esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The AIC and ABER of bandwidth-constrained link with signal modulation of M-QAM has been derived in a closed form. For a given bandwidth-constrained link, we find that choosing a suitable modulation order M can reduce the bandwidth limited ABER caused by the channel of turbulent absorption seawater. Seawater scattering, bubbles, physio-chemical properties of seawater, and pointing errors are other important factors affecting optical communication in seawater, but seawater scattering, bubbles and physio-chemical properties of seawater are very complex factors affecting optical communication transmission [25; 26; 27; 28; 29], so we only consider a POV in clean or ignore the complicated factors of optical communication problems in offshore underwater channels. Therefore, the optimization of source parameters and AIC of underwater wireless optical communication absorption link with carrier of POV and M-QAM are investigated under Rytov approximation. In Section 2, normalized amplitude of POV in weak absorptive turbulence is presented. In Section 3, the bandwidth limited ABER of POV carrier link using M-QAM is derived. In Section 4, the AIC of the POV carrier and bandwidth-constrained link using M-QAM is proposed. Based on the numerical simulation of bandwidth-limited ABER of M-QAM link and the AIC of the link, the optimized POV source parameters and the influence of seawater turbulence and absorption on the AIC of the communication link are given in Section 5, respectively. Finally, conclusions are given in Section 6.
## 3 Results and Discussion  
### Physico-Chemical Composition  
The results of the physico-chemical analysis of the experimental cheeses and brines during the ripening-preservation period are shown in Tables 1 and 2. The type of containers (TC and SST) did not have a statistically significant (\(p\) > 0.05) effect on the content of total solids, moisture, fat, protein, ash, salt, and pH between both the WBC and the brines of the two groups (TC and SST).  
After 10 days of ripening, the pH of the cheeses kept in TC and SST decreased, at 4.43 and 4.45, respectively, which is desirable. At 60 days onwards, both white brine cheeses (kept in TC and SST) had the desired pH (up to 4.5). The same pH (<4.6) was observed in the respective brines too. This pH value is particularly important in order to prevent microbial spoilage.  
At 60 days onwards, when the cheese is considered ready for consumption, the average moisture content of the cheeses being ripened in TC and SST ranged from 53.04 to 56.34% and 54.13 to 54.63%, respectively. Both cheeses (TC and SST ripened), are classified as quality A, as set out in Greek legislation [20].  
During the ripening-preservation process, the fat content (expressed on dry matter) of both cheeses was similar. At 60 days, the fat content of TC- and SST-ripened cheees ranged from 51.64 to 53.50% and 52.18 to 53.74%, and that remained constant up to 90 days. Based on their fat content, the cheeses from both groups are classified as quality A based on Greek legislation [20].  
The protein content (expressed on dry matter) of both TC- and SST-ripened cheees was similar (\(p>0.05\)); during ripening-preservation, and after the 60 th day, this ranged from 34.26 to 35.84% and 34.80 to 36.24%, respectively. Both groups presented their maximum value at day 90. In addition, the protein content of the respective brine, was seen to increase continuously up to day 90, when a maximum value for both brines was recorded. This probably indicates the transfer of proteins from the cheese to the brine.  
The salt concentration in both cheese groups increased over time and, on the 60 th day onwards, ranged from 2.77 to 3.19% and 2.90 to 3.24% in TC and SST, respectively. No significant statistical differences (\(p>0.05\)) were observed in the salt concentration of SST-ripened cheees compared to the TC cheees. This can be confirmed by the salt content of the brine which, in both cases, decreased up to the 90 th day.  
The ash content of the cheeses (in TC and SST) was similar (\(p>0.05\)) and increased over time and varied from 3.29 to 3.74% and 3.44 to 3.81% for TC and SST, respectively. The physico-chemical characteristics and the pH of TC- and SST-ripened cheees, throughout their ripening-preservation process, agree with those described in recent research works [21; 22; 23; 24].
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 2 Experimental Program
### The Assessment Technique
#### 2.3.3 Microstructural Analysis  
To capture the healing progress of the AAS specimens and check the ability of microbes to precipitate calcite crystals, scanning electron microscopy (SEM) was applied. Crushed powder specimens were scanned in an electron microscope equipped with an energy-dispersive X-ray analysis (EDX) detector. To explore the produced chemical compounds, such as hydration products and calcium carbonate precipitation, an X-ray diffraction test (XRD) was performed.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation  
The proposed methodology introduces a novel strategy for a Smart Farm Surveillance System (SFSS) that leverages IoT and machine learning technologies to detect wildlife and human intrusions. This strategy offers enhanced accuracy and responsiveness compared to traditional monitoring systems such as using wooden scarcorows to repel animals. For enhanced object recognition and quick user warnings, the suggested system takes advantage of cloud computing. Figure 2 presents the methodology of the proposed system.  
To begin, live footage from the agricultural fields is effortlessly sent to the cloud servers using microcontrollers. These cloud servers perform two functions: they save real-time agricultural field imagery and they host machine learning algorithms that recognize animals and possible incursions. The implementation of the machine learning algorithms on cloud servers allows for the execution of intrusion detection in real time.  
When an incursion occurs, whether it is caused by animals or people entering the field, the system initiates an alarm mechanism meant to repel these invaders and inform surrounding persons. This warning method provides a quick reaction to any illegal presence, both brightening away possible dangers and informing individuals in the affected area.  
In the case of animal or human invasions, the cloud server sends an SMS alert to the field owner as an added layer of protection and for user convenience. This timely notice provides the customer with a thorough understanding of any security breaches in their agricultural assets.  
To examine the overall performance of the system, a thorough evaluation is performed to determine the feasibility and efficacy of the suggested strategy. A detailed study of the AI models' capacity to identify intrusions is required for performance measurement. End-to-end testing of the system includes testing its video transmission, intrusion detection, and the effectiveness of its alarm systems. This comprehensive assessment guarantees that the system runs smoothly and offers the appropriate degree of security and intrusion detection.
## 4 Discussion
### AbdoMan Phantom  
Finally, the accuracy of the quantification was assessed by comparing the measured activity in the liver lesions and in the liver compartment with the known activity within the phantom measured at preparation. As a general rule, our quantification procedure provided excellent results for the liver compartment, where the measured activity overestimates the expected activity by +1%. Similar results were obtained for the liver lesions where the measured activity was slightly underestimated in the 40 mm sphere (\(-2\%\)) and overestimated in the 20 mm sphere (+4%). The poorest result was obtained for the 40 mm hollow sphere, with an underestimation of \(-20\%\) in the measured activity. This underestimation can be explained in part by imperfect correction for partial volume effects, representing a major correction factor in very small objects. From a clinical point of view, this issue is of particular relevance for wide HCC lesions with hypoxic necrotic core, for which SPECT system may not be able to estimate the activity gradient in the circular corona. Apart from this slight discrepancy, our results compare well with those obtained by [PERSON] et al. [15] and confirm that the SPECT/CT Symbia Intevo system along with the xSPECT software is likely to provide excellent activity estimates. Data on the quantitative accuracy are detailed in Table 4. In particular, the most striking result to emerge from this study is that the proposed acquisition protocol is capable of recovering the activity concentration even in very small objects (+4% in the 20 mm sphere).  
The present findings might have important implications for dosimetry studies in RE, where accurate activity quantification in small lesions is essential to obtain reliable dose estimates. Further work needs to be done to assess the capability of modern SPECT systems and up-to-date reconstruction software to estimate the absorbed dose in 3D printed anthropomorphic phantoms. In addition, it is a question of future research to investigate the use of 3D printed phantoms provided with real clinical data (e.g., physical model of the patient's tumor), with the possibility of including personalized radiobiological parameters in the dose assessment model.
## 4 Results on ML/DL-Based Adaptive Modulation
### Overview of Adaptive Modulation Strategies  
An overview of ML/DL techniques, optimizers, and training examples for single-carrier and multi-carrier UWA communication systems, implementing adaptive modulation, is presented in Tables 10 and 11. The detailed descriptions of the various parameters listed in these tables were provided earlier in Section 3.1. Notably, RL methods like table lookup and Q-learning do not use typical optimizers. Instead, they follow rules from the Bellman equation to adjust learning step by step.  
It can be observed from Tables 10 and 11 that SC-UWA systems utilize ML models such as SVM, KNN, LDA, BRT, MLR, MLP, CNN, and RL for measured as well as simulated datasets. In some cases, no dataset is used, particularly in RL approaches. In MC-UWA systems, adaptive modulation techniques involve models like A-kNN, CNN, and RL-based approaches. Training examples range from measured data to simulated datasets, with Bellhop being a common simulation tool. Some RL studies do not use a dataset, focusing instead on theoretical evaluations. Overall, SC-UWA techniques employ a mix of traditional ML models and deep learning approaches, while MC-UWA techniques integrate more RL methods and simulation-based training. The diversity in optimization strategies and training datasets highlights the evolving nature of adaptive modulation in UWA communication.  
To summarize, SC-UWA systems for adaptive modulation use a mix of traditional ML and DL methods. They often rely on measured data. Some RL approaches skip datasets and use theory. MC-UWA systems use more simulation and RL methods. Bellhop is commonly used for channel modeling. Training sources range from real to synthetic data. This reflects the variety in adaptive modulation strategies for UWA communication.
## 2 Methods and Equipment
### Holographic Particle Image Quality Indicators  
To assess the influence of distortions considered in this study (non-stationary coherent noises and the twin-image effect) on the image quality of a particle reconstructed from a digital hologram, it is necessary to use quantitative indicators of the particle image quality. The most common parameters used for this are MSE [26], SNR or PSNR [27], and image intensity dispersion \(\sigma\)[28]. These parameters are applicable when the reference image is used, and in this case, they allow comparing the noisy test image with the reference image (without noise).  
In a holographic study of real particles under natural conditions, for example, plankton and other marine particles, it is impossible to compare the reconstructed image with the reference image. Moreover, the accurate determination of the size and shape of the particle based on its holographic image [24; 25], as well as its recognition, require a clear definition of the image boundary, which, in turn, requires accurate digital focusing, i.e., to determine the value z at which the image is of the highest quality (sharp). To assess the digital focusing quality of reconstructed particle images, we will use the quality indicators of the particle image reconstructed from the digital hologram--boundary contrast K and boundary intensity jump P at the boundary of the particle image [29]. For this purpose, let us define areas with much smaller width than the particle size along the boundary of the reconstructed image of the particle from the external (background) and internal sides of the particle image. The boundary contrast and boundary intensity jump characterize the quality of boundary detection: \(K=\frac{\langle I_{\mathrm{int}}\rangle}{\langle I_{\mathrm{ext}}\rangle}\)--boundary contrast and \(\Delta I=\frac{\langle I_{\mathrm{int}}\rangle-\langle I_{\mathrm{ext}} \rangle}{I_{\mathrm{av}}}\)--boundary intensity jump, where \(\langle I_{\mathrm{int}}\rangle=\frac{\sum_{i=1}^{n}  
intensity of the external area; \(I_{i}\)--intensity of i pixel; \(n\), \(m\)--number of pixels of the internal and external areas, respectively; and \(I_{av}\)--average image intensity.  
To assess the possibility of recognizing the shape of a particle (for flat model particles with known parameters), the study uses the distinctive ability \(\delta_{s}=\frac{|\,s_{im}-s|}{s}\) over the area and \(\delta_{l}=\frac{|\,l_{im}-l|}{l}\) over the length of the boundary, where \(l_{im}\)--length of the boundary of the particle holographic image, \(s_{im}\)--area of the particle holographic image, \(l\)--length of the boundary of the model particle itself, and \(s\)--its area [23]. We will use the distinctive ability indicators in laboratory experiments with flat model particles with known geometric parameters.
## 1 Introduction
### Basic Model \(M_{0}\)
We note that at least a gross sense of direction may be technologically possible by smart and/or multiple antennas. Regarding the extra cost of the antennas and circuitry, we note that certainly such a cost is introduced but as technology advances this cost may lower. (ii) It can estimate the distance from a nearby particle that did the transmission (e.g. via estimation of the attenuation of the received signal). (iii) It knows the direction towards the sink \(S\). This can be implemented during a set-up phase, where the (very powerful in energy) sink broadcasts the information about itself to all particles. (iv) All particles have a common co-ordinates system. Notice that GPS information is assumed by this model. Also, the global structure of the network is not assumed known. This model is used in the PFR protocol.
## 3 Proposed Scheme
### ANN-Based DoS Detection  
We employ an artificial neural network (ANN) for AV local training, comprising input, hidden, and output layers, as shown in Figure 5. The ANN architecture consists of 25 layers with validation and testing data of 15% each. The input layer comprises multiple neurons, which are connected to each neuron of the hidden layer, and similarly, the hidden layer neurons are linked to the output layer. At each layer, the output can be calculated after passing the neuron's current value through an activation function. In the ANN, we utilized the scaled conjugate gradient (SCG) as a training algorithm. We also trained the ANN model using another training algorithm, such as Levenberg-Marquardt (LM) or Bayesian Regularization (BR); however, the most suitable results were achieved by the SCG training algorithms. The output of the \(i_{th}\) hidden layer's neuron can be calculated using Equation (5) below. In Equation (5), \(\omega 1_{j,i}\) represents the weight of the \(i\)th neuron of \(j\)th hidden layer, \(\alpha_{i}\) represents the \(i\)th input features, and \(\beta_{1}\) and \(\beta_{2}\) represent the biases of the hidden and output layers, respectively.  
\[HL_{j}^{V_{i}}=\frac{1}{1+e^{-(\sum_{i=1}^{m}\omega 1_{j,i}^{V_{i}}a_{i}+\beta_{1})}} \tag{5}\]  
In Equation (6), \(\phi_{j}^{V_{i}}\) represents the output of the \(j\)th neuron of the output layer for vehicle \(V_{i}\), and \(\beta_{2}\) represents the bias of the output layer. After obtaining the output \(\phi_{j}^{V_{i}}\) from the output layer, the predicted output is compared with the targeted output \(TO_{k}^{V_{i}}\), as given in Equation (7), to calculate the error \(E_{V_{i}}\) between the predicted and target outputs.  
\[\phi_{j}^{V_{i}}=\frac{1}{1+e^{-(\sum_{i=1}^{m}H_{j}^{V_{i}}\omega _{i}+\beta_{2})}} \tag{6}\]  
\[E_{V_{i}}=\frac{1}{2}\sum_{k=1}^{q}{(TO_{k}^{V_{i}}-\phi_{k}^{V_{i}})}^{2} \tag{7}\]  
If the learning criteria are not met, then the weights are back-propagated to the hidden layer. The weights of the hidden layers are updated using the current weights \(\omega 1_{j,k}^{V_{i}}(t)\) by adding the change in weights, which is measured using the \(\Delta\omega 1_{j,k}^{V_{i}}\) multiplied by some constant factor \(\eta\). The updated weights are represented by \(\omega 1_{j,k}^{V_{i}}(t+1)\) for the hidden layer using Equation (8).  
\[\omega 1_{j,k}^{V_{i}}(t+1)=\omega 1_{j,k}^{V_{i}}(t)+\eta\Delta\omega 1_{j,k}^{V_{i}} \tag{8}\]  
Upon updating the weights of the hidden layer, all weights are collected in a matrix form known as the trained model of the ANN algorithm and further utilized for the testing phase.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
### Paper Contributions  
As communication systems are rapidly becoming part of daily life, the end user expects the modern systems to provide high reliability, secure transfer of data, and high data rates, and should be low cost. Much has been enabled by the use of OCNs for single wavelength applications, but support for multiple users is still a challenge to accommodate the large number of anticipated users for future systems [20]. Hence, a multi-channel OCN model is presented in this paper. The dispersions, such as CD and PMD, generated due to multi carrier transmission over OCNs, are successfully mitigated by implementing FFE and DFE methods. The proposed model shows significant results in terms of BER, optical signal-to-noise ratio (OSNR), and quality factor (Q-factor). The prime contributions of this paper are given as follows:  
1. Multi-channel transmission degrades the OCN's performance due to extra induced dispersion in the system. Hence, a simple network model is proposed based on FFE and DFE elements. An electronic equalizer (EE) component is installed in the proposed system model for the above-mentioned techniques. As a result, this procedure has made the proposed model simple and cost effective.
2. FFE and DFE are investigated in this set up analytically based on their block models. The models are designed individually and jointly for both FFE and DFE techniques. From simulation results, it is achieved that joint operation of FFE and DFE has more efficiency against dispersion instead of working individually.
3. The proposed model is also analyzed in terms of eye-diagrams. The eye-diagrams clearly show that the equalization improves the quality of multi-channel transmission, and hence improved performance can be achieved as compared with the OCN without equalization. BER below \(10^{-5}\) and Q-factor of more than 5 are achieved through simulation, which shows better efficiency as compared with other models, which were presented in [14; 15; 16; 17; 18].  
The rest of the paper is organized as follows. Section 2 explains the proposed network architecture. Section 3 describes the technical background. Section 4 consists of results and discussion. The paper is concluded in Section 5.
## 3 Results
Considering the limited population of 40 patients, we applied statistical analysis aimed at investigating potential differences in polar and azimuthal values between Group 1 (difference between polar and azimuthal values obtained by the clinical method and the planned reference values) and Group 2 (difference between polar and azimuthal values obtained by the proposed method and the planned reference values). Mann-Whitney U test (\(\alpha\) = 0.05) reported a non-significant difference among the two groups, both for polar and azimuthal angles. All statistical analyses were performed in Matlab (MATLAB and Statistics Toolbox Release R2020b, The Mathworks, Inc, Natick, MA, USA). In the boxplot analysis (Figure 10), the median (IQR) values for Group 1 were \(-1.52^{\circ}\) (IQR = \(1.48^{\circ}\)) for polar angles and \(-0.28^{\circ}\) (IQR = \(1.28^{\circ}\)) for azimuthal angles, and for Group \(2-1.38^{\circ}\) (IQR = \(1.28^{\circ}\)) and \(-0.11^{\circ}\) (IQR = \(1.08^{\circ}\)) for the polar and azimuthal angles, respectively. \begin{table}
\begin{tabular}{c c c c} \hline
**Diseased Eye** & **Fixation Eye** & **Polar**\([^{\circ}]\) & **Azimuth**\([^{\circ}]\) \\ \hline R & R & 28 & 80 \\ R & R & 28 & 120 \\ R & R & 28 & 170 \\ R & R & 30 & 110 \\ R & R & 28 & 110 \\ R & R & 28 & 40 \\ R & R & 26 & 70 \\ R & R & 27 & 120 \\ L & L & 25 & 10 \\ L & L & 25 & 115 \\ L & L & 28 & 130 \\ L & L & 30 & 70 \\ L & L & 26 & 130 \\ L & L & 26 & 90 \\ L & L & 26 & 0 \\ L & L & 28 & 30 \\ L & L & 29 & 112 \\ \hline \end{tabular}
\end{table} 
Table 2: Fixation angles of the simulated clinical-like patients considered in the study. Left eye (L) and right eye (R). \begin{table}
\begin{tabular}{c c c} \hline
**Diseased Eye** & **Fixation Eye** & **Polar**\([^{\circ}]\) & **Azimuth**\([^{\circ}]\) \\ \hline R & L & 28 & 270 \\ R & R & 28 & 230 \\ R & R & 28 & 260 \\ R & R & 28 & 310 \\ R & R & 28 & 228 \\ R & R & 28 & 270 \\ R & R & 28 & 320 \\ L & R & 28 & 350 \\ L & L & 25 & 190 \\ L & L & 25 & 250 \\ L & L & 25 & 315 \\ L & L & 26 & 300 \\ L & L & 26 & 280 \\ L & L & 26 & 200 \\ \hline \end{tabular}
\end{table} 
Table 3: Patients with downwards fixation angles. Left eye (L) and right eye (R). One patient used the healthy eye as the fixation eye as he was unable to see the ETS fixation light with the diseased eye.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 1 Introduction
It is possible to improve the accuracy and speed of algorithms in indoor environments without the need for GPS signal by replacing Bluetooth-based transmitters instead of Wi-Fi and displaying the location of objects connected to the network on a local map;
2. Using passable point mapping methods, unusable paths can be eliminated in location searches to increase the accuracy and speed of the system response;
3. Based on data processing algorithms in fingerprint methods, noise effects can be neutralized, and accuracy can be increased using the sampled and evolved data of users.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 2 The Use of Neural Networks in the Global Positioning System
### Attitude Determination Using NN  
[PERSON] [30] has implemented the GPS as an interferometer for free posture estimation and carrier phase correlations. In computational methods, basic vectors are calculated by using the least square or Kalman filtering technique. However, if the baseline vector keys are produced using the least squares method, the actual posture results are intrinsically noisy. The KF makes an effort to reduce the estimation errors' error variance. Although it necessitates a comprehensive previous recognition from the measurement noise and process noise covariance matrices, it will yield the best results. The KF will be replaced by a neural network state estimator that will be used in the attitude determination mechanism to estimate the attitude angles from noisy raw attitude solutions. When there is statistical knowledge uncertainty, using the neural network estimator instead of the Kalman filtering approach increases robustness.