esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 4 Deep CNN-Based Detector
### DCNN Type-I: Training with Accurate CSI  
Figure 3 shows the first training method of DCNN-based detector with accurate CSI, which is denoted as DCNN type-I. In the training phase, a data set including both the imperfect CSI \(\hat{\mathbf{H}}_{UM}\) and the accurate  
Figure 2: Details of DCNN model.  
CSI \(\mathbf{H}_{UM}\) is fed into the learning model. The loss function is defined as the mean square error between the output of the model \(\widetilde{\mathbf{H}}_{UM}\) and the accurate CSI \(\mathbf{H}_{UM}\), i.e.  
\[\epsilon_{I}=\frac{\|\widetilde{\mathbf{H}}_{UM}-\mathbf{H}_{UM}\|^{2}}{N_{U}N _{M}}. \tag{12}\]  
During the DCNN model training, the loss function is calculated batch by batch and used to optimize the weight and the bias of the DCNN model [43]. Please note that the accurate CSI is necessary for DCNN type-I, which is used to calculate the model loss function. Thus, the application of DCNN type-I is limited, because in some practical scenarios, it is difficult to obtain the accurate CSI especially in the wireless MIMO communications. Therefore, another type of DCNN is proposed to overcome this limitation.
## 3 Results and Discussion
### In-Situ Electro-Chlorination Experiment with CFD Simulation
The pipe diameter is 2 m and the flow rate is 14 m\({}^{3}\) s\({}^{-1}\). The figure shows 10 m of the pipeline. (**a**) The simulation result when the cathode is placed on the surface of the pipe wall. (**b**) The simulation result when the cathode is placed on the inside of the pipe with 100 mm of distance from the pipe wall. Figure 9: Comparison of in-situ electro-chlorination and direct chlorine injection in full-scale applications. (**a**) Estimated chlorine concentrations along the pipe surface. (**b**) Relative chlorine demands. The cooling pipes of power plants are often large both in diameter and length, so they can carry a large volume of water [37]. Nevertheless, the biofouling induced by marine organisms is more common near the intaking region [38]. Therefore, intensive chlorination is often practiced at the intaking region. The main disadvantages of this practice are consumption of large amounts of chlorine so as to increase chlorine concentration of whole cooling water and difficulty in controlling chlorine levels at the end of cooling systems. On the other hand, in-situ electro-chlorination can produce chlorine along the pipe wall to save chlorine dose. In fact, the direct chlorine injection often uses electrolysis to produce chlorine on site, therefore, the operational costs of both methods can be compared with the amount of chlorine produced. Reported operational expenditure (OPEX) of on-site electro-chlorination is approximately 1,400,000 USD per year in the case of 18 m\({}^{3}\) s\({}^{-1}\) of cooling water flow [39]. In that case, approximately 424,000 USD per year could be saved. As shown in Figure 9, several electrodes can be installed at a certain distance along a pipeline. Since chlorine is generated in-situ via electrochemical process, a control scheme (e.g., the one based on an oxidation-reduction potential (ORP) sensor [40]) can be easily implemented [41]; this will be a future study.
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 4 Experimental Results
### Cross-Dataset Testing  
In addition to intra-dataset testing on the VIPL-HR, MR-NIRP, and UBFC-rPPG datasets, we also conducted cross-dataset testing to assess the SS-Net's generalization capabilities. We directly tested the model trained on the VIPL-HR dataset on the MR-NIRP dataset. This decision was due to the fact that the VIPL-HR dataset is more challenging and contains more complex scenarios. Thus, ensuring that models trained on one dataset can effectively generalize to others is straightforward. Table 4 shows the comparison between our technique and SOTA methods.  
The results demonstrate that our method outperforms other methods. Specifically, compared to traditional methods, our approach maintains a significant advantage in heart rate prediction. Compared with PhysFormer [17] and PhysNet [12], our proposed method achieves a superior MAE of 2.67 bpm and 2.05 bpm, respectively, in HR estimation on the MR-NIRP dataset and outperforms the methods of [PERSON] et al. [20] by 4.20 bpm in RMSE.
## 1 Introduction
They developed a model and implemented the virtualised system, integrated solar power generation units, and battery energy storage systems with the proposed grid architecture. The virtualization of the proposed grid architecture addressed issues related to photovoltaic penetration, back-feeding, and irregularity of supply. Their simulation results revealed the effect of renewable energy integration into the grid and highlighted the role of batteries that maintain the stability of the system. The understanding is that it gives the indication that no feasibility study or other work form have been realised on assessing the impact of EVs on the design and effectiveness of the electric distribution grid with DG including a photovoltaic (PV) system and energy-storage system (ESS) in SGs. Therefore, considering the EV concerns and challenges in the context of the electric distribution grid, the objective of this work is to determine the likely effect that electric vehicles which are already in circulation, and whose penetration is expected to increase a lot in the future, will have on the distribution network. This study is in two stages: first, an evaluation of the impact of EVs on the already existing distribution networks, and secondly, gathering of the most significant data related to an uncoordinated charging strategy. Finally, a management model of the production and consumption of electricity, which couples a PV system with an ESS, is proposed to improve the critical aspects obtained from the first simulations. This solution is based on an algorithm that controls the charging and discharging of a battery according to the photovoltaic production to which it is coupled.
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 1 Introduction
[21] used the OTSU method thresholding difference images to obtain a map of snow changes in the Indian Himalayan region, which sensitively detected snow changes and successfully distinguished snow-covered areas from unchanged snow-free forest areas, and which performed consistently and effectively in the validation of the results. [PERSON]. [22], in solving the problem of imaging behind occluders for novel NLOS imaging problem, used either OTSU or manually adjusted thresholding methods to segment the image into illuminated and shadowed regions, ultimately reconstructing hidden scenes embedded in fairly complex visible scenes without the use of sophisticated, dedicated equipment or significant calibration. In the process of applying the wavelet transform modulus maxima method for edge detection, this paper convolves a convolution kernel generated by a two-dimensional Gaussian function firstly with the target image, which is also the CWT (continuous wavelet transform). Based on the convolution result, the modulus value and magnitude angle of the image pixel have been calculated, and then the modulus maxima in the image pixel are determined according to the magnitude angle. Then, the threshold value is calculated automatically using OTSU. Comparing the threshold value and the modulus maxima, we can remove the pseudo-edges of the image to obtain the final edge image. The edge detection results of Canny algorithm and Laplace algorithm are shown, and detection results of the three were evaluated with information entropy and SSIM as the evaluation indexes. The results demonstrate that the detection quality and detection efficiency of the algorithm in this paper have been improved. To fully illustrate this, images with more complex backgrounds were processed by the three algorithms. The results still show that the information entropy and SSIM of the wavelet transform modulus maxima method are the highest, which demonstrates that this algorithm has obvious advantages when processing images with more complex backgrounds.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure  
### Static Communication Ranges  
In a V2V communication between two adjacent vehicle nodes, the received power can be expressed as;  
\[P_{r}=\begin{cases}\frac{P_{1}P_{0}}{d^{\alpha}},&\text{LOS}\\ \frac{P_{1}P_{0}}{kP_{SL}d^{\alpha}},&\text{NLOS},\end{cases}\] (1a) where \[P_{0}\] is path-loss at unit reference distance, \[d>0\] is inter-vehicle distance between two adjacent vehicles, \[\alpha\] is path-loss exponent.  
While in a LOS (1a), for the NLOS link case, a bus shadowing effect is considered for \[k\] big vehicles (buses), each causing a path-loss \[P_{SL}\] as in [13] while blocking the communication link of the intended receiving vehicle (with \[\hat{P}_{r}\] ) in (1b). Thus if \[P_{r}\], or \[\hat{P_{r}}\] is determined by transmitted power, \[P_{t}\] and \[\hat{P_{SL}}\] (bus shadowing effect), then the distance between vehicle, \[d\] is considered the transmission range \[R\], hereafter referred to as deterministic transmission range and expressed from ( 1 ) as \[R=\begin{cases}\left(\frac{P_{t}P_{0}}{P_{r}}\right)^{\frac{1}{2}},&P_{r}\geq 0 \\ \left(\frac{P_{t}P_{0}}{kP_{SL}\hat{P_{r}}}\right)^{\frac{1}{2}},&\hat{P_{r}} \geq 0,\end{cases}\] (2a) for ( 2a ) without bus shadowing (WBS) and ( 2b ) with bus shadowing effect, respectively.
## 1 Introduction
Furthermore, multi-channel detection can be achieved using multi-core fibers. However, integrating RWG sensors on a fiber-tip is still challenging. One typical difficulty is due to the small size of the fiber core. In the case of on-chip RWGs, relatively long gratings are needed to achieve strong enough resonance. Conversely, with a fiber-tip RWG, only power from a limited number of grating periods around the core can be coupled back to the core mode resulting in a weak peak intensity, which may be difficult to detect considering inherent background noise. In their work from 2000 [22], [PERSON] and [PERSON] demonstrated a -18% transmittance notch obtained with silicon nitride (Si\({}_{3}\)N\({}_{4}\)) grating films and deposited on a fiber tip using interference lithography, although the read-out of the signal was difficult because of the high level of background noise. Later, in 2018, regarding enhanced peak intensity, the same group reported the realization of an improved fiber-tip RWG sensor based on a multi-mode fiber (MMF) with the collection of the transmitted signal [23]. In practical sensing applications, probe-type fiber RWGs, with the use of the reflected broadband signal for detection, are preferable both in biochemical sensing and for real-time medical diagnostics. Since low coupling efficiency between single-mode fiber (SMF) and MMF would hinder direct employment of existing fiber-optic interrogation systems for input and read-out of signals, it is of importance to further enhance the resonant properties of RWG filters on an SMF facet. In this work, we propose the use of an FP cavity surrounding the grating to improve the reflection peak intensity of the fiber-tip RWG sensors. At the same time, the footprint of the RWG sensor is strongly reduced. Using resonant enhancement, the designed novel RWG sensor has a reflection peak intensity of about three times that of a normal quasi infinite RWG. The high sensitivity, along with the compact sensor's dimensions, would allow for multi-channel sensing using multi-core optical fibers and could have good potential for refractive index sensing in small volumes such as in biomedical applications.
# Evaluation Method for Rock Burst Hazards in Strip Filling of Working Faces in Deep Coal Mines
## 5 Discussions  
With the transfer of coal mining to deep coal resource mining, the mechanical properties and engineering response characteristics of coal and rock mass will change significantly under the influence of 'three high and one disturbance', resulting in frequent rock burst accidents. Strip-filling mining has significant advantages in rock movement and rock burst prevention and control, surface subsidence control, and the improvement of the coal resource recovery rate. It is one of the key technologies for deep coal resource mining. However, for the working face with strip coal pillars and low filling rate, there is still a risk of rock bursts. The evaluation of rock burst risk under this condition is the basis and key to preventing this type of rock burst.  
In this paper, a method for evaluating the effectiveness of filling and anti-impact is proposed for the 'risk area-hazard level' of rock bursts under the condition of strip-filling mining, which can qualitatively analyze whether the filling is effective in anti-impact. At the same time, a method for evaluating the impact risk of a strip-filling mining face based on fuzzy mathematics is constructed, which provides some guidance for the prevention and control of rock bursts in deep well strip-filling mining faces.  
In this paper, the impact risk of strip-filling mining faces is evaluated, and some valuable theories are obtained. However, the impact risk evaluation method proposed in this paper is aimed at the strip-filling mining face. Whether the impact risk assessment under other conditions is applicable needs further study. At the same time, the mechanism and technology for preventing and controlling rock bursts by filling mining, how to effectively use the gangue in the goat, and how to improve the filling rate need further research to provide a guarantee for the safe mining of deep coal resources.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 4 Evaluation
### Model Optimization  
In Section 4.3, it can be found that 60% of the training data is enough to build a robust model. In this paper, the dataset is divided into 6:4; that is, 60% of the web shell and normal scripts is used to train the model, and the remaining 40% is used for testing. In order not to lose generality, this paper randomly extracts 60% of the scripts in all family web shells and mixes them with the random samples of 60% of the normal samples as a labeled training set. The advantage of doing this is to ensure that the characteristics of each family can be learned by the classifier. During training, SVM uses the radial basis kernel function because the web shell classification in this paper is a multidimensional nonlinear problem; the use of the radial basis kernel function can quickly and efficiently build a model.  
In order to optimize the model, the grid search is used to determine the best parameters for SVM training. In the SVM training phase, the two main parameters that need to be determined are C and \(\gamma\), where parameter C is the penalty coefficient. If C becomes larger, it means that the intensity of the punishment becomes larger, resulting in a situation where the model is not flexible enough and the generalization ability becomes weak. Conversely, if C becomes smaller, then the punishment strength will naturally become smaller, and the model is prone to underfitting. Another parameter \(\gamma\) determines the distribution of the data after mapping into a new feature space. Similar to the case of parameter C, if \(\gamma\) is too large, the model does not learn the content of the vector, but only remembers the support vector itself, which leads to a decline in the generalization ability and is prone to overfitting. If \(\gamma\) is too small, the data distribution in the new feature space will be too smooth, resulting in underfitting. In this paper, the SVM convergence coefficient is fixed at 0.01, the parameter \(\gamma\) is fixed at 0.01, and the change of parameter C and the final classification accuracy of the model is shown in Figure 5. It can be seen from Figure 5 that when the value of the parameter C is 0.8, the classification effect is the best. Similarly, when the SVM convergence coefficient is fixed at 0.01 and the parameter C is fixed at 0.8, the change in parameter \(\gamma\) and the final classification accuracy of the model is shown in Figure 6. It can  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(r_{n}\) & 2.604 \\ \(r_{m}\) & 3.025 \\ \(dc_{m,n}\) & 17.531 \\ \hline \end{tabular}
\end{table}
Table 2: Cluster separability.  
Figure 4: The relationship between different percentages of samples for training and classification accuracy, with/without feature selection (Fisher score).  
be seen from Figure 6 that when the value of the parameter \(\gamma\) is 0.03, the classification effect is the best. In summary, C = 0.8 and \(\gamma=0.03\) are used in this paper.
## 1 Introduction
However, many organizations face data silos, inconsistent formats, or proprietary systems that limit cross-domain integration and slow down development cycles [14; 15; 16]. Digital twin frameworks are rapidly becoming foundational in modern engineering, offering scalable, secure, and standardized ways to replicate physical systems virtually. The strategic importance of architectural and security guidelines for ensuring the robust deployment of DTs across cyber-physical networks has been emphasized in recent surveys [17]. In alignment with the goals of Industry 4.0 and sustainable European manufacturing,dedicated projects have shown how DTs can enhance intelligent lighting systems through real-time monitoring and AI integration [18]. This is particularly relevant for automotive applications, where DTs are increasingly used to support diagnostic processes and predictive maintenance. One approach demonstrates the use of a 3D-simulated environment with camera-integrated virtual lighting systems for classifying headlamp states using neural networks, with an accuracy of 80% under real-world conditions [19]. Another method combines digital signal analysis and machine learning to detect short-circuit LED failures, with precision exceeding 99% [20]. These targeted implementations complement broader efforts to model vehicle environments and behaviors for safer, more efficient road operation through DT-enabled visual recognition and simulation techniques [19]. In addition, organizational resistance to technological change, combined with skill shortages in simulation-based validation, can hinder adoption. The implementation of a DT-SiL-HiL framework requires not only advanced tooling and infrastructure but also a workforce trained in systems modeling, real-time simulation, and cyber-physical system design. Bridging these gaps demands investment in upskilling, as well as cultural alignment within engineering and quality assurance teams to embrace model-based verification methodologies [21; 22]. This paper addresses these multi-dimensional challenges by proposing a process-oriented framework that strategically integrates SiL, HiL, and DT technologies into a cohesive validation environment for automotive headlamp systems. The aim is to define a unified and traceable methodology that aligns simulation-based testing with system requirements and industry standards.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 6 _Ulva_ sp. as Environmental Danger  
_Ulva_ spp. is widely recognized for generating uncontrolled, fast, and massive mass production known as \"green tides.\" The free-floating shards can operate as a nucleus to produce green floods in new sites, offering enormous ecological and economic risks [4]. There are several examples in the literature: in Belfast (1911), Laguna of Venice (1930), Galicia (1999), New Zealand (1930s, 1991-1993, 1998, and 2003-2007), Maine through Long Island Sound in the USA (reports in 1958, 1973, 2000, 2004, 2005, 2009, and 2013), Bahia de la Paz in California (2013 to 2015), Tokyo Bay (1995, 1999, 2002, 2004), Brittany and Florida coasts (2008), Australia (2012), Yellow Sea in China (from 2007 to 2019), and Muttukadu Lagoon, India (2023) [223; 224; 225; 226; 227].  
During the bloom event, the fast-growing seaweed begin vying for sunlight and nutrients, causing ecosystem harm through hypoxia and acidity and interfering with the variety and richness of the habitat [228].  
Algal blooms are natural in the environment but have increased significantly due to pollution. According to [PERSON] and [PERSON] [229], during El Nino circumstances (e.g., 2007), when nutrient-rich deep waters upwell offshore and reach the harbor, Ulva blooms are especially severe [223]. In summary, increased urbanization has led to changes in the global carbon cycle [230], increased coastal eutrophication [231; 232], altered meteorological patterns [233; 234], and overfishing [235], which act together in intricated paths to affect nearshore habitats [236].  
This also shows that algal blooms will increase in different parts of the world, but, like the old saying, \"In every difficulty there is always an opportunity.\" One of these opportunities is to create more sustainable measures for overfishing. According to [PERSON] et al. [237], there are depleted sections of top predator populations, indicating increases in smaller fish and significant covering of ephemeral algae. This allows consideration of the need for broader ecosystem processes and trophic interactions [223].  
However, if we compare with other seaweed such as red and brown algae, _Ulva_ sp. cannot be cultivated in deep waters due to the light intensity need. At the commercial level, the most interesting seaweed belongs to the genera _Porphyra_, _Fucus_, _Chondrus_, _Laminaria_, _Gracilaria_, _Sargassum_, _Palmaria_, _Kappaphycus_, _Gelidium_, _Spirulina_, and others, i.e., red and brown seaweeds. This means that _Ulva_ sp. is not very well explored scientifically and economically.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Modulation Techniques
#### 5.2.3 Gaussian Frequency Shift Keying in BackCom  
GFSK uses the Gaussian-shaped frequency shifting technique to modulate information in BackCom. GFSK is considered for modulation, in a monostatic full-duplex Bluetooth Low Energy (BLE) compatible BackCom system, for mobile devices [97]. BLE utilizes a 1 Mbps Gaussian-shaped binary shift keying modulation technique. The positive frequency deviation of more than 185 kHz above the channel's center frequency represents \"1\" bit, while a negative frequency deviation of more than 185 kHz shows a \"0\" bit in each of the 40 BLE channels. Figure 21 delineates the frequency plan for creating BLE-compatible backscatter messages. Usually, BLE backscatter tags are made to reflect carrier signals in a manner whereby the resulting sub-carriers contain frequency components within a particular choice of BLE channel.
## 2 Materials and Methods  
Figure 1 represents the block diagram of the QD multi-spot position detection system based on OFDM. The modulated signal light is simultaneously illuminated on the detector target surface. The photocurrent signal is amplified by a trans-impedance amplifier (TIA) to obtain a voltage signal, and the analog signal is converted into a digital signal through the ADC. The DC signal is converted into an AC signal through the digital DC-AC conversion module to achieve DC isolation processing of the signal. Finally, FFT is performed on these signals to obtain the amplitude value of the photocurrent signal in each quadrant. The spot center positions are calculated using the QD position calculation equation.