esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Recommendations for Future Works and Challenges  
Indicators of PCM-concrete's thermal performance and its composition may now be visualised thanks to the updated research, as summarised in Tables 1-3. The following is an example of a collection of suggestions for further study:  
1. Further research is needed to investigate novel PCM materials with increased latent heat and acceptable phase change temperatures for particular environments, climates, and applications.
2. The distribution of PCM inside the concrete mixture has an essential effect on the rate of heat transfer. Thus, a specific line of research to optimise the distribution and content of PCM is required to increase thermal conductivity and boost heat transfer effectiveness.
3. Examining the durability and suitability of PCMs for use with various concrete mixes is interesting research to consider any potential volume changes and micro-cracking that may occur during phase transitions.
4. Future research is needed to assess the real freeze-thaw performance of Microencapsulated mortar since this property depends not only on the heat cycles but also on the material's strength.
5. More research into the effects of specimen size, material composition, and environment on PCM's thermal efficiency, as well as cost-effectiveness analysis and durability evaluation, is needed.
6. To get an accurate prediction of the wall of an encapsulated ceramsite-based encapsulated thermal storage aggregate, it will be necessary to conduct a long-term examination of thermal storage stability and to design an adequate matching temperature programme control system.
7. The same methodology may be used in the study of encapsulated PCMs with increased heat storage capacity. The suggested mix design allows for the construction and examination of models of large-scale buildings.
8. Examine the best building orientation and architectural layout for optimising the advantages of PCM-enhanced concrete in various temperature zones.
9. Conducting an economic evaluation to determine whether PCM integration in the concrete industry is cost-effective is important. However, it should consider both immediate building costs and long-term energy savings.  
Conceptualisation, F.L.R. and M.A.A.-O.; methodology, F.L.R., A.D. and A.H.; software, H.B.M.; validation, F.L.R., L.F.A.B., H.B.M. and A.H.; formal analysis, M.A.A.-O., L.F.A.B. and M.A.E.; investigation, F.L.R.; resources, A.D. and A.H.; data curation, F.L.R., A.D. and M.A.A.-O; writing--original draft preparation, F.L.R., M.A.-O., A.D., L.F.A.B., H.B.M., M.A.E. and A.H.; writing--review and editing, F.L.R., M.A.A.-O., A.D., L.F.A.B., M.A.E. and A.H.; visualisation, F.L.R.; supervision, F.L.R. and A.D.; project administration, F.L.R. and A.D.; funding acquisition, A.H., L.F.A.B. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The financial support of Kerbala University and the University of Warith Al-Anbiya in Iraq is gratefully acknowledged.  
The authors declare no conflict of interest.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
###### Abstract  
Haze significantly degrades image quality by reducing contrast and blurring object boundaries, which impairs the performance of computer vision systems. Among various approaches, single-image dehazing remains particularly challenging due to the absence of depth information. While Vision Transformer (ViT)-based models have achieved remarkable results by leveraging multi-head attention and large effective receptive fields, their high computational complexity limits their applicability in real-time and embedded systems. To address this limitation, we propose MLKD-Net, a lightweight CNN-based model that incorporates a novel Multi-Head Large Kernel Block (MLKD), which is based on the Multi-Head Large Kernel Attention (MLKA) mechanism. This structure preserves the benefits of large receptive fields and a multi-head design while also ensuring compactness and computational efficiency. MLKD-Net achieves a PSNR of 37.42 dB on the SOTS-Outdoor dataset while using 90.9% fewer parameters than leading Transformer-based models. Furthermore, it demonstrates real-time performance with 55.24 ms per image (18.2 FPS) on the NVIDIA Jetson Orin Nano in TensorRT-INT8 mode. These results highlight its effectiveness and practicality for resource-constrained, real-time image dehazing applications.  
Keywords:single-image dehazing; convolutional neural networks; multi-head large kernel attention; real-time inference
# Analysing Mouse Skin Cell Behaviour under a Non-Thermal kHz Plasma Jet
Depending on the dosage of nitrite (NO\({}_{2}\)\({}^{-}\)) and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), one can obtain both positive (cell proliferation, cell differentiation, angiogenesis) and negative (apoptosis, necrosis, growth arrest) effects on cells [1; 2; 9; 14]. Both positive and negative effects can be useful in different applications. For example, in cancer treatment, it is useful to induce damage to target cells, but in open wound treatment, cells should not be damaged at all. Depending on the required result, there are different approaches to treating cells: direct and indirect. The direct approach involves direct plasma treatment of biological cells while they are inside the cell culture medium. Indirect plasma treatment means that a liquid medium is treated with plasma (a plasma-treated medium (PAM)) and is subsequently added to the cells. One difference between these approaches is the species--produced in the gas phase--that reach and interact with the cells [15]. In the case of direct plasma treatment, both short- and long-lived species will interact with cells. In contrast, with an indirect approach, only long-lived species produced in the gas phase will interact with cells. However, this does not imply that there are no short-lived species inside the liquid; they form again when long-lived species decompose and recombine. Both direct and indirect plasma treatments influence cell processes and morphology changes [16]. This research aimed to create a simple single-electrode, kHz-driven plasma jet and examine its effect on L929 murine fibroblast cells. The motive was to explore this type of plasma jet's usability for topical skin treatment--and consequently for wound healing or gene delivery--and then compare this source with other similar approaches and investigations. To do so, we chose argon plasma, which is relatively unexplored, for direct cell treatment on healthy mouse skin cells. Direct plasma treatment was chosen in order to shorten the procedure, as indirect plasma-treated cells must be incubated in PAM for at least 24 h. Argon was chosen over helium gas because of its affordability and processing sustainability. Moreover, under the same conditions, argon plasma has higher electron density but lower electron temperature, which was considered an advantage for direct cell treatment [17].
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Algorithm Design  
As shown in Algorithm 1, an online data transmission and energy management (DTEM) method is proposed to obtain optimal solutions for energy-efficient data transmis  
Figure 2: Weighted bipartite graph: the transformed link assignment.  
sion problems. The designed algorithm consists of four steps. Firstly, we can obtain the optimal solution for the EHO problem (lines 1-5). Secondly, the solution for the DACO problem is proposed to obtain an optimal data acquisition rate (lines 6-13). Then, we formulate a many-to-many matching problem to solve the problem of link allocation (line 14). Based on this, we can obtain the optimal solution for the LTO problem (lines 15-19). Finally, the data queue \(\mathcal{D}_{i}(t+1)\) and energy queue \(E_{i}(t+1)\) are calculated based on the obtained optimal solutions, i.e., \(\rho_{i}^{*}(t)\), \(\varphi_{i}^{*}(t)\), \(\delta_{ij}^{*}(t)\) and \(\gamma_{i}^{*}(t)\) (lines 20-22).  
```
Input :\(\mathcal{D}_{i}(t),E_{i}(t),\forall i\in M\). Output :\(\varphi^{*}(t),\varphi^{*}(t),\delta_{ij}^{*}(t),\gamma_{i}^{*}(t),\mathcal{D} (t+1),E(t+1)\). /* Solution for the EHO problem */
1 for\(i\in M\)do
2 if\(\rho_{i}(t)(\tau-\omega_{i}(t))\leq\mathcal{B}-E_{i}(t)\)then
3\(\rho_{i}^{*}(t)=\rho_{i}(t)\).
4 else
5\(\rho_{i}^{*}(t)=(\mathcal{B}-E_{i}(t))/(\tau-\omega_{i}(t))\).
6 /* Solution for the DACO problem */
7 for\(i\in M\)do
8\(F_{L}=(V\varphi_{\max})/(\tau D_{i}(t)\varphi_{\max}-\tau P_{T}(E_{i}(t)- \mathcal{B}))-1\).
9 if\(F_{L}\geq\varphi_{\max}\)then
10\(\varphi_{i}^{*}(t)=\varphi_{\max}\).
11 else
12\(\varphi_{i}^{*}(t)=0\).
13 else
14\(\varphi_{i}^{*}(t)=F_{L}\).
15 /* Solution for the LTO problem */
16 Solve the LAO problem to obtain \(\delta_{ij}^{*}(t)\).
17 for\(i\in M\)do
18 if\(\sum_{j=1}^{N}\delta_{ij}(t)==1\)then
19\(\gamma_{i}^{*}(t)=\zeta_{i}(t)\).
20 else
21\(\gamma_{i}^{*}(t)=0\).
22 /* Update data queue and energy queue */
23 for\(i\in M\)do
24 Compute \(\mathcal{D}_{i}(t+1)\) according to (6).
25 Compute \(E_{i}(t+1)\) according to (8).
```  
**Algorithm 1**DTEM  
We analyze the time complexity of the DTEM algorithm including three parts, i.e., EMO, DACO, and LTO. Since the problems EMO and DACO are independent for each user satellite and can be solved by distributed computing. Therefore, the time complexity of the LTO problem can be regarded as that of DTEM, which is determined by the KM algorithm. Hence, the complexity of the DTEM algorithm is to solve the KM algorithm in the worst case, i.e., \(O((\max[K*N,M])^{3})\).
# Key Roles of Plasmonics in Wireless THz Nanocommunications--A Survey
## 5 Plasmonic THz Wireless Nanoscale Link Components
### THz Band Nanotransceivers
#### 5.2.1 THz Band Transmitters  
For the last few years, silicon photonic devices have been widely adopted for equipping current THz band transceivers, with desired features such as large bandwidth, high transmission power and high detection sensitivity. However, high path losses at the THz band is still considered a major unsolved challenge. As far as concerns THz band transmitters, photonic III/V (InP) devices, such as UTC-PDs with mW power levels at 300 GHz [135], and quantum cascade lasers (QCLs) [136], are considered as state of the art sources. The latter, however, requires an external laser for optical electron pumping, thus limiting the area overhead, while also performing poorly at room temperature [137]. SiGe-based heterojunction bipolar transistors (HBTs) have been also proposed in [138], for equipping THz transmitters operating at 820 GHz.  
QCLs at the THz band, such as plasmonic QCLs, or THz QCLs [139] with metallic cavities [140], which belong to the plasmonic laser family, may be a promising candidate for THz transmission, but researchers have to tackle with tunability potential at these high frequencies. Recently, a new tuning mechanism called the antenna feedback mechanism, has been developed for single mode metal-clad plasmonic lasers, based on the principle that the refractive index of the laser's surrounding medium affects the resonant cavity mode as much as the refractive index of gain medium inside the cavity [141]. This mechanism leads to the generation of hybrid SPPs propagating outside the cavity of the laser with a large spatial extent. The emission frequency of the plasmonic laser and its tunability potential are dependent strongly on the effective propagation index of the SPP mode in the surrounding medium, by coupling the resonant SPP mode to a highly directional far field radiation pattern, and integrating it, with the hybrid SPPs of the surrounding medium [142].  
Such an antenna feedback principle is shown in Figure 17. Specifically, the general principle of a DFB can be implemented in a THz QCL by introducing periodic slits or holes in its metallic cladding. A parallel-plate metallic cavity is illustrated (Figure 17a). Due to the periodicity of conventional DFBs there is a phase-mismatch between successive apertures for SPP waves on either side of the cladding (Figure 17b). The designed grating period of antenna-feedback effectively couples a single-sided SPP wave that travels in the surrounding medium with the SPP wave traveling inside the active medium (Figure 17c). The antenna-feedback scheme leads to a buildup of in-phase condition at each aperture between counter propagating SPP waves on the either side of metal-cladding. Emission from each aperture adds up coherently and constructively to couple to far-field radiation in the z direction (Figure 17d).  
Figure 16: Plasmonic transceiver block diagram.
## 4 Experimental Results
### Experimental Results in Scenario 1  
Figure 9 first demonstrates the proposed vehicular VLC system's ability to transfer driving dynamics data (e.g., the acceleration data in this experiment) between the leading and middle vehicles within a tunnel. The figure highlights that the communication link between the two vehicles remains highly reliable, with a maximum error of 0.76, even during hard braking events where acceleration sharply decreases. Moreover, Figure 9 illustrates the functionality of the data exchange mechanism described in Section III-F. As shown, the middle vehicle compares its own acceleration data with the data received from the leading vehicle to decide which value to transmit to the trailing vehicle. The experiment confirms that the middle vehicle successfully selects and transmits the smaller of the two values, ensuring the accuracy of the information passed along the chain. Finally, Figure 9 provides a comparison between the data transmitted by the middle vehicle and the data received by the trailing vehicle. This experiment, conducted using a scenario where vehicles are driving at regular intervals and the leading vehicle makes a sudden stop, demonstrates that the VLC-based chain network enables reliable communication in the tunnel, effectively transmitting hard braking information to prevent chain collisions.  
Figure 8: Experimental environments.
## 3 System Performance Analysis  
Many factors affect the system performance of wireless networks. The authors of [34] investigated the causes of OP at the BS, such as brief BS power supply variation, preventive BS activity state transition due to excessive temperature increase or decrease inside the BS rack, auto-recovery software and hardware failure, and temporal cell interference and congestion. The downlink MIMO-NOMA model with superposition transmission at BS and SIC at the terminal devices and the SIC processing is adopted in receiver side [35]. Therefore, our study considered OP at the receivers when the receivers could not successfully decode messages in the received signals. We analyze the system performance of the network model depicted in Figure 1 and delivers novel closed-forms of OP, system throughput and EE expressions at coupled relays \(\mathcal{R}=\{R_{1},R_{2}\}\) and devices \(D_{n}\) for \(n=\{1,\ldots,N\}\).
## 4 Experiments
### Experimental Results
#### 4.5.3 Number of Residual Blocks in Dynamic Temporal Module  
To determine the appropriate number of residual blocks in the model, we selected different numbers of residual blocks and performed experiments. The experimental results are presented in  
Figure 9: **(a)** Test Mean RMSE of 12 steps versus the number of training epochs on PeMSD7 dataset. **(b)** Fault-tolerance comparison on PeMSD7 dataset.  
Figure 10a. As the number of residual blocks increases, the prediction performance of the model improves. However, after the number of residual blocks reaches 4, the accuracy of the model does not continue to improve or even becomes worse, and the training time of the model also increases greatly. Finally, four residual blocks are used in the dynamic temporal module of our model.
## 5 Results and Discussions  
### Unit Root Tests Results  
Table 2 reports the results of the Augmented Dickey-Fuller unit root test and Phillips-Perron unit root test with intercept, and then both intercept and trend.  
Augmented Dickey-Fuller unit root test results and Phillips-Perron unit root test results indicated that the significance of variables at 1%, 5%, and 10%, and none of the variables was integrated with the order of I(2); then, the ARDL model employed. Numerous cointegration approaches are available in empirical literature to test cointegration between the series, but the ARDL bounds testing is considered to be superior and preferable due to its various advantages such as: (i) no need for all of the variables in the system to be of an equal order of integration; (ii) it is an efficient estimator, even if samples are small and some of the regressors are endogenous; (iii) it allows the variables to maybe have different optimal lags; and (iv) it employs a single reduced-form equation. Thus, we opted for the ARDL approach to cointegration because of its simplicity and its suitability to models where the involved variables are of mixed order of integration [70].
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### In-Office Therapeutic Treatments of Dentin Hypersensitivity
#### 6.2.1 Potassium Salts Gels  
Potassium salts-containing products for in-office treatments are marketed mainly in the form of gels. In particular, potassium-based agents facilitate the elevation of potassium ion (K\({}^{+}\)) concentration in nerve endings, thereby diminishing the nerve's capacity to transmit sensory stimuli and modifying its action potential [2,100]. When these salts are applied to the exposed dentin, they increase the concentration of K\({}^{+}\) in the tubules and surrounding tissues, influencing the normal depolarization and repolarization processes of nerve cells [101]. More specifically, nerve cells rely on the movement of various ions, including Na\({}^{+}\) and K\({}^{+}\), to generate and transmit electrical signals, known as action potentials. When a nerve is stimulated, Na\({}^{+}\) ions rush into the cell, causing depolarization, which is a change in the electrical potential of the cell membrane. This depolarization triggers the transmission of the nerve impulse along the nerve fiber. Potassium ions play a crucial role in repolarizing the nerve cell after the action potential has passed. They move out of the cell, helping to restore the cell's resting electrical state. However, when potassium salts are present in high concentrations, as in the case of potassium-based agents used for treating DH, the movement of K\({}^{+}\) becomes disrupted. The elevated concentration of K\({}^{+}\) outside the nerve cells interferes with their ability to repolarize efficiently. This results in a dampened or altered response to sensory stimuli. Essentially, the nerve cell becomes less responsive to subsequent stimuli, reducing its capacity to transmit pain signals caused by external triggers like temperature changes or mechanical pressure. This mechanism contributes to the reduction of DH by impeding the transmission of nerve impulses associated with pain sensation [102].  
Figure 2: Occlusion of the orifices of the dentin tubules after treatments for dentin hypersensitivity. The rate of occlusion is measured by the reduction of the diameter of the tubules (%). (**a**) 0% reduction, (**b**) 50% reduction, and (**c**) 100% reduction of the diameter of the tubules.
# Unexpected Ferromagnetism--A Review
## 2 Examples of \(d^{0}\) Ferromagnetism
### Non-Magnetic Oxides
#### 2.2.2 MgO  
Magnesium oxide (MgO) has a well-known electronic configuration with empty \(d\)-orbitals and no free electrons. However, regardless of these known parameters, MgO was observed to display room temperature ferromagnetic behavior, mostly attributed to defects and vacancies, either intrinsic or generated by dopants [27; 28; 29].  
Studies on MgO thin films have shown that the room temperature ferromagnetic behavior is not due to contaminants but rather due to cation vacancies [30]. This was confirmed by later studies on undoped MgO thin films in which the exhibited magnetism was correlated with the Mg vacancies. Moreover, it was added that there is a link between the crystallinity of the material and the ferromagnetic behavior, in regard to the fact that the magnetism can be enhanced with the decrease in crystallinity [31; 32].  
A recent study on the matter was conducted on inkjet-printed MgO thin films deposited on Si substrates. After spectroscopic investigations, it was concluded that the magnetization displayed by the MgO thin films was not due to contamination but rather due to Mg vacancies. Moreover, it was observed that vacancy concentrations could be adjusted by the pH value of the ink [33].  
Room-temperature ferromagnetism on nanosized powders was also investigated. In a study on MgO powders achieved by thermal decomposition, different phase compositions, morphologies, and magnetic properties were investigated. It was concluded that the room temperature ferromagnetism exhibited as a result of several factors, such as Mg vacancies, the different phase compositions attained by different calcination temperatures, and the degree of crystallinity [34].
## 2 Mathematical Formulation
### Model Interpretation  
The optimization model defined in (1) to (9) can be interpreted as follows: Equation (1) corresponds to the objective function regarding the simultaneous maximization of the active power consumption in all the nodes in the network; Equations (2) and (3) outline the application of [PERSON]'s theorem to all the nodes in the network (i.e., the apparent power definition to each node) [36], which generates the classical active and reactive power balance constraints; inequality constraint (4) defines the voltage regulation limits in all the nodes of the grid (this constraint is generally defined by regulatory policies and utility operative practices); inequality constraint (5) limits the maximum size of the dispersed generator that can be connected at node \(i\); inequality constraint (6) defines the maximum number of dispersed generators that can be installed along the distribution network; inequality constraint (7) defines the maximum percentage of dispersed generation penetration in the entire distribution network; restriction (8) indicates the binary nature of the decision variable \(x_{i}\); and inequality constraint (9) represents the positiveness definition of the loadability coefficient.  
**Remark 1**.: _The core complication of the optimization model (1)-(9) corresponds to the non-convexity of the solution space due to the presence of products among decision variables and trigonometric functions in the active and reactive power balance constraints, in addition to the binary nature of the decision variable \(x_{i}\). As a result, it is necessary to use advanced optimization techniques in conjunction with its solution, as presented in the following section._