esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Ankle Joint Torque Prediction Results  
The muscular model-based ankle torque prediction results are shown in Figure 12. In this figure, the prediction results and the torque recorded by the force plate are distinguished by red and blue lines, respectively. According to the data comprising this figure, it can be seen that the values of RMSE and VAF are, respectively, 4.9 Nm and 0.86. The calibrated parameters for the different muscles of subject B are given in Table 5. It is noticeable from this table that, although there is no guarantee that each parameter is correct with the proposed optimal method, the multiplication of these parameters is relatively accurate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** \\ \hline \(p_{a}\) of SOL 1  & \(16.84\pm 7.51\)(\(<\)0.01) & \(8.88\pm 9.356\)(\(<\)0.01) & \(3.75\pm 1.02\)(0.826) & \(2.00\pm 1.07\)(0.03) & \(9.61\pm 4.70\)(0.03) \\ \(p_{d}\) of SOL & \(-1.13\pm 3.71\)(\(<\)0.01) & \(0.70\pm 0.82\)(0.031) & \(0.73\pm 0.60\)(0.017) & \(0.56\pm 0.43\)(0.17) & \(0.93\pm 1.06\)(0.03) \\ VAF & \(64.99.04\)(\(<\)0.01) & \(67.07\)(\(7.0\)(\(<\)0.01) & \(77.6\)/\(88.1\)(0.185) & \(59.3\)/\(832\)(\(<\)0.01) & \(73.7\)/\(88.7\)(0.23) \\ \(p_{a}\) of GAS & \(2.97\pm 1.79\)(0.108) & \(0.67\pm 0.52\)(0.021) & \(6.46\pm 1.94\)(0.087) & \(1.88\pm 1.05\)(\(<\)0.01) & \(6.11\pm 2.50\)(0.397) \\ \(p_{d}\) of GAS & \(-0.13\pm 0.98\)(\(<\)0.01) & \(0.18\pm 0.09\)(0.428) & \(0.33\pm 0.30\)(0.029) & \(1.11\pm 1.40\)(0.43) & \(0.35\pm 0.48\)(0.09) \\ VAF & \(67.59\)(\(3.0\)(\(<\)0.01) & \(76.9\)/\(82.7\)(0.027) & \(90.09\)/\(53.0\)(0.045) & \(60.1\)/\(80.1\)(\(<\)0.01) & \(79.3\)/\(86.4\)(0.47) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The rest part mean reflex gains for all five subjects.  
Figure 11: Reflex model prediction results for the rest part of subject A over the six trials. The blue lines are the muscle activation calculated from the measured sEMG signals. The red lines are the prediction results using individually calibrated reflex gains. The yellow lines are the prediction results using mean reflex gains.  
The correlation coefficient between muscle activations and ankle joint torque is greater than 0.95. This implies that muscle activations and torque are highly linear with each other. Also, this can be further demonstrated by noting that \(f_{l}\) and \(f_{v}\) in Equation (8) are almost constant (\(L_{CE}<<L_{CE0}\), \(V_{CE}<<V_{CE0}\)). Thus, Equation (8) can be simplified as follows:  
\[\tau=\sum_{i}c_{i}a_{i} \tag{13}\]  
where \(i\) represents different muscles. \(c_{i}\) can be obtained by regression algorithm.  
In Figure 12, the prediction result is depicted as an orange line. Based on the data comprising the prediction result in this figure, it can be seen that the values of RMSE and VAF for the prediction results are 4.0 Nm and 0.88, respectively. These results indicate that a relatively high accuracy ankle joint torque can be obtained under the condition of 'ankle strategy' push-recovery movement by using the muscle activation level.
## 3 Compressive Sensing
### Compressive Sensing Applications  
In this part, we introduce several examples of combining CS with practical systems and applications, such as wireless channel estimation, network tomography and magnetic resonance (MR) image reconstruction. In channel estimation, a wideband signal reveals the actual response of the wireless channel, which has a discrete nature consisting of multipath components; while the response is smoothed out for narrowband communication systems, which makes the impulse response of the wireless channel tend to be sparse for a larger bandwidth [26]. In link delay estimation of network tomography, only a limited number of links have large delays in typical networks. If considering a vector composed of delays of all links, the vector will be sparse [27]. While the MR image shows sparsity after Fourier transformation, CS can be used for image reconstruction. In fact, the original paper of [PERSON] [10] was largely motivated by the reconstruction of MR imaging.
## 5 Experiments and Discussion  
### Experimental Data  
As mentioned in Section 3.1, the experimental data in this paper are drive test data from the Shuo Huang Railway LTE-R network from September 2015 to August 2018, as well as the corresponding EARR data for communication services with a QCI value of 1 exported by the OMC system. Then, we preprocess these data as follows.  
Firstly, we extract the data sequences of the key performance indicators (RSRP, RSRQ, and SINR) from the drive test data of each cell, based on the PCI value. Then, using the Lagrangian interpolation to interpolate these three data sequences into sequences with a length of 500, we regard the \(3\times 500\) matrix as a record in the dataset. Finally, we label these records based on the EARR data for communication services with a QCI value of 1 for each cell. If the EARR value is greater than 2%, the cell is marked as an abnormal cell, otherwise, the cell is marked as a normal cell. Through the above process, we obtained the detection dataset of LTE-R cells with degraded communication performance, or the degradation detection dataset for short.  
In our experiment, 70% of the data were selected as the training set, and the remaining 30% were used as the test set. Detailed information regarding the degradation detection dataset is shown in Table 3.
## 5 Analysis of Key Factors Influencing the Cyclic Operation Time
### Degree of Intelligence  
The higher the intelligence level, the less human participation in observation, decision-making, and manipulation; therefore, the shorter the time gaps in actions of cylinders and supports. In addition, all actions are regulated by the intelligent control of the master computer throughout the operations. The execution of actions is controlled by the hydraulic support electrohydraulic control system, thereby reducing the number of workers required at the working face and improving the overall efficiency. For the above reasons, the time of cyclic operation is the shortest for intelligent solid backfilling. The relationship between the time of cyclic operation and the intelligence index is plotted in Figure 11.  
It can be deduced from Figure 11 that the time of cyclic operation in mechanized and intelligent solid backfilling technologies is irrelevant to the intelligence index. However, the time of cyclic operation in automated solid backfilling technology is inversely proportional to the intelligence index. At an intelligence index of 4, the times of cyclic operation for mechanized, automated, and intelligent solid backfilling technologies are 3.13, 2.22, and 1.92 h, respectively. Compared with mechanized filling, the backfilling efficiency of automated and intelligent operations is improved by 32% and 39%, respectively.
## 4 Computational Experiments and Analysis  
Considering that a higher frequency of identical target components increases the algorithm's computational complexity, and that the vehicle's surface roughness must closely match real-world conditions, the incident wave is set to a frequency of \(f=6\) GHz. Unless otherwise specified, the following simulation settings are used throughout this study: the incident angle is \(\theta_{i}=30^{\circ}\), and the vehicle body material is \(Mg_{3}Fe\), with a calculated dielectric constant of \(\varepsilon=121.6-j28.8\). The wheel material is \(Ni\), and its dielectric constant, as computed in [30], is \(\varepsilon=29-j8.9\). The root mean square (RMS) height of the vehicle surface roughness is \(\delta=0.05\lambda\). The snow layer has a depth of \(15\lambda\) and a moisture content of \(30\%\), corresponding to a dielectric constant [23] of \(\varepsilon=14.8-j0.65\). The soil moisture content is \(30\%\), from which the relative dielectric constant [28] of the soil is calculated to be \(\varepsilon_{s}=8.0-j37.7\). The rough surface scattering is simulated over \(30\) independent realizations.
## 3 Data Acquisition and Pre-Processing
### Prototype Design
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{4}{c}{**Vision Sensors**} \\ \hline  & Brand Name & Resolution & Sensor & Height & Distance \\ \hline
1 & ArduCAM MT9F001 & 4384 \(\times\) 3288 & 1/2.3 inch CMOS & 8 cm & 19.5 cm \\
2 & Huawei P9 Lite & 3120 \(\times\) 4160 & Sony IMX214 Exmor RS & 16 cm & 30 cm \\ \hline  & \multicolumn{4}{c}{Weight sensor} \\ \hline
3 & AccuPost PP-70N & 10 g–32 kg, USB 2.0/3.0 supported Windows 10 & \\ \hline  & \multicolumn{4}{c}{Illuminance sensor} \\ \hline
4 & Ambient light sensor & Arduino BH1750 ambient light sensor & \\ \hline  & \multicolumn{4}{c}{Controlling embedded system} \\ \hline
5 & Embedded system & Arduino Uno (ATmega-328), 8-bit, 16 MHz & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of multiple sensors used for building the laboratory setup as a prototype of images based on supermarket self-checkouts for weight and image data acquisition. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Fruit/Vegetable**} & \multicolumn{2}{c}{**Nomenclature**} & \multicolumn{2}{c}{**Avg. Weight (kg)**} & \multicolumn{2}{c}{**Average Illuminance (LS1-LS4) Lux**} \\ \hline
1 & Brown onion & ONIBRXXXX & 0.212 & 526.17 & 524.84 & 523.56 & 522.57 \\
2 & Carrot & CARROXXOX & 0.064 & 525.02 & 527.20 & 522.95 & 524.39 \\
3 & Caulflower & CABCAXXXX & 0.419 & 526.34 & 525.12 & 525.35 & 523.08 \\
4 & Continental cucumber & CUCCOXXX & 0.014 & 533.08 & 525.70 & 529.26 & 525.94 \\
5 & Creme potato & POTCRXXXX & 0.140 & 527.94 & 523.90 & 525.08 & 527.10 \\
6 & Drumhead cabbage & CABDRXXXX & 0.833 & 534.03 & 528.22 & 523.58 & 529.09 \\
7 & Granny Smith apple & APPGSXXXX & 0.164 & 523.79 & 522.65 & 525.75 & 526.06 \\
8 & Iceberg lettuce & LETICXXXX & 0.432 & 531.46 & 525.87 & 530.42 & 526.30 \\
9 & Lady finger banana & BANLEXXXX & 0.125 & 523.67 & 522.55 & 523.45 & 526.62 \\
10 & Mandarin & MANDAXXXX & 0.138 & 525.85 & 526.88 & 529.81 & 526.49 \\
11 & Navel orange & ORRAAXXX & 0.138 & 524.97 & 526.92 & 528.67 & 523.33 \\
12 & Packham pear & PEAPAXXX & 0.150 & 529.43 & 530.63 & 530.13 & 535.69 \\
13 & Pink lady apple & APPPLXXXX & 0.326 & 529.32 & 523.56 & 522.13 & 535.77 \\
14 & Strawberry & BERSTXXXX & 0.012 & 525.81 & 525.63 & 522.00 & 527.37 \\
15 & Tomato & TOMFIXXXX & 0.132 & 523.55 & 523.56 & 526.91 & 532.98 \\ \hline \hline \end{tabular} \(\chi\)XXXX: denotes the count of the samples per class.  
\end{table} 
Table 3: A description of the classes in the obtained dataset, nomenclature, and illuminance values obtained with sensors (LS1–LS4). Figure 2: A schematic of the proposed prototype: (**a**) Laboratory setup depicting the placement of vision sensor, illuminance sensors, weight sensor, and fruit or vegetable sample, and (**b**) A proposed placement of sensors on a typical supermarket self-checkout kiosk.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 4 Poly(3,4-ethylenedioxythiophene)-Based Electrodes
### 3D Nanostructures  
PEDOT-based 3D nanostructures can be manufactured by modifying conventional synthetic routes. Here, [PERSON] et al. fabricated 3D Au-PEDOT-graphene aerogels using the in situ polymerization of EDOT on 3D graphene aerogels and the electrodeposition of Au on the surface [131]. This composite nanostructure is a highly sought after material with a 3D PEDOT-based surface via PEDOT coating of the 3D graphene skeleton. Antibodies for detecting prostate cancer factors were introduced on top of this material to generate highly sensitive biosensor electrodes. [PERSON] et al. synthesized PEDOT nanofiber-based 3D networks using tetrabutylammonium perchlorate (TBAP) soft templates (Figure 10a) [132]. The nanofibers' network structure possessed a more active surface area to facilitate the binding of lactate dehydrogenase. The group noted improved sensitivity of the electrode to the target analyte (i.e., lactate). [PERSON] et al. modified VDP of 3,4-ethylenedioxythiophene (EDOT) on the  
Figure 9: **(a)** PANI:PSS/silk fibroin core/shell wires derived via wet spinning and photo crosslinking reactions for use as ascorbic acid biosensors [123]. Copyright 2019, MDPI. **(b)** PEDOT nanow whiskers/Au nanoparticles–hemoglobin composite electrodes for H\({}_{2}\)O\({}_{2}\) detection via electrodeposition [126]. Copyright 2013, Royal Society of Chemistry. PMMA surface (Figure 10b) by controlling the temperature and pressure conditions of the reaction, thereby generating nanorods on the fibers' surface [133]. The resulting 3D PEDOT nanofiber-based membranes were applied for dopamine detection through the immobilization of the human dopamine receptor (hDRD1). The sensing performance of PEDOT-based biosensors are suggested in Table 3.  
Figure 10: (**a**) The 3D PEDOT nanofiber network from the tetrabutylammonium perchlorate (TBAP) soft template for lactate detection [132]. Copyright 2020, Elsevier. (**b**) Multidimensional PEDOT nanofiber membranes via a modified vapor deposition polymerization method for dopamine detection [133]. Copyright 2016, American Chemical Society.
# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 2 Related Work  
Deep learning-based methods have previously been applied successfully to a range of medical imaging problems [15; 16; 17; 18; 19], including heart segmentation [1]. Two important challenges, ACDC [4] and MMWHS [3], are good benchmarks for measuring segmentation performance. In both cases, variations of the popular U-Net [20] is the preferred choice of researchers.  
A significant point of study was the comparison of 2D and 3D methods. It has been shown that the performance depends highly on the acquisition methods [1; 10]. Three-dimensional models are negatively affected by large inter-slice gaps and misalignments, whereas 2D models cope better in these situations. The results of [PERSON] et al. [10] confirm this on the ACDC dataset, with the ACDC data having a thickness of 5-8 mm and sometimes an inter-slice gap of 5 mm. The main drawback of using 2D models is that they cannot learn any inter-slice dependencies since they work slice-by-slice. On the other hand, on the MMWHS dataset, the best-performing models are 3D-based [3; 21]. In [22] the authors proposed a novel intelligent framework designed ad hoc for enhancing MR image segmentation results of MRI data characterized by an underlying bimodal histogram.  
Another significant topic is the two-step segmentation, where the region of interest (containing the heart) is first detected via a localization procedure, which is subsequently fed to the segmentation network. Different localization procedures were used, such as: regressing to the center of the heart, then cropping a fixed-size 3D region around the center, large enough to enclose all segmentation labels on every image from the training set. This strategy was proved effective as it was used to obtain the best results of the MMWHS challenge by [PERSON] et al. [21].  
Several other works experimented with similar ideas, for instance, by using a Faster RCNN network [23] or constructing a bounding box using three CNNs [24] that determine the presence of the heart independently in axial, coronal, and sagittal slices of the image volume.  
These methods operate in a 3D scenario, aiming to obtain a 3D box which encompasses the entire heart. Other methods proposed to locate the heart by focusing on motion or heart beats, based on the changes in pixel intensities [25; 26]. A somewhat simpler approach is proposed by [PERSON] et al. in [27], where they use the output of a standard segmentation model to extract the ROI for another model, using the boundaries of the initial prediction. They used information from adjacent slices to compute the ROI for a single 2D slice.
## 2 Materials and Methods
### Methods
#### 2.3.3 Climatic Yield Anomalies  
Crop yield may be influenced by the weather, pests, diseases, cultivation techniques, and management. To reflect the effect of climate change on crop yield, the Hodrick-Prescott (HP) filter method [46] was used to decompose crop yield (Y) into trend yield (Yt) and climatic yield (Yc). Yt reflects the long-period yield component of the agricultural production level, and Yc reflects the short-period yield component influenced by meteorological elements. (Yc/Yt) \(\times\) 100% is the climatic yield anomalies, which is not affected by time, space and agricultural technology level, and is comparable among different regions.
# Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach
## 1 Introduction
These studies take point-based crashes as the research object and used point-based spatial clustering analysis method, which is effective to detect hazardous locations by clusters of crashes. The above studies provide a foundation for the research content of this paper. However, previous studies neglected some issues by using spatial data mining methods. First, these studies mainly focus on the point-based spatial clustering to find hotspots of crashes, and thus cannot directly identify RCHC. Second, some studies neglected the road-road or crash-road spatial relationships that affect accuracy of the result of spatial data mining. To solve the issues, first, this paper focuses on the line-based spatial clustering method and takes linear roads as the research object, which can directly identify RCHC. Second, in this study, road-road and crash-road spatial relationships are applied in spatial data mining methods. Crashes are spatially aggregated as the attribute of the count of crashes of road (ACCR) by considering the road-crash geometric and attribute relationships. Then, a spatial weight matrix of roads (SWMR) [26; 27; 28] is established based on the road-road topological and geometric relationships. The ACCRs and SWMR are used as the input parameters in the cluster and outlier analysis (local Moran's I) to improve accuracy of the result of spatial data mining. The aim of this study is to (a) create the accurate SWMR of complex road network respect to overpass crossing or underpass crossings to support further spatial statistics analysis (e.g., high-low clustering, hotspots analysis) (b) identify the RCHC to help transportation authorities and safety specialists to identify and prioritize roads that require more safety attention to reduce crashes. The rest of the paper is organized as follows. Section 2 presents the methodology used in this study. Section 3 describes the spatial data, including the traffic crashes and roads of Polk County, Iowa used in this study. Section 4 illustrates and discusses the results by using the methodology within the study area. Section 5 recommends future work. Finally, Section 6 concludes the paper.
## 4 Results and Discussions
### Effect of Channel Outlet Configuration
The heat transfer levels of OC1 and OC5 were relatively close, which was 2.1-13.0% lower than that of OC4 case. While for the OC2 and OC3 cases, the improvement of \(Nu\) gradually slowed down with increasing \(Re\). Specifically, the heat transfer performances of OC3 and OC1 were at the same level within the \(Re\) range of 10,000-50,000, while the \(Nu\) of OC3 was obviously lower than that of the OC1 case when the \(Re\) reached 100,000. Among the five channel outlet configurations, OC2 presented the worst heat transfer performance, and the \(Nu\) difference between OC2 (baseline) and OC4 was up to 37.4%. Figure 6: Streamlines and TKE distributions on the longitudinally middle sections for different outlet configurations, \(Re\) = 30,000. As is shown in Figure 8b, except for the friction decrease of OC1, OC2, and OC3 cases as the \(Re\) was increased from 10000 to 30000, the friction of all other cases almost remained unchanged, indicating that the \(Re\) has little effect on the flow resistance of the trailing edge channels studied in the paper. However, the flow resistance was significantly affected by the channel outlet configuration. The friction was within the range of 0.605-0.623 for the OC1 case, and was obviously increased when introducing the second passage (OC2 and OC3). The relative increment of friction factor as 35.3-40.6% for the OC2 case and 27.6-28.7% for the OC3 case, compared with the OC1 case. This was mainly caused by the impingement effect and appearance of a closed vortex, which has been described in detail in Figure 6. When the top region outflow was applied, the impingement and vortex were eliminated, thus the flow resistance was significantly reduced, which was 69.9-70.2% lower than that of the OC1 case. Additionally, the friction coefficient can be further decreased using both sides extractions (OC5), and the friction coefficient was decreased by 100000. Figure 8: Variations of Nusselt number and friction with \(Re\) for different channel outlet configurations. Figure 7: Nusselt number distributions on the endwalls for different outlet configurations, \(Re\) = 30,000. Therefore, the drag reduction effects can be effectively achieved by the top region outflow and both sides extractions. In general, the adoption of second passage will weaken the heat transfer performance and simultaneously increase the flow resistance for the cooling of the trailing edge with lateral slots, thus thie should be avoided in practical applications; while the outlet configuration of top region outflow reduces the friction and enhances heat transfer, and is recommended as a priority in the design of the trailing edge cooling structure; although the outlet configuration of both sides extractions has a lower friction coefficient, it produced the worse heat transfer characteristics compared with the OC4, and should be considered in the light of specific circumstances.
## 5 Feasibility and Evaluation
### Preferences and Capability-Based Adaptation  
Adapting security changes autonomously by only considering the impact of the risks faced is a security risk itself. In such cases, changing security parameters may negatively affect service attributes, such as throughput or latency, and thing resources. Re-configurations based only on the security context of a threat without assessing its impact on the service requirements can cause unnecessary adaptation that may cause serious problems [PERSON] et al. (2015). Such strategies can cause service disruption in environments like IoT or WSN, where the main driving technologies are battery-powered and resource-limited devices. Therefore, other factors, such as the device and application capabilities, as well as service requirements, need to be considered while an adaptation strategy is decided, as performed in EDAS.  
Nonetheless, in a user-centric service, such as IoT-eHealth, the user, _i.e._, the patient, as well as the medical staff, preferences should also be assessed while new security settings are adapted. The EDAS runtime adaptation ontology stores these capabilities, requirements and preferences before system start-up and transforms them into metrics with respective utilities against a particular _SecurityProperty_. This enables the adaptation engine to choose a mitigation action from the available action pool, such that its weighted utility has a maximum value for a given user using a particular service in a specific risk situation.