esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 4 Modeling System Approach
### General Framework of the Simulation  
In order to gain a deeper understanding of the communication performance of the satellite network, a unified maximum service time strategy simulation was adopted to simulate the call test between the earth station and the satellite, minimizing the number of link switches in the process from the earth station to the satellite. The general flow of the simulation is shown in Figure 4, and the specific steps are as follows:  
Figure 3: Global population density by provincial administrative division. The above global population density data are derived from the WorldPop database. [[https://hub.worldpop.org/geodata/listing?id=76](https://hub.worldpop.org/geodata/listing?id=76)]([https://hub.worldpop.org/geodata/listing?id=76](https://hub.worldpop.org/geodata/listing?id=76)) (accessed on 20 December 2022) [25].  
* The ground stations A to B send service requests sequentially in the calling order, and the corresponding satellite with the longest service time within the visual range is selected by combining the Time strategy, in which the satellite node connected to ground station A is called source satellite C and the satellite node connected to ground station B is called target satellite D;
* ISLs are constructed according to different connection strategies, resulting in different topologies for the entire mega constellation network;
* After setting the time slice size, the link connections of satellites in each time slice are recorded in the matrix K of ISLs, and the shortest path P from C to D in the LEO satellite network is obtained by [PERSON]'s algorithm, with a static network topology table generated in different time slices;
* Analyze the resource allocation of the remaining channels of all satellites in the shortest path P. If the remaining channels are not zero, access to the current call is allowed while the total number of channels in the path is subtracted by one. If there are no remaining free channels, the call is blocked directly at the current moment, and the result is recorded as a call failure;
* Continue to access new call requests and repeat the above process until there are no new ones.  
When a new call request is generated and accessed, the existing call queue needs to be updated, and two main influencing factors need to be taken into account. Firstly, when implementing dynamic switching of ISLs in particular time slices, it should be ensured that the calls to be switched are made in continuous time Y and that the shortest path P from C to D also changes continuously within time Y. Secondly, as the satellite is operating at high speed, the angle between the ground base station and the satellite needs to be kept within the minimum elevation angle criteria. If the minimum elevation angle is exceeded, it is necessary to re-judge whether the ISL needs to be switched.
# Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging
## Materials and Methods
### Image Correction  
The HSI system used in this study acquired 224 spectral bands (\(\lambda\)) with 789 \(\times\) 512 spatial pixels due to the acquisition software (Lumo Scanner), as shown in Figure 1b. The spectral radiance cube generated by the HSI system includes the sensor's noise, illumination and several other atmospheric effects (air particles, light effects, particle size, etc.), which can cause false results [40]. Therefore, to exclude those effects, actual reflectance of the object was calculated by using the imperial line method [41]. For reflectance calculation a dark and white reference is required to remove the atmospheric effects. Thus, white reference \(W\) was captured by scanning a white tile and the dark reference \(b\) was captured by obscuring the camera lens fully using an opaque black cap. All the samples were prepared in the same way using Equation (1).  
\[R=\frac{Rad-b}{W-b} \tag{1}\]  
where \(R\) is the normalized accurate reflectance cube; \(Rad\) represents spectral radiance captured by HSI-system, with 0% reflectance for \(b\) and 99.9% for \(W\). Furthermore, each minced meat spectrum absorbs some portion of light, which cannot be observed through normal visualization [2; 42]. Therefore, the absorption values of minced meat were computed by using Equation (2) [14; 43];  
Figure 1: Laboratory protocol (Specim Fx 10) used in this study for minced meat data acquisition.  
\[A=\log\frac{1}{R} \tag{2}\]  
where \(A\) and \(R\) represent absorption and accurate reflectance cube, respectively. The calculated absorption spectrum represents the composition of chemical bonds; for instance, the 430 nm wavelength band contains information about absorption of hemoglobin pigments, the 500-600 nm one about respiratory pigments and 760 nm, 940 nm and 996 nm about the second overtone of O-H [32; 44] in the minced meat spectrum.
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 3 Proposed Work
### Implementation  
Both the above Algorithms 1 and 2 were implemented using Python code. All the components were created in Python code. The tests were performed on a machine running Windows 11 Professional with an Intel (R) Core i7 CPU and 8 GB of RAM. We assume that units of a fixed size are broadcasted and accessed from the XML stream. The number of buckets is then used to calculate the access time. The sample code is attached for the reference:  
```
import xml.etree.ElementTree as ET import os # Read in the XML data stream xml_file ='example.xml' tree = ET.parse(xml_file) root = tree.getroot()
```  
**Algorithm 3**The implementation of the proposed scheme for  
Choose a partitioning criterion, such as document size, document type, or document content partition_criterion = 'document_size'
For each document in the XML data stream, determine the value of the chosen criterion document_values = {} for document in root.findall('document'): if partition_criterion == 'document_size': value = os.path.getsize(document.get('file_path')) elif partition_criterion == 'document_type': value = document.get('type') elif partition_criterion == 'document_content': value = document.find('content').text else: raise ValueErrorError('Invalid partitioning criterion')
Create a partition for each unique value of the chosen criterion if value not in document_values: document_values[value] = []
Assign each document to the partition that corresponds to its value of the chosen criterion document_values[value].append(document)
Output the partitions as separate XML data streams for value, documents in document_values.items(): root_partition = ET.Element('documents') for document in documents: root_partition.append(document) partition_file = f'partition_partition_criterion].value].xml' ET.ElementTree(root_partition).write(partition_file) If partitioning by document_size, each document element should have a file_path attribute, a type attribute, or a child element named content. It reads the XML data stream, determines the partitioning criterion, calculates the value of the chosen criterion for each document, creates a partition for each unique value, and assigns each document to the partition corresponding to the value of the chosen criterion. The partitions are then generated as separate XML streams.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 5 Experiments
### Impact of Hyper-Parameters  
The sensitivities of hyper-parameters in loss functions are analyzed in this section: i.e., the weight of camera classification loss \(\lambda_{cam}\) and gradient penalty \(\lambda_{gp}\), which control the relative importance of different losses when generating camera style transferred features (Section 3.2), and the weight of generated feature loss \(\lambda_{gen}\), which controls the contribution of generated features when jointly training (Section 4.3). The experiments are carried out on the validation set, and the results are shown in Figure 5-7.  
When \(\lambda_{cam}=0.3\), the proposed model reaches its best results. It is worth mentioning that the camera style information is not identity-related, so the weight of camera classification loss \(\lambda_{cam}\) should not be too large. Otherwise, the generator in the generative model tends to learn overcharged camera information, which does not exist in the input identity feature and corrupts the training process.  
\(\lambda_{gp}\) is introduced into the network to maintain 1-Lipschitz continuity. The proposed model performs best when \(\lambda_{gp}=1.0\).  
The best performance can be obtained when \(\lambda_{gen}=0.5\). Assigning an excessive value for \(\lambda_{gen}\) reduces the result. This suggests that the proposed generative model can act as an effective data augmentation scheme in the appropriate range.  
Figure 5: Hyper-parameter analysis of the weight of the camera classification loss \(\lambda_{cam}\) on Market-1501. Best performance achieved when \(\lambda_{cam}=0.3\).  
Figure 6: Hyper-parameter analysis of gradient penalty \(\lambda_{gp}\) on Market-1501. Best performance achieved when \(\lambda_{gp}=1.0\).
## 3 Experiments and Discussions  
In this section, we collected four-day data to compare the accuracies of PPP-B2b orbit and clock to those of IGS RTS products. The positioning accuracies of PPP-B2b on dual-frequencies and single-frequency PPP are further studied with static and dynamic tests.
## 2 Materials and Methods
### Volatile Compounds  
The EVOO samples (2 g), spiked with 100 mg of internal standard solution of 4-methyl-2-pentanol dissolved in refined sunflower oil (50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), were placed in a 20 mL glass vial, tightly capped with a polytetrafluoroethylene (PTFE) septum, and held for 5 min at 40 \({}^{\circ}\)C to allow for the equilibration of the volatiles in the headspace. After the equilibration time, the septum covering each vial was pierced with a solid-phase microextraction (SPME) needle, and the fiber was exposed to the headspace for 40 min. When the process was completed, the fiber was inserted into the injector port of the GC. The temperature and time of the pre-concentration step, performed in a HT280T (HTA s.r.I, Brescia, Italy), were automatically controlled by the software HT-COMSOFT (HTA s.r.I). The SPME fiber (2 cm length and 50/30 \(\mathrm{\SIUnitSymbolMicro m}\) film thickness) was from Supelco (Bellefonte, PA, USA) and consisted of a stable flex stationary phase divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). The fiber was previously conditioned following the instructions of the supplier.  
Determination of Volatile Compounds  
The volatiles absorbed by the fiber were thermally desorbed in the hot injection port of a GC for 5 min at 260 \({}^{\circ}\)C with the purge valve off (splitless mode) and were then injected onto a TR-WAX capillary column (60 m \(\times\) 0.25 mm i.d., 0.25 \(\mathrm{\SIUnitSymbolMicro m}\) coating; Teknokroma, Barcelona, Spain) of a Shimadzu GC-2010 Plus gas chromatograph with a flame ionization detector (FID) (Shimadzu, Kyoto, Japan). The carrier gas was hydrogen with a flow rate of 1.5 mL/min. Detector temperature was 280 \({}^{\circ}\)C, and the oven temperature was held at 40 \({}^{\circ}\)C for 10 min and then programmed to increase by 3 \({}^{\circ}\)C/min to a final temperature of 200 \({}^{\circ}\)C, where it was held for 10 min. The data were recorded and processed with GC solution Ver. 2 Workstation Software (Shimadzu, Kyoto, Japan). Each sample was analyzed in triplicate. The identification of the volatile compounds was performed by comparison of the retention times with Sigma standards and quantified by interpolation on the calibration curves. The calibration curves were constructed using sunflower oil spiked with volatile compounds standard (Sigma) in a range from 10 to 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. The linear regression coefficients (R) were in a range from 0.975 to 0.994.
###### Abstract  
Root-knot nematode (RKN) _Meloidogyne javanica_ presents a great challenge to Solanaceae crops, including potato. In this study, we investigated transcriptional responses of potato roots during a compatible interaction with _M. javanica_. In this respect, differential gene expression of _Solanum tuberosum_ cultivar (cv.) Mondial challenged with _M. javanica_ at 0, 3 and 7 days post-inoculation (dpi) was profiled. In total, 4948 and 4484 genes were detected, respectively, as differentially expressed genes (DEGs) at 3 and 7 dpi. Functional annotation revealed that genes associated with metabolic processes were enriched, suggesting they might have an important role in _M. javanica_ disease development. MapMan analysis revealed down-regulation of genes associated with pathogen perception and signaling suggesting interference with plant immunity system. Notably, delayed activation of pathogenesis-related genes, down-regulation of disease resistance genes, and activation of host antioxidant system contributed to a susceptible response. Nematode infestation suppressed ethylene (ET) and jasmonic acid (JA) signaling pathway hindering JA/ET responsive genes associated with defense. Genes related to cell wall modification were differentially regulated while transport-related genes were up-regulated, facilitating the formation of nematode feeding sites (NFSs). Several families of transcription factors (TFs) were differentially regulated by _M. javanica_ infestation. Suggesting that TFs play an indispensable role in physiological adaptation for successful _M. javanica_ disease development. This genome-wide analysis reveals the molecular regulatory networks in potato roots which are potentially manipulated by _M. javanica_. Being the first study analyzing transcriptome profiling of _M. javanica_-diseased potato, it provides unparalleled insight into the mechanism underlying disease development.  
root-knot nematode; _Solanum tuberosum_; differentially expressed genes; defense response 1
## 3 Methods
### Anomaly Detection in Tag Space  
Text corpora consisting of a collection of structured textual units like blogs, emails, abstracts _etc._, have a closer resemblance to the natural language interactions prevalent between human subjects inthe society. Hence, a question worth investigating is if the idea of using contextual information from semantic networks to accomplish the task of anomaly detection could also work at the word level and not just at the topic level. We investigated this question by conducting the anomaly detection experiments on tags collected from YouTube videos.  
We use the \"Normalized Google Distance\" as our semantic measure in this case due to the noisy/colloquial nature of user supplied tags. Our dataset consisted of 50 sets of tags with one anomalous word each. Table 1 shows five such sets with the anomalous word in bold. The tag labeled anomalous was the one that the majority of our human reviewers (\(N\) = 10) agreed upon to be anomalous. The effectiveness of our technique is judged based on the number of times the most anomalous tag adjudged by the human reviewers is matched by the one flagged by our technique.  
Suppose a video \(V\) is accompanied by a set of \(n\) tags \(I=i_{1},i_{2},i_{3},.....,i_{n}\). In order to find the most atypical tag in this set, we used a modified version of the K-farthest neighbor algorithm [1], as described below. We calculated the distance of a tag from every other tag in the set and the sum of these distances would result in a score that quantifies the overall divergence of a tag from the rest of the set. Based on value of the parameter \(k\) (1 in our case), the top \(k\) most divergent tags are flagged as anomalous. We believe that an approach similar to this could be applied in ontologies, taxonomies, concept hierarchies _etc._, to detect the presence of anomalous content.  
```
Input: Tag-Set \(T\), number of anomalies \(k\) Output: Set Of Anomalous Tags  Initialize the array \(Scores\) to zero {This array will contain the divergence of each tag from the rest of the set.} {Find the Score for each tag.} while i=1 to \(n\)do while j=1 to \(n-1\)do \(D\)=\(NGD(i,j)\) \(Score(i)\)+=\(D\) {Find the Score for one tag.} endwhile endwhile Sort \(Score\) in Descending Order  Output Top \(k\) tags
```  
**Algorithm 2** The Semantic Anomaly Detection Algorithm For Tags  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tag Set Number** & **Tag Set** \\ \hline
1 & Education, Tutorial, Teacher, Class, **Somersault** \\
2 & Yoga, Health, Exercise, **Shocking** \\
3 & Lonely, Island, Holiday, Adventure, **Rap** \\
4 & Cute, Babies, Funny, Laughter, **Blood** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of Tag-Sets (Visually determined anomalous tag In Bold).
## 2 Methods
### Principal Component Analysis (PCA)
#### 2.1.2 Principal Component Analysis of Carbonation Depth  
To apply PCA to the carbonation depth prediction model, six influential factors were selected: the compressive strength of the concrete, service life, carbon dioxide concentration, working stress, temperature, and humidity. The following steps were taken for PCA:  
(1) The first step in PCA involved standardizing the correlative matrix of the dataset from the input variables to many samples. The coefficient matrix was then calculated, and the secular equation was derived from it; finally, the eigenvalues \(\lambda_{i}\)(\(i\) = 1, 2,..., 6) and eigenvectors of the correlation matrix were obtained.  
(2) The variance contribution proportion VCP (\(\beta_{i}\)) was calculated according to the eigenvalues (\(\lambda_{i}\)) of the covariance matrix, and the contribution of cumulative variance proportion CVCP (\(\eta_{i}\)) was calculated from the cumulative sum of the variance contribution proportion. VCP (\(\beta_{i}\)) and CVCP (\(\eta_{i}\)) can be calculated using the following equations:  
\[\beta_{i}=\frac{\lambda_{i}}{\sum\limits_{j=1}^{p}\lambda_{i}} \tag{1}\]  
\[\eta_{i}=\frac{\sum\limits_{k=1}^{i}\lambda_{k}}{\sum\limits_{k=1}^{p}\lambda _{k}} \tag{2}\]  
where \(\beta_{i}\) is the variance contribution proportion and \(\eta_{i}\) is the cumulative variance contribution proportion.  
(3) The third step of PCA involved identifying the principal components. This is typically accomplished by checking whether the cumulative value of the component variance percentage (CVCP) satisfies a specific standard or whether the eigenvalue is above 70-80% [17]. Once the principal components are identified, a matrix composed of the corresponding eigenvectors of the eigenvalues of every principal component is generated. This matrix is referred to as the component projection matrix or score coefficient matrix. The original dataset can then be converted into a reduced-dimension sample matrix and projected using this projection matrix.  
(4) The correlation coefficient was calculated between the sequences as follows:  
\[F_{i}=\alpha_{1i}X_{1}+\alpha_{2i}X_{2}+\ldots+\alpha_{pi}X_{p} \tag{3}\]  
where \(F_{i}\) is the principal component and \(\alpha_{i}=(\alpha_{1i},\alpha_{2i},\cdots,\alpha_{pi})\) is the relational degrees of the principal component with the initial variables.  
The relational coefficient is \(\sqrt{\lambda_{i}}\alpha_{ji}\) between the principal component and \(X_{j}\) variables; the coefficients in Equation (1) should satisfy \({\alpha_{1i}}^{2}+{\alpha_{2i}}^{2}+\cdots+{\alpha_{pi}}^{2}=1\), \(i=1,2,\cdots,p\).
## 3 Description of the Systematic Impact/Aptitude Land Planning Model
### Diagnosis and Territorial Analysis
#### 3.2.3 Determination of Land Capacity for Proposed Activities in an Environmental Land Unit  
We developed a new index to assess the land capacity for different proposed activities within each environmental land unit. The updated index is notable for incorporating a native plant species as a limiting factor, having a greater impact than other non-native species. Consequently, a new extended phytobiological value was created, with the calculation detailed below. This value emphasized the ecological importance of endemic species in maintaining biodiversity and conservation, the limited area size, and the human effects from the proposed activities. This index is relevant in any circumstance where a species is at risk. Our initial assumption was that the study area needed to exhibit the endemic species in their natural habitat. As a result, the lack of endemism is considered a disadvantage. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PPPV** & **FPPV** & \begin{tabular}{c} **Color on the** \\ **Map** \\ \end{tabular} & \begin{tabular}{c} **General Use** \\ **General Use** \\ \end{tabular} & \
\begin{tabular}{c} **Loss of** \\ **Heritage** \\ \end{tabular} &
\begin{tabular}{c} **Land** \\ **Capacity** \\ \end{tabular} \\ \hline
81\% to 100\% & Maximum & Green & Protection & Very high & Very low \\ \hline
61\% to 80\% & Notable & Blue & Extensive use & High & Low \\ \hline
41\% to 60\% & Medium & Orange & Extensive use & Medium & Medium \\ \hline
20\% to 40\% & Low & Yellow & Intensive use & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Final presented phytobiological value (FPPV) categories used in this work. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **PWFi** \\ \hline Singularity & 2 \\ \hline Diversity & 1 \\ \hline Representativeness & 1 \\ \hline Originality & 0.6 \\ \hline Landscaping & 0.6 \\ \hline Soil protection & 0.6 \\ \hline Economic–cultural value & 0.6 \\ \hline Current state of conservation & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter weighting factors (PWFi). To evaluate this, we established six additional value classifications for each parameter, indicating positive and negative evaluations (Tables 5 and 6). In order to align with the value categories in Table 2, we continued to use the same three-value structure of high, medium, and low. The latest evaluation assigns favorable values to factors that improve land capacity (high +5, medium +3, and low +1) and unfavorable values to factors that diminish land capacity (high \(-5\), medium \(-3\), and low \(-1\)).