esa-sceva/satcom-chunk-collection huggingface dataset:
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 5 Simulations and Results
### Wireless Channel Model  
We assume a wireless channel subject to quasi-static fading. The instantaneous signal-to-noise ratio (SNR), measured by a node \(j\) from a packet received from a node \(i\), is given by [55]:  
\[\gamma_{ij}=|h_{ij}|^{2}\,\frac{P_{r}}{N}, \tag{1}\]  
where \(P_{r}\) is the average received power, \(N\) is the noise power, and \(h_{ij}\) is the channel gain. The channel fading \(h_{ij}\) follows the Nakagami-\(m\) distribution, while \(|h_{ij}|^{2}\) follows the Gamma distribution. The probability density function of \(\gamma_{ij}\) is, thus,  
\[f_{\gamma_{ij}}(x)=\frac{\left(m/\overline{\gamma}_{ij}\right)^{m}x^{m-1}}{ \Gamma(m)\,\,e^{mx/\overline{\gamma}_{ij}}}, \tag{2}\]  
with \(\Gamma(a)=\int_{0}^{\infty}y^{a-1}\,\,e^{-y}dy\) being the Gamma function and \(\overline{\gamma}_{ij}=P_{r}/N\). The average received power is given by  
\[P_{r}(d_{ij})\,\,[\mathrm{dBm}]=P_{r}(d_{0})\,\,[\mathrm{dBm}]-10\,\alpha\log _{10}\left(\frac{d_{ij}}{d_{0}}\right)+S, \tag{3}\]  
where \(d_{ij}\) is the distance between nodes \(i\) and \(j\), \(P_{r}(d_{0})\) represents the power received at a reference distance \(d_{0}\), the parameter \(\alpha\) is the path loss exponent, and \(S\) represents the shadowing, which follows a zero-mean Gaussian distribution with variance \(\sigma^{2}\) in dB [56]. We assume a homogeneous environment, with the path loss exponent \(\alpha=3\), Nakagami-\(m\) fading with parameter \(m=2\), and shadowing variance \(\sigma^{2}=5\,\mathrm{dB}\), for all nodes.
## 2 Literature Review
### Action Space and Training Algorithm  
Signal controllers at real intersections primarily employ RTSC methods that allow a phase to last for any number of seconds, subject to certain constraints [2; 34]. However, many researchers have restricted this flexibility to ease training by creating a coarse-grained action space. Specifically, they allowed the green time for each phase to be only in multiples of 5 s, 7 s, 10 s, 20 s, etc. [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. In other words, this means phase green time could only be one of a few numbers, with large intervals between them, e.g., \((7\cdot\delta)\), where \(\delta\in\{1,2,3,4,5\}\). This approach has limited the potential of MFDRL methods to achieve greater performance advantages compared to the existing methods operating on a second-by-second basis.  
In scenarios where signal operations follow a fixed phase sequence, \"phase duration\" was a suitable action definition for MFDRL [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. It means choosing a green time for the current phase. However, it is non-trivial to combine the \"phase duration\" definition and second-by-second signal operation. In the case of a single-ring phase structure, such a definition leads to a discrete action space containing only tens of green time choices. For example, 26 choices at one-second intervals if minimum and maximum greens are 15 s and 40 s, respectively. But regarding DRBPS, an agent needs to determine the green time for each of the concurrent phases in two rings. As a result, the action becomes the permutation of green times for two phases. The number of selectable actions grows quadratically to \(26^{2}=676\). Exploring such a large action space is very challenging for an agent.  
The deep Q-network (DQN) and its variants are typical value-based MFDRL training algorithms [44]. They are competitive even with the newer algorithms, such as soft actor-critic when handling discrete actions [45; 46]. The DQN variants were very common in the previous RTSC studies. But to the best of our knowledge, this paper is the first to propose an algorithm that expands the Q-dimension of a DQN variant [25] by using the technique described in [47], to address the large action space caused by DRBPS with one-second green time intervals.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 5 Joint Optimization Scheme for Communication Rate
### Optimization of Three-Dimensional Trajectory for Relay Drones  
To study the optimization problem of the three-dimensional trajectory, \(Q(k)\), of the relay drones, it is necessary to fix the power allocation factor, \(\lambda^{(c-1)}(k)\), of the cluster drones during the \((c-1)\)th round of iterations within time slot \(k\) and the modulation method \(U^{(c)}(k)\) of the relay drones that was just updated in the \((c)\)th round, as well as the transmission power \(P^{R,(c)}(k)\). Consequently, the original optimization problem \(C_{1}\) can be simplified as follows:  
\[\begin{split} C_{6}:\max_{\{Q\}}:&\ R_{S}^{C,Q}\\ (24)-&\eqref{eq:p:p:p:p:p:p:p:p  
The \(log_{2}(\cdot)\) is a monotonically increasing concave function, the quadratic transformation of the partition in problem \(C_{6}\) must be a concave function, and in Reference [28], the optimization problem \(C_{8}\) after the quadratic transformation of problem \(C_{6}\) is a convex problem, which can be solved by applying the CVX toolkit in order to obtain the global suboptimal solution.
## 4 System Methodology
### Preprocessing, Feature Extraction and Feature Ranking
#### 4.2.1 Time-Frequency Features  
Wavelet transform is used in decomposing the EEG signals into several sub-bands. Here, the Daubechies mother wavelet is selected because it was proven in the literature to be suitable for decomposing EEG signals [51]. The level of decomposition is selected as 10 to capture all the signal coefficients.
## 4 Methodology
### Overview of the Deep Learning Models Used for Classification
#### 4.2.4 Residual Networks  
Introduced by [PERSON] and colleagues, Residual Networks (ResNets) tackled the challenge of training deeper networks effectively [63]. At their core, ResNets employ residual blocks that aim to learn residual functions, simplifying the learning process by focusing on residual mappings. These blocks, through skip connections, allow the network to adjust identity mappings by a residual, enhancing gradient flow and addressing the vanishing gradient problem.  
In a residual block, the network is tasked with learning the residual function \(F(x)\) instead of the original function \(H(x)\). Mathematically, this is represented as:  
\[F(x)=H(x)-x, \tag{4}\]  
where \(F(x)\) is the residual function that the block learns, \(H(x)\) is the desired underlying mapping, and \(x\) is the input to the block. The output of the residual block is then given by:  
\[\text{Output}=F(x)+x=\left(H(x)-x\right)+x=H(x), \tag{5}\]  
where Output is essentially the original function \(H(x)\), with the skip connection ensuring that the input \(x\) is directly added to the learned residual function \(F(x)\). This mechanism allows gradients to bypass one or more layers, thereby mitigating the vanishing gradient problem and facilitating the training of much deeper networks.  
Figure 5: Xception architecture.  
The architecture of ResNets includes various depths such as ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152, each denoting the number of layers in the network. Furthermore, there are _wide_ ResNet versions that expand the number of filters in each layer, thereby enhancing performance at the cost of increased computational demands [63]. The 50-layer deep ResNet model is visualized in Figure 6. Here, the residual connections can be seen along with the variable depth layers repeated, corresponding to the depth of the architecture chosen.  
ResNets have profoundly influenced subsequent deep learning models by offering a robust solution to the challenges associated with training deep networks, balancing performance with computational efficiency. The introduction of residual blocks has set a precedent for many modern architectures, underscoring the importance of efficient gradient flow and effective training of deep models.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 3 Results
### Extraction of Bisphenols from Food Samples  
Based on the above results, poly(MEO2 MA) microgel was employed as a sorbent for selective extraction of BPF, BPE, BPA and BPB from liquid food samples. Preliminary research with sample preparation of solid parts of food was performed with liquid organic solvents. When organic extracts were applied directly into microgel, it resulted in very poor retention of bisphenols. On the other hand, the evaporation of the organic solvents caused some loss of bisphenols. For that reason, only liquid samples were chosen for further studies.  
As listed in Table 1, very good recoveries with the relative standard deviations (RSD) of 0.21-5.01% were obtained for all liquid food samples spiked with 50 \(\upmu\)g\(\cdot\)L\({}^{-1}\) mixture of bisphenols. Slightly poorer recoveries (70.5-84.9%) were obtained for BPF and BPE in fruit canned samples, which were caused by the weaker retention of these bisphenols, as around 10% of the spiked concentration of BPF and BPE were found in the effluent of each spiked food sample. Due to the relatively high complexity of food samples such as those tested, in some cases, the peaks from bisphenols were overlapped by the peak from the matrix, which made it impossible to assess recovery; this happened for the determination of BPB in mushroom or peach sample.  
In five of the analyzed samples (pineapple, peach, mandarin, mushroom and pickles), the recovery for BPA was significantly higher than 110%, which resulted in the conclusion that they might be contaminated with BPA. Further experiments with the preconcentration of food extracts (without spiking) confirmed our suspicions. Figure 4 presents the LC-FLD chromatograms of the following: (A) liquid pineapple sample after the microgel preconcentration (visible peak for BPA, \(t_{\text{R}}\) = 6.39 min); (B) is the chromatogram of the liquid pineapple sample before the preconcentration procedure; and (C) presents the chromatogram of liquid pineapple sample spiked with 0.5 mg\(\cdot\)L\({}^{-1}\) of four analyzed bisphenols, where the retention times were as follows: 3.03 min (BPF), 4.39 min (BPE), 6.36 min (BPA) and 11.2 min (BPB). After the extraction procedure, the detected and calculated concentration of bisphenol A was 6.2 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for pineapple, 8.5 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for mandarin, 11 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for mushroom, 12 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for pickles and 22 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for peach (Table 2). These results are below the migration limits set for BPA (50 \(\upmu\)g\(\cdot\)kg\({}^{-1}\)) [31].  
Figure 4: LC-FLD chromatograms of (A) liquid pineapple sample after microgel preconcentration (with the visible peak for BPA, \(t_{\text{R}}\) = 6.39 min), (B) liquid pineapple sample before preconcentration procedure and (C) liquid pineapple spiked with the standard solution of bisphenols (0.5 mg\(\cdot\)L\({}^{-1}\)).
## Results and Discussion
### Stability  
The stability of GrRAC was previously examined by determining caffeine, obtaining results of good consistency with a relative standard deviation (RSD) of 1.41% (\(n=3\)); this outcome suggested that the cork-graphite composite sensor can be reused. A similar stability assessment was carried out in the case of Pb(II) by recording a series of voltammetric analyses over 10 days. No significant changes in the Pb peak current were observed after five measurements over that time period (Pb(II) = 15 uM; RSD = 1.52%, \(n=5\)). The cork-graphite composite sensor was washed and stored at 25 \({}^{\circ}\)C after each experiment.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 3 Experiment and Results
### Target Detection Experiment  
In this experiment, 164 real and 394 pseudo-side-scan sonar images were used, and each image contained only one target. Target positions in the images were annotated using LabelImg software (v.1.8.6). Shipwreck and aircraft target images were labeled as 0 for \"ship\" and 1 for \"plane\", respectively, and the classifications and detection boxes were saved in XML files. During the experiment, an early stopping mechanism was used to prevent overfitting and save training time. The training parameters were set as the input image size of 640 \(\times\) 640 pixels, a batch size of 16, an initial learning rate of 0.01, a momentum of 0.937, and a weight decay of 0.0005. The optimizer was stochastic gradient descent, and the number of training epochs was set to 200.
# A 3D Two-Phase Landslide Dynamical Model on GIS Platform
## 2 Methods  
As a result of advancements in remote sensing technology and computer capabilities, GIS has emerged as a predominant platform for simulating landslides and debris flows [34; 37]. Within the GIS framework, expression formats primarily comprise vector data and grid data formats. Among these, the grid data format proves optimal for representing fields and aligns well with the Eulerian description approach. Particularly for flow-like models on GIS platforms, the grid networks of DEM facilitate streamlined and accurate calculations [38]. Therefore, we have devised a solution to construct a three-dimensional landslide dynamic model based on DEM. At the same time, we borrowed from the SPH kernel function to transform the simulation results into two dimensions, allowing for their visualization within a GIS platform. The comprehensive process is methodically segmented into five distinct phases, as shown in Figure 1: (1) DEM correction, (2) geometric modeling, (3) numerical calculation, and (4) post-processing.
## 2 Methods: Experiment and Numerical Simulation
### Numerical Models
#### 2.2.4 Modeling of Composite Laminates  
The finite element model of SMA-reinforced composite laminates is generated and analyzed by ABAQUS/Explicit, and the FE model of composite laminates is identical to the experimental setup (see Figure 1). Here, the impactor is modeled as a cylindrical rigid body, and since the mass density of the spherical impactor is 7800 kg/m\({}^{3}\), the mass density of the cylindrical impactor entered in Abaqus should be converted to 2012903 kg/m\({}^{3}\) based on the actual model shape and size of the impactor. The fixed boundary condition is adopted with 48 fully constrained supports that are applied symmetrically to the upper and lower surfaces at the edges of the specimen and the circular rings clamp regions, and the displacements and rotation angles in the x, y, and z direction are all set as zero. Fiber and resin are considered as a whole, and SMA wires are used as reinforcement; both are meshed using an eight-node linear brick, reduced integration hourglass control (C3D8R). Furthermore, to improve computational efficiency and reduce computational time, the accuracy of the mesh sizes and densities is needed. It should be noted that some mesh elements will be eliminated from analysis, such as when a specified mesh element lost its load capability and was accompanied by significant deformation after the impact. Figure 4 shows the finite element model of the laminate under impact loading. Figure 5 shows the schematic diagram of different types of laminates under different impact energies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Symbols** & **Interlayer Interfaces** & **SMA–Layer Interfaces** \\ \hline Cohesive damage (GPa/m) & \(K_{int}\), \(K_{ss}\), \(K_{ss}\) & 15, 15, 15 & 1.5, 1.5, 1.5 \\ Initiation (MPa) & \(\sigma_{nr}^{0}\), \(\tau_{s}^{0}\), \(\tau_{t}^{0}\) & 123, 96, 96 & 12.3, 19.6, 19.6 \\ Evolution (N/m) & \(G_{nr}^{c}\), \(G_{s}^{c}\), \(G_{t}^{c}\) & 0.831, 1.99, 1.99 & 0.167, 0.398, 0.398 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The cohesive parameters values adopted in simulation.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 2 THM Coupling Model  
The continuum theory says that in the space occupied by matter, matter is continuously distributed without gaps. The macroscopic quantities can be acquired by partial differential equation (PDE) methods, and the microscopic structure of substance can be omitted [42]. The theory of mixtures was developed in light of modern continuous-media physics, which extended the continuum mechanics principle to simulate multiphase systems in several permeable continuum media [43].  
General assumptions are as follows:  
1. The partly saturated medium is assumed to be a polyphase system (solid, liquid and gas). The space of the solid skeleton is partly filled with gas, and partly with liquid water.
2. The multiphase medium is considered as a mixture. In its compound, each phase is continuous and any spatial point is presumed to be occupied concurrently by a physical point of each phase.
3. Gas-phase density is a function of temperature and pressure, according to the ideal gas state equation.
4. Fluid seepage follows [PERSON]'s law and does not undergo chemical reactions with solid particles. The saturated vapor pressure follows [PERSON]'s humidity law, and the diffusion of vapor follows the generalized Fick Law.
5. The deformation of porous media satisfies the small-deformation assumption, and the Terzaghi effective stress principle is applicable to unsaturated media.
6. It is assumed that the solid, liquid and gas phases keep partial thermal equilibrium.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Key Concept and Applications
#### 4.5.4 Digital Twins in Aquaponics  
[PERSON] et al. [100] described a decision support system for urban agriculture that utilizes a DT framework and ML to optimize aquaponics. The system employs sensors for monitoring parameters such as water quality, including pH and turbidity, and other critical variables like DO, temperature, and TDS. The paradigm of a Dynamic Data-Driven Application System (DDDAS) is implemented, with data processed through a Raspberry Pi. The simulation operates as a series of modules, covering aspects such as fish feed, TDS, fish weight gain, pH, nitrates, and plant growth, ensuring a comprehensive and integrated approach to managing urban agriculture systems.  
[PERSON] et al. [101] introduced an economic smart aquaponic system leveraging IoT and DT technologies to enhance sustainability and efficiency in aquaculture and hydroponics. The system integrates physical sensors for the continuous monitoring of key parameters, including pH, temperature, water level, DO, and sunlight intensity, managed by a Raspberry Pi. Data are synchronized in real-time using the Firebase platform, facilitating remote supervision and control via IoT. The system prioritizes water quality over irrigation requirements, aiming to reduce labor costs and enhance productivity. This approach simplifies operation, improves resource management, and lowers human intervention by approximately 70%.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 1 Introduction
### UMK Camera  
For the photogrammetric survey, [PERSON] and his team chose a metric camera UMK 6.5/1318 with a super wide lens SuperLamegon PI 5.6/64. From the photogrammetric perspective, for re-processing of the analogue images, it was important to study the used camera.  
UMK cameras (Universal Messungs Kammer) were metric cameras with high-quality lenses manufactured by Carl Zeiss Company in Jena, formerly East Germany. As a first camera of the series, UMK 10/1318 with a wide-angle lens Lamegon 8/100A was designed. The designation of UMK 10/1318 refers to the focal length of the used lens (10 cm) and to the dimension of the storing medium (\(13\times 18\) cm). As a universal UMK 10/1318 was not ideal for some photogrammetric applications, Carl Zeiss Company came with other types of UMK cameras. There were manufactured cameras UMK 20/1318 and UMK 30/1318 with longer focal lengths but also camera UMK 6.5/1318 with a super wide-angle lens SuperLamegon PI 5.6/64.  
The lenses of cameras were focusable in steps, except of Super-Lamegon, which had a fixed-focus set to 8 m. UMK cameras were equipped with a shutter and adjustable aperture. The camera was possible to rotate by +/-15\({}^{\circ}\) and +/-30\({}^{\circ}\) upwards or downwards.  
The construction of cameras was designed to have long-term steady parameters of interior orientation with low distortion. The parameters of interior orientation were usually calibrated in laboratories.  
As a storing medium, glass plate negatives with dimension \(13\times 18\) cm (useful dimension was \(120\times 166\) mm) or roll-film 19-cm wide were used. The glass plate negatives were possible to use in portrait orientation or landscape orientation. A frame with fiducial marks that were projected to the  
Figure 1: Padise Abbey in 2017.  
image was placed in front of the images. It was possible to make marks on the frame (e.g., calibrated camera constant of camera, position of an image in stereo-pair) [20].
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 5 Conclusions  
In this paper, the SAS-YOLOv3-tiny algorithm is proposed to solve the problem that the original lightweight algorithm YOLOv3-tiny was low at accuracy. Even though YOLOv3-tiny has a faster speed and fewer parameters, its detection accuracy needs to be improved. First of all, the lightweight Sandglass-Residual module based on depthwise separable convolution and channel attention mechanism was constructed to replace the original convolution layer while the max-pooling layer was replaced with the convolution layer of stride two, which could reduce the number of parameters and improve detection performance. Furthermore, the detection performance is further improved when three-scale feature prediction is utilized. Next, the improved spatial pyramid pooling module was merged behind the backbone network to extract expressive features. Finally, we utilized CIoU to improve the loss function, which also improved the location effect. In conclusion, for the validation set, SAS-YOLOv3-tiny made P from 70.7% to 73.2%, made R from 73.3% to 80.2%, made mAP from 73.7% to 81.6% and made F1 from 71.9% to 76.4%. For the test set, SAS-YOLOv3-tiny had good generalization, and it performed better than the original YOLOv3-tiny at the expense of 0.7 ms speed, which was comparable to YOLOv4-tiny in terms of detection accuracy; compared with the heavyweight algorithms YOLOv3 and YOLOv4, SAS-YOLOv3-tiny had a great advantage in speed although its detection accuracy was not as good as theirs. The experimental results and contrast curves reveal that the improved methods can strengthen the effect of detection. The next work is to expand the safety helmet dataset based on the dataset in this paper and further improve the detection accuracy while maintaining a lower number of parameters and speed.  
Conceptualization, X.H. and R.C.; methodology, R.C. and X.H.; software, R.C.; validation, X.H. and R.C.; formal analysis, X.H. and R.C.; investigation, R.C., X.H. and Z.W.; resources, X.H. and Z.Z.; data curation, R.C. and X.H.; writing--original draft preparation, R.C.; writing--review and editing, X.H. and R.C.; visualization, X.H. and R.C.; supervision, X.H.; project administration, X.H. and Z.Z.; funding acquisition, X.H. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (61572023, 61672467).  
**Institutional Review Board Statement:** Not applicable.  
Figure 7: The detection results of some test images.