esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Exoperoxidase Production Over a Time Course  
The _Ensifer_ strain was evaluated for extracellular enzyme production with and without hydrogen peroxide for 144 h and the findings are shown in Figure 3. The findings showed that _Ensifer adherens_ NWODO-2 attained optimum peroxidase production (\(12.19\pm 1.05\) U mL\({}^{-1}\)) at 48 h, corresponding to the late logarithmic growth phase with specific productivity of \(12.76\pm 1.09\) U mg\({}^{-1}\) (Figure 3a,b). However, there was a decline in exoperoxidase production as from 72 h, which corresponds to the early stationary phase. More so, exoenzyme activity (\(23.10\pm 0.82\) U mL\({}^{-1}\)) was detected without the use of hydrogen peroxide and was optimal at 72 h, with specific productivity of \(12.76\pm 0.45\) U mg\({}^{-1}\) protein (Figure 3a,b). The production of peroxidase by _Ensifer adhaerens_ NWODO-2 is growth-associated as the enzyme activity increased significantly at the logarithmic phase with optimum productivity attained at 48 h (Figure 3). The sharp decrease in peroxidase production at 72 h may probably be associated with nutrient depletion or proteolytic activities [22; 55]. This finding is contradictory to previous related  
Figure 2: Effect of lignin monomers and nitrogen supplements on exoperoxidase production by _Ensifer adhaerens_ NWODO-2. (**a**) Effect of lignin monomers on exoperoxidase production. AL: alkali lignin (Control), GA: guaiacol, VALC: veratryl alcohol, VAN: vanillin, VA: vanillic acid, FA: ferulic acid. Analysis of variance: F = 1023, \(p\) value = \(p<0.0001\), \(p\) value summary = *** (highly significant), R squared = 0.9988. (**b**) Effect of nitrogen supplementation on exoperoxidase production. YE: Yeast Extract (Control). Analysis of variance: F = 17940, \(p\) value = \(p<0.0001\), \(p\) value summary = *** (highly significant), R squared = 0.9999.  
reports, where optimal production of peroxidase by other bacteria species was achieved at 72 h of incubation [23; 35; 36]. This discrepancy may be a result of bacterial growth rate, culture medium and inoculum size. The detection of an extracellular enzyme activity without the use of hydrogen peroxide, the typical peroxidase activator, is noteworthy as this may suggest probable lactose production by _Ensifer alhaerens_ NWODO-2 [25].
## 4 Discussion
### Results of Surface Roughness Experiments  
End milling with backwards and forwards sweeping (fifty in our case) using a ball-end tool tip and a constant radial depth (ar) should leave an anisotropic surface signature. This anisotropic signature should be generated by the scallop leaves on the surface by the combination of the tool tip geometry and two consecutive sweeps (Figure 9). Nevertheless, the milled surface observations (Figure 4) revealed isotropic surface topologies on all the samples. The isotropic topology was confirmed by additional roughness measurements parallel to the feed rate direction. These results suggest that ball-end tool geometry is not a key parameter that impacts roughness in end milling mode and highlights the importance of the tool diamond grits not taken into account by the geometrical model proposed and described in Figure 9. In end milling mode, very few diamond grits are in contact with the machined surface and the tool wear is caused by a null cutting speed near the tool axis, increasing the pullout/wear of the grits. This phenomenon can explain the low roughness values observed on the milled surfaces with the two tools. The abrasive grains lost from the tool during milling can be displaced and compressed between the surface to be machined and the tool, causing three-body abrasion. The resulting phenomenon, called lapping [32], is consistent with our results and leads to low roughness parameters and an isotropic surface topology.  
To summarize, the isotropic topology is left by the milling toolpath (backwards and forwards sweeping) associated with the lapping phenomena caused by diamond grits immersed in the coolant.  
With both tools, the 3D parameters Sz are four times greater, on average, compared to the 2D Rz parameters. This can be explained by a larger number of extreme values found on surfaces than on linear profiles.  
It was also noticed that the standard deviations (SD) were lower for the Ra and Sa parameters. The Ra and Sa parameters are based on an arithmetic mean, so extreme points have a limited influence on Ra and Sa. Rt, Rz, Sq, and Sz are extremum parameters. Due to the intrinsic definition/formula of these roughness parameters, giving a lot of weight to the maximum points, Rt, Rz, Sq, and Sz are very sensitive to these maximum values, which is why there SD values are higher.
###### Abstract  
The running biomechanics of running shoes have been extensively investigated. However, there is limited knowledge about the use of bionic shoes compared to neutral shoes, along with the velocities involved in their use. The aim of this study was to examine the biomechanical alterations associated with various running velocities of bionic shoes. By removing different thicknesses of the forefoot section, bionic shoes created a more natural shape--close to that of a human foot. The study included 16 heel strike runners running at 10 km/h, 12 km/h and 14 km/h in bionic shoes and neutral shoes, respectively. A two-way ANOVA and SPM1d were employed for examining kinematic and kinetic differences. Regarding the results for the shoes, increased ROM was observed for the bionic shoes for the hip (\(p<0.001\)) and ankle joints (\(p<0.001\)). Ankle positive work (\(p<0.001\)) and negative work (\(p=0.042\)) also showed significant differences. Regarding the velocity results, hip ROM (\(p<0.001\)) increased and peak knee angular velocity (\(p=0.018\)) increased, while knee ROM (\(p=0.023\)) decreased. The interaction effects only existed in hip (\(p=0.031\)) and ankle (\(p=0.008\)) ROM. The results of this study suggested that the impact of running propulsion in the bionic shoes was minimal. However, with increased velocities, the bionic shoes demonstrated the ability to absorb more force, created a more stable training environment, and contributed to injury prevention for the hip and ankle joints.  
footwear; running velocity; bionic shoes; running biomechanics
## 3 Materials and Methods
### Data Collection
#### 3.3.2 Definition of Features
* Traffic Analysis: this involves monitoring the traffic characteristics and keeping track of the number of bytes that are sent or received by each node;
* Dropping ratio monitoring: observing the dropping rate of packets can help in the identification of nodes that selectively drop or discard incoming packets, indicating malicious behavior;
* Throughput monitoring: black hole attack typically drops a large amount of data, which may decrease significantly the throughput, making it an important metric to monitor for detection purposes. The set of features recorded in our dataset are summarized in Table 2: CTRLPackets, Count RREQ, CountRREP, CountRERR, Throughput, Sent Pckts, Received Pckts, and DroppingRatio.
# Composing Multiple Online Exams: The Bees Algorithm Solution
## 4 Experimental Design and Results
Regarding the success rate, the BA achieved an average success rate of approximately 90% across the various test cases, significantly outperforming the RA and SA, which failed to obtain any successful solutions for the majority of the test cases. The PSO methods, though, had a 100% success rate for all test cases. In terms of the overlap percentage, the BA achieves better results in this experiment compared to the previous one on the same question bank (Table 3), as is discussed in more detail in Section 5. The performance of the algorithms when varying the required difficulty levels using the small question bank is presented in Table 6. Similar to previous experiments, both PSO methods exhibited higher success rates than the BA in all instances, except for the instance in which \(DLR\) = 0.3, where both PSO methods significantly underperformed in comparison to the BA. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Algorithm** & **Number of** **Exams** & **Difficulty Level** & **Number of** **Runs** & **Successful Solutions** & **Average Fitness** & **Std Deviation** & **Average Overlap** \\ \hline \multirow{6}{*}{**BA**} & 100 & 0.3 & 10 & 888 & \(7.31\times 10^{-5}\) & \(2.38\times 10^{-4}\) & 0.47 \\  & 100 & 0.4 & 10 & 905 & \(4.15\times 10^{-5}\) & \(4.82\times 10^{-5}\) & 0.39 \\  & 100 & 0.5 & 10 & 922 & \(4.00\times 10^{-5}\) & \(3.36\times 10^{-5}\) & 0.36 \\  & 100 & 0.6 & 10 & 895 & \(4.39\times 10^{-5}\) & \(4.45\times 10^{-5}\) & 0.39 \\  & 100 & 0.7 & 10 & 888 & \(4.88\times 10^{-5}\) & \(7.89\times 10^{-5}\) & 0.50 \\ \hline \multirow{6}{*}{**PMPSO**[2]} & 100 & 0.3 & 10 & 1000 & \(4.89\times 10^{-5}\) & \(2.89\times 10^{-5}\) & 0.015 \\  & 100 & 0.4 & 10 & 1000 & \(4.86\times 10^{-5}\) & \(2.89\times 10^{-5}\) & 0.010 \\  & 100 & 0.5 & 10 & 1000 & \(4.72\times 10^{-5}\) & \(2.84\times 10^{-5}\) & 0.009 \\  & 100 & 0.6 & 10 & 1000 & \(4.70\times 10^{-5}\) & \(2.89\times 10^{-5}\) & 0.010 \\  & 100 & 0.7 & 10 & 1000 & \(4.73\times 10^{-5}\) & \(2.87\times 10^{-5}\) & 0.014 \\ \hline \multirow{6}{*}{**SPSO**[2]} & 100 & 0.3 & 10 & 1000 & \(4.92\times 10^{-5}\) & \(2.93\times 10^{-5}\) & 0.015 \\  & 100 & 0.4 & 10 & 1000 & \(4.80\times 10^{-5}\) & \(2.91\times 10^{-5}\) & 0.010 \\  & 100 & 0.5 & 10 & 1000 & \(4.90\times 10^{-5}\) & \(2.88\times 1  
clarity. From these figures, it can be observed that the BA demonstrated better performance in terms of average fitness in two instances (\(DLR\) = 0.3 and 0.5), while it had a worse average fitness than the PSO methods in the remaining three instances. However, it is worth noting that the BA consistently outperformed the RA and SA in all instances. Additionally, the PSO methods exhibited slightly better standard deviations than the BA in most cases. Again in this experiment, the desired threshold for the overlap percentage was still not achieved by the BA.
## 4 Conclusions  
Two biochars from sorghum distillers grain were prepared through KOH and NaOH activation followed by pyrolysis for the first time. They both presented enhanced NH4\({}^{+}\)-N adsorption capacity compared with the original biochar, owing to the significantly enhanced specific surface area after activation. The KOH-activated biochar had higher adsorption capacity than did the NaOH-activated biochar because of its much higher content of functional groups. The kinetics of NH4\({}^{+}\)-N adsorption onto KOH-activated biochar followed a pseudo-second-order model, and the adsorption equilibrium data suggested that NH4\({}^{+}\)-N adsorption fitted the Freundlich model well. The thermodynamic parameters demonstrated that the adsorption process was endothermic and spontaneous. The maximum adsorption capacity was found to be 14.34 mg/g at 45 \({}^{\circ}\)C, which was satisfactory when compared with other biochars prepared from different feedstocks. This work demonstrates that chemically activated biochar from sorghum distillers grain is a novel and efficient adsorbent for NH4\({}^{+}\)-N removal.  
Conceptualization, D.H., C.L., and G.W.; methodology, D.H. and T.P., writing--original draft preparation, D.H. and T.P., writing--review and editing, D.H., C.L., Y.W., and G.W. This research was funded by the National Natural Science Foundation of China (31700515). This work was kindly supported by the National Natural Science Foundation of China (31700515). The authors declare no conflict of interest.
# State-of-the-Art Internet of Things in Protected Agriculture
## 3 The Crucial Technologies of IoT in Protected Agriculture
### Data Transmission Technology
LPWAN technologies target a diverse set of applications with varying requirements in protected agriculture and therefore it should provide some sort of quality of service (QoS) over the same underlying LPWAN technology. However, current LPWAN technologies provide no or limited QoS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wireless Technology** & **Wireless Standard** & **Frequency Band** & **Network Type** &
\begin{tabular}{c} **Transmission Range** \\ \end{tabular} & **Data Rate \&Power** \\ \hline \multirow{2}{*}{Wi-Fi} & IEEE802.11 & \multirow{2}{*}{2.4 GHz, 5–60 GHz} & WLAN & 20–100 m & 1 Mb/s–6.75 \\  & a/c/b/d/g/n & & & Gb/s, 1 W \\  & Bluetooth & & & & 1 Mb/s–48 Mb/s, \\  & (formerly IEEE) & 2.4 GHz & WPAN & 10–300 m & 1 w \\  & 802.15.1 & & & & \\
6 LowPAN & IEEE 802.15.4 & 908.42 MHz/2.4 GHz & WPAN & 20–100m & 20 Kb/s–250 Kb/s, \\  & Sigfox & 908.42 MHz & LPWAN & \(<\)50 km & 10–1000 b/s, N/A \\  & LoRaWAN & LoRaWAN & Various & LPWAN & \(<\)15 km & 0–350 Kb/s, N/A \\  & NB-IoT & 3 GPP & 180 KHz & LPWAN & \(<\)15 km & 0–200 kb/s, N/A \\  & 2G-GSM, GPRS & & & & 2G: 50–100 kb/s \\  & 3G-UMTS, & 865 MHz, 2.4 GHz & GERAN &
\begin{tabular}{c} Entire cellular \\ area \\ \end{tabular} & 3G: 200 kb/s \\  & 4G: 0.1–1 Gb/s, 1 W \\  & IEEE 802.15.4 & 2400–2483.5 MHz & Mesh & 0–10 m & 250 Kbps, 1 mW \\  & ISO/IEC 13157 & 13.56 MHz & Point to Point & 0.1m & 424 Kbps, 1–2 mW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of popular wireless technologies and basic parameters. Meanwhile, mobile cellular communication technology has undergone four generations of technology updates and the fifth-generation mobile communication technology (5G) was announced in 2016, which will make large file transfer such as agricultural images and audio a reality and bring new impetus to the agricultural IoT. From the perspective of future research, the research of wireless sensor networks focuses mainly on communication, energy conservation, network control and its application in agricultural monitoring.
## 2 Materials and Methods
### Measurement of Intracellular Peroxynitrite  
Intracellular peroxynitrite production was measured after LPS treatment and X-ray irradiation. RAW264 cells were seeded in 96-well cell culture plates at a density of 2 \(\times\) 10\({}^{4}\) cells/well and incubated overnight. Cells were treated with 0 or 1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL LPS for 24 h at 37 \({}^{\circ}\)C and subjected to Cell MeterTM Fluorimetric Intracellular Peroxynitrite Assay Kit *Green Fluorescence* (AAT Bioquest, Inc., Sunnyvale, CA, USA). This kit contains a specific fluorescent probe for peroxynitrite detection that emits green fluorescence upon reaction with peroxynitrite. Briefly, the cells were irradiated with 20 Gy X-rays as described above. Four hours after X-ray irradiation, cells were washed with phosphate-buffered saline (PBS) and a fluorometric analysis was performed using an Infinite M200 microplate reader (Tecan Group Ltd., Mannedorf, Switzerland). The excitation and emission wavelengths were 490 and 530 nm, respectively.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Deep Learning(DL) Methods
#### 4.10.2 Convolutional Sparse Representation  
Sparse representation has been widely used in various image fusion. Due to the modeling burden and computational cost, traditional sparse representation has always been performed on local image patches rather than on the entire image. The concept of convolutional sparse coding (CSC) originates from the deconvolutional networks proposed by [PERSON] et al. [152]. Its fundamental principle is to get an image's convolutional decomposition with a sparsity constraint. As an image representation approach, CSC is also termed as convolutional sparse representation (CSR). On the contrast to the conventional sparse representation, the sparse representation of an entire image can be computed in the CSR model. In this CSR model, the obtained representation is single-valued and optimized over the entire image [153; 154]. [PERSON] et al. [153] introduced the CSR into the field of medical image fusion on MRI/CT. [PERSON] et al. proposed a novel fusion method based on convolutional sparse representation (CSR) to fuse the mis-registered GFP and phase contrast images in biomedical image fusion [155; 156].
# Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection
###### Abstract  
With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data. The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification but less dependent on other features. In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection. Different from the existing research, our method takes full advantage of knowledge about the dataset itself and the differences between datasets, which makes the features selected have a higher correlation with the classification, improves the classification accuracy, and reduces the data dimension. Experimental results on the UCI machine learning repository datasets confirmed the performance and effectiveness of our method. Compared to the existing method, smaller subsets of features and an average of 1% higher classification accuracies were achieved.  
Keywords:feature selection; fuzzy set; rough set; membership function +
Footnote †: journal: Applied Sciences
## 8 Conclusions  
This work aimed at extending the benefits of IoT to people living in areas with limited or no Internet connectivity, by collapsing the LoRaWAN servers normally spread in the cloud into a single box, the BS, that provides all the required functionalities. It can use TTN gateways and servers wherever they are available, even on an intermittent basis, or it can behave as a complete stand-alone system. Users interact with the proposed BS using their own devices over WiFi connectivity.  
The end-devices are very cheap and battery powered, while the moderate power required by the always on Base Station can be supplied by an affordable photovoltaic power system. The single channel gateway is very inexpensive and power miser (therefore well suited for small communities), while the eight channels offers more capabilities at the expense of increased power consumption. The proposed storage for the prototype is a 16 GB SD card which can be expanded to add additional content. The inexpensive and widely available Raspberry Py is the core of the Base Station, and proved to be adequate for the required communication tasks. Two types of BSs were tested, a very inexpensive single channel device, that can handle moderate traffic, and a more expensive eight channels one that can be deployed when the traffic grows significantly. The BS slaves surrounding the BS master are interconnected via their packet forwarder and extend the community network allowing synchronization of content and also enabling roaming for mobile nodes.  
A few of the many prospective IoT applications are sketched in the scenario's section. Future work will aim to address other applications for community networks that can leverage this kind of infrastructure to alleviate the existing disparity of opportunities in many countries such as those discussed here.  
Conceptualization, P.A.B., M.Z. and J.D.; methodology, P.A.B.; software, P.A.B.; validation, M.Z. and J.D.; formal analysis, P.A.B.; investigation, P.A.B., M.Z. and J.D.; writing-original draft preparation, P.A.B. and E.P.; writing-review and editing, M.Z. and J.D; visualization, J.D.  
This research received no external funding.  
We are grateful to the African Center of Excellence in Mathematical Science and Applications (CEA-SMA), IMSP, Dangbo, Republic of Benin, which supported this work, implemented at ICTP's Marconi Laboratory (ICT4D Lab) in Trieste, Italy.  
The authors declare no conflict of interest.
Applied sciences  
Development and Application of a Motion Analysis Protocol for the Kinematic Evaluation of Basic and Functional Hand and Finger Movements Using Motion Capture in a Clinical Setting--A Repeatability Study  
**[PERSON] 1,2,+\(\copyright\)**, [PERSON] 2,3, [PERSON] 2,4**,  
**[PERSON] 1 and [PERSON] 1**  
1 Division of Plastic Surgery and Hand Surgery, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; [EMAIL_ADDRESS] (L.R.); [EMAIL_ADDRESS] (M.C.)  
2 Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093 Zurich, Switzerland; dianajermann@bluewirch. (D.J.); [EMAIL_ADDRESS] (R.L.)  
3 Biomechanics Research Laboratory, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia  
4 Human Performance Lab, Schulthess Clinic, Zurich, Lengghalde 2, 8008 Zurich, Switzerland  
* Correspondence: [EMAIL_ADDRESS]  
Received: 19 August 2020; Accepted: 11 September 2020; Published: 16 September 2020  
**Featured Application: Potential applications of the presented motion analysis protocol are clinical evaluations of joint function, e.g., for comparisons between different treatment regimens or evaluation of pre-postoperative improvements. Furthermore, the provided repeatability measures serve as a base for meaningful data interpretation as well as for comparison with different biomechanical hand models.**  
**Abstract: The purpose of this study was to develop a motion analysis protocol that allows the simultaneous assessment of all hand and finger joint movements. The objective was to demonstrate repeatability for future clinical applications in functional assessments. This study includes selection of marker positions, movement tasks, kinematic approaches and a comparison of the two most commonly used finger marker sets. By using a test-retest measurement of the range of motion in twenty healthy volunteers, the repeatability of the developed protocol was validated. Estimated errors of the presented method ranged from 1.2\({}^{\circ}\) to 6.4\({}^{\circ}\). Finger joint angles derived from the marker set with two markers per segment showed better repeatability (3.7\({}^{\circ}\)) than with markers located on the joints (5.1\({}^{\circ}\)). Given the high repeatability found, the presented method appears to be suitable for clinical applications. For the fingers, measurement repeatability can be improved by using at least two markers per segment. Within this study, advanced kinematic approaches, such as functional determination of joint centers and axes, are applied to the analysis of hand movements. The provided standard values and estimate of the minimal detectable differences provide a valuable basis for meaningful data interpretation and may be used for future comparison with other protocols.**  
**Keywords: finger kinematics; hand kinematics; functional joint coordinate system; motion analysis protocol; minimal detectable difference; range of motion; repeatability; motion capture**