esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Comparison and Analysis
### Comparison of Heuristic Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multicolumn{2}{c}{_Numerical_} & \multicolumn{8}{c}{**APRD**} & \multicolumn{8}{c}{**CT(s)**} \\ \cline{3-11} \multicolumn{2}{c}{_Example_} & **C1S1** & **C1S2** & **C1S3** & **C2S1** & **C2S2** & **C2S3** & **C3S1** & **C3S2** & **C3S3** & \\ \hline \multirow{3}{*}{\(n\)} & 20 & 16.28 & 17.48 & 17.77 & 9.85 & 12.28 & 21.65 & 15.12 & 14.73 & 22.57 & 238.42 \\  & 30 & 32.41 & 21.09 & 28.42 & 28.03 & 7.11 & 20.69 & 21.17 & 23.95 & 27.08 & 357.78 \\  & 50 & 18.50 & 38.11 & 19.54 & 31.87 & 17.72 & 19.57 & 17.31 & 17.85 & 17.29 & 503.44 \\  & 100 & 23.08 & 20.09 & 29.91 & 24.18 & 28.64 & 19.46 & 13.54 & 17.94 & 24.86 & 1129.22 \\ \hline \multirow{3}{*}{\(m\)} & 4 & 27.17 & 31.31 & 30.16 & 30.90 & 18.99 & 35.62 & 10.04 & 17.47 & 25.92 & 544.87 \\  & 6 & 35.18 & 38.35 & 36.34 & 36.75 & 21.22 & 30.80 & 25.92 & 26.91 & 36.17 & 631.96 \\  & 8 & 26.78 & 21.84 & 34.00 & 26.29 & 18.42 & 16.51 & 23.05 & 27.04 & 29.15 & 909.66 \\ \hline \multirow{3}{*}{\(a\)} & 4 & 46.46 & 46.42 & 48.53 & 39.44 & 19.33 & 31.22 & 28.80 & 36.75 & 37.87 & 721.32 \\  & 6 & 27.65 & 32.14 & 25.11 & 35.29 & 30.23 & 29.70 & 25.00 & 22.13 & 31.39 & 725.63 \\ \cline{1-1}  & 8 & 16.16 & 18.21 & 22.00 & 19.20 & 16.19 & 20.45 & 13.34 & 15.59 & 22.54 & 781.91 \\ \hline \end{tabular}
\end{table} 
Table 1: The APRD value of each policy on the comparison instances. indicate that the Nearby Choice (NC) strategy performs best when AGVs are scarce, while the Sequential Choice (SC) strategy maintains stable performance across different machine and job configurations. Additionally, we observed that standby strategies significantly influence scheduling efficiency in high-load scenarios, whereby the Next Task Standby (NTS) strategy proved most effective in reducing idle time. This analysis highlights the importance of selecting AGV scheduling strategies based on specific production constraints, ensuring adaptability to different manufacturing environments. Future work could extend this study by incorporating real-time dynamic factors such as AGV battery life and unexpected job arrivals.
## 3 Results and Discussion
### Commoner and Composition Sequence Distribution Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Sample** & \(D\) & \(R\) & \(L_{V}\)\({}^{R}\) & \(L_{V}\)\({}^{R}\) & \(K\) & \(P_{\rm PV}\)\({}^{a}\) & \(P_{\rm PV}\)\({}^{a}\) & \(P_{\rm PV}\)\({}^{a}\) & \(P_{\rm BB}\)\({}^{a}\) & \(P_{\rm BB}\)\({}^{a}\) & \(A\) & \(B\) & \(X\) & **Model \({}^{c}\)** & **Sequence Distribution** \\ \hline PHBW-46 & 1 & 1 & 1.29 & 1.29 & 3.43 & & & & & & & & & & & & & & \\  & & & & & & & 0.35 & 0.65 & 0.19 & 0.81 & 2.33 & & & & (i) & \\ PHBW-61 & 1 & 1 & 1.28 & 1.28 & 3.55 & & & & & & & & & & & & \\  & & & & & & 0.35 & 0.65 & 0.17 & 0.83 & 2.64 & & & & & & & \\  & & & & & & & 0.35 & 0.65 & 0.17 & 0.83 & 2.64 & & & & & (ii) & \\ PHBW-78 & 1 & 1 & 1.30 & 1.30 & 3.32 & & & & & & & & & & & \\  & & & & & & 0.35 & 0.65 & 0.20 & 0.80 & 2.23 & & & & & (ii) & \\  & & & & & & & & & & & & & & & \\ PHBW-84 & 1 & 1 & 1.25 & 1.25 & 4.02 & & & & & & & & & & (i) & \\  & & & & & & & 0.34 & 0.66 & 0.15 & 0.85 & 2.88 & & & & (ii) & \\ \hline \end{tabular}  
\({}^{a}\) The estimated errors in the values of \(P_{\rm PV}\) are \(<0.085\). \({}^{b}\), \(r_{\rm BB}\)\({}^{a}\), \(P_{\rm PV}\) and \(r_{\rm PV}\)\({}^{b}\), \({}^{c}\)-explent representant data (0, 0i), and (ii) are calculated values by Bernoulli model, first-order Markovian model, and mixture of two Bemoulin random copolymers model, respectively.  
\end{table} 
Table 6: Parameters \(D\), \(R\), experimental number average sequence lengths of HV units (\(L_{V}\)\({}^{R}\)), number average sequence length of randomly distributed HV units in copolymer (\(L_{V}\)\({}^{R}\)), ratio between the concentration of HV and HB units (\(\lambda\)), four conditional probabilities (\(P\)\({}^{b}\)\({}^{c}\)) and the reaction index (\(r_{172}\)) for PHBW operational days 46, 61, 78 and 84. The diad and triad sequence distribution of PHBV biopolymer samples were calculated using three models including (i) Bernoullian, (ii) first-order Markovian, and (iii) a mixture of two Bernoullian random copolymers (explanation of the models is given in Supplementary Material Equations (S4)-(S6) [8; 29]. The result of molar fractions of diad, and 3 HV-centered triads of 3 HB and 3 HV for PHBV-46, -61, -78, and -84 is listed in Table 7.  
The PHBV samples sequence distributions followed the Bernoullian statistics model. The results confirm that the PHBV samples were all random copolymers and these results are in agreement with those reported previously [8; 29].
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 7 Discussion and Algorithm Variations
### Inclusion of Ancillary Maps for Improved Specular Point Accuracy Over Ocean and Land  
It is possible to further improve the accuracy of the specular point estimation with the inclusion of ancillary data maps of height variations with respect to the WGS84 ellipsoid reference. Over ocean the inclusion of a global mean sea surface map, such as those provides by the National Space Institute of Denmark [30], has been demonstrated to significantly improve the specular point estimation accuracy over ocean [31]. Additionally, land digital elevation maps are available such as the NASA Shuttle Radar Topography Mission (SRTM) data sets [32] which can greatly improve the accuracy of open loop tracking over land surfaces. It is recommended to apply a filter to the SRTM maps to create continuous and relatively smooth surfaces, which are more amenable to quick conversion of the specular point algorithm.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 3 Performance Analysis
### Outage Probability for RIS-DH Scheme
#### 3.1.1 NCCS Distribution  
As reported in [58], the probability density function (PDF) of \(Z^{2}\) is given as follows:  
\[f_{Z^{2}}(x)=\frac{1}{2\sigma^{2}}\biggl{(}\frac{x}{\lambda}\biggr{)}^{-\frac {1}{4}}e^{-\frac{x+\lambda}{2\sigma^{2}}}I_{-\frac{1}{2}}\biggl{(}\frac{\sqrt{ x\lambda}}{\sigma^{2}}\biggr{)}, \tag{10}\]  
Figure 3: The flowchart to calculate the main system metrics for the RIS-T scheme.  
where \(\lambda=(\frac{N\pi}{4})^{2}\), \(\sigma^{2}=N(1-\frac{\pi^{2}}{16})^{2}\) and \(I_{v}(\bullet)\) denotes the Bessel functions of the first class with order \(v\)[60]. Moreover, the PDF of \(\eta\) can be expressed as follows.  
\[\begin{array}{l}f_{\eta}(x)=\frac{e^{-\frac{\lambda}{2\sigma^{2}}}}{2\sigma^{ 2}\bigg{(}\frac{\rho_{S}}{d_{1}^{2}d_{2}^{2}}\bigg{)}^{\frac{3}{2}}}\Big{(} \frac{x}{\lambda}\Big{)}^{-\frac{1}{4}}\\ \times e^{-\frac{d_{1}^{2}d_{2}^{2}x}{4\rho_{S}\sigma^{2}}}I_{-\frac{1}{2}} \left(\frac{\sqrt{d_{1}^{2}d_{2}^{2}x\lambda}}{\sqrt{\rho_{S}}\sigma^{2}} \right).\end{array}. \tag{11}\]  
Based on ([60], Eq. 8.445), the cumulative distribution function (CDF) of \(\eta\) is written as follows:  
\[\begin{array}{l}F_{\eta}(x)=1-Q_{\frac{1}{2}}\left(\frac{\sqrt{ \lambda}}{\sigma},\sqrt{\frac{d_{1}^{2}d_{2}^{2}x}{\rho_{S}\sigma^{2}}}\right) \\ =\sum_{k=0}^{\infty}\frac{e^{-\frac{\lambda}{2\sigma^{2}}}}{k!\Gamma \left(k+\frac{1}{2}\right)}\bigg{(}\frac{\lambda}{2\sigma^{2}}\bigg{)}^{k} \gamma\bigg{(}k+\frac{1}{2},\frac{d_{1}^{2}d_{2}^{2}x}{2\rho_{S}\sigma^{2}} \bigg{)}.\end{array} \tag{12}\]  
where \(Q_{m}(a;b)\) is the Marcum Q-function [61] and \(\gamma(\bullet,\bullet)\) is gamma incomplete [60].  
Based on the CDF in (12), the closed-form of \(P_{out}\) can be expressed as follows.  
\[\begin{array}{l}P_{DH,out}=\sum_{k=0}^{\infty}\frac{e^{-\frac{ \lambda}{2\sigma^{2}}}}{k!\Gamma\left(k+\frac{1}{2}\right)}\bigg{(}\frac{ \lambda}{2\sigma^{2}}\bigg{)}^{k}\\ \times\gamma\bigg{(}k+\frac{1}{2},\frac{\gamma_{th}\sigma_{3}d_{ 1}^{3}d_{2}^{3}}{2\sigma^{2}\rho_{S}(\varpi_{1}-\gamma_{th}\varpi_{2})}\bigg{)}.\end{array} \tag{13}\]
# Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions
## 3 Results
### Ranking of Varieties  
The F-forcing parameter was evaluated for 36 varieties, only in the Lisboa wine region, where the sample is significantly larger than in the other regions. Furthermore, only the phenology models for varieties with EF* above or equal to 0.5 are considered in this analysis, as they provide a better adjustment to the phenological data of a given variety and are thereby more likely to reveal the actual response of that variety to the accumulated thermal conditions (F-forcing). Therefore, 19 individual models were considered for budburst, 35 for flowering, and 28 models for veraison. As such, in the evaluation of the F parameter, a classification of grapevine varieties into early, intermediate, and late varieties was performed according to their thermal accumulation. As stated previously, this classification is based on the quartiles of the empirical distribution of the F-parameters (Q1: early, Q2 + Q3: intermediate, and Q4: late varieties).  
Figure 6 shows the grapevine varieties ranked as a function of their F-forcing parameter for budburst and in ascending order. For this case, cv. _Avesso_ (F = 50.57) is the least thermally demanding. For the intermediate varieties, there are five red and four white varieties considered, being the red grape variety cv. _Pinot Nori_ (F = 56.03) associated with the lowest forcing, whereas cv. _Viosinho_ (F = 59.66) reveals the highest forcing. Amongst the late varieties, cv. _Dona Maria_ (F = 66.70) is the most heat demanding. Overall, white varieties are earlier than red varieties, with lower accumulation values. For flowering (Figure 7), the earliest variety is cv. _Espadeiro_ (F = 38.84). For the intermediate varieties, cv. _Tinto Cao_ (F = 43.18) is the first to achieve flowering, whilst cv. _Avesso_ (F = 46.06) is the latest. Among the late varieties, cv. _Azal Branco_ (F = 48.78) is the most heat demanding. For veraison (Figure 8), cv. _Touriga Franca_ (F = 60.27) is the least demanding variety. Early varieties are mostly red, except for cv. _[PERSON]_ (F = 66.11). For the intermediate varieties, cv. _Baga_ (F = 66.22) is the least heat demanding, being cv. _Moscatel Galego_ (F = 73.19). Concerning the late varieties, cv. _Avesso_ (F = 84.23) is the most heat demanding among all the studied varieties. This outcome for cv. _Avesso_ is remarkable, as it is the lest heat demanding variety regarding budburst but is also the most demanding variety when considering veraison.  
Figure 5: Representation of the generalized sigmoid models (GSM) for each phenological stage: budburst (blue), flowering (yellow), and veraison (green).  
Figure 6: Ranking of the F measure for budburst by grapevine variety in the Lisboa wine region. Only models with efficiency coefficient (EF) \(\geq\) 0.5 are shown.  
Figure 7: Ranking of the F measure for flowering by grapevine variety in the Lisboa wine region. Only models with EF \(\geq\) 0.5 are shown.
## 4 Real-Time Threat Detection and Response
### Deployment of ML Models at the Network Edge  
\(\bullet\)**Edge-Based ML Inference:** By running ML models on multi-access edge computing (MEC) nodes, base stations, or IoT gateways, anomalies can be detected locally, and responses can be executed in near real-time. This approach reduces the round-trip time to a central server and mitigates potential bottlenecks in network bandwidth. For example, NVIDIA's Jetson family of devices and Google Edge TPU provide hardware-accelerated inference capabilities at the edge [41].  
\(\bullet\)**Adaptive and Federated Learning:** Rather than training one global model, federated or distributed learning allows multiple local models to share knowledge without transmitting sensitive data to a central server [42]. Such an approach not only preserves data privacy but also enables continuous adaptation to evolving threats. An example tool is Federated TensorFlow (part of TensorFlow Federated), which can be integrated into edge applications to continually update intrusion detection models without aggregating raw data centrally.  
\(\bullet\)**Integration with Existing Security Platforms:** Real-time detection at the edge can complement traditional Security Information and Event Management (SIEM) systems by offloading some detection logic. Tools such as Zeek, Suricata, and Snort can be deployed in conjunction with edge-based ML pipelines to provide multi-layer detection and enhance coverage [43].
###### Abstract  
Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. RFs were extracted from manual segmentations of AAAs using PyRadiomics. Recursive feature elimination (RFE) reduced features for model optimisation. A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Accuracy was evaluated via cross-validation, with uncertainty quantified using dropout (MLP), standard deviation (RF), and 95% prediction intervals (XGBoost). External validation used independent data from two centres. Ground truth growth rates were calculated from serial ultrasound (US) measurements or CT volumes. Results (3): From 93 initial RFs, 29 remained after RFE. The MLP model achieved an MAE \(\pm\) SEM of 1.35 \(\pm\) 3.2e\(-\)4 mm/year with the full feature set and 1.35 \(\pm\) 2.5e\(-\)4 mm/year with RFE. External validation yielded 1.8 \(\pm\) 8.9e\(-\)8 mm/year. RF, XGBoost, and SVM models produced comparable accuracies internally (1.4-1.5 mm/year) but showed higher errors during external validation (1.9-1.97 mm/year). The MLP model demonstrated reduced uncertainty with the full feature set across all datasets. Conclusions (4): An MLP model leveraging [18F]FDG PET-CT radiomics accurately predicted AAA growth rates and generalised well to external data. In the future, more sophisticated stratification could guide individualised patient care, facilitating risk-tailored management of AAAs.  
**Keywords:** abdominal aortic aneurysm; positron emission tomography-computed tomography; machine learning; radiomics
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 4 Proposed Method  
In this section, an improved likelihood probability method is proposed to solve the optimization problem shown in (15). Owing to its mathematical intractability, the optimization problem is difficult to solve. Hence, the realized samples of the likelihood probabilities were leveraged. Using these samples, the combined weights in the optimization problem were trained using the least-squares solution, which resulted in an improved likelihood probability.
###### Abstract
Then, monitoring and controlling the quantity of fluoride in public water supplies is crucial to preventing exposure to high fluoride levels. Additionally, it is crucial to make sure that the fluoride levels in private wells and other water supplies are periodically checked. Fluoride can be removed from water using treatment techniques if it is found in high concentrations [17; 23; 28; 29; 30]. Adsorption, as a medium for separating mixtures, has seen considerable advancement and is often touted as a simple and affordable means to eliminate excess fluoride from drinking water [12; 20; 22; 31; 32]. Despite its cost-effectiveness and extensive exploration for F\({}^{-}\) removal, transitioning this method to commercial scale necessitates an adsorbent that meets the criteria of availability, quantity, and economic and environmental feasibility. With this perspective, research is fundamental for the search of new adsorbents and optimization of separation conditions. Some review papers concerning the adsorption process for fluoride removal have been published [27; 31; 33; 34; 35]. However, none of them deeply investigated the parameters affecting the adsorption process. In this context, the aim of this review is to provide a systematic, critical, updated, and more in-depth analysis of the specific parameters to be considered for fluoride removal by adsorption, such as pH, adsorbent dosage, surface area, pore size of the adsorbent, temperature, and competing ions. This review is structured following the scope of the articles and is organized into three main topics, including general principles for fluoride adsorption, factors influencing the adsorption of this compound, fluoride adsorption capacity and engineering implications. The objectives and key results obtained by the authors of the selected articles were thoroughly analyzed and appropriately compared.
## 3 Theoretical Calculation and Result Verification  
### Establishment of Water Gushing Model  
During the construction of a tunnel, drainage of groundwater will cause the groundwater level to drop and damage the ecological environment of the tunnel site. After a long period of constant flow pumping, the diving surface forms a relatively stable drop funnel near the tunnel, with a shape similar to an inverted cone [28,29].  
Assuming that the initial water level is \(H\), the inner and outer diameters of the tunnel support structure are \(r_{0}\) and \(r_{1}\), the thickness of the grouting reinforcement ring is \(l-r_{1}\), and the tunnel seepage volume is \(q\), when seepage is stable. When tunnel drainage is stable, the diving flow can be regarded as a steady flow. Ignoring replenishment along the tunnel axis, the phreatic flow can be regarded as a one-dimensional flow, and the tunnel drainage calculation model is established, as shown in Figure 3.  
Figure 1: Space simplified model of permeability of surrounding rock.  
Figure 2: Spatial gradual change curve of permeability of surrounding rock.  
In Figure 3, C shows the position of the influence radius in the tunnel cross section when seepage is stable; B, A, E and F, respectively, indicate the outer edge of the disturbance zone in the tunnel cross section, the outer edge of the grouting ring, the outer edge of the supporting structure and outer edge of supporting structure. Areas FE, EA, AB and BC represent the supporting structure, grouting circle, disturbance zone and ordinary surrounding rock section, respectively, and the permeability coefficients of each area are \(k_{\rm s}\), kg, \(k_{\rm d}\) and \(k_{\rm r}\), respectively. Among them, areas FE and EA are regarded as homogeneous media without considering the discrete type of permeability coefficient; area AB is a disturbance zone, and the permeability coefficient of surrounding rock is affected by two factors of excavation disturbance and in situ stress; area BC is only affected by in situ stress. The surrounding rock is considered to be layered and homogeneous, and the permeability coefficient increases with the increase in the vertical distance from the tunnel. The permeability coefficient of the surrounding rock in section AB and BC is determined by Equation (1).
## 2 Data and Data Preprocessing
### Data Preprocessing  
This paper extracts the maximum magnitudes of earthquakes in the North-South Seismic Belt regions on a monthly basis from January 1970 to December 2023. However, as shown in Figure 2, the time series of monthly maximum magnitude data are relatively poor, which may make it difficult for deep learning models to learn patterns effectively. VMD can decompose data with poor time series characteristics into modes with relatively good time series properties. In earthquake prediction, many scholars have achieved promising results using machine learning and deep learning methods based on seismological parameters calculated from earthquake catalog data [37; 38]. In this study, the VMD method is employed to decompose the monthly maximum magnitudes of the northern and southern seismic Belt regions into multiple modes, and then each mode is combined with the calculated seismological parameters to create new datasets. For each dataset, this study predicts the maximum magnitude for the following month using a rolling time window approach (the time window in this paper is measured in months, and the size of the time window indicates the number of months). Finally, the predictions from multiple modes are summed to obtain the final prediction result. Through experiments, this paper finds that decomposing into six modes yields better results. Figure 3 shows the decomposition results for the first two samples when the time window is set to 12, as well as the decomposition results for each mode across the entire dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Time**}} & \multicolumn{10}{c}{**Seismological Parameters**} \\ \cline{2-10}
**Period** & **T (Days)** & **M\({}_{\text{mean}}\)** & **dE\({}^{1/2}\)(10\({}^{8}\)ergs)** & **a** & **b** &
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 3 RMCSat Mission
### Performance Requirements and Constraints
#### 3.1.2 Radio Noise Interference Measures  
For solar flux fluctuations to be distinguishable, radio noise interference must be sufficiently minimized. A fixed satellite orientation with respect to Earth is necessary to remove an additional degree of variation, and to keep terrestrial thermal emissions in the lowest gain region of the primary antenna beam. A suitable secondary antenna with a wider beam pointing earthward is required to capture Earth emissions for comparison and subtraction of the thermal emission from primary antenna measurements. The monitoring of two channels, at 2785 MHz and 2815 MHz, allows for the identification of RFI signals. A software-defined radio (SDR) architecture is used to allow flexibility in signal collection bands. If persistent mission-disabling interference is experienced by RMCSat within the sampled bandwidths, the use of an SDR payload, reconfigurable from the ground, allows satellite operational channels to be adjusted in response, separating sampling frequencies from the sources of disruptive RFI.