esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Algal Growth in Different Initial pH Treatments
In all treatments, the results showed that a large amount of COD was consumed by microalgae under a mixotrophic mode, indicating that the large consumption was associated with consumption of residual glucose and some other organic matters from the pretreated ADPE. During pretreatment, fungi can significantly degrade larger complex organic matters to smaller simple ones with short chains in water [49; 50], which can be easily utilized by microalgae for algal growth [51; 52]. These simple organic compounds and glucose assimilated by microalgae resulted in a large amount of COD consumption in the pretreated ADPE. In pH 7.0 and 8.0 treatments, the increases of TN and COD concentrations on the final day may be relevant to pH values below 5.0 during the experiment (Figure 7b). Levels of pH below 5.0 have been shown to induce cell damage in _Chlorella_[42]. Dissolved organic matter (DOM) can be released from the dead and disrupted algal cells [53], among which those induced by decreasing algal yield may result in much DOM release and consequent increase of TN and COD concentrations. These results suggested that the initial pH of 9.0 was the optimum treatment for removing NH4+-N, TN, TP, and COD from the pretreated ADPE. Figure 7: Growth curves of NCU-39 (**a**) and pHs (**b**) at different initial pHs in 5-fold diluted anaerobic digestion piggery effluents (ADPEs) pretreated by 3-day fungal decolorization. Tap medium was used as a control. Error bars represent standard deviations (\(n=3\)). Based on nutrient concentrations on the final day (Figure 8) and those in 5\(\times\) diluted raw ADPE (Table 1), the maximum removal rates of NH4\({}^{+}-\)N, TN, TP, and COD were calculated as 88%, 58%, 65%, and 77%, respectively, in the pretreated ADPE at initial pH of 9.0. This study achieved a significant process optimization in 5\(\times\) low-diluted ADPE decolorized by NU-27 for three days and subsequent microalgae cultivation by NH4\({}^{+}\)-tolerant strain at initial OD as 0.3 and pH of 9.0 for ADPE remediation. The process optimization provided the optimum conditions for algal growth among the treatments, which allowed maximum algal yield and nutrient removal from the ADPE. However, _Lichtheima_ sp. as a pathogenic fungus implied a concrete risk for human health, and its application in microalgae-based wastewater treatment should receive careful evaluation [54]. Therefore, further study is required to select a non-toxic and eco-friendly fungus, such as white rot fungi [55], for ADPE decolorization. In addition, because fungi and microalgae are high-efficient organisms on heavy metal removal from diluted solutions [56; 57], further exploration of their performance on heavy metal removal from ADPE is expected.
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 5 Evaluation
### Inspection Report Footprint
When the machine is getting old, we collect more observations than numerical readings. This turns into an increasing size of the report proportional with the aging. More details about statistics of the report sizes are available in Table 2.  
An Internet connection is needed to send the report from the mobile app to the cloud module for processing and persistence. Thus, sending a report is not a concern when traditional Internet access such as cellular networks (over edge, 3G, 4G, or 5G) or broadband access (over WiFi) is available. However, as mines are usually located in remote locations, a cellular/fixed Internet connection may not be available. For this reason, we evaluate using satellite communication equipment to send data to the cloud. Usually, this type of communication requires a data plan billed per MB of data. Assuming that one MB of data sent via satellite communication costs USD 6.80 ([[https://www.bluecosmo.com/inmarsat-bgan-monthly-service-plan.html](https://www.bluecosmo.com/inmarsat-bgan-monthly-service-plan.html)]([https://www.bluecosmo.com/inmarsat-bgan-monthly-service-plan.html](https://www.bluecosmo.com/inmarsat-bgan-monthly-service-plan.html)), accessed on 15 December 2021) Table 3 shows the estimated costs of sending one inspection report using this communication technology to the cloud, net of overheads such as IP, TCP, TLS, and HTTP headers.
# A Low-Profile Wideband Linear-to-Circular Polarization Conversion Slot Antenna Using Metasurface
###### Abstract  
A new low-profile wideband linear-to-circular polarization conversion microstrip slot antenna based on a metasurface for C-band satellite communication applications is proposed in this paper. The metasurface basically consists of four unit cells with parasitic square cross gaps arranged in a \(2\times 2\) layout. By loading the metasurface on the microstrip slot antenna, linearly polarized (LP) waves from the source antenna are converted into circularly polarized (CP) waves. Then, by etching three more parasitic square cross gaps in the middle of the metasurface, enhanced impedance bandwidth and axial ratio bandwidth (ARBW) are achieved. Furthermore, an equivalent circuit and a phase analysis are presented to explain how a wide ARBW is realized by the metasurface. A final model with an overall size of \(36\times 36\times 3.5\) mm\({}^{3}\) (approximately \(0.65\lambda_{0}\times 0.65\lambda_{0}\times 0.06\lambda_{0}\) at 5.5 GHz) was designed and fabricated. The measured S\({}_{11}\) bandwidth and 3 dB ARBW were 39.25% from 4.28 GHz to 6.37 GHz and 17.77% from 5.18 GHz to 6.19 GHz, respectively. As a result, the proposed antenna shows great potential for satellite communication applications due to its low profile and compact structure, wide impedance bandwidth, and wide axial ratio bandwidth.  
metasurface; low-profile; wideband; polarization conversion; microstrip slot antenna +
Footnote †: journal: materials  
0000-0001-9991-5000][PERSON]  
0000-0002-4882-2888][PERSON]  
0000-0002-4882-2888][PERSON]
## 4 Experimental Materials
### The RPL Performance Metrics  
In this section, we introduce four main metrics to measure RPL performance while adopting two different objective functions (OF0, ETX). These metrics provide an important impact on the routing process as RPL performance is changing according to the network topology and the hardware platform. We measured the following RPL metrics:  
* **Control Traffic Overhead**: RPL has three different control messages as described before (DIO, DAO, DIS), the total of all control messages sent from node clients to the root node measures the control traffic overhead metric.
* **Packet Delivery Ratio (PDR)**: This metric is used to measure the total received packets at the root node compared with the total sent packets from clients. The higher PDR percentage of successfully delivered messages to the root node during the test, the higher routing protocol performance.
* **Network Latency (End-to-End delay)**: It is considered as the total time taken for a packet to travel from client node to the root node (destination).
* **Energy Consumption**: Power estimation is a very important factor in lossy networks as it indicates the lifetime of WSN. In this study, we use a percentage radio on time of the radio to reflect the consuming power in the sensor nodes.
## 5 Results and Discussion
### Failure Case Analysis  
While the proposed method performs robustly across a variety of low-light scenes, we observed several failure cases where enhancement results were suboptimal. These failures fall into three common categories:  
1. Extreme Darkness with Minimal Structure: In some near-black inputs with severely low exposure and little structural information, the model tends to over-amplify noise or produce unnatural brightness gradients. This is likely due to the lack of such extreme examples in the training distribution and the limited ability of the attention mechanism to differentiate noise from faint structure.
2. Strong Color Casts or Sensor Artifacts: Images captured with low-end sensors or under unbalanced lighting conditions (e.g., yellow streetlights) occasionally result in amplified color distortions. The model attempts to \"normalize\" the color but may hallucinate incorrect tones due to the absence of ground-truth cues.
3. Over-saturation in Highly Reflective Regions: When enhancing scenes that contain bright highlights (e.g., signs, metallic surfaces), the decoder may produce overexposed regions with loss of detail. This can be attributed to limited dynamic range handling in the current architecture.  
To visually illustrate some of the limitations discussed above, Figure 7 presents representative failure cases where the model struggles under particularly challenging low-light conditions.  
To address these limitations, future research could explore  
* Raw image enhancement pipelines to preserve low-level detail and dynamic range.
* Scene-aware attention modules that condition enhancement on contextual priors (e.g., geometry or semantic features).
* Noise-aware learning objectives that penalize hallucinated structures in ambiguous regions.
* Augmenting training data with synthetically generated extreme darkness scenarios and sensor artifacts.  
While our model achieves substantial gains in efficiency over large-scale networks, it exhibits slightly higher FLOPs compared to some ultra-compact architectures (e.g., Zero-DCE, [PERSON] et al. [13]). This trade-off is primarily due to the inclusion of perceptual and attention mechanisms that improve structural fidelity and texture preservation. We argue  
Figure 7: Example of failure case observed in challenging low-light scenarios. (**Left**): Over-amplification of noise in severely underexposed regions. (**Center**): Color distortion caused by unbalanced or low-CR illumination. (**Right**): Detail loss due to overexposed highlights. This case highlights limitations in generalization to extreme inputs.  
that this marginal increase in FLOPs is justified by the notable gains in LPIPS and subjective human evaluation scores, which reflect enhanced perceptual quality--a key requirement in many vision-critical applications.
## 2 Background Information
### Generation Mechanism of Hydrodynamic Noise
#### 2.1.1 Turbulent Boundary Layer Noise
The geometry of the object and the thickness of the fluid boundary layer directly affect the turbulence intensity, and consequently, the intensity of sound wave radiation. Secondly, the flow velocity \(U\) exhibits a close correlation with the turbulence energy, and an increase in turbulence intensity is typically accompanied by a higher noise level. The disturbance caused by turbulence is proportional to the flow velocity, and the radiated noise increases with  
comprehend how flow velocity, boundary layer thickness, and fluid viscosity synergize in the noise generation process. In recent years, numerical simulations and experimental analyses have demonstrated that the vortex structures within the turbulent boundary layer have a direct impact on noise radiation. Particularly under high-Reynolds-number conditions, the more complex the turbulence structure, the higher the noise level [25; 26; 27]. [PERSON]'s acoustic analogy theory provides a fundamental explanation for the generation mechanism of turbulent noise, stating that the pressure fluctuations caused by fluid disturbances propagate through the surface of the moving body into the external flow field. [PERSON] [28] calculated the flow field using large eddy simulation (LES) and a scale-adaptive simulation (SAS) turbulence model, and solved the variational formulation of [PERSON]'s acoustic analogy through the finite element method, obtaining the distribution of the turbulent noise sound field. [PERSON] [29] employed a hybrid numerical method that combines LES with [PERSON]'s acoustic analogy to successfully simulate flow-induced noise at low Mach numbers. The results were validated against data from publicly available literature, confirming their accuracy. Studies have shown that this hybrid method is feasible and reliable for engineering applications.
## 3 Internal Load Monitoring
### Questionaires and Diaries  
Ahletes and exercise practitioners frequently use questionnaires and training diaries as simple and cost-effective tools to gain insights into training loads for individual or upcoming sessions. These tools collect subjective data; thus, it is important to complement the information with data from objective methods for monitoring training loads (i.e., physiological or performance metrics) to improve accuracy [5; 72; 73]. Among the primary questionnaires and diaries, the following stand out: the training diary, profile of mood states (POMS), recovery-stress questionnaire for athletes (RESTQ-Sport), and total quality recovery (TQR). These tools can be applied to athletes, trained individuals, or those beginning their exercise journey [72; 73; 74].  
In training diaries, the individual describes everything that occurred during the training session, such as difficulties, pain, and more, and is used more commonly by athletes than by physical exercise practitioners [72; 73]. The POMS questionnaire consists of 65 items that assess six mood states: vigorous activity, anxiety-tension, dejection-depression, anger-hostility, fatigue-inertia, and confusion-bewilderment [56; 58]. The RESTQ-Sport comprises 76 questions divided into 19 scales, of which 7 assess overall stress, 5 evaluate overall recovery, 3 assess sport-related stress, and 4 evaluate specific recovery issues [72; 75]. Lastly, the TQR assesses the individual's recovery over the last 24 h by asking \"how do you feel now?\" on a scale from 6 to 20, where 6 means \"not recovered at all\" and 20 means \"completely recovered\" and is widely used between training sessions [72]. The TQR in its modified form with a 0-10 scale can also be found and used [76].
## 3 Results and Discussion
### Previous Works and Future Investigations  
Previous studies have investigated the role of the hydrogeological parameters on the potential impacts induced by UPSH in the underground medium [16,30]. The present investigation goes further and shows how the \(K\) controls the potential environmental impacts on surface water bodies and on the global \(\eta\) of the plant. While the results are useful to understand the system behavior and to define screening methodologies, further investigation should be done regarding not only the influence of other hydrogeological parameters, such as the effective porosity, but also considering heterogeneities in the underground medium. In addition, the importance of the shape and volume of the available underground cavity should be assessed.  
The results showed that, under certain circumstances, the discharge of large volumes of the pumped groundwater into surface water bodies would be needed. In addition, the effects on the piezometric head of pumping and discharge may alter the interactions between the groundwater and nearby surface water bodies. Consequently, it would be advisable to investigate how UPSH may influence the e-flows of nearby rivers [51] by considering UPSH plants during the environmental flow assessment processes.  
This paper also investigated, for the first time, the relevance of the elevation of the piezometric head in terms of \(\eta\) and the environmental impacts. The results showed that the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TOP Scenario** & \(\Delta\eta\) **(\%)** & **MIDDLE Scenario** & \(\Delta\eta\) **(\%)** \\ \hline TOP-3 & \(-37.3\) & MID-3 & \(0\) \\ TOP-4 & \(-12.6\) & MID-4 & \(-0.1\) \\ TOP-5 & \(-3.1\) & MID-5 & \(-0.6\) \\ TOP-6 & \(-0.4\) & MID-6 & \(-0.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variations in the efficiency (\(\Delta\eta\)) with respect to an ideal reference scenario.  
relative elevation of the piezometric head was crucial for the operation of a UPSH plant, and for this reason, it should certainly deserve more investigation in the future.  
Finally, we assessed the influence of the water exchanges on the global \(\eta\) of UPSH plants. To date, only one study had investigated the relation between groundwater exchanges and \(\eta\)[32]. However, this previous investigation only considered the influence of groundwater exchanges on the \(\eta\) of pumps and turbines. While the findings of this previous study were relevant, the reported variations of \(\eta\) were much lower than those obtained in this paper.  
The current investigation went further and considered the variations in the global \(\eta\) of UPSH by comparing the energy consumed for pumping water from the underground reservoir and that obtained by discharging water from the upper reservoir. Considering that the \(\eta\) of the pumps and turbines depends on the head difference between reservoirs, it is advisable to investigate the effect on their \(\eta\) of different positions for the piezometric head as in the present paper.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 4 Rain Detection
### Optimization of the Heterogeneity Filter(s) for Rain Detection
Figure 10 shows the rain detection percentage as a function of rain rate for different wind speed regimes with respect to the polarization channels considered for the detection. As observed, no matter the polarization configuration, rain is more likely detected when the rain rate is high. For all rain rate regimes, the background sea state is found to have an impact on the detection. For a given rain rate, the higher the wind speed, the less we can detect rain in the signal. In particular, for rain rates larger than 10 mm/h, the detection rate increases significantly when the wind speed decreases. Furthermore, the benefit of merging the two polarization channels for rain detection is evident, since the percentage of rain detected pixels increases from about 20-50% to up to 70% when both co- and cross-polarization channels are combined. Figure 11 shows the detection rate as a function of incidence angle for different rain rates. It shows that the rain detection increases with incidence angle independent of the applied heterogeneity filter. These results confirm that C-band SAR measurements are more sensitive to rain for high incidence angles as documented in Section 3. Figure 11: True positive rate of rain detection percentage as function of incidence angle for the polarization channels considered with respect to different rain rates. The first bin at 32\({}^{\circ}\) is affected by artefacts linked to recurrent invalid data at near range. Figure 10: True positive rate of rain detection percentage as a function of rain rate for different wind speed regimes with respect to the polarization channels considered. (**a**) VV-generated filter, (**b**) VH-generated filter and (**c**) dual-pol filter. In general, the higher detection rate of rain obtained when the two polarizations are combined show their complementary. This suggests that the different sensitivity of the co- and cross-polarization channels to rain rate could be due to different scattering mechanisms.
## 3 Results and Discussion
Both collected temporal signals were recorded by a 2 GHz bandwidth digital oscilloscope (Tektronix, MSO 5204B, Beaverton, OR, USA). The measured pulse durations (FWHM) of the pump and the idler light are 24.47 ns and 18.85 ns, respectively. The idler pulse width is narrower than the pump pulse width resulting in the temporal gain narrowing effect in the OPO process [26]. Hence, the short pulse duration and high pulse energy bring about the MIR 6.45 um laser having a high peak power of about 358.6 kW at the maximum output level. The measurement of beam quality factors M\({}^{2}\) for the idler beam at 6.45 um was carried out at the output energy of 4.5 mJ. The exit idler beam radii in the horizontal (\(x\)) and vertical (\(y\)) directions were measured at different distances from a CaF\({}_{2}\) convex lens of \(f=100\) mm after OC mirror by a commercial MIR charge-coupled device (CCD) camera (Spiricon, PY-III-HR-C-A). The plot in Figure 6 displays the measured beam caustic along the \(x\) (black squares) and \(y\) (red dots) transverse directions in terms of Gaussian approximation. By fitting the Gaussian beam standard expression, the beam quality values were calculated to be about 11.19 and 7.84 in \(x\) and \(y\) directions, respectively, corresponding to an average beam quality factor of M\({}^{2}=9.52\). The insets of Figure 6 show the two-dimensional (2D) and  
\[P_{i}(x,y,z,t)=i\frac{d_{eff}\omega_{i}}{cn_{i}}A_{p}(x,y,z,t)A_{s}^{*}(x,y,z,t) \exp(i\Delta kz) \tag{3}\]0 Measured spectra of the pump and signal waves. three-dimensional (3D) spatial beam intensity profiles. It also indicated that the generated beam of the MIR wave has a good Gaussian spatial distribution, which is crucial for surgical applications. It is worth mentioning that the stability of the idler output energy was also evaluated by an energy meter when the output energy was ~4.5 mJ. The measured results are shown in Figure 7, which demonstrates that the fluctuation of idler output energy was less than \(\pm\)0.5% over 165 min. The excellent stability of the MIR light source is essential to the ablation of biological tissues. The stability of MIR laser output could be potentially further improved by controlling the stable output of the pump source. \[P_{i}(x,y,z,t)=i\frac{d_{eff}\omega_{i}}{cn_{i}}A_{p}(x,y,z,t)A_{s}^{*}(x,y,z,t) \exp(i\Delta kz) \tag{3}\]1 The temporal pulse profiles of the pump wave and the MIR idler wave. \[P_{i}(x,y,z,t)=i\frac{d_{eff}\omega_{i}}{cn_{i}}A_{p}(x,y,z,t)A_{s}^{*}(x,y,z,t) \exp(i\Delta kz) \tag{3}\]2 Beam caustic measurement of generated MIR idler laser at 6.45 μm in \(x\) and \(y\) directions at an output pulse energy of 4.5 mJ. Insets: the measured 2D (**top**) and 3D (**bottom**) idler spatial beam intensity profiles.
## 3 Map Construction Method
### Road Centerline Extraction
#### 3.1.2 Road Division  
In order to distinguish between road segment areas and intersection areas, it is necessary to classify road triangles. Road triangles can be classified into three types by the number of their neighboring triangles: if the triangle has only one neighboring triangle, it belongs to Type I, as shown in Figure 4a, which means this triangle is mainly located at the end of a road; if the triangle has two neighboring triangles, it belongs to Type II, as shown in Figure 4b, which means it is mainly located within a road segment; while if the triangle has three neighboring triangles, it belongs to Type III, as shown in Figure 4c, which means that it is located at the intersection. Based on such rules, the classification of the triangle types can initially be used to distinguish between road segment triangles and road intersection triangles.  
To further delineate the intersection area, a circle with the road width as the radius is determined using the gravity center of the intersection triangle as the center of the circle. The intersection of the circle and the road edge is used as the road intersection boundary point (as shown in Figure 5a, the points \(P_{1},\ P_{2},\ P_{3},\ P_{4},\ P_{5},\ P_{6}\)). These road intersection boundary points are connected in turn to determine the s  
Figure 4: Triangle-type division. (**a**) Type I; (**b**) Type II; (**c**) Type III.