esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Architecture of Modified AlexNet-33 (MAlexNet-33)
#### 4.2.3 The Combination of Layer and Parameter Modification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Activation Function**} & \multirow{2}{*}{**FC6**} & \multirow{2}{*}{**FC7**} & \multicolumn{2}{c}{**Num. of Layers**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Epochs**} & \multicolumn{2}{c}{**Network**} \\  & & & & & & **Size** & **Size** \\ \hline ReLU & 384 & 192 & 23 (n.conv4) & None & 30 & 21.19 MB & 84.63\% \\ Leaky ReLU (0.01) & 384 & 192 & 23 (n.conv4) & None & 30 & 21.19 MB & 83.05\% \\ Leaky ReLU (0.01) & 576 & - & 20 (n.conv4) & None & 30 & 27.98 MB & 83.80\% \\ Leaky ReLU (0.01) & 1064 & - & 20 (n.conv4) & Equ. & 30 & 45.99 MB & 84.66\% \\ ReLU & 1064 & - & 20 (n.conv4) & Equ. & 30 & 45.99 MB & 82.54\% \\ ReLU & 576 & - & 20 (n.conv4) & Equ. & 30 & 27.99 MB & 83.63\% \\ Leaky ReLU (0.01) & 576 & - & 20 (n.conv4) & Equ. & 30 & 27.99 MB & 83.71\% \\ Leaky ReLU (0.01) & 576 & - & 22 (w.conv4) & Equ. & 30 & 30.64 MB & 85.00\% \\ Leaky ReLU (0.01) & 384 & - & 22 (w.conv4) & 24 & 30 & 23.56 MB & 85.76\% \\ Leaky ReLU (0.01) & 384 & 172 & 23 (n.conv4) & 24 & 30 & 21.16 MB & 84.76\% \\ Leaky ReLU (0.01) & 384 & 172 & 27 (gconv64) & 24 & 30 & 17.34 MB & 86.89\% \\ Leaky ReLU (0.01) & 192 & 86 & 27 (gconv64) & 48 & 30 & 13.59 MB & 85.61\% \\ Leaky ReLU (0.01) & 384 & 172 & 33 (gconv32) & 24 & 30 & 14.33 MB & 87.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results for the combination of layer and parameter modifications. Figure 7: Training accuracy and losses graph (**Left**) AlexNet; (**Right**) Modified AlexNet with less parameters. and decreasing the network size by over 40%. Aside from the improvement in resource requirements, decreasing the network size also returned a notable improvement in the inference execution time, provided that they are factors linearly related to one another.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 2 Related Work
The authors in [29] presented a Probabilistic Geographic Routing (PGR) that uses only local information, link reliability, and residual energy to probabilistically forward data after selecting a candidate's nodes and assigning these probabilities to these candidates. Distributed Heuristic Algorithm (DHA) was presented in [30]. DHA is a probabilistic scheme based on three distances parameters to select the forwarder nodes, the distance between the sender node and the sink, the distance between the forwarder node and the sink, and the maximum distance between the sender node and the sink.
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 3 Landing Environment  
CBA landing is a complex process during which the CBA must traverse the airspace behind the carrier deck and eventually land on a runway of limited length, which is quite different from a land-based landing environment. During CBA landing, the flight altitude relative to sea level changes greatly. Also, the CBA is in the troposphere when it lands, where relatively significant airflow occurs, and the CBA is extremely susceptible to the carrier's air wake on approach and so is greatly challenged by the resulting complex aerodynamic environment. Finally, the flight deck experiences pitching, rolling and phugoid motions due to wind waves, so the ideal touchdown point thereon is a 6 DOF spatial motion point, which can also seriously affect the landing safety.
## 7 Conclusions  
In this paper, we proposed an effective DSSL framework for InF-DH scenarios named AMT, which solves the problem of inaccurate positioning caused by inadequately labeled samples for fingerprint positioning. In the DSSL framework, we operate AMT by assigning the EMA weights of the student model to the teacher model and regularizing the consistency loss of two models. Additionally, we have also proposed novel implicit random augmentation methods in terms of amplitude and temporal features of CIR data in AMT to enhance the performance of neural networks. By adding random perturbation to these critical positioning features in the training process, continuous new data can be generated artificially from existing data.  
From conducting the simulation, we conclude that the deep-learning method has a superior performance compared with the estimation method in the heavy NLOS scenario, which can achieve a sub-meter level accuracy, and using CIR for deep learning-based positioning can achieve a higher positioning accuracy than using other measurements proposed in the 5G NR standard. By analyzing the performances of the DSSL methods, the numerical results show that using regularization in consistency loss improves the neural network's ability to resist perturbation, which helps learn effective features in unlabeled data, especially in the case of small samples. Meanwhile, the interaction in model weights for consistency regularization methods improves the convergence of neural networks.  
Additionally, results also show the robustness of AMT by using different numbers of labeled data, and its performance gain in the smaller samples is more obvious.  
By using AMT, we find the inherent similarity between image processing and indoor positioning, and we also verify the effectiveness of image processing methods when applying them to the positioning field. However, the work does not involve using the angle feature for positioning because of the single receiving antenna set in the dataset. Since multiple receiving antennas can bring more channel information for positioning, we plan to explore the generalization of our proposed method in multiple-receiver situations in future studies.  
Conceptualization, P.C.; Methodology, P.C. and Y.L.; Resources, B.Y.; Data curation, W.L. and J.W. ([PERSON]); Writing--original draft preparation, P.C., Y.L., W.L., J.W. ([PERSON]) and B.Y.; Writing--review and editing, P.C., Y.L. and J.W. ([PERSON]); Supervision, J.W. ([PERSON]), B.Y. and G.F.; Project administration, B.Y.; Funding acquisition, J.W. ([PERSON]) and P.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by China Telecom Research Institute.  
Restrictions apply to the availability of these data. Data were obtained from OPPO Research Institute and are available at wireless-intelligence.com (accessed on 6 November 2023) with the permission of OPPO Research Institute.  
The authors declare no conflicts of interest.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 5 Results Validation
### Validation of Optimization Results for Temperature Sensors  
In the validation process of optimization of the positions and orientations of the temperature sensors, the considered monitored load is the maximal temperature value in the whole structure. The load range of the Dirichlet boundary condition between 0 and 50 \({}^{\circ}\)C for each load case location is considered.  
For each of the 500 training trials of the temperature prediction model, the root mean square error (RMSE) between the predictions and targets for all the reference data was calculated. Statistical comparison between the accuracy of prediction models based on measurements from sensors in random positions and orientations against sensors in their optimal positions is presented in Table 2. The minimal value of all the trials for optimal positions is lower than the minimal value for random positions. The maximal value, average value, and standard deviation for optimal positions is lower than the corresponding values for random positions by an order of magnitude. The histograms of RMSE for all the training trials for both cases are presented in Figure 27. The results prove that the aim of the implemented optimization method was achieved, as it allowed us to obtain a prediction of much greater accuracy than positioning the sensors randomly.  
\begin{table}
\begin{tabular}{c c c} \hline
**Title 1** & **Random** & **Optimal** \\ \hline Minimal value & 0.0076 & 0.0052 \\ Maximal value & 3.0844 & 0.3101 \\ Average & 0.2115 & 0.0198 \\ Standard deviation & 0.4854 & 0.0201 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical comparison of prediction accuracy of 500 training trials of temperature prediction model based on sensor measurements with sensors in random and optimal positions and orientations; all values in \({}^{\circ}\)C.
## 3 Frustule Modification: Techniques and Applications
### Frustule Metalization for Applications in Plasmonics
The bio-derived mesocapsules can easily flow inside a microfluidic channel and reach an interrogation site. The combined effect of high density of embedded nanoparticles, photonic crystal-induced enhancement of the plasmonic field, efficient analyte capture by the frustule porous matrix, and effective mixing with analyte in the microfluidic channel allowed reaching single molecule detection for Rhodamibe 6G and a detection limit of \(1\times 10^{-9}\) M of benzene and chlorobenzene compounds in tap water with near real-time response. Figure 6: (**a**) Transmission Electron Microscopy (TEM) image showing the details of a _P. multistriata_ valve with indications of its characteristic features. Several periodic subpatterns, whose overall interaction provides an efficient coupling with incident optical radiation, can be identified. (**b**) Atomic Force Microscopy (AFM) scan of a metalized valve showing its 3D morphology, in particular the extruded edge wall with its peculiar blade profile. (**c**) Optical image and relative Raman map of a single metalized valve covered with a biphenyl-4-thiol (BPT) monolayer used to estimate the EF. Most of the signal comes from the lateral, extruded edge. Gold thickness: 40 nm. (**d**) BPT average spectra outside (black) and on (red) the metalized valve after excitation at 785 nm. (**a**,**b**) reproduced with permission from Ref. [80]. Copyright 2018 American Chemical Society. In addition to SERS, other plasmonic effects can be induced by a suitable metalization of diatom frustules. [PERSON] et al. [82] obtained gold replicas of _Coscinodiscus asteromphalus_ valves by means of a combined process, comprising an amine-amplifying surface functionalization followed by a rapid, electroless gold deposition. A final selective removal of the underlying silica by dissolution in a HF solution allowed yielding freestanding gold structures that retained the geometry and morphology of the starting valves. These replicas showed transmission maxima and refelction minima at infrared wavelengths that cannot be found in the starting diatom silica valves nor in flat non-porous gold films used as references. This extraordinary optical transmission (EOT) effect has been ascribed, as confirmed by numerical simulations based on a surface plasmon interference model introduced by [PERSON] et al. [83], to the combined effect of the periodic hole pattern inherited by _C. asteromphalus_ valve and the involved gold chemistry, which induces the generation of surface plasmons and consequent interference with incoming light. Surface plasmon-mediated EOT usually finds applications in integrated optics and in chemical and biological sensing.
# Vehicular Visible Light Communication for Intersection Management +
## 4 V2X Cooperative System Evaluation
### V-VLC Experimental Evaluation  
As an example, the I2V MUX signals received and decoded (on top of the figures) by the receiver of each vehicle \(a\), \(b\), \(c\), and \(d\) are shown in the figures, for the four-legged (Figure 8a,b) and for the split (Figure 8c,d) intersections, at different frame times. In the right side, the received channels for each vehicle are identified by its 4-digit binary codes and associated positions in the unit cell. On the top, the transmitted channels packets [R, G, B, V] are decoded.  
The results show that the information pattern changes as the receiver moves between generated point regions. Figure 8a displays the MUX signals assigned to two IM messages received by Vehicles \(a\) and \(b\). Assuming that vehicle \(a\), driving in the right lane (Figure 8a) enters Cell C\({}_{4,2}\) by enter #2 (\(t^{\prime}{}_{a}\), Phase1, green pose), goes straight to E to position #8 (\(t^{\prime\prime}{}_{a}\); Phase 1, green pose), then this vehicle enters the crossroad through #8 (\(t^{\prime\prime}{}_{a}\)) and leaves it in the exit #2 at \(t^{\prime\prime\prime}{}_{a}\), as displayed in the figure, always keeping the same direction (E). Vehicle \(b\) approaches the intersection after having asked permission to cross (\(t_{b}\)) and only receives authorization when vehicle \(a\) has left the intersection (end of Phase 2, \(t^{\prime\prime\prime}{}_{a}\)). Then, Phase 3 begins with vehicle \(b\) heading to the intersection (W) (pose red) while vehicle \(a\) follows its destination towards E (green pose). In Figure 8b, the normalized MUX signal responses and the assigned decoded messages from vehicle \(c\) inside the intersection are displayed at \(t^{\prime\prime}{}_{c}\) and \(t^{\prime\prime\prime}{}_{c}\). The data show that vehicle c, driving in the left lane, receives an order to turn left (NE) at #7 and continues in this direction across position #1 toward the north exit (Phase 2, violet pose).  
Figure 8c shows two requests made by vehicles \(a\) (green pose; #2E; \(t^{\prime}{}_{a}\)) and \(b\) (blue poses; #6; \(t^{\prime}{}_{b}\)) to the cross Intersection 2, Phase 1. In Figure 8d, the correspondent messages to cross Intersection 1, Phase 3, from vehicle \(b\) (red pose; #7W; \(t_{1}{}^{\prime}{}_{b}\)), vehicle \(d\) (violet pose; #1E; \(t_{1}{}^{\prime}{}_{d}\)) and vehicle \(b\) (red pose; #1 SW; \(t_{1}{}^{\prime\prime}{}_{b}\)) are displayed.  
In both intersections, before the request of vehicle \(d\) to cross Intersection 1, the IM is aware through the request made by its leader \(c\) that a follower is approaching (\(d\)). In the four-legged intersection, vehicle \(b\) must wait for the vehicle \(d\) to cross, while three actions must be taken in the split intersection to promote smooth movement avoiding congestions and delays: changing the synchronism of Intersection 2, delaying the simultaneous passage of vehicles \(b\) and \(a\) at Intersection 2 (Phase 1) and finally, allowing the joint passage of vehicles \(b\) and \(d\) at Intersection 1 in the same phase of vehicle 1 (Phase 3).
## 3 Results and Discussion
### Durability: Acid Resistance of Roa- Base Binders  
As shown in Figure 3, when the amount of waste in the binder is increased, the acid attack coefficient increases. For specimens CP-20-OR-80, CP-20-SC-80, and CP-20-E-80, the acid attack coefficient is greater than 1. Part of the Ca present in waste and cement in an acidic environment dissolves in water so that Ca\({}^{2+}\) reacts with SO\({}_{4}\)\({}^{2-}\), producing CaSO\({}_{4}\) -2H\({}_{2}\)O. As shown in Figure 4, the acid attack caused the generation of a gypsum layer on the surface and inside the specimen, giving the specimens a white appearance. This formed gypsum has a higher compressive strength than the material before the acid attack [56]), so in cases of high waste dosage, it increases compression resistance. However, in materials with higher initial compressive strength and lower porosity (higher density), the gypsum induces higher volumetric expansions and leads to the spalling of the surface layers, producing a diminution after the attack [57].  
Figure 3: Resistance to the acid attack of hydraulic road binders. Note: CP: Portland cement; SC: scallop shells; E: eggshell; OR: biomass fly ash.  
\begin{table}
\begin{tabular}{c c c} \hline
**Composition** & **Density (kg/m\({}^{3}\))** & **CS (MPa)** \\ \hline CP-100 & 1857 \(\pm\) 22 & 42.6 \(\pm\) 1.2 \\ \hline CP-92.5-SC-7.5 & 1813 \(\pm\) 21 & 37.8 \(\pm\) 1.3 \\ \hline CP-20-SC-80 & 1449 \(\pm\) 17 & 2.5 \(\pm\) 0.3 \\ \hline CP-92.5-E-7.5 & 1800 \(\pm\) 25 & 33.2 \(\pm\) 0.9 \\ \hline CP-20-E-80 & 1507 \(\pm\) 18 & 2.6 \(\pm\) 0.3 \\ \hline CP-92.5-OR-7.5 & 1846 \(\pm\) 19 & 23.3 \(\pm\) 0.8 \\ \hline CP-20-OR-80 & 1651 \(\pm\) 11 & 1.4 \(\pm\) 0.2 \\ \hline \end{tabular}
\end{table}
Table 3: Physical and mechanical properties Note: CP: Portland cement; SC: scallop shells; E: eggshell; OR: biomass fly ash.
# Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
## 3 Methodology
### Data Preprocessing  
Medical data usually suffer from different problems (i.e., the existence of null values or extreme values (outliers)) due to the sensor, network, and data entry failure. When it comes to building an ML model, dataset problems have a negative effect on ML and DL models. The main objective of this stage is to increase data quality by handling missing values and outliers.  
* Data encoding: In the utilized dataset, a combination of categorical and numeric features exists. Unfortunately, two feature-selection techniques--ML and DL--perform better with numeric features than categorical ones. Therefore, we utilized the label encoder module in the Scikit-learn library to encode all categorical features.
* Filling missing values: Several statistical methods have been developed to deal with missing data, but it mainly depends on how much data is missing and how important the missing feature is [40]. Classic statistical techniques such as mean, maximum, and mode perform well with a low percentage of missing values (5% to 10%), with increasing percentage of missing values (20-50%); other complex techniques like expectation maximization [41] are appropriate. In our study, this is due to the low percentage of missing values. The feature means are used to impute missing values.
* Removing outliers: Outliers are values that lie far from the normal range of all feature values. It considers a critical problem in building a robust and generalized model [42]. In our current study, all data is analyzed from a statistical point of view to specify the outliers and then ensure the outlier's values from a medical point of view. All outliers in the utilized data were replaced by feature mean.
## 2 Methodology and Modeling  
To analyze the effectiveness of passive solutions for achieving a suitable thermal environment in a single-family dwelling, simulations were conducted using the Trace 3D Plus v6.00.106 software by Trane [29]. This software utilizes EnergyPlus [30]--which applies the heat balance method [31; 32]--for annual energy simulation and was selected for its advanced capabilities in modeling and simulating thermal performance in buildings.  
Three countries with distinct climates (Portugal, Brazil, and Turkey), each presenting the challenge of high air temperatures for the thermal environment, were chosen for this study. By examining these diverse climatic conditions, the study aims to provide a comprehensive understanding of how passive design strategies can be optimized to improve the thermal environment across different geographical locations and also reduce electrical energy consumption associated with the use of air conditioning units.  
Some initial parameters were common to all simulations, as shown in Table 1. The chosen method integrates both load and energy considerations, offering a comprehensive understanding of how the building's thermal performance affects overall energy demand, in addition to calculating the thermal load.  
The Conduction Transfer Function (CTF) algorithm offers a simple approach and is suitable for most residential building applications [33]. A ceiling height of 3 m reflects the design of older houses, which were built with higher ceilings.  
The simulation model incorporates several key variables, including external wall, window, and roof materials, infiltration rate, window-to-wall ratio, building orientation, and shading. These parameters are varied to assess their impact on the thermal environment in residential buildings.  
The methodology developed in this study is designed to be adaptable for diverse climatic conditions and building types beyond the specific locations analyzed (Brazil, Portugal, and Turkey). By allowing modifications to input parameters--such as building orientation, insulation levels, glazing properties, and ventilation rates--the simulation model can be adjusted to reflect a variety of climate zones and structural characteristics. This flexibility enables its application in other geographic and climatic contexts, where specific values for thermal environment ranges, U-values, the solar heat gain coefficient (SHGC), and air changes per hour (ACH) can be input according to local standards or research needs. As such, this model serves as a scalable framework for evaluating passive design strategies in regions facing different temperature extremes, building regulations, and energy efficiency goals.
# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
## 2 Materials and Methods
### Method
#### 2.3.2 BP Neural Networks  
BP (back propagation) neural network is a concept put forward by [PERSON] and [PERSON] in 1986 [19]. It is a multilayer feedforward neural network trained according to the error back propagation algorithm. The structure of BP neural network contains input layer, hidden layer, and output layer, out of which, there are one or more layers in the hidden layer. Each neuron in two adjacent layers is connected to all neurons, while there is no connection between neurons in the same layer. In this manner, the BP neural network can deal with more complex computational tasks. Since the BP neural network has the back-propagation mechanism, the mean square of the difference between the actual output and the expected output can be regarded as an error signal to propagate back along the network in supervised learning. During the propagation process, the weight of each layer will be adjusted. This process ends when the error is lower than the target value [20]. In this work, we utilize the BP neural network to construct oleic acid content prediction model.  
For the 80 groups of rapeseed samples, the hyperspectral features are regarded as the input layer, while the oleic acid content of rapeseed is used as the output layer in BP neural network. The number of hidden layer nodes \(p\) is based on the range given by the empirical Equation (10). By using a trial-and-error method for multiple training (100 training times for each sample), the optimal number of nodes is 9.  
\[p=\sqrt{k+m}+a \tag{10}\]  
\(k\) is the number of input layer units; \(p\) is the number of hidden layer nodes; \(m\) is the number of output layer units; and \(a\) is the constant of 1-10. Set the number of iterations as 1000 and the learning accuracy as 0.01. The 64 samples are then randomly selected as the training set and the leftover 16 samples are regarded as the test set. By using the Trainlm training method [21] with cross-validation, the BP network predication model can be constructed and optimized to select the best model parameters.