esa-sceva/satcom-chunk-collection huggingface dataset:
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 5 Conclusions  
In this paper, a fusion anti-interference scheme based on a deep complex network and blind source separation is proposed, aiming at the actual problem of 5G signal interference in underground mines. By the feedback trigger loop design of two functions of interference monitoring and signal-to-noise separation, the interference components in the underground 5G signals could be adaptively suppressed, and the anti-interference ability of the underground wireless radio frequency signals could be enhanced. The feasibility of the above method was verified through experiments, and it was proven that the interference sources, such as burr interference signals and crosstalk signals, could be effectively suppressed under the premise of ensuring the main communication performance indicators of the mine 5G communication systems. It can achieve the anti-interference and reliability of mine 5G communication systems, and provide technical support for mine 5G communication and intelligent mine construction.  
Conceptualization, L.Z. and W.Y.; methodology, Y.J.; software, Y.J.; validation, L.Z., Y.J. and Q.Z.; formal analysis, W.F.; investigation, W.Y.; resources, W.Y.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, Q.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Communication delay tests: (**a**) Communication delay without fusion anti-interference, and (**b**) Communication delay with fusion anti-interference.  
**Funding:** This research was funded by the National Natural Science Foundation of China (51874299); and the Special Project of Science and Technology Innovation and Entrepreneurship Fund of Tiandi Technology Co., Ltd. (2022-TD-ZD001, 2022-2-TD-ZD001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method
### Joint Losses  
We employ two joint loss functions, namely the cross entropy (CE) loss [37] and the triplet loss [38] for training our proposed framework. The CE loss is applied for the view prediction task and is calculated as follows:  
\[L_{ce}=-\sum_{n=1}^{N}\sum_{V=1}^{V}y_{n}log(P_{n,v})w.r.t.P_{n,v}=\frac{e^{ \widetilde{P}_{viewn,v}}}{\sum_{V=1}^{V}e^{\widetilde{P}_{viewn,v}}} \tag{18}\]  
where \(N\) represents the total number of silhouette sequences and \(y_{n}\) denotes the discrete ground value of the \(n\)-th sequence.  
Next, the triplet loss is employed to augment the discriminative capability of the features. For each triplet of gait silhouette sequences \((Q,P,N)\), where \(Q\) and \(P\) belong to the same subject, and \(Q\) and \(N\) are from the different subjects, the triplet loss can be calculated as follows:  
\[L_{trip}=\frac{1}{K}{\sum_{i=1}^{K}\sum_{l=1}^{n}max\Big{(}m-d_{ij}^{-}+d_{ij}^ {+},\,0\Big{)}d_{ij}^{-}}. \tag{19}\]  
where \(K\) represents the number of triplets, \(d_{ij}^{-}=\left\|f_{final,j}^{Q_{i}}-f_{final,j}^{N_{i}}\right\|_{2}^{2}\) and \(d_{ij}^{+}=\left\|f_{final,j}^{Q_{i}}-f_{final,j}^{P_{i}}\right\|_{2}^{2}\).  
Finally, to obtain the overall loss function, we joint the CE loss and the triplet loss as follows:  
\[L=L_{trip}+\lambda_{ce}L_{ce} \tag{20}\]  
where \(\lambda_{ce}\) is a hyper-parameter.
## 3 Basics of Acoustic Target Tracking
### Simulation Model  
For our simulations we used the VisualSense simulator [30] that builds on and leverages Ptolemy II [31]. Simulation was done with Ptolemy II version 6.0.2. We used 40 motes with a single sink node. All sensor nodes were spread with normal distribution in 2-dimensional square fields with variation of X position in [0, 500] meters range and Y position in [0, 500] meters range, and the target object rotated 10 times in spiral form in this field passing a unique route in each run such that all parts of simulation field were traversed. Simulation was run for 400 seconds and a target object regularly broadcasted specific acoustic signals in two seconds periods. All sensor nodes were assumed equipped with acoustic sensors, and the acoustic signals of the target object were assumed detectable by all sensor nodes that the target was located in their sensing coverage. Target tracking was carried out 200 times during simulation. The sink had radio communication radius of 240 meters and other motes had an equal radio communication range of 120 meters. We assumed perfect routing without any packet losses.  
We used three techniques in our simulations: (1) highly accurate time synchronization with \(10^{\text{-}12}\) seconds precision, (2) target tracking by using the information of only three different sensing modes in each set of simultaneous equations, and (3) a simple formal majority voter. We used a variation of formal majority voter presented in [32] for fusing the information of target tracking. The mean of spatially distributed 3-dimensional vectors of spatio-temporal information of the target object was computed first. A nearest vector to the mean vector was chosen as a representative. A vector was then randomly selected from a group of candidate vectors whose Euclidian distance was lower than a specific threshold value (similarity parameter) \(\sigma\) from the representative vector; 0.4 was assigned to the \(\sigma\) parameter.
## 3 Results
### Analysis of Active Constituents by FTIR and UV-Vis  
For analyzing the types of functional groups present in plant extracts, FTIR spectroscopy is an important characterization that provides the structural information in plants. In this experiment, dried powders of mushroom extracts were used. The 900-4000 cm\({}^{-1}\) complete FTIR spectra are shown in Figure 3A, denoting several functional groups that are normally observed in mushrooms, such as broad bands at 3300 cm\({}^{-1}\) of -OH, and at 1640 cm\({}^{-1}\) of C = C stretching, respectively. The -OH functional group indicates phenol. Figure 3B shows the magnified spectra in the 900-1500 cm\({}^{-1}\) range. New functional groups in this range were generated after exposure to plasma. For instance, the band at 1421 cm\({}^{-1}\) can be attributed to -OH bending, while the bands at 1090 cm\({}^{-1}\) and 1039 cm\({}^{-1}\) can be attributed to C-O stretching.  
Figure 3: Fourier transform infrared spectra and UV-Vis analysis of extracts of nonthermal gas plasma-treated mushroom-derived extracts. Fourier transform infrared spectra of _F.__belutipes_ extract **(A)** 900–4000 cm\({}^{-1}\) and **(B)** 900–1500 cm\({}^{-1}\). Black line, control (0 s not exposed to plasma); red line, exposed to plasma for 30 s; blue line, exposed to plasma for 60 s; **(C)** UV-Vis absorption spectra of _F.__belutipes_ extracts. Black line, control (0 s not exposed to plasma); red line, exposed to plasma for 30 s; and blue line, exposed to plasma for 60 s.  
We also analyzed the extracts using UV-Vis spectroscopy. The color of chemicals directly affects absorption in visible and ultraviolet ranges. Figure 3C shows absorption peaks mainly in the region of 230-400 nm in the UV-Vis spectra of all three extracts. This region is closely associated antioxidant components such as phenols, flavonoids, and aromatic compounds [25]. Natural polyphenols and flavonoid components have absorbance maxima in the ranges of 260-350 [25] and 280-350 [26] nm, respectively. Although all samples produced peaks in these regions, absorbance was higher for the 30 and 60 s samples as compared with control samples at the same wavelength. The peak was more obvious for the 30 s than the 60 s group, which was consistent with results obtained from the phytochemical analysis.
###### Abstract  
Most of the world's 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO\({}_{2}\) gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018-2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.  
volcano monitoring; Sentinel missions; Convolutional Neural Network (CNN); Synthetic Aperture Radar (SAR) imaging; InSAR processing; infrared remote sensing; SO\({}_{2}\) gas emission +
Footnote †: journal: Remote Sensing, 11, 1528; doi:10.3390/rs11131528
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Ablation Studies
#### 4.4.2 OSM Ablation  
We can see that the OSM data greatly help to improve the results, especially for the positional recall rates. The starting strategy has a large influence on the final result, with the model using OSM and with _lon - 1_ achieving the best results for the AlignNet models (Table 4). We show that the right strategy allows the model to obtain competitive results compared to the state of the art.
## 6 Model Building and Analysis
### Verification of the Developed Model  
To verify the developed model, 20% of the data set was left for testing purposes. To determine whether the model is verified or not when using results comparison as in this study, two terms can be used to determine the validity of the model, Average Validity Percent (AIP), and Average Invalidity Percent (AIP). The AVP represents the validation percent out of 100, and AIP represents the prediction error [49]. These two terms are shown in Equations (7) and (8).  
\[\text{AIP}=\left(\sum_{i=1}^{n}\left|1-\left(\frac{E_{i}}{C_{i}}\right)\right| \right)\star 100/n \tag{7}\]  
\[\text{AVP}=100-\text{AIP} \tag{8}\]  
where AIP is Average Invalidity Percent, AVP is Average Validity Percent, \(E_{i}\) is Estimated value, and \(C_{i}\) is Actual value.  
Table 9 shows the validation data with a comparison of categories of actual organization rank and the predicted category of organization rank. The Average Invalidity Percentage is 36.667%. Therefore, the average validity percentage is 64.334%. Thus the model is stable.  
Figure 12: Membership function assigned to the Output variable.
## 3 Proposed Scheme: MC-TIRP
### Trust Evaluation Component  
In this paper, trust evaluation is classified into local trust evaluation and global trust evaluation. First, local trust evaluation derives local trust based on the trustworthiness, QoS, and energy value evaluated by each node and learns by receiving it as a reward. This method detects and excludes malicious nodes through flexible thresholds in the process of deriving local trusts and transfers mission-critical data to a trusted alternative path. It also proposes a flexible weighting method to ensure reliability while managing the energy of resource-constrained devices. Global trust evaluation is calculated based on the local trust value of the nodes included in the multi-path, and it learns by deriving a global trust for the entire path and receiving it as a reward. As a result, the optimal path can be learned and the data with the highest mission criticality can be transferred to a safe path.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 2 Materials and Methods
### Field Observations  
During three separate field campaigns in October 2017, August 2019, and October 2019, we collected ground verification data at locations throughout Northern and Southern Belize, along both the mainland coastal zone and the BBR. In total, we visited 339 ground control points throughout the country, recording the land cover type (i.e., mangrove or non-mangrove) and geographic coordinates at each point with 3 m accuracy. Using high-resolution (1 m or finer) Google Earth imagery captured in 2020, we checked each ground control point to verify that the land cover type at the point had not changed between the original collection date and 2020. We used these ground control points to \"train\" and refine our classification of the remotely sensed data, as outlined in further detail below.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 3 PEB Computation and Channel Estimate
### CRB for Position  
CRLB for the position is computed from the inverse of FIM \(\mathbf{J}_{\mathbf{\eta}_{b,k}}\) corresponding to positioning parameters \(\mathbf{\bar{\eta}}_{b,k}=[p_{x^{*}}^{k},p_{y^{*}}^{k},g_{b,k^{*}}^{R},g_{b,k}^{I}]\). To do that, we first obtain position parameters from channel parameters, using the geometric relationship between a BS-user pair [12], as illustrated in Figure 1. For user \(k\) and BS \(b\), parameters \(\mathbf{\eta}_{b,k}\) and \(\mathbf{\bar{\eta}}_{b,k}\) are related as: \(\tau_{b,k}=\|\mathbf{q}_{b}-\mathbf{p}_{k}\|/c\), \(cos(\
## 4 Drug Resistance Mechanisms in TB
### Human Errors and Advances in MDR-TB Management  
Human errors may contribute to the development of drug-resistance because of the improper use of anti-TB drugs. Two pathways lead to the development of genetic resistance: (i) primary resistance, when a person is infected with a drug-resistant strain, and (ii) acquired resistance, when a person infected with a drug susceptible strain is inadequately treated with drugs, allowing the selection of resistant mutants [13]. The first case mostly occurs in highly crowded communities (e.g., prisons), or in countries with high MDR-TB prevalence, where it is essential to rapidly diagnose and treat patients, so as to reduce transmission [15]. In the second case, it is essential to follow the WHO recommendations on how to adequately treat the TB patients whose disease is caused by a drug-susceptible strain. The clinicians need also to ensure that infection control measures are established, particularly when MDR-patients are hospitalized [26].  
To implement the Stop TB Strategy (developed from the DOTS framework), WHO identified a number of factors contributing to poor treatment outcomes, including the acquisition of acquired drug-resistant TB [13]. They were: (i) Inappropriate treatment by health care providers (inappropriate or absent guidelines, poor training of physicians and nurses, sub-optimal education of patients, poor management of adverse drug reactions, no monitoring of treatment, poorly organized or funded TB control programs); (ii) Inadequate drug supply (poor quality medicines, stock-outs, poor storage conditions, wrong dose or combination); (iii) Inadequate drug intake or treatment response by patients (lack of information on treatment adherence, adverse effects, malabsorption).  
Common clinical errors in MDR-TB management, particularly in developing countries, include the addition of a single drug to a failing regimen, failure to recognize existing drug resistance, failure to provide directly observed therapy and to manage nonadherence, suboptimal dosages of second-line drugs to decrease side effects, drug treatment based on clinical facts while waiting for DST results [13; 14]. In any case, it is important to know that only drug combinations decrease the risk of selection of resistant strains.  
Since the treatment of MDR/XDR-TB cases is difficult, WHO recommended that their management be performed by a multidisciplinary team (TB Consilium) at local, regional and/or national levels, including experts (e.g., clinicians, microbiologists, public health officers) with different professional backgrounds [13; 15]. In medium and high incidence countries, TB Consilia are important for accessing second-line drugs and/or new drugs BDQ and DLM.  
A comparative analysis of the TB Consilia for management of difficult-to-treat MDR/XDR TB cases in Europe and Latin America has been reported [27]. In October 2018, a Global TB Consilium was launched by the Global TB Network, with the goal to provide to a clinician a response on difficult TB cases within 48 h [15; 28].
###### Abstract  
Pickering emulsions are emulsions stabilized by solid particles, which generally provide a more stable system than traditional surfactants. Among various solid stabilizers, bio-based particles from renewable resources, such as micro- and nanofibrillated cellulose, may open up new opportunities for the future of Pickering emulsions owing to their properties of nanosize, biodegradability, biocompatibility, and renewability. The aim of this research was to obtain oil-in water (O/W) Pickering emulsions using cellulose microfibers (CMF) produced from cotton cellulose linters by mechanical treatment through a high-pressure homogenizer. The O/W Pickering emulsions were prepared with different O/W ratios by mixing edible oil (sunflower oil) with water containing CMF at concentrations of up to 1.0 wt%. The apparent viscosity of the separated emulsion phase was measured. Results showed the feasibility of using low concentration of CMF for preparing and stabilizing Pickering emulsions, with the apparent viscosity of the emulsion phase increasing 60-90 times with respect to the sunflower oil, for a shear rate of 1 s\({}^{-1}\). In addition, theoretical nutrition facts of the emulsions were calculated and compared with other fats used in foods, showing that they can be a promising low-calorie product containing dietary fiber, replacing trans and saturated fats in foods.  
Keywords:cellulose microfibers; Pickering emulsions; sunflower oil; rheology; emulsification index; food applications +
Footnote †: journal: Applied Physics Letters
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 3 Key Technologies
### Entity Object Model Design  
Satellites, rockets, and probes are used to carry out space missions such as launch, observation, and detection, and become the actual participants in space activities. Furthermore, these man-made space targets will become the main body of VSEs that needs to be supported and expressed because of the inability of a large number of human beings to enter into space at present. Figure 5 shows the four kinds of information required for describing a working BeiDou navigation satellite: (i) Attribute; this contains the name, id, category, and country of the object. (ii) Structure; this mainly describes the geometric form and the change. In addition to the model construction of the target, it also includes the change of overall form following the action of the components, such as the directional rotation of the solar panels during the operation. (iii) Position; determining the location of the target in the space-time coordinate system. The motion of the space target often follows a certain dynamic model. (iv) Attitude; according to the missions' need, the attitude of the satellite should be adjusted frequently such as the direction of the satellite antenna to the earth needs to ensure the visibility of the communication link. Therefore, a unified object model was designed, which consisted of the attribute, ephemeris, geometric model, and behavior model, which is shown in Figure 6.  
Based on the multiple ephemeris segment, the ephemeris elements are used to determine the location of the target object at any time. Each ephemeris section contains a description of the timeline, coordinate system, and orbital information. In addition, three forms of orbits are supported in Sino-InSpace: Kepler, two-line element (TLE), and discrete track points. The first two orbits have their own prediction model which is used to calculate the position. For the description of the shape, the geometric model is composed of components that are constituted by some primitives based on the tree structure; the primitives are the smallest parts of the model consisting of dots, lines, planes, and textures. In addition, the primitive can also be a model file constructed by commercial software (e.g., 3 ds Max). The behavior model is used to describe the movements of the overall and internal  
Figure 4: Space environment octree subdivision method.  
components of the target object at different stages. Each phase contains a description of the timeline, action object, and behavior type, which includes three types of operations such as translation, rotation, and scaling.
# Shared Tuned Mass Dampers for Mitigation of Seismic Pounding
## 2 Conceptual and Mathematical Model of Shared TMDs (STMD)  
For conceptual convenience, let us consider two single degree of freedom (SDOF) systems connected by a shared TMD as shown in Figure 1. The SDOFs are simplified representations of buildings A and B, and their masses, stiffnesses, and damping coefficients are denoted as \(m_{A}\), \(m_{B}\); \(k_{A}\), \(k_{B}\); and \(c_{A}\), \(c_{B}\), respectively. The mass, stiffness and damping coefficient of the tuned mass damper are denoted by \(m\), \(k\), and \(c\), respectively. An alternate scheme is to connect the TMD mass to building A with a dashpot and to building B with a spring.  
The system mass, stiffness, and damping matrices are given below.  
\[\mathbf{K}=\left[\begin{array}{ccc}k_{A}+k&0&-k\\ 0&k_{B}&0\\ -k&0&k\end{array}\right] \tag{1}\]  
\[\mathbf{M}=\left[\begin{array}{ccc}m_{A}&0&0\\ 0&m_{B}&0\\ 0&0&m\end{array}\right] \tag{2}\]  
\[\mathbf{C}=\left[\begin{array}{ccc}c_{A}&0&0\\ 0&c_{B}+c&-c\\ 0&-c&c\end{array}\right] \tag{3}\]  
and the state matrix is defined as  
\[\mathbf{S}=\left[\begin{array}{ccc}0&\mathbf{I}\\ -\mathbf{M}^{-1}\mathbf{K}&-\mathbf{M}^{-1}\mathbf{C}\end{array}\right] \tag{4}\]  
where \(\mathbf{I}\) is an identity matrix. The performance (or cost) function, as defined by [PERSON] et al. (2001) is the trace of a matrix \(\mathbf{L}\), which satisfies the following Lyapunov equation.  
\[\mathbf{L}\mathbf{S}+\mathbf{S}^{\mathrm{T}}\mathbf{L}+\mathbf{Q}=0, \tag{5}\]  
where \(\mathbf{Q}\) is a weighting matrix that assigns different importance to the displacements (and/or velocities) at the different degrees of freedom of the system. The design problem is then to estimate \(k\) and \(c\) that minimize the trace of \(\mathbf{L}\), denoted hereafter by \(J\). The weighting matrix is taken as \(\mathbf{Q}_{11}=1\) and all other elements as 0.