esa-sceva/satcom-chunk-collection huggingface dataset:
# Separation of Albumin from Bovine Serum Applying Ionic-Liquid-Based Aqueous Biphasic Systems
###### Abstract  
In this work, the extraction and separation of bovine serum albumin (BSA) from its original matrix, i.e., bovine serum, was performed using a novel ionic-liquid-based aqueous biphasic system (IL-based ABS). To this end, imidazolium-, phosphonium-, and ammonium-based ILs, combined with the anions' acetate, arginate and derived from Good Buffers, were synthesized, characterized, and applied in the development of ABS with \(\mathrm{K_{2}HPO_{4}/KH_{2}PO_{4}}\) buffer aqueous solutions at pH 7. Initial studies with commercial BSA revealed a preferential migration of the protein to the IL-rich phase, with extraction efficiencies of 100% obtained in a single-step. BSA recovery yields ranging between 64.0% and 84.9% were achieved, with the system comprising the IL tetrabutylammonium acetate leading to the maximum recovery yield. With this IL, BSA was directly extracted and separated from bovine serum using the respective ABS. Different serum dilutions were further investigated to improve the separation performance. Under the best identified conditions, BSA can be extracted from bovine serum with a recovery yield of 85.6% and a purity of 61.2%. Moreover, it is shown that the BSA secondary structure is maintained in the extraction process, i.e., after being extracted to the IL-rich phase. Overall, the new ABS herein proposed may be used as an alternative platform for the purification of BSA from serum samples and can be applied to other added-value proteins.  
a +
Footnote †: journal: selected topics  
Academic Editor: [PERSON]
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Node Subversion Threat: Impact on Multicast Groups  
Because the networks we consider are deployed in a hostile environment, the nodes may only be afforded limited physical protection. Unless they are tamper-resistant (typically not the case), we must consider the threat of an adversary capturing a node, obtaining its cryptographic material, and reprogramming it to do their bidding. Even worse, the obtained keys and other cryptographic material may be loaded onto a more capable device that will work as a _clone_ posing as the captured node in the network. This may be, e.g., a device with a stronger transceiver module that is powered externally or capable of carrying out a wormhole attack.  
Given this threat, we make a distinction between internal and external adversaries. An internal adversary is one that has gained access to the cryptographic material (keys) of at least one network node (Figure 3). An attack by an internal adversary corresponds to the scenario described above, where a node is captured and turned against the network. (It may also correspond to successful cryptanalytic efforts by an adversary to learn the keys. Assuming strong cryptographic primitives, this scenario may have a negligible probability.) On the other hand, an external adversary is one that has no access to the keys used by the network nodes. In both cases, we assume that the adversary acts as a man in the middle; they may delete or modify any message sent by node \(i\) to node \(j\) independently for each pair \((i,j)\), as well as send arbitrary messages to any node \(i\). We naturally extend the notation of internal and external adversaries to multicast groups. For a multicast group \(\mathcal{R}\), we say that an adversary is internal if they have compromised one of the receivers in \(\mathcal{R}\) (the case of a compromised sender is beyond the scope of this work) and external otherwise, even if they know the keys of other nodes outside the group.
## 3 Various System Configurations Studied
### Configuration Type II: Two Isolated DCB Sources
Furthermore, in this configuration, the number of redundant states is minimized. As a result, at lower modulation levels, the inverter may cause the overcharging of low-voltage capacitors in specific regions. The main drawback of this configuration with two iso-linked DC bus sources is the risk of overcharging the DC buses. To prevent this issue, additional components must be integrated.
## 3 Proposed Scheme: MC-TIRP
### Trust Evaluation Component
#### 3.2.2 Global Trust Evaluation  
Global trust evaluation is a method of receiving a reward from a destination node when the mission-critical data requirements are satisfied. Each mission-critical application has different requirements that must be met [14; 17; 18]. Therefore, although local trust evaluation between nodes is vital, a reward system is activated when the requirements along the entire path are satisfied. The global trust provides the average value of local trust, as shown in Equation (8). The global trust is calculated as the average of the local trust values of the nodes included in each path, and as a result, the trust value of the entire path can be obtained.  
\[GlobalTrust=\frac{1}{n}\sum_{k=1}^{n}{LocalTrust_{p}(t)}. \tag{8}\]  
In the case of end-to-end delay, global trust is compensated when the communication requirements of IoT applications are less than 100 ms [16] and the \(PTV\) (Path Trust Value) is higher than 0.65 [7] (0 \(\leq\) PTV \(\leq\) 1). We assign the minimum \(PFR\) of the nodes in the trusted route to the \(PTV\), as shown in Equation (9) [19; 20]. \(PTV\) values can verify that the measured path trust values meet the trust requirements of mission-critical data.  
\[PTV=minPFR \tag{9}\]  
In this global trust evaluation, the reward is calculated when the packet sent from the sensor node to the destination satisfies the mission-critical data requirements. This ensures the reliability of data that meets the requirements of mission-critical applications.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 2 Dataset  
### Problems with Existing Solar Filament Datasets  
Deep learning-based methods rely on dataset annotations to extract the features of filaments. Unreliable annotations can lead to errors in the detection results.  
Currently, there are two publicly available solar filament datasets. One is the dataset maintained by the Huairou Solar Observing Station (hereinafter referred to as Dataset 1) [23]. The other is the dataset published in reference [24] (hereinafter referred to as Dataset 2).  
Dataset 1 uses a threshold segmentation method for annotation. There are numerous errors in the annotations. Dataset 2 uses a manual annotation method, but a considerable number of filaments are missed. Figure 1 shows randomly selected images from the aforementioned two solar filament datasets. In Figure 1, the yellow rectangles represent incorrect annotations where non-filamentary parts are falsely annotated as filaments. The white rectangles indicate erroneous annotations where sunspots are mistakenly annotated as filaments, and the red rectangles indicate missed annotations for solar filaments.  
These incorrect annotations in the dataset can easily affect the accuracy of solar filament detection results. Therefore, we felt it necessary to build a new dataset.
## 2 Design and Fabrication of the Current Leads  
As shown in Figure 1, a pair of current leads were installed to supply electrical current to the superconducting magnet's top plate at room temperature and the magnet itself at the liquid helium temperature. For a superconducting magnet that operates at 4.2 K, a vapor-cooled current lead can be a major source of heat leakage. Thus, design optimization is essential. The basic design concept for an optimum vapor-cooled lead is to remove the heat by funneling cold helium vapor through the lead.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Conductor & NbTi & – \\ Conductor length & 6.06 & km \\ Central magnetic field & 5.01 & tesla (T) \\ Operating current & 45 & A \\ Inductance & 15 & H \\ Magnetic field energy & 15.2 & kJ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the low temperature superconducting magnet.  
Figure 2: Structure of hybrid current leads.
## 3 Methodology
### Structured Data Classification Framework  
Figure 4 illustrates the components of the structured data classification and grading framework, comprising three main modules: the hierarchical classification library building module, the keyword augmentation module, and the data classification and grading module. The hierarchical classification library building module is responsible for building industry-specific data classification and grading libraries, which are designed based on industry compliance standards. The keyword augmentation module leverages NLP technology to expand the keywords present in the industry data hierarchical classification library. Additionally, it trains the synonym discrimination model to enhance the library's capabilities. Lastly, the data classification and grading module utilizes the keywords, rules, dictionaries, and synonymous discriminative models from the hierarchical classification library. These components collectively enable the module to classify and grade structured data. The resulting output includes sensitive data types and their respective grades organized by columns.
## 2 The Proposed Approach
### Derivation of the Optimal Solution to the Considered Problem  
In the previous parts of the layout process described, the intermediate path segments along the motion between task frames were determined, and the task-based kinematic performance index was structured. Given these, the objective function of the considered problem was defined. In order to conduct the search for the optimal solution, a Genetic Algorithm is deployed.  
In order to utilize the GA, the variables of the optimization problem must be appropriately represented. Here, a chromosome consisting of six digits of real numbers is utilized. These six digits correspond to the position of {L} with respect to {B} as [_x,y,z_] coordinates and the Z-Y-Z Euler angles of {L} with respect to {B}. As such, the six digit chromosome is presented as:  
\[\underbrace{x\quad y\quad z}_{position}\underbrace{z\quad\theta_{z}\quad\theta_{ y}\quad\varphi_{z}}_{orientation}, \tag{10}\]  
The value ranges for the chromosome's genes were: \(x\), \(y\), \(z\) \(\in\) [0.1, 0.5] (in meters) and \(\varphi_{z}=\theta_{y}=0\), \(\theta_{z}\in\) [\(-\pi/2\), \(\pi/2\)].  
With respect to the GA operators utilized:  
* Reproduction was conducted in an elitist manner, ensuring the survival of the best individuals in the next generations (60% of the population).
* Crossover was conducted using a one-point crossover method. The parents pairs were arbitrarily selected and the cut point was also arbitrarily defined. Here, the population selected corresponded to the 20% of the total population.
* Mutation was conducted in a single point manner. A random digit was selected and reset to an arbitrary value. Again 20% of the population was selected to undergo mutation.  
Given the objective function defined in Equation (8), the fitness function of the GA can be defined as:  
\[\mathrm{fitness}=\frac{1}{\mathrm{R_{v}}\left({}^{\mathrm{B}}\mathrm{P_{L}}, \mathrm{\theta}_{-}\right)}, \tag{11}\]  
Additionally, it is subject to minimization. Two conditions of search process termination were included, i.e., a maximum allowed generations number and a maximum number of iterations where there was no change in the fitness function value. The first was setto 100 generations and the second to 10 generations. A population of 40 individuals was utilized in each generation.  
The outputs of the design part are the optimal location and orientation of all the task frames relative to [B]. These are the inputs to the experimental implementation (Figure 1). The outputs of the experimental implementation are the manipulator's joint velocities during task execution.
## 2 Materials and Methods
### Antibiotic film Activity  
Biofilms were formed based on a microtiter plate test described by [PERSON] et al. [35], with some modifications. The plates, prepared as described above, were incubated aerobically at 37 \({}^{\circ}\)C for 18-24 h. After inoculation, the culture was centrifuged (3000\(\times\) g, 4 \({}^{\circ}\)C, 10 min) and washed, and the pellet resuspended. The cell concentration was adjusted to 1 \(\times\) 10\({}^{8}\) cells/mL using a Neubauer counting chamber. For biofilm formation, 2 mL of the cell suspensions were transferred to 24-well flat tissue culture plates with steel coupons (1 cm \(\times\) 1 cm). To allow for biofilm formation, the culture plates were incubated aerobically for 24 h on a horizontal shaker at 120 rpm and 37 \({}^{\circ}\)C. Negative controls were performed using wells containing only SDB medium.  
In order to mimic clinical endodontic retreatment, biofilms were immersed in the following conventional irrigating solutions: in 3% NaOCI for 5 min followed by 17% EDTA for 1 min, and finally in the isolated solvents (MEK, TCE, OOil) and mixtures (MEK/TCE or MEK/OOil) for 5 min each. Negative controls were performed using, sequentially, 3% NaOCI for 5 min, 17% EDTA for 1 min, and phosphate-buffered saline for 5 min. We also used a control culture of _C. albicans_ SC5314 without any treatment. After this procedure, the biofilm cells were scraped, and their cultivability was assessed by colony-forming unit (CFU) calculation. In brief, 1 mL of fresh saline solution was added to each well plate, and the biofilms were scraped from the steel coupons. The biofilm cells suspensions were then serially diluted in saline solution and plated onto SDA plates. After 24 h of aerobic incubation at 37 \({}^{\circ}\)C, the cultured cell count was performed. Values of cultivable sessile cells were expressed as Log CFU per area (Log CFU/cm\({}^{2}\)).  
The biofilm cultures, with and without treatment with the conventional irrigating sequence of NaOCI and EDTA, and the solvent mixtures were also observed by scanning electron microscopy (SEM) in an EDAX Nova nanoSEM 200 equipment. In brief, samples were carefully washed three times with distilled water, dehydrated by immersion in solutions of increasing ethanol concentrations of 70, 95, and 100% (\(v/v\)) for 10, 10, and 20 min, respectively, and placed in a sealed desiccator. Afterward, samples were mounted in aluminum pin stubs with electrically conductive carbon adhesive tape (PELCO Tabs(tm)). Samples were sputter-coated with gold and observed on a Phenom Charge Reduction Sample Holder (CRH) at 5 Kv and a spot size of 3.3, with a desktop SEM coupled with energy-dispersive X-ray spectroscopy (EDX) analysis (Phenom ProX with EDX detector, Phenom-World BV, Eindhoven Netherlands). All procedures are standardized in our laboratory.
## 4 Simulations Results and Discussion with Performance Evaluation
### Simulation Results  
In this section, a comparative analysis of proposed protocols and existing protocols are discussed. Several active nodes per layer, EC, delay, RPER and PRR are considered verify the performance of the proposed protocols. The detail of each performance parameter is presented as follows:
# eHealth Integrity Model Based on Permissioned Blockchain +
## 2 Blockchain Based eHealth Integrity Model
### Adding and Removing Records  
The algorithm for adding record \(r\) or entry \(e\) to blockchain \(B\) is straightforward:  
1. obtain a covert ID and public blockchain encryption key \(Pk_{ID}\) for a user's public ID (from central eHealth service);
2. obtain last transaction number \(j\) of a user with the covert ID (from central eHealth service);
3. prepare transaction \(t\) Type \(1|2|3\), Subtype \(a\), with hash \(h\) of record \(r\) or entry \(e\), transaction number \(j=j+1\), and current time;
4. encrypt \(t\) using \(Pk_{ID}\);  
Figure 1: Integration of the Blockchain-based eHealth Integrity Model (BEIM) into a service-oriented architecture.  
send \(t\) to \(B\); and
6. verify if \(t\) is embedded in \(B\); if not, repeat from Step 2.  
When it is necessary to remove a record, it is required to add a Subtype \(b\) transaction to a blockchain using the algorithm for removing a record. The algorithm is initiated by a user with a role that has removal access right. The input for the algorithm is a user's public ID, record \(r_{j}\) (or entry \(e_{j}\)) that is intended for removal, and removal order \(RO_{j}\). The algorithm is as follows (compare with Figure 2):  
1. calculate hash \(h_{j}\) of record \(j\);
2. obtain a covert ID for a user's public ID and user's blockchain encryption key pair \((Pk_{ID},Dk_{ID})\);
3. select all transactions \(T\) with the covert ID from blockchain \(B\);
4. decrypt \(T\) using \(Dk_{ID}\);
5. verify if a transaction from \(T\) contains \(h_{j}\). If not, return \(false\);
6. find in \(T\) last transaction number \(j\) of a user with the covert ID;
7. prepare transaction \(t\), Type \(1|2|3\), Subtype \(b\) with hash \(h\) of record \(r\) or entry \(e\), transaction number \(j=j+1\), and current time;
8. encrypt \(t\) using user's public blockchain encryption key \(Pk_{ID}\);
9. send \(t\) and \(RO_{j}\) to \(B\) (blockchain nodes only accept \(t\) when \(RO_{j}\) matches \(h_{j}\)); and
10. verify if \(t\) is embedded in \(B\); if not, repeat from Step 3.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 3 Results
### Survey C  
Another 2D survey was conducted on an 80 cm by 80 cm area on the Type II wall specimen, as shown in Figure 1b. Again, the measurement step was 5 mm in both directions. Figure 8 shows the reconstructed image at a depth of 2.3 cm in the HH polarization channel. The vertical post, horizontal girt, and the inclined brace are well imaged. Different from the post and girt, the inclined brace shows variable phase in a short section--i.e., bright at portions A and C while dark in the middle portion B. After the measurement, we removed the covering plywood board and a photo of the wooden structure under it is shown in Figure 9. We found that the inclined brace had been completely crushed. There are two opening cracks at parts A and C and a downward deflection of about 2 cm at part B. Therefore, the variable phase of the brace in Figure 8 is the electromagnetic response of the severe deformation of the brace after the crushing. The distance from the surface of the deformed wooden brace to the radar sensor varies along it. As a result, the time delay--i.e., phase of the reflected signal varies. However, the cracks on the top surface of portions A and C, as well as the crack on the bottom surface of portion B of the brace are hardly identifiable in the reconstructed images.  
Figure 10 shows the Pauli-RGB image generated from the full polarimetric dataset after SAR processing in each polarization channel. Due to the fact that, in contrast to the conventional 2D SAR case, in the subsurface SAR case we are dealing with 3D imaging, we cannot simply apply a conventional interpretation of the Pauli color coding. Nevertheless, in the composite RGB image we can easily note the differences in scattering behavior between the damaged and undamaged parts. The intact post and girt show no obvious variations in their color representation. On the contrary, a significant variation can be observed for the diagonal wooden brace. The parts A and C of the brace show green colors, while the colors change into blue for the crushed part B in the middle. Also, the contributions of the reflections from the corrugated steel sheet on the bottom side are easy to identify in the color image.  
Figure 8: Reconstructed SAR image of the 2D survey on the Type II wall specimen in Figure 1b at a depth of 2.3 cm from the wall surface in HH polarization channel.  
Figure 9: Photo of the deformed brace under the decorating wood board of specimen I.
# Effect of the Planetesimal Belt on the Dynamics of the Restricted Problem of 2 + 2 Bodies
## 2 Formulation of the Model  
The restricted 2 + 2 body problem consists of two primaries, \(M_{1}\) and \(M_{2}\), with unit-less masses \(1-\mu\) and \(\mu\), respectively. They are assumed to move on circular Keplerian orbits around their common center of mass under their mutual gravitational force. Two infinitesimal bodies, \(P_{1}\) and \(P_{2}\), of dimensionless masses \(\mu_{1}\) and \(\mu_{2}\), respectively, move in the gravitational field while mutually attracting each other without perturbing the primaries. The perturbed mean motion \(n\) can be considered as in [15; 16; 17; 18],  
\[n^{2}=1+\frac{2M_{b}r_{c}}{(r_{c}^{2}+T^{2})^{\frac{3}{2}}},\]  
where \(M_{b}\) is the total mass of the planetesimal belt, and \(r_{c}\) is the dimensionless reference radius of the planetesimal belt. The gravitational potential of the planetesimal-belt-like system is expressed as in [19; 20],  
\[\varphi_{b}(R,z)=\frac{M_{b}}{(R^{2}+[a+\sqrt{z^{2}+b^{2}}]^{2})^{\frac{1}{2}}},\]  
where \(R\) is the minor body's radial distance, \(a\) is the flatness parameter, \(b\) is the core parameter, and \(z\) is the coordinate of the planetesimal belt in a direction of \(Z\)-axis. The potential reduces to the system of a point mass with the condition \(a=b=0\). Restricting the condition to the \(XY\) plane (i.e., \(Z=0\)) and defining \(T=a+b\), we have the unit less potential written as,  
\[\varphi_{b}(R,0)=\frac{M_{b}}{(R^{2}+T^{2})^{\frac{1}{2}}}.\]  
According to the formulation of the restricted 2 + 2 body problem given by [5; 21; 22], R2 + 2 BP is characterized by three parameters, \(\mu\), \(\mu_{1}\) and \(\mu_{2}\), which are the mass parameters of \(M_{2}\), \(P_{1}\), and \(P_{2}\), respectively. We consider a perturbed R2+2 BP in the rotating coordinatesystem. Let \((\mu,0,0)\), \((\mu-1,0,0)\), \((x_{1},y_{1},z_{1})\), and \((x_{2},y_{2},z_{2})\) be the coordinates of \(M_{1}\), \(M_{2}\), \(P_{1}\), and \(P_{2}\), respectively, in a rotating frame, as shown in Figure 1.  
Using dimensionless variables and considering the effect of the planetesimal belt, the restricted \(2+2\) problem is described by differential equations [5]:  
\[\ddot{x_{i}}-2n\dot{y_{i}} =\frac{1}{\mu_{i}}\frac{\partial U}{\partial x_{i}}, \tag{1}\] \[\ddot{y_{i}}+2n\dot{x_{i}} =\frac{1}{\mu_{i}}\frac{\partial U}{\partial y_{i}},\] \[\dot{z_{i}} =\frac{1}{\mu_{i}}\frac{\partial U}{\partial z_{i}},\]  
\[U=\sum_{i=1}^{2}\mu_{i}\left[\frac{1}{2}n^{2}\left(x_{i}^{2}+y_{i}^{2}\right) +\frac{1-\mu}{r_{1i}}+\frac{\mu}{r_{2i}}+\frac{1}{2}\frac{\mu_{3-i}}{r}+\frac {M_{b}}{\left(R_{i}^{2}+T^{2}\right)^{\frac{1}{2}}}\right]. \tag{2}\]  
where  
\[n^{2}=1+\frac{2M_{b}r_{c}}{\left(r_{c}^{2}+T^{2}\right)^{\frac{1}{2}}},\quad \mu=\frac{M_{2}}{M_{1}+M_{2}},\quad\mu_{i}=\frac{m_{i}}{M_{1}+M_{2}}\]  
\[r_{c}^{2}=1-\mu+\mu^{2},\quad R_{i}^{2}=x_{i}^{2}+y_{i}^{2},\]  
\[r_{1i}^{2} =(x_{i}-\mu)^{2}+y_{i}^{2}+z_{i}^{2},\] \[r_{2i}^{2} =(x_{i}-\mu+1)^{2}+y_{i}^{2}+z_{i}^{2},\] \[r^{2} =(x_{1}-x_{2})^{2}+(y_{1}-y_{2})^{2}+(z_{1}-z_{2})^{2}.\]