esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 4 EMF Exposure Level and 5G Technologies
### EMF Evaluation Methodology
#### 4.2.1 EMF Exposure Evaluation Framework  
In this section, we present state-of-the-art frameworks for evaluating EMF exposure across different categories. The aim is to provide researchers and practitioners with an overview of current methods and best practices for assessing EMF exposure in various environments. We discuss the applicability, advantages, limitations, and examples associated with each category, as well as relevant evaluation metrics. As shown in Figure 4, EMF exposure evaluation can be broadly classified into three main categories:  
* Geometric stochastic: This approach uses path loss modeling to estimate EMF exposure levels at various locations within the coverage area. It accounts for factors such as distance, terrain, and building materials to predict signal attenuation and EMF levels. Studies employing this approach include [72; 73; 74; 75; 76; 77].
* Deterministic: The deterministic approach uses ray-tracing techniques to model the propagation of radio waves in a given environment. It provides a detailed assessment  
Figure 4: EMF exposure evaluation categories and metrics.  
of EMF exposure, considering reflections, diffraction, and scattering. This method has been applied in [63; 78].
* Experimental: The experimental approach involves direct measurement of EMF exposure using specialized instruments and devices. While highly accurate, it can be time-consuming and costly. Relevant works employing this method include [49; 50; 51; 52; 53; 54; 56; 57; 58; 59; 71; 79].  
Each category offers unique advantages and limitations. Table 3 provides a comparative summary of these approaches, highlighting key distinctions.  
Despite advancements in EMF exposure evaluation, there remains a research gap concerning frameworks tailored for RIS deployments. Although RIS technology plays a crucial role in enhancing wireless communication systems, studies specifically addressing its impact on EMF exposure are limited. In [80], a new RIS-assisted 5G mmWave network planning framework is proposed. This framework uses machine learning (ML) classification for multi-RIS clustering to optimize RIS placement and improve network efficiency while minimizing the number of deployed RISs. The study provides a comparative analysis of passive and active RIS impact on EMF exposure in densely populated urban areas, revealing that the exposure from multiple active RISs is approximately 7.5 times higher than that from passive RISs. While the exploration of RIS technology in EMF-aware radio resource management has begun, it remains underdeveloped. Section 4.3 reviews existing research addressing EMF-aware radio resource management with specific consideration of RIS technology.
## 4 Performance Evaluations
### Simulation Results
#### 4.2.1 Regular Deployment
Figure 1(b) shows the ALE results according to Table 4 where the number of anchor nodes increases from 30 to 70. It can be seen that the ALE change ranges of these algorithms are not violent, and the trend of ALE decreases gradually as the number of anchor nodes increases. As a result, LSRR-LA exhibits the best performance. Figure 3 shows an overall summarization of the six competing methods for the three anisotropic networks when \(R\) = 100 m and \(M\) = 20%. In contrast, LSRR-LA demonstrates the best positioning accuracy for the three different networks, and meanwhile keeps a small standard deviation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **M = 30/13.7\%** & **M = 40/18.3\%** & **M = 50/22.8\%** & **M = 60/27.4\%** & **M = 70/31.9\%** \\ \hline \multicolumn{6}{c}{**Communication Radius \(R\) = 100 m**} \\ \hline DV-hop & 290.1/90.1\% & 285.2/92.2\% & 285.9/93.7\% & 271.4/94.2\% & 284.4/95.0\% \\ Amorphous & 408.1/92.9\% & 398.1/94.4\% & 400.4/95.5\% & 385.0/95.9\% & 398.4/96.45\% \\ MDS-MAP & 339.3/91.5\% & 329.7/93.2\% & 328.0/94.5\% & 331.8/95.2\% & 326.9/95.7\% \\ PDM & 85.6/66.4\% & 75.3/70.4\% & 68.1/73.7\% & 66.4/76.2\% & 63.5/77.7\% \\ LSVR & 78.4/63.3\% & 75.3/70.4\% & 77.1/76.8\% & 75.4/79.0\% & 78.4/81.9\% \\ LSRR-LA & 28.8 & 22.3 & 17.9 & 15.8 & 14.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the ALE under different numbers of anchors in the regular distribution network. Figure 2: The ALE results of C-shaped regular networks. Figure 4 illustrates the localization results of the six methods and highlights the localization error of each node, when the communication radius \(R=100\) m and anchors \(M=20\%\) (under regular distribution). The blue circles indicate the estimated positions of the nodes and the blue lines describe the localization errors. The length of these lines is proportional to the localization errors. In addition, three-dimensional graphics are used to visualize the performance in order to show the localization results more clearly. In Figure 5, the three-dimensional (3D) localization results of LSRR-LA are compared with those of the classic DV-Hop method. The results prove that LSRR-LA outperforms DV-Hop, especially at the edge of these regions. Figure 4: The localization results of the six algorithms in regular distribution networks (\(R=100\) m, \(M=20\%\)). Figure 5: _Cont_. Figure 3: Comparison of ALE when \(R=100\) m and \(M=20\%\).
###### Abstract  
This study focuses on optimizing machining parameters in the micro-milling of AlSi10 Mg aluminum alloy produced via the powder bed fusion additive manufacturing process. Although additive manufacturing enables complex geometries and minimizes material waste, challenges remain in reducing surface roughness and cutting forces during post-processing. Micro-milling experiments were conducted using spindle speeds up to 60,000 rpm, with varied feed rates and cutting depths. Cutting forces (Fx, Py, and Fz) were measured using a Kistler-9119 AA1 mini dynamometer, while surface roughness (Ra) was evaluated with a Nanovea-ST400 3D optical profilometer. Five advanced machine learning models, random forest regressor (RFR), gradient boosting regressor (GBR), LightGBM, CatBoost, and k-nearest neighbors (KNN), were employed to predict cutting forces and surface roughness, with CatBoost achieving the highest predictive accuracy (R\({}^{2}>\) 0.96). Among all models, CatBoost achieved the best predictive performance, with test R\({}^{2}\) values exceeding 0.96 for both force and Ra estimations. Experimental and ML-based results demonstrated that higher feed rates and depths of cut increased cutting forces, particularly in the Fx direction, while elevated spindle speeds reduced forces due to thermal softening. Surface roughness was minimized at lower feed rates and higher spindle speeds. The optimal machining conditions for achieving Ra < 1 \(\upmu\)m were identified as ap = 50 \(\upmu\)m, n = 30,000 rpm, and fz = 0.25 \(\upmu\)m/tooth. This integrated approach supports precision machining of AM aluminum alloys.  
additive manufacturing; AlSi10 Mg; micro-milling; cutting forces; surface roughness; machining optimization; powder bed fusion; machine learning +
Footnote †: journal: Applied Sciences
## 5 Data Analysis Method
### Averaged Power Spectral Densities  
To make the spectrogram data more useful, a closer look at the frequency content of each signal can help improve future machine learning classification and also confirm how well the radar data matches with the accelerometer measurements. This is where power spectral density (PSD) becomes helpful.  
For tests involving multiple passengers--where there were few sudden changes during recording--an average PSD was used to highlight the main frequency components in the signal. In contrast, the PRND (PARK, REVERSE, NEUTRAL, DRIVE) tests had more frequent changes, since the gear shifted every 10 s. So, the recording was split into sections based on each gear mode, leaving out the short periods during the actual shifts. An average PSD was calculated for each gear mode segment to highlight the frequency patterns linked to PARK, REVERSE, NEUTRAL, and DRIVE. This method could help machine learning models learn the differences in frequency content between these modes.  
Because vibration-related micro-Doppler effects are generally symmetrical, and accelerometer signals are completely symmetrical around positive and negative frequency components, focusing only on the positive frequencies of the Doppler data can make comparison easier. Similar to how the Short-Time Fourier Transform (STFT) or Fast Fourier Transform (FFT) can give both double-sided and single-sided spectral densities, the single-sided spectral density \(G_{XX}\) is shown in Figure 8 and is defined as  
\[G_{XX}=\frac{2}{T}|X_{m}|^{2}. \tag{6}\]  
In equation (6), \(T\) is the signal's time duration and \(X_{m}\) is the linear spectrum. The linear spectrum is found by applying FFT to the windowed time series and multiplying it by the frequency resolution \(\Delta f\).  
In MATLAB (R2023b), the FFT function calculates the linear spectrum by multiplying the FFT result by the sampling period. However, this process may vary in different software tools. For the averaged PSD, the signal is divided into overlapping segments. The spectral density \(G_{XX}\) is calculated for each segment. Then, all the \(G_{XX}\) values are added together and divided by the number of segments to get the final \"averaged\" spectral density.  
Even though short-lived changes (transients) are not averaged in this part of the analysis, they are still important. These quick events may contain features useful for identifying gear shifts or detecting unusual behavior, as shown in later figures. Averaging is avoided in these cases because it would smooth out the rapid frequency changes, making it harder to detect important details in the spectrum during these brief moments.  
Figure 8: Single-sided averaged spectral density. The sampling frequency is \(f_{S}\).
## 3 Results and Discussion  
### Evaluation of Network Kinematic Positioning Performance  
To evaluate the kinematic positioning performance of the basic network configuration \"3+1+1\", various distances between the seafloor base stations and heights of the subsurface buoy were simulated. As the user can obtain single-epoch kinematic positioning by receiving acoustic signals from at least three stations, we analyzed the number of effective positioning grids (EPGs) and the position dilution of precision (PDOP) with at least three available station numbers (ASN). Figure 2a shows the percentage of EPGs in the total grid number of 39,015. For the same subsurface buoy height, the EPG varied significantly with the seafloor base station distance. When the distance was less than 17 km, the EPG decreased slowly as the distance increased. However, when the distance was greater than 17 km, the circumscribed circle radius of the seafloor base stations array was greater than 10 km, and the EPG decreased rapidly. For the same seafloor station distance, the EPG changed gently with the subsurface buoy height. The EPG values were relatively larger when the subsurface buoy height was above 1.5 km. Overall, the EPG reached its maximal value of 18,486 when the seafloor base station distance was 1 km and the subsurface buoy height was 1.5 km.  
Figure 2: User positioning indicators of the “3+1+1” network configuration. (**a**) Heat map of the percentage of EPGs with the number of available stations \(\geq\)3. (**b**) Box chart of the PDOP variation with the seafloor base station distance and the subsurface buoy height. The red line denotes the median, the blue box denotes IQR (interquartile range), the solid black lines denote the maximum value and minimum value, respectively, and the \(\pm\)1.5 \(\times\) IQR criterion is used for outlier detection and removal. The shaded area denotes the steep variation of the PODP.  
Figure 2b shows the PDOP value variation with the seafloor base station distance and the subsurface buoy height. According to Equation (5), due to the inversion of **N**, outliers of PDOP value may occur. We detected and removed the PDOP outlier when its value exceeded \(\pm\)1.5 \(\times\) IQR (interquartile range), which amounted to approximately 7% of all of the PODPs [37]. Figure 2b indicates that, when the subsurface buoy height is lower than 1.5 km, the PDOP value changes slowly with the base station distance. However, when the height is higher than 1.5 km and the distance changes from 7 km to 9 km, the PDOP values change notably. Overall, the results show that, when the subsurface buoy height is less than 1.5 km, the PDOP mainly changes with the seafloor base station distance. Otherwise, the PDOP fluctuates with the subsurface buoy height and the station distance.
## 3 Route Determination for Ship Passage Using Capability Plots  
The determination of a vessel's route between two geographical points (the starting and ending points of the route) is one of the initial tasks in planning any maritime operation, for example as an anchoring operation at an anchorage site. Vessels designated to keep their position at anchor are typically equipped with thruster systems that are considerably less powerful than those on vessels equipped with a DP system. This difference means that in higher wind speeds, they cannot maneuver freely using only their thrusters, resulting in increased energy consumption and a prolonged travel time along the route. To minimize the impact of environmental forces on the vessel's hull and thereby reduce the energy consumption, the use of the vessel's CP and [PERSON]'s algorithm can be advantageous.
# Introduction to Photonics: Principles and the Most Recent Applications of Microstructures
## 2 Applications of Photonics  
Photonics have uses in almost every aspect of our life, ranging from daily life to highly innovative science. For instance, information processing, telecommunications, light detection, metrology, lighting, spectroscopy, photonic computing, holography, medical field (surgery, vision correction, health monitoring and endoscopy), fighting machinery, visual art, agriculture, laser material processing, robotics, and biophotonics. Similar to the way electronics have been used extensively since the creation of earlier transistors of 1948, the exceptional use of photonics continuously increases. Economically significant uses of photonic devices include fiber optic telecommunications, optical data storage, displays, optical pumping of high-power lasers and laser printing. Prospective applications of photonics are practically limitless and include medical diagnostics, organic synthesis, information, and communication, as well as fusion energy [9; 10]:  
* Telecommunication: optical down-converter to microwave, and optical fiber communications.
* Medical applications: laser surgery, poor eyesight correction, tatto removal and surgical endoscopy.
* Manufacturing processes in industries: involves the use of laser in welding, cutting, drilling, and many surface modification techniques.
* Building and construction: smart structures, laser range finding, and laser leveling.
* Space exploration and aviation: including astronomical telescopes.
* Military operations: command and control, IR sensors, navigation, mine laying, hunt and salvage, and discovery.
* Metrology: range finding, frequency and time measurements.
* Photonic computing: printed circuit boards, and quantum computing.
* Micro-photonics and nanophotonics.  
These typically include solid-state devices and photonic crystals [11]. In simple terms, photonics is currently solving and addressing the challenges of a modern world. Photonics enhances the quality of life; it safeguards our health, security, and safety, it drives our economic growth, and it creates jobsas well as global effectiveness. Photonics technology enhances the quality of life in many areas. Specific areas are communications and information technology, advanced manufacturing, defense, health and medicine, and energy [12; 13]. Photodetectors are used to detect light. They can be very slow, as in the case of solar cells that are used in harvesting sunlight energy, or very fast like photodiodes that are very fast and are employed in communications in conjunction with digital cameras. Numerous others centered on quantum, thermal, photoelectric and chemical areas also exist. Photonics likewise involves research on photonic systems. The term photonics system has found its application in optical communication systems [14].
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Anti-Recompression and Anti-Requantization Test
#### 4.3.2 Anti-Requantization Attack  
In this paper, experiments are performed on videos under different QP (quantization parameter) for the heavy compression attack. In the experiment, the initial QP is 20, and then a requantization attack with QP from 16 to 23 is performed to calculate the NC and BER values for the extracted watermarked images. The results are shown in Figure 7, the abscissa is the QP value, and the error rates of the extracted watermarks after requantization are all below 0.14, indicating that this algorithm can resist the requantization attack well, especially when the QP is reduced.
## 4 Experiments and Analysis
### Analysis of Our System  
Assuming that there are \(n\) features from \(X_{1}\) to \(X_{n}\), \(e_{new}\) is the new coming instance, \(e_{nearest}\) is the closest instance to the new coming one, and we take _Euclidean distance_ as the similarity measure method:  
\[Dist_{nearest}=\text{sqrt}(\sum{iw_{i}\times(e_{new}(Xi)-e_{nearest}(Xi))^{2}}) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Experiment** & **Number of Input** & **Minimum** & **Maximum** & **Minimum** & **Maximum** & **Average** & **Hit Rate** \\
**Number** & **Conditions** & **Error** & **Error** & \(|Error|\) & \(|Error|\) & \(|Error|\) & \\ \hline
1 & 1 & \(-4\) levels & 0 level & 0 level & 4 levels & 1.52 levels & 0.00 \\
2 & 2 & \(-4\) levels & 0 level & 0 level & 4 levels & 1.52 levels & 0.00 \\
3 & 3 & \(-4\) levels & 1 level & 0 level & 4 levels & 1.25 levels & 0.27 \\
4 & 4 & \(-4\) levels & 1 level & 0 level & 4 levels & 1.16 levels & 0.36 \\ \hline \end{tabular}
\end{table}
Table 5: Comparisons among the case-based FCTF reasoning systems with different number of input conditions.  
Algorithm starts from \(X_{1}\),  
\[Dist_{nearest}=\mathrm{sqrt}((e_{new}(X_{1})-e_{nearest}(X_{1}))^{2})=|e_{new}(X _{1})-e_{nearest}(X_{1})| \tag{5}\]  
If \(X_{1}\) is not related to the problem (\(X_{1}\
otin P_{f}\), where \(P_{f}\) is the set of the problem features), the _hit rate_ would be very low regardless whether _Dist_ is below our threshold. Otherwise, if \(Dist_{nearest}<\alpha\), Output \(e_{nearest}(Y)\), And we add another \(X_{2}\),  
\[Dist_{nearest}=\mathrm{sqrt}((e_{new}(X_{1})-e_{nearest}(X_{1}))^{2}+(e_{ new}(X_{2})-e_{nearest}(X_{2}))^{2}) \tag{6}\]  
Analysis: if \(X_{i}\in P_{f}\) and \(Dist_{nearest}<\alpha\), _Hitting rate_ would increase at the ideal conditions; if \(X_{i}\in P_{f}\) and \(Dist_{nearest}>\alpha\), _Hitting rate_ may increase through increasing the value of \(\alpha\). If \(X_{i}\
otin P_{f}\) and \(Dist_{nearest}<\alpha\), \(Dist_{nearest}\) is not influenced by adding the noisy attributes. However, the _hit rate_ of the system would not increase; If \(X_{i}\
otin P_{f}\) and \(Dist_{nearest}>\alpha\), \(Dist_{nearest}\) is influenced by adding the noisy attributes, and the results would be proved as a failure.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 6 Application and Evaluation
### Example: JobDeciderWorkflow  
We envisage a workflow called JobDeciderWorkflow as an example to explain the applicability of our approach to AWS Step Functions. For readability, we do not present its JSON-style specification directly but instead illustrate the specification in Figure 5. The main purpose of this workflow is to run a job in a cost-effective way, either with Lambda (if the job is small to medium-sized) or ECS (if the job is large). Assume that each job of this workflow can be decomposed into multiple parts. All main stages (except \"start\" and \"end\") are implemented as Lambda functions and play the following different roles: the workflow controllers (i.e., \"Job Decider\" and \"Job Complete?\"), job runners (i.e., \"Run Job in Parallel\"), job submitter (i.e., \"Submit Job to ECS\"), time keeper (i.e., \"Wait X Second\"), status checker (e.g., \"Get Status\") and event emitter (i.e., \"Emit 'Successful'\" and \"Emit 'Failed\"). If the \"Job Decider\" decides that the job should be run in Lambda, by exploiting the easy parallelisation of this service, a set of parallel threads are spawned to handle different parts of the job. If the \"Job Decider\" decides that the job should be submitted to ECS and run by it, two loops are used to guarantee the quality of job completion: The inner loop monitors the status of the job, and the outer loop resubmits the job if it has failed. This workflow example manifests many common characteristics of AWS Step Function workflows.
## 2 Materials and Methods  
### Study Area  
The target watershed of this study is the Yongdam Dam, located in the upper reaches of the Geum River in South Korea, between 35\({}^{\circ}\)35\({}^{\prime}\) N to 36\({}^{\circ}\)00\({}^{\prime}\) N and 127\({}^{\circ}\)20\({}^{\prime}\) E to 127\({}^{\circ}\)45\({}^{\prime}\) E. It encompasses Chuncheongnam-do, Jeollabuk-do, and Gyeongsangnam-do, including the counties of Muju, Jinan, and Jangsu. The study area exhibits a temperate climate, and its precipitation patterns are significantly influenced by the East Asian monsoon system, with notable rainfall during the summer months. The watershed area covers 930 km\({}^{2}\), which accounts for approximately 9.5% of the total Geum River basin. The Yongdam Dam watershed mainly consists of eight sub-watersheds, five inflowing streams, and the Yongdam Dam, which has a height of 70 m, a length of 498 m, and a total storage capacity of 815 million m\({}^{3}\). Also, it has a total of 22 operational observation stations for the production of hydro-meteorological data, including water level/flow, precipitation, soil moisture, and evapotranspiration (Figure 1).
# A Direction-Sensitive Microwave Sensor for Metal Crack Detection
## 2 Sensor Design
### Realization of Improved Current Distribution  
As the current distribution of the rectangular patch resonator with direct feeding is limited to a narrow area, the crack occurred in the middle of the corresponding area on the metal grounding patch will have a better detection performance than the crack that  
Figure 4: The current distribution of sensor with a single feeding port. (**a**) Current distribution without crack. (**b**) Current distribution with crack along oy axis. (**c**) Current distribution with crack along ox axis.  
Figure 5: Crack direction detection of designed dual-port sensor.  
occurred on the edge. So, a coupled feeding method is adopted in this paper to improve the current distribution on the patch resonator as shown in Figure 6a, and the current distribution for direct feeding is also given for the convenience to compare.  
We can see from the simulation results that the current density of coupled feeding is much larger than that of direct feeding. Additionally, the coupled feeding has a lager coverage area with uniform current distribution. In addition, the current on the edge adjacent to the direct feeding port has a messy direction, which limits the crack detection area of the sensor. Compared with the direct feeding method, the current distribution on the resonator will be improved greatly for the coupled feeding, and this will lead to a better detection performance and a larger coverage area.