esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods  
### Satellite Data  
Land surface temperature (LST) images retrieved from observations of the MODIS sensors on board Terra and Aqua satellites were used. LST data were downloaded from ORNL DAAC archive ([PERSON] et al., 2016), selecting MOD1A2 (Terra) and MYD11A2 (Aqua) 8-day LST products with 1 km resolution. These LST products are composed of daily 1 km LST values averaged during an 8-day period; cloudy pixels for each image were masked out. Terra satellite passes over Thailand at around 10:00 am and 22:00 (local time), whilst Aqua satellite passes at around 14:00 and 2:00 am; local time is +7:00 from UTC time. In this work, observations from 2003 until the end of 2016 were selected.  
Since Thailand is in the tropical zone and has very few cloud-free images during the rainy/monsoon season, in this study, satellite LST images during two intervals within each year were acquired: three months during winter (November-January) and summer (February-April), excluding the six rainy months (May-October). Overall, 336 daytime and 392 nighttime MODIS-Terra/Aqua LST images were processed for a total of 728 raster LST maps of 1 km spatial resolution for each city.  
Therefore, the whole dataset will allow an analysis of the SUHI temporal variation at three timescales: diurnal, seasonal, and multiyear. The diurnal variation is represented by the four times that MODIS passes (daytime: 10:00 and 14:00; nighttime: 22:00 and 02:00), while the seasonal variation is examined by comparing the summer and winter maps. Overall, all the selected images (24 in summer and 28 in winter for each city and for each year) were averaged to get seasonal LST mean values (i.e., winter and summer separately) at the four diurnal times, from which the SUHI was extracted.  
To identify the rural buffer for the SUHI computation, MODIS Land Cover Type Product (MCD12Q1) with 500 m spatial resolution was used. The MCD12Q1 products yearly categorize the land using combined Terra and Aqua MODIS observations to identify different land cover classes.  
Land use and land cover (LULC) maps for each city were also derived from cloud-free Landsat-4/Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), and Landsat-8 Operational Land Imager (OLI) images with a spatial resolution of 30 m downloaded from USGS archive ([PERSON] et al., 2013). A total of 126 scenes from 2003 to 2016 were used to extract the urban area extent, exploiting the higher spatial resolution with respect to the MCD12Q1 products. The land covers were classified into four broad types (i.e., built-up land, water body, agriculture, and forest, Figure 2) applying a supervised maximum likelihood classification approach ([PERSON] et al., 2013; [PERSON] et al., 2014). The user and producer accuracy ([PERSON] et al., 2014) for urban and agriculture classes--evaluated by high-resolution images incorporated in GoogleEarth Pro(r) and reported as mean value over all the Landsat maps--are as follows: user (urban) = 0.80; user (agriculture) = 0.94; producer (urban) = 0.95; producer (agriculture) = 0.84.
## 4 The PI-Based Task Offloading Algorithm
### Consensus and Task Removal
#### 4.3.2 Task Removal  
After reaching a common \(\mathbf{R}_{s}\) through the consensus process, there still exist task conflicts. Thus, the task removal process is conducted on each satellite \(s\in S\) to eliminate task conflicts in \(\theta_{s}\).  
For satellite \(s\in S\), we first obtain pending removal tasks \(\Psi_{s}=\{t\in\theta_{s}\mid\ W_{st}\
eq s\}\). Tasks in \(\Psi_{s}\) are those which exist in \(\theta_{s}\) but are believed not to be offloaded to \(s\), according to \(\mathbf{W}_{s}\). Then, a local removal impact vector \(\mathbf{R_{s}}^{\bullet}=[R_{s1}{}^{\bullet},R_{s2}{}^{\bullet},\ldots,R_{sn}{}^{ \bullet}]^{\mathsf{T}}\) is calculated by \(\theta_{s}\), where entry \(R_{st}{}^{\bullet}=\mathcal{R}(\theta_{s}\ominus t)\) for \(\forall\ t\in\theta_{s}\), and otherwise, \(R_{st}{}^{\bullet}=\infty\). Next, a task \(t\in\Psi_{s}\) will be removed from \(\theta_{s}\) when  
\[\max_{t\in\Psi_{s}}\{R_{st}^{\bullet}-R_{st}\}>0 \tag{24}\]  
If condition (24) is met, it implies the presence of a conflicting task with a higher removal impact value, indicating a worse offloading assignment. Then, task \(t^{\prime}=\operatorname*{argmax}_{t\in\Psi_{s}}[R_{st}{}^{\bullet}-R_{st}]\) will be removed from \(\theta_{s}\) and \(\Psi_{s}\). After that, \(R_{s}{}^{\bullet}\) are updated and (24) is evaluated again. This process is continued until \(\Psi_{s}=\emptyset\) or (24) is no longer met. Finally, the remaining tasks in \(\Psi_{s}\) will be retained in \(\theta_{s}\), and we set \(W_{st}=s\) and \(R_{st}=\mathcal{R}(\theta_{s}\ominus t)\) for each \(t\in\Psi_{s}\). The task removal process is summarized in Algorithm 3.  
```
Input: task sequence \(\theta_{s}\), and vectors \(\mathbf{R}_{s}\) and \(\mathbf{W}_{s}\). Output: new task sequence \(\theta_{s}\), new vectors \(\mathbf{R_{s}}^{\prime}\) and \(\mathbf{W_{s}}^{\prime}\).
1. Obtain pending removal tasks \(\Psi_{s}=\{t\in\theta_{s}\mid\ W_{st}\
eq s\}\);
2. Calculate local removal impact vector \(\mathbf{R_{s}}^{\bullet}=[R_{s1}{}^{\bullet},R_{s2}{}^{\bullet},\ldots,R_{sn}{}^{ \bullet}]^{\mathsf{T}}\);
3.while\(\Psi_{s}\
eq\emptyset\) and (24) is met
4. Obtain task \(t^{\prime}=\operatorname*{argmax}_{t\in\Psi_{s}}[R_{st}{}^{\bullet}-R_{st}]\);
5. Remove \(t^{\prime}\) from \(\theta_{s}\) and \(\Psi_{s}\);
6. Update \(\mathbf{R_{s}}^{\bullet}\);
7.end
8.for each remaining tasks \(t\in\Psi_{s}\)
9. Update \(W_{st}=s\);
10. Update \(R_{st}=\mathcal{R}(\theta_{s}\ominus t)\);
11.end
12. Let \(\theta_{s}{}^{\prime}=\theta_{s}\), \(\mathbf{R_{s}}^{\prime}=\mathbf{R_{s}}\) and \(\mathbf{W_{s}}^{\prime}=\mathbf{W_{s}}\);
13. Output \(\theta_{s}\), \(\mathbf{R_{s}}^{\prime}\), and \(\mathbf{W_{s}}^{\prime}\).
```  
**Algorithm 3** Task Removal
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 3 Notations and Preliminaries
### Federated Learning
#### 3.1.3 FedAVG  
FedAvg is a basic algorithm in federated learning that balances the need for collaborative model training with the requirement for data privacy. Algorithm 1 shows the working of FedAVG [7]. After the first client server interaction, the server collects the updated model parameters from the selected clients. The server computes the new model parameters by averaging the model parameters received from clients. The aggregation is usually performed using a weighted average, using the weights corresponding to the number of samples on each client.
## 2 Reconfigurable Holographic Surface
### Holographic Principle  
The RHS controls the leaky waves generated by each radiation element by constructing a holographic pattern (recording the interference between the reference wave and the target wave) and, by superimposing the leaky waves of each element, can obtain a beam in the desired direction.  
As shown in Figure 2, we adopt Cartesian coordinates, where the \(xoy\) plane coincides with the RHS surface, and the \(z\)-axis is vertical to the RHS surface. We assume that the holographic surface has \(X\times Y\) radiation elements and \(K\) feeds. In the \((x,y)\)-th radiation elements, the phase of the reference wave excited by the \(k\)-th feed and the phase of the object wave radiated in the direction of \((\theta_{0},\varphi_{0})\) are respectively denoted as  
\[\Psi_{ref}\Big{(}\mathbf{r}_{x,y}^{k}\Big{)}=\exp\Big{(}-j\mathbf{k}_{s}\cdot \mathbf{r}_{x,y}^{k}\Big{)}, \tag{1}\]  
\[\Psi_{obj}\big{(}\mathbf{r}_{x,y},\theta_{0},\varphi_{0}\big{)}=\exp\Big{(}- j\mathbf{k}_{f}(\theta_{0},\varphi_{0})\cdot\mathbf{r}_{x,y}\Big{)}, \tag{2}\]  
where \(\mathbf{r}_{x,y}\) is the position vector of the \((x,y)\)-th radiation element, \(\mathbf{k}_{f}\) is the propagation vector of the leaky wave in the desired direction, \(\mathbf{r}_{x,y}^{k}\) is the distance vector from the \(k\)-th feed to the \((x,y)\)-th radiation element, and \(\mathbf{k}_{s}\) is the propagation vector of the reference wave.  
According to the holographic interference rule, the interference between reference wave and object wave can be denoted as  
\[\Psi_{intf}\Big{(}\mathbf{r}_{x,y}^{k},\theta_{0},\varphi_{0}\Big{)}=\Psi_{ obj}\big{(}\mathbf{r}_{x,y},\theta_{0},\varphi_{0}\big{)}\Psi_{ref}^{*} \Big{(}\mathbf{r}_{x,y}^{k}\Big{)}. \tag{3}\]  
When the holographic pattern is excited by the reference wave, the resulting beam will satisfy \(\Psi_{intf}\Psi_{ref}\propto\Psi_{obj}\big{|}\Psi_{ref}\big{|}^{2}\); as such, a beam in the desired direction is formed.  
Note that, when the phase difference increases, \(\mathbf{Re}\Big{(}\Psi_{intf}\Big{)}\) will decrease, which meets the requirements of amplitude control, so we construct the normalized amplitude with \(\mathbf{Re}\Big{(}\Psi_{intf}\Big{)}\) to represent the radiation amplitude of each radiation element. Therefore, the radiation amplitude of the \((x,y)\)-th radiation element in the direction \((\theta_{0},\varphi_{0})\) can be expressed as  
\[M\Big{(}\mathbf{r}_{x,y}^{k},\theta_{0},\varphi_{0}\Big{)}=\frac{\mathbf{Re} \Big{[}\Psi_{intf}\Big{(}\mathbf{r}_{x,y}^{k},\theta_{0},\varphi_{0}\Big{)} \Big{]}+1}{2}. \tag{4}\]  
Setting the radiation amplitude of each reference wave according to (4) will get the object wave in the desired direction \((\theta_{0},\varphi_{0})\). The proof is given in Appendix A.
# A Modified Methodology for Generating Indoor Navigation Models
## 2 Research Objective and Methods
In the MPRSS method, the network model is developed by:  
1. adopting a set of base points {P\({}_{\text{b}}\)} from the floor plan (e.g., points representing the vertices of walls and partition walls),
2. generating a zigzag line with the Constrained Delaunay Triangulation (CDT) tool for constructing a Triangulated Irregular Network (TIN) based on {P\({}_{\text{b}}\)},
3. selecting edges {E\({}_{\text{zigzag}}\)} from the TINs located inside the segmented structure S,
4. mapping the midpoints {P\({}_{\text{Mid}}\)} of edges {E\({}_{\text{zigzag}}\)},
5. generating a Voronoi diagram (VD) based on a set of midpoints {P\({}_{\text{Mid}}\)},
6. segmenting object S based on VD, i.e., transforming S into a set of segments S\(\rightarrow\) {SS},
7. assigning a point from the [P\({}_{\text{Mid}}\)] set to every segment [SS],
8. generating the axis of structure S by developing a topological neighborhood model based on a set of segments {SS} and {P\({}_{\text{Mid}}\)}; in this solution, only the relations between segment edges and the left and right polygon are analyzed. The generated axis of structure S is based on points {P\({}_{\text{Mid}}\)}, which are regarded as identification nodes. In the described approach, the MAT algorithm, which is popularly used to determine the axes of polygonal objects, has been replaced with simple transformations. The presented method for generating segments [SS] and object axes was applied only to corridors during the construction of an indoor navigation model. When the developed network covers an entire floor, additional network edges should be generated based on segment-room relations. When room entrances are taken into account, segment-entrance-room relations should be generated. In this case, the network generation method is referred to as the Middle-Point Relation Structure Segment Entrance (MPRSSE) approach. The MPRSS and MPRSSE methods will be presented in detail based on mock data models. A new methodology termed Middle-Point Relation Structure Segment Entrance Modification (MPRSSEM)  
Figure 2: Theoretical background for generating object axes: (a) midpoints {P\({}_{\text{Mid}}\)} of the zigzag line for mapping the object axis, (b) segmentation of object S into a set segments S\(\rightarrow\) {SS} based on the midpoints of the zigzag line and generation of the topology network: the middle line. was proposed to combine the MPRSS and MPRSSE approaches. The relations from the MPRSS model (segment-room) were written in the MPRSSEM model as (segment-entrance-room) relations. The MPRSS, MPRSSE, and MPRSSEM topological-semantic networks modeling navigation routes on a single floor are represented by graphs. The shortest routes can be generated by describing the edges of a graph with edge lengths in the adopted units of measurement. Route lengths represent the differences in the results. In the proposed methodology, GIS tools were used to transform data. The shortest routes were generated with the Distkri algorithm.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 3 Bayesian Networks and Markov Boundaries
### The d-Separation Criterion  
In order to determine whether two variables of a Bayesian network are independent or not, given a conditioning set of other variables, the directional separation (d-separation) criterion must be applied [2]. By definition, two variables are independent given the conditioning set if all the paths between their corresponding nodes in the network contain either:  
* A node with diverging edges, which is in the conditioning set, or
* A node with converging edges, which is outside the conditioning set and also all of its descendants are outside the conditioning set.  
If there is a path in the network that connects the two nodes but contains no node for which any of the above conditions is broken, then the two variables are dependent even after the conditioning set is given [2]. In this case, the nodes that represent the variables are said to be d-connected, as opposed to d-separated.  
Conversely, a path that contains a node for which the above conditions hold is considered a \"blocked\" path, thus it will not convey dependence. If all possible paths between two specific nodes are blocked given the conditioning set, then the nodes are d-separated, thus the variables they represent are conditionally independent given the conditioning set.  
Because the d-separation criterion simply evaluates to either \"dependent\" or \"independent\" when applied to two variables and a conditioning set, it is obvious that Markov Boundary Discovery algorithms can use d-separation just as easily as using a statistical test of independence. As stated earlier, applying the d-separation criterion requires the Bayesian network.
# QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
## 4 Proposed Solution
### VANET Vehicles
#### 4.3.2 Fog Nodes  
Fog nodes are used to provide nearby processing and storage to VANET Vehicles messages/information. SDN nodes and the main SDN controller are placed on fog nodes to reduce response time.
# Non-Real-Time Wireless System for Lightning Effect Measurements
## 3 Conception of Measuring System
This is a significant drawback since it requires a clock within the device to clock a stable signal at such a high frequency. The disadvantages also include the jitter, i.e., the lack of time synchronization between systems operating at high frequency (for communication without confirming the correctness of the transmitted data). The advantage is a single transmission line and no dynamic hazards from parallel connection. The dynamic and functional hazard of the parallel transmission, the second solution, is the generation of errors due to the different path lengths for all transmitted bits [42]. For the parallel transmission of 2 bytes, it is necessary to provide 16 transmission lines between the ADC and RAM. The different lengths of the paths result in a different propagation time of the signal (for high frequencies and significant length differences), which can lead to a situation where some bits are read from the current data frame and some from the previous (because they arrived late). The use of parallel transmission for the proposed solution has a decisive advantage: it allows to work at a speed of 30 MHz, because 2 bytes are written in the same clock cycle. One really reliable method is good synchronization, which is not applicable in some cases. So circuits should be carefully designed to have equal delays in each path [18]. The disadvantage, however, is the size of the data bus itself, which occupies a significant area and can cause mutual interference [43; 44]. It is essential to use a cover to protect the separated area from the influence of an external electromagnetic field. Shielding effectiveness is affected by the physical properties of the metal. These may include conductivity, permeability, solderability, thickness, and weight. Electrically dominant waves are reflected by highly conductive metals (copper or aluminum) but magnetically dominant waves are absorbed/suppressed by a less conductive metal such as steel [45; 46].
Molecules2014199369-9379; doi:10.3390/molecules19079369 molecules ISSN 1420-3049 www.mdpi.com/journal/molecules Article Determination of Phenol Compounds In Surface Water Matrices by Bar Adsorptive Microextraction-High Performance Liquid Chromatography-Diode Array Detection [PERSON] and [PERSON]. [PERSON] * University of Lisbon, Faculty of Sciences, Chemistry and Biochemistry Department and Center of Chemistry and Biochemistry, Campo Grande Ed. C8, 1749-016 Lisbon, Portugal; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +351-217-500-899; Fax: +351-217-500-088.  
Received: 13 March 2014; in revised form: 26 June 2014 / Accepted: 27 June 2014 / Published: 3 July 2014  
**Abstract:** Bar adsorptive microextraction combined with liquid desorption followed by high performance liquid chromatography with diode array detection (BA\(\upmu\)E-LD/HPLC-DAD) is proposed for the determination of trace levels of five phenol compounds (3-nitrophenol, 4-nitrophenol, bisphenol-A, 4-\(n\)-octylphenol and 4-\(n\)-nonylphenol) in surface water matrices. By using a polystyrene-divinylbenzene copolymer (PS-DVB) sorbent phase, high selectivity and efficiency is achieved even against polydimethylsiloxane through stir bar sorptive extraction. Assays performed by BA\(\upmu\)E(PS-DVB)-LD/HPLC-DAD on 25 mL water samples spiked at the 10.0 \(\upmu\)g/L levels yielded recoveries over \(88.0\%\pm 5.7\%\) for all five analytes, under optimized experimental conditions. The analytical performance showed good precision (RSD \(<\) 15%), detection limits of 0.25 \(\upmu\)g/L and linear dynamic ranges (1.0-25.0 \(\upmu\)g/L) with determination coefficient higher than 0.9904. By using the standard addition method, the application of the present method to surface water matrices allowed very good performances at the trace level. The proposed methodology proved to be a suitable alternative to monitor phenol compounds in surface water matrices, showing to be easy to implement, reliable, sensitive and requiring a low sample volume.  
**Keywords:** bisphenol-A; alkylphenol;nitrophenol; bar adsorptive microextraction; floating sampling technology; HPLC-DAD; surface waters
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results
### Evaluation Indicators  
Indicators are used to evaluate the overall system classification performance of the suggested model's network. Accuracy, precision, recall, and specificity were the metrics we utilized in this part of the evaluation of the classification algorithm we developed. Accuracy was a measurement of how accurate the model was, precision was a measurement of how well the ships were categorized, and recall was a measurement of how effectively negative samples were recognized. Specificity was the metric used to quantify how various ship classes were correctly classified. The F-measure (F1) was determined by adding the marks for accuracy and recall (8). The formulations for these metrics are as in the following equations:  
\[\text{Accuracy}\,=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{4}\]  
\[\text{Precision}\,=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Recall}\,=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
\[\text{Specificity}\,=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{7}\]  
\[\text{F1}=2\times\text{Precision}\times\frac{\text{Recall}}{\text{Recall}+ \text{Precision}} \tag{8}\]  
In the equations of classification evaluation (4)-(7), true positive (TP) indicates a correctly classified ship, false positive (FP) indicates an incorrectly classified ship, false negative (FN) indicates a correctly classified ship for members, and true negative (TN) indicates a correctly classified ship for members in Equations (4)-(7). Both the total number of the test samples that were correctly predicted and the total number of test samples that were correctly labelled were the same.
# Tire Defect Detection via 3D Laser Scanning Technology
## 3 Experiments and Discussion
### Comparison with X-ray-Based Methods  
Mainstream methods formulate tire defect detection as a semantic segmentation task. Specifically, typical methods are based on X-ray images, and binary labels are predicted to identify defect areas. Although these methods have achieved successful results on several types of tire defects, we argue that they are not suitable for the inspection of bulge-like defects, as discussed in Section 1. The problems of these methods lie in the following: 1. Two-value masks are not suitable for practical defect detection, which requires clear marks such as bounding boxes instead of segmented pixels. 2. The annotation of segmentation datasets is time-consuming and laborious, making it hard to construct large-scale datasets. 3. Tiny defects are difficult to capture using X-ray scanners.  
To justify the aforementioned observations, we captured additional X-ray images based on the tires used in the 3D tire datasets. As shown in the first column of Figure 6, unfortunately, identifying defects in these images is hard. We tested several state-of-the-art methods, including EDRNet [26], BASNet [27], U2 Net [28], PiCANet [29], and SCRN [30], on our captured X-ray images. Qualitative results are shown in Figure 6, from which we can clearly find that locating defects from segmented results was hard, impeding practical applications.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 3 Design and Implementation
### Call Setup in Mobile Station  
There are normal setup and emergency setup procedures described by 3 GPP 24.008. Depending on the subsequent network interconnecting from the cellular network, a proper setup process can be used. In any case, every call should have the calling party address in a proper format. When a gateway connects to a cellular network, it should format the setup message to generate and include the PAN in the A number field, which will be used by the cellular network to forward to the destination. Between mobile and circuit switched network, proper procedures are described for the setup and for the emergency setup. For example, Table 9.54/3 GPP TS 24.008 [30] summarises the messages for circuit-switched call control.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Country Code**} & **National Destination Code (Optional)** & **Subscriber Number** \\ \cline{2-3}  & **National (Significant)** \\ \hline cc = 1-3 digits & maximum = 15-cc = 12 to 14 digits \\ \hline International public telecommunication number for geographic areas (maximum 15 digits) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number structure for geographical area (adapted from [29]).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**X (t)**} & **Y (1)** & **ABCDe (5)** & **JKLMN (5)** \\ \hline \multirow{4}{*}{Network ID (0–9)} & \multirow{4}{*}{Application ID (0–9)} & North Coordinate & West Coordinate \\ \cline{3-4}  & & Degrees + minutes + seconds & Degrees + minutes + seconds \\ \cline{1-1} \cline{3-4}  & & Minutes + seconds & Minutes + seconds \\ \cline{1-1} \cline{3-4}  & & Proprietary LID & Proprietary LID \\ \hline \hline \end{tabular}
\end{table}
Table 3: PAN Presentation Numbering Scheme: Example (12 digits). X (1 st digit for network ID), Y (2 nd digit for application ID), ACBDE (3 rd to 7 th digits), JKLMN (8 th to 12 th digits)