esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 6 NRA Crossing and Sense for Circular Arcs
### Sense of NRA Crossing for Circular Arcs  
As before, in case there is an NRA intersection, its sense must be determined, and expression (43) can be used and adapted for the case of arcs:  
\[I_{C}=-\operatorname{sgn}\{(\theta_{2}-\theta_{1})r\cos[\theta_{1}+t_{c}( \theta_{2}-\theta_{1})]\}. \tag{58}\]  
Expression (58) can be further simplified avoiding the need to calculate the trigonometric function. Knowing that \(r>0\) and being \(\theta(t_{c})=(\theta_{1}+t_{c}(\theta_{2}-\theta_{1}))\) mod \(2\pi\), we have:  
\[I_{C}=\begin{cases}\operatorname{sgn}(\theta_{1}-\theta_{2})&\Leftarrow- \frac{\pi}{2}<\theta(t_{c})<\frac{\pi}{2}\\ \operatorname{sgn}(\theta_{2}-\theta_{1})&\Leftarrow\text{Otherwise}\end{cases}. \tag{59}\]  
Also, as occurred for the linear segments, the particular cases of \(t_{c}=0\) or \(t_{c}=1\) correspond to \"half intersections\" (one extremity of the arc is over the NRA) and, therefore, the \(I_{C}\) calculated with (59) must be adjusted the same way, as given by (45), or formally stated: \(t_{c}=0\lor t_{c}=1\Rightarrow I_{C}\gets 0.5\times I_{C}\).
## 3 Design of the EASIST Transmission System  
### The Actuators  
#### 3.1.1 EMPU  
The primary transmission system of EMPU is shown in Figure 5. The basic working principle is as follows:  
The asynchronous motor drives the steel cable reel to rotate through the reducer and pulls ERSD along the longitudinal through the steel cable. The steel cable reel also rotates the power cable reel through the chain so that the power cable can move following ERSD. In this process, the EMPU controller controls the speed of the asynchronous motor. The system regulates steel cable tension through the hydraulic cylinders mounted on the fixed pulley.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 4 Homography Estimation for a Non-Center-Aligned Image Pair
### Dataset of Non-Center-Aligned Image Pair  
We chose 118,000 images from the trainval set of the MS-COCO 2014 dataset to generate training samples for the homography estimation, and 10,000 images from the test set of the MS-COCO 2014 dataset to generate test samples. As shown in Step 1 in Figure 6, all images selected from the MS-COCO 2014 dataset were converted to single-channel gray-scale images and three samples were generated from each selected image to increase the capacity of the resulting dataset. To do this, three rectangles (blue rectangle in Figure 6) of size \(w\times h\) (\(w\geq 128,h\geq 128\)), excluding a boundary region of \(\frac{w}{4}\) pixels in the x-direction and \(\frac{h}{4}\) pixels in the y-direction, were randomly selected from each image. A random perturbation in the range of \(\frac{w}{4}\) pixels for the x-coordinate and \(\frac{h}{4}\) for the y-coordinate was added to the pixel coordinates of each corner of the rectangle, and the perturbation values of the four corners were the target four-point homography values (green quadrilaterals in Figure 6). In Step 2, the target four-point homography was warped to generate image_patch_b of size \(128\times 128\) pixels. In Step 3, the image_a (resized to \(128\times 128\) pixels) and image_patch_b pair, along with the target four-point homography values, were fed as inputs to the network.  
Figure 5: HomographyFpnNet results on test images. The corners of the green quadrilaterals are the ground truth and the corners of the red quadrilaterals are the predicted results.
# Generation of Data-Driven Expected Energy Models for Photovoltaic Systems
## 2 Methodology
### Data  
The PVROM database contains 1.3 million data points of hourly production data across 176 sites in the United States [23], spanning multiple states (Figure 1) and generally ranging between 2017 and 2020. The database contains hourly measurements of expected energy in kiloWatt-hours (kWh), irradiance (Watts per square meter; \(\frac{\text{W}}{\text{m}^{2}}\)), ambient temperature, and module temperature; site-level direct current (DC) capacity is provided by the industry partners. The DC capacity (\(C_{DC}\)) for the sites within the database span from 37.8 kilowatts (kW) to 130,000 kW; a majority of the sites (140) are under 10,000 kW, with 67 of those sites under 1000 kW. A subset of the sites (100) contain industry-partner-provided expected energy estimates generated from proprietary models; these values serve as a basis for model validation activities (see Section 2.5).
## 4 Conclusions  
In this review, we reported recent studies that have used molecular docking and in vitro studies in metastatic BC for drug discovery. We divided the studies into five main categories: \"Systematic repositioning of drugs/molecules\", \"natural drugs\", \"new synthesized molecules\", \"combinations of drugs\", and \"drug latentiation\".  
The studies in the systematic repositioning of drugs/molecules category generated new clinical indications for old known drugs or molecules, such as ropinirole, small molecular compound SYSU-21598, etoposide, thionine, and 2,4-diacetylphloroglucinol, reporting a new possible application in metastatic BC. In addition, natural products such as speurotin A, atranorin, eugenol, astragalus membranacetus, 21 plant-derived inhibitors, noscapine, shikonin, plantamajoside, flavipin, and 13 new synthesized molecules were analyzed and proposed as effective drugs.  
Another possible application of molecular docking is studying combinations of drugs and drug latentiation in metastatic BC. We proposed, as promising combinations of drugs, quinacrine and curcumin, ITH-47 and ESE-15-ol, and vitamin E and paclitaxel. The drug latentiation procedures demonstrated that the three gefitinib-based derivatives are more potent than gefitinib.  
Overall, the computational and experimental results included herein reported a good consistency and demonstrated that molecular docking and in vitro studies should be used as complementary methods, which, together, can increase the knowledge for drug discovery and development. Indeed, molecular docking generates a binding score that shows the affinity between drugs and targets and in vitro studies investigate the biological responses. However, the binding affinity energy obtained by a single docking algorithm could be inaccurate due to incorrect ligand poses. A better strategy could be the use of combined scores obtained by two or more docking algorithms and/or the use of molecular dynamics. Molecular dynamics is a computational method that describes the dynamic behavior of a biological complex as a function of time. These methods could be more powerful approaches to investigate the novel biological aspects of disease mechanisms, providing a combined procedure to increase innovation in the pharmaceutical industry and to discovery novel therapies for unmet medical needs.  
Conceptualization, C.C.; methodology, C.C.; writing--original draft preparation, C.C.; supervision, I.C.; project administration, I.C.; funding acquisition, I.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding  
The authors declare no conflict of interest.
## 6 Conclusions  
In this research, the influence of shear load on the stress state evolution and bearing capacity of inclined pillars was investigated systematically. Three paths of stress evolution were revealed, and then a shear strength model was proposed to evaluate the bearing capacity of inclined pillars. These conclusions have been summarized as follows.  
1. There are three kinds of stress evolution paths of an inclined pillar, which are the stress paths controlled by the area extraction ratio, inclination, and the in-situ stress ratio; the stress paths controlled by the three factors satisfy a straight line, an arc, and a curve, respectively.
2. The change of load property of an inclined pillar is the essence of the dip effect on strength. With the increase of inclination angle, the compressive load is transformed into a shear load. As a result, the bearing capacity of the pillar is reduced and the risk of instability is increased.
3. A shear strength model was proposed to evaluate the bearing capacity of inclined pillars. The model is multiplied by a vertical pillar strength model and a dimensionless coefficient that is named the contribution factor of the shear load. The model was used to evaluate the bearing capacity of some inclined failed and stable pillars, and the results are consistent with the actual situation.
4. The bearing capacity of an inclined pillar is affected by the in-situ stress ratio and dip angle; when the dip angle \(\theta\)\(\in\)(0\({}^{\circ}\), 90\({}^{\circ}\)) and the in-situ stress ratio is less than 1.0, the larger the in-situ stress ratio, the greater the pillar bearing capacity. When the in-situ stress ratio is greater than 1.0, the larger the in-situ stress ratio, the smaller the pillar bearing capacity. In giving an in-situ stress ratio, the bearing capacity of the pillar decreases firstly and then increases with the increase of dip angle.  
Conceptualization, L.S. and Y.Y.; methodology, B.L.; formal analysis, N.H.; writing--original draft preparation, L.S. and B.L.; writing--review and editing, B.L., N.H. and P.L.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Projects in Hubei Province (grant number 2020 BCA082), the Natural Science Foundation of Hubei Province (grant number 2020 CFB123), and the State key R & D Projects (2020 YFC1909101).  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflict of interest.
# Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB
## 4 Yahoo! Cloud Serving Benchmark (YCSB)  
The YCSB benchmark performs an essential role in the process of developing and testing data management systems. The YCSB has become the _de facto_ standard benchmark to compare and measure the performance of different NoSQL databases under various workloads. The YCSB architecture is illustrated in Figure 5, which consists of the YCSB Client (composed of workload executor), the client threads, and the statistics module. YCSB Client is a Java application that generates the data to be loaded into the target databases and the operations representing the workloads. In basic operations, the workload executor triggers several client threads. Each thread performs a series of operations that make calls to the database interface layer to load the database, known as the load phase, and to execute the workloads, known as the transaction phase. The database interface layer converts simple requests, such as read requests, from client threads into database calls. Threads measure latency and throughput achieved by their operations and pass these measurements to the statistics module. At the end of the workload execution, the statistics module aggregates these measurements and returns the execution time, the average latency, the latency in percentage, and a histogram or a time series of latencies [21; 22]. The client receives two properties (name/value pairs) that define the operation to be performed [23]:  
* Workload properties: define the workload; for example, the combination of reading and writing, the distribution to be used, and the size and number of fields in a record;
* Runtime properties: define the specific properties of a workload execution; for example, the database interface layer uses the properties used to initialize this layer, such as the database service hostname and the number of client threads. In this work, we used the standard workloads provided by YCSB, called YCSB Core Workloads, which are described in Table 1. The standard workloads are from workload A to workload F. We defined two new workloads, G and H, which are similar to workloads B and C. However, workloads B and C focus on the read operation, and workloads G and H focus on the update operations. Figure 4: GridFS mechanism (based on [19]). In Table 1, we can see the distributions of operations and the type of distribution used. In all workloads, the data size was always 10 KB because we defined a number of twenty fields and each field has 500 bytes. The numbers of records were defined by stipulating a tenfold increase, starting with 100,000, 1 million, and 10 million records. The number of threads also varied between 1, 3, and 6. When running a workload, the operations, distribution, and data size parameters always remain constant. The parameters that vary are the number of records and the number of threads. For example, a workload with one hundred thousand records and one thread, or one hundred thousand records and three threads, or one hundred thousand records and six threads, etc., for the remaining records. For each workload, three executions
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
###### Abstract  
Hard-and-software integrated systems such as command and control systems (C\({}^{4}\)ISR systems) are typical systems that are comprised of both software and hardware, the failures of such devices result from complicated common cause failures and common (or shared) signals that make classical reliability analysis methods will be not applicable. To this end, this paper applies the Goal-Oriented (GO) methodology to detailed analyze the reliability of a C\({}^{4}\)ISR system. The reliability as well as the failure probability of the C\({}^{4}\)ISR system, are reached based on the GO model constructed. At the component level, the reliability of units of the C\({}^{4}\)ISR system is computed. Importance analysis of failures of such a system is completed by the qualitative analysis capability of the GO model, by which critical failures of hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are ascertained. This method of this paper contributes to the reliability analysis of all hard-and-software integrated systems.  
command and control system; GO methodology; reliability model; reliability analysis +
Footnote †: journal: Applied Sciences
# Effect of Cooking Methods on Bioactive Pigments in Purple Carrots (_Daucus carota_ L.)
## 2 Materials and Methods
### Antioxidant Activity (AA)  
The AA in terms of free radical scavenging activity was determined in the extracts by the DPPH\({}^{\bullet}\)[19] and ABTS\({}^{\bullet}\)[20] assays with slight modifications. In both cases, a BioTek Synergy Mx multi-mode microplate reader with BioTek's Gen 5 TM software (BioTek Instruments Inc., Winooski, VT, USA) and 96-well microplates (Thermo Fisher Scientific, Madrid, Spain) were used for the analyses. The AA data were always calculated by interpolating the results into a Trolox standard curve ranging between 0 and 1000 \(\upmu\)mol\(\cdot\)L\({}^{-1}\). When necessary, the concentration of the extracts was diluted to be adapted to this linear range. For both assays, the results were expressed as milligrams of Trolox equivalents per g of DW. All analyses were performed in quadruplicate. The specific experimental details for each assay were as follows:
## 2 Materials and Methods
### Clinical Deployment Considerations
#### 2.8.1 Real-Time Performance  
The proposed system exhibits fast inference times due to its use of an optimized Deep MLP and a lightweight autoencoder structure. On a standard machine (Intel i7 CPU, 16 GB RAM, NVIDIA GTX 1660 GPU), the average prediction time per sample is about 8 milliseconds. This meets real-time diagnostic needs in most clinical workflows. Batch processing can further boost throughput when analyzing multiple patient records.
## 2 Materials and Methods
### Study Selection Process  
We performed a worldwide systematic review of studies reporting on COVID-19 and psoriasis, using three electronic medical databases-PubMed, EMBASE, Web of Science considering articles dated 1 January 2019 through 31 December 2021.  
The search terms were selected to identify studies describing the relation between COVID-19 and psoriasis.  
The keywords used were \"COVID-19 AND psoriasis\", \"COVID-19 AND psoriasis AND biologists\", \"COVID-19 AND psoriasis AND systemic treatments\", \"SARS-CoV-2 vaccations AND psoriasis\".  
All selected databases were searched from their respective inception, without any time interval. In addition, we searched by hand the reference lists of other relevant articles on COVID-19 and psoriasis.  
In this first phase 298 records were identified from selected database. Records after duplicates removed were 237. Among the selected record, none was marked as ineligible by automation tools. Relevant studies were then selected. This process occurred in three phases. In the first phase, four researchers ([PERSON], F.D., E.M., G.R.) independently selected articles based on title. Any disagreements were resolved by consulting a senior researcher (A.O.). In the second phase, abstracts were evaluated. Two members of the research team ([PERSON], F.D.), independently evaluated each abstract. The research group resolved all discrepancies through unanimous consent. Ninety-five articles were excluded and 142 were evaluated for full-text analysis. Among them 15 manuscripts were not retrieved.  
The third phase consisted of critical appraisal of the full text of the 127 selected papers. Ultimately, 82 studies were selected for the final qualitative synthesis. To be included into our review, studies had to be focused on COVID-19 course in psoriasis patients, on psoriasis course in patients affected by COVID-19, on effect of vaccines against SARS-CoV-2 infection in psoriasis patients, on systemic treatment strategies in psoriasis patients during pandemic. All included studies had to be published in English, with abstract available. No restrictions on study design were considered, and randomized controlled trial, case-control study, cross-sectional study, and case series were included. Articles were excluded from our review for only three reasons: reason 1 = review article (12 reports had been excluded following this reason), reason 2 = case reports (20 reports had been removed for this reason),reason 3 = reports in languages other than English (13 reports had been neglected for this reason).
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Monitoring of Environmental Pollution (MoEP)
The use of UAVs for the environmental monitoring tasks is conditioned by significant economic losses and a threat to public health [43]; it is expected that the use of UAVs will increase the intensity and quality of control over emissions into the atmosphere, which can reduce industrial environmental violations [37], reduce the number of smog days [45], and reduce greenhouse gas emissions into the atmosphere [46]. The estimated annual expenses are USD 1555 million. It is necessary to develop the methods of operational control of hazardous substances emissions at the production facility and quality assessment of reclamation works based on heterogeneous data received from the UAV board. The following problems should be solved in this area: increasing flight time, payload, accuracy, and sensitivity of sensors; and data processing software, including computer vision systems (to assess the quality of  
Figure 4: Environmental monitoring tasks and UAVs capabilities. Source: Generated by the authors. -remediation work) when performing crop protection and fertilization tasks. The evaluated economic effect of IUAVT employment is USD 87.9 million (see Appendix A Table A3).
# CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification
## 3 Methodology  
### Overview  
The model architecture of this paper is shown in Figure 1, which is divided into two main parts: feature extraction (see Section 3.2 for details) and prediction structure (see Section 3.4 for details), where the prediction structure contains \"SRS\" feature fusion (see Section 3.3 for details). Due to the data imbalance problem in the relation classification datasets, we use the Focal loss function (see Section 3.5 for details) instead of the traditional Cross-Entropy loss function. For a given sequence data, the identifiers <e1>, </e1> and <e2>, and </e2> from the original data are used to mark the position information of the entity. Based on the position information of the entities, the relationship span between the entities can be truncated, and we use the relationship span and the complete sentences as the input to the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** &
\begin{tabular}{c} **Feature Extraction** \\ **Framework** \\ \end{tabular} & **Classification Method** & **Year** \\ \hline Att-Pooling-CNN [23] & Attention + CNN & Linear + SoftMax & 2016 \\ BERT-LSTM [24] & BERT + LSTM & Linear + SoftMax & 2019 \\ TRE [25] & Transformer & Linear + SoftMax & 2019 \\ BERTEM +MTB [26] & BERT & Linear + SoftMax & 2019 \\ D-BERT [27] & BERT & Linear + SoftMax & 2021 \\ BERT with Entity, convolution & BERT & Linear + SoftMax & 2021 \\ and max-pooling [28] & BERT & Linear + SoftMax & 2019 \\ R-BERT [10] & BERT & Linear + SoftMax & 2019 \\ CRSAtt(Our) & SpanBert & Multi-Class Attention + SoftMax & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of our model and the attention-based mechanism models.  
In the feature extraction part, we use the pre-trained SpanBert [30] as the feature extraction framework. We extract whole_feature, which represents the semantics of the whole sentence, and span_feature, which represents the semantics of the relational span, by using the feature extraction architecture. whole_feature goes through a mask extractor to obtain the feature vectors **e1** and **e2** of entity1 and entity2, and a fully connected layer to refine the vector **w** representing the semantics of the whole sentence. span_feature goes through a fully connected layer to refine a vector **s** representing the semantics of the relational span. After extracting the feature vectors **w**, **s**, **e1**, and **e2**, we input these vectors to the attention-based prediction structure to calculate the probability distribution of each relation R. Finally, we feed them to the softmax classifier to obtain the prediction results.
## 2 Methods  
To address the challenges of high computational complexity and large memory consumption in PSInSAR processing for large-scale, high-density urban areas, we propose a fully automated processing framework based on data block partitioning, independent sub-block PSInSAR processing, and sub-block result merging. First, the method takes as input the following SAR-derived datasets: co-registered SAR images, temporal and spatial baseline files, differential interferograms after terrain phase removal, intensity images, coherence maps, slant range, and incidence angle parameters. Based on these inputs, the proposed method consists of three key stages: regular grid-based data block partitioning, independent PSInSAR processing for each sub-block, and global spatially consistent merging of results. In this workflow, regular grid partitioning employs an adaptive grid size adjustment and boundary expansion mechanism to ensure controllable memory usage and sufficient overlap for point matching in subsequent merging. Independent PSInSAR processing for each sub-block transforms the high-dimensional global matrix operations into parallelized low-dimensional sub-problems. The global merging stage integrates common PS points from overlapping regions and topological relationships of connected components to construct a weighted least squares adjustment model to eliminate systematic biases from the sub-block processing. This enables the complete retention of PS point information and seamless integration of the PS dataset. The workflow of the proposed method is illustrated in Figure 1.