esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Experimental Set-Up
#### 3.4.2 Optimization Assays  
For the optimization assays, 5 mL of ultrapure water (pH 5.5) were added to a sampling glass flask and spiked with 100 \(\upmu\)L of a mix solution of all six TCAs (500.0 \(\upmu\)g L\({}^{-1}\)), resulting in a final concentration of 10.0 \(\upmu\)g L\({}^{-1}\). Afterwards, a conventional Teflon magnetic stirring bar and a BA\(\mu\)E device were introduced in the same flask. The microextraction process was performed through floating sampling technology using a multi-point agitation plate (Variomag HpL Labortechnik AG Multipoint 15, Oberschleissheim, Germany) at room temperature (25 \({}^{\circ}\)C). After microextraction, the BA\(\mu\)E devices were removed from the glass flasks with clean tweezers and a clean Kimwipe. For the back-extraction stage, the BA\(\mu\)E devices were placed into glass vial inserts having 90 \(\upmu\)L of an organic solvent following by ultrasonic treatment (42 \(\pm\) 2.5 kHz, 100 W) at room temperature (25 \({}^{\circ}\)C). Afterwards, 10 \(\upmu\)L of a DIF solution (IS; 10,000.0 \(\upmu\)g L\({}^{-1}\)) were introduced to the glass vial inserts, resulting in a final concentration of 1000.0 \(\upmu\)g L\({}^{-1}\). In the case of the PP devices and after the back-extraction stage, these are removed from the vial before being sealed and proceeding to the instrumental analysis. In the case of the nylon devices this step is not required. Blank assays without spiking were also performed. Unless specified, each assay was done in triplicate. Several parameters were studied to optimize the extraction efficiency following an OVAT strategy. In this approach all variables are fixed, except one, and the extraction efficiency is studied at several levels of this parameter. The selected parameters for the optimization process were desorption solvent (MeOH, ACN, and MeOH/ACN (50/50%, \(v/v\)) and time (15, 30, and 45 min), stirring speed (750, 990, and 1250 rpm), pH (2.0, 5.5, 8.0, and 12.0), organic modifier (MeOH: 0, 5, 10, 15, and 20%), matrix ionic strength (NaCl: 0, 5, 10, 15, and 20%), equilibrium time (1, 2, 3, 5, and 16 h) and sample dilution effect using different volumes (1.5, 5.0, 10.0, 25.0, and 40.0 mL), in accordance with previous reports [23; 29; 30; 31; 32; 33; 34; 35; 36].
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 2 Theoretical Design of CLSDAT
### Determining the Adaptive Threshold  
During the iteration process, the optimal circle center needs to be determined based on the distance between each iteration's circle center and the standard center. After completing the entire iteration process, the minimum distance value among all distances is selected as the optimal circle center, allowing for an adaptive search to be carried out for the best circle center. The minimum distance value in Figure 6 corresponds to the 21 st iteration.
## 5 Design of the PV System Dynamics
### Reference Filter Design  
Since the \(P\)&O reference \(v_{po}\) exhibits step changes with infinite derivatives, an additional block must be inserted into the MPPT block to limit the reference derivative, which is needed to ensure that both reachability conditions (24) and (26) are fulfilled. The block diagram of Figure 3 shows the internal structure of the MPPT block, which includes a filter to limit the derivative of the reference provided by the P&O algorithm (\(v_{po}\) signal), thus providing a MPPT signal (\(v_{mppt}\)) with a derivative that ensures the global stability of the SMC.  
This paper proposes a first-order filter to limit the maximum derivative of the MPPT reference \(v_{mppt}\). Considering that positive and negative variations of the P&O signal \(v_{po}\) are steps and that the first-order filter provides the same output derivative for positive and negative inputs, the derivative of the MPPT reference will be the same for increments or decrements in the \(v_{po}\) signal. Therefore, the first step is to define the maximum filter derivative (\(SR_{f}\)) that fulfills both \(SR_{p}\) and \(SR_{n}\) limits given in (24) and (26), which is calculated as follows:  
\[SR_{f}=\max\bigl{(}SR_{p},|SR_{n}|\bigr{)} \tag{31}\]  
Figure 3: Block diagram of the MPPT block.  
Figure 2: P&O algorithm: flowchart, behavior, and waveforms.  
The transfer function \(G_{f}\) of a first-order filter is reported in (32); such an expression also shows the Laplace representation of the \(v_{po}\) steps with amplitude \(\Delta v_{po}\) and the Laplace calculation of the MPPT reference \(v_{mpt}\).  
\[G_{f}=\frac{V_{mpt}(s)}{V_{po}(s)}=\frac{\Omega}{s+\Omega}\quad \Rightarrow\quad V_{mpt}(s)=G_{f}\cdot\frac{\Delta v_{po}}{s} \tag{32}\]  
Applying the inverse Laplace transformation leads to the time waveform of \(v_{mpt}(t)\) given in (33) and the time-derivative given in (34). The maximum value of \(\frac{d\sigma_{mpt}}{dt}(t)\) is obtained for \(t=0\)\(s\), and such a maximum value must be set equal to \(SR_{f}\), which enables the calculation of the filter parameter \(\Omega\), as given in (35), to ensure a derivative of \(v_{mpt}\) lower than \(SR_{f}\) for \(t>0\).  
\[v_{mpt}(t)=\Delta v_{po}\cdot\left(1-e^{-\Omega\cdot t}\right) \tag{33}\] \[\frac{dv_{mpt}}{dt}(t)=\Delta v_{po}\cdot\Omega\cdot e^{-\Omega \cdot t}\] (34) \[\Omega=\frac{SR_{f}}{\Delta v_{po}} \tag{35}\]  
Finally, designing the reference filter using (35) will ensure the SMC's global stability.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
###### Abstract  
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things--IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems' architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.  
IoT; cloud-based operating systems; edge computing; machine learning; federated learning; task offloading; security and privacy; blockchain technology +
Footnote †: journal: Future Internet
## 3 Problem Formulation
### Problem Definition  
The EEMsMIAJSP addressed in this work consists of multiple stages, including processing, transportation, and assembly. The notations defined for use in the EEMsMIAJSP mathematical model are shown in Table 1. The problem is defined as follows. In the processing stage, transportation stage, and assembly stage, there are \(N_{M}\) flexible processing machines \(\mathbf{M}=\left\{M_{1},\cdots,M_{m},\cdots,M_{N_{M}}\right\}\), \(N_{T}\) transportation vehicles \(\mathbf{V}=\left\{V_{1},\cdots,V_{t},\cdots,V_{N_{T}}\right\}\) with the same load capacity \(G_{max}\), and \(N_{K}\) assembly machines \(\mathbf{K}=\left\{K_{1},\cdots,K_{k},\cdots,K_{N_{K}}\right\}\), respectively. There is a product set \(\mathbf{P}=\left\{P_{1},\cdots,P_{z},\cdots,P_{N_{P}}\right\}\) that needs to be manufactured, where each product \(P_{z}\in\mathbf{P}\) is composed of a set of various jobs \(\mathbf{J_{z}}\) and should undergo an assembly operation set \(\mathbf{A_{z}}\) to be assembled into the final product. Each job \(J_{j}\in\mathbf{J_{z}}\) has a release time and consists of a set of processing operations \(\mathbf{O_{j}}\). All operations \(O_{j}^{i}\in\mathbf{O_{j}}\) must be completed before \(J_{j}\) can be transported in batches by vehicles with the load capacity constraint. Once all jobs \(J_{j}\) belonging to a product \(P_{z}\) have been transported to the assembly stage, the assembly of \(P_{z}\) can begin. In the processing stage, each operation \(O_{j}^{i}\) has a set of compatible machines \(\mathbf{M_{j}^{i}}\) from which one machine is selected for processing. Once the operation is completed, the next operation for job \(J_{j}\) is processed according to the specified sequence. In the transportation stage, a fleet of vehicles \(\mathbf{V}\) is available to transport jobs from the processing stage to the assembly stage. Each vehicle \(V_{t}\in\mathbf{V}\) departs from the processing shop and must return after completing its transportation tasks. The vehicles are subject to capacity constraints and are allowed to make multiple trips, with each trip carrying a batch of components produced in the processing stage. In the assembly stage, once all components belonging to product \(P_{z}\) are ready, all operations \(A_{z}^{a}\in\mathbf{A_{z}}\) are sequentially completed according to the assembly process.
# Effects of Various Extraction Factors on Protein Yield of Haliotis discus hannai (Abalone)
## 3 Results and Discussion  
### Protein Assay and Buffer Effects  
There are significant reasons to use buffer solution for protein extraction, including improving the stability of protein molecules, facilitating the isolation of protein molecules from non-soluble matrix components, and protecting the integrity of the target proteins [14]. In addition, several key factors need to be considered for an appropriate buffer, including solubility, chemical stability, efficiency in the chosen pH, and permeability to the extractionmatrix. Generally, PB with a pH range between 5.8 and 8.0 is used to stabilize the proteins and is usually used to solubilize the target proteins. Tris-HCl buffer has a pH range of 7.0-9.0 and can extract cytoplasmic proteins [15]. In addition, Tris-HCl buffer can preserve the physiological ionic strength. However, it is known that the pH of Tris-HCl buffers is highly dependent on the temperature and the concentration. Between PB and Tris-HCl, PB was more effective in extracting protein from abalone. Protein yield was 60.8 \(\upmu\)g/mg with PB and 28.7 \(\upmu\)g/mg with Tris-HCl (Figure 1A). Therefore, PB was selected to examine the effects of other extraction factors. Our results are in good agreement with another study that compared the effects of PB and Tris-HCl on the extraction of various stress-related biochemical and physiological proteins in _Populus deltoids_, the eastern cottonwood. In that study, PB showed higher efficiency for soluble proteins, catalase, glutathione reductase, and superoxide dismutase [14].  
Regarding sonication time, the protein yield was increased by ultrasonication for up to 60 s. However, sonication for 120 s showed an adverse effect (Figure 1B). This result indicates that the excessive application of ultrasonication could adversely affect the extraction of abalone protein, which could be due to protein denaturation and the occurrence of cavitation in the extraction solution after long-term ultrasonication [16]. Regarding protein denaturation, [PERSON] et al. [17] found that ultrasound energy can denature proteins, especially on the surface of meat. This surface denaturation can rearrange the hydrophobic and hydrophilic amino acid groups on the surface, which influences the solubility of proteins [18].
# N-Heterocyclic Carbene-Gold(I) Complexes Targeting Actin Polymerization
## 3 Results
### Docking Studies  
To first evaluate the binding poses and the calculated affinities between **AuL4** and **AuL7** to the protein actin, we performed molecular docking simulations, using as a target the three-dimensional structure of actin [22]. Affinities of the two compounds to the protein were calculated by AutoDock, according to the expression \(K_{\text{i}}\) = exp (deltaG/(R*T)). As discussed in our previous works [29; 30; 31], clusterization of the outcomes from our simulation runs, together with the visual inspection of the ligand:protein binding sites, were considered as markers of the quality of the interaction.  
**AuL4** and **AuL7** share the same actin binding site with latrunculin B (Figure 3), previously determined by X-ray crystallography [22]. Among the two compounds, **AuL4** is positioned within the nucleotide binding site of actin, forming hydrogen bonds with protein residues Asp211, Lys215, and Tyr306. Its chloride atoms are involved into halogen bonds with Glu214, while the gold atom is coordinated by Lys336. The binding site is stabilized by hydrophobic interactions with residues Met16, Met305, and Tyr306. On the other hand, **AuL7** is positioned with part of its structure exposed to the solvent. This compound forms hydrogen bonds with actin residues Asp157, Thr303, and the peptide nitrogen atom of Gly182. Arg201 form a halogen bond with a chlorine atom of the ligand, which is also stabilized by hydrophobic interactions with Met16, Met305, and Tyr306. The Au atom is coordinated to Glu214.
## 2 Theoretical Analysis of the Directivity Response
### Far Field Radiation of Plane Waves
In this case, the wavefronts of the airborne sound wave propagate in parallel to the surface, and grazing radiation is present, in which the bending waves can supply maximum power to the surrounding fluid [16]. The coincidence frequency \(f_{c}\) of rectangular plates can be described with Equation (4), which depends on the ratio of mass per unit area \(m^{\prime\prime}\) to the bending stiffness \(B\):  
\[f_{c}=\frac{c_{A}^{2}}{2\pi}\sqrt{\frac{m^{\prime\prime}}{B}}. \tag{4}\]  
In the case of \"supersonic radiation\" or \"above coincidence\" \(k_{x},k_{y}<k\), the whole surface is a radiator. The wavenumber \(k_{z}\) for the radiated sound field can thus be described by  
\[k_{z}=\sqrt{k^{2}-k_{x}^{2}-k_{y}^{2}}. \tag{5}\]  
The acoustic wavelength is smaller than the bending wavelength, and the acoustic short circuiting of the local sources on the panel does not occur. Instead, the entire panel radiates sound to the acoustic far field. Figure 4 shows that the radiation to the side dominates, whereas only a small part is radiated on-axis. Figure 4: Radiation of the mode \(1\times 1\) and mode \(1\times 7\) to visualize the frequency-dependent impact of several sound sources of a complex mode and the different radiation mechanisms for mode \(1\times 7\), depending on the acoustic wavelength \(\lambda_{A}\).
###### Abstract
Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is two times less than that of the typical SLAM algorithm (3.4%) [13]. [PERSON] et al. (2022) presented a 2D LiDAR sensor-based system for measuring retaining wall deformation. Laboratory experiments were performed using a simulated deformation model to evaluate the displacement measurement performance of the system, which had a root-mean-square error of 2.82 mm at approximately 20 m. The economic feasibility of the system was analyzed, which revealed that the system was economically feasible, with a benefit/cost ratio and breakeven point of 3.52 and 2.71 years [14]. [PERSON] et al. (2023) mentioned an experiment using LiDAR to survey for soft ground settlement measurements at a large land reclamation site, showing a very large settlement up to 10 m with 6.23% root-mean-square error in terms of vertical accuracy, predicted long-term settlement, and consolidation [15]. Thus, LiDAR is totally reliable for measuring the displacement of building structures. However, two major disadvantages of using LiDAR equipment for construction site monitoring are its high cost and the large volume of 3D data it generates. Commercial high-price LiDAR is usually used for applications requiring high accuracy, such as autonomous vehicles, surveying, mapping, aerospace, industrial automation, and environmental monitoring. Moreover, commercial LiDAR can measure large areas with high resolution and has better noise filtering capabilities. However, commercial high-price LiDAR measures more unnecessary data than region of interest because most LiDAR equipment supports 360\({}^{\circ}\) scanning. On the other hand, low-cost LiDAR are to be used to meet economic feasibility and observation targets, such as concrete surfaces and concrete wall. Practically, the degree of noise on the surface and edges will increase the error range for measured data and reduce reliability. Nevertheless, noise of the measured data can be revised by applying a line or plane fitting to the measured point cloud data, which improves noise level on the edges or planes within the point clouds. The use of low-cost LiDAR significantly enhances cost-effectiveness for projects. Generally, the cost for commercial high-price LiDAR is about USD 5000 while low-cost LiDAR is only USD 500, which basically saves up to 90%. In this study, it was presented that a real-time monitoring system for concrete structures was constructed using LiDAR equipment and a displacement extraction algorithm. The reliability of the extracted data was verified through simultaneous measurement and cross-analysis of a traditional LVDT and a LiDAR. In addition, linear fitting was applied to correct the instrumentation noise generated by non-contact sensors, thereby improving the accuracy of the extracted displacement. As such, the algorithm to which linear fitting was applied derived accurate quantitative values for the displacement of the structure through data processing analysis.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 2 Materials and Methods
### SPME Sampling of Exocarp and Mesocarp  
To investigate the volatile chemical composition of the [PERSON]'s hand fruit without modification, thin slices of exocarp (\(\sim\)2 g) and mesocarp (\(\sim\)2 g) were individually placed into a 20 mL glass vial with PTFE-coated silicone septum. The sampling was performed by SPME technique following [PERSON] et al. [20], with some modifications. An SPME device from Supelco (Bellefonte, PA, USA) with 1 cm fiber coated with 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) was used. After an initial conditioning phase of the fiber, at 270 \({}^{\circ}\)C for 20 min, it was exposed to the equilibrated sample headspace for 30 min at 40 \({}^{\circ}\)C to capture the volatile compounds. After sampling, the SPME fiber was inserted into the GC injector maintained at 250 \({}^{\circ}\)C for the thermal desorption of the adsorbed compounds.
## 2 Method and Model
### Simulation Model  
Figure 1 shows the domain settings around this girder, and this scaled girder's structural damping ratio and vertical natural frequency are 0.5% and 2.807 Hz, respectively. The total size of the computational domain is \(18B\times 28D\), where \(B\) and \(D\) are the width and height of the scaled girder model separately. The scaled model is one-twentieth the size of the original structure, which is 32 m wide and 5.2 m high. The computational domain is divided into four sections: rigid domain, dynamic-mesh domain, wake domain, and outer domain. The rigid domain has a width of \(1.12B\) and a height of \(1.25D\), and it travels in lockstep with the bridge girder. The rigid domain is filled with a structural mesh with  
fifteen boundary layers to ensure the accuracy of the simulation results near the girder. The dynamic-mesh domain is \(3B\times 10D\) in size and is filled with a dynamic mesh made up of triangular grids. Because there may be a vortex street behind the bridge girder, the grids in wake domain are densified to clearly reflect this phenomenon. The outside domain, distant from the girder, is filled with bigger structured girds in order to increase computing efficiency, as shown in Figure 2.  
The wind attack angle is considered as \(+5^{\circ}\) in this simulation. In this case, the left and lower directions of the girder are the sources of the incoming wind, while the right and higher directions represent the wind flow's outflow. The velocity inlets are defined as the left and lower boundaries, with a uniform wind speed of 9.1 m/s, a turbulent intensity of 0.02, and a turbulent viscosity ratio of 2; the pressure outlets are defined as the right and top boundaries, with a relative pressure of zero; and the no-slip wall boundary is defined as the surface of the streamlined box girder. Moreover, [PERSON] et al. (2018, 2020) [17; 27] performed a rigorous independent assessment of the grid and time step of this simulation, and the procedure of VIV numerical simulation by combining structural dynamics equation with fourth-order Runge-Kutta method was also detailed in the references, and the correctness of the numerical simulation results has been validated by comparing with wind tunnel test data.  
Figure 1: Computational domain sketch.  
Figure 2: Meshing Scheme.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 5 Results and Analysis
Increased Tier 3 resolution drives the FSO link towards pencil-beam emission, requiring more Tier 3 configurations over a given area; thus, the optimal Tier 2 resolution increases to again better utilize Tier 2 in the search. To reiterate the point from above, this analysis presents a qualitative example of the tradeoffs; however, quantitative analysis depends on the uniformity of coverage, hardware characteristics (e.g., steering velocity), and the feedback latency of the control messaging protocol between AP and UD, which may be pipelined. Accordingly, this analysis demonstrates tradeoffs in design parameters, but the optimal search time ultimately depends on the protocol characteristics. Figure 8: Optimal Tier 2 resolution (i.e., \(\alpha_{2}\)) and Tier 3 range (i.e., \(\beta_{3}\)) for systems where relative switching times are related by: \(t_{2}/t_{3}=10\) (**top**); \(t_{2}=t_{3}\) (**middle**); and \(t_{2}/t_{3}=0.1\) (**bottom**).