esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Research Methodology
#### 2.2.5 Validation Methods  
The results of the maize extraction in Huinong District were proven to be correct by comparing the data on the maize area with real-world data and statistical yearbooks. The classification results of various machine learning classification methods were evaluated using quantitative statistics. Ground validation points were used to establish a confusion matrix, which was then utilized to calculate Overall Accuracy (OA, %), Producer Accuracy (PA, %), User Accuracy (UA, %), and the Kappa coefficient [20]. The Overall Accuracy refers to the proportion of pixels acquired throughout the categorization process compared to the total number of pixels involved. The formula is derived from the calculation of Equation (1). The Producer Accuracy refers to the likelihood that the ground truth reference data for a specific category are accurately classified within a given classification. The formula is computed using Equation (2). The percentage of test points correctly classified within  
Figure 3: Random forest algorithm flow.  
a particular category on the classification map serves as the indicator of User Accuracy. Equation (3) is a representation of this calculation. There are two kinds of consistencies that Kappa coefficients look at: the agreement between the sampling and the reference classification and the agreement between the automatic classification and the reference data. The Kappa coefficient is categorized into five distinct groups to denote varying degrees of consistency: 0.0 to 0.20 represents a very low level, 0.21 to 0.40 indicates an average level, 0.41 to 0.60 signifies a moderate level, 0.61 to 0.80 denotes a high level, and 0.81 to 1.00 represents an almost perfect level of consistency. The calculation formula is presented in Equation (4).  
\[OA=\frac{\sum_{i=1}^{n}P_{ii}}{N}\times 100\% \tag{1}\]  
\[PA=\frac{P_{ii}}{P_{+i}}\times 100\% \tag{2}\]  
\[UA=\frac{P_{ii}}{P_{i+}}\times 100\% \tag{3}\]  
\[Kappa=\frac{N\times\sum_{i=1}^{n}P_{ii}-\sum_{i=1}^{n}\left(P_{i+}\times P_{ +i}\right)}{N^{2}-\sum_{i=1}^{n}\left(P_{i+}\times P_{+i}\right)} \tag{4}\]  
where \(n\) is the total number of columns in the confusion matrix, which is the total number of categories; \(N\) represents the total number of samples used for precision assessment; \(P_{ii}\) represents the number of samples in row \(i\) and column \(i\) of the confusion matrix; \(P_{+i}\) represents the total number of samples in column \(i\); \(P_{i+}\) represents the total number of samples in row \(i\).
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Machine Learning Methodology  
The complex, non-linear relationship between cement stabilization parameters and unconfined compressive strength (UCS) makes machine learning (ML) ideal for predicting UCS in cement-treated soils [32; 57]. Supervised regression algorithms were used to develop predictive models for UCS based on the experimental data collected in this study.  
The comprehensive machine learning methodology implemented in this study follows a systematic four-phase approach designed to develop a robust uncertainty quantification (UQ) system for UCS prediction, as illustrated in Figure 3. This workflow begins with initial algorithm screening using a conventional train-test split to identify the most promising approaches, followed by rigorous evaluation through nested cross-validation with Bayesian hyperparameter optimization via Optuna (version 4.3.0). The methodology then transitions to a sophisticated two-stage training architecture where the optimal Random Forest model serves as the primary predictor while a dedicated uncertainty model learns to estimate prediction error magnitudes. This systematic progression from broad algorithm exploration to specialized uncertainty quantification ensures both high predictive accuracy and reliable confidence interval estimation, culminating in a deployed prediction system that provides engineers with both central UCS estimates and calibrated uncertainty bounds for informed decision-making in cement-stabilized soil applications.  
Figure 3: Sequence diagram illustrating the ML methodology.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### Route Management  
Node \(i\) resolves the local minimum problem by checking the distance to the sink of the node from which it previously received the packet (assume Node \(j\)) with that of its virtual cluster's LMR node. If Node \(j\) is closer to the sink than its' LMR node, Node \(i\) sends back the packet to Node \(j\), piggybacked with a local minimum alert message. Upon successful reception of the packet by Node \(j\) or the LMR node, Node \(i\) changes its status to a non-eligible VCG member node. On the other  
Figure 6: Receiver contention prioritization and backoff scheme.  
hand, Node \(j\) or the LMR node will seek for a new path for the packet by performing the VCG-based data forwarding initiative determination, followed by the receiver contention prioritization, as earlier explained in Section 3.5 and Section 3.6, respectively.  
In the event of the failure of an LMR node, which is detected if the VCG members fail to receive the still-alive packet after \(z\) consecutive times, the VCG node closest in position to the previous LMR node is selected as the next LMR node. To achieve this, each member of the VCG will run a random NLMR timer with a value between \(CW_{\min}\) and \(\left(CW_{\min}+\left(\frac{CW_{\min}}{2}\right)^{a}\right)\). Upon the expiry of the timer, the node pronunces itself as the new LMR node, reflecting the nodes proximity to the previous LMR node. The closer it is, the faster it pronounces itself. It is important to note that since the LMR nodes do not necessarily take part in packet forwarding, the failure of the LMR node is also a rare occurrence. This makes the scheme a robust one with the ability of sustaining a routing path for as long as is feasible.
## 2 Materials and Methods
### Apparatus, Settings, and EEG Preprocessing  
The experimental protocol was programmed with PsychoPy [30], and stimuli were displayed on a 22-inch monitor controlled by a personal computer (PC). The EEG was recorded using a 128-sensor HydroCel GSN Electrical Geodesics Inc. (EGI) Net. The EEG data were referenced to vertex Cz, and impedances were kept below 45 k\(\Omega\) whenever possible (maximum: 75 k\(\Omega\)). Eye blinks and movements were monitored by horizontal and vertical bipolar electrooculography (EOG) electrodes. The EEG and EOG data were sampled at 1000 Hz and filtered with a band-pass filter (0.02-100 Hz). The preprocessing of EEG data was performed using EEGLAB toolbox [31]. EEG signals were first band-pass filtered (Finite Impulse Response filter, 0.5-50 Hz, EEGLAB). For the rest of the channels, we performed the independent component analysis (ICA, Infomax, EEGLAB) for the concatenated EEG data and applied the ADJUST algorithm [32] to identify and remove artifact components, including horizontal eye movement, vertical eye movement, eye blinks, and generic discontinuities.  
For the purposes of the analysis, we focused on 29 electrodes covering frontal, central, parietal, occipital, and temporal scalp (Figure 4): FP1(22), FP2(9), F3(24), F4(124), Fz(11), F7(33), F8(122), FT7(39), FC3(29), FCz(6), FC4(111), FT8(115), C3(36), C4(104), T3(45), T4(108), CP3(42), CP4(93), CPz(55), TP7(50), TP8(101), P3(52), P4(92), Pz(62), T5(58), T6(96), O1(70), O2(83), and Oz(75). Please note that the number in the parenthesis refers to the original electrode number of the EGI 128-sensors.  
Figure 4: 128 channel map of the HydroCel GSN. The 29 channels marked in black were used for analysis.  
Figure 3: Trial sequence for the resting and emotion-induction states. EEG = electroencephalography.
# Percutaneous Needle Electrolysis Accelerates Functional Muscle Regeneration in Mice
## 3 Results
### Endplate Noise Recording in Muscles Injured with Bupivacaine  
Special care was taken to achieve only a partial lesion of the gastrocnemius muscles. The first record was made in the right gastrocnemius (control record, untreated). The left gastrocnemius was then recorded (experimental record, treated with BPV). For the assessment performed 72 h after the administration of BPV, a decrease of up to half of the number of areas with endplate noise was observed (Control: 3.0 \(\pm\) 0.01; BPV: 1.33 \(\pm\) 0.33; % of variation compared to the control: 49.89% \(\pm\) 9.42, n = 36 muscular areas from three mice; \(p\) = 0.002; Figure 4a). Thereafter, at seven days, there were 25% fewer areas with endplate noise (Control: 8.0 \(\pm\) 0.01; BPV: 5.67 \(\pm\) 0.33; % of variation compared to the control: 75.00% \(\pm\) 7.22, n = 36 muscular areas from three mice; \(p\) < 0.001; Figure 4a). Seeing that the electromyography values were still not normalized, experiments were performed 10 days post-treatment with BPV. However, at 10 days, the muscles had still not completely recovered (Control: 7.33 \(\pm\) 0.88; BPV: 6.00 \(\pm\) 0.58), as they still presented 20% fewer areas with endplate noise (% of variation compared to the control: 82.13% \(\pm\) 3.57, n = 36 muscular areas from three mice; \(p\) = 0.022; Figure 4a).  
In the analysis of the frequency of the endplate noise, recording 72 h after the administration of BPV (control: 92.85 \(\pm\) 3.07; BPV: 54.08 \(\pm\) 8.21), a decrease of 40% was observed compared to the control limb (% of variation compared to the control:58.38% \(\pm\) 8.86, n = 36 muscular areas from three mice; \(p\) = 0.013; Figure 4b). Regarding the assessment performed at seven days, there was still 23% less frequency of endplate noise (control: 123.23 \(\pm\) 6.87; BPV: 94.64 \(\pm\) 8.89; % of variation compared to the control: 76.87 \(\pm\) 2.25, n = 36 muscular areas from three mice; \(p\) = 0.002; Figure 4b). However, at 10 days after inoculation with BPV, the control values of endplate noise were achieved (control: 136.29 \(\pm\) 3.91; BPV: 122.02 \(\pm\) 1.40; % of variation compared to control: 89.52% \(\pm\) 1.26, n = 36 muscular areas from three mice; Figure 4b).  
Figure 4: Electromyography of muscles injected with bupivacaine. Comparative recordings were performed between the gastrocnemius treated with saline solution (left, control) compared to the one injected with BPV (right, experimental) of each animal. At 72 h and 7 and 10 days, needle electromyography was performed. The number of areas presenting endplate noise was evaluated (**a**). During the endplate noise recording, the frequency was studied as the number of potentials that appeared per second, expressed in Hz (**b**). For each column, n = 36 muscular areas from 3 animals. Values are expressed as the mean \(\pm\) SEM. * \(p\) < 0.05 compared to control values.
## 2 Applications of HREM
### HREM in Embryo Research  
HREM was used for analysing whole embryos or embryonic organs and tissues of various species, including humans. However, the highest impact in this field was gained from phenotyping mouse embryos and from the selection of HREM as the tool for phenotyping mouse mutants harvested at embryonic day (E) 14.5 in the Deciphering the Mechanisms of Developmental Disorders (DMDD) program.
## 5 Discussion
Comprehensive simulation experiment verification results show that the pilot intention identification model established in this paper is reasonable, stable, and effective. However, in view of a preliminary study of pilot intention, there are still many areas that need to be strengthened in this study. The follow-up research work is as follows:  
Figure 11: The same degree of intention identification results of BP, ELM, SVM, and CSA-ELM. Figure 10: Intention identification accuracy of BP, ELM, SVM, and CSA-ELM methods. (1) The pilot's intention follows the evolutionary laws of generation, transfer, and reduction. Therefore, as a preliminary study of pilot intention identification, this paper is only a basic identification of whether the pilot generates an intention to change the operating attitude of the aircraft. Here, the intention to affect the flight attitude needs to be further determined. Thus, in the follow-up study on pilot intentions, a detailed division of pilot intentions is needed. Based on the determined pilot intention, the law of transfer between different intentions and even the attenuation law will be studied so as to identify the pilot's intention more comprehensively, specifically, and accurately in the follow-up study. (2) To improve the efficiency of intention identification, this paper only considers the process of generating the intention of the pilot when performing the airfield traffic pattern. The impact of other complex flight environments on the pilot such as crosswind, engine failure, landing gear failure, and other more complex situations during flight missions should be analyzed in future research. The model used in flight simulation is an abstract expression of the actual system, which has a certain subjectivity. Therefore, in future research, we need to pay more attention to the objectivity of the problem and consider the comprehensive impact of multiple situation effects on establishing a model. In addition, we also need to further improve the consistency between the model simulation results and the actual situation. (3) In the process of establishing the identification model, the experimental data used in this paper have a certain deviation from the actual data because of objective factors such as time, experience of experimenters, and accuracy of equipment, which leads to some errors in the pilot intention identification results. Furthermore, it is still necessary to improve the experimental methods and expand the experimental sample size to make the intention identification results more accurate in future research.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 4 The Proposed Approach for Defining a Discomfort Index
### Defining the Thermal Discomfort Index
* Using the temperature model described in Section 4.3 and the water utilization forecast, the water temperature is obtained for the next 12 h.
* An interruption of \(k\) minutes is simulated and the temperature for the next 12 h is obtained using the proposed temperature model. * Since \(T_{max}\), \(T_{min}\), \(T_{comf}\) are known, Equation (5) is applied to compute \(TDI(I,u,w)\) for the current realization \(w\) and all uses. * An auxiliary variable \(S_{uses}(w)\) is defined by Equation (6). \[S_{uses}(w)=\sum_{u\in UI(w)}TDI(I,u,w) \tag{6}\]  
Finally, after computing \(S_{uses}(w)\) for each realization, TDI is computed as the empirical expected value defined in Equation (7). \[TDI(I)=\sum_{w=1}^{w=100}S_{uses}(w)/100 \tag{7}\]  
Figure 7: Graphical representation of TDI.
## 5 Conclusions and Future Work  
In this hybrid framework (DRSM-CNNFIS), the diabetic retinopathy stage identification process provided robust performance in all classes (normal DR (NDR), mild DR (MDR), moderate (MoDR), severe DR (SDR), and proliferative DR (PDR)), with an overall accuracy of 93%. Membership functions were constructed and tuned based on labelled data [14] that increased only by 3.6-fold compared to the original data. In the first step, the convolutional neural network was used to obtain the output vector of image features. In the next step, a fuzzy rule-based system was implemented based on human expert knowledge to measure the stage of DR. The final framework showed better performance compared with existing CNN models and ensemble models mentioned in the related work: DenseNet-201, Inception-ResNet-V2, Inception-V3, ResNet-50, Xception, Majority Vote Ensemble, and Average Ensemble. The proposed system showed better results and robust performance for multiclass classification with a weighted average of 0.9371, 0.9281, and 0.9296 for precision, recall, and D1-score, respectively, using the five-fold cross-validation method. In the future, automated method for rule extraction in the fuzzy rule-based system based on the training data will be implemented.  
Implementing the proposed model in clinical practice at hospitals and ophthalmology offices will enable regular automated diagnostic measurement, which will save time and cost, and increase the chance of early stage diagnosis, as the results of the proposed model provide stable accuracy for all classes. Furthermore, dealing with noisy or low-quality data can open the door for the future of using mobile phone cameras for such applications.  
The proposed model overcomes data dependency problems for deep learning models by using human expert (ophthalmology consultant) knowledge, but the limitation of this work can be seen in different aspects, such as the variation of the performance using different devices and the ethical aspects of health application automation.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 3 Proposed Methodology
### Two-Fold Synchronized Dataset  
The final seismic signal dataset consists of time-series data collected from our low-cost system, described in Section 3.2, strategically placed in a region prone to seismic activity. This dataset primarily comprises seismic signals associated with earthquake events. These signals exhibit a wide spectrum of characteristics, encompassing various magnitudes and frequencies. Of particular significance is the inclusion of ambient noise originating from passing vehicular traffic. This environmental noise component, stemming from the dataset's proximity to a roadway, introduces a distinctive dimension to our dataset. While seismic signals provide insights into genuine ground motion events, the presence of vehicular noise poses a challenge that reflects real-world scenarios and presents a challenge to our machine learning model.  
To ensure the dataset's reliability, we cross-referenced our recorded signals with data from established seismographs from NOA, known for their accuracy and trustworthiness. The purpose of this dataset is to test both the stochastic and amalgamated models, in order to verify their ability to distinguish between earthquake signals against vehicular noise signals. The dataset was recorded and saved into 84 distinct csv files, which then were visually inspected, and each data point was labeled either as an earthquake or noise (irrespective of being vehicular or otherwise). Later, the same procedure as with the previous datasets was performed to prepare it for the testing phase. In order to further support the reproducibility of our work, the data of this dataset are available online ([[https://drive.google.com/drive/folders/16](https://drive.google.com/drive/folders/16) uKG9 eq1 kkf9 Xpk39T96 HHBtZ06N6]([https://drive.google.com/drive/folders/16](https://drive.google.com/drive/folders/16) uKG9 eq1 kkf9 Xpk39T96 HHBtZ06N6) qq?usp=drive_link, accessed on 13 September 2023).
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 2 Primary Classifications of Electrical Energy Losses
### Technical Energy Losses  
Technical losses of electricity can mainly be attributed to technical elements such as power transmission and distribution lines and transformers. These categories exhibit the highest quantitative losses of electricity. Therefore, technical energy losses can be defined as the physical properties of the components that comprise the distribution system, manifesting as a natural occurrence in lines and transformers during the flow of electrical power. A property of technical losses is their quantifiable nature, allowing for their estimation through calculation [6].  
These losses, classified as active energy losses, can result in substantial economic repercussions over time. They manifest in various forms within power systems. These losses manifest in the following ways:  
1. Technological own consumption: This term denotes the quantity of active energy losses that are generated by the transmission and distribution processes, under the conditions that have been established for the designed installations.
2. Deviations from the designed operating regime give rise to technical energy losses, primarily attributable to inadequate sizing of the network and improper operation.
3. Commercial losses--this category of technical losses is caused by errors introduced by the metering and organization groups on the energy record.  
Figure 2: A categorization of energy losses within the power system.  
On average, electricity losses in the networks of a system vary between 10% and 15% of the total electricity production. These losses are contingent upon the structure of the network under analysis, the conditions under which it is operated, and other considerations.  
The technical losses of active energy-summing technology's own consumption (TOC) are characterized and localized as follows:  
* The location of the magnetic field is contingent upon the thermal effect resulting from the electric current. This phenomenon is observed within the conductors of power lines and the windings of transformers.
* The presence of a magnetic field is evident in the magnetic core of transformers and autotransformers, contingent on the existence of eddy and hysteresis currents.
* The corona effect, on the other hand, leads to the localization of electric fields in power lines with a voltage of at least 220 kV.
* The electric field is localized in power lines in medium-voltage cable and in lines in cable where the voltage Un \(\geq\) 60 kV [7].  
The technical type of energy loss is principally based on the analysis of electromagnetic field theory. Consequently, the loss of electrical energy entering the conductor lines is converted into thermal-type energy, which is supplied by the electromagnetic field. The construction types of electrical transmission cables result in varying types of active losses on the lines, which differ from one category to another [8].  
The active energy losses in power lines are influenced by several factors and can be classified into different types, as outlined below:
## 3 Landing Trajectory Data Correction
### Landing Trajectory Correction
#### 3.3.2 Trajectory Correction Based on Projection and Ground Speed  
Based on the above analysis, we further combine the runway projection and ground speed information during the landing phase to improve the correction of the aircraft's landing trajectory. First, the touchdown point is selected and projected onto the runway  
Figure 3: Trajectory correction based on runway projection.  
as a reference point. Second, based on the ground speed, the distance between the next trajectory point and the reference point is calculated. Then, combining this distance with the runway direction, the coordinates of the next trajectory point are determined. Additionally, the trajectory point where the aircraft's ground speed is reduced to 30 knots is projected onto the runway. Finally, the trajectories of the other points between the two reference points are corrected based on the ground speed. The final correction results are shown in Figure 4, where the corrected trajectory aligns with the runway and exhibits a more uniform variation, which is more consistent with actual situations.