esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and output spaces. However, the prediction performance decreases when the specific data points with geometric features are used. The CGK clustering solves the above situation by generating valid clusters considering the geometric attributes of data in input and output spaces with the aid of the Mahalanobis distance. However, it is necessary to generate rational information granules (IGs) because there is a significant change in performance according to the context generated in the output space and the shape, size, and several clusters generated in the input space. As a result, the rational IGs are obtained by considering the relationship between the coverage and specificity of IG using the genetic algorithm (GA). Thus, the optimized CGK-based fuzzy granular models with the aggregated structure are designed based on rational IGs. The prediction performance was compared using the two databases to verify the validity of the proposed method. Finally, the experiments revealed that the performance of the proposed method is higher than that of the previous model.  
CGK clustering; information granules; granular model; aggregated structure; optimization 2022
## 4 Results and Discussion
### Validation
However, the basic profiles remain the same when a comparable plot was produced that \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{3}{c}{**Average Dwelling Heat Loss W/K**} \\ \hline  & **DEFF 08 \({}^{\text{a}}\)** & **Model** & **Difference** \\ Walls & 97.1 & 98.7 & 2\% \\ Windows \& Doors & 61.5 & 64.3 & 4\% \\ Roof & 18.6 & 15.4 & \(-\)21\% \\ Floor & 20.5 & 21.0 & 2\% \\ Ventilation & 49.0 & 51.2 & 4\% \\ Total & 246.7 & 250.6 & 2\% \\ \hline \multicolumn{3}{c}{**Average Annual Dwelling Energy Consumption (kWh)**} \\ \hline  & **DEFF 08** & **Model** & **Difference** \\ Total & 20,305 & 20,466 & 1\% \\ \hline \hline \end{tabular} \({}^{\text{a}}\) Domestic Energy Fact File 2008 [28].  
\end{table} 
Table 3: Model validation comparison. excluded non-thermal energy requirements. Lights and appliances are a larger percentage of the overall energy consumption for heat pump fitted properties due to the fact that these properties use less energy for the same thermal load than the equivalent gas boiler fitted property. Despite this, it does not significantly affect the results. Removing one element of the energy consumption of a property would not provide a realistic picture, particularly as the presence of lighting and appliances contributes to the overall heat gains for the property--_i.e._, if it is included for heat gains, it should remain for energy consumption. Although from an energy consumption perspective, heat pumps operating even at a relatively low level of expected performance realise fairly significant reductions in energy consumption, the increased running cost implication of these results is significant; despite the reduction in energy use, the higher cost of electricity compared with gas, not to mention the capital cost of the heating hardware, may negate any perceived financial savings. These results are only likely to be of interest in terms of planning nationwide power generation strategies as a reduced electrical energy requirement translates to a reduced power generation capacity, _i.e._, less power stations. The results could be of interest when targeting non-centrally heated properties once the cost of energy is factored in. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Heating** \\ **Technology** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Efficiency** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean Annual** \\ **Energy** \\ **Consumption (kWh)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Percentage** \\ **Decrease** \\ **Compared** \\ **with Gas** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Percentage** \\ **Compared** \\ **Resistive Electric** \\ \end{tabular} } \\ \hline Gas Boiler & 76\% & 20,400 & - & - \\ Resistive Electric & 100\% & 15,650 & 23\% & - \\ Air Source HP & 215\% & 8,900 & 56\% & 43\% \\ Ground Source HP & 234\% & 8,200 & 60\% & 48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Annual energy consumption per property fitted with different heating technologies. Figure 4: Annual energy consumption per property.
## 2 G-LiHT Design and Instrumentation
### Irradiance Spectrometer  
Downwelling irradiance is measured with an Ocean Optics (Dunedin, FL, USA) USB4000-VIS-NIR spectrometer. Light energy is transmitted to the spectrometer through an upward looking opaline glass cosine diffuser with a 180\({}^{\circ}\) field of view (FOV). The cosine diffuser is mounted on top of the aircraft, where there is an unobstructed view of the sky. A custom mount was designed for the leading edge of the Cessna 206 (Figure 5), which integrates both the GPS antenna and cosine diffuser. A 3 m long, 100 \(\upmu\)m diameter optical fiber delivers the light energy through a 25 \(\upmu\)m entrance slit and a multi-bandpass order-sorting filter, where it is dispersed with a fixed grating across a 3,648-element Toshiba linear Charge Coupled Device (CCD) array. The spectrometer covers the spectral range from 350 to 1,100 nm with a native optical resolution to \(\sim\)1.5 nm (full width half maximum, FWHM). Power and communications are transferred through a USB 2.0 connection to the computer. The downwelling radiometer is operated continuously during flight with a 33 ms integration time, a 30 scan average and 3\(\times\) spectral binning, which is equivalent to a 1 Hz data acquisition rate. The unit is cross-calibrated with the imaging spectrometer to enable atmospheric characterization of downwelling irradiance, which is used to compute the at-sensor reflectance product.
###### Abstract  
Safety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such parts becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe the recent trends of error surface and responds efficiently to the local gradient for precise spare prediction results marked by lumpiness. Introduction of the momentum term allows the proposed ANN model to ignore small variations in the error surface and to behave like a low-pass filter and thus to avoid local minima. Using the whole collection of aviation spare parts having the highest demand activity, an ANN model is built to predict the failure of aircraft installed parts. The proposed model is first optimized for its topology and is later trained and validated with known historical demand datasets. The testing phase includes introducing input vector comprising influential factors that dictate sporadic demand. The proposed approach is found to provide superior results due to its simple architecture and fast converging training algorithm once evaluated against some other state-of-the-art models from the literature using related benchmark performance criteria. The experimental results demonstrate the effectiveness of the proposed approach. The accurate prediction of the cost-heavy and critical spare parts is expected to result in huge cost savings, reduce downtime, and improve the operational readiness of drones, fixed wing aircraft and helicopters. This also resolves the dead inventory issue as a result of wrong demands of fast moving spares due to human error.  
lumpy demand forecasting; aviation; machine learning; spare part demand prediction
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Machine Learning in WSNs
#### 1.2.2 Unsupervised Learning  
Unsupervised learning focuses on uncovering hidden patterns or structures within unlabelled data. In the context of WSNs, unsupervised learning techniques are utilised for clustering sensor nodes based on spatial proximity or similarity in sensed data and anomaly detection, which involves identifying abnormal or unexpected sensor readings. The absence of a requirement for labelled data makes unsupervised learning particularly valuable in large-scale or dynamically changing WSN deployments [13; 14; 15; 16; 17]. The main strength of unsupervised learning for clustering is its ability to operate without labelled data and adapt to changing network topologies. However, some algorithms (e.g., K-Means) may require the number of clusters to be specified in advance, and their performance can be sensitive to initial conditions or parameter choices.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Makespan for DRFTSA Using Regular Dataset with Fixed Number of VMs  
This subsection clearly presents the calculation of makespan for the proposed DRFTSA by giving input from a real-time dataset, i.e., Google Cloud Jobs. In Section 5.1, as mentioned previously, we categorize datasets as regular and large datasets and 100 to 600 tasks are used in the regular dataset and then the makespan of DRFTSA is calculated. Any task scheduler's performance depends on the makespan and here in our research when we choose a suitable VM for a task by considering the priorities of both tasks and VMs based on electricity cost effects and makespan. We choose a VM which can process the task according to its processing capacity and therefore there is no chance of an increase in execution time and failures. It directly impacts performance of the task scheduler as, if makespan is lowered or minimized for a task scheduler, then we can say that performance of the scheduler is good and it is defined as the time taken to execute a task on a virtual machine. If the execution time for a task on a VM is increased, it results in poor performance of the scheduler. Therefore, in our research work while developing DRFTSA, we calculated makespan by using a regular dataset with a fixed number of VMs and generated tasks. The simulation ran for 50 iterations to check the efficacy of our proposed DRFTSA against PSO, GA, and ACO approaches. Makespans generated for PSO with 100, 200, 300, 400, 500, 600 tasks are 684.3, 712.54, 732.57, 812.78, 836.9, 895.87, respectively. Makespans generated for GA with 100, 200, 300, 400, 500, 600 tasks are 725.62, 756.09, 791.36, 854.03, 912.36, 953.47, respectively. Makespans generated for ACO with 100, 200, 300, 400, 500, 600 tasks are 545.17, 759.97, 834.18, 871.22, 891.22, 932.18, as shown in Table 3, respectively.  
Makespans generated for DRFTSA with 100, 200, 300, 400, 500, 600 tasks are 421.22, 503.12, 519.96, 485.78, 505.88, 456.27, respectively. From Table 4 and Figure 4 it is evident that DRFTSA minimized makespan compared to state-of-the-art approaches while running with a regular dataset and fixed number of VMs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of the Entity** & **Value of the Entity** \\ \hline Tasks & 1000 \\ Length of Tasks & 750,000 \\ Memory of Physical Host & 16 GB \\ Storage Capacity of Physical Host & 1 TB \\ Network Bandwidth of Physical Host & 500 MBPS \\ Number of VMs & 50 \\ Memory of VMs & 2 GB \\ Network Bandwidth of VMs & 20 MBPS \\ Operating System of VMs & Ubuntu \\ Type of Hypervisor & Monolithic \\ Name of Hypervisor & Citrix Xen \\ Number of Datacenters Used & 10–80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration settings used for DRFTSA in simulation.
## 4 Materials and Methods
### Model Formulation

Table 5: Definition of decision variables. \[\sum_{k\in K}a_{4l}s^{\prime}_{khj_{\
u}}\leq q^{\prime}_{\upsilon}truck_{bhjl} \quad\forall v\in V;h\in H;l\in L;j\in J \tag{8}\] \[\sum_{k\in K}a_{kl}s^{\prime}_{khj_{\
u}}\geq q^{\prime}_{\upsilon} \Big{(}truck_{bhjl}-(1+min_{\upsilon})\Big{)}\quad\forall v\in V;h\in H;l\in L; j\in J\] (9) \[\sum_{v\in V}s^{\prime}_{khj_{\
u}}\leq p_{hj}\sum_{i\in I}s_{kih} \qquad\forall k\in K;h\in H;j\in J\] (10) \[x_{h}\in\{0,1\}\qquad\forall h\in H\] (11) \[truck_{vhjl},\,\,\delta_{ki},y_{ih},s_{kih},s^{\prime}_{khj_{\
u }}\geq 0\qquad\forall v\in V;h\in H;l\in L;j\in J;k\in K \tag{12}\]  
As Equation (3) is not linear, we adapted the linear reformulation proposed in [23] for multinomial logit choice probabilities to the gravity model. In this work, we set the parameter \(\varphi_{ih}\) shown in Equation (13). \[\varphi_{ih}=\frac{\frac{\frac{\varphi_{h}^{\delta}}{\pi^{\prime}_{\delta}}}{ \frac{\varphi_{h}^{\delta}}{d_{ij}^{\delta}}}}{\sum_{j\in I}\frac{\varphi_{j} ^{\delta}}{d_{ij}^{\delta}}}\qquad\quad\forall i\in I;h\in H \tag{13}\]  
The above linear reformulation introduces a non-negative variable \(\overline{y_{i}}\), which is the accumulative choice probability of farmer \(i\in I\). This reformulation adds the constraints sets defined in Equations (14)-(16) to our model, and replaces Equation (3). \[\overline{y_{i}}+\sum_{h\in H}y_{ih}\leq 1\qquad\forall i\in I \tag{14}\]  
\[y_{ih}-\frac{\varphi_{ih}x_{h}}{1+\varphi_{ih}}\leq 0\quad\forall i\in I;h\in H \tag{15}\]  
\[y_{ih}-\varphi_{ih}\overline{y_{i}}\leq 0\qquad\forall i\in I;h\in H \tag{16}\]  
Notice that the value of the parameter \(\widehat{p_{hj}}\) for a given hub is independent of the other open hubs since hubs are not competing among each other to supply cities.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.8. Wavenis  
Wavenis is a wireless protocol architecture created as a proprietary technology by Coronis systems and promoted by the Wavenis Open Standard Alliance [78]. Wavenis is specifically designed to provide an ultra-low power and long-range wireless solution for a vast range of Machine to Machine (M2M) applications such as industrial process control, environmental monitoring and healthcare monitoring. In the majority of M2M applications, devices are expected to have low data rates and to operate on battery. However, recharging or replacing batteries not an easy task in many situations, saving battery power without compromising reliability is an important challenge. Moreover, a high link budget is needed to achieve adequately long-range communication in a number of M2M applications. Wavenis is an appropriate candidate to provide solution for these challenges. The main features of Wavenis technology include power conservation, reliability, network coexistence and resistance against interference. Wavenis operates worldwide in the 433 MHz, 868 MHz and 915 MHz ISM bands. It supports different data rates of 4.8 kb/s, 19.2 kb/s and 100 kb/s, uses GFSK modulation scheme and employs fast Frequency-Hopping Spread Spectrum (FHSS) technology. The MAC layer of the Wavenis protocol consists of two transmission techniques: synchronous and asynchronous. In the synchronous communication networking mode nodes are equipped with a combination of CSMA and TDMA channel access schemes. In this case, a randomly computed time slot is allocated to a node willing to acquire the channel. Prior to transmission in the allocated time slot, the node listen to the shared medium to check for any on-going transmission. If the shared medium is occupied by other nodes, the node calculates a new time slot for its next transmission. However, asynchronous communication networking applies in applications where reliability plays an important role such as security systems and in such applications CSMA/CA mechanism is used [75].
# Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance
###### Abstract  
This paper presents an approach to enhance electronic traveling aids (ETAs) for people who are blind and severely visually impaired (BSVI) using indoor orientation and guided navigation by employing social outsourcing of indoor route mapping and assistance processes. This type of approach is necessary because GPS does not work well, and infrastructural investments are absent or too costly to install for indoor navigation. Our approach proposes the prior outsourcing of vision-based recordings of indoor routes from an online network of seeing volunteers, who gather and constantly update a web cloud database of indoor routes using specialized sensory equipment and web services. Computational intelligence-based algorithms process sensory data and prepare them for BSVI usage. In this way, people who are BSVI can obtain ready-to-use access to the indoor routes database. This type of service has not previously been offered in such a setting. Specialized wearable sensory ETA equipment, depth cameras, smartphones, computer vision algorithms, tactile and audio interfaces, and computational intelligence algorithms are employed for that matter. The integration of semantic data of points of interest (such as stairs, doors, WC, entrances/exits) and evacuation schemes could make the proposed approach even more attractive to BSVI users. Presented approach crowdsourcings volunteers' real-time online help for complex navigational situations using a mobile app, a live video stream from BSVI wearable cameras, and digitalized maps of buildings' evacuation schemes.  
electronic travelling aids; guided navigation; blind and severely visually impaired; social networking; crowdsourcing; computer vision +
Footnote †: journal: Applied  
doi: 10.3390/appr12010523
# Recent Spatiotemporal Changes of Subhourly Extreme Rainfall Events in Seoul
## 4 Results
### Spatial Pattern Changes in Extreme Rainfall Events
Red triangles indicate a significant positive spatial autocorrelation with a 5% significance level based on [PERSON]’s \(I\) index values while blue circles indicate insignificance correlation. The red linear lines indicate statistically significant increasing and decreasing trends, respectively, detected at the 10% significance level. The black dashed line means linear regression line without statistically significant trend. Additionally, ** and * indicate statistically significant trends at the 5% and 10% significance levels, respectively. A high CV indicates significant dispersion in AMP values across individual rain gauge stations within a given year. In contrast, a high [PERSON]'s \(I\) signifies strong spatial autocorrelation of these data. In other words, high CV and Moran's \(I\) values suggest that AMPs are concentrated in a narrow area within the domain. Analysis revealed that both CV and Moran's \(I\) for subhourly durations, such as 30 and 45 min, showed statistically significant increases over time at the 5% significance level. This finding indicates an increasing frequency of localized heavy rainfall events, or intense downpours, during subhourly durations in Seoul. As shown in Figure 6i, for a 6 h duration, [PERSON]'s \(I\) often exhibited statistical significance annually. Although [PERSON]'s \(I\) values showed an increasing trend, the MK test revealed no statistical significance. Figure 7 provides a reillustration of Figure 5i. In Figure 7, the linear slope is nearly zero, indicating that the CV series exhibited almost no trend. By connecting the CV values of years with high CVs using a red dashed line and those of years with low CVs using a blue dashed line, we observe a gradual increase in the variability of CV values. A high CV value indicates the occurrence of strong, localized heavy rainfall that contributed to the formation of the AMP. In contrast, a low CV value implies that the AMP occurred evenly across Seoul because of large-scale systems, such as typhoons and monseons, rather than localized heavy rainfall. Observing the red and blue dashed lines reveals that for the 6 h duration, rainfall events may be influenced by both localized heavy rainfall and large-scale systems. Since urban flooding typically occurs during subhourly durations, a 6 h duration implies a high probability that urban flooding has already taken place. However, when the drainage basin is large, the 6 h duration may warrant consideration. For areas or hydraulic structures affected by the 6 h duration in Seoul, a more detailed analysis is necessary to determine appropriate design criteria and implement damage mitigation measures.
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 5 Experiment  
### Experimental Set Up  
In order to verify the effectiveness of our pruning optimization method, we implemented it in Java and verified in the Linux environment. For the experiment, a PC (Intel Xeon E5-2620 V2, 16 GB memory, 2 TB disk) running Ubuntu 16.04 64-bit was used.  
The experiments used the global AIS (Automatic Identification System) [19] data from 1 July to 5 July 2012 as the data source, in which each AIS message was regarded as a spatial location of a moving object at a timestamp. We randomly extracted some data from the data source to form five datasets. The statistical information is shown in Table 3. When constructing a 3 DR-tree for a dataset during the experiment, it was considered that the motion track between the spatial positions with a long time interval may be significantly different from the real motion track, according to the maximum transmission time interval of the AIS message, we made the following provisions: If an AIS message had an adjacent message within six minutes of the time interval, the motion track between the spatial positions of the two messages was taken as a trajectory segment. Otherwise, it was regarded as a special trajectory segment with the same start time and end time. We evaluated the performance of BFT with TDS [10] and CDQLTD [12]. The query performances were evaluated under different data scales, different time ranges and different distance thresholds. The evaluated metrics included the following four aspects:  
1. Number of the accessed nodes: The sum of the number of index nodes and the number of trajectory segments checked during pruning;
2. Selectivity: The ratio of the number of candidate trajectory segments obtained by pruning to the number of trajectory segments contained in the dataset;
3. Pruning Latency: The execution time of the pruning step during a query;
4. Query Latency: The execution time of a query.  
For each experiment, we performed 50 queries, and used the average as the experiment result. By default, we used AIS05 as the experimental dataset, 24 h as the query time range, and two nautical miles as the distance threshold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trajectory** & **Candidate Quantity** & **Candidate Quantity** & **Candidate Quantity** \\
**Segment** & **(BFT)** & **(TDS)** & **(CDQLTD)** \\ \hline \(l_{1}\) & 0 & 1 & 15 \\ \(l_{2}\) & 2 & 2 & 15 \\ \(l_{3}\) & 8 & 10 & 15 \\ \(l_{4}\) & 10 & 11 & 15 \\ \(l_{5}\) & 4 & 4 & 15 \\ \(l_{6}\) & 6 & 7 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of pruning results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **No. of Moving Objects** & **No. of AIS Messages** \\ \hline AIS01 & 73,490 & 10,000,000 \\ AIS02 & 95,732 & 20,000,000 \\ AIS03 & 110,566 & 40,000,000 \\ AIS04 & 136,932 & 80,000,000 \\ AIS05 & 152,807 & 160,000,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of Datasets.