esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 2 Network Layout  
To accommodate many users, multi-channel transmission model is one of the promising solutions. However, this model also contributes to the additional dispersion effects of the system. The proposed layout in this work is of an effective equalizer for the OCN, in order to minimize the effects from the optical transmission and increase the system's performance. In this section, the proposed network architecture is explained, as shown in Figure 1, to demonstrate the system components and their parametric values, which have been used to analyze the multi-channel OCN transmission. The elements such as pseudo random bit sequence (PRBS) generator, continuous wave laser (CWL), non-return to zero (NRZ) pulse shaper, Mach-Zehnder modulator (MZM), optical DWDM multiplexer (MUX), single mode fiber (SMF), erbium doped fiber amplifier (EDFA), optical Gaussian filter, polarization controller (PC) to reduce the polarization dependent losses, positive intrinsic negative (PIN) photodiode, band pass Gaussian filter, signal analyzers, EE, and 3R-regenerator (3R-regenerator means re-amplification, re-shaping and re-timing) are used in the proposed system for simulations.  
In the OCNs, the data are transmitted over fiber in the form of light. For each user unit, the PRBS module generates stream of bits and defines the data rate of the system. These generated bits are then converted to electrical signals using NRZ module whose output is given as input to the MZM modulator. MZM is used as an external modulator which modulates the input radio frequency (RF) data signal on to the optical wavelength. The MZM is biased at quadrature point to provide maximum linearity in data modulation. The output of the MZM from each user unit is combined using a DWDM multiplexer, whose output is transmitted over SMF from the central station to the base station. The 16 total channels are combined to make a composite DWDM signal, which is transported over the single fiber link. The fiber consists of constant 0.2 dB/km attenuation losses; hence, increasing the fiber length will result in increasing the propagation losses and, consequently, CD and PMD effects will also be increased. To compensate the attenuation losses, EDFA amplifier (10 dB gain for every 50 km SMF span) is used in the system. At the other end of transmission, an optical filter is used to retrieve the data for a particular user (to perform performance analysis). The use of optical filter, and a RF filter after the PIN diode, removes the out-of-band noise from the user's data. PIN photodiode is used at the receiver end to convert optical data into electrical signals. Due to high dispersion losses, the data will not be in its original form after covering a long distance. Therefore, equalization techniques have been used to retrieve the original form of data. Re-amplification, re-shaping, and re-timing are the functions embedded in the 3R-regenerator. The purpose of BER analyzer is to investigate received signals in terms of BER, Q-factor and eye-diagrams.
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 3 Methodology
### Network Topology Diagram  
A network topology diagram in Figure 4 illustrates the physical layout of routers, subnets, and connections between network components. To arrive at this network diagram, we used Cisco Packet Tracer. All the components that were connected by wired means used Copper Straight-Through wires available within Packet Tracer. The laptops to be connected wirelessly to the Wireless Router were first adapted by installing the Linksys-WPC300N module. The installation process requires turning off the laptop, installing the module, and then turning the laptop on again. This module provides one 2.4 GHz wireless interface suitable for connection to wireless networks. The module supports protocols that use Ethernet for LAN access. The connection between Router2 and the wireless router was conducted using Copper Cross Over wire whereas the connections between the three routers Router0, Router1, and Router2 were conducted using Serial DCE cables. It was necessary to install the HWIC-2T which is a Cisco 2-Port Serial High-Speed WAN Interface Card, providing two serial ports. To proceed with the installation, we turned each router off, installed the serial ports, then turned the router on again.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interface** & **Router0** & **Router1** & **Router2** \\ \hline GigabitEthernet0/0 & 192.168.5.193 & NA & 192.168.5.129 \\ \hline Subnet Mask & 255.255.255.224 & NA & 255.255.255.192 \\ \hline GigabitEthernet0/1 & NA & NA & 192.168.5.1 \\ \hline Subnet Mask & NA & NA & 255.255.255.128 \\ \hline Serial0/3/0 & 192.168.5.225 & 192.168.5.226 & 192.168.5.230 \\ \hline Subnet Mask & 255.255.255.252 & 255.255.255.252 & 255.255.255.252 \\ \hline Serial0/3/1 & NA & 192.168.5.229 & NA \\ \hline Subnet Mask & NA & 255.255.255.252 & NA \\ \hline \hline \end{tabular}
\end{table}
Table 5: Router Interface Configuration.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 4 Distribution Matching and Sphere Shaping Architectures
### Geometric Interpretation of the Shaping Architectures  
Output sequences of CCDM have a fixed composition and thus, all have the same sequence energy \(nE\), i.e., they are located on the \(n\)-dimensional shell of squared radius \(E^{\circ}=nE\). We note that there are multiple compositions that lead to the same sequence energy and thus, the corresponding shell is only partially utilized by CCDM, as shown in Figure 6 (left). With multiple compositions at its output, MPDM makes use of multiple partly filled \(n\)-shells, as in Figure 6 (middle). The average symbol energy as well as the squared radius of the outermost shell that is utilized by MPDM depend on the actual set of considered compositions. Finally, \(n\)-sphere shaping employs all sequences inside the \(n\)-dimensional sphere of squared radius \(E^{\bullet}\), as shown in Figure 6 (right). Note that for simplicity, we have in this explanation neglected the constraint that any practical binary scheme can only address a power-of-two number of shaped sequences. When all three approaches enclose the same number of sequences at a fixed \(n\), their average energy as in (6) satisfy \(E_{\text{ccdm}}\geq E_{\text{mpdm}}\geq E_{\text{spsh}}\). Therefore, at any blocklength, SpSh makes use of the set of sequences having the least average energy and thus, it is the most energy-efficient scheme. This observation will later be confirmed by the rate loss analysis in Section 5.1.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 3 Data Description
For instance, temperatures were from \(13.9\)\({}^{\circ}\)C to \(35.1\)\({}^{\circ}\)C, and concentrated around 20 to 30 \({}^{\circ}\)C (Figure 2d). Furthermore, relative humidity was concentrated in high values, showing the common behavior in a tropical environment (Figure 2e). Moreover, particulate matter was concentrated in low values for EN4 because it is located inside a campus surrounded by a forest; however, there are two peaks in 28 and 50 \(\mu\)g/m\({}^{3}\), which are caused by a rock mine near the campus (Figure 2g). In addition, it can be noticed that the distribution of the experimental path loss is Gaussian-bell-shaped as expected [12] (Figure 2h). Finally, the distributions of consumed energy for ENs 1, 2, and 4 are similar; nevertheless, the EN3 has its energy concentrated around 0.1 J, which was caused by the fixed SF = 10 that guaranteed that the received signal could be demodulated. Regarding the packet delivery rate (PDR) of each EN, we obtained 95.1%, 85.2%, 81.6%, and 86.35% for EN1, EN2, EN3, and EN4, correspondingly. These PDRs can be explained from the SNRs obtained for each EN, as depicted in Figure 2j. According to Table 2, varying the SF allows the signal power level to fall below the noise power level up to \(-20\,\)dB. Furthermore, as we will see in Section 4.4, we distributed the SF uniformly for each EN from 7 to 10. In that way, we obtained PDRs according to the SNR of each EN. For instance, EN1 achieved SNRs over 0 dB, so getting a PDR of 95.1% is expected because many packages were delivered successfully. On the other hand, we notice that EN3 achieved the lowest PDR (81.6%) because the mean SNR is approximately \(-15\,\)dB, so using a low SF can cause a loss of packets. The dataset also includes the Effective Signal Power (\(ESP\)) metric, which is defined as the signal power in the receiver without including the noise power (Equation (3)) and the noise power \(P_{n}\) (Equation (4)) [39]:  
\[ESP_{(dB)}=RSSI_{(dB)}+SNR_{(dB)}-10\cdot log_{10}(1+10^{\frac{SNR_{(dB)}}{10}}) \tag{3}\]  
\[P_{n(dB)}=RSSI_{(dB)}-10\cdot log_{10}(1+10^{\frac{SNR_{(dB)}}{10}}). \tag{4}\]  
The \(ESP\) and \(P_{n}\) are relevant metrics by which to evaluate the quality of LoRaWAN radio links instead of \(RSSI\) and receiver sensitivity (traditionally, \(RSSI>Sensitivity+LM\)) because successful demodulation is achieved when \(ESP_{(dB)}>P_{n(dB)}-SNR_{limit}+LM\)Thus, the \(ESP\) and \(P_{n}\) empirical distributions are depicted in Figure 3, where it can be noticed that the \(ESP\) is always under the \(P_{n}\), concluding that LoRaWAN can withstand very adverse channel conditions. Figure 2: Empirical distributions of the collected variables per node. In **(a)**, we depict the SF usage from 7 to 10. In **(b)**, we show the number of collected samples at each distance. In **(c)**, we show the number of collected samples at each frequency. In **(d-j)**, we depict the experimental distributions per node for temperature, relative humidity, barometric pressure, particulate matter, path loss, energy, and SNR, correspondingly.
# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
###### Abstract
The performance of JPEG-LS improves with simpler image scenes, whereas in more complex scenes, the prediction and context updating become more intricate, leading to a decrease in compression speed. JPEG-LS is only applicable to 8-bit and 12-bit images and is not suitable for images with higher bit depths. The complexity of image redundancy removal and entropy coding, along with limitations in pixel bit depth, restricts the application of JPEG-based algorithms in line-scan imaging with high data rates and high pixel bit depths. This study focuses on 14-bit line-scan infrared panoramic images. Unlike traditional area-array images, each row of the image is generated by the same photosensitive element, leading to stronger inter-column correlation. Conventional JPEG series image compression methods do not take into account the characteristics of the line-column scanning image. Based on the characteristics of 14-bit line-scan infrared panoramic images, this paper analyzes the feasibility of removing spatial redundancy through inter-column differencing. The inter-column differencing DPCM prediction method is used to replace the complex wavelet transform in JPEG2000 for removing spatial redundancy in the image. This paper also designs an improved Huffman coding scheme to replace the complex entropy coding in JPEG 2000. The improved Huffman coding simplifies the image compression process by using a code table. Additionally, a method for generating the code table is proposed, simplifying the compression process by avoiding pixel statistics in entropy coding. Based on the proposed methods, a low-complexity lossless compression algorithm based on the code table is ultimately implemented using a simple lookup method. The structure of this paper is as follows: Section 2 introduces related work on Huffman coding, Section 3 analyzes methods for redundancy removal in line-scan infrared panoramic images, Section 4 presents the code-table-based lossless compression method proposed in this paper, Section 5 provides the experimental results, and Section 6 concludes the paper.
## 2 Related Work
### Approaches to Disturbance Handling and Prediction in Greenhouse Control  
In greenhouse NMPC, disturbance management can be approached either reactively (feedback-only) or proactively (predictive). While traditional NMPC often relies on current disturbance measurements (feedback-only), incorporating multi-step forecasts from AI models enables proactive adaptation and can significantly enhance control performance. The importance of weather forecast horizons has been investigated, for instance, in [PERSON]'s work, which, although RL-focused, highlights the impact of forecast horizons [21]. [PERSON] et al. have modeled weather forecast errors and analyzed their impact on automated greenhouse climate control systems [22].  
ML has demonstrated significant advancements in general weather forecasting, as indicated in recent surveys [5,23]. For specific greenhouse applications, [PERSON] et al. successfully applied a Recurrent Neural Network (RNN) with an LSTM model for temperature prediction, further illustrating the promise of ML for microclimate forecasting in precision agriculture [4]. Hybrid models that integrate neural networks with first-principles dynamics offer promising accuracy and robustness for real-time disturbance and state prediction in complex systems [23]. Recent advances in Bayesian deep learning, such as Bayesian Neural ODEs, have enabled greenhouse models to quantify prediction uncertainty, supporting robust decision-making and stochastic control [24]. However, the effective integration of these advanced ML-based predictions into NMPC and the quantification of their benefits alongside other operational aspects, such as computational load, remain active areas of research. Furthermore, robust NMPC formulations that inherently attempt to handle uncertainty can increase computational demands, raising concerns for real-time feasibility in smart greenhouse automation.
## 4 Three-Dimensional Modeling of Structural Planes Based on B-Spline Interpolation
### Curve Fitting  
Because the raw structural data consists of discrete sampling points, curve fitting is first required to generate continuous and smooth lines. Several alternative interpolation methods--such as triangulated irregular networks (TINs), radial basis functions (RBFs), and Kriging--were considered. However, these approaches either lacked sufficient smoothness (e.g., TINs), incurred high computational costs (e.g., Kriging), or offered limited local control (e.g., RBFs).  
B-spline interpolation was selected for its favorable balance between computational efficiency, geometric continuity, and local controllability, making it particularly well-suited for modeling irregular yet continuous geological surfaces. In this study, cubic spline interpolation is employed to fit the sampled points, using segmented third-order polynomials that ensure continuity in function value, first derivative, and second derivative across data points. The general mathematical formulation is as follows:  
\[S(x)=a_{i}+b_{i}(x-x_{i})+c_{i}(x-x_{i})^{2}+d_{i}(x-x_{i})^{3} \tag{4}\]  
where \(x_{i}\) are the data points, and \(a_{i}\), \(b_{i}\), \(c_{i}\), and \(d_{i}\) are coefficients determined by interpolation and smoothness constraints.  
The implementation was carried out in Python using the CubicSpline function from the SciPy library. The process includes: (1) reading coordinate data from stored files, (2) performing spline interpolation to construct smooth curves, and (3) visualizing the result with Matplotlib.  
The interpolated curves of different stations for structural plane l were plotted in a 3D coordinate system, and the same process was repeated for other structural planes. An example is shown in Figure 8.  
To evaluate interpolation quality, the mean squared error (MSE) was used:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}{(y_{\text{true}}-y_{\text{pred}},i)}^{2} \tag{5}\]  
where \(n\) is the number of points, and \(y_{\text{true}}\) and \(y_{\text{pred}}\) are the actual and predicted Y-values.  
The threshold of 75 sampling points was determined through an empirical analysis aimed at balancing model complexity and reconstruction accuracy. As illustrated in Figure 9,the mean squared error (MSE) decreased notably with increasing point density and tended to stabilize beyond 75 points. This threshold thus represents a practical trade-off between interpolation precision and computational efficiency. The fitting results are compared to the original data in Figure 10, demonstrating excellent agreement.  
Figure 8: Fitted curves from sampled structural lines.  
Figure 10: Comparison between fitted and actual curves.  
Figure 9: Relationship between MSE and number of sampled points.
# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 3 Experiments
### Evaluation Indices  
As evaluation indices, we selected the peak signal-to-noise ratio (PSNR) [27] and structural similarity index (SSIM) [27] for the objective assessment.  
PSNR evaluates the pixel difference between the separated image and the real image. The PSNR is defined as follows:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right)=20\cdot\log_{ 10}\left(\frac{MAX_{I}}{\sqrt{MSE}}\right), \tag{5}\]  
where \(MAX_{I}\) is the maximum value representing the color of the image point; a higher PSNR value indicates a smaller distortion.  
From the perspective of image composition, the SSIM regards the structural information as independent of brightness and contrast, which reflects the properties of the object structure in the scene, and the distortion is measured as a combination of three different factors of brightness, contrast and structure. SSIM is defined as follows:  
\[\text{SSIM}(\mathbf{x},\mathbf{y})=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2 \sigma_{xy}+c_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left( \sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}\right)}, \tag{6}\]  
where \(\mu_{x}\) is the average of \(\mathbf{x}\), \(\mu_{y}\) is the average of \(\mathbf{y}\), which are estimates of brightness; \(\sigma_{x}^{2}\) is the variance of \(\mathbf{x}\), \(\sigma_{y}^{2}\) is the variance of \(\mathbf{y}\), and \(\sigma_{xy}\) is the covariance of \(\mathbf{x}\) and \(\mathbf{y}\), the variance is an estimate of contrast, and covariance is a measure of structural similarity. Moreover, to maintain stability, two constants, \(c_{1}=\left(k_{1}L\right)^{2}\) and \(c_{2}=\left(k_{2}L\right)^{2}\), are added, \(L\) is the dynamic range of pixel values, \(k_{1}=0.01\), and \(k_{2}=0.03\) The range of the SSIM values is [0, 1].
###### Abstract  
Concentrations of ambient particulate-bound polycyclic aromatic hydrocarbons (pPAHs) were measured in PM\({}_{10}\) samples collected at roadside, industrial and urban background sites in Bangkok between May 2013 and May 2014. The annual average PM\({}_{10}\) concentrations were not significantly different between the roadside (56.4 \(\pm\) 27.3 ug m\({}^{-3}\)) and industrial (51.0 \(\pm\) 31.1 ug m\({}^{-3}\)) sites. The lowest annual mean PM\({}_{10}\) was observed at the urban background site (39.8 \(\pm\) 22.2 ug m\({}^{-3}\)). Seasonal variations of pPAHs were observed at the three sampling sites. The total pPAHs ranged between 1.09 and 13.10 ng m\({}^{-3}\) (mean 4.85 \(\pm\) 2.51 ng m\({}^{-3}\)), 1.49 and 9.39 ng m\({}^{-3}\) (mean 3.84 \(\pm\) 2.01 ng m\({}^{-3}\)) and 0.77 and 5.20 ng m\({}^{-3}\) (mean 2.28 \(\pm\) 1.16 ng m\({}^{-3}\)) at the roadside, industrial and urban background sites, respectively. The observed annual average benzo[a]pyrene concentrations were 0.47 \(\pm\) 0.39 ng m\({}^{-3}\), 0.35 \(\pm\) 0.27 ng m\({}^{-3}\) and 0.24 \(\pm\) 0.19 ng m\({}^{-3}\) at the roadside, industrial and urban background sites. Long-term carcinogenic health risk of inhalation exposure expressed as the toxicity equivalent to benzo[a]pyrene concentrations were calculated as 0.83, 0.72 and 0.39 ng m\({}^{-3}\) at the industrial, roadside and urban background sites, respectively. The composition of pPAHs plays an important role in the carcinogenicity of a PAHs mixture.  
benzo[a]pyrene; GC-MS; PM10; polycyclic aromatic hydrocarbons; risk assessment Article
## 4 Simulation and Discussions
### Short-Term Prediction  
Based on the load transfer function calculated above, the short-term forecast was made selecting the JONSWAP spectrum, which can be expressed as follows:  
\[S(\omega)=487[1-0.287\ln(\gamma)]\frac{H_{\rm s}^{2}}{T_{p}^{4}}\omega^{-5}\exp \left(\frac{-1948}{T_{p}^{4}}\right)\gamma^{\exp\left(\frac{-(0.199\omega T_{p }-1)^{2}}{2\gamma^{2}}\right)} \tag{7}\]  
where \(\gamma\) and \(\sigma\) represent the spectral peak lifting factor and the spectral width parameter, respectively. When \(\gamma\) is equal to 1, the above formula degenerates into the P-M spectrum. Based on statistical data of the sea state where the platform operates, the wave parameters were selected as follows: significant wave height \(H_{\rm s}\) = 2.5 m, period \(T_{p}\) = 9.0 s and peak lifting factor \(\gamma\) = 2.0.  
It can be seen from Table 3 that the maximum of \(Fx\) and \(Fz\) appeared near 75\({}^{\circ}\), which is consistent with the direction of the maximum RAO. Furthermore, the maximum of \(Fy\) also appeared near 75\({}^{\circ}\), whereas the direction of the maximum RAO appeared near 60\({}^{\circ}\). By analyzing the shape of RAO under different wave directions (Figure 10b), it was found that the spectral width of RAO increases rapidly with increases in the wave direction, but the peak value of 60 degrees was close to 75 degrees, resulting in an increasing trend in the variance of the transfer function, defined by \(m=\int_{0}^{\infty}S(\omega)d\omega\). According to the relationship between the significant value \(x_{1/3}\) and variance \(m\) in the Rayleigh distribution (\(x_{1/3}=2\sqrt{m}\)), the maximum significant value of the load finally appeared at 75\({}^{\circ}\). It can be concluded that the wave direction of the maximum short-term significant value of the platform was not the same as the direction where the maximum of RAO appeared. It is suggested that all the wave directions should be searched in order to obtain an accurate maximum short-term significant value when calculating the connector loads.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
###### Abstract  
Addressing the issues of weak echo signals and strong background interference in the laser detection of ships' wakes, an analysis of the laser backscatter detection characteristics of ships' wakes has been conducted. Based on the Monte Carlo method, a simulation model for the dual-mode fusion detection of ship wake bubbles using laser technology was constructed under different target characteristics. A dual-mode fusion detection system for ships' wakes was designed, and an indoor experimental platform for the dual-mode fusion detection of ship wake bubbles using laser technology was established. To address problems such as a wide range of echo signal intensity changes, severe signal fluctuations, low resolution, poor image contrast, and blurred target edge information, an algorithm based on multi-timescale hierarchical fusion signal processing and temporal difference accumulation image processing was proposed. Verification experiments for ship wake detection were conducted, which revealed that the dual-mode fusion detection method for ship wake bubbles using laser technology can effectively enhance the detection signal-to-background ratio and counter the maneuvering evasion of underwater weapons by ships. It achieved high sensitivity, large dynamic range, high resolution, and a wide field of view detection and real-time signal processing of ship wake bubble targets of different magnitudes against a strong reverberation background. The effectiveness of the dual-mode fusion detection mode was validated, providing theoretical support for the overall system design and parameter settings.  
ship wake; laser dual-mode detection; backscattering; image processing