esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 2 Related Work
### Generalization to Generic Shapes  
The generalization of the problem for domains beyond polygons (simple or non-simple) can be carried out by using the winding number approach. However, the literature has not shown a definitive viable implementation to do it, which includes the case of [PERSON] in [19], who gives a methodology to calculate the winding angle associated with a circular arc. But [PERSON]'s approach counts on the winding number technique described in (1), with its ambiguity for points over segments, which in the case of arcs would be points over the chord of the arc. That is not viable since the point will not even be on the boundary, and the calculation may fail depending on the sense of the circulation! Let us consider the example in Figure 7 with \(a=(0,0)\). The contribution for the winding number only by the arc \(p_{1}\)\(\sim\)\(p_{2}\) depends on the circulation sense, as happens with any segment, but in case of this arc, the value is not symmetric: it is \(\pi\) or \(0!\) Since the contributions of the other two segments are symmetric depending on the circulation sense (\(\pm\pi\)), this all adds up to ambiguity in detecting whether \(a\) lies inside or outside the region, which is absurd because point \(a\) is clearly inside the region.  
Actually, the problem of point location in regions that may have curve segments was first mentioned, although not actually solved in practice, by [PERSON] and [PERSON] in 1981 [21], and the general problem is seen by [PERSON] in 1983 as a challenge that may require a completely new approach:  
[...] While the algorithm [proposed by [PERSON]] can be adapted to certain other situations (for example, when all internal regions are star shaped), the general problem of optimal search in subdivisions formed from arbitrary curve segments may require a totally new approach. ([22])  
Figure 7: Case where point \(a\) is without doubt included in the delimited region, including a circular arc, independently of the circulation sense, but which could fail by the application of the traditional winding number technique proposed by [19].  
The winding number approach has been a well-accepted solution for the problem of point location, and the calculation of the winding number of a polygonal curve \(\Gamma\) around some point \(a\) is easy to calculate by determining the number of intersections of \(\Gamma\) with the real (horizontal) axis, as first pointed out in [23] and later implemented into a specific algorithm by [1]. However, in the literature, nothing specific is added concerning the winding number of other curves for the purpose of point inclusion, although algorithms exist to calculate the winding number contribution of Bezier curves [24].
## 4 Results and Discussions
### Wavelet Coherence Analysis of Daily Global Solar Radiation with Air Pollutants  
Many environmental factors, especially air pollutants, have a negative impact on the efficiency of photovoltaic power generation. In this section, we investigate the correlation between global solar radiation and major air pollutants (including CO, NO\({}_{2}\), O\({}_{3}\), PM\({}_{2.5}\), PM\({}_{10}\), and SO\({}_{2}\)) in Harbin from January 2014 to December 2020. Figure 7a-f illustrates the corresponding research results. It is evident from the figure that the coherence between global solar radiation and several air pollutants primarily manifests on the annual cycle time scale. Among them, five air pollutants, including CO, NO\({}_{2}\), PM\({}_{2.5}\), PM\({}_{10}\), and SO\({}_{2}\), exhibited very similar correlations with global solar radiation. Over the time span of 256 to 512 days, the arrows in their wavelet coherence spectra consistently point towards the northwest, at an angle ranging from 210 to 225 degrees. This indicates that all five air pollutants are approximately negatively correlated with global solar radiation. Additionally, only O\({}_{3}\) demonstrated a positive correlation with global solar radiation on the annual cycle time scale, and there was no obvious dependency between them.  
Figure 7: _Cont._  
After conducting the aforementioned analysis, we observed a negative correlation between air quality and global solar radiation in Harbin on the whole. This finding aligns with the fact that Harbin experiences a cold winter climate, limited sunshine duration, and the significant emission of air pollutants due to fossil fuel-based heating practices.
## 2 Proposed Method
### Algorithm  
Algorithm 1 illustrates the proposed algorithm's process. \(\xi\), \(\mathcal{L}\) and \(\varphi\) represent the encoder, decoder and score-guided network, respectively; \(\sigma_{1}\) and \(\sigma_{2}\) are the noise parameters of the auto-encoder and score-guided network. These parameters are initialized randomly and optimized in the training iteration (Steps 3-13) to minimize the loss in Equation (10). Specifically, the representation learner learns the data representation \(\mathcal{Z}\) through \(\mathcal{Z}=\xi(x)\) in Step 6. Then, the scoring network learns the anomaly scores s through \(\mathcal{S}=\varphi(\mathcal{Z})\) in Step 8. Then, Step 9 calculates the loss function, and Steps 10 and 11 take a backpropagation algorithm and update the network parameters. Data representation and anomaly score distribution are optimized during training. Given a trained model, anomaly scores can be directly calculated given new data samples.  
Figure 6: The brief flow of the model from data processing to training model to testing.  
```
Input: Training set \(\left\{x_{j}\right\}_{n}\), \(x_{i}\in R^{d}\) Output: An optimized damaged detection network M Training :
1. Randomly initialize the network parameters of the \(\xi\), \(\mathcal{L}\), \(\varphi\)
2. Initialize the noise parameter \(\sigma_{1},\sigma_{2}\)
3. for\(\in\) MaxEpoch do
4. Divide input data into batches
5. for\(x\in\) each batch do
6. \(Z\gets Z=\xi(x)\)
7. \(\xi\gets\xi=\mathcal{L}(Z)\)
8. \(\mathcal{S}\gets\mathcal{S}=\varphi(Z)\)
9. Calculate loss function L by Equation (10)
10. Update the parameters of \(\xi\), \(\mathcal{L}\) by Equations (4) and (5)
11. Update \(\sigma_{1}\),\(\sigma_{2}\) by Equations (11) and (12)
12. endfor
13. endfor
14. return Optimized Model M Testing :
15. Input \(x_{test}\)
16. \(S_{test}=\text{M}(x_{test})\)
17. return anomaly score \(S_{test}\)
```  
**Algorithm 1** Damage Detection Network
## Appendix A \"Anchor\" Proposition 1 Derived from Pelgrom's Transistor
Now, denoting the coefficient of inverse proportionality between the relative variance and the mean current by \(\sigma_{uu}^{2}\), yields  
\[\mathrm{var}^{\mathrm{rel}}\{I_{s}\}\equiv\sigma_{I_{s}}^{2}/\langle I_{s} \rangle^{2}=\sigma_{uu}^{2}/\langle I_{s}\rangle \tag{11}\]  
which amounts to the inverse proportionality formulation (97). Multiplying both sides of the second equality in (11) by \(\langle I_{s}\rangle\) and cancelling \(\langle I_{s}\rangle\) then establishes the validity of the \(\mathrm{var}I_{s}\propto\langle I_{s}\rangle\) key rule (95) in Proposition 1 (the proportionality of the source current variances and means):  
\[\mathrm{var}\{I_{s}\}\equiv\sigma_{I_{s}}^{2}=\sigma_{uu}^{2}\langle I_{s} \rangle,\ \ \ s=0,1,2,\ldots,S-1. \tag{12}\]  
\(\square\)  
An even simpler derivation of Proposition 1 is possible in the special case of current steering eDACs of the BiWgt type, in which the currents form a geometric sequence with ratio 2, i.e., \(\mathbf{I}=\{I_{u},2I_{u},4I_{u},\ldots,2^{B-1}I_{u}\}\) and wherein the various source currents are realized by parallel connection of nominally identical unary currents sources, \(I_{1}=I_{u}\), \(I_{2}=I_{u}^{(1)}+I_{u}^{(2)}\), \(I_{3}=I_{u}^{(1)}+I_{u}^{(2)}+I_{u}^{(3)}+I_{u}^{(4)}\), \(I_{s}=I_{u}^{(1)}+I_{u}^{(2)}+I_{u}^{(3)}+\ldots+I_{u}^{(2^{s-1})}\), with all \(I_{u}^{(i)}\) having a common same mean. In this case as the \(I_{u}^{(i)}\) currents are IID random variables, it is seen that  
\[\mathrm{var}I_{s}=\mathrm{var}I_{u}^{(1)}+\mathrm{var}I_{u}^{(2)}+\mathrm{var }I_{u}^{(3)}+\ldots+\mathrm{var}I_{u}^{(2^{s-1})}=2^{s-1}\mathrm{var}I_{u}^{(1)} \tag{13}\]  
and the mean of \(I_{s}\) is also the sum of the means:  
\[\langle I_{s}\rangle=\left\langle I_{u}^{(1)}\right\rangle+\left\langle I_{u }^{(2)}\right\rangle+\left\langle I_{u}^{(3)}\right\rangle+\ldots+\left\langle I _{u}^{(2^{s-1})}\right\rangle=2^{s-1}\left\langle I_{u}^{(1)}\right\rangle \tag{14}\]  
Taking the ratio of the variance and the mean we see that for all \(s=0\), \(1\), \(2\), \(\ldots\), \(S-1\) we have:  
\[\mathrm{var}I_{s}/\langle I_{s}\rangle=2^{s-1}\mathrm{var}I_{u}^{(1)}/(2^{s-1 }\left\langle I_{u}^{(1)}\right\rangle)=\mathrm{var}I_{u}^{(1)}/\left\langle I _{u}^{(1)}\right\rangle=\mathrm{const} \tag{15}\]i.e., we conclude that in BiWgt eDAC current sources structures wherein there is built a large array of unary sources which are bunched up in subsets of 1, 2, 4,... and are connected in parallel within each subset, then  
\[\text{var}I_{\text{s}}\propto\langle I_{\text{s}}\rangle \tag{10}\]  
Remarkably, this derivation holds irrespective of the dependence on the mismatch variance on the area of the transistor (i.e., the RSTD \(\propto\frac{1}{\sqrt{\text{WL}}}\) formula need not hold for (10) to be true in this case, as the derivation of (10) made no assumption except that the unary sources have identical means and are independent; in fact, any other formula for the relative variance as a function of area would have worked all the same.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### 5G and IoT Sensor Technologies for UAVs
#### 2.3.3 Communication Systems  
Managing and controlling tasks for UAVs are performed through communication systems and networks [33]. In the case of multiple drones, technologies are required to allow them to communicate with each other for safety reasons. There are different types of communications, and some of the main types used in UAVs are listed in Table 1. An extensive list of network protocols and communication techniques for generic IoT devices can be found in Reference [34]. Based on the coverage range, available data rates, and latency specifications, it is evident that 5G technology would impact drones' communications, enabling several worldwide applications. In such a conceptual model, UAVs can form infrastructureless dynamic network segments of the IoT architecture, which are interconnected to the core network for the provision of demanding services, such as surveillance multimedia streaming [35].
# Multi platform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
## 4 Discussion
The study's strengths are its high precision and delivery of results. Computer vision technology can provide accurate and real-time data on the physical fitness of schoolchildren, representing a significant advance in promoting healthy lifestyles in the school population. Furthermore, integrating computer vision technology in physical education represents an innovation that can modernize and improve traditional assessment methods in school systems in various world regions. Future studies should implement virtual platforms to assess physical fitness at the school level. A limitation of this study is the visual noise in the video capture that can influence the accuracy of the computer vision algorithm and be affected by uncontrolled factors. Furthermore, the acceptance of technology by schoolchildren and teachers may vary, which would influence the effective implementation of these proposals. In future work, it is recommended that this research branch be deepened by developing an algorithm or training a convolutional neural network to recognize bodies in unnatural positions. Currently, the algorithms and models provided by LightBuzz and the Azure Kinect SDK have difficulty recognizing bodies in intense stretch positions, as was the case for the sit-ups flexibility test. Therefore, developing models that correctly infer a body in unnatural positions would expand the possible tests computer vision systems can evaluate.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Classification Accuracy Evaluation  
In this study, we evaluated the accuracy of the classification results by using the pixel-based confusion matrix method; this method utilizes a standard accuracy evaluation matrix with n rows and n columns. As the most commonly used evaluation indicators in image classification research, the producer's accuracy (PA) and user's accuracy (UA) were derived for each class, and the overall accuracy (OA) and Kappa index of agreement (KIA) were also calculated to assess the performance of the rubber plantation classification. These indicators are calculated as follows:  
\[\text{OA}=\frac{\sum_{i=1}^{k}N_{ii}}{N}, \tag{1}\]  
\[\text{PA}=\frac{N_{kk}}{N_{+k}}, \tag{2}\]  
\[\text{UA}=\frac{N_{kk}}{N_{k+}}, \tag{3}\]  
\[\text{KIA}=\frac{N\sum_{i=1}^  
that this period corresponded to the defoliation and leaf-extraction periods of the rubber trees; thus, this period was determined to be the best temporal window for remote sensing monitoring, and this result was consistent with previous research conclusions [7,8]. The NDVI values of rubber trees were the lowest on the 49 th day, and it could be inferred that the NDVI values of rubber trees remained lowest between the 49 th and 65 th days. Based on this information, high-quality PlanetScope images of the target region that met the time requirements were queried, and an image taken on 27 February 2019 (day 58), was selected. The rubber trees enter mature leaf period from April to December, so any PlanetScope image taken in this period with good quality and no clouds could be chosen. We selected an image taken on 8 June 2019 for use in this study.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 2 Background
### Conventional V2X Communication Scheme in 5.9 GHz Band
In IEEE 802.11p, the default transmission bandwidth for frame transmission is 10 MHz. Doubled timing parameters are adopted compared to other IEEE 802.11 systems [13] because of a larger delay spread in the vehicular communication environment.
## 4 Conclusions  
In this study, a new and efficient way to synthesize the cefpime derivatives Asp-CFM and Glu-CFM has been designed and experimentally confirmed. It was shown that modification of the cefpime molecule can be achieved by a direct coupling reaction between Fmoc-Asp-anh or Boc-Glu-anh and the amine group of the thiazole ring of the CFM molecule. Modification of the CFM molecule at this site (via the thiazole ring) does not affect the biological activity of the antibiotic (its affinity to bacterial cells), which has already been determined [29]. Mild acidic/basic conditions can be used for the deprotection reaction of the amine group (removal of the Fmoc or Boc groups) which is crucial for the possibility of further coupling with any chelator in the ester (-NHS or -SCN) form.  
The new NODAGA-Glu-CFM conjugate was radiolabeled with gallium-68. The obtained [68]Ga-NODAGA-Glu-CFM radioconjugate was formed with a high yield and specific activity within the range of 0.68-0.87 MBq/nmol, as well as exhibiting full stability in human serum.  
In conclusion, on the basis of these results, it can be considered that the [68]Ga-NODAGA-Glu-CFM radioconjugate meets all the physicochemical requirements for radiopharmaceuticals. Bearing in mind that the modification of cefpime via the amine group of the thiazole ring does not affect its affinity to bacterial cells [29], the newly developed and tested radioconjugate can be considered as a new promising radioconjugate for the diagnosis of bacterial infections which, despite its promise, requires further biological research. This is the first report on the labeling of the antibiotic cefpime with gallium-68 and a cefpime-based diagnostic [68]Ga-radiopharmaceutical with potential use in PET imaging of bacterial infections in diabetic foot syndrome.  
Conceptualization, P.K. and E.G.; methodology, P.K. and K.Z.-M.; investigation, P.K. and K.Z.-M.; writing--original draft preparation, P.K., K.Z.-M. and E.G.; writing--review and editing, P.K., K.Z.-M. and E.G.; supervision, P.K. and E.G.; project administration, P.K.; funding acquisition, P.K.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Center (Poland), grant number 2017/01/X/ST4/00925.  
Not applicable.  
The contribution of student [PERSON] was made within the framework of the National Centre for Research and Development Project No. POWR.03.02.00-00-1009/17 (radiopharmaceuticals for molecularly targeted diagnosis and therapy, RadFarm, Operational Project Knowledge Education Development 2014-2020, co-financed by the European Social Fund).  
The authors declare no conflict of interest.
# Removal of Antibiotics by Biochars: A Critical Review
## 3 Antibiotics and Bacterial Cytology  
Antibiotics are a diverse group of chemicals comprising as many as 38 different groups by chemical structure [5]. However, the most frequently used antibiotics may be grouped according to their chemical structure, such as \(\beta\)-lactams, sulfonamides, tetracyclines, quinolones, and macrolides [12; 49; 56]. The specific chemical composition of antibiotics is designed to selectively attack bacterial cells without affecting human cells, inspired by selective staining of bacterial cells [19].  
In order to understand the mode of action of antibiotics, it is also necessary to analyze the chemical composition of the targeted bacteria. Most bacteria can be broadly classified as Gram-positive or Gram-negative bacteria according to Gram staining. Gram-positive bacteria contain a thick cell wall (10-80 \(\upmu\)m) of peptidoglycan layers, while Gram-negative bacteria contain a thinner cell wall (7.5-10 \(\upmu\)m) of peptidoglycan as well as an additional lipopolysaccharide membrane. Human cells do not contain peptidoglycan cell walls. Therefore, the cell walls of bacteria are the first target of antibiotics. Generally, Gram-positive bacteria are less resistant to antibiotics than Gram-negative bacteria due to their cell wall structure. Apart from bacterial cell walls, the other three major targets of antibiotics are DNA replication, protein synthesis (50 s and 30 s ribosomes), and folic acid metabolism of the bacteria (Table 1).  
Figure 2: The co-occurrence map of the keywords “biochar” and “antibiotic” in the Web of Science (WOS).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Antibiotics** & **Mechanism** & **Spectrum** & **Target Bacteria** & **Chemical Structure** & **Chemical** \\  & & & & & **Formula** \\  & & & & & **Functional** \\  & & & & & **Groups** \\ \hline Penicillins & Inhibition of cell wall synthesis & Broad-spectrum, last resort, narrow-spectrum & Gram-positive bacteria (first- and second-generation) & &  
The cellular structure and organization of the bacteria are helpful in understanding bacterial diseases and in the development of antibiotics. Therefore, bacterial cytology is briefly analyzed below.
# Maximizing the Capacity of Edge Networks with Multicasting
## 7 Multicast Network Capacity
### Capacity  
After we acquire the presentation of the optimal copy number in the two multicast schemes, the multicast capacity of the edge caching network can be presented as follows.  
**Theorem 5**.: _The lower limitation on the system capacity with the last-hop multicast can be calculated by_  
\[C=\frac{Wm}{Q_{1}\sqrt{\frac{m}{\beta}}\frac{p_{i}}{C_{i}}}. \tag{14}\]  
where  
\[C_{i}=\begin{cases}0;&p_{i}\leq 0\\ \sqrt{\frac{Q_{1}P_{i}}{\beta(m-s)}};&0<p_{i}\leq\frac{n^{2}\beta(m-s)}{Q_{1}} \\ n;&p_{i}\geq\frac{n^{2}\beta(m-s)}{Q_{1}}\end{cases}\]  
Correspondingly, Theorem 6 presents the system capacity in the complete \(k\)-array multicast.  
**Theorem 6**.: _The upper limitation on the system capacity with the complete \(k\)-array multicast can be calculated by_  
\[C=\frac{4 Wm}{Q_{2}\sqrt{n}\sum\limits_{i=1}^{m}\frac{p_{i}}{C_{i}}}, \tag{15}\]where  
\[C_{i}=\begin{cases}0;&p_{i}\leq 0\\ \sqrt{\frac{Q_{2P_{i}}}{\beta(m-s)}};&0<p_{i}<\frac{n^{2}\beta(m-s)}{Q_{2}}\\ n;&p_{i}\geq\frac{n^{2}\beta(m-s)}{Q_{2}}\end{cases}\]
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 1 Introduction
### IoMT and Wearable Devices  
Internet of Medical Things (IoMT) refers to the integration of IoT (Internet of Things) technology into the healthcare sector [1]. This term indicates the use of connected devices, such as wearable sensors and smart medical equipment, to collect and exchange data to improve clinical decision making [2, 3] (Figure 1A). Access to near-real-time data may alert users of emergency alterations in biomarkers, enabling timely medical interventions [4]. Moreover, IoMT represents an opportunity for empowering patients to monitor their own health data while potentially reducing patient care costs [5]. The combination of IoMT and telemedicine has the potential to improve patient care by allowing remotemonitoring [6]. This scenario eliminates costly in-person visits, leading to more efficient and cost-effective care [7; 8]. However, IoMT faces some challenges. For example, the data integration between IoMT networks and Electronic Medical Records (EMRs) can be difficult to establish [9; 10]. As IoMT devices generate large volumes of data in various formats, the lack of standard protocols complicates integration into EMR systems. Furthermore, differences in technology infrastructure across healthcare organizations and varying data-sharing regulations contribute to these difficulties. Privacy concerns and strict compliance requirements impose additional constraints on data transfer and storage, creating barriers to full integration [9; 10; 11; 12].  
Moreover, healthcare institutions may struggle to collect all EMRs from different stakeholders due to data quality issues and administrative requirements, which is a major challenge in achieving interoperability [11]. Some studies have highlighted the potential privacy issues and patient data security [12; 13]; thus, implementing appropriate data security strategies is essential to protect sensitive patient data [14]. Additionally, IoMT devices require high-quality evidence of their performance and accuracy before being used for clinical and diagnostic purposes.