esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.4 Energy Consumption/Harvesting  
In [112], author propose a Q-learning and a deep Q-learning algorithms for cooperative networks to user devices and the Small Base Station (SBS) due to different complexities. Results show greater energy saving performance of these approaches over existing methods. In [113], a DRL approach is proposed for optimizing energy consumption in 6 Gnetworks. This model takes mobility into account and accelerates block verification. The reward function considers the total consumed energy for transmission and caching. In this paper, also, a security study is conducted, with the model providing security and privacy protection, while maintaining low-energy consumption. The proposed algorithms achieves 86% of successful content caching requests against 76% of a conventional greedy algorithm and 5% of a random content caching approach.  
In [114], the authors propose two DRL-based algorithms for energy harvesting: one hybrid-decision-based actor-critic learning (Hybrid-AC) algorithm and one multi-device hybrid-AC (MD-Hybrid-AC) algorithm for dynamic computation offloading scenarios. Hybrid-AC applies an improvement in the actor-critic architecture. In this approach, the actor outputs offloading ratio and local computation capacity and the critic evaluates these continuous outputs with discrete server selection. MD-Hybrid-AC applies centralized training with decentralized execution in the scenarios. The model constructs a centralized critic for output server selections, and considers the continuous action policies of all devices for actor. Simulation results show that the proposed algorithms have a significant performance improvement compared with conventional and can maintain good balance between time and energy consumption.  
In [65], a Deep Q-Network (DQN) based algorithm for energy consumption is proposed. Furthermore, the authors develop a RL algorithm for minimization of prediction error, in order to address a battery's energy prediction challenge. Finally, a two-layer RL network approach is developed to solve the joint access control and battery prediction issue. In this approach the first RL layer deals with the battery's energy prediction and the second, depending on the output of the first layer, produces the access policy of the system. Simulation results show that the three proposed RL algorithms can achieve better performances compared with existing approaches in terms of optimizing energy consumption, sum rate and minimizing the prediction loss.  
In [115], a multi-agent DRL-based framework was proposed for power control and maximization of throughput in energy-harvesting super IoT systems. Furthermore, a DNN based for distributed online power control is developed to study the policies in the system. Simulation results show the efficiency of the proposed power control policies, outperforming conventional optimal approaches like Markov decision process, and also achieving throughput close to optimal.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Unintended Data Disclosure Issue  
Within the robot or user sensing space, the presence of any adults who are not supposed to be listening to the conversation between the robot and the user would be considered an unintended listener. People could walk in, people could already be present within the sensing space (which went undetected), and if it is a public area, any adults in the public space within the sensing area are also considered unintended unauthorised individuals. Either the robot or the user, or both, should be able to recognize information leaking scenarios and situations. If public space is considered, then the forms of information leakage may not only be due to the presence of unintended adults, but could also be the presence of technological recording systems such as CCTV, which record both audio and video.
# A Model for Working Environment Monitoring in Smart Manufacturing
## 1 Introduction
As has been stated, a large amount of research is devoted to developing methods, tools, and technologies for smart manufacturing systems, but there is a need for more comprehensive research that will deepen our understanding of issues related to working conditions for humans. Our research aims to contribute to filling this gap by proposing a model and testing platform for working environment monitoring in smart manufacturing. The contribution of this paper is a model for the working environment in smart manufacturing systems. The model was designed with the idea of including emerging technologies and contemporary approaches in building complex cyber-physical systems. The model had to be easily developed in the form of testing platform. For that purpose, open-source hardware and software were used for platform development. The model and the platform were tested for their validity and applicability. The experimental environment and tools are described in the paper. The test results show that the platform is efficient and suitable for creating different scenarios for the particular use cases. The rest of the paper is structured as follows. Section 1 gives the introduction. Section 2 outlines related work. Section 3 gives the model overview, with the detailed description of its components. In the same section, a model of data analytics and usage of the proposed sensor network is given for indoor dust pollution monitoring. Section 4 presents the experiment, the testing platform, and platform validation scenarios. Section 5 gives the results and analyses, and Section 6 gives the concluding remarks.
## 2 Common SWIR Imaging Technologies  
### Fluorescent Imaging  
Fluorescent (or fluorescence) imaging is a commonly used technique in the field of biomedicine, employed to image samples ranging from the cellular level on a microscope slide to larger tissue samples in vivo [21]. In clinical settings, fluorescence imaging is now employed as a tool for intraoperative guidance during surgeries in various configurations suitable for both open and laparoscopic surgery with potential applications, including  
Figure 2: Categoriesof ML-powered SWIR techniques in the biomedical field.  
tumor delineation, metastasis detection, and nerve visualization multiplexing [22]. Biomedical images generated in the SWIR frequency range offer striking contrast, precisely outlining tumor boundaries. Surgeons utilize these images to guide intricate procedures, ensuring accurate tumor removal while minimizing impact on healthy tissues. These compelling images not only demonstrate the immediate clinical relevance of SWIR fluorescence in enhancing surgical precision, but also emphasize its potential to revolutionize intraoperative decision making. This application captures the large interest of readers by highlighting the tangible benefits of SWIR fluorescence imaging in improving surgical outcomes and paving the way for more effective and targeted interventions in the field of personalized medicine. Fluorescent imaging has also been applied to pharmacology, offering real-time monitoring of drug distribution within the vasculature of animals [23].  
In this method, fluorophores within the sample are illuminated by light at the excitation wavelength, and the resulting emitted fluorescent light is captured by an SWIR camera [21]. Figure 3a illustrates the schematic of the epi-illumination SWIR fluorescent (SWIRF) imaging technique. A common approach for illuminating the sample is the wide-field configuration, where the entire sample is illuminated simultaneously, see Figure 3a [21; 24]. However, as the emitted light from different points within the sample interacts, the signals received by the camera are semi-quantitative. For quantitative results, the fluorescence molecular tomography (FMT) method is recommended (Figure 3b), involving the raster scanning of the sample surface with a narrow light beam [21; 24]. FMT, subsequently, employs an inverse modeling approach to generate a three-dimensional tomogram depicting the distribution of fluorophores within the sample. One key downside of FMT, however, is the relatively long acquisition and data processing times.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission
### Atmospheric Attenuation
We present the results for rain rates of up to 10 mm/h to account for this. From Figure 7a, it is evident that the losses resulting from rain remain relatively small across all geographical locations. For instance, the highest loss among all the rain models, assuming a distance of 100 m, is 2.93 dB, as observed in the Japan model. This model serves as the upper limit for rain losses, explaining its widespread usage in the literature. As expected, the losses increase as the distance between the transmitter and receiver increases. For instance, using the France model and assuming a high rain-rate, the loss of 2.93 dB at a propagation distance of 100 m jumps to 8.78 dB and 14.63 dB for distances of 300 m and 500 m, respectively. Considering that rain rates are less than 3 mm/h in most practical cases [88], the losses for a typical propagation distance through rain in vertical/slant links (i.e., a few hundred of meters) are not expected to exceed 7 dB. Several observations can be made upon comparing the losses caused by fog and rain in Figures 6 and 7. First, in conditions of very low visibility, the attenuation losses attributed to fog can significantly impact system performance. In the presence of dense fog, attenuation can reach hundreds of dBs. In moderate visibility and clear weather conditions, the losses are typically a few dBs, which are comparable to the losses caused by rain. It can also be noted that rain-induced losses are wavelength independent, whereas fog-induced losses are wavelength dependent, with higher wavelengths experiencing less attenuation. So far, we have discussed attenuation loss and geometrical loss, which basically determine the average received power in an FSO communication system. There are other parameters that can affect the instantaneous received power, which will be discussed in detail in the following subsections.
## 5 Experimental Results
### Experimental Procedure and Methods
Experimental Determinations Concerning the Effect of Pulse-Width Variation on the Light Intensity Output  
In order to understand the effect of pulse-width variation at the VLC receiver level, the emitter was positioned at 2 m distance, and a square signal with a frequency of 10 kHz was transmitted. The irradiance and the amplitude at the optical receiver output were measured, decreasing the duty cycle from 50% to 0.1%, with the PDA100A optical detector gain adjusted for 20 dB. The representation depicted in Figure 11a shows that the irradiance is directly proportional with the duty cycle rate until around 2%, when the intensity of light is too low to be detected by the irradiance meter (i.e., under 0.1 \(\upmu\)W/cm\({}^{2}\)). On the other hand, the amplitude of the received signal is stable at around 100 mV until the duty cycle reaches 3%. Because the PDA100A has a bandwidth of 800 kHz when the gain is adjusted for 20 dB, at low duty cycle values, the signal starts to lose its squareness, appearing the effect of pulse overshoot. As such, the amplitude of the deformed pulse starts to increase until the duty cycle is lowered to around 1%, as seen in Figure 11b. Continuing to decrease the duty cycle, the pulse starts to drop in amplitude until the duty cycle reaches around 0.12%, when the LED lights of the emitter were unable to keep up with the fast-switching rate. Taking all this into consideration, at low duty cycle values, it is expected to see an impact on the maximum distance as well. Additionally, one can see that depending on the environment illuminance, with a duty cycle below 1%, the results can be considered as \"lights-off.\" It should be specified that this represents a particular case of light dimming in which the lamps are perceived as \"off,\" while the communication is still going on with ultra-short pulses. However, because the processing power necessary to decode a message transmitted with a duty cycle below 1% is too demanding, this proof of concept will be done with experiments in the 1% to 40% range. \begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment type** & **Equipment** \\ \hline Spectral analyzer & Sekonic C800 \\ Irradiance meter & Delta Ohm HD 2302.0 with LP 471 RAD Probe \\ Oscilloscope & Tektronix TBS 2104 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Equipment used during the tests. Figure 10: Schematic of the experimental setup. Experimental Determinations Concerning the Effect of Pulse-Width Variation on Communication Performances  
This section presents the experimental results showing the effect of the light dimming on the VLC link BER performances for indoor and outdoor setups.
## 5 Methodology, Experiments and Results
### Proposed Methodology  
The operation of applications for various architectures is examined. The data were analyzed using several approaches: visual analysis, association rules, and regression tree. The first step was a visual analysis based on the data presented in the charts. However, it is burdensome and in more cases, quite difficult due to the lack of automation. In some cases, it may even be sufficient, and it will be possible to draw specific conclusions based on it. It does not always make sense to use complex solutions to analyze a particularly small amount of data. In this article, we used two next solutions. The standard path of data analysis was used: data acquisition (Section 5.1.1), data preprocessing (Section 5.1.2), implementation of algorithms (Section 5.1.3) and data analysis (Section 5.2).
###### Abstract  
For hybrid direct sequence/frequency hopping (DS/FH) spread spectrum signals, even if the relative motion speed between the transmitter and receiver remains constant, the Doppler frequency will vary due to the continuous hopping of the carrier frequency. Under high dynamic conditions, the first-order and second-order change rates of the Doppler frequency attached to the received signal further increase the Doppler frequency agility, making it difficult for the carrier tracking loop to maintain steady-state tracking. To address these issues, a high dynamic velocity locked loop (HD-VLL) is proposed in this paper. Specifically, the accumulated phase tracking error caused by acceleration and jerk is first analyzed. Subsequently, to compensate for this phase tracking error with the system clock, the proposed loop adds an acceleration compensation module and a jerk compensation module. However, this results in the output of the high dynamic loop filter being updated with the system clock, which contradicts the multiplexing design of a traditional loop filter for parallel signal processing, making the hardware implementation of an HD-VLL impractical. Therefore, this contradiction leads us to design an HD-VLL-based multi-carrier NCO (HD-VLL-NCO). The HD-VLL and HD-VLL-NCO are simulated, revealing the HD-VLL's superior dynamic adaptability and steady-state tracking, while the HD-VLL-NCO achieves comparable accuracy with the appropriate truncation bit width.  
DS/FH; carrier tracking; high dynamic; velocity locked loop
# Sustainability Audit of University Websites in Poland:
## 4 Results
### University Clustering by Sustainability Characteristics  
The sustainability typology of the websites was developed using cluster analysis with the k-means algorithm. The clustering employed six indicators (diagnostic variables) that describe the sustainability and technical characteristics of the websites: the Ecograder Score, Ecograder CO\({}_{2}\), page assets (MB), Kastor Global Score, Kastor CO\({}_{2}\), and Ecol Impact Score. All variables were standardised to convert them to a common scale. This prevented indicators with larger absolute values from dominating the clustering process.  
Figure 3 shows the 63 websites clustered based on their sustainability and technical parameters, reduced to two dimensions with a principal component analysis (PCA). Principal component analysis reduced the number of dimensions while preserving the maximum statistical information. As a result, it was possible to present the measured values, such as CO\({}_{2}\) emissions, website size, and SEO audit results, on two independent axes, PC1 and PC2, that best describe the qualitative variance among the websites (Figure 3). This helps with cluster visualisation and identification of websites with similar sustainability and technical profiles. Principal component analysis synthesises and aggregates information rather than reducing it. Each data point represents a website (university). Its colour reflects one of three clusters. The clusters in the diagram are clearly distinct, which confirms proper classification.  
Figure 2: Matrix of correlations between indicators. Source: original work with Green Web Audit Assistant AI.  
The analysis identified three distinct groups of websites: Cluster 1: websites with the lowest CO2 emission indicators, small sizes (page size), and high sustainable design scores. These websites achieved the best scores from Ecograde and Kastor Green, which indicates they were designed following good sustainability optimisation practices. Cluster 2: websites with moderate technical and sustainability parameters. They exhibit middling CO2 emissions, an average Ecol Impact Score, and a partially optimised structure. Cluster 3: these websites have undoubtedly higher CO2 emission indicators, large asset sizes, often over 10 MB, and low sustainability indicator values. Their Ecograde scores are low, and CO2 emissions per page load reach high values.  
The clustering was verified by calculating the silhouette coefficient for the three clusters. A result of 0.374 indicates a moderate but acceptable cluster cohesion and separation, confirming correct classification. This score is consistent with typical values for exploratory analysis of datasets with limited dimensionality and highly diverse objects.
## 2 Fracture Parameter Solution Method Based on the Postprocessing Routine of ABAQUS
### The Advantages of the Present Method  
The conventional J-integral can be expressed as follows:  
\[J\ =\ \int_{A}\ (\sigma_{ij}u_{j,1}-W\delta_{1i})q_{,j}dA+\frac{1}{2}\int_{A }\sigma_{ij}S_{ijkl,1}\sigma_{kl}qdA, \tag{25}\]  
In the conventional J-integral, given by Equation (25), the derivatives of the material parameters are involved. Thus, the material properties must be differentiable when using the J-integral to solve the fracture parameters. However, in the interaction integral given by Equation (12), there are no derivative terms of material parameters. This means it does not require differentiable material properties. Instead, it only needs to calculate the difference between the material parameters at the integration point and the crack tip.  
For complex material interface cases, the interaction integral can be expressed as follows:  
\[I\ =\ \int_{A}\ (\sigma_{ij}u_{j,1}^{\rm aux}+\sigma_{ij}^{\rm aux}u_{j,1} -\sigma_{jk}^{\rm aux}\varepsilon_{jk}\delta_{1i})q_{,j}dA+\int_{A}\sigma_{ ij}[S_{ijkl}^{\rm tip}-S_{ijkl}(\mathbf{x})]\sigma_{kl,1}^{\rm aux}qdA+I_{\rm interface}^{ \rm s}, \tag{26}\]  
After solving the interfacial integral \(I_{\rm interface}^{\rm s}\) in a curve coordinate system based on the material interface, it was found that \(I_{\rm interface}^{\rm s}\ =\ 0\), meaning the material interface makes no contribution to the interaction integral. Therefore, the interaction integral form derived from Equation (12) also applies to the cases with material interfaces. Compared with the J-integral, the method proposed in this paper can solve the crack problems in heterogeneous materials whose material properties are discontinuous.  
The present integral interaction method is deeply embedded in the finite element simulation platform using the postprocessing routine of ABAQUS. This makes the traditional finite element method capable of dealing with the fracture problems in heterogeneous materials with complex material interfaces.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### Second Set of Tests
#### 5.3.2 Method Two  
The results of method two are illustrated in Figure 18. Unlike the first set of tests, where 34 kHz was the optimum frequency of excitation, in these tests, 37 kHz achieves better results. It is not possible to detect Defect 2 without any outliers using the 30-kHz frequency, since the required minimum threshold level, 0.125, is higher than the maximums that are reported in Figure 13. Nonetheless, using the two other frequencies, Defect 2 can be detected. From the margins, it is evident that 37 kHz achieves the highest _safe zone_ margin required for detection, where both the minimum and maximum thresholds that are required are respectively lower and higher than the ones required for other frequencies. Furthermore, when the defect size decreases, the limits are not affected significantly, which suggests that when using 37 kHz, smaller defect sizes than 4% CSA can be detected. In fact, in this region, defects with CSAs as small as 2% can only be detected using this frequency using both method two and method three.  
Compared to method one, although the limits have been increased in the case of Defect 2, it can be seen that it is not affected as much, since method two is capable of detecting defects using 34 kHz without any outliers and 30 kHz with outliers. Nonetheless, this method also performs best in the absence of flexurals in the same location as the defect signal.  
Figure 17: Minimum, average, and maximum values of method one of the second test cases.
## 4 Results and Discussion
### Tunnel Axial Force  
Figure 12 illustrates the axial forces at tunnel transverse sections before and after excavation. It can be seen that the excavation-induced unloading effect above the tunnels results in considerable variations of the tunnel axial force distribution. Before the excavation, the axial forces at the side walls of the tunnels are larger than these at the crowns and inverts. The axial forces at the upper part of the tunnels are slightly smaller than these at the lower part. The maximum axial force values for the left and right tunnels are 654.88 kN and 663.71 kN, which are recorded at measurement points L9 and R8, respectively, both of them were close to the inverts of the tunnels. After excavation, the axial forces at all tunnel parts are decreased to varying degrees. The variation in axial forces at side walls of the tunnels is significantly greater than that at the crowns and inverts. The locations of the maximum axial force are basically unchanged. As can been seen from Figure 10, the axial forces at or near the side walls of the tunnels are the most sensitive to the excavation unloading, with reductions of 194.72 kN and 181.72 kN at the L5 and R12 regions. The axial forces undergo excavation-induced decreases of about 31.96% and 29.65%, respectively, compared with before excavation. The variations of axial forces at crowns and inverts of tunnels are not significant. In this case, axial forces at the left tunnel crown and the invert are decreased by 7.05% and 4.84%, respectively, while these at right tunnel the crown and invert axial forces are decreased by 9.05% and 6.11%, respectively.