esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Smartphone Size and Hand Size on Grip Posture in One-Handed Hard Key Operations
## 5 Conclusions  
The present study examined preferred grip postures on smartphone mock-ups in different screen sizes of 3.0\"-7.0\" by users with different hand sizes in one-handed hard key operations while simulating major smartphone tasks while standing in a laboratory experiment. The grip postures were encoded by the locations (left side: L, right side: R, top: T, bottom: B, front: F, and back: K) on a smartphone and the number of fingers at each contact location. A total of nine grip postures were identified as those used for hard key operations, and out of those nine, three postures (L3-R1-K1: 70.0%, L4-R1: 13.3%, L3-R1-T1: 12.0%) were found to be dominant. Changes in grip posture frequency distribution by smartphone size and hand size were identified--the larger the smartphone size or hand size, the higher the frequency of L3-R1-K1. The grip posture frequency distribution by smartphone size would be of use to determine the optimal locations of hard keys on a smartphone of a particular size. The findings of the present study need to be verified further by users with hand sizes beyond the hand size range of the present study and use contexts other than standing.  
**Author Contributions:** Conceptualization, Y.C. and H.Y.; methodology, Y.C., J.P., and H.Y.; software, J.P. and Y.C.; validation, Y.C., X.Y. and W.L.; formal analysis, Y.C. and W.L.; investigation, Y.C. and J.P.; data curation, Y.C. and J.P.; writing--original draft preparation, Y.C. and X.Y.; writing--review and editing, W.L. and H.Y.; visualization, Y.C.; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was jointly supported by the research programs (2017M3C1B6070526; 2018R1A2A2A 05023299) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST), those (No. 10063384; R0004840, 2017) of the Ministry of Trade, Industry, and Energy (MOTIE) under Industrial Technology Innovation Program, and the Biomedical Research Institute Fund, Chonbuk National University Hospital.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
### Algorithms for Optical Particle Sensors
#### 1.2.2 Application of Standard Algorithm to Plantower Sensors  
The standard algorithm in Equation (2) was first applied to Plantower PMS 5003 sensors used in PurpleAir PA-II monitors [4; 19]. These studies tested 33 PurpleAir monitors within 500 m of 27 regulatory FEM/FRM monitors in California, finding the CF to be 3.0. The algorithm was therefore named ALT-CF3, where the \"ALT\" suggests an alternative algorithm to those supplied by Plantower and the \"CF3\" is the calibration factor found in these two studies. Therefore, the algorithm as applied to the PurpleAir monitors using Plantower PMS 5003 sensors is equal to  
\[\text{ALT-CF3}=3(\text{aN1}+\text{bN2}+\text{cN3}) \tag{3}\]  
where the a, b, and c coefficients are those in the above table.  
This algorithm is freely available on the PurpleAir API site, where it has been renamed \"pm2.5 alt\". A major study of 3000 indoor air monitors selected pm2.5 alt as the only algorithm to be used. The study is expected appear soon in Proceedings of the National Academy of Sciences (PNAS).  
A more recent study showed that both the PMS 1003 and PMS 5003 sensors have an identical (within 2%) CF of 3.4 [20], leading to the revised Equation (3)  
\[\text{ALT-CF3.4}=3.4(\text{aN1}+\text{bN2}+\text{cN3}) \tag{4}\]  
where a, b, and c are still unchanged from the values in Table 2 above, but the CF is changed from 3 to 3.4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bin** & **D\({}_{\text{p}}\) (Geom. Mean) (um)** & **Volume (um3/100)** & **Mass (Density = 1)** \\  & & & **(ug/m3/100)** \\ \hline
0.3–0.5 μm & 0.387298335 & 0.000304183 & a = 0.000304183 \\ \hline
0.5–1 μm & 0.707106781 & 0.001851201 & b = 0.001851201 \\ \hline
1–2.5 μm & 1.58113883 & 0.020697059 & c = 0.020697059 \\ \hline \hline \end{tabular}  
This CF of 3.4 has been used in several studies and is used in this study as well. For persons wanting to use this most recent value, it is sufficient to multiply the value given in the API site by 3.4/3, or about a 12% increase in PM\({}_{2.5}\) values.  
An advantage in using equations of the general form of Equation (2) above is that it allows an estimate of the contribution made by each size category to the total mass (PM\({}_{2.5}\)). For example, typical values of N1, N2, and N3 occurring in a Santa Rosa home were determined from a full-year dataset from 1 January 2021 to 31 December 2021. These are entered into Equation (4) to determine the fractional contribution of each bin to total PM\({}_{2.5}\) during this period (Table 3 and Figure 1).
## 1 Introduction
Guided Wave technology faces limitations over long distances, Metal Magnetic Memory is restricted to ferromagnetic materials and susceptible to magnetic interference, and In-Line Inspection requires specific pipeline configurations, potentially disrupting operations [8; 19; 21; 23; 39; 40]. Given these limitations, coupled with the scarcity of research focused on pipeline inspection for water supply systems in mining or rural settings, a comprehensive assessment of suitable inspection techniques is needed. This study addresses this research gap by evaluating and comparing the performance, detection range, and operational suitability of GW, MMM, and ILI for detecting leaks and defects in mining water supply pipelines. This study facilitates more effective technology selection, contributing to long-term pipeline reliability, safety, and integrity management in mining systems. By demonstrating the benefits of a tailored technology approach for the unique operational challenges of mining environments, this research provides practical guidance for technology selection and supports the industry's sustainability and efficiency goals.
## 3 Case Study
### SKAB Dataset  
#### 3.2.1 Datasets Description  
The Skoltech Anomaly Benchmark (SKAB) [34], extensively utilized in industrial anomaly detection, encompasses a variety of subsystems within a simulated industrial environment. This includes an array of sensors, such as those for temperature, pressure, and flow rates. SKAB's dataset comprises multiple features in each time-series sample, reflecting diverse sensor readings. It includes a baseline of normal operational data, alongside a series of artificially introduced anomalies to simulate industrial faults (Figure 7). These anomalies, present in both the training and testing subsets, are designed to replicate realistic conditions. SKAB consists of 34 time-series sets, with one anomaly-free set and 33 others transitioning from normal to anomalous states due to induced faults in the pumping system. The detection of anomalies, originating from various sources like valves and pumps, is the primary focus of this dataset. Despite their subtlety, as illustrated in Figure 8, these anomalies highlight the complexity of detection tasks. Originating from measurements of a single system, SKAB's datasets are characterized by highly correlated channels, where anomalous data form a significant portion of each set.  
In preparation for the application of the GOPML algorithm, the SKAB dataset was methodically preprocessed. This involved segmenting the dataset using a sliding window approach, where each window comprised 30 consecutive data points to adequately capture the dynamics of the industrial processes, with a step size of 15 points ensuring temporal overlap. Each of these windows was treated as an individual task, forming a structured dataset \(D=\{x^{(i)},y^{(i)}\}\) that correlated inputs with their respective labels, vital for representing both normal operations and the 33 different types of anomalies present in SKAB. This task-specific formation allowed for nuanced pattern recognition and adaptation of the GOPML algorithm to the complexities of each anomaly type, enhancing its diagnostic capabilities. Additionally, clustering of these tasks based on feature similarity was conducted, aligning with GOPML's methodology to facilitate more effective gradient aggregation and parameter updates within the algorithm.  
Figure 6: Comparative fault classification accuracy across various scenarios in the TEP dataset.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 3 Experimental Analysis
### Comparion and Analysis of the Proposed Model with Other Models
#### 3.4.1 Comparison of the Proposed Model with Transformer Variant Models  
The proposed model is compared with CARD, Autoformer, Informer, NSTransformer, and Autoformer models. The comparison of model prediction curves is shown in Figure 15, and the evaluation metrics for the test set are compared in Table 5.  
In Figure 15, red represents the model proposed in this article, and the blue solid line represents the actual wind power data. From Figure 15, it is evident that the model proposed in this article has the highest degree of fitting with real wind power data in terms of prediction results. In Table 5, the EWMA-KAN-Transformer model shows a reduction in MAE and RMSE by 4.05 MW and 7.96 MW and an increase in R\({}^{2}\) by 4.19% compared to the CARD model. Compared to the Autoformer model, the MAE and RMSE are reduced by 1.42 MW and 1.48 MW, and R\({}^{2}\) is increased by 0.55%. Compared to the Informer model, the MAE and RMSE are reduced by 0.38 MW and 1.30 MW, and R\({}^{2}\) is increased by 0.48%. Compared to the NSTransformer model, the MAE and RMSE are reduced by 0.05 MW  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE/MW** & **RMSE/MW** & **R\({}^{2}\)/\%** \\ \hline KAN-Transformer & 4.46 & 7.72 & 98.61 \\ EWMA-KAN-Transformer & 4.38 & 7.37 & 98.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of test set evaluation metrics between the KAN-Transformer model and the EWMA-KAN-Transformer model.  
Figure 15: Comparison of prediction curves of Transformer variant models and EWMA-KAN-Transformer model.  
and 0.79 MW, and R\({}^{2}\) is increased by 0.28%. This demonstrates that the proposed model achieves the highest prediction accuracy among the four Transformer variant models.
###### Abstract  
Wireless Sensor Networks (WSNs) continue to provide essential services for various applications such as surveillance, data gathering, and data transmission from hazardous environments to safer destinations. This has been enhanced by the energy-efficient routing protocols that are mostly designed for such purposes. Gateway-based Energy-Aware Multi-hop Routing protocol (MGEAR) is one of the homogenous routing schemes that was recently designed to more efficiently reduce the energy consumption of distant nodes. However, it has been found that the protocol has a high energy consumption rate, lower stability period, and poorer data transmission to the Base station (BS) when it was deployed for a longer period of time. In this paper, an enhanced Heterogeneous Gateway-based Energy-Aware multi-hop routing protocol (HMGEAR) is proposed. The proposed routing scheme is based on the introduction of heterogeneous nodes in the existing scheme, selection of the head based on the residual energy, introduction of multi-hop communication strategy in all the regions of the network, and implementation of energy hole elimination technique. All these strategies are aiming at reducing energy consumption and extend the life of the network. Results show that the proposed routing scheme outperforms two existing ones in terms of stability period, throughputs, residual energy, and the lifetime of the network.  
wireless sensor networks; heterogeneous; hazardous environment; energy efficient +
Footnote †: journal: Information  
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## 3 Results
### Computational Efficiency  
The time for the 10 runs of the optimization procedure was recorded for each algorithm, each function (F01-F12), and each number of dimensions (\(D\) = 5, 10, 30, 50). Some objective functions are harder to calculate and the corresponding optimization problem also becomes harder and more time consuming. Figure 9 shows the time needed for the optimization of each function, for each method, and for \(D\) = 5, 10, 30, 50. It is obvious that problem F06 is much harder than the other problems, because of the complexity of this objective function. The time in Figure 9 is in logarithmic scale.  
Figure 8: Convergence history of the various optimizers for 50 dimensions (\(D\) = 50), for all 12 objective functions (average of 10 runs).  
The computational time needed by PROS is much smaller than the one needed by GA, PSO, or DE in most of the cases, with the exception of function F06. In addition, it is slightly larger than the one of the PRS method, which, however, shows very poor performance in terms of the final optimum achieved. The PROS method is much faster than all three established optimization algorithms (based on the same number of function evaluations) in almost all the problems, with only very few exceptions.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 4 An Overview of Deep Learning Techniques
### Models Used for Extracting Temporal Data  
CNNs can be applied to video data in the same way as image data, where every frame is treated as a separate image. On the other hand, if the temporal information needs to be extracted from the video, other approaches can be used. For example, CNNs can be combined with an RNN model [25], where the CNN model is used for extracting the image features, and the RNN model is utilized for capturing temporal dependencies. This method is known as the fusion approach, where the model architecture consists of convolutional layers, LSTM layers, and finally fully connected and softmax layers [26]. The CNN and LSTM models can be fused in three ways. A feature-level fusion can be implemented by combining the outputs after the features are extracted by the sequence of convolution and LSTM layers from the input data. After the fusion, a classifier can be used to develop a feature-level fusion model. Score-level fusion is another type of fusion that can take place between or after the softmax and fully connected layers. In this type of fusion, the input data are passed through the CNN, LSTM layers, and a classifier, resulting in a probability score. The fusion of multiple scores from different models is a score-level fusion. The decision-level fusion is similar to a score-level fusion, except that the fusion is performed after the network's prediction. This type of fusion is entirely associated with the network's predicted output and is not related to the score/probability used for the decision.  
Another deep learning architecture suitable for extracting temporal features is a 3D CNN [27]. The 3D CNN comprises several consecutive layers of 3D convolutions and operates by convolving a four-dimensional kernel over a four-dimensional data input in both space and time. These four dimensions for the input data and kernel come from the temporal dimension (i.e., the number of video frames), two spatial dimensions, and the channel dimension (e.g., an RGB image has three channels).
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 1 Introduction
### Vision and Pillars of Industry 5.0 and Society 5.0  
Industry 5.0 is a term used to describe the next phase of industrial development, which is characterized by the integration of advanced technologies such as Artificial Intelligence, the Internet of Things, and robotics with human labor to create more efficient and productive manufacturing processes. The vision of Industry 5.0 is to create a manufacturing environment that is both highly efficient and sustainable while also promoting social and economic benefits for workers and communities. The technological pillars of Industry 5.0 include, among others, the following [10]:  
* Cyber-physical systems (CPS): this refers to the integration of physical and digital systems, allowing for real-time monitoring and control of manufacturing processes [11].
* Artificial Intelligence (AI) and Machine Learning: AI algorithms can be used to analyze vast amounts of data to identify patterns and optimize manufacturing processes, improving productivity and reducing waste [12].
* The Internet of Things (IoT): The IoT connects devices and sensors throughout the manufacturing process, providing real-time data on the status of equipment and materials [13].
* Additive Manufacturing (AM): AM technologies such as 3D printing allow for the creation of complex and customized parts, reducing waste and increasing efficiency [14].
* eXtended Reality (XR): XR technologies can be used to train workers and provide real-time information on the manufacturing process, improving safety and productivity [15].  
As such, the vision of Industry 5.0 is to create a manufacturing environment that is highly efficient, sustainable, and socially responsible, promoting the well-being of both workers and communities while driving economic growth (Figure 1).
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 1 Introduction  
Land subsidence (LS) is defined as the settlement of the ground, which can occur gradually or rapidly, and which usually occurs in an area with unconsolidated alluvial deposits. It is well known to occur in many cities in Indonesia, such as Jakarta [1, 2], Bandung [3, 4], Semarang [2, 5], and Pekalongan [6, 7]. Many studies have been conducted using various methods to measure the subsidence rate along with the possible cause in the cities; however, the most prominent cause of LS is the ground being subjected to groundwater overexploitation in an unconsolidated alluvial or basin-fill aquifer [8, 9]. In addition, LShas negative environmental and socioeconomic impacts, such as infrastructure damage, worsening flooding, loss of housing, and reduced groundwater capacity.  
While LS is ongoing in the aforementioned cities, the fact that groundwater exploitation due to industrial, household, and agricultural needs is not limited to those cities does not rule out the possibility that land subsidence will still occur in other areas. For example, LS also occurs in Cirebon, a city with a population of approximately 350.000 residents on the north coast of West Java Province, with rate of 25 mm/year [10]. Moreover, in addition to groundwater, oil and gas exploitation is also known to cause LS in Indonesia [8], and the distribution of oil and gas fields is spread widely throughout the Indonesian archipelago.  
Mapping and monitoring of LS in Indonesia has been performed geodetically using various methods, such as Global Navigation Satellite System (GNSS) observation [10,11], leveling [12], and Interferometric Synthetic Aperture Radar (InSAR) [1,10]. However, most of the studies are focused on one It can be understood that ground surveys have limitations in the survey area, but even InSAR observation cannot be easily performed for a very large area, especially when performing time series analysis.  
A large area of LS mapping in Indonesia was performed in Java and Sumatra Island using ALOS PALSAR data from 2006 to 2011, and it revealed two other places in Java Island previously unmentioned, that is, Blanakan and Bungbulang [8], but it did not have updated information for most of the locations. However, the case in Cirebon is not mentioned in the paper. A recent study also focused on the northern coast of Java Island, where many large cities are located [11,13], but the LS remains unknown for the rest of the island. The lack of LS information from other places, the fact that a large number of populations are using groundwater for many activities, and the impact caused by LS are the motivations for this study. On the other hand, although global LS mapping was performed in 2023 [14,15], the discussion was undertaken only for large cities, as mentioned before. This study aims to provide the recent status of LS in Java, but it is not limited only to large cities. Furthermore, in this study, we also try to find new areas of LS because of the rapid development in Java over the last decades.
## 2 Materials and Methods  
Cefepime was a gift from the Public Central Teaching Hospital of Medical University of Warsaw. Fmoc-Asp-OH was purchased from Bachem AG (Bubendorf, Switzerland), Boc-Glu-anhydride was purchased from Fluorochem Ltd (Hadfield, UK). Macrocyclic chelator 2,2'-(7-(1-carboxy-4-((2,5-dioxopyrrolidin-1-yl)oxy)-4-oxobutyl)-1,4,7-triazonane-1,4-diyl)diacetic acid (NODAGA-NHS) was purchased from CheMatech (Dijon, France), other chemicals and solvents were purchased from Merck (Darmstadt, Germany) and used as obtained. \({}^{68}\)Ga radionuclide (emitter \(\beta^{+}\), 1/2 = 67.7 min, E\({}_{\text{gmax}}\) = 1.9 MeV, in the form of [68]GaCl\({}_{3}\) in 0.1 M HCl solutions) was eluted from the commercially available GalliaPharm\({}^{686}\)Ge/\({}^{68}\)Ga generator (Eckert and Ziegler, Berlin, Germany). Deionized water was prepared in a Hydrolab water-purification system (Hydrolab, Straszyn, Poland). Human serum was a gift from the Regional Centre for Blood Donation and Blood Treatment in Warsaw, Poland.  
HPLC analyses, separations and purifications were carried out using a VWR-Hitachi LaChrom Elite HPLC system which consisted of a pump L2130, column thermostat L-2350, UV diode array detector (DAD) L-2455 and the EZChrom Elite data system. The radioactivity was monitored using a 3 \(\times\) 3'' NaI(Tl) scintillation detector Raytest Gabi Star (Straubenhardt, Germany). The separation of the obtained products was accomplished on a LiChrospher\({}^{\circledr}\) 100 RP-18 analytical column (5 um particle size, 100 A pore size, 4.6 mm \(\times\) 250 mm) from Merck (Darmstadt, Germany) and a SUPELCOSIL\({}^{\circledr}\) LC-18 semi preparative column (5 um particle size, 120 A pore size, 10 mm \(\times\) 250 mm) from Sigma-Aldrich Co. LLC (Darmstadt, Germany). The solvents were as follows: solvent A, 0.1%(\(v/v\)) trifluoroacetic acid (TFA) in water; solvent B, 0.1% (\(v/v\)) trifluoroacetic acid (TFA) in acetonitrile. The gradient conditions are presented in Table 1. The chromatograms were performed using UV (220-400 nm) or \(\gamma\)-detection.  
The radiochemical purity (RPC) of the synthesized [68]Ga]Ga-NODAGA-Glu-CFM radioconjugate was analyzed by instant thin-layer chromatography (ITLC) using a glass microfiber chromatography paper impregnated with a silica gel (Agilent, Santa Clara, CA, USA), and 0.2 M citrate buffer, pH 5.0, was used as the developing solvent. After completing development, the distribution of radioactivity on the strips was determined using a Storage Phosphor System Cyclone Plus (Perkin-Elmer Life and Analytical Sciences, Downers Grove, IL, USA).  
\({}^{1}\)H NMR spectra were registered with a Bruker Avance Neo 400 spectrometer in DMSO-d\({}_{6}\) solvent. During the measurements, the temperature was stabilized at 295 K and the \({}^{1}\)H signals were calibrated on the residual solvent signal (2.5 ppm).  
The IR spectrum was recorded on a Thermo Scientific Nicolet iS10 FT-IR spectrometer in the range of 4000-400 cm\({}^{-1}\) using a KBr pellet.