esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Injecting Physics into Models
#### 3.4.2 Model Selection (Architecture-Constrained Technique--ACT)
Firstly, the number of networks is important when multiple sub-models have been identified as part of the problem understanding (see Section 3.3.1). In such a case, it is recommended to train a different network for each of these sub-models. This has been done, for example, in [100, 136], where parallel models were used for thermo-mechanical modelling and concrete spalling evaluations, and in [85], where coupled models were used for wildfire modelling (see Section 3.3.1). Secondly, the number of neurons per layer and the number of hidden layers determine the network's capacity to learn complex relationships. A deeper network (more layers) with more neurons can capture highly nonlinear interactions but may require careful regularisation to prevent overfitting. This does not directly contribute to integrating physics into the model; however, it is nevertheless also from a PISM perspective recommendable to align the complexity of the neural network with the complexity of the underlying physical model. Lastly, the choice of activation functions plays a particularly important role in PINNs. In cases where the loss function depends on higher-order derivatives of the output, the activation function should be capable of producing meaningful higher-order derivatives. For example, in heat transfer analysis using PINNs, a linear activation function is not recommended because it would fail to generate the required derivatives for additional loss terms associated with PDE constraints for conduction [14]. On the other hand, using simpler activation functions in neural networks improves computational efficiency, prevents vanishing/exploding gradients, and ensures better gradient flow for stable training. Functions like ReLU are preferred as they enable faster optimisation and reduce the risk of overfitting compared to complex, saturating functions like sigmoid or hyperbolic tangent [144]. Another way to embed physical constraints in neural networks is through the network architecture itself. A recent study implemented an architecture-constrained technique by introducing a custom entropy-based layer derived from principles of statistical mechanics into the neural network structure [84]. Specifically, the authors designed a differentiable entropy layer that computes Shannon entropy over spatial vegetation patterns, capturing landscape complexity associated with wildfire risk. This entropy measure was not used as a post-processing tool or loss term, but rather as an internal part of the model's forward pass, influencing subsequent predictions.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Rapid Reading Software  
Recently, EndoCapsule [91] introduced a rapid reading software [92] to reduce the number of uninformative frames from endoscopy videos. The rapid reading software decreases the time taken to process the video and increases the efficiency of the diagnosis process. A study [93] has shown a reduction of 64% in reading times associated with a 0.93 sensitivity in the diagnosis of lesions in preselected cases. The application of such software for other diagnostic videos is still an open challenge for researchers.
# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 2 Related Works
In publication [26], the aim was to detect the driver's hands while using a mobile phone or when they were placed on the steering wheel. The authors proposed a modified version of Fast-RCNN for hand, smartphone and steering wheel detection. Then, using geometric relationships, the system determined whether the driver had his hands on the steering wheel or was using a smartphone. The effectiveness of smartphone detection reached 94%, and the detection of hands on the steering wheel was effective in 93% of cases. Another example of a hand detection algorithm combined with position determination using CNN was presented in publication [27]. The authors suggested that methods of detecting hands and their position and orientation also helped computers understand human intentions and provide guidance for more complex tasks. The proposed CNN model was tested on selected sets (e.g., Oxford Hand Dataset--13,050 hand images) and achieved a sensitivity of 99-100% at the stage of generating the proposed hand areas for position analysis. In publication [28], the authors tested many detectors and, as a result, proposed a hand recognition method using the Yolov7 and Yolov7x models. The Oxford Hand Dataset was again used for testing. The following results were achieved: 84.7% precision and 79.9% recall. In light of the research and examples described above, a hand detection method based on deep learning and convolutional neural networks is proposed. A set of over 21,000 thermal images and a model based on a 15-layered convolutional network were prepared. The network was optimized and a set of hyperparameters is proposed to ensure high training efficiency and speed. The system presented here completely and automatically analyses images obtained from a mobile thermal imaging camera and detects hot objects as well as the risk of burning hands. Its task will be to increase the safety of visually impaired or blind people during their everyday activities. The proposed system is an innovative application of thermal imaging because there are no similar solutions on the market combining mobile technologies and thermal image analysis using CNN. The main reason for proposing the CNN network was the known and confirmed high effectiveness in the process of image classification, object detection and semantic segmentation. It results from the image analysis mechanism used (based on the hierarchical arrangement of convolutional layers and the use of receptive fields) and effective and automatic feature extraction [29; 30]. The use of CNN was made possible by providing a set of over 21,000 images.
## 3 Methodology  
### Multi-Collinearity Test  
Multi-collinearity problems can cause when there is a correlation between two or more variables in a regression model. This problem can cause the calculations to be false, and the logistic parameters are incorrect and or inexact [38]. As the surface factors used in this study, five independent variables (slope, profile curvature, plan curvature, TWI, and SPI) calculated from elevation were used. Applying variables derived from one raster data to a regression model may cause multi-collinearity problems [17]. Therefore, the determination of multi-collinearity is an important step in detecting flood vulnerability using a logistic regression model. The variance inflation factor (_VIF_), one of the indicators used to determine multi-collinearity, can be calculated using the coefficient of determination (\(R^{2}\)) as in Equation (4).  
\[VIF=\frac{1}{1-R^{2}} \tag{4}\]  
[PERSON] [39] stated that variables can be judged to have multi-collinearity when VIF is greater than 10. Table 1 shows that there is no multi-collinearity problem as the VIF values for the six independent variables of the surface factors ranged from 1.099 to 2.679 (Table 2). Therefore, it was confirmed that six surface factors can be used as independent variables in logistic regression analysis to calculate flood vulnerability.
# Scaled Modeling and Measurement for Studying Radio Wave Propagation in Tunnels
## 5 Scale Model Experiments in the SHF and the MMWs Regime
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Horn Antenna** & **Frequency (GHz)** & **Gain (dBi)** & \
\begin{tabular}{c} **H-Plane 3 dB** \\ **Beamwidth (deg)** \\ \end{tabular} &
\begin{tabular}{c} **E-Plane 3 dB** \\ **Beamwidth (deg)** \\ \end{tabular} \\ \hline A-INFO & 1 & 4.97 & 71 & 70 \\ X-Band Gray & 10 & 15.7 & 33 & 30.6 \\ X-Band Black & 10 & 10.8 & 61.2 & 47.7 \\ \hline A-INFO & 10 & 12.69 & 35 & 33.4 \\ W-Band & 94 & 24 & 8.8 & 8.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Antennas used in the real tunnel experiments and their counterparts used in the respective scale models for frequencies of 1, 10, and 94 GHz. As with the field experiments, the experiment was performed by measuring the power as a function of distance between the antennas inside the tunnel. Figure 7 shows a comparison between the simulation and the experiments for the normalized results of wave propagation for frequencies of 10 and 94 GHz. Figure 6: Scale tunnel model built out of Formica of rectangular profile with a height of 0.2 m and width of 0.1 m (**a**) a schema, and (**b**) a real tunnel model. Figure 7: Normalized results of the scale model experiments and simulation as a function of distance for frequencies of (**a**) SHF 10 GHz, and (**b**) MMW 94 GHz. The same dielectric pror4 perties as in the field experiment were used in the simulations for both frequencies and of the scaled tunnel model in the laboratory. For this experiment, in addition to the measured power inside the tunnel, the power permeating through the walls of the tunnel laboratory model was also monitored. The transmitting and receiving antennas were placed inside the tunnel. For the 94 GHz experiment, the measured permeating power was negligible, while for 10 GHz some leakage was observed. Figure 7 demonstrates a relatively good agreement between the measurement results and those obtained from the simulations. The main difference is revealed in the dips, and especially in the case of the received power at the W-band experiments, where the wavelengths is 3 mm. These discrepancies are explained due to the limited dynamic range and the sensitivity of the detection. Further, note that the received power was measured in discrete locations along the tunnel. Given these differences, the results demonstrate that link budget estimations using down scaled model are sufficiently reliable for practical scenarios. The results obtained during the scaled experiments show that precise scale models, constructed to maintain the physical properties of the ratio of wavelength to tunnel dimensions, can be used to simulate and predict the wave propagation in tunnel environments. The results also show that simulations based on the ray tracing models correlated well with the field experiments and the scaled experiments.
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 2 System Model
### Caching Model  
Based on the characteristics of LEO satellite orbits, we assume that all satellites within the environment move in the same direction. Satellite access to edge users follows a Poisson distribution with an average arrival rate of \(\lambda\). When an edge user accesses a satellite node, a content request is generated. Considering the segmented nature of content requests, we assume that, within one service cycle of a satellite, user requests can be completed. Therefore, in a single segment request, the edge user and fog satellite node are unique. The speed of satellites is dependent on the orbit altitude, and satellites at the same altitude move at the same speed.  
We denote satellite nodes and edge users, respectively, as \(s=\{1,2,\dots,N\}\) and \(u=\{1,2,\dots,K\}\). The requested content is represented as \(o=\{1,2,\dots,M\}\). We assume that the cache priority between different contents is equal, primarily considering user demands for cache decisions. We consider that each user may have preferences for several content types, with preference levels defined as \(\alpha\in[0,1]\). A higher value of \(\alpha\) indicates a higher preference level for the user, also implying a higher probability of the user requesting the corresponding content.  
The inter-satellite links of LEO satellites utilize laser for communication, offering an extremely high bandwidth and ultra-low latency, which can be negligible in the overall delay. Therefore, as shown in Figure 2 in our model, we assume that, when cached content exists in neighboring satellites, it also indicates a cache hit. If cached content is absent in all the satellites accessed by the current user and neighboring satellites, the satellite needs to request content from the cloud. In this case, due to the additional satellite-to-ground transmission process, we consider the transmission delay for cached content to double.
# Decentralizing Private Blockchain-IoT Network with OLSR
## 4 Decentralization of IoT Blockchain System  
### Motivation  
As mentioned, blockchain technology brings decentralization to IoT systems. However, the underlying network is normally built on top of the wireless infrastructure network. For example, Figure 1 shows a typical private blockchain-IoT, which runs over a Wi-Fi network. It is obvious that all the communication of blockchain nodes in the system relies on the access point. Hence, all packets generated by blockchain connections are transmitted and received via the access point. When a link failure happens, the associated blockchain node will be removed (from the system) even though it is still fully functional. If the access point fails due to power shortage or damage, the whole system is corrupted. Consequently, all the decentralization merits disappear. Hence, this system incurs the single point of failure issue in the network layer. To further enhance the adoption capability of IoT blockchain, it is necessary to solve this problem.  
Turning the underlying network of IoT blockchain into ad hoc one is a potential method to handle the issue. The ad hoc network does not have a centralized point as in the infrastructure one. Moreover, each node in an ad hoc mode can receive and forward messages, facilitating decentralization. By dispatching packets by network nodes rather than a specific centralized point, the blockchain connections become self-organized. Another merit is that the blockchain-IoT network, which is normally limited by the access point's communication range, can be extended by adding new IoT nodes. However, all the packets traverse through several nodes to a destination require an ad hoc routing protocol. More importantly, the routing may allow IoT nodes to search for adaptable, dynamic routing paths, recovering from network failure. Since the ad hoc routing is lightweight and low performance, it is important to investigate its operation with blockchains. In the next sections, we present our approach with OLSR.
## Instrument Description
### RF Receivers
#### 2.2.1 DC Offset Compensation  
The fast comparators used for digitizing the down-converted signals have an instrumental DC bias of only a few millivolts, but this DC offset becomes important for the latter post-processing when employing the PARIS interferometric technique. The resulting complex amplitude waveforms are displaced from the origin [11], which effectively reduces the SNR (Signal to Noise Ratio) of the waveforms. When using the clean-replica processing this effect does not appear because the GNSS chipping codes are effectively DC free, and the DC bias is eliminated when computing the cross-correlation between the signal and the chipping code.  
The function of the DC offset compensation circuit is to add a DC voltage to the signals with the opposite sign and the same amplitude as the instrumental DC offset. In a closed-loop approach, the instrument controller measures the DC component of the digital signals in real-time and for sequential periods of 1 s, and then generates a pulse-width modulated (PWM) signal for each in-phase and quadrature component, from which the DC offset compensation circuit extracts the DC value and subtracts it from the analogue signal.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
###### Abstract  
The term \"Agriculture 4.0\" emerged from the term \"Industry 4.0\" like amany other \"4.0\" terms. However, are Industry 4.0 technologies and concepts really applicable to agriculture? Are the benefits that Industry 4.0 brings to industrial use cases transferable to livestock farming? This paper tries to answer this question for the three dominant sectors of livestock farming in Central Europe and Germany: Poultry, pig fattening, and dairy farming. These sectors are analyzed along with the eight most relevant Industry 4.0 benefits. The results show that only part of the Industry 4.0 benefits are relevant for livestock farming in a similar manner as in industrial production. Due to basic influences between industrial and livestock farming use cases, some of the benefits must be adapted. The presence of individual living animals and the strong environmental impact of livestock farming affect the role of digital individualization and demand orientation. The position of livestock farming within the value chain minimizes the need for flexibilization. The introduction and adoption of Industry 4.0 concepts and technologies may contribute significantly to transforming agriculture into something that may be called Agriculture 4.0. Technologies are indispensable for this development step, but vocational education and open-mindedness of farmers towards Industry 4.0 is essential as well.  
livestock farming; animal husbandry; Industry 4.0; Agriculture 4.0 +
Footnote †: journal: Applied Sociology  
0000-0001-9993-3300]H.M.Kraft
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.4 Resource-Constrained IoT Devices  
IoT devices have limited resources (battery, computational power, storage and memory capacity). It is not feasible to rely only on these devices to fulfill all the needed computational operations. FNs will carry out most of the computational tasks on behalf of resource-constrained IoT devices, hence reducing these devices' complexity, deployment costs and energy consumption.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 4 Experimental Verification
### RCS Reduction Performance  
The RCS reduction performance of the prototype can be observed from the simulated and measured results. The simulated results are obtained using the CST 2021 commercial simulation software with plane wave excitation and open boundary conditions. It is seen that the integration of AFST on top of the RTA effectively reduces the out-of-band RCS under both the TE and TM polarized incident waves, as shown in Figure 17. Specifically, the 10 dB RCS reduction bandwidth for low and high frequencies is 52.5% and 43.8%, respectively. The RCS reduction performance under oblique incidences is given in Figure 18. It is found that the RCS reduction performance of low frequency is relatively stable at oblique incidence, while the RCS reduction performance of high frequency has band shift. In general, in the case of a 30\({}^{\circ}\) oblique incidence, the designed low-RCS 1-bit RTA can maintain a relatively stable RCS reduction performance.  
Figure 16: Simulated and measured beam scanning performance at 7.5 GHz in **(a)** E-plane and **(b)** H-plane.  
Figure 17: Simulated monostatic RCS of the 1-bit RTA with and without AFST for both TE and TM polarization.  
Figure 15: Simulated and measured broadside radiation patterns at 7.5 GHz. **(a)** E-plane. **(b)** H-plane.
The multicast tree, which operates between a sender and its connecting nodes, uses a few methods. Accordingly, on each network, the built-in SDN software maintains the scaling and renovation of the network. Optimizers are deployed nearer to the edge server and the main server to manage the network traffic that varies from low to very high. Different types of traffic are grouped and the forwarder has been selected in the network through multicast mode and video streaming takes place through it. In this proposed model, the speed of the node and the errors of conversion of the receiving packets have been calculated, the packets with the least changes and the video packets with the least delay have been found and sent through multicast routing mode. To regulate the transmission, the local best and global best nodes are selected in the particle swarm optimization mode and the packets sent. In this paper, Section 2 examines the literature survey, revealing the background ideas proposed for the MSVPC method, which states that video packets are transferred through a forwarder selected through PSO and data transmitted over SDN-enabled devices on the 5G network. Section 3 describes the design and methodology of MSVPC, Section 4 presents the results and discussions and, finally, Section 5 concludes with the influences and future challenges that need to be addressed.
## 5 Evaluation
### Performance Analysis
The DC-SoC model not only maintains a low count of distorted transactions but also ensures a high propagation success rate, blocking over 72% of malicious transactions. This demonstrates DC-SoC's strong resistance to Sybil and Byzantine attacks, ensuring network reliability even under adverse conditions. Thus, while BlockP2P-EP may show superior initial performance, DC-SoC offers superior long-term resilience and security, particularly as the network scales or faces higher malicious peer ratios.
# A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems
###### Abstract  
In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.  
unmanned aerial vehicles; OFDM; ranging; LS channel estimation; pilot; pseudorandom code +
Footnote †: journal: Advances in Neural Information Processing Systems