esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Parameter Analysis
#### 6.3.2 Assistant Storage Analysis  
The range of the maximum size of assistant storage in a vehicular node is from 3 MB to 30 MB, and the numbers of satisfied requests due to V2V communications in the whole simulation time are displayed in Figure 7.  
Figure 7 shows that the larger the assistant storage is, the more contents are delivered because of the supplementary storage through V2V communications. However, after the size of assistant storage reaches 24 MB, the performance remains stable. This is because the delivery is still affected by the mobility model of vehicles, the bandwidth competition at roadside units and other factors. Searching for this turning point through some pilot study is helpful to achieve a full use of the assistant storage at vehicular nodes.  
Figure 6: Results with different storage cycles. (**a**) access ratio; (**b**) average access delay.  
Figure 7: Results with different maximum sizes of assistant storage of a vehicular node.
## 3 Results
### ARIMA Model and Error Metric Analysis  
Table 1 presents the seasonal ARIMA model. It was built as the best model with the lowest AIC, as shown in Table 1. There were four main lagged AR and MA variables. The first set of AR and MA variables lagged by one time step (AR.L1 and MA.L1, respectively). The second set lagged by 12 and 24 time steps (AR.S.L12 and AR.S.L24). The coefficient column represents the weight or importance of each set of AR and MA variables and how the variables impact the time series to be stationary. The coefficients of the AR and MA terms are less than 1, and the sum of the coefficients of the two seasonal AR terms is less than 1. The \(p\)-values are statistically significant as \(<\)0.001 in the AR, MA, and seasonal ARIMA models (\(p<0.05\)) of the lagged AR and MA terms, which significantly affect the  
Figure 6: Cross-validation using an autocorrelation function, estimated density function, and quantile–quantile analysis. (**A**) Standardized residual was analyzed over 20 years, and downtime increased due to major component replacements between 2006 and 2007. (**B**) KDE (kernel density estimation) with a histogram in a normal distribution. (**C**) Verification for the normal distribution using a Q-Q analysis. (**D**) ACF shows that the stationary status was satisfied by gradually converging to zero in a correlogram.  
model forecast. All standard deviation errors show values close to zero. The value for sigma squared (\(\sigma^{2}\)) was calculated, indicating the variance of the residual. This means that the normality of the residual against the alternative of non-normality was tested for the ARIMA model.  
Table 2 presents the error metric analysis using the MSE, RMSE, and MAPE. The MAPE is less than 10%.
## 4 Discussion
### Environment Characteristics and Their Impact on Divergence
#### 4.1.2 High-Divergence Environments: Strategic Bottlenecks and Divergent Policies  
Conversely, environments prone to high divergences, such as Qbert, Boxing, KFM, and Breakout, often feature strategic bottlenecks, multiple viable strategies, or complex state dependencies that amplify the effects of stochasticity.  
* Boxing is a highly interactive, opponent-dependent game. An agent might learn an aggressive rushing strategy, while another learns a defensive, counter-punching style. These are two distinct but viable approaches that lead to entirely different patterns of interaction, creating separate clusters in the observation space and varied performance outcomes.
* Qbert and KFM contain significant exploration challenges and \"bottlenecks.\" In KFM, an agent that fails to learn how to defeat a specific enemy type will be trapped in early-level states, while an agent that succeeds will unlock a vast new region of the observation space. This creates a sharp bifurcation in experience and performance. Similarly, the unique board structure in Qbert presents many locally optimal paths, causing agents to specialize in different sections of the pyramid.
* Breakout is a classic example of an environment with a critical strategic bottleneck: learning to tunnel the ball behind the brick wall. Agents that discover this strategy enter a new, high-scoring phase of the game with a completely different set of observations. Agents that fail to discover it remain trapped in a low-scoring, repetitive gameplay loop, leading to extreme performance variance.
## 4 Results and Discussion  
### ANFIS Model  
The ANFIS model has been applied to predict the advance rate of shield machine during the tunneling process. To apply this model, three effective parameters (CT, SC, and CR) were set as inputs and AR is set as output. In ANFIS modeling, there are two main stages: the generation of pattern vector and the pattern formation with an input vector and its corresponding target vector. The data range of both input and output is significant and cannot be neglected in different parameters of operating ranges. Thus, all datasets were normalized in the range of (0, 1) to simplify the design procedures using the following equation [53]:  
\[X_{n}=\frac{(X-X_{\text{min}})}{(X_{\text{max}}-X_{\text{min}})} \tag{13}\]  
where \(X\) and \(X_{n}\) are the measured and normalized values, respectively, and \(X_{\text{min}}\) and \(X_{\text{max}}\) denote the minimum and maximum values of \(X\), respectively.  
In this study, the 200 datasets have been categorized into two subsets randomly. 80% of the whole dataset utilizing to create ANFIS structure are selected as training set and the other 20% utilizing for verifying the models are chosen as testing set, following the recommendation of [PERSON] [54]. Figure 7 displays the architecture of ANFIS with three input parameters and one output. For ANFIS model, the number and kind of the membership functions (MFs) and epoch number should be provided. As mentioned in the previous literature, there are no explicit methods or formulas to predict the necessary membership functions [55; 56; 57]. Thus, the MFs were estimated by way of trial and error. The best estimates were acquired from the Gaussian type (Table 3). The Takagi-Sugeno method was applied because of its higher reliability and computational efficiency for developing a systematic  
Figure 6: Principal components analysis for some parameters in this study.  
technique to construct fuzzy rules from the input-output dataset. The initial fuzzy inference system (FIS) was generated using fuzzy c-mean clustering method. MATLAB software was applied with the Genfis3 function to adjust the initial fuzzy inference system of the model. Figure 8 shows the correlation coefficient for the training and testing dataset. This figure illustrates that the relation between the measured and the predicted AR are more applicable in training dataset than in testing dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ANFIS Parameter Type** & **Characteristic/Value** \\ \hline Membership function (MF) type & Gaussian \\ Fuzzy structure & Takagi-Sugeno-type \\ Output MF & Linear \\ Number of fuzzy rules & 8 \\ Number of Epoch in ANFIS & 200 \\ Minimum Improvement & \(1\times 10^{-5}\) \\ Type of initial fuzzy inference system & Genfis 3 \\ Initial step size & 0.01 \\ Step size decrease rate & 0.9 \\ Step size increase rate & 1.1 \\ Number of training data pairs & 160 \\ Number of testing data pairs & 40 \\ Training method & GA \\ Maximum number of generations & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANFIS-GA model’s analytical details.  
Figure 7: The ANFIS network-based architecture.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 2 System Model
### Channel Model  
Differently from the traditional terrestrial network, in the satellite-ground transmission scenario, the channel between the satellite and the ground user is not only affected by the distance loss caused by the line-of-sight (LoS) propagation, but also by the atmospheric fading, the shadow effect caused by obstacles, and the scattering. We use the shadowed-Rician (SR) fading channel, which is widely used in both S-band and ka-band satellite channels for data transmission [31; 32; 33]. We assume that the satellite uses a single transmitting antenna. According to [31], the probability density function (PDF) of the SR channel gain \(f_{h}(r)\) is expressed as  
\[f_{h}(r)=\left(\frac{2b_{0}m}{2b_{0}m+\Omega}\right)^{m}\frac{1}{2b_{0}}\exp \left(-\frac{r}{2b_{0}}\right)\cdot{}_{1}F_{1}\left(m,1,\frac{\Omega r}{2b_{0 }(2b_{0}m+\Omega)}\right), \tag{1}\]  
where \(b_{0}\), \(m\), \(\Omega\) denote the average power of scatter component, the Nakagami-\(m\) parameter, and LOS component, respectively. The term \({}_{1}F_{1}(a,b,c)\) is the first kind of confluent hypergeometric function. In particular, for the parameter \(m\), when \(m\) is small, this indicates that there are more obstacles and shadows between the satellite and the ground, which may not meet the conditions for establishing the LoS link. When \(m\) tends to infinity, this indicates that the link between the satellite and the ground has almost no obstacles and blockages, which satisfies the conditions of the LoS link. In this case, the SR distribution will be downgraded to the Rayleigh distribution [33].  
According to the different size of the fading, paper [31] divides the channel model into three states, which are infrequent light shadowing (referred to as light), frequent heavy shadowing (referred to as heavy), and average shadowing (referred to as average).
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
###### Abstract  
Wireless sensor networks have been attracting research attention for the past decade and will continue to be a hot topic due to the emerging trend of Internet-of-Things (IoT). Edge computing for IoT (Edge-IoT) is a promising framework that can help low-powered sensor networks to conduct complex computational tasks. Different from the existing works that focus on cooperative task execution for edge and sensor networks, in this paper, we investigate the problem of reliable data transmission in edge-aided sensor networks. Firstly, we discuss how edge servers can help to improve the data transmission of sensor networks. Secondly, we propose a forwarding scheme for edge nodes to forward packets according to coverage and corresponding interference. Thirdly, we propose an edge-based error recovery approach. By employing edge servers for data transmission and error recovery, the efficiency and reliability of data transmissions can be largely improved.  
sensor networks; edge-aided computing; data transmission; reliability +
Footnote †: journal:
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Intercomparisons with Reference Wind Speeds
#### 3.1.2 ERA5 Reanalysis Wind Intercomparison
(**c**) Log density scatterplot between NOAA wind speed and ERA5 matchup reference winds. (**d**) RMSD and bias between the two wind speeds as a function of the reference wind. Note the change in scale for (**c**) needed to include NOAA’s outliers. Figure 9 shows probability distribution functions (PDFs) of the FDS winds separated by a CYGNSS flight model (FM), a CYGNSS antenna (starboard or port), and GPS block type. There is no significant variation in the PDFs between GPS transmitters or CYGNSS receiver hardware, indicating that the hardware calibration algorithms are robust across both CYGNSS and GPS satellites. Geographical Bias Distribution  
Figure 10 shows the geographic distribution of FDS bias with respect to ERA5. The figure is derived from a single month of data (December 2023) but is indicative of typical bias distributions throughout the mission. There is a small positive bias in the Intertropical Convergence Zone, likely due to the presence of persistent precipitation (see Section \"Precipitation Dependence\" for discussion). A previous investigation addressed the impact of precipitation on GNSS-R ocean observations and found that both splash-induced surface roughening and the outflow of downdrafts caused by rain can introduce wind speed retrieval biases ([PERSON], 2017). Other isolated regions of biases can be seen that may be due to radio frequency interference (RFI) from supplemental navigation sources. For example, the high bias seen in the Mediterranean is at a geometry consistent with the location of one of the geostationary satellites in the Indian Space Research Organization's NavIC constellation. Similar characterizations were noted in a 2019 study ([PERSON], 2017). However, the RFI environment can be quite dynamic and may have changed in the intervening time. Figure 8: Time series of FDS RMS and mean difference (RMSD and Bias) with respect to ERA5. Figure 9: PDFs of v3.2 FDS winds by (**a**) CYGNSS satellite Flight Model (FM), (**b**) port/starboard antenna, and (**c**) GPS block type, with ERA5 winds as the reference.
## 5 Event Detection
### Data-Driven Event Detection  
Events related to air quality typically occur over a period of time, examples include showers (short humidity peak), social events (increased humidity and CO2) or windows left open (ventilation has lower effect). As such, time series event mining techniques are particularly interesting, which evaluate time series subsequences rather than individual data points. One domain-agnostic state-of-the-art technique is the Matrix Profile (MP) [12], which can be used to look for time series discords and motifs. Discords are sequences that are unique in a time series, i.e., behavior that is novel or anomalous. Motifs are sequences that are well preserved (repeated) in time series, i.e., behavior that is repeated throughout a series. Various extensions have been suggested for the MP, including variants for streaming data [13], multi-dimensional data [14], missing data [15] and noisy data [16], making it a well suited technique to monitor real-world data. Our first data detector uses the MP to find discords in incoming data, since discords consist of previously unseen patterns, we name this detector the _unknown pattern detector_.  
Per Healthbox, we assigned one unknown pattern detector. This instance ingested the CO2 and relative humidity signals for all rooms. For each of these signals, a streaming MP instance compared the incoming signal against the most recent available year of data. For performance reasons, the incoming signals were subsampled to 1 sample per minute and blocks of missing values were replaced by a single marker value. The subsequence lengths were chosen in function of the events we wanted to detect: 1 h (60 samples) for the relative humidity metric and 8 h for CO2. Additionally, we applied a correction for the noise present in the data and added a restraint that ensured peaks would only be matched with other peaks, more details can be found in the work by [PERSON] et al. [17].  
Figure 2: Overview representing how data- and knowledge-driven techniques can be combined in an anomaly and/or fault detection system.
# New Miniature Narrow Band Microstrip Diplexer for Recent Wireless Communications
###### Abstract  
Using Kappa substrate material, a compact microstrip diplexer is developed in this research with two separate channels based on the coupled junction and two bandpass filters functioning in independent frequency bands. Each filter comprises an input/output feed line and a number of resonators with different impedances. The diplexer's frequency response was modeled and optimized using the Sonnet EM solver. At 2.84 and 4.08 GHz for TX/RX channels, the insertion loss is better than 1 dB for both channels, while the return loss values are 21.2 and 17 dB for transmit and receive filters, respectively. The microstrip diplexer has miniature dimensions of 24 mm \(\times\) 18 mm with highly narrow bands and band isolation of more than 35 dB. The simulated scattering parameters are in agreement with the measured ones.  
microstrip; diplexer; UIR; SIR; Kappa substrate; negative group delay; narrow bands Article
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 2 Background
### Laplacian Mesh Smoothing
#### 2.1.2 Adaptive Laplacian Mesh Smoothing  
Adaptive Laplacian mesh smoothing is proposed based on ordinary Laplacian mesh smoothing. Generally, in ordinary Laplacian mesh smoothing, invalid elements may be created in the concave areas of the mesh, which results in a mesh quality that does not improve. To solve this problem, adaptive Laplacian mesh smoothing [23] adds a step to judge whether the mesh quality improves after every update of the nodal location. If the new smoothing location improves the mesh quality, the new location is retained; if not, it is recalculated. This approach provides a guarantee for improving the mesh quality compared with ordinary mesh smoothing.  
The quality of an entire mesh model depends on the quality of all tetrahedrons in the mesh; and the quality of a tetrahedron can be defined by different metrics, such as mean ratio, aspect ratio, dihedral angle, circumradius-shortest edge ratio, etc. According to these defined metrics, the adaptive Laplacian algorithm will automatically determine whether the new node is invalid. This mechanism of checking can avoid invalid iterative calculations compared with the ordinary Laplacian algorithm. Figure 2 illustrates the smoothed positions of a sample node in the concave area of a tetrahedral mesh when using ordinary Laplacian mesh smoothing and adaptive Laplacian mesh smoothing.  
Figure 1: A simple illustration of improving tetrahedral mesh quality using Laplacian smoothing.
## 3 Results and Discussions
### Influence of pH and Ionic Strength  
The adsorption behavior of U(VI) is highly dependent on the pH of the solution (Figure 6), indicating that the adsorption of U(VI) by PVKAP is dominated by surface complexation [53]. These experiments were carried out under the condition of \(C_{\text{U(VI)initial}}\) = 36.0 mg L\({}^{-1}\), \(T\) = 293 K, 11 different pH values (pH = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, respectively) and three different ionic strengths (0.1 M NaNO\({}_{3}\), 0.01 M NaNO\({}_{3}\), 0.001 M NaNO\({}_{3}\)). When the pH value of the solution increased from 2.0 to 6.0, the removal rate of U(VI) by PVKAP increased from \(\sim\)39.0% to \(\sim\)96.2%, and then decreased to \(\sim\)74.0% as the pH increased to 12.0. This result could be clarified by the intrinsic properties of the adsorbents and the speciation of U(VI) in the solution. When pH \(<\) 6, U(VI) is mainly present in solution in the form of UO\({}_{2}\)\({}^{+}\), UO\({}_{2}\)(OH)\({}^{+}\) and (UO\({}_{2}\))\({}_{3}\)(OH)\({}^{5+}\)[54], the sharply increased removal rate of PVKAP could be attributed to the capture of UO\({}_{2}\)\({}^{2+}\) with a negatively charged active site on the surface of PVKAP. When pH \(>\) 6, the positively charged U(VI) speciation gradually decreases with the increased pH value, while the negatively charged speciation of U(VI) (e.g., (UO\({}_{2}\))\({}_{3}\)(OH)\({}_{7}\)\({}^{-}\)) is gradually increased, thereby causing an increasing electrostatic repulsion between PVKAP and U(VI), and resulting in a decrease in the adsorption capacity [55; 56; 57; 58; 59].  
In addition, the adsorption behavior of PVKAP capturing U(VI) at different ionic strengths is shown in Figure 6. According to the reported literatures [29; 60], the dependence of adsorption behavior on the ionic strength is an indirect method to distinguish the inner layer and the outer surface complexation. As shown in Figure 6, at the three ionic strengths (0.1 M NaNO\({}_{3}\), 0.01 M NaNO\({}_{3}\), 0.001 M NaNO\({}_{3}\)), the adsorption behavior of PVKAP is similar and there is no obvious adsorption boundary. Therefore, the inner layer complexation dominates the efficient capturing of U(VI) by PVKAP [29], which could be explained by the strong complexing ability of phosphonic acid groups and carboxyl groups on the surface of PVKAP.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 4 System Model
### Energy Consumption Model  
IoT sensor nodes operate in four modes; transmission, reception, sleep, and idle. The total energy consumed by the sensor node is the energy consumed during these four modes. Transmission mode is the one in which the sensor node transmits data to either the coordinator or other nodes. The total energy consumed by the sensor node in this phase depends on the size of the transmitted data [41]. In reception mode, the sensor node receives data from other nodes, such as the coordinator in our model, and the total energy consumed in this mode depends on the amount of data received by the sensor node. The idle mode is the one in which a node is not sending nor receiving packets, and energy is mainly spent to power the circuit. In sleep mode, the majority of the sensor circuitry is turned off, and the energy consumption of the sensor becomes minimal, depending on the sleep period duration. Thus, the total energy consumed by the sensor node is equal to  
\[E_{c}=E_{tx}+E_{rx}+E_{slap}+E_{idle}, \tag{12}\]  
where \(E_{c}\) is the total energy consumed during superframe, \(E_{tx}\) is the energy consumed during data transmission, \(E_{rx}\) is the energy consumed during data reception, \(E_{slap}\) is the energy consumed during sleep mode, and \(E_{idle}\) is the energy consumed during idle periods.  
\[E_{tx}=V\times I_{tx}\times t_{tx}=V\times I_{tx}\times\frac{L}{R}, \tag{13}\]  
where \(E_{tx}\) is the energy consumed in transmitting the data, \(V\) is sensor battery voltage, \(I_{tx}\) is the current consumed during the transmission, and \(t_{tx}\) is the time required for transmission, which is equal to the ratio of packet size \(L\) to the data rate \(R\).  
The reception mode energy consumption consists of energy consumed during the beacon received by the sensor nodes and given as:  
\[E_{rx}=V\times I_{rx}\times t_{rx}=V\times I_{rx}\times\frac{Beacon}{R}, \tag{14}\]  
where \(I_{rx}\) is the current required during the reception, and \(t_{rx}\) is the reception time.  
Energy consumed during sleeping mode is given as:  
\[E_{sleep}=V\times I_{sleep}\times t_{sleep}, \tag{15}\]  
\[t_{sleep}=BI-SD=2^{BO-SO}, \tag{16}\]  
where \(I_{sleep}\) is the current drawn during the sleep period. \(BO\) and \(SO\) are beacon order and superframe order values, respectively. Thus, the total energy consumed during a superframe is equal to:  
\[E_{c}=V\left[(\frac{L}{R})(I_{tx})+(2^{BO-SO})I_{sleep}+(\frac{Beacon}{R})(I_ {tx})\right]. \tag{17}\]