esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
###### Abstract  
In recent years, reversible data hiding (RDH) has become a research hotspot in the field of multimedia security that has aroused more and more researchers' attention. Most of the existing RDH algorithms are aiming at continuous-tone images. For RDH in encrypted halftone images (RDH-EH), the original cover image cannot be recovered losslessly after the watermark is extracted. For some application scenarios such as medical or military images sharing, reversibility is critical. In this paper, a reversible data hiding scheme in encrypted color halftone images (RDH-ECH) is proposed. In the watermark embedding procedure, the cover image is copied into two identical images to increase redundancy. We use wet paper code to embed the watermark into the image blocks. Thus, the receiver only needs to process the image blocks by the check matrices in order to extract the watermarks. To increase embedding capacity, we embed three layers in the embedding procedure and combine the resulting images into one image for convenience of transmission. From the experimental results, it can be concluded that the original image can be restored entirely after the watermarks are extracted. Besides, for marked color halftone images, our algorithm can implement high embedding capacity and moderate visual quality.  
reversible data hiding; image encryption; color halftone image; wet paper code +
Footnote †: journal: Applied Sciences
# An IoT System for Remote Monitoring of Patients at Home
## 6 Buddy-ACK Authentication Scheme
Figure 13 shows the Buddy-ACK authorization scheme process proposed in this study. The Scheme 1 has the following sequences:  
(Storage sequence)  
1. PHDa sends measured biomedical data to IoTs through an IoT network. 2. IoTs receives the data, and Splits the data into two parts to store separately
3. IoTs sends the separation information (i.e., the locations of the two parts) to Ioauth and deletes the separation information from IoTs, and Ioauth stores the separation information received from IoTs  
(Retrieval sequence performed by Spm)  
1. Spm sends Ek(IMEIm) to IoTs to request [PERSON]'s biomedical data
2. IoTs requests ACK of PHDa to initiate the Buddy-ACK authorization process
3. PHDa sends Ek(IMEIa) to IoTs as an ACK
4. IoTs sends (Ek(IMEIm) + Ek(IMEIa)) to IoTauth
5. IoTauth performs Dk(Ek(IDms) + Ek(IDa)) to determine whether it is the legitimate buddy, and IoTauth sends the related separation information to IoTs
6. IoTs locates two parts of [PERSON]'s biomedical data using the received separation information, and Assembles them to send to Ms  
(Retrieval sequence performed by PHDa)  
1. PHDa: PHD of patient A SPm: Smartphone of Medical staff Ms related to A IMEIa: IMEI of PHDa IMEIm: IMEI of SPm IoTs: IoT server IoTauth: IoT authentication server Ek(IMEIm): Encryption of IMEIm with the private key k of IoTauth Dk(IMEIa + IMEIm): Decryption of (IMEIa + IMEIm) with the private key k of IoTauth  
**Scheme 1. ** Buddy-ACK authorization scheme. Retrieval sequence performed by PHDa is very similar to Retrieval sequence performed by Spm, except that \"Spm\" and \"Ek(IMEIm)\" are replaced by \"PHDa\" and \"Ek(IMEIa)\", respectively. Figure 13: Buddy-ACK authorization scheme process.
## 3 Results and Discussion
### Going from NIPS to VIPS: A Parametric Study
#### 3.2.3 Effect of Exposure Time  
A clear gradual change of the surface structure of membranes prepared using 12% PVDF, 60% RH and casting thickness of 500 \(\upmu\)m is shown in Figure 6. This change translated into the in trend of CA shown in Figure 7A. A drastic change of surface structure was observed at exposure time between 1 and 2 h, in which the structure changed from a flat surface to a spherulitic-like structure (Figure 6). This structure is responsible for the substantial increase in CA up to 137\({}^{\circ}\). However, the increase in CA was also mirrored by an increase in BP (Figure 7), which diminished the LEP value. The LEP data shows a gradual decrease with exposure time (Table 4), which is in agreement with the SEM and the BP results (Figures 6 and 7).  
Increasing exposure time enlarged the membranes' MPS, which reached a plateau at 1 h, then jumped substantially at 2 h (Figure 7B). This is mainly because of the drastic change of membrane structure discussed above. Also, significant increase of BP only occurred after a certain exposure time (30 min). These findings suggest that extending the exposure time can significantly change the surface hydrophobicity without substantially changing the BP. In other words, it is possible to increase the LEP and MPS, which implies a larger MD flux at an assumed constant pore density, by extending the exposure time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**RH (\%)** & **Exposure Time (min)** & **MPS (\(\upmu\)m)** & **BP (\(\upmu\)m)** & **CA (\({}^{\circ}\))** & **LEP (kPa)** \\ \hline - & 2 & 0.057 & 0.10 & 91.0 & 118.34 \\
60\% & 5 & 0.060 & 0.13 & 94.3 & 167.66 \\ - & 10 & 0.137 & 0.78 & 104.5 & 137.18 \\ - & 2 & 0.073 & 0.17 & 93.6 & 141.17 \\
80\% & 5 & 0.126 & 0.85 & 97.7 & 108.86 \\ - & 10 & 0.140 & 2.90 & 98.2 & 105.66 \\ \hline \end{tabular}
\end{table}
Table 4: Membranes properties with casting thickness of 500 \(\upmu\)m and PVDF concentration of 12% (\(w/w\)).  
Figure 5: Surface SEM images of membrane samples prepared under different RH and different exposure times. Casting thickness: 500 \(\upmu\)m, polymer solution: 12% \(w/w\) PVDF.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### NETCONF-Light: A Lightweight Protocol for Resource-Constrained Devices  
NETCONF-Light [77] is a lightweight version of the NETCONF protocol specifically designed for resource-constrained devices. It provides a streamlined subset of NETCONF operations to facilitate configuration management, including the ability to install, manipulate, and delete network configurations as described in Algorithm 2. The key operations of NETCONF-Light (\(O\)) can be expressed as  
\[O_{NETCONF}=\{\text{install, modify, delete}\} \tag{15}\]  
These operations are applied to device configurations (\(D\)), and their effect is to transition the device to a desired configuration state:  
\[O_{NETCONF}:D\rightarrow\text{State} \tag{16}\]  
where  
* \(D\): the set of device configurations.
* State: the target state after applying the operations.
# Optimizing Service Placement for Microservice Architecture in Clouds
## 4 Placement Algorithm
### Application Partition
#### 4.1.1 Binary Partition  
The idea of the binary partition algorithm is to continuously perform binary partition on the application until the resource demands from each part do not exceed \(\alpha\) or no part contains more than one service. The pseudocode is shown in Algorithm 2. The basic process can be described as follows. The algorithm continuously checks the resource demands of each part in current application partition \(P\). The initial partition is \(P=\{S\}\) where the entire application is treated as one part. If the total resource demands of a part \(\mathcal{S}_{i}\) in \(P\) exceeds the threshold \(\alpha\) and part \(\mathcal{S}_{i}\) contains more than one service, the part is selected to be partitioned into 2 parts (binary partition). It first constructs a graph \(G=(V,E)\) based on \(\mathcal{S}_{i}\), where the nodes represent services and the weights of edges represent the traffic rate. As mentioned in Section 3, if we repeatedly perform the contraction algorithm many times, we can obtain a minimum cut with high probability. Considering both the partition quality and the partition speed, we choose to perform the contraction algorithm \(n\) times in our algorithm (in offline manner, it can be set to run \(n^{2}\log n\) times to get a minimum cut with high probability). Then, according to the minimum cut \(G_{min}\), we get from the contraction algorithm, it partitions the \(\mathcal{S}_{i}\) into two parts \(\{\mathcal{S}_{x},\mathcal{S}_{y}\}\). This process would be repeatedly performed until the resource demands from each part do not exceed threshold \(\alpha\) or no part contains more than one service.
## 2 Materials and Methods
### Material Characterizations  
The particle size of the Si waste was measured using a laser particle size analyzer (LA-960, Horiba, Ltd., Kyoto, Japan). The composition of the Si waste was analyzed with an X-ray diffractometer (D8 Advance, Bruker, Billerica, MA, USA) using Cu K\(\alpha\) radiation at 40 kV and 40 mA. The X-ray diffraction (XRD) results were collected from 10\({}^{\circ}\)\(<\) 20 \(<\) 100\({}^{\circ}\) at a scan rate of 2\({}^{\circ}\)/min. A scanning electron microscope (SEM, Model Inspect S50, FEI, OR, USA) was used to examine the surface morphology of Si micropillars. Quantitative measurement of the average base diameter of micropillars was performed using ImageJ software (National Institutes of Health, USA). Elemental analysis of the Si anode after 100 cycles was carried out using energy-dispersive X-ray (EDX) equipped with this SEM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Laser Type** & **Nd:YAG Laser** \\ \hline Wavelength (nm) & 532 \\ Environment & In air \\ Beam profile & Gaussian \\ Beam diameter (\(\upmu\)m) & 85 \\ Pulse width (ns) & 48.4 \\ Repetition frequency (kHz) & 10 \\ Average power (mW) & 300, 600 \\ Laser fluence (mJ/cm\({}^{2}\)) & 529, 1057 \\ Scanning speed (mm/s) & 1 \\ Line spacing for area irradiation (\(\upmu\)m) & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser irradiation conditions.
## 3 Simulation Analysis of High-Energy Pulse EMAT
### Characteristic of Magnetic Flux Density Distribution in High-Energy Pulse Excitation  
In the condition that the lift-off distance \(h\) is 0 mm, the magnetic flux density distribution of the high-energy pulse EMAT in different directions was simulated and analyzed.  
Figure 4a,b are the magnetic flux density distribution of the high-energy pulse EMAT in different directions (\(\mathbf{B}_{\rm x}\) is the horizontal component of magnetic flux density and \(\mathbf{B}_{\rm y}\) is the vertical component of magnetic flux density) of the steel plate surface. From Figure 4a, when the measured point is exactly below the axis of the excitation coil wire (such as \(x=30.5\)), the amplitude corresponding to the horizontal component \(\mathbf{B}_{\rm x}\) of magnetic flux density is the highest. When the measured point is exactly under the middle of the two adjacent wires of the meander coil (such as \(x=31.425\)), the value \(\mathbf{B}_{\rm x}\) is 0. From Figure 4b, the distribution of \(\mathbf{B}_{\rm y}\) is exactly the opposite to the distribution of \(\mathbf{B}_{\rm x}\). In Figure 4b,  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Steel plate size width \(\times\) height (mm) & 300 \(\times\) 1 \\ Lift-off distance (mm) & 0-4 \\ Circular section wire diameter (mm) & 1 \\ Coil turn number & 4 \\ Frequency \(f\) (kHz) & 220 \\ Young modulus \(E\) (GPa) & 200 \\ Poisson ratio \(\mu\) & 0.3 \\ Conductivity \(\gamma\) (MS/m) & 99.3 \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of the model.  
Figure 3: The characteristics of high-energy pulse excitation. (**a**) Current in excitation coil of the high-energy pulse EMAT simulation; (**b**) The spectrum of high-energy pulse EMAT excitation.  
the measured point exactly below the excitation coil wire axis (for example, \(x=30.5\)) corresponds to 0. Whereas, in the position exactly below the middle of the two adjacent wires of the meander coil (for example, \(x=31.425\)), the amplitude is the highest. Based on the comparison between Figure 4a,b, it can be seen that the magnetic flux density in the horizontal direction is larger than that in the vertical direction by an order of magnitude. In other words, the contribution of the magnetic flux density is mainly dominated by the horizontal component of magnetic flux density. According to the aforementioned, in ferromagnetic materials, only when the direction of the magnetic field is horizontal, the magnetostrictive force may become the main mechanism of action. It is critical to determine when the Lorentz force and magnetostrictive force is the main mechanism of transduction.
# Analysis and Design of Tunable THz 1-D Leaky-Wave Antennas Based on Nematic Liquid Crystals
## 2 Theoretical Analysis
### Tunable Leaky Waveguide Filled with NLC
Similarly, the tilt angle, which is generally spatially variant as \(\delta(x,z)\), can be approximated by its 2-D spatial average \(\bar{\delta}\equiv\frac{1}{d_{x}d_{z}}\int\!\int\!D_{\rm{D}z}\,\delta(x,z)\mathrm{ d}x\mathrm{d}z\) (where \(D_{xz}\), \(d_{x}\), and \(d_{z}\) are the spatial domain of the LC cell along the \(xz\)-plane and its dimensions along the \(x\)- and \(z\)-axes, respectively) that describes intermediate biasing states--namely, \(0^{\circ}<\bar{\delta}<90^{\circ}\). The components of the NLC permittivity tensor are still provided by Equation (6), where \(\delta\equiv\bar{\delta}\). Since we are assuming the optical axis of the NLC to switch in the \(xz\)-plane, only the TM leaky modes are affected by the application of a voltage (as can be inferred from the characteristic TE-TM admittances of the NLC layer: see, e.g., Ref. [38]), thus, motivating the choice of having PMC lateral walls. The variation of the phase constant of the TM leaky mode through voltage biasing will affect the beam angle through Equation (1), thus, producing the desired beamsteering property at a fixed frequency. The scan rate of the TM leaky mode with respect to variation of the relative permittivity \(\mathrm{SR}:=\mathrm{d}\theta_{0}/\mathrm{d}\varepsilon_{\rm{r}}\) can be found from Equation (5) and reads:  
\[\mathrm{SR}\simeq\csc{(2\theta_{0})}/2, \tag{7}\]  
which reveals that the angular sensitivity is higher when the antenna radiates at angles close to either broadside or endfire. However, 1-D LWAs do not usually radiate efficiently at such angles, and a trade-off has to be accepted [1].
## 3 Results and Discussion of Biosensor Formation and Its Validation
### Robustness  
The robustness of the method relates to the influence of slight changes in the analytical procedure on the stability of the results. Table 3 summarizes the changes in the analytical procedure and their influence on the quantification value.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Biomarker**} \\ \cline{2-7}  & \multicolumn{4}{c}{**VEGF-A**} & \multicolumn{4}{c}{**FGF-2**} \\ \cline{2-7}
**Parameter** & \multicolumn{4}{c}{**C\({}_{\text{RM}}\) [pg/mL]**} & \multicolumn{4}{c}{**C\({}_{\text{RM}}\) [pg/mL]**} \\ \cline{2-7}  & **4** & **20** & **50** & **80** & **5** & **20** & **50** & **80** \\ \hline X\({}_{\text{C}}\) [pg/mL] & 3.94 & 21.36 & 52.08 & 80.40 & 4.97 & 22.70 & 53.60 & 80.07 \\ SD & 0.47 & 1.19 & 2.36 & 0.80 & 0.52 & 1.74 & 1.75 & 0.87 \\ CV [\%] & 11.9 & 5.6 & 4.5 & 1.0 & 10.5 & 7.6 & 3.3 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Precision of the developed analytical method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Biomarker**} \\ \cline{2-7}
**Change in** & \multicolumn{2}{c}{**Description of the Change in the**} & \multicolumn{2}{c}{**VEGF-A**} & \multicolumn{2}{c}{**FGF-2**} \\ \cline{2-7}
**Analytical Procedure** & \multicolumn{2}{c}{**Analytical Procedure**} & \multicolumn{2}{c}{**X\({}_{\text{C}}\)**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**X\({}_{\text{C}}\)**} & \multicolumn{2}{c}{**SD**} \\  & \multicolumn{2}{c}{**[pg/mL]**} & \multicolumn{2}{c}{**[pg/mL]**} & \multicolumn{2}{c}{**[pg/mL]**} & \multicolumn{2}{c}{**[pg/mL]**} \\ \hline - & \multicolumn{2}{c}{Real sample} & 45.74 & 0.82 & 5.67 & 0.84 \\ T\(\uparrow\) & sample heated in an incubator for 2 h (40 \({}^{\circ}\)C) & 31.61 & 0.54 & 3.82 (<LOQ) & 0.39 \\ T\(\downarrow\) & sample chilled in the refrigerator for 2 h (4 \({}^{\circ}\)C) & 44.75 & 0.29 & 5.72 & 1.05 \\ pH \(<\) 7.40 & pH = 4.99 & 38.07 & 0.98 & 3.02 (<LOQ) & 0.72 \\ pH \(>\) 7.40 & pH = 9.50 & 29.06 & 0.44 & 3.15 (<LOQ) & 0.47 \\ shot and gun & sample prepared immediately before & 45.30 & 0.71 & 5.65 & 0.53 \\  & \multicolumn{2}{c}{the analysis} & 30 s & 22.47 & 0.22 & \textless{}LOD & - \\ Analysis time & 2 min & 37.21 & 0.87 & \textless{}LOD & - \\ (interaction between & 6 min & 46.12 & 0.76 & 4.97 & 0.19 \\ ligand and analyte) & 8 min & 45.09 & 0.69 & 5.63 & 0.46 \\ \hline \hline \end{tabular}  
* temperature increase; T\(\downarrow\)
- temperature lowering  
\end{table}
Table 3: Effect of changes in the analytical procedure on the quantification result.  
Both developed analytical methods showed no robustness to elevated temperature and pH values different from 7.40. The time of sample preparation was not found to have a significant influence on the analysis result. Optimum antibody-analyte interaction times were 6 min for VEGF-A and 8 min for FGF-2.
## 2 Methodology
### Available Data  
Data from three sites with rigid inclusions were used. These inclusions were constructed using an auger that displaces the soil laterally. In all three sites, the Enteco E6050 type drill was used. An onboard Menard Emparex data acquisition system recorded depth, penetration rate, rotational speed, torque, and thrust roughly every 8 cm. All the inclusions had a diameter of 360 mm.  
Site 1 is formed by an anthropic fill (silty sands, 1.60-2.20 m thick) that lies above a thin organic soil layer (silty sands, 0.4-1.0 m). Below that, there is an alluvial layer of fine sand with silt (very loose, 1.60-2.20 m), followed by altered granite, which becomes more compact with depth. Overall, the soil is homogeneous, predominantly sandy, and the water table lies near the surface. In Site 2 the top layer is an anthropic fill (sands and clayey sands) about 6 m thick. Below it, alluvial materials form interlayered clays to sands extending to about 30 m. The deepest detected layer is a dense, highly compact sand (Pliocene), which the soil treatment does not reach. Water levels have been observed between 2 and 8 m deep at different times. Overall, this site is more heterogeneous than Site 1, with varied materials and a fluctuating water table. Lastly, the soil of Site 3 is essentially a dredged fill (sandy with some fines and gravel) 4-8 m thick, placed for sea reclamation. Below it lies quaternary sands that become denser with depth. The water table is shallow, influenced by sea level. Overall, the terrain is predominantly sandy and fairly homogeneous.  
In these worksites, a series of DPSH-B type penetrometers were executed according to the UNE-EN-ISO22476-2 standard [63]. The result of these tests is the number of blows required for penetrations in 20 cm sections and can be plotted against depth (Figure 1).  
The features and target variable for the algorithms are included in Table 3.  
Further information about the available data in these works can be found in the following reference [64].  
To maintain a certain consistency in the study, the drilling and penetrometer data have been converted to the AGS4 format [65]. This format is intended to collect geotechnical data and can be easily converted to a Pandas data frame [66] through a dedicated library [67].  
Once the data was adapted, a blow value was associated based on the closest penetrometer.
# Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-\(\omega\)
## 1 Introduction
### Research Focus  
Global particle swarm optimizer with inertial weights (GPSO-\(\omega\)) is a variant of PSO algorithm, which is a stochastic optimization technique used to solve complex optimization problems. QPGPSO-\(\omega\), which is an extension of GPSO-\(\omega\), was utilized in [53] for the solution of ED-IEEE standard (3, 6, 13, 15, 40, and 140) units for Korean grid thermal test systems under various constraints. QPGPSO-\(\omega\) outperformed several other methods in solving the EDP, showcasing superior results. However, in this study, QPGPSO-\(\omega\) is applied to address the challenges of EDPs specifically related to the integration of RESs and the consideration of CEED. The main focus of this study is as follows:  
* Validation of the QPGPSO-\(\omega\) methodology for solving only RES-based EDs with resource variability.
* Validation of the QPGPSO-\(\omega\) methodology for solving regional load sharing dispatch.
* Utilization of QPGPSO-\(\omega\) to solve the CEED problem for ten-unit TPPs, which is a significant challenge in power systems due to various constraints, such as VPL effects and losses.
## 7 Results
### Specialist Interviews  
#### 7.2.1 Interviewee [PERSON]  
Interviewee \"[PERSON]\" possesses a multifaceted background in system development, IT architecture, and robotics engineering. However, [PERSON]'s involvement in C-ITSs came later in their career. [PERSON] also has experience in cybersecurity. When confronted with the concept of CLC, [PERSON] did not have prior knowledge of it, and similar to [PERSON], would utilize the Internet to source information about it. [PERSON] identified significant challenges in C-ITSs and cryptographic technologies, specifically concerning the limitations of resources. These constraints often manifest as performance issues, such as difficulties related to hardware computations. [PERSON] saw parallels between these challenges and those faced in robotics, particularly in terms of securing data and maintaining performance as devices become interconnected. The future of these issues, however, remained unclear to [PERSON]. For instance, [PERSON] pondered the impact of further advancements in quantum computing. [PERSON] also speculated that the evolution of technology, like quantum computing and cloud services, might necessitate different types of security controls. The adoption of these new security controls could prove challenging, owing to the difficulty in distinguishing between quality requirements and business requirements. Moreover, performance issues stemming from new security controls might also act as deterrents to their adoption.  
According to [PERSON], one of the keys to successfully integrating new security controls in their organization would be securing buy-in from standardization bodies. [PERSON] stressed the need for the organization to recruit personnel with competence and knowledge in cryptography. This would facilitate a better understanding and faster adoption of new security controls. Furthermore, involvement and active participation in innovation are crucial for the success of the organization. [PERSON] also pointed out that a significant barrier to the adoption of new controls is the organization's tendency to adhere to previously approved security solutions (e.g., traditional PKI solutions). Another crucial factor for embracing innovation, [PERSON] indicated, falls on the management's responsibility. The ability to understand and encourage innovation could be pivotal in driving the organization's work towards adopting new security controls.  
Figure 4: Trend analysis of CLC in C-ITSs articles over time, using linear regression to predict the number of articles for 2023. Q1 marks the number of articles collected up to and including Q1 2023.