esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Noise Generation and Insertion  
In this study, Laplace noise was incorporated into the simulated dataset to introduce stochastic variability, thus mirroring the inherent randomness and uncertainty present in natural reservoir properties. Laplace noise, also known as double exponential noise, is characterized by a probability density function that is exponentially decaying on either side of the mean (the location parameter).  
The choice of Laplace noise was driven by its utility in scenarios where the data are expected to have heavier tails. This leads to a higher likelihood of observing extreme deviations from the mean. This feature is particularly advantageous in geological simulations where extreme values can represent rare but significant events, such as reservoir discontinuities or anomalous porosity and permeability zones. Additionally, the Laplace distribution's sharper peak enhances the model's sensitivity to subtle changes in the input parameters, providing a more robust framework for evaluating the impact of small fluctuations on the modeled reservoir properties.  
Figure 3: Dataset before adding noise.  
The formula for the probability density function (PDF) of the Laplace distribution is given by the following:  
\[f(x|u,b)=\frac{1}{2b}exp\bigg{(}-\frac{|x-\mu|}{b}\bigg{)}, \tag{1}\]  
where \(x\) represents the variable, \(\mu\) is the location parameter, and \(b\) is the scale parameter, which determines the spread of the distribution.  
In this work, \(b\) is set to 10% of the standard deviation of each variable in the dataset. This means that the noise added to each variable is scaled according to the variability (standard deviation) of that variable but at a reduced magnitude (only 10%). This approach ensures that the noise magnitude is proportionate to the inherent variability of each variable, thereby preserving the relative scale of fluctuations across different variables.  
Figure 4 demonstrates that, despite the introduction of noise, the relationships between the variables are preserved and remain consistent with the original data scatterplots shown in Figure 3.
###### Abstract  
Fish feed delivery is one of the challenges which fish farmers encounter daily. The main aim of the feeding process is to ensure that every fish is provided with sufficient feed to maintain desired growth rates. The properties of fish feed pellet, such as water stability, degree of swelling or floating time, are critical traits impacting feed delivery. Some considerable effort is currently being made with regard to the replacement of fish meal and fish oil with other sustainable alternative raw materials (i.e., plant or insect-based) with different properties. The main aim of this study is to investigate the motion and residence time distribution (RTD) of two types of solid feed pellets with different properties in a cylindrical fish tank. After experimental identification of material and geometrical properties of both types of pellets, a detailed 3D computational fluid dynamics (CFD) study for each type of pellets is performed. The mean residence time of pellets injected at the surface of the fish tank can differ by up to 75% depending on the position of the injection. The smallest residence time is when the position is located at the center of the liquid surface (17 s); the largest is near the edge of the tank (75 s). The maximum difference between the two studied types of pellets is 25% and it increases with positions closer to the center of the tank. The maximum difference for positions along the perimeter at 3/4 tank radius is 8%; the largest residence times are observed at the opposite side of the water inlet. Based on this study, we argue that the suitability of different solid feed pellets for aquaculture systems with specific fish can be determined, and eventually the pellet composition (formula) as well as the injection position can be optimized.  
recirculating aquaculture systems (RAS); fish feed; residence time distribution (RTD); 3-dimensional (3D) simulation 2020 10 6954 10.3390/app10196954
## 5 Results and Discussion
### Results  
The software was also used to test multiple real-world problems in the industry using multiple scenarios. The research was conducted in two stages:  
* The analysis of the need for a buffer in production;
* The analysis of the possible buffer configuration scenarios.  
In the first step, a set of simulations was conducted to find where the greatest need for a buffer between the operations was. So, in this set of simulations, a GA without buffer limitations was executed 100 times. The ten best results were taken into consideration to find out the buffer needs (Table 6). The buffer is shown as a number of pieces, and since all three products are similar, the buffer capacity is same for all three products.  
As can be seen in the last row in Table 6, the requirements for the buffer are the largest between the first and second operations and between the fourth and fifth production stages. Otherwise, the smallest buffer requirements is between the third and fourth production stages. These results were taken into consideration for the next stage, i.e., determining the optimal buffer configuration.  
In the second stage, we tested six possible buffer configurations (BC1-BC6). The configurations were determined according to the data from Table 6, and the situations in the production facility and six possible configurations were taken into consideration (Table 7). For each scenario, the GA was executed 100 times with the same parameters as in 5.1.  
According to the simulation results, the best makespan was achieved with the BC1 buffer configuration, both in an average value for 100 consecutive simulations and with the best overall result. The BC1 buffer configuration is also the only buffer configuration that meets the specified condition of there being an on-time delivery.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 2 Analysis  
On the other hand, the orthogonal frequency-division multiplexing (OFDM) has been more and more used for long-haul optical transmission [1; 2; 3], where the real-time digital signal processing has enabled signal rates to surpass 10 Gbit/s, with high spectral efficiency and flexible network design.
## 4 Simulation Results  
This section presents numerical examples of the developed control scheme, _i.e._, simultaneous system voltage profiles improvement as well as static voltage stability margin enlargement, on a sample power network. The simulation program is coded using MATLAB(r) and implemented on a personal computer which has a CPU with Intel(r) Core(tm2) Duo 2.66 GHz and a memory with 4 GB.  
The IEEE 30-bus system is used as an example to show the effectiveness of the proposed scheme. This sample system consists of 41 transmission lines, 6 VAR sources, and 24 loads. The system data including line parameters and bus data are given in [29]. For all examples, the permissible value adopted for voltage magnitude is set at 0.95 \(\leqslant\)\(|V|\)\(\leqslant\) 1.05 p.u., and the security margin for the test system is set as \(VSM\)\(\geqslant\) 0.3 p.u.. These are the threshold levels for triggering the proposed control algorithm. The voltage profile improvement index \(x^{rms}\) used in this research is expressed by the root mean square value of voltage changes at all load buses, _i.e._,  
\[x^{rms}=\left(\frac{1}{m}\sum\limits_{j=1}^{m}||x_{j}||_{2}^{2}\right)^{1/2} \tag{11}\]  
where \(m\) is the number of load buses in the test system, and \(x_{j}\) for each \(j=1,\,2,\,\ldots\,\,\,m\) denotes the resulting voltage change at each of the load buses. In this test system, \(m\) is set to be 24. Note that the smaller the value of \(x^{rms}\), the better the voltage profile will be.  
In order to verify the performance of the presented methodology to power system secondary voltage control, we have studied a lot of experiments. These simulation cases include different load levels, different load patterns, various pilot-bus selections, and various branch outage contingencies. Among those investigated cases, some typical test results for the scenarios given in Table 1 are briefly summarized in the following.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **PMU Locations at** & **Load Change Pattern** & **Line Outage Contingency** \\ \hline I & \#30 & Single bus & / \\ II & \#3, \#12 & All buses & / \\ III & \#24, \#29 & Several buses & Line \#27–\#28 \\ IV & All & Several buses & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Case studies for the IEEE 30-bus system (“#” Indicates” number” & “/” indicates ”None”).  
Figure 5: Illustration of the proposed control scheme.
## 3 Crash Prediction Testing Methodology
### Data Acquisition  
Representative test scenarios are required to test Pre-Crash systems. One of the biggest challenges in scenario mining for ADAS/AD and safety systems is the collection of high quality and in-depth real-world data on a large scale. Test scenario data sources on macroscopic, mesoscopic and microscopic levels are presented by [PERSON] et al. [26]. A modified and expanded illustration with additional types of data sources and concrete dataset examples is shown in Figure 5.  
A common limitation is the inverse proportionality between the amount of data and the data depth. While the official accident statistics cover all traffic accidents recorded by the police, the depth of data is usually not sufficient to define concrete test scenarios. More important for test scenario extraction is not the amount, but the combination of the representativeness of the data and the data depth. These properties are fulfilled by in-depth accident data bases. The GIDAS database contains detailed information on German traffic accidents with injured participants. The accidents are recorded at the scene of accident and reconstructed in order to obtain information about all five phases of the accident, as shown in Figure 1. The GIDAS PCM (Pre-Crash Matrix) database contains a big sample of reconstructed GIDAS Pre-Crash scenarios in a specific PCM format. It describes the Pre-Crash phase over about 5 s before the collision and contains the information on participants, their dynamics and the environment [27]. Currently, the GIDAS PCM database contains 11,074 collision scenarios, 40% of which are car to car collisions [28]. The PCM scenarios are used for the simulative testing of driver assistance and active safety systems in a virtual environment [29]. However, GIDAS does not include accidents without injured persons, nor near accident scenarios. Naturalistic driving studies and field operational tests collect real world data during regular driving. Nevertheless, critical situations and even a small number of collisions also occur in large traffic data collection campaigns. For example, the  
Figure 5: Representation of driving scenario sources and examples of concrete datasets. The illustration is modified and expanded from [26].  
SHRP2 study contains over 36,000 regular driving, near collision and collision events [30]. However, the collisions represent only a small part of the SHRP2 dataset. Modern vehicles are equipped with advanced vehicle dynamics, and interior and environmental sensors, which enables the collection of real-world driving data on a mesoscopic level. The amount and depth of the data is flexible and depends on the vehicle equipment and the definition of the data collection campaign. Thus, the highest possible amount of data corresponds to the entire vehicle fleet, which would vastly exceed conventional data sources. However, the protection of personal data must be ensured.
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## Appendix B Merge Search Parameter Value Selection  
The Merge search (MS) parameters of interest are the mixed integer program (MIP), time limit (120 and 300 s), the number of iterations in ant colony optimisation (ACO) (500, 1000, 2000 and 5000 iterations) and the number of random sets to split into (2, 4 and 8 sets) (Note that a larger number of split sets implies more diversity, especially since the sets are obtained randomly.) Ten runs per instance were conducted and all runs were conducted for 30 min with 10 cores. (The number of cores is also a parameter of interest, but these experiments are conducted in Section 5.4 instead). The results are presented in Table A1. For each parameter of interest, the results are averaged across the results obtained with all values of all the other parameters.  
Regarding the MIP time limit, we see that, overall, 120 s is preferable, particularly for eight machines or more. For four and six machines, there are not many differences in the results, but 300 s is slightly preferable for six machines.  
The results concerning the number of iterations in ACO are straightforward, with a setting of 5000 iterations providing the best average results across all machines. This suggests that a higher number of ACO iterations could be considered. However, for the 20 machine problem instances, only four iterations of MS complete with 5000 iterations. More ACO iterations will cause--in these cases--that the MS component does not contribute significantly to the final solutions.  
The amount of random splitting is almost independent of machine size. Marginally, the smallest number of sets is most beneficial (three out of six problem instances).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Machines**} \\ \cline{2-7}  & **4** & **6** & **8** & **10** & **12** & **20** \\ \hline \hline ACO Iter. & & & & & & \\ \hline
500 & 66.76 & 240.34 & 790.28 & 703.33 & 2729.48 & 9700.10 \\
1000 & **66.73** & 239.38 & 727.79 & 671.92 & 2636.11 & 9631.67 \\
2000 & **66.73** & 238.90 & 692.31 & 644.35 & 2546.22 & 9411.80 \\
5000 & **66.73** & **238.68** & **670.24** & **633.83** & **2454.07** & **9026.49** \\ \hline \hline \end{tabular}
\end{table}
Table A1: The results of experiments conducted to determine which set of parameter values lead to the best solutions for MS. For each parameter, the results are averaged across the results obtained by the settings of all other parameters.
# Geometrical Parametric Study on Steel Beams Exposed to Solar Radiation
## 4 Finite Element Modeling  
### Heat Conduction and Thermal Boundary Conditions  
As disclosed in Section 2, the main cause of temperature rising is solar radiation which also heats up the air temperature. The warming occurs within light hours, in which the intensity of solar radiation and the temperature of air are functions of time. The radiation from the Sun reaches the exposed surfaces directly as beam rays or indirectly as reflected radiations from the ground and other close surfaces. During the dark night times, the air becomes colder due to the absence of the warming source (Sun), which cools faster than concrete surfaces and works as a cooling load via surface convection. Another cooling load is the heat dissipation from warm surfaces to atmosphere via long-wave radiations. The abovementioned heating and cooling loads summarize the thermal boundary loads that should be simulated in the finite element analysis of externally exposed structures and members, which were all successfully modeled in the current finite element thermal analysis as detailed in previous studies [32; 44]. On the other hand, the heat conduction from surfaces to body core during warming time or vice versa during cooling time is the main process, for modeling which the Fourier heat conduction equation (Equation (1)) was utilized. This equation solves for the temperature as a function of time and position (coordinates) and is dependent of material's density (\(\rho\)), specific heat (\(C_{\rm P}\)) and the thermal conductivity (\(k\)) [16]:  
\[k\bigg{(}\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial y ^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\bigg{)}=\rho C_{\rm P}\frac{ \partial T}{\partial t} \tag{1}\]  
The convection cooling (\(q_{\rm c}\)) between the beam external surfaces and the moving air in the field environment is represented in Equation (2), where \(T_{\rm s}\) represents the temperature of the beam's surface and \(T_{\rm a}\) represents the air temperature, while h is the convection coefficient, which is represented as a function of wind speed (\(v\)). It is important to address the used formula (Equation (3)) for the convection coefficient as there are several empirical formulas in the literature for I-beam sections. This formula was adopted from previous studies where it was used successfully to model the convection between air and beam surfaces:  
\[q_{\rm c}=h(T_{\rm s}-T_{\rm a}) \tag{2}\]  
\[h=5.6+4v(v\leq 5{\rm m}/{\rm s}) \tag{3}\]
## 5 Conclusions  
SAR images are inherently degraded by the phase interferences of received 2-D backscattered signals, which results in a high-frequency multiplicative noise known as speckle. Speckle reduction methods are applied before texture information extraction with the help of spatial de-speckling filters. Since the performance of speckle noise suppression must be consistent with the application of de-speckling filters in order to preserve fine details and subtle textures, a novel speckle analysis method, as well as de-speckling filter categorization and evaluation, have been proposed in this research study. The categorization resulted in three major groups of spatial filters: spectral filters, system resolution enhancement filters, and image resolution improvement filters. This research study also introduces spatial de-speckling filter implementation results based on their performance in the best- and the worst-case applications, according to their categories. The results of this research study can also be used as an evaluation protocol for spatial filter performance and speckle removal evaluation. The sample filters in this study are Frost, Kuan, Gamma, Lee, Lee sigma, Local adaptive median, and Local sigma, and all the simulation results were derived based on the spectral analysis, the system resolution, and the image resolution criteria. The results clearly show that selecting a proper spatial de-speckling filter for the best noise suppression is completely dependent on the category of interest. However, the superiority of the performance is dependent on the quality objective metric comparison.  
[PERSON] contributed to the research experiments, simulations and writing of the paper. [PERSON]-[PERSON] conceived this research and was responsible for the reviewing of the paper and the results.  
This research was funded by Korean Polar Research Institute (KOPRI), grant number PE19120 (Research on analytical technique for satellite observation of arctic sea ice).  
The research was supported by the Korea Polar Research Institute (KOPRI) and we are thankful to them for providing all the facilities for this research study's investigations.  
The authors declare no conflicts of interest.
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
###### Abstract  
Early warning systems (EWSs) are crucial for optimising predictive maintenance strategies, especially in the industrial sector, where machine failures often cause significant downtime and economic losses. This research details the creation and evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical machine faults. The proposed system is designed to process time-series data collected from an industrial printing machine's embosser component, identifying error patterns that could lead to operational disruptions. The dataset was preprocessed through feature selection, normalisation, and time-series transformation. A multi-model classification strategy was adopted, with each LSTM-based model trained to detect a specific class of frequent errors. Experimental results show that the system can predict failure events up to 10 time units in advance, with the best-performing model achieving an AUROC of 0.93 and recall above 90%. Results indicate that the proposed approach successfully predicts failure events, demonstrating the potential of EWSs powered by deep learning for enhancing predictive maintenance strategies. By integrating artificial intelligence with real-time monitoring, this study highlights how intelligent EWSs can improve industrial efficiency, reduce unplanned downtime, and optimise maintenance operations.  
early warning system; time-series analysis; LSTM; predictive maintenance; big data analytics Article
# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 3 Results and Discussion
### Discussion of Challenges and Recommendations
#### 3.3.3 Data Storage and Analytics  
Measurement to feedback: Raw sensor measurements can be difficult to reduce into actionable recommendations for farmers.  
_Project managers_: While measurements are relatively easy to display on a dashboard, correlating them with crop growth and other effects requires input from a topical expert. Accessing the rightexpertise can be a challenge in itself, but resources at universities and agricultural extension agencies can often give some direction.  
_Technologists_: Take a human centered approach by having farmers and agriculture experts from the area provide input on (a) what kind of recommendations would be useful and actionable and (b) the best means for the farmer to receive the recommendation.  
Equity of access: The ownership of data collected is often overlooked in IoT projects and can lead to disagreements among stakeholders.  
_Project managers_: Negotiate data access with project partners and funders. While it is important to maintain community access to data, service providers and funders may have requirements for data access.  
_Funders_: Communicate early with the project implementers and have clear guidelines on data access, privacy, ownership, and sharing mechanisms of the data with the community.
## 1 Introduction
[PERSON] _et al._[3] provided a comprehensive survey on WBSNs. They surveyed WBSNs on PHY, MACand network layers and classified routing protocols based on their strategies: temperature-aware, cross layer and clustering. In this paper, however, we focus only on the temperature-aware routing protocols, because temperature rise directly affects human health and safety, as studied in [4, 5]. The antenna radiation and node circuitry operation generate and release heat to their surroundings, which can be harmful to the human body if a safety threshold level is surpassed. The Federal Communications Commission (FCC) standard states that the upper limit of specific absorption rate (SAR) for near-field exposures is 1.6 W/kg for any tissue averaged over one gram, whereas the International Commission on Non-Ionizing Radiation Protection (ICNIRP) defines the upper limit of 2.0 W/kg for 10 g of tissue. The study in [4] translates these SAR values into temperature rise, and the maximum values of possible temperature increase in the human head and brain are 0.31 \({}^{\circ}\)C and 0.13 \({}^{\circ}\)C for the FCC standard and 0.60 \({}^{\circ}\)C and 0.25 \({}^{\circ}\)C for the ICNIRP standard, respectively. The study in [5] also indicates that a temperature rise of 0.1 \({}^{\circ}\)C is high enough to trigger intense human body thermoregulatory responses. That is, the temperature rise is the most important factor in designing a routing protocol for WBSNs, and how a routing protocol operates has a direct effect on the node activities, which, in turn, affect the temperature rise. To the best of our knowledge, no survey focusing on the temperature-aware routing protocols in WBSNs has been conducted. The rest of the paper is organized as follows. Section 2 presents the challenging open issues in WBSNs. In Section 3, the explanation of each routing protocol surveyed in this paper is provided. The comparative discussion of the routing protocols is presented in Section 4. Finally, this paper is concluded in Section 5.