esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
(a) TEM micrograph of a typical as-made Bi\({}_{2}\)Te\({}_{3}\) nanoparticle with a lateral cross-sectional size of about 500 nm; (b) high resolution micrograph showing lattice fringes; and (c) Fourier-transform of the selected square region in (b) showing crystalline nature of Bi\({}_{2}\)Te\({}_{3}\) nanoplatelet. Figure S6. High resolution XPS Spectra of as-made Bi\({}_{2-x}\)Sb\({}_{x}\)Te\({}_{3}\) samples, for the regions Bi 4f, Sb 3d, and Te 3d. Table S2. Transport properties for nanostructured bulk Bi\({}_{2-x}\)Sb\({}_{x}\)Te\({}_{3}\): mass density (\(\ell\rho\)) in percentage of the theoretical density, total thermal conductivity (\(k_{tot}\)), Lorentz number (\(L_{0}\)), electronic thermal conductivity (\(k_{el}\)), and lattice thermal conductivity (\(k_{lat}\)). Figure S7. Hybrid thermoelectric film (\(1.5\times 5\) cm\({}^{2}\)) with Ag paste contact. The films are made by blending the as-made thermoelectric nanoparticles with the polymer PVDF. Figure S8. Microstructure of the as-made Bi\({}_{2}\)Te\({}_{3}\) nanoparticles-PVDF hybrid thermoelectric film at different magnifications. PVDF is visible as a dark shade in the rightmost micrograph, showing perfusing nanoparticles. Figure S9. Device characteristic of the large area film made with Bi\({}_{2}\)Te\({}_{3}\) nanoplatelets and PVDF. The resultant transport qualities for the film are S = \(-46.12\) uV/K, \(\sigma\) = 0.6013 S/m, leading to a power factor of PF = \(1.284\times 10^{-3}\) uW/mK\({}^{2}\). **Author Contributions:** [PERSON] conceived the presented idea. [PERSON], [PERSON], and [PERSON] contributed to conceptualization and methodology, material synthesis, and original draft preparation. [PERSON], [PERSON], and [PERSON] contributed to physicochemical characterization. [PERSON] and [PERSON] processed the material. [PERSON] and [PERSON] validated the thermoelectric properties. [PERSON] and [PERSON] performed the XPS analysis. All authors discussed the results and reviewed and edited the manuscript. [PERSON] and [PERSON] supervised the work. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Swedish Energy Agency (Energimyndigheten, 43521-1) and in part by Swedish Research Agency Council (VR, 2018-03462) as well as EC-H2020 (UncorrelaTEd Grant ID: 863222). SB acknowledges support by Scientific and Technological Research Council of Turkey (TUBITAK, 216M254) and Scientific Research Projects Coordination Unit of Istanbul University (BAP, 21809 and 32641). **Acknowledgments:** MST acknowledges the financial support from Olle Engkvist Foundation (SOEB, 190-0315) for the establishment of microwave synthesis facilities. **Conflicts of Interest:** The authors declare no conflict of interest.
## Example Analysis
### Evaluation Metrics  
The forecasts are evaluated using the MAE, sMAPE, and MASE. The MAE is a commonly used metric, representing the mean value of the sum of absolute differences between actual and forecasted. Meanwhile, sMAPE is another effective metric used, which is an alternative to Mean Absolute Percent Error (MAPE) when there are zero or near-zero demand for items; sMAPE self-limits to an error rate of 200%, reducing the influence of these low volume items [9; 53].  
\[\text{MAE}=\text{mean}(|y_{n}-\overset{\wedge}{y}_{n}|), \tag{21}\]  
\[\text{sMAPE}=\text{mean}\left(200\frac{|y_{n}-\overset{\wedge}{y}_{n}|}{|y_{ n}|+|\overset{\wedge}{y}_{n}|}\right), \tag{22}\]  
Furthermore, this paper also uses the MASE proposed by [PERSON] and [PERSON] [54]. The molecular part of Equation (23) is defined as the mean absolute error for the test set, and the denominator part is defined as the mean absolute error of a benchmark method on the training set. The MASE needs a scaling factor that can provide the help of the error measured by a benchmark method, where normally the naive method is used. When we calculate the MAPE, we first need to select the naive method as a benchmark method, which uses the last known reference value as forecast  
\[\text{MASE}=\frac{\text{mean}(|y_{n}-\overset{\wedge}{y}_{n}|)}{\text{mean}(| y_{i}-\overset{\wedge}{y}_{i-m}|)}, \tag{23}\]  
where the denominator part of Equation (23) is the analytical form of naive method based on the training set. The \(m\) represents the forecast period. When the MASE < 1, the proposed method performs better than the naive method. When the MASE > 1, the proposed method is inferior to the naive method [9; 53].
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 3 The Immunological Function of Exosomes That Are or Are Not Associated with Soriasis
### Non-Immune Cell-Derived Exosomes  
#### 3.2.1 Keratinocyte-Derived Exosomes  
Keratinocytes (KCs) are the main constituents of the epidermis and are critical for the first defense against the invasion of foreign pathogens. However, KCs participate in the development of psoriasis. The terminal differentiation of KCs is incomplete and the proliferation of KC stem cells is dysregulated in psoriasis, resulting in preferential activation and proliferation of the cells. Similar to exosomes derived from other cells, the contents of KC-derived exposomes include chemokines, miRNAs, MHC molecules, etc., which depend on the parent cell status and stimulus [41; 70]. KC-derived exosomes are closely related to the development of psoriasis. Treatment of normal KCs with exosomes collected from IL-17-treated KCs upregulated their expression of psoriasis-related genes [40]. Additionally, it was found that exosomes released from environmental pollutant-stimulated keratinocytes induced significant release of proinflammatory cytokines and chemokines in HaCaT cells through the AhR signaling pathway [10].  
On the other hand, exosomes derived from KCs also play important roles in cellular crosstalk, thus regulating various cellular functions associated with psoriatic skin inflammation. For example, miRNAs in psoriatic KC-derived exosomes induced T cell polarization and enhanced their differentiation into Th1/Th17 cells, thus promoting psoriasis development [42]. Additionally, KC-derived exosomes altered dendritic cell phenotypes and cytokine production [43]. [PERSON] et al. also found that KC-derived exosomes activated the NF-\(\kappa\)B and p38 MAPK pathways, leading to the release of NETs and production of IL-6, IL-8 and TNF-\(\alpha\) by neutrophils. These exosomes further exacerbated psoriatic skin lesions in imiquimod (IMQ)-induced mice [44]. Furthermore, exosomes released from KCs regulated extracellular matrix (ECM) factors in dermal fibroblasts, activated various signaling pathways affecting fibroblast migration, and upregulated the fibroblast gene FGF-2 in a dose-dependent manner [71; 72]. In addition, macrophage polarization may be mediated by intercellular communication with keratinocytes. [PERSON] et al. revealed that leucine-rich \(\alpha\)-2-glycoprotein 1 (LRG1)-enriched exosomes isolated from the primary epidermis of psoriatic mice activated macrophages via a TGF-\(\beta\) receptor 1 (TGF\(\beta\)R1)-dependent process, thus accelerating psoriasisiform skin lesions [11]. Another study showed that miR-4505 was highly expressed in the skin tissue of patients with psoriasis. Vitamin D receptor-deficient keratinocyte-derived exosomes (Exos-shVDR) containing miR-4505 significantly promoted macrophage proliferation and polarization towards M1 phenotype while inhibiting macrophage apoptosis. These results imply that miR-4505 may be important for the effects of Exos-shVDR on macrophage function to occur [45].
## 2 Materials and Methods
### Statistical Optimization Methodology  
Box-Behnken design (BBD) was utilized in the response surface methodology (RSM) to optimize the adsorption process of MB with BCA-Mag/AB. Thus, three independent variables, including contact time, BCA-Mag/AB dosage, and pH, were optimized using BBD. The experimental design and data processing were carried out using Design-Expert 13.0. The values displayed in Table 1 serve as inputs (factors) at various levels of BBD.  
The working ranges of the adsorption key parameters were selected based on preliminary experimental tests. The relationship between the inputs (ads  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Codes** & **Variables** & **Level 1 (–1)** & **Level 2 (0)** & **Level 3 (1)** \\ \hline A & Adsorbent dose & 0.02 & 0.06 & 0.1 \\ B & Contact time & 20 & 85 & 150 \\ C & pH & 2 & 6 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Codes and ranges of independent variables at their respective levels.  
the output (MB removal) is primarily represented by the following expression to determine the optimal working conditions (Equation (1)):  
\[\mathrm{Y}=\beta_{0}+\sum\beta_{1}\mathrm{X}_{1}+\sum\beta_{1}\mathrm{X}_{1}^{2 }+\sum\sum\beta_{1\mathrm{J}}\mathrm{X}_{1}\mathrm{X}_{\mathrm{J}} \tag{1}\]  
The details of the BBD matrix and the results related to the removal of MB are provided in Table 2.  
For the MB removal experiments, 50 mL of MB solution with a fixed concentration of 100 mg/L was placed in a 100 mL Erlenmeyer flask along with a specific amount of BCA-Mag/AB. Subsequently, the flasks containing MB solutions were mixed in a shaker at a speed of 150 rpm for a period of time. After the completion of the adsorption process, BCA-Mag/AB was separated from the mixture using a 0.45 \(\upmu\)m syringe filter. The amount of MB in the solution was determined using an Agilent Cary 60 spectrophotometer at a wavelength of 663 nm. The percentage of MB removal was calculated using the following equation (Equation (2)):  
\[\mathrm{\%R}=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{e}})}{\mathrm{C}_{0}} \times 100 \tag{2}\]  
This equation calculates the percentage of MB removal; C\({}_{0}\) represents the initial concentration, while C\({}_{\mathrm{e}}\) indicates the concentration of MB at equilibrium.
## 4 Experiment Results and Analysis
### Control Performance Analysis of the Pure ASP Algorithm
#### 4.1.2 Tracking Performance for Sound Enhancing
Thus, \(L_{TE,\text{RMS}}\) of the C6 order after control was achieved by 1.4 dBA, whereas that before control was 11.0 dBA as presented in Table 3.  
In the case of the C9 order, as shown in Figure 8e,f, \(L_{TE}(f)\) under 3800 r/min (570 Hz) were significantly improved from a range of \(-25\) to 2 dBA to that of \(-4\) to 2 dBA by the ASP control. However, it was observed that there was \(-10\) dB of \(L_{TE}(f)\) at around 4000 r/min (600 Hz) due to the leakage value of 0.98. Moreover, there was little difference in \(L_{TE}(f)\) before and after controls above about 4800 r/min (690 Hz). This was because the ASP control was restricted using the leakage term to protect the control stability from the increased uncertainties at these higher frequencies. Thus, as displayed in Table 3, \(L_{TE,\text{RMS}}\) after control was obtained by 7.5 dBA, approximately half that before control, but that was about 4-6 dBA higher than that of the C3 and C6 orders.
## 5 Discussion  
This section synthesizes the main findings from the reviewed studies on predictive maintenance, underscoring both theoretical and practical contributions. Methodological and technical innovations are discussed, along with persistent challenges such as data standardization, model scalability, and industrial validation. Additionally, future research directions are addressed, emphasizing the need for robust, interdisciplinary strategies in this rapidly evolving domain. Energy Efficiency and Sustainability: Focus is placed on strategies aimed at reducing energy consumption (Consumption Reduction) and minimizing environmental impact (Environmental Impact). By proactively detecting anomalies, operators can implement timely interventions to conserve resources and extend asset lifespans, aligning with broader sustainability goals [10, 11, 52, 53, 62]. In particular, deep learning-based condition monitoring has demonstrated significant gains in reducing overhead energy usage by enabling just-in-time maintenance scheduling [51, 63]. Furthermore, advanced controllers dynamically adjust machinery operating conditions in response to real-time feedback from IoT sensors, thereby preventing excessive loads and unnecessary power consumption [8, 9]. Hybrid approaches have further revealed that integrating data-driven prognostics with physics-based energy models enhances the identification of operational _sweet spots_, where both productivity and energy sustainability converge [13, 43, 44]. Additionally, multi-objective optimization frameworks [12] have been employed to address the dual aim of throughput maximization and power minimization, proving beneficial for conveyor belt systems and communution circuits alike [4, 53, 54]. Sectorial Applications and Critical Failures: Emphasis is placed on _Early Detection_ (e.g., vibrations, temperature, pressure) and _Reduction of Unexpected Downtime_. By leveraging sensor data and advanced analytics, mining operations can predict and prevent failures before they escalate, reducing unplanned maintenance and improving resource allocation [8, 9, 21, 22, 23]. For instance, the implementation of neural networks for high-frequency vibration signals in sub-level saving operations has led to a reduction in failure rates by up to 40% [52, 56]. These technologies have also been extended to critical assets in other do
## 3 Analysis and Design of the Force Control System
### Design Method  
Considering the stability analysis presented in the preceding section and the design curve in Figure 5, one can observe a compromise between stability and error attenuation. Selecting a big value of control magnitude \(|G_{c}(j\omega)|\) might result in an acceptable force error but this magnitude may deteriorate the stability.  
The proposed design method for the controller \(G_{c}=K_{p}+K_{d}s\) is obtained, when one includes in the curves of Figure 5 the maximum value of magnitude/gain \(K_{GMmax}\) that preserves stability (see Figure 6). The set of gains \(K_{p}\) presented in Figure 6 is selective since it considers only the gains \(K_{p}\) that guarantee stability. The values of \(e_{ss}\) corresponding to the gain \(K_{GM_{max}}K_{p}\) are the minimum values one can have in the force error considering the disturbance magnitude \(M\) and the stability margin. Therefore, the curves in Figure 6 provide a better design method for the controller \(G_{c}(s)\) compared with the curves in Figure 5.  
Selecting Gains of \(G_{c}\) Considering Stiffness \(K\)  
From Equation (7), one can observe how the product of \(K\) and \(|G_{c}(j\omega)|\) affects the gain margin. Therefore, when the control gains in Equation (2) are selected/adjusted, one should consider the value of stiffness \(K\) in order to preserve the relative stability of the system.  
Assume the stiffness \(K\) can be modified/adjusted (adding elasticity to the end-effector using a spring). Then, from Equation (7), the magnitude \(|L(j\omega)|\) contains two adjustable terms (its parameters are at hand); the first one is the controller \(G_{c}\) and the second one is the stiffness \(K\).  
If the goal is to preserve a desired gain margin, one should keep the magnitude \(|L(j\omega)|\) as close as possible to its value associated with the desired gain margin. Therefore, when the control gains are adjusted (or the stiffness is adjusted), one should keep a balance between the magnitude of the controller \(G_{c}\) and the value of stiffness \(K\). For example, if the magnitude of \(G_{c}\) increases, one should balance/compensate this change with a decrease in \(K\) to preserve the desired magnitude \(|L(j\omega)|\) associated with the desired gain margin.  
Therefore, in order to preserve a stable contact force, the following relations between the stiffness and the controller exist:  
* For a rigid surface/environment, a compliant controller is needed, i.e., \(K>>|G_{c}|\).
* For a compliant surface/environment, a rigid controller is needed, i.e., \(K<<|G_{c}|\).
## 5 Mathematical Simulation Analysis  
### Trajectory and Sensor Data Simulation  
The flight trajectory is set as follows, and the simulation time is 540 s. In the simulation, the flight trajectory of the aircraft is simplified, and the maneuvering operation is carried out during the flight. The initial angle and velocity are 0, the starting point is the Aerospace Laboratory of Dalian University of Technology, the initial latitude, longitude and height are 38.8880\({}^{\circ}\), 121.541724\({}^{\circ}\), 380 m, and the trajectory changes are shown in the following Figure 5.  
In order to express the position clearly, the position increment information is drawn, with the change relative to the original position; \(\Delta L\)at represents the latitude increment, \(\Delta L\)on represents the longitude increment and \(\Delta H\)gt represents the height increment.  
Since the increase of nodes only changes the complexity and amount of calculation, we assume that the inertial network is composed of two inertial navigation nodes in the simulation process, which are the main node cg installed at the center of gravity of the aircraft and a local node \(i\) located at the wing of the aircraft. The sensor accuracy of the two nodes is shown in Table 1.  
The sensors of the two nodes have sensor errors, different from the node cg, and the sensors of the node \(i\) are also affected by the elastic deformation. The sampling values of the two nodes are shown in the following Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Parameters** & **Node cg** & **Node i** \\ \hline Gyroscope bias error (\({}^{\circ}\)/h) & 0.01 & 0.04 \\ Gyroscope noise (\({}^{\circ}\)/sqrt (h)) & 0.003 & 0.17 \\ Accelerometer bias error (ug) & 100 & 200 \\ Accelerometer noise (\({}^{\circ}\)/sqrt(h)) & 25 & 30 \\ Gyroscope sampling frequency (Hz) & 100 & 100 \\ Accelerometer sampling frequency (Hz) & 100 & 100 \\ Markov correlation time (s) & 0 & \([10;20;30]\) \\ Markov process variance (min) & 0 & \([6;-10;7]\) \\ Gyroscope installation error (min) & 0 & \([5;\,0;\,0]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of inertial sensors.  
Figure 5: The figure of the real trajectories: (**a**) Pitch angle diagram of real trajectory; (**b**) 3D diagram of real trajectory.  
Figure 6: Data collected by sensors of node cg and node \(i\): (**a**) The data comparison diagram collected by the gyroscopes of node cg and node \(i\); (**b**) The data comparison diagram collected by the accelerometers of node cg and node \(i\).
## 5 Discussion and Future Directions
### Limitations of SCI Device-Based Therapies
#### 5.2.2 Wireless Data Logging and Adaptability  
Wireless data logging is increasingly recognised as a critical feature in modern medical devices, allowing for real-time monitoring, adaptive feedback, and personalised rehabilitation strategies. Yet, most SCI devices reviewed in this paper lack wireless data logging capabilities, which significantly limits the clinical usability and long-term adaptability. The lack of wireless logging comes with several limitations. One of the key limitations is in device evaluation and troubleshooting, as without data logging, device engineers do not have enough information to make improvements to drive innovation and progression. Without real-time data, devices cannot adjust stimulation and parameters based on patient-specific responses, which limits their ability to tailor the therapy to the individual's needs [49]. This inability to adapt to patient response can delay clinical decision making when treating SCI. Manual data retrieval delays feedback loops, preventing timely adjustments in therapy and reducing the responsiveness to change in the patient's condition [50].  
As an exception, MyrdMove FES systems and the EksoGT exoskeleton both allow for non-real-time wireless data logging through a cloud-based tracking system or software platforms [20; 21; 36]. Furthermore, the NEO wireless SCI has partial wireless logging capabilities. Although this device uses Bluetooth for signal transmission, it still requires external electrode adaptors and lacks full integration with any wireless data platforms [46]. This limits the device's ability to personalise patient therapy over time. While the ultrasonically powered FES implant is self-powered and biocompatible, it is dependent on an external ultrasonically powered probe to activate the device. The device lacks wireless telemetry, which means that it cannot be logged or transmitted in real time. This will limit the device's adaptability and limit patient progress. The SMART implant mentioned by [PERSON] et al. [31] can harvest energy from body motion and monitors spinal fusion progress. Yet, it does not have wireless data transmission, as it requires a manual probe-based interrogation. This prevents the ability to track healing dynamically or integrate with remote monitoring systems. The i360 device, although capable of 360-degree stimulation and recording, lacks wireless data logging and must be manually accessed for signal extraction. This reduces the potential for real-time feedback and patient-specific stimulation protocols. Similarly, DARPA BCI has a 64-channel system; however, it needs wires to log data back to the computer. Without wireless logging, the system cannot adapt stimulation based on real-time neural feedback, reducing its usability and adaptive potential.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 3 Experimental Results and Discussion  
### Dataset  
The \"UCMerced_LandUse Dataset\" is widely used in the field of computer vision and machine learning for land use classification tasks. This dataset was created by the University of California, Merced, and it consists of high-resolution aerial images of various land use and LC classes, as given in Table 2. It is commonly used for tasks such as image classification, object recognition, and segmentation. Researchers and practitioners often use the UCMerced_LandUse Dataset for training and evaluating ML algorithms, especially for image classification tasks related to land use and LC mapping. It provides a valuable resource for testing the effectiveness of different models and techniques in remote-sensing and computer vision applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **Inception-v3** & **DenseNet-121** & **ResNet-50** \\ \hline Image dimensions & 224 \(\times\) 224 & 224 \(\times\) 224 & 224 \(\times\) 224 \\ \hline Optimizers & SGD & SGD & SGD \\ \hline Batch size & 32 & 32 & 32 \\ \hline No. of epochs & 50 & 50 & 50 \\ \hline Loss & CC & CC & CC \\ \hline Activation function & SoftMax & SoftMax & SoftMax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed model hyper-parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Class** & **No. of Images** & **Train Images** & **Test Images** \\ \hline
1 & Agricultural & 1000 & 800 & 200 \\ \hline
2 & Airplane & 1000 & 800 & 200 \\ \hline
3 & Baseball diamond & 1000 & 800 & 200 \\ \hline
4 & Beach & 1000 & 800 & 200 \\ \hline
5 & Buildings & 1000 & 800 & 200 \\ \hline
6 & Chaparral & 1000 & 800 & 200 \\ \hline
7 & Forest & 1000 & 800 & 200 \\ \hline
8 & Freeway & 1000 & 800 & 200 \\ \hline
9 & Golf course & 1000 & 800 & 200 \\ \hline
10 & Harbor & 1000 & 800 & 200 \\ \hline
11 & Intersection & 1000 & 800 & 200 \\ \hline
12 & Mobile home park & 1000 & 800 & 200 \\ \hline
13 & Overpass & 1000 & 800 & 200 \\ \hline
14 & Parking lot & 1000 & 800 & 200 \\ \hline
15 & River & 1000 & 800 & 200 \\ \hline
16 & Runway & 1000 & 800 & 200 \\ \hline
17 & Storage tanks & 1000 & 800 & 200 \\ \hline
18 & Tennis court & 1000 & 800 & 200 \\ \hline \multicolumn{3}{c}{**Total Images**} & **18,000** & **14,400** & **3600** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detail distribution of dataset.