esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Convergence Process of the EESA Algorithm for Three Scenarios  
The convergence process of the EESA algorithm to achieve the optimal results at different \(LPSP^{max}\) values is illustrated in Figure 12. All three scenarios were tested on 100 iterations. It is shown in Figure 12 that the EESA reduced TAC during the initial iterations, which showed the efficiency of the proposed hybrid algorithm towards achieving the objective function.  
From Figure 12, it is noticed that due to the high number of decision variables (\(N^{pv}\), \(N^{wt}\), and \(N^{b}\)) involved in the PW-WT-battery hybrid system, the convergence process was achieved at later iterations. However, for the PV-battery and WT-battery systems with fewer decision variables, the EESA achieved the convergence process earlier, as displayed in Figure 12 and Figure 12, respectively.
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 6 Results and Discussions
### The Effect of Power Law Index, Volume Fraction of Porosity, Thermal Gradient on Whirl Frequencies  
The whirl frequencies of the porous FG rotor-bearing system at a shaft speed of 4000 rpm are calculated for different power law indices, volume fractions of porosity and temperature gradients in this work. The results are tabulated in Table 8. As the power law index increases, the whirl frequencies of the FG rotor system decrease. The whirl frequencies decrease with the increase in the temperature gradient as expected due to the reduction in stiffness at higher thermal gradients. The split between 1 BW-1 FW and 3 BW-3 FW is minimal compared to the split between 2 BW and 2 FW. Campbell diagrams for k = 0.5, \(\alpha\) = 0, 0.1 and \(\Omega\) = 4000 rpm, 10,000 rpm at different temperature gradients are shown in Figure 10a-d. The split between 1 FW-1 BW and 3 FW-3 BW is barely detectable in the figures.  
Figure 9: (a) Variation in DNF of an FG rotor system at different thermal gradients without porosity. (b) Variation in DNFs of a porous FG rotor system for \(\alpha\) = 0.2 at different thermal gradients.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Modes AT = 0**} & \multicolumn{4}{c}{\(\alpha=0\)} & \multicolumn{4}{c}{\(\alpha=0.1\)} & \multicolumn{4}{c}{\(\alpha=0.2\)} \\ \cline{2-13}  & \(\mathbf{k=0.5}\) & \(\mathbf{k=1}\) & \(\mathbf{k=5}\) & \(\mathbf{k=10}\) & \(\mathbf{k=0.5}\) & \(\mathbf{k=1}\) & \(\mathbf{k=5}\) & \(\mathbf{k=10}\) & \(\mathbf{k=0.5}\) & \(\mathbf{k=1}\) & \(\mathbf{k=5}\) & \(\mathbf{k=10}\) \\ \hline  
W & 269.545 & 267.809 & 265.352 & 265.026 & 272.155 & 269.810 & 266.436 & 266.015 & 275.932 & 272.755 & 268.107 & 267.536 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Whirl frequencies of a porous FG rotor–bearing system for different values of k, \(\alpha\) and AT.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 3 Results
### Field Tests
#### 3.2.1 Communications Performance  
In Figure 10, we have a close-up vision of how the position reports were received and the strength of the signal. In most cases the reports were under the 120 dBm mark, except for some good line of sight reports. The gateways were located on the south side of the mountains, 12 km from the furthest point.  
The distances achieved ranged from 10 to 12 km to both gateways with almost no difference in signal strength between them. The elevation profile (Figure 11) depicts the position of the gateway and the range of altitude covered with the tracker. It also shows some obstacle at 8.6 and 3 km. Depending on the distance and height the contribution to path loss is variable. RSSI in the tracking area goes from \(-115\) to \(-135\) dBm.  
Figure 9: Peak signal-to-noise ratio (PSNR) distribution vs. codebook centroids. (Sampling Freq. = 100 Hz, Window size = 1 s).
## 2 Operation and Control
### Operation Support Tools
#### 2.5.2 Distortion in the Electrical Grid  
The reliability of the system is analyzed by probability in the work of [PERSON] et al. [105]. The study has been validated in distribution feeders, which integrate manageable and non-manageable DG.  
[PERSON] et al. [106] present a tool to detect vulnerabilities with probabilistic line outages and overloads with direct current load flow and linear programming dispatch of generation. The tool is validated and presents excellent results.  
The prediction of faults in distribution sections can be addressed with distribution fault anticipation (DFA) algorithms, as presented by [PERSON] and [PERSON] [107]. This prediction is fundamental to anticipate problems, and the one presented by the authors produces formidable results.  
[PERSON] and [PERSON] [108] formulate and solves the problem of placing additional PMUs into a power grid (based on swarm optimization algorithm). The excess of devices is not beneficial, the authors show that with the exact devices, the results are great.  
Some works employ distributed detection schemes, such as the work of [PERSON] et al. [109], which employs a distributed general-anomaly detection (DGAD) scheme. This type of scheme is formulated in a simple way, and its operation helps to keep the grid stable and secure.  
Continuing with this, [PERSON] and [PERSON] [110] use an application based on gray wolf optimizer (GWO) mixed with an advanced pattern search, to address the insecurity of the system in SG.  
The robustness of the system and the cost of guaranteeing it do not follow a linear relationship, as presented by [PERSON] et al. [111], and this must be taken into account. The authors show that excessive spending is not necessary to maintain a secure electrical grid.  
[PERSON] and [PERSON] [112] propose a hybrid system (fuzzy logic and ANN) to detect sources of harmonics in the system. This AI-based detection allows to guarantee stability.  
Photovoltaic systems can cause harmonic distractions in the grid, as demonstrated by [PERSON] et al. [113]. These authors propose a new model to characterize the accommodation capacity according to the distortion, and this model is validated in a real environment. It is necessary to take into account the harmonic distortion caused by renewable generation sources.
## 3 Proposed Solution to Predict the AoA
### System Overview and Assumptions
#### 3.1.2 Key Technical Contributions  
Key technical contributions and enablers within O-RAN Split 7-2x include the following:  
* Parallel distributed inference: The system executes lightweight Weighted LR prediction at the O-RU (for immediate reaction) and advanced curvature-adaptive SVR at the O-DU (for robust, longer-term modelling), running in true parallel to eliminate the inherent latency of reactive model switching.
* Dominance-enforced confidence-curvature weighted fusion: LR and SVR predictions are fused in real-time via a dynamic weighting algorithm that incorporates statistical spread (confidence), trajectory curvature, and instantaneous windowed prediction error. Whenever a clear winner is present (as determined by MSE), the fusion \"dominance-enforced\" overrides the privileges of that predictor, ensuring that the most reliable result drives beam updates at each slot.
* Autonomous MUSIC-based fallback: Upon detection of a sharp AoA jump or drop in prediction confidence, the O-RU immediately triggers a localised MUSIC estimation within a reduced angular sector, providing rapid and robust AoA refinement and protecting against outlier or adversarial conditions.
* Jump-aware window clustering: When sudden AoA jumps are detected within the sliding window, the regression window is split, and predictions are recalculated only on the most recent post-jump segment, reducing prediction error during abrupt trajectory changes.
* Modular and practical integration: The prediction architecture is fully modular and designed for seamless integration with standard O-DU scheduling and control logic. No changes are required at the O-RU's hardware level, facilitating practical adoption and interoperability within existing O-RAN deployments.  
Overall, this architecture leverages the flexibility of O-RAN Split 7-2x to optimise both responsiveness and accuracy, enabling predictive AI-driven beam management that is well-suited for 5G and future 6G RANs.
# Efficient Capture of Sr\({}^{2+}\) Ions by a Layered Potassium Neodymium Phosphate
## 2 Materials and Methods
### Methods
#### 2.2.3 Batch Adsorption Experiments  
Adsorption studies were performed using the batch method. Before the adsorption experiments, we prepared the required Sr\({}^{2+}\) solution. A certain amount of SrCl\({}_{2}\)-6H\({}_{2}\)O was dissolved in ultrapure water and then diluted to different concentrations of Sr\({}^{2+}\) solution. Such as in a typical adsorption experiment, a certain amount of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) powder was weighed in a glass bottle and added with a suitable concentration of Sr\({}^{2+}\) solution (\(V/m\) = 1000 mL/g, \(V\) is the solution volume, \(m\) is the mass of K\({}_{3}\)Nd(PO\({}_{4}\))\({}_{2}\) powder), and then the mixture was stirred using a magnetic stirrer at 800 r/min for 12 h at room temperature. After stirring, the supernatant was filtered with a polyethersulfone (PES) needle filter with a pore size of 0.22 \(\upmu\)m. In order to ensure that the concentration of metal ions in the solution met the testing requirements of the ICP-MS/ICP-OES instrument, the filtered solution was diluted with 2% HNO\({}_{3}\). This can avoid problems such as clogging the atomizer.  
In the adsorption isotherm study, the aqueous solutions with different initial Sr\({}^{2+}\) concentrations (12.565-756 mg/L) were used under neutral conditions. The kinetic experiment was performed in a neutral solution with an initial Sr\({}^{2+}\) concentration of 3.650 mg/L. The suspensions were taken at selected reaction times (0, 2, 5, 10, 20, 30, 60, 90, 120, 180, 360, 720, and 1440 min). In pH-dependent experiments, the pH range of Sr\({}^{2+}\) solutions were from 3.07 to 12.17. The initial Sr\({}^{2+}\) concentrations were from 4.445 to 4.750 mg/L. ICP-OES was used to determine the concentration Sr\({}^{2+}\) ions in the solutions before and after Sr\({}^{2+}\) adsorption, and ICP-MS was used to determine the leaching concentration of Nd\({}^{3+}\) in the solutions in the pH-dependent experiments.
## 3 Methodology
### Complex Network Analysis and Girvan-Newman Method
#### 3.2.4 Clustering Coefficient  
Clustering is a measure of how well a network is aggregated. The clustering coefficient is calculated while using the local clustering coefficient of the specific node \(i\). The local clustering coefficient is a measure of how well the neighboring nodes are clustered around the specific node \(i\).  
\[C_{i}=\frac{\text{number of triangle}}{\text{number of triplet}}=\frac{\Delta_{i}}{k_{i }(k_{i}-1)/2} \tag{10}\]  
where \(\Delta_{i}\) is the number of triangles for the sub-network of node \(i\) and its neighborhood and \(k_{i}\) is the number of neighboring nodes. \(C_{i}\) denotes the local clustering coefficient of node \(i\), and their average value is the clustering coefficient of the network.  
\[C=\frac{1}{N}\sum_{i=1}^{N}C_{i} \tag{11}\]
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Scheme Overview  
The proposed PPDB consists of seven phases: **System Initialization**, **Registration**, **Reports Generation**, **Data Aggregation**, **Data Reading**, **Billing and Arbitration**. The overview flow is as follows:  
* **System Initialization:** PKG boots the entire system and generates system parameters.
* **Registration:** PKG generates and distributes keys for the entity requesting registration.
* **Reports Generation:** SM first encrypts the electricity consumption data, then sends the ciphertext to the corresponding FN.
* **Data Aggregation:** FN aggregates the valid ciphertext and then sends the aggregated ciphertext to AC.
* **Data Reading:** AC first pre-decrypts the received data, then sends the pre-decrypted ciphertext to SP. SP recovers the sum of electricity consumption data from the pre-decrypted data.
* **Billing:** FN computes the user's electricity billing ciphertext and sends the aggregated billing ciphertext to AC at the end of a billing cycle. AC first pre-decrypts the received billing ciphertext, then sends the pre-decrypted ciphertext to the SP. The SP decrypts the ciphertext to obtain each user's bill.
* **Arbitration:** AC uses the auxiliary information sent by the SP to arbitrate disputed bills.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 3 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror  
The proposed 3D estimation process follows the flow shown in Figure 2. In this process, a known spherical mirror is assumed to be placed in the scene, and images of the mirror are captured using an omnidirectional camera.  
Figure 2: The Process of a 3D Estimation Based on a Catadioptric Imaging System with an Omnidirectional Camera and a Spherical Mirror: 1. Segmentation of the mirror image region in an omnidirectional image by network. Estimation of the 3D position of a spherical mirror from the shape of the mirror image (Section 4). 2. Estimation of the 3D information of the shooting scene based on the 3D position of the spherical mirror. Searching for the omnidirectional image part corresponding to the mirror image using the incident light rays from the object obtained by the 3D position information of the spherical mirror, using stereo matching (Section 5).  
To estimate the 3D information in the catadioptric imaging system, the determinion of the 3D position of the spherical mirror is crucial. This method estimates the mirror's position by analyzing the mirror image observed in the omnidirectional image. The mirror region is obtained by applying the mirror region segmentation network discussed in Section 2.2 to the omnidirectional image. An ellipse is fitted to the mirror region, and the 3D position of the spherical mirror is estimated based on the shape of the ellipse.  
The 3D information of the captured scene is estimated using the estimated 3D position information of the spherical mirror. The process begins with backward projection, which determines the reflection points on the spherical mirror's surface and the incident light ray directions from the objects in the scene. Next, to obtain the 3D information observed from multiple viewpoints, the mirror image at each reflection point and the corresponding area in the omnidirectional image are searched. Stereo matching along the ray directions, estimated through backward projection, is performed to calculate the similarity of visibility. The 3D information with the highest similarity is selected. The similarity of visibility is computed using a color histogram, considering the distortion of the mirror image caused by the spherical shape. This process is applied to all pixels of the spherical mirror in the omnidirectional image.  
By following this flow, the proposed method enables the estimation of 3D information in the scene captured by the catadioptric imaging system, leveraging the 3D position estimation of the spherical mirror and the analysis of the mirror reflections.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.2 Human-Robot Interaction and Control
The EEG signals were acquired using a Neuroscan (Compumedics Neuroscan, Abobtsford, Australia) device equipped with eight electrodes, while the AR display was facilitated by the utilization of HoloLens 2. For the preprocessing portion, three subfilters with different bandpass ranges were designed, running at 7-17 Hz, 16-32 Hz, and 25-47 Hz, respectively, while for the classification process, the filter bank convolutional neural network (FB-tCNN) was employed. During the cue-guided task involving the robotic arm, all subjects demonstrated a grasping success rate of 87.50%. Additionally, ITR achieved a value of 159.40 bits/min. [PERSON] et al. [43] explored the potential of a projected AR system combined with an SSVEP-based BCI to aid human-robot interaction. They employed the NextMind BCI device, which used NeuroTags as flickering visual stimuli in order to enable users to control a robotic arm for pick-and-place tasks. The system was tested with 22 healthy participants, evaluating usability and robustness through metrics such as the System Usability Scale (SUS) and NASA-TLX. The study found that the adaptive positioning of visual stimuli was significantly more effective and preferred over a nonadaptive linear approach.
# Assessment of Postbiotic, Mundticin-like Substance EM 41/3 Application in Broiler Rabbits
## 3 Results
### Phagocytic Activity, GPx and Biochemistry  
PA values from blood reached 57.13 \(\pm\) 3.14% at day 0/1 (Table 3). At day 21, PA increased in rabbits of the EG compared to PA measured at day 0/1 (\({}^{\text{th}}\)\(p\) < 0.05). But PA also increased in rabbits of the EG compared to rabbits in the CG (\({}^{\text{th}}\)\(p\) < 0.05). And PA value was significantly higher in rabbits of the EG at day 21 compared to PA value in rabbits of the EG at day 21, Day 42--EG, experimental group at day 42 (3 weeks of MLS EM 41/3 cessation), Day 42--CG, control group at day 42. Caecum-staphylococci, Day 42--EG: Day 42--CG \({}^{\text{th}}\)\(p\) < 0.05. At day 21, the values of GPx increased in both groups of rabbits (EG, CG) (Table 3) compared to day 0/1. However, GPx was higher in rabbits of the CG than in rabbits of the EG (no oxidative stress stimulation). At day 42, GPx values decreased; in the EG, GP values almost reached initial levels (Table 3), and still lower in the EG than in the CG. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Day 21 (\(n\) = 4)—EG** & **Day 21 (\(n\) = 4)—CG** & **Day 42 (\(n\) = 4)—EG** & **Day 42 (\(n\) = 4)—CG** \\ \hline Caecum & & & & \\ \hline Enterococci & 0.93 \(\pm\) 0.05 & 0.93 \(\pm\) 0.05 & 1.43 \(\pm\) 1.05 & 0.09 \(\pm\) 0.0 \\ \hline Lactic acid bacteria & 1.82 \(\pm\) 1.41 & 2.55 \(\pm\) 1.93 & 1.05 \(\pm\) 0.17 & 0.95 \(\pm\) 0.06 \\ \hline Staphylococcus & 3.96 \(\pm\) 0.29 & 4.07 \(\pm\) 0.51 & 2.48 \(\pm\) 1.72 \({}^{\text{b}}\) & 4.09 \(\pm\) 0.30 \({}^{\text{a}}\) \\ \hline Amyloidic streptococci & 4.15 \(\pm\) 0.21 & 3.96 \(\pm\) 0.39 & 4.43 \(\pm\) 0.19 & 4.21 \(\pm\) 0.16 \\ \hline Coliform bacteria & 2.42 \(\pm\) 1.25 & 1.32 \(\pm\) 0.33 & 3.54 \(\pm\) 1.04 & 2.42 \(\pm\) 1.25 \\ \hline Appendix & **Day 21 (\(n\) = 4)—EG** & **Day 21 (\(n\) = 4)—CG** & **Day 42 (\(n\) = 4)—EG** & **Day 42 (\(n\) = 4)—CG** \\ \hline Enterococci & 2.54 \(\pm\) 1.07 & 0.93 \(\pm\) 0.05 & 0.90 \(\pm\) 0.00 & 0.90 \(\pm\) 0.0 \\ \hline Lactic acid bacteria & 3.30 \(\pm\) 1.63 & 2.55 \(\pm\) 1.93 & 0.90 \(\pm\) 0.00 & 0.95 \(\pm\) 0.06 \\ \hline Staphylococcus & 3.78 \(\pm\) 0.24 & 4.07 \(\pm\) 0.51 & 3.59 \(\pm\) 0.23 & 4.09 \(\pm\) 0.30 \\ \hline Amyloidic streptococci & 4.47 \(\pm\) 0.42 & 3.96 \(\pm\) 0.39 & 4.66 \(\pm\) 0.38 & 4.21 \(\pm\) 0.16 \\ \hline Coliform bacteria & 3.97 \(\pm\) 0.17 & 1.32 \(\pm\) 0.33 & 4.95 \(\pm\) 0.11 & 2.42 \(\pm\) 1.25 \\ \hline \hline \end{tabular}  
* Average value of bacteria in CFU/g (log 10) \(\pm\) SD; Day 0/1 means sampling before MLS EM 41/3 application, Day 21—EG, experimental group at day 21 (3 weeks of MLS EM 41/3 application), Day 21—CG, control group at day 21, Day 42—EG, experimental group at day 42 (3 weeks of MLS EM 41/3 cessation), Day 42—CG, control group at day 42. Caecum-staphylococci, Day 42—EG: Day 42—CG \({}^{\text{th}}\)\(p\) < 0.05.  
\end{table} 
Table 2: Bacterial counts in caecum and appendix of rabbits after MLS EM 41/3 application and its cessation.
## 3 Results and Discussion
### Representative Size for Fine Aggregate (FA) Geometry Characterization  
In the indirect methods, measurements of the FA bulk properties were conducted on the graded aggregates. However, the direct methods are based on the direct measurement of the individual aggregates. In this case, it is necessary to find out a representative size for FA to eliminate the effect of particle size and represent the geometric characteristics of graded FA. Theoretically, the aggregates  
Figure 8: Flow time of different FAs.  
Figure 9: Particle index of different FAs.  
with the determined representative size should have the maximum correlation with the graded FA in terms of the geometric characteristics. Determination of the representative size can be achieved with resort to the gray relation analysis (GRA) method. The underlying mechanism of GRA method is to calculate the relation degree between reference sequence and comparative sequence. The greater the relation degree is, the higher the correlation will be.  
In this research, 3 FAs (FA 2, FA8 and FA10) were selected as the study object to investigate the representative size by means of the GRA method. Firstly, the mono-sized (2.36 mm, 1.18 mm, 0.6 mm, 0.3 mm, 0.15 mm, 0.075 mm) aggregates for each kind of the selected FAs were collected by sieving. For comparison, three kinds of graded aggregate for each kind of the selected FAs were blended according to upper, middle and lower gradations (see Figure 10) recommended by \"Technical Specification for Construction of Highway Asphalt Pavements\" (JTG F40-2004).  
Then, the uncompacted void content test, flow time test and ASTM D3398 test were conducted on both mono-sized and graded aggregates, respectively. On basis of the test results, the GRA method was used to calculate the relation degree between the mono-sized and graded aggregates for all the three tests separately. But for the sake of comparing and analyzing, the calculation results for different tests were combined as shown in Figure 11.  
Figure 11: Comparison of the gray relation degree of different mono-size.  
Figure 10: The recommended aggregate gradations.  
Figure 11 indicates that in the flow time test and ASTM D3398 test, the particle size of 0.6 mm exhibits the strongest relation with graded aggregate. In the uncompacted void content test, the particle size of 2.36 mm exhibits the strongest relation with graded aggregate, followed by 1.18 mm and 0.6 mm. In general, the 0.6 mm aggregates have the highest characteristic similarity with the graded fine aggregates. Thus, 0.6 mm was determined as the representative size of FA and used in the following FA geometric characterization.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
###### Abstract  
This work investigates the impact of rainfall on cellular communication links, leveraging smartphone-collected measurements. While existing studies primarily focus on line-of-sight (LoS) microwave propagation environments, this work explores the impact of rainfall on typical signal metrics over cellular links when the LoS path is not guaranteed. We examine both small-scale and large-scale variations in signal measurements across dry and rainy days, considering diverse locations and time windows. Through statistical and spectral analysis of a large dataset, we uncover novel insights into how rainfall influences cellular communication links. Specifically, we observe a consistent daily fluctuation pattern in key cellular metrics, such as the reference signal received quality. Additionally, spectral features of key mobile metrics show noticeable changes during rainfall events. These findings, consistent across three distinct locations, highlight the significant impact of rainfall on everyday cellular links. They also suggest that the widely available by-product signals from mobile phones could be leveraged for innovative rainfall-sensing applications.  
Keywords: rainfall; LTE signal; signal strength indicator; signal measurements +
Footnote †: journal: