esa-sceva/satcom-chunk-collection huggingface dataset:
# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 2 Related Work
has been evaluated in a real WMN environment and exhibits the most common design features with our dynamic approach (e.g., SDN-based centralized control over real WMN). Nevertheless, the main difference between our approach with [23] is decision-making regarding the dynamic conditions of WMN. In particular, our work aligns the functionality of NDN with the decisions of a dynamic WMN routing protocol, offering a rapid response of the NDN network to frequent topology changes (e.g., due to link failures). Most of the aforementioned works are not validated in real-world test beds. Moreover, there is no prior work that addresses the challenging communication issues of deploying NDN in real-world mesh networks, using the SDN approach, as illustrated in Table 1.  
As our proactive strategy, there are also other works that utilize clustering methods for NDN routing. In [24], authors propose an NDN routing protocol for wireless networks using clustering to reduce the number of nodes considered for route discovery. Moreover, in [25], a routing protocol is presented based on clustering to reduce routing overhead among the network nodes. In [26], the authors studied the integration of NDN-based IoT with Edge computing, introducing an algorithm that employs clustering to improve NDN routing. In contrast with our cluster-based approach, the above works are not evaluated over real network environments, and their routing decisions do not take into account WMN network characteristics (e.g., the quality of the wireless links). In work, [6] authors use clustering for NDN service placement and investigate ICN advances by conducting experiments over a smart-city WMN test-bed [4]. The decision-making for the service placement takes into account compute resources (i.e., CPU) and bandwidth among the network nodes. In contrast to this work, we investigate the best NDN path selection considering RSSI and delay metrics among the network nodes, as we target efficient NDN communication over WMN in terms of delay and reliability. From the literature review, we observe that most cluster-based routing works (as the above) mainly focus on excluding monitoring data or characteristics of the network (e.g., considered network nodes) that affect the routing decision, aiming to reduce the data communication latency or costs. Compared to these works, we approach cluster-based routing differently, using a multi-cluster approach as an evaluation quality measure to select the best NDN path (i.e., the clustering results are taken into account for the path selection). The employment of a clustering-based approach, which intuitively requires stationary data structures, corresponds with the results of an extensive experimental analysis over a real wireless network with static nodes (described in Section 4).
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 2 Related Work  
### Technical Background  
Because the PRNU associated with a sensor is unique, using it as an important reference basis for SCI has inherent advantages. Specifically, a real image can be represented as [7]:  
\[I=I_{0}+KI_{0}+\theta, \tag{1}\]  
where \(I_{0}\) represents an ideal image without any noise; \(I\) represents the output real image; \(K\) represents the factor of SPN on \(I_{0}\) with a multiplicative rule; and \(\theta\) represents random noise. Here, the spectral of \(K\) is very similar to that of WGN.  
The extraction of the reference PRNU can be divided into two stages, filtering and combination [6]. In the filtering stage, the main aim is to denoise \(m\) images taken by one camera to obtain \(m\) noise residuals \(W_{i}\)[6].  
\[W_{i}=I_{i}-F(I_{i}), \tag{2}\]  
where \(F\) is the denoising filter, and \(W_{i}\) denotes the noise residual of \(I_{i}\), \(i=1,2,\cdots,m\). It is crucial to obtain the noise residual \(W_{i}\) which contains PRNU component in the filtering stage.  
In the combination stage, the \(m\) noise residues associated with one camera need to be combined to obtain a reliable reference PRNU noise \(R\), mainly by the mean and maximum likelihood estimation (MLE) methods. The mean method [6] is shown as follows,  
\[R=\sum_{i=1}^{m}W_{i}\bigg{/}m, \tag{3}\]  
The MLE method [7] extends Equation (3), weighting the noise residues with the original image as follows,  
\[R=\sum_{i=1}^{m}W_{i}I_{i}\bigg{/}\sum_{i=1}^{m}I_{i}^{2}. \tag{4}\]
# A Vibrational Energy Harvesting Sensor Based on Linear and Rotational Electromechanical Effects
## 2 Device Description
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline
**Known** & & \\ \hline Magnet masses, \(m_{\text{zz}}\), \(n=1,2\) & \(1.52\), \(0.453\) & g \\ Linear spring constants, \(k_{\text{zz}}\), \(n=1,2\) & \(649\), \(160\) & kg s\({}^{-2}\) \\ Planar spring radius, \(l\) & \(10\) & mm \\ Magnetic flux density peak amplitude, \(B\) & \(1\) & T \\ Damping, \(c_{\text{zz}}\), \(n=1,2\) & \(5\times 10^{-2},5\times 10^{-2}\) & kg s\({}^{-1}\) \\ Number of coil turns, \(N\) & \(650\) & Unitless \\ Coil area, \(A\) & \(25\pi\) & mm\({}^{2}\) \\ Coil resistance, \(R_{c}\) & \(60\) & \(\Omega\) \\ Coil inductance, \(L_{c}\) & \(1.3\) & H \\ Load resistance, \(R_{l}\) & \(50\) & \(\Omega\) \\ \hline
**Guesses** & & \\ \hline Magnet radii, \(\rho\) & \(1\) & mm \\ Magnet height, \(h\) & \(5\) & mm \\ Magnet volume, \(V_{n}\) & \(\pi\rho^{2}h=15.7\) & mm\({}^{3}\) \\ Magnetizations, \(M_{\text{zz}}\), \(M_{\text{my}}\), \(M_{\text{zz}}\), \(n=1,2\) & \(5\times 10^{4}\), \(10^{4}\), \(10^{4}\) & A m\({}^{-1}\) \\ Spring radii, \(l_{11}\), \(l_{12}\), \(l_{21}\), \(l_{22}\), \(2,8,3,7\) & mm \\ Damping, \(c_{1}\), \(c_{2}\) & \(0.05\), \(0.05\) & N s m\({}^{-1}\) \\ \hline
**Particle Swarm Optimized Fit Values** & & \\ \hline Linear spring constants, \(k_{11}\), \(k_{12}\) & \(559\), \(461\) & kg s\({}^{-2}\) \\ Linear spring constants, \(k_{21}\), \(k_{22}\) & \(130\), \(161\) & kg s\({}^{-2}\) \\ Moments of inertia, \(J_{1}\), \(J_{2}\) & \(2.2\times 10^{-2}\), \(4.8\times 10^{-2}\) & kg m\({}^{-2}\) \\ \hline
**Unconstrained Derivative-Free Optimized Static Values** & & \\ \hline \(x_{1}(0),x_{2}(0)\) & \(0.05\), \(-24.14\) & μm \\ \(\theta_{1}(0),\theta_{2}(0)\) & \(0.23\), \(0.08\) & degrees \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physicalparameters used in the simulation which approximately match those of the experiment. Figure 2: Lumped parameter one-dimensional model with ODE parameters of the device shown in Figure 1. The top magnet is represented by the mass \(m_{1}\). The combination bottom magnet and coil is represented by mass \(m_{2}\).
# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
## 3 Results  
Experimental data were obtained from three subjects, denoted as signers, which recorded the 24 static letters of the ASL alphabet using the MPS. For each letter, about 400 samples were recorded by each signer, keeping the hand at approximately the same orientation within the operational volume of the MPS. The orientation of the hand is defined assuming that an interlocutor is viewing from the \(xy\) plane of the MPS.  
Additional samples were recorded by rotating and moving the hand in different orientations while performing each letter. These additional samples were used to characterize the performance of Model C to show the feasibility of recognizing gestures even from different viewpoints and orientations. In Model C, the letters that differ only by the rotation of the hand have been aggregated into the same class. This is because it is not possible to discriminate them using a reference coordinate system that is aligned with the hand. Specifically, the letters that are aggregated in the same class are the couples D-P and H-U.  
The results obtained by Model A, Model B, and Model C are shown by the confusion matrices in Figures 12-14, respectively. Furthermore, a summary of the classification accuracy achieved by the three considered models on data acquired by the signers individually  
Figure 11: Block diagram of the proposed method.  
and on the global MPS dataset is provided by Table 5. The global dataset is built by combining data of all three signers.  
Figure 12: Confusion matrix showing the classification performance of the proposed method using Model A, where only fingertip positions are used as inputs to the SVM classifier model.  
Figure 13: Confusion matrix showing the classification performance of the proposed method using Model B, where 21 hand landmarks, reconstructed by the kinematic model in Section 2.2, are used as inputs to the SVM classifier model.  
For benchmark purposes, an additional experiment is conducted by performing both training and testing of the SVM on the data from the MPS exclusively. Thus, the data from the image dataset processed with the MediaPipe library are not used. Specifically, training is performed on MPS data from Signer 1 and Signer 2, whereas testing is performed on MPS data from Signer 3. The goal of this additional experiment is to study the best possible performance of the proposed method, since in this case, we operate on data acquired by the same system. Such additional benchmark experiment resulted in an accuracy of 95.66% for Model A, 97.71% for Model B, and 98.17% for Model C.
## 3 Results and Discussion
### Economical Assessment  
A preliminary cost assessment was performed to compare the economic feasibility of the three setups shown in this work. The compared conditions were the ones applied to real wastewater. The quality criterion required for comparing the processes was 80% removal of the model compound. On intensive processes, the most important parameter in terms of costs is operational expense (OPEX); on the contrary, for extensive processes, the most relevant cost is the capital (CAPEX). As both technologies are intensive processes, only the OPEX was considered.  
The volume of treated water was 350 m\({}^{3}\) year\({}^{-1}\) based on [PERSON] et al. [53], who set it as a reference in a mid-scale textile industry environment. For photo-Fenton, the cost for electricity was 0.1 \(\upt\) kWh\({}^{-1}\); it was 0.325 \(\upt\) L\({}^{-1}\) for the H\({}_{2}\)O\({}_{2}\) solution 35%, 0.71 \(\upt\) L\({}^{-1}\) for Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\bullet\)H\({}_{2}\)O and 3.5 \(\upt\) L\({}^{-1}\) for EDDS (35% w/v) [54]. The GAC cost is estimated to be between 2100 and 2500 \(\upt\) ton\({}^{-1}\) for fresh material and 1200 and 1600 \(\upt\) ton\({}^{-1}\) for the regenerated material.  
Table 3 shows the calculated price of each process per m\({}^{3}\). The most expensive was the photo-Fenton as a stand-alone technology. While the costs for the reagents were similar to those of the combination with GAC, the energy consumption was 6 times higher as a longer irradiation time was needed (33 s). The GAC filtration stand-alone was the cheapest, making the NRS Carbon GA 0.5-2.5 the most economically convenient. The GAC cost for the combination was slightly higher for the NRS Carbon GA 0.5-2.5 than the CarboTech  
Figure 8: Bed volume achieved after reaching different target compound removal.  
DGF 8x30 GL due to the quicker exhaustion of the first one. Facing the preliminary assessment, GAC columns seem the most suitable process with respect to the elimination of indigo carmine; however, a deeper study of other costs aside from operational ones (such as the disposal of the exhausted-not-regenerated GAC), environmental costs and risks should be carried out. The specific CECs will define the process capability for their removal (i.e., diclofenac was proven to be easily removed by photo-Fenton but not easily adsorbed in GAC), and so a quality criterion for limits of discharge could determine the final choice.
## 7 Applications in SDN-based VANETs
### Emergency Vehicle Signal Pre-emption  
This application is utilized deliberately to turn the signal light of the particular RSUs to a green light and to turn the other traffic signs to red. This component is used to diminish the waiting time of emergency vehicles at the red light sign.
# Analysis of Potent Odour-Active Volatile Thioids in Foods and Beverages with a Focus on Wine
## 3 Thiol Isolation--Extraction and Derivatisation
### Selective Extraction with Metal Ions  
Thiol-specific extraction methods are based on the strong affinity between thiols and metal ions, such as mercury (Hg\({}^{+}\)) and silver (Ag\({}^{+}\)). In fact, the word mercaptan, which is the historical synonym for thiol, arose from the Latin term _cercurium captan_, which means mercury-seizing [20]. Organomercurial compounds have long been applied for thiol-specific extraction (Table 1, Entries 1-4) [21; 22] by reversibly binding with thiols and forming stable Hg-thiol complexes (mercaptides). The thiol moiety can then be replaced from Hg-thiol complexes by an excess of other thiol (e.g., glutathione [21], cysteine [22], or d,l-dithiothreitol [23]) during an elution step, which releases the thiols of interest. Specific organomercurial compounds that have been used in this manner include 4-hydroxymercuribenzoate (usually referred to as \(p\)-hydroxymercuribenzoate and abbreviated \(p\)-HMB) [21; 22; 24], phenylmercuric chloride [25], and 4-aminophenylmercuric acetate [23]. These agents efficiently and selectively bind thiols; for instance, the reaction between \(p\)-HMB, the most popular organomercurial reagent, and thiols in wine requires less than 90 s, and, more importantly, \(p\)-HMB does not react with thioesters, sulfides, or disulfides [21]. Pioneered for wine with the identification of varietal aroma compounds that are associated with box tree odour, the early developed \(p\)-HMB extraction method (Table 1, Entry 1) requires pH adjustment on a large volume of sample (1000 mL), followed by LLE with organic solvent prior to thiol extraction with aqueous \(p\)-HMB solution [21]. While using this extraction method, 4-MSP was identified for the first time in the plant kingdom (a Sauvignon wine). For quantitation purposes that require cleaner thiol extracts, a strong anion exchange column was introduced as a clean-up step after the extraction of thiols from the organic phase with \(p\)-HMB solution, prior to eluting with cysteine solution to release the thiols (Table 1, Entry 2) [22]. This approach has been modified and used for thiol extractions in beer [12] and cheese [26]. One limitation of applying this extraction method is that it requires pH adjustment at several points (in the raw sample, during extraction, and in pooled extracts [22]), and the high pH conditions could induce thiol oxidation or unwanted changes in the sample matrix, such as the formation of quinones that can react with thiols. Therefore, the \(p\)-HMB extraction method has been modified to avoid tedious pH adjustment of wine by using a Tris buffer solution (pH \(>\) 7) [24].
## 2 Numerical Methods
### Eulerian Approach Based on Continuum Properties
#### 2.1.1 Reynolds Averaged Navier-Stokes Solver
The computational domain is divided into two parts: one is the rotating part for the flow driven by the propeller, and the other is the flow field elsewhere around the submarine body. The entire grid distribution with the zoomed grids around the propellers is presented in Figure 3. The value of \(y^{+}\) is set to 1 to accurately simulate the blockage effect and wake flow caused by the wall boundary layer on the submarine body and cavitation tunnel as well as the tip vortex flow of the propeller. The numbers of grids in the rotating part for the propeller and the non-rotating part for Suboff body are 5.8 and 4.6M, respectively. The conservation equations of mass and momentum for unsteady incompressible RANS can be written as  
\[\
abla\left(\overrightarrow{\rho u}\right)=0. \tag{2}\]  
\[\frac{\partial}{\partial t}\left(\overrightarrow{u}\right)+\
abla\left( \overrightarrow{u}\otimes\overrightarrow{u}\right)=-\frac{\
abla p}{\rho_{0} }+\frac{1}{\rho_{0}}\
abla.\overline{\tau}+\rho\overrightarrow{g} \tag{3}\]  
Here, \(\rho\), \(\overrightarrow{u}\) and \(\overrightarrow{g}\) are liquid density, pressure, velocity vector and gravitational acceleration, respectively. \(\otimes\) denotes the tensor product and \(\overline{\tau}\) means the viscous stress tensor. The commercial code STAR CCM+ is used to numerically solve Equations (2) and (3) [12; 14]. The numerical discretization is based on a finite volume-based solver. The second-order accuracy schemes are used for time and spatial derivatives. The SIMPLE method for coupling the pressure and velocity is used to satisfy the mass conservation. Reynolds stress model (RSM) is employed as the turbulent model because it accurately simulates the boundary layer and vortex structure [12]. The Schnerr-Sauer cavitation model [28] is used to consider the mass transfer between liquid and vapor. The volume of fluid (VOF) method [29] is applied to identify the cavitating flow region. The number concentration density of nuclei is considered using the Rayleigh-Plesset equation [30]. However, the detailed distribution of nuclei according to various nuclei size cannot be accounted for by the Eulerian model.
# New Insights into the Neuromyogenic Spectrum of a Gain of Function Mutation in SPTLC1
## Materials and Methods
### Proteomic Profiling of immortalized Lymphoblastoid Cells
#### 2.2.2 Proteomic Analysis  
Samples were analyzed using an UltiMate 3000 RSLC nano UHPLC connected to a QExactive HF mass spectrometer with the total amount of peptide administered always being 1 \(\upmu\)g. The samples were first separated on a 75 m \(\times\) 50 cm, 100, C18 main column with a flow rate of 250 nL/min and a linear gradient consisting of solution A (99.9% water, 0.1% formic acid) and solution B (84% acetonitrile, 15.9% water, 0.1% formic acid) with a pure gradient length of 120 min (3-45% solution B). The gradient was composed as follows: 3% B for 20 min, 3-35% for 120 min, followed by three wash steps, each lasting 3 min and ranging from 95% buffer B to 3% buffer A. The instrument was left to equilibrate for 20 min after the final washing procedure. MS data were acquired utilizing an in-house generated spectrum library in a data independent acquisition (DIA) mode. Each sample was combined with an adequate amount of iRT standard before examination (Biognosys, Schlieren, Switzerland). Full MS scans were obtained at a resolution of 60,000 (Orbitrap) from 300-1100 \(m/z\), with the polysiloxane ion at 445.12002 \(m/z\) serving as the lock mass. Maximum injection time was set to 20 milliseconds and the automatic gain control (AGC) was set to 3E6. Following the full MS scans, 23 DIA windows were acquired at a resolution of 30,000 (Orbitrap) with an AGC of 3E6 and an nCE of 27. Each DIA window covered a range of 28 \(m/z\) with 1 \(m/z\) overlap, starting at 400 \(m/z\). For proteomic analysis, samples from two healthy individuals and the sample from the patient were processed. After the preparation of immortalized lymphoblastoid (see Section 2.1), samples were propagated (cell culture) to 2 \(\times\) 3 samples for controls and 1 \(\times\) 3 samples for the patient. In total, we processed 9 samples independently in our proteomics sample preparation and analysis pipeline.
## 3 Results  
### The SMAP FT Global Data Domain  
The SMAP R16 global grid FT products encompass \(\sim\)51.4% (\(\sim\)73 million km\({}^{2}\)) of the global land area (Figure 2). The SMAP FT global grid products cover the same area as the heritage FT-ESDR [31], encompassing all land areas where seasonal frozen temperatures are estimated to be a significantconstraint on annual ecosystem productivity. The map in Figure 2 shows areas where the NPR and FT-SCV algorithms are applied. The NPR domain (in green) encompasses areas where seasonal frozen and thawed conditions are persistent enough to generate stable NPR reference thresholds and where the FT reference difference is large enough for suitable algorithm performance (NPR > 0.1). The FT-SCV domain encompasses more sou The overall accuracy from the 36 km and 9 km FT data records is presented in Table 1. These results summarize the SMAP FT validation assessments against WMO weather station observations for both northern (\(\geq\)45\({}^{\circ}\)N) and global domains over the two-year record. Overall, the PM overpass FT classification results from the 9 km and 36 km products exceed the targeted 80% accuracy threshold for the SMAP baseline mission requirement [8]. The AM FT apparent accuracy is lower than the PM accuracy for both the 36 km and 9 km products, but generally above the 70% targeted accuracy threshold of the minimum mission. The lower FT classification agreement over the northern (\(\geq\)45\({}^{\circ}\)N) domain may be attributed to a larger number of daily FT variations over the annual cycle and to wetter snow conditions during seasonal transitions [7; 51], where the L-band snow volume emission becomes significant in the presence of even small amounts of snow liquid water [52]. However, the distribution of other physical landscape factors can degrade the Tb signal-to-noise and influence the associated FT accuracy pattern [51; 53].  
Figure 3: Seasonal pattern of SMAP daily global mean FT classification accuracy (%) in relation to in-situ SAT measurement based FT estimates from global weather stations for AM (top) and PM (bottom) overpass results; blue and red lines denote the respective 36 km (L3_FT_P) and 9 km (L3_FT_P_E) grid results.  
Figure 2: The SMAP global FT algorithm domain. The respective portions of the global FT classification domain using normalized polarization ratio (NPR) and single Tbv channel (FT-SCV) algorithms are denoted in green and orange. Areas outside the SMAP global FT domain are shown in white and grey.  
Figure 4: The spatial distribution of SMAP global FT classification agreement (%) from L3_FT_P[E] AM and PM overpass results in relation to the SAT-based daily FT observations from global weather stations for 2016.