esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Mechanical Properties of Coal-Rock Models  
The bulk modulus and shear modulus in the coal-rock compound models were simulated in the Mechanical Properties of the Force module. Figure 6 shows the simulation results of the bulk modulus and shear modulus of the anthracite-rock model and coking coal-rock model under different calcite and kaolinite mineral contents. The bulk modulus of the anthracite-rock model decreases with the addition of calcite and tends to decrease with increasing calcite mineral content. The bulk modulus increases with the addition of the kaolinite mineral and tends to increase with increasing mineral content. The bulk modulus of the coking coal-rock model increases with the addition of calcite and kaolinite and tends to increase with increasing mineral content. Note that under the same mass percentage, the influence of kaolinite on the change in the bulk modulus and shear modulus of the two coal rock models is greater than that of calcite on the change in the two mechanical properties. The ratio of bulk modulus to shear modulus (E/G) is often used to estimate the brittle or ductile behaviour of materials. A high E/G represents plasticity, and a low E/G represents brittleness [52]. When minerals exist, the brittleness of the molecular model increases, and the brittleness index increases with increasing mineral content [53]. In addition, the atomic representation of coal-rock compound models is presented on a nanometre scale, which cannot illustrate the macroscopic mechanism of rockbursts and coal and gas outbursts. Larger coal-rock compound models will be constructed in our future work.  
Figure 5: Adsorption heat of methane at 273.15 K in anthracite–mineral model (**a**) and coking coal–mineral model (**b**).
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 3 Results and Analysis
### Analysis of the Accuracy of Measurement
#### 3.1.2 Comparison of the Results of the Other Four Measurement Methods with the 3-D Method
The primary reasons for the increased accuracy of the image processing method were as follows: (a) the image processing method regarded the pod as an irregular cylindrical body composed of several inclined cylinder slices with different diameters, which objectively restored the characteristics of the irregular long-chain bead shape for the pod; and (b) the models of measured values were corrected using image processing, and the 3-D methods were established, which substantially improved the accuracy of measurement. The use of a high-precision 3-D method to measure the actual area of pods had not been previously reported. The high accuracy of such a 3-D method directly determines the high accuracy of our image processing method. Based on this analysis, we suggest that correcting the measurement results by establishing a correction model is necessary. We introduced the correction step into the image processing method to determine whether more accurate measurements could be obtained by correcting other measurement methods. The comparative analysis was conducted using the same modeling sample and detection sample, considering that the calculations of the Clark formula, Leng formula, and quasi-cylinder side area methods require different coefficients configured with pod length and pod width because modeling and correction using the same samples would lead to the same results. Thus, only the Clark formula method was selected as a representative for comparison. After the calculations, a significant linear correlation was observed between the measurement results of the modeling samples produced by the [PERSON] formula, flattening scanning, and the 3-D method. The results of modeling and accuracy analysis are shown in Table 3. After correction, the measurement accuracy of the Clark formula and flattening scanning methods were significantly improved. The average relative error was 2.72% and 2.74%, respectively, and the average RMSE was 1.15 cm\({}^{2}\) and 0.87 cm\({}^{2}\), respectively. Comparatively, the modeling correlation coefficient of the image processing and flattening scanning methods was higher than that of the Clark formula method. The smallest average value of relative error was obtained using the image processing method. The flattening scanning method generated the smallest average value of the RMSE, followed by the image processing method. The Clark formula method yielded the largest value. A comprehensive analysis suggests that even if the other measurement methods are corrected, the image processing method still has comparative advantages in the correlation with the actual value, relative error, and degree of dispersion.
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 4 Results and Experiments
### Performance Evaluation in Relation to Baseline Models  
Our study aimed to evaluate the effectiveness of the proposed deep TL model for diagnosing AD and its early stages and compare its performance with that of state-of-the-art CNN models reported in the literature. The classification performance of the models was assessed in terms of accuracy, sensitivity, precision, and the F1 score. The proposed Xception + PPC model exhibited superior accuracy compared with the baseline model, achieving a 6.65% increase. Furthermore, the VGG-16 + PPC, VGG-19 + PPC, and ResNet-50V2 + PPC models demonstrated accuracy improvements of 3.7, 4.19, and 5.42%, respectively. The incorporation of multiple dense layers in the PPC contributed to enhanced learning ability and accuracy. PPC also exhibited improved detection rates and stability for AD classification. The baseline models showed lower performance on the test set, with accuracies ranging from 85% to 89% as well as lower sensitivity and  
Figure 6: Confusion matrix from Xception + PPC model.  
F1 scores. Several factors, including dataset variations, overfitting, and challenges in feature extraction from TBM images, contribute to the underperformance of base models. A comparison of the computational costs revealed that the Xception + PPC model had a longer training time (3130.86 s) than the base model. However, our primary focus was on improving the accuracy of the method, which showed significant enhancement after the incorporation of PPC. Overall, our proposed method outperformed the baseline models in terms of classification performance and demonstrated potential for accurate AD diagnosis (Table 8).
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
###### Abstract  
This paper investigates the bending resistance of an innovative lightweight composite floor system, LWT-FLOOR. The system consists of built-up cold-formed steel elements that are spot-welded and connected to the concrete slab using demountable shear connectors. As the system under investigation is new, the existing standards do not provide guidelines for calculating its bending resistance. This paper gives an overview of different analytical approaches and a comparison of calculated results using bending capacities from the numerical parametric study. Within the numerical parametric study, the influence of the height of the steel beam, the type and degree of shear connection, the connection between the steel elements, and the thickness of the corrugated web used for the bending capacity are investigated. Numerical results for the full shear connections resulted in lower bending capacities than the analytically calculated plastic bending resistances. However, numerically obtained bending capacities for partial shear connections were found to exceed the calculated characteristic non-linear bending resistances and bending resistances for partial shear connections. The obtained results will comprise the basis for further experimental tests, which will support the search for an optimal analytical approach for the bending resistance of the proposed composite system.  
numerical analysis; analytical approaches; bending resistance; composite beam; cold-formed steel sections; demountable shear connection Article
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.1.2 Enhanced Gradient
The autoencoder is used to  
\[\
abla_{W_{ij}} = COV_{d}(v_{i},h_{j})-COV_{m}(v_{i},h_{j}) \tag{11}\]6 GBRBM blocks model. reconstruct the input data that do not have a class label. The output of the autoencoder is defined as follows:  
\[\
abla_{W_{ij}} = COV_{d}(v_{i},h_{j})-COV_{m}(v_{i},h_{j}) \tag{11}\]7  
The deep autoencoder neural network architecture is depicted in \[\
abla_{W_{ij}} = COV_{d}(v_{i},h_{j})-COV_{m}(v_{i},h_{j}) \tag{11}\]8 The deep autoencoder, as seen in Figure 4, consists of an encoder phase and a decoder phase. The number of neurons for each layer of the network must be determined after the number of layers is chosen. Unfortunately, the optimization process is quite time-consuming because there is no set range for the number of neurons in each layer. It should be noted that the bottleneck layer includes the neural network's most fundamental input properties, and that the performance of the neural network depends on these features. In other words, the bottleneck layer's neuron count needs to be precise, the size of the bottleneck layer exceeds 8, and the detection accuracy is becoming saturated. As a result, in the DNN core architecture, the fifth network layer's neuron count is set to 8, where both the encoder and the decoder share a similar structure. Once the hidden layers have been unrolled, the decoded output can be reconstructed as:  
\[\
abla_{W_{ij}} = COV_{d}(v_{i},h_{j})-COV_{m}(v_{i},h_{j}) \tag{11}\]9  
After that, the mean-square-error (MSE) cost function is used in the fine-grained stage to optimize the proposed algorithm through the backpropagation algorithm:  
\[+ \langle v_{i}\rangle_{dm}\
abla_{c_{j}}+\langle h_{j}\rangle_{dm} \
abla_{h_{i}},\]0  
\[+ \langle v_{i}\rangle_{dm}\
abla_{c_{j}}+\langle h_{j}\rangle_{dm} \
abla_{h_{i}},\]1 Structure of Encoder and Decoder in Deep Neural Network. where \(N\) represents the sample number of data input. Autoencoder training is unsupervised since it seeks to recreate the input data without any reference to the class labels. The final layer of the encoder network, depicted in Figure 4, is referred to as a bottleneck layer, and a softmax classifier is put behind it after features have been extracted using GBRBMs and an autoencoder. In particular, we use the softmax classifier to assess the likelihood that the input belongs to specific classes. Ref. [21] proposed a loss function defined as:  
\[+ \langle v_{i}\rangle_{dm}\
abla_{c_{j}}+\langle h_{j}\rangle_{dm} \
abla_{h_{i}},\]2  
where \(w_{o}\) and \(c_{o}\) denote the encoder parameters and \(y\) and \(y_{0}\) represent the predicted and real labels. Equation (20) represents the loss function using the enhanced gradient algorithm with the backpropagation algorithm as shown in Algorithms 1-3.
## 4 Performance Evaluation
### Evaluation Setup  
#### 4.2.1 IEEE 802.11a OPNET Model PHY/MAC Adaptation to IEEE 802.11p PHY/MAC Parameters  
As previously mentioned, the simulation testbed for FPBR and DDOR was programmed in the OPNET Modeler simulator. This is a well recognized simulation suite widely used in the industry and academia for the evaluation of communication networks, [35, 70, 71]. However, by the time this research work was completed, a specific model for the IEEE 802.11p standard was not included in the OPNET Modeler (v.16.0). Nevertheless, because of the relevance of the IEEE 802.11p standard within the context of V2V communications, the simulation setup used an adaptation of the IEEE 802.11a model such that all PHY/MAC settings and parameters correspond to those found in the IEEE 802.11p standard. This approach was previously used in [35] for the evaluation of AODV and DSR in VANETs equipped with IEEE 802.11p transceivers.  
As mentioned in the introduction, the MAC layer in IEEE 802.11p adopted the IEEE 802.11e EDCA mechanism. This mechanism defines four different classes of traffic with different distributed coordination function (DCF)-CSMA/CA parameters, [20, 35, 36]. In this paper IEEE 802.11p DCF parameters corresponding to best effort traffic over service channels (see [36]) were used to modify the IEEE 802.11a OPNET model. Therefore, the minimum and maximum contention windows sizes, and the time slot length were adjusted to IEEE 802.11p best effort traffic values. Similarly, the DIFS value was replaced by the corresponding AIFS value. Regarding the PHY layer adaptation, the bandwidth and operating frequency of the IEEE 802.11a OPNET model were adjusted to 10 MHz and 5.880 GHz respectively, as defined for IEEE 802.11p standard. Additionally, the data rates of the IEEE 802.11 aOPNET model were adjusted to those defined by the IEEE 802.11p standard. For the rest of this paper the modified model will be referred as adapted IEEE 802.11a/p model.
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 4 Evaluation
### Controller Parameter Setting and Evaluation
#### 4.2.1 Evaluation of Adjustment Time and Overshoot Criterion  
Figure 7 shows the response of the ADS temperature sensor calibration system to step control signal input with different control strategies. We set the expected temperature calibration accuracy at 0.5% steady-state error. For the step signal input, the original system  
Figure 6: Finite element simulation and S-K iterative fitting of temperature dynamics.  
without adding the controller has no overshoot but the adjustment time is very long at 183.9 s. The PID controller is added to the original system and the adjustment time is shortened to 104.8 s; however, the system has an overshoot of 9.4%, which does not meet the requirement of overshoot \(\sigma\)% \(\leq\) 5%. Using the TFB PID parameter adjustment method, the system's dynamic performance is greatly improved and the adjustment time is only 59.2 s. However, the controller output signal of this method is not suitable for calibrating the system, which will be explained in detail later. Then, we use the Fuzzy PID control method, the overshoot of the system is 10.9%, and the adjustment time is 112.4 s. It does not obtain satisfactory stability and dynamic performance. Using the MCA PID controller design method, the overshoot of the system is only 0.7%, and the adjustment time is 87.7 s, achieving better stability and dynamic performance.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 5 Results  
The developed smart city platform framework envisions a citywide deployment over the city of Aveiro. Making use of the developed network elements and the multi-technology employment, it leverages the multi-gateway MAC in the LoRa protocol in order to ensure reliable communications within this technology, and the WiFi opportunistic and delay-tolerant network to increase the heterogeneity of the platform.  
Versatility and adaptability denote core characteristics of the described platform. Its implementation endows it with a modular design capable of integrating new features, e.g., new communication technologies, additional sensory equipment, new network functionalities, and new mobile entities, conferring a dynamic nature to it. Therefore, heterogeneity is a key aspect of the platform, since it is able to cope with distinct network elements and contrasting features, for instance the distinct speeds of mobile nodes. Such properties allow the deployment of a large variety of smart city applications. Being a smart city platform, scalability was taken into consideration during its design and implementation. A real use case scenario was used to evaluate our smart city multi-wireless technology with an opportunistic communications platform.
# Populating the Mix Space: Parametric Methods for Generating Multitrack Audio Mixtures
## 2 Theoretical Framework
### Generating Gain Vectors by Sampling the Mix-Space
#### 2.2.1 Simple Mixing Model  
From here, the example mixing session described is an 8-track session, containing vocals, guitars, bass and drums [15]. For \(n=8\) tracks, the gains required for the equal-loudness mix (once all audio tracks have been normalised in perceived loudness) are distributed around the following \(\mu\)--each track gain is equal to \(n^{-2}\), such that \(|\mu|=1\).  
\[\mu=[0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ \ 0.3536]\]  
Previous studies have indicated that, while a good initial guess, presenting each track at equal loudness is not an ideal final mix. As suggested by three recent PhD theses on the topic [15; 16; 17], vocals are often the loudest element in a mix. To this equal loudness configuration, a vocal boost is added according to p.157 of [16], i.e., a boost of 6.54 dB. This addition of 6.54 dB to the vocal track produces the following vector, where track 8 is vocals.  
\[\mu=[0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ \ 0.3536\ \ 0.3536\ \ 0.3536\ \ \ 0.3536\ \ \ 0.7507]\]  
Figure 5: Three sets of mixes, drawn from the mix-space. This shows the effect of varying the concentration parameter \(\kappa\), that a larger value results in less diversity.  
If the previous vector was, then it is clear that this point is no longer on the unit 7-sphere. To project the point back onto the unit 7-sphere, the vector is normalised by dividing by the \(\ell_{2}\) (Euclidean) norm, resulting in the following.  
\[\mu=[0.2948\ \ 0.2948\ \ 0.2948\ \ 0.2948\ \ 0.2948\ \ 0.2948\ \ 0.2948\ \ 0.2948\ \ 0.6259] \tag{7}\]  
This vector is the new \(\mu\) on the unit 7-sphere about which a set of mixes will be generated. The result is shown in Figure 6a. Each mix generated draws a gain value for each track such that the \(\ell_{2}\) norm is equal to 1. Note that the median values closely match the vector \(\mu\), as expected. Of course, there may not exist a mix which has these median values. This specific value of \(\kappa\) was chosen to avoid generating negative gains, achieved through trial and error. For a distribution which produces negative gains, the absolute value could be taken to avoid inverting the phase of the tracks. Ignoring phase, a gain of \(g\) is perceptually equal to \(-g\), meaning that the shape of the distribution would be altered if negative gains were included.
## 3 Results
### Inertia Measurements  
Figure 8 shows the results of the inertia measurements. The underlying measurement data are listed in Appendix B. The determined inertias for the vehicles including the driver were in a range between 19 kgm\({}^{2}\) and 43 kgm\({}^{2}\) for the velomboibles. Compared with the velomboibles, trikes had a lower inertia of about 12 kgm\({}^{2}\) to 23 kgm\({}^{2}\). The observed standard deviations in the vehicle measurements were in the range of 1% to 4% (mean 2.7%) of the measured value.  
A comparability of the determined values with other vehicle types was achieved via the radius of inertia \(r_{I}\) (Equation (15)) and the related radius of inertia \(r_{I,rel}\) (Equation (16)). The radius of inertia provides information about the mass distribution or compactness of the mass. A small value indicated a higher mass concentration at the COG. Since vehicles with larger wheelbases usually have larger radii of inertia, a relativization can be made using the related radius of inertia.  
\[r_{I}=\sqrt{\frac{I_{z}}{m}} \tag{15}\]  
\[r_{I,rel}=\frac{r_{I}}{l} \tag{16}\]  
The radius of inertia and the related radius of inertia of the vehicles are shown in Figure 9. The radii of inertia for velombiles were typically in the range of 0.44 m to 0.55 m except for vehicle 7, which showed slightly higher values. Trikes were below this range with values between 0.36 m and 0.43 m. The wheelbase-related radii of inertia ranged  
Figure 8: Yaw inertia measurement results.  
from 0.32 to 0.42, whereby the lower values were more typical of trikes and the medium and higher values were more typical of velombiles. Values for wheelbase-related radii of inertia for passenger cars are given as 0.43 to 0.53 by [24]. A smaller related radius of inertia essentially indicates higher yaw agility of the vehicles in comparison with passenger cars (neglecting e.g., the tire characteristics).  
Figure 10 shows the relative shares of the vehicle empty mass in the total mass and the vehicle empty moment of inertia in the total moment of inertia. According to the measurements, the vehicle accounted for about 18% to 40% of the total mass of the vehicle-driver combination. Due to the electric drive, the heavy vehicle 9 had a slightly higher mass ratio for light drivers. The vehicle mass was, in any case, lower than the mass of the driver. In contrast to mass, inertia showed a much larger relative influence on the vehicle. The shares of vehicle inertia in the total inertia ranged between 48% and 78% for velombiles and between 25% and 35% for the trikes without electric drive (vehicles 11 and 12). So while the empty vehicle exerted less influence on the mass, the inertia was more influenced by the vehicle. Therefore, it seems possible to significantly influence the total moment of inertia of the vehicle-driver combination by changes of the vehicle design for velombiles.  
Figure 10: Mass ratio (**top**) and yaw inertia ratio (**bottom**) between empty vehicles and vehicles with drivers.  
Figure 9: Yaw inertia radius (**top**) and related yaw inertia radius (**bottom**) of the vehicles.
## 2 Experimental Setup and Procedure
### Measurement
#### 2.3.3 Calculation Method of Flow Characteristics  
According to the Reynolds decomposition expression, the instantaneous velocity \(u\) of the flow can be decomposed into the time-averaged velocity \(\overline{u}\) and fluctuating velocity \(u^{\prime}\), i.e., \(u=\overline{u}+u^{\prime}\), where the  
Figure 3: Measurement locations: (**a**) locations of measurement sections; (**b**) measurement locations arrangement in each section (all the dimensions are in centimeters).  
time-averaged velocity of the measuring point \(\overline{u}=\frac{1}{N}\sum_{1}^{N}u_{i}\), and \(N\) refers to the sampling number of the flow velocity in measuring time. The average velocity \(U\) of measuring section is defined as  
\[U=\frac{1}{A}\int_{0}^{2}\int_{0}^{2}\overline{u}dydz \tag{1}\]  
where \(A\) refers to the transverse measurement section area.  
The dimensionless Reynolds shear stress of the \(XOY\) section was expressed as:  
\[R_{XY}=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}(u_{i}-\overline{u_{i}})(v_{i}- \overline{v_{i}})}}{U} \tag{2}\]  
The longitudinal dimensionless turbulence intensity was expressed as:  
\[I_{X}=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}(u_{i}-\overline{u_{i}})(u_{i}- \overline{u_{i}})}}{U} \tag{3}\]  
In a similar way, the transverse and vertical time-averaged velocity (\(\overline{v}\), \(\overline{w}\)), dimensionless turbulence intensity (\(I_{Y}\), \(I_{Z}\)) and dimensionless Reynolds shear stress (\(R_{YZ}\), \(R_{XZ}\)) could also be computed.