esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Classification Detection Rate  
The performance comparison and experimental results are described in the Tables 5 and 6. The results of the proposed method are compared with the experimental results of [19; 29; 30; 31] in Table 5, whereas Table 6 presents the results of different classifiers. The comparison is performed based on three factors such as data sizes, detection rate and average training time per sample. It can be observed from the results that the SPLR model is much more effective due to the comparable classification accuracies. There are some other observations from the Table 5 that need to be highlighted. First, if the KDD'99 datasets are used, the SPLR has achieved better performance in detection rate (97.6%) and the FAR is 0.34%. However, it takes less training time (11.6 s) to build a model while the average training time per sample is (0.000001 s) than the VFDT method. Second, considering the fact that other classifiers such as genetic programming, multivariate adaptive regression splines, naive Bayes and VFDT are good classifiers, the proposed (SPLR) classifier shows an impressive performance in terms of feature selection, improve classification accuracy, detection rate, training time and average training time per sample. Third, it needs to be emphasized that the SPLR's model-based classifier can perform discriminative feature selection during the classifier training phase, which provides an interesting and integrated solution for IDS classification problems.
## 3 System Model
### LEO Satellite Constellation  
In the LEO constellation system, we focus on a conventional polar orbit with an inclination of approximately \(90^{\circ}\). An LEO satellite network is defined as a graph \(G=(V,E)\), where \(V\) is the set of satellites and \(E\) is the set of ISLs. A typical \(N\times M\) constellation comprises \(N\) orbits and each orbit contains \(M\) satellites. The orbital planes are separated from each other by the same angular distance of \(2\pi/(2\times N)\). The satellites in the orbital plane are separated from each other by the same angular distance of \(2\pi/M\). Considering zero eccentricity for each orbit results in the satellites revolving in circular orbits. Following other studies, we considered the usual type of ISLs: (i) each satellite has two intra-plane ISLs (links with adjacent satellites in the same orbit plane) and (ii) two inter-plane ISLs (links with satellites in the adjacent orbit).  
The topology of the LEO satellite constellation is shown in Figure 1. The dashed line represents the intra-plane ISL, and the dotted line in Figure 1 represents the inter-plane ISL. The location of each satellite can be represented by \((v_{n,m})\); \(n\) is the plane index and \(m\) is the satellite index, where \(n\in\{0,1,\cdots,N\}\) and \(m\in\{0,1,\cdots,M\}\). Each satellite has four directional ISLs, denoted by \(E=\{e^{\mathrm{dir}}_{n,m}|\mathrm{dir}_{v_{n,m}}\in\{\mathrm{up,left,down,right}\}\}\). LEO satellites in the polar region cannot establish an inter-plane ISL with adjacent satellites. The polar constellation has a seam because of the opposing relative velocities between the satellites. Thus, cross-seam ISL connections are unavailable. Therefore, satellites within the polar region have two ISLs and satellites near the seam have three ISLs.
## 3 Mathematical Models and Validations
### Model Validation  
The ANSYS Fluent MSMD module could automatically identify the quantities of series or parallel connections from the 3D model of the BTMS based on the correct busbars and cells configurations. The air was set as an incompressible ideal gas with a viscosity of temperature-dependent function following Sutherland's Law. The ECM settings are listed in Table 2.  
The experimental data from a published paper by [PERSON] et al. [38] were adopted to verify the heat generation and transfer models in this study. Figure 4a shows the validation model of the experimental test rig of 32 pieces (4 \(\times\) 8) with 18,650 cylindrical cells in an acrylic wind tunnel. The cells are connected in 4p8s connection: every 4 cells in one column are connected in parallel and all 8 columns are connected in series. The experiments were conducted at flow rates of 0.6-4 m/s. Figure 4b shows the CFD mesh effect of the validation model. The horizontal and vertical cell centre distances are both 22 mm. The distance between the cell centre and the acrylic wall is 13 mm. The inlet air temperature and the ambient temperature were 20 \({}^{\circ}\)C. The three discharging rates were 0.5, 1C, and 2C.  
Figure 4: Model validation: (**a**) validation model; (**b**) CFD mesh; (**c**) battery surface temperature contour and air velocity streamlines at 4 m/s 20 \({}^{\circ}\)C inlet air during 0.5C discharging; (**d**) the comparison between the experimental data in Ref. [38] and the validation results in this study at 3 different discharging rates.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Battery ECM Parameters** & **Values** \\ \hline Nominal Cell Capacity (Ah) & 4 \\ Specified C-Rate & 2 \\ Max Stop Voltage (V) & 4.3 \\ Min Stop Voltage (V) & 2.5 \\ Initial SoC & 1 \\ Reference Capacity (Ah) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electric circuit model Settings.  
Figure 4c shows the battery surface temperature contour and air velocity streamlines at 4 m/s 20 \({}^{\circ}\)C inlet air during 0.5C discharging. Figure 4d compares the reference experimental results and the simulation results during 0.5C, 1C, and 2C discharging. The average errors of all five velocity scenarios during 0.5C, 1C, and 2C discharging are 0.12 K, 0.47 K, and 0.37 K, respectively. The standard deviations are 0.35 K, 0.94 K, and 0.33 K, respectively. As a result, the comparison successfully demonstrated the accuracy and reliability of the theoretical model to predict both heat generation and transfer of the thermal performances of an air-cooling BTMS in this study.
# Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization
## 2 Conventional Wastewater Treatment Technology
### Pure-Oxygen Activated Sludge Process  
Compared with air aeration, pure-oxygen aeration, also known as enriched-oxygen aeration, has the following characteristics:  
1. The oxygen content of air is generally 21%; the oxygen content of pure oxygen is 90-95%, and the partial pressure of oxygen is 4.4-4.7 times higher than that of air, so pure oxygen aeration can greatly improve the diffusion capacity of oxygen in the mixed liquid.
2. The oxygen utilization rate can be as high as 80-90%, while the air-erated activated sludge method is only about 10%, so the volume of gas required to achieve the same oxygen concentration can be greatly reduced.
3. Activated sludge concentration (MLSS) can reach 4000-7000 mg/L, so the volume load can be greatly increased at the same organic load.
4. The sludge index is low, only about 100, which is not prone to sludge swelling;  
Figure 3: Process flow chart of adsorption-regeneration activated sludge method.  
(5). High treatment efficiency and short aeration time.  
(6). The amount of residual sludge produced is small.  
There are three types of pure oxygen aeration pool: (1) multistage- sealed, in which oxygen is introduced into the pool from the closed top cover, the sewage from the first stage is pushed forward step-by-step, oxygen flows from a centrifugal compressor through a hollow shaft into a rotary impeller, which mixes the sludge and oxygen in the pool to keep full contact, so that the sludge can greatly absorb unused oxygen and biochemical reaction metabolites from the previous level of discharge. (2) The old aeration tank is reformed, and a curtain is set on the pool. Not only pure oxygen enters, but also compressed air. Some tail gas is discharged, and can also be recycled. (3) Open pure oxygen aeration tank. The technological process is shown in Figure 4. When the reference values of the various design parameters used in the treatment of urban sewage are the same as 2.2, the removal rate of BOD, COD and TSS can reach 90% [28; 29; 30; 31; 32].  
By supplying pure oxygen, the process can greatly improve the diffusion capacity of oxygen in solution and reduce the volume of gas, thus reducing aeration time, aeration energy consumption and carbon footprint. However, it still cannot achieve good sustainable development and utilization of resources in the disposal of sludge operation management. In addition, the structure and management requirements of the equipment are relatively high, which increase the operating costs of the treatment process.
## 3 Results and Analysis
### Spatial and Temporal Distribution Characteristics of EVI and GPP
Specifically, the annual change rates are as follows: for croplands, EVI increases at a rate of 0.002246 per year (Figure 5a) and GPP at a rate of 1.452164 gC/m\({}^{2}\) per year (Figure 5f); for forests, EVI increases at a rate of 0.002021 per year (Figure 5b) and GPP at a rate of 1.319233 gC/m\({}^{2}\) per year (Figure 5g); for shrubs, EVI increases at a rate of 0.002277 per year (Figure 5c) and GPP at a rate of 1.832376 gC/m\({}^{2}\) per year (Figure 5h); for grasslands, EVI increases at a rate of 0.001014 per year (Figure 5d) and GPP at a rate of 0.705504 gC/m\({}^{2}\) per year (Figure 5i); and for wetlands, EVI increases at a rate of 0.000604 per year (Figure 5e) and GPP at a rate of 0.813150 gC/m\({}^{2}\) per year (Figure 5j). Figure 4: Inter-annual changes in EVI and GPP in different climate regions (**notes:** (**a-e**): inter-annual variation in EVI in the whole region, arid region, semi-arid region, sub-humid region and humid region; (**f-j**): inter-annual variation in GPP in whole region, arid region, semi-arid region, sub-humid region and humid region).
## 4 Results and Discussion
### Coverage Prediction via Ray-Tracing
#### 4.3.2 Non-Line-of-Sight Link NLOS1  
Figure 9 shows the coverage map with the received power at every grid point for the access point located at the same location as the TX antenna of scenario NLOS1. In Figure 9a, an omnidirectional TX antenna with a gain of 3 dBi is considered, which is equivalent to the PL measurement setup, whereas in Figure 9b a directional TX antenna with a gain of 23 dBi and an HPBW of 18\({}^{\circ}\) is considered, which is similar to the horn antenna used as RX antenna in the PL measurement setup. Figure 9a also visualizes the best reflected  
Figure 8: Measured packet error rate as a function of modulation and coding scheme index for the different measurement scenarios.  
paths. The simulated minimum PL at the grid point corresponding to the RX antenna of scenario NLOS1 is the same for the omnidirectional and directional antennas, but the throughput estimation takes into account the antenna gain, which is higher for a directional antenna, and therefore the directional antenna has an advantage. Two paths exist with a minimum PL of 98 dB. The first path consists of a ceiling reflection, the second path reflects on the wall of the upper-right corner, i.e., from where the photograph in Figure 3b is taken. This second path is also captured with the TG platform, i.e., TX beam 35.2\({}^{\circ}\), RX beam 22.5\({}^{\circ}\) with PL 98.4 dB, as can be seen in Figure 7. Two paths have a total PL of 111 dB. The first path undergoes three reflections with a small incident angle (two wall reflections and one window reflection) before reaching the receiver. The second path undergoes five reflections, including one ceiling reflection. Two more paths, with PL values of 117 dB and 120 dB, undergo five reflections. For this scenario, the ray-launching algorithm misses the reflected path with AoA 95\({}^{\circ}\) and PL 87 dB, as well as the diffracted path with AoA 11\({}^{\circ}\). The throughput estimation for the grid point corresponding to the RX location of NLOS1, is 2.5 Gbps, which is lower than the throughput based on SNR (3.85 Gbps) but larger than the estimation based on PER (1.25 Gbps).
## 3 Results
### Mineralization  
To ensure a more comprehensive study, the mineralization of the dye was examined in the two homogeneous techniques that proved to be the most effective. The reduction in the COD values of the treated dye solution indicates the mineralization of dye molecules along with color removal. In Figure 14, a substantial decrease is observed in the COD of the solution with time.  
Figure 13: The % degradation of Nile Blue dye, with different homogeneous and heterogeneous photocatalytic techniques after 60 min of treatment (C\({}_{0}\) = 15 mg L\({}^{-1}\), C\({}_{\text{Fe}}\)\({}^{+2}\) = 2 mg L\({}^{-1}\), C\({}_{\text{cataly}}\) = 100 mg L\({}^{-1}\), C\({}_{\text{oxidant}}\) = 200 mg L\({}^{-1}\)).  
In the UV/Fe\({}^{2+}\)/H\({}_{2}\)O\({}_{2}\) system, the chemical oxygen demand (COD) is completely reduced from 20 mg L\({}^{-1}\) to 0 mg L\({}^{-1}\), indicating 100% mineralization of the organic compounds. This high efficiency can be attributed to the enhanced generation of reactive hydroxyl radicals (\({}^{\bullet}\)OH), which is promoted not only by the continuous regeneration of Fe\({}^{2+}\) ions via the photoreduction of Fe\({}^{3+}\) ions under UV light, but also by the direct activation of hydrogen peroxide through UV irradiation [47]. The slight increase observed at the beginning may be due to the fact that the dye does not degrade instantly into CO\({}_{2}\) and water, but produces intermediates that require oxygen for further breakdown, thus temporarily increasing the COD. Additionally, if a portion of the added oxidant remains unreacted during the initial stages of the treatment, it may contribute to an apparent increase in the COD value, as it can consume dissolved oxygen in the solution.  
In the case of the UV/Fe\({}^{2+}\)/S\({}_{2}\)O\({}_{8}\)\({}^{2-}\) system, the COD is reduced by only 30% (from 20 to 14 mg L\({}^{-1}\)) relative to its initial value, indicating that the process is less efficient than the conventional photo-Fenton reaction. This lower performance may be due to the slower generation rate of reactive sulfate radicals (SO\({}_{\bullet}\)\({}^{\bullet-}\)) compared to hydroxyl radicals (\({}^{\bullet}\)OH), as well as the more complex activation mechanism of persulfate, which typically requires longer reaction times to achieve complete mineralization [48].
# The Fusion of MRI and CT in the Planning of Bracotherapy for Cancer of the Uterine Cervix
###### Abstract  
Introduction: tumors of the uterine cervix are among the most common carcinomas in women. Intracervical brachytherapy is an indispensable part of curative treatment. Although the tumor is significantly more recognizable in MRI than in CT, the practical application of MRI in brachytherapy planning is still difficult. The present study examines the technical possibilities of merging CT and MRI. Materials and Methods: the treatment files and imaging of all 53 patients who had been irradiated by image-guided adaptive brachytherapy (IGABT) between January 2019 and August 2021 at the Department of Radiotherapy of the Hannover Medical School were evaluated, retrospectively. Patients were treated first with an external beam radiotherapy (EBRT) combined with simultaneous chemotherapy. After an average of 4.2 weeks, the preparation for IGABT began. The clinical target volume (CTV) for brachytherapy was contoured first in an MRI acquired before starting EBRT (MRI 1) and once more in a second MRI just before starting IGABT (MRI 2). Then, after inserting the intravaginal applicator, a CT-scan was acquired, and the CTV was contoured in the CT. Finally, the recordings of MRI 1, MRI 2, and the CT were merged, and the congruence of CTVs was quantitatively evaluated. Results: the CTV delineated in MRI 2 was, on average, 28% smaller than that in MRI 1 after an average applied radiation dose of 42 Gy. The CTV delineated in the CT covered an average of no more than 80.8% of the CTV delineated in MRI 2. The congruence of CTVs was not superior in patients with a smit sleeve in the cervical channel, with a 3D-volumetric MRI or with a contrast-enhanced sequence for MRI. Conclusion: the anatomical shape and position of the uterus is significantly changed by introducing a vaginal applicator. Despite the superior delimitability of the tumor in MRI, brachytherapy cannot be reliably planned by the image fusion of an MRI without a vaginal applicator.  
cervical cancer; radiotherapy; brachytherapy; MRI; IGABT
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 5 Results  
In this section, a comparative analysis is conducted that is based on analytical simulations of two state of the art protocols with the proposed protocol. The major performance parameters selected in this study are:  
1. Throughput--No of packets successfully received at the sink.
2. Stability Period--Duration before the energy of the first node depletes completely and node dies.
3. Network Lifetime--Total time duration till the last node's energy drains completely and all nodes in a network died.
4. Thermal Dissipation--Heat energy generated by the internal sensor circuitry and due to electromagnetic signals. This needs to be controlled for a hazard-free deployment of WBAN.  
The evaluation of TAEO routing protocol is carried in comparison to ATTEMPT (Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop ProTocol) [19] and SIMPLE (Stable increased-throughput multi-hop protocol for link efficiency in wireless body area networks) [32].
## 2 Related Work
### UAV-Based Delivery Systems and Integration with Transport Networks  
Drone-based delivery systems have evolved from prototype experimentation to structured deployments led by both industrial actors and academic researchers. Several companies have demonstrated UAV use for short-range logistics, and studies have proposed various delivery architectures addressing routing, task assignment, and drone-vehicle coordination [7; 14].  
A particularly promising approach involves hybrid delivery architectures, where UAVs are paired with terrestrial vehicles, such as trucks or vans, to increase delivery coverage and optimize fleet efficiency. In these models--commonly formalized as variants of the traveling salesman problem with drones (TSPD) or vehicle routing problems (VRPs)--the drone is used for short detours from the main vehicle's path [5; 14].  
An alternative line of work investigates the integration of UAVs with public transport systems (PTSs), such as buses or trains. In these systems, drones \"ride\" on public vehicles and deploy near the delivery locations. Several studies have modeled this concept using deterministic or time-dependent routing approaches, and some have considered mission constraints such as delivery time windows or limited battery capacity [10; 11]. Other works explore the use of PTS as mobile recharging platforms, enabling drones to extend their autonomy through scheduled energy replenishment [16; 17].  
These contributions demonstrate the potential of UAV-PTS synergy, but their focus remains primarily on feasibility and high-level routing logic, often abstracting away the physical and energetic realism of drone behavior during deployment.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 2 Test Environment and Methods
### Metamodel
#### 2.1.2 Neural Network (NN) Metamodel  
This metamodel employs the \"neuralnet\" package ([[https://github.com/bips-hb/neuralnet](https://github.com/bips-hb/neuralnet)]([https://github.com/bips-hb/neuralnet](https://github.com/bips-hb/neuralnet)), accessed on 23 May 2023) for R [35] and uses a back propagation algorithm. Specifically, the neural network used here consists of input neurons \(x_{IN,1},x_{IN,2},\ldots,x_{IN,n_{IN}}\) corresponding to the inputs, output neurons \(x_{OUT,1},x_{OUT,2},\ldots,x_{OUT,n_{OUT}}\) corresponding to the number of objectives, and \(n_{H}\) layers of hidden neurons, where \(x_{i,j}\) correspond to the \(j\)th neuron on the \(i\)th hidden layer. Input neurons interact only with neurons on the first hidden layer, and neurons on each hidden layer interact only with neurons on the following hidden layer, with neurons on the final hidden layer interacting with the output neurons.  
As a result, in a system with \(N_{H}\) hidden layers, where the \(i\)th hidden layer has \(n_{i}\) neurons, the value of the first output neuron (dropping the \(OUT,1\) notation for clarity) might be expressed as:  
\[x=f(w_{0}+\sum_{j=1}^{n_{N_{H}}}w_{j}*x_{N_{H}}). \tag{3}\]  
Every neuron has a weight for each neuron on the previous layer, and a constant neuron. The weighted sum of neuron values on the previous layer (for output neurons, the previous layer is the \(N_{H}\)th hidden layer) is the input to \(f()\), the activation function. In this case, the activation function is the logistic function: \(f(u)=\frac{1}{1+e^{-u}}\) as it gives final outputs in the interval \([0,1]\) and the metamodel works on inputs and objectives scaled to this interval.  
In keeping with the design intent to minimally adjust component settings, the main default options are used for training the model. In particular, the algorithm used is the resilient backpropagation with weight backtracking by [36] on two layers of hidden neurons (five in the first layer and three in the second).
## 2 Data and Method
### Data
#### 2.1.3 Satellite AIS Data  
AIS data for the study area during the 2020 fishing season were obtained from the satellite service provider ORBCOMM ([[https://www.orbcomm.com/en/solutions/maritime/ais-data](https://www.orbcomm.com/en/solutions/maritime/ais-data)]([https://www.orbcomm.com/en/solutions/maritime/ais-data](https://www.orbcomm.com/en/solutions/maritime/ais-data))) to evaluate the performance of deep learning methods for detecting illuminated fishing activities [30]. We filtered information from AIS message types 1 (scheduled position report), 2 (assigned scheduled position report), 3 (special position report responding to an interrogation), 18 (standard Class B equipment position report), 19 (extended Class B equipment position report), and 27 (long-distance message), all of which included a timestamp, longitude and latitude, MMSI number, speed, and course. Squid fishing vessels authorized to operate in the central to east Pacific were listed by the North Pacific Fisheries Commission (NPFC). This list was downloaded from the NPFC ([[https://www.npfc.int/compliance/vessels](https://www.npfc.int/compliance/vessels)]([https://www.npfc.int/compliance/vessels](https://www.npfc.int/compliance/vessels))) and used to filter and extract the relevant vessels from the AIS data based on their unique MMSI numbers.