esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 3 Smart Cities' Technologies
### Standards/Open Standards
Additionally, it must be noted that there is overlap in standards subjects across different organizations, which enables different perspectives and ideas to be applied to the same subject and users to choose the most suitable for them. \begin{table}
\begin{tabular}{c c} \hline \hline
**Title of Standard** & **Subject** \\ \hline ISO 37106 [195]—Sustainable cities and communities—Guidance on establishing smart city operating models for sustainable communities: & \\ This standard provides guidance on establishing smart city operating models to promote sustainability in communities. & \\ ISO 37101 [196]—Sustainable development in communities—Management system for sustainable development—Requirements with guidance for use: This standard specifies requirements for a management system for sustainable development in communities, providing guidance on implementing sustainable practices. & Organizational \\ ITU-T Y.4000 [197] series—Smart sustainable cities: This series of standards provides an overview of smart sustainable cities, covering concepts, principles, and frameworks for the planning, development, and implementation of smart city initiatives. & \\ ITU-T Y.4051 [198]—Vocabulary for smart cities and communities: This standard defines key terms and concepts related to smart sustainable cities, providing a common vocabulary for stakeholders involved in smart city projects. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of standards for smart cities.
## 2 Motivating Applications and Use Cases
### Real-Time Situation Analysis  
Real-time situation analysis is also a computationally intensive real-time application. It requires access to video surveillance cameras in a target area to collect video data, vast amounts of computing and storage resources to execute computationally intensive video analysis tasks for situation analysis, and actuators to perform necessary actions. Video sensors are assumed to be either deployed in the target area or mobile robots or micro-drones equipped with video sensors are rented for data collection.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 3 Results and Analysis
### Relations between Strength and Stiffness  
Figure 7 reveals a strong correlation between physical properties, characterized by high regression values. A well-fitted power diagram effectively represents the relationship between the \(q_{u}\)-\(G_{0}\) graph in Figure 7a and Equation (5), exhibiting a high regression coefficient (R\({}^{2}\) = 0.89). Furthermore, Figure 7c illustrates the relationship between \(E\) and \(G_{0}\) for all the mixtures under examination, as described by Equation (6), which displays a power diagram with a notable coefficient of regression of R\({}^{2}\) = 0.88.  
Equations (3) and (5) play an essential role in determining the \(G_{0}\) of specimens prepared at the specified value of \(\eta/(X_{\mathrm{ir}})^{0.15}\) = 29, and they are linked to the relevant parameters of UCS and \(E\) in Equations (3) and (4). The significance of Figure 7a,c lies in the fact that, at different GP and sintered gypsum amounts, \(q_{u}\) and \(E\) can be determined using Equations (5) and (6) for various clay blends over specific curation through the non-destructive assessment of \(G_{0}\).  
\[q_{u}=8.508{G_{0}}^{0.7757} \tag{5}\]  
\[E=0.6374{G_{0}}^{0.8799} \tag{6}\]  
Figure 6: The relationships between the normalized \(G_{0}\) and the modified P/B index for all curing durations and different amounts of sintered gypsum with **(a)** 0% GP, **(b)** 4% GP, **(c)** 8% GP, **(d)** 12% GP, **(e)** all tested samples in one chart, and **(f)** all GP-improved samples with additives in one chart for both samples’ dry densities.
# The Effect of Varying Out Beverage Ratios on the Characteristics of Fermented Dairy-Oat Beverages
## 3 Results
### The pH Value During the Fermentation  
Initially, all samples exhibited a pH within the range of 6.8-7.0 (Figure 2), characteristic of unfermmented dairy and dairy-oat beverages. Over time, the pH systematically decreased due to the production of lactic acid by lactic acid bacteria. In the initial phase (0-1.5 h), differences between samples were minimal, but from the third hour of fermentation, these differences became pronounced. The sample without the addition of oat beverage (0%) demonstrated the most rapid pH decline, reaching the lowest value (-4.2) after 6 h of fermentation. Increasing the proportion of oat beverage resulted in a slower acidification rate; the higher the oat content, the higher the pH at any given point during fermentation. Oat beverage contains less lactose and more starch, which may influence the slower fermentation rate. Additionally, the presence of fiber, plant proteins, and mineral compounds in oats can enhance the buffering capacity of the beverage, leading to smaller pH fluctuations and slower acidification. It is possible that the high oat content may alter the availability of nutrients for bacteria, affecting their growth and lactic acid production. Samples containing 75% and 100% oat beverage achieved the highest final pH (-4.5-4.6), suggesting that fermentation proceeded more slowly or less intensely in these samples.  
Figure 1: Lactose content (means and SD) during the fermentation.
# Assessment of Pesticide Contamination of Groundwater from Titu-Sarata Plain, Romania
## 2 Materials and Methods
### GC-ECD Analysis of OCPs  
Analyses were performed on a Varian 450 GC system coupled with an electron capture detector (ECD). The capillary column used to separate the compounds was an Agilent CP-Sil-5 CB 100% dimethylpolysiloxane (30 m length, 0.25 mm internal diameter, and 0.25 \(\upmu\)m film thickness), with a stationary phase consisting of 5% diphenyl and 95% dimethyl siloxane. The oven temperature program of the GC-ECD was as follows: initial temperature of 130 \({}^{\circ}\)C (1 min isotherm); increase at 5 \({}^{\circ}\)C min\({}^{-1}\) to 170 \({}^{\circ}\)C (0 min); increase at 3 \({}^{\circ}\)C min\({}^{-1}\) to 180 \({}^{\circ}\)C (0 min); increases at 5 \({}^{\circ}\)C min\({}^{-1}\) to 210 \({}^{\circ}\)C (2 min); and, finally, increase at 0.5 \({}^{\circ}\)C min\({}^{-1}\) to 218 \({}^{\circ}\)C (5 min isotherm).  
Extraction method  
A volume of 500 mL of water sample and 100 mL of solvent petroleum ether purchased from Sigma Aldrich (Darmstadt, Germany) were put in a 1000 mL Erlenmayer vessel and stirred for 15 min on a magnetic stirrer at 1100 rpm, after which the solution was transferred to a separation funnel and left to rest for the separation of the phases. The obtained extract was collected in a conical flask. The aqueous layer was extracted by adding 50 mL of petroleum ether using a similar procedure. The obtained extracts were passed through a glass column filled with anhydrous sodium sulfate purchased from Sigma Aldrich (Darmstadt, Germany) and subsequently moistened with petroleum ether, after which the obtained extract was concentrated to 1 mL in a TurboVap500 concentrator and analyzed by GC-ECD.  
Reference-grade standards were purchased from LGC Dr. Ehrenstorfer (Gibraltar, UK) with a purity range 98.2-99.9%.  
The method was validated according to correlation coefficient (r\({}^{2}\)), recovery, and sensitivity.  
The correlation coefficient (r\({}^{2}\)) was assessed using a five-point calibration curve over the range of 2.5-25 \(\upmu\)g/L, running in duplicate with each set of samples.  
The sensitivity of the method was determined by the limit of quantification (LOQ) based on the lowest measurable signal corresponding to a signal-noise ratio of 10:1 and by the limit of detection (LOD) based on the lowest detectable signal corresponding to a signal-noise ratio of 3:1.  
The validation parameters and their acceptance criteria are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**OCPs**} & **Linearity** & **Correlation** & **Recovery** & **LOD** & **LOQ** \\  & **(\(\upmu\)g/L)** & **(r\({}^{2}\))** & **(\%)** & **(\(\upmu\)g/L)** & **(\(\upmu\)g/L)** \\ \hline Aldrin & 2.5–25 & 0.9995 & 86.8 & 0.0116 & 0.0386 \\ alfa-Endosulfan & 2.5–25 & 0.9993 & 103.5 & 0.0101 & 0.0336 \\ pp’DDE & 2.5–25 & 0.9967 & 97.5 & 0.0104 & 0.0346 \\ Dieldrin & 2.5–25 & 0.9968 & 81.5 & 0.0110 & 0.0366 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tests and acceptance criteria in determining the OCP concentrations.
## 6 Conclusions and Future Research
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & **Energy** & & **Costs** & & **Emissions** & & **Thermal** \\  & & & & & & & & & **Discomfort** \\ \hline \multirow{4}{*}{**Fitting**} & \multirow{2}{*}{**Unit**} & **Unit** & **kWh** & **\%** & **EUR** & **\%** & **CO\({}_{\text{2 eq}}\)** & **\%** & **h** & **\%** \\ \cline{3-10}  & & **Reference** & 155,220 & - & 39,483 & - & 50,548 & - & 26 & - \\  & **Cost PRBC** & 152,590 & \(-1.7\%\) & 38,057 & \(-3.6\%\) & 48,897 & \(-3.3\%\) & 32 & 23.1\% \\  & **CO\({}_{\text{2}}\) PRBC** & 154,320 & \(-0.6\%\) & 38,725 & \(-1.9\%\) & 48,795 & \(-3.5\%\) & 30 & 15.4\% \\ \cline{2-10}  & **Reference** & 62,056 & - & 15,639 & - & 20,622 & - & 0 & - \\  & **Cost PRBC** & 65,119 & 4.9\% & 15,747 & 0.7\% & 20,839 & \(1.1\%\) & 0 & 0\% \\  & **CO\({}_{\text{2}}\) PRBC** & 65,299 & 5.2\% & 16,089 & 2.9\% & 20,599 & \(-0.1\%\) & 0 & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Annual performance results. Author Contributions:Conceptualization, G.C. ; methodology, E.G. and G.C. ; validation, G.C. ; data curation, G.C. ; writing--original draft preparation, G.C. ; writing--review and editing, E.G. and G.C. ; supervision, A.M.P. All authors have read and agreed to the published version of the manuscript. Funding:The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \"First Call for H.F.R.I. Research Project to support Faculty members and Researchers and the procurement of high-cost research equipment grant\" (Project Number: 4104). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Nomenclature RES & Renewable Energy Sources HVAC & Heating, Unentilation, and Air-Conditioning System DR & Demand Response CHP & Combined Heat and Power AWHP Air-to-Water Heat Pump TES Thermal Energy Storage TRNSYS & Transient System Simulation Tool MPC Model-Predictive Control PRBC Predictive Rule-Based Control KPI Key Performance Indicator COP Coefficient of Performance PMV Predicted Mean Vote
## 2 Literature Review
### ANFIS and Multilayer FNN (MFNN) Models and Optimization Algorithms
Percent bias: \[PBIAS=\left[\frac{\sum\limits_{i=1}^{n}\left(Y_{i}^{obs}-Y_{i}^{sim}\right)*100}{ \sum\limits_{i=1}^{n}\left(Y_{i}^{obs}\right)}\right].\] (11)
2. Standard deviation of RMSE observations \[RSR=\frac{RMSE}{STDEV_{obs}}=\frac{\left[\sqrt{\sum\limits_{i=1}^{n}\left(Y_{ i}^{obs}-Y_{i}^{sim}\right)^{2}}\right]}{\left[\sqrt{\sum\limits_{i=1}^{n}\left(Y_{ i}^{obs}-Y_{i}^{mean}\right)^{2}}\right]}.\] (12)
3. Nash-Sutcliff efficiency \[NSE=1-\left[\frac{\sum\limits_{i=1}^{n}\left(Y_{i}^{obs}-Y_{i}^{sim}\right)^ {2}}{\sum\limits_{i=1}^{n}\left(Y_{i}^{obs}-Y^{mean}\right)^{2}}\right].\] (13)
4. RMSE (root-mean-square error) \[v_{l}(t)=[x_{l}(t)-x_{*}]\times f_{l},\]0 (14)  
\(Y_{i}^{obs}\) is observed data, \(Y_{i}^{sim}\) is simulated data, \(N\) is the number of data, and \(Y^{mean}\) is the average value of the data.
## 4 Experimental Results  
Before conducting experimental measurements, the verification and validation activities were deliberately separated to fulfill their respective objectives. Specifically, verification was performed during the design phase by using CAD-based simulation to check mechanical feasibility, component motion limits, and sensor alignment within Autodesk Inventor. This ensured that the system design satisfied initial measurement conditions.  
Subsequently, validation was carried out through experimental precision tests using both a standard flat platform and real marine propeller blades. Repeated measurements under different operating modes were statistically analyzed to confirm the system's actual performance, repeatability, and measurement precision.  
This two-stage approach aligns with standard engineering definitions: verification refers to ensuring that \"the system is built correctly\", while validation confirms that \"the correct system is built\" and functions as intended in real environments.  
Experiments were performed in which the measurement platform architecture illustrated in Figure 1 was employed to examine the four propeller blade measurement points shown in Figure 7. To explore the effect of measurement mode on measurement accuracy, this study employed the continuous measurement mode and single-point measurement mode. The measurement processes under each mode were recorded and analyzed using the corresponding video documentation (continuous measurement mode video [18] and single-point measurement mode video [19]).  
The results obtained under the two measurement modes were compared in terms of accuracy, repeatability, and operational efficiency. The findings served as a critical basis for subsequent system optimization and measurement mode selection.
# Human-Centered Dynamic Service Scheduling Approach in Multi-Agent Environments
## 2 Related Work
However, the above literature requires precise knowledge of the progress of a task at a specific point in time. This type of full observability can only be achieved by continuously monitoring the agents' actions or the state of the workspace with a wide range of sensor settings. Moreover, such approaches primarily rely on manually specified task descriptions for the deployment of static jobs and do not consider incorporating new incoming tasks into the system and dynamic changes in the execution order of tasks. Alternatively, Ref. [11] uses the largest total amount of processing time first strategy and schedules with a tabu-search algorithm to minimize the makespan of the jobs with tree-formed precedence constraints. However, the study did not consider the agent heterogeneity. The service robot must be able to accept new requests from the user and create a newly ordered task model in real time. Therefore, task scheduling for service robots must embody dynamic task sequencing (modeling) and dynamic task allocation. Ref. [23] suggests a heuristic algorithm based on a greedy for dynamic scheduling for tasks. It reduced the total workload but could not adjust the specific timing and task sequency. Recently, studies have been conducted on service robots used in smart homes. A knowledge-based framework for object search [24] and ontology-based smart home architecture [25; 26] have been proposed. Probabilistic inference has been applied to hybrid task planning to address task failures [27]. These studies combined intelligent space and semantic task models to dynamically create and sequence a new task but focused on context-aware services and did not address the scheduling aspect. In addition, all the executions were dedicated to a single robotic agent. In summary, the limitations of the previous robot task scheduling approach are as follows. (1) Existing methods determine an optimal schedule based on maximum productivity, but service robots must prioritize user demands and specifications over efficiency. (2) Only environmental variabilities such as delays and robotic failures are considered, and the user activity dynamics, such as user requests, are not considered. (3) They require precise knowledge of the overall progress via constant monitoring. (4) The previous robot task scheduling method could not accept new tasks or user requirements and build newly sequenced task models in real time. Therefore, we believe that a new approach to task scheduling for multiple service robots is essential to ensure the accessibility of service robots in daily environments. We propose a systematic scheduling framework that can dynamically adjust and manage a schedule according to user requests for the effective provision of services.
## 3 Data Processing Methods and Its Result  
To achieve the aims of this study, several accessible datasets, including marine gravity data, seismic data, bathymetric and topographic data, magnetic data, and other ancillary data, were sensibly revised and reviewed. Such an assessment will improve our understanding of the situation in the area under investigation. Several profiles of marine potential field data in this study were obtained from a databank, such as the GEODAS and BGI databases [15]. These data were integrated with data from the Egyptian General Petroleum Company [30], which were issued as a combination of Bouguer gravity maps of Egypt at a 1:500,000 scale obtained by collecting all accessible gravity data (Figure 2a). An elementary free-air gravity map was created to provide explanation of the investigated area (Figure 2b). To achieve morphotectonic relief, some profiles stayed as developed by the BGI. These data were collected from the available ETOPO database [16] to create the major geographies of the bathymetric and topographic map shown in Figure 2a. In addition, available magnetic data were acquired from the GEODAS database. [17]. A total magnetic intensity map (Figure 2c) was created to confirm a qualitative interpretation of the investigated area. The locations of the datasets are shown in different colours in Figure 1a. Moreover, to improve and evaluate the 2D model of marine gravity data, the results from deep seismic sounding experiments were acquired along with three seismic profiles (P1-P1\({}^{\prime\prime}\), P2-P2\({}^{\prime\prime}\), and P3-P3\({}^{\prime\prime}\)) from the study area. The Geological Survey of Cyprus approved these investigations to determine the structure of the deep sedimentation throughout the study area. The three seismic regional construction outcome profiles were recycled to provide layer geometry and thickness. They allowed for the preliminary evaluations of the strata densities laterally; the three seismic profiles intersect the foremost geological feature of the area under investigation and were used to assess a tectonic model in support of this district. The locations of the three seismic profiles are shown by thick red lines (Figure 1b).
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 3 Theoretical Background
### Clustering
The clusters labelled 1-3 depict traffic fluctuations of different amplitudes--from the smallest ones in cluster 1 to the medium ones in cluster 3. Clusters 4 and 5 describe major increases or drops in the throughput with a non-zero mean change and high variance. The OPTICS algorithm identifies clusters as dense areas separated by less dense ones. Density-based algorithms can discover clusters with arbitrary shapes--an advantage over partition-based algorithms such as KMeans, which can only identify sphere-shaped clusters. OPTICS is based on the same concept as DBSCAN [42], but it overcomes one of DBSCAN's weaknesses--the inability to identify meaningful data clusters with varying densities. This is particularly relevant in network trace clustering because, as illustrated in Figure 5, the density of data points varies among distinct clusters. To handle this problem, OPTICS uses a reachability distance in addition to the core distance used in DBSCAN. These distances depend on two input parameters: \(e\), the distance around an object that constitutes its \(e\)-neighbourhood, and _minPts_, a minimum number of points defining the \(e\)-neighbourhood. Deterministic clustering methods, such as KMeans and OPTICS, are standard tools for finding groups in data. However, they lack the quantification of uncertainty in the estimated clusters. AutoClass is a clustering algorithm based on the probabilistic model that assigns instances to classes probabilistically. AutoClass is based on a mixture of models and assumes that all the data points are generated from a mixture of a finite number of probability distributions with unknown parameters. The mixture of models can be compared to the generalization of KMeans clustering to incorporate information about the covariance of the data. To build the probabilistic model, the AutoClass algorithm automatically determines the distribution parameters and the number of clusters. Figure 5: Exemplary two-dimensional clusters for mean and variance features extracted from a fragment of the differenced M2 trace using KMeans algorithm. The clusters labelled 1–3 group traffic fluctuations of different amplitudes. Clusters 4 and 5 describe more significant changes in the network throughput.
## 5 Discussion
### RQ1: Common Types of Attacks on Blockchain Technology and Their Impact  
Blockchain technology faces several common attack types that compromise security and integrity:  
* Smart contract vulnerabilities: Reentrancy attacks, integer overflow, and weak access control mechanisms lead to financial fraud and unauthorized transactions.
* Denial-of-service (DoS) attacks: DDoS blockchain state storage attacks cause network congestion and transaction delays, impacting system reliability.
* Consensus attacks: 51% attacks, selfish mining, and long-range attacks exploit mining power to reverse transactions and double spend assets.
* Oracle manipulation: Flash loan exploits and price manipulation enable attackers to control asset prices and execute fraudulent trades in DeFi applications.
* Cryptographic attacks: Quantum cryptographic threats, identity theft, and weak key management lead to unauthorized access and data breaches.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference(s)** & **Attack Category** & **Impact Result** \\ \hline
[241] & Scalability Security & High fees and slow transactions due to scalability issues. \\ \hline
[163, 242, 243, 244, 245, 246, 247, 248, 249, 250, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 282, 284, 286, 287, 289, 280, 283, 285, 286, 287, 288, 289, 291, 280, 285, 287, 288, 289, 292] & Software Testing & Security vulnerabilities overlooked, undetected software vulnerabilities, and unreliable security testing results. \\ \hline
[293, 294] & Supply Chain Attack & Malfunctioning electronic components and compromised software due to supply chain attacks. \\ \hline
[295] & Threat Intelligence & Slow response to cyber threats due to inadequate threat intelligence. \\ \hline
[296, 297, 298, 299] & Transaction Manipulation & Financial loss, disruption of services, unauthorized transactions, and financial manipulation. \\ \hline
[300] & Trust Exploitation & False service discovery and compromised interactions due to trust exploitation. \\ \hline
[301] & Trusted Execution Environment & Vulnerabilities in trusted execution environments. \\ \hline
[109, 123, 278, 302, 279, 303, 304, 305, 306, 307, 308, 309, 300, 300, 301, 302] & Various (studies that discuss attacks from multiple categories) & Online fraud, identity theft, unauthorized access to ECUs, denial of service (DoS), financial losses, privacy violations, data breaches, network collapse, theft of NFTs, and unauthorized code execution. \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._* Privacy violations: Data leaks in EHRs and IoT privacy breaches due to weak encryption mechanisms expose user-sensitive data.
* IoT security weaknesses: IoT device hijacking, unauthorized access, and industrial IoT intrusions result in compromised network security.  
These attacks cause financial losses, data breaches, network disruption, and erosion of trust in blockchain systems.