esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Methods
#### 2.3.2 NMR Test  
The theory of NMR technology measurement is based on the interaction between the magnetic properties of atomic nuclei and an applied magnetic field, which are used to characterize the chirality of fluid-bearing pores in rocks. After the sample is placed into the magnetic field, a radio frequency pulse of a specific frequency is emitted. The hydrogen proton (H proton) absorbs the energy to resonate, and when the radio frequency pulse ends, the H proton releases the energy absorbed at the time of resonance, and the change in power can be detected by the unique coil, which is the signal of nuclear magnetic resonance. The energy release rate is different for samples with other properties, and the difference in movements can be visualized to reflect the changes in the internal microscopic pore structure of the rock.  
The changes in pore structure can be analyzed by \(T_{2}\) mapping after freeze-thaw cycles. According to the NMR principle [33], the total NMR transverse relaxation rate \(1/T_{2}\) can be expressed as  
\[\frac{1}{T_{2}}=\frac{1}{T_{2 free}}+\rho_{2}\bigg{(}\frac{S}{V}\bigg{)}_{ porosity}+\frac{D(\gamma GT_{E})^{2}}{12} \tag{1}\]  
where \(T_{2 free}\) is the fluid free chirp time, ms; \(S\) is the pore surface area, cm\({}^{2}\); \(V\) is the pore volume, cm\({}^{3}\); \(\rho_{2}\) is the transverse surface chirp intensity, \(\mu\)m/ms; \(D\) is the diffusion coefficient; \(\gamma\) is the spin-to-magnetism ratio, rad/(S*T); \(G\) is the magnetic field gradient, Gs/cm; \(T_{E}\) is the echo time, ms.  
In the presence of only a single type of fluid in the pore space, the volume relaxation time is much larger than the area relaxation time, so \(T_{2 free}\) can be neglected; when the magnetic field is uniform, and a short \(T_{E}\) is also used, the diffusion relaxation can also be ignored so that the above equation can be simplified as  
\[\frac{1}{T_{2}}=\rho_{2}\bigg{(}\frac{S}{V}\bigg{)}_{ porosity} \tag{2}\]  
From Equation (2), it can be seen that there is a correlation between the distribution of transverse relaxation time \(T_{2}\) of the material and the pore size information, and there is a positive correlation growth between the two. The transverse relaxation time of the material is determined by the ratio of pore surface to volume (\(S/V\)) and the surface relaxation strength of the fabric together.  
In the test process, it is necessary to calibrate the instrument with the standard oil samples with known NMR signals and then carry out NMR relaxation measurements on the normal mudstone samples in the initial state and under different freeze-thaw cycling conditions, according to the size of the porosity of the mudstone samples, the \(T_{2}\) relaxation time, and the change of the recorded signal intensity, to analyze the evolution of the pore structure and porosity characteristics of the mudstone under the action of the freeze-thaw cycle. This is carried out to investigate the damage law of the mudstone under the conditions of freezing and thawing and to reveal the mudstone freezing and thawing microimpairment mechanism.
## 2 Results and Discussion
### Antioxidant Activity
#### 2.4.1 Radical Scavenging Activity
The EC\({}_{50}\) values of STP2, STP3, and STP5 were lower than those of peptides from protein hydrolyses of miuiv croaker swim bladders (GFEPY: 0.87 mg/mL; FYKWP: 1.92 mg/mL; FTGMD: 3.04 mg/mL;GFYAA: 3.03 mg/mL; FSCLR: 3.35 mg/mL) [47], croceine croaker muscle (VLYEE: 0.693 mg/mL; MLMR: 0.993 mg/mL) [55], bluefin leatherjacket heads (WEGPK: 3.223 mg/mL; GPP: 4.668 mg/mL; GVPLT: 2.8819 mg/mL) [39], and skate cartilage (FIMGPY: 1.61 mg/mL; GPAGDY: 1.66 mg/mL; IVAGPQ: 1.82 mg/mL) [37]. However, the EC\({}_{50}\) values of STP2, STP3, and STP5 were higher than those of peptides from protein hydrolys of croceine croaker muscle (YLMR: 0.450 mg/mL) [55], swim bladder of miuuy croakers (FPYLRH: 0.34 mg/mL; GIEWA: 0.30 mg/mL) [47], round scad (HDHPVC: 0.265 mg/mL; HEKVC: 0.235 mg/mL) [56], monkfish muscle (FLHRP: 0.101 mg/mL; LMGQW: 0.042 mg/mL) [57], and croceine croaker scales (GFRGTIGLVG: 0.463 mg/mL; GPAGPAG: 0.099 mg/mL; GFPSG: 0.151 mg/mL) [58]. Therefore, STP2, STP3, and STP5 can serve as superoxide anion radical scavengers to eliminate radical damage together with SOD in organisms. 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid (ABTS) Cation Radical Scavenging Assay  
2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid (ABTS) is a popular measure of the antioxidant capacities of peptides because ABTS is converted to its radical cation by the addition of sodium persulfate, which shows a blue color with the absorbance at 734 nm. As shown in Figure 5D, the ABTS cation radical scavenging ratios of five antioxidant peptides (STP1-STP5) increased with an increasing concentration ranging from 0.1 to 5.0 mg/mL, but their activities were still lower than that of GSH at the same concentration. The EC\({}_{50}\) values of STP2 and STP5 were 0.41 and 0.21 mg/mL, respectively, which were significantly lower than those of the other three peptides. Furthermore, the EC\({}_{50}\) values of STP2 and STP5 were lower than those of peptides from protein hydrolys of salmon (FLNEFLHV: 1.548 mg/mL) [44], skate cartilages (FIMGPY: 1.04 mg/mL; GPAGDY: 0.77 mg/mL; IVAGPQ: 1.29 mg/mL) [37], bluefin leatherjacket heads (WEGPK: 5.407 mg/mL; GPP: 2.472 mg/mL; GVPLT: 3.124 mg/mL) [39], and grass carp skin (VGGRP: 0.465 mg/mL) [49]). These results indicated that the five antioxidant peptides (STP1-STP5), especially STP2 and STP5, could effectively inhibit the ABTS cation radical chain reaction by converting it to the colorless form.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 5 Discussion and Results
### Testing Data with the Predictive Model  
After obtaining the predictive model, we can assess the accuracy with actual test data. For example, in Figure 13, we can observe part of the result of the model on the test data in which the classification is unknown at the outset, showing the precision obtained by the application of the predictive model.  
Most of the results are accurate between 99.9% and 100%, so the model is consistent with the actual data obtained. As a result, we can state that positioning sensors, such as the accelerometer, effectively detect the falls of people. Furthermore, its association with the GPS (SIM 808) and temperature sensors, among others, can contribute to improving the quality of life of people living in remote areas where there is no mobile network coverage. The combination of LoRa networks overcomes this lack of mobile network coverage, so the use of strategically placed Gateways can ensure complete coverage of those regions.  
Figure 12: Sample of the test data file.  
Figure 13: Results were obtained by applying the model to the test group.  
Furthermore, it keeps people connected to the central system, which will be in operation with authorized entities for that purpose, namely, security forces, fire brigades, and families.  
It should be noted that the SIM808 has an interface for a mobile SIM card. Thus, the system presented here is perfectly viable and functional when the current mobile network (2G/3G/4G) can be used. In addition, it can be used in LTE-M and NB-IoT technology (standard to be integrated into 5G), where mobile networks are used for communications, using low resources, even in low coverage locations. However, they always require a paid mobile communications plan, with increased costs for the user.
## 4 Experiments  
### Dataset  
In order to illustrate the proposed system, the experiments were conducted using the Le2i fall detection dataset [28], which represents a realistic video surveillance setting taken by a single RGB camera. The frame rate was 25 fps and the size was \(320\times 240\). The video sequences presented typical difficulties, such as occlusions, clutter, and textured backgrounds. In the video scenes, falls and normal daily activities were simulated at different locations, such as home and office. Different types of fall events were recorded to include falls caused by a loss of balance, as well as forward and backward falls. In the dataset, 20 videos were randomly selected to confirm the effectiveness of the proposed system. In the video sequences, four healthy subjects including three males and one female performed the simulated falls. Some of the video sequences used in the experiments are as shown in Figure 10.  
Figure 10: Example video sequences which include the falls.
## 5 Conclusions  
The EKF and GRA methods use the gain matrix and the covariance matrix in their operations. This makes them robust in their function to measurements subject to gross error. However, the EKF gain matrix is created considering the historical covariance matrix. In contrast, the GRA covariance matrix, using an equivalent weighting matrix, suppresses the results of measurements of relative bearings and distances subject to gross error. The differences indicated result in an inconsistency in the accuracy of coordinate position estimation by both methods, based on the same measurement results taken relative to the beacons. This inconsistency becomes apparent as the number of measurements (relative bearings and distances) increases and the so-called position lines with favourable (accuracy) crossing angles appear.  
Therefore, these methods should be used interchangeably to improve the accuracy of position estimation. The mean limiting error of the GRA estimation of position coordinates may be used for this purpose. Its value should be estimated as a result of experimental simulation studies conducted for a specific navigation system using the information on the distribution of beacons, e.g., obtained from an electronic navigation chart.  
Figure 25: Graph of mean error \(M_{xy}\) of GRA-estimated position coordinates during 100 SV crossings, relative to triangle marks as a function of time.  
**Author Contributions:** Conceptualization, K.N.; methodology, K.N.; software, K.N.; validation, K.N.; formal analysis, P.S.; investigation, K.N.; resources, K.N.; data curation, P.S.; writing--original draft preparation, K.N. and P.S.; writing--review and editing, K.N.; visualization, K.N. and P.S.; supervision, K.N.; project administration, P.S.; funding acquisition, K.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The results presented in this article were obtained as a part of the project titled \"Hydroacoustic positioning system for unmanned autonomous underwater vehicles\" (no. DOB-SZAFIR/01/B/003/04/2021), co-financed by the Polish National Centre for Research and Development (NCBR).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 5 Conclusions  
We studied the first step of obtaining the needle point cloud for needle pose and position estimation: the needle segmentation in OCT images. We proposed two methods: a conventional needle segmentation method based on morphological features and a fully convolutional neural networks method. These are two typical machine learning methods in image segmentation, manually feature designing based method (MF method) and automatically feature learning based method (FCN method). We analyzed our evaluation results based on the segmentation performance and runtime requirement. Two important insights from our experiments are that the deep learning method generates the discriminative model very well and conventional machine learning method can achieve real-time performance very easily. We believe that our work and insights will be highly helpful and can be referenced as a fundamental work for future needle segmentation research in OCT images. It is also worth to note that the FCN method can segment not only the surgical tools and but also the retinal tissue, which provides additional information to guide the positioning of the surgical tools, such as an alarm warning on the distance between the tool and underlying retinal tissue.  
In future, we would like to collect more data and apply the state-of-the-art deep learning model on them. Moreover, we will build up a specific tiny needle tip recognition model in order to overcome the shortness in current performance. A future design can integrate two methods to speed up the processing with better performance. The conventional method could detect the indexes of B-scan images with needle preliminarily in a short time, and then the deep learning is used to segment these B-scan images accurately.  
**Acknowledgments: Part of the research leading to these results has received funding from the European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under grant agreement No. 720270 (Human Brain Project), and also supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. The authors would like to thank Carl Zeiss Meditec AG, for providing the opportunity to use their ophthalmic imaging devices and [PERSON] for part of code contributions.**  
**Author Contributions: [PERSON] proposed the idea, designed the experiments and wrote the paper; [PERSON] performed the experiments; [PERSON] provided OCT imaging devices and experimental suggestions; [PERSON] and [PERSON] provided computation hardware support and suggestions; [PERSON] and [PERSON] gave the surgical knowledge and experience; [PERSON] gave the helpful feedback and supported the research. [PERSON] provided experimental materials and designed the experiments.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
###### Abstract  
Wireless local area network (WLAN) is a technology that combines computer network with wireless communication technology. The 2.4 GHz and 5 GHz frequency bands in the Industrial Scientific Medical (ISM) band can be used in the WLAN environment. Because of the development of wireless communication technology and the use of the frequency bands without the need for authorization, the application of WLAN is becoming more and more extensive. As the key part of the WLAN system, the antenna must also be adapted to the development of WLAN communication technology. This paper designs two new dual-frequency microstrip antennas with the use of electromagnetic simulation software--High Frequency Structure Simulator (HFSS). The two antennas adopt ordinary FR4 material as a dielectric substrate, with the advantages of low cost and small size. The first antenna adopts microstrip line feeding, and the antenna radiation patch is composed of a folded T-shaped radiating dipole which reduces the antenna size, and two symmetrical rectangular patches located on both sides of the T-shaped radiating patch. The second antenna is a microstrip patch antenna fed by coaxial line, and the size of the antenna is diminished by opening a stepped groove on the two edges of the patch and a folded slot inside the patch. Simulation experiments prove that the two designed antennas have a higher gain and a favourable transmission characteristic in the working frequency range, which is in accordance with the requirements of WLAN communication.  
WLAN; microstrip antenna; dual-frequency; HFSS +
Footnote †: journal: Sensors
## 3 MPPT Control Algorithms
### Perturb and Observe  
The P&O algorithm is the most utilized MPPT algorithm for solar PV panels, due to its ease use and high conversion efficiency [12], although compared to other MPPT algorithms such as the Incremental Conductance, the generated power and other parameters such as the output voltage of the PV are susceptible to bigger oscillations and take more time to achieve steady state. This algorithm is a hill climbing method, because the generated power increases when the voltage increases, until the MPP is reached, when MPP is reached the generated power decreases when the voltage increases. A flowchart of the algorithm implemented in the Simulink model for the P&O is presented in Figure 10, while the Figure 11 shows how the duty cycle changes regarding the voltage variation and power generated by the solar panel.  
As Figures 10 and 11 summarize, the P&O algorithm compares and measures the PV voltage and generated power between iterations and changes the duty cycle accordingly. If the generated power is equal between iterations the duty cycle is not changed, else the PV voltage is compared and the duty cycle will be decreased or increased, depending on the needs until it reaches MPP. In the Simulink model, when the duty cycle is changed its value can be increased or decreased by 0.00025, else it will remain unchanged between iterations. Due to this low value, it is expected that it will take a longer time to adapt to the changing environment conditions, but it will have low oscillations when the MPP has reached steady state conditions.  
Figure 9: Maximum power point (MPP) of a solar panel with (**a**) the solar irradiation variation, (**b**) the ambient temperature variation.
## 8 Implementation and Experiments
### Deep Learning Experiments
Interestingly, some authors stick to traditional CNN architectures but focus on variations of the learning algorithm itself, using ensemble or federated learning. For example, [PERSON], [PERSON], and [PERSON] [46] apply two networks with the so-called EfficientNet-b0 architecture. The two networks are trained on disjoint data subsets (bagging) and then as an _adaptive ensemble_ by performing the fine-tuning of the trainable combination layer. The authors achieve a 99.612% accuracy for CIFAR-10. Despite the consistency of hmdl and Keras observed in our experiments in terms of accuracy and convergence, we must fairly note the evident superiority of Keras (TensorFlow) over hmdl in terms of time efficiency. In many cases, the hmdl implementation was slower by at least an order of magnitude. For structures with multiple convolutional layers, the Keras fit times were roughly 40 times shorter (despite the application of numba.cuda in hmdl) in the case of the NVIDIA Quadro M4000M device and roughly 90 times shorter in the case of the NVIDIA Grid A100 device. The main reason working in favor of TensorFlow's efficiency is the highly optimized cuDNN low-level library that directly supports DNN-related primitives present within TensorFlow and accelerates GPU computations, convolutions, and pooling in particular. On the other hand, some possible reasons working against hmdl time efficiency (in spite of numba.cuda) may include Python's objected-oriented overhead, wrapping costs for Numba, and CPU-GPU memory management.
## 2 Related Studies
Instead of surveying a large number of protocols, we have described a few significant modified asynchronous MAC protocols [28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. We have also discussed current issues that have arisen because of the implementation of asynchronous duty cycles in opportunistic routing. The main contributions of this paper are summarized as follows:  
* We highlight the main reasons behind energy drainage while applying asynchronous duty-cycled MAC functionalities to opportunistic routing protocols in WSNs;
* We have provided a review of the asynchronous duty-cycled MAC protocols that have been proposed up until the present time, and explain their data transmission procedure with an illustrative diagram. We have also discussed their main contributions and limitations regarding energy consumption;
* We have presented a literature review of opportunistic routing protocols and their evaluation, while explaining their base MAC mechanisms;
* We have focused on energy consumption, and have analyzed and compared the OR protocols based on their addressed challenges, deployed networks, base MAC functionalities, decision metrics, advantages and disadvantages.
## 5 Conclusions  
This paper studies multiple-BDS signal reflection detection for multiple moving targets in the sea. It analyzes the influence of multiple-target Doppler phases and proposes the MDTC method to suppress influence under target estimation and not under target estimation. The results show the proposed method can effectively restore target peaks under the influence of four kinds of Doppler frequencies in GNSS-R detection, where the Doppler frequency variation range is \(-\)1000-1900 Hz. For further study of target peak detection, simulations of point target and ship target detection were performed to verify the effectiveness of the proposed peak identification method and range inversion methods. The peak identification results of four satellites and three ship targets showed corresponding peaks which matched the RCS data of detected targets. The range inversion results show a better resolution of target peak detection results, which can obtain the specific RCS distribution of ships in the hundred-meter class. At last, noise and clutter backgrounds were added to simulation 2 under 10 s pulse accumulation, which shows some influence on the sidelobe of target 2 and target 3, and the three target main peaks can still be detected relatively clearly. In the future, we will study the detection simulation of small-scale targets in the sea and detection with various satellite detection angles.  
Conceptualization, methodology, formal analysis, writing--original draft preparation, X.L.; data curation, L.W. and W.J.; writing--review and editing, X.L., J.L. and M.Z.; supervision, M.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62171351, No. 6217011640, No. 62001343, No. 41901267, and No. 41701386 and the China Postdoctoral Science Foundation under Grant No. 2021T140532.  
The satellite coordinates based on the geodetic coordinate system were obtained from [[http://www.csno-tarc.cn/datacenter/ephemeris](http://www.csno-tarc.cn/datacenter/ephemeris)]([http://www.csno-tarc.cn/datacenter/ephemeris](http://www.csno-tarc.cn/datacenter/ephemeris)) (accessed on 1 January 2021). The signal PRN parameters were obtained from literature [27; 28].  
The authors declare no conflict of interest.