esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### On-Chain Data Storage Methods
#### 2.1.2 Smart Contract Based Data Storage  
Smart contracts are self-executing code segments that operate on the blockchain and are triggered when predefined conditions are met. This mechanism enables secure and decentralized data storage and management. Smart contract-based storage methods are particularly suitable for managing small datasets on-chain.  
* _Smart Contract Data Storage_: Stores small-sized data directly on the blockchain and is commonly used for NFT metadata, ERC-20 token information, and similar applications. While it provides security and decentralization, it incurs high transaction costs and is not suitable for large-scale data storage [60; 61].
* _Ethereum State Storage_: Essential for managing dynamic data in DeFi applications. It enables fast access but can lead to blockchain bloat over time, affecting long-term scalability [62; 63].  
Apart from smart contracts and database structures, blockchain technology also integrates persistent data storage and transaction-related metadata management. These techniques enhance blockchain functionality and accommodate various application scenarios.
## 2 Research Aims, Scope, and Novelty  
Many two-layer concrete beams were theoretically and experimentally analyzed. The span of the TLBs varied from 70 cm to 8 m. Experiments have demonstrated that concrete TLBs are very effective bending elements. The main difference in the present research is that it is focused on the experimental investigation of TLBs' linear creep behavior during the first 90 days of long-term static loading.  
The main aims of the present study are the following:  
* Experimental investigation of the linear creep effect in TLBs;
* Applicability of the previously proposed algorithm [15] to obtained experimental results;
* Analyzing the linear creep effect on the interaction between the TLB layers;
* Studying the influence of cracks on TLB specimens under long-term loading (90 days).  
To investigate the above-mentioned aspects, six TLB specimens were designed, produced, and tested under constant long-term loads. To obtain experimental data that are not affected by the contribution of compressed and shear reinforcements, the TLB specimens were designed with tensile reinforcement steel bars only. The tests were performed under controlled temperature and humidity conditions in a special climate room. Deflections in the midspan of each specimen were measured under long-term loading at four-point bending.  
The novelty of the present research is that for the first time, the linear creep effect in TLBs is experimentally investigated. The obtained results allow proper adaptation of known design approaches for single-layer reinforced concrete beams in order to use them in case of two-layer bending elements.
## 6 Conclusions and Perspectives  
This paper mainly investigated the application of the combined PSVM model based on SVM and PSO in the prediction of ultimate axial capacity \(N_{u}\) of square CFST short columns. A large number of axial compression tests were utilized to obtain the experimental data needed by the prediction model. The reliability of the experimental dataset was ensured by input selection and data checking. PSO algorithm was used to optimize the parameters of SVM to achieve the optimal PSVM model. The prediction performance of the trained model was verified by an independent test set, and compared with that of the other three benchmark models and two design expressions. The prediction effect of each method was quantified by five evaluation criteria. Simulation results show that the proposed PSVM model has obvious advantages in \(N_{u}\) value prediction, as described below:  
* The SVM optimized by PSO can accurately capture the complex nonlinear relationship between the seven input variables and the ultimate axial capacity. Thus, both model training and test results are great, and the accuracy rate is more than 90%.
* The PSO algorithm can rapidly converge after less than 100 iterations, and the MSE value corresponding to the optimal parameter combination is small, indicating that PSO is suitable for optimizing the SVM parameters.
* Compared with the other three MLAs and two expressions, the evaluation indexes of PSVM are superior. The excellent prediction performance of PSVM can reflect the enormous potential of combining mechanical property experiments with artificial intelligence algorithms.
* The proposed prediction error correction method is helpful to improve the prediction performance of PSVM. Additionally, the sensitivity analysis results are expected to simplify the design of square CFST short columns.  
As such, it is believed that the proposed PSVM model can be suitably applied in engineering. Not only can the model predict the ultimate axial capacity, but can be used to estimate other mechanical properties of square CFST short columns, such as ultimate pure bending moment and fire resistance.  
**Author Contributions:** Supervision and funding acquisition, M.L.; Conceptualization and investigation, Q.R.; Formal analysis and model validation, M.Z.; Data acquisition and preprocessing, Y.S. and W.S.  
**Funding:** This research was jointly funded by the National Natural Science Foundation of China (Grant No. 51879185), the National Natural Science Foundation for Excellent Young Scientists of China (Grant No. 51622904), and the Tianjin Science Foundation for Distinguished Young Scientists of China (Grant No. 17 JCJQJC44000).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Total Residual Oxidant in Direct OCI\({}^{-}\)-Injection and Electrolysis
#### 3.2.1 Direct OCl\({}^{-}\)-Injection  
The formation of ClO\({}_{3}\)\({}^{-}\) and ClO\({}_{4}\)\({}^{-}\) when OCl\({}^{-}\) was directly added to seawater was evaluated by varying OCl\({}^{-}\) concentration (0.5, 0.7, 10, 50, and 150 mg/L). Figure 5 shows the pattern of ClO\({}_{3}\)\({}^{-}\) formation during the chlorination of WS and ES as a function of contact time. ClO\({}_{3}\)\({}^{-}\) was not detected when the OCl\({}^{-}\) amount added to the seawater samples was low (i.e., 0.5 mg/L). On the other hand, 816 \(\upmu\)g/L and 808 \(\upmu\)g/L of ClO\({}_{3}\)\({}^{-}\) could be observed for WS and ES, respectively when the amount of OCl\({}^{-}\) added to the seawater was 150 mg/L. This result could be explained by Equations (8)-(17). ClO\({}_{3}\)\({}^{-}\) can be formed via either direct or indirect reactions: via either (1) the reaction between OCl\({}^{-}\)/HOCI and H\({}_{2}\)O to form ClO\({}_{3}\)\({}^{-}\) or (2) the reaction of added OCl\({}^{-}\)/HOCI with Cl\({}^{-}\) in seawater to form OCl\({}^{-}\), ClO\({}_{2}\)\({}^{-}\), and then to form ClO\({}_{3}\)\({}^{-}\). Regardless of the amount of OCl\({}^{-}\) added to the water samples, the ClO\({}_{3}\)\({}^{-}\) formation occurred within the reaction time of 1 min. Then, the ClO\({}_{3}\)\({}^{-}\) concentration slightly decreased by 8-17% due to Equations (18)-(20).  
In the case where ClO\({}_{3}\)\({}^{-}\) is present at a high level, ClO\({}_{4}\)\({}^{-}\) can be formed through the reaction of ClO\({}_{3}\)\({}^{-}\) with H\({}_{2}\)O (Equation (18)). Figure 6 shows the time profiles of ClO\({}_{4}\)\({}^{-}\) formation during the chlorination of WS and ES as a function of contact time. ClO\({}_{4}\)\({}^{-}\) was not detected from the samples when the OCl\({}^{-}\) concentration was \(\leq\) 10 mg/L. No significant difference was observed in the formation of ClO\({}_{4}\)\({}^{-}\) between WS and ES. When the seawater samples were added with 50 and 150 mg/L OCl\({}^{-}\), however, ClO\({}_{4}\)\({}^{-}\) of 0.36 and 3.44 \(\upmu\)g/L, respectively could be observed. These results indicated that at a low concentration OCl\({}^{-}\) might not generate ClO\({}_{4}\)\({}^{-}\) in seawater.  
Figure 4: Proposed pathways of ClO\({}_{3}\)\({}^{-}\) and ClO\({}_{4}\)\({}^{-}\) formation during seawater chlorination.  
Figure 5: Effect of OCl\({}^{-}\) dose on ClO\({}_{3}\)\({}^{-}\) formation during chlorination of (**a**) WS and (**b**) ES (volume: 0.4 L; temperature: 20 \(\pm\) 1 \({}^{\circ}\)C; salinity: 28.9 PSU for WS and 28.7 PSU for ES; pH: 8.3 for WS and 8.0 for ES).
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 4 Action Space
### Q-Value Estimation  
Estimating the Q-value is of fundamental importance to RL approaches. Traditional methods estimate the Q-value of a state [150; 207] or a state-action pair [45; 165; 294; 296; 298]. By comparing the Q-values of reachable states, the agent can select the action leading to the highest expected return.  
Function approximation methods can use non-linear models, such as deep neural networks, to estimate the Q-values of each state [138; 153; 160; 297] or state-action pair [84; 91; 149; 164], even if the number of states is infinite. For example, to predict state-action pair values, ref. [290] uses a long-short term memory (LSTM) model and ref. [160] uses a deep neural network with an attention mechanism.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Group** & **Features** & **References** \\ \hline \multirow{3}{*}{Heuristic selection} & Dispatching & [16; 18; 22; 27; 30; 34; 37; 41; 42; 53; 62; 66; 93; 120; 169; 171; 209; 242; 242; 244, 264; 289; 306; 307; 308] \\ \cline{2-3}  & Local rules & [29; 33; 181; 183; 197; 203; 208; 260; 270] \\ \hline \multirow{3}{*}{Multiple selections} & Single-action-encoded & [59; 64; 116; 117; 189; 205; 233; 275; 309] \\ \cline{2-3}  & Multiple decisions & [25; 114; 123; 127; 147; 168; 193; 196; 201; 210; 228; 243; 245; 289; 307] \\ \cline{2-3}  & Resources & [23; 26; 28; 47; 59; 64; 82; 86; 95; 99; 100; 103; 123; 135; 147; 162; 182, 191; 226, 239; 307] \\ \hline \multirow{3}{*}{Variable-sized output} & & [19; 47; 65; 184; 185; 243; 289; 292] \\ \cline{2-3}  & Number output & [140; 202; 207; 247; 310] \\ \cline{2-3}  & Continuous value & [23; 58; 77; 92; 95; 98; 101; 105; 106; 164; 199; 214; 224; 225; 227; 228; 236] \\ \cline{2-3}  & Discretized continuous values & [88; 127; 141; 193; 311; 312] \\ \cline{2-3}  & Hybrid strategy parameters & [144; 159; 218; 235; 252; 259; 261; 263; 286; 298; 313] \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Value-based approaches, which compute Q-values for states or state-action pairs, can apply a softmax layer to the value outputs and use the result as an expected return probability distribution for each action [30; 78]. This works even for tabular approaches [73]. Instead of searching for the maximum Q-value, the agent can directly select a candidate option form a list. Policy gradient models' policy outputs the action selection probability for the said list [20; 79; 178; 206; 208], also using a softmax layer.
## 3 Results and Discussion  
The results of performed experiments that involved determining physical, mechanical, and mechanical fracture parameters are displayed in Figures 7 and 8. The mean values accompanied by the sample standard deviations (error bars) are shown for all investigated characteristics. Although the test specimens were produced in three different batches, the variability of results is low, which proves the possibility to produce the composite with reproducible properties. As a result of the non-traditional curing conditions of ambient temperature and high RH \(\geq\) 95% for a long period, the results of strength, fracture, and fatigue characteristics are hard to compare with other AAS or ordinary Portland cement (OPC) materials. For this reason, the results are partially compared in terms of materials of similar composition cured under different conditions.  
Figure 7: The mean values of shrinkage (**a**), mass loss (**b**) at RH of (55 \(\pm\) 10)%, dynamic modulus of elasticity and rigidity (**c**), and dynamic Poisson’s ratio (**d**) at RH \(\geq\) 95% of the investigated alkali-activated slag (AAS) composite during the specimen’s aging.
## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Aspect** & **Urban IoT Environments** & **Rural IoT Environments** \\ \hline Connectivity & High bandwidth, low latency & Intermittent, low bandwidth, high latency \\ Power supply & Stable and continuous & Often limited, solar or battery-powered \\ Infrastructure & Dense, centralized & Sparse, decentralized \\ Physical access & Regular maintenance possible & Remote, infrequent access \\ Cybersecurity support & Managed networks, centralized monitoring & Limited supervision, distributed and offline nodes \\ Environmental factors & Controlled (indoor, urban) & Unpredictable (terrain, weather, obstacles) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Urban vs. rural IoT environments: key differences. supports the generation of datasets composed of both raw network traffic and preprocessed logs suitable for further analysis. The remainder of this work is structured as follows: Section 2 reviews relevant work on rural IoT testbeds, datasets, and cybersecurity. Sections 3 and 4 describe the architecture of the Smart Rural IoT Lab, the experimental scenarios, and the methodology used for traffic capture and preprocessing. Section 5 presents the results of the experimental evaluation of the implemented scenarios and dataset generation. Section 6 analyzes the implications of these results. Finally, Section 7 concludes the paper by summarizing the main contributions of the laboratory and outlining future research directions.
# An Internet of Things Model for Improving Process Management on University Campus
## 2 Materials and Methods  
For the development of the IoT model, it is important to know the environment in which it is intended to be applied, as well as the infrastructure of the campus in which the university carries out its activities [15]. A university campus has features that can be exploited or reused by the IoT architecture. Therefore, to guarantee its operation, it is necessary to guarantee navigation. Within the campus, this can become a problem since the services are on an intranet, and for them to leave, they must go through the network security layer [16]. In this layer, both inbound and outbound access to the network is filtered and monitored. This process causes navigation to be restricted, with port blockages in place to maintain network security [13]. Such restrictions and blockages can directly affect the IoT devices that are being applied to the campus by subtracting them from functionalities or rendering them useless. To avoid any problem in the design of the model, it is important to identify the IoT requirements and the network architecture available to the university under consideration [17].  
The IoT requirements established for the proposed model are as follows:  
* Security: IoT introduces new security risks and challenges at the level of devices, platforms, operating systems, communications, and connected systems [18]. IoT security is even more complicated in dealing with basic \"things\" that do not allow sophisticated security mechanisms.
* IoT analytics: IoT business models exploit the information collected from things in many ways in order to understand customer behavior, improve products, and provide new services [19]. IoT requires new analytical approaches involving new tools and algorithms [20].
* Management of IoT devices: things require management and monitoring of the device, the firmware, software updates, reporting, and security management. Therefore, the tools must be scalable for thousands and millions of devices [21].
* Low-power and short-range networks: selecting a wireless network for an IoT device requires balancing various contradictory requirements such as battery life, bandwidth, range, device costs, operational costs, etc. Such low-power and short-range networks dominate IoT wireless connectivity [22].
* IoT processors: processors and architectures for IoT devices define their capabilities. The different characteristics sought, hardware cost, software cost, updates, and encryption must be considered.
## 3 Parametric Study  
In the parametric analysis, a comparative study of S-parameter performance has been carried out by varying the dimensions of triangular slot width (\(S\)) and SSRR slot width (\(t\)) of the radiating patch as illustrated in Figure (a)a,b. From Figure (a)a, it can be understood that one of the rejection band frequencies shown in Table 2 can be shifted by varying the dimension '\(S\)' on the main radiating patch. At \(S\) = 0.65 mm, 0.9 mm, and 1.15 mm, the corresponding higher rejection-band frequencies are tuned between 6.72-9.1 GHz, 6.77-8.7 GHz, and 6.73-8.5 GHz, respectively, although the lower rejection-band between 4.2-5.45 GHz remains the same in all cases. The higher operational band is also varied accordingly. The second analysis has been performed by varying the dimensions of parameter '\(t\)' and it can be seen in Figure (b)b, that the middle operational band was shifted. At \(t\) = 0.4 mm, 0.5 mm, and 0.6 mm, the middle operational band is tuned between 5.4-6.5 GHz, 5.45-6.77 GHz, and 5.45-6.98 GHz respectively.  
Figure 8: Simulated 3D RPs at (a) 3.5 GHz (b) 6.1 GHz (c) 9.25 GHz.  
Figure 9: Simulated S-Parameter performance by varying (a) Triangular slot width (\(S\)) (b) SSRR slot width (\(t\)).  
From Figure 9a,b, it can be seen that the higher operational band can be controlled by varying the parameter dimension '\(S\)', and the middle operational band can be controlled by changing the dimension of the parameter '\(t\)'. Table 3 shows the summarized value of the operational and rejection band by varying '\(S\)' and '\(t\)'.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 5 Numerical Results  
In this section, a wireless communication system with [PERSON], [PERSON] and [PERSON] are considered. Under the system model, some typical results will be presented. To evaluate the secure performance of the system, the average strengths of Alice-Bob link and Alice-Eve link are used as the performance metrics. The two performance indicators are given as  
\[\begin{cases}E(\gamma_{\text{B}})=&\frac{A_{\text{B}}P}{2\alpha_{\text{B}}^{ 2}}\Gamma(\alpha_{\text{B}}+2)\sum\limits_{j=1}^{\beta_{\text{B}}}a_{\text{B}} \bigg{(}\frac{\alpha_{\text{B}}\beta_{\text{B}}}{\alpha_{\text{E}}\beta_{\text{ B}}\beta_{\text{B}}\alpha_{\text{B}}}\bigg{)}^{-\frac{\alpha_{\text{B}}+j+4}{2}} \Gamma(j+2)\\ E(\gamma_{\text{E}})=&\frac{A_{\text{E}}P}{2\alpha_{\text{E}}^{2}}\Gamma( \alpha_{\text{E}}+2)\sum\limits_{k=1}^{\beta_{\text{E}}}a_{\text{E},k}\bigg{(} \frac{\alpha_{\text{E}}\beta_{\text{E}}}{\alpha_{\text{E}}\beta_{\text{E}}\alpha _{\text{E}}}\bigg{)}^{-\frac{\alpha_{\text{E}}+j+4}{2}}\Gamma(k+2)\end{cases} \tag{47}\]  
In this section, the exact simulation results, the simulation results of the lower bound, and the theoretical results of the lower bound of different performance indicators will be shown. To facilitate the accuracy analysis, the relative error is employed as a useful measure. Here, three relative errors are defined as follows  
\[\left\{\begin{array}{l}e_{1}=\frac{|S-T_{\text{Low}}|}{S}\times 100\%\\ e_{2}=\frac{|S_{\text{Low}}-T_{\text{Low}}|}{S_{\text{Low}}}\times 100\%\\ e_{3}=\frac{|S-T|}{S}\times 100\%\end{array}\right. \tag{48}\]where \(S\) and \(T\) denote the exact simulation value and theoretical value, \(S_{\rm Low}\) and \(T_{\rm Low}\) denote the simulation value and theoretical value of the lower bound.
## 2 Materials and Methods
### Data Preparation  
A quality and consistency check of the AIIR dataset was carried out in the first phase of the data preparation to filter out typos and false records. Inconsistent records (outliers), such as soil samples with high carbonate content and low pH, were excluded from the dataset. We then selected those records from the AIIR dataset that corresponded to agricultural parcels of intensive (i.e., high fertilizer use) cultivation. The selection was made based on the amount of fertilizers applied, and records containing at least 125 kg \(\times\) ha\({}^{-1}\) of nitrogen and 30 kg \(\times\) ha\({}^{-1}\) of active phosphorus input were kept. In this way, the analysis of the current assessment focused on data from intensively cultivated fields.  
Winter wheat (_Triticum aestivum_), maize (_Zea mays_) and sunflowers (_Helianthus annuus_), which have been sown on up to 80% of the croplands in Hungary [46] in recent decades, were selected for the productivity evaluation. In order to establish a common basis for the analysis, the yield data of these three main crops from each parcel of the dataset were normalized to a scale of 1 to 100. For the same reason, the GPP values were also normalized to a scale of 1 to 100. Normalization was applied to all wheat, maize and sunflower yield data in the five years covered by the AIIR database and to all cropland pixels in the GPP dataset.  
The AIIR database with normalized yield data and the normalized GPP dataset were integrated with the climate geodatabase into a single geodatabase using geographical coordinates as unique identifiers. The result was a georeferenced dataset created to include all soil, climate, management and yield data. Productivity analysis was carried out using information of the georeferenced pixels, including their geographical coordinates.  
The GPP data, originally produced at 500 m resolution, were downscaled to 100 m resolution and normalized to values between 1 and 100. The downscaling was performed by the nearest neighbor resampling method The SRTM data, which were originally produced at 30 m resolution were generalized to 100 m resolution using the bilinear interpolation technique.  
All datasets were converted to the Uniform National Projection System (EOV) to create a coherent geodatabase.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 3 Materials and Methods
### Impact of Controller Energy Consumption on a Data Center Network  
A data center that uses the SDN paradigm to structure its core network will have many controllers. This is necessary for redundancy and load balancing reasons. With the SDN paradigm, the separation between network and servers is blurry since the SDN controllers may be implemented on virtual machines running on Servers. These servers account for most energy consumption on a data center, as reported by [PERSON] et al. (2019). The amount of energy consumed by SDN controllers poses severe limitations on previous works that centered energy measures on data-plane only. This paper focuses on the control-plane to add this variable to the energy-aware solutions.
## 2 Materials and Methods
### Anthropometric Measurements  
The following anthropometric parameters were measured: body weight, height, and lower limb length. All measurements took place on the same day and followed a consistent sequence outlined below. Body weight was measured to the nearest 0.1 kg using an electronic scale (Seca, Hamburg, Germany), with participants standing barefoot and wearing shorts or light clothing. Height was measured with a wall-mounted stadiometer and a sitting height table (GPM-Swiss Made). These measurements were taken while participants were barefoot. Lower limb length was assessed using a tape measure, as outlined by [PERSON] et al. (2014), from the anterosuperior iliac spine to the center of the medial malleolus of the same leg, with the participant in a supine position.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Control Group** & **F11+ Group** \\  & **(_n_ = 11)** & **(_n_ = 13)** \\ \hline Age (years) & 24.27\(\pm\) 1.00 & 24.38\(\pm\) 1.71 \\ Body mass (kg) & 85.18 \(\pm\) 8.84 & 79.69 \(\pm\) 10.72 \\ Height (cm) & 181.27 \(\pm\) 6.08 & 178.85 \(\pm\) 8.22 \\ Lower limb length (cm) & 100.09 \(\pm\) 7.06 & 94.52 \(\pm\) 4.05 \\ \hline \end{tabular}
\end{table}
Table 1: Subject characteristics at the start of the study (Pre-Test). Data are presented as mean \(\pm\) standard deviation (SD).