esa-sceva/satcom-chunk-collection huggingface dataset:
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### Pre-Configured Protection Schemes
#### 2.3.5 The Multi-Path Protection Scheme  
The multi-path protection (MPP) scheme is another important survivability technology that can be used to resist multiple failures. For the MPP scheme, a data stream is split into multiple low-rate streams, each of which is routed and assigned to a separate path. At present, research about the MPP scheme mainly focuses on multipath configurations. Besides, other research focuses on improving the spectrum efficiency or extending the application range of the MPP scheme.  
**Survivable Multipath Configuration**: For the MPP scheme, the first important research is to conduct routing and spectrum allocation for each light path. In [111; 112], the authors defined the static survivable multipath routing and spectrum allocation (SM-RSA) problem in OFDM-based flexible optical networks. In [113], the authors proposed a new survivable MPP scheme in OFDM-based optical networks and studied the static SM-RSA problem. In [114], the authors proposed a novel energy efficient multipath-based survivability scheme against single link failure in a static traffic scenario. In [115], the authors studied the protection schemes for MPP to ensure 100% restoration against single-link failures. In [116], the authors proposed an OpenFlow-based multipath protection scheme for elastic optical networks.  
**Optimization for Survivable Multipath**: To improve the spectrum efficiency of the MPP scheme, shared protection can be applied among multi-paths. In [117], the authors proposed a spectrum-efficient shared-protection MPP (S-MPP) scheme against multiple failures in a flexible-grid optical network. The MPP scheme can also be applied in virtual optical network (VON) mapping and network virtualization. In [54], the authors proposed a survivable routing and resource assignment scheme with MPP and SRLG in VON to get less resource consumption and enough protection against physical failure. In [118], the authors proposed a survivable multipath routing and spectrum assignment scheme for anycast traffic in CDNs against multiple node failures for less resource consumption and enough protection. In [119], the authors proposed a multipath protection-based virtual network function placement and scheduling scheme in elastic optical data center networks. It outperformed conventional protection methods in terms of spectrum and computing resource utilization as well as blocking probability.  
Figure 6: The p-cube scheme: (**a**) p-cube configuration; (**b**) traffic recovery in p-cube A; (**c**) traffic recovery in p-cube B [96].  
In [120], the authors proposed a survivable multipath virtual network embedding scheme against multiple failures.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 2 Shearer Cutting Unit Detection in Infrared Thermal Images
### Feature Capture of a Shearer Cutting Unit in Infrared Thermal Images
#### 2.2.3 Location Detection  
In Section 2.2.2, the features of a shearer cutting unit are listed, but these features cannot fully represent the shape and accurate position of the cutting part. In this paper, the minimum enclosed rectangle method was used to measure the features in the image; as such, the location of the shearer cutting unit could be reasonably determined. The concrete steps are as follows:  
1. First, we used the bwlabel function in MATLAB to search the connected components in the image, that is, the white block area or the cutting-unit features.
2. Then, the regionprops function was used to measure the properties of the white block area, and the BoundingBox function was used to make the minimum rectangle containing all cutting-unit features. The location information of the rectangle in the image could be obtained, which was a 1 \(\times\) 4 matrix of [\(a\), \(b\), \(c\), \(d\)]. Then, the position of the shearer cutting unit in the image processed by morphology could be roughly determined, as shown in Figure 5. In the matrix, \(a\) and \(b\) are the coordinates of point B in the image, and \(c\) and \(d\) are the length and width of the matrix, respectively.  
Figure 4: Morphological processing results of the binary image: (**a**) Corrosion operation; (**b**) Swell operation.  
Figure 5: Enclosed rectangle of shearer cutting unit.
# Numerical 3D Modeling: Microwave Plasma Torch at Intermediate Pressure
## 3 Governing Equations
### Equations of the Plasma Model  
In plasma convection-diffusion and in the plasma reaction process, the electric field vector, electron diffusion coefficient, and electron mobility affect the electron density and electron density energy. Equations (9) and (10) describe the changes in the electron density with time and spatial distribution, while Equations (11) and (12) describe the changes in the electron density energy with time and spatial distribution. The electron density change rate can be described by the drift-diffusion formula, as shown in Equations (9)-(12):  
\[\frac{\partial n_{\varepsilon}}{\partial t}+(\mathbf{u}\cdot\
abla)n_{\varepsilon }+\
abla\cdot\mathbf{\Gamma}_{\varepsilon}\ =\ R_{\varepsilon} \tag{9}\]  
\[\mathbf{\Gamma}_{\varepsilon}\ =\ -(\mu_{\rm e}\cdot\mathbf{E})n_{\rm e}-D_{\rm e} \cdot\
abla n_{\rm e} \tag{10}\]  
and  
\[\frac{\partial}{\partial t}(n_{\varepsilon})+\
abla\cdot\Gamma_{\varepsilon}+ \mathbf{E}\cdot\Gamma_{\varepsilon}\ =\ R_{\varepsilon}-(\mathbf{u}\cdot\
abla)n_{\varepsilon} \tag{11}\]  
\[\mathbf{\Gamma}_{\varepsilon}\ =\ -(\mu_{\varepsilon}\cdot\mathbf{E})n_{\varepsilon}-D_{ \varepsilon}\cdot\
abla n_{\varepsilon} \tag{12}\]  
where symbols \(\mathbf{\Gamma}_{\varepsilon}\) and \(\mathbf{\Gamma}_{\varepsilon}\) express electron flux and electron energy flux, respectively; terms \(n_{\rm e}\), \(\mu_{\varepsilon}\), \(n_{\varepsilon}\), \(\mu_{\varepsilon}\), and \(\mathbf{u}\) denote the electron density, electron mobility, electron energy density, electron energy mobility, and velocity vector of the fluid, respectively. Symbol \(D_{\rm e}\) signifies the electron diffusion coefficient, \(D_{\varepsilon}\) denotes the electron energy diffusivity, and \(R_{\varepsilon}\) and \(R_{\varepsilon}\) express the electron rate expression and energy loss or gain, respectively, because of inelastic collisions [32].
### Motivation and Related Literature
[23], authentication is based on a logistic regression model assisted by multiple landmarks at different locations that use multiple antennas to estimate the RSSI of the transmitter. In both previous papers, a static environment is considered; moreover, the required computation load cannot be affordable for low-complexity devices such as the one we consider. In ref. [24], a kernel least mean square scheme is presented, where the dimensionality of the multiple-attribute authentication is reduced by the kernel function that is able also to track the variations in time. Other sophisticated ML algorithms proposed for PLA based on Q-Learning and neural networks as in [25] cannot be suitable for resource-constrained scenarios, such as the one of interest in this work. Approaches more suitable for an IoT context for their low computational complexity are based on one-classical ML classification schemes, as in [26; 27; 28]. Different PLA algorithms are presented and compared in [26], based on classical hypothesis testing and on ML, particularly nearest neighbor (NN) and support vector machine (SVM) algorithms. The proposed solutions exploit the characteristics of a set of parallel wireless channels (modeled as an OFDM transmission). These ML approaches are unsupervised, allowing a clustering algorithm to decide the nature of the received packets. Channel coefficients are fixed. In refs. [27; 28], two ML approaches based on SVM and k-means clustering are investigated in two different contexts, such as in multiple input multiple output (MIMO) stationary systems [27] or in unmanned aerial vehicle (UAV) aided wireless systems [28]. Channel variations are not considered.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 6 Conclusions  
We proposed an improvement method of human pose estimation for scenes with occluded bodies by using RGB-D images. The proposed method iteratively fused body joint information extracted from both color and depth images, thereby enhancing the final joint representation. To evaluate the performance in occlusion-prone environments, the proposed method introduced a method for extracting occluded samples from conventional pose estimation test datasets. The performance of the proposed method was quantitatively assessed using the constructed occlusion test set. The experimental results demonstrated that the proposed approach improved pose estimation accuracy in occluded environments using both color information and topological cues to detect human joints effectively. Specifically, the proposed method improved most per-joint AP values in the occlusion test set. Furthermore, the mAP increased by 22.2% and 23.4% compared to OpenPose and HigherHRNet, respectively. These results demonstrate the effectiveness of the proposed method  
Figure 8: Visualization of confidence maps for joints in occluded scenes.  
in pose estimation for scenes with body-to-body occlusions. The proposed pose estimation method is expected to be particularly beneficial in real-time applications where occlusions frequently occur, such as surveillance video analysis, action recognition, and autonomous driving. Moreover, beyond human pose estimation, the method can be extended to structurally defined objects, offering a highly efficient and generalizable approach to object detection in occlusion-prone environments.  
Conceptualization, J.-h.Y. and S.-k.K.; software, J.-h.Y.; writing--original draft preparation, J.-h.Y. and S.-k.K.; supervision, S.-k.K. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP), grant funded by the Korean government (MSIT) (IITP-2025-RS-2020-II201791, 100%), by the BB21 plus funded by Busan Metropolitan City and Busan Techno Park, and by a Dong-eui University Grant (202501170001).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Data and Methods  
### Datasets  
GNSS RO measurements from the following satellite missions are used in the present work: CHAMP [32], COSMIC [33], Gravity Recovery and Climate Experiment (GRACE) [34], METeorological Operational satellites (METOP) -A [35], and the successive METOP-B/C [36]. The post-processed level two dry temperature profiles (atmPrf files) for the years 2007-2020, which are provided by the COSMIC Data Analysis and Archive Center (CDAAC, [[https://cdaac-www.cosmic.ucar.edu/cdaac/products.html](https://cdaac-www.cosmic.ucar.edu/cdaac/products.html)]([https://cdaac-www.cosmic.ucar.edu/cdaac/products.html](https://cdaac-www.cosmic.ucar.edu/cdaac/products.html)), last accessed on 27 August 2021), are used to study the long-term changes in global GW activities in the lower stratosphere, i.e., at the altitude range of 20-35 km. Temperature profiles from near the ground up to about 60 km, which are with a vertical resolution better than 1 km, are available in these atmPrf files. The RO dry temperature data are with sub-Kelvin accuracy in the lower stratosphere, while their errors increase significantly at altitudes higher than 35 km due to the a priori information used in the inversion process and residual ionospheric effects [37; 38]. The consistency, mission independence, and good precision among the data products from different GNSS RO missions, as well as compared to radiosonde data, have been revealed in many previous studies [39; 40; 41; 42].  
In addition, the monthly zonal wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset, which is the latest product of the ECMWF, are used to present in detail the relationship between the temporal variations of GW Ep and QBO over the tropics in Section 4. The detailed information about the ERA5 dataset is available on the web site [[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)) (last accessed on 27 August 2021).
## 4 Validation of Surface Roughness Model and Process Parameter Optimization
### Establishment of Surface Roughness Model Considering Milling Cutter Vibration
#### 4.1.1 Establishment of the Vibration Model of Milling Cutter
An independent system of ordinary differential equations is obtained as follows [43]:  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]3  
where \(Q_{r}(t)\) is the generalized force corresponding to the canonical coordinate \(q_{r}(t)\). When \(F_{x}(t)\) acts on the end of the milling cutter, the generalized force \(Q_{r}(t)\) can be expressed as:  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]4  
Combined with the previous studies, the response of Equation (12) is [44; 45]:  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]5  
Substituting \(q_{r}(t)\) into the canonical equation (Equation (12)):  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]6  
For the fixed-hinged beam system, \(q_{r0}=\dot{q_{r0}}=0\). Ultimately, the vibration response at the end of the milling cutter in the \(x\)-axis direction is:  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]7  
During the milling process, the milling force is a force that varies from moment to moment. Milling force and vibration displacement exist in a mutually coupled relationship. To find the milling cutter displacement in the vibration state, the time \(t\) is discretized into multiple tiny moments. For the period \(t_{i}\leq t<t_{i+1}\), the milling force in the \(x\)-direction is approximated by considering it as a constant force (see Figure 23):  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]8  
\[w_{r}=\frac{{{\lambda_{r}}^{2}}}{L^{2}}\sqrt{\frac{EJ}{\rho A}} \tag{2}\]9 The discrete model of the milling force. For the convenience of calculation, the formation function is only taken to the first three orders. The discretized equation can be expressed as:  
\[\lambda_{r}=\left(r+\frac{1}{4}\right)\pi \tag{3}\]0  
Similarly, the vibration response of the milling cutter in the \(x\)-direction can be found. Therefore, the vibration response of the end of the milling cutter during milling can be expressed in vector form as:  
\[\left[ \begin{array}{c}d(x,L,t_{i})\\ d(y,L,t_{i})\end{array} \right]=\sum_{r=1}^{3}\frac{Y_{r}(L)}{w_{r}}\left(sh \beta_{r}L-\frac{sh\lambda_{r}}{\sin\lambda_{r}}\sin\beta_{r}L\right)\sum_{m=1 }^{i}\sin w_{r}(t_{i}-t_{m})\Delta\tau\left[ \begin{array}{c}-F_{x}(t_{i})\\ F_{y}(t_{i})\end{array} \right] \tag{19}\]  
It is worth noting that the coordinate systems of the dynamometer and the milling cutter are different. Regarding the \(x\)-direction, the directions specified in both coordinate systems are the same. Regarding the \(y\)- and \(z\)-directions, they are opposite in both coordinate systems.
## 3 Results
### Datasets
#### 3.1.2 Voice Instructions Dataset (VID)  
To assess the performance of different STT models and web services, we collected audio recordings featuring voice commands from children aged 4 to 8, all in Spanish, the language used in all our experiments. These recordings included a variety of commands, such as _move to the white cylinder, go down_, and _go up_, among others, aimed at controlling the robot to perform a wide range of tasks. We gathered 311 audio recordings, creating a substantial dataset for evaluating the robot's task execution capabilities. This set is named VID. The recordings exhibit varying sound qualities, as we requested parents to use their smartphones for the recordings, resulting in a diverse range of audio qualities within the dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Subset** & **Number of Images** \\ \hline Training & 3500 \\ Validation & 500 \\ Testing & 1001 \\ \hline Total & 5001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of images across training, validation, and testing subsets within the synthetic dataset, which includes three classes: cylinder, star, and cube. The dataset comprises 24,568 instances of cubes, 24,064 instances of cylinders, and 24,098 instances of stars.  
Figure 4: Image samples from the synthetic dataset used to train the object detector model.  
Observing that numerous recordings in the original dataset commenced directly with a child's voice--an approach generally less favorable for STT systems--we created an alternative version (VIDIS). In this version, we added 1 s of silence at the start of each recording. This adjustment was made to explore whether introducing a brief period of silence could enhance the STT system's performance, particularly considering the abrupt beginning of most of the original recordings.
# An Approach to Improve the Resolution of DLP 3D Printing by Parallel Mechanism
## 2 Design Principle and Overall Scheme
### Design of 3D Printing Scheme  
The commonly technology using DLP 3D printing can be divided into top-down projection stereolithography and bottom-up projection stereolithography according to the projection position.  
As shown in Figure 2b, the object is solidified at the bottom of the resin tray with a transparent window in the bottom-up projection system, which can well control the thickness and printing precision. However, the printed parts are affected by the separation force during the pull-up process, and the additional disengagement materials are needed to refill. The top-down projection stereolithography (Figure 2a) adopts mask pattern projected to the free liquid surface to cure objects, and the structure is relatively simple and flexible, avoiding separation force. Therefore, the top-down projection molding method is used for the design of 3D printer in the study. The overall design scheme is shown in Figure 3.  
The whole printing scheme is mainly composed of slicing system, control system and mechanical system. In the slicing system, the preprocessing software is used to slice and generate the 3D model slice data in cli. format. And the computer in the control system processes the slice data, controls the projector to irradiate the mask, and communicates with the controller to realize the orderly movement of the four-axis motor in the mechanical system. The parallel mechanism in the mechanical system drives the projection to move in the effective workspace. The position of the projection lens is changed, and the best  
Figure 3: Overall design scheme.  
Figure 2: (a) The top-down projection stereolithography; (b) The bottom-up projection stereolithography.  
projection surfaces at different positions are projected onto the raised forming platform to print the object.
# A Review on Bioactive Compounds and Pharmacological Activities of _Citrus unshiu_
## 4 Pharmacological Activities of Satsuma Mandarin
### Anti-Inflammatory Activity  
Some experiments have assessed the anti-inflammatory activity of _C. unshiu_ bioactives. The anti-inflammatory activity of the aqueous and methanol peel extracts of _C. unshiu_ (hesperidin, naringin, and rutin) were investigated in LPS-stimulated RAW 264.7 macrophages. The extracts exhibited anti-inflammatory activity by reducing the release of LPS-stimulated PGE2, NO, iNOS, COX-2, TNF-\(\alpha\), and IL-1\(\beta\) in macrophages [45]. The inhibitory effects of _C. unshiu_ peel on the production of inflammatory cytokines/mediators were investigated in LPS-induced RAW 264.7 cells. The dried peel was dissolved in 1000 mL distilled water, extracted at 115 \({}^{\circ}\)C for 3 h, and then freeze-dried at 4 \({}^{\circ}\)C. The active compounds hesperidin and naringin were identified in _C. unshiu_ peel by high-performance liquid chromatography. These compounds significantly inhibited NO, PGE2, IL-6, TNF-\(\alpha\) production, iNOS and COX-2 expression, as well as p38, ERK, JNK, MAPK, and NF-kB phosphorylation, in macrophage cells [105]. The anti-inflammatory effects of dried immature _C. unshiu_ fruit extracts by 70% ethanol and ethyl acetate elution were evaluated in LPS-stimulated RAW 264.7 cells. The extracts exhibited anti-inflammatory activity by decreasing iNOS and COX-2 protein expression through the suppression of MAPK and NF-kB signaling pathways [106]. An experiment evaluated the anti-inflammatory effects of _C. unshiu_ aqueous extracts rich in hesperidin using RAW 264.7 cells/mouse peritoneal macrophages in vitro and LPS-induced systemic inflammation mice. RAW 264.7 cells and mouse peritoneal macrophages were treated with _C. unshiu_ at different concentrations (101, 102, 103, 104 \(\mu\)g/mL) for 24 h, followed by LPS induction for 6 h. Mice were assigned to control, LPS, and LPS plus _C. unshiu_ groups. The LPS plus _C. unshiu_ group was orally administered _C. unshiu_ extracts at a dose of 300 mg/kg for 7 days, while the other groups received water only. Treatment with _C. unshiu_ extracts inhibited LPS-induced NO, iNOS, TNF-\(\alpha\), IL-6, IL-1\(\beta\), and CCL2 expression in RAW 264.7 cells and mouse peritoneal macrophages through downregulating the JNK, NF-kB, and p38-MAPK signaling pathways. The treatment of _C. unshiu_ raised the expression of the anti-inflammatory cytokine IL-10 while decreasing the expression of TNF-\(\alpha\) and IL-6 in comparison to other groups [107].
# A Review of Machine Learning Applications in Land Surface Modeling
## 5 Techniques of Machine Learning  
ML methods are automated or semi-automated techniques of data inference without any prior assumptions (data-driven). ML techniques can be broadly classified into (1) supervised learning and (2) unsupervised learning. Supervised learning is based on a priori specification of output and one or more inputs. In unsupervised learning, there is only input and no outputs, and the model is aimed to discover patterns in the data. Most applications in land surface models are based on supervised learning, where neural networks or other machine learning models are trained to provide a known output from the model. The supervised ML techniques can be further classified into two categories of (a) traditional ML methods and (b) DL based methods. In this section, we discuss the specific ML techniques used by the previously mentioned papers (for different purposes), with brief details, under these two categories. Our goal was to identify the most popular ones from these two categories and include further detailed description about those. The discussion is also summarized in Table 1. Following convention, ANN (traditional) is considered as networks with single hidden layer and DL-ANNs are the networks with multiple hidden layers.