esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Strain Distributions  
The strains of pile specimens were measured to evaluate the damage states of piles under cyclic displacements. For the sake of brevity, only strain distribution for the CP-2, RP-8 and HP-9 specimens are presented in Figures 11-13, respectively.  
As seen in Figures 11a and 12a, the tensile and compressive strain distributions of the CP-2 and RP-8 specimens were symmetrical at 2 to 5 mm loading steps, which indicates that the soil-pile system remained elastic. When the displacement loading increased to 8 mm, the tensile and compressive strain distributions started to behave asymmetrically. The compressive strain was continued to increase while the tensile strain remained almost the same as the load reached 8 mm. This is mainly because the concrete started to crack after 5 and 8 mm loading steps at the buried depth of 4.5 D (0.7 m) for the CP-2 and RP-8 specimens and thereby, there was a substantial decrease in the tensile strain of piles after these loading steps, as shown in Figures 11b and 12b. Similar behavior was also observed for the PHC specimens [28].  
Figure 13a shows the tensile and compressive strain distributions of the HP-9 specimen were symmetrical up to 25 mm loading, which indicates that the H-shaped steel pile has more favorable flexibility than the common RC and PC piles. Moreover, as Figure 13b,c demonstrates, the tensile and compressive strains of the HP-9 specimen start to decrease at 70 mm loading at 0.7 mm buried depth. It indicates that the H-shaped steel pile showed a substantial elastic-plastic deformation capacity.  
Figure 14 presents the cracking and crushing displacements of the CP-1 to CP-5, PC-6 to PC-7, RP-8 specimens as well as the PHC-1 to PHC-4 specimens from the reference [28]. It can be observed from Figure 14a that for the CP-1 to CP-3 specimens the cracking displacement increased with the increase of reinforcement ratios. The cracking displacement of the CP-2, PC-6 to PC-7 and PHC-1 to PHC-4 specimens increased with the increment of the prestressing level. It can be concluded that the larger reinforcement ratio and prestressing level will result in a better concrete cracking resistance for CP, PC and PHC pipe piles in IAJBs. This is the key aspect of designing CP and PC pile foundations for crack resistance in IAJBs. Furthermore, the crushing displacement of concrete was around 40 mm of displacement loading, as illustrated in Figure 14b.  
Figure 10: Inflection point depth of the specimens.  
Figure 11: Strain distribution of CP-2 specimen. (**a**) Compressive and tensile strain distributions. (**b**) Tensile strain versus loading history. (**c**) Compressive strain versus loading history.
###### Abstract  
In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.  
Internet of health things; edge computing; Fog and Cloud computing; Mist computing; energy efficiency; fuzzy logic; computing capacity; load balancing +
Footnote †: journal: _Sensors_  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-000  
To that end, this paper introduces a pioneering solution known as the Mist-based fuzzy healthcare system (MFHS), which addresses the crucial aspects of processing time and energy efficiency within IoT networks. MFHS leverages the concept of the Mist layer, an intermediate layer that strategically manages the allocation of IoT data resources in the initial stages of data processing. By efficiently distributing computational tasks and data-processing activities, MFHS optimizes the utilization of resources, leading to reduced processing times and enhanced energy efficiency. This innovative approach has the potential to significantly improve the overall performance of IoT networks, ensuring that healthcare systems and other industries can leverage the full benefits of IoT technology.  
The paper comprises six sections, where the first section critically examines and analyses recent studies relevant to MFHS. The second section provides a comprehensive review of previous studies that leveraged fuzzy logic systems to address IoT network challenges. Section 3 offers an overview of the motivation and contributions of MFHS, while Section 4 presents the detailed methodology employed in MFHS. Section 5 showcases the results obtained from the implementation of MFHS, including a comparative analysis with existing approaches. Finally, the concluding section summarizes the key findings and highlights the implications of MFHS in advancing IoT network efficiency within healthcare systems.
## 3 Results
### Colorectal Cancer Patients and Controls
Figure 2: ROC curve analysis for the studied parameters in CRC and Control groups: (**a**) UA (95%CI: 0.53–0.47); (**b**) lactate (95%CI: 0.56–0.77); (**c**) Na\({}^{+}\) (95%CI: 0.66–0.84); (**d**) Cl\({}^{-}\) (95%CI: 0.50–0.72); (**e**) osmolality (95%CI: 0.66–0.85). A significant difference in plasma uric acid concentrations depending on BMI was established only in the CRC group, where it was higher in the overweight subgroup with BMI \(\geq\) 25. At the same time, the overweight CRC subgroup showed lower levels of uric acid than the overweight Controls (Table 3). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Na\({}^{\star}\)**} & **Cl\({}^{-}\)** & **Osm** & **Lactate** & **UA** & **BH** & **BW** & **BMI** & **Age** \\ \hline
**Na\({}^{\star}\)** & - & 0.68 *** & - & ns & ns & ns & ns & ns \\ \hline
**Cl\({}^{-}\)** & - & 0.67 *** & \(-\)0.35 ** & ns & ns & ns & ns & ns \\ \hline  & **Osm** & - & \(-\)0.27 * & ns & ns & ns & ns & ns \\ \cline{2-8}  & & **Lactate** & - & ns & ns & ns & ns \\ \cline{2-8}  & & & **UA** & - & 0.45 *** & 0.58 *** & 0.49 *** & ns \\ \cline{2-8}  & & & & **BH** & - & 0.76 *** & 0.33 * & ns \\ \cline{2-8}  & & & & **BW** & - & 0.86 *** & ns \\ \cline{2-8}  & & & & **BMI** & - & ns & \\ \cline{2-8}  & & & & & **Age** & - \\ \hline \end{tabular} Legend: Osmolality, Osm; uric acid, UA; body height, BH; body weight, BW; Body Mass Index: BMI. Correlation significance as follows: * \(p\) \(<\) 0.05; **_p_ \(<\) 0.01; *** \(p\) \(<\) 0.001.  
\end{table} 
Table 4: Correlation between studied parameters in CRC patients. \begin{table} \begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & **CRC** & \multicolumn{2}{c}{**Control**} \\ \multicolumn{1}{c}{**Variable**} & **\textless{}25** & \(\geq\)**25** & **\textless{}25** & \(\geq\)**25** \\ \hline Uric acid \(\mu\)mol/L & 293.92 \(\pm\) 15.25 ** & 312.70 \(\pm\) 18.87 \({}^{\ddagger}\) & 277.63 \(\pm\) 22.58 & 314.13 \(\pm\) 15.92 \\ Lactate mmol/L & 2.34 \(\pm\) 0.17 & 2.10 \(\pm\) 0.13 & 2.68 \(\pm\) 0.25 & 3.09 \(\pm\) 0.30 \\ Na\({}^{\star}\) mmol/L & 131.00 \(\pm\) 0.64 \({}^{\ddagger}\) & 122.57 \(\pm\) 0.58 & 133.13 \(\pm\) 0.35 & 133.33 \(\pm\) 0.36 \\ Cl\({}^{-}\) mmol/L & 102.46 \(\pm\) 0.53 \({}^{\ddagger}\) & 96.14 \(\pm\) 0.70 & 104.17 \(\pm\) 0.30 & 103.71 \(\pm\) 0.36 \\ Osmolality mmol/kg & 266.82 \(\pm\) 0.28 \({}^{\ddagger}\) & 249.67 \(\pm\) 1.19 & 271.05 \(\pm\) 0.66 & 272.39 \(\pm\) 1.06 \\ \hline  & & & & & \\ \end{tabular} 
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**\(p\) \(<\) 0.01 vs. BMI \(\geq\) 25 from the same group (BMI \(<\) 25 CRC/BMI \(\geq\) 25 CRC; BMI \(<\) 25 Control/BMI \(\geq\) 25 Control); \({}^{\  
A significant positive correlation between BMI and body weight with UA was established (r = 0.49, \(p\) < 0.001 and r = 0.58, \(p\) < 0.001). Body height also appeared to correlate significantly with UA levels in the CRC group (r = 0.45, \(p\) < 0.001). Lactate and Cl\({}^{-}\) were identified to be in negative correlation (r = \(-\)0.35, \(p\) < 0.01).
## 3 Results and Discussion
### Morphological and Structural Analyses  
Figure 2 shows a representative XRD pattern taken from networked SnO\({}_{2}\) NWs. All the peaks located at 2\(\theta\) = 26.55, 33.85, 51.85, 54.75, 61.75, 64.85 and 65.95\({}^{\circ}\) can be indexed as the (110), (101), (211), (220), (301), (112), and (311) planes of SnO\({}_{2}\), respectively, with a tetragonal rutile structure (JCPDS Card No. 88-0287) [39]. The high intensity of the SnO\({}_{2}\) Bragg peaks demonstrates the highly crystalline nature of the synthesized SnO\({}_{2}\) NWs.  
Figure 1: (**a**) Digital photograph of the set up for sensing. (Inset: X-ray sensor). (**b**) Schematic showing the set up for X-ray sensing.  
In Figure 3a-c, FE-SEM micrographs with different magnifications are provided. It is evident that NWs are grown selectively on interdigitated electrodes due to the catalytic activity of Au. In particular, Figure 3a indicates a plan-view low-magnified FE-SEM micrograph of networked SnO2 NWs, showing a selective growth of SnO2 NWs. Figure 3b shows a higher magnification image, where long SnO2 NWs with a relatively dense structure and a straight-line morphology can be seen. Figure 3c clearly shows the formation of ultrathin SnO2 NWs, where the diameter of the SnO2 NWs is estimated to be 50-60 nm.  
Figure 3: (**a–c**) Field-emission scanning electron microscopy (FE-SEM) micrographs of SnO2 NWs with different magnifications. Transmission electron microscopy (TEM) analyses results; (**d**) low-magnification bright-field TEM image; (**e**) high-resolution lattice image.  
Figure 2: Representative X-ray diffraction (XRD) pattern of SnO2 nanowires (NWs).  
To further examine the crystalline quality of synthesized NWs, TEM analysis was performed. A low-magnification bright-field image of a single NW is presented in Figure 3d. Obviously, the NW is very smooth at the surface and has a single-crystalline nature without any defects, including stacking faults or dislocations. The high-resolution lattice micrograph, presented in Figure 3e, again demonstrates the single-crystalline quality. The spacing between the parallel fringes is \(\sim\)0.340 nm, which can correspond to the (110) plane of SnO\({}_{2}\)[40].
###### Abstract  
Field and laboratory investigations were conducted to characterize bacterial diversity and community structure in a badly contaminated mangrove wetland adjacent to the metropolitan area of a megacity in subtropical China. Next-generation sequencing technique was used for sequencing the V4-V5 region of the 16s rRNA gene on the Illumina system. Collectively, Proteobacteria, Chloroflexi, Planctomycetes, Actinobacteria and Bacteroidetes were the predominant phyla identified in the investigated soils. A significant spatial variation in bacterial diversity and community structure was observed for the investigated mangrove soils. Heavy metal pollution played a key role in reducing the bacterial diversity. The spatial variation in soil-borne heavy metals shaped the spatial variation in bacterial diversity and community structure in the study area. Other environmental factors such as total carbon and total nitrogen in the soils that are affected by seasonal change in temperature could also influence the bacterial abundance, diversity and community structure though the temporal variation was relatively weaker, as compared to spatial variation. The bacterial diversity index was lower in the investigated site than in the comparable reference site with less contaminated status. The community structure in mangrove soils at the current study site was, to a remarkable extent, different from those in the tropical mangrove wetlands around the world.  
Keywords: mangrove; 16s rRNA; microbial diversity; community structure; heavy metal; spatial variation +
Footnote †: journal: Applied Sciences
## 4 Discrete Time Markov Chain Models and Analysis
After that, the throughput \(S\) can be calculated as seen in [14]:  
\[S=\frac{P_{s}P_{tr}E[P]}{\left(1-P_{tr}\right)\sigma+P_{tr}P_{s}T_{s}+P_{tr}(1 -P_{s})T_{c}} \tag{7}\]  
where \(P_{tr}=1-\left(1-\tau\right)^{n}\) is the probability that one or more transmissions are tried in a particular time slot, \(P_{s}=\frac{n\tau\left(1-\tau\right)^{n-1}}{P_{tr}}=\frac{n\tau\left(1-\tau \right)^{n-1}}{1-\left(1-\tau\right)^{n}}\) is the probability that just one transmission is occurred and thus a successful transmission happens in a particular time slot, and \(E[P]\) is the average length of payload. In addition, \(\sigma\), \(T_{s}\), and \(T_{c}\) represent the time slot duration, the average time when a successful transmission happens, and the average time when a collision happens, respectively. \(T_{s}\) and \(T_{c}\) can be expressed depending on whether RTS/CTS is enabled or not, which is denoted as subscript letters of on and off of, as follows. \[\begin{cases}T_{s,\text{off}}=Header+DIFS+PAYLOAD\\ \qquad+2\sigma+SIFS+ACK\\ T_{c,\text{off}}=Header+DIFS+PAYLOAD+\sigma\\ \qquad+\ \  
As mentioned in Section 4 and Figure 4a, an amount of transmitted data can be boosted if the auxiliary link has been idle during \(T\)th. If there is no single-link device on the link 2, where the auxiliary link of the MLD is formed as shown in Figure 4, the transmitted data is always doubled. However, if there are single-link contenders, such as legacy stations, on the link 2, transmitted data may not get a chance to access the auxiliary link. For this case, we can revise the throughput expression as follows.  
Since the DCF operates on a time-slotted manner, we let this number of checking slots as \(N\)th slots, which can be represented as \(N\)th \(=\lceil T\text{th}/\sigma\rceil\). Then we can express throughput equation for SLA referring to Equation (7), \(S_{ SLA}\) as follows:  
\[S_{ SLA}=\frac{P_{s, SLA}P_{tr, SLA}E[P]\bigg{(}1+\left(1-P_{tr,single}\right)^{N\text{th}} \bigg{)}}{(1-P_{tr, SLA})\sigma+P_{tr, SLA}P_{s, SLA}T_{s}+P_{tr}(1-P_{s})T_{c}} \tag{10}\]  
where \(P_{tr,single}\) represents the probability that there is at least one transmission from single-link devices on the link where the auxiliary link is formed in a randomly selected slot. In case when there are single-link devices, \(P_{tr,single}\) can be obtained empirically or numerically by means of the [PERSON]'s Markov Chain model. Otherwise, \(S_{ SLA}\) just becomes doubled compared to conventional single-link DCF Markov Chain model. The other parameters, such as \(\sigma\), \(T_{s}\), and \(T_{c}\) can be derived in the same way as given equations expressed in Equations (8) and (9).
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Evaluation Metric  
The grasp evaluation metrics are evaluated according to grasp rectangles, so the grasp configuration map needs to be converted into grasp rectangles at first. The grasping configuration is considered effective when the following two conditions are satisfied [8,9].  
1. The Jaccard index between the predicted grasping rectangle \(g\) and the ground truth of grasping rectangle \(g_{gt}\) is greater than 0.25, expressed as \[J\big{(}g,g_{gt}\big{)}=\frac{\big{|}g\cap g_{gt}\big{|}}{\big{|}g\cup g_{gt }\big{|}}>0.25\] (14)
2. The variance between the predicted grasp angle \(\varphi\) and the ground truth of grasp angle \(\varphi_{gt}\) is less than 30\({}^{\circ}\), expressed as \[\varphi_{A}=\big{|}\varphi-\varphi_{gt}\big{|}<30^{\circ}\] (15)
## 2 Material and Methods  
### Study Area and Sample Collection  
The ISO-1 peat column sampling (64\({}^{\circ}\)19\({}^{\prime}\)16\({}^{\prime\prime}\) N, 40\({}^{\circ}\)41\({}^{\prime}\)01\({}^{\prime\prime}\) E) was conducted within the large Ilassky marshland, which is situated \(\sim\)20 km to the south of Arkhangelsk city. The samples were taken in the eastern part of the Ilassky marshland, which was subjected to drainage in 1969-1972 by open drainage to remove excess moisture from the road-bed during highway construction [30]. The sampling location and photo of the drained peat-bog area are shown in Figure 1.  
In morphological terms, an ISO-1 site is an area of convex oligotrophic bog in a state of effective drainage. The vegetation cover includes a moss-lichen layer, represented by _Pleurozium schreberi_ green mosses, _Politrichum commune_, _Dicranum bergeri_ and _D. Polysteum_, with a slight admixture of sphagnum mosses and _Cladonia_ lichens; a herb shrub layer, composed of _Vacinium uliginosum_, _V. myrtillus and Ledum pulsatre_; and a tree layer, consisting of _Pinus sylvestris_ up to 7 m high.  
The ISO-1 peat core is 1.5 m thick. The peat is homogeneous in its botanical content and consists of Sphagnum Moss as well as a few admixtures of shrubs, cotton-grass and pine wood throughout the peat profile. The degree of decomposition (R) varies between 7-35% and increases with depth. It should be noted that a slight increase in the R in the subsurface layer (0-10 cm), caused, apparently, by activation of the microbiological degradation of peat organic matter in an area of increased aeration. The basement rocksare a moraine of medium-textured structure, as the formation of the peat deposit began immediately after glacier retreat [31]. The groundwater level was between \(-9\) and \(-42\) cm throughout the study period.  
The unbroken peat core up to 50 cm was removed by a PVC pipe. Following transportation to the laboratory, this peat core was divided into 2 cm layers, excluding the top 0-4 cm layer, and readied for next analysis steps. The peat samples were dried and reduced to a fraction of 0.5-2 mm by a series of sequential grinding in a rotary mill and sieving through a sieve with holes of an appropriate diameter. The obtained samples were used to assess the physicochemical parameters: active and exchange acidity of peat, water soluble salts, organic matter and ash content and C, H, N-elemental analysis. To determine the specific activity of \({}^{137}\)Cs, \({}^{210}\)Pb and \({}^{234,235,238}\)U, the peat samples were additionally ground and sieved to a fraction of 0.1 mm. To estimate the botanical composition, the level of degradation and the natural saturation of humidity, the peat samples without sample preparation (with an undisturbed structure and natural moisture) were used. A summary of procedures for determining the radionuclide activity and physical and chemical values is given on the following pages.
## 4 Discussion  
We evaluated the performance of the vision transformer encoder at different positions integrated with the lightweight framework. The experimental results show that integrating the vision transformer encoder in the neck (Position 4 and Position 5) can make the model more efficient. In this section, we analyze the visualization results, that is Bounding-box generated before Non-Maximum Suppression (NMS).  
Figure 11 shows an offshore scene; we can see that the bounding box generate by YOLOv5 + ViT(4) is more focused on the target. This shows that the integrated vision transducer encoder in the neck effectively improves the detection performance.  
Figure 12 proves the above point again, integrated vision transformer encoder in the neck (YOLOv5 + ViT(4)) effectively reduces false alarms caused by river bank textures.  
Figure 13 shows the comparison with the different scales of YOLOv5. Interestingly, although the YOLOv5 + ViT(5) model parameters are less than others, it also effectively avoids false alarms. Compared with the optical image, the SAR image has only one gray channel, and the ocean remote sensing has an only gray background. The features are relatively single, so there is no need to obtain redundant feature extraction. Furthermore, the YOLOv5 detector is easily disturbed by the noise in the SAR image, mistakenly detected noise as a ship, and leads to a false alarm phenomenon occurring. The detector combines the vision transformer and suppresses the false alarms. From this point of view, YOLO \(-\) ViTSS has an anti-noise ability. This is because, due to the integration of the vision transformer encoder, the model not only pays attention to the pixel information of the corresponding channel but also pays attention to the context information, which effectively reduces the false alarm caused by noise, which is suitable for SAR images with inherent coherent noise especially. We call the proposed model, which fuses with the vision transformer module in the neck: YOLO with the Vision Transformer encoder for SAR Ship Detection(YOLO \(-\) ViTSS). Experiments show that YOLO\(-\)ViTSS has good anti-noise performance and is very lightweight and energy-saving, it can be deployed on spaceborne computers with  
Figure 11: An offshore visualization scenario. Each subgraph represents the Bounding-box generated before NMS in the different models.  
Figure 12: A complex visualization scenario. Each subgraph represents the Bounding-box generated before NMS in the different models.  
limited computing power for online training and on-orbit detection, transplanting the YOLO-ViTSS to the remote sensing satellite platform will be an important future work. Furthermore, another important future work is exploring interpretability and setting up cross-validation training, it will be an effective technical route to further improve the generalization ability of the model and reduce the parameters.
## 4 Robot Modeling
### Dynamic Model Identification
The model parameters derived from the above-described identification process are indicated in Table 2. From these results, it can be stated that: (i) as expected, the two models \(G_{i}(s)\) obtained for each link are stable; (ii) gains of the models (coefficient \(a_{0}\)) are of the same order of magnitude; and (iii) the values obtained for the parameter \(\alpha\) for each of the models are very similar. Figure 7 shows a comparison of the experimental data and the frequency response of the identified model for each robot link (denoted as \(P_{L_{q}}(j\omega)=H(j\omega)G_{1_{l_{q}}}(j\omega)\), being \(L_{q}=\{L_{1},L_{2},L_{3}\}\) the link and \(G_{1_{l_{q}}}(s)\) the dynamics of each link with the form of (5)). As can be observed, differences between experimental and model frequency responses appear at frequencies higher than 50 rad/s, i.e., outside of the range of interest. In order to compare and evaluate the goodness of the obtained models, besides \(J\), additional performance indices were calculated: mean square error (MSE), mean absolute deviation (MAD), maximum deviation (MD), and coefficient of determination \(\mathrm{R}^{2}\), all of them calculated in the same way as in [55]. They are given in Table 3, where the best fits are in bold. To this respect, except for the third link, the best results are obtained using the model \(G_{1}(s)\) given by (5). However, for the third link that model is also the best in terms of \(J\) and MD, but not if another of the remaining indices is considered. Figure 7: Comparison of experimental and model frequency responses of the AEF swimming robot by exciting the first actuator and measuring the deflection at the end of each link.