esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Linear Dimensions  
The linear dimensions of the samples were measured, including the external dimensions of the full sample and the sample with a hole, as well as the internal diameter of the hole in latter sample.  
The measured dimensional error (\(dm\)) is the difference between the measured dimension and the nominal dimension. The dimensional error (\(dm\)) includes, among other factors, the error due to linear shrinkage (\(ds\)) and the error from light bleed. The shrinkage error was determined using the following Equation (4):  
\[ds=s\times\text{nominal dimension} \tag{5}\]  
The remaining part of the dimensional error (\(dl\)), associated among others with light bleed, was calculated as the difference, as follows:  
\[dl=dm-ds \tag{6}\]  
The results of \(dl\) are shown in Figure 18. The error caused by light bleed should be positive for external dimensions and negative for internal dimensions. Theoretically, as the exposure time increases, light bleed should become larger, and therefore the magnitude of the deviation (its absolute value) should also increase. The results of the tests largely reflect these assumptions. The deviations for external dimensions with short exposure times are negative. This may be caused, among other factors, by printing errors related to matrix resolution and print repeatability.  
In Figure 18, a trend is visible, indicating that the error value depends significantly on the interaction between exposure time and the type of dimension. The error values (\(dl\)) for the external dimension of the solid and hole samples are similar. To investigate the significance of the aforementioned factors on the error value (\(dl\)), a regression model was developed. Through factor significance analysis in regression, the statistical significance of factors such as dimension type and the interaction between exposure time and dimension type on the value of \(dl\) was confirmed (Table 4). The variability of these parameters accounts for 88% of the total observed variability in the \(dl\) parameter. The influence of sample type on \(dl\) was not statistically significant.  
Figure 18: Errors due to light bleed for solid and hole samples.  
For the samples with hole, the error \(dl\) (Figure 18) for the external dimension is not symmetrical around zero (it is symmetrical around the value of \(-0.045\)), so it cannot be easily attributed to light bleed. The values of contour shifts relative to the nominal dimension result from the combined effects of shrinkage and light bleed. Therefore, to compensate for dimensional errors, it was proposed to first compensate for shrinkage (samples labeled as scaled). After measuring the dimensional errors \(dm\) on the scaled samples, an appropriate offset can be determined. This procedure has been verified, and the results of this verification are presented in Section 3.5. An extension of this research area related to improving the accuracy of printed models could be concerned with the reduction of shrinkage and offset correction steps into a single stage.
## 5 Conclusions
### Limitations and Further Research  
This study faces several limitations that may affect its findings. First, the source selection was restricted by publication date. Since the review focused on recent research advances, only literature published from 2020 onwards was considered. This choice may have overlooked older, yet still valuable, studies on sustainable transport solutions relevant to potential crises. Another limitation is that the review drew exclusively from available scientific publications. Consequently, the findings may not fully capture the real-world challenges of implementing the proposed innovations, potentially overlooking obstacles, such as societal resistance to new technologies. There are also limitations related to source selectivity, as the review was based on research from only four scientific databases. This approach may have excluded other significant studies in less widely used databases or articles not indexed in these databases. Therefore, the review may not present a complete picture of the current state of knowledge on the topic.  
This study also indicated possible directions for future research in sustainable transport solutions, which are also relevant in the context of possible crises. Given the results obtained, empirical studies on the effectiveness of implementing individual innovations based on geographical location and social diversity would be appropriate. These analyses could examine cost-benefit assessments, environmental impact, and societal acceptance of these solutions. Another critical area for future research involves developing hypothetical scenarios to prepare the transport sector for various crises and associated disruptions. The literature review also highlighted the importance of advancing research on the integration of autonomous technologies with urban infrastructure due to their potential to reduce emissions and improve energy efficiency. Finally, investigating the impact of international cooperation on transport technology development would be beneficial, particularly regarding access to renewable energy sources, legal regulations, and potential trade barriers.  
Conceptualization, S.S.; methodology, S.S.; formal analysis, A.B.; data curation, S.S.; writing--original draft preparation, A.B. and S.S.; writing--review and editing, A.B. and S.S.; visualization, S.S.; supervision, A.B.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
## 2 Measurement Principles and Device
### Repeatability  
Measurement repeatability is one of the fundamental characteristics of the measurement device. In order to obtain a good value with a small standard deviation, the resolution of the FTIR should be 16 cm\({}^{-1}\) and the aperture 8 mm.  
The single measurement process is as follows: 1. Measure the radiation signal of the liquid nitrogen blackbody; 2. measure the radiation signal of the isothermal blackbody; 3. measure the radiation signal of the measured sample five times. Then, the emissivity is calculated according to Formula (8), and the standard deviation computed as shown in Figure 8.
## 3 Results
### Positioning Performance of the Dual Systems
The convergence time of  
Figure 11: Distribution of the convergence time and RMS of static PPP solutions for the single systems (G is short for GPS, E is short for Galileo, and C is short for BDS-3) and dual-system combinations (GE is short for GPS combined with Galileo, GC is short for GPS combined with BDS-3, and CE is short for BDS-3 combined with Galileo) in September 2022. (**a**) Horizontal component of convergence time, (**b**) Horizontal component of RMS, (**c**) Vertical component of convergence time, (**d**) Vertical component of RMS. the EC combination is 15.5 min. All three dual-system combinations outperform GPS-only. It is clear that the RMS of PPP solutions in the vertical component is vastly better than in the horizontal component. Both the GE and GC combinations are obviously superior to GPS-only in the horizontal component. The median RMS of the two combinations is 2.19 and 1.97 cm, respectively. The median RMS of the CE combination is 2.61 cm. The CE combination performed better than BDS-3-only and Galileo-only, but not as well as GPS-only. For the vertical component, only the GE combination performed better than GPS-only. It is 3.58, 4.03, and 4.75 cm for the GE, GC, and CE combinations, respectively. The GC and CE combinations performed better than BDS-3-only and Galileo-only. As is well known, the accuracy of kinematic PPP is clearly lower than that of static PPP for various solutions due to the weaker model. Therefore, the RMS statistics of kinematic PPP are four times as large as static PPP. However, the system combination significantly reduces the error of kinematic PPP. For convergence time, all three dual-system combinations have a shorter convergence time than the GPS-only PPP solutions. For RMS, it is clear that with Galileo combined, GPS performs better than without. On the other hand, with BDS-3 combined, GPS does not perform as well as without.
## 2 Materials and Methods
### Analytical Methods  
The pH was determined using a pH meter. N-NO3\({}^{-}\) and P-PO4\({}^{3-}\) contents were determined using spectrophotometric methods. The N-NH4\({}^{+}\) and Kjeldahl nitrogen content were checked by performing titration (Buchi K-355). The total organic carbon (TOC) was measured with a Multi N/C 2100. BOD5 was determined using the manometric method. Volatile fatty acids, the phenol index, and COD was determined using spectrophotometric methods with a HACH DR4000. Dissolved oxygen was measured using an oxygen meter with a CO-01 Elmetron. The specific surface area (SBET) of zeolite was determined by using a micromerities accelerated surface area and porosimetry analyzer system with an ASAP 2020, Micromerities, Atlanta, GA, USA.  
The measurements were conducted in triplicate. All data are presented as arithmetical means. The relative standard deviation (RSD) was between 5 and 10%.  
The removal efficiency (\(\eta\)) and maximum removal efficiency (\(\eta_{\rm max}\)) of pollutants, expressed as a percentage, were calculated using Equation (1):  
\[\eta=\frac{\rm C_{i}-C_{f}}{\rm C_{i}}100\% \tag{1}\]  
where \(\eta\) i \(\eta_{\rm max}\) refer to the removal efficiency (%) and \(\rm C_{i}\) i \(\rm C_{f}\) are the initial and final concentrations of contaminants (mg/L). The sorption capacity of the sorbents (q\({}_{\rm max}\)) was calculated using Equation (2):  
\[q=\frac{(\rm C_{i}-C_{f})}{\rm m}V \tag{2}\]  
where q is the sorption capacity (mg/g), \(\rm C_{i}\) i \(\rm C_{f}\) refer to the initial and final concentrations of contaminants (mg/L), V is the sample volume (L), and m is the mass of sorbent (g). The ammonium nitrogen removal rate (\(\eta_{\rm N}\)) was calculated according to Equation (3):  
\[\eta_{\rm N}=\frac{\rm C_{i}-C_{f}}{t} \tag{3}\]  
where \(\eta_{\rm N}\) is the removal rate (mg/L d\({}^{-1}\)), \(\rm C_{i}\) i \(\rm C_{f}\) refer to the initial and final concentrations of contaminants (mg/L), and \(t\) is the time in days.
## 4 Results and Analysis
### Optimization Results of Vehicle Detection Robustness
This confirms that image semantic information extracted by ISE can strengthen SDM's understanding of degraded image content, thereby effectively improving restoration fidelity. Image semantics features outperform textual semantics features since visual features capture spatial details, while a single text description may correspond to multiple objects distributed across different image regions. Further analysis shows that incorporating SegE leads to substantial metric improvements. This stems from SegE's inclusion of critical traffic element attributes--including category, position, and size--which ensures the restored images preserve original traffic elements without arbitrary modifications. As the core component of TDIRM, SegE significantly enhances both the fidelity and consistency of restored images.
## 2 Materials and Methods
### Validation Phase  
To validate the functionality of the network structure and ensure the proper allocation of network requests, we propose the following process. In this study, we conduct a repeatability analysis to assess the likelihood of soliton generation along a specific path. We replicate the soliton generation process 200 times to achieve this objective by utilizing distinct initial field values. These initial values are obtained by applying complex Gaussian noise with a standard deviation of \(\sigma_{\text{noise}}=10^{-9}\). Subsequently, we examine the final state of the path for each repetition to verify the formation of DKS. The experiment involves counting the number of peaks obtained in the field intensity and confirming the successful generation of the comb. The results from each repetition are recorded in a histogram, enabling a thorough analysis of the path [42]. Additionally, we conduct a Monte Carlo experiment to determine the accuracy percentage in classifying the required optical comb type in a set of network requests. To achieve this, we randomly obtain 70% of the data for training and 30% for testing to quantify the results. Using the test data, we compute the confusion matrix for the SVM classifier and compare its performance with that of other popular methodologies in the literature. We calculate the average behavior and standard deviation from the confusion matrices obtained in each simulation.  
We calculate the number of rejections against the number of customers and the required bandwidth to observe the network scope and identify the variables that increase rejections in requests. We also compute the amount of bandwidth assigned in the \(n\) requests and the bandwidth blocking ratio (see Equation (4)):  
\[BBR=\sum_{i=1}^{N}\frac{BWR_{i}}{BWS_{i}} \tag{4}\]  
Here, \(BWR\) is the total bandwidth rejected, and \(BWS\) is the total bandwidth of the requests. These metrics allow us to verify the network's performance. Finally, we propose two state-of-the-art methods to compare their performance with that of our methodology; these methods are First-Fit (FF) and Random Wavelength Assignment (RWA).
# A Survey on Cybersecurity in IoT
## 3 Data Integrity and Privacy
### Advanced Cryptographic Techniques for Data Integrity  
Traditional methods such as hash functions [80; 81; 82], digital signatures [83; 84; 85], and message authentication codes (MACs) [86; 87; 88] are commonly utilized to ensure data integrity by verifying that data have not been altered. However, the resource constraints of IoT devices necessitate the adaptation of these techniques. Lightweight cryptographic algorithms like lightweight block ciphers (e.g., PRESENT, SPECK) and reduced-round hash functions have been specifically designed to operate within the limited computational capabilities of IoT devices [89; 90; 91]. Despite these adaptations, ensuring the integrity of data in transit and at rest remains challenging, particularly in environments where devices are exposed to physical tampering or where secure key management is problematic [92].
## 2 Background and Related Work
### Power Consumption Adjustment of Datacenters
[45] also considered that the workloads of large-scale datacenters are variable, believing that a coordinated resource management and power management approach could help datacenters to use renewable energy more effectively and proposed a green-power-aware virtual machine migration strategy to manage resources and power in green datacenters powered by mixed supply of both grid and renewable energy. The results illustrated that the holistic power consumption of both IT devices and cooling devices in the datacenter could be controlled towards the variation of the mixed power supply. Based on the above relevant research and also our prior work, we regard datacenters as adjustable and controllable loads in the smart grid, which might help the grid to keep its stability and improve its efficiency.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 2 IEEE 802.11 ax Use Cases in Fifth-Generation Radio Access Network on Unlicensed Bands (5G-U)  
Based on current research trends [5] and developments within IEEE 802.11 ax HEW [3], several future use cases for 802.11 ax can be identified. Figure 1 shows some of the use cases proposed for HEW in 5G-U [7]. These include a high throughput HEW in the form of a gigabit ethernet connection replacement, improved network capacity with multi-user multiple-input and multiple-output (MU-MIMO) transmissions, using HEW as a backhaul for local area networks (LAN), and supporting highly dense scenarios (such as an office building, stadium, train, etc.).
## 4 Discussion
### RQ3: How Should Psychometrics Assess Emotional Experience?
#### 4.5.1 Electrophysiological Correlates in Artificial Intelligence  
Classification of emotions is a field that has been extensively researched in recent years. It has gained relevance since artificial intelligence algorithms such as machine learning [85; 86] and deep learning [87; 88] could widen the understanding of human behavior and be implemented for ER. From the selected literature, only four studies implemented artificial intelligence algorithms for emotion classification. Two of them only implemented EEG for the acquisition of electrophysiological signals [26; 59]. The others used EDA combined with other techniques, such as ECG [27] and EEG [32]. From these studies, the best classification accuracy was the algorithm that received differential entropy of EEG in the five neural bands as input [26]. The second-best result was observed in a study using ICA for spatial filtering in EEG signals [59]. From this analysis, it could be observed that filtering in time/space domains gave the best results when trying to correlate EEG signals to emotions. EEG reported better results compared to other studies combining different techniques. In fact, reliable accuracies above 80% have been previously reported in studies using EEG for emotion classification and with a sample size of at least 10 participants [89]. Contrary to the evidence aforementioned, [32] reported that EEG alone was not a good physiological indicator for emotion classification.  
Accuracy, the most common classification metric, was reported in all studies. Only in [27] and [32], confusion matrices on the performance of classifiers reported recognition rates. Other classification metrics, such as recall, precision and F1 score that extend the analysis of true positives, true negatives, false positives, and false negatives, were not reported [90]. Comparisons of recognition rates based on these metrics could give a better representation of the performance of classification algorithms with the given electrophysiological data.  
From the revised literature, classes in terms of valence [26] and arousal [59] showed a better identification performance. Another finding was that in these studies, classes were set in continuous emotions (e.g., from pleasant to unpleasant), including a neutral case, rather than in discrete emotions (anger, disgust, fear, happiness, neutral, sadness).
## 1 Introduction
### Construction of Demagnetization Fault Indications
#### 2.1.2 Building of Demagnetization Fault Identified Signal  
The selection of the demagnetization fault identified signal (DFIS) sets the stage for an efficient and accurate demagnetization fault diagnosis. The DFIS should include sufficient information about the condition of permanent magnets to enable the detection and identification of demagnetization faults.  
The voltages measured by the toroidal-yoke coils are the back EMF, which are induced by the stator yoke flux linking the search coil. The voltage signals obtained from three toroidal-yoke coils cannot be directly utilized for the detection of demagnetization faults and the identification of demagnetization fault modes, and must be further processed to achieve the DFIS.  
Based on the voltage signals measured by three toroidal-yoke coils, three DFISs are constructed to identify the occurrence and mode of demagnetization faults, thereby achieving the localization of demagnetized permanent magnets. The main identification signal (SIm) is obtained by subtracting the residual voltage in TYC1 from TYC2 (residual voltage is the difference between the measured voltage and the voltage under the healthy condition of the machine); the first auxiliary identification signal (SIa1) is obtained by subtracting the residual voltage in TYC1 from TYC3; and the second auxiliary identification signal (SIa2) is obtained by subtracting the residual voltage in TYC2 from TYC3.
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 4 Design
### Markov Decision Process  
In the MATD3 algorithm, each UAV has its own agent, and they collaborate to accomplish the path-planning task. As shown in Figure 4, each agent receives state information and, based on this information, decides on the next action. We model the UAV path-planning problem as a multiagent reinforcement learning problem, with reinforcement learning model \(<\)\(S\), \(A\), \(\tau\), \(R\), \(P\)\(>\). Suppose there are \(m\) UAVs that need path planning; the state (\(s_{i}\)) of each UAV can be represented as its position. Each UAV needs to take an action (\(a_{i}\)) to move to the next position. The policy (\(\pi_{i}\)) of each UAV can be represented as a probability distribution mapping the current state to the action space.
# Simultaneous Lightwave and Power Transfer for Internet of Things Devices
## 4 Discussion
### Active Area vs. Data Rate  
Mono-crystalline and multi-crystalline Si SCs are reaching their theoretical efficiency limits. Some novel SC technologies, GaAs SCs, for example, can offer a large 3-dB-bandwidth [7] but at the expense of the detection area. However, using a large array of these SCs that can be controlled independently can provide a large detection area while offering a large detection bandwidth. A comparison between the active areas of various SC technologies and the demonstrated date rates in the literature can be seen in Table 3. Using large active area receivers can ease the strong requirements of alignment in FSO and increase the resilience to beam wandering effects arising from atmospheric turbulence [18]. Using solar panels has been equally shown to be helpful to detect laser beams propagating through underwater channels [19]. In a harsh environment, such as the underwater, fulfilling the pointing, acquisition, and tracking (PAT) is challenging given the presence of turbulence arising from random variation of the refractive index in the propagation media. Relying on large-area solar panels can help relax PAT for underwater  
Figure 8: (**a**) Diagram of the wake-up using a UAV, (**b**) the device being woken up by a drone, (**c**) oscilloscope outputs of the infrared sensor (in light blue) and the interruption (in dark blue).  
optical wireless communication but at the expense of the data rate that is constrained by the resistance-capacitance limit. OFDM-based modulation can provide access to higher data rates compared to what is provided by OOK. Another idea to investigate is integrating fused tapered optical fibers with small-active area SCs. This technique has been proven to significantly increase the field of view of small-active area OWC receivers [20]. Due to their weak wavelength dependence, tapered fiber bundles can be potentially used for different SC technologies.