esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Proposed Mechanism  
The removal of KP in distilled, ground, and drinking water using the CTG-500 photocatalytic membrane was found to have through filtration and photodegradation under light irradiation. During the filtration process, water molecules and KP contaminants are filtered through the pores of the membrane. The water molecules, because of their small diameter of 0.27 nm, were able to pass through the CTG-500 (with a pore size of 324 nm, Figure 4g,h) while the significantly larger KP was retained on the surface of the membrane. Additionally, the adsorption of KP onto the surface of CTG-500 was facilitated by hydrogen bonding, electrostatic interactions, and non-electrostatic forces that existed between the photocatalytic membrane and KP. In the case of non-electrostatic interactions, the attraction is between the GCN component of the photocatalytic membrane and free OH groups of KP. The structure of GCN, like that of nitrogen-doped carbon nanotubes and conductive polymers, such as polyaniline, consists of aromatic rings with delocalized \(\pi\) electrons; capable of forming \(\pi\)-\(\pi\) non-electrostatic interactions [25; 57]. Hydrogen bonding was formed through the free hydroxyl groups on KP and the hydroxyl groups of CA [58; 59].  
Photodegradation was also key in the removal of KP, particularly for the self-cleaning characteristic of CTG-500 as it resulted in lower fouling and recyclability. This was possible because the CuO/TiO\({}_{2}\)@GCN (1:9) particles were successfully loaded onto the surface of CA to form the photocatalytic membrane. The photodegradation occurs via the absorption of visible light by the CuO dopant, yielding excitation of its valence band (VB) electrons. The bulk of the excited electrons then migrated into the conductive band (CB) through to the conductive GCN, which served as an electron sink. Some of the remaining electrons were able to react with the adsorbed O\({}_{2}\) molecules yielding superoxide radicals (O\({}_{2}\)') which could react with KP. Nevertheless, as it was shown by studying the effect of scavengers, the electrons had a minimal role in the photodegradation of KP (Figure 11). On the other hand, the positively charged holes on the VB of CuO were left free to react with the adsorbed water  
Figure 11: Photodegradation of KP in deionized water using CTG—500 membrane in the presence of various scavengers.  
molecules to form hydroxyl radicals and hydronium ions. These species were the main actives responsible for the photodegradation of KP. The process is represented in Figure 12.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## Appendix C Proof of Theorem 3  
The battery capacity \(\mathcal{B}\) is derived when the residual energy of satellite \(i\) is insufficient for data transmission and ISL is inactive, i.e., \(E_{i}(t)\leq E_{\max}^{c}\) and \(\delta_{ij}(t)=0\). Thus, we have \((P_{t}/\xi_{\max})(\mathcal{B}-E_{i}(t))-\mathcal{D}_{i}(t)\leq 0\) when \(\delta_{ij}(t)=0\), i.e.,  
\[\mathcal{B}\leq E_{i}(t)+\mathcal{D}_{i}(t)\frac{\xi_{\max}}{P_{t}}.\] (A4)  
To ensure the ISL cannot be established when \(E_{i}(t)\leq E_{\max}^{c}\), we maximize \(\mathcal{D}_{i}(t)\) in (A4). Then, we have  
\[\mathcal{B}=E_{\max}^{c}+\mathcal{D}_{\max}\frac{\xi_{\max}}{P_{t}}.\] (A5)
# Free Vibration Analysis of Triclinic Nanobeams Based on the Differential Quadrature Method
## 3 Solution Procure  
### Generalized Differential Quadrature Method (GDQM)  
In what follows, the GDQM is proposed as a numerical method to solve the equations of motion to free the above-mentioned vibration problem of nanobeams, due to its fast convergence and accuracy as largely demonstrated in the literature for different demanding applications [47; 48; 49; 50; 51; 52; 53]. The GDQM discretizes the partial derivative of a function with respect to a variable by a weighted linear sum of function values at all grid points in that direction. This approximation yields the following relation [7],  
\[\left.\frac{\partial^{r}f(\zeta,\eta)}{\partial\zeta^{r}}\right|_{(\zeta,\eta )=(\zeta_{i},\eta_{j})}=\sum_{m=1}^{N_{\zeta}}A_{im}^{\zeta(r)}f(\zeta_{m}, \eta_{j})=\sum_{m=1}^{N_{\zeta}}A_{im}^{\zeta(r)}f_{mj} \tag{27}\]  
for \(i=1,\,2\,\ldots,\,N_{\zeta};j=1,\,2\,\ldots,\,N_{\eta}\); and \(r=1,\,2\,\ldots,\,N_{\zeta}-1\).  
According to this technique, two important factors should be considered, namely, the appropriate distribution of grid points and weighting coefficients for discretization purposes.  
As far as the first key aspect is concerned, different distributions could be selected, involving both uniform or not uniform discretizations, whose numerical performances have been largely discussed and compared in literature [54; 55]. In this research the Chebyshev-Gauss-Lobatto sampling point rule is selected, due its high accuracy and fast convergence, and defined by the following relation  
\[\frac{\zeta_{i}}{a}=\frac{1}{2}\left\{1-\cos\left[\frac{(i-1)\pi}{N_{\zeta}- 1}\right]\right\};\frac{\zeta_{j}}{b}=\frac{1}{2}\left\{1-\cos\left[\frac{(j- 1)\pi}{N_{\eta}-1}\right]\right\} \tag{28}\]  
for \(i=1,\,2\,\ldots,\,N_{\zeta};j=1,\,2\,\ldots,\,N_{\eta}\); and \(r=1,\,2\,\ldots,\,N_{\zeta}-1\).  
As far as the weighting coefficients are concerned, the following expressions for the first and second derivatives are considered  
\[A_{ij}^{\zeta}=\left\{\begin{array}{ll}\frac{1}{L_{\zeta}}\frac{M(\zeta_{i} )}{(\zeta_{i}-\zeta_{j})M(\zeta_{j})}&\text{for}\,i\
eq j\\ -\sum\limits_{\begin{array}{c}N_{\zeta}\\ -\end{array}}^{N_{\zeta}}A_{ij}^{\zeta}&\text{for}\,i=j;\,i,j=1,2,...,N_{ \zeta}\\ j=1&i\
eq j\end{array}\right. \tag{29}\]
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Evaluation Criteria
#### 5.2.2 Nmi  
[PERSON] et al. [39] proposed a standardized mutual information index NMI (Normalized Mutual Information Index) for community detection in complex networks. When we want to measure the difference between the community structure obtained by the algorithm and the known real community structure, NMI is generally used as the evaluation index. NMI applies to many types of complex networks and is also applicable to evaluating the bipartite network's detection results overlapping communities. The value of NMIis between 0 and 1. The larger the NMI value is, the closer the algorithm's community structure is to the real community. The calculation formula of NMI is shown in (31).  
\[H(X)=I(X:Y)+H(X|Y), \tag{27}\]  
\[H(Y)=I(X:Y)+H(Y|X), \tag{28}\]  
\[H(X,Y)=H(X|Y)+I(X:Y)+H(Y,X), \tag{29}\]  
\[I(X:Y)=\frac{1}{2}(H(X)-H(X|Y)+H(Y)-H(Y|X)), \tag{30}\]  
\[NMI=1-\frac{1}{2}\bigg{(}\frac{H(X\mid Y)}{H(Y)}+\frac{H(Y\mid X)}{H(X)}\bigg{)}. \tag{31}\]  
\(I(X:Y)\) represents the mutual information between \(X\) and \(Y\), \(H(X)\) represents \(X^{\prime}s\) entropy information, and \(H(X\mid Y)\) represents \(X^{\prime}s\) and \(Y^{\prime}s\) entropy information under certain conditions. When \(X=Y\), \(H(X\mid Y)=0\), then NMI reaches the maximum value of 1; otherwise, when \(X\) and \(Y\) are completely different, \(H(X\mid Y)=1\), then NMI is the minimum value of 0.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 4 Experiments and Discussion
### Focus on the Hard-to-Track Sources  
As mentioned above, there are four indicators used to evaluate the model. They are mainly utilized to figure out which leaking source is tough to track.  
Table 3 shows the potential leaking sources whose indicator values are less than 0.99. Sources with indicator values less than 0.95 are marked by '-'. In large-scale data classification, precision and recall tend to be mutually restrictive, which means in most cases, if precision is high, recall will be low, and if recall is high, precision will be low. However, from the table above, the values of precision, recall, and _F1-score_ are close to each other. This means the model learns well from the datasets and it can balance the precision and recall.  
Thus, to improve the overall performance of classifying the tough classes above, different weights are set for them when the cross-entropy loss is calculated. The model tends to fit classes with higher weights theoretically. However, implementing balanced cross-entropy loss makes all classes indicator values decrease. Its poor performance may result from setting a priori weights, which is clumsy for such a complex model. Focal loss can dynamically adjust weight coefficients. Via experiments, it can exactly further improve the overall performance of the model from 98.86 to 98.93%. The corresponding indicators for each bad-performance class also improve, as shown in Table 4. Although some indicators decrease compared to Table 3, there are still more of them that improve from a global perspective.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Source No.** & **Precision** & **Recall** & **F1-score** \\ \hline
25 & 0.97 & 0.97 & 0.97 \\
27 & 0.96 & 0.97 & 0.97 \\
38 & 0.97 & 0.97 & 0.97 \\
39 & 0.98 & 0.98 & 0.98 \\
40\({}^{\text{-}}\) & 0.92 & 0.93 & 0.93 \\
41\({}^{\text{-}}\) & 0.93 & 0.92 & 0.92 \\
42 & 0.98 & 0.98 & 0.98 \\
44 & 0.96 & 0.97 & 0.96 \\
46 & 0.97 & 0.96 & 0.97 \\
47 & 0.97 & 0.97 & 0.97 \\
50 & 0.97 & 0.97 & 0.97 \\
51 & 0.97 & 0.97 & 0.97 \\
52\({}^{\text{-}}\) & 0.88 & 0.89 & 0.89 \\
53\({}^{\text{-}}\) & 0.88 & 0.89 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The indicators of some potential sources with indicator values less than 0.99. Sources with indicator values less than 0.95 are marked by ‘-’.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### Target Distortion Optimization Performance
#### 5.3.3 Power and Bandwidth Trade-Off  
A trade-off exists between power and bandwidth usage for each expected video quality, as the minimal resource solution is generally not unique. However, the valid ranges for power and bandwidth for a given distortion are constrained. The trade-off curves shown in Figure 8 correspond to the average texture and depth maps. The curve boundaries vary with different predefined distortions. In general, for lower target PSNR values, the required power and bandwidth usages are comparatively minimal. The power resource is high  
Figure 6: Bandwidth usage savings and PAR influence: (**a**) \(SNR_{chk}\) = 20 dB (PSNR\({}_{target}\) = 50 dB); (**b**) \(SNR_{chk}\) = 15 dB (PSNR\({}_{target}\) = 45 dB); (**c**) \(SNR_{chk}\) = 10 dB (PSNR\({}_{target}\) = 40 dB); (**d**) \(SNR_{chk}\) = 5 dB (PSNR\({}_{target}\) = 35 dB).  
near the \(N_{sub}\) lower boundary. At this point, a slight increase in bandwidth dramatically reduces the power. Nonetheless, allocating more bandwidth after a certain threshold does not reduce the power further. Hence, it is suggested that a minimum power and bandwidth usage pair is adopted close to the curve's turning point, where a slight change in one factor noticeably affects the other.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Operational Considerations
#### 5.2.5 Aeronautical Information  
One of the most important aspects of pre-flight planning for a BVLOS operation is to ensure that all aeronautical information is taken into account. At the most basic level, this involves checking what airspace the operation will be within, the requirements of that airspace, and how to comply with those requirements (or if it is even possible to comply with those requirements). Some of the key sources of aeronautical information for unmanned aircraft operators include the Aeronautical Information Publication (AIP), AIP Supplements, Visual Navigation Charts (VNCs), and Notices to Airmen (NOTAMs) [20]. Each one of these provides information that is relevant for different time intervals. AIPs provide aeronautical information that is of a lasting nature and is essential for air navigation, while the AIP Supplements provide temporary changes to information published in the AIP [20]. VNCs are probably the most used of the sources of aeronautical information because these visually depict this information, including airspace boundaries, aerodrome locations, special use airspace, and other useful information [21; 30; 86]. Reading VNCs has been established as a key risk mitigation because of how it assists with flight planning and situational awareness of the surrounding airspace [85]. Another source of aeronautical information, that unmanned aircraft operators should check prior to operation, are NOTAMs. Unlike other sources of aeronautical information, NOTAMs are issued daily and contain information about temporary changes to aeronautical information or procedures, as well as any temporary hazards that should be taken into account when planning a flight [21; 87]. This has also been identified as a key risk mitigation step that unmanned aircraft operators should take to help mitigate airborne risks [36]. The issuance of NOTAMs may also a useful safety precaution for BVLOS operations, as this will make manned aircraft pilots aware of the ongoing operation, the operating area, and times of operation. The importance of this would depend on what airspace and what altitude the operations take place within. When operations are taking place in airspace that is shared with manned aircraft, then, it would be advisable to issue a NOTAM.
## 6 Evaluation
### Simulated Hardware and Software Stack  
The simulated environment was designed with low, mid-range, and high processing power devices for IoT, MEC, and CC layers, respectively. For IoT devices, we chose Arduino Mega 2560, with five operating frequencies and corresponding supply voltages for DVFS. The MEC servers were simulated on top of 5 Raspberry Pi 4 Model B boards, each board with a Quad-core Cortex-A/2 1.5 GHz (ARM v8) 64-bit, summing a total of 20 CPU cores per server. These CPU cores have three operating frequencies and corresponding supply voltages. Table 3 specifies the voltage-frequency pairs and the capacitance of the underlying hardware architecture of IoT and MEC devices. These combined values are used to calculate the power consumed by a device according to the selected values.  
For CC, we chose data centers with Intel Xeon Cascade Lake processors of 2.8 GHz per CPU core, reaching up to 3.9 GHz with _Turbo Boost_ on (Technical information can be found in the data sheets of the electronic components). Here, there are no voltage or  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hardware** & **Voltage-Frequency Pairs** & **Capacitance** \\ \hline \multirow{4}{*}{IoT device} & (5 V–16 MHz), (4 V–8 MHz), & \\  & (2,7 V–4 MHz), (2.3 V–2 MHz), & 2.2 nF \\  & (1.8 V –1 MHz) & \\ \cline{1-1}  & (1.2 V–1500 MHz), (1 V–1000 MHz), & \\ \cline{1-1}  & (0.825 V–750 MHz), (0.8 V–600 MHz) & 1.8 nF \\ \hline \hline \end{tabular}
\end{table}
Table 3: Device variables for power calculation.  
capacitance variables. Instead, the resulting power is used, 13.85 Watts and 24.28 Watts, for configuration with and without Turbo Boost, respectively.  
The network throughput was configured to achieve up to 1 Gbps speed and latencies to 5 ms, for both 5G and fiber optic communications [36; 37]. The simulated applications are two vehicular applications that were described originally in [PERSON]'s Ph.D. thesis [38]. Application 1 represents the image recognition of vehicle registration plates on roads. It has a high workload and a high task creation rate. Application 2 is a vehicle-to-vehicle communication to avoid car collisions. It is a critical-mission application with a low workload, low data, and a hard deadline. Because of this, the task generates rates are faster, in comparison with Application 1. Application 2 creates more tasks than Application 1 for the same interval time, but each task has lower processing requirements. Table 4 shows the characteristics of each application.
# Aortic Pressure Control Based on Deep Reinforcement Learning for _Ex Vivo_ Heart Perfusion
## 3 Results
### AoP Change Conditions  
In order to assess the ability of the proposed controller to respond effectively to an extreme situation, namely a sudden change in target aortic pressure, we set \(R_{CA}\) to 1.2 mmHg\(\cdot\)s/mL and \(HR\) to 100 bpm. Moreover, we define step sizes of 10 mmHg, 20 mmHg, and 30 mmHg. In order to provide a basis for comparison, we conduct a series of experiments with the proposed method, as well as two commonly used methods from the control field: the PID and the fuzzy PID control methods. We modify parts (c) and (d) of Figure 3 to apply a PID controller and a fuzzy PID controller, respectively, while maintaining the configuration of parts (a) and (b). In the case of the PID method, the proportionality constant \(K_{p}\) is set to 0.94, the integral constant \(K_{i}\) is set to 0.30, and the derivative constant \(K_{d}\) is set to \(-\)0.10, following a process of parameter tuning. In the case of the fuzzy PID method, the parameters are optimized in accordance with specific fuzzy rules through a process of several iterations, resulting in the selection of \(K_{p}\) = 0.83, \(K_{i}\) = 0.10, and \(K_{d}\) = 0.10 as coefficients for a step size of 30 mmHg. In the PD-DDPG method, the network can converge in less than 400 timesteps.  
The results of the simulation, in terms of the AoP and mean AoF, are presented in Figure 6. The results demonstrate that the PID control method requires a control time of 26.9 s and exhibits a pressure overshoot of 15.6 mmHg when the \(MAP_{ref}\) undergoes a sudden change of 30 mmHg. This is mainly because the parameters of the PID method cannot be modified according to the varying perfusion conditions. In comparison, the fuzzy PID method exhibits superior performance. The pressure smoothing time is 19.3 s, the overshoot is 10.8 mmHg, and the overshoot pressure is lower than that of the PID method. In contrast, the PD-DDPG method exhibits a slight overshoot of 1.3 mmHg when controlling the aortic pressure change. Moreover, the AoP could be rapidly smoothed within 4.2 s, and the mean AoF overshoot is less than 1.1 mL/s. This indicates that the  
Figure 5: Tracking result during step changes in \(HR\). (**a**) MAP tracking results; (**b**) \(HR\): 80 bpm to 100 bpm; (**c**) \(HR\): 100 bpm to 90 bpm.  
PD-DDPG method is capable of maintaining smooth pressure and flow changes within a relatively short period of time. This is primarily due to the advantages of the agent in learning the optimal control strategy.
###### Abstract
This paper proposes a novel cluster-centric routing protocol that establishes network connectivity through dynamic clustering. This method adapts cluster configuration based on shared nodes between clusters. Additionally, a heuristic func  
Figure 1: Store-carry-forward architecture in OppNet. range of the cluster head, effectively transforming a fragmented network into an ad hoc network. As a result, our proposed protocol offers significant performance improvements for OppNets, making it a compelling solution for managing transportation routes and determining optimal paths between source and destination nodes. This paper presents the following key contributions:  
* Adaptive Clustering with Extended Radio Range: We propose an adaptive clustering method that leverages a new heuristic function to increase the cluster head's radio range, thus enhancing network efficiency. This approach allows the OppNet to function similarly to a TCP/IP network, making it suitable for dynamic network topologies. * Infrastructure-Independent Clustering for Efficient Routing: We introduce a novel common member constraint-based routing algorithm for clustering. This algorithm facilitates the creation of optimal clusters, enabling faster data transmission and shorter paths between source and destination nodes. Notably, this technique eliminates reliance on network infrastructure like roadside units (RSUs), which can introduce delays and routing inefficiencies. * Demonstrated Performance Improvement: We present a method that achieves significant improvements across various performance metrics, including delivery ratio (approximately 28% increase), end-to-end delay (approximately 42% decrease), throughput (approximately 45% increase), hop count (approximately 44% decrease), and reachability (approximately 80% increase).
## 3 Results  
### General Variation in Pollution  
The spatial distribution of the annual average NO\({}_{2}\) concentrations in the air above Poland varies strongly. The lowest concentrations occur in southern Poland (the Carpathian Mountains), and in northern Poland (lake districts and coastal areas). Low levels also occur in Eastern Poland (Figure 3). In contrast, high concentrations, exceeding 40 \(\mathrm{\SIUnitSymbolMicro}\)mol/m\({}^{2}\), occur in central Poland and parts of southern Poland. The highest pollution occurs in the vicinity of the Belchatow Power Plant and the Upper Silesian Conurbation (approx. 80 \(\mathrm{\SIUnitSymbolMicro}\)mol/m\({}^{2}\)), as well as in the agglomerations of Krakow and Warsaw (approx. 70 \(\mathrm{\SIUnitSymbolMicro}\)mol/m\({}^{2}\)). High levels occur in the air over the agglomerations of Wroclaw, Opole, Lodz, Poznan, and Tarnow (50-60 \(\mathrm{\SIUnitSymbolMicro}\)mol/m\({}^{2}\)). Power plants at Kozienice, Turow, and the German Janschwalde (border area west of Zielona Gora) also have a similar impact on air pollution with NO\({}_{2}\). Lower but still noticeable pollution (40-50 \(\mathrm{\SIUnitSymbolMicro}\)mol/m\({}^{2}\)) occurs in Szczecin, the Trojmiasto, Radom, the Plock refinery, the Gogow and Legnica copper works, and the Konin and Polaniec  
Figure 2: Relationship between the satellite and ground data (annual averages): **(A)**—2019, **(B)**—2020.  
power plants. As a result of lockdown restrictions, the year 2020 saw quite a marked reduction in areas with the highest concentrations of NO\({}_{2}\) in the air. It was particularly noticeable in the case of the Belchatow power plant, the Upper Silesian Conurbation, and the agglomerations of Warsaw and Krakow. By contrast, in the following year, the areas with the highest concentrations (>60 \(\mu\)mol/m\({}^{2}\)) were larger than before the pandemic (Figure 3).  
Figure 3: Spatial diversity of the mean annual NO\({}_{2}\) concentrations in the air over Poland in 2019, 2020, and 2021: 1—national parks, 2—landscape parks, 3—tourist regions, and 4—health resorts.  
Over the course of the year, there is significant variation in the monthly mean NO\({}_{2}\) concentrations in the air. They are the highest in February and October, and the lowest in July (Table 1). The maximum levels recorded in Poland in months other than June and September are above 80 \(\upmu\)mol/m\({}^{2}\), and they are greater than 140 \(\upmu\)mol/m\({}^{2}\) in February.  
The lowest mean annual concentrations occurred in national parks and tourist regions, followed by health resorts and landscape parks (Figure 4). In provincial capitals, they were 50% greater than in the other areas. It was found that the amount of pollution in tourist reception areas in 2020 decreased by 5% in national parks, 7.7% in health resorts, and 7.8% in provincial capitals. In 2021, by contrast, there was an increase by 1.2-2.0% in national parks, landscape parks, and health resorts; by 3% in tourist regions; and by 6% in provincial capitals, in relation to 2019 (Figure 4).