esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 2 Materials and Methods
### Research Methodology
#### 2.3.2 CLUE-S Model  
The CLUE-S model is an improved model for small regions based on the CLUE model by [PERSON] et al., of Wageningen University in the Netherlands. Within the CLUE-S model, a raster is used as the study unit, and the LU type on the raster indicates the LU status [19; 20; 21]. The CLUE-S model consists of nonspatial and spatial analysis modules. The nonspatial module is based on the analysis of natural, social, economic, and policy driving factors of LUC, and other models or methods are used to calculate the LU demand for each simulation year; the Markov model is chosen to calculate the land demand data in this study.  
In the CLUE-S model, the development probability of each type of LU needs to be found using a logistic regression equation; the auto-logistic model, which uses neighborhood correlation to construct spatial autocorrelation weights, has better predictive ability than the general logistic model in finding the development probability of each LU type. The auto-logistic equation was first proposed by [PERSON] [22] and the general expression is as follows:  
\[\mathrm{log}\left(\frac{\mathrm{P_{i}}}{1-\mathrm{P_{i}}}\right)=\mathrm{S_{0 }}+\mathrm{X\beta}+\mathrm{r}\sum\
olimits_{j=1}^{n}\mathrm{y_{i}}\mathrm{w_{ ij}} \tag{2}\]  
where X represents a vector consisting of a set of driving factors; \(\mathrm{y_{i}}\) represents the state of the event as a binary variable; and \(\mathrm{w_{ij}}\) represents the spatial weight value.  
In this study, the weight matrix is constructed with the help of neighborhood factors, and the spatial weights are set as [23; 24]  
\[\mathrm{w_{ij}}=\frac{\sum\
olimits_{n=1}^{N}\mathrm{S_{jn}}}{N} \tag{3}\]  
where \(\mathrm{S_{jn}}\) is the similarity of each neighboring raster around raster j to j, with 1 for the same LU type and 0 otherwise, and N is the total number of neighbors of neighboring raster j. To test the effect of regression analysis, the receiver operation characteristic (ROC) method can be used to evaluate the driving factor for the explanatory ability of each type. Generally, it is considered that the ROC is greater than 0.7, which indicates that the driving factor has a good explanatory ability for the type [25].
# Material Design in Implantable Biosensors toward Future Personalized Diagnostics and Treatments
## 3 Classification of Implantable Biosensors Based on Material Design
### Fiber-Based Implantable Biosensors
One of the approaches to improve the sensitivity and limit of detection (LOD) in fiber-based implantable biosensors is the use of organic electrochemical transistors (OECTs), which can magnify the signals in contact with their targets [67]. Nevertheless, OECTs for in vivo applications are scarce given the mechanical incompatibility between OECTs and organ tissues. To overcome this lack, all-in-one structured OECTs were proposed (Figure 6A) [67]. All fiber OECTs can detect different chemicals, including dopamine (Figure 6B) and glucose (Figure 6C). Moreover, the fiber OECT's ability for in vivo monitoring was examined by implanting a platform in a mouse brain where the fluorescent images after 7 days of implantations demonstrated no noticeable immune reactions (Figure 6D-G). The CNF materials cannot be implanted in the body without any supporting layer due to their soft nature. The use of rigid auxiliary materials is another approach to overcome the instability of soft fiber materials in in vivo applications. However, these materials can produce further tissue damage during removal due to micromotions of the soft biosensor and the lengthy removal procedure. Still, they are indispensable for implanting flexible biosensors [68]. To use the advantage of both soft and rigid materials in implantable sensors, microfiber neural probes (MFNPs) were introduced. MFNPs are core-shell structures with three layers comprising alterable, insulation, and soft neural layers (Figure 7) [68]. MFNPs can create environments similar to biological tissues, with the ability to be three-dimensionally folded, bent, or wrapped to form various shapes after contact with liquids. Therefore, dry-MFNPs could be directly implanted, and the wet-MFNPs, by absorbing water in implanted tissues, can later form stable interfaces with dynamic tissues [68]. Therefore, MFNPs are good systems that can be used to implant soft biosensors. Figure 5: Carbon nanotubes (CNT) fibers-based implantable biosensor. (**A**) Structure of the CNT assembly and formation of the electrode with recognition and insulation layers. (**B**) Implantation of the CNT fiber-based biosensor for local monitoring. Reproduced with permission from ref. [65]. ©2020 Nature Publishing Group.
## 4 Conclusions  
The CNTs were oxidized to form f-CNTs with different levels of carboxylation. The SEM and TEM images show CNT incorporation into the drug crystals, and their presence was seen inside as well as outside the crystals. Based on Raman, XRD, and DSC measurements, it was evident that the f-CNTs did not change the crystal structure or the melting point. The dissolution rate was significantly enhanced with the incorporation of f-CNTs. By decreasing the C:COOH ratio in the f-CNTs, dissolution rates increased. Additionally, higher levels carboxylation showed lower values of these parameters. Therefore, it is evident that by varying the level of carboxylation, it is possible to control the dissolution rate of the hydrophobic drugs. This paper presents a novel approach to controlling the release rate by altering the level of CNT carboxylation. It is also possible that the degree of carboxylation can be used to alter other aspects of drug delivery, such as targeting capabilities.  
Conceptualization, K.C. and S.M.; methodology, K.C. and S.M.; software, K.C.; formal analysis, K.C.; investigation, K.C.; data curation, K.C.; writing--original draft preparation, K.C.; writing--review and editing, S.M.; project administration, S.M.; and funding acquisition, S.M.  
This research was funded by a grant from the National Institute of Environmental Health Sciences (NIEHS) under Grant No. R01 ES023209. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NIEHS.  
The authors declare no conflict of interest.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
In the non-symmetrical uplink of 155.52 Mb/s, the frame format is composed by a concatenation of 53 ATM cells and the addition of 3-bytes overhead in front of each ATM cell. Needless to say, the constant of 125 us is kept for the duration of a frame in all cases. _QoS:_ In uplink, QoS provisioning is realized by distinguishing traffic priority classes in each ONU/ONT, according to the service level agreement (SLA). SLA specifies traffic parameters (e.g., lowest bandwidth requirement and jitter) that a user requires from the BPON. The traffic priorities (from high to low) are named: fixed (bandwidth is offered without demand to delay sensitive traffic), assured (bandwidth after demand), non-assured (bandwidth, if available but not guaranteed), and best effort (if remaining uplink bandwidth is available; not guaranteed). Based on SLA, each ONU/ONT performs intra-ONU/ONT scheduling and queue management to define the bandwidth requirement for each traffic class. On the other hand, the OLT, after having received the bandwidth requests from the ONUs/ONTs, decides the allocated bandwidth (according to SLA) and offers transmission opportunities, performing inter-ONU/ONT scheduling. _Security:_ In the downlink, security is provided through a key exchange process. The ONU/ONT is notified by the OLT (issuing a PLOAM cell) to send the key, and then the OLT employs the advanced encryption standard (AES) mechanism to encrypt the data and send it to the ONU/ONT (the only one that knows the key) [6].
## 4 Morphology of the Blastocyst and Quality Assessment
### Blastocyst Grading Approaches in IVF
#### 4.2.1 Manual/Traditional  
Manual grading was first initiated using visual evaluation [37] by inter and intra-observation based on blastocyst morphological appearance. Prospectively, manual annotation has been found effective for certain measures, i.e., cell numbers, fragmentation, and symmetry in a non-invasive embryo selection process [44]. However, there is a tendency to biased results in manual grading system amongst inter and intra-rater decision [12], and this method is highly prone to subjectivity [31] in judgement of embryo quality. To overcome this problem, more than one rater may need to evaluate a single embryo as proven by [45]. The study has shown that high degree of inter-embryologist and intra-embryologist variability in scoring embryos, likely due to the subjective nature of traditional morphology grading, and this may ultimately lead to less precise disposition decisions and the discarding of viable embryos.
# Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China
## 3 Polarization Results and Earthquakes
### Polarization Results of the Jinta Earthquake
That was 5 days prior to the earthquake and lasted for nearly 2 months afterward (Figure 2a). The maximum polarization value of \(Y_{\rm ZH}\) during that period was approximately 1.58, which exceeded the threshold value (\(m+2\sigma\)) by 85%. A similar increase in the \(Y_{\rm ZG}\) was also observed in this plot (Figure 2b). The Dst index and a reference station of CNH were selected for comparisons, as shown in Figure 2c-e. Figure 2e shows that the absolute value of the Dst index is small and almost always less than 30 nT over the period that \(Y_{\rm ZH}\) and \(Y_{\rm ZG}\) of the JYG station abnormally increase. The calculation results yield correlation coefficients between \(Y_{\rm ZH}\), \(Y_{\rm ZG}\), and Dst index of 0.32 and 0.43, respectively, which implies no correlation between these polarization ratios enhancements and Dst or geomagnetic  
Figure 1: Distribution of geomagnetic stations and earthquakes at (**a**) the national scale, (**b**) KSH area, (**c**) JYG and GLM area. activities. To further confirm these findings, we obtained \(Y_{\rm ZH}\) and \(Y_{\rm ZG}\) of CNH observatory as a reference station, which is 2185 km from the epicenter. No significant enhancements of \(Y_{\rm ZH}\) and \(Y_{\rm ZG}\) were detected in the CNH station (Figure 2c,d), which implies that the enhancements of \(Y_{\rm ZH}\) and \(Y_{\rm ZG}\) in JYG station may be regional and associated with the Jinta \(M_{\rm s}\) 5.4 earthquake.
## 4 Results and Discussion
### Coherent Noises Associated with the Impact of Medium Inhomogeneities during Hologram Recording
#### 4.2.3 Discussion of Results in Section 4.2  
The averaging methods discussed in the section show that when averaging over a small number of frames, it is possible to achieve a noticeable improvement in the quality of the image reconstructed from a hologram. At the same time, the method of averaging is not that critical here: over digital holograms or reconstructed images of particles. The physical reason for limiting the correction possibilities in the case of Gabor holography is the inability to achieve large phase shifts when forming a sample for averaging, as is carried out in the case of non-in-line holography [39]. However, the use of several correction methods can improve the reliability of particle recognition from holographic images.  
The limitations on the length of the volume registered by the holographic camera make it possible to achieve a larger effect. Figure 18 shows holographic images of the caliber model particle in the extreme position at the illuminating module porthole (the most unfavorable point) at different lengths of the studied volume (in a marine experiment, the length of the studied volume of a submersible DHC was successively reduced to increase the distinctive ability of the DHC in turbid seawater). The obtained results made it possible to choose the length of the studied volume (494 mm) with an acceptable quality of the reconstructed image (Figure 18c,d).  
Figure 17: Dependence of the length of the studied volume on the medium turbidity according to the distinctive ability of 0.2 for a 500 \(\times\) 500 \(\upmu\)m model particle.  
Figure 18: Holographic images of the caliber model particle at the illuminating module porthole at different lengths of the studied volume: (**a**) 694 mm, (**b**) 594 mm, and (**c**) 494 mm. (**d**) Real plankton individual at the hologram recording stage near the illuminating module porthole (Barents Sea, Zelenets Bay, 2022).  
However, in this case, it is necessary to be prepared to significantly reduce the recording volume and, as a result, the sampling of particles for analysis. Thus, the adaptation of the DHC to the in situ conditions of Lake Baikal to eliminate turbulent flows led to the fact that this volume was 0.02 L instead of the nominal 0.5 L [42].
# ACapMed: Automatic Captioning for Medical Imaging
## 2 Background
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Input** & **Output** & **Dataset** \\ \hline Template-based method: kernel ridge regression and classification [11] & chest X-ray images & description & PADChest dataset \\ \hline Retrieval-based method: Lucene Image Retrieval (LIRE) + multi-label classification and topic modeling [12] & medical images & description & ImageCLEF + ROCO datasets \\ \hline Deep-learning-based method: show and tell encoder–decoder model [13] & natural images & captions & Pascal VOC 2008, Flickr8k, Flickr30k, MSCOCO \\ \hline Deep-learning-based method: encoder–decoder model [1] & medical images & diagnosis report & IU X-ray + own created dataset \\ \hline Deep-learning-based method: encoder–decoder model with CNN-based multi-label classifier and RNN as decoder [14] & chest X-ray images & medical report & IU X-ray dataset \\ \hline Deep-learning-based method: encoder–decoder with attention, show, attend and tell model [16] & natural images & captions & Flickr8k, Flickr30k and MS COCO \\ \hline Deep-learning-based method: encoder–decoder with attention, decoder with GRU [15] & medical images & descriptions & ImageCLEF 2021 \\ \hline Deep-learning-based method: merge model with a variational auto-encoder [17] & chest X-ray images & medical report & IU X-ray dataset \\ \hline Deep-learning-based method: encoder–decoder with SSA-based hyperparameter optimizer [18] & natural images & captions & MSCOCO and Flick8K \\ \hline Hybrid method: topic-guided attention mechanism [19] & lateral and frontal chest X-ray images & description & IU X-ray dataset \\ \hline Hybrid method: abnormality graph learning with retrieval technique [20] & lateral and frontal chest X-ray images & medical report & IU X-ray dataset \\ \hline Hybrid method: method based on a Retrieval-Generation Reinforced Agent [6] & lateral and frontal chest X-ray images & medical report & IU X-ray + ChexPert dataset \\ \hline Hybrid method: retrieval-based method combined with generation-based method [21] & natural images & captions & MSCOCO \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of some state-of-the-art methods on image captioning.
## 5 Conclusions  
A method for estimating the minimum split DC-link capacitance value required in a family of three-level, three-phase bidirectional AC-DC power converters operating with arbitrary power factor under constrained zero-sequence component was proposed and experimentally validated in the paper. The analysis is based on the previously revealed impact of arbitrary power factor operation on the behavior of split DC link voltages. A detailed examination of the distinction between operation with leading and lagging power factor and the corresponding influence on the capacitance sizing process was carried out. The proposed design approach was validated through closely matching simulations and experiments, confirming the accuracy of the developed analytical framework. Future work on the subject includes analysis of system operation under unbalanced AC-side voltages and/or nonlinear AC-side currents under both restricted and non-constrained zero-sequence injection.  
Conceptualization, A.K. and S.K.; methodology, Y.S., M.S., A.Y. and A.K.; software, Y.S. and V.Y.; validation, Y.S. and V.Y.; formal analysis, A.Y. and A.K.; investigation, Y.S. and M.S.; resources A.K.; data curation, Y.S. and V.Y.; writing--original draft preparation, Y.S.; writing--review and editing, A.Y. and A.K.; visualization, Y.S.; supervision, M.S., A.Y. and A.K.; project administration, S.K. and A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Israel Ministry of Energy.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 11: AC-side and DC-side voltages. Rated load, \(\cos(\varphi)=0.5\) (lagging). Voltage scale: 20 V/div; Time scale: 5 ms/div.
## 2 Data and Preprocessing  
### FLUXNET Data  
The FLUXNET 2015 dataset ([[https://fluxnet.fluxdata.org/data/FLUXNET2015-dataset/](https://fluxnet.fluxdata.org/data/FLUXNET2015-dataset/)]([https://fluxnet.fluxdata.org/data/FLUXNET2015-dataset/](https://fluxnet.fluxdata.org/data/FLUXNET2015-dataset/)) (accessed on 1 August 2022)) includes multiple temporal scales (e.g., half-hourly, hourly, daily, weekly, monthly, and yearly) of observations, which contains the flux data of carbon, water, and energy collected from 212 sites around the globe. Data were quality-controlled and processed using uniform methods to improve the consistency and intercomparability across sites [38; 39]. In this paper, a total of 171 sites (1104 site-years) with high quality data (\"NEE_QC\" \(>\) 0.75) were selected from the FLUXNET 2015 dataset (Figure 1), and the variables of the daily \"GPP_NT_VUT_MEAN\" and \"SW_IN_F\" were selected as the reference daily GPP (g C m\({}^{-2}\) d\({}^{-1}\)) and shortwave radiation (SR; W m\({}^{-2}\)). In addition, the daily PAR was calculated using the site observed shortwave radiation according to the empirical formula (i.e., PAR = 0.45 \(\times\) SR \(\times\) 0.0864; MJ m\({}^{-2}\)). More details about the FLUXNET 2015 dataset can be found in [PERSON], Trotta [38].
## 1 Introduction
This research article mainly presented a miniaturized broadband-multiband planar monopole antenna with a clockwise spiral shape of two branches for Beyond-5G, 5G, 4G, V2X, DSRC, WiFi 6 band, WLAN, and WiMAX application in autonomous vehicles and mobile IoT communication system device.
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 1 Introduction
The present study proposes a comprehensive evaluation method for social infrastructure planning regarding accessibility, as shown in Figure 1. This method consists of two steps: (1) evaluating the overall accessibility of social infrastructure in a region such as a neighborhood (town) or city by computing the Accessibility Index (AI) and (2) identifying the social infrastructure \"blind area\" of the region. AI is an index representing accessibility to a region's social infrastructure. A social infrastructure blind area is a residential area with poor access to social infrastructure. The overall research process was conducted using Geographic Information Systems (GIS). Data collection, manipulation (such as joining the spatial and non-spatial data), spatial-query analysis, and spatial display were performed using GIS. In addition, a navigation application programming interface (API) was employed to calculate the walking time and vehicle travel time between a residence and a social infrastructure facility. The social infrastructure facilities investigated in this study are elementary schools, parks, childcare facilities, kindergartens, sports facilities, and libraries. Elementary schools, neighborhood parks, childcare facilities, kindergartens, neighborhood sports facilities, and neighborhood libraries are town-level facilities that citizens can generally use within walking distance. City parks, city sports facilities, and city libraries are city-level facilities relatively more extensive than the town-level facilities, and citizens usually drive to use them. The reasons for setting up the research facilities are as follows: First, these facilities are usually provided and operated mainly by the public; thus, more careful planning is required. Second, we selected the facilities that can guarantee data reliability. Data on the information of facilities, such as usage, size, and location, were obtained through local governments. Finally, the proposed method was applied to a real case in Namdong-gu, Incheon, South Korea.
# Optical Flow Estimation with Occlusion Detection
## 5 Experiments  
We performed experiments on the MPI-Sintel dataset [43]. The MPI-Sintel dataset is a challenging evaluation benchmark, which is obtained from an animated movie. It contains multiple sequences with large motions and specular reflections. Two versions are provided: the Final version contains motion blur and atmospheric effects, while the Clean version does not include these effects. MPI-Sintel contains 1041 training image pairs for each version and provides dense ground truth. F-measure is usually considered as the measures of occlusion detection. In addition, the average of endpoint errors (AEE) are the mean EPE (endpoint errors) over all pixels in the dataset, which are normally as the measures of optical flow estimation. LDOF [7] and FlowFields [6] are classic and effective optical flow estimation algorithms, which will be used as baseline in this paper.