esa-sceva/satcom-chunk-collection huggingface dataset:
# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 4 Proposed Workflow
### Blob Detection  
Starting from the complement of the image \(I_{PRE}\), a multiscale blob detection step based on the scale-normalized LoG (Laplacian of Gaussian) was employed [14] (see Figure 6):  
\[L=\sigma^{2}(G_{xx}(x,y,\sigma)+G_{yy}(x,y,\sigma)) \tag{1}\]  
where:  
\[G(x,y,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{2}\]  
Moreover, considering a specific scenario in terms of image acquisition conditions (e.g., imaging device properties, zoom factor, and so forth), the typical sizes of leukocytes could be easily estimated. Then, all the parameters of the scale-normalized LoG (i.e., \(\sigma\) values) could be set to work properly in the specified range of scales. As first, the input image was convoluted with a series of scale-normalized LoG with increasing \(\sigma\) values (\(\sigma_{1}\), \(\sigma_{1}\),..., \(\sigma_{N}\)) obtaining \(N\) output images \(I_{\sigma_{1}}\), \(I_{\sigma_{2}}\),..., \(I_{\sigma_{N}}\). The range of \(\sigma\) applied in the convolutions was strictly related to the radii of the blobs that had to be detected (from \(r_{min}\) to \(r_{max}\) in our tests). It is worth noting that the Laplacian achieved a maximum response to a binary circle of radius \(r\) when \(\sigma=r\sqrt{2}\).  
Once the three-dimensional (\(x\), \(y\), \(\sigma\)) scale-space was generated, local maxima were detected and considered as candidate blobs. To retain only the strongest local maxima, filtering based on an adaptive threshold, defined as follows, was then applied:  
\[Th_{blob}=t\times V_{max} \tag{3}\]  
where \(V_{max}\) is the maximum value computed in the scale-space and \(t\in[0,1]\).  
Finally, a non-maxima suppression step was employed to refine the final results, as shown in Figure 6.  
Figure 5: (**a**) Original RGB image, (**b**) grayscale image, and (**c**) preprocessed image exploiting both the hue and saturation channels.
## 6 Conclusions  
In summary, this paper presents the work on building a complete set of simulation tools for optimizing the field-null configuration to achieve robust plasma breakdown on HL-3. First, a forward simulation model of the tokamak circuit is developed based upon the Simulink(tm) framework, including the coils, their power supplies and the passive conductive structures. The model has been calibrated, and the calculated coil currents can perfectly match the experimental values. With the developed model, an iterative approach is developed for optimizing the field-null configuration, which naturally includes the eddy currents in passive structures in a self-consistent way and can provide the CS and PF current waveforms that generate the optimum field-null configuration inside a given region. Finally, a PID feedback controller was designed and integrated with the model to form a closed-loop system, and simulation FB control experiments have demonstrated the feasibility of the designed field-null configuration and the corresponding coil current waveforms. These results build a solid base for applying the optimum field-null recipe to HL-3, which could help improve the quality and robustness of plasma breakdown on HL-3. This can help increase the machine productivity and could also assist in robustly achieving higher plasma currents by lowering the loop voltage at breakdown and saving flux consumption. The proposed method could also provide an alternative and more flexible way for designing the field-null configurations for future tokamaks like ITER.  
Conceptualization, B.L. and B.Y.; Methodology, M.H. and Y.C.; Software, M.H. and Y.C.; Validation, M.H., B.Y., X.Z., P.L. and B.L.; Formal analysis, Y.C. and X.X.; Investigation, M.H. and Y.C.; Data curation, X.X., X.Z., J.Y., J.S. and P.L.; Writing--original draft, M.H.; Writing--review & editing, B.Y., Y.C. and B.L.; Visualization, M.H. and Y.C.; Supervision, B.Y., B.L. and X.J.; Project administration, B.Y. and Y.C.; Funding acquisition, B.Y., Y.C., X.X., B.L. and X.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National MCF Energy R&D Program of China under Grant No. 2024 YFE03020004, the National Natural Science Foundation of China under Grant No. 12175056, the National MCF Energy R&D Program of China under Grant No. 2022 YFE03010004, and the National MCF Energy R&D Program of China under Grant No. 2022 YFE03020003. And The APC was funded by the National MCF Energy R&D Program of China under Grant No. 2024 YFE03020004.  
Ethical review and approval were waived for this study as it is not concerned with medical or biological issues.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
**Acknowledgments:** The author sincerely thanks the team of the HL-3 operation and control for helpful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Frequency Hopping Techniques
### Channel-Aware, Reduced-Hop-Set Techniques
#### 5.2.3 Clipped Matched Frequency Hopping  
The Clipped Matched Frequency Hopping (CMFH) is an evolution of the MFH technique [29]. The channel gains are first clipped at a certain threshold, proportional to the maximum gain, and then, the clipped channel gains are decreased by this threshold. That means any channel gain less than or equal to the threshold, \(\xi\text{max}({{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{                                                             }}}}}}}}}}}}}}}}}}  
The MFH technique is then applied to the new clipped power metrics, instead of using directly \(\left\lvert H_{k}\right\rvert^{2}\) as it is done in the MFH technique. The CMFH technique will result in a hopping set with more concentrated channels than in the MFH technique, but with higher gains.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 5 Segmented Global Path Planning
### Door and Auxiliary Navigation Point  
The door area is a high-risk area for navigation failure. Firstly, the feasible road is blocked by the wrong obstacles (shown as the dots in the red circle in Figure 4a) because of the sensor noise and calibration error, so that robot cannot correctly perceive the environment. Secondly, the shortest path through the door is usually a circle trajectory (red line in Figure 4a), so the motor and control errors like drift or slippage easily lead to the collision between the robot and the door frame.  
For the first problem of sensor error, we can find that the noise can be cleared when robot directly stand in front of the door to perceive the environment (as shown in Figure 4b), so as to plan a correct path correctly. For the second problem, it is obvious that controlling the robot to cross the door along the center line of the door area, can ensure the maximum distance between the robot and the door frame to ensure the maximum safety. Therefore, we propose to use the dominant direction of the room and the coordinates of the door endpoints to generate ANPs on both sides of the door and modify the robot's path to pass through the ANPs. This greatly improves the safety and success rate of the robot crossing door. The specific algorithm is described in Algorithm A2 in Appendix A.  
In the process of building the semantic topological map based on the previous work [35], the semantic information of rooms (Room\({}_{1}\) and Room\({}_{2}\)), dominant directions of the room (L), and coordinates of the door endpoint (E\({}_{1}\) and E\({}_{2}\)) have been generated, as shown in Figure 5a. Then, we make the perpendicular line A\({}_{1}\)A\({}_{2}\) through the midpoint O of line-segment E\({}_{1}\)E\({}_{2}\), as shown in Figure 5b. Note that the length of OA\({}_{1}\) and OA\({}_{2}\) both are equal to \(\Delta\)D and is related to door width and robotic size. A\({}_{1}\) and A\({}_{2}\) are used as the ANPs on both sides of the door. Furthermore, the room's semantic information is stored in the ANP, as the form of the room which ANP is in, the room which ANP connects to. Additionally, the orientation of ANPs are parallel to L but change with navigation  
Figure 4: Navigation failure because of perceptual error: (**a**) The perceptual error in the door area results in the navigation failure. (**b**) Correct perceptual location can eliminate the perceptual error.  
directions. For example, in Figure 5c,d, the orientation is up when the robot trajectory is bottom-up, and it is down when the robot trajectory is top-down.
## 3 Results and Discussion  
### Surface Properties of UF and MF Membranes  
Table 1 summarizes the surface properties of the two ultrafiltration (UF) membranes (PES-UF and PVDF-UF) and one microfiltration membrane (PTFE-MF) used in this study. The pore size of the membranes was provided by the manufacturers. The values of zeta potential were \(-\)35 mV for PES-UF and \(-\)37.4 mV for PVDF-UF. The zeta potential of the PTFE-MF membrane could not be determined, but the value reported in the literature is \(-\)22 mV [32]. The contact angles of the membranes were less than 50\({}^{\circ}\), indicating that these membranes are all hydrophilic. In terms of roughness, the PES-UF membrane exhibited a smooth topology (average surface roughness (Ra) = 63.6 \(\pm\) 15.4 nm). PVDF-UF and PTFE-MF membranes indicated a relatively rougher surface (188.8 \(\pm\) 61.5 and 247.8 \(\pm\) 54.3 nm, respectively) with a height variation of hundreds of nanometers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Membranes**} & **MWCO a** & **Zeta-Potential** & **Contact Angle** & **Roughness** & **Permeability** \\  & **(Pore Size *)** & **(mV)** & **(°)** & **(Ra, nm) b** & **(LMH/bar)** \\ \hline PES-UF & 100 kDa & \(-\)35.0 \(\pm\) 3.7 & 47.5 \(\pm\) 1.1 & 63.6 \(\pm\) 15.4 & 120 \(\pm\) 2 \\ PVDF-UF & 100 kDa & \(-\)37.4 \(\pm\) 2.4 & 36.9 \(\pm\) 0.6 & 188.8 \(\pm\) 61.5 & 921 \(\pm\) 21 \\ PTFE-MF & 0.3 \(\mu\)m * & \(-\)22 \(\times\) & 37.0 \(\pm\) 0.3 & 247.8 \(\pm\) 54.3 & 8923 \(\pm\) 104 \\ \hline \end{tabular} a
[FOOTNOTE:a]Footnote a: footnotetext: MWCO indicates nominal molecular weight cut-off. b  
\end{table}
Table 1: Surface properties of the membranes.
## 3 Review of WBASN MAC Protocols for IEEE 802.15.4 and IEEE 802.15.6
### Duty-Cycle-Based Approaches  
Various research studies have optimised the performance of the slotted mode of IEEE 802.15.4 in terms of latency and energy consumption by adjusting the duty cycle mechanism accordingly. These approaches deal with channel access management during the active and inactive periods of the superframe to make power utilisation efficient. The advantage of this approach is to increase the network lifetime by managing the duty cycle period.  
AMFE fine-tunes the duty cycle by considering the occupancy rate of the superframe order (SO). The dynamic superframe adjustment algorithm [86] adjusts the duty cycle using two parameters, including the superframe occupancy rate of the SO and the collision rate. Duty cycle adoption [87] utilises the buffer occupancy and queuing delay statistics to optimise the duty cycle mechanism. The adaptive algorithm optimises the dynamics [88] and adjusts the superframe's active periods using the comparison number of received packets in different superframes. The duty-cycle learning algorithm [89] improves the duty-cycle mechanism based on the offered traffic load.
###### Abstract  
In this study, diethylenetriamine-functional magnetic core-shell polymer modified graphene oxide (DETA-MPs-GO) was prepared via precipitation polymerization and amidation reaction, and it was characterized by transmission electron microscopy (TEM), Fourier-transformed infrared spectroscopy (FTIR), and X-ray diffractometer (XRD). Subsequently, a magnetic solid-phase extraction (MSPE) procedure was applied to the as-synthesized DETA-MPs-GO for the detection of nine fungicides in fruit samples, prior to ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). The homogenized fruit samples, spiked with D-labelled internal standards, were firstly extracted by 5 mL of acetonitrile twice and then purified by DETA-MPs-GO adsorbents. The optimization of the adsorption and elution conditions of DETA-MPs-GO toward fungicides was carried out to attain a satisfactory adsorption performance and desorption efficiency. The adsorption mechanism was carefully investigated, and the results revealed that a synergistic adsorption mechanism, including hydrogen bond and a \(\pi\)-\(\pi\) stacking interaction, was confirmed. Moreover, the limits of quantitation (LOQs) of the proposed approach were in the range of 0.01 to 0.30 \(\upmu\)g/kg under the optimum conditions. The average recoveries at three spiking levels were 84.9% to 105.2%, with relative standard deviations (RSDs) varying from 0.8% to 8.2% (\(n=6\)). The developed method was successfully utilized for the screening and detection of fungicides in 81 fruit samples purchased from markets. A detailed survey was carried out about the concentration distribution, types of fungicides, and combined use of fungicides in different fruits.  
diethylenetriamine-functional magnetic core-shell polymer modified graphene oxide (DETA-MPs-GO); magnetic solid-phase extraction (MSPE); synergistic adsorption mechanism; ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS); fungicides +
Footnote †: journal: J. Mol. Sci.
## 3 Results  
We included 12 athletes, mean aged 20.5 \(\pm\) 6.2 years, with a mean height of 180.8 \(\pm\) 7.9 cm, of the same team competing in the Second Division of the Italian National Volley Championship. All the athletes had COVID-19 mild-infection in the same frame time (January 2021) and they were isolated for 25 days. Paracetamol was administered for symptoms control. The most prevalent symptom was fever reported by 7 athletes, followed by headache (6 athletes), loss of taste or smell (5 athletes), muscle aches (4 athletes), and cough (2 athletes). The mean duration of symptoms was 4.0 \(\pm\) 2.1 days.  
The electromyographic analysis of the knee stabilizer muscles showed at T1 a significant delay of the RF activation time (426 \(\pm\) 188 vs. 152 \(\pm\) 106 ms; \(p\) = 0.001); VM (363 \(\pm\) 192 vs. 140 \(\pm\) 96 ms; \(p\) = 0.019); BF (229 \(\pm\) 60 vs. 150 \(\pm\) 63; \(p\) = 0.006); MH (T0: 231 \(\pm\) 88 vs. 203 \(\pm\) 89 ms; \(p\) = 0.011). Further details are shown in Table 1.  
At T1 the BIA showed a statistically significant reduction of muscle mass (41.24 \(\pm\) 3.06 vs. 39.38 \(\pm\) 2.63 kg; \(p\) = 0.003); fat mass weight (19.82 \(\pm\) 4.45 vs. 18.4 \(\pm\) 4.04 kg; \(p\) = 0.009), ECM (22.36 \(\pm\) 1.98 vs. 23.43 \(\pm\) 2.38 kg; \(p\) = 0.035), BCM (32.92 \(\pm\) 4.53 vs. 31.27 \(\pm\) 3.81 kg; \(p\) = 0.007), and phase angle (7.81 \(\pm\) 0.72 vs. 7.02 \(\pm\) 0.44 degrees; \(p\) = 0.003). Further details on the BIA evaluation are depicted by Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **T0** & **T1** & \(p\)**Values T1-T0** \\ \hline RF (ms) & 426 \(\pm\) 188 & 152 \(\pm\) 106 & 0.001 \({}^{*}\) \\ VM (ms) & 363 \(\pm\) 192 & 140 \(\pm\) 96 & 0.019 \\ BF (ms) & 229 \(\pm\) 60 & 150 \(\pm\) 63 & 0.006 \({}^{*}\) \\ MH (ms) & 231 \(\pm\) 88 & 203 \(\pm\) 89 & 0.011 \({}^{*}\) \\ \hline \end{tabular} Continuous variables are expressed as means \(\pm\) standard deviations. Statistical analysis performed to assess intra-group differences was paired sample \(t\)-test; \({}^{*}\) = \(p\) < 0.0125. Abbreviations: RF: rectus femoris; VF: vastus media; BF: biceps femoris; MH: medial hamstrings.  
\end{table}
Table 1: Differences in pre-activation time (ms) of knee stabilizer muscles before COVID-19 infection (T0) and at the return-to-training day (T1) comparing the same study population (n = 12).  
Figure 2: Timeline of this retrospective study on volleyball athletes affected by COVID-19.  
At T1 we found a negative trend, even though not significant, in terms of CMJ flight time (0.56 \(\pm\) 0.04 vs. 0.55 \(\pm\) 0.03 s; \(p\) = 0.113) and height (35.7 \(\pm\) 12.3 vs. 35.2 \(\pm\) 11.9 cm; \(p\) = 0.103); and SJ (flight time 0.467 \(\pm\) 0.15 vs. 0.461 \(\pm\) 0.14 s; \(p\) = 0.144) and height (32.04 \(\pm\) 5.13 vs. 31.25 \(\pm\) 4.67 cm; \(p\) = 0.140) after COVID-19.
# Development, Implementation and Evaluation of An Epidemic Communication System
## 2 Related Works and Issues
However, if the number of drones increases too much in a limited space, the problem of physical contact between drones will arise. Epidemic communication, which is not dependent on transmission time, has issues that must be considered in determining the optimal number of drones for operation, as well as the flight speed of the drones. These are discussed in Ref. [14]. There are few wireless devices capable of performing epidemic communication operations. For example, Transfer Jet is a device that may be used for epidemic communication. This is a technology being standardized in the IEEE802.15.3e [31; 32], and it is specialized for close-proximity wireless transfer [33; 34]. Another, Wi-Fi Direct [35] of Wi-Fi Alliance standard is a technology that enables simple direct connections and has been implemented in smart phones and notebook PCs, etc. These technologies eliminate or simplify the authentication and association process, and the time spent for the connection procedure between wireless stations is extremely short. Therefore, epidemic communication may be implemented, because these can achieve pure communication of the infected part. However, although these devices are implemented in commercial products, they are very difficult to obtain as devices for development. Therefore, in this study, we did not use these unique devices, but used wireless LAN (Wi-Fi) devices that are readily available and can be developed universally. Although wireless LANs can be easily implemented, the development was conducted after considering issues such as connection time, since authentication and association procedures are used in the communication protocols. The purpose of this study is to establish the implementation method for epidemic communication, and although maximum performance may not be achieved, the final result is that the design policy and assumed behavior can be realized, and providing this information is a contribution to other researchers.
## 4 Case Analysis
### Simulation Attack and Performance Analysis
#### 4.4.3 Analysis of the Impact of Random Attacks on Network Performance  
After focusing on protecting and maintaining 16 critical nodes, we simulated a random attack on the transport network and analyzed the impact of the damage to the remaining 68 common nodes on the overall network performance, as shown in Figure 7.  
From Figure 7, it can be seen that the trends of network efficiency and network connectivity show the same randomness under random attacks. However, after the failure  
Figure 6: Deliberate attack on key nodes results graph.  
Figure 7: Random attack on key nodes results graph.  
of important nodes, these two indicators show a significant decline. This confirms that the decline in network performance will accelerate with the failure of important nodes. Compared with the network performance changes brought about by deliberate attacks, the decay rate of network performance under random attacks is slower. This also implies that after focusing on protecting and maintaining the key nodes, when nodes in the network fail randomly, the overall performance decline trend will slow down, exhibiting a certain degree of resilience.  
From Figure 7, it can be observed that under random attacks, network connectivity and natural connectivity still maintain similar change characteristics, and the overall rate of network performance paralysis remains nearly unchanged, displaying a linearly declining trend. This indicates that the importance of nodes has little effect on connectivity. The decrease in network efficiency and maximum connectivity subgraph is initially faster and then slows down. After maintaining some stability, the largest connected subgraph drops sharply, indicating that after randomly attacking some important nodes, network performance deteriorates significantly. Compared to deliberate attacks, the overall decline rate of network performance is noticeably slower. To reduce the performance to around 50%, nearly 40 stations need to be damaged, whereas, with deliberate attacks, nearly 16 stations can achieve the same effect. This shows that after protecting the key nodes, the network exhibits a certain resilience.
## 3 System Configurations and Modelling
### Performance Indicators
The heating and cooling is converted into electricity using a reference efficiency for a conventional heat pump or chiller electrically driven. The generic mathematical expression of this performance indicator is as follows: \[EqES=\left(Q_{H}/COP_{ref}+Q_{C}/EER_{ref}\right)Q_{WH}{}^{-1}.\] (4) In the equation above, the value of the reference coefficient of performance (\(COP_{ref}\)) utilized to convert the heating produced into electric terms depends on the temperature at which the heating is produce. Thus, the COP of a reference heat pump for the production of hot water at 40, 50, and 60 \({}^{\circ}\)C were set to 3.7, 3.1, and 2.5, respectively, as typical values for a water-to-water heat pump [36]. To convert the cooling produced to electric terms, we used a reference energy efficiency ratio (\(EER_{ref}\)), which is defined as the ratio of the cooling to the electricity consumption of the refrigeration unit. Since the cooling is produced at the same temperature in all the configurations considered (6 \({}^{\circ}\)C), a single value of \(EER_{ref}\) was set to 4.1 [37]. * Avoided greenhouse gas emissions (\(AGHGe\)), in kg-CO\({}_{2,\text{eq}}\)/MWh. This indicator accounts for the amount of GHG emissions avoided per MWh of wasted heat recovered: \[AGHGe=EF.EqES,\] (5) where \(EF\) is the reference emissions factor that represents the amount of equivalent CO\({}_{2}\) emissions due to electricity production, and it was set to 0.298 kg-CO\({}_{2,\text{eq}}\)/kWh, which is a typical value for an electricity mix integrated by different energy sources (e.g., Spanish electricity mix). * Exergetic efficiency (\(\eta_{ex}\)). This indicator provides information regarding the \"quality\" of the energy products obtained from the waste heat recovered. It is defined as follows: \[\eta_{ex}=\frac{Q_{H}(1-T_{0}/T_{DH})+Q_{C}(1-T_{DC}/T_{0})}{Q_{WH}(1-T_{0}/T_ {WH})}\times 100\%.\] (6)
* The subscript \"0\" denotes the reference environment temperature, which was set to 298.15 K.  
With the aim of comparing the performance of all the configurations from a common framework, all performance indicators above were referenced to a mass flow rate of 1 kg s\({}^{-1}\) in the heat transport network.