-Aware EFL-Based Model Aggregation Policy in Edge Computing
### Primary Objectives and Algorithm Designs
To prevent this issue, the priority and congestion detection are pivotal to consider with the high-impact features of \(\tau_{m}\) and output of MLSTM-based estimation. Moreover, to enable resource-efficient model computing, each edge aggregator requires elastic resource placement orchestrated through virtualized and softwarized management entities such as NFV orchestrator (NFVO), virtual network function (VNF) manager (VNFM), and VIM. By allowing modifiable properties in each instance descriptor, NFVI consists of efficient virtual mapping allocation that is extensively controlled by an SDN-enabled controller connected to the proposed modules. Addressing throughput-maximized model communications involves activating model updates as opposed to sharing raw data. Moreover, real-time scheduling is further extended in the following section. As illustrated in Figure 2, the interactivity of three main tiers in our proposed IIoT-EFL architecture is given, including participant, edge, and controller tiers. Each tier can be described as follows:* **Participant tier**: The selection criteria are listed, including the non-malicious user status, sufficiency of resource capacities, and high-quality image sensing contributions. Real-time wireless data planes in SDN-enabled architectures involve various end-user equipment, including robotic image sensing nodes, manufacturing nodes, surveillance videos, and tracking/monitoring applications. * **Edge tier**: Virtual resource edge aggregators adapt to the formulated industrial service type and class-\(m\) prioritization orchestrated through the virtualization layer. This orchestration directly follows the controller tier. \(M_{n(t)}\) values are loaded into global loss minimization processes directed towards the edge-aggregated parameters \(M_{e(t)}\). * **Controller tier**: With multi-services in IIoT-EFL-based applications, the final averaged learning model \(M_{m}^{G(e)}\) for each application type is independently computed following the scheduling policies of the defined congestion states from MLSTM-CP output and controller-based orchestration in VIM. The procedural flow of EFL-based IIoT applications entails four fundamental steps: 1. Distributing model structures and hyperparameters for each matching service type between distinct aggregators \(e\) and local nodes \(n\). 2. After obtaining the primary model, local computing optimizes model parameters using local data, followed by uploading the refined model parameters back to the corresponding edge aggregator. 3. By collectively gathering multi-type local model parameters, the edge-aggregated models are executed and subsequently categorized based on their respective service types. This categorization sets the stage for the final averaging process during the last-index iteration. 4. Each service class computes the final learning model by dynamically adopting the aggregation and scheduling policies outlined by the proposed controller.
## 3 Material and Methods
### System Design
#### 3.1.3 Location-Dependent ID  
In most digital systems, users often use IDs to prove their identity [37; 38]. However, it does not guarantee that the physical person who provides the ID information is the digital user matched with the ID.  
For matching the physical people and their digital twin, the system generates location-dependent IDs that represent the user's unique identity in real time, on site. The IDs are generated using the user's identity, location, and time information which are managed in the digital twin. The ID ensures the identity of the user in a certain location and time. In other words, the ID provides more detailed information about who the user is. Therefore, the users can identify the matching person by only exchanging the ID in a contactless situation.
## 4 Discussion
### Strengths and Limitations  
This scoping review provides three key contributions: (1) It highlights the innovative integration of VR and aromatherapy as a promising approach to enhancing the well-being of older adults by engaging multiple senses, (2) it ensures scientific rigor by following the JBI guidelines, and (3) it strengthens credibility through the involvement of patient partners and interdisciplinary clinicians to ensure relevance. One limitation of the review is the exclusion of studies published in languages other than English or Chinese, potentially overlooking relevant research from other linguistic contexts.
## 3 Multi-Parametric Formulations
### Distributionally Robust mp-MILP and Reformulation
* Step 3: For the \(m\)-th CR with its corresponding piece of \(v_{1m}^{R}(\theta)\), build the following MILP, to find a new \(y\) that gives a better optimal value, i.e., \[\underset{x,y,\theta}{\min}c^{T}x\] \[\text{s.t.}Ax+By\leq b+F\theta,\;\theta\in CR_{m}\] (23) \[c^{T}x\leq v_{1m}^{R}(\theta),\|y-y_{1}\|_{2}^{2}\geq 1\]  
where the last constraint excludes the solutions that have been found, and multiplies as the iteration process. The optimal solution of (23), if feasible, is \(y_{1m}^{*}\), which, along with the \(m\)-th CR, solves an mp-LP like (20), whose optimal value function is \(v_{1m}^{R}{}^{*}(\theta)\). Then, the pointwise minimum comparison between \(v_{1m}^{R}{}^{*}(\theta)\) and \(v_{1m}^{R}(\theta)\) gives \(v_{2m}^{R}(\theta)\), as well as the corresponding CRs. After checking \(M_{1}\) CRs, the optimal value function of mp-MILP (19) updates to \(v_{2}^{R}(\theta)\) with \(M_{2}\) CRs. * Step 4: With \(v_{m2}^{R}(\theta),m=1,\cdots,M_{2}\) and its corresponding \(m\)-th CR, go back to Step 3, until (19) finds no new for \(M_{K}\) CRs, the algorithm terminates, and the solution of mp-MILP (19) is concluded as follows: \[v^{R}(\theta)=\left\{ \begin{array}{c}v_{1}\theta+\mu_{1},\theta\in CR_{M_{1}} \\ \vdots\\ v_{K}\theta+\mu_{K},\theta\in CR_{M_{K}}\end{array} \right.\] (24) where \(v_{k}/\mu_{k}\) is the slope/intercept of the \(k\)-th piece of \(v^{R}(\theta)\). Actually, \(v^{R}(\theta)\) is a pointwise minimum comparison among a group of piecewise linear convex functions, and every single CR in (24) is a convex set, while some CRs may share the same \(v_{k}/\mu_{k}\). Interested readers are recommended Reference [32].
# Usage of V2X Applications in Road Tunnels
## 3 Methodology  
For V2X communication testing, a wide range of methods can be applied. Some of the methods involve a technical approach [34], and some directly relate to specific use cases, such as communication with traffic lights [35], etc. There are many cases related to testing of communication among vehicles, but also to infrastructure equipment, pedestrians, cyclists, etc. Some of the cases also respond to the development of autonomous vehicles  
Figure 1: Utilization of Dead Reckoning.  
Figure 2: V2X Platooning [33].  
and their role in transportation [36; 37]. Various devices can be used, such as tools for the assessment of validity [38], compliance with standards [39; 40], or security aspects [41]. For the purposes of the experimental analysis, the functional test in the field was performed including communication logging for further subsequent technical post-analysis.
## 4 Multi-Physics Simulation Analyses of GMM-FBG Current Sensor
### Temperature Drift Characteristics of GMM-FBG Bragg Wavelength
The temperature-drifted curves of the current-Bragg wavelength-sensing characteristics obtained by multi-physics finite-element simulations are numerically fitted with \(T_{r}\) = 273 K as the reference temperature to reach a temperature-drift function representing the sensing performances of the GMM-FBG current sensor as follows: \begin{table}
\begin{tabular}{c c c c c} \hline
**Material Parameters** & **Quartz Core** & **Glass Cladding** & **Epoxy Resin Binder** & **GMM Rod** \\ \hline Thermal expansion coefficient \(10^{-6}\)/K & 0.55 & 1.3 & 60 & 12 \\ [PERSON]’s modulus \(10^{9}\) Pa & 90 & 55 & 2.4 & 6.0 \\ Poisson’s ratio & 0.35 & 0.28 & 0.38 & 0.45 \\ \hline \end{tabular}
\end{table} 
Table 2: Material characteristics specified for simulating thermal strains in a GMM-FBG optical modulator. Figure 6: Temperature-drifted sensing characteristics of the GMM-FBG current sensor, which are represented by the Bragg wavelength differential as a function of excitation current at \(-\)50–150 \({}^{\circ}\)C temperatures. \[\Delta\lambda(I,T)=K_{\rm f}\lambda_{\rm B}(\varepsilon_{\rm s}-a(T-T_{\rm r})) \left(1-\exp[\frac{-(bl+c)^{2}}{gT/T_{\rm r}}]\right) \tag{9}\]  
where \(K_{\rm f}\) denotes the FBG optical modulation sensitivity, \(\lambda_{\rm B}\) represents the Bragg wavelength under zero excitation, \(\varepsilon_{\rm s}\) symbolizes the saturated magnetostrictive strain of the GMM material, \(I\) and \(T\) are the detected electrical current in unit A and working temperature in unit K, respectively, and the fitting constants are resolved to \(a=7.6\,\times 10^{-4}\), \(b=0.025\), \(c=17.8\), and \(g=758\). According to the fitted Formula (9), the measuring scale and range of the GMM-FBG current sensor can be calibrated for different working temperatures.
# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 2 Materials and Methods
### Robot Overview
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Link** & **Mass (kg)** & **Center of Mass \({}_{xyz}\) (m)** & \multicolumn{3}{c}{**Ixyz (kg m\({}^{2}\))**} \\ \hline
1 & 3.7 & \(\begin{bmatrix}0&-0.02561&0.00193\\ 0.2125&0&0.11336\\ \end{bmatrix}\) & \(\begin{bmatrix}0.00375&0.00765&0.00765\\ 0.0085&0.208&0.208\end{bmatrix}\) \\
3 & 2.33 & \(\begin{bmatrix}0.15&0&0.0265\\ 0&-0.0018&0.01634\\ \end{bmatrix}\) & \(\begin{bmatrix}2.46\times 10^{-4}&0.00719&0.00719\\ 9.09\times 10^{-4}&0.00119&0.00119\\ \end{bmatrix}\) \\
5 & 1.219 & \(\begin{bmatrix}0&0.0018&0.01634\\ 0&0&-0.00159\end{bmatrix}\) & \(\begin{bmatrix}9.09\times 10^{-4}&0.00119&0.00119\\ 9.09\times 10^{-4}&0.00119&0.00119\end{bmatrix}\) \\
6 & 0.1879 & \(\begin{bmatrix}0&0&-0.00159\\ 0&0&-0.00159\end{bmatrix}\) & \(\begin{bmatrix}1.22\times 10^{-4}&8.21\times 10^{-5}&8.21\times 10^{-5}\end{bmatrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dynamic parameters for the links of a UR5 robotic arm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Link** & \(Jm\) **(kg m\({}^{2}\))** & \(G\) & \(B\) **(Nms rad\({}^{-1}\))** & \(Tc\) **[\(-\), \(+\)] **(Nm)** \\ \hline
1 & \(1.87\times 10^{-8}\) & 101 & \(1\times 10^{-4}\) & \(\begin{bmatrix}0.076&-0.076\\ 0.083&-0.082\\ \end{bmatrix}\) \\
3 & \(1.87\times 10^{-8}\) & 101 & \(1\times 10^{-4}\) & \(\begin{bmatrix}0.078&-0.077\\ 0.014&-0.014\\ \end{bmatrix}\) \\
4 & \(2.7\times 10^{-5}\) & 101 & \(1\times 10^{-4}\) & \(\begin{bmatrix}0.014&-0.014\\ 0.020&-0.019\\ \end{bmatrix}\) \\
6 & \(2.7\times 10^{-5}\) & 101 & \(1\times 10^{-4}\) & \(\begin{bmatrix}0.020&-0.019\\ 0.021\end{bmatrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dynamic parameters for the motors of a UR5 robotic arm. current for the joint \(i\), respectively. To estimate the motor current from the torque, a linear model proposed in [38] is used, where the torque constants and an offset are obtained, with which they establish a relationship between the modeled torque and the measured current. These parameters are proposed to compute the inverse process and estimate the motor current from a modeled torque. Equation (2) is reformatted as Equation (3):  
\[\hat{\tau}=K_{ii}^{T}ia_{i}+b_{i}, \tag{3}\]  
where the term \(b_{i}\) represents the offset of the linear model for the respective joint \(i\). The values of the parameters for \(K_{ii}^{T}=[7.5,6.8,7.1,2.8,3.2,3.4]\), and for \(b_{i}=[-0.61,16,4.5,0.84,0.064,0.099]\). The UR5 robot is commonly utilized in cooperative tasks and robotics research projects. Additionally, there are toolboxes available for different platform software which facilitates programming and controlling the robot. Moreover, there exists a publicly accessible dataset specifically based on this robot, which has been utilized for exploring and studying robot accuracy degradation.
# A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities
###### Abstract  
Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed.  
Keywords:Software-Defined Networking; in-band control; survey; network bootstrapping; fault recovery; routing; distributed control +
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