esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
###### Abstract
The data size, priority, and transmission scheduling of various physiological signals are also considered, the data are compressed at the network edge and the transmission bandwidth of each physiological signal is assigned through a scheduling algorithm. The proposed method ensures data transmission frequency and quality of service for different types of physiological signals, as well as simplifies data transmission traffic and improves data transmission efficiency. The proposed scheduling method can simplify the data transmission traffic and increase the data transmission efficiency. The major contributions and key features of this paper are: (1) explore the architecture of the group health monitoring system on basis of the relay-based wireless body area network (WBAN); (2) address routing and relay selection schemes for the proposed architecture; (3) devise the specific health monitoring architecture based on the IoMT, considering signal prioritization and transmission scheduling through the hierarchical clustering method to organize sensors by setting weights, and applying data compression techniques at the network edge. The rest of the paper is organized as follows: Section 2 discusses the related works. Section 3 explains the functionality and operation of the proposed clustering methods, compression technique, and scheduling algorithms. Section 4 presents the experiment scenario and analyzes the experiment results. Section 5 draws conclusions and future work.
## 4 Conclusions  
The objective of this study was to clarify the possibility of numerically predicting pollutant transport in a dynamic and complex hydrologic environment and to investigate the characteristics of pollutant transport under both naturally and artificially induced groundwater flows. We attempted to reproduce the changes in 1,4-dioxane concentrations in groundwater observed in the monitoring wells using a quasi-3D flow and transport simulation that considered the multiple sources and spatiotemporal changes in hydraulic conditions.  
The reproduced data and monitoring data (over a period of five years) for all monitoring wells generally exhibited good fitting results, suggesting that it is possible to predict the 1,4-dioxane concentration by considering groundwater flow. From the distribution of 1,4-dioxane, an obvious accumulation of 1,4-dioxane along with the impermeable wall was observed in these artificial activities, impermeable walls, and pumping well installments.  
Passive treatment suggested that 1,4-dioxane moves with groundwater flows, and its concentration gradually decreases over time. The concentration of 1,4-dioxane was relatively high near the impermeable wall. With active treatment, the 1,4-dioxane concentration in the groundwater in most areas was \(\leq\)10\({}^{-3}\) mg/L, and no longer accumulated near the impermeable wall. It is understood that the pumping actions during active treatment may result in a more dynamic groundwater flow than during passive treatment, promoting 1,4-dioxane transport to areas where it would otherwise be difficult to reach.  
The study suggests the potential for the prediction and remediation of pollution at illegal waste dumping sites. However, further extensive studies on the complex geological structure of illegal waste dumping sites, dynamic seasonal rainfall, and groundwater changes are encouraged. In order to evaluate such complex environments, it is essential to improve prediction accuracy using more advanced models [39] and to analyze detailed field data.  
Conceptualization, T.P. and K.N.; Methodology, T.P. and K.N.; Software, T.P.; Validation, T.P.; Formal analysis, T.P.; Investigation, T.P.; Resources, T.P.; Data curation, T.P. and K.N.; Writing--original draft preparation, K.N. and T.P.; Writing--review and editing, N.W., J.W. and T.K.; Visualization, T.P.; Supervision, T.K.; Project administration, T.P. and K.N.; Funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by DOWA Holdings funding and GP-RSS International join graduate program of Tohoku University, and the authors acknowledge the academic support of Prof. [PERSON] at Tohoku University.  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
This research was supported by the International Join Graduate Program in Resilience and Safety Studies, Tohoku University, Japan.  
The authors declare no conflict of interest.
## 3 Results
### Speed and Robustness Evaluation  
In the present study, two snow sculpture scenarios are selected, corresponding to the lighting conditions at 7:00 a.m. (with little sunlight) and 10:00 a.m. (with much sunlight) respectively. By using structure from motion method with patch-based multi-view stereopsismethod processing(PMVS-SFM) brought forward by [PERSON] et al. [25], laser scanning method using Leica RTC360 and the method proposed in this paper, 3D reconstruction is performed, with the results given in Figures 12 and 13, respectively.  
It can be seen from the test results that for snow sculpture with complicated surface texture, a dense 3D point cloud generated with PMVS-SFM image reconstruction method of [PERSON] contains more invalid areas, preventing it from well reproducing the original appearance of the snow sculpture. The measurement grows even poorer with the increase of exposure to sunlight. Laser scanning method is less sensitive to sunlight, but provides a relatively sparse point cloud with vague details. Moreover, its laser measurement accuracy of surface texture of the snow sculpture is affected by secondary reflection characteristics of the surface. By comparison, the method proposed in this paper successfully addresses the impact of sunlight and secondary reflection of snow, thus leading to more satisfactory imaging. The benefits include clearer texture and higher stability and accuracy.  
Meanwhile, the average execution time of 1000 frames is calculated to be 21.3 ms. It is enough to ensure real-time nature of the normal video capture operation at a speed of 25 frame/s.  
Figure 12: Three-dimensional reconstruction of snow sculptures at 7:00 a.m. (**a**) Snow sculpture front; (**b**) 3D reconstruction results using PMVS-SFM; (**c**) 3D reconstruction results using laser scanning method;(**d**) 3D reconstruction resultsusing our method.  
Figure 13: Three-dimensional reconstruction of snow sculptures at 10:00 a.m. (**a**) Snow sculptures side; (**b**) 3D reconstruction results using PMVS-SFM; (**c**) 3D reconstruction results using laser scanning method; (**d**) 3D reconstruction resultsusing our method.
## 3 Markup languages
### Markup languages for graphics and images  
With the increasing requirements of retrieval of images and graphics, there are a number of markup languages specific to graphics which have been introduced. Firstly, SVG [24], Scalable Vector Graphics, is a novel XML-based language for describing two-dimensional graphics at the level of graphical objects rather than individual points. SVG is now a W3C (World Wide Web Consortium) [25] standard. SVG is becoming increasingly popular due to its inherent advantages over raster image formats. SVG can work with three types of graphical objects: vectorial graphical shapes (such as lines and primitive shapes), text and images (equivalent to raster image retrieval). The creation of SVG was attributed from other two XML applications for encoding vector information. They are VML (Vector Markup Language) [26] and PGML (Precision Graphics Mark-up Language) [27]. VML defines a format for vector information together with additional markup to describe how that information may be displayed and edited, and is currently used by Google Maps for rendering vectors when running on IE 5.5 or late. On the other hand, PGML is designed to meet precision needs of graphics artists besides simple vector graphics needs of casual users. Both VML and PGML were submitted to W3C as proposed standards in 1998, but failed, and then joined and improved upon to create SVG. X3D (eXtensible 3D) [28], the successor to VRML (Virtual Reality Modelling Language) [29], was introduced for real-time three-dimensional computer graphics and is now an ISO standard. X3D features many extensions to VRML such as Humanoid Animation, NURBS and GeoVRML, the ability to encode the scene using XML syntax as well as the Open Inventor-like syntax of VRML97, and enhanced application programmer interfaces (APIs). In recent years, SVG and X3D have been utilised in engineering domain such as design and manufacturing systems, especially for representation of product models and provision with complex engineering data for applications such as CAD/CAPP/CAM [30]. Commercial industry has recently also developed some XML-based format for lightweight 3D model. For examples, 3D XML enables users to access and share accurate 3D data quickly and easily [31] through a sophisticated 3D graphics compaction algorithm based on NURBS surface mathematics. PLM XML [32], an XML-based PLM format, is created and supported by UGS. The objective of PLM XML is to integrate collaborative product lifecycle processes by offering a standardised protocol for data interoperability.
## 5 Results and Discussion
### SAR Impulse Response Function  
The proposed calibration approach also has an impact on the impulse response function achieved during SAR processing. Figure 11 illustrates the improvement achieved for a reference target imaged at C-band: the principal improvement lies in the flattening of the range spectral profile, shown in the middle column, which is due to the response correction introduced in Section 4.1. A further, but less significant improvement is due to the flattening of the azimuth spectral profile, shown on the right, which is due to the C-band antenna pointing correction that is illustrated in more detail in Figures 6 and 9.  
Figure 11: A comparison of the 2D SAR impulse response function of a reference target imaged in VV polarisation at C-band. The top row represents the nominal performance. The IRF in the bottom row. meanwhile, is the one obtained when the estimated calibration corrections are taken into account. The plots in the second and third columns show the normalized power spectrum in range and in azimuth. The red dashed lines indicate the signal bandwidth.  
Figure 10: The impact of the pointing estimate of Figure 9 on the backscatter amplitude in an independent SAR acquisition over the city of Landsberg am Lech.
## 3 Predictive Research
### Scenario Analysis  
The planning of scenarios (i.e., descriptions of potential events) helps individuals and organizations to move away from linear thinking and to be able to better cope with external change [39]. [PERSON] et al. [40] point out that scenarios \"challenge existing assumptions, identify novel lines of inquiry, and enable new research opportunities to emerge\" (p. 70). In this paper, we do not differentiate explicitly between scenario planning, scenario thinking, or scenario analysis, but use only the latter term to refer to tools, techniques, and methods that allow organizations to better understand (potential) future developments and to adjust their strategies accordingly. [PERSON] et al. [41] point out that scenario planning techniques have been around for several decades and provide an overview of their origins and evolution in long-range business planning. They classify the methodologies into three main schools, namely intuitive-logics models, La Prospective models, and probabilistic modified trend models. This classification illustrates the versatility and coverage of scenario techniques and, from a methodological standpoint, their inclusive nature. The details of the respective approaches are far beyond the scope of this paper and can easily be found in academic as well as practitioner literature [39; 42; 43], but it is especially their methodological flexibility that is noteworthy and that allows for the straightforward integration of numerous qualitative and quantitative approaches.  
Scenario techniques are also capable of exploring the impact of black swan events, which are deemed to be unpredictable or highly improbable yet nonetheless to have massive consequences [38]. Consequently, we argue that exploratory academic research that is not focused on averaging and finding the most likely development or \"significant\" relationships might be well-suited to identifying such improbable events by leveraging the wisdom of the crowd or dedicated experts. Therefore, blockchain researchers (or researchers who are generally interested in the impact of technology) are well advised to have a closer look at scenario analysis. As a further benefit, this approach can also be used to develop systems or devise strategies with the goal of actively shaping the future. This might be of interest for organizations and policy makers alike.  
Figure 2 illustrates a scenario funnel with a timeline starting at the blockchain status quo and the most likely development (the trend scenario) shown as a dotted line. Depending on contingency factors, alternative blockchain scenarios are possible that can either be perceived as positive or negative. Quite naturally, other kinds of alternative scenarios are possible that lie between the respective extreme positions as shown in the figure. While existing research is (implicitly) interested in identifying the trend scenario, a fully fledged scenario analysis will uncover numerous possible developments.  
Figure 2: Scenario development.
## 3 Results and Discussion
### Implications from EIS Measurements  
The recorded impedance spectra deliver 46 measurement points at different frequencies. For the illustration of the impedance change at different frequencies the 1 kHz, 78 Hz, 10 Hz and 0.1 Hz measurements, which are also marked in Figure 2, were chosen. As can be seen from Figure 6 and from Table 5, the strongest relative increase is detectable for the lowest frequency. The deviation between 1 kHz and 78 Hz with superimposed DC discharge current is marginal and would not lead to a divergent conclusion on the SoH of the battery. It can be noted that with a superimposed discharge current the 1 kHz measurement shows a relative increase compared to the initial value, while with 0 DC spectra deliver lower relative values. Nevertheless, for these impedances at different frequencies also battery 2-024 and battery 3-008 are showing higher increases (see Table 5) and subsequently would be detected as the once more stressed within the string. Although the differences in voltage and temperature of the continuous measurement were within the range of accuracy, the slightly higher voltage of 2-024 and the higher voltage in phase 1 as well as the higher temperature for 3-008 could be a first explanation.  
The development of the transient frequency, the point in the spectrum where the imaginary part of \(|\,\mathbb{Z}^{1}\) gets 0, could be used as an indicator for SoC as described by [23] or even for SoH. However, it delivers no valid result as the measurement even with a starting frequency of 6.25 kHz have almost no zero crossing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Battery No.** & **String No.** & **Hioki BT3554** & **1 kHz \(\mu\)EIS 0 DC** & **LEM Sentinel 2** & **78 Hz \(\mu\)EIS with \(-\)120 DC** \\ \hline
2-014 & 2 & \(-6.57\%\) & \(-6.70\%\) & \(+16.46\%\) & \(+6.45\%\) \\
2-018 & 2 & \(-7.29\%\) & \(-7.53\%\) & \(+6.14\%\) & \(+10.82\%\) \\
2-024 & 2 & \(-2.54\%\) & \(-5.62\%\) & \(+5.16\%\) & \(+13.51\%\) \\
3-004 & 3 & \(-2.31\%\) & \(-0.98\%\) & \(+28.30\%\) & \(+0.001\%\) \\
3-008 & 3 & \(+11.92\%\) & \(+11.08\%\) & \(+25.02\%\) & \(+15.07\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of relative change of the impedances registered by the different devices over 568 days in comparison for all investigated batteries.
## 4 Experiment Results and Analysis
### Evaluation Criteria  
We use mean average precision (mAP) as the evaluation metric in our experiments. mAP, as the best evaluation metric for the target detection task, combines the accuracy of detection category and location. The calculation of mAP is shown in Equation (6).  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i} \tag{6}\]  
In Equation (6), \(n\) presents the number of classes of EMs; \(AP\) is determined by the area under the accuracy-precision curve. The calculations of accuracy and precision are related to true positive (TP), true negative (TN), false positive (FP), false negative (FN), and intersection over union (IoU). The description of TP, TN, FP, and FN is shown in Table 2. The calculations of accuracy and precision are shown in Equations (7) and (8), respectively.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{7}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{8}\]
# Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor
## 4 Experimental Results
### Deep Neural Network Training and Comparisons  
The design of the neural networks was done through iterative testing where the initial configuration was based on empirical estimations. During the iterative design phase, different layer sizes and hyper-parameters were tested. The best result was obtained with the architecture of input layer 12 neurons, 5 hidden layers with unit size of 24, 48, 48, 48, 24 neurons, and, finally an output layer of 8 neurons. All input and hidden layers use an activation function \(tanh\). For the output layer, \(Softmax\) activation function is selected. The Loss Function is configured as Categorical Cross-entropy, and, as a learning optimizer, the Adam algorithm is selected.  
The DNN model is programmed using Python libraries and Tensor-flow Keras [37]. Then, the data set is divided into a 70/30 relationship for training and testing in 180 epochs. The data set contains the force/torque values of the two sensors. It must be considered that every time the DNN model is trained, even with the same data, different results of performance are expected.  
The performance of the algorithm for training/testing is shown in Figures 8 and 9. These graphs present the accuracy of the DNN, where the blue line represents the training data and the orange line the testing data. As seen in the one sensor case, the test data  
Some other machine learning algorithms based on the sci kit-learn [38] were designed to train/test different classification techniques and compare how well they fit the data. A list of tested machine learning algorithms with this data set is presented as follows:  
* Random Forest
* K-Nearest Neighbors
* Decision Tree
* Multi Layer Perceptron
* Deep Neural Networks  
In order to demonstrate the difference between using only one F/T sensor versus two F/T sensors, the classification model uses either 6 features of the peg sensor or the 12 features of both peg and hole sensors.  
To show the results of the training/testing of the machine learning models, a comparison of their performance is presented in Table 3. It can be seen that the threshold algorithms based on statistical metrics performs worst. The best model corresponds to the Deep Neural Network followed by the Random Forest. The metrics reported in the table consider the accuracy defined as the number of correct predictions divided by the total number of predictions. The second metric is the F1-Score that represents the harmonic mean between precision and recall metrics.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 4 Experiment Results
### Recognition Accuracy under Incomplete Training Samples  
In the last subsection, all samples at the 17\({}^{\circ}\) depression angle over a full 0\({}^{\circ}\)-360\({}^{\circ}\) range of the aspect view are used to train the classifiers. In the real-world tasks, the data cannot cover all conditions of the targets. In this experiment, a certain percentage of samples at the 17\({}^{\circ}\) depression angle are randomly selected to construct the training set, and all the samples at the 15\({}^{\circ}\) depression angle are tested. The feature space, PCA\({}^{3}\), is adopted here, which is to say that the dimensions of samples are reduced to the extent where the accumulative contribution rate is approximately equal to 80% before classification.  
Figure 6 shows the recognition rates under different percentages of training samples. The recognition rates of the two classifiers rise with the increasing percentage of the training samples.  
Figure 5: The recognition rates under different unconventional feature spaces; (**a**,**b**) are two forms of the recognition rates. Down-sample\({}^{3}\) refers to reducing the dimensions of the cropped image to a size of 1600 by down-sample. Similarly, Down-sample\({}^{2}\), Down-sample\({}^{1}\), PCA\({}^{4}\), PCA\({}^{3}\), PCA\({}^{2}\) and PCA\({}^{1}\) stand for sizes of 400, 100, 400, 125, 100 and 25, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Down-Sample\({}^{3}\)** & **Down-Sample\({}^{2}\)** & **Down-Sample\({}^{1}\)** & **PCA\({}^{4}\)** & **PCA\({}^{3}\)** & **PCA\({}^{2}\)** & **PCA\({}^{1}\)** \\ \hline SVM & 86.79\% & 86.73\% & 84.39\% & 71.84\% & 74.71\% & 74.40\% & 59.19\% \\ SRC & 72.93\% & 89.76\% & 81.83\% & 70.34\% & 78.89\% & 78.86\% & 54.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The recognition accuracy under different unconventional feature spaces.  
In other words, both the classifiers are sensitive to the number of samples in the training set. Additionally, with the decreasing percentage of the training samples, the recognition accuracy of SVM descends faster than that of SRC, so SRC is more robust than SVM in this experiment.