esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
For the ACO-OFDM branch in the PEADO-OFDM and ADO-OFDM schemes, the difference in BER performance becomes obvious when \(N_{ACO}\) decreases for the reason that the power of PEADO-OFDM signals is lower than ADO-OFDM signals and the difference is increasing with the decrease in \(N_{ACO}\). In Figure 5c, the overall BER performances of the PEADO-OFDM and ADO-OFDM schemes with different \(N_{ACO}\) in the clear water are performed. For any \(N_{ACO}\), the PEADO-OFDM scheme has a better overall BER performance than ADO-OFDM and ADO-OFDM schemes with different \(N_{ACO}\) in the same water. Figure 5: BER performance comparison of **(a)** the DCO-OFDM branch, **(b)** the ACO-OFDM branch and **(c)** the overall dual-branch ADO-OFDM and dual-branch PEADO-OFDM with different \(N_{ACO}\) in coastal water channel. mance than the conventional ADO-OFDM scheme. To obtain an overall BER of \(10^{-3}\), the PEADO-OFDM scheme achieves about 0.16 dB, 0.29 dB and 0.28 dB gains compared with the enhanced ADO-OFDM scheme when \(N_{ACO}=1024\), 512 and 256, correspondingly. For the other UOCM channels, the same analysis can obtain similar results. The proposed modified PEADO-OFDM scheme for the UOWC system not only achieves a lower BER performance on communications, but also allows for easier system scaling. For example, it can be further extended to a multi-channel parallel communication system by using a multi-wavelength transmitter with a blue/green range or spatial division. Such a UOWC system design with the proposed highly efficient scheme proves the communication potential of blue/green LDs, which will play an important role in future high-speed UOWC, integrated systems for underwater drones and underwater information interaction applications.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
###### Abstract  
In order to combat the impulse noise and channel time-frequency selective fading, it is essential to use channel interleavers. Among them, the block interleaver has been widely used in terrestrial broadcasting systems such as DVB-T2 and ATSC 3.0, since it can better reflect the mapping relationship of data units on time-frequency resources. Due to the limited value of the interleaving structure parameter, the existing block interleavers cannot fully exploit time and frequency diversities, which leads to a loss of partial diversity gains. To address this problem, this paper proposes a generic block interleaver for channel interleaving through jointly considering the time and frequency diversities. First, row crossing is performed according to a certain interval, which will increase the interleaving distance; then, cyclic shift is used to achieve a greater time diversity gain. The proposed method can achieve flexible parameter configuration, which can effectively increase the scalability of the parameters and make the interleaver more versatile. Theoretical analysis shows that a larger interleaving distance is obtained and the multiplicity of minimum distance is significantly reduced. The simulation results of the fixed and mobile reception scenarios in the terrestrial broadcasting systems demonstrate the effectiveness of proposed interleaver compared to existing block interleavers.  
block interleaver; time and frequency interleaving; terrestrial broadcasting; interleaving distance; cyclic shift +
Footnote †: journal: 2022, 12, 2025. [[https://doi.org/10.3390/app12042025](https://doi.org/10.3390/app12042025)]([https://doi.org/10.3390/app12042025](https://doi.org/10.3390/app12042025))
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 4 SA with Incomplete Cost Evaluation Applied to EIT  
EIT is a noninvasive imaging modality that provides safe medical imaging through a non-ionizing method. This technique converts its internal electrical conductivity distribution into a grayscale value, defining an image. Assuming there is no access to the interior of the body, the conductivity distribution must be inferred from electrical potential measurements taken on the surface of the body [28].  
Several medical applications use EIT, which include the continuous observation of pulmonary function to assess the effects of therapeutic maneuvers on the regional distribution of ventilation [29,30], as well as monitoring cardiac activity [31]. Several nonclinical applications also use EIT, such as crack detection on mechanical components, detection of minerals in the soil, soil pollution monitoring, the visualization of multiphase flow, and real-time monitoring of industrial processes [32,33].  
Figure 3: Plot of the functions with two variables. (**a**) Sphere, (**b**) [PERSON], (**c**) [PERSON], (**d**) [PERSON], (**e**) [PERSON], (**f**) [PERSON], (**g**) [PERSON].  
The EIT reconstruction problem can be solved by minimizing the difference between the observed data and a simulated impedance problem. The use of objective function derivatives is not recommended, as the EIT reconstruction is a nonlinear, nonconvex, and often ill-posed optimization problem. The objective function requires the solution of several Finite Element Method (FEM) problems, making it very computationally expensive. This work proposes a new SA approach in which the objective function is partially evaluated, while ensuring the boundaries of the proposed algorithm [34]. SA with an incomplete cost evaluation applied to EIT considers that the objective function returns an interval and a value. The value is an estimate of the precise objective function value and the interval contains the precise objective function value. It is shown below that the acceptance and rejection decision is not as simple as shown in Algorithm 2.
## 2 Proposed Methodology
### Problem Formulation of Fixture Layout and Topology Optimization  
The objective function of this research is to minimize the total deformation normal to the plane of the workpiece, while maintaining individual nodes deformation up to 2 mm. This research does not include clamping force and area as parameters in minimizing deformation in sheet metals. The following Equation (1) represents the objective function for Fixture layout optimization. Equations (2) and (3) represent the constraining condition and clamp, ranges respectively. The problem formulation of fixture layout optimization is represented as:  
\[\text{Minimize}\ \sum\delta_{i} \tag{1}\]  
\[\delta_{max}\leq\ 2\ \text{m}\quad i=1,2,3\ldots,n \tag{2}\]  
\[\text{Subject to}\ a_{j}\leq x_{j}\leq b_{j}\qquad j=1,2,3\ldots,t \tag{3}\]  
\[\begin{array}{c}c_{j}\leq y_{j}\leq d_{j}\\ g_{j}\leq z_{j}\leq k_{j}\end{array}\]  
where \(\delta_{i}\): nodal deformation  
\(n\): number of finite elements  
\(t\): primary plane clamps  
\(a\), \(b\), \(c\), \(d\), \(g\), \(k\): Limit of clamps _in_\(x\), \(y\), \(z\) direction  
Minimizing compliance, as shown in Equation (4), is the objective function [38]. Equation (5) represents the law of elasticity. Equation (6) shows displacement constraint,  
Figure 1: General procedure of proposed method.  
whereas Equation (7) represents mass constraint. Equation (8) indicates volume constraint. Equation (9) represents the density range of elements [35]. The problem formulation of topology optimization is represented as:  
\[\text{Minimize}\;\;\text{C}(\rho_{i})=\text{F}^{T}\text{U} \tag{4}\]  
\[\text{Subject to}\;\;\text{F}=\text{K}(\rho)\text{U}\quad i=1,2,\ldots,n \tag{5}\]  
\[\text{u}\;\leq\;\overset{-}{\text{u}} \tag{6}\]  
\[\text{m}=\overset{-}{\text{M}}*\text{m}_{\text{o}} \tag{7}\]  
\[\sum\;v_{i}\;\rho_{i}\;\leq\text{V} \tag{8}\]  
\[0<\rho_{min}\leq\rho_{i}\leq 1 \tag{9}\]  
where C: compliance of structure  
F: global load vector  
\(\rho_{i}\): density of \(i\)th element  
U: global displacement vector  
K: global stiffness matrix  
n: number of finite elements  
u: displacement value at point of interest  
\(\overset{-}{\text{u}}\): presetting max allowable value  
m: mass after topology optimization  
\(\text{m}_{\text{o}}\): initial mass  
\(\overset{-}{\text{M}}\) : mass fraction constraint  
\(v_{i}\): volume of \(i\)th element  
V: total volume of structure
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 2 Materials and Methods
### Subjects and Experimental Set-Up  
The presented pilot analyses were carried out on a group of 23 healthy young adults (age 28.3 \(\pm\) 4.0 years) aiming at setting up a processing pipeline suited to clinical applications employing CW-fNIRS instrumentation. Experimental data were acquired at IRCCS Fondazione Don Carlo Gnocchi, Milan. The instrument was the NIRScoutX 32 \(\times\) 32 (NIRx Medizintechnik, Berlin, Germany). This system performs functional measurements at 760 and 850 nm wavelengths and employs LEDs as the source technology and avalanche photodiodes as detectors. The head cap consisted of 32 detectors and 32 sources locations, making a total of 102 measurement channels between them with a resulting sampling frequency of 1.9531 Hz. The mean source-detector distance, referring to the virtual co-registration of this configuration in the Colin27 atlas in AtlasViewer software package [45], was 3.46 cm (Figure 1).  
The functional task consisted of a motor-grasping task where participants were asked to alternatively move their left or right hand by repeatedly squeezing sponge balls placed  
Figure 1: Employed fNIRS probe configuration in the current study. (**Left**) Red and green dots indicate the respective location of LED sources and fiber optics detectors according to the 10–10 reference system labeling. (**Right**) Virtual co-registration of source (red dots) and detector (blue dots) locations over the Colin27 anatomical template in the AtlasViewer software package [45].  
in their palms. Together with the similar version of finger-tapping, this functional task is widely used in fNIRS literature for assessing cortical activation associated with motor functions over motor and somato-sensory areas mapping the hand [46]. A block-design paradigm was adopted, and a schematic representation of the timing is presented in Figure 2. Participants had to sit still in a dim light room watching a stimulus presentation screen with a cross placed at its center, which represented the resting condition. Conversely, the task condition was the repeated left vs. right grasp and release of the sponge ball when the cross blinked at 0.8 Hz over the left vs. right side of the screen. The task condition lasted 10 s followed by 20 s of the resting condition, while stimulus presentation was randomized between the hand movement and repeated 10 times. No adverse conditions were simulated during the data acquisition.
## 4 Results
### Test Analysis of Dynamic Penetration  
The test results for the solid dynamic penetration before and after grouting of No. 1-3 piles are shown in Figures 10-12.  
Figure 9: Test result of transmission wave in the No. 2 pile: (**a**) original data; (**b**) processed Data.  
It can be seen from Figure 10 to Figure 12 that the strength of the undisturbed soil at the bottom of the No. 1 pile and the No. 3 pile is relatively large, and relatively speaking, the strength of the undisturbed soil at the bottom of the No. 2 pile is the worst. For example, when the number of hammer blows is 10, the cumulative penetration of the undisturbed soil dynamic penetration of the pile bottom before grouting is 8.8 cm and 9.1 cm for the No. 1 pile and No. 3 pile, respectively, and the cumulative penetration degree for No. 2 pile is 10.5 cm. It is 19.3% and 15.4% larger than the No. 1 pile and No. 3 pile, respectively. This shows that the bearing capacity of the adjacent piles on the same site is still different due to the slight differences in geological and construction conditions [25]; the slope of the curve in the figure is gradually gentle, that is, the single penetration depth of the three piles  
Figure 11: Comparison of dynamic penetration test of No. 2 pile before and after grouting.  
Figure 12: Comparison of dynamic penetration test of No. 3 pile before and after grouting.  
Figure 10: Comparison of dynamic penetration test of No. 1 pile before and after grouting.  
gradually decreases with the increase in the number of hammer blows. The first hammering depths of the three piles were 1.5 cm, 1.2 cm, and 1.3 cm, and the last hammering depths were 0.8 cm, 0.9 cm, and 0.6 cm, which shows that as the soil at the pile base is continuously hammered and compacted, the strength becomes larger, which conforms to the general law of dynamic penetration [23; 24].  
The grouting amount of the No. 1 pile is 500 kg. The dynamic penetration results before and after grouting are shown in Figure 10. After the No. 1 pile base is grouted, the number of hammer blows required to reach 10 cm is 17 times. The cumulative depth of dynamic penetration before grouting has reached 10.3 cm, while the cumulative depth after grouting is only 7.7 cm.  
The grouting amount of the No. 2 pile is 250 kg. The dynamic penetration results before and after grouting are shown in Figure 11. After the No. 2 pile base is grouted, the number of hammer blows required for dynamic penetration to reach 10 cm is 13 times. The cumulative depth of dynamic penetration before grouting has reached 10.5 cm, while the cumulative depth after grouting is only 8.2 cm.  
Although the No. 3 pile was not grouted, a dynamic penetration comparison test was also completed at the same time. As can be seen from Figure 12, the number of hammer blows in both tests was about 12, and the curves are roughly coincident, which is consistent with reality.
## 6 Virtual Sensor Design
To settle the design problem, the system (46) is transformed into a Linear Parameter-Varying (LPV) form:  
\[\mathbf{e}_{d,k+1}=\sum_{i=\{f,r\},j=\{l,r\}}p_{x,i,j}\mathbf{X}^{i,j}\mathbf{e}_{d,k}+\mathbf{ Z}\bar{\mathbf{w}}_{k}, \tag{56}\]  
where  
\[\mathbf{X}^{i,j}=\mathbf{I}-\mathbf{L}\mathbf{E}^{i,j} \tag{57}\]  
with  
\[\mathbf{E}^{f,l}=T_{s} \begin{bmatrix}0&-1&0&0&0\\ -p_{f,l}&R_{e}&0&0&0\\ -p_{f,r}&0&R_{e}&0&0\\ -p_{r,l}&0&0&R_{e}&0\\ -p_{r,r}&0&0&0&R_{e}\end{bmatrix} ,\mathbf{E}^{f,r}=T_{s} \begin{bmatrix}0&0&-1&0&0 \\ -p_{f,l}&R_{e}&0&0&0\\ -p_{f,r}&0&R_{e}&0&0\\ -p_{r,l}&0&0&R_{e}&0\\ -p_{r,r}&0&0&0&R_{e}\end{bmatrix} ,\] \[\mathbf{E}^{r,l}=T_{s} \begin{bmatrix}0&0&0&-1&0\\ -p_{f,l}&R_{e}&0&0&0\\ -p_{r,l}&0&0&R_{e}&0\\ -p_{r,r}&0&0&0&R_{e}\end{bmatrix} ,\mathbf{E}^{r,r}=T_{s} \begin{bmatrix}0&0&0&0&-1 \\ -p_{f,l}&R_{e}&0&0&0\\ -p_{f,r}&0&R_{e}&0&0\\ -p_{r,l}&0&0&R_{e}&0\\ -p_{r,r}&0&0&0&R_{e}\end{bmatrix} .\]  
Thus, Theorem 1, can be reformulated in the following fashion:  
**Theorem 2**. : _The system (56) is strictly quadratically bounded for all \(\mathbf{E}_{k}\) and all allowable \(\bar{\mathbf{w}}_{k}\in\mathcal{E}_{w}\) if there exist \(\mathbf{N},\mathbf{P}\succ\mathbf{0}\) and \(0<\alpha<1\), the following conditions are satisfied:_  
\[ \begin{bmatrix}-\mathbf{P}+\alpha\mathbf{P}&\mathbf{0}&\mathbf{P}-(\mathbf{E}^{i,j})^ {T}\mathbf{N}^{T}\\ \mathbf{0}&-\alpha\mathbf{Q}_{w}&\mathbf{R}^{T}\\ \mathbf{P}-\mathbf{N}\mathbf{E}^{i,j}&\mathbf{R}&-\mathbf{P}\end{bmatrix} \prec\mathbf{0},\] \[i=\{f,r\},j=\{l,r\}, \tag{58}\]  
_with \(\mathbf{R}= \begin{bmatrix}\mathbf{P}&-\mathbf{N}\mathbf{W}\end{bmatrix} \)._  
Finally, the design procedure of virtual sensors boils down to:Offline:
1. Select \(Q_{w}\) in (47). 2. Select \(0<\alpha<1\) and obtain the gain matrix \(\mathbf{L}\) of (43) by solving (58) and then substituting \(\mathbf{L}=\mathbf{P}^{-1}\mathbf{N}\). Online:
1. Set \(\hat{\mathbf{d}}_{0}\) and \(k=0\). 2. Obtain \(\hat{\mathbf{d}}_{k+1}\) with (43). 3. Set \(k=k+1\) and go to _Step 1_.
# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 3 Introduction to the P2E-YOLOv5 Network
### High-Resolution Small-Target Detection Head  
To address the challenges posed by the small target size and low resolution of the transmission line, this paper proposes the addition of a high-resolution P2 detection head to the Head part of YOLOv5. This enhancement aims to strengthen the feature fusion between shallow features of small targets and deep semantic information, thereby improving the feature extraction ability for small targets.  
In the original YOLOv5 network model, the backbone network obtains 5-layer (P1, P2, P3, P4, P5) feature expressions after 5 down-sampling operations, with Pj indicating a resolution of \(1/2\)\({}^{j}\) of the input image. In the Neck part of the network, top-down FPN and bottom-up PAN structures are used to achieve feature fusion at different scales, and object detection is performed using the detection head on the three-level feature map P3, P4, and P5.  
However, when dealing with the small-target data set in transmission lines, the pixel scale of these targets is often less than 32 \(\times\) 32. As a result, after multiple down-sampling operations, most of the detailed features and spatial information are lost, making it challenging for the higher resolution P3 layer detection head to effectively detect these small targets.  
To achieve the detection of the aforementioned small targets, a new detection head is introduced in the P2 layer feature map of the YOLOv5 network model, as illustrated in Figure 4.  
Firstly, the detection head has a high resolution of 160 \(\times\) 160 pixels, enabling it to detect tiny targets as small as 8 \(\times\) 8 pixels. This high resolution makes it highly sensitive to smaller targets, enhancing the ability to detect them accurately. The new high-resolution P2 detection head only requires two down-sampling operations on the backbone network, retaining extremely rich shallow feature information of small targets.  
Secondly, in the Neck part, the top-down features of the P2 layer are merged with the same-scale features of the backbone network, allowing for the fusion of shallow features  
Figure 3: The main network structure of P2E-YOLOv5 network consists of the following three parts: Backbone (backbone network), Neck (neck network), and Head (detection head).  
and deep semantic features. This fusion process results in output features that effectively combine multiple input features.  
Lastly, the integration of the P2 layer detection head with the original three detection heads effectively addresses the issue of scale variance. The P2 layer detection head is derived from low-level, high-resolution feature maps, which contain abundant detail features and semantic information related to small targets.  
As a result, this detection head becomes highly sensitive to detecting small targets in transmission lines. Although incorporating this detection head increases the computational complexity and memory requirements of the model, the substantial enhancement it brings to the detection accuracy of small targets justifies its inclusion.
## 3 Hybrid Business System Model
### Analysis for System SNR  
OFDM can convert a wireless channel into a flat channel across different sub-carriers, with several consecutive sub-carriers forming a spectral sub-bandwidth. We assume that the fading of the channel is roughly the same within a sub-band and that different sub-bands are independent of each other. If, during a slot interval, the URLLC service shares the transmission sub-channel with the mMTC service, the URLLC service will experience interference from the AIoT device transmitter, which can interfere with the URLLC service. Based on this analysis, when the URLLC link \(h\) and the mMTC link \(j\) are on the same sub-channel, the Signal Interference Plus Noise Ratio (SINR) of the URLLC service is calculated as follows:  
\[SINR_{h,n}=\frac{P_{n}^{h}g_{h,n}}{\frac{\gamma}{J}\rho_{j,n}P_{n}^{j}g_{j,n}+ \sigma^{2}}, \tag{1}\]  
where \(P_{n}^{h}\) is the power required to transmit the \(h\)-th URLLC service in the \(n\)-th bandwidth, \(g_{h}\) denotes the channel coefficient, and \(g_{j,n}\) is the interference coefficient caused by the transmission of the \(j\)-th mMTC service in \(n\)-th sub bandwidth. \(\rho_{j,n}\in\{0,1\}\) represents the sub-bandwidth allocation control coefficient: when \(\rho_{j,n}=1\), it denotes that the \(j\)-th mMTC service can use the \(n\)-th sub bandwidth for transmission, and \(\rho_{j,n}=0\) represents that the \(n\)-th sub bandwidth cannot be used. \(\sigma^{2}\) is the noise power spectral density. Similarly, the noise of the mMTC service mainly comes from the URLLC service in the same bandwidth. Then, when the \(j\)-th mMTC service uses the \(n\)-th bandwidth for transmission, the SINR of the mMTC service is calculated as follows:  
\[SINR_{j,n}=\frac{P_{n}^{j}g_{j,n}}{P_{n}^{h}g_{h,n}+\sum\limits_{\
eq j}^{J} \rho_{j,n}P_{n}^{j}g_{j^{\prime},n}^{j}+\sigma^{2}}, \tag{2}\]  
Figure 1: Multi-layer Collaborative Resource Scheduling Framework for Hybrid Service Coexistence System.  
where \(P_{n}^{j}\) is the power required to transmit the \(j\)-th mMTC service in the \(n\)-th bandwidth. \(\sum\limits_{j^{{}^{\prime}}\
eq j}^{l}\rho_{j^{{}^{\prime}},n}^{j}p_{n}^{j}g_{j^{{}^ {\prime}},n}\) represents the noise impact of the remaining mMTC services on the current mMTC service in the \(n\)-th bandwidth. \(\rho_{j^{{}^{\prime}},n}^{j}\) is the bandwidth allocation control coefficient for the remaining mMTC services. If \(\rho_{j^{{}^{\prime}},n}=1\), it means that more mMTC services can be added to the current sub-bandwidth for transmission.
# Towards a Glass New World: The Role of Ion-Exchange in Modern Technology
## 2 Glass Strengthening: The Ion-Exchange Contribution
### Chemical Strengthening: Applications
The effect of silver was however better, and a 20% photoluminescence intensity increase, assigned to energy transfer from silver clusters to Ho\({}^{3+}\) ions, was measured. Silver ion-exchange has been reported to enhance NIR emission also when not using rare earths, as with Bi-doped silicogramanate glasses [148]. In fact, lower valence Bi ions are responsible for NIR fluorescence, so that increasing their presence can improve the low efficiency of fibre amplifiers based on this element. In this case, the effect of silver ions introduced in the glass by ion-exchange followed by thermal annealing is not linked to energy transfer processes, but rather to the valence reduction effect on the Bi\({}^{3+}\) ions. This can be efficiently achieved only by introducing silver ions by ion-exchange, since the required valence state of silver is given by the process itself. The authors showed that Bi emission in the visible range was reduced and NIR emission increased by about twofold this process. Figure 3: Photoluminescence emission of Eu\({}^{3+}\) doped sodium-aluminosilicate glass as a function of the silver ions density. Reprinted with permission from [60] © The Optical Society. However, the presence of silver does not always enhance photoluminescence emission. In fact, as described for Ce-doped soda-lime silicate glasses by [PERSON] et al. [149], silver causes the reaction:  
\[\text{Ce}^{3+}+\text{Ag}^{+}\rightarrow\text{Ce}^{4+}+\text{Ag}^{0}.\]  
This in turn not only changes the emission photoluminescence spectrum but also its intensity, since Ce\({}^{4+}\) ions do not emit and even absorb both the UV excitation and the photoluminescence produced by Ce\({}^{3+}\) ions. In the case of photo-thermo-refractive (PTR) glasses, however, the same type of reaction has been exploited by [PERSON] et al. [74] to test the potential of these sodium-zinc-aluminosilicate glasses co-doped with Ce and Sb to serve as wavelength converters for enhancing the efficiency of PV cells. The authors tested the possibility of producing neutral silver molecular clusters in PTR glasses both by irradiating with UV radiation samples already doped with silver in bulk or by Ag\({}^{+}\)/Na\({}^{+}\) ion-exchange. In both cases an annealing step followed the first phase of the process. As a result, in all cases, silver molecular crystals were obtained, but their efficiency was different depending on the process. The PV cells placed behind the best sample obtained by ion-exchange reduced their overall efficiency, even if only slightly, whereas the sample with silver atoms dispersed at the time of initial melting showed a slight efficiency improvement respect to the as-synthesised cover glass.