esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 4 Methods
### Spectrum Oversampling  
In Figure 10, the impact of a displacement of 800 \(\upmu\)m on the spectrum is demonstrated, where the peak of the distribution shifts by 0.498 nm. Given the resolution of the spectrometer, which is 0.16 nm, this spectral shift results in a calculated displacement sensitivity of approximately 250 \(\upmu\)m. However, this sensitivity is constrained by the intrinsic resolution limit of the spectrometer, defined by the smallest wavelength interval it can reliably detect. To enhance the sensitivity and improve the precision of the displacement detection, the detected spectrum can be oversampled. This technique effectively increases the apparent resolution of the spectrometer, allowing for finer distinctions between spectral peaks. The oversampling process is achieved by linear interpolation between the actual data points obtained from the spectrometer. Linear interpolation works by estimating the values between the discrete data points that are directly measured by the spectrometer. If the spectrometer measures at wavelengths \(\lambda_{i}\) and \(\lambda_{i+1}\), linear interpolation constructs intermediate values, based on the assumption that the data between these points vary linearly. Mathematically, for a given point, \(\lambda k\), between \(\lambda_{i}\) and \(\lambda_{i+1}\), the interpolated value S(\(\lambda  
The result of oversampling is a denser spectrum with more closely spaced data points, allowing for a more precise determination of the spectral peak position and improved sensitivity in detecting small displacements. This is particularly beneficial in applications where sub-nanometer shifts in the spectrum need to be detected but the physical resolution of the spectrometer is insufficient.
## 3 Results and Discussion
### The Electrocatalytic Reduction and Detection of 2,4-Dinitrotoluene
#### 3.3.5 Linear Sweep Voltammetry  
The electrochemical behavior of the modified electrode towards reduction of 2,4-DNT was investigated by linear sweep voltammetry (LSV) where current is measured at the working electrode. LSV response of the modified/Au electrode shows a linear relation between the concentrations of 2,4-DNT vs. time examining the reduction current (Figure 8a); the modified Au electrode was assessed by LSV for the detection of 0.1 mM 2_A_-DNT in PBS (pH 7.4) with the applied electrode potential range between \(-0.2\) to \(-0.8\) V and a slow scan rate of 50 mV/s. The modified electrode in PBS without 2,4-DNT observed no reduction peak in LSV whereas for 2,4-DNT it detected two stepwise reduction peaks around \(-0.49\) V and \(-0.68\) V in LSV (Figure 8b). The LSV response shows the number of nitro group reduction peaks in the aromatic compound.  
Consequently, the number of nitroaromatic groups can be easily detected with the help of modified electrode thus showing its prowess and high efficiency for the electrochemical detection of 2,4-DNT. The two-step reaction requires six electrons and six protons for the reduction of the nitro group. The initial reduction of the nitro group, via 2 e\({}^{-}\) and 2 H\({}^{+}\) exchange (removal of water molecules) to a nitroso derivative (Reaction 1) (Figure 9). The nitroso compound was further reduced to a hydroxylamine via exchange of 2 e\({}^{-}\) and 2 H\({}^{+}\) (Reaction 2) and finally to the aromatic amine via elimination of water molecules (Reaction 3). The procedure requires in total 12 e\({}^{-}\) and 12 H\({}^{+}\) for the reduction of the two nitro groups into corresponding amines.  
Figure 8: (**a**) LSV of modified Au electrode potential is swept linearly with respect to time and, (**b**) LSV of modified Au electrode in 0.1 M PBS (pH 7.4) in the presence of DNT and absence of DNT at the slow scan rate (50 mV/s).  
Figure 9: Reaction mechanism involved in the reduction of the aromatic (Ar) nitro group (1) reduction of nitro group into nitroso derivative, (2) conversion of nitroso group into hydroxylamine and (3) finally the conversion of hydroxylamine into aromatic amine.  
The modified electrode showed good results towards detection of 2,4-DNT which proved that Zn-MOF-8@AgQDs composites efficiently worked for sensing. The reproducibility of the Zn-MOF@AgQDs modified Au electrode was evaluated by using CV in presence of 0.2 uM 2,4-DNT for 3 times using the same electrode and no significant variation was observed in terms of over potential and current response; the response time of the developed sensor being <2 s.  
The schematic representation for the synthesis of Zn-MOF-8@AgQDs and the proposed redox reaction mechanism during the sensing of 2,4-DNT is given in Figure 10. Based on these results, the Zn-MOF-8@Ag/AuE shows a good potential for the detection of 2,4-DNT.
## 3 Results and Discussion
### Comparative LCA of Hydrogen Production: Biogas Reforming vs. Agricultural Residue
#### 3.2.4. Particulate Matter and Acidification Effects  
Fine particulate matter formation (FPMF) is significantly higher in biogas reforming (0.0243 kg PM\({}_{2.5}\) eq) compared to gasification (0.00151 kg PM\({}_{2.5}\) eq). This suggests that biogas reforming contributes more to air pollution, with potential respiratory health risks. Additionally, terrestrial acidification (TA) is markedly greater for biogas reforming (0.0120 kg SO\({}_{2}\) eq) than for gasification (0.0004788 kg SO\({}_{2}\) eq), reinforcing the lower environmental burden of gasification. These results align with findings of [PERSON] et al. (2021) [PERSON] et al. (2021) who demonstrated that hydrogen production from biogas results in higher emissions of sulfur oxides and nitrogen oxides, leading to increased acidification potential. Conversely, agricultural residue gasification minimizes these emissions due to more controlled combustion conditions and efficient gas cleaning systems.
# A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications
## 3 NFC Sensors
The gas sensors show an optic response that is read through high-resolution digital color detectors. A white LED is used as the common optical excitation source for all sensors. The responses of the sensors were calibrated and fitted to simple functions, thus allowing fast prediction of gas concentration. Another gas sensor detection system was presented in Reference [69]. Sensitized single-walled carbon nanotubes (SWCNTs) whose resistance changes with gas concentration (NH\({}_{3}\)) were inserted in series with the NFC IC. The effect of the gas causes the tag to detune. When gas concentration is high, power transfer is insufficient for effective smartphone-tag communication and the tag is unreadable. An NFC bicycle tire-pressure measurement system (BTPMS) was presented in Reference [70]. The sensor comprises an ASIC that integrates an on-chip capacitive pressure and temperature sensor, an RFID interface for HF/NFC, and EEPROM. The IC is soldered with wire-bonding to a FR4 PCB with the antenna. The tag is incorporated into the bicycle tire. A marker on the tire's exterior indicates the position of the NFC BTPMS, and therefore, the NFC readable area. The pressure can be read using an ISO 14443 RFID-compatible reader. As the sensor presents linear dependence, a two-point calibration technique is sufficient for sensor calibration. Low-cost monitoring systems are in demand for irrigation control at home, in greenhouses, or at garden centers. A low-cost, batteryless, NFC-powered device capable of measuring volumetric water content (soil moisture), temperature, and relative humidity was recently presented in Reference [71] (see Figure 13). The tag was based on a M24 LR04E-R from an ST NFC IC connected to a low-cost microcontroller (Attinvy85 from Atmel). The data are shown on a smartphone application or uploaded to the cloud for sharing or storage. The temperature is measured using an I\({}^{2}\)C temperature sensor (LM75A), while air humidity is detected by reading the analog output from the HIH-5030 humidity sensor from Honnewey, designed for measuring soil volumetric water content. Capacitance measurement is based on a low-power timer 555 working as an oscillator, and a diode detector whose output is measured by the ADC of the microcontroller. The external circuitry requires less than 1 mA at 3 V to operate. A procedure was presented for calibrating the sensor based on a simple expression whose coefficients can be experimentally obtained. Figure 14 shows a measurement taken with the system. This reference shows that conventional low-power sensors can be integrated within the NFC tag for the new generation of IoT devices. Figure 13: Soil moisture NFC tag being powered and read by a smartphone, which retrieves the sensed data and the tag unique identifier (UID) to identify the species [71].
# A Nighttime and Daytime Single-Image Dehazing Method
## 4 Experimental Results and Discussion
### Qualitative Comparison
#### 4.1.2 Daytime Hazy Images  
We chose the different prior methods of [PERSON] et al. [9], [PERSON] et al. [14], and [PERSON] et al. [12], the dehazing method based on deep learning of [PERSON] et al. [21] and the recent dehazing methods of [PERSON] et al. [15] to carry out daytime dehazing experiments. When using [PERSON] et al.'s [9] method, it is easy to overestimate the illumination, causing the image to be too dark (Figure 6D5), and one side is dark when there are far and near scenes (Figure 6D4,D8). [PERSON] et al.'s [12] method also has the same problem. [PERSON] et al.'s [14] method does not handle the sky area well and produces noise (Figure 6D1,D6,D10) and color artifacts (Figure 6D7). [PERSON] et al.'s [21] method in some areas of complex pictures, such as trees, deepens the color of the image and causes the details to be unrecognizable  
Figure 5: Comparison of nighttime dehazing [9; 21; 24; 25; 26].  
(Figure 6D7,D9,D10). [PERSON] et al.'s [15] method produces color artifacts at the junction of the sky area. For example, the sky at the junction with the mountain turns yellow (Figure 6D3) or green (Figure 6D9) as it is affected by the color of the mountain. By contrast, MF Dehazer does not have these defects. The sky, lake and the near and far scenes are handled more naturally; the color is closer to the original image, and it looks visually pleasing.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 3 Results
### Perfect Stability of the 2 nd Doppler Moment (Spectrum Width)  
In the absence of atmosphere (no wind, refraction or turbulence) and for a non-rotating antenna, a null spectrum width and radial velocity could be expected; hence, one Digital Number (DN) would  
Figure 1: Morphology of the mountainous Cimetta backscattering radar bin: small cottages, the hut, the chairlift and the 90 m tall metallic tower.  
be sufficient to describe both the 2 nd and the 1 st Doppler moments. Indeed, no variability at all is found in the spectrum width over the 5 clear sky days of the training data set or over the eleven clear sky days of the evaluation data set (see Section 4). All the (288 \(\times\) 16) samples have the same Digital Number (DN = 1). However, this Digital Number does not correspond to the correct interval expected from theory that is [0.12-0.18] m/s (DN = 2). This inconsistency will be further investigated together with the radar manufacturer. All considered, any DN value between 1 and 3 could probably be acceptable for meteorological applications (\(\pm\)0.06 m/s).
## 2 Materials and Methods
### N-Way Wilkinson Power Dividers Based on Slow-Wave Structures  
As is well known, 1-to-N-way WPDs consist of N branches which have characteristic impedances of \(\text{Z}_{0}\sqrt{N}\), as shown Figure 3. Although they provide better size reduction than that of the cascaded structures, they suffer from the narrow band and poor isolation between output ports [28].  
Equivalent circuit models for conventional 2-, 4-, and 8-way cascaded WPDs are illustrated in Figure 4a-c, respectively. As can be seen from Figure 4, conventional 2-way WPDs comprise 2 quarter-wavelength \((\lambda/4)\) transmission lines with the characteristic impedances of \(Z_{0}\sqrt{2}\). The same transmission line length and impedance values are also valid for 4- and 8-way WPDs, as depicted in Figure 4b,c. In order to obtain high isolation between the output ports, an isolation resistor of \(2Z_{0}\) is connected between 2 output ports.  
Figure 3: Conventional 1-to-N-way WPD.  
Figure 2: (**a**) Equivalent circuit model for the calculation of S\({}_{32}\). (**b**) Even-mode half-circuit model. (**c**) Odd-mode half-circuit model.  
In addition, the input/output ports must have characteristic impedances of \(Z_{0}\) for good impedance matching [28].  
In order to reduce the overall circuit size, quarter-wavelength transmission lines can be capacitively or inductively loaded by different approaches, as shown in Figure 5. Due to the increase in the capacitance or inductance per unit length of the transmission line, the phase velocity on it decreases, so such a transmission line can be called a slow-wave  
Figure 4: Conventional cascaded WPDs: (**a**) 2-way; (**b**) 4-way; (**c**) 8-way.  
structure. Among the slow-wave structures shown in Figure 5, slit-loaded and meandered transmission lines are widely preferred for different kinds of microwave circuits because of their easy fabrication processes. However, the fabrication of interdigital capacitor-loaded transmission lines are more difficult than the others since they may need extra via connections. The number of narrow slits and meandered sections can be determined with respect to the desired center frequency. Here, it should be taken into consideration that a transmission line with narrow slits and meandered sections should exhibit similar behavior to a quarter-wavelength transmission line having the characteristic impedance of \(Z_{0}\sqrt{2}\). Thus, the designed WPD would have optimum performance in terms of return loss and isolation. For this purpose, all dimensions and the number of narrow slits and meandered sections were determined by using the parameter sweep in Sonnet Software. Investigations into the related parameters are described in the following sections.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments  
In this section, a series of experiments are described to demonstrate the effectiveness of the Flame-SeaFormer model in flame situation detection. The model's performance is evaluated on two datasets: Our_flame_smoke (a private dataset) and FLAME (a public dataset). Widely used image segmentation models from the general domain, including FCN, Unet, Deeplab v3+, SegFormer, and GMMSeg, are chosen as comparative models. In comparison to these five methods, Flame-SeaFormer achieves superior performance in accurately detecting the state of flames.
### Related Works
#### 1.2.2 Contour or Edge Approach  
Using edges to track the 3D object in a monocular RGB camera is also feasible and fast. There are multiple proposed edge-based methods [16; 17]. RAPID [16] incorporates a predefined set of control points to match certain contours to an image. As RAPID exclusively leverages the edges of an image, it is able to perform only on high-contrast edges, where the edges of the object are clear. Additional features, from an image, for instance, can be adopted to make the method more robust. One of these features is a direction-based pose validation scheme that increases the robustness of edge-based tracking by exploiting the motion of the object in a video [18]. However, the challenge arises when the method is used in clutter edges. To tackle this issue, [PERSON] et al. [19] utilizes 2D-3D correspondences by searching for the feature based on the location of the object in the previous scene. Nevertheless, this method needs to handle false 2D-3D correspondences. Additionally, SLET [20], the refiner that we modify and integrate into this paper, includes the color features of the 3D object projected on the 2D scene. Furthermore, it accounts for the occlusion of multiple 3D objects in the scene, as well as edge confidence. Since SLET is an edge-based algorithm, the 6 DoF computational gains in terms of speed are considerable. However, within the field of woodworking, metallic tool heads are characterized by a shiny surface with patterned edges. These features have the potential to disrupt tracking when relying on edge and color cues. As a result, specific adjustments are necessary for the optimal use of edge-based object tracking.
# Dependence of Dispersion on Metamaterial Structural Parameters and Dispersion Management
## 4 Light Pulse Propagation in the MM
### Impact of \(\beta_{2}\), \(\beta_{3}\), and \(\beta_{4}\)  
In conventional media, the influence of \(\beta_{4}\) can be ignored as it is so small. Section 3 shows that \(\beta_{4}\) in the MM may be larger than that in the ordinary materials, however, it is just in \(10^{-3}\) order of magnitude around \(\overline{\omega}_{D}\), three orders of magnitude smaller than \(\beta_{3}\) (Figure 1f). What is its impact?  
There are two groups of data in Table 3, in which \(|\beta_{2}|\), \(\beta_{3}\), and \(\beta_{4}\) are not much different, except for the different sign for \(\beta_{2}\). In Figure 6, the red line represents the portion of the pulse waveform only under the impact of \(\beta_{2}\) and \(\beta_{3}\), while the blue one represents the similar waveform under the simultaneous impact of \(\beta_{2}\), \(\beta_{3}\), and \(\beta_{4}\). It is shown from a comparison to the details after the magnification of the red and blue lines at \(z=21\) km that for \(\beta_{2}<0\) in Figure 6a, the blue line is lower than the red line, representing a somewhat inhibition of \(\beta_{2}\) to the pulse broadening, while for \(\beta_{2}>0\) in Figure 6b, it is opposite, representing a little aggravation of \(\beta_{2}\) to the pulse broadening. We also find that \(\beta_{4}\) has no effect on the position of pulse splitting.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 3 Communication  
Communication plays a crucial role in swarms due to multiple factors. No advanced swarm behaviour is possible without communication among the swarm members, both those belonging to the same species and those of different species. Furthermore, monitoring systems utilise synchronous reporting about the state of the environment, asynchronously reporting about occurring events, or offloading measured sensor data to a data hub. Specifically, within the subCULTron swarm, the aMussels periodically report on the current status of the underwater environment, notify other agents if an event has occurred, and transfer collected sensor data to aPads after they surface. These features require the aMussels to be capable of information exchange both while submerged and on the water surface. Communication in the swarm can be split into two categories: surface and underwater communication.
## 3 Results
### Monitoring of the Acidification Process  
TTA and pH values recorded throughout the whole process are graphically represented in Figure 2.  
Immediately after dough preparation, the average pH was 6.04 \(\pm\) 0.01. After 3 h of fermentation, there was a pH drop ranging from 0.36 (C2) to 1.44 (B3) across all trials. After 6 h, the lowest pH value (4.33) was observed in the B3 trial, while the trial inoculated with _L. mesenteroides_ (C1) had a slightly higher pH of 4.67. On the other hand, C2 and B2 trials showed pH values above 5.0. This trend continued after 9 h of fermentation, with pH values decreasing further for B1 and C1 by 0.3 and 0.37, respectively. After 12 h of fermentation, the pH values for C1 (4.24), B1 (4.35), and B3 (4.21) were similar and lower than those for the C2 and B2 trials (5.39 and 5.19, respectively). TTA values showed an inverse trend compared to the pH values. TTA values tended to increase as pH decreased, requiring more 0.1 N NaOH to neutralize and achieve the characteristic color change for the analysis [24]. At the end of the fermentation process, the highest TTA value was recordedin the B3 trial (10.48 mL of NaOH 0.1 N), while the C2 trial had the lowest value (5.12 mL of NaOH 0.1 N).
# Recycling 3D Printed Concrete Waste for Normal Strength Concrete Production
## 3 Results and Discussion
### Compressive Strength
#### 3.1.2 Influence of Water Content  
To demonstrate the impact of water content variation in the mixing process, we opted for the mix specified in Table 5. This particular mix features diverse water content levels while maintaining the same recycled aggregate (RA), replacement level, and concrete strength class. For the purpose of this comparison, C40-RA50, C40-RA67, C30-RA50, and C30-RA67 have been selected as reference mixes. These reference mixes share a similar water-cement ratio (w/c), with the mixes having 0% replacement of RA within their respective strength class [see, Table 2].  
Initially, we compared the measured mean compressive strength against the required minimum mean compressive strength for the respect strength class concrete, as illustrated in Figure 9.  
A prominent observation from Figure 9 indicates that the utilization of higher amounts of water, particularly for the 50% replacement level, fails to meet the required minimum compressive strength for the targeted strength class. Consequently, this leads to a downgrade in the strength class, as detailed in Table 5. Conversely, in the case of a lower amount of water used for the 67% replacement, although it satisfies the conformity of the mean compressive strength, the slight difference in the measured compressive strength results in a change in the concrete strength class, as indicated in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mix Designation**} & **Density** & \(f_{cm,cbe,test,H}\) & \multirow{2}{*}{**Strength Class [15]**} \\  & **(kg/m\({}^{3}\))** & **(MPa)** & \\ \hline C40-RA50 & 2220 & 75 & C45/55 \\ C40-RA50w+ & 2174 & 60 & C35/45 \\ C40-RA67 & 2174 & 75 & C45/55 \\ C40-RA67w\(-\) & 2196 & 74 & C40/50 \\ C30-RA50 & 2155 & 49 & C30/37 \\ C30-RA50w+ & 2128 & 37 & C20/25 \\ C30-RA67 & 2165 & 58 & C30/37 \\ C30-RA67w\(-\) & 2147 & 59 & C30/37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Influence of water amount in the C40 and C30 mix series.  
Figure 9: Influence of water content on compressive strength.  
Therefore, altering the water content in the mix results in a decrease in compressive strength compared to the reference mix. Additionally, the pre-saturation of the recycled aggregate in 3D-printed concrete waste is deemed unnecessary before mixing.