esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Overview of the Studied Appalachian Region  
The Appalachian region is characterized by its mountainous terrain. High truck traffic presents unique challenges for air pollution monitoring [10]. Steep landscapes can lead to increased UFP pollutants. As diesel trucks move uphill and downhill, this leads to high UFP concentrations. This phenomenon poses a significant risk to communities near transportation corridors. Additionally, the combination of increased traffic from diesel-powered vehicles, rugged terrain, and industrial activities such as mining and unconventional natural gas drilling (UNGD), creates a complex environmental scenario [7]. Currently, this remains underexplored in air quality research [10].  
Current methodologies for monitoring UFP concentrations rely on advanced instruments and sophisticated modeling techniques. While accurate, these techniques are often expensive and impractical for widespread use in resource-limited settings [11]. This limitation hinders efforts to assess community-level exposure risks and implement effective mitigation strategies. Moreover, the lack of studies examining the interplay between heavy diesel truck traffic, traffic pattern, topography, truck speed, engine size and truck loading conditions in rural and semi-urban regions limits the ability to generalize findings from urban areas to Appalachian regions [11].  
This study sought to address these critical gaps by developing a simplified, cost-effective methodology to estimate UFP concentrations in relation to heavy-duty diesel truck traffic volume, specifically in areas with both flat and complex terrain. In this study, topography is significant because diesel truck uphill and downhill movement, engine size, loading condition, and truck speed in complex terrain may affect UFP concentrations. Trucks that climb steep inclines need more engine power, which increases fuel burning and raises UFP concentrations in complex terrains. By establishing a correlation between UFP levels, traffic patterns, and topographical features, this research aims to provide actionable data for policymakers, urban planners, and public health professionals. The findings will support the development of targeted interventions to reduce air pollution exposure, mitigate health risks, and enhance the quality of life for populations in challenging topographical environments. This work is particularly relevant as regions like Appalachia continue to experience industrial growth and increased transportation activity, intensifying the need for effective air quality management solutions. The central aim of this paper is to develop a correlation equation between heavy-duty truck traffic volume and ultra-fine particulate matter concentration (as determined by the number concentration and real-time size distribution measurements).
## 2 Study Area, Data, and Methodology
### Data and Methodology
#### 2.2.3 Methodology  
In the present study, nighttime and daytime LST data were used for the quantification of temperature profiles over 20 cities with a population of more than 0.5 million, in India and surrounding countries. The SUHI effect was measured using LST observations. To quantify the SUHI effect, urban and non-urban areas of the selected urban cities were separated using the MODIS global urban extent product. The non-urban/rural region was defined around the urban area using a buffer of 10 km. The SUHI was calculated by Equation (1) as the difference in LST between the urban extent and the surrounding rural region. The SUHI effect was computed from the MODIS monthly composites from 2000 to 2023. We determined the SUHI by comparing the mean temperature between the urban and rural areas, referred to as \(\mathrm{SUHI_{Mean}}\) as given below [23; 93]:  
\[\mathrm{SUHI_{Mean}}=\mathrm{Mean\ LST_{urban}}-\mathrm{Mean\ LST_{rural}} \tag{1}\]
## 4 Results
### Comparison between RRI and HRV Models  
We compared the performances of the HRV and RRI models to test whether the prediction was improved by implementing a deep learning approach. Table 8 lists the difference in the performance metrics between the RRI and HRV models that used the same method. The differences were calculated by subtracting the HRV model value from that of the RRI model. We performed a two-sample t-test to statistically compare the metrics between the two models.  
The RRI model outperformed the HRV model in terms of BAC, precision, and F1-score when using hybrid loss with ADASYN. However, there was no significant difference in QWK between the two models when using the same method. We observed a significantly higher BAC in the RRI model than in the HRV model when the hybrid loss was implemented with or without ADASYN, and opposite trends in BAC were observed when the classification loss was implemented. We observed a significantly higher QWK in the RRI model than in the HRV model when hybrid loss was used, and the opposite trend in QWK was observed when the classification loss was used. The accuracy differed significantly in three methods, with the HRV model outperforming the RRI model when using hybrid loss with ADASYN. The precision differed significantly in two methods, with the RRI model outperforming the HRV model when using hybrid loss with ADASYN. The F1-score differed significantly in three methods, with the RRI model outperforming the HRV model when using hybrid loss with ADASYN.  
Specifically, the RRI model outperformed the HRV model in terms of BAC and F1-score when using hybrid loss with ADASYN, suggesting better performance in addressing class imbalance. Considering all four methods, these results suggest that the RRI and HRV models show comparable performance in predicting age.  
Figure 8: Confusion matrices of combined models based on the methods in the test dataset.
## 4 Results and Discussion  
### Rock-Breaking Characteristics Analysis  
The stress contour maps, expressed in MPa, during the rock-breaking process when the cylindrical, axe, and triangular prism cutters are utilized as the front row correspond respectively to the first, second, and third rows depicted in Figure 9. Due to the difference in cutter structure, the cylindrical cutter mainly uses the edge to shear the rock, while the axe and triangular prism cutters have a convex ridge-like structure that not only shears the rock but also crushes the center of the rock. Hence, the rock-breaking efficiency of the axe cutter and the triangular cutter surpasses that of the cylindrical cutter.  
Figure 8: Finite element model.  
Figure 9: Stress contour of the PDC cutter during the rock-breaking process.  
The red circle in Figure 9 evidently demonstrates that the cylindrical cutter, due to shearing, contacts a larger volume of rock compared to the remaining two cutters, which may lead to a change in the damage mode of the rock from plastic small block damage to large block shedding. This causes the cutter to repeatedly cut the rock chips, resulting in a decrease in the efficiency of the bit and an increase in the probability of vibration. The axe cutter has a convex ridge-like structure, and when it comes in contact with the rock, it splits the rock and distributes it on both sides of the cutter, effectively solving the problem of rock balling, which can make the rock break in a smaller volume compared to the cylindrical cutter and improve the rock breaking efficiency. Comparing the maximum stress in the contour, it is found that the stress on the rock is significantly greater when the axe cutter is used as the front row. Upon computation, it has been discovered that the maximal stress experienced by rocks when fractured using an axe cutter shows an increase of 8.9% and 3.2% when compared to fracturing with a triangular prism cutter and a cylindrical cutter, respectively. This observation implies that an axe cutter can more easily reach the yield stress of rocks, thus effectively addressing the issue of a cylindrical cutter struggling to penetrate hard rocks. It indicates that the contact between the axe cutter and the rock is more likely to reach the yield stress of the rock, which can solve the problem that the cylindrical cutter does not easily penetrate the hard rock. The rock-breaking mode of the triangular prism cutter is similar to that of the axe cutter, but the crushing effect on the rock is not as strong as that of the axe cutter. From the comparison of the red circle position in the figure, it can be found that the rock separation effect under the action of the triangular prism cutter is not as obvious as that of the axe cutter.  
A comprehensive analysis of the rock-breaking characteristics of the cutters mentioned above reveals that axe cutters are suitable for the front row of the cutter blades of the PDC drill bit, and cylindrical and triangular prism cutters may be more suitable for the back row.
## 4 Experiments and Results
### Data Generation  
The first stage of the experiments involves signal generation. It is assumed that multiple SUs and a single PU are moving at a velocity of \(v=3\) km/h, and their initial positions are randomly chosen within an area of 250 m \(\times\) 250 m. As a result, the users' positions change over a time period of \(\Delta t=5\) s. Each occupied band \(N_{B}\) has bandwidth \(B_{W}\) of 10 MHz, and the PU can simultaneously use 1 to 3 bands. Additionally, \(P=23\) dBm, \(\beta=10^{3.453}\), \(\alpha=3.8\), \(\sigma=7.9\) dB, and \(N_{0}\) is randomly chosen between \(-114\) and \(-174\) dBm/Hz. The ratio of leaked power to adjacent bands, \(\eta\), is 10 dBm, resulting in leaked power to adjacent bands being half of the PU signal power. For the experiments, 42,000 instances were generated, divided into 80% for training and 20% for testing. For the transformer method, the training dataset was divided into 80% for training and 20% for validation. A total of 1024 samples per second of the signal were generated. In Table 3, the parameters and their respective values are presented.
# Possible Blockchain Solutions According to a Smart City Digitalization Strategy
## 3 Digitalization Strategy of City Governance
### Water Resource Management Application  
Another example of the use of blockchain technology is water resource management to improve the quality of urban life [53]. The solution consists of collecting data using IoT sensors, storing data in the cloud and sharing information with users. Citizens can take an active role and participate in decision-making regarding water resource management. The app uses smart contracts, and contract execution depends on decisions made publicly by the community. In this way, access to information and involvement of citizens in making correct and transparent decisions is ensured.
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 3 Results and Discussion
### Comparison with Other Adsorbents

Table 3: Ammonium, nitrate, and phosphate adsorption capacities of different BCs and HCs formed under different conditions. Based on the data in Table 3, it appears that the BCs and HCs employed in this study share similar sorption capacities with other adsorbents used in earlier investigations. Higher NH4+ adsorption was observed in HCs derived from oak [11] and HCs activated with KOH [62] compared to HCs derived from other biomass sources. Our HC30s from sugarcane bagasse adsorbed far less ammonium than those in Table 3. When compared to wheat straw BC [34], BC500 was at least ten times more efficient in removing nitrates from the environment. The sorption capacities of the BCs and HCs examined here are comparable to those of previously investigated adsorbents, as shown in Table 3. Compared to HCs generated from other biomass sources, HCs activated with KOH and HCs derived from oak exhibited much greater NH4+ adsorption. Table 3 displays the ammonium adsorption capacities of several HCs. Sugarcane bagasse HC30 exhibited the lowest value. BC500 was shown to be at least ten times more effective than wheat straw BC at adsorbing ammonium. Table 3 shows that the BCs and HCs used here compare favourably to other adsorbents in terms of their sorption capacities. KOH-activated HC and oak HC were shown to have much greater NH4+ adsorption than HCs generated from other biomass sources. Table 3 shows that, compared to the other HCs, sugarcane bagasse HC30 was much less effective at adsorbing ammonium. BC500 was at least 10 times more effective at adsorbing ammonium than BC produced from wheat straw. For the best NO3- adsorption results, use BC [55] produced from refined sugarcane bagasse. In acidic solutions, adsorption was shown to be more efficient than in basic ones. Table 3 suggests that, among the BCs, BC500 had the highest nitrate adsorption rate. The highest PO43- adsorption capacity of the modified sugarcane BC was achieved through slow pyrolysis at 350 \({}^{\circ}\)C with Al enrichment, similar to what was carried out in a prior work [66]. The BCs' improved P adsorption capacity can be attributed to the material being doped with Al cations, which facilitate bonding between ions and carboxylic groups. Surface irregularity and specific surface area are proven to have a major impact on BCs' adsorption performance [67]. According to Table 3, alternative HCs, such as sewage sludge HC, KOH-activated sewage sludge HC, and oak-based HC, are more efficacious than the sugarcane bagasse HC30 used in the present study [66]. As a result, the adsorption capacity differs substantially depending on the type of biomass, the degree to which it was pre-treated or activated, and the conditions under which it was manufactured.
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 4 Experimental Results
### Wi-Fi Communication  
The communication system is based on point-to-point wireless communication (wireless P2P). To do that, the standard IEEE 802.11 is used. This standard operates in the 2.4 GHz free band and the protocol used to send the data is the User Datagram Protocol--Internet Protocol (UDP-IP). The UDP-IP allows low latency in the transmission because only a simple acknowledgement of message receipt is required. In the case there are some corrupted data, the next sent corrects them since the sampling time is 10 ms and the non-lineal sliding control is robust under perturbations and it allows errors in the data transfer maintaining the system stable.  
The grid-connected PV system has sensors connected to a low-cost dSPIC30F4011 microcontroller, which is the responsible for reading the sensors. This dSPIC is connected to a EM203 module. The EM203 is a device that converts the 10/100 Base Ethernet communication of serial communication up to 115.2 kbps and vice versa. The EM203 module sends the signals to the PC, where the controller is implemented and the control signals are calculated. Then, the control signals are sent to the EM203 module to send them to the dsPIC, where the PWM signals are generated.  
The temperature, irradiance, voltage and current sensors are connected to the dsPIC by means of an ADC port. The ADC has a resolution of 12 bits and it allows a maximum sampling time of 200 ksps. The data from the sensors are sent to the EM203 module by the microcontroller with a frequency of 100 Hz via serial communication port with Tx and Rx signals. Then, the EM203 sends the packet signal, using the UDP-IP protocol, via Wi-Fi to the PC. The serial frame structure and field size sent from the system to the PC is presented in Figure 9. In Figure 9a), a sending from the dsPIC to the PC is depicted. First, a start is sent and then the data from the sensors are sent, the voltages, currents, temperature and irradiance. Finally, the end is sent to notify that the updated data have been sent. The Figure 9b) shows the sending between the PC and the microcontroller. When tha calculations have finished, the control signal is sent. In this work, this signal is sent each 10 ms. The dsPIC receives the signal and generates the PWM regarding the received value. The PWM signal has a resolution of 10 bits and its frequency is 20 kHz.  
Figure 9: Serial frame structure and field size: (**a**) sending from the dsPIC to the PC; (**b**) sending of the control signals from the PC to the dsPIC.
## 5 Forward Simulation Results of Different Salt Cavity Shapes
### Influence of Cavity Height  
The timing of salt mining can also influence the growth of salt cavity height, as evidenced by on-site operational tests showing height difference ranging from several meters to several hundred meters between the two cavities of the buttoned well. These differences are attributed to production time and the sequence of water injection rounds. Understanding the response laws of varying salt cavity heights in seismic profile is crucial for accurately interpreting the true reflection patterns of old cavities.  
Figure 14: Seismic record profiles of variable density at different cavity diameter.  
Figure 17 shows the seismic record profiles of variable density at different cavity heights. Obviously, the top surface of an 80-m-diameter cavity is clearly visible across various cavity heights. This top surface consistently exhibits a prominent valley reflection feature, while the interior of the cavity and the sediment bottom appear blurred. The entire cavity presents a circular pattern. As the cavity height increases, the reflection energy at the lower portion of the circular wave reflection diminishes, leading to a more blurred appearance. Importantly, the reflection characteristics of the horizontal channel remain unaffected by cavity height, consistently demonstrating a strong amplitude reflection pattern characterized by \"one valley and one peak\".  
Figure 18 illustrates a superposition diagram combining waveform and salt cavern models with different cavern heights. As the cavity height increases, the reflection characteristics of both the cavity's top surface and the bottom of the horizontal channel remain largely unchanged. The cavern top consistently aligns with a prominent trough reflection, while the bottom of the connecting channel maintains a corresponding strong reflection peak, regardless of the cavity height. Notably, since the cavern height in the vertical seismic record is not magnified, utilizing the reference level of the horizontal channel to calculate the cavern height provides an accurate representation of its true dimensions. Additionally, the peak reflection of the residue's top surface consistently appears as the first peak beneath the two peaks of the cavity's top surface, regardless of changes in cavity height. Moreover,  
Figure 16: The relationship between the amplification factor of wave width and cavity diameter.  
Figure 15: Congruent diagrams of waveform seismic record profiles and cavity models with different cavity diameter.  
regardless of how the cavity height changes, the top surface of the residue always remains in the upper half of the circular waveform.
## 3 Feedback Cooling of the Movable Mirror
For \(0\leq r<\frac{1}{2}\), it is noted that the effective cavity decay rate \(\kappa_{eff}=\kappa(1-2r)\) is decreased with increasing the reflective coefficient \(r\) of the beam splitter. Therefore, in the presence of the feedback (\(0\leq r<\frac{1}{2}\)), it is possible to make the value of \(|G|/\kappa_{eff}\) larger than unity even when the value of \(|G|/\kappa_{eff}\) is less than unity in the absence of the feedback. We still use the parameters from the recent experiment [34]: \(\lambda=\frac{2\pi c}{\omega_{l}}=1554.35\) nm, \(\kappa=2\pi\times 423\) MHz, \(g_{0}=2\pi\times 869\times\sqrt{2}\) kHz, \(\omega_{m}=2\pi\times 5.25\) GHz, \(\gamma_{m}=2\pi\times 13.816\) kHz. Moreover, we choose the effective cavity detuning \(\Delta=\omega_{m}\), the initial temperature of thermal environment \(T=30\) mK, and the power of the input laser \(\wp=1.3\) mW. The structures of the spectra of \(S_{Q}(\omega)\) and \(S_{cout}(\omega)\) are determined by the solutions of the equation \(d(\omega)=0\). The real and imaginary parts of the solutions of \(d(\omega)=0\) determine the locations and linewidths of the normal modes of the optomechanical system, respectively. We plot the real parts \(\mathrm{Re}(\omega)\) and the imaginary parts \(\mathrm{Im}(\omega)\) of the solutions of \(d(\omega)=0\) versus the reflection coefficient \(r\) of the beam splitter in Figures 3 and 4. When \(r\leq 0.043\), \(\mathrm{Re}(\omega)\) has two identical values, so there is no normal mode splitting in \(S_{Q}(\omega)\) and \(S_{cout}(\omega)\), however, \(\mathrm{Im}(\omega)\) has two different values, thus the lifetime splitting appears [35]. When \(r>0.043\), \(\mathrm{Re}(\omega)\) has two different values, and the difference between them is increased with increasing \(r\), thus the normal mode spitting occurs in \(S_{Q}(\omega)\) and \(S_{cout}(\omega)\), and the peak separation becomes larger with increasing \(r\), whereas \(\mathrm{Im}(\omega)\) has two equal values, the opposite of \(\mathrm{Im}(\omega)\) is decreased with increasing \(r\), which implies two normal modes in \(S_{Q}(\omega)\) and \(S_{cout}(\omega)\) have the same linewidths, and their linewidths are reduced with increasing \(r\). Figure 4: The imaginary parts of the solutions of \(d(\omega)=0\) versus the reflection coefficient \(r\) of the beam splitter. Figure 3: The real parts of the solutions of \(d(\omega)=0\) in the domain \(\mathrm{Re}(\omega)>0\) versus the reflection coefficient \(r\) of the beam splitter. **Author Contributions:** Conceptualization, S.H. and A.C.; methodology, S.H. and A.C.; software, S.H. ; formal analysis, S.H. ; writing--original draft preparation, S.H. ; writing--review and editing, S.H. and A.C.  
**Funding:** This research was funded by Science Foundation of Zhejiang Sci-Tech University (grant numbers 18062121-Y, 17062071-Y), and by the National Natural Science Foundation of China [grant number 11775190]. **Acknowledgments:** [PERSON] would like to thank [PERSON]. [PERSON] helpful discussions. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Hardware Design of Dual-Phase Sensitive Lock-in Amplifier Circuits  
### Pre-Differential Amplifier Circuit  
The task of the target signal channel is to amplify the input signal to ensure that the amplitude of the target signal reaches the operating voltage required by the detection circuit, while minimizing the interference of various types of noise and DC drift, so that the signal input to the correlator channel is maintained in a low-noise and low-distortion state. In this paper, a Texas Instruments model OPA656 operational amplifier was used, which has excellent characteristics including high gain-bandwidth product, low input bias voltage, and low input voltage noise. The preamplifier circuit in the target amplifier uses differential amplification. As shown in Figure 4, at the signal input, two voltage followers with gain are introduced to achieve efficient amplification of the input voltage, and then the differential amplification circuit is used for differential operation to output the processed signal, which is designed to effectively increase the amplitude of the target signal.  
When this circuit \(R_{2}\) = \(R_{3}\), \(R_{4}\) = \(R_{5}\), \(R_{6}\) = \(R_{7}\), its structure is approximated to an upper and lower symmetrical structure; then, the output signal \(U_{o}\) is given in Equation (13):  
\[U_{O}=\frac{R_{6}}{R_{4}}\bigg{(}1+\frac{2R_{2}}{R_{1}}\bigg{)}(U_{+}-U_{-}) \tag{13}\]  
where \(U_{-}\) and \(U_{+}\) are electrical signal inputs.  
Figure 4: Schematic diagram of differential amplifier.  
Figure 3: Schematic diagram of a dual-phase sensitive lock-in amplifier.