esa-sceva/satcom-chunk-collection huggingface dataset:
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 3 Method Design and Implementation
### Backbone of the Feature Extraction Modules  
Drawing upon the concepts of the DenseNet architecture, the backbone of the feature extraction network, denoted as DNET in Figure 5, is composed of dense blocks, transition layers, and convolutional pooling layers.  
Figure 4: Architecture of the proposed approach.  
Figure 3: Architecture of Feature Pyramid Network (FPN).  
This study incorporates dense blocks into the conventional CNN architecture. As illustrated in Table 2, the network receives image data with dimensions of \(320\times 240\times 3\) as input. After traversing the CONV1 layer, comprising convolution and pooling operations, the result is a feature map of size \(160\times 120\times 64\). Subsequently, it passes through the DEN1 dense block. The dense block DEN1 is composed of conventional \(1\times 1\) and \(3\times 3\) convolutions integrated with channel-wise concatenation operations. As illustrated in Table 2, the input feature map is subjected to \(1\times 1\) and \(3\times 3\) convolution operations. Subsequently, the resulting feature maps are merged with the input feature map at the channel level to generate the final feature map. This process is iterated twice, leading to the generation of the ultimate feature map following the DEN1 module.  
Subsequently, the feature map undergoes processing by the TRAN1 transition layer, which is comprised of a \(1\times 1\) convolution and a \(2\times 2\) average pooling operation. The feature map of size \(160\times 120\times 256\), following processing by the transition layer TRAN1, is transformed into a feature map of dimensions \(80\times 60\times 128\). Then, the feature map is forwarded through the DEN2 dense block, resulting in an \(80\times 60\times 512\) feature map. Subsequently, this feature map undergoes processing by the TRAN2 transition layer, yielding a \(40\times 30\times 256\) feature map. Finally, the feature map undergoes processing in the DEN3 dense block, leading to the generation of a \(40\times 30\times 1024\) feature map.  
The design of the DEN dense blocks in the network structure enables the connection of each layer's feature information to all subsequent feature layers through \"shortcut connections\". High-level feature maps have the capability to readily access the information that has been extracted by low-level feature maps. This enables each layer to directly tap into feature maps from all preceding layers. This contributes to the efficiency of reusing features, facilitates gradient propagation, and enhances the overall performance of the model. The configuration of dense blocks serves to elevate the model's utilization rate of features, diminish the parameter count, and bolster the transmission of features and information flow throughout the network.  
Figure 5: Architecture of DNET backbone.
## 3 Results
### Setting Time and Missing Data Analysis Results  
The setting time \(T_{ST}\) is the minimum time interval between a patient's admission time and their first vital sign data entry. Table 8 shows the mean setting times of Pulse pressure (SBP, DBP), Okimetry, Respiratory rate, Pulse rate, and Temperature. As the average setting time in most vital signs was around 3 h \(T_{ST}=3\), we set 3 h as our setting time in this research. Figure 4 visualizes the averaged setting time between patient's first vital sign data entry and ICU admission. In other words, if the time gap of the prediction task was less than 3 h, a lot of missing data may occur, thus affecting the model performance. In another word, we only consider data collected after \(T_{ST}=3\) h of the patient's admission time.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 2 Related Work
So far, there is only a small number of P4 implementations for congestion management mechanisms in the literature, and most of them have been demonstrated on software switches. In contrast, for monitoring purposes, there are many novel concepts based on P4 technology, and quite a few have been prototyped on hardware platforms. The work in [20] proposes a monitoring mechanism for detecting microbursts in datacenter networks at line rate. It has been implemented for the P4 hardware switch Tofino. In [21] the authors present a P4 implementation for the BMv2 software switch that monitors the network state in real-time without requiring probing packets. In a similar context the authors of [22] introduce a P4-based mechanism for the BMv2 that leverages bloom filter for both flow tracking on a single device and full path tracking. The work in [23] proposes IDEAFIX, a monitoring mechanism that identifies elephant flows in IXP networks by analyzing flow features in edge switches. IDEAFIX has been implemented for the P4 BMv2 software switch.
## 4 Results and Discussion
### Performance Analysis  
As presented in Tables 2-8, regardless of the case, all the classifiers could be used to provide high classification accuracy. On one hand, the proposed method can be employed to realize a maximal classification accuracy at 100% in Cases 1 and 2. On the other hand, it still provides a high classification accuracy of 99.98% in Case 3, as well as a maximum accuracy of 98.35% in Case 4, as shown in Table 2. Furthermore, combining all the features from PF1 to PF4 resulted in better performance than the others in all cases, as shown in Table 7. One plausible explanation is that the information transmission must be distributed to prevent the effects of noise, transmission channel blocking, etc. Another possible explanation is that PF1 contains more significant features than the others, such as healthy versus seizure, seizure-free versus seizure, healthy versus seizure-free versus seizure, etc. Table 8 presents the confusion matrix corresponding to the four cases.  
Generally, the performance of this automated seizure detection method can be analyzed using three criteria:  
* Detection sensitivity (SEN). This method successfully detected 1562, 1549, 1583, and 1406 of 1592 healthy events, which amount to detection sensitivities of 98.12%, 97.30%, 99.43%, and 88.32%, respectively. The detection sensitivity can be as high as 100% and as low as 99.94%.
* False positives (specificity; SPE). This method successfully detected 1589, 1589, 1588, and 1585 of 1592 seizure events, which amount to detection sensitivities of 99.81%, 99.81%, 99.75%, and 99.56%, respectively. A good seizure detector must achieve maximum seizure detection sensitivity while minimizing false detections. Even with high sensitivity, a large amount of false detections can be problematic for the patient and medical staff in real-life applications. In this study, the false positives were as low as 0.00% and as high as 9.36%.
* Classification accuracy (CA) is considered as a statistical measure by which the performance of a classification algorithm can be evaluated. It is computed by dividing the number of correctly classified samples by the total number of samples. This method was successfully used to obtain accuracies as high as 99.91% and as low as 93.41%.  
Therefore, strong universality has been proven and can be considered as the greatest aspect of this method. Furthermore, the results of these four cases illustrate the robustness of this method, which accordingly has the potential to be applied to clinical cases with a high classification accuracy.
# MIMO Over-the-Air Computation for Distributed Estimation
## 1 Introduction
In contrast, this paper explicitly considers the target function of the distributed estimation problem with the MIMO-AirComp framework. The rest of the paper is organized as follows. Section 2 briefly presents the technical background of the MIMO-AirComp. In Section 3, we present the MIMO-AirComp framework to compute various target multifunctions over the fading channel. Section 4 illustrates how the proposed MIMO-AirComp framework efficiently computes the target function of the distributed estimation. Section 5 evaluates the performance of the proposed approach compared to the conventional separated approach. Finally, we conclude the paper in Section 6.
## 5 Conclusions  
In this study, we characterized the reliability of using PPG-based pulse contour analysis to support clinical decision. For this purpose, we applied our self-developed SCN4 ALL telemedical system and used a multidirectional approach to explore and characterize the possible measurement errors in depth in order to establish the reliability of this diagnostic tool. We showed that if we use a PPG-based telemedicine system, which is proven to track artificial signals with high repeatability, it can analyze most pulse contour parameters (e.g., stiffness index, reflection index, left ventricular ejection time index) with high precision in human measurements. This allows high-fidelity evaluation of these parameters and the detection of small cardiovascular alterations. However, correct evaluation of some parameters derived from the second derivative of the pulse wave (i.e., aging index, d/a) can be hindered by pathophysiological alterations or normal variants of the pulse wave which make c and d point identification difficult. To handle this limitation, we recommend the introduction of the c-d point detection ratio in pulse wave analysis and the consideration of second derivative parameters only if its value is acceptable. In summary, we can claim that PPG-based pulse wave analysis is a reliable measurement tool and meets the requirements set for cardiovascular telemonitoring devices. Clearly, further, large-population studies are warranted to establish the guidelines for its application in patient follow-up.  
Conceptualization, D.K., S.K., and Z.M.; methodology, F.A., D.K., and Z.M.; validation, S.K., and Z.M.; investigation, F.A., Z.M., D.K., and D.W.; resources, S.K.; data curation, D.S.V. and K.I.L.; writing--Original draft preparation, F.A., D.K., and Z.M.; writing--Review and editing, Z.M. and D.S.V.; visualization, F.A., K.I.L., and D.W.; supervision, Z.M.; project administration, F.A. and D.W.; funding acquisition, D.K. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by E-Med4 All Europe Ltd.; Z.M. receives financial support from the Higher Education Institutional Excellence Program of the Ministry of Human Capacities in Hungary, within the framework of the Molecular Biology thematic program of the Semmelweis University, and D.S.V. receives financial support from the Thematic Excellence Programme (TKP) of the Ministry of Innovation and Technology of Hungary, within the framework of the BIOImaging Excellence programme at Semmelweis University.  
The authors acknowledge the skilled contribution of [PERSON], who provided and prepared the datasets for analysis. Authors express their special thanks for the great work of [PERSON], who wrote the automated algorithm and leads the development of the software that is the core of the given telemedicine system.  
D.K., F.A., K.I.L., S.K., and Z.M. are in financial terms with E-Med4 All Europe Ltd. (D.K. and S.K. as co-owners, F.A. as employee, and K.I.L. and Z.M. as subcontractors). D.S.V. and D.W. did not receive compensation for their contribution by financial or any other means.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Types of Accelerometers
#### 1.3.3. Laser Accelerometer  
A laser accelerometer uses a laser to measure changes in speed/direction. It has a frame with three orthogonal input axes and multiple proof masses. Each proof mass is supported by a predetermined surface. A flexible beam holds each proof mass and allows the proof mass to move along its axis. When there is an acceleration towards the input axis, the proof mass bends the beam. This movement shifts the blanking surface across the light ray, which partially blocks the light beam [7].
# Electrochemical Biosensors Based on Conducting Polymers: A Review
###### Abstract  
Conducting polymers are an important class of functional materials that has been widely applied to fabricate electrochemical biosensors, because of their interesting and tunable chemical, electrical, and structural properties. Conducting polymers can also be designed through chemical grafting of functional groups, nanostructured, or associated with other functional materials such as nanoparticles to provide tremendous improvements in sensitivity, selectivity, stability and reproducibility of the biosensor's response to a variety of bioanalytes. Such biosensors are expected to play a growing and significant role in delivering the diagnostic information and therapy monitoring since they have advantages including their low cost and low detection limit. Therefore, this article starts with the description of electroanalytical methods (potentiometry, amperometry, conductormetry, voltammetry, impedometry) used in electrochemical biosensors, and continues with a review of the recent advances in the application of conducting polymers in the recognition of bioanalytes leading to the development of enzyme based biosensors, immunosensors, DNA biosensors, and whole-cell biosensors.  
conducting polymers; biosensors; electrochemistry; bioanalyte +
Footnote †: journal: Applied Sciences
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 4 DWT Reconstruction and Analysis
### Analysis of Non-Stationary Wind Data Using Optimal Decomposition Levels in DWT
The reconstructed power spectral density results demonstrate that wavelet reconstruction effectively removes low-frequency structures from the wind. The distribution of turbulent energy is defined by the turbulent wind spectrum in the frequency domain. According to the Chinese wind resistance specification, the Kaimal spectrum is applied to the longitudinal power spectrum, while the Panofsky spectrum is used for the vertical power spectrum. The Kaimal and Panofsky power spectra are described by formulas (9) and (10), respectively. \[\frac{nS_{u}(n)}{\left(u_{*}\right)^{2}}=\frac{200f}{\left(1+50f\right)^{5/3}} \tag{16}\]  
\[\frac{nS_{w}(n)}{u_{*}^{2}}=\frac{6f}{\left(1+4f\right)^{2}} \tag{17}\]  
\(n\) is the frequency of wind (Hz), \(u_{*}\) is the friction velocity, and \(f\) is the Monin similarity coordinate. \(S_{u}(n)\) is the wind power spectrum. The longitudinal wind spectrum and the Kaimal spectrum exhibit inconsistencies at both low and high frequencies. At low frequencies, the measured wind spectrum under the stationary wind speed model is lower than the Kaimal spectrum. At high frequencies, the measured and reconstructed wind spectra are significantly higher than the Kaimal spectrum, suggesting that the energy contribution of small vortex structures in the measured wind field is not well captured by the Kaimal spectrum. The vertical wind spectrum exhibits relatively strong consistency with the Panofsky spectrum. At low frequencies, the vertical wind spectra measured under the stationary wind speed model are slightly higher than the Panofsky spectrum, whereas at high frequencies, they align well with the Panofsky spectrum.
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 6 Conclusions  
The study aimed to convey an overview of the principal players of RS technology, including satellites, RPAs, and sensors, in this new era of the 5 th industrial revolution (of agriculture 5.0); thus, highlighting its importance in achieving SDG-2. Satellites occupy a prominent place in RS technology that can be appended with IoD to maximize its potential in assuring sustainable agriculture. Satellite imagery data are freely accessible, and RPA technology becoming more and more economical with each passing day. Integrated application of these RS components and training ML models is what is expected from this 5 th revolution. Few studies have been done on cohesive applications of AI in this field, such as automation in the fruit grading process, crop variety identification, visual representation of data analysis in the mobile applications, etc. Moving forward, the aim is to improve resource use efficiency. Similarly, there is a need to address the limitations of AI in terms of model training, standardization, and oversimplifications of reality.  
With the novel technologies come the novel challenges; initiating novel research projects addressing challenges of data privacy, slow data processing, lack of historical maps, cost of sensors, standardization, processing complexity, training, etc., can effectively contribute to, not only the dissemination of novel technologies, e.g., IoT, IoE, IoD, ML, etc., but also adoption, thereby promising a sustainable agriculture 5.0.  
[PERSON] conceptualized the idea. [PERSON] and [PERSON] contributed in the design, preparation, and writing of the manuscript, followed by revisions from [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the projects: \"VIRTUOUS\" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, \"SUSTAINABLE\" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the \"Project of Excellence\" from _Junta de Andalucia_ 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
Not applicable.  
Images facilitation by Graniot, an agricultural remote-sensing-based firm in Spain, is acknowledged (Figures 3 and 4). Similarly, cooperation from MC Biodrone, an agricultural company in Spain, is highly appreciated in terms of facilitating vegetation indeices maps for olive crops (Figures 6 and 7).  
The authors declare no conflict of interest.
## 2 Experimental Procedure  
### Electrospinning Process  
The most common and easy approach to make nanofibers is the electrospinning method, as listed in the literature. In recent years, the electrospinning technique has attracted much attention as a capable method to produce fibers with nanoscale diameters. Figure 1 and Table 1 show the schematic diagram of the electrospinning device and PAN nanofiber manufacturing conditions for manufacturing nanofibers in this experiment, respectively. In this process, the polymer solution at the end of syringe tip is in the form of a hemispherical droplet. The standard electrospinningning equipment consists of a high-voltage power supply, a metallic capillary, and a grounded collector as a counter electrode. Polymer solution is extruded from an orifice to form a small droplet in the presence of an electric field [27]. As mentioned above, when an electric field is applied, the charge or dipole orientation is induced on the surface of the air layer and the solution for the hemispherical droplet surface, mutual charge repulsion or dipole causes a force directly opposite of the surface tension. The electrically-charged solution jets are ejected from the orifice when the electrostatic force is larger than a threshold point. The feed rate can be adjusted, for example, with a syringe pump. The polymeric fluid tends to stretch out and form a continuous fiber until it reaches a grounded collector. Thus, the hemispherical surface of the fluid at the tip of syringe elongates to form a cone shape known as a Taylor cone. When the electric field reaches a critical value at which the repulsive electric force overcomes the surface tension force, a charged jet of the solution is ejected from the syringe tip. In this study, the electrospinningning was carried out using the Nano NC Company NNC-ESP100 (Salamander Pumps, Sunderland, Britain). The solutions used in the electrospinning experiments were prepared using PAN polymer, DMF solvent purchased from Sigma Aldrich and Macron Company (Seoul, South Korea). The solution sample including 1 g PAN and 10 mL DMF was physically stirred for 24 h (350 rpm, 40 \({}^{\circ}\)C) in order to produce a PAN polymer solution, the viscosity of the prepared solution was also measured by using a viscometer. The PAN polymer solution was spun at a flow rate of 0.3 mL/h, the distance between the syringe tip and the collector plate was kept at 4 cm for five hours. The high-voltage NNC-ESP100 power supply was used to provide a voltage of 10 kV.
## 5 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**TE** & **Methods** & \(\mathbf{N}\) & \(\mathbf{p_{c}}\) & \(\mathbf{CPU_{T}}\) & \(\mathbf{err_{min}}\) & \(\mathbf{\Phi}\) & \(\mathbf{\Phi^{\prime}}\) & \(\mathbf{n_{inv}}\) & \(\mathbf{n_{total}}\) \\ \hline
**TE1** & \(2^{nd}NM\) & 10 & 1.99 & 2.504327 & 1.038 \(\times\)\(10^{-103}\) & 10 & 10 & 10 & 40 \\  & \(4^{th}NR\) & 6 & 3.99 & 1.957957 & 5.384 \(\times\)\(10^{-207}\) & 12 & 12 & 12 & 48 \\  & \(5^{th}ACT\) & 6 & 4.99 & 2.397614 & 2.280 \(\times\)\(10^{-289}\) & 18 & 12 & 12 & 60 \\  & \(5^{th}MBJ\) & 6 & 4.99 & 2.241982 & 1.095 \(\times\)\(10^{-315}\) & 12 & 12 & 6 & 48 \\  & \(5^{th}PJ\) & 6 & 4.99 & 2.237277 & 0 & 12 & 12 & 12 & 48 \\  & \(8^{th}SA\) & 5 & 7.79 & 2.218581 & 0 & 15 & 10 & 5 & 50 \\  & \(8^{th}MBJ\) & 5 & 7.79 & 2.092063 & 0 & 15 & 10 & 5 & 50 \\  & \(8^{th}PJ\) & 5 & 7.90 & 2.074542 & 0 & 15 & 10 & 10 & 50 \\  & \(11^{th}PJ\) & 4 & 10.95 & 2.060324 & 4.362 \(\times\)\(10^{-154}\) & 16 & 8 & 8 & 48 \\ \hline
**TE2** & \(2^{nd}NM\) & 8 & 2.00 & 3.023825 & 3.928 \(\times\)\(10^{-145}\) & 8 & 8 & 8 & 128 \\  & \(4^{th}NR\) & 5 & 4.03 & 2.589849 & 2.988 \(\times\)\(10^{-291}\) & 10 & 10 & 10 & 160 \\  & \(5^{th}ACT\) & 4 & 5.15 & 2.916807 & 5.083 \(\times\)\(10^{-102}\) & 12 & 8 & 8 & 144 \\  & \(5^{th}MBJ\) & 4 & 5.15 & 2.840291 & 5.083 \(\times\)\(10^{-102}\) & 8 & 8 & 4 & 128 \\  & \(5^{th}PJ\) & 4 & 5.12 & 2.790838 & 5.714 \(\times\)\(10^{-121}\) & 8 & 8 & 8 & 128 \\  & \(8^{th}SA\) & 4 & 8.01 & 3.331801 & 0 & 12 & 8 & 4 & 144 \\  & \(8^{th}MBJ\) & 4 & 8.80 & 3.082510 & 0 & 12 & 8 & 4 & 144 \\  & \(8^{th}PJ\) & 4 & 8.60 & 3.071780 & 0 & 12 & 8 & 8 & 144 \\  & \(11^{th}PJ\) & 3 & 11.78 & 3.089214 & 9.138 \(\times\)\(10^{-106}\) & 12 & 6 & 6 & 120 \\ \hline
**TE3** & \(2^{nd}NM\) & 9 & 2.00 & 2.907378 & 1.010 \(\times\)\(10^{-107}\) & 9 & 9 & 90 \\  & \(4^{th}NR\) & 5 & 4.00 & 2.401273 & 1.010 \(\times\)\(10^{-107}\) & 10 & 10 & 10 & 100 \\  & \(5^{th}ACT\) & 6 & 5.00 & 3.457730 & 0 & 18 & 12 & 12 & 138 \\  & \(5^{th}MBJ\) & 5 & 3.92 & 3.086405 & 7.523 \(\times\)\(10^{-106}\) & 10 & 10 & 5 & 100 \\  & \(5^{th}PJ\) & 5 & 3.92 & 3.017444 & 2.109 \(\times\)\(10^{-143}\) & 10 & 10 & 10 & 100 \\  & \(8^{th}SA\) & 5 & 6.02 & 3.3776025 & 0 & 15 & 10 & 5 & 115 \\  & \(8^{th}MBJ\) & 5 & 6.02 & 3.327364 & 0 & 15 & 10 & 5 & 115 \\  & \(8^{th}PJ\) & 4 & 5.86 & 2.945875 & 1.938 \(\times\)\(10^{-104}\) & 12 & 8 & 8 & 92 \\  & \(11^{th}PJ\) & 4 & 8.09 & 3.231956 & 4.484 \(\times\)\(10^{-228}\) & 16 & 8 & 8 & 104 \\ \hline
**TE4** & \(2^{nd}NM\) & 8 & 2.00 & 7.892696 & 4.963 \(\times\)\(10^{-114}\) & 8 & 8 & 8 & 240 \\  & \(4^{th}NR\) & 5 & 3.99 & 7.841182 & 1.410 \(\times\)\(10^{-228}\) & 10 & 10 & 10 & 300 \\  & \(5^{th}ACT\) & 5 & 4.05 & 11.754446 & 2.580 \(\times\)\(10^{-195}\) & 15 & 10 & 10 & 375 \\  & \(5^{th}MBJ\) & 5 & 4.02 & 14.253392 &