esa-sceva/satcom-chunk-collection huggingface dataset:
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 3 Treatment of OA
### Surgical Intervention  
Individuals with severe OA (significant joint degradation, extreme discomfort, and function impairment), who have failed to improve with pharmacological and non-pharmacological therapies, are candidates for surgery [102; 103].  
The first surgical methods to restore structural stability include joint debridement using arthrotomy or arthroscopy. These procedures aim to eliminate loose cartilage, meniscus fragments, cartilage shaving, and osteophytes. It has been shown that these approaches provide specific improvements in pain reduction and functional capacity [103]. Arthroscopy is still the most commonly performed operation globally to aid in the mechanical mobility of the afflicted stiff knee [103; 104; 105].  
For those with OA who have reached the end stage of the disease, joint replacement is considered the last resort. Hip and knee replacement surgeries are excruciatingly painful and necessitate a lengthy recovery period. Furthermore, complete knee replacement produced negative consequences such as pulmonary embolism, surgical-associated infections, and mortality in some cases [87; 102; 105].  
Marrow stimulation methods, such as subchondral drilling, include the penetration of subchondral bone, which results in bleeding, migration of bone marrow stem cells to the site of damage, and the formation of a blood clot [106].  
The addition or removal of a tiny amount of bone above or below the joint to reduce pain and slow the degenerative process is known as osteotomy [102; 105].  
Conservative procedures, frequently reserved for young patients with early-stage lesions, leave damaged tissue behind. The anticipated outcome of arthroscopic lavage and cartilage debridement is alleviating symptoms by eliminating inflammatory debris and cytokines [102; 105]. Arthrodesi, or articulation-joining surgery, can be performed to boost strength and decrease pain.  
Cellular therapy has been utilized to treat cartilage since the 1980s. [PERSON] and colleagues first described an autologous chondrocyte implant (ACI) technique in 1994 [107]. Because it combines surgical and cell culture procedures, this procedure necessitates two rounds of surgery. Despite promising therapeutic outcomes, ACI has a few downsides. The principal disadvantages of periosteal flap surgery include delamination and periosteal hypertrophy. Another disadvantage of ACI, caused by fibro cartilage disorganization, is structural variability.  
Scientists are exploring strategies to make the existing chondrocyte phenotype more stable in vitro to find novel cells for cartilage tissue engineering. These methods are intended to restore joint tissue and reduce inflammation. MSCs are currently the most promising cell source for cartilage tissue development [108; 109].  
Table 1 lists the treatment methods of OA, the mode of administration, and their main therapeutic effects in degenerative disease.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network
### Underground WSNs  
Underground WSNs have also been considered terrestrial WSNs in the last decade. In this network, two types of wireless devices are considered, such as on-ground transceiver devices, while these devices avoid communication under the soil. In the soil sensor device, these are generally connected to gain access to data [40]. However, underground WSNs require a high cost to design, implement, and maintain the special sensor for the network communication. Mostly, application sensors are deployed under the ground for agriculture and minefields to monitor conditions in the soil, environment, infrastructure, border patrol, and security monitoring [41]. Recently, underground applications consist of agriculture for large-scale environmental monitoring to improve farm choice and to detect accidental infection such as soil monitoring, underground environments, and levels of water information under the surface [42]. On the other hand, WSNs are used to monitor underground environments such as mines and miners' security to transfer information in case of a disaster. However, the most difficult function is to certify safe working situations in coal mines underground where unconditional environments such as dust, gas, and water monitoring exist [43]. Underground WSNs have design routing protocols for their specificapplications such as underground routing protocol (URP), underground opportunistic routing (UnOR), and bounce routing in tunnels (BRIT) [44]. Some existing studies examine underwater WSNs for pipeline and mine monitoring [45; 46]. A time-driven data model such as temperature readings from the pipe's environment and the pipe surface has the ability to provide crucial data for the leakage's identification and localization. When water leaks from a pipe in to the underground, the local temperature profile in the ground might vary when compared to values measured at other nodes far away from the leak [45]. Similarly, event-driven data models are used for safety, of which coal mine productivity is a top focus, and the model performs an essential part in it. Coal mining is mostly done underground, when geological structures are more difficult. As coal mining progresses to deeper levels, the volume of gas emitted rises, increasing the danger of coal and gas outbursts. Other natural causes, such as rock breaks, coal dust bursts, and water leaks also create tragedies in deep mines. WSNs have the potential to observe and analyze changing, hostile, and unfamiliar situations. As a result, a mesh WSN with multi-parameter monitoring in underground coal mines is created, functioning as a replacement to the cable monitoring system (CMS), depending on advanced technologies [46].
# A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs
## 5 Experimental Study
Figure 3 graphically summarizes the total inverse link capacity that was obtained when the UAVs are placed following a Naive heuristic versus the total inverse link capacity obtained when the BR-ILS algorithm is applied. The Naive heuristic consists of dividing the area in equal parts depending on the number of UAVs. In particular, for three UAVs, the area is divided into three equal subareas. Following, the centroid of each subarea is found and the UAVs are placed as close as possible to the centroid. Finally, the users in each subarea are assigned to the selected UAVs. Notice the improvement that is obtained in terms of the total cost with the adoption of the proposed methodology. On average, the total link capacity (or, similarly, the total cost) is improved by 12.24% when the UAVs are placed by using BR-ILS. Figure 3: Comparison between the total cost (inverse link capacity) obtained with a Naive heuristic versus the total cost resulting from the application of the proposed BR-ILS algorithm. Figure 2: Network with 20 users and the resulting optimal placement of 3 UAVs.
## 4 Results and Discussion
### Machine Learning Models  
The training dataset for the ML model used in this study consisted of 70% (n = 700) of the 1000 total commercial districts, and 30% (n = 300) were employed as the testing dataset. The training data were then used to model three different years. From the derived model, the dependent variable, the number of crime reports per commercial area, of the test dataset was predicted. As mentioned in Section 3.3, the best-performing model was selected for our final model. Among several ML models assessed, the top five highest-performing models, along with our benchmark model (linear regression), are shown in Table 2.  
The training results show that the Extra Trees Regressor had the highest performance among the five models in 2019, with an R\({}^{2}\) of 0.4626. The difference in RMSE, a measure of how close each model's predictions are to the true value, was negligible for all models. Although the MAPE, which describes the performance of the regression model, indicated that the Random Forest Regressor performed the best, the difference was minimal compared with the Extra Trees Regressor. Since the Extra Trees Regressor had a relatively high explanatory power, this model was chosen. The 2020 model training results also demonstrate that the Extra Trees Regressor had the highest explanatory power of 43.61%. RMSE values were the same for all models, and Bayesian Ridge had the lowest MAPE with a value of 0.4421, even though the difference from other models was not significant. Thus, the model that had the highest R\({}^{2}\) (Extra Trees Regressor) was adopted for this year. The Extra Trees Regressor's R\({}^{2}\) was also the highest in the 2021 model training results, whereas no differences exist for RMSE. In MAPE, every model received a value lower than 0.5, but the Random Forest Regressor yielded the lowest value of 0.4273.
# Predicting Electromagnetic Performance Under Wrinkling in Thin-Film Phased Arrays
## 2 Wrinkling of Step-Stiffness Structure and Dual-Domain Displacement Mod
### Wrinkling Mechanism of Step-Stiffness Structure
Assuming an infinitely long film is subjected to a uniform \(1^{\circ}\) rotation deformation about the x-axis, the resulting wrinkle wavelength can be calculated as 14.3 mm. The finite element simulation results yielded an approximate wavelength of 15.9 mm, demonstrating the validity of the FEM method. The subsequent analysis was carried out using the finite element method verified in this section because the analytical solution is difficult to obtain due to the complexity of the boundary conditions. The approximately 8% deviation in wrinkle amplitude is primarily caused by the numerical initial imperfection imposed via the buckling eigenmode: this seeding mechanism generates a non-uniform wrinkle pattern that amplifies boundary wrinkle growth and biases the predicted amplitudes. A secondary contribution stems from localized stress concentrations around the clamping fixtures, which induce additional non-uniform wrinkles that propagate inward and further perturb the membrane's central region.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
### Contributions
problems and then develop an analytical solution framework grounded in Lagrangian duality theory and implement a dynamic IT update mechanism to iteratively approach the Pareto boundary. * **Simulation validation**: Through numerical simulations, we validate the efficacy of our proposed scheme. The results demonstrate that our approach surpasses baseline methods in terms of training convergence speed, cross-cell performance balance, and test accuracy. Moreover, it achieves stable convergence within a limited number of iterations, underscoring its practicality and effectiveness in complex multi-task edge intelligence scenarios. The remainder of this paper is structured as follows. Section 2 introduces the parallel multi-task Air-FL system model. Section 3 examines the influence of aggregation errors on cell-level local optimality gaps, formulates a power control optimization framework, and presents the concept of the Pareto boundary. Section 4 proposes both a centralized power control optimization scheme and a distributed power control optimization scheme based on IT constraints. Section 5 provides numerical results to validate the performance of our proposed scheme. Section 6 concludes the paper.
## 2 Extended Harmonic-Modal Hybrid (XHMH) Approach
### Parametric Damping in Stratified Media  
As just discussed, by assuming a proportional damping, the dynamical problem can be fully diagonalized, and, in presence of nonlinearities, the last are linearized and transformed into an effective extra-loading.  
However, when addressing the dynamics of stratified soils, the damping of each layer is different and can depend on the mechanical solution itself in the nonlinear case.  
For facilitating the computation, one is tempted to consider a parametric damping by decomposing its associated global matrix \(\mathbf{C}\) as  
\[\mathbf{C}=\zeta_{1}\mathbf{C}_{1}+\dots+\zeta_{\mathcal{L}}\mathbf{C}_{ \mathcal{L}}, \tag{17}\]  
where \(\mathcal{L}\) is the number of layers composing the stratified media, and \(\zeta_{1},\dots,\zeta_{\mathcal{L}}\) the associated damping parameters.  
The main consequence of such a decomposition is that a fully diagonalization is not possible anymore. Thus, the most immediate route consists in considering the whole damping as an effective loading by evaluating it at the previous iteration solution of the nonlinear solution procedure as discussed in the previous section. However, such a route compromised the convergence in most of the numerically analyzed cases.  
For that reason, we consider in what follows the usual harmonic formulation:  
\[\left(-\omega^{2}\mathbf{M}+i\omega\left(\zeta_{1}\mathbf{C}_{1}+\dots+\zeta_ {\mathcal{L}}\mathbf{C}_{\mathcal{L}}\right)+\mathbf{K}\right)\mathcal{U}= \mathcal{F}, \tag{18}\]  
that as indicated cannot be fully diagonalized. However, in order to compute efficiently the parametric solution \(\mathcal{U}(\omega,\zeta_{1},\dots,\zeta_{\mathcal{L}})\) by invoking the PGD [10; 11], an appealing alternative consists in using a reduced modal basis, which are the modes \(\mathbf{\phi}_{i}\) related to the highest eigenvectors associated with the eigenproblem (8), grouped in the truncated matrix \(\tilde{\mathbf{P}}\), which allows defining the amplitude approximation \(\mathcal{U}\approx\tilde{\mathbf{P}}\tilde{\mathbf{\xi}}\), which introduced into Equation (18) and premultiplying by \(\tilde{\mathbf{P}}^{T}\) reads  
\[\left(-\omega^{2}\tilde{\mathbf{P}}^{T}\mathbf{M}\tilde{\mathbf{P}}+i\omega \tilde{\mathbf{P}}^{T}\left(\zeta_{1}\mathbf{C}_{1}+\dots+\zeta_{\mathcal{L} }\mathbf{C}_{\mathcal{L}}\right)\tilde{\mathbf{P}}+\tilde{\mathbf{P}}^{T} \mathbf{K}\tilde{\mathbf{P}}\right)\tilde{\mathbf{\xi}}=\tilde{\mathbf{P}}^{T }\mathcal{F}, \tag{19}\]  
or  
\[\left(-\omega^{2}\tilde{\mathbb{M}}+i\omega\left(\zeta_{1}\tilde{\mathbf{C}}_ {1}+\dots+\zeta_{\mathcal{L}}\tilde{\mathbf{C}}_{\mathcal{L}}\right)+\tilde{ \mathbb{K}}\right)\tilde{\mathbf{\xi}}=\tilde{\mathbf{f}}. \tag{20}\]
## 4 Experiments and Analysis
### Experiment I: Semantic Segmentation Model Applicability
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**UNet**} & \multicolumn{3}{c}{**PSPNet**} & \multicolumn{3}{c}{**LinkNet**} \\ \cline{3-10}  & & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** \\ \hline \multirow{3}{*}{(a)} & Only Pre-trained & 81.63\% & 75.31\% & 78.34\% & 62.27\% & 79.82\% & 69.96\% & 53.42\% & 86.01\% & 65.91\% \\  & AD-DAF & 92.16\% & 81.65\% & 86.59\% & 93.49\% & 72.97\% & 81.96\% & 90.86\% & 76.17\% & 82.87\% \\ \cline{1-1}  & Only Pre-trained & 64.05\% & 91.66\% & 75.41\% & 71.35\% & 81.19\% & 75.95\% & 64.32\% & 94.91\% & 76.68\% \\ \cline{1-1}  & AD-DAF & 90.75\% & 84.48\% & 87.51\% & 88.39\% & 79.73\% & 83.84\% & 90.81\% & 86.87\% & 88.79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results on the three semantic segmentation models in two different target domains. Figure 2: Two high-resolution remote sensing images for cross-domain testing. (**a**) The first image from Google Images at Shenzhen, China, with 500 \(\times\) 500 pixels and a spatial resolution of 1 m for urban area. (**b**) The third image from ISPRS semantic segmentation dataset at Postdam, Germany, with 6000 \(\times\) 6000 pixels and a spatial resolution of 0.05 m for suburban area. Figure 3 shows the building extraction effects of our AD-DAF framework with three different models in two different target domains. In this Figure, the first and second rows correspond to the results of the first image and the second image, respectively. The white area indicates the building footprint. As shown in Figure 3, our AD-DAF framework can extract some building objects under all the three models. Compared with the other two models, the building outlines extracted by our AD-DAF framework with the UNet model are clearer. This is consistent with the results of the quantitative metric F1 in Table 1.
# Dynamics and Control of Typical Orbits around Saturn
## 4 Frozen Orbits  
Before analyzing frozen orbits, we first investigate the orbits at the critical inclination. The traditional design of the orbits at critical inclination around Earth just considers the \(J_{2}\) term by which the first order secular variation of eccentricity and argument of perigee are [15; 16]:  
\[\dot{e}_{1}=0,\] \[\dot{\omega}_{1}=-\frac{3 ntj_{2}R_{2}^{2}}{2a^{2}(1-e^{2})^{2}}( \frac{5}{2}\sin^{2}i-2).\]  
Different from the methods which only considered \(J_{2}\), we still need to calculate the perturbation caused by \(J_{2}\) and \(J_{4}\) terms. Though the magnitude of \(J_{2}\) term exceeds \(J_{3}\) term 1000 times, \(J_{4}\) term still plays an important role in aspheric perturbation. Thus, the conditions for the orbits at the critical inclination should be combined \(\omega_{1}\) with \(\omega_{2}\):  
\[\dot{e}_{1}=\dot{e}_{2}=0,\] \[\dot{\omega}=\dot{\omega}_{1}+\dot{\omega}_{2}=0. \tag{24}\]  
Figure 8: The semimajor aixs compensation period \(\Delta T_{C}\)(hours) for repeating ground track orbits where \(a\in[62,\)268, 62,468] km.  
Figure 7: The semimajor aixs compensation \(\Delta a\)(m) for repeating ground track orbits where \(a\in[62,\)268, 62,468] km.  
However, the critical inclination cannot satisfy most missions for remote sensing satellites. For the purpose of missions, we must consider the frozen orbits around Saturn. Frozen orbits are the orbits which own small constant eccentricity and constant argument of perigee while arbitrary inclination and orbiting altitude can be chosen. The frozen orbits we designed which have small eccentricity must consider first order long-period terms [23]. The main parts of long-period terms are [16]  
\[\begin{split}\dot{e}_{1}^{l}&=-\frac{J_{3}R_{e}}{2J_{ 2}p}\sin i(1-e^{2})\sin\omega,\\ \dot{\omega}_{1}^{l}&=-\frac{J_{3}R_{e}}{2 eJ_{2}p}(1 -\frac{e^{2}\cos^{2}i}{\sin i})\cos\omega.\end{split} \tag{25}\]  
Then the complete conditions for frozen orbits are combined by Equations (24) and (25) [23].  
\[\begin{cases}\dot{\epsilon}=\dot{e}_{1}+\dot{e}_{2}+\dot{e}_{1}^{l}=0,\\ \dot{\omega}=\dot{\omega}_{1}+\dot{\omega}_{2}+\dot{\omega}_{1}^{l}=0.\end{cases} \tag{26}\]  
Using \(\omega=90^{\circ}\) or \(\omega=270^{\circ}\) to solve Equation (26), respectively, we learn that only when \(\omega=270^{\circ}\) can the eccentricity \(e\) be positive. In Figure 9, the values of eccentricity are presented for different combinations of semimajor axis and inclianition.
## 1 Introduction
Another research group attempted to encapsulate ZnPc into polycaprolactone (PCL) using a solvent emulsification-evaporation method to obtain nanoparticles with a diameter size of 187 nm [13]. However, neither of these previous studies reported on the cellular uptake of the particles obtained nor on the resultant cytotoxicity of the encapsulated dye. It is known that the addition of substituents in the \(\alpha\) and \(\beta\) positions of ZnPc impacts the photophysical and photochemical properties of this system. Furthermore, new substituents can influence cell uptake and/or subcellular localization. Although hydrophobic (lipophilic) PSs have a  
Figure 1: Common (\(\alpha\) and \(\beta\)) vs. IUPAC systems for labeling metal (M) phthalocyanines. higher tendency to permeate cell membranes, their tendency to undergo aggregation in aqueous media makes them ineffective without a suitable delivery system. Hydrophobic PSs also tend to remain longer in the patient's body compared to hydrophilic PSs. These observations have caused PSs with amphiphilic properties through conjugation with water-soluble or amphiphilic polymers to become attractive in PDT studies [6; 13; 14; 15; 16; 17]. While hydrophobic properties are essential to enhancing PS distribution in a tumor, water solubility is needed for intravenous administration and blood circulation of a PDT sensitizer. Adding a measure of water solubility can also be achieved by using biodegradable nanoparticles as a vehicle for delivery of the hydrophobic sensitizers to target cells. In addition to more effective transport of the sensitizer in serum and interstitial fluid, obtaining small nanoparticles should enhance passive permeation into encapsulated tumors to deliver the sensitizer to targeted cancer cells. Sensitizer encapsulation into nanoparticles also reduces the amount of dye needed for treatment, which subsequently reduces potential side effects. In addition, successful encapsulation will allow future modification of the nanoparticle surface to target specific cell types in order to reduce and/or prevent disease onset. With the above points in mind, the goal of the present work was to enhance cellular uptake through modification of the ZnPc system and encapsulation of the resulting PS into nanoparticles. Specifically, _para_-methylbenzoyl groups were added to ZnPc to form ZnPcBCH\({}_{3}\) and this dye was encapsulated into PLGA-b-PEG (Figure 2) nanoparticles. The choice of ZnPc analog was based on ease of synthesis and potential to enhance solvent solubility and potential to give suitable particle sizes following encapsulation. Following encapsulation, the photophysical and biological properties (cell uptake, dark and light toxicity) of free and encapsulated ZnPcBCH\({}_{3}\) were evaluated.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Assessment of Water Availability and Demand  
#### 2.5.1 WEAP Model Set-Up  
It is a fact that hydrological process simulation models focusing on only water supply issues are probably not adequate or satisfactory. Therefore, a software tool called \"Water Evaluation and Planning model (WEAP)\", developed by the Stockholm Environment Institute (SEI), which addresses the water supply and demand issues, as well as challenges and concerns for limited water resources and policies on sustainable water use [23], was applied for enabling the assessment of water availability and demand within the Lam Phaniang River Basin. In detail, essential information related to the irrigated area, domestic and industrial sites, irrigation water requirements, and streamflow for both historical (2000-2017) and future conditions (2021-2100) were imported into the WEAP model. Current and future climate and land use scenarios were utilized in the WEAP model to investigate the water supply and demand relations through scenario evaluation. As a prerequisite to the WEAP model set-up, the watershed was delineated and divided into several sub-basins using Arc Hydro Tools in ArcGIS 10.5 software, based on the 30-m Digital Elevation Model (DEM) and stream network. In consequence, the Lam Phaniang River Basin was then delineated and divided into 20 sub-basins (see more details in Figure 1). There are nine different types of physical components (so-called \"nodes\"), i.e., Demand Site, Catchment, Groundwater, Flow Requirement, Reservoir, Rivers, Transmission Link, Runoff/Infiltration Link, and Return Flow Link, that were incorporated in the WEAP model, together with GIS-shapefiles of catchment and sub-district boundaries, river networks, and surface water storage, in order to capture the features of water systems. The daily climate data during 2000-2017 and 2018-2100, i.e., minimum and maximum temperature, and rainfall, were used to calculate crop and irrigation water demand, as well as streamflow estimates.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 2 Methodology
### Problem Formulation  
This section introduces the principle of channelization and the channel division method. The limitations of channelized receivers are explained.