esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamentals of Aerial Wireless Sensor Networks for Oceanographic Monitoring.
### Oceanographic Sensors  
There are many types of sensors for monitoring oceanographic parameters (physical, chemical and biological). The right choice of sensor depends on the requirements defined by the user and the requirements imposed by the characteristics of the area where they are to be deployed. These requirements include the measurement range within which the parameter is to be measured, the place where the sensor is to be deployed, sensitivity, linearity, accuracy, precision, resolution, measurement rate, power consumption and deployment time. The parameters most commonly measured in a marine environment and the measurement units used are shown in Table 2. In addition, depending on what sensor is used, it is essential to consider its position within the node and the depth at which it will be working. For example, to determine the temperature profile of a water column several sensors will have to be placed at different depths on the same vertical line. Figure 3 shows four commercial oceanographic sensors.  
\begin{table}
\begin{tabular}{c c} \hline
**Measured Parameter** & **Unit** \\ _Temperature_ & \({}^{\circ}\)C, \({}^{\circ}\)F \\ _Pressure_ & mmHg \\ _Salinity (Conductivity)_ & g/L \\ _Water speed_ & m/s \\  & FTU (Formazin Turbidity Unit) \\ _Turbidity_ & NTU (Nephelometric Turbidity Units) \\  & JTU (Jackson Turbidity Unit) \\  & mg/L SiO\({}_{2}\) \\ _Chlorophyll_ & g/L \\ \hline \end{tabular}
\end{table}
Table 2: Common oceanographic sensors.  
On the other hand, the sensor node may be equipped with surface sensors (Table 3), which are normally used to determine the state of the water surface or the atmosphere. These conditions may be important when setting up a sampling strategy. For example, in the event of bad atmospheric conditions the sensor node may decide to raise the sampling frequency to assure more precise monitoring of the environment.  
\begin{table}
\begin{tabular}{c c} _Dissolved oxygen_ & mg/L \\ _Nitrate_ & mg/L \\ _pH_ & pKa \\ _Swell_ & Height: (metres) \\ _Blue-Green Algae Phycocyanin_ & Direction:(degrees) \\ _Ammonium/ammonia_ & Relative Fluorescence Units \\ _Chloride_ & mg/L \\ _Rhodamine_ & \(\mu\)g/L \\ _Hydrocarbons_ & ppm \\ \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Examples of commercial sensors for oceanographic use (photographs reproduced with the owners’ permission): (a) EMS-SBE—16 plus V2 SEACAT Temperature and conductivity (pressure optional) sensors; (b) NORTEK—Profiler Aquadopp (AquaPro); (c) YSI 6600V2 sonde. YSI 6136 and YSI 6025 turbidity and chlorophyll sensors; (d) AANDERAA—Oxygen Opdote 4835. Dissolved oxygen sensor.
## 4 Conclusions  
In conclusion, we fabricated reduced graphene oxide (rGO) co-catalyst on the silicon nanowire (SiNW) photocathode system. The surface nanostructuring of the SiNW photocathode showed an enhanced property for hydrogen evolution reaction (HER). Moreover, deposition of rGO co-catalyst on the SiNW showed 158 times higher conversion efficiency than that of bare planar Si without any co-catalyst and also indicated 3.47 times higher efficiency than bare SiNW, respectively. As a result, rGO displayed remarkable catalytic activity, meant for photoelectrochemical HER on SiNW photocathodes. We believe that the optimization of interface engineering between the co-catalyst and the photoelectrode in this study will pave the way to realize metal-free carbon-based catalysts with high efficacy for effectual solar-driven hydrogen fuel production.  
Conceptualization, U.S. and J.M.; Methodology, U.S.; Validation, U.S. and J.M.; Formal Analysis, Y.S.; Investigation, U.S.; Resources, U.S.; Data Curation, U.S.; Writing-Original Draft Preparation, Y.S. and J.J.; Writing--Review & Editing, J.J., U.S., and J.M.; Visualization, Y.S.; Supervision and Project Administration, J.M. and U.S.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF-2017M3A7B4049176, 2018R1C1B6001267, and 2018R1A5A1025224).  
Following is result of a study on the \"Leaders in INdustry-university Cooperation+\" Project, supported by the Ministry of Education and National Research Foundation of Korea.  
The authors declare no conflicts of interest.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Network Model  
To effectively design and evaluate our model's essential constituents, we need to evaluate the network prerequisites in terms of handling communication latency. This is crucial for Algorithm 2, where a _resolve module_ is triggered to hosts with anomalous connections as part of M0-4. In this section, we abstractly describe our consideration of a dynamic _step model_[48] network simulation approach using the network state we derived through the process in Sections 3.2 and 4.1. We place fixed latency estimation values used to consider routing decision time as routing delay (\(R_{d}\)), router passing cycle-time as switch delay (\(S_{d}\)), link passing cycle-time as link propagation delay (\(L_{d}\)), and link bandwidth (\(L_{b}\)).  
All packets sent at time interval \(\Delta t\) use a modeled average latency \(L\) or fixed network parameters. For different network parameters, the implementer would need to recompute \(L\) and cover expanded areas, such as routing algorithms or different policies. Applying the queue and regression models from [49], we quantify source-destination packet behavior in our model network and describe the _resolve module_ latency to a selected destination host as follows:  
\[L_{i}=f(P)+\xi(D) \tag{2}\]  
where \(f(P)\) represents the minimal source-destination path physical latency accumulated by a particular packet. We assume \(f(P)\) is given only by network constraints, e.g., the distance between hosts, meaning that there is no resource contention among packets. \(\xi(D)\) represents the dynamic contention interaction of packets given network constraints, e.g., link bandwidth.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack Label** & **Attack Types** & **Instance Count** & **Total Instances** \\ \hline \multirow{8}{*}{DoS} & Neptune & 101,201 & \\ \cline{2-4}  & Back & 2203 & \\ \cline{2-4}  & teardrop & 979 & \\ \cline{2-4}  & Apache2 & 737 & 386,203 \\ \cline{2-4}  & Mailbomb & 293 & \\ \cline{2-4}  & Smurf & 280,790 & \\ \cline{2-4}  & IPsweep & 1247 & \\ \hline \multirow{4}{*}{Probe} & Nmap & 231 & \\ \cline{2-4}  & Mscan & 996 & 3514 \\ \cline{1-1} \cline{2-4}  & Portsweep & 1040 & \\ \hline \multicolumn{4}{c}{Grand Total} & 389,717 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected KDDCUP’99 dataset [39].  
```
0: All hosts \(S\), time-intervals \(t\)
0: Total Cyberattack Effect (TCE%)
1: Start Timer
2:for\(i\gets 1,t\)do
3:for\(j\gets 1,S\)do\(\triangleright\) hosts ordered by IP address
4:if\(\texttt{Timer}>t_{i}\)then\(\triangleright\) Check in intervals
5:break;
6:endif
7:if Solve (14), then
8:\(DefenseModule\) (Solve (2))\(\triangleright\) Section 4.8
9:\(\forall j_{(TCP)}\leftarrow\) Solve (21) Color Blue;
10:\(\forall j_{(TCP)}\leftarrow\) LineWidthSize Single;
11: Solve (16)
12: TCE[i] ++= Solve (17)
13:endif
14:endfor
15:endfor
16:Stop Timer
17:return (TCE)
```  
**Algorithm 2** Visualization of defense effects (\(S,t\))
## 2 Light Color Spaces
### Color Space CIE1931  
We consider the CIE1931 color space because of its simplicity. The tristimulus values (X, Y, and Z) represent the three primary color quantities of the three-component additive color model needed to produce a target color [19].  
\[X=\int S(\lambda)\;\overline{x}(\lambda)d\lambda,\;Y=\int S(\lambda)\; \overline{y}(\lambda)d\lambda,\;Z=\int S(\lambda)\;\overline{z}(\lambda)d\lambda \tag{1}\]  
In (1), \(\lambda\) indicates the wavelength (nanometers) of the monochromatic light for each corresponding primary color. \(\overline{x}(\lambda)\), \(\overline{y}(\lambda)\), and \(\overline{z}(\lambda)\) are the three color matching functions for the CIE1931 color space, and \(S(\lambda)\) is the spectral power distribution of a light source. The three normalized tristimulus values can be determined by  
\[x=\frac{X}{X+Y+Z},\;y=\frac{Y}{X+Y+Z},\;z=\frac{Z}{X+Y+Z} \tag{2}\] \[x+y+z=1 \tag{3}\]  
Using these normalized values, a CIE1931 color space chromaticity diagram can be produced [19]. The xy-plane of a chromaticity diagram is shown in Figure 2. The CIE1931 chromaticity diagram is not perceptually uniform since it is derived from the human-eye response function.  
To determine the color triple (X, Y, Z) from the (x,y) coordinates of the CIE1931 color space, information for Y (luminance) must be known. We can calculate the tristimulus values using (2) and (3) [19].  
\[z=1-x-y,\;X=\frac{x}{y}Y,\;Z=\frac{z}{y}Y \tag{4}\]  
The (x, y) coordinates represent the colors in the CIE1931 color space. The colors can also be represented by the proportion of R, G, and B in the RGB color space; the wavelength of a monochromatic light source in these three channels is 700 nm, 546 nm, and 436 nm, respectively. The XYZ and RGB coordinate systems have a linear relationship with each other, thus one can be transformed into the other [19].  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=\frac{1}{0.17697}\left[\begin{array}{ccc}0.4900&0.3100&0. 2000\\ 0.17697&0.81240&0.01063\\ 0.0000&0.0100&0.9900\end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right] \tag{5}\]  
\[\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{ccc}0.4185&-0.1587&-0.0828\\ -0.0812&0.2524&0.0157\\ 0.0009&-0.0025&0.1786\end{array}\right]\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right] \tag{6}\]
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Silver Sulfide Films  
Nanostructured n-type silver (I) sulfide (Ag\({}_{2}\)S) films were deposited onto fluorine-doped tin oxide (FTO)-coated glass [14]. Photoelectrochemical studies revealed that high series resistance caused a very low value of power conversion efficiency. Nanoscale highly-conducting Ag\({}_{2}\)S films with thicknesses of 299 nm were produced under specific conditions, such as immersion time = 25 s, deposition cycle = 50 cycles, and rinse time = 15 s [15]. The obtained films were homogeneous and polycrystalline with the most intense peaks along (100) the planes based on the field emission sacking electron microscopy (FESEM) and the X-ray diffraction (XRD) analysis. The energy dispersive X-ray analysis (EDX) spectra confirmed silver richer in these films. The films (thickness = 0.22 \(\mu\)m) were synthesized onto amorphous glass at 27 \({}^{\circ}\)C by using thiourea (SC(NH\({}_{2}\))\({}_{2}\)) and silver (I) nitrate (AgNO\({}_{3}\)), which were annealed under various temperatures. The XRD data supported the recrystallization process, causing bigger grain sizes at higher annealing temperatures. Electrical resistivity (2.58 to 1.99 \(\times\) 10\({}^{4}\)\(\Omega\cdot\)cm) and the band gap (1.07 eV to 1 eV) were reduced with increasing temperatures (from 373 to 573 K) [16]. The deposition of Ag\({}_{2}\)S (silver sulfide) films took place on glass slides by using a complexing agent (ammonia (NH\({}_{3}\))) at 40 \({}^{\circ}\)C [17]. Films showed monoclinic phases and preferred orientations corresponding to the (120) plane. Improvements in crystallinity and the red shift in absorption edge could be observed when the deposition cycle increased from 20 to 50 cycles. Thiourea and silver nitrate were used to produce thin films under 30 deposition cycles, at 27 \({}^{\circ}\)C; the rinse time was 10 s, and the immersion time was 15 s [18]. The film thickness and crystallite size were 135 and 21.38 nm, respectively. In the optical studies, the absorption edge was observed at 620 nm, showing a band gap of 2.09 eV. Other researchers have highlighted that these films could be used in sensitized solar cells due to the non-toxic materials [19], excellent short circuit currents, and compatibility with the polysulfide electrolyte [20].
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking
#### 3.2.4 Point of Interest Recognition  
After acquiring the most salient object in the previous step, the next task is to determine whether this object is a POI and, if so, recognize the POI in question. To achieve this, the bounding box derived from the segmentation mask is used to crop the image, focusing on the most salient object. This cropped image serves as the input for the POI recognition process.  
As discussed in Section 3.1, a POI reference database is constructed, containing one or more reference images for each POI. These images are not used for training or fine-tuning a machine learning model, as the goal is to maintain a flexible and easily extensible methodology that can handle new POIs and races without requiring retraining. Instead, a keypoint matching approach is employed, enabling training-free recognition of POIs. This matching procedure consists of several steps.  
First, the reference images of POIs within a 500 m radius of the helicopter's GPS position are retrieved. This saves computational resources and ensures that the matching process focuses on relevant nearby POIs. Both the cropped image of the most salient object and the retrieved reference images are then resized to a width of 300 pixels. This size strikes a balance between maintaining a high-enough resolution for accurate feature point calculation and achieving efficient processing speeds.  
Next, the keypoints and descriptors of the resized images are determined using the SuperPoint algorithm [40]. SuperPoint is a fully convolutional neural network that extracts points and descriptors in a single forward pass, offering both speed and superior performance compared with traditional keypoint detectors and descriptors, such as SIFT. To optimize future iterations of the pipeline, the keypoint information of the reference images is automatically cached and loaded.  
Finally, the SuperPoint descriptors of the salient object's crop and reference images are matched using SuperGlue [16], a graph neural network designed to find correspondences between two sets of sparse image features. Figure 5 illustrates the keypoints and matches between the salient object crop and a reference image. Each point match is assigned a confidence score; these scores are summed to obtain a total match score per reference image. The reference image with the highest total confidence, exceeding a threshold of 10, is selected as the matched POI. If the highest total confidence falls below 10, no match is found, and the pipeline is exited, waiting for the next iteration, as shown in Figure 5.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## 5 Conclusions  
In this paper, we studied the outage performance of vehicles equipped with multiple antennas in NOMA-V2X networks over Nakagami-m fading channels. We derived closed-form expressions for outage probabilities of vehicles considering two different SIC operations, i.e., pSIC and ipSIC. Based on this result, we investigated the impacts of various design parameters to the outage performance of the NOMA-V2X system in various aspects. This investigation provides the theoretical guidelines for the actual design of the NOMA-V2X communication system. Analyses and simulations demonstrate that NOMA-V2X can achieve significant outage performance improvement with a high fading parameter, a high number of antennas, and a perfect SIC operation. In addition, we found that the limiting impact of an imperfect SIC can keep outage performance at an acceptable level in the real applications of NOMA-V2X system.  
Conceptualization, D.-T.D., T.-T.T.N., C.-B.L. and J.W.L.; methodology, D.-T.D. and T.-T.T.N.; formal analysis, C.-B.L. and J.W.L.; writing-original draft preparation, D.-T.D.; writing-review and editing, J.W.L.; funding acquisition, J.W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2019R1F1A1042599).  
The authors declare no conflict of interest.
## 3 Results  
### Analysis of the Average Emission Factor  
The duration curves for the two scenarios shown in Figure 5 illustrate the wide range of the developed AEFs. The median of the annual AEF\({}_{\text{fc}}\) decreases continuously due to the forced phase-out of coal combustion in both scenarios. The coal phase-out is clearly shown by a jump in the duration curves from 2035 onwards. Merely in the BAU scenario, the AEF\({}_{\text{fc}}\) decreases only insignificantly from 2040 on. Caused by the phase-out of bioenergy, despite meeting the emissions cap, the median even rises slightly to 2045. The spread of the values and also the peak AEF\({}_{\text{fc}}\) remain constantly at a high level and only reach lower values in 2050 in the scenario CAP, where the highest AEF\({}_{\text{fc}}\) is then 100 gCO2 eq/kWhel. The AEF\({}_{\text{fc}}\) can drop much further in CAP and reach almost 0 gCO2 eq/kWhel, because the expansion of RES and electricity storage massively increases. Due to the applied calculation method of GHG emissions from electricity storage and must-run restrictions, reaching exactly 0 gCO2 eq/kWhel in these scenarios is almost impossible. Furthermore, for imports from the EU for the years 2045 and 2050, in some hours the power is already being produced exclusively emission-free. Overall, in 2050, CAP in 3709 h and BAU in 660 h will fall below 10 gCO2 eq/kWhel.  
Figure 5: (**a**) Distribution of the AEF\({}_{\text{fc}}\) in 7 milestone-years in the two scenarios BAU and CAP shown in boxplots. Whisker length \(w=1.5\); \(q_{3}+w\cdot(q_{3}-q_{1})<Outlier<q_{1}-w\cdot(q_{3}-q_{1})\). This corresponds approximately to 99.3 percent coverage if the data is normally distributed; (**b**) yearly duration curves of the corresponding scenarios and milestone-years. Model results.  
With a GHG reduction of 74% in BAU, the average annual AEF\({}_{\rm fc}\) by 2050 falls only slightly to 183 g\({}_{\rm CO2 eq}\)/kWh\({}_{\rm el}\). While the other duration curves show a clear reduction of the emission factors in the peak values from year to year, those of 2025 in the BAU scenario are initially higher than the present-day peak values. This fact has to be credited to the phase-out of nuclear energy and its substitution by increased power production from lignite-and gas-fired power plants. The situation is similar in the years 2045 and 2050 with the decreasing amount of bioenergy. The peak values of the AEF\({}_{\rm fc}\) when no renewables are fed into the grid result from the adjustment with gas-fired power plants, whereas in 2040, bioenergy could still contribute a part to that.  
When calculating the AEF\({}_{\rm{Ica}}\) with the corresponding LCA-Ef (as shown in Table 1), a similar course of the duration curves and distribution of the values can be seen. However, the average annual AEF\({}_{\rm{Ica}}\) in all milestone-years is between 30 and 60 g\({}_{\rm CO2 eq}\)/kWh\({}_{\rm el}\) higher than the AEF\({}_{\rm fc}\). Because of the continuously decreasing AEF until 2050, the average annual AEF\({}_{\rm{Ica}}\) in 2020 is still less than 10% higher than the AEF\({}_{\rm fc}\), while in 2050 it is twice as high in the CAP scenario.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Global Spectrum Planning
#### 6.1.2 Planning Constraints  
In addition to the factors mentioned above, there are several key considerations that should be taken into account during spectrum planning.  
Demand constraints: User demand constraints primarily pertain to users' QoS requirements, such as bandwidth, delay, and BER.  
Interference constraints: Interference constraints include several factors that must be considered during system spectrum planning. These include interference avoidance with GEO satellites, CCI outside the system, CCI among SBSs within the system, and CCI among beams of an SBS. Specific management strategies can be employed to address these constraints; for example, SBS reselection can be used for interference avoidance with GEO satellites, and beam scheduling can manage inter-beam interference within an SBS. Additionally, inter-satellite interference within the system and CCI outside the system are key factors that must be carefully considered during spectrum planning.  
Link status constraints: In satellite communication systems, the influence of rain attenuation must be carefully considered when establishing connections between SBSs (small base stations) and users/gateways. SBSs with shorter rain paths and lower attenuation levels are typically better choices for users, as they can provide more reliable and consistent connectivity. Furthermore, the selection of SBSs has a direct impact on spectrum planning, as it influences factors such as frequency allocation, signal strength, and overall network performance.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### The Disengagement Maneuver  
As a result, their mean motions also diverge, with the lower satellite orbiting faster, gradually altering the relative angular separation of the satellites. To maximize phasing efficiency, the change in orbital energy \(\Delta E\) must also be optimized. The change in energy is derived from the vis-viva equation, Equation (1), as:  
\[\Delta E=\frac{1}{2}\Delta v^{2}+v_{0}\Delta v\cos(\alpha) \tag{1}\]  
where \(\alpha=0\) represents a tangential disengagement along the velocity vector, and \(\alpha=\frac{\pi}{2}\) represents a radial disengagement. Since \(v_{0}\)\(\Delta v\cos(\alpha)\) is the dominant term, then tangential disengagement at perigee, where \(v_{0}\) is highest, maximizes energy change. The magnitude of \(\Delta v\) affects phasing times and is incorporated into the optimization algorithm, where it needs to be large enough to sufficiently alter orbital velocity and minimize the risk of post-disengagement collisions while also governing the initial separation rates during phasing. This study focuses, in part, on determining the required \(\Delta v\) magnitude but does not address the disengagement mechanism design.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Routing in VANETs  
The routing protocol in VANETs uses intermediate vehicles as relays to transmit the data packets from a source vehicle to the destination.