esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal and Thermomechanical Characterization of Polypropylene-Seed Shell Particles Composites
## 2 Materials and Methods
### Composites Characterization  
The composites were characterized by differential scanning calorimetry (DSC) using a Perkin Elmer model DSC8000, using a 10 mg samples that were encapsulated in aluminum sample holders that were hermetically sealed, performing a double scan in order to delete the thermal history of the composites, the first from \(-\)40 to 300 \({}^{\circ}\)C with a heating rate of 20 \({}^{\circ}\)C/min; later, the material was left in isothermal mode at a temperature of 200\({}^{\circ}\)C for 5 min, during which it was reported the crystallization temperature (T\({}_{\rm c}\)) and the A\({\rm H}\)c of crystallization; samples were subsequently cooled at a rate of 20 \({}^{\circ}\)C/min to \(-\)40 \({}^{\circ}\)C, and again the material was left isothermally for 5 min to subsequently carry out a second scan up to 200\({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C/min, taking the data of the second scan for the  
Figure 1: Appearance of JA (**a**) and PI (**b**) particles.  
analysis, and reporting the melting temperature (T\({}_{\rm m}\)) and the melting \(\Delta\)H\({}_{\rm m}\) to calculate the % of crystallinity (%C) using the following formula:  
\[\rm{Xc}=(\Delta\rm{H_{f}}/\Delta\rm{H^{o}_{f}})\times 100 \tag{1}\]  
For the above calculation, 207 J/g was used as the reference value for \(\Delta\rm{H^{o}_{f}}\); this is the melting enthalpy per mass unit in a 100% crystalline PP [23].  
The thermal stability of the composites was evaluated by means of thermogravimetric analysis (TGA), for which a TA Instruments model Q600 was used, 10 mg of sample was used in a platinum crucible. The temperature range was from 30 to 700 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min in an atmosphere of N\({}_{2}\) with a flow of 100 mL/min.  
The dynamic mechanical properties of the composites were determined using a TA Instruments model Q800, with a dual cantilever clamp, the sample dimensions were 20 \(\times\) 10\(\times\) 3.2 mm\({}^{3}\), in a temperature range of \(-\)40 to 150 \({}^{\circ}\)C, with a heating rate of 5 \({}^{\circ}\)C/min and a fixed frequency of 1 Hz.The storage modulus (E\({}^{\prime}\)) and the tan \(\delta\) were reported. These conditions were stablished based on the previous work of our group [25].  
The composites structural properties were evaluated with an X-ray diffraction analysis, it was carried out in a Bruker model D8 Advance, with a range of 5 to 40\({}^{\circ}\) on the 2 \(\Theta\) scale, with a step size of 0.02\({}^{\circ}\), a voltage of 40 kV, a current of 40 mA, and a radiation of Ka from Cu (0.15405 nm).
## 2 The Proposed Method and Theoretical Background
### Fractional Fourier Ambiguity Function  
As a linear operator, fractional Fourier transform (FRFT) can realize the counter-clockwise rotation at any angle of the signal around the TF axis, and the \(p\)-order FRFT is given by:  
\[\text{FRFT}_{p}(u)=\int_{-\infty}^{+\infty}K_{2}(u,t)s(t)\text{dt} \tag{31}\]  
in which \(K_{2}(u,t)\) represents a kernel function, and it is given as follows:  
\[K(u,t)=\left\{\begin{array}{l}\sqrt{\frac{1-\cot\beta}{2}}\exp\Big{\{}j \Big{[}\big{(}u^{2}+t^{2}\big{)}\frac{\cot\beta}{2}\Big{]}-ut\csc\beta\Big{\}},\beta\
eq n\pi\\ \delta(u-t),\beta=2n\pi\\ \delta(u+t),\beta=(2n\pm 1)\pi\end{array}\right. \tag{32}\]  
in which \(\beta=p\pi/2\). From Equation (32), it can be seen that FRFT degenerates into the traditional FT when \(p=1\).  
According to PCT, a polynomial frequency modulation signal is defined as \(s_{1}(t)=A_{1}\cdot\exp\big{[}j\big{(}\omega t+at^{2}\big{)}\big{]}\), in which \(A_{1}\) and a represent the amplitude and phase coefficient, and the corresponding AF is given as follows:  
\[\text{AF}_{s_{1}}(\tau,\omega)=\int_{-\infty}^{+\infty}s_{1}(t+\frac{\tau}{2}) {s_{1}}^{*}(t-\frac{\tau}{2})\exp(-\text{j}\omega t)\text{dt} \tag{33}\]  
in which \(\tau\) represents the time delay.  
The autocorrelation function of \(s_{1}\) is shown as follows:  
\[R_{s_{1}s_{1}}(t,\tau)=s_{1}(t+\frac{\tau}{2}){s_{1}}^{*}(t-\frac{\tau}{2}) \tag{34}\]  
Therefore, the FFAF is defined as follows:  
\[\text{AF}_{s_{1}}^{*}(\tau,\omega)=\int_{-\infty}^{+\infty}R_{s_{1}s1}(t,\tau )K(u,t)\text{dt} \tag{35}\]Based on the above analysis, the correlation function takes the maximum value when \(\tau\) = 0.  
\[R_{s_{1}s_{1}}(\tau)=E\{s_{1}(t)\cdot s_{1}(t+\tau)\}=\sqrt{2\pi}A_{1}^{2},\tau=0 \tag{36}\]  
in which \(E\{\cdot\}\) represents the expectation operator. Meanwhile, a frequency modulation signal \(s_{2}(t)=A_{2}\cdot\exp\left[\mathrm{j}(\omega+\triangle\omega)t+at^{2}\right]\), in which \(\triangle\omega\) represents the variation of frequency, is constructed.  
Based on the orthogonality of \(s_{1}(t)\) and \(s_{2}(t)\), the corresponding cross-correlation function can be obtained as follows:  
\[R_{s_{1}s_{2}}(\tau\to 0)\approx 0 \tag{37}\]
## 3 Measured Parameters and Signal
### Structure of the Plant
For the control system design purposes, all of the measurable signals were divided into three groups: control signals, disturbances, and system outputs (states). Besides those already described, the signals that are denoted in the structure are: \(E_{w}\)--ratio of volumetric flow of collected air from the cooling fans to taken air volumetric flow, \(F_{h}\)--volumetric flow of water mist to feed, control voltage, H\({}_{g}\)--humidity of the taken air streams, \(H_{d}\)--humidity of air collected from the cooling fans, \(H_{n}\)--fresh feed humidity, \(T_{g}\), \(T_{r}\), \(T_{d}\)--main, recycle, and additional air streams temperature, respectively, \(P_{g}\), \(P_{r}\), \(P_{d}\)--main, recycle, and additional air streams pressure, respectively, \(E_{f}\)--active power of the inverter. Authors distinguish two types of inputs presented in Figure 6: control signals (left-side inputs) and disturbances (top-side inputs). A role of a certain input signal follows from the control system structure. Different objectives which need to be met by the control system impose the introduction of multi-rate control scheme. Multi-rate control arises in time domain and follows from different control horizon that is related to the certain control objective. Thus, hierarchical structure of the control system is formed (Section 4). Some signals are not presented in Figure 6. These quantities are not used directly when the control system operates. However, they were measured and analysed at different stages of research to determine static and dynamical models that have been used in control, optimization, or fault detection algorithms design. An example is the distribution of magnetic field strength in the working chamber as  
Figure 6: General structure of the plant. a function of basic and output frequency set on the inverter (see Section 4.4). Model of the distribution serves as a constraint of energy optimization.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 5 Discussion
### Practical Implications  
The findings of this study offer valuable practical applications for Arabic poetry analysis and AI-driven literary tools. The high accuracy achieved using half-verse data shows that meter classification can be effectively automated without requiring full verses, enhancing its real-world applicability. Moreover, the ability of DL and transformer models to learn metrical patterns supports the development of educational technologies that allow researchers and students to rapidly analyze and classify Arabic poetic meters.  
This research contributes to Arabic NLP, particularly in tasks that demand rhythmic-sensitive analysis. Demonstrating that half-verses suffice for precise classification reduces processing demands and broadens access to AI-driven solutions in Arabic literary and language technologies.
# Osteoarthritis--The Role of Mesenchymal Stem Cells in Cartilage Regeneration
## 4 MSC-Based Therapy for OA
### Sources of MSCs
#### 4.2.6 IPFP for OA Treatment  
The study of IPFP, a form of fatty tissue, has gained significant attention due to its potential to reduce inflammation and slow cartilage degeneration. The IPFP is located inside the knee's anterior compartment. It is an intra-capsular component that houses around 20 cm\({}^{3}\) of AD-MSCs. The collection of these data may occur during either arthroscopic or open knee surgery [255; 256].  
IPFP-MSCs may serve as a source of autologous stem cells. These MSCs show greater chondrogenic capacity than BM-MSCs and AD-MSCs [257]. IPFP-MSCs are characterized by the presence of cell markers such as CD9, CD10, CD13, CD29, CD44, CD49e, CD59, CD105, CD106, and CD166 [258]. In addition, these cells are capable of aplo-, chondro-, and osteogenic differentiation [258; 259].  
[PERSON] et al. [260] injected IPFP-MSCs intra-articularly directly into the knees of rabbits with experimental OA. The IFP-MSCs that were used had undergone in vitro expansion and cultivation. They were then supplied in a single dosage of 1 million cells suspended in 1 mL of media 12 weeks after the surgical procedure. Rabbits that received IPFP-MSCs had significantly reduced levels of cartilage degradation, osteophyte development, and subchondral sclerosis compared to the control group at a postoperative period of 20 weeks.  
IPFP adipocytes treated with PRP and HA induced chondrogenesis and inhibited adipocyte-mediated inflammation [251]. IPFP is also a significant source of perivascular stem cells (PSCs). IPFP contains both of these types of stem cells. Due to their ability to maintain the structural integrity of the telomere, IPFP-PSCs retain their unique and tenacious growth capacities even after several expansions [261; 262]. The chondrogenic activity of PSCs extracted from IPFP was much higher than that of PSCs obtained from subcutaneous adipose tissues.  
Coculture of chondrocytes and IPFP-MSCs in the presence of chitosan/HA nanoparticles enhanced chondrogenic efficiency, indicating that coculture in the presence of appropriate stimuli may aid in cartilage regeneration in an osteoarthritic knee [263].  
[PERSON] et al. [264] found that treatment with IPFP-MSCs improves clinical symptoms and reduces pain after three months, with the highest levels of success happening after six months of treatment.
## 4 Case Study
### IEEE RTS-24 Node Case Study System
#### 4.2.4 Impact of Energy Storage Costs on Planning Results  
Due to the current development of energy storage technology, the cost of energy storage is relatively high. The high cost of energy storage limits the allocation of more energy storage in planning models with economic optimality as the objective function. This section further discusses the impact of energy storage costs on the coordination planning of transmission network and energy storage.  
On the basis of Case 3, the energy storage cost coefficient is gradually changed from 0.2 to 1.2 in intervals of 0.2, and the model is solved to obtain the impact of energy storage cost on energy storage configuration results and the impact on transmission construction and wind and solar curtailment, as shown in Figures 5 and 6.  
From Figure 5, it can be seen that as the energy storage cost coefficient decreases, the power capacity and energy capacity of the energy storage increase. When the energy storage cost coefficient decreases from 1.2 to 1.0, the power capacity and energy capacity of the energy storage increase from 138.98 MW and 1001.90 MWh to 224.34 MW and 1466.71 MWh, respectively, representing increases of 61% and 46%. However, the line planning scheme remains unchanged during this process, and the cost of wind and solar curtailment decreases by 272 million, indicating that the configuration of energy storage increases the flexibility of system down-regulation and up-regulation, thereby increasing the integration of wind and solar power generation. When the energy storage cost coefficient is less than 1.0, the rate of increase in power capacity and energy capacity of the energy storage significantly accelerates. From Figure 6, it can be seen that the cost of wind and solar curtailment and the cost of line expansion decrease as the energy storage cost coefficient decreases. Especially when the energy storage cost coefficient decreases from 0.8 to 0.6, the cost of line expansion decreases from 443 million to 368 million, a decrease of 16.9%. This indicates that the increase in energy storage capacity can effectively reduce redundant lines in the original power transmission line planning, increase system flexibility, and integrate more wind and solar power generation, while also delaying the upgrade of power transmission lines.
## 3 Experimental Results and Discussion  
This section evaluates the performance of the proposed algorithm. For indoor fall research, testing datasets are publicly available; however, it is difficult to find testing datasets for outdoor falls because very little research has been done until now. We therefore recorded 38 testing video clips, consisting of 4271 frames in total, at three different locations including both indoor and outdoor environments with six people to evaluate our fall detection method. With the datasets, the performance in terms of sensitivity, specificity, accuracy, and error was measured for a quantitative evaluation. The frame rate of our testing dataset is 12 fps and the resolution of the images is 720 \(\times\) 404 pixels. Video recording was performed using a simple smart phone camera (iPhone 5s) and multiple camera angles were used. A total of 27 fall scenes depict the falling postures, such as falling front, back, side (right and left), and other motions based on the orientation of the fall. Eleven confounding scenes include sitting, squatting, and bending, which are the motions that make it difficult to distinguish between a fall and normal motion. Prior to the main experiments, we conducted pilot experiments to find the optimum values of thresholds used for our algorithm with 24 separate datasets. We used the Matlab 2017 version to simulate the fall detection algorithm.
## 1 Introduction
In order to improve the classification accuracy, an improved Skewness Decision Tree SVM was proposed by determining the classification order using the Euclidean distance between the nearest sample vectors of the multi-class samples [21]. However, the nearest sample vectors of the multi-class samples may be an interference point, which would decrease the classification accuracy. In order to address such a limitation, an improved SDTSVM algorithm has been proposed with the classification order determined by the sum of the Euclidean distances between the multi-class sample centers. The ISDTSVM algorithm can improve the classification accuracy and overcome the influence of interference point by considering the distribution characteristics of the samples. In this study, our main contribution is to propose an improved SDTSVM algorithm and the ISDTSVM algorithm has been applied to the classification of the weak magnetic signals of steel cord conveyor belt defects. The classification order of the ISDTSVM algorithm is determined by the sum of the Euclidean distances between the multi-class sample centers and the parameters are optimized by the inertia weight of the PSO algorithm. The ISDTSVM algorithm can efficiently improve the classification accuracy of the steel cord conveyor belt defects. It has an important significance for preventing fracture accidents of the steel cord conveyor belt.
## 2 Proposed Method
### Feature Extractor  
The Residual Convolutional Neural Network (ResNet) effectively addresses the issue inherent in traditional Convolutional Neural Networks (CNNs), where an overly deep convolutional architecture may exacerbate gradient vanishing or explosion during the deep learning process [17]. In this study, ResNet-50 is deployed as the feature extractor within the RASAN-W framework, demonstrating robust capabilities in extracting fault features from bearing fault samples across both source and target domains. The foundational element of the ResNet-50 model described in this study is the residual block. Within each residual block lies a succession of convolutional layers, a batch normalization layer (BNL) [18], a global average pooling layer (GAP) [19], and the Rectified Linear Unit (ReLU) [20] activation function. The architecture of ResNet-50 includes multiple 3 \(\times\) 3 convolutional layers and several residual blocks, and finally outputs feature information through a global average pooling layer. Specifically, assuming that there is an input sample \(x_{i}\), the corresponding feature extractor output feature is \(G_{f}(x_{i};\theta_{f})\), where \(G_{f}\) represents the feature extractor and \(\theta_{f}\) is a differentiable parameter in the feature extractor.
## 6 Conclusions
### Implications  
Academics acknowledge the crucial role of PBs in reducing carbon emissions in the construction industry and the significant contributions of PBSC participants involved in design, construction, and operation. However, there has been limited examination of the relationships between participants in the full PBSC regarding strategy implementation. This study differs from previous ones by not only examining the impact of external interventions on decision-making but also considering the interactions among various PBSC participants in implementing abatement strategies. Furthermore, stochastic perturbation factors undoubtedly impact participants' decision-making in the PBSC, yet few studies have analyzed decision-making in this context. This study analyses the impact of each parameter on the change in each participant's strategy in the context of stochastic perturbation to bring the conclusions closer to reality.  
The model's parameter setting considers stochastic perturbation factors, participants' capabilities, PBSC synergy status, external agencies' behavior, and environmental information, with conclusions drawn through simulation, offering insights into promoting participants' emission reductions, optimizing the PBSC, and innovating management models. At the same time, it can provide suggestions for PBSC participants, such as contractors, builders, and operators, to respond to external changes and choose strategies. It can also serve as a decision-making basis for external organizations such as governments, financial institutions, and universities.  
In conclusion, this study expands the research scope on implementing carbon reduction strategies by PBSC participants, offers a modeling framework for studying PBSC, and provides insights into carbon reduction and supply chain construction for PBs. It also inspires decision-making by both participants and external organizations.
## 5 Conclusions  
In the near future, IoT-based systems will have an important role in several application domains and common IoT communication technologies will be broadly disseminated. For example, LPWANs technologies may be seen as a key technology enabler in several IoT domains due to their low implantation cost, and high compatibility with resource-constrained devices. Moreover, they provide long-range communications with low power consumption. Presently, three major LPWAN communication technologies--LoRaWAN, Sigfox, and NB-IoT--are being widely used in several IoT application domains, which have been covered in detail in this article, namely: smart cities, smart transportation, smart healthcare, and smart manufacturing. Furthermore, each application domain has its own different singularities and requirements, being the provision of location information an important feature in asset tracking and management. Using LPWAN technologies, it is possible to perform communications and localization simultaneously, reducing not only the implantation cost but also the IoT device energy consumption. More specifically, the LoRaWAN technology presents several important strengths that make it more appropriate for communication and localization in IoT-based ecosystems. Therefore, this article focuses the exploration of low-cost GNSS-free outdoor localization techniques, with a focus on the LoRaWAN technology. Following, the four main open issues of IoT-based systems that affect the provision of location information are introduced and discussed:  
* Energy Consumption: it is an important limitation in IoT-based systems. The main portion of energy consumption is related to connectivity, typically during the data transmission periods. To decrease data transmission, resource-aware and context-aware solutions are becoming an attractive subject for researchers [137]. * Security and Privacy: LPWAN technologies contain important security and safety vulnerabilities These vulnerabilities are exploited by malicious entities and lead to great damages. This becomes more important in some IoT categories (Industry 4.0, smart city) and it is a drawback. Therefore, providing a reliable security mechanism based on their limitations is a challenging and open issue task [15; 138].
## 2 Design of the 3.7 kW WCS  
According to regulations on the power of SAE J2954, WPT1 (Wireless Power Transmission level 1) with 3.7 kW WCS is selected as a design product. The overall system is composed as shown in Figure 1. The primary side consists of a grid input, a power factor correction (PFC) circuit, an inverter, a primary compensation topology, and a primary coil, which are arranged on the ground; the secondary side is composed of a secondary coil, a secondary compensation topology, a rectifier, a filter and the battery arranged at the chassis of vehicle. The common part of primary and secondary side is the coupling coils, between which energy is transmitted through a coupling magnetic field.  
The overall circuit structure is built according to the components in Figure 1, as shown in Figure 2. The input voltage of system is 220V AC. After rectification and boosting by PFC, the output DC voltage of PFC reaches 425V, and then the inverter is used to change the 425V DC voltage to 85 kHz AC voltage. Then, the energy is coupled through the coupling coils and compensation circuit to rectifier and filter of the secondary side. For the on-board battery, the charging DC rated voltage is 400V, and the maximum output power of WCS is required as 3.7 kW. According to the respective function in Figure 2, parameters of these electrical modules are designed and determined.