esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The GCF, referred to simply as the contrast, is calculated before and after filtering. The contrast gain for all the images was calculated using Equation (4). \[\text{Contrast Gain}=\frac{\text{Avg Filtered Contrast }-\text{Avg Unfiltered Contrast}}{\text{Avg Unfiltered Contrast}}\ast 100\% \tag{4}\]  
The average luminosity and contrast gains for the seven samples are given in Table 2. It is observed that the average luminosity gain for the 3 filters is nearly identical at more than 50%, while the average contrast gain is more than 100%, according to the Matkovic metric [22]. The same findings are applicable to the remaining 53 samples. For the whole test suite, the average luminance before and after filtering are approximately 36 and 58, respectively, while the average contrast before and after filtering are about 0.4 and 1.13. However, since the Matkovic contrast is derived from the absolute difference, and its weightings are subjectively derived from the feedback of participants, it is fair to conclude that the contrast and luminosity of the images in our experiments were enhanced without paying too much attention to the numeric values. The approach was executed using an AMD 5900 HS processor on the MATLAB R2021b platform, and the mean execution time was nearly 2 s for all filters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Before Filtering** & \multicolumn{2}{c}{**After IDF**} & \multicolumn{2}{c}{**After AF**} & \multicolumn{2}{c}{**After MF**} \\ \hline  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline
1 & 69.1 & 23.2 & 128.4 & 15.1 & 128.3 & 15.8 & 128.4 & 13.45 \\ \hline
2 & 74.3 & 23.0 & 128.6 & 14.8 & 128.3 & 14.9 & 128.7 & 14.75 \\ \hline
3 & 109.6 & 15.2 & 128.6 & 16.4 & 128.5 & 16.8 & 128.7 & 16.21 \\ \hline
4 & 92.8 & 20.9 & 128.5 & 22.5 & 128.5 & 22.8 & 128.3 & 21.64 \\ \hline
5 & 70.9 & 18.3 & 128.4 & 17.9 & 128.2 & 18.3 & 128.7 & 17.07 \\ \hline
6 & 93.4 & 22.4 & 128.6 & 19.9 & 128.2 & 20.5 & 128.9 & 17.5 \\ \hline
7 & 68.9 & 35.1 & 128.7 & 15.6 & 128.4 & 15.7 & 128.6 & 15.5 \\ \hline Average & 82.71 & 22.6 & 128.54 & 17.46 & 128.34 & 17.83 & 128.61 & 16.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The means and standard deviations of some images before and after filtering.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 3 Experimental Verification
### Simulation with Specified Traffic
#### 3.2.1 Scenario I, Unexpected Braking in Current Lane  
In this scenario, the front vehicle in current lane \(V_{sF}\) take an emergency braking at the same time of lane change. The deceleration of brake is selected as \(-2\) m/s\({}^{2}\), \(-3\) m/s\({}^{2}\) and \(-4\) m/s\({}^{2}\) respectively, and the duration of brake is 3 s. Depending on the lane change algorithm, \(V_{H}\) can choose to increase speed with the potential danger of colliding with \(V_{sF}\) or \(V_{tR}\), or abandon the current motion and return to the original lane. Simulation results by using different methods A, B and C are compared in Table 4. The check mark with a tick means a successful lane change, a circle means the vehicle abandons the lane change maneuver, and a cross means a collision with other vehicles.  
A detailed demonstration with \(-4\) m/s\({}^{2}\) emergency braking is shown in Figure 7. In the graphs depicting the velocity and acceleration of host vehicle, the dark shades represent the areas within strict parameter constraints, and the light shades represent those within loose constraints. It can be observed that apart from the longitudinal velocity, other values rarely exceed the strict constraints during simulation, and all values stay within the loose constraints. For the last graph, the upper and lower boundaries of the shaded area obtained by the length of front vehicle and host vehicle, which means any curve that come in contact with the shaded area indicate a collision.  
From the results of simulation C in Figure 7, \(V_{H}\) collides with \(V_{sF}\) at \(t=2.8\) s. In the simulation A and B, \(V_{H}\) abandons the lane change maneuver and switches back to \(L_{1}\) with deceleration. Compared with the results of B, the simulation A has smaller deceleration, faster response to unexpected event and longer safe distance to front vehicle.
## 3 Photoelectric Scene Design and Verification
### Concrete Equivalent Exposure Scene Design and Validation  
#### 3.2.1 Equivalent Exposure Scene Design  
According to Equation (7), the product of \(\mathrm{L_{in}}\) and s can serve as the equivalent exposure of the field environment, and equivalent exposure can be controlled by adjusting both ambient illuminance and camera exposure time. Twenty-one scenes of equivalent exposure were designed, as shown in Table 2. In Table 2, in the exposure time control scenes (EC), the illuminance remained constant (100 k), and the camera exposure time was set from 100 ms to 2500 ms every 120 ms. In the illumination control scenes (IC), the exposure time remained constant (200 ms), and the ambient illumination varied from 50 k to 1250 lx by increments of 60 lx.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scene**} & \multicolumn{2}{c}{**Exposure Time Control**} & \multicolumn{2}{c}{**Illumination Control**} & \multirow{2}{*}{**Equivalent**} \\ \cline{2-2} \cline{4-6}  & \multirow{2}{*}{**Illuminance/lx**} & **Exposure** & **Exposure** & **Illuminance** & **Illuminance** \\  & & **Time/ms** & **Time/ms** & **Designed/lx** & **\&**de Facto/lx** \\ \hline
1 & & 100 & & 50 & 50.3 & 10 \\
2 & & 220 & & 110 & 110.8 & 22 \\
3 & & 340 & & 170 & 170.3 & 34 \\
4 & & 460 & & 230 & 229.4 & 46 \\
5 & & 580 & & 290 & 207.5 &
### System Planning and Configuration  
This section outlines the principles for planning and configuring PEDF systems by analyzing different application scenarios. The key principles include energy matching, flexibility, economic, low-carbon emissions, and reliability. A dual-layer model for planning and operating is proposed to assist in configuring the system's topology, load, photovoltaic components, energy storage, and critical interactive devices.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Synchronization and Doppler Compensation  
The proposed UWA OTFS system employs a linear frequency modulation (LFM) signal for preamble detection and synchronization. However, relying solely on this preamble is inadequate for a UWA OTFS system designed for long delay spreads and significant Doppler effects, due to the requirement that the channel be underspread for OTFS to operate effectively. Therefore, a Doppler compensation method is proposed [29; 30].  
First, we resample the received signal as \(y(t)=r(\frac{t}{1+\delta})\), where \(\hat{a}\) is the general Doppler scaler:  
\[y(t)=\sum_{i}^{p}h^{\prime}e^{j2\pi\frac{cf_{i}}{1+\delta}}s(\frac{t-\tau^{ \prime}_{i}}{1+\hat{a}})e^{j2\pi v^{\prime}_{i}(t-\tau^{\prime}_{i})}+\omega( \frac{t}{1+\hat{a}}), \tag{11}\]  
where \(v^{\prime}_{i}=\frac{v_{i}}{1+\hat{a}}\) and \(\tau^{\prime}_{i}=\tau_{i}(1+\hat{a})\).  
Second, we multiply \(y(t)\) by \(e^{-j2\pi\hat{t}}\) to remove the CFO, where \(\hat{e}\) is the residual Doppler shift.  
We estimate \(\hat{a}\) and \(\hat{e}\) according to [31]. Through Doppler compensation, the influence of residual Doppler is significantly reduced, allowing the underspread channel assumption of Equation (6) to be satisfied.
## 3 Results and Discussions
### Metal Ion Extraction Performance in the Homologous Series of JNPs
Judging purely by the number of ligands a particle can carry, the metal ion uptake capacity for each technology is also expected to decrease with the decrease in ligand carrying capacity of the particulate material, in the following order: IIPs > core-shell IIPs surface immobilized ligands > surface immobilized ligands and ligand brushes > ion-exchangers and biomasses. For example, the reported ion uptake capacity by JNPs for Zn(II), 4 mg/g, see Figure 4, is higher than the non-chelating physical adsorbing biomasses, whereas the reported maximum ion uptake capacity of biomasses by biosorption of Zn(II) was found to be around 1.688 mg/g for eucalyptus bark, 1.028 mg/g for mango bark and 0.45 mg/g for pineapple fruit peel at a particle size of 0.5 mm [21]. On the other hand, it is expected that the ion-imprinted polymer nanoparticles (IIPs-NPs) offer the best ion uptake capacity, whereas, for the Co(II) for example, the maximum loading capacities reported ranged between 78.3 to 96.6 mg/g by core-shell nanoparticles obtained by constructing an IIP shell of Co-polyacrylamide amide onto SiO\({}_{2}\) nanoparticles [22] and 74 mg/g by an IIP shell of Co-dithizone/poly(methacrylic)acid [23] onto magnetic Fe\({}_{3}\)O\({}_{4}\) core nanoparticles. On the other hand composite chelating IIPs-NPs constituted by a shell of polyamidoxime and a silica core PAO/SiO\({}_{2}\), showed a maximum Cu(II) ion uptake of 10 mg/g [24], which is comparable to the Cu(II) ion uptake by JNP-bPEI of 6 mg/g, see Figure 4. Even so, the Ni(II) ion absorption capacity of an ion-imprinted polymer (IIP) obtained by copolymerization of a Ni-dithizone complex with 4-vinylpyridine and ethyleneglycoldimetheacrylate (EGDMA) is 1.3 mg/g [25] The value found for Ni(II) for JNP-bPEI in this study is higher by a factor 1.5, see Figure 4. [PERSON] et al. [20] have reported ion extraction capacities between 20 and 40 mg/g for Cu(II), Co(II), Ni(II) and Cr(VI) for polystyrene nanoparticles modified as in the current case with a bPEI ligand brush or corona Thus, it can be concluded that the JNP-bPEI exhibit a good ion extraction or ion-uptake capacity, comparable to the extraction capacity of other ligand carrying micro- and nanoparticles obtained with other technologies, in the expected performance range.
# Combined Investigation of Indoor Climate Parameters and Energy Performance of a Winery
## 3 Results
### Energy Analysis  
#### 3.2.1 Present Situation  
In Figure 5, one may see the monthly consumption of energy according to the model, as well as according to the energy bills. The agreement is quite satisfactory, given the discussion in Section 2.3 for the related uncertainties and modeling difficulties; the deviation  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{4}{c}{**Winemaking (3/10/2019)**} & \multicolumn{4}{c}{**Non-Winemaking (13/12/2018)**} \\ \cline{2-9}  & _Office_ & _Vinification Area_ & _Storage_ & _Out_ & _Office_ & _Vinification Area_ & _Storage_ & _Out_ \\ \hline Benzene & 0.46 & 1.76 & 0.81 & 0.46 & 27.51 & 16.23 & 12.49 & 3.89 \\ \hline Toluene & 0.77 & 1.20 & 3.28 & 0.52 & 32.42 & 21.93 & 13.20 & 3.37 \\ \hline Octane & ND & ND & ND & 0.08 & 1.07 & 1.14 & 0.79 & 0.25 \\ \hline Ethylbenzene & 0.19 & 0.10 & 0.96 & 0.06 & 2.16 & 1.14 & 0.60 & 0.21 \\ \hline p,m-xylene & 0.52 & 0.33 & 3.24 & 0.20 & 6.67 & 3.92 & 2.16 & 0.55 \\ \hline o-xylene & 0.24 & 0.16 & 1.73 & 0.09 & 3.57 & 2.11 & 1.15 & 0.32 \\ \hline a-pinene & 1.13 & 0.47 & 6.85 & ND & 5.40 & 1.22 & 9.49 & 0.03 \\ \hline l,2,4-trimethylbenzene & 0.22 & 0.14 & 0.82 & 0.10 & 1.81 & 1.57 & 0.47 & 0.18 \\ \hline d-limonene & 0.22 & 0.06 & 2.88 & ND & 273.76 & 7.38 & 7.32 & 0.38 \\ \hline Naphthalene & 0.06 & 0.04 & 0.06 & 0.01 & 0.47 & 0.22 & 0.12 & 0.12 \\ \hline Hexane & 0.25 & 0.52 & 0.47 & 0.20 & 14.19 & 15.41 & 6.31 & 6.94 \\ \hline Trichloroethylene & ND & ND & ND & ND & ND & ND & 0.15 \\ \hline Tetrachloroethylene & ND & ND & 0.01 & ND & ND & ND & 0.07 \\ \hline Styrene & 0.10 & 0.05 & 0.69 & 0.02 & 0.94 & 0.44 & 0.80 & 0.25 \\ \hline b-pinene & ND & ND & ND & ND & 0.37 & ND & ND & ND \\ \hline
3-carene & 0.46 & 0.33 & 3.36 & 0.04 & 9.99 & 1.98 & 10.98 & 0.41 \\ \hline l,3,5-trimethylbenzene & 0.09 & 0.07 & 0.43 & 0.04 & 3.53 & 1.85 & 0.54 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 7: VOC concentrations (in \(\upmu\)g/m\({}^{3}\)) for the different winery rooms and period.  
on annual level being 16.1%. Estimated consumed energy reaches 0.42 kWh/L, which is in agreement to the values proposed in [5] for the English wineries in average; it should be noted though that in [6], significantly lower values are proposed through the case study of an Italian winery.  
In Figure 6a, the share of the main processes to the total energy consumption is presented on monthly basis; Figure 6b presents the annual share. It is evident that the cooling and refrigeration processes, together with heating, constitute the most important share, in agreement with the findings of the existing literature [5,6].
## 2 Description of the Proposed Method
### Optimazation Problem to Determine Operating Points
#### 2.1.2 Optimization Formulation and Solving Method  
The vectors of the phase angle and voltage magnitude are represented as \(\theta\) and \(\mathbf{V}\), respectively. The output references of VSC and LCC HVDCs can be represented by vector form, as \(\mathbf{P_{VSC}}\), \(\mathbf{Q_{VSC}}\), and \(\mathbf{P_{LCC}}\). Note that the reactive power of LCC HVDC, \(\mathbf{Q_{LCC}}\), can be represented in terms of \(\mathbf{P_{LCC}}\) and \(\mathbf{V}\) using Equations (2)-(5), so it is not necessary to include \(\mathbf{Q_{LCC}}\) with the unknown vector. Therefore, the unknown vector, \(\mathbf{X}\), can be defined as:  
\[\mathbf{X}=\Big{[}\;\;\mathbf{P_{VSC}^{T}}\;\;\;\mathbf{Q_{VSC}^{T}}\;\;\; \mathbf{P_{LCC}^{T}}\;\;\;\mathbf{\theta^{T}}\;\;\;\mathbf{\Psi^{T}}\;\;\Big{]} ^{T}. \tag{10}\]  
The purpose of the proposed problem is to minimize the system loss. Since conversion loss is not included in MIDC systems, only the line loss of AC systems is considered in the objective function. The objective function of the optimization is represented as:  
\[\text{minimize}\;f=\sum_{j=n}^{N}\{P_{GEN}(n)+P_{VSC}(n)+P_{LCC}(n)-P_{LOAD}(n)\} \tag{11}\]  
where \(N\) is the number of buses. Note that the difference between the active power injected to the network, \(P_{GEN}\), \(P_{VSC}\), \(P_{LCC}\), and \(P_{LOAD}\) is the same as the system loss. Only LCC and VSC HVDCs are considered as frequency-supporting resources in this paper, with the assumption that conventional generators do not participate in frequency support. However, conventional generators can be considered in the optimization problem with a little modification, because the generators can be represented as active and reactive power sources similar to the VSC HVDC system. Additionally, the security constraint of the conventional generators can be represented by simple inequality of active and reactive power [33].  
To solve the optimization problem, an interior-point method is used [34; 35]. For the proposed optimization problem, an interior-point method cannot guarantee global optimality. However, as the solution found by the method is better than a trivial solution, the interior-point method can be successfully applied to the proposed optimization problem.
## 2 Materials and Methods
### Water Supply Mode  
The urban water supply network includes both intermittent and continuous supply, while agricultural water supply encompasses rotation irrigation, supplementary irrigation, and random supply. By combining urban and agricultural water supply systems, three irrigation water supply modes for agricultural pipeline networks are proposed [28].  
* Segmented water supply  
In this mode, the pipeline network is divided into multiple independent sub-regions, each supplied with centralized water from separate pump stations or water sources. This mode is suitable for areas with significant elevation differences that require zoned water distribution. Each group is supplied with centralized water at designated time intervals. During the irrigation season, the entire pipeline network is operated with continuous water supply through the main pipelines, while water supply to the sub-main pipelines and branch pipes alternates, with control managed by the branch valves. The corresponding flow calculation formula for the branch valves is as follows:  
\[Q_{i}=\frac{10 mA}{3600\eta Tt}, \tag{1}\]  
Figure 1: Schematic diagram of irrigation pipe network.  
where \(Q_{i}\) is the design flow rate of the \(i\)-th branch pipe, m\({}^{3}\)/s; \(m\) is the irrigation quota, mm; \(A\) is the irrigation area of the \(i\)-th branch pipe, ha; \(T\) is the irrigation cycle, d; \(t\) is the daily working time, h; \(\eta\) is the irrigation water utilization coefficient.
* Uniform water supply. During peak water usage periods, when the originally designed segmented water supply system fails to meet production demands, a uniform water supply mode is implemented to address issues such as water and time competition. In this mode, the water distribution is evenly allocated to each node based on demand through a balanced network design. This mode is suitable for areas with stable demand distribution and flat terrain. The design flow rate of the branch valve in the uniform water supply mode is still calculated according to Formula (1).
* Random water supply. In the random water supply mode, water distribution is adjusted in real-time or on an unplanned basis, lacking systematic planning. This mode is suitable for emergency situations or scenarios with significant demand fluctuations (e.g., disaster relief). In the irrigation network, the opening and closing of each outlet in the pipeline system are independent of the operational status of other outlets and are not constrained by time or location. The design flow rate \(Q\) at each branch pipe is calculated using the First Clement formula [29; 30]. \[Q=p_{i}{\sum_{k=1}^{n}Q_{i}}+U{\sqrt{p_{i}(1-p_{i})}}{\sqrt{{\sum_{k=1}^{n}( Q_{i})}^{2}}},\] (2) where \(Q_{i}\) is the water flow rate of the \(i\)-th branch valve; \(N\) is the number of nodes downstream of the pipeline; and \(U\) is the percentile related to the normal distribution function. When the number of nodes \(n>100\), the guarantee rate of irrigation water during peak periods is 90%, and \(U\) is taken as 1.282; \(p_{i}\) is the probability of opening the \(i\)-th water intake.
## 5 Conclusions and Future Work  
In this paper, a path model for mobile anchor node aided localization in Wireless Air Pollution Monitoring System (WAPMS) named the Arrow-Curve path model was proposed. The model guarantees full coverage area. It achieves high position accuracy results. It has a small path length and consumes low power and can handle obstacles met in its way. The experimental results of the model in both simulation environment and realtime environment are compared to the H-Curve model. The results of simulation environment are more ideal than the realtime environment as it is an environment with zero noise effects. Arrow-Curve achieves average position error results lower than H-Curve by 10.6% in the simulation environment. The Arrow-Curve achieves an average position error lower than H-Curve by 5% in outdoor and 10% in the realtime environment, and the proposed model has higher efficiency in terms of average position error, standard deviation of position error, position coverage ratio, path length, number of pausing points, and power or energy consumption. It achieves high results in indoor environment with high obstacles compared to outdoor environment because of the nature of the path, in which it moves around any obstacle to overcome the obstacle problem. The model can be used in many applications, such as in healthcare and any indoor applications. In future  
Figure 28: Energy dissipated (\(\upmu\)J) corresponding to resolution (L) in both one mobile anchor node and fixed anchor nodes: (a) Arrow-Curve path model; (b) R-Curve path model.  
Figure 27: Energy dissipated (\(\upmu\)J) corresponding to resolution (L) in both simulation and real environment: (a) Arrow-Curve path model; (b) R-Curve path model.  
work, to reduce power dissipated, we plan to use different mobility techniques and find the optimal number of mobile anchor nodes for more scalability and program each of them to work at a period of time and cause the others to sleep to reduce power consumption, and we can also use other wireless sensor nodes that allow other wireless communication protocols that consume less power, such as the Zigbee wireless technology protocol.  
[PERSON] performed the experiments, designed the methodology, participated in conceptualization, software implementation, hardware testing, formal analysis, and writing the original draft. [PERSON] contributed to conceptualization, supervision, writing, reviewing, and editing. [PERSON]-K. contributed to conceptualization, supervision, writing, reviewing, and editing. [PERSON] contributed to supervision, writing, reviewing, and editing. [PERSON] contributed to supervision, writing, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Image Analysis  
All TNC and VNC images were reviewed by two radiologists with 12 (CF) and 9 (GA) years of experience in CTA on PACS workstations (Picture Archiving and Communication System; Centricity Radiology, GE Healthcare, Milwaukee). Only axial TNC and VNC images were assessed for qualitative and quantitative analysis.  
Quantitative Analysis. The comparison of attenuation and noise between TNC and VNC images was made by drawing circular regions of interest (ROI) on both dataset images at the same level.  
ROIs of approximately 0.5 cm\({}^{2}\), as large as possible for vessel caliber and avoiding vessel wall, vessel defects, plaques, and stent materials at five arterial segments, were drawn.  
ROIs were positioned within the infra-renal abdominal aorta, common iliac artery, external iliac artery, femoral artery, and popliteal artery lumens.  
Mean attenuation values in Hounsfield units (HU) and the corresponding standard deviation (SD) were measured as shown in Figure 2. Measurements were performed using transverse standard and MPR images and repeated three times to minimize sampling inaccuracies related to breath or movement artefacts.  
Qualitative Analysis. The two radiologists, blinded to clinical information and previous radiology reports of the examined patients, independently evaluated the quality of the CT images. The qualitative analysis was performed by using a 4-point Likert scale (\(4=\text{excellent},3=\text{good},2=\text{moderate but sufficient for diagnosis, and 1 = non-diagnostic) and included overall image quality, signs of residual contrast medium, possible subtracted calcifications, and stent structures.  
The scores obtained with the two protocols were then compared and the inter-observer agreement was assessed, too. The specific artefacts were scored as yes/no.  
Figure 2: Arterial phase, true non-contrast (TNC) and virtual non-contrast (VNC) of dual-energy CTA run-off acquisition. In both TNC and VNC images, a circular region of interest (ROI) is placed at the level of the abdominal aorta, common iliac artery, external iliac artery, superficial femoral artery, and popliteal artery, avoiding vessel walls and plaques.