esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Nuclear Segmentation Evaluation Metrics  
To evaluate the overall score for nuclear instance segmentation, three metrics widely used in the literature were used: Ensemble Dice [40], Aggregated Jaccard Index (\(AJI\)) [41], and Panoptic Quality (\(PQ\)) [42]. Ensemble Dice computes and aggregates Dice coefficient (\(DICE\)) per nucleus, which is defined as:  
\[DICE=2\times\frac{X\cap Y}{|X|+|Y|} \tag{3}\]  
where \(X\) denotes the ground truth and \(Y\) is a prediction. It is usually used to calculate the similarity of two samples. \(AJI\) calculates the ratio of an aggregated intersection cardinality and an aggregated union cardinality between the ground truth and the prediction. \(PQ\) for nuclear segmentation is defined as the product of the Detection Quality (\(DQ\)) and the Segmentation Quality (\(SQ\)):  
\[PQ=DQ\times SQ \tag{4}\]  
The Detection Quality (\(DQ\)) can be interpreted as the F1-score, which is widely used to evaluate instance detection. At the same time, segmentation quality (\(SQ\)) informs how close each correctly detected instance is to its matched ground truth. The following formulas describe these measures:  
\[DQ=\frac{|TP|}{|TP|+\frac{1}{2}|FP|+\frac{1}{2}|FN|} \tag{5}\] \[SQ=\frac{\sum_{(x,y)\in TP}IoU(x,y)}{|TP|} \tag{6}\]  
where \(x\) stands for a ground truth segment, \(y\) is a prediction segment and \(IoU\) denotes intersection over union. \(PQ\) provides a direct insight into the detection quality of individual nuclear instances and the segmentation of each detected nucleus. If the \(IoU(x,y)>0.5\) each \((x,y)\) pair is unique [42]. This property allows to divide the predicted and ground truth segments into the following sets: true positives (TP), false positives (FP), and false negatives (FN), which represent correctly matched pairs of segments, mismatched predicted segments, and mismatched ground truth segments, respectively.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 4 Performance Evaluation
### Evaluation Metrics  
* Delivery Ratio: it is the ratio of successfully received packets using the network server to the overall sent packets by all participating devices over the whole simulation time. The metric is evaluated according to the following equation. \[Delivery\ Ratio=\frac{\sum\textit{rcvdPackets by NS}}{\sum\textit{ sentPacket by EDs}}\] (2)
* Total received packets by network server from out-of-range devices using the proposed mechanism compared to the total sent uplink traffic from the same devices.
* Amount of received packets by the main gateway' LoRa receiver that is below the sensitivity of the gateway itself. The receiver sensitivity expresses the capability of the gateway radio receiver to extract the transmitted signal and, hence, indicates the weakness of the signal. The metric indicates the ability of the proposed system to decrease the overall lost traffic due to arriving below the sensitivity of the gateway.
* Average power consumption by an end device per day. It is calculated according to the following equation. \[Power\ Consumption=\frac{\left(\frac{\sum\textit{consumed Power by EDs}}{\sum\textit{EDs}}\right)}{\textit{simulation duration(days)}}\] (3)
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
## 4 Simulation and Application of the Algorithm  
This section presents the simulation and experiment for self-collision avoidance by a robot using the proposed algorithm. The robot has dual 7-DOF arms, as shown in Figure 1. To avoid collision of the links with one arm, the joints were set as indicated in Table 1. According to the specifications of the robot, the maximum velocity of the end-effect was below 2.5 m/s.  
The collision model was constructed with 10 spheres centered at 10 control points on each arm, as illustrated in Figure 7. The red lines in Figure 7 represent the distances between the control points on different arms. The diameters of the spheres are listed in Table 2.  
As the collision model is built, the sensitivity index \(\beta_{lr}\) is calculated according to Section 2. The new joint velocities \(\dot{\theta}_{new,k}=[\dot{\theta}_{k,1}..\dot{\theta}_{kj-1},\dot{\theta}_{kj} ]\in R^{j}\) (\(k=l\) or \(r\)) are then calculated for self-collision avoidance when one spherical volume collides with another spherical volume.
# A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams
## 5 Conclusions  
We present a new method implemented in a GIS-based framework to detect hot and cold spots from social streams. A lightweight document emotion classification method is applied to measure the relevance of pleasant and unpleasant emotions in documents where a document is related to a subzone and a time frame. A fuzzy partition is used to fuzzify pleasant and unpleasant emotions' relevance. Subzones classified with pleasant (unpleasant) emotion relevance over a prefixed threshold are labeled as cold (hot) spots in all time frames.  
We have applied the FESC framework to an urban study area given by the municipalities of the northeastern area of the province of Naples (Italy), analyzing the citizens' discomfort due to the presence of heat wave phenomena in the summer seasons of the years from 2020 to 2022. The findings indicate that the study area's two hot spots are located in areas with large densities of people and buildings. On the other hand, the identified cold patch is marked by an urban region with a lower population density than the research area's average.  
The results obtained confirm the effectiveness of our approach, which detects hot and cold spots on the basis of the hidden knowledge inserted in social networks connected to the perception of comfort and discomfort of citizens in the presence of the analyzed phenomenon. The hot and cold spot detection algorithms in the literature only use measurement or collected data, in some cases insufficient to represent the critical issues actually perceived and present in the area.  
In the future, we plan to use our methodology in various settlements to analyze the discomfort of citizens in the presence of various types of climatic and environmental phenomena. In addition, we intend to compare our method with the traditional hot and cold spot detection algorithms by integrating datasets consisting of geo-referenced event points with hidden knowledge extracted from social networks.  
Conceptualization, B.C., F.D.M. and V.M.; methodology, B.C., F.D.M. and V.M.; software, B.C., F.D.M. and V.M.; validation, B.C., F.D.M. and V.M.; formal analysis, B.C., F.D.M. and V.M.; investigation, B.C., F.D.M. and V.M.; resources, B.C., F.D.M. and V.M.; data curation, B.C., F.D.M. and V.M.; writing--original draft preparation, B.C., F.D.M. and V.M.; writing--review and editing, B.C., F.D.M. and V.M.; visualization, B.C., F.D.M. and V.M.; supervision, B.C., F.D.M. and V.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data sharing is not applicable.  
The authors declare no conflict of interest.
## 3 The Impact of Size and Dynamic Limitations
### Impact on Fuel Cell Performance
Moreover, as observed in the experimental results in Figure 7, the degradation rate of the fuel cell is higher under high-power conditions. In Figure 8d, strict current density limitations result in a longer time for the fuel cell to reach its maximum power. For both power designs, an increase in the minimum operating power and a decrease in the intensity and amplitude of power changes can be observed. Based on the analysis in Figure 8c, it can be inferred that the degradation rate of the fuel cell decreases in this case, but the equivalent hydrogen consumption increases. According to the simulation results, low-power fuel cells are unable to maintain the charge characteristics of the power battery. Figure 8: Power variation in fuel cells under CHTC. **(a)** The rate of change in current density is 1. **(b)** The rate of change in current density is 0.1 **(c)** The rate of change in current density is 0.01. **(d)** The rate of change in current density is 0.001.
## 3 Results and Discussion
### Dynamic Changes during Processing Analyzed via GC-IMS  
#### 3.2.1 GC-IMS Topographic Plots in Different Processing Stages of SFABT  
In order to facilitate the identification of volatile components and their variation regularity in SFABT during the manufacturing process, the technique known as GC-IMS was employed to extract comprehensive IMS insights from the examined samples. The volatile components in SFABT are shown via topographic plots (Figure 2A), with the ordinate signifying the retention time, and the abscissa capturing the ion migration time [15]. A red vertical line positioned at abscissa 1.0 represents the reactive ion peak (RIP), which has been pre-normalized. The background of each topographical profile is rendered in pristine blue, with the color signifying the intensity levels of the volatile compounds. Specifically, shades of red designate higher concentrations, while white demonstrates diminished levels. The deeper the shade, the higher the signal intensity. To facilitate visualization and comparative analysis, a spectral diagram obtained from fresh leaves was chosen as the benchmark, and the corresponding spectrum displayed by processed tea samples was deducted from the referential data. If a specific volatile compound remained consistent, the post-deduction background would appear white. The presence of red and blue implies that the concentration of certain volatile components was either heightened or reduced relative to the reference sample. Most of the signals were distributed in a retention time of 100-400 s and a drift time of 1.0-1.8. It is evident from Figure 2B that certain compounds diminished in SFABT as the processing phases progressed, and novel substances emerged, likely owing to the intricate biological and chemical mechanisms inherent in the fabrication process [6].
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 2 Materials And Methods
### Datasets
#### 2.2.2 Land Registry Data  
The difficulty of reaching the official government data is one of the main reasons we aim to acquire LUM solely using openly and widely available point-based data. Nevertheless, we need ground truth data from the official sources to evaluate the results and compare the predicted LUM values.  
Hence, we legally and ethically gathered 650,000 official parcel data in the form of shapefiles from the Turkish land registry website ([[https://parselsorgu.tkgm.gov.tr](https://parselsorgu.tkgm.gov.tr)]([https://parselsorgu.tkgm.gov.tr](https://parselsorgu.tkgm.gov.tr)), accessed on 15 May 2020) via various web automation and web scraping techniques on the cloud in a fair usage manner. We used Python's Selenium package to implement the scraper and deployed it on the Google Cloud. We utilized virtual machines with 2 GBs of RAM and 20 GBs of storage on the cloud and managed 100 servers for two months to make the data acquisition faster. We also used Google Cloud Storage to consolidate all the collected data.  
The crawled data were packaged as compressed shapefiles containing various information about the land, including but not limited to the borders, area, and land type. These shapefiles were later used to calculate the ground truth results.  
One of the vital information included in the official land registry data is the land type labels. After scrutinizing the labels, we found that the following labels can be used to infer the residential areas: home, apartment, housing, domicile, and villa.  
Therefore, we labeled the areas associated with the above tags as residential and others as non-residential.
## 2 Methods and Infrastructure
The effective S4 is calculated by taking the ratio of the standard deviation of \(C/N_{0}\) to the mean of \(C/N_{0}\) over a 1-minute period as:  
\[S4=\frac{\left[\left\langle(C/N_{0})^{2}\right\rangle-\left\langle(C/N_{0}) \right\rangle^{2}\right]}{\left\langle(C/N_{0})\right\rangle^{2}}. \tag{1}\]  
This algorithm is different from the standard routines to compute \(S4\), where signal intensity \(I\) is used in Equation (1) instead of \(C/N_{0}\)[21]. However, a benchmarking indicated the effective \(S4\) values computed by Equation (1) are highly correlated with the \(S4\) generated from the NovAtel GPStation-6 receiver, a commercial off-the-shell (COTS) ionospheric TEC and scintillation monitor. The STK visualization system can visualize the satellite trajectory in either real-time or replay mode. More details of these systems of VTFTFIB have been introduced in [18]. The new version of the VTFTFIB (or VTFTFIB 2.0) is an extension of the VTFTFIB by adding the Galileo relevant components, including a Spinert GSS-7800 Galileo signal generator, an upgraded version of navigation and control system, and a multi-constellation version of ionospheric remote sensing system. The GSS-7800 signal generator is capable of simulating Galileo (E1, E5a, E5b, E5 AltBOC) RF signals. The GSS-8000 GPS signal generator and GSS-7800 Galileo signal generator have been synchronized together to simulate multi-constellation (GPS + Galileo) scenarios. The new navigation and control system is designed to handle both GPS and Galileo data from the OEM628 receiver. The previous ionospheric remote sensing is expanded to process and generate Galileo TEC, _Ne_, and effective S4. Figure 2: Total electron content (TEC) processing algorithm flowchart.
# Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages
###### Abstract  
This paper proposes a model which uses the greedy algorithm to select the optimal intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD), namely the greedy empirical mode decomposition (GEMD) model. The optimal IMFs can more sufficiently represent the characteristics of damage bearings since the proposed GEMD model effectively selects some IMFs not affected by noise. To validate the superiority of the proposed GEMD model, various damage types of motor bearings were shaped by electrical discharge machining (EDM) in this experiment. The measured motor current signals of various types were decomposed to IMFs by using EMD. Then the optimal IMFs can be obtained by using the proposed GEMD model. The results show that the Hilbert-Huang transform (HHT) spectrums when using the optimal IMFs become easier in the detection system than when using all IMFs. Simultaneously, the detection accuracy of motor bearing damages is increased by using the features extracted from the lower complexity HHT spectrum. The average detection accuracy can be also improved from 69.5% to 74.6% by using the features extracted from the GEMD-HHT spectrum even in a noise interference 10 dB  
gredy algorithm; empirical mode decomposition; intrinsic mode function; Hilbert transform Article Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages
## 5 HAWT Blade Design
### Blade Plan Shape and Quantity  
The ideal plan form of a HAWT rotor blade is defined using the BEM method by calculating the chord length according to Betz limit, local air velocities and aerofoil lift. Several theories exist for calculating the optimum chord length which range in complexity [1, 4, 10, 12], with the simplest theory based on the Betz optimisation [Equation (3)] [1]. For blades with tip speed ratios of six to nine utilising aerofoil sections with negligible drag and tip losses, [PERSON]'s momentum theory gives a good approximation [1]. In instances of low tip speeds, high drag aerofoil sections and blade sections around the hub, this method could be considered inaccurate. In such cases, wake and drag losses should be accounted for [4, 12]. The Betz method gives the basic shape of the modern wind turbine blade (Figure 2). However, in practice more advanced methods of optimization are often used [12, 13, 14].  
\[\begin{array}{l}\text{r}=\text{ radius (m)}\\ \text{n}=\text{Blade quantity}\\ \text{C}_{\text{L}}  
Generally, in practice the chord length is simplified to facilitate manufacture and which involves some linearization of the increasing chord length (Figure 4). The associated losses signify that simplification can be justified by a significant production cost saving.  
For optimum chord dimensioning (Equation 3) the quantity of blades is considered negligible in terms of efficiency. However, in practice when blade losses are considered a 3% loss is incurred for two bladed designs [1] and a 7%-13% loss for one bladed design [6] when compared to three blades. A four bladed design offers marginal efficiency increases which do not justify the manufacturing cost of an extra blade. Tower loading must also be considered when choosing the appropriate blade quantity [6]. Four, three, two and one bladed designs lead to increased dynamic loads, respectively [16].  
Figure 4: Efficiency losses as a result of simplification to ideal chord length [15].  
Figure 3: Optimal blade plan shape for alternate design tip speed ratios and number of blades [1].  
The imposing size and location of wind turbines signify that the visual impact must be considered. Three bladed designs are said to appear smoother in rotation therefore more aesthetically pleasing. Faster one and two bladed designs have an apparent jerky motion [1]. Three bladed rotors are also thought to appear more orderly when in the stationary position [17].
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 5 Experiment and Evaluation
### Performance and Security Analysis
#### 5.4.1 Performance Improvements  
Given the above numerical results in terms of processing time and running time resource usages, our PoENF consensus is more computationally efficient than the PoW-based methods. Such a lightweight property of PoENF is promising for reducing energy consumption in mining processes and can lower demands on system capability for participants. Thus, resource-limited IoVT devices can directly work as validators (miners) rather than depending on support from an intermediate consensus layer by outsourcing mining tasks on fog networks or cloud servers. Compared with these hardware dependent solutions, such as REM based on Intel SGX and PoR requiring large local storage, our PoENF consensus relies on a platform independent algorithm to extract ENF-containing multimedia signals from recordings as ENF proofs. Therefore, it is promising to address heterogeneity issues as we integrate blockchain technology with IoVT systems that include multiple non-standard platforms.  
EconLedger achieves communication efficiency by executing consensus protocol within a random selected PoENF committee. Such a small scale consensus network imposes low levels of data transfer overhead on IoVT systems at the network of edge, which has limited bandwidth. In addition, communication complexity for each validator is linearly scaled to PoENF committee size, as shown in Figure 6. Thus, limited data transmission also means lower energy consumption on devices during communication handling tasks. Unlike non-scalable BFT-based solutions that rely on a pre-fixed set of validators, EconLedger aims to improve scalability by requiring a randomly elected consensus committee to delegate other nodes of the network. As a tradeoff, EconLedger is actually a partially decentralized blockchain network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ethereum** & **Tendermint** & **EconLedger** \\ \hline \(tx\)**committed time (s)** & 4.6 & 3.0 & 5.5 \\ \hline \(tx\)**rate (tx/s)** & 124 & 334 & 436 \\ \hline CPU **usage (\%)** & 103 & 38.6 & 15.2 \\ \hline
**Memory usage (MB)** & 1200 & 72 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative evaluation of launching transactions on different blockchain platforms.  
In EconLedger, raw data are saved into off-chain storage deployed on a DDB network, while only references of data are encapsulated into transactions that are finalized on distributed ledger (on-chain storage). As a reference is a fixed length of hash value disregarding format or size of the source data, such light transactions can be used to verified complicated data in use over IoVT systems, such as multimedia recordings, contextual information, and trained models, etc. Moreover, each tx has fixed and small size such that a block can record more txs. As a result, the txs rate increased given that the block confirmation time is stable.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Obtaining Pure Oxides and Metals
#### 3.7.1 Cementation  
Gallium can be isolated from neutral solutions using zinc dust. As a result of this reaction, gallium is \"replaced\" in the solution with zinc, which dissolves during the process while gallium is precipitated [96].  
Zinc or aluminum are most often used for indium cementation [96], with the best results achieved in chloride solutions. Reducing agents are used both in the form of plates and powders. The zinc plate proves to be low efficiency; however, Zn powder performs well. In the case of aluminum, both the plates and the powder work well, and virtually complete recoveries of indium could be achieved within 10-60 min--depending on the pH and temperature of the solutions. The produced sponge can be removed easily from the Al plates and efficiently melted under a protective atmosphere, vacuum, or salt flux. The purity of the produced sponges can generally be above 99.9%, and the reduction on the Al plates produces the purest indium [97]. The cemented sponge can be vacuum melted to produce an indium anode block of \(>\)99.9% purity used for electrorefining. Galvanostatic and potentiodynamic results show that the optimal electrolyte conditions are 50 g/dm3 of In, 1 M of free Cl\({}^{-}\) ions, and a pH of 1. The cathodic deposition of Zn, Mn, and Fe is negligible; however, the prior removal of Cu and Sn from the electrolyte is required for producing high-purity indium [41].  
The method of cementation that uses the aluminum plate is highly effective in recovering indium from the pregnant leach solution after processing of the waste indium phosphide (InP). When the reaction temperature is maintained at 85 \({}^{\circ}\)C, the indium sponge precipitates and can be separated easily from the surface of the aluminum plate, thus simplifying the cementation process. The cementation process can achieve over 90% recovery efficiency for indium in its metallic sponge form [42].
## 2 System Modeling
### NB-IoT Energy Consumption Modeling Based on 3 GPP Standards  
As shown in Table 1[14], the total energy consumption can be broken down into four main blocks for an IoT device to operate: \(E_{Communication}\) required energy to communicate the data, which is typically 60% of the total; \(E_{\text{Collection}}\) for collecting (6-20% of the total) and \(E_{\text{Processing}}\) (15-30%) for processing collected data. Furthermore, a small portion of the energy (1-6%), which we can call \(E_{system}\), is consumed to wake up the machines periodically or run a real-time operating system. Summing all these energy terms together expresses the total consumed energy of the devices \(E_{Device}\) as can be seen in the following expression:  
\[E_{\text{Device}}=E_{\text{Communication}}+E_{\text{Collection}}+E_{\text{Processing }}+E_{\text{System}} \tag{6}\]  
In order for the IoT device to perform the tasks, it needs to wake up each time and complete collection, processing, and communication of data in a particular time that we can call \(T_{Recording}\). Additionally, \(T_{\text{messaging}}\) can be defined as the required time that IoT devices need to communicate the processed message.  
The data processing and communications operations for the smart metering application can be split into three steps:  
* Energy consumption measurement of each circuit in the building;
* Applying compression techniques to the collected data to reduce the size of data using smart meter hardware
* Updating the Energy Data Center (EDC) information by transmitting the compressed data or detecting an unusual situation to activate an alarm, by creating a data packet of duration Message \(T_{messaging}\).