esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The patients maintained a good oral hygiene. None of the patients suffered significant changes in periodontal indices during the two time periods. No bleeding on probing (BP) or probing pocket depth (PPD) were detected 24 h before or after the initiation of the orthodontic treatment.  
GCF analysis revealed detectable values for IL1\(\beta\) at both time periods. We were able to identify IL6 only on a third of the sites, the other values being below the test detection threshold. Thus, the mean value identified for IL6 at T0 is 0.14 pg/\(\mu\)L, and for T1 1.27 pg/\(\mu\)L, values that were not taken into consideration in the statistics. Moreover, the identified values for IL6 were lower than those identified for IL1\(\beta\).  
The inflammation caused by the orthodontic movement is followed by an increase in both gingival crevicular levels and inflammatory mediators. Table 1 presents the values 24 h before (T0) and after (T1) the initiation of the orthodontic treatment for gingival crevicular volume and for IL1\(\beta\) levels. Both of these parameters showed increased values at T1. Between the two time periods, there were significant changes both in the crevicular fluid rate (\(p\) = 0.001) and in IL1\(\beta\) levels (\(p\) = 0.009).  
Tables 2 and 3 show a comparison of IL1\(\beta\) values by age groups in the two time periods. Our results indicate higher levels of IL1\(\beta\) in adolescents than in young adults before and after the initiation of the orthodontic treatment. In adolescents, the mean value for IL1\(\beta\) raised from 22.77 pg/\(\mu\)L at T0 to 42.96 pg/\(\mu\)L at T1, while in the age group represented by young adults, the concentration of IL1\(\beta\) raised from 11.28 pg/\(\mu\)L at T0 to 17.93 pg/\(\mu\)L at T0. Adolescents had a statistically significant higher level of IL1\(\beta\) 24 h before (\(p\) = 0.031) and after the initiation of orthodontic treatment (\(p\) = 0.006).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Gingival** & **Gingival** & **IL1\(\beta\)-T0** & **IL1\(\beta\)-T1** \\  & **Crevicular Volume** & **Crevicular Volume** & **(pg/\(\mu\)L)** & **(pg/\(\mu\)L)** \\ \hline Mean & 0.57 & 0.95 & 15.67 & 27.94 \\ SE & 0.05 & 0.10 & 2.21 & 3.92 \\ SD & 0.23 & 0.46 & 9.89 & 17.54 \\ Minimum & 0.28 & 0.35 & 6.42 & 13.09 \\ Maximum & 0.93 & 1.54 & 37.64 & 69.91 \\ Confidence level (95\%) & 0.11 & 0.21 & 4.62 & 8.21 \\ \hline \hline \(p\) value & & 0.001 & & 0.009 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gingival crevicular volumes (\(\mu\)L), IL1\(\beta\) values (pg/\(\mu\)L) before and after 24 h after the initiation of orthodontic treatment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Age** & **17-28 Years** & **11-16 Years** \\ \hline Mean & 11.28 & 22.77 \\ SE & 1.01 & 4.30 \\ SD & 3.66 & 12.17 \\ Minimum & 6.42 & 6.47 \\ Maximum & 17.58 & 37.64 \\ Confidence level (95\%) & 2.21 & 10.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: IL1\(\beta\) values (pg/\(\mu\)L) in adolescents (11–16 years old) and young adults (17–28 years old) at T0.
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 2 GNSS Processing
### POD Strategy  
Orbits of the BDS-2 and BDS-3 satellites, and associated clock solutions, are computed with a modified version of the Positioning And Navigation Data Analyst (PANDA) software package [25], developed at the GNSS Research Center of Wuhan University, China. In this study, the observations from BDS-2 and BDS-3 are jointly processed in a unified processing strategy. We take three consecutive days as one orbit arc and process BDS data of these days to obtain a long arc solution in a batch mode. For each three-day solution, the ionospheric-free linear combinations of B1 and B3 code and phase observations are used to form basic observation equations to eliminate the ionospheric delay. The a priori orbits are taken from the broadcast ephemeris provided by the iGMAS. The BDS-2 and BDS-3 satellite orbit parameters, which include the initial position and velocity, solar radiation pressure (SRP) parameters, satellite clock offsets, station coordinates, receiver clock biases, float ambiguities, and 2-h ZTD (zenith total delay) parameters are estimated together in a weighted least-squares approach. The receiver antenna phase center offset (PCO) and phase center variation (PCV) values are not considered for BDS-3 observations as they are not available. For BDS-2 satellites, the PCO and PCV corrections provided by WHU (Wuhan University) are used resulting in improved performance [23]. For BDS-3 satellites, the PCO corrections are provided by the Operation Control Department, and no PCV corrections are available. For further processing options, see Table 3.
# Campus City Project: Challenge Living Lab for Smart Cities
## 2 Campus City Initiative Main Components  
### Smart City Verticals: Smart Mobility, Water and Energy Definitions  
Mobility, Energy and Water are the main vertical axes of a smart city, united under a common premise: reducing economic and environmental costs, and saving time through the use of data, information and telecommunication. The citizens' quality of life and their perception of the city they live in will improve through intelligent systems that can optimize the administration of resources and inform them about the status and availability of mobility, water and energy resources.  
Smart Mobility--a series of initiatives, policies and actions whose main objective is to promote cleaner, safer and more efficient forms of transportation and to facilitate mobility via public or private transportation throughout the city.  
Smart Water--the use of data acquisition systems, prediction and cognition models, as well as information systems to allow better decision-making by the users and the water infrastructure agency in terms of its accumulation, monitoring, distribution and traceability.  
Smart Energy--to achieve a transition towards greater balance in the distribution and use of energy from renewable sources (sun and wind among others) and fossil fuels, with the purpose of polluting less and improving energy consumption through the use of environmentally friendly and safer technologies.
## 4 Conclusions  
In these studies, sustainable, high-performance self-compacting concrete mixes providing good rheological and mechanical properties were developed. In all concretes GGBS was used as a cement replacement in the amount of 46% by cement weight and a constant water to binder ratio of 0.32. The following conclusions can be drawn from the conducted study:  
* Workability of high-performance self-compacting concrete decreases as the content of polypropylene fibres increases. The T\({}_{500}\) slump flow time is approximately 4 s for the reference HPSCC. In the other hand, the longest flow time was obtained for 0.125% PP-fibre-reinforced PPHPSCC. The blocking factor ranges from 0.63 to 0.94 and the increasing fibre fraction decreases passing ability.
* Polypropylene fibres have no statistically significant effect on the 28 day compressive strength of high-performance self-compacting concrete at the volume fractions used in this investigation. On the other hand, the addition of 0.05%, 0.075%, and 0.125% volume fraction of PP fibres increases the 7 day compressive strength by 31%, 13.5% and 11.5%, respectively. Minor effects are noted for the minimum and maximum volume fraction of PP fibres (for 0.025% and 0.25%) used in this study.
* Polypropylene fibres have noteworthy effects on the splitting tensile strength. The 7 day and 28 day strength improvements of PPHPSCC range from 11.5% to 38%, and from 15.5% to 44.5%, respectively, at the PP fibre volume contents of 0.025% to 0.25% in comparison with reference HPSCC. Greater increases in the splitting tensile strength are noted after 28 days of water curing.
* Polypropylene fibres affect the flexural strength significantly. The addition of 0.025%, 0.05%, 0.075%, 0.125%, and 0.25% volume fractions of PP fibres increases the flexural strengths at 7 and 28 days by 12.5% and 9.5%, 29% and 18%, 33.5% and 28.5%, 45% and 42%, 16.5% and 12%, respectively, in comparison with control HPSCC. In this case, greater increases in strength occur for the 7 day flexural strength.
* The strength models developed for HPSCC accurately predict 7 day and 28 day compressive strengths, tensile splitting strengths, and flexural strengths.  
Figure 15: Correlation between flexural strength and splitting tensile strength for HPSCC and PPHPSCCs.  
* In future works, studies of fracture parameters and durability properties of high performance self-compacting concrete containing ground granulated blast furnace slag and polypropylene fibers, as well as an evaluation of their microstructure, morphology, and pore structure, are planned.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, P.S., upon reasonable request.  
The author would like to thank the CEMEX Company for donating the materials for this study. The careful review and constructive suggestions by the anonymous reviewers are gratefully acknowledged.  
The author declares no conflict of interest.
## 7 Experimental Results
### Original BEE
#### 7.1.2. CH Rate  
Comparing the CH percentage in Figure 6, we can see that BEE has similar number of CHs as HEED. In HEED, the CH percentage increased to 1 at the end. This is because all the sensors still alive were no longer in each other's cluster range and then every sensor was required to be CH to transmit data directly to the BS. It has been proved that in HEED the elected CHs can be well distributed in the network and cover the whole sensing area. The case that two CHs in each other's cluster range is rare. Since BEE does not need more CHs than HEED, the CHs elected through improved iteration CH election process in BEE can also cover the whole sensing area.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 1 Introduction to Unguided Optical Communication (UOC)
### Revolution of UOC
However, addressing all these research barriers is of the utmost importance to build a network infrastructure capable of handling near-future data traffic. Therefore, a comprehensive review of these challenges together, from their initial status to today's mature and the state of art developments, is essential to allow the readers to comprehend them. Several review/review papers addressing focused (very narrow) and limited issues on sub-topics of UOC technology have been reported in the literature [10, 11, 12, 13, 14]. Recently published review papers have addressed only a few topics [9, 10, 11, 12, 13, 14]. However, minimal review works reported on a comprehensive evaluation of UOC systems/techniques, all the way back to ancient man's fire to present-day AI-supported UOC technology. This coextensive review will benefit many readers considering that near-future high-data-rate wireless networks will benefit from the integration and operation of UOC systems individiually and together (combined version) with RF signals. To the best of our knowledge, no review paper covers the entire roadmap of UOC, as in Figure 1.  
In this review paper, we cover a thorough investigation on the complete evolution of UOC technologies right from the prehistoric man's fire-based communications. Brief historical developments of communication technologies and the working principles and installation strategies of UOC systems are covered in Sections 1.2 and 1.3, respectively. Section 2 reviews FSO communications, channel effects and mitigation techniques. Hybrid FSO-RF systems and the achievements therein are covered in Section 3. Sections 4 and 5 present optical water communications and wireless optical communications, respectively. Section 6 outlines the recent developments in UOC with AI, and energy harvesting, whereas Section 7 addresses next-generation UOC networks and several associated research challenges. Finally, Section 8 concludes this review paper.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 4 Generative AI-Based IDS for IoV
### Standardized Evaluation and Benchmarking Challenges  
Despite promising results, comparing Generative AI-based IDS models across studies remains challenging due to the absence of standardized evaluation protocols. Many surveyed papers report different combinations of metrics, often calculated on distinct datasets or under varied experimental setups. In several instances, key performance indicators such as latency, computational overhead, or false negative rates are omitted entirely, making direct comparison difficult and potentially misleading.  
Furthermore, the use of diverse datasets introduces additional variability. These datasets differ in terms of complexity, attack types, feature richness, and their representativeness of real-world vehicular environments--all of which significantly influence the reported performance of IDS models.  
To ensure fair and reproducible evaluation, the establishment of standardized benchmarking frameworks is essential within the domain of generative IDS for the Internet of Vehicles. Future research efforts should adopt unified evaluation protocols that include a core set of metrics reported consistently across studies. In addition, the development of publicly available vehicular datasets that reflect a wide range of real-world scenarios and attack types is crucial. Open-source implementations of IDS models and experimental setups should also be encouraged to promote transparency and support comparative analysis.  
Adopting such standardized practices would significantly improve the reliability of performance evaluations, facilitate meaningful cross-study comparisons, and accelerate the translation of generative IDS technologies from experimental research to real-world automotive applications.
## 1 Introduction
### High-Mass X-Ray Binary Pulsars  
X-ray pulsars are rapidly spinning neutron stars (NSs) that emit X-rays modulated by their spin periods. These pulsars are found in NS X-ray binary (XRB) systems with high-mass or low-mass companion stars from which the NS accretes material. The material falls onto the star's magnetic poles, forming bright spots ('hotspots') that emit X-ray photons. As the NS rotates, the X-ray beams sweep across space, appearing as regular pulses to any observer. The pulse period tells us how fast the neutron star is spinning, and changes in this period over time reveal details about the interaction of the NS with its companion star [20; 21].  
High-mass X-ray binaries (HMXBs) are systems in which the NS companion is a massive (\(>\)8\(M_{\odot}\)) hot star, usually of spectral type O or B. A special type of HMXB is the subset of Be X-ray binaries in which the companion is a Be star. Be stars are a class of B-type stars that show Hydrogen emission lines (e.g., H\(\alpha\)) in their optical spectra (hence, the 'e' in the class name). They form large decretion disks around them due to material shed from their rapidly rotating surfaces enhanced by processes thought to be related to stellar pulsations [22; 23]. This disk, composed of partly ionized gas, is held together by centripetal, gravitational, and pressure forces, coupled by turbulent viscosity, and extends outward in the star's equatorial plane. The disk is rich in hydrogen, which produces the characteristic H-alpha emission line, which is a key diagnostic feature for identifying and studying Be stars. The H-alpha emission varies with the size and density of the disk, often linked to the star's activity level. The decretion disk is also responsible for excess infrared emission due to free-free and bound-free radiation, helping astronomers trace the disk's size, structure, and evolution. This disk is not static; it can grow, shrink, or even become disrupted, depending on interactions with the binary companion. Decretion disks are crucial in driving the observed X-ray behavior in Be-XRBs. When the NS passes through or interacts with the disk material, then the process of accretion occurs leading to powerful X-ray outbursts [24]. These interactions are especially significant near the periastron of the binary orbit, resulting in regular Type I outbursts or more intense longer, irregular Type II outbursts when the disk extends significantly.
## 2 The Summation Method
This approximation has been implemented and compared to the octave band SPL attenuation calculated as a function of time of propagation with Equation (5) and \(N=9\), see Figure 3. The approximation yields attenuation that is systematically too low for longer times of propagation, and for the octave bands 2 kHz and 4 kHz, the attenuation decreases after reaching a maximum of around 18 dB. It must be concluded that this approximation cannot be used. [PERSON] et al. [13] also found that this approximation fails to estimate accurate decay curves for octave bands. Figure 3: Octave band air attenuation as function of time of sound propagation (20 \({}^{\circ}\)C, 50% RH). Full lines: Calculated for octave band with the summation method. Dashed lines: The approximation method in ANSI S1.26 Annex E [9]. Figure 2: Air attenuation at various frequencies as function of time of sound propagation (20 \({}^{\circ}\)C, 50% RH). Full lines: Calculated for octave band with the summation method. Dashed lines: Pure-tone attenuation at center frequency of octave band. Dot-dot-dashed lines: Pure-tone attenuation at center frequency of lower one-third octave band within the octave. It appears that the problem of air absorption in octave bands is complicated, and none of the approximations found in the literature can offer a valid solution to this problem. The best solution is the summation method, but it is quite heavy in terms of calculations when applied to every single time step in the impulse response.
## 5 Modes of Operation
### STARDUST (Subpayload 2) Camera Real-Time Operation
#### 5.2.1 Real-Time Attitude Determination  
The star tracker algorithm was split between a Lost In Space (LIS) algorithm, which used no a priori attitude knowledge, followed by a tracking mode algorithm which only used the star measurements and time between images. Both outputs provide the attitude in the Earth Centered Inertial (ECI) reference frame for every image captured. All design choices were targeted to maximize image capture by decreasing the computation time at the cost of lower pointing accuracy. Examples of this included the implementation of a tracking mode, which reduced the need for the computationally expensive LIS algorithm, as well as the requirement to use a minimum of three stars rather than four or more to determine the attitude. LIS would be performed every 5 min, based on inertial ground observations in which the accuracy for tracking mode was decreased to above 5 degrees error.  
The LIS algorithm started with detecting and centroiding the stars in the image, using a simple threshold and Center Of Mass (COM) calculation. Next, the planar area and moment method was used for pattern matching. The onboard star catalog containing the same pattern of stars with their inertial measurements used a magnitude threshold of three and roughly a third of the full Bright Star 5 catalog [33] based on the expected pointing direction for the camera. Finally, using the three brightest star's body frame and ECI vectors, the Quaternion Estimator (QUEST) algorithm was applied to output the final attitude measurement. The algorithms are described in detail in [34]. Tracking mode first applied a Gaussian blur and gamma correction to the image to help with noise reduction and star detection respectively. Next, the centroid of the stars was found and compared with the stars in the previous image using a nearest neighbor search. The unit vector representation in the body frame of the stars was used to estimate the angular rate between the images [35]. This angular rate was then converted to an updated attitude based on only the measurements of the stars.  
Figure 5: Block diagram outlining the closed-loop image acquisition application once the payload is powered on.
## 3 Results and Discussion
### Prediction of Plant Yield
#### 3.2.1 Linear Regression  
LR models applied to the four datasets produced R\({}^{2}\) values ranging from 0.18 to 0.41 (Table 4), indicating limited explanatory power. Data 2 and 4, in particular, exhibited lower AIC values, suggesting a more suitable balance between fit and complexity than  
Figure 1: Multiple plots (scatter, histogram, and Spearman correlation) for all variables in the dataset. The upper triangle displays Spearman correlation coefficients with significance levels denoted by asterisks (*, **, and ***, representing \(p\)-values \(\leq\) 0.05, \(\leq\)0.01, and \(\leq\)0.001, respectively). The value at the intersection of the lines indicates the correlation between the variables. The lower triangle presents scatterplots for each pair of variables. The diagonal shows histograms of each variable’s distribution.  
those for Data 1 and 3. However, introducing RMSE provides additional insight into the model's predictive performance, with Data 2 and 4 showing lower errors, thus reinforcing their efficacy.  
Despite the moderate explanatory power demonstrated by all models, the consistency in results between the transformed and untransformed predictors for Data 2 underscores the model's robustness (lowest RSE, AIC, and RMSE). This aspect is particularly noteworthy as it highlights the model's capability to effectively capture the underlying relationship between the predictors and PY, regardless of the scale or distribution of the predictor variables. However, the model's explanatory power is limited, as the three variables collectively explain only 24% of the variation in the data. Analysis of the coefficients reveals that T has the most substantial impact on PY (62.42), followed by NC (\(-\)8.05), and then D (2.05).  
The diminished predictive capacity of LR underscores the need for advanced modeling techniques to more accurately estimate PY in the context of highly variable crop yield data characterized by high variability and multifactorial influences [61; 62]. LR typically yields modest results in such scenarios [63]. Moreover, the inherent variability in crop yield, characterized by its specificity to each period and its intrinsic nonlinearity, further complicates prediction [64].
## 4 ML-Based Resource Allocation
### Deep Learning Approach  
Deep learning (DL), also known as deep network, is a part of machine learning where several layers of a neural network are used to mimic the behavior of the human brain. Artificial neural networks (ANN), recurrent neural networks (RNN), convolutional neural networks (CNN), deep neural networks (DNN), etc., are the different existing DL algorithms. One of the common and most widely used algorithms in the field of deep learning is DNN which is a collection of computational functions and an activation matrix that significantly improves the computational efficiency and provides better accuracy [39]. In DNN, each neuron generates its output when input meets certain conditions, and one neuron's output feeds into the next neuron's input to complete the chain [40]. In this way, the output of each layer is determined by the multiplication of input, weights, and the addition of a bias.  
Deep learning has emerged as a powerful tool for addressing complex optimization problems in wireless communication systems, particularly in resource allocation. Traditional methods often require solving complicated optimization problems, which can be computationally expensive and difficult to implement in real-time environments. Due to flexibility, lower computational time, and the capability to achieve near-optimal performance without solving the complicated optimization problem explicitly [41; 42; 43], we propose a DQN-based DL algorithm.