esa-sceva/satcom-chunk-collection huggingface dataset:
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
## 3 Results
### Detection of Water Adulteration Using Lacunarity Analysis  
Regardless of the concentrations of added water and NaCl, the lacunarity demonstrates a consistent decreasing trend concerning the normalized box size \(\ln(\epsilon)\) for dried drop patterns, see Figure 9. Furthermore, through a comparison of the maximum lacunarity values at low \(\ln(\epsilon)\) of the curves for each NaCl concentration, we observed that the lacunarity decreases with increasing NaCl concentration, irrespective of the milk type. We utilize the estimated lacunarity values at \(\ln(\epsilon)=-4.6\) (\(l=2\) pixels) to conduct discriminant analysis based on ROC curves, enabling differentiation between milk and milk with water content. Notably, for deposits with \(\phi=0\) wt%, the effectiveness exceeds 0.98 in detecting  
Figure 8: Reproducibility of milk deposit patterns with varying NaCl and water concentrations. Dry deposits of whole and lactose-free milk with different water concentrations (0, 20, 40, 60, and 80%) at \(\phi_{N}=0\) wt% **(a)**, 2 wt% **(b)**, and 4 wt% (\(\epsilon\)). Each condition is shown to illustrate the reproducibility of the patterns.  
20% added water in whole milk but declines to above 0.75 when detecting 20% added water in lactose-free milk, see Figure 10a. Comparatively, deposits with \(\phi=2\) wt% exhibit effectiveness values above 0.99, irrespective of milk type and added water concentration (Figure 10b). In contrast, deposits with \(\phi=4\) wt% and 20% added water demonstrate effectiveness values lower than 0.85, but increase to above 0.97 for milk containing 60% and 80% added water, see Figure 10c.
## 4 Results
### MI System Architectures
#### 4.2.2 AR as an Interface  
As an interface, AR is used not only to visualize data for an agent, but also to allow an agent to provide input to an MI system. When AR is used as an interface, the agent can manipulate one or more IoT devices by interacting through the AR interface on an interaction device, such as a smartphone, tablet PC, or HMD (Figure 4c). Likewise, an agent can control IoT devices (as indicated by a dotted arrow in Figure 4d) while watching AR content from another entity (Figure 4d). The difference between the two patterns is what is used an identifier: the pattern that is depicted in Figure 4c uses an IoT device as an identifier, whereas the pattern depicted in Figure 4d uses another entity as an identifier.  
The MI system that was developed by [PERSON] et al. [10] is a case that employs the architecture displayed in Figure 4c. The AR mobile application connected to their MI system can recognize an AR marker placed on a miniature centrifuge. By scanning the AR marker, the agent can monitor the data that are sent by sensors installed on the motor of the centrifuge, and can control the motor by touching a button on the mobile screen.  
As an example of the pattern that is depicted in Figure 4d, [PERSON] et al. [40] developed a companion application used in educational lab kit activities that was tested in elementary, middle, and high school. The companion application helps students perform physical activities in an AR environment that require assembling circuits from a printed floor plan. The printed floor plan contains AR markers in order to visualize AR content on it. Some of the AR content is data collected by IoT devices that are installed within the school building, such as the temperature, humidity, and energy consumption rate. To receive the data, the agent must scan an AR marker that is attached to IoT devices using the companion application. Once the IoT devices are scanned, the agent can send commands to them to control light-emitting diode (LED) light.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### City-based Models  
For vehicular networks, the Manhattan grid mobility model is frequently mentioned (e.g., [PERSON] et al., 2007, [132] and [PERSON] and [PERSON], 2008, [133]), and sometimes, it is also known as the city section mobility model (see [PERSON] et al., 2002, [77]). In the city section mobility model, a road network is shown with the roads on a 90\({}^{\circ}\) grid pattern similar to that used in many cities in the United States of America. Node movements are confined to the roads. Nodes select destinations within the city and move along the streets at speeds governed by the local speed limits, taking an appropriate route to their destination. On the contrary, the Manhattan grid model does not have any node destination. Rather, when a node reaches a crossroads, it will randomly select one of the three possible options with probabilities configured for each of the following directions: left turn, right turn, and straight ahead. Speeds may also randomly change randomly but are subjected to previous speeds and are limited to the speed of the node ahead if there is one.
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 3 Proposed Work
### Proposed Algorithm  
In this section, we are proposing two, Algorithms 1 and 2, for the proposed XML querying over wireless broadcast channels.  
This algorithm divides the XML data stream into multiple partitions based on a specified criterion, such as document size, type, or content. These partitions can be used for a variety of tasks, such as improving query performance, balancing loads, and distributing data across multiple wireless broadcast channels.  
```
Inputs: XML data stream, wireless broadcast channels, query Outputs: Query results
1. Partition the XML data stream into several partitions based on criteria, such as document size, type, or content.
2. Assign each partition to a different wireless broadcast channel, ensuring that the load on each channel is balanced.
3. Broadcast the XML data partitions over their respective channels.
4. Implement a query routing mechanism that can route the queries to the appropriate channel or combination of channels that contain the relevant XML data partition. 1. If using query decomposition: 1. Split the query into sub-queries based on the relevant partitions. 2. Send each sub-query to the appropriate channel that contains the relevant XML data partition. 3. Combine the results from each channel to generate the final result. 2. If using query caching: 1. Check if the query and its results are already cached in the mobile device. 2. If cached, retrieve the results from the cache and return them. 3. If not cached, send the query to the appropriate channel that contains the relevant XML data partition and cache the results for future queries. 3. If using query broadcasting: 1. Broadcast the query to all channels. 2. Collect the results from each channel. 3. Combine the results to generate the final result. 5. Return the query results.
```  
**Algorithm 1**An algorithm for the proposed scheme for improving XML querying over wireless broadcast channels  
```
Inputs: XML data stream Outputs: Partitions of XML data stream
1. Read in the XML data stream.
2. Choose a partitioning criterion, such as document size, document type, or document content.
3. For each document in the XML data stream, determine the value of the chosen criterion.
4. Create a partition for each unique value of the chosen criterion.
5. Assign each document to the partition that corresponds to its value of the chosen criterion.
6. Repeat steps 3-5 for all documents in the XML data stream.
7. Output the partitions as separate XML data streams.
```  
**Algorithm 2**An algorithm to partition an XML data stream into several partitions based on criteria
## 3 Data and Methodology
### Data Source and Processing
#### 3.1.2 Meteorological Data  
Meteorological data, including daily mean temperature, sunshine time, solar radiation, etc., from 2001 to 2019, were acquired from the China Meteorological Administration ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). In order to maintain a consistent resolution between meteorology data and remote-sensing data from space, the Anuspline interpolation method was used to interpolated point data into raster data with the same spatial resolution as NDVI, FPAR and other data based on 25 meteorological stations in Northeast China.
## 3 Background and Theory
### SVM with Polynomial Kernel Classifier  
The fourth supervised machine learning classifier is the support vector machine (SVM) with a polynomial kernel [33]. In EEG, it is beneficial when the relationship between input features and target classes is non-linear. The polynomial kernel allows the SVM to project the input data into a higher-dimensional feature space where a linear separator can be found. This kernel function is defined as (24):  
\[K\big{(}\mathbf{x},\mathbf{x}^{\prime}\big{)}=\left(\gamma\,\mathbf{x}^{\top}\mathbf{x}^{\prime }+r\right)^{d} \tag{24}\]  
where the following apply:  
* \(K(\mathbf{x},\mathbf{x}^{\prime})\) is the kernel function measuring similarity between two feature vectors;
* \(\mathbf{x}\) & \(\mathbf{x}^{\prime}\) are input feature vectors derived from EEG signals;
* \(\gamma\) is a scaling factor that adjusts the influence of the features (kernel coefficient);
* \(r\) is a constant that controls the trade-off between higher-order and lower-order terms;
* \(d\) is the degree of the polynomial, determining the complexity of the decision boundary.  
The decision function for the SVM with a polynomial kernel is expressed as (25):  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}y_{i}K(x_{i},x)+b \tag{25}\]  
where the following apply:  
* \(N\) is the number of support vectors selected during training;
* \(\alpha_{i}\) are Lagrange multipliers obtained by solving the SVM optimization problem;
* \(y_{i}\) are the class labels of the training samples;
* \(\mathbf{x}_{i}\) are the support vectors from the training data;
* \(b\) is the bias term or intercept.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 3 Numerical Simulations and Discussion
### Mission Applications  
The aim of this subsection is to analyze the time variation of the parameters of the osculating orbit and the corresponding optimal control laws in two mission scenarios, each of which were obtained with a different value of \(\beta\). For exemplary purposes we select a value of \(\beta\) for the DT case and a different one for the transfer with a single SWA. Note that the general characteristics of the time variation of the two control variables \(\{\tau,\,\alpha_{n}\}\) can be shared with other mission scenarios obtained with different values of \(\beta\).
# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 2 Materials and Methods  
### Database  
Seven experimental datasets were selected from the literature [15; 16; 17; 18; 30; 31; 32] for the current study. These studies were considered suitable, as a variety of absorbents or biosorbents were used to remove _As_(III) from contaminated aqueous solutions or ground/ wastewater. In general, the adsorption or biosorption experimentation was followed by modeling _As_ removal based on several input parameters, including initial _As_ concentration, adsorbent dose, pH, contact time, agitation speed, temperature, and others.
###### Abstract  
Background: Minimally invasive surgery has improved patient outcomes but raises environmental concerns due to carbon emissions from disposable instruments, energy consumption, and hospital resource use. This study evaluates the carbon footprint of robot-assisted and laparoscopic nephrectomies. Methods: A multicenter prospective study analyzed 162 patients undergoing robot-assisted or laparoscopic partial (RAPN, LPN) and radical nephrectomy (RARN, LRN). Life cycle assessment (LCA) quantified CO\({}_{2}\) emissions from instrument use, sterilization, and energy consumption. Results: Robot-assisted procedures had lower CO\({}_{2}\) emissions than laparoscopic approaches. Emissions from instrument disposal were 9.04 kg in RAPN vs. 12.14 kg in LPN (\(p\) = 0.02) and 8.74 kg in RANN vs. 11.45 kg in LRN (\(p\) = 0.03). Despite higher intraoperative energy use, robot-assisted surgery produced 3-4 kg less CO\({}_{2}\) per procedure due to reusable instruments and shorter hospitalization. Conclusions: Robot-assisted nephrectomy reduces environmental impact by minimizing waste and hospital stays. Efforts should focus on reusable instruments, energy efficiency, and sustainability initiatives to mitigate surgical carbon footprints while ensuring high-quality care.  
**Keywords:** minimally invasive surgery; robot-assisted surgery; laparoscopic surgery; nephrectomy; carbon footprint; life cycle assessment (LCA); greenhouse gas emissions; surgical sustainability; reusable instruments
## 5 Distributed Heterogeneous Cluster Task Planning Based on Nash Equilibrium
### Construction of a Task Planning Model for Heterogeneous Clusters
Regarding the multi-satellite collaborative detection mission system, the Nash equilibrium strategy set requires that all participating mission nodes in the system satisfy the following condition:  
\[f_{ei}(\sigma_{i}^{*},\sigma_{-i}^{*})\geq f_{ei}(\forall s,\sigma_{-i}^{*}), \ \ s\in S_{i}\ \ \sigma_{i}^{*}\in S_{i} \tag{18}\]  
Here, \(f_{ei}\) represents the comprehensive effectiveness evaluation function for node \(i\), \(f_{ei}\) denotes the optimal strategy for node \(i\), and \(S_{i}\) is the strategy space of node \(i\). The notation \(-i\) refers to all nodes in the system except node \(i\). The comprehensive effectiveness function of the \(i\)-th node is not only related to its own decision variables but also influenced by the decision variables of other nodes \(-i\). According to the definition of Nash equilibrium, when each sub-node solves its problem, it requires the strategy information of other nodes. Therefore, a communication network among nodes is necessary for distributed task planning, enabling sufficient information exchange. Figure 4 shows solution process for distributed task planning based on Nash equilibrium theory.
## 2 Basic Measurement Techniques for Localization in WSNs  
The localization algorithms for WSNs depend on various measurement techniques. There are many factors that affect the accuracy of the localization algorithms and consequently, the choice of the localization algorithms to be used in various applications. For example, network architecture, sensor density in an area, number of anchor nodes, geometric shape of the measurement area, sensor time synchronization, and the signaling bandwidth among the sensors are the key factors to be taken into account while designing a localization algorithm. However, it is the type of measurement and the corresponding precision that fundamentally determines the accuracy of localization algorithm.  
Measurement techniques in WSNs localization can be broadly classified into three categories [50]. Angle of Arrival (AOA) measurements, distance related measurements and the Radio Signal Strength (RSS) profiling techniques. Figure 1 shows the classification. In the subsequent discussion, we briefly discuss these techniques along with their limitations in different WSNs applications.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 5 Real Scenarios Evaluation
### Analysis of Results
#### 5.2.1 Ideal Conditions without FEC Protection  
In Zone I, under ideal conditions, every packet sent by the RSU is correctly received by the client vehicle for all the combinations of coding modes and tiles per frame. So, we have a PLR value of 0%. Consequently, no tile is missing in any of the sequences and the quality of the reconstructed video sequences is the quality directly provided by the encoding/decoding process (shown in Table 2). The coding efficiency of each one of the encoding modes prevails, so, in the absence of packet loss obtained in Zone I, the LP and IPx modes obtain the best PSNR values.  
In Zone II, for the non-protected bit streams, we encounter a packet loss burst corresponding to the area where neither of the RSUs signals arrive. The average PLR in this zone has a narrow range (from 1.98 to 2.01%). The TLR has also a narrow range (from 1.95 to 2.39%). As opposed to the results shown in Figure 5 for probabilistic random packet losses (where the TLR was higher than the PLR, especially in 1 and 2 til/rm layouts), here, the PLR is always very similar to the TLR, even when a low number of tiles per frame is used. The reason is that, when isolated losses occur (like those generated by random packet loss), the real loss of one isolated packet (a low PLR) entails the loss of the whole tile (which may mean the effective loss of several packets and a high TLR), but, when bursty losses occur, like in this case, the loss of consecutive packets that belong to the same tile does not entail an increase in the TLR. In this zone, the best two modes regarding the PSNR value (averaging the whole range of til/rm layouts) are LPI4 and IPPP, with 33.74 dB and 33.71 dB values, respectively. The IPx25 pctTIL and IPxpattern modes also show good performance. As the TLR is not pronounced, all the encoding modes have PSNR values over 30 dB, even the LP, IPx, and IPx25 pctCTU modes, which showed poor performance in the presence of packet loss. Under these conditions, the AI mode obtains the lowest PSNR value. Comparing the different tiles per frame layouts, no layout performs much better than the others, regarding the PSNR value.  
In Zone III, where the signals of two of the RSUs overlap, the PLR is around 22-23%. As the two RSUs are 1 km away from each other, they cannot \"see\" each other (because the coverage radius of the wireless devices is 500 m). These collisions produce a high loss rate in the overlapped area, which is wider than the shaded area in Zone II. Some bursts of data loss appear in this zone. As in Zone II, the TLR values are only slightly higher than the PLR values. However, in this zone, the TLR values are very high and this fact produces very low values for PSNR. The same behavior as that for Zone II is observed regarding the tiles per frame layouts: all of them show very similar performance. Therefore, we can conclude that when bursty losses occur, the til/rm layout does not have any influence on the PLR value, nor on the PSNR value.
## 5 Empirical Case Study: Courier Company in Bangkok
### Towards Balancing Vehicle's Total Working Time  
_Route balance_ is the difference between the longest and the shortest among all the vehicle working times in the problem (route time range). In the route adjustment algorithm, the vehicle working time limit has been taken as an algorithm parameter. In this section we demonstrate the importance of finding _optimal working time_ to achieve vehicle's total working time balance.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 3 Game Theoretic Framework and Utility Function Design
### Information Feedback from Neighboring Small Cells  
Note that, to compute the utility on a subchannel in Equation (10), the serving SBS of a link needs to gather information of all other links in the SCN that are utilizing the particular subchannel. Here, it is assumed that the transmission on a link interferes with all other links over the common subchannel. However, this is not always true in SCNs where the interference sources are dominant only within a neighborhood due to low transmitting power of SBSs [25]. In this regard, we consider each MS aperiodically listens the network to construct a neighbor relation table (NRT) that contains the identity of its neighboring SBSs, and sends back to its serving SBS. For this purpose, the automatic neighbor relation framework introduced in the third-generation partnership project (3 GPP) can be utilized to discover neighboring SBSs of the MS [34]. Here, each SBS broadcasts a unique physical cell identity (PCI), the identifier of the SBS, which the MSs can detect if the reference signal receive power (RSRP) is greater than some threshold value (\(RSRP_{TH}\)). For simplicity of evaluation, we assume that the RSRP from a SBS is greater than the \(RSRP_{TH}\) within their coverage range. Now, the MS lists all the detected PCIs in its NRT, indicating them as its neighboring SBSs, and sends the NRT to the serving SBS. With the PCIs information in the NRT, the serving SBS can establish X2 interface connections with the neighboring SBSs of the MS in order to receive necessary information feedback while computing the utility (the X2 interface allows for inter-cell signaling between SBSs) [35]. Hence, to compute the utility on a subchannel, the serving SBS of a link needs information feedback only of those links originating from the neighboring SBSs that are utilizing the particular subchannel.  
Now, the utility function in Equation (10) of link \(i\) is modified as  
\[u_{i}^{\text{MC}}(s_{i}=k^{\prime},\mathbf{s}_{j})=W_{k^{\prime}}(s_{i},\mathbf{s}_{j} )-W_{k^{\prime}}(\mathbf{s}_{j}) \tag{11}\]  
Here, \(j\in\mathcal{J}_{i}\subset\mathcal{M}\) is the set of links originating from the neighboring SBSs of link \(i\). The utility function in Equation (11) for link \(i\) is calculated with the feedback of information only of the links in set \(\mathcal{J}_{i}\) that are also using subchannel \(k^{\prime}\), i.e., for which the strategy is \(\mathbf{s}_{j}=k^{\prime}\), thus further reducing the computational complexity to compute the utilities.
## 3 Results and Discussion
### Application of MW-SPM and HPLC-FD to the Analysis of Capsules  
The validation results that were obtained revealed that both MW-SPM and HPLC-FD are appropriate for routine analysis of DUV and SEL in their capsules. Both methods were applied to the quantitation of DUV and SEL in commercialized copiktra capsules for DUV and laboratory-made capsules for SEL. The mean recovery values that were obtained for the nominated concentrations were \(\sim\)100% for both DUV and SEL by both MW-SPM and HPLC-FD (Table 5). This result indicated that both methods are appropriate for routine accurate determination of DUV and SEL in QC laboratories.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Taken Conc. a**} & \multicolumn{2}{c}{**Relative Standard Deviation (\%) b**} & \multirow{2}{*}{**Recovery (\% \(\pm\) SD) a**} \\ \cline{3-3}  & **Intra-Assay, n = 3** & & \\ \hline \multicolumn{4}{c}{MW-SPM} \\ \hline DUV & & & \\
20 & 1.29 & 2.23 & 100.2 \(\pm\) 1.8 \\
100 & 1.02 & 1.82 & 101.4 \(\pm\) 2.1 \\
160 & 1.62 & 2.41 & 98.5 \(\pm\) 1.8 \\ SEL & & & \\
20 & 1.82 & 2.14 & 100.2 \(\pm\) 2.2 \\
100 & 1.46 & 2.05 & 99.4 \(\pm\) 1.9 \\
160 & 2.01 & 2.27 & 102.5 \(\pm\) 2.4 \\ \hline \multicolumn{4}{c}{HPLC-FD} \\ \hline DUV & & & \\
0.5 & 1.24 & 1.41 & 101.4 \(\pm\) 1.2 \\
1.5 & 0.89 & 1.22 & 100.2 \(\pm\) 1.5 \\
3.0 & 0.98 & 1.54 & 99.8 \(\pm\) 1.2 \\ SEL & & & \\
0.5 & 1.32 & 1.58 & 101.4 \(\pm\) 1.4 \\
1.5 & 1.04 & 1.04 & 98.8 \(\pm\) 1.5 \\
3.0 & 1.52 & 1.72 & 100.2 \(\pm\) 1.8 \\ \hline \end{tabular} a Taken concentrations are ug/well and ug/mL for MW-SPM and HPLC-FD, respectively. b Values are the mean of three determinations.  
\end{table}
Table 4: Precision and accuracy of the proposed MW-SPM and HPLC-FD for determination of DUV and SEL.