esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation  
### Simulation Environment and Design  
The simulation was developed following the conceptual model described by the flow diagrams in Figures 2 and 3 in the proposed solution. The initial step was to design a modular code with subsystems interacting with each other to mimic the baseline IEEE 802.11  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **IEEE 802.11** & **BiMMAC** & **AMMAC** & **SA-MMAC** & **E-SA-MMAC** \\ \hline Issue of & & & & & \\ multi-channel hidden & NA & Very less & No & No & No \\ terminal & & & & & \\ Control channel & & & & & \\ overhead & NA & Less & High & High & Very High \\ More than one data & & No & Yes & No & Yes & Yes \\ frame in a handshake & & & & & \\ Number of & & & & & \\ transceivers & 1 & 1 & 1 & 1 & 1 \\ Exclusive control & & & & & \\ channel needed & NA & Yes & No & No & No \\ Channel selection * & Random & Last Used & Random & Last Used & Last Used \\ * Subject & & & & & \\ availability of & & & & & \\ common channels. & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of features.  
Figure 3: Flow diagram of the proposed adaptive MMAC protocol.  
protocol and to verify the accuracy of the coding method. The simulation environment is done on Python 3 with each protocol compartmentalized in separate python files. A parent python program calls each protocol to execute simultaneously with a given set of configurations, such as the maximum number of stations, increment increase in stations per round, number of wireless channels available, contention window, and other parameters described in the simulation parameters.  
Each protocol python file involves running functions that mimic a network of a varying number of stations competing to access a wireless channel to transfer data to another station. This is modeled using array structures consisting of frames up to the maximum number of frames allowed as per the predefined simulation time. Another array is configured with random backoff periods from the 0 to \(CW_{max}\) window for each collision that occurs. Collisions are modeled as situations when multiple stations select the same frame time to send the packet, and a collision counter is assigned to each station in another array to store historical contention results. This collision counter imposes a greater backoff penalty for stations to ensure fairness in the algorithm. Nodes fairing badly with subsequent collisions are not able to access a wireless data transmission channel, thus access delay increases and fairness decreases.  
After the contention mechanism, the model moves to the success event function with an assigned data transmission channel for two frame time units. This function, depending on the protocol, determines whether the station receiving the transmission is able to send a bidirectional data transmission in the same transmission window post-handshake. This function is the primary difference in the code between the protocols, with the exception of E-SA-MMAC and Max-E-SA-MMAC, as they differ in contention windows during a failure event scenario.
## 3 Results and Discussion  
### Volatile Profile and Composition  
A total of 29 volatile compounds were isolated, identified and quantified in the headspace of the three wines under study, using HS-SPME and GC-MS and GC-FID. These compounds were grouped into 7 chemical families: _(i)_ esters (\(n=14\)), _(ii)_ aldehydes (\(n=5\)), _(iii)_ alcohols (\(n=3\)), _(iv)_ acids (\(n=4\)), _(v)_ terpenes (\(n=1\)), _(vi)_ alkanes (\(n=1\)), and _(vii)_ norisoprenoids (\(n=1\)) (Table 1). The most important finding was the predominant role played by the ester family in wines B and C, while acids and terpenes were the most abundant families in _wine A_ (Figure 1). This pattern of chemical families seemed to imply that the first two wines (\(B\) and C) were more complex and had more intense fruity and sweet flavor notes as compared to the third one (\(A\)), which was characterized by high levels of freshness and sourness. This experimental finding is essential to state that the type of inner coating of the clay-_tinaja_ (through different micro-oxygenation patterns) plays a key role in determining the final volatile profile of the wine and probably its sensory profile. It will be very interesting to design future studies controlling different levels of micro-oxygenation through the use of different inner coatings and/or clay materials and studying their effects of the wine characterization and quality.
## 4 Results and Discussion
### Analysis of the Results of Testing Mortar in the Hard State
#### 4.2.2 Dynamic Modulus of Elasticity  
The results of the testing of the dynamic modulus of elasticity E\({}_{\text{D}}\), at the age of 28 days, are shown in Figure 7.  
Based on the presented diagram, the following conclusions can be drawn:  
* The results of testing the dynamic modulus of elasticity of treated mortar mixtures at the age of 28 days show that series containing electrofilter ash have a lower dynamic modulus of elasticity by 26.72% and 26.26%, respectively. This is in line with claims made by other authors that electrofilter ash has a pronounced impact on reducing the values of the modulus of elasticity [27];  
Figure 7: Dynamic modulus of elasticity (E\({}_{\text{D}}\)) of samples aged 28 days.  
* The addition of synthetic fibers in the series has a minimal impact on the change in the dynamic modulus of elasticity. In both series A and B, samples containing fibers exhibit only a slight increase in the dynamic modulus (the largest percentage difference being 0.5%), which can be primarily explained by the low percentage participation of microreinforcement within the cement matrix.  
Taking into account the displayed values of the tested dynamic modulus of elasticity (E\({}_{\text{D}}\)) and compressive strength (fp), from the following diagram (Figure 8), it can be concluded that there is a good correlation between these parameters in both the quadratic parabolic functional dependency, with a correlation coefficient of r\({}^{2}\) = 0.8888, and in the linear dependency, where r\({}^{2}\) = 0.8678.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 5 High-Speed Scenario
### Location Division  
The entries of high-speed railway stations are important areas that should be carefully considered regarding the access policies. The main refinement is to use different access policies according to the station size. The area division of large-scale and small-scale stations are presented in Figure 9.  
For large-scale high-speed railway stations, such as Nanjing south station and Suzhou station in Jiangsu province, the waiting rooms are quite large with several indoor stations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Public Network** &
\begin{tabular}{c} **Private Network** \\ **(High-Speed Railway)** \\ \end{tabular} \\ \hline defaultpagingcycle & 1280 ms & 320 ms \\ tReselectionIntraEUTRA & 2 s & 0 s \\ \hline \hline \end{tabular}
\end{table}
Table 14: Parameters related to cell reselection.  
Figure 8: Cellular network performance with different frequency band policies. **(a)** CDF vs. RSRP. **(b)** SINR vs. RSRP.  
Figure 9: Access polices of large-scale and small-scale high-speed railway stations.  
Parts of indoor stations belonging to public networks do not incorporate with private networks. Therefore, we deploy the transition network on the platform. The UEs can access the transition cellular networks when they are outside the waiting rooms. Then, all UEs access the private networks of the high-speed railway after they are on the train. For small-scale stations, such as Zhenjiang station and Changzhou station, there are one or two indoor stations incorporating with the private networks. Therefore, the indoor stations should add neighbor areas with the private networks. UEs on the train can directly access the private networks as shown in Figure 9. Based on these policies, success access rates of more than 90% can be achieved for the private networks.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Retrieval and Processing of Orthoinagery from Geoportal
#### 3.4.1 Determining the Maximal Available Resolution  
For each road segment, the script calculated its bounding box in EPSG:2180. This defined the area for retrieving orthoimagery from Geoportal's 'SkorowidzeWgRozdzielczosci' service. The WMS 'GetMap' request targeted layers with resolutions of 5 cm, 10 cm, and 25 cm per pixel. A GML response was parsed to determine the highest available resolution for the segment based on the smallest numerical value mapped to fetched resolution names.
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 5 Numerical Results
### Capacity Curves
Figure 7: Mutual information vs. snr when \(\kappa=0.95\) for \(\phi=\pi/2\) (blue dashed lines), \(\phi=0\) (red dashed lines) and \(\phi^{*}=0.9538\) (black solid lines) for improper \(M\)-QAM signals. The capacity of the improper Additive White Gaussian Noise (AWGN) channel using Gaussian codewords and the asymptotic envelope of the curves are also depicted. Figure 8: Mutual information vs. snr when \(\kappa=1\) (maximally improper) for \(\phi=\pi/2\) and \(\phi^{*}\) for improper constellations with \(M=4\) and \(M=16\) signal points. The capacity of the improper AWGN channel using Gaussian codewords is also depicted. Figure 9: Capacity comparison between proper and improper (with \(\kappa=0.95\)) \(M\)-QAM discrete constellations. For the improper \(M\)-QAM, the optimal \(\phi^{*}=0.9538\) at high snr is used. The proper and improper Gaussian capacity is also depicted.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 3 Fluctuation-Enhanced Gas Sensing: A Brief Historical Overview
theoretically demonstrated that the product of frequency by the power spectral density of the gas sensing layer resistance fluctuations often has a maximum which is characteristic of the gas, thus providing a valid method for gas classification. They experimentally confirmed this property in the case of the detection of NO\({}_{2}\) and O\({}_{3}\) using a WO\({}_{3}\) sensing layer. [PERSON] et al. explored the possibility of using FES in electronic tongues [62]. When carbon nanotubes gained importance in the field of sensors and there was a strong growth in their applications, the FES technique was also applied to increase the sensitivity of these materials [63; 64; 65; 66] and also the noise characteristics of graphene, which shows, with respect to the vast majority of ordinary semiconductors, a peculiar behavior of the flicker noise power spectral density as a function of the charge carrier density, have been  
Figure 3: Schematic diagram for fluctuation-enhanced chemical sensing. investigated for possible application in FES [67]. FES has also been applied to amperometric gas sensor measurements [68; 69]. In order to take advantage of FES potentialities, special low-noise measurement hardware and software to acquire data and perform analysis are required. Although an in-depth discussion on this topic will be made in Section 6, here we briefly emphasize that the FES devices have followed the evolution of electronics, therefore several solutions have been presented in literature, such as, for example, a small, USB-powered device [70], tested on Taguchi and carbon nanotube based gas sensors, and a complete mixed-signal system [71]. Today we live in the era of the Internet of Things (IoT), which provides opportunity to continuously monitor different aspects of our lives in a cost-effective way. Since FES is a promising method to increase the selectivity and sensitivity of the sensors, an attempt to realize a complete wireless sensor node system based on Wi-Fi communication has also been made [72] which is a significant step toward the possibility of using fluctuation-based sensing methods in everyday life.
## 3 Numerical Model  
### Physical Model  
The main structure of the CP-\(\pi\) RFA consists of a central channel, a central distribution hole, an adsorbent layer, an outer distribution hole and an outer channel, as shown in Figure 3a, with the parameters listed in Table 2. The physical model was established with 1/4 part of RFA as the calculation area, since the cylindrical axisymmetric structure with two symmetry planes, as shown in Figure 3b. The schematic of the CP-\(\pi\) RFA including the important size are shown in Figure 3c. The origin of coordinates was set on the center of the end of the air inlet of the adsorber, with the \(X\)-axis and \(Z\)-axis along the radial direction and axial direction of the cylindrical structure. The adsorbent layer was full filled with the adsorbent in the spherical shape of 1.6 mm diameter, and the adsorption bed porosity is 0.4.  
In addition, it is convenient to combine O\({}_{2}\) and Ar species into one pseudo species O\({}_{2}\)-Ar and treat air as a binary mixture of N\({}_{2}\) and O\({}_{2}\) with a mole fraction of 79: 21 for calculations in the model, because of almost the same capacity of O\({}_{2}\) and Ar in the experiment adsorbent.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Values** & **Parameter** & **Values** \\ \hline \hline Molar feed composition & O\({}_{2}\)-N\({}_{2(21-79\%\text{ by volume})}\) & Inner diameter of adsorber (mm) & 211 \\ \hline Diameter of adsorber (mm) & 219 & Adsorption bed width (mm) & 61.5 \\ \hline Sorbent filling height (mm) & 187 & Outer channel width (mm) & 25 \\ \hline Entrance radius (mm) & 12 & Central channel width (mm) & 13 \\ \hline Entrance length (mm) & 40 & Flow distributor width (mm) & 3 \\ \hline Opening ratio of outer channel (\%) & 27\% & Diameter of central channel hole (mm) & 2 \\ \hline Opening ratio of central channel (\%) & 17\% & Diameter of outer channel hole (mm) & 8 \\ \hline Vent length(mm) & 40 & Vent radius (mm) & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the CP-\(\pi\) RFA.  
Figure 3: Physical model of the radial flow adsorber of centripetal RFA (CP-\(\pi\) RFA), (**a**) structure of the CP-\(\pi\) RFA, (**b**) physical model of the CP-\(\pi\) RFA, (**c**) the schematic of the CP-\(\pi\) RFA.
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 7 (Al)GaN PCSS Devices
### PCSS performance  
Switching jitter is found to be inversely proportional to field, as illustrated in Figure 12. To achieve the promise of ps-scale jitter in Mn-doped GaN switches, field strengths of \(>\)40 kV/cm are needed. The doping species is also found to play a significant role in switching performance, as shown in Table 3. While a delay between laser triggering and closing of the switch exists for all devices, this is both larger and more variable in Fe-doped samples. Delay during the successful triggering of GaN:Fe switches was significantly longer than for the GaN:Mn devices, suggesting lower photo-carrier generation rates in Mn-doped devices compared with Fe-doped devices at this trigger wavelength of 800 nm.  
Below 25 kV/cm and 22.5 u\(\parallel\) of laser pulse energy at \(\lambda\) = 800 nm, the switch operates in the linear mode; however, when the field and pulse energy exceed these values, a nonlinear switching mode is observed (see Figures 13 and 14a). These switching events are confirmed both in air (where surface flashover occasionally occurs) and in an atmosphere of insulating SF\({}_{6}\). The location and near-bandgap emission wavelength of the flashover, and the current filamentation typical of high-gain switching, are confirmed by wavelength-resolved camera images as shown in Figure 13. The energy requirement to initiate nonlinear conduction, holding field constant, also depends superlinearly on the laser trigger wavelength  
Figure 11: Optical micrographs of fabricated GaN:Mn PCSS devices in (**a**) lateral geometry, (**b**) vertical geometry with coaxial heat-sink, and (**c**) plan view of high-hole-density anode {center} and circular backside cathode {outer circle}.  
Figure 10: Predicted electronic structure and deep defect levels for magnetic 3D dopants in cubic GaN. All of the 3D dopants are energetically favored to substitute in the Ga sublattice. The first two transitions for V (VB + 1.39/2.07 eV), Cr (VB + 2.44/3.07 eV), Mn (VB + 1.46/1.61 eV), Fe (VB + 0.71/2.80 eV), and Co (VB + 0.88/1.54 eV) are illustrated schematically within the room-temperature GaN bandgap (re-generated from Ref. [20]).
# Evaluation of Workability and Mechanical Properties of Bottom Ash Aggregate Concrete
###### Abstract  
The purpose of this study is to evaluate the workability and mechanical properties of concrete containing bottom ash, which is an industrial byproduct, as an aggregate. Twelve concrete mixtures, including concrete containing bottom ash aggregate (CCBA), are classified into three groups of _W/C_ 0.45, 0.375, and 0.3 according to the target strength. Each group includes four mixtures in which natural aggregate and bottom ash are applied as fine and coarse aggregates according to particular ratios. On the basis of the test results, a reliable model to predict the 28-day compressive strength of CCBA is proposed by applying regression analysis. The proposed model points out that when applying bottom ash as aggregate, the concrete density is lowered, and _W/C_ should be reduced to offset the lowered compressive strength. As a result of comparing experimental values with the existing design code models, it is found that while the _fib_ model accurately predicts compressive strength development and tensile resistance capacity in general, the ACI 318 code overestimates the elastic modulus at the oven-dried density (\(\rho_{c}\)) range of 1840 kg/m\({}^{3}\) and above. Accurate prediction of the mechanical properties of CCBA requires the measurement of additional experimental data that consider _W/C_ and \(\rho_{c}\) as important parameters.  
bottom ash; lightweight concrete; density; workability; mechanical properties +
Footnote †: journal: Applied Physics Letters  
_Article_  
Evaluation of Workability and Mechanical Properties of Bottom Ash Aggregate Concrete
## 6 Results and Discussion  
The classification models, namely, VGG-19, Inception V3, and ResNet-50, are trained employing the principle of tuning and then tested on three datasets using the Google Colaboratory (Tesla T4, CUDA Version: 11.2). The imagery benchmark datasets used in this research work are divided into two parts, one for training purposes and the other one for testing purposes. A 70%-30% distribution of each of these datasets was employed in this work. A 10-fold cross-validation technique for such datasets is also taken to evaluate the performance of each of such distinct classifiers which are tuned during the training phase. A variety of evaluation metrics are employed to measure the performance in the experiments, including Root-Mean-Square Error (RMSE) [70], Kappa Statistic [71], True Positive Rate (TP-Rate), recall, False Positive Rate (FP-Rate), accuracy, precision, and F-measure. In essence, accuracy, precision, True Positive Rate (TP-Rate), False Positive Rate (FP-Rate), Recall, and F-measure are derived from the confusion matrix [72] of the individual classifier.  
It is to be noted that optimal performance for each of the trained classifiers is achieved after 40 epochs though this performance comparison scheme proposed in this research work provides test results of each of these classifiers for 10, 20, and 30 epochs. The Supplementary Materials contain the test results considering 10, 20, and 30 epochs. Performance analysis is comprised of two parts. The first part describes the performance comparisons of the classifiers developed on different evaluation measures such as accuracy, weighted kappa statistic, and root-mean-square error (RMSE). Meanwhile, the second part deals with performance comparisons of each individual classifier with respect to TP-Rate/Recall, FP-Rate, precision, and F-measure. Standard deviation is also presented in the test results. The detailed descriptions of these statistical metrics which are used as tools for measuring performance are provided below.
###### Abstract  
In this paper, approximate lag synchronization (LS) and anticipating synchronization (AS) between two unidirectionally coupled hyperchaotic Chen systems without time-delay coupling are analytically investigated. Firstly, the synchronization condition for exact LS in two unidirectionally coupled hyperchaotic Chen systems with time delay in signal transmission is analytically obtained. Under such conditions, approximate LS and AS are discussed by replacing the true time-delay terms with their Taylor expansions up to the third order.Differently from other research studies, the condition for exact LS is derived by regarding LS as a special type of generalized synchronization (GS), which has nothing to do with the value of the time delay. It is convenient to individually change the value of the lag and anticipation time of approximate LS and AS without considering the synchronization condition. Our study shows the power of a new method for recreating the past signals or predicting the future signals of a hyperchaotic Chen system by using its current signals. The results provide a simple way to eliminate the negative effects of time delay in the signal transmission between two hyperchaotic systems.  
lag synchronization; anticipating synchronization; hyperchaotic Chen system; drive-response systems +
Footnote †: journal: Applied Sciences
# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 6 Results and Analysis
### Bandwidth Analysis  
Table 8 presents the bandwidth values for both the GWR and MGWR models. In the MGWR model, the scales of the variables differ, whereas the GWR model can only provide an average of the action scales for each variable.  
Figure 4: Comparison between the GWR and MGWR model fittings.  
Table 8 shows that most of the explanatory variables exhibit spatial variations in their impact on the riding demand, with only a few variables having consistent effects across the entire study area. For instance, the bandwidth of the roadway facilities, trip attractions, and other variables are high, indicating their impact on the entire region. However, the values of intersection facility bandwidth, quantity of intersections, and other variables are low, which indicates the limited impact range. These indicators only affect the riding demand in the adjacent area, but do not affect the riding demand in the nearby area.