esa-sceva/satcom-chunk-collection huggingface dataset:
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 6 Land-Based Systems Implementation-Premise: Ultrasonic Broken Rail Detector (UBRD)
### Defect Detection Principle and Employed Signal Processing Method  
The transducers of each transmitter generate an acoustic signal that propagates through the rail; this signal travels in both directions and can reach receivers located at a significant distance from the transmitter. The received signals are filtered, amplified, and processed. Rail integrity between the transmitter and each receiver is confirmed as long as an acceptable signal is received. If a clean break occurs in the considered stretch of rail, the corresponding receiver will no longer be reached by the transmitted signal and will generate an alarm [50; 53]. Receivers detect valid signals on the basis of specific criteria concerning signal frequency, burst length, burst repetition interval, and burst train continuity.  
Severe continuous noise (such as that generated by an approaching train) at a receiver will affect signal detection, regardless of the efficiency of the employed detector. Under such circumstances, the receiver will generate the message \"train in section\", will stop processing its input signals, and will remain idle until the noise intensity decreases; then, it will return to its normal conditions.  
This system is commercialized under the name \"RailSonic-ultrasonic broken rail detector\". The system is not classified \"Fail Safe\", although in Ref. [50], it is claimed that many fail safe principles have been incorporated in order to correctly detect most of equipment failures; this allows for correctly identifying these events and are, at the same time, to avoid false alarms.
# An Adaptive Back-Off Mechanism for Wireless Sensor Networks
## 2 Review of Related Work
### Critical Technologies of S-MAC Protocol
#### 2.1.2 Collision and Overhearing Avoidance  
It is of great importance for contention-based protocols to guarantee an effective collision and overhearing avoidance mechanism. S-MAC employs both physical and virtual carrier sense [15]. Physical carrier sensing is performed by listening to the channel for possible transmissions, while virtual carrier sensing is realized by the node's recording of the value of a sender's remaining transmission time, which is contained in a variable called the network allocation vector (NAV).  
Figure 1: Periodic listen and sleep mechanism; SYNC: synchronization.  
The transmission between transmitter and receiver is controlled by a fourfold handshake mechanism. The transmitter firstly sends a request-to-send (RTS) packet to its receiver. After successfully receiving this packet, the receiver sends a clear-to-send (CTS) packet as a reply. After receiving the CTS packet, the transmitter begins to send the data packet, and the receiver replies with an acknowledgement (ACK) packet as the sign of a successful transmission. The value of NAV is loaded and updated in all these four types of packets. When neighbor nodes overhear these packets and obtain the NAV value, they will immediately turn to sleep state until the transmission is complete. In this way, the hidden terminal problem and data collision is avoided to some extent.
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 3 Hardware Architecture of DDFS
### Multi-Core DDFS  
Multi-core DDFS [6] is able to increase the sampling rate and the frequency of the output signal. Its main idea is to use different cores to calculate simultaneously the values of functions corresponding to different offset phases. Its architecture is shown in Figure 6.  
There are two main differences between the multi-core architecture and the single-core architecture. The first one is that the input to each core is the MFCW (multicore frequency control word), which can be calculated from Equation (13). The another difference is the addition of an adder after the phase accumulator for adding the phase offset which can be represented by Equation (14) for each core between the different cores.  
\[MFCW=M*FCW \tag{13}\]  
\[offset_{j}=j*FCW \tag{14}\]  
where \(M\) is the number of cores, \(j\) denotes the \(j\)th core, and \(1\leq j\leq M\). With such an architecture, the sin function can be sampled M times in one clock cycle, increasing the sampling rate by a factor of \(M\).  
Figure 5: The hardware architecture of proposed DDFS.
## 2 2D Wave Field Reproduction in 3D Space Using a Single Circular Array
A loudspeaker can be modeled as a monopole, and the wave field \(S_{s}\) reproduced by a circular array can be expressed as  
\[S_{s}(\mathbf{x},k)=\int_{0}^{2\pi}R_{s}D(R_{s},\phi_{s})G(r-R_{s},\phi-\phi_{s}) \,d\phi_{s}, \tag{7}\]  
where \(R_{s}\) is the radius from the point of origin to the loudspeaker array, \(D\) is the complex amplitude of each loudspeaker, and \(G\) is the free field Green's function, which is expressed as follows:  
\[G(r-R_{s},\phi-\phi_{s})=\frac{e^{jk|\mathbf{x}-\mathbf{x}_{s}|}}{4\pi|\mathbf{x}-\mathbf{x}_{ s}|}, \tag{8}\]  
where \(\mathbf{x}_{s}\) is the loudspeaker position. Equation (7) can be interpreted as a circular convolution and converted as follows by the convolution theorem [24]:  
\[\tilde{S}_{s}(r,m)=2\pi R_{s}\tilde{D}(R_{s},m)\tilde{G}(r-R_{s},m), \tag{9}\]  
where \(\tilde{G}(r-R_{s},m)\) is the circular wave spectrum of the monopole, which can be expressed as  
\[\tilde{G}(r-R_{s},m)=\sum_{n=|m|}^{M}jkj_{n}(kr)h_{n}^{(1)}(kR_{s})A_{nm}^{2} P_{nm}^{2}, \tag{10}\]where \(h_{n}^{(1)}(\cdot)\) is the first-kind spherical Hankel function of the \(n\)-th degree, \(j_{n}(\cdot)\) is the first-kind spherical Bessel function, and \(P_{nm}\) is the associated Legendre function; \(A_{nm}\) can be expressed as  
Figure 1:0  
We substituted Equation (6) with Equation (9) to match the sound pressure of the desired wave field in the reference circle using the loudspeakers. Figure 1:1  
As aforementioned, in the denominator of Equation (12), for the Bessel zero problem, \(J_{m}(kR_{mic})\) generates zero for every specific frequency, which affects the driving signal and the performance of wave field reproduction. The circular wave spectrum of the driving signal obtained from Equation (12) can be converted to the complex amplitude of each loudspeaker through the inverse discrete spatial Fourier transform. As shown in Equation (12), the driving signal was determined by three variables: \(r_{ref}\), \(R_{mic}\), and \(R_{s}\), which are related to the wave field reproduction error. To reproduce the wave field using the 2.5D wave field reproduction method, it is common to set \(r_{ref}\) to a value close to zero [24]. However, to reproduce the wave field using 2D spatial information in 3D space with minimum error, it is necessary to confirm the relation between wave field reproduction error and these three variables. Because the wave field reproduction error can be considered as the criterion of the \"anti\" wave field of the noise field that the circular array can produce, the ANC noise reduction performance can be predicted. Accordingly, we can organize the ANC system by setting the \(r_{ref}\), \(R_{s}\), and \(R_{mic}\) values.
There is still growing worldwide interest in the antioxidant ability of phenolics of this genus. Several studies have reported that many _Centaurea_ species, such as _C. cyanus_ [PERSON] [19], _C. bornmuelleri [PERSON]_ [20], _C. drabifolia subsp. drabifolia_ and _C. lycopifolia_ [21], _C. amaena_ [PERSON]. & [PERSON] and _C. aksoyi_ [PERSON] & [PERSON] [1], _C. pseudoscabosa_ [PERSON] subsp. _Aranatica_ (Azn.) [PERSON], _C. pulcherrina_ Wild var. _pulcherrina_ [PERSON], _C. salicifolia_ Bieb. ex [PERSON] subsp. _abbrevianta_ [PERSON]. [PERSON], and _C. babylonica_ (L). [PERSON] et al. [12] exhibited remarkable antioxidants properties that allow for their use as a potential source of new nutraceutical products. The imbalance between the production and elimination of (ROS) and (RNS), leading to an increase in steady-state or transient ROS concentration, with consequences of cellular physiology, has been defined as oxidative stress [22]. Among the main ROS, hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) has revealed DNA oxidative injury in cells and lipid peroxidation [23]. Thereby, the ultimate target of the present research was to investigate whether _Centaurea_ plant extracts are able to alleviate H\({}_{2}\)O\({}_{2}\)-caused oxidative damage on the human Fibroblast HS-68 cell line. No information on biological properties (i.e., antioxidant ability and protective effect against oxidative stress in cells) was reported so far on of aerial parts of _Centaurea kroumirensis (Coss. )_ and _Centaurea sciula_ L. subsp _sciula_ to the best of author's knowledge. Hence, the current study presents the first report on the in vitro antioxidant properties of these two _Centaurea_ species, one of them being endemic to Tunisia flora. The main objectives were to evaluate the total phenolic content, to assess the bioactive activity through in vitro assays and in cells. The effect of different conventional and non-conventional extraction methods on the antioxidant abilities of both _Centaurea_ species has been studied in order to make systematic comparison and select the extract with high antioxidant power for further studies. Moreover, correlation between antioxidant capacities and total phenol content and were evaluated.
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies
### Spraying Reactor  
Spraying liquid in a gas chamber is another method to mix two immiscible phases phase in a gas-liquid system. [PERSON] et. al. studied the formation of methane hydrate in a spraying reactor by spraying water into a high-pressure methane container [54,55]. There were many attempts to develop the proposed setup by [PERSON], and to scaled-up systems, by [PERSON] et al. [56] and [PERSON] et al. [57]. Using this design (Figure 9), [PERSON] and his team studied different parameters on hydrate formation such as initial pressures, water volumes, gas-supply modes, and nozzle-atomising angles by measuring the hydrate-formation rate, induction time, gas consumption, heat of formation, and gas-storage density.  
Figure 8: Schematics of unidirectional growth apparatus.  
Figure 9: Experimental apparatus for water-spray design.  
The design is made by five main sub-systems:  
Gas-supply: To supply gas and maintain the pressure.  
Refrigeration system: To keep the reactor at low temperature by dissipating the released latent heat during hydrate formation.  
Reactor chamber: The hydrate-formation chamber.  
Spraying system: water sprays as small particles into the gas phase  
Data-logging system: Pressure, temperature and mass flow will be recorded during the reaction time.  
Increased the gas/water interfacial area and use of a simpler reactor design (with no need for mechanical stirrer) are the main advantages while the difficulty in dispensing the produced latent heat of hydrate formation, together with limited hydrate-to-water volume ratios, are identified as the main disadvantages of this design. [PERSON] (2018) and [PERSON] (2018) suggested the use of a steadily-chilled copper block as the cooling plate in front of the spraying system inside the gas chamber to collect the released heat and minimize the heat-dispersion problem.
## 3 Results and Discussion
### Characterization of the DP Composite
#### 3.1.3 Chemical Structure of the DP Composite  
Figure 6 shows the FT-IR curves of paraffin, diatomic and DP. Paraffin shows obvious absorption peaks at positions of 718 cm\({}^{-1}\), 1466 cm\({}^{-1}\), 1473 cm\({}^{-1}\), 2849 cm\({}^{-1}\) and 2918 cm\({}^{-1}\). The absorption peak at 718 cm\({}^{-1}\) is related to the rocking vibration of CH\({}_{2}\)[39]. The asymmetric stretching vibration of the C = O functional group causes a peak at 1466 cm\({}^{-1}\)  
Figure 5: TGA and derivative of the TGA (DTG) curves: (**a**) paraffin and (**b**) diatomite/paraffin.  
and the peak at 1473 cm\({}^{-1}\) is related to the asymmetric stretching vibration of the CH\({}_{2}\) group [30]. The weak absorption peaks at 2345 cm\({}^{-1}\) and 2461 cm\({}^{-1}\) are caused by the CO\({}_{2}\) antisymmetric stretching mode [40], which is affected by the atmospheric CO\({}_{2}\), and the absorption peaks at 2849 cm\({}^{-1}\) and 2918 cm\({}^{-1}\) are ascribed to the stretching vibrations of CH\({}_{2}\) and CH\({}_{3}\), respectively [30]. Diatomite has four distinct absorption peaks at 455 cm\({}^{-1}\), 791 cm\({}^{-1}\), 1092 cm\({}^{-1}\), and 3444 cm\({}^{-1}\), and the first three are related to the bending vibration of Si-O (455 cm\({}^{-1}\)), the bending vibration of Si-O-H (791 cm\({}^{-1}\)), and the asymmetric stretching vibration of Si-O-Si (1092 cm\({}^{-1}\)), respectively [25; 41]. The broadband of 3444 cm\({}^{-1}\) corresponds to the vibration of the O-H mode in the physical adsorption of H\({}_{2}\)O, and the bending vibration of the water molecules retained in the diatomaceous silica matrix [42]. The absorption peaks of DP distribute at positions of 455 cm\({}^{-1}\), 791 cm\({}^{-1}\), 1092 cm\({}^{-1}\), 1465 cm\({}^{-1}\), 2354 cm\({}^{-1}\), 2850 cm\({}^{-1}\) and 2916 cm\({}^{-1}\), which are contained within the peaks of paraffin or diatomic. This indicates that there is only a simple physical adsorption between paraffin and diatomite. Therefore, the DP composite is chemically compatible.
# Challenges for the Implantation of Symbiotic Nanostructured Medical Devices
## 5 Perspectives  
For the appropriate long-term stable function of implanted symbiotic medical devices, it is essential to develop a system that includes a membrane interface that maintains unfettered duplex communication of materials with the body; this remains a major challenge for the field of implantable enzymatic biofuel cells. Clearly, it is necessary to consider the technical electrochemical aspects to improve the energy density, power density, and voltage output of the enzymatic biofuel cell. However, the technical aspects of operational stability must be considered in parallel with developing the enzymatic biofuel cell as a biocompatible system for implantation. Such a biocompatible system for stable long-term function requires the combination of a 3D structured bioelectrode with a stable porous protective membrane. The most promising directions are for the integration of 3D bioelectrodes that utilize some form of cross-linked chitosan to provide the optimal environment for the stabilization of enzymes combined with a protective membrane, such as those formed with PVA as the basis. This type of approach will most likely optimize the design of gel-like polymer membranes to minimize the fibrous encapsulation of implanted enzymatic biofuel cells [41]. Such an approach could include the possibilities of either excluding biofouling with the PVA-based membrane or promoting cell adhesion and hence integration with the body, e.g., by using modified chitosan or polyelectrolyte coatings [42; 43]. Indeed, even materials like porous silicon could contribute to biocompatible systems after the porous silicon is stabilized to avoid degradation in the body [44].  
This practical experience of designing enzymatic biofuel cells for long-term and stable performance when implanted provides directions to the general challenge of biocompatibility for symbiotic implanted devices. Such symbiotic systems extend the classical biocompatibility concept to include this functional requirement for the continuous two-way (duplex) communication of materials with the body. It is probable that utilizing such a biomimetic approach could enable scientists to be more efficient in mimicking what is happening at the molecular and cellular levels to create a porous membrane that allows for the long-term exchanges of molecules between an implanted device and the body (see Figure 1). Having this concept in mind will guide the research in a new field between medical implant and regenerative medicine to create functional symbiotic devices for long periods of implantation.  
All authors contributed information that guided the perspectives discussed in this article. The initial draft was written by [PERSON]. All authors contributed to the editing of revised drafts. All authors have read and agreed to the published version of the manuscript.  
This work was supported financially by grants from the French Agence Nationale de la Recherche (ANR-15-CE05-0003-01, and ANR-16-CE19-0007-03) and the French Investissements d'Avenir (ANR-10-NANO-03-01).  
The authors declare no conflict of interest.
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
###### Abstract  
In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian's entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians' head movement tracklets, and the pedestrians' whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole body bounding boxes are calculated, and the Hungarian algorithm is used to match the above calculation results. Finally, according to the matching results, the head bounding boxes in the head tracklets are replaced with the whole body bounding boxes, and the whole body motion tracklets of the pedestrians in the dense scene are generated. Our method can be performed online, and experiments suggested that our method effectively reduces the negative effects of false negatives and false positives on the tracker caused by severe occlusion in dense scenes.  
head tracking; intersection over containment; Hungarian algorithm; deep learning +
Footnote †: journal: 2023, 13, 440. [[https://doi.org/10.3390/app13010440](https://doi.org/10.3390/app13010440)]([https://doi.org/10.3390/app13010440](https://doi.org/10.3390/app13010440))
## 2 Methods and Models
### Methods and Plans
#### 2.2.3 Low-Temperature Carbon Dioxide Adsorption Method  
The micropores of carbon molecules with diameters ranging from 0.5 nm to 1 nm need to be filled at a relative pressure ranging between 10\({}^{-7}\) and 10\({}^{-5}\). However, this corresponds to a slow diffusion rate and adsorption equilibrium, whereas the CO\({}_{2}\) adsorbate can be quickly filled and reaches adsorption equilibrium at 273.15 K and 3.48 MPa of saturated vapor pressure. Therefore, the low-temperature carbon dioxide adsorption method is typically used to test the pore size structure of micropores with a width of less than 2 nm [21].  
The Autosorb iQ fully automatic physical adsorption instrument was utilized, with reference to the standard GB/T 21650.3-2011, \"Pore size distribution and porosity of solid materials by mercury porosimetry and gas adsorption. Part 3: Analysis of micropores by gas adsorption\" [22], for the purpose of testing. Approximately 2 g of the sample was initially weighed and loaded into a sample tube.  
Vacuum degassing was conducted for 12 h, and 99.99% high-purity CO\({}_{2}\) was used as the adsorbent for adsorption experiments at a temperature of 273.15 K.
## 4 Discussion
### Uncertainty Analysis  
In this study, the BEPS model was used to detect CUE extremes and analyze their forming climate conditions on a global scale. That is because the study requires long time series data, but MODIS GPP and NPP dataset production only started from the year 2000, which is not suitable for analyzing the CUE extremes and comparing them with the result of this study. In addition to the diagnostic model, some prognostic models could be used to simulate global and long-time series productivity datasets, but the prognostic models have large uncertainties in simulating the plant structure and carbon cycle, and the performance of the BEPS model can do a similar or better job than the prognostic models [19]. Therefore, we conducted the analysis based on the BEPS model. Although the BEPS model was validated in previous studies [10; 22], there are still some uncertainties. Furthermore, the BEPS model was driven by remote sensing data, which included the land cover change and some human activities on the terrestrial ecosystem [10]; thus, we did not consider the effect of land cover change and human activities on CUE extremes.  
In addition, the study only considered the climate conditions on CUE extremes, but other factors, such as soil conditions and nitrogen deposition, could also influence the CUE extremes [37]. Therefore, to better understand CUE extremes and their forming environmental conditions, future studies could consider more environmental factors.
## 2 Principles of the Proposed Method
### Optimization of Computational Complexity of the CD Estimation Method Based on FrFT  
In order to obtain the optimal order of the FrFT, the traditional two-dimensional search method uses the transform order as a variable to perform a grid search within a set range. In order to improve the measurement accuracy, more samples and a smaller search interval are required, resulting in a higher computational complexity. For example, when the search accuracy reaches 0.00001 and the optimal order is searched in the range of (\(-\)1,1), 200,000 searches are required in principle. In order to reduce the complexity under the premise of the accuracy of CD estimation, and when the number of samples is a certain value, an optimal order search method based on a stepwise process is proposed. For the new search method, the search process is divided into two parts: coarse search and fine search for multi-step adjustment. Therefore, the search range is gradually reduced. Figure 5 shows a flowchart of a new search method for the optimal transform order based on the stepwise approach.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & **Chirp Signal (Time Domain)** & **CD (Frequency Domain)** \\ \hline Expression & \(E(t)=A\exp(j(\pi kt^{2}+2\pi f_{0}t+\varphi))\) & \(G(z,\omega)=G(0,\omega)\exp(\frac{(iD\lambda^{2}\omega^{2}z)}{4\pi c})\) \\ Estimated Parameters & \(k\) & \(D_{\lambda}z\) \\ Transform Order & \(p\) & \(Q\) \\ Relationship between Order and Parameters & \(k=-\cot(p\cdot\frac{\pi}{2})\frac{d\omega}{dt}\) & \(D_{\lambda}z=(N-1)dt^{2}\frac{c}{\lambda^{2}}\tan(Q\frac{\pi}{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the chirp signal in the time domain and CD.  
Figure 4: The relationship between the CD value and different transform orders of (**a**) PM-QPSK and (**b**) PM-16 QAM signals under FrFT.  
First, use a larger order search interval to perform a coarse search in the initial search range to obtain a coarse result, then a small range search is carried out near the rough value, and so on until the initial set search precision is searched and a more accurate optimal order is obtained. For example: first, perform a coarse search in the interval (\(-\)1,1) with a step of 0.01, and the number of searches is 200 to obtain a coarse transform order. Then, carry out the second step search in the range of plus or minus 0.5 near the order with a step of 0.001. The number of searches is 1000 to obtain a more accurate and optimal change order, and finally step in the range of plus or minus 0.05 with a step of 0.0001. A fine search is performed, and the number of searches is 1000 to obtain the optimal transform order at this precision. It can be seen that, under the condition of the same search precision, the number of searches for the optimal stepwise search method is 2200, compared with 20,000 for the one-step search method, which greatly reduces the computational complexity.