esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To improve the low-frequency isolation performance of optical platforms, an electromagnetic active-negative-stiffness generator (EANSG) was proposed, using nano-resolution laser interferometry sensors to monitor the micro-vibration of an optical platform, and precision electromagnetic actuators integrated with a relative displacement feedback strategy to counteract the positive stiffness of pneumatic springs within a micro-vibration stroke, thereby producing high-static-low-dynamic stiffness characteristics. The effectiveness of the method was verified by both theoretical and experimental analyses. The experimental results show that the vertical natural frequency of the optical platform was reduced from 2.00 to 1.37 Hz, the root mean square of displacement was reduced from 1.28 to 0.69 \(\upmu\)m, and the root mean square of velocity was reduced from 14.60 to 9.33 \(\upmu\)m/s, proving that the proposed method can effectively enhance the low frequency isolation performance of optical platforms.  
active negative stiffness; optical platform; laser interferometry sensor; electromagnetic actuator +
Footnote †: journal: Applied Sciences
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 2 Related Work
The paper indicates that nodes move randomly, but does not specify the exact mobility model employed, including whether the movement pattern of attacker nodes differs from that of regular nodes. The study focuses on key performance metrics such as the packet delivery ratio, throughput, and end-to-end delay to assess the severity of the attacks. The research methodology involves conducting multiple simulation runs under controlled conditions to analyze the behavior of black hole attacks. While the paper provides valuable insights into the impact of these attacks on network performance, it has certain limitations. The study's primary focus on black hole attacks restricts its scope. Additionally, the absence of detailed information about the mobility model and the lack of statistical analysis to support the findings weaken the overall strength of the conclusions. Despite these limitations, the paper offers a foundation for understanding the effects of black hole attacks on MANETs. The use of simulation and clear performance metrics strengthens the research. However, future studies could benefit from a broader exploration of different attack types, a diverse range of mobility models, including specific characterizations for attacker movement, and robust statistical analysis to enhance the overall contribution to the field. This study introduces a novel approach to evaluating the impact of various attacks on MANETs. Unlike previous research, we focus on a scenario where normal nodes are stationary while attackers exhibit mobility. By employing both random and uniform distribution topologies, we provide a comprehensive analysis of network resilience under different spatial arrangements. This research investigates the effects of four distinct attack types: dropping, delay, sinkhole, and a combined black hole attack (incorporating dropping and sinkhole). Through this comparative analysis, we aim to identify vulnerabilities in an effort to enhance the robustness of MANETs against malicious threats.
## 4 Platform Design and Development
### Interface Design and Application  
#### 4.4.1 Interface Design  
Based on the workflow of architects and GIS users, this study provides an interface that is intuitive, operational, and convenient. As this platform has a unique UI design, it meets the needs of professionals to improve the communication between the platform and users. This platform is called IM Fusion (information modeling fusion), which is an interactive platform used to build a virtual world and amplify the VR display based on real world data. As a city has many different elements, integrating historical building models with geographic information requires that the real world is considered, thus, digital city models are complex systems. Therefore, this study took architects and GIS users as the user objects, and only selected the model information required by these two different fields for application in cultural heritage conservation as the display objects of IM Fusion.  
There are two kinds of applications on this platform (Figure 13). Architectural professionals engaged in cultural heritage conservation have focused on historical buildings and surrounding buildings; however, such structural information is not easily obtained by architects. On the contrary, for professionals in the field of geographic information, as building models lack details, not all buildings can be accurately and individually edited. Therefore, through IM Fusion, this study connects the workflow of architects and GIS users to enhance data sharing and collaboration in different fields.  
The user interface of the platform has setting options for architects and GIS users, respectively (Figure 14), and scenes can be selected according to the users' professional field or the content they seek.  
Therefore, the historical building model will appear after the user selects the architect option in the menu screen, which is designed according to the habitual workflow of architects, while Taiwan's terrain will appear when the GIS User option is selected. The field of GIS was started from the analysis of large scale data (Figure 15).  
In order to improve the communication between architects and GIS users, this study designed different options for different users in IM fusion (Figure 16); the three on the left are the common instructions used by architects, which are model scene, single building information scene, and old city block scene in order. The three geographic information scenarios in the middle are the common instructions used by GIS users, which are global terrain, region or country, and administrative region. The three on the right are the future development functions for the platform. The integrated VR interactive interface can be used for virtual presentation, immersive devices, or multiplayers. In the overall process of scene transformation, users can enter the platform with any identity and select each scene.  
Figure 11: Unity reading database and the volume model.  
Figure 12: Flow chart of integrating volume building and attribute data by C# interactive program script.  
Figure 13: Serial flow chart of architects and GIS users.
## 3 Pre-Deformed Projection Content with Simulation-Based Fitting Method
### Coordinate and Shape Detection for the CCD Image  
To obtain the coordinate and shape of real objects with the CCD, the first task is to determine \(\mathbf{H}^{C\to S}\) in Equation (2). According to the 8-point algorithm [19], the corners of four letters, such as CQNU printed on A4 paper in Figure 2a, are defined as the four feature points, and have homogeneous coordinates in \(\boldsymbol{O}^{S}\) (unit: cm) of  
\[\boldsymbol{p}_{\boldsymbol{O}^{S}}^{\text{4L}}=\Big{\{}[0,0,1]^{\prime}_{ \boldsymbol{O}^{S}},[26.6,-0.4,1]^{\prime}_{\boldsymbol{O}^{S}},[0.2,16.7,1]^{ \prime}_{\boldsymbol{O}^{S}},[26.9,16.4,1]^{\prime}_{\boldsymbol{O}^{S}}\Big{\}} \tag{5}\]  
Figure 2: Illustrations of CCD-based detection of coordinates and shapes of real objects on projection plane. (**a**) The captured CCD image before calibration, where four points are given for estimating the homography matrix; (**b**) the image after calibration; (**c**) the DLP display of the coordinate of a cover; and (**d**) identifying two magnets.  
After getting their corresponding coordinates \(\mathbf{p}_{\mathbf{O}^{C}}^{\mathbf{\mathrm{H}}\mathbf{\mathrm{C}}}\) in \(\mathbf{\Gamma}^{C}\), the inverse of \(\mathbf{\mathrm{H}}^{C\to S}\) or \(\mathbf{\mathrm{H}}^{S\to C}\) is  
\[\mathbf{\mathrm{H}}^{S\to C}=\begin{bmatrix}0.645&0.072&-81.260\\ 0.063&0.975&-72.926\\ -5.0e-5&-1.2e-4&1\end{bmatrix} \tag{6}\]  
Obviously, the sheer element of \(\mathbf{\mathrm{H}}^{S\to C}\) in the AR system is 0.072 or 0.063 rather than 0 due to the misalignment angle of the CCD. Furthermore, \(\mathbf{\mathrm{H}}^{S\to C}\) is used to calibrate the optical structure deformation, as shown in Figure 2a,b, where the deviation for horizontal axis in \(\mathbf{O}^{5}\) before and after calibration is illustrated. Therefore, the estimated homography \(\mathbf{\mathrm{H}}^{S\to C}\) based deformation correction allows students to configure the CCD easily without the requirement of high precision operation.  
When the CCD and screen are assumed to be fixed tightly, \(\mathbf{\mathrm{H}}^{S\to C}\) is a time-invariant transformation and its estimated value keeps constant; thus, the real-world coordinates of objects on the projection screen can be measured in real-time by using Equation (2) without repeating the estimation of \(\mathbf{\mathrm{H}}^{S\to C}\). In addition, the type of real object is identified from the CCD image by the classical neural network method, such as the function of CascadeClassifier in the OpenCV library. Figure 2c,d demonstrate the projection display of the measured coordinate and identified objects including a cover and two magnets (M1, M2), respectively, and the results suggest that the estimated coordinates have a relative error of less than 4% and the maxima difference of about 3 mm; moreover, the response time of coordinate and object detection running on the BB controller is less than 0.7 s when the image resolution is 640 \(\times\) 360 pixels. Therefore, the detection of shape and position for real objects provides an accurate and fast input to the DLP-based AR system.
## 3 Results and Discussion  
### Characterization of LDH Powder  
#### 3.1.1 Morphology  
The morphology was characterized by SEM as shown in Figure 1. It can be seen that both the two samples C-high and C-low presented three different zones in the SEM images as shown in Figure 2(a1,a2), where zone A,D corresponded to dark area, zone B,E corresponded to light area, and zone C,F corresponded to aggregates. These three different zones were enlarged at high magnifications in Figure 2(b1-d1,b2-d2), respectively. Figure 2(b1,b2) showed uniform and dense distribution of the LDH platelets on the substrates, while Figure 2(c1,c2) showed sparse and sporadic distribution. For Figure 2(d1,d2), the LDH platelets aggregated seriously. The size distribution in Figure 3 also suggested serious aggregation phenomenon, which was in good accordance with the SEM result. It can be seen that both the LDH platelets synthesized at a high or low concentration presented the typical hexagonal shape (Figure 4), which was in good accordance with the literature [5, 10, 11, 12]. According to inset images, the width (L) and the thickness (d) of the hexagon of C-high in Figure 2(b1) were found to be 118 \(\pm\) 15 \(\upmu\)m and 17.2 \(\pm\) 3.1 \(\upmu\)m, respectively, where the corresponding data for C-low in Figure 2(b2) were found to be 120 \(\pm\) 20 \(\upmu\)m and 19.0 \(\pm\) 3.4 \(\upmu\)m, respectively. It is clear that the change of reactant concentration did not have an obvious influence on the morphology and size of the synthesized LDH platelets.  
TEM characterization was also carried out to show more details. It is clear that the LDH platelets possessed a hexagonal shape as shown in the SEM results. The observed lattice spacing in the high resolution TEM in Figure 5c,f indicated good crystallinity of the synthesized LDH [13]. The Fast Fourier Transform (FFT) image further confirmed the good crystallinity.
## 2 Materials and Methods
### Surfaceome Analyses
#### 2.3.2 LC-MS/MS and Label-Free Quantification  
The tryptic peptides were concentrated and purified using ZipTip C18 (Millipore) and equivalent quantities of peptides from each sample were submitted to an Easy-nLC 1000 nano-LC system (Thermo Scientific), coupled with a quadrupole Orbitrap mass spectrometer (Q ExactiveTM, ThermoElectron, Bremen, Germany) equipped with a nanoelectrospray ion source (Easy-SprayTM, Thermo Scientific), as previously reported [16]. The liquid chromatography (LC) separation was performed in an Easy-SprayTM column capillary with a 25-cm bed length (C18, 2-um beads, 100 A, 75-um inner diameter, Thermo Scientific), using a flow rate of 300 nl/min. The peptides were eluted with a 2%-30% gradient of solvent (composed of 100% acetonitrile and 0.1% formic acid) in 60 min. The obtained mass spectrometry (MS) raw files were submitted to MaxQuant software (MaxQuant v.l.6.1.0) [17] for protein identification and label-free quantification (LFQ) using an in-house database composed of _S. aureus_ ATCC25923 protein sequences (CP009361 and CP009362) [12] in forward and reverse. Carbamidomethyl (C) was set as a fixed and methionine oxidation as a variable modification. A first search peptide tolerance of 20 ppm and a main search error of 4.5 ppm were used. Trypsin without proline restriction enzyme option was used with two allowed miscleavages. The minimal unique + razor peptide number was set to 1, false discovery rate (FDR) to 0.01 (1%) for peptide and protein identification, and label-free quantitation with default settings was used. Known contaminants provided by MaxQuant and those identified in the samples were excluded from further analysis. Missing value imputation for principal component analysis (PCA) was performed for proteins identified in at least 2/3 replicates in at least one sample group using Perseus [18] v.1.5.6.0 by random draws from a low-abundance-adjusted normal distribution. Statistical comparisons of the LFQ levels were also carried out in Perseus using a \(t\)-testwith a permutation-based false discovery rate of 0.05. Further statistical analyses were performed in IBM SPSS Statistics version 25 (IBM; Armonk, New York, US). Surfaceome patterns were illustrated in Perseus (v. 1.6.2.3) by standardizing (z-score) the log\({}_{2}\)-transformed LFQ abundances across the samples and carrying out complete-linkage hierarchical clustering according to Lp norm distance (\(p=1.5\)) both row- and columnwise. The mass spectrometry surfaceomics data are deposited to the ProteomeXchange Consortium via the PRIDE [19] partner repository with the dataset identifier PXD016344.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Deployment Related Problems in WSNs  
Wireless sensor network deployment problem encompasses the determination of positions for sensor nodes in order to achieve intended coverage, connectivity and energy efficiency while keeping the number of nodes as minimum as possible [102]. Optimal deployment of WSN guarantees sufficient quality of service, increased network life time and minimum cost [24]. Table 4 depicts mathematical formulations of some commonly used objectives related to deployment of wireless sensor network nodes. In the following, different objectives related to optimal deployment of wireless sensor networks are elaborated.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Ref.** & **Objectives** & **Equation** & **Details** \\ \hline
[9] & Arrangement to & & \(e_{i}\) represents the energy \\  & maximize the & & consumed at each node; \\  & area of coverage, & & \(N\) denotes number of \\  & minimize the & & sensor nodes; \(NC_{j}\) is a \\  & net energy & & non-coverage penalty \\  & consumption, & & parameter; \(h_{j}\) is variable to \\  & maximize the & & & indicate if demand point j \\  & network lifetime, & & & is not covered; \(D\) \\  & and minimize the & & & represents set of \\  & number of deployed & & & demand points. \\  & sensor nodes & & & \\ \hline
[108] & Maximize & & & For \(C_{jH}=0\), sensor \(j\) is \\  & connectivity & & disconnected. It means that \\  & and minimize & & sensor \(C_{jH}\) is not within \\  & energy & & the scope of cover. \(E_{min}\) is \\  & consumption of & & the minimum energy \\  & the network & & consumption; \(P_{j}\) is the \\  & & & transmission power level of \\  & & & sensor \(j\). \\ \hline
[111] & Optimal sensing, & & & First objective function is \\  & coverage and & & & used for total sensing area, \\  & network lifetime & & & second objective function \\  & & & & is used for network life and \\  & & & & third objective function is \\  & & & & used for moving cost of \\  & & & sensor nodes. \\ \hline
[112] & Coverage & & & Vector function \(z\) \\  & and lifetime & & \(\min\) & \(z(x)=[z_{1}(x),z_{2}(x),.....,z_{M}(x)]\) \\ \hline
[128] & Maximize & & & \(A_{i}\) is the area covered by \(i^{th}\) \\  & Coverage & & \(\max\limits_{coverage}f1=\dfrac{U_{i=1,.....,N}}{A}A_{i}\) & & node; \(N\) is total number of \\  & and Lifetime & & \(\max\limits_{lifetime}f2=\dfrac{T_{failure}}{T_{max}}\) & & node and \(A\) is the area of \\  & & & & region of interest. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Deployment related objectives in wireless sensor networks.
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 1 Introduction  
The Japanese scientist [PERSON] of Tokyo University was the first to use the term \"nano-technology\" in a 1974 conference to describe semiconductor processes such as thin film deposition and ion-beam milling exhibiting characteristic control on the order of a nanometer. His definition was that \"nano-technology\" mainly consists of the processing of separation, consolidation and deformation of materials by one atom or one molecule [1].  
Nanotechnology is the science and technology of making and using very small structures called nanomaterials. As the scales of the constructions become smaller, the conventional methods of making these structures--lithography, etching, micromolding--reach physical limits and it becomes extremely difficult to apply these top-down methods at nanoscale dimensions. To overcome the limitations and create smaller and ordered structures, a so-called \"bottom-up\" approach must be used. As the component size decreases in nanofabrication, the \"bottom-up\" approach is being increasingly used in the preparation of nanomaterials, which are materials with at least in one dimension less than 100 nm in size.  
The ionizing radiations by virtue of very high energies of \(\gamma\) photons (0.66-1.25 MeV) or accelerated electrons (several keV-10 MeV) and their ability to penetrate into solid materials generate free radicals homogeneously in the media they pass through. This allows irradiation of monomers or polymers in any state at--or if needed, below--room temperature. These unique properties make ionizing radiation a very useful tool for the preparation of nanomaterials [2,3].  
Nanomaterials are currently categorized into four types:  
1. Carbon-based nanomaterials;
2. Metal-based nanomaterials;
3. Polymer-based nanomaterials;
4. Composite nanomaterials.  
Quantum dots can be considered as new-generation nano-systems with hybrid metalloid-polymer structures which can be included under metal-based nanomaterials. Materials prepared from graft copolymers with nanoscale graft chain lengths can also be considered as nanomaterials and will be discussed under a separate title.  
Nanomaterials are synthesized by one of the three different approaches: chemical, physical or biological methods. Although various techniques are employed under one of these three approaches for the synthesis of nanomaterials, ionizing radiation is seldom mentioned as either a standalone technique or in combination with any conventional technique as a tool for the preparation of polymeric nanomaterials. The aim of this short review is meant to bring awareness of the advantages of using ionizing radiation for the synthesis of nanomaterials.
## 4 Simulation Results and Discussion
### Initial Case  
We generated the initial decision matrix shown in Table 2 with considering ten satellites of the Starlink constellation by SpaceX. Based on this data, we computed the initial criteria weights shown in Table 3, with each algorithm weighting the criteria according to its weighting technique. For each criterion, the sum of the weights is equal to 1. These weights influence the subsequent process of selecting the target satellite for the handover. Figure 3 graphically compares the weights obtained. We observe that, for all three TOPSIS variants, upload speed has the highest weight among the criteria, making it the most important criterion in our case based on the given data. Next, ping is followed by download speed and download speed followed by packet loss, which has a slightly lower weight.  
Table 4 presents the initial scores values obtained with each TOPSIS variant under study, and Figure 4 shows a visual comparison of these values. The scoring process,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citeria** & **SD-TOPSIS** & **Entropy-TOPSIS** & **Importance-TOPSIS** \\ \hline Upload speed & 0.2943 & 0.3412 & 0.3493 \\ Download speed & 0.2151 & 0.1812 & 0.1935 \\ Ping & 0.2954 & 0.3282 & 0.2974 \\ Packet loss & 0.195 & 0.1492 & 0.1597 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Criteria weights values for the initial decision matrix.  
Figure 3: Criteria weights for the initial decision matrix.  
following the assignment of initial weights, is crucial in selecting the target satellite for handover. Each algorithm calculates satellite scores on a scale from 0 to 1 based on criteria weights. From Table 4 and Figure 4, it can be seen that Satellite 5 is given the highest score by the three TOPSIS variants, which means that they all rank it as the best alternative for handover.
## 2 Materials and Methods
### Determination of Free Sulfhydryl (-SH) Group Content  
Disulphide bonds in hempseed protein may undergo a disulphide displacement reaction with thiol during alkaline extraction. Determining the -SH content can provide information on the dynamic changes in disulphide content during the extraction. The free sulfhydryl group content in different supernatants collected during alkaline extraction was determined using DTNB reagent according to a previously reported method [42] with slight modifications. Briefly, the extracted hempseed protein solution supernatant was diluted with Tris-Gly standard buffer solution (86 mM Tris, 90 mM glycine, and 4 mM EDTA, pH 8.0) to 2 mg/mL. For free sulfhydryl content measurement, 40 uL of DTNB (4 mg/mL) solution was added into a 4 mL aliquot of each hempseed protein solution. The mixture was vortexed and incubated at room temperature for 15 min and subjected to spectrometric analysis. The absorbance of the samples was recorded at 412 nm (Cary 50 bio UV-Vis, Agilent, Australia). The solution mixture of Tris-Gly standard buffer containing protein supernatant with a total volume of 4 mL was used as a protein blank. The -SH content per gram of soluble protein was calculated using Equation (3) [43]:  
\[\text{SH }(\upmu\text{mol}/\text{g})=\frac{10^{6}\times\text{A}\times\text{D}}{ 1.36\times 10^{4}\times\text{c}} \tag{3}\]  
where A is the measured absorbance at 412 nm, D represents the dilution factor, \(1.36\times 10^{4}\) M\({}^{-1}\)cm\({}^{-1}\) is the molar extinction coefficient, and c is the initial protein concentration (2 mg/mL).