esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Programming Implementation of the Configuration and Measurements (PLC)  
For a widespread application of economic MPC in power systems, it is crucial to not only reduce the modelling effort but as well to reduce the commissioning time and the training needs of commissioning staff. In the presented implementation, the commissioners do not need to be trained MPC experts, because the system configuration is conducted entirely on the PLC within the widely used IEC 61131 programming style using the Codesys V3 runtime system [11]. Since 2013, the third and current edition of the standard (IEC 61131-3) allows for object-oriented programming. A PLC program contains function blocks (FBs) that can be linked together in graphical programming languages such as the function block diagram language (FBD). These FBs correspond to classes in the object-oriented programming paradigm.  
The developed program contains preconfigured FBs for all component types. Figure 3 shows the FBs (classes) of the PLC program. For every device in the power system, one instance of a corresponding FB has to be instantiated by the commissioner. All device parameters, that describe the corresponding device (e.g., maximum charge power, minimum and maximum SOC/energy content of a BESS, etc.), must be passed to the FB instance. Furthermore, the measurements from the power system are passed from I/O components of the PLC to the FB instances. All FBs contain methods for the publication of parameters and measurements on the communication bus, which can then be accessed by the edge computer in order to assemble the equation system, depending on the system configuration and parametrisation. The IEC standard allows extensions of function blocks, a concept that is similar to inheritance. This allows commissioning engineers to extend function blocks in order to incorporate functionalities, that are additional to the presented core properties and methods and add to the flexibility that is required to adapt the general model in concrete microgrids. This includes device-specific battery degradation models.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 4 Discussion
### Cross-Layer Design Has to Be Considered  
In CRAHN, integrating the information from multiple layers in the MAC protocol can bring various benefits to the network. For example, the MAC layer can use the PHY layer information about channel sensing and interference detection to gradually build a model of the channel occupancy history. Channel sensing has been integrated with MAC in [83], but other actions such as interference detection and collaboration at the PHY layer can be useful. Although this model may vary with location and time of the day, it can help MAC protocols to update the strategy for selection of frequencies and the corresponding transmission. Assuming the channel occupancy model, the CR user can queue packets to be communicated through the off periods of duty-cycled PUs, which can help the CR user to conserve energy and also reduce the chance of interference with PUs.
## 5 Experiments
### Comparison of Different Models' Results
#### 5.2.3 Results Visualization  
To facilitate the observation of the prediction accuracy of different models under different tasks, a visualization diagram of Comparison of predicted wave forms of different models is drawn in Figure 8.  
Due to the fact that short-term predictions only predict waveform trends within the next few hours, the prediction time window is short and inconvenient to observe. Here, the rolling prediction method was used to predict the wind power data over two weeks, and the image is shown in Figure 8a. It can be observed that the proposed G-Former model is closest to the waveform of GroundTruth.  
In medium-long-term prediction, experiments take the input sequence length of 96 and the predicted sequence length of 96 as an example to visualize and obtain the image, as shown in Figure 8b. It can be seen that, in the medium- to long-term prediction process, G-Former has the best prediction waveform.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 1 Introduction
Non-ideal effects, including dead time, photon-detection efficiency (PDE), dark count rate (DCR), afterpulsing, and crosstalk, affect the performance of practical SPAD-based receivers. Dead time is the period during which the SPAD is insensitive to incoming photons and unable to detect any subsequent events. In particular, because of the nonlinearity induced by dead time, SPAD receivers' observed photon counts deviate significantly from the Poisson distribution [11]. As a way to reduce the dead time effect and hence increase photon-counting capabilities, a SPAD array is widely used that outputs the superposition of the photon counts of each individual SPAD, and the overall photon counts can be further approximated as Gaussian [12]. SPADs have become increasingly popular in recent years due to their high sensitivity and low noise characteristics, making them ideal for use as optical receivers in various applications such as quantum key distribution [13], LiDAR [14], and time-of-flight (ToF) sensing [15]. SPADs are characterized by several key performance metrics including PDE, array size, dead time, afterpulsing, DCR, and crosstalk. For instance, [16] reported a detection efficiency of 30% and a timing jitter of 60 ps for a 25-um-diameter SPAD at 1550 nm. A dead time compensation model has been presented in [17] by considering reset generated off-pixel which also showed significant PDE improvement. The authors of [18] presented a cost-effective 0.35-um SPAD in the visible wavelength range, aimed at speeding up the sensing of detector ignition (reduced down to a few hundred picoseconds) which reduces afterpulsing through active quenching of the avalanche current buildup. Furthermore, a recent study [19] demonstrated a high photon-detection efficiency of \(>\)19% for a 10-um-diameter SPAD at 850 nm. Other studies reported on cross-talk characterization of high-fill-factor SPAD arrays and showed that the average cross-talk probability is well below 1% for the shallow-junction SPAD structure with 15.6-\(\upmu\)m pitch and 39.9% fill factor [20]. These findings suggest that SPADs have the potential to be employed as photon-counting receivers with high detection efficiency and low noise levels, making them suitable for various applications in photonics. In this work, we investigate the performance of a SPAD-based VLC system that supports dimming control. In particular, we focus on the bit error rate (BER), data rate, and sensitivity performance. Note that the sensitivity is usually quantified by the minimum detectable signal (MDS) power, which is the minimum optical power required to achieve a specified BER. For a SPAD-based optical receiver, the MDS power can be calculated based on the device's noise characteristics and the system's operating parameters.
## 3 Study Area and Data Used
### Data Used  
#### 3.2.1 Well Yields  
Groundwater potential assessment is generally done by the evaluation of well yields data [14; 19; 85; 86]. In this study, the well yield data of 227 drilled wells was obtained from the national project of the Vietnam Academy for Water Resources (VAWR) and used for model development. The well data included: (1) water level at a non-operated and operated situation, (2) structure of the wells, (3) measurement of temperature and TDS (total dissolved solids), (4) yield, and (5) location. This dataset was randomly divided into separate sets such that 70% of the data was used for model building and the remaining data (30%) was used for model validation. The yield value of 1.6 l/s was selected as the threshold value for distinguishing between potential and non-potential areas of Dak Lak Province in terms of the groundwater potential.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 4 Evaluation
### Perceptual Listening Tests
#### 4.3.1 Test Paradigms  
The first listening test followed the multiple stimulus test with hidden reference and anchor (MUSHRA) paradigm, ITU-R BS.1534-3 [49]. The reference was a direct HRIR convolution, and medium and low anchors were low-pass filtered versions of the reference stimulus with an \(f_{c}\) of 7 kHz and 3.5 kHz, respectively. The other 6 stimuli were the binaural Ambisonic renders for three Ambisonic  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\psi\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(\theta\) (\({}^{\circ}\)) & 180 & 50 & 118 & 0 & 180 & 62 & 130 & 0 \\ \(\phi\) (\({}^{\circ}\)) & 64 & 46 & 16 & 0 & 0 & \(-16\) & \(-46\) & \(-64\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spherical coordinates of test sound locations.  
Figure 7: Sagittal Plane Localisation Model [44] plots of binaural Ambisonic rendering, with and without Diffuse-Field Equalisation (DFE): (**a**) \(M=1\), no DFE, (**b**) \(M=3\), no DFE, (**c**) \(M=5\), no DFE, (**d**) \(M=1\), with DFE, (**e**) \(M=3\), with DFE and (**f**) \(M=5\), with DFE. Plots show greater clarity (bolder shading) with DFE implementation.  
orders, with and without DFE. For each trial, the listener was asked to rate the 9 stimuli in terms of timbral similarity to the reference. The 8 test sound locations were repeated giving a total of 16 trials. The presentation of stimuli and trials was randomised and double blind.  
The second listening test was an AB comparison. Participants were presented with two sets of three consecutive stimuli (corresponding to Ambisonic renders of 1 st, 3 rd and 5 th-orders), one set of which was diffuse-field equalised, and were asked to rate them in terms of timbral consistency. The 8 test sound locations were repeated with a different arrangement of the Ambisonic orders (the first was 1, 3, 5 and the second was 1, 5, 3), giving a total of 16 trials. The presentation of trials was randomised and double blind.
## 4 Results and Discussion
### Comparison Experiments with Advanced Baseline Models
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Prediction Models** & **MAE (KW)** & **RMSE (KW)** & **CORR** \\ \hline \multirow{8}{*}{\#1} & **DHGAR** & **76.135** & **114.295** & **0.915** \\  & DGCRN & 80.146 & 122.548 & 0.894 \\  & AGCRN & 82.719 & 125.675 & 0.885 \\  & iTransformer & 86.429 & 131.178 & 0.842 \\  & Crossformer & 87.995 & 133.784 & 0.839 \\  & TSmixer & 83.443 & 127.342 & 0.878 \\  & RLinear & 84.137 & 128.795 & 0.867 \\  & [PERSON]’s model & 83.715 & 127.159 & 0.876 \\  & [PERSON]’s model & 85.632 & 130.544 & 0.861 \\  & SVM & 95.314 & 145.199 & 0.787 \\  & ARIMA & 94.178 & 144.328 & 0.795 \\ \hline \multirow{8}{*}{\#2} & **DHGAR** & **67.424** & **93.418** & **0.924** \\  & DGCRN & 70.295 & 99.145 & 0.902 \\  & AGCRN & 71.342 & 101.697 & 0.897 \\  & iTransformer & 75.638 & 108.245 & 0.823 \\  & Crossformer & 77.199 & 110.334 & 0.802 \\  & TSmixer & 75.117 & 109.121 & 0.821 \\  & RLinear & 72.612 & 103.447 & 0.874 \\  & Mi’s model & 74.253 & 107.889 & 0.837 \\  & Yu’s model & 73.942 & 105.244 & 0.855 \\  & SVM & 82.166 & 121.749 & 0.765 \\  & ARIMA & 83.194 & 123.586 & 0.743 \\ \hline \multirow{8}{*}{\#3} & **DHGAR** & **84.216** & **121.547** & **0.901** \\  & DGCRN & 86.147 & 125.334 & 0.874 \\  & AGCRN & 88.322 & 131.556 & 0.862 \\  & iTransformer & 95.745 & 144.624 & 0.786 \\  & Crosformer & 96.947 & 146.154 & 0.774 \\  & TSmixer & 92.547 & 137.014 & 0.807 \\  & RLinear & 93.326 & 139.984 & 0.799 \\  & [PERSON]’s model & 91.177 & 135.647 & 0.819 \\  & [PERSON]’s model & 90.894 & 135.024 & 0.836 \\  & SVM & 102.322 & 155.789 & 0.714 \\  & ARIMA & 101.479 & 154.324 & 0.709 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The time series statistical analysis indexes of all contrast predictors. Figure 6: Experimental results of different models for dataset #2.
## 4 Case Study
### Pattern Recognition and Prediction Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mathcal{A}\) & **Precision** & **Recall** \\ \hline Classification Decision Tree & 0.998 & 0.838 & 0.814 \\ Logistic Regression & 0.996 & 0.829 & 0.486 \\ SVM & 0.993 & 0.000 & 0.000 \\ Naive Bayes & 0.685 & 0.019 & 0.871 \\ Random Forest & 0.997 & 1.000 & 0.543 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of classification algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mathcal{A}\) & **Predicted** & **0** & **1** \\ \hline Classification Decision Tree & 0 & 9918 & 11 \\ \cline{2-4}  & 1 & 13 & 57 \\ \hline Logistic Regression & 0 & 9922 & 7 \\ \cline{2-4}  & 1 & 36 & 34 \\ \hline SVM & 0 & 9929 & 0 \\ \cline{2-4}  & 1 & 70 & 0 \\ \hline Naive Bayes & 0 & 6789 & 3140 \\ \cline{2-4}  & 1 & 9 & 61 \\ \hline Random Forest & 0 & 9929 & 0 \\ \cline{2-4}  & 1 & 32 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for classification algorithms. than 824, RX_X12 (a variable in the dataset indicating received packet) is greater than 2786.5 and FADE_X1 is smaller or equal to 16,044, then the terminal will be disrupted and present a data mismatch greater than 0. This was an interesting finding, because it shows that data mismatches depend on errors in other terminals. This implied that errors might propagate through the network. This finding also supported the experience of the satellite engineers in that each network has its own behavioural pattern. The other characteristics considered essential in disruption prediction are: RX_X8, TX_X6 (A transmit variable), RX_X18, MIS_X6, MIS_X1, MIS_X8, and SNR_X1 (a signal to noise variable). A disruption is usually observed when the traffic indicators are low (RX_X18, RX_X8, TX_X6) or when downstream signal quality is low (SNR_X1). Because a decision tree provides a transparent view of the decision process, it enabled clearer understanding of why a specific phenomenon occurs and the dependencies between a target variable and other variables. 2. **Regression:** In this work, regression was used to estimate the exact value of a target characteristic which was disrupted (1) and not disrupted (0). RMSE, the root mean squared error, was used to evaluate how close the prediction is to the actual value. Figure 5: Classification decision tree for terminal data, with IS_DISRUPTED variable as a target. The smaller value, the better the prediction. Three regression algorithms, regression tree, linear regression, and SVM regression, were assessed in the experiments. Using other terminal attributes, regression was used to predict the value of the RX_15 terminal variable. A nonzero value of RX_15 is a sign of serious problems occurring in the network. It was discovered that the regression tree had the best performance among other methods, as it creates the smallest error (Table 4).
## 4 Implementation Issues, Results
### Simulation Results  
Below, the optimal curves for performance indices considered are included:  
* index \(J_{1}\) (\(J_{1}^{*}=1401\)): \[x^{*}(t) = -8.4274\exp^{-\frac{t}{3.25}}+8.4274\exp^{\frac{t}{3.25}}\,,\] \[y^{*}(t) = -30.8138\exp^{-\frac{t}{3.25}}+30.8138\exp^{\frac{t}{3.25}}\,,\] \[w_{x}^{*} = 67.40\exp^{\frac{t}{3.25}}\,,\] \[w_{y}^{*} = 246.50\exp^{\frac{t}{3.25}}\,,\] \[t_{f} = 4.0889\,\mathrm{min}\,;\]
* index \(J_{2}\) (\(J_{2}^{*}=17\),\(240\)): \[x^{*}(t) = 400t\,\,\mathrm{(known\,in\,advance)}\,,\] \[y^{*}(t) = -1.3513\exp^{-\frac{t}{3.25}}+1.3513\exp^{\frac{t}{3.25}}\,,\] \[w_{y}^{*} = 10.80\exp^{\frac{t}{3.25}}\,,\] \[t_{f} = 13.9888\,\mathrm{min}\,;\]  
Figure 8: Reference signal minimizing \(J_{2}\).  
Figure 6: Solution minimizing \(J_{3}\).  
Figure 7: Reference signal minimizing \(J_{1}\).
## 4 Discussion  
### Parameter Settings  
In order to evaluate the performance of proposed strategy, we used SUMO to simulate an urban road environment, and vehicles moved along the urban road like real scenes. The arrival rate of vehicle was 600 vehicles/h, and the maximum speed of vehicle was 80 km/h. An interface, named TraCI(Traffic Control Interface), was used to connect SUMO to Python. The 3-hour moving information of vehicles was collected by Python through the interface. Python used the moving information to perform the algorithm simulation and to compare the simulation results with those described by the authors of [19]. In our simulation, the channel model is expressed as \(\beta\cdot P_{i}\cdot r^{-\alpha}\) with a path loss exponent \(\alpha\) of 4,channel fading gain \(\beta\) of \(10^{-2}\), channel bandwidth \(B\) of 1.5 MHz, and power of Gaussian noise \(P_{n}\) of \(-110\) dBm. The transmission rate of the network can be derived by the Shannon formula. The average rate between vehicles was set as 9 Mbps, and the average between vehicle and RSU was set as 20 Mbps. Other simulation parameters are shown in Table 2. The Average Delay was used to describe the average time duration between the vehicle send requests and when the vehicle obtains the desired content. The Request Success Ratio was used to describe the average ratio that can be derived by dividing the total number of requests by the number of successful requests.
## 4 Our Hybrid Quantum-Classical Optimization Algorithm
### Parallel ACO Algorithm with Cooperating Ant Colonies
#### 4.1.1 ACS Algorithm  
Pheromone Initialization  
In the ACS, the initial tour cost is determined by creating a greedy tour, and the pheromone on each link is set as:  
\[\text{Pheromone}[i,\ j]=\tau_{i,j}=1.0/(cityCount\times tourCost) \tag{13}\]  
where _cityCount_ is the total number of nodes.  
Figure 2: Optimization of a tour by partitioning of the entire path and assembly of the optimized disjoint sub-tours Sn.  
Tour Construction  
In the ACS, each ant \(k\) at node \(i\) decides to go to an unvisited node \(j\) in the \(t\)th iteration by following the pseudorandom proportional action choice rule described as follows:  
if the generated random number (between 0 and 1) \(>q\),  
select the node j with the highest value of \(\tau_{i,j}\times\left[\left.\eta_{i,j}\right]\right|^{\beta}\)  
else  
the probability of visiting node j from the unvisited set \(N_{j,k}\) is :  
\[p_{i,j}^{k}(t)=\frac{\left[\tau_{i,j}(t)\right]^{\alpha}\left[\left.\eta_{i,j} \right]\right]^{\beta}}{\sum_{l\ \in\ N_{i,k}}\left[\tau_{i,l}(t)\right]^{\alpha}\left[\left.\eta_{i,j} \right]\right]^{\beta}}\ j\in\ N_{i,k} \tag{14}\]  
where \(\eta_{i,j}=1/dist(i,j)\) is a popular heuristic, and \(\alpha\) and \(\beta\) are the hyper-parameters of the ACO system. The parameter \(\alpha\) contributes to the weight given to a trail according to the pheromone on it, whereas \(\beta\) takes into account the distance of the next unvisited node that can be selected from the current node. The typical value of \(\alpha\) is 1, and \(\beta\) is usually selected between 2 and 10. Note that a higher \(\beta\) favors the exploration of different paths [29].  
Pheromone Update  
In the ACS, only pheromones on the trails of the global best tour are updated as:  
\[\tau_{i,j}^{(t+1)}=\tau_{i,j}^{(t)}(1-\rho)+\rho/costOfGlobalBestTour \tag{15}\]  
There is no evaporation of pheromone values in the ACS algorithm.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 4 Simulation Results and Discussions
### Highway Vehicle Speed Prediction Using Greenshields Models Based Fuzzy Logic System  
Traffic status data, including traffic flow and traffic density, collected from the segment between Dingjin and Rende on the Sun Yat-Sen Highway in Taiwan [42], are used to illustrate the design of Greenshields model-based fuzzy logic systems for dual-mode vehicle speed prediction. Separate fuzzy systems are developed for congested and non-congested modes to model the relationships among traffic flow, traffic density, and vehicle speed. To build an effective fuzzy system, the variable ranges are defined based on empirical data, and the model parameters are adjusted to align with actual traffic conditions on the specified highway segment.  
The simulations are performed using MATLAB_R2021b, which provides a visual interface for adjusting parameter values during model setup and for dynamically evaluating the performance of the fuzzy logic system throughout the design process. In the design specifications, the two input variables--traffic flow and traffic density--are expressed as percentages, ranging from 0% to 100% of their respective maximum values on the segment. The output variable, vehicle speed, ranges from 0 to 130 km/h. The relationship between vehicle speed and traffic density in the congested mode is shown in Figure 17, displaying a linear correlation consistent with the theoretical model in Figure 3. The relationship between vehicle speed and traffic flow in the congested mode is depicted in Figure 18, illustrating a quadratic relationship that corresponds to the theoretical model in Figure 4. Similarly, the curve of vehicle speed versus traffic density and the graph of vehicle speed versus traffic flow in the non-congested mode are presented in Figures 19 and 20, respectively.  
Figure 17: Relationship curve between vehicle speed and traffic density in congested mode.  
The results show that the fuzzy logic models are highly consistent with [PERSON] theoretical models. Moreover, the dual-mode fuzzy logic system provides valuable insight into the dynamic traffic status at specific locations. Using this system, real-time vehicle speeds can be accurately predicted, closely approximating actual conditions. These results confirm the feasibility and effectiveness of the proposed scheme.  
Figure 19: Relationship curve between vehicle speed and traffic density in non-congested mode.  
Figure 20: Relationship curve between vehicle speed and traffic flow in non-congested mode.  
Fuzzy logic is a mathematical framework for dealing with vague or ambiguous information. It allows for the representation of imprecise data and the use of linguistic variables. Fuzzy logic systems employ fuzzy rules to capture expert knowledge and express connections between factors. Hence, fuzzy logic is suitably used in systems where precise mathematical modeling is difficult.