esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Comparison Experiments with Similar Algorithms  
We also conducted comparison experiments with an algorithm from the literature [24,25]. The experimental parameter settings are consistent with the comparison literature, and the data values of the comparison literature are taken from the original paper. The paper [24] is the first quantization coefficient that embeds the watermark into the subblock by selecting the subblock for embedding the watermark based on the video texture information and motion vector entropy. The paper [25] achieves the embedding of the watermark by modifying the positive and negative signs of the non-zero DCT coefficients of the chromaticity subblock. The algorithm in this paper differs from the comparison algorithm in that the modulation can be determined according to the video and watermark characteristics after selecting the subblock to achieve a more robust and invisible video watermark.
## 3 Results and Discussion
### Optical Properties
#### 3.5.1 UV-Vis-NIR Analysis with Tauc's Plots
As a result, \(\Delta\)E can be determined by plotting the incident photon energy (\(h\
u\)) versus ln\(\alpha(h\
u)\) and taking the inverse of the straight line's slope. Table 2 shows the predicted values for the B0H set of samples, which corroborate the existence of indirect or phonon-assisted transformations, as well as defects caused by the presence of Ho\({}^{3+}\)-ions in B0H glass. \(\Delta\)E stands for structural stability and the lower the \(\Delta\)E, the greater the structural stability [36; 37; 38]. The steepness parameter (S) [39] represents the relaxation of the exciton--phonon due to the expansion of optical absorption. This can be determined using the formula:  
\[S=\frac{kT}{\Delta E} \tag{10}\]  
where, \(k\) is Boltzmann constant, \(T\) is the room temperature and \(\Delta E\) is Urbach energy. A lower S value suggests that temperature was less of a consideration. Table 2 summarizes the results. Figure 8 depicts an indirect energy (Tauc's) and the Urbach energy plots.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 3 Proposed Algorithm  
The MMSE detection algorithm has near sub-optimal detection performance when applied to large-scale MIMO systems, which generates a high exponential level of complexity. The algorithm is difficult to re-engineer in hardware devices. This paper applies the CJGS iterative algorithm to signal detection in massive MIMO communication. The scheme can be broken down into three parts, initialization, CJ joint processing, and GS iterative estimation, and the block method of CJGS detection is shown in Figure 2. During the initial stage, the optimal initial value is selected to speed up convergence. In the CJ joint processing phase, the CG iteration is used to select the optimal search direction and is combined with Jacobi iteration to reduce the number of subsequent iteration cycles. In the GS iteration estimation phase, Gauss-Seidel iteration is used to converge quickly, thus improving the overall detection performance of the massive MIMO system.
## 2 Identification of Mechanical Parameters of Rock Samples in Static Tests
### \(\mu\)CT Data Acquisition and Analysis  
Tomographic imaging devices (XCT, \(\mu\)CT, X-ray CT) utilized in industrial applications rely on X-ray spectroscopy. A detailed description has been formulated in a published paper [35] and despite more than two decades passing, the operating principle remains current and significant. The examined object undergoes thorough inspection in a dedicated chamber of the device. It is placed between the radiation emitter, commonly referred to as the tube, and the detector. Device control occurs through the precise determination of the object's position between the lamp and the detector, inputting voltage and current intensity values generating radiation (power), and specifying the operational characteristics of the detector. The proper selection of parameters enables results to be obtained which facilitate further 3D reconstruction of the object. In contrast to medical tomography, where patient comfort and health are limiting factors, in industrial examinations, the detector and lamp remain stationary during the inspection, while the sample undergoes rotation along a vertical axis. Thousands of measurements of radiation beam attenuation passing through the examined object are conducted during a single scan. The examination was conducted using a microfocus X-ray tube with parameters set at 140 kV and 165 \(\mu\)A. The core inside the tomographic chamber is illustrated in Figure 4.  
The next step involves the reconstruction of a 3D volume. During a full rotation, the device generates a large number of high grayscale images (e.g., GE Phoenix v | tomex | m (General Electric Company, Hurth, Germany) utilizes 14 bits). It is a common practice to employ sophisticated algorithms during reconstruction, including correction for beam hardening, automatic geometry calibration, and geometry optimization [36; 37]. The reconstructed object can undergo visual inspection through the analysis of 2D and 3D cross-sectional images, as well as further volumetric analysis. It should be noted that all measurements were taken under precisely the same detector settings, voltage, and with the manipulative table in an unchanged position. The results were obtained through reconstruction using a consistent algorithm: employing the bhc+ filter (factor 6.4), auto agc, and reconstruction optimization. Volumetric analysis began by determining the isosurface of the material. The material was visible due to its higher density. Next, porosity analysis was performed based on the VGDefX/Only Threshold procedure (VGSTudio MAX 3.5.1). Total core porosity was determined. The isovalue parameter for porosity was 26.480, and filtration was applied. Significant spots were considered larger than 8 voxels.  
Figure 3: Methodology for determining strain energy (\(W_{s}\)).
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
## 2 Image Reconstruction
### MLEM Method  
The MLEM algorithm, well-known for image reconstruction in nuclear medicine CT, is derived under the assumption that both the nuclear disintegration process and measurement noise follow a Poisson distribution. It has been proven that the generalized KL-divergence between the forward projection \(Az(n)\) and the measured projection \(y\),  
\[\text{KL}(y,Az(n)):=\sum_{i=1}^{I}y_{i}\log\frac{y_{i}}{A_{i}z(n)}+A_{i}z(n)-y _{i} \tag{3}\]  
decreases as the iteration \(n\) progresses [18; 19]. Here, \(z(n)\) represents the pixel values at iteration \(n\), \(A_{i}\) is the \(i\)th row vector of the projection operator \(A\), and \(y_{i}\) is the \(i\)th element of the projection vector \(y\) (\(i=1,2,\ldots,I\)). The iterative algorithm of the MLEM method can be described as the following recurrence relation for the elements \(z_{j}\) of the pixel vector \(z\) (\(j=1,2,\ldots,I\)):  
\[z_{j}(n+1)=z_{j}(n)\frac{\sum_{i=1}^{I}A_{ij}\frac{y_{i}}{A_{i}z(n)}}{\sum_{i= 1}^{I}A_{ij}} \tag{4}\]  
for \(n=0,1,2,\ldots,N-1\), where \(A_{ij}\) denotes the element in the \(i\)th row and \(j\)th column of the matrix \(A\) (\(i=1,2,\ldots,I\), \(j=1,2,\ldots,J\)), and the initial pixel values \(z(0)\) are chosen as a constant vector \(x^{0}\) with positive elements.
## 3 Selection of Materials for Wearable Antennas  
The efficiency of wearable antennas depends on their smooth functionality when affixed to the human body. Wearable antenna technology predominantly utilizes WBANs. As a result, carefully selecting appropriate substrate material that possesses both conductive and non-conductive properties is an essential component during constructing these antennas. The employed substrate must be adequate to position the antenna near the human body. Several researchers have extensively researched the most suitable substrate material for this purpose [49]. This work introduces a small wearable antenna created from jeans and denim materials for designing an antenna for integration into smart wearable textiles, operating at a frequency of 3.4 GHz. Simulations and actual implementations assess the antenna's performance. This study examines the impact of humidity on the resonance frequency of a microstrip textile antenna. It specifically focuses on the features of the substrate material and how environmental factors affect the antenna's performance. This research looks at how well the ring resonator and quarter-wavelength stub resonator methods work for testing the dielectric properties of textiles made in Nigeria. It also verifies their suitability as substrates for designing wearable antennas [50]. This research examines the design of a small, adaptable, and slim ultra-wideband antenna for wearable devices, with a focus on its compatibility with WBANs and an analysis of its performance when bent [12, 51, 52]. Several different adaptable substrates and conductive materials are used in the construction of antennas. As shown in Figure 3, the substrate is selected due to its dielectric qualities, resistance to mechanical deformations (such as bending, twisting, and wrapping), compatibility with miniaturization, and longevity in situations exposed to the elements [53]. Alternatively, the selection of conductive material, which is determined by the material's electrical conductivity, impacts the antenna's performance, including the antenna's radiation efficiency [54].  
Figure 3: Selection of various materials for design of wearable antennas.
# Tilted Beam Fabry-Perot Antenna with Enhanced Gain and Broadband Low Backscattering
## 4 Discussion  
To compare and analyze the proposed work with similarly reported works, Table 4 is presented. In all previous works, it can be observed that the peak radiation is oriented towards the broadside, unlike the proposed antenna. The upfront advantage of the proposed antenna over other works is its tilted beam radiation at an angle of 13\({}^{\circ}\), achieved in parallel with the RCS reduction, as well as gain enhancement. When gain is compared, although it is slightly lower than other designs, nevertheless, the cavity size as well as the cavity height is also smaller as compared with other designs [24]. More importantly, the achieved gain value is also at a tilt angle, and the lower value of gain can be attributed to scanned angle loss [30]. The average RCS reduction is better than most of the works, and the RCS reduction bandwidth is also wider as compared with other designs. The proposed antenna, in particular, can benefit applications in the military realm where low radar signature platforms are of utmost importance. One example is a Side-Looking Airborne Radar (SLAR), which employs side looking antennas [18]. Multiple input multiple output (MIMO) technology is also paving its way into military applications [5,6], and as such, the proposed work can be extended towards realization of low RCS MIMO antennas drawing diversity gain achieved through pattern decorrelation [31]. In fact, for any general military application where off-the horizon communication is desired, the proposed low RCS tilted beam antenna would be more beneficial over a low RCS broadside beam antenna.  
Figure 19: Normalized monostatic RCS angular response for xoz plane with y-pol incident wave at 8.8 GHz. (**a**) Reference antenna; (**b**) Proposed antenna.
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 1 Introduction
In the performance optimization loop for wireless body area networks (WBANs), we used a full-wave numerical electromagnetic code with a numerical model of the human body to calculate the objective function components, such as the path loss or the off-body radiation pattern. Standard body models are available in electromagnetic simulation programs, which are made for a human body in the standing (or lying) positions. It is possible to reposition these models into different poses with specialized programs (e.g., Remcom VariPose [17]), but because of the lack of a suitable programming interface, the software cannot be run automatically within an optimization loop. Our optimization procedure is currently adapted for two body postures: sitting and standing. For this purpose, two simplified models of a human body consisting of a set of cylinders are proposed. These were generated swiftly and automatically in Remcom XFdtd [18], which is a commercial package implementing full-wave Finite-Difference Time-Domain (FDTD) electromagnetic simulator, for each optimization step using the available scripting language. Our approach is flexible because it can be also used in the future for other body postures. The major goal of our current study was to develop a strategy for the automatic optimization of the location of WBAN nodes for varying body positions of the user. We used the Remcom XFdtd simulation program in this research, which utilizes the FDTD method, and the numerical human body model which we have elaborated. The model was verified by us experimentally [19]. We found that simulation results obtained for the 2.4 GHz ISM frequency band with the heterogenic NMR Hershey human body model in XFdtd are similar to the results of measurements obtained for a human subject and for the simplified 6-cylinder phantom.
## 2 Materials and Methods  
### Study Area and Sample Collection  
Seawater samples were collected from seven Portuguese ports with high maritime traffic: Povoa de Varzim (41.376120, \(-\)8.766945); Leixoes (41.195238, \(-\)8.684177); Aveiro (40.645899, \(-\)8.727098); Figueira da Foz (40.146848, \(-\)8.849176); Peniche (39.355422, \(-\)9.375479); Setubal (38.521228, \(-\)8.887277); and Sines (37.950219, \(-\)8.864599). All samples were collected during low tide, 50 cm above sediment level. Physicochemical parameters (temperature, pH, salinity, conductivity, and dissolved oxygen) were measured using a multiparameter water meter (Hanna HI 9828; Hanna Instruments, Villafranca Padovana, Italy). At each sampling location, water samples were collected for bacteria isolation and for chemical analyses (butylins and metallic elements). Samples were processed within 8 h of collection.
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 4 Reconfiguration Formation Algorithm
### Peak-Search Method  
Figure 3 shows a block diagram of the peak-search method. Initially, the parameter \(I\) is set as two for the reconfigurable mesh-tree formation. The designated root of the reconfigurable mesh-tree executes the connection link block to calculate the total number of links in the mesh-shaped topology. In the hop length block, the average query hop is defined as the ratio of total number of hops over the number of nodes in the mesh-shaped topology. The query hop is defined as the hop length from each M/S node to the designated node. Finally, the link performance is calculated and compared with its previous \(I\) layer in the optimum decision block. This algorithm is iterated until the optimum number of layers \(I\) is determined.  
To realize the peak-search method, a topology configuration algorithm is implemented in the root node of the reconfigurable mesh-tree. During the tree-shaped topology formation phase, a new parameter \(c\) is designed as the counter of the hop length from each downstream master to the designated root. With \(k=I=2\) and \(c=0\), the root node initially propagates these two parameters in the downstream direction. The new masters (M/S nodes) then increase \(c\) by one and decrease \(k\) by one until \(k=0\) in the downstream direction.  
During the return connection phase, a new parameter \(r\) is also added to represent the number of return links of each M/S node. When each return master connects with additional M/S nodes, the parameter \(r\) can be increased by one. Finally, each return master passes its node ID, \(c\) and \(r\) to the designated root when each master finishes the return connection phase. The root node thus collects sufficient information to calculate the average query path length, the total number of nodes and the total number of return links for the optimum peak-search method decision.  
When the optimum I is not located, the root node continuously propagates these two parameters with \(k=I+1\) and \(c=0\) in the downstream direction to increase the tree-shaped subnet by one more layer as well as to generate new masters. During the return connection, only the new masters can connect with their neighboring M/S nodes and increase the return link parameter \(r\). This procedure is repeated until the optimum \(I\) is determined. When the M/S node cannot connect with its downstream master, it resets parameter \(r=0\) and sends its ID to the root node to cancel the return connection.  
Figure 3: Block diagram of the peak-search method. \(I\), the optimum number of layer.  
From this point on, each root node, either in the mesh-subnet or in the tree-subnet, distributively executes the peak-search method to determine the optimum I layer when the sizes of subnets grow. If the complete network topology is acquired at the root node during the maintenance phase, the shortest path of the \((I-1)\), I, and \((I+1)\) layer can be computed, and the optimal layer can be determined.
## 4 Results  
### Petrographic and Chemical Characterization of Carbonate Rocks before SC CO\({}_{2}\) Injection  
The Puerto de Palombera Formation mainly consists of a heterogeneous, recrystallized carbonate breccia that presents a high percentage of macroporosity (\(\approx\)30%) in surface outcrops due to the dissolution of evaporate minerals. The partial recrystallization resulted in a matrix of highly varying grain size from micrite up to crystals of 200 \(\upmu\)m. The angular-subangular clasts are composed of sparite and could be intra-, extra- or bioclasts. Their size varies between 0.1 mm and \(>\)2 mm (Figure 3a). The high calcite/dolomite ratio and the textural features suggest a dolomitization, followed by a posterior de-dolomitization process (Figure 3b). Calcite filled veins and pores are frequently observed and open fractures and intercrystalline porosity affect both fragments and matrix (Figure 3c). Geode type open pores are also present (Figure 3d). The optical porosity measured by optical image analysis (OIA) on thin sections is \(\approx\)4.1 \(\pm\) 0.15% and the average pore size is (\(\approx\)20 um).  
The major element composition of the samples before CO\({}_{2}\) exposure was determined by XRF (Table 1 Rock before CO\({}_{2}\) column). Table 1 shows the values in weight % and their uncertainty range.  
The optimal mineral composition corresponding to the XRF analyses and the rock description was obtained applying numerical correction. The initial mineral composition of the studied rock was 92.8 wt % calcite, 4.49 wt % dolomite, 1.33 wt % quartz, 1.21 wt % illite, 0.17 wt % pyrite and some trace of gypsum.
## 3 Background
### Feature Extraction  
#### 3.5.1 Term Frequency-Inverse Document Frequency (TF-IDF)  
TF-IDF tackles a specific issue that may not occur frequently in our corpus but is immensely significant. The TF-IDF value escalates in proportion to the frequency of a word's occurrence in a document, but it reduces in relation to the number of documents in the corpus containing the word [49]. TF-IDF consists of two sub-parts, which are:  
* Term Frequency (TF), which is calculated by Equation (7), represents the frequency of occurrence of a term within the entire document, which can be interpreted as the likelihood of discovering a particular word in the document. It is computed as the number of occurrences of a word \(w_{i}\) in a review \(r_{j}\), relative to the total number of words in the review \(r_{j}\);
* Inverse Document Frequency (IDF) is a metric that evaluates the frequency of a term across the documents in a corpus, as described by Equations (8) and (9). It emphasizes rare words that appear in only a few documents throughout the corpus and results in a high IDF score. The value of IDF is obtained by log-normalizing the ratio of the total number of documents \(D\) in the corpus to the number of documents that contain the term \(t\). \[\textit{tf-idf}(t,d)=tf(t,d)\times idf(t)\] (7) If \(smooth\_idf=False\), the formula for calculating \(idf(t)\) is the following: \[idf(t)=\log\frac{n}{df(t)}+1\] (8)  
Figure 2: The process of breaking down the utility matrix into matrices of latent factors.  
If \(smooth\_idf=True\) (this prevents zero division), the formula for calculating _idf(t)_ is the following:  
\[idf(t)=\log\frac{n+1}{df(t)+1}+1 \tag{9}\]  
where \(n\) is the total number of documents in the corpus and \(df(t)\) is the document frequency of \(t\); the document frequency is the number of documents in the corpus that contain the term \(t\).
# Evaluation of Symmetrical Exercises in Scoliosis by Using Thermal Scanning
###### Abstract  
(1) Background: Scoliosis affects about 3% of the population and the number of children diagnosed with this condition is increasing. Numerous studies have been conducted in recent years to observe the effectiveness of rehabilitation specific exercises for this condition. In the present study we aim to observe if symmetrical exercises activate the back muscles in the same way in the case of children with mild scoliosis and those without postural deviations; (2) Methods: We used the thermal imaging camera, which allows a non-invasive, painless investigation that provides real-time information about muscle activity. The study qualitatively assessed muscle activation during exercises. In this study, 30 children were divided into two groups: 15 children diagnosed with mild scoliosis and 15 children without postural deviations; (3) Results: Acquisition of images after each exercise revealed an imbalance in the functioning of the back muscles in children with scoliosis, with areas of higher temperature after exercise on the convexity side of the scoliotic curve. In the second experiment in which children with scoliosis performed the required exercises under the supervision of a physiotherapist, they showed a symmetrical activation of the back muscles on the right and left side of the back; (4) Conclusions: In children without postural deviations, symmetrical exercises activate the muscles equally on the right and left sides of the back. In the case of children with scoliosis, the symmetrical exercises indicated in the rehabilitation programs should be performed only under the supervision of a physiotherapist to properly activate the back muscles.  
thermal imaging camera; exercise efficiency assessment; scoliosis +
Footnote †: journal: Applied sciences