esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Topology Control
### Post-Deployment
#### 4.3.3 Connectivity Maintenance Protocols  
There are many energy-aware routing protocols. In order to balance the connectivity and lifetime of the network, a good energy-aware protocol should: first, schedule as many nodes as possible to turn off; second, keep the network connected; third, offer as much total capacity as the original network; fourth, power saving should inter-operate correctly with whatever routing system [88]; and fifth, the algorithm needs to be distributed and highly efficient.  
In a power saving technique for multi-hop ad hoc wireless networks called SPAN [88], a node volunteers to be a coordinator if it discovers that two of its neighbors cannot communicate with each other directly or through an existing coordinator. The Coverage Configuration Protocol (CCP) [15] protocol is a decentralized protocol that only depends on the local states of the sensing neighbors. CCP is able to guarantee both coverage and connectivity if the sensing range is less than half of the communication range, but if the sensing range is more than half of the communication range, CPP integrates with SPAN [88] to provide both coverage and connectivity. Probing Environment and Adaptive Sleeping (PEAS) [95] schedules a required number of nodes as awake by adjusting the sleep period in the presence of failures. It sends the probing messages to the nodes within the probing range, which is a value lower than the communication range. Any nodes within its probing range will send back a reply message. The sleeping nodes keep working only if they do not receive the reply messages. In this way, it keeps the network connected. The work in [27] presents a greedy algorithm: it starts from a complete graph, then reduce nodes to reach the desirable connectivity, or starts from an empty graph and later connects edges until the desirable connectivity is reached; then, it cuts nodes, but maintains the same connectivity. The second step is to reduce the redundant nodes.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### User Experience
#### 8.5.4 Social Interaction and Complexity in the Metaverse  
Social interaction is one of the most promising but also challenging aspects of the metaverse [299]. It allows users to connect with others, form relationships, collaborate, and engage in shared experiences across vast virtual spaces. However, the complexity of social interactions in immersive environments brings its own set of UX challenges. In physical reality, social cues such as eye contact, body language, and tone of voice are vital for smooth communication, but replicating these nuances in a virtual environment remains difficult. Developers need to focus on creating natural communication systems that replicate these subtle social cues as much as possible [300]. For instance, spatial audio--where sound emanates from the direction of a virtual avatar--helps users better engage with conversations, creating a sense of presence and immersion. Facial tracking and emotion mapping (enabled by newer VR/AR headsets) can also enhance avatar realism, making social interactions more intuitive.  
However, there are potential downsides to social interaction in the metaverse, such as the risk of harassment or exclusion [301]. Developers need to implement safety features such as personal space bubbles, mute/block functions, and reporting mechanisms to ensure that users feel safe while interacting with others in the virtual world. Moreover, AI-driven content moderation systems can help identify and mitigate toxic behaviors in real-time, ensuring a positive social experience for all users. Also, creating inclusive social spaces that accommodate users of diverse backgrounds, abilities, and cultures is key to the success of the metaverse. By facilitating customizable avatars that represent various ethnicities, body types, and disabilities, developers can promote diversity and ensure that all users feel represented and included in virtual environments.
## 3 Problem Statement and Input Data Representation Methods
### Traffic Data and Their Unique Characteristics
#### 3.1.2 Temporal Dependencies  
Temporal dependencies are typically categorized into two main types in data representation: sequentiality and periodicity. Sequentiality represents the natural order of data over time, which in traffic prediction means that data are collected in chronological order, with each data point timestamped later than the previous one, similar to the stacked vector method for representing spatial dependencies, where consecutive temporal units are stacked into a single vector to indicate that temporally closer data are more related. Periodicity involves the recurring patterns displayed by data, such as daily cycles (peak and off-peak periods within a day) or weekly cycles (differences in traffic patterns between weekdays and weekends). Representing data with periodicity allows models to capture these regularly occurring patterns, enabling accurate predictions for similar future periods.
## 4 Results and Discussion  
### Flow Patterns  
Figures 6-10 show the spanwise normalized vorticities diagrams and the time-averaged streamline diagrams at midspan plane for flow past three cylinders arranged in an equilateral-triangle configuration at different spacing ratio \(L/D\) at \(Re=3\times 10^{6}\). Clockwise and counterclockwise vorticities are displayed by red and blue colors, respectively. The flow  
Figure 4: Comparison of pressure distribution on a single cylinder between the present study and published data [44; 45; 47; 48].  
Figure 5: Comparison of skin friction coefficient (\(C_{f}\)) distribution on a single cylinder surface with the published experimental and numerical data [44; 46; 49].  
patterns are classified according to \(L/D\) based on the careful examination of Figures 6-11 and other parameters such as \(Cd\), \(Cl\), and \(St\). Five different flow patterns were summarized and categorized. The naming of the flow patterns is partially referenced to [PERSON] [21] who found them at \(Re=50\)-\(300\). It is worth noting that the specific features and critical spacing ratios are widely divergent according to results from [PERSON] [21], on account of the four-orders-of-magnitude difference in Reynolds number.  
The specific characteristics of each flow pattern are as follows.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS with Advanced Sensors
#### 3.2.2 FAS Based on Smartphones  
Compared with general-purpose FAS methods that focus on accuracy and generalization issues [15], FAS on smartphones places more emphasis on practical issues in real-world applications, such as security, usability, and practicality. Therefore, FAS on smartphones can utilize various sensors (e.g., camera, microphone, speaker, accelerometer, etc.) to detect spoof faces. Meanwhile, FAS on smartphones often requires participants to evaluate their systems in the real-world using prototypes they implement on smartphones. It is also important to note that in current FAS research, most detection methods remain one-way, where the system passively receives input images or video streams and then performs liveness or spoof detection. In contrast, FAS methods on smartphones are often interactive and two-way in nature. That is, the system actively issues instructions or stimuli, such as playing sounds, emitting light, or gaze responses, and the user is required to produce a genuine response. This interaction enables more reliable spoof detection. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Ref.
# A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark
## 3 The SSiGraM Approach
### Optimizing Support Evaluation
#### 3.4.1 Load Balancing  
The support evaluation process is implemented in parallel at subgraph scale, which means that each subgraph will be sent to an executor in the Spark cluster for support evaluation. The search space is highly dependent on the subgraph's CSP domain size. Nevertheless, new subgraphs may have different domain sizes which result in the phenomenon that some executors may finish searching fast while others are very slow. The final execution time of the whole cluster depends upon the last finished executor.  
To overcome this unbalance, generally, the subgraphs distributed to various executors must have roughly the same domain sizes. Algorithm 2 illustrates the detailed process. Because the domain of the present subgraph is incrementally generated from the parent subgraph's domain of last iteration, we save the domain sizes of all subgraphs in each iteration. Then, according to the saved domain sizes of parent subgraphs, new generated subgraphs are re-ordered and partitioned to different executors (Lines 6-9).  
Let \(n\) be the number of nodes of subgraph \(S\), i.e., the domain size. Load balancing can be done in \(\mathcal{O}(n)\) time.  
```
0:RDD of subgraph set \(SRDD\), Spark cluster parallelism \(n\), Spark context \(sc\)
0:\(SRDD_{balance}\): balanced RDD of subgraph set
1:functionLoadBALancing(\(SRDD\),\(n\),\(sc\))
2:\(S_{balance}\leftarrow\varnothing\)
3:\(S\gets SRDD\)
4: sort \(S\) in descending domain size order
5: initialize \(n\) empty partitions
6:for\(s\) in \(S\)do
7:\(S_{balance}(p)\leftarrow\)smallest partition
8: add \(s\) to partition \(S_{balance}(p)\)
9:endfor
10:\(SRDD_{rp}\gets sc.parallelize(S_{balance},n)\)
11:return\(SRDD_{balance}\)
12:endfunction
```  
**Algorithm 2** Load Balancing
## 3 Results  
The results of the conducted studies of the mass concentrations of nitrogen oxides released from the explosion of the model composition without the addition of catalysts are shown in Figure 4.  
Figure 3: Scheme for measuring emissions of nitrogen oxides (photo by the author [PERSON]).  
The concentration of nitrogen oxides significantly exceeds the maximum permissible concentration of nitrogen dioxide in Russia (2 mg/m\({}^{3}\)).  
The results of using various catalysts are shown in Figure 5.  
After the comparison with the maximum permissible concentration, it can be noted that zinc carbonate (ZnCO\({}_{3}\)) is the most effective filler for reducing nitrogen oxide emissions and it allows you to reduce the concentration of released nitrogen oxides.  
The results of the research on the content of nitrogen oxides in the explosion products of the explosive pentaerythntol tetranitrate without the use of catalysts are presented in Table 1.  
Figure 4: Dependence of the concentration of nitrogen oxides in the explosion products of a model explosive (compiled by the author [PERSON]).  
Figure 5: Dependence of the concentration of nitrogen oxides in the explosion products on the filler used (compiled by the author [PERSON]).  
After adding zinc carbonate to the stemming in the amount of 20%, the results of the study to determine the concentration of nitrogen dioxide were as follows (Table 2):  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**t, s** & **Concentration NO2, mg/m3** & **1** & **2** & **3** & **4** & **5** \\ \hline
0 & & 380 & 402 & 398 & 356 & 412 \\ \hline
15 & & 360 & 387 & 402 & 334 & 400 \\ \hline
30 & & 352 & 366 & 387 & 320 & 380 \\ \hline
45 & & 334 & 340 & 366 & 334 & 376 \\ \hline
60 & & 318 & 332 & 354 & 340 & 366 \\ \hline
75 & & 306 & 323 & 348 & 345 & 340 \\ \hline
90 & & 292 & 305 & 332 & 323 & 321 \\ \hline
105 & & 280 & 298 & 320 & 320 & 310 \\ \hline
120 & & 276 & 280 & 312 & 318 & 298 \\ \hline
135 & & 264 & 282 & 295 & 315 & 290 \\ \hline
150 & & 258 & 278 & 280 & 310 & 288 \\ \hline
165 & & 240 & 266 & 275 & 287 & 280 \\ \hline \end{tabular}
\end{table}
Table 2: The concentration of nitrogen oxides in the explosion products of pentaerythitol tetranitrate with 20% zinc carbonate ZnCO\({}_{3}\) in the stemming (compiled by the author [PERSON]).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**t, s** & **Concentration NO2, mg/m3** & **1** & **2** & **3** & **4** & **5** \\ \hline
0 & & 653 & 360 & 550 & 670 & 480 \\ \hline
15 & & 579 & 560 & 470 & 580 & 440 \\ \hline
30 & & 564 & 533 & 482 & 585 & 421 \\ \hline
45 & & 548 & 521 & 451 & 530 & 389 \\ \hline
60 & & 470
# Interoperability of Digital Tools for the Monitoring and Control of Construction Projects
## 4 Results and Discussion
### Cyber-Physical Systems
#### 4.1.2 Communication and Collaboration Technologies (CT)  
Industry 4.0 generates a large amount of data. This information needs to be processed, analyzed and used efficiently, which demands solid technological management. By definition, this data management must occur in real time and needs human support. [58]. In this sense, the quality of the information generated, its efficiency, the format in which it is transferred, its applications and its subsequent uses are key elements for effective management [35] for correct processing to provide meaningful information [59]. The application of virtual collaboration in construction necessitates the use of various novel technologies and communications, collaboration software and visualization applications to create a better collaborative environment. The goal of planning and executing virtual collaboration is to provide a communication platform that has the potential to share essential visual information to support communication and knowledge sharing among planners, engineers and other team members [60].  
It is widely accepted that to improve productivity and performance in construction, the industry requires an integrated collaborative approach to project execution. Collaborative environments provide tools that support the exchange of information among different applications, simultaneous access to data and the sharing of information sources across a network to enable collaboration among different users and support for all teams, addressing the challenges of fragmentation. The correct management of information is crucial to achieve these objectives [60]. Although there has been rapid growth in the development of collaborative tools and systems in recent years, especially in communication, visualization, information and knowledge management, the adoption and application of these tools has been slow and with mixed levels of success. Therefore, a well-defined methodology for collaborative work is needed [60].  
A large amount of stored and processed data requires a robust storage system, which allows for access almost anywhere and by any stakeholder, inside or outside a network, enabling global data management, control and analysis. [61]. This system is particularly complex in an industry that is characterized by fragmentation and cascade planning of processes and performance stages of its disciplines and professionals. These barriers must be overcome, both inside and outside an organization, by actively involving suppliers in the logistics and innovation processes [62].
# Feasibility Analysis of Industrial High-Temperature Heat Pumps in Greece
## 3 Results and Discussion
### Sustainability Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Ambient-Source HTTP**} & & & & \\ \hline
**T\({}_{\text{process}}\)** (\({}^{\circ}\)C) & **100** & **110** & **120** & **130** & **140** & **150** & **160** & **170** & **180** \\ \hline
**COP** & 1.924 & 1.748 & 1.593 & 1.453 & 1.325 & 1.204 & 1.090 & - & - \\ \hline
**E\({}_{\text{el}}\) (MWh)** & 1366 & 1503 & 1650 & 1809 & 1984 & 2182 & 2412 & - & - \\ \hline
**LCC (MEUR)** & 3.690 & 4.048 & 4.430 & 4.845 & 5.303 & 5.821 & 6.421 & - & - \\ \hline
**LCOE (EUR/kWh)** & 0.0562 & 0.0616 & 0.0674 & 0.0737 & 0.0807 & 0.0886 & 0.0977 & - & - \\ \hline
**CO\({}_{2}\) emissions (tn/y)** & 344.3 & 378.9 & 415.7 & 455.7 & 500.0 & 549.9 & 607.8 & - & - \\ \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline
**WHS-HTHP at the Temperature of 75 \({}^{\circ}\)C** & & & & & & & & \\ \hline
**COP** & 3.285 & 2.87 & 2.533 & 2.25 & 2.006 & 1.788 & 1.59 & 1.405 & 1.229 \\ \hline
**E\({}_{\text{el}}\) (MWh)** & 800 & 916 & 1038 & 1168 & 1310 & 1470 & 1653 & 1870 & 2138 \\ \hline
**LCC (MEUR)** & 2.211 & 2.513 & 2.831 & 3.172 & 3.543 & 3.960 & 4.438 & 5.007 & 5.707 \\ \hline
**LCOE (EUR/kWh)** & 0.0337 & 0.0383 & 0.0431 & 0.0483 & 0.0539 & 0.0603 & 0.0676 & 0.0762 & 0.0869 \\ \hline
**CO\({}_{2}\) emissions (tn/y)** & 201.6 & 230.8 & 261.5 & 294.3 & 330.1 & 370.4 & 416.5 & 471.4 & 538.9 \\ \hline
**Natural Gas Boiler** & & & & & & & & \\ \hline
**E\({}_{\text{B}}\) (MWh)** & & & & & & & & & \\ \hline
**LCC (MEUR)** & & & & & & & & & \\ \hline
**LCOE (EUR/kWh)** & & & & & & & & \\ \hline
**CO\({}_{2}\) emissions (tn/y)** & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results for the different systems. if electricity prices increased, the financial viability of HTTP would be restricted, while in cases with lower electricity prices, HTTP technology would be a more viable solution.
## 3 Proposed Solutions
### The Selection of a Listening Window to Establish a Connection with the Device
#### 3.1.1 Linear Increasing of Time Window  
We propose to listen for an incoming packet using a linearly growing time window. We set the first window to \(x_{1}=2\cdot(\alpha\cdot\sigma)\), the second window to \(x_{2}=2\cdot(2\cdot\alpha\cdot\sigma)\), and the third window to \(x_{3}=3\cdot(2\cdot\alpha\cdot\sigma)\), where \(\sigma\) is the standard deviation of the normal distribution defining the clock-acceptable random values of the desynchronized device over appropriate period \(X[s]\), and \(\alpha\) is a scaling factor. The center of the listening window is set as the point where we expect the data to be transmitted assuming a complete lack of clock desynchronization. The listening session is carried out as a three-window rule, the length and location in time are presented in Figure 2.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 4 Experimental Results
### Methodology  
We conducted experiments based on the Common Test Conditions (CTC) defined by ISO/IEC MPEG Video Coding [23]. This helps us to ensure a fair comparison between different methods for immersive video coding. To carry out the experiments, we used the TMIV--Test Model for MPEG Immersive Video 16.0 [23]--which is designed to implement the MPEG Immersive Video coding standard. Used test sequences are listed in Table 1.  
For depth estimation performed at the decoder side, we used Immersive Video Depth Estimation (IVDE [4]), which is used in MIV experiments conducted by ISO/IEC MPEG Video Coding. We use our modification of publicly available IVDE 8.0. Once depth maps are estimated, the TMIV renderer is utilized to produce virtual views that are positioned in the same position as all views of the test sequences used. To measure the quality of the virtual views in comparison to the real views of the sequences, PSNR and IV-PSNR metrics [16] are utilized. Eventually, BD-rate (Bjantegaard delta [24]) is computed to assess the percentage change in the bitrate required to attain the same quality for the tested techniques relative to the reference method.  
The tested methods were also evaluated based on their computational complexity. These evaluations are presented as a runtime ratio when compared to DSDE, which was performed without hierarchical alpha-expansion.
# Chemical Images on Fingerprints Revealed with Mass Spectrometry
## 3 Results
### Endogenous Compounds MSI  
An ion at \(m/z\) 265.1420 was observed in all fingerprints acquired. From the DESI/Orbitrap measurement, this signal was identified as the dodecyl sulfate anion of SDS, a surfactant usually employed in cleaning formulations. This ion was used as an instrumental reference (lock mass, red signal in Figure 2) in LFM-analysis, achieving high mass accuracy, even with regular TOF mass detectors (resolution lower than 20,000).  
Figure 1: Caffeine on target. CHCA matrix (**A**) vs. TiO\({}_{2}\)NP (**B**). On the MALDI target, 1 \(\upmu\)L of nanoparticle suspension was drop-cast (0.5 mg/mL, 0.5 \(\upmu\)g spotted) and 2.5 nmol of caffeine (1 \(\upmu\)L, 2.5 mM water solution) was added. The mass spectrum in (**A**) is dominated by matrix ions in the low mass range, while nanoparticles in (**B**) produce a cleaner spectrum, favoring cationization over protonation.  
Five different LFM reconstructions, obtained by MALDI-MSI, are shown in Figure 3A-D depicts palmitoleic, palmitic, oleic, and stearic acids, respectively. Figure 3E is the chemical image obtained by mapping the relative abundance of SDS anions.  
Figure 4 shows the experimental case of two overlapped LFMs. Two different people touched the MALDI target after a short time, and P25 was then used for MALDI imaging. Figure 4A is the optical image of both LFMs, obtained with an optical stereoscope equipped with a 2\(\times\) camera. Figure 4B shows the LFMs reconstruction obtained by extracting the signal due to SDS (C\({}_{12}\)H\({}_{25}\)SO\({}_{4}\), \(m/z\) 265, blue) from the MSI dataset. Figure 4C,D shows the molecular distribution of myrsitic acid (C\({}_{14}\)H\({}_{28}\)O\({}_{2}\), \(m/z\) 227, green) and arachidonic acid (C\({}_{20}\)H\({}_{40}\)O\({}_{2}\), \(m/z\) 311, red), respectively. These fatty acids were expressed differently in the two donors, so their relative abundance allowed the full deconvolution of both LFMs. Figure 4E is an example of a background chemical image obtained by extracting an unidentified recurring ion (\(m/z\) 65, grey) from the background noise. This MSI excludes the presence of artifacts or false positives in the LFM reconstruction.  
Figure 3: Endogenous compounds in LFMs. (**A–D**) Palmitoleic, palmitic, oleic, and stearic acids, respectively. (**E**) The distribution in negative ion mode of SDS.  
Figure 2: Endogenous compounds in LFMs: both saturated and unsaturated fatty acids (Table S1) were identified as [M-H]– deprotonated molecular ions, based on the high resolution spectra. The ion at \(m/z\) 265.1420 marked in red is a ubiquitous trace compound on the human skin, SDS, and was used as the instrumental reference.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
## 2 Methods
### Soil-Water Characteristic Curve Model  
An accurate description of the soil-water characteristic curve over a wide suction range is the basis for the estimation of the hydraulic conductivity. Many SWCC models have been proposed [34]. A four-parameter SWCC equation was proposed by [PERSON] and [PERSON] [35], which can effectively describe the SWCC over the wide suction range by  
Figure 1: Soil-water interaction mechanisms: (**a**) soil-water characteristic curve; (**b**) hydraulic conductivity curve (modifications of [25; 31; 33]). Parameters \(\Psi_{a}\), \(\Psi_{f}\) and \(\Psi_{r}\) are the air-entry value, transition suction and residual suction, respectively. Parameter \(k_{\omega}\) is the soil hydraulic conductivity, and parameters \(k_{c}\) and \(k_{a}\) are the capillary and adsorptive components of the hydraulic conductivity, respectively.  
introducing a correction function. [PERSON] and [PERSON]'s equation, in terms of suction (\(\Psi\)) and degree of saturation (\(S_{r}\)), is written as follows:  
\[S_{r}=\frac{C(\psi)}{\left\{\ln\left[e+\left(\frac{\psi}{\alpha}\right)^{n} \right]\right\}^{m}} \tag{1}\]  
\[C(\psi)=1-\frac{\ln\left(1+\frac{\psi}{C_{r}}\right)}{\ln\left(1+\frac{10^{6} }{C_{r}}\right)} \tag{2}\]  
where \(e\) is the natural constant; \(\alpha\) is a parameter that is related to the air-entry value; \(m\) and \(n\) are parameters that are associated with the SWCC shape; C(\(\Psi\)) is the correction function; \(C_{r}\) is not the precise residual suction value. It is more reasonable to consider \(C_{r}\) as a fitting parameter that is associated with the residual suction [36].