esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Overall Performance of Partial Nitrification on Treating Livestock Wastewater
One of the reasons might be that the concentration of common NOB genera in the SBRs was too low to be identified by 16S rRNA gene-sequencing equipment; another reason was that the special NOB species, such as _Marichromatum_ which was considered as a kind of heterotrophic NO2\({}^{-}\)-N oxidation bacteria [25], presented in two SBRs to induce the oxidization of NO2\({}^{-}\)-N. The persistent high-COD environment in the reaction cycle was considered to be one of the reasons for the growth of _Marichromatum_ bacteria. According to Figure 6, the percentage of AOB and NOB decreased in both of IASBR and CASBR on Day 45 compared with the seed sludge. The high concentration of SS in the original livestock wastewater could dilute the density of bacteria in the reactors and inhibit the propagation of AOB and NOB in the start-up stage. In this period, the decreased pH could also affect the activity of AOB and NOB [26]. In the steady-state stage (Day 109), the quantity of _Nitrosomonas_ in IASBR rapidly recovered from 9.3% to 11.8%, but only a 0.4% increase appeared in CASBR. It shows that the growth of AOB could be accelerated in the intermittent aeration condition. As for _Marichromatum_, 1.0% and 2.2% of the increase was observed in IASBR and CASBR from Day 45 to Day 109, respectively. It shows that the NOB activity recovered in the long-term livestock wastewater treatment process. However, in IASBR, due to the inhibition of the intermittent aeration strategy, the recovery of NOB was lower than that in CASBR.
## 5 Conclusions  
In this paper, an effective data acquisition clustered protocol using compressive sensing for heterogeneous WSNs in terms of energy and transmission range is proposed (EDACP-CS). In EDACP-CS, each sensor node independently elects itself as a cluster head based on its residual energy and distance form the BS. CS measurements are obtained via cluster heads. Distributed Wavelet Transform (DWT) is used as the sparsifying matrix and \(i.i.d\) Gaussian with zero mean and unit variance is used as the sampling matrix. The simulation results reveal that our method decreases the energy consumption and therefore, prolongs the network lifetime and the stability period compared with EECDA and LEACH-Hetero protocols.
# Edge and Cloud Computing in Smart Cities
## 4 Application Domains
### Disaster Management and Emergency Response  
Edge-cloud computing significantly enhances disaster response by providing real-time situational awareness, predictive analytics, and rapid resource deployment. Edge nodes facilitate immediate hazard detection by processing sensor data from surveillance cameras, unmanned aerial vehicles (UAVs), and environmental sensors. Instantaneous risk assessment enables authorities to make informed decisions and deploy emergency resources efficiently [151; 152; 153].  
Cloud services assist in large-scale coordination by aggregating multi-source data, refining disaster prediction models, and optimizing evacuation strategies. AI-driven emergency response systems prioritize rescue operations based on real-time impact assessments, ensuring that relief efforts are allocated to the most affected regions [154; 155].  
UAVs equipped with edge processors play a vital role in disaster monitoring. They assist in search and rescue missions through AI-enhanced object recognition. These advancements ensure faster response times, improved victim detection, and efficient resource utilization in crisis scenarios [156; 157].
## Materials and Methods
### System Design
#### 2.2.4 Dispersion Compensation  
Group delay dispersion mismatch between the sample and reference arms can negatively affect OCT images by causing the full-width at half maximum (FWHM) of a surface to expand. Spectral phase expansion can be written as:  
\[\phi(\omega)=\sum_{n}\frac{1}{n!}\phi^{(n)}(\omega_{0})(\omega-\omega_{0})^{n}, \tag{2}\]  
where \(\phi(\omega)\) [radians/second] is the Taylor series for about the center optical frequency, \(\omega\) (\(\omega=2\pi\
u\)). The zero-order term describes the phase difference at the center optical frequency. The first-order term, \(\phi^{(1)}(\omega_{0})\), contains the inverse group velocity, that is \"group delay\", and units are in seconds. The second-order (quadratic) term, \(\phi^{(2)}(\omega_{0})\), is the group delay dispersion (GDD), and the units are s\({}^{2}\).  
Without dispersion compensation lenses in the reference arm, the SAR-OCT system (Figure 1) would have dramatic GDD mismatch and loss of longitudinal resolution. To characterize SAR-OCT dispersion mismatch between sample and reference arms, a series of measurements were recorded in which mirror were placed in sample and reference arms. With only a single collimating lens in the reference arm, each lens of the sample arm was added, one-at-a-time, in proper order and dispersion mismatch measured.  
Table 1 shows the second-order term to characterize dispersion after successive lenses and the contribution of each lens individually. Total GDD dispersion mismatch (5.01 \(\times\) 10\({}^{-28}\) s\({}^{2}\)) was measured. Two achromatic lenses were selected to match this dispersion and placed in an afocal configuration in the reference arm of the interferometer thereby reducing total dispersion mismatch by 5X (9.50 \(\times\) 10\({}^{-29}\) s\({}^{2}\)). This reduction in dispersion was sufficient for high-quality retinal imaging.  
The amount of dispersion introduced by the murine eye is sufficiently small to forego dispersion compensation for the ocular components. Length of the mouse eye is relatively short compared to that of humans, and the refractive index of the ocular media is nearly constant over the wavelength range of the OCT source.
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 2 Materials and Methods  
### GNSS RO Overview  
Many previous studies, e.g., [1; 2; 3; 27] described the observing principle of the RO technique. Only a brief overview of the observation technique is given here, focusing on the parts pertinent to the atmospheric 1D-Var. The primary observables are the GNSS signal's phase and amplitude recorded by a receiver onboard a LEO satellite during an occultation event, which occurs over a brief period (a few minutes) when a GNSS satellite sets or rises behind the Earth's atmospheric limb from the standpoint of the LEO satellite. By relating the time-frequency information content of the occulted signal to the time-varying occultation geometry, one can determine a profile of the bending angle as a function of the impact parameter, e.g., [52; 53]. While the ionosphere exerts considerable influence on the ray's bending, the bulk of the first-order ionospheric effect can be eliminated by using two carrier frequencies and forming a linear (the so-called ionosphere-free) combination of the dual-frequency observations at some point in the data processing chain.  
The inverse Abel transform of the bending angle gives the refractive index [54; 55]:  
\[n(a)=exp\bigg{\{}\frac{1}{\pi}\int_{a}^{\infty}\frac{\alpha(x)dx}{\sqrt{x^{2}- a^{2}}}\bigg{\}}. \tag{1}\]  
where \(\alpha\) is the bending angle, \(a\) is the impact parameter, and \(n\) is the refractive index that can be approximated in the neutral atmosphere as follows [56]:  
\[N\equiv 10^{6}(n-1)=k_{1}\frac{p}{T}+k_{2}\frac{e}{T^{2}}. \tag{2}\]  
where \(N\) is the refractivity, \(T\) is the temperature in K, \(p\) is the (total) atmospheric pressure in hPa, and \(e\) is the water vapor pressure in hPa. The coefficients are \(k_{1}\) = 77.6 hPa K\({}^{-1}\) and \(k_{2}\) = 3.73 10\({}^{5}\) K\({}^{2}\) hPa\({}^{-1}\).  
In a dry atmosphere, the moisture term (second rightmost) vanishes and as a result the refractivity is proportional to the air density \(\rho_{D}\) as per the ideal gas law:  
\[\rho_{D}=\frac{N}{k_{1}R_{d}}. \tag{3}\]  
where \(R_{d}\) (=287.058 J K\({}^{-1}\)kg\({}^{-1}\)) is the specific gas constant for dry air. Assuming further that the atmosphere is in a hydrostatic balance, one can obtain the dry air pressure as follows:  
\[p_{D}(z)=p_{D}(z_{TOA})+\int_{z}^{z_{TOA}}\rho_{D}gdz. \tag{4}\]  
where \(g\) is the acceleration due to Earth's gravity, \(z\) is the height, and \(z_{TOA}\) is the height of the top of the atmosphere. It should be pointed out that \(p_{D}\) must be distinguished from the partial pressure of dry air in a moist atmosphere and the same applies to \(\rho_{D}\). Finally, one can obtain the dry temperature based on the ideal gas law:  
\[T_{D}=\frac{p_{D}}{\rho_{D}R_{d}}. \tag{5}\]  
The sequential application of Equations (3)-(5) is the traditional dry retrieval mentioned earlier. With the promise that the temperature and pressure in Equation (2) are closely known, the water vapor is separable. This corresponds to the traditional humidity retrieval.
## 4 Results and Discussion  
### Operating Costs as the Objective Function  
The optimization objective is to minimize the operating costs, in which the results obtained from the simulations are shown in Figures 6-8.  
As can be seen from Figure 6, the buried pipe flow rate first decreases from approximately 14 m\({}^{3}\)/h to 8.5 m\({}^{3}\)/h and then increases to 11 m\({}^{3}\)/h, with 18:00 as the demarcation point. Additionally, the buried pipe flow rate is high when the electricity price is low, while it is low when the electricity price is high.  
Figure 7 depicts the heat storage capacity and consumers' heat load of each period after optimization. According to the profile of the heat storage with time, the working conditions of the heat storage tank can be mainly divided into three stages: 2:00-10:00 and 20:00-24:00 are both the heat release stage, while 12:00-20:00 is the heat storage stage. The heat storage capacity first changes from approximately 300 kW to 0 in 2:00-10:00, then from 0 to 150 kW in 12:00-20:00, and then to 0 in 20:00-24:00. Compared with the profile of the heat load, the heat load is smaller during the heat storage period and basically at a high level during the heat release period. In contrast, compared with the time-of-use electricity price curve, the heat storage period is basically the valley electricity period, but the heat release period is basically the peak electricity period. Therefore, it can be assumed that the operating mode of the heat storage tank at this time is jointly determined by the consumers' heat load and the electricity price.  
Figure 6: Buried pipe flow and electricity price in each period when the objective function is operating costs.  
Figure 7: Heat storage capacity and heating load in each period when the objective function is operating costs.  
Figure 8 illustrates the power consumption of each period after optimization; it can be seen that the variation in the heat pump power consumption is very small and the variation trend in the total power consumption is mainly determined by the water pump power consumption. It can be seen from Figure 7 that 21:00-10:00 is the higher power consumption period of the system, which is mainly due to the larger flow rate of the buried pipe at this time, resulting in the higher power consumption of the pump on the ground source side. Overall, the system is designed to reduce operating costs by increasing the heat supply and storing heat during the valley electricity period as well as releasing heat during the peak electricity period, thereby shifting the power consumption of the system over time.
## 5 Performance Evaluation
### Experiment 1  
In this experiment, both flows have the same transmission rate. The rate of both flows varies from 280 kbps to 560 kbps with step of 20 kbps. Figure 7 shows the jitter performance of specific real-time flow, network throughput, average end-to-end delay and packet loss ratio due to both mechanisms _versus_ offered load.  
From Figure 7a, it is seen that BLJCAR can improve the jitter performance substantially as compared with the ONC policy because it considers the queue state of coding node in packet coding algorithm and could better optimize the trade-off of effect of packet waiting delay and network coding on jitter, resulting in improved long-term jitter benefits.  
From Figure 7b,d, it is observed that BLJCAR can improve the network throughput and packet loss rate as compared with the ONC policy because it delays packets to wait for the arrival of a future coding opportunity, which allows more data transmissions in each time unit.  
From Figure 7c, it also shows that BLJCAR scheme outperforms the ONC policy in average end-to-end delay because the effective queue management mechanism of BLJCAR can result in improved jitter benefits, the transmission competition is greatly reduced and the queuing delay of packets is reduced.  
Figure 7 shows that BLJCAR is suitable for the single rate case.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 7 Simulations and Results
### Vehicular Speed vs. V2P Capacity  
For total V2P throughput we have taken the SINR of each V2P link and calculated the sum from all V2P UE. Using the equation below:  
\[\sum_{\
u=1}^{\
u}\Bigl{[}\log_{2}(1+\gamma_{\
u})\Bigr{]} \tag{15}\]  
An increasing speed of vehicles lowers the throughput of the V2P links. Figure 4 below shows the performance of increasing speed. The very critical and critical pedestrians still get a higher capacity compared to a normal pedestrian. The benchmark technique used for comparison is the method proposed in [9], where the pair matching is done using Hungarian Algorithm and optimum power is found using bisection search. We have used the same method in our scenario and compared the result with our proposed method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Operating Frequency & 2 GHz \\ Bandwidth & 10 MHz \\ Cell Radius & 500 m \\ Vehicular Speed & 60–140 km/h \\ Discount Factor (n) & v.c = 0.8, c = 0.5, n = 0.3 \\ Maximum Transmit Power & 23 dBm \\ AWGN Noise & \(-\)114 dBm \\ SINR Thresholds (\(\gamma_{0}\)) & 5, 7, 10, 15 dBs \\ Number of Vehicular User Equipment (VUEs) & Variable \\ Number of Cellular User Equipment (CUEs) & Variable \\ Learning Rate (x) & 0.5 \\ D2D Communication Radius & 50 m \\ Highway Length & 1 km \\ Number of Lanes & 1 \\ Number of Resource Blocks & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 4: Vehicular Speed vs. V2P throughput.
## 3 IJA: Incremental Join Algorithm
### Incremental Join Algorithm Strategy  
Figure 2 shows the flow of IJA. The sink node in IJA is a node happened query and responsible for gathering the query results from each region similar to previous researches. The different point compared to the previous researches is that the result to be routed with each region is a kind of join results just participated in the query processing. For this, suppose that nodes in each region could know information of tables to be participating in join operations from query. Each node performs the local  
join operation based on the query information. For instance, the table \(R\) information in a query would join within \(S\) table existed in region S. In case of table \(S\), it is relatively in the opposite direction. Therefore, we can get the information to perform local joins at each region through a query. If there is no longer local join operation in a region from query, then no further operation is needed to rout and deliver to the final node. In addition, owing to processing a unit of query, the communication cost could be remarkably reduced.  
The steps for IJA are the following:  
1. Send an event pair of _R_(or _S_) to other part. Only send the pair to _R_egion_ S to make a semi table \(\mathrm{P_{R}}\) (or \(\mathrm{P_{S}}\)) at the counterpart.
2. Perform join operation at each region to produce a semi table. Send the semi table to reg\({}_{i}\)on F in case joining results exist.
3. Perform join operation with semi tables from R and S respectively. Send the join results to the query sink.
4. At the query sink node, the query can get the result within region F not to compute all of R and S computations.  
The incremental join algorithm's objective is to produce a smaller semi table by processing the join operation in the counter region because it needs to be decided whether the event pair is useful to join at the region or not. The rest of operations to process at the regions such as selecting a center location of the regions, routing protocol etc, is based on [7, 13]. The number of the join nodes at semi table join region is decided by \(\mathrm{P_{R}}\) and \(\mathrm{P_{S}}\) arrived at \(\mathrm{N_{H}}\) region. Therefore given memory m for a node, the node number at semi table region is as follows:  
\[\mathrm{|N_{H}|=(|P_{R}|+|P_{S}|)\,/\,m}\]  
The different point with other algorithms is just sending the pair whenever a pair is occurred with insert or update operation. To compute the communication cost, \(\mathrm{|N_{F}|}\) is as follows:  
\[\mathrm{|N_{F}|=(\begin{array}{cc}\sum&|C(R_{i})|+\sum&|C(S_{j})|\end{array} )\,/\,m}\]  
Figure 2: Incremental join algorithm.  
\[\mathrm{n_{i}}\in\mathrm{N_{R}}\qquad\mathrm{n_{j}}\in\mathrm{N_{S}}\]  
where \(|\mathrm{C(R_{i})}|\) is the number of join candidate pairs arrived from node \(\mathrm{n_{i}}\in\mathrm{N_{R}}\). \(|\mathrm{C(S_{j})}|\) is the number of join candidate pairs arrived from node \(\mathrm{n_{j}}\in\mathrm{N_{S}}\).
###### Abstract  
This work studied the effect of the injector spray angle (SA) on the combustion and emissions of a 4-stroke port-injection natural gas-diesel dual-fuel (NG-Diesel DF) marine engine to determine the optimal SA for the fuel injector, aiming to reduce exhaust gas emissions while keeping the engine performance. Three-dimensional (3D) simulations of the combustion process and emission formations occurring in the engine cylinder in both diesel and DF modes were carried out using the AVL FIRE R2018a code. The engine's in-cylinder temperature, pressure, and emission characteristics were analyzed. To clarify the effect of the injector SA on the combustion and emission characteristics of the engine, only the injector SA has been varied from 145 to 160\({}^{\circ}\). Meanwhile, all other boundary conditions for the simulations and operating conditions of the engine have remained unchanged. The simulation results have been compared and showed a good agreement with the engine experimental results. The study has successfully investigated the effects of the injector SA on the combustion and emission characteristics of the engine. A better SA for the fuel injector, to reduce the NO emissions (145\({}^{\circ}\)) or soot and CO\({}_{2}\) emissions (150\({}^{\circ}\)), while keeping the engine power almost unchanged, without the use of any exhaust gas post-treatment equipment, has also been suggested.  
spray angle; combustion; emission; NG-diesel engines; dual-fuel engine Article
## 5 Results  
### Preliminary Research  
The operational data of training helicopters from 2012-2019 were analyzed in terms of the utilization of the service potential of the entire aircraft fleet, level, and duration of specific maintenance, the occurrence of faults and failures, in particular where they were detected, how they were rectified and the time required to restore serviceability, the time taken to operate the equipment until failure (flight hours), and the waiting time for spare parts when a replacement was necessary. Based on operating/maintenance data, the following associations were analyzed: the stages in operation/maintenance at which failures were detected (Figure 1). It was noted that most defects are detected during the flight. This may indicate that the helicopter was affected during the flight, which is, unfortunately, the most dangerous situation.  
Figure 1: Percentage distribution of faults according to detection place.  
Summarizing the conducted analysis (Figures 1-4), it can be concluded that most defects were detected in flight or during ongoing maintenance, and, during preventive maintenance, only a fifth of all defects were detected. For 19% of all defects, the time to rectify them took more than 5 days, and, for a further 18%, the time took more than 30 days. In total, 50% of all faults were rectified by replacement with a new unit. For 28% of all faults rectified by replacing the unit with a new one (taken from stock), the waiting time for a spare part was more than 30 days. From these partial conclusions, one main conclusion arises: the main reason for the lack of adequate availability of helicopters for flight training is the lack of parts in stock. This observation prompted the exploration of methods to improve this situation.
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 2 Preliminaries and Proposed Method
### Framework  
The proposed framework is shown in Figure 2. Carrier images are images generated by the protected model. A fixed watermark is input into the frequency perturbation generation  
Figure 2: The diagram illustrates our proposed image watermarking framework, comprising a frequency generation network \(F\) and a watermark extraction network \(E\). In our research, we use carrier images (i.e., the image generated by the generating network to be protected) with dimensions of \(256\times 256\times 3\). The watermark is set to \(128\times 128\times 3\), and the size of the frequency perturbation generated by network \(F\) is also \(128\times 128\times 3\). Before adding this perturbation to the frequency domain of the carrier image, we must first pad it to match the dimensions of the carrier image, which are \(256\times 256\times 3\). We applied the FFT operation with a shift (centering on low frequency) to transform the image into the frequency domain, and subsequently used IFFT to return the frequency domain representation to the spatial domain.  
network \(F\), and we expect \(F\) to generate the frequency perturbation \(f\). As shown in Figure 2, we constrain the dimensions of \(f\) to \(128\times 128\times 3\) and add it to the low-frequency domain of the carrier image. To match the frequency domain of the carrier image, we apply padding to the frequency perturbation (the filled area is set to 0). To obtain the frequency domain of the carrier image, we employ the Fast Fourier Transform (FFT). We add \(f\) to the frequency domain and subsequently perform an Inverse Fast Fourier Transform (IFFT) to convert it back to the spatial domain, resulting in the marked image. Similarly, our objective is to make the marked image as similar as possible to the carrier image, subject to the constraints of the loss function, while minimizing the impact of the frequency perturbation on the marked image. Lastly, we input the marked images into a watermark extraction network \(E\), with the expectation that \(E\) learns how to extract watermarks from marked images. To prevent overfitting of the watermark extraction network \(E\), we provide it with some negative samples from which we aim to extract noise. The above goals will be achieved through joint training of the watermark extraction network and the frequency perturbation generation network.  
In our paper, we employ a Unet-like network structure [33] for the frequency perturbation generation network \(F\), with the input and output dimensions set to \(128\times 128\times 3\). For the watermark extraction network \(E\), we utilize a ResNet-like architecture similar to the one used in paper [34], with input and output dimensions of \(256\times 256\times 3\) and \(128\times 128\times 3\). To transform signals from the spatial domain to the frequency domain and vice versa, we employ fundamental signal analysis techniques, namely the FFT for the former and the IFFT for the latter.