esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study presented a system for Blind Image Quality Assessment (BIQA) that integrates feature extraction, feature selection, and support vector machine (SVM) regression. The incorporation of four separate types of image features, as well as the usage of the Information Gain attribute approach for feature selection, contribute to the regression model's increased performance. Using well-known benchmark datasets such as TID2013, CSIQ, and LIVE, this shows that the proposed framework outperforms previous techniques in terms of accuracy and durability. Furthermore, this study performed extensive tests to investigate the effect of various types of features and feature selection methodologies on the framework's performance. The system improves the resilience and accuracy of the quality prediction model by including a wide set of image properties. Furthermore, using the Information Gain approach for feature selection minimizes the complexity of the feature space and improves the regression model's performance. As a whole, the proposed methodology outperforms the competition across many benchmark datasets, the proving  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**BIQA Models** & \multicolumn{3}{c}{**LIVE Dataset**} & \multicolumn{3}{c}{**TID2013 Dataset**} & \multicolumn{3}{c}{**CSIQ Dataset**} \\ \hline  & **LCC** & **SROCC** & **KROCC** & **RMSE** & **LCC** & **SROCC** & **KROCC** & **RMSE** & **LCC** & **SROCC** & **KROCC** & **RMSE** \\ \hline FRIQUEE [25] & 0.9411 & 0.9347 & 0.7817 & 9.2061 & 0.7688 & 0.6926 & 0.5161 & 0.7965 & 0.9069 & 0.8815 & 0.7077 & 0.1113 \\ NFERM [26] & 0.9463 & 0.9427 & 0.8063 & 8.8021 & 0.7465 & 0.6747 & 0.4976 & 0.8301 & 0.8658 & 0.8213 & 0.6394 & 0.1298 \\ BRISQUE [27] & 0.9482 & 0.9436 & 0.8005 & 8.6605 & 0.6213 & 0.5739 & 0.4149 & 0.9668 & 0.8311 & 0.7403 & 0.5590 & 0.1442 \\ BLINDS II [28] & 0.9370 & 0.9298 & 0.7754 & 9.5072 & 0.6511 & 0.5723 & 0.4137 & 0.9403 & 0.8134 & 0.7528 & 0.5652 & 0.1522 \\ CORNIA [29] & 0.9473 & 0.9452 & 0.7953 & 8.7478 & 0.7451 & 0.6542 & 0.4770 & 0.8247 & 0.8044 & 0.7325 & 0.5464 & 0.1554 \\ DIIVINE [30] & 0.9134 & 0.9120 & 0.7487 & 11.096 & 0.7294 & 0.6735 & 0.4947 & 0.8504 & 0.8077 & 0.7594 & 0.5718 & 0.1546 \\ Proposed Model & **0.9569** & **0.9456** & **0.8143** & **4.6769** & **0.9453** & **0.9289** & **0.7851** & **0.4006** & **0.9409** & **0.9263** & **0.7804** & **0.0932** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Comparison of Proposed Method with Existing BIQA Techniques.  
its efficacy and robustness in blind image quality evaluation. These findings illustrate the major benefits of the proposed methodology and help to develop the discipline. It is crucial to highlight, however, that the suggested approach has limitations, such as the dependency on certain datasets and the need for future feature extraction technique development. A future study should solve these constraints and investigate the framework's use in additional image-processing tasks.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Transparency with the User  
Reliable and susceptible communication is required when we are executing a task at remote distance. Therefore, the user needs to experience a specific level of immersion to deliver the desired activities when communicating remotely. These desired activities must be user-friendly in real time and be effectively executed in the remote environment to create the appropriate acknowledgement to the user. Without this non-cognitive transparency, the efficiency and authenticity of this communication loop cannot be guaranteed [54; 55; 56]. When a measurable round-trip time delay appears, complete transparency becomes nearly impossible. Additionally, there is a need for correct simulation of physical interaction when the user tries to mirror the virtual environment with the remote environment.  
Another challenge is the transparency of the system being affected by lossy haptic data compression and communication delay. Thus, the perceptual dead-band-based kinaesthetic data compression approach could be used to improve the quality of learning for haptic teleoperation in a TI environment [57].
## 2 Literature Review and Hypothesis Development
### Privacy Concerns About ARSGs  
The recording and sharing functionalities of ARSGs raise serious privacy concerns, which can be categorized into five main areas: surveillance and consent, data security, ethical considerations, location tracking, and data retention. First, ARSGs allow users to capture audio, images, and video of people and surroundings, creating continuous surveillance risks [4]. Their discreet design often prevents individuals from realizing they are being recorded, limiting their ability to provide informed consent [4]. Second, recorded data is vulnerable to security breaches and unauthorized access [27]. Without strong encryption, personal information may be exploited for malicious purposes, such as identity theft or unauthorized disclosure [5]. Robust security protocols are essential to safeguard sensitive data. Third, ARSGs allow users to share recorded content widely online, often without considering the privacy rights of those depicted [28]. This raises ethical issues involving consent, dignity, and potential social consequences like reputational harm or harassment [2]. The ease of content sharing amplifies these risks. Fourth, many ARSGs incorporate GPS and location tracking technologies to enhance functionality [5]. However, collecting and sharing location data without explicit consent heightens privacy and security risks, necessitating transparent data policies and user control over location information [4]. Finally, uncertainty about data ownership and storage further complicates privacy concerns. Content stored on manufacturer or third-party servers may limit users' control over their own data, increasing the risk of misuse or unauthorized access [2]. Clear data ownership guidelines and user rights are crucial for protecting individual privacy.
## 3 Results
### Bed Resistance  
The experimental data were used to determine the average lengths and heights of the dunes under the different slopes, and these were analyzed alongside corresponding the bed resistance measurements, as illustrated in Figure 10.  
On the same slope, an increase in flow intensity generally corresponds to an increase in dune height, with bed resistance exhibiting a significant positive correlation with dune height. Notably, the maximum bed resistance recorded was 0.0204 on both the 2% and 4% slopes. However, the dune heights at these slopes were 12.2 cm and 13.8 cm, respectively, indicating that although bed resistance increases with flow intensity, the dune height does not consistently follow this trend.  
The relationship between bed resistance and dune length under varying flow conditions across the three slopes is not clearly defined. From the experiments results, there is no direct and obvious correlation between bed resistance and the dune length.  
To further examine the patterns of change in bed resistance, the correlation between measured bed resistance and bedload transport intensity was analyzed and is illustrated in Figure 11. This figure reveals a significant positive correlation between bed resistance and  
Figure 10: Relationship between dune morphology parameters and bed resistance. (**a**) J = 1%, (**b**) J = 2%, and (**c**) J = 4%.  
bedload transport intensity. Within the same slope, the data from the different experiment groups are distributed across distinct regions; however, they exhibit a consistent overall trend of increasing bed resistance with heightened bedload transport intensity.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 4 Results
### Visual Inspection and Corrosion Classification on Fastening System Components
#### 4.2.2 Clips  
For the PPE, DEPP, and Zagreb 21-CTT fastening systems, corrosion was observed at the lowest part of the clips. For the PPE fastening system, the maximum reduction in the clip cross section was from 13 mm to 5.5 mm. For the DEPP fastening system, the cross section of the clips decreased from 13 mm to 6.5 mm, and for the Zagreb 21-CTT fastening system, the current resulted in a reduction of the cross section from 13 mm to 9 mm. In the case of the ZG 3/2 fastening system, no corrosion was observed on the clips. However, severe corrosion deterioration was observed on the steel rib in direct contact with the clip. Therefore, it was assumed that the current flowed from the clip into the steel rib and from the steel rib into the electrolyte (water). Because the same type of deterioration was observed in both clips of each tested sample, the condition of one clip from each sample after the test was completed is shown in Figure 13. In addition, the condition of the steel rib after the completion of the test is shown for the ZG 3/2 fastening system.  
The percentage of cross-sectional thinning of the clips at the points of most severe clip degradation was calculated using the reference diameter and the diameter measured after completion of the laboratory test. Based on the corrosion percentage, the corrosion level was classified as follows:  
* Low corrosion level: cross-sectional thinning of up to 25%
* Medium corrosion level: cross-sectional thinning of 25% to 50%
* High corrosion level: cross-sectional thinning of more than 50%.  
The reference height for all the clip types was 13 mm. The corrosion classification of the rail clips for all the test samples is presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Fastening System**} & \multicolumn{2}{c}{**Total Corroded Area**} & \multicolumn{3}{c}{**Corrosion Level [\%]**} \\ \cline{2-6}  & **[mm\({}^{2}\)]** & **\%** & **Low** & **Medium** & **High** \\ \hline ZG 3/2 & 3267.5 & 10 & 36 & 31 & 33 \\ \hline PPE & 3434 & 10 & 23 & 55 & 22 \\ \hline DEPP & 1505 & 4 & 8 & 92 & 0 \\ \hline Zagreb 21-CTT & 5369 & 16 & 65 & 35 & 0 \\ \hline \end{tabular}
\end{table}
Table 4: Levels of corrosion observed on the rails in all fastening system components.  
Figure 12: Condition of the rails after testing was completed: (**a**) ZG 3/2, (**b**) PPE, (**c**) DEPP, (**d**) Zagreb 21-CTT.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Other Type
#### 3.6.4 Compliant Structure  
[PERSON] et al. [95] introduced a stiffness combination-compatible constant force mechanism consisting of flexible components to design an isolator, assembled in the form of Figure 18, in which a folded beam provides positive stiffness while an articulated bent beam provides negative stiffness, which is more compact, lighter, and easier to fabricate compared to a QZS isolator consisting of mechanical components. Similarly, [PERSON] et al. [96] proposed a compact tripod-type QZS isolation based on a flexible constant-force mechanism with a form factor of only 240 \(\times\) 240 \(\times\) 130 cubic millimeters, which is capable of effectively suppressing vibrations above 9 Hz when carrying a load of 6 kg. [PERSON] et al. [97] proposed a near-zero-frequency flexible curved beam support, which utilizes the nonlinear negative stiffness provided by the curved beam support to neutralize the nonlinear positive stiffness of the helical beam support. The stiffness can be adjusted by translational transformation of negative stiffness structures. Experiments show that the structure achieves near-zero vibration isolation from 0.8 Hz onwards.
## 3 Architecture
### Data-Ingestion Component  
In our case, the primary data source for PQ monitoring is a relational database which concentrates the data reported by nearly 800 PQM across the CFE-DCO power distribution network. Data comprises multivariate time series (31 variables plus timestamp). Variables include three-phase active and reactive power, voltage and current values and unbalances, total harmonic distortion (THD), and many others. Time series in the database extend up to several years at a 10-min resolution. This massive volume of information makes any complex query on the relational database impractical, which motivated the design of a column-based replica database for MIRD. It is worth noticing that building a NoSQL database was far out of the initial scope of the project; therefore, a Parquet archive was designed to persist the different stages of data through the pipeline. Parquet is a columnar file format optimized for storage and query, compatible with many big-data computing frameworks [18].  
Figure 3 shows the data-ingestion component of MIRD. The time series in the relational database represent the input for this module. A Parquet file called Incomplete Datasets keeps a record of all the PQM/date combinations that did not contain enough data in the relational database. MIRD must include those datasets in the pipeline at a later date. SparkSQL scripts extract the required time series according to the pipeline execution date. This extraction process includes all the elements included in the Incomplete Datasets archive. The database aggregates information by PQM and date and stores them separately at 10-min, 1-h, and 1-day resolutions, keeping the same schema as the original database. The next component in the pipeline receives hourly data, although 10-min and daily data are kept for other processes eventually executed outside the pipeline, e.g., visual segmentation or customized queries on the time series. Preserving redundant copies of data aggregated at different time resolutions (10-min, 1-h, and 1-day) is a common NoSQL practice that provides the system with increased query performance at the cost of disk space. The data-ingestion component of MIRD is executed daily, either automatically or via a provided manual-override operation.  
Figure 3: Data-ingestion component of MIRD. The pipeline ingests all time series at day \(t\). It records unavailable time series on the Incomplete Datasets archive. At day \(t+1\), they are either recovered or kept in the archive for further recovery.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 3 Independent Vortices Search
### Characteristics of the IVS  
The IVS starts from the MVS, aiming to make it simpler and more efficient. We propose three strategies through which IVS differs from MVS.  
The first major difference is the complete elimination of Equation (25), which changes the vortex center positions, based on the vortex with the best result. By doing so, each vortex can proceed toward a minimum, whether local or global. This is intended to minimize the local solutions, as we can add more vortices to explore solutions independently. Figure 2 presents a hypothetical situation in which the vortices follow their paths towards the minima.  
Figure 2: A hypothetical optimization scenario with Independent Vortices Search. The convergence of the radius of each vortex follows its investigation to a (local or global) minimum. Points represent the candidate solutions generated by each vortex.  
The second feature of IVS is the replacement of Equation (20) for each additional vortex. The first vortex has an initial solution defined by applying Equation (20), whereas the remaining, by Equation (26), are randomly distributed in the search space:  
\[\mu_{0j}^{l}=\epsilon\cdot\left(x_{max,j}-x_{min,j}\right)+x_{min,j}, \tag{26}\]  
where \(\mu_{0j}^{l}\) is the initial solution for the \(j\)th parameter of the vortex \(l\), with \(l>0\), \(x_{min,j}\), and \(x_{max,j}\) are the respective minimum and maximum limits of the \(j\)th parameter, \(and\epsilon\) is a random real value in the interval [0, 1].  
The third modification considers maintaining the amount of candidates per vortex when \(L>1\) (where \(L\) is the total number of vortices). In the MVS proposal [33], the experiments presented five centers and divided the number of candidates between them. IVS, however, fixes the quantity of candidates for each vortex. To compensate for the computational increase, IVS decreases the number of cycles performed to keep the total Fitness Evaluations (FEs) the same.  
It is still possible to verify that the first two strategies have the possibility of a higher diversity of the candidates generated. By removing Equation (25), each vortex is allowed to go ahead with its search, not dragging them close to a vortex, with better overall results at the moment. The second strategy (Equation (26)) allows for greater diversification while generating the first results. This is because the MND, which uses the center of the vortex as the mean for candidate generation, generates more solutions near the center. With the centers of vortices more widely distributed, IVS should not concentrate candidates in only a part of the search space.
## 2 Frost-Heave Model and Thermosiphon Model
### TM Model: Porosity Rate Function  
The typical frost-heave models of frost-susceptible soils include the SP, FROST, PC-Heave, and TM-type porosity rate function models. The comparison between each model and the corresponding laboratory-model test results are illustrated in Figure 2[28]. In this comparison study, [PERSON]'s [29] laboratory experiment was compared with each frost-heave model; the laboratory experiment was conducted using a 150 mm soil sample by applying temperatures of \(-1.4\)\({}^{\circ}\)C and 0.7 \({}^{\circ}\)C to the top and bottom of the sample, respectively. Observably, among the models compared with the laboratory-derived experimental results, the PC-Heave and porosity rate function models can optimally simulate the frost heave of the soil.  
The frost-heave model employed in this study is the porosity rate function, which is a TM-type model that defines the frost expansion of the soil according to the changing porosity rate with a change in temperature. The core function of the porosity rate function is as follows:  
\[\dot{n}=\frac{\dot{n}_{m}}{gT}\bigg{(}\frac{T-T_{0}}{T_{m}}\bigg{)}^{2}\cdot e^ {1-(\frac{T-T_{0}}{T_{m}})^{2}}, \tag{3}\]  
where \(\dot{n}_{m}\) implies the maximum growth rate of the porosity, \(T_{m}\) and \(g_{T}\) depict the temperature and temperature gradient at the maximum porosity growth rate (\(\dot{n}_{m}\)), respectively, and \(\dot{n}_{m}/g_{T}\) possesses a constant value depending on the type of soil [23].  
The porosity rate (\(\dot{n}\)) tends to vary depending on the type of soil, as illustrated in Figure 3a[25]. As shown in Figure 3a, the maximum porosity growth rate (\(\dot{n}_{m}\)) of silt tends to be greater than that of clay. This maximum porosity growth rate is the key value that determines the frost-heave amount of soil in Equation (3). In Figure 3b, it can be seen that the maximum porosity growth rate affects the maximum frost-heave amount. These results come from numerical analysis in which the maximum porosity growth rate value is arbitrarily changed but the other conditions are set to remain identical.  
Figure 2: Accuracy of frost-heave models.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 5 Quantitative and Qualitative Analysis
### Architectural Comparison  
Table 2 shows a qualitative comparison among mobile network architectures implemented using current 4G EPC, using standard centralized SDN OpenFlow where the traffic must wait for the Controller response to be routed, and using the 5G proposed architecture in terms of: (1) forwarding response time; (2) traffic engineering possibilities; (3) GTP-U use requirement; (4) traffic detection flexibility; (5) specific treatment for QoS traffic; and (6) controller processing requirement.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Plan Executor Module Technical Implementation
#### 3.1.4 Error Handling  
The framework addresses specific failure scenarios through differentiated handling mechanisms. Task failures due to satellite malfunctions or communication disruptions are automatically detected through telemetry monitoring and reintegrated into subsequent scheduling cycles with elevated priority based on deadline proximity. The system prevents cascading failures through its modular architecture, where component failures remain isolated within their respective modules. Resource constraint violations trigger immediate rescheduling with updated resource models derived from actual telemetry data, ensuring realistic resource allocation in subsequent planning cycles [89]. The multi-trigger rescheduling approach enables the system to respond to various operational disruptions while maintaining scheduling efficiency and preventing task loss in dynamic operational environments.
## 3 Node Key Construction Technology
### Cantilever Truss Node  
#### 3.3.1 Node1 Structural Design  
The steel column GKZ1, the upper chord SXG1, the horizontal beam GKL1, the oblique abdominal rod FG1A-D, and the horizontal rod FG2 form Node 1, as shown in Figure 6. A size 800 mm \(\times\) 720 mm \(\times\) 40 mm is used to construct the GKZ1 as a round steel pipe column, and a concrete-filled steel tube column is used to improve the vertical bearing capacity of the frame column. A size of1000 mm \(\times\) 300 mm \(\times\) 20 mm \(\times\) 35 mm for an H-shaped solid beam is used to construct the horizontal beam GKL1. The size of the upper chord SXG1 is 1500 mm \(\times\) 1500 mm \(\times\) 80 mm \(\times\) 80 mm box-shaped steel plate, and the size of the horizontal rod FG2 is a 700 mm \(\times\) 700 mm \(\times\) 60 mm \(\times\) 60 mm box-shaped steel plate. The sizes of the oblique rods FG1A to D are all 700 mm \(\times\) 700 mm \(\times\) 40 mm \(\times\) 40 mm box steel plates. In the design of Node 1, the steel column GKZ1 is responsible for bearing vertical loads to provide vertical support. The column is also used to resist lateral shift and tilt of the structure. The drawings are shown in Table 1. The upper chord SXG1 is used to connect the steel column and the horizontal beam. The horizontal beam GKL1 is mainly used to transfer the upper load, the bending moment, and the shear force. The diagonal bars FG1A to D connect the adjacent nodes in the form of diagonal lines to obtain a triangular stable structure, which serves as the main lateral force resistance system and increases the stability of the nodes. The horizontal bar FG2 mainly provides lateral stability, shares horizontal loads, and enhances the overall stiffness of Node 1. The node is composed of eight components with complicated forces. The design fully takes into account the impact of seismic action, and it can have a double-K node structure with more uniform stress distribution [22]. The maximum stress value is located at the part where the upper chord flange is connected to the steel column. This purpose can ensures that the node has sufficient safety and can effectively absorb and disperse the seismic energy when a rare earthquake occurs. The degree of damage to the structure is reduced, and the seismic performance of the whole structure is improved.  
Figure 4: (a) Key nodes of the cantilever truss; (b) Typical truss diagram of the connecting bridge.  
Figure 5: SHELL63 unit geometry.