esa-sceva/satcom-chunk-collection huggingface dataset:
# Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives
## 3 Mapping from the Wikidata Semantic Model to HERO
### Mappings between Properties and Mappings Between Complex Patterns  
In this section, we present the mappings between Wikidata and HERO patterns involving properties. In some cases, the mapping is straightforward, since the correspondence is defined between two properties. In other cases (actually, the most interesting ones), a Wikidata property, or a Wikidata pattern, corresponds to a complex representation in terms of HERO. As mentioned before, we tried to use the algorithm developed to map categories (Section 3.1) to obtain mappings between Wikidata and HERO properties, but the results were not so encouraging. Considering the 380 properties defined in HERO, only 57 of them were mapped by the algorithm to a Wikidata property and, analyzing these mappings, we observed that only 15 of them were correct. As a consequence of these results, we analyzed the structure of the Wikidata semantic model, to individuate groups of properties that are noteworthy for our domain. A first property that needs to be mapped is the Wikidata _instance of_ (P31) property, which relates individuals with the categories to which they belong. As reported in Table 3, _instance of_ (P31) is mapped to _rdf.type_ (mapping 1). Such a mapping states that any Wikidata triple _<XX, instance of, (P31)?Y>_ can be translated in HERO as _<X, rdf.type, Map-c(?Y)>_, where _Map-c(?Y)_ is the HERO category corresponding to the Wikidata category _?Y_, according to the mappings between categories (Section 3.1), or one of its super-categories (actually, the HERO triple should formally be _<Map-i(?X), rdf.type, Map-c(?Y)>_, where _Map-i(?X)_ represents the individual in the PRiSMHA triplestore corresponding to the Wikidata individual _?X_. For the sake of simplicity, in this section, we assume that Wikidata and PRiSMHA can formally share instances). The function _Map-c_ is computed as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Classes** & **Individuals** \\ \hline P(CN\(\mid\)N) & 0.94 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimated probability that a “no correspondence” answer is correct. _Map-c:_  
_input: a Wikidata category WdC;_  
_output: a HERO category equivalent to WdC or to a super-category of WdC_  
_T. Add WdC to a FIFO queue Q_  
_2. For each C in Q:_  
_a. If a correspondence is defined for C,_  
_return the HERO category specified by the mapping_  
_b. Add the parents of C to Q_  
_3. return HEROentity/if WdC and all its ancestors do not map to any HERO cate-gory,_  
_//the most general HERO category is returned._  
In order to get the parents of a Wikidata category (C), we use the following SPARQL query, which retrieves all the fillers (objects) for the Wikidata properties _instance_ of (P31) and _subclass of (P279)_, in statements where the subject is the Wikidata category C.  
select * where {  
select?c where {
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 5 Results
### User Traffic Prediction Module Results
#### 5.1.2 Transformer and Temporal Convolutional Network Results  
The architecture chosen for the Transformer and TCN hybrid approach resulted from trials conducted with both the Transformer model and TCNs and each one separately. Just as in the case of our LSTM model, these models were trained to a maximum of 100 epochs with a mechanism for early stopping, meaning that 100 epochs were not reached during the learning process.  
Table 3 illustrates the calculated error of the Transformer and TCN hybrid approach. Even though the error seems to be larger than that of our LSTM model, there is a qualitative distinction that makes the hybrid approach more promising. That distinction is that the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**1 LSTM Layer** & \multicolumn{2}{c}{**2 LSTM Layers**} & \multicolumn{2}{c}{**3 LSTM Layers**} \\ \hline
**AbsError** & **Percent** & **AbsError** & **Percent** & **AbsError** & **Percent** \\ \hline
3225 \(\pm\) 153 & 0.52\% \(\pm\) 0.06 & 1059 \(\pm\) 47 & 0.17\% \(\pm\) 0.02 & 1592 \(\pm\) 70 & 0.25\% \(\pm\) 0.03 \\ \hline \hline \end{tabular} Comparison between different architectures for our LSTM user traffic model in terms of absolute error and its percentage.  
\end{table}
Table 2: Long short-term memory trials.  
Figure 4: User traffic in active users predicted for the next 60 time steps of an instance in the dataset with the LSTM model.  
latter architecture captures better the directionality and trends of the time series of the user traffic.  
In Figure 5 we can see the predicted next 60 time steps of user traffic of an instance of the dataset. This figure shows that the first time steps are not accurately predicted as they were with the LSTM model. The main advantage of this approach is the trends can be predicted as far as the 40 th to 60 th time step as seen in Figure 5. These trend captures can be seen across the figure, for instance, in time steps 7 to 18 and time steps 24 to 30. This is a very promising result due to the fact that if the erroneous artifacts are eliminated, the results can become by far superior to the Long Short-Term Memory model. An example of the erroneous artifacts is that in time steps 20 to 24 of the graph in the figure.
## 3 Results and Discussion
### Analysis of Fire Severity Classification Accuracy for Different Indices
The TOP 10 WF1 for dVIs based on the pre- and post-fire image combination are shown in Table 2. Among the dDI category, the highest accuracy was achieved with the combination of the shortwave infrared band at 1627 nm and the near-infrared band at 1224 nm, with an accuracy of 78.33%. Among the dRI category, the highest accuracy was achieved with the combination of the shortwave infrared band at 1543 nm and the near-infrared band at 1241 nm, with an accuracy of 82.75%. Among the dNDI category, the highest accuracy was achieved with the combination of the shortwave infrared band at 2048 nm and the near-infrared band at 1106 nm, with an accuracy of 83.39%. The analysis of the pre- and post-fire image combination indicated that the dNDI category provided the best classification accuracy, followed by the dRI category, and the dDI category yielded the lowest accuracy. The optimal classification accuracy for dVIs was 83.39%.
## 2 Materials and Methods
### Methods
#### 2.3.2 Index Construction  
To assess the ability of different indices in identifying fire severity, the band composition of the ZY1E/F AHSI was considered, and an exhaustive method was used for index construction. The vegetation index (VI) construction rules followed those of Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), and Normalized Difference Vegetation Index (NDVI), respectively, creating a difference index (DI) category, ratio index (RI) category, and normalized difference index (NDI) category. The pre-fire and post-fire index differences were calculated for each index, denoted as dDI, dRI, and dNDI. In total, there were 10,878 indices for each category, amounting to 65,268 indices. The formulas for all indices are presented in Equations (5)-(8).  
\[DI=b_{2}-b_{1} \tag{5}\]  
\[RI=\frac{b_{2}}{b_{1}} \tag{6}\]  
\[NDI=\frac{b_{2}-b_{1}}{b_{2}+b_{1}} \tag{7}\]  
\[\text{d}VI=VI_{post-fire}-VI_{pre-fire} \tag{8}\]  
In Equations (5) to (7), \(b_{1}\) and \(b_{2}\) represent different bands of the ZY1E/F AHSI, with \(b_{2}\) having a wavelength greater than \(b_{1}\). In Equation (8), dVI denotes the difference in index values before and after the fire for each index, where \(VI_{pre-fire}\) and \(VI_{post-fire}\) represent the index values before and after the fire, respectively.
## 4 Strain Results and Analysis
### Strain Analysis
#### 4.2.2 Peak Strain Attenuation Law  
According to Equation (9), the peak strain of the representative test point was fitted with the scaled distance, and the attenuation curves are shown in Figures 4-6. Note: The TNT equivalent conversion was not performed when calculating the scaled distance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**The Peak Tensile Strain (PTS) and Peak Compressive Strain (PCS)/10\({}^{-6}\)**} \\ \cline{2-13}  & \multicolumn{2}{c}{**50 (g)**} & \multicolumn{2}{c}{**75 (g)**} & \multicolumn{2}{c}{**100 (g)**} & \multicolumn{2}{c}{**125 (g)**} & \multicolumn{2}{c}{**150 (g)**} & \multicolumn{2}{c}{**175 (g)**} & \multicolumn{2}{c}{**200 (g)**} \\ \cline{2-13}
**Point** & \multicolumn{2}{c}{\(\overline{R}\)=7.3} & \multicolumn{2}{c}{\(\overline{R}\)=6.4} & \multicolumn{2}{c}{\(\overline{R}\)=5.8} & \multicolumn{2}{c}{\(\overline{R}\)=5.4} & \multicolumn{2}{c}{\(\overline{R}\)=5.1} & \multicolumn{2}{c}{\(\overline{R}\)=4.8} & \multicolumn{2}{c}{\(\overline{R}\)=4.6} \\  & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) & (m/kg\({}^{-13}\)) \\ \hline  & **PTS** & **PCS** & **PTS** & **PCS** & **PTS** & **PCS** & **PTS** & **PCS** & **PTS** & **PCS** & **PTS** & **PCS** \\ \hline
21H & 46 & 121 & 86 & 151 & 73 & 160 & 99 & 239 & 143 & 318 & 162 & 340 & 182 & 356 \\
21Z & 42 & 79 & 56 & 116 & 68 & 90 & 71 & 109 & 75 & 119 & 78 & 114 & 76 & 121 \\
23H & 40 & 153 & 62 & 82 & 65 & 210 & 94 & 315 & 129 & 419 & 115 & 345 & 177 & 480 \\
23Z & 137 & 103 & 139 & 97 & 141 & 1  
the scaled distance ranged from 6 to 11, and the axial PTS values of 23Z and 33Z were approximately equal, which also indicated a strong correlation between the PAS and overall deformation.
## 4 Results and Discussion
### Limitations and Future Perspectives
The essential data required for this sub-model includes the number and type of both traditional and alternative vehicles, their average fuel or energy consumption, carrying capacity, acquisition and maintenance costs, and operational ranges. The delivery operations sub-model will encompass stocks such as the number of deliveries and total shipments, modulated by flows representing delivery rates. These rates will be dynamically influenced by delivery pressure and effective delivery time, with explicit linkages to the utilization of alternative and traditional vehicle types. Data acquisition for this component will necessitate daily delivery counts, average delivery times, vehicle load factors, typical route lengths, and the frequency of access to specific urban zones. Future iterations of this sub-model will aim to differentiate between Business-to-Business (B2B) and Business-to-Consumer (B2C) last-mile deliveries for a more granular analysis. In terms of urban congestion and access management, the model will incorporate key stocks such as traffic and congestion levels and the consumption rate of parking areas. Flows within this sub-model will represent traffic generation, vehicle circulation, and parking utilization, directly impacted by Urban Vehicle Access Regulations (UVARs), time restrictions for parking, dynamic management of parking areas (including dynamic opening and closing mechanisms), and dynamic access opening and closing. The crucial variable of \"Time to circulate\" will be included to explicitly link congestion back to overall delivery efficiency. Data requirements for this segment will cover traffic volumes, average speeds, congestion levels stratified by time and location, parking occupancy rates, and enforcement data pertinent to UVARs. Finally, the environmental and societal impact sub-model will quantify outcomes such as transport emissions, air pollution levels, noise pollution, and the broader quality of urban life. Flows within this section will be calculated based on fossil fuel consumption, the number of kilometers traveled by traditional vehicles, and the volume of alternative shipments, directly assessing the attainment of relevant 2030 Agenda Sustainable Development Goals (SDGs), specifically SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). The essential data for this part includes local air quality indices (e.g., PM2.5, NOx), observed noise levels, and Greenhouse Gas (GHG) emission factors for various vehicle types. Furthermore, this framework could be broadened to incorporate wider socio-economic ramifications--such as job creation, economic growth, and social equity--in order to provide a more thorough understanding of the implications of urban logistics policies and access management. Additionally, future work could integrate this viewpoint with other types of interventions affecting city logistics, such as the establishment of consolidation centers.
## 3 Results  
### Procedure 1 Results  
With Equation (7), we establish the error between vectors \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\), or between vectors \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\), where \(q=8\) is the number of elements in \(A_{sp_{1}}\). The error between vectors \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\) is expressed as \(E_{sp_{1}}(A_{sp_{1}},\hat{A}_{sp_{1}})\), and that between vectors \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\) as \(E_{sp_{1}}(A_{sp_{1}},\hat{A}_{sp_{1}})\). The errors between \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\) (a vector directly modeled with the data for modeling) for \(sp_{1}\) values [60,65,75,85,90] are as follows: \(E_{60}(A_{60},\hat{A}_{60})=0.0222\) s, \(E_{65}(A_{65},\hat{A}_{65})=0.0519\) s, \(E_{75}(A_{75},\hat{A}_{75})=0.0550\) s, \(E_{85}(A_{85},\hat{A}_{85})=0.0238\) s, and \(E_{90}(A_{90},\hat{A}_{90})=0.0128\) s. The errors between \(A_{sp_{1}}\) and \(\hat{A}_{sp_{1}}\) (a vector indirectly modeled with the data for modeling, without the data for validating) for \(sp_{1}\) values \(\{70,80\}\) are as follows: \(E_{70}(A_{70},\hat{A}_{70})=0.1817\) s and \(E_{80}(A_{80},\hat{A}_{80})=0.2505\) s.  
\[E_{sp_{1}}=\frac{\sum_{k=1}^{k=q}\left|A_{sp_{1}}(k)-\hat{A}_{sp_{1}}(k) \right|}{q} \tag{7}\]  
The mean error of the \(A_{sp_{1}}\) vs. \(\hat{A}_{sp_{1}}\) cases is \(\overline{E}=mean([E_{60}\ E_{65}\ E_{75}\ E_{85}\ E_{90}\ ])=0.0332\) s, a lesser value than the mean error between the \(A_{sp_{1}}\) vs. \(\hat{A}_{sp_{1}}\) cases, which is \(\overline{E}=mean([E_{70}\ E_{80}\ ])=0.2162\) s. This is an expected result as vectors \(\hat{A}_{sp_{1}}\) were modeled directly with the data for modeling, i.e., with \(A_{sp_{1}}\) for \(sp_{1}\) values [60,65,75,85,90], while vectors \(\hat{A}_{sp_{1}}\) were indirectly modeled with the data for modeling, but without the data for validating, i.e., without \(A_{sp_{1}}\) for \(sp_{1}\) values \(\{70,80\}\). Figure 1 shows the graphs of the compared vectors in this section.
## 2 Analytical Properties of 5-Point-Window \(L_{1}\) Splines
When \(\triangle z_{1}-\triangle z_{0}>0\), \(\triangle z_{2}-\triangle z_{1}>0\) and \(\triangle z_{3}-\triangle z_{2}>0\), all five points are convex. When \(\triangle z_{1}-\triangle z_{0}>0\), \(\triangle z_{2}-\triangle z_{1}<0\) and \(\triangle z_{3}-\triangle z_{2}>0\), the five points \"oscillate.\" As shown in Table 1, there are 27 cases to consider, of which, due to symmetry, only 10 cases need be analyzed. We will analyze the location of \(b_{2}^{*}\) in these 10 cases. Recall that \(b_{2}^{*}\) is the unique optimal solution after applying the choice procedure to resolve nonuniqueness, if it occurs. **Remark. ** The portions of the following results related to linearity (Cases 1, 2, 4, 5, 6, 11 and 12 and cases that are equivalent to these cases) overlap with analogous linearity results in [22]. In the present paper, however, these linearity results are presented in a wider context where not only linearity but also convexity and oscillation, measured by increases and decreases in the \(\Delta z_{i}\), are considered. Recall that from equation (1), we have  
\[\delta_{2}=\frac{z_{3}-z_{1}}{x_{3}-x_{1}}.\]  
**Case 1. ** In this case, \(\triangle z_{0}=\triangle z_{1}=\triangle z_{2}=\triangle z_{3}\) and \(c_{1}=c_{2}=0\). From Lemma 3,  
\[\frac{\mathrm{d}G_{1}(b_{2})}{\mathrm{d}b_{2}}+\frac{\mathrm{d}G_{2}(b_{2})}{ \mathrm{d}b_{2}}=\left\{ \begin{array}{cl}\frac{10}{3}&\text{if }b_{2}-\triangle z_{1}>0,\\ -\frac{10}{3}&\text{if }b_{2}-\triangle z_{1}<0.\end{array} \right.\]  
The unique optimal solution is therefore \(b_{2}^{*}=\triangle z_{1}\). **Case 2. ** In this case, \(\triangle z_{0}=\triangle z_{1}=\triangle z_{2}<\triangle z_{3}\), \(c_{1}=0\) and \(c_{2}>0\). From Lemma 3,  
\[\frac{\mathrm{d}G_{1}(b_{2})}{\mathrm{d}b_{2}}+\frac{\mathrm{d}G_{ 2}(b_{2})}{\mathrm{d}b_{2}}>\frac{5}{3}>0 \text{if }b_{2}-\triangle z_{1}>0,\] \[\frac{\mathrm{d}G_{1}(b_{2})}{\mathrm{d}b_{2}}+\frac{\mathrm{d}G_{ 2}(b_{2})}{\mathrm{d}b_{2}}\leq-\frac{5}{3}<0 \text{if }b_{2}-\triangle z_{1}<0.\]The unique optimal solution is \(b_{2}^{*}=\triangle z_{1}\). **Case 4. ** In this case, \(\triangle z_{0}=\triangle z_{1}<\triangle z_{2}=\triangle z_{3}\), \(c_{1}=0\) and \(c_{2}=0\). From Lemma 3,  
\[\frac{\mathrm{d}G_{1}(b_{2})}{\mathrm{d}b_{2}}+\frac{\mathrm{d}G_{2}(b_{2})}{ \mathrm{d}b_{2}}=\left\{ \begin{array}{cl}\frac{10}{3}&\text{if }b_{2}-\triangle z_{2}>0,\\ 0&\text{if }\triangle z_{1}\leq b_{2}\leq\triangle z_{2},\\ -\frac{10}{3}&\text{if }b_{2}-\triangle z_{1}<0.\end{array} \right.\]  
Any solution in \([\triangle z_{1},\triangle z_{2}]\) is optimal. Since \(\triangle z_{1}<\delta_{2}<\triangle z_{2}\), the unique solution (the \(b_{2}\) in the optimal interval closest to \(\delta_{2}\)) is \(b_{2}^{*}=\delta_{2}\).
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 5 Discussion
### Specifications  
* Data Capture: The immediate collection of data from virtual reality activities, such as choosing items and doing experiments, is crucial for offering precise and prompt recommendations [53].
* The SAAS model performs data processing by utilizing its trained capabilities to analyze user preferences and produce appropriate recommendations.
* The recommendations produced are transformed into lifelike speech by sophisticated Text-to-Speech (TTS) technology, guaranteeing a seamless and coherent delivery by the non-player characters (NPCs).
* NPC Interaction: Non-player characters (NPCs) in the virtual reality (VR) environment are specifically programmed to engage with users in a manner that closely resembles normal human discourse. These NPCs offer suggestions and guidance in a way that imitates realistic human interaction [54; 55].
## 4 System Architecture
### The Workflow of Distributed Bandwidth Enforcer  
The distributed bandwidth enforcer resides in the hypervisor of every compute server, and it is composed of the following components: the Rate Limiter component, the Congestion Controller component and the Congestion Detector component. Together, these components provide the isolation of network performances among tenants and congestion control for each tenant. Furthermore, each component also has its own specific function. The Rate Limiter component can limit the sending rate of senders. The Congestion Detector component can be used to detect the packet losses, based on which, it can determine whether there is any congestion at the receivers. The Congestion Controller component decides a proper rate of every flow sent by the senders. For the non-blocking VN, the congestion can be prevented by limiting the sending rate of each sender as allocated, and in this case, the bandwidth guarantees are enforced. On the other hand, for blocking VN, congestion may occur anywhere in the network due to the unpredictable traffic behavior of the tenant. It is impossible to achieve performance isolation only by limiting the sending rate of each sender. For the purpose of preventing congestion and improving network efficiency, the Congestion Detector component at the receiver is used to detect packet loss first. Then, the Congestion Controller component analyzes and calculates the proper rate for all senders, and the result is used as the feedback. In the meantime, this feedback is delivered to the corresponding senders. At last, the senders adjust their sending rate according to the feedback.
## 3 Results and Discussions
### Optimization of Fungal Primer Pairs  
Of the five fungal ribosomal-specific primer pairs designed for this study (Figure 1b), two pairs amplified fungal ITS1, one pair ITS2, and two pairs the entire fungal ITS region (Table 2). The ITS unit is 500-700 bp long with two subunits (ITS1 and ITS2) of 250-400 bp each, separated by the 5.8S unit. To prepare the amplicon library for HTS, any one of the three ITS region may be targeted, and we initially tested all three options and found that all ITS primer pairs resulted in the best, strongest, clearest outcome at this step except for the primer pair targeting the ITS1 region (Figure 2).  
The PCR outcomes guided us in making a decision about primer choice (Table 2). The band position should match the expected number of base pairs (bp; Figure 3a-c,f,i); an absence of band indicated that the desired ITS-DNA sequence had not been amplified (Figure 3e,f), while undesired band positions (Figure 3f,h) suggested primer mismatches or primer-dimers, which are considered to result from hybridization between two primers (Figure 3d,e). Faint bands were interpreted as inefficient amplification and were adjustments were made by changing the annealing temperature (Figure 3g) and in some cases by changing the number of cycles during amplification (Figure 3f,g).  
In PCR, the formation of primer dimers is one of the most common problems, which it has been suggested is caused by hybridization within and between primer pairs [27; 28]. Many solutions for avoiding them are suggested in the literature, including: careful primer design, hot start PCR, touch-down PCR, changing the amount and concentration of primers and template DNA, and changing the annealing temperature, duration, and number of PCR cycles [29; 30; 31; 32; 33; 34; 35]. We changed the annealing temperature, duration, number of cycles and amount of template DNA, resulting in a single band without primer-dimers from each reaction (for detailed settings, please see Table 2).
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
###### Abstract  
Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.  
applied deep learning; digital microscopy; diatom identification; diatom classification; microscope automation +
Footnote †: journal: Applied Sciences