esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Settings  
In this paper, we used low-resolution palmprint images including both contact-based and contact-less databases. The typical HKPU version 2 was employed as contact-based palmprint images and the 2D contact-free HKPU [62] and Indian Institute of Technology Delhi (IITD) Touchless Palmprint Database [63] were used as the contact-less ones.  
The typical contact-based HKPU contains 7752 gray-scale images corresponding to 193 subjects or 386 palms. The subjects were 131 males and 62 females. The minimum number of samples corresponding to each palm was 10, collected at two different time intervals. To have a consistent number of samples assigned to each class, we utilized 10 samples and ignored the extra ones. Although it is mentioned by the database owner that there are approximately 18 samples for each person, it should be noted that the existing samples of the 150\({}^{th}\) individual are 11.  
In 2D contact-free HKPU, each individual contributed five hand images in the first session, followed by another five in the second session. The database currently has 3540 hand images.  
The IITD palmprint image database includes hand images from 230 users, all the images are in bitmap (*.bmp) format. Subjects in the database were in the age group 12-57 years. Five to six images from each person's palm exist in the database.  
The simulation was performed in Python programming language, and the TensorFlow [64] framework was exploited to implement the CNN methods. Based on the documentation, it is recommended to leave the parameters of networks at their default values, except sometimes for the learning rates. In our experiment, the Adam optimizer was exploited with the good default settings by the tested machine learning problems, for \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\) in the CNN architecture of REM and FEM. Besides, according to the documentation, the recommended learning rates are 1, 0.01, 0.001, and 0.0001. We used 70% of the palmprint images as the training set and the remaining as the test set for palmprint ROI extraction.
###### Abstract  
The underground utility tunnel in a soft foundation is generally affected by the serious disturbance of the vehicle load during the operation period. Therefore, in this study, for the typical utility tunnel engineering in Suqian City of Jiangsu Province, China, field tests were conducted to monitor the performance of the utility tunnel structure in a soft foundation affected by the ground traffic loads during the operation period. Based on the test results, the datasets whose number is 15,376, composed of the five main disturbance factors (four vehicle operating load parameters and one operating time parameter), and the corresponding two main structure responses (displacement and stress) have been constructed. Based on the obtained datasets, using the proposed new deep learning model called WO-DBN, in which the seven hyperparameters of a deep belief network (DBN) are determined by the whale optimization algorithm (WOA), the safety responses of the utility tunnel structure have been predicted. The results show that for the prediction results, the average absolute error for the displacement is 0.1604, and for the stress, it is 12.3726, which are not significant and can meet the requirement of the real engineering. Therefore, the deep learning model can accurately predict the performance of the utility tunnel structure under a vehicle load and other disturbances, and the model has good applicability.  
field test; safety response; tunnel structure; vehicle load; WO-DBN Article of Utility Tunnel Performance in a Soft Foundation during an Operation Period Based on Deep Learning  
1  
[PERSON]  
Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China  
Alaneda College, Peralta Community College District, Oakland, CA 94606, USA  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-025-83772037
# Low-Density Geopolymer Composites for the Construction Industry
## 1 Introduction  
Portland cement is currently the most important building material in the world. Despite the obvious advantages, Portland cement needs to be replaced with more environmentally friendly building materials; b its manufacture causes 8% of the global carbondioxide emissions [1]. Furthermore, geopolymer composites perform better than conventional cement-based composites [2; 3; 4]. The quest for substantial construction resources is becoming ever more urgent, and geopolymer composites show promising alternative green solutions.  
Geopolymers are suitable as building materials in many ways, including favorable mechanical and physical properties, high compressive strength, low thermal conductivity, etc. Light-weight geopolymers are light-weight and have even better thermal conductivity and sound insulation [5]. These excellent properties are useful as both wall and wall cladding materials as they are able to minimize heat loss and improve sound insulation in buildings. However, little attention is paid to the subject in the literature. The main objective of the current work is to investigate several potential light-weight geopolymer composites for wall and wall cladding materials. In addition, a material that can absorb electromagnetic signals is desirable. Electromagnetic waves such as cell phone signals, Wi-Fi, etc., are indispensable for modern life. The benefits of these electromagnetic signals are undeniable. However, there are hundreds of studies showing the relationship between electromagnetic radiation and health risks: being continuously exposed to these electromagnetic (EM) radiations, people can face several health risks. Wi-Fi signal (2.4 GHz) has been shown to significantly damage sperm count [6]. Male sperm counts can decrease by over 50% [7], accompanied by sperm quality in all advanced technology countries. ER radiation has negative effects on mental health and sleeping quality [8; 9; 10]. In addition, electromagnetic radiation impairs the functioning of electronic devices and damages electronic storage devices [11]. EM radiation could lead to information leaks or hacking [12; 13]. Metals are reasonable solutions for shielding strong electromagnetic radiation because of their high electrical conductivity. However, the use of metals for EM shielding requires high installation and maintenance costs [14] and can be challenging to be employed for construction.  
The current work investigated the use of light-weight geopolymers as alternative building materials. The following work evaluated the mechanical strengths, the thermal conductivity, and the density of the geopolymer composites studied. In addition, the electromagnetic shielding ability of geopolymers reinforced by the carbon grid was evaluated.
## 4 Validation of the Suggested Circuit
### Results Obtained with Practical Setup  
The validation of simulation results requires practical values or results that are obtained by developing a practical setup as shown in Figure 9. This realization of the front stage bridge rectifier circuit is ensured by using four diodes (RHRG3040) that have abrupt reverse recovery characteristics. They are employed to lower the switching losses as their reverse recovery charge or time very is low. The high-frequency H-bridge inverter is constructed with four high switching frequency transistors (IRF840). Their voltage and current rating are almost 500 V and 5 A, respectively. Four gate driving circuits are realized with gate driving chips (EXB840) and four isolated dc supplies. An input capacitor of 1 uF filters any abrupt variation in the incoming input voltage. An inductor of 500 uH is employed at the intermediate stage to tackle the problem of possible shoot-through that may arise due to the switching action of the complementary operated switching transistors. Arduino controller is employed as a PWM generator, and the generated control signals need not to synchronize with the polarity of the input voltage. Therefore, a few lines of code generate these control signals.  
In summary, solid-state devices and passive components employed in practical setup are also represented Table 4.  
The main target of this developed laboratory prototype is to confirm the power quality concern of the input current and generation of high-frequency output voltage. These results  
\begin{table}
\begin{tabular}{c c} \hline
**Device/Component Name** & **Number/Value** \\ \hline Diode & RHRG3040 \\ \hline Transistor & IRF840 \\ \hline Gate driver & EXB840 \\ \hline Input capacitance & 1 uF \\ \hline Series inductance & 500 uH \\ \hline Load impedance & 80 \(\Omega\) \\ \hline Switching frequency & 25 kHz \\ \hline Arduino & UNO \\ \hline \end{tabular}
\end{table}
Table 4: Design parameters and components employed for real results.  
Figure 9: Practical setup.  
or targets are ensured by setting the switching frequency for switching transistors to 25 kHz with a 50% duty cycle. This duty cycle ensures that output is non-inverted and inverted alternatively for 50% of the switching interval with any polarity of the input voltage as can be viewed from Figure 10a. The output current also follows a similar behavior that can be viewed in Figure 10b with the reference of output voltage. Figure 10c shows the output voltage and output current in a magnified form around the peak value of the input voltage. Here it can be observed that for the switching period of 40 us, the output is positive for 20 us, and then it is negative for 20 us interval. The current conduction path of the input current is always non-interrupted, so the input current is continuous with low distortion, as can be viewed from Figure 10d. Here input current is being represented in the red color waveform with respect to the input voltage (blue color waveform). This depicts the improved power quality of the input current.
## 2 Mathematical Model of VTOL Platform with PI Control
### PI Controller for Actuator Model  
An input voltage of 4V to 8V is applied to the DC motor to operate the propeller for the VTOL system. The PI controller is applied as an inner-loop controller, as shown in Figure 2. Figure 2 shows that the PI controller and VTOL system are connected in a cascaded fashion. The input to the actuator is voltage \(V_{m}\) and the output is the motor current \(I_{m}\). The motor current \(I_{m}\) is added to the PI controller as a feedback signal. The mathematical expression for the PI controller can be rewritten as  
\[v_{m}(t)=k_{p}(i_{ref}(t)-i_{m}(t))+k_{i}\!\int{(i_{ref}(t)-i_{m}(t))dt} \tag{8}\]  
Taking the Laplace transform \(\mathcal{L}\) of Equation (8),  
\[V_{m}(s)=k_{p}(I_{ref}(s)-I_{m}(s))+\frac{k_{i}}{s}(I_{ref}(s)-(I_{m}(s)) \tag{9}\]  
Considering the closed-loop configuration for the actuator model, placing the value of \(V_{m}(s)\) from Equation (7) in Equation (9), we have  
\[I_{m}(s)(L_{m}s+R_{m})=I_{ref}(s)(k_{p}+\frac{k_{i}}{s})-I_{m}(s)(k_{p}+\frac{k _{i}}{s}) \tag{10}\]  
By simplifying Equation (10), one has  
\[I_{m}(s)(L_{m}s+R_{m})+I_{m}(s)(k_{p}+\frac{k_{i}}{s})=I_{ref}(s)(k_{p}+\frac{ k_{i}}{s}) \tag{11}\]  
Figure 2: Block diagram of the VTOL system and actuator model based on PI controller.  
Finally, rearranging Equation (11), we obtain the following expression:  
\[\frac{I_{m}(s)}{I_{ref}(s)}=\frac{k_{p}s+k_{i}}{s^{2}+s\big{(}R_{m}+k_{p}\big{)} \frac{1}{L_{m}}+\frac{1}{L_{m}}k_{i}} \tag{12}\]  
Hence, the proportional and integral gains of the PI controller can be obtained by solving Equations (5) and (12) as  
\[k_{p}=-R_{m}+2\zeta\omega_{n}L_{m},\text{ and }\omega_{n}^{2}L_{m}=k_{i} \tag{13}\]  
The block diagram of the VTOL system and actuator model based on the PI controller is shown in Figure 2.
## 4 Results and Discussion
### Performance Comparison with Imbalanced Dataset
Most algorithms showed a recall of the mid-to-high 90% range, and in particular, the high consumption group of the SVM algorithm showed that the recall, which dropped to 66.4%, increased up to 85.6%. In Figure 2, which compares the recall between the cases of using the refined and imbalanced datasets and the case of using our framework, it is confirmed that our framework shows good performance, even in real-world situations. This means that, although the existing algorithms alone cannot properly classify the propensity of OTT consumers encountered in the real world, our proposed algorithm facilitates proper classification of data reflecting real-world situations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ML** & & & **Refined Dataset** & & & **Imbalanced Dataset** & \\
**Algorithms** & **Class** & **Recall** & **Precision** & **F-Measure** & **Recall** & **Precision** & **F-Measure** \\ \hline \multirow{3}{*}{J48} & Low & 0.941 & 0.950 & 0.946 & 0.992 & 0.984 & 0.988 \\  & Medium & 0.955 & 0.933 & 0.944 & 0.917 & 0.917 & 0.917 \\  & High & 0.948 & 0.955 & 0.952 & 0.915 & 0.966 & 0.940 \\ \hline \multirow{3}{*}{KNN} & Low & 0.951 & 0.933 & 0.942 & 0.999 & 0.967 & 0.983 \\  & Medium & 0.958 & 0.967 & 0.962 & 0.852 & 0.992 & 0.916 \\  & High & 0.946 & 0.962 & 0.954 & 0.863 & 0.997 & 0.925 \\ \hline \multirow{3}{*}{PART} & Low & 0.925 & 0.872 & 0.898 & 0.991 & 0.961 & 0.976 \\  & Medium & 0.886 & 0.905 & 0.895 & 0.825 & 0.945 & 0.881 \\  & High & 0.901 & 0.958 & 0.929 & 0.839 & 0.958 & 0.894 \\ \hline \multirow{3}{*}{SVM} & Low & 0.961 & 0.965 & 0.963 & 0.999 & 0.940 & 0.968 \\  & Medium & 0.973 & 0.961 & 0.967 & 0.843 & 0.988 & 0.909 \\ \cline{1-1}  & High & 0.975 & 0.980 & 0.978 & 0.664 & 0.994 & 0.796 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between refined data and imbalanced data by ML algorithms.
## 4 Proposed Framework
### Layer 1--Data Capture  
In the first layer of the architecture, called the data capture layer, the processing flow starts through an agent located in the Kubernetes cluster nodes responsible for running the application containers. This agent collects the system calls of the desired containers using the Sysdig tool and records them in a local file on the node's file system, avoiding dependency on external services in this task [33]. When capturing system calls from a container through Sysdig, it is possible to specify which attributes of each system call invoked will be collected through filters. These attributes will later be used to build the features that will compose the dataset of system calls. Sysdig provides a list of filters available for use in its documentation [37] and for demonstrative purposes, the filters listed in Table 2 were used to capture the system calls of containers.  
Figure 5: Proposed architecture with five layers and it’s tools [33].  
As system calls are written to a file, a service provided by the Filebeat tool reads each new line added to the file and sends them to Redis, an in-memory database external to the cluster [33]. To provide secure communication between Filebeat and Redis, a password is required to authenticate to Redis before data is received by this service. This password must be parameterized in the Filebeat configuration. Still in the data capture layer, Redis acts as a cache, giving resilience and high performance to the flow of system calls captured in real time from containers running [33]. Different queues containing system calls can be created in Redis for each monitored container. Queues remain in Redis until Logstash, a Layer 2 service in the architecture, reads and removes data from the queues. In this way, the data only persists in Redis in memory until it is consumed by layer 2, giving way to the entry of new system calls. To prevent this workload from running on cluster Kubernetes, Redis has been configured on a host dedicated to this service. If necessary, the Redis server can be scaled horizontally to work as a cluster, offering greater performance in the data stream.
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 4 Simulation Results  
In this section, to assess the performance of the designed PI-PBC with GPEBO, related simulation research is conducted by using the software Matlab. The circuit parameters are given in Table 1. The structure of the proposed sensorless controller is shown in Figure 3.  
First, the tracking performance is verified by considering several step changes in \(x_{2\star}\). Next, the robustness against the parameter perturbations is investigated. Finally, the phase portrait is given to show the global convergence.
###### Abstract  
With the rapid deployment of intelligent transportation systems in real-life applications, both dedicated short-range communications (DSRC) and cellular Vehicle-to-Everything (C-V2X), utilized to enable V2X communication, are undergoing extensive development to meet the quality of service (QoS) demands of advanced vehicular applications and scenarios. Compared to C-V2X, which lacks fully validated effective reliability, DSRC has undergone extensive field testing worldwide, ensuring its practicality. IEEE 802.11 bd, as the next-generation V2X (NGV) standard within DSRC, is expected to greatly exceed the performance of its predecessor, 802.11p. However, existing studies mention that the ambient traffic environment will influence the performance of V2X due to the cyber-physical properties of V2X. To fully assess the advancements of NGV, this study presents a comparative analysis of IEEE 802.11 bd and IEEE 802.11p, focusing on dynamic traffic conditions. Specifically, the technical advancements of the IEEE 802.11 bd standard are first theoretically examined, emphasizing significant enhancements in aspects like modulation and coding schemes, coding rates, and channel coding. Subsequently, these critical technical enhancements are implemented in Veins, a simulation framework for the Internet of Vehicles (IoV), encompassing large-scale dynamic traffic scenarios. The simulation results indicate that the IEEE 802.11 bd standard significantly enhances the data transfer rate compared to IEEE 802.11p, achieving a stable twofold increase. Furthermore, the data transmission latency is reduced by over half compared to IEEE 802.11p, while the data transmission reliability experiences a noteworthy 20% enhancement. Notably, the enhanced data transmission mode of the IEEE 802.11 bd standard requires an increased signal-to-noise ratio (SNR). Additionally, this research evaluates the data dissemination properties in the IoV and finds that the traffic volume has a limited impact on the data propagation speed.  
Internet of Vehicles; IEEE 802.11 bd; IEEE 802.11p; data transfer rate; transmission reliability Article  
[PERSON], [PERSON], X. A Comparative Study of IEEE 802.11 bd and IEEE 802.11p on the Data Dissemination Properties in Dynamic Traffic Scenarios  
1
## 4 Results and Discussion
### New Pictogram Events  
Studying the general habits of users while registering new pictograms, we observed the distribution of image sources for the creation of pictograms with the **source** field (see Table 5). We could see that 55.87% of the pictograms were downloaded from the arasaac repository, 37.31% from the device's gallery, and the final 6.82% directly captured  
\begin{table}
\begin{tabular}{c c} \hline
**Day** & **Total** \\ \hline Monday & 14.76\% \\ Tuesday & 13.40\% \\ Wednesday & 18.18\% \\ Thursday & 21.69\% \\ Friday & 12.25\% \\ Saturday & 11.48\% \\ Sunday & 8.24\% \\ \hline \end{tabular}
\end{table}
Table 13: Test 3: Percentage of usage on different weekdays.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Day** &
\begin{tabular}{c} **Social** \\ **Interaction** \\ \end{tabular} & **Nutrition** & **Leisure** & **Care** & **Emotions** & **Health** \\ \hline Monday & 20.69\% & 10.57\% & 15.52\% & 12.00\% & 12.52\% & 11.15\% \\ Tuesday & 10.36\% & 14.03\% & 13.90\% & 14.08\% & 15.67\% & 22.23\% \\ Wednesday & 23.26\% & 16.36\% & 15.36\% & 17.20\% & 16.53\% & 14.58\% \\ Thursday & 26.46\% & 18.30\% & 21.61\% & 20.16\% & 21.76\% & 17.02\% \\ Friday & 9.16\% & 13.25\% & 13.79\% & 13.68\% & 13.52\% & 13.00\% \\ Saturday & 5.82\% & 15.35\% & 11.87\% & 14.49\% & 11.26\% & 12.90\% \\ Sunday & 4.26\% & 12.13\% & 7.96\% & 8.38\% & 8.75\% & 9.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Test 3: Usage of categories on different weekdays.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Samples** & **3 s Reiteration** & **5 s Reiteration** \\ \hline Social interaction & 20.394 & 11.03\% & 13.73\% \\ Nutrition & 20,340 & 15.08\% & 16.05\% \\ Leisure & 14,038 & 10.03\% & 12.10\% \\ Care & 9273 & 23.66\% & 34.45\% \\ Emotions & 4513 & 19.79\% & 21.58\% \\ Health & 2861 & 20.90\% & 23.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Test 4: Percentage of repetitions of sentences.  
from the app. This indicates that users find it valuable to have an accessible source of standardized pictograms.  
Another relevant behavior refers to how commonly pictograms of an equal category are created simultaneously. Table 16 shows the proportion of pictograms belonging to each category, which were created along with pictograms in the same category.  
These results suggest that for categories like nutrition or social interaction, users tend to create most of the pictograms they need at the same time, while for categories like care or health, they prefer to add new pictograms when they need them.
## 2 Background
### IEEE Std 1855-2016 and JFML  
The IEEE Std 1855-2016 [56] presents the Fuzzy Markup Language (FML) based on the syntax of the well-defined XML meta-language to represent FLS in a human-readable and hardware-independent way [57]. FML includes an extensible schema that defines the basic components of different types of FLS--including Mamdani, Tsukamoto, and Takagi-Sugeno-Kang (TSK) [58]; also including the most recent AnYa [59].  
An FLS described in FML can be divided into the following main five components: fuzzy knowledge base; fuzzy rule base; inference engine; fuzzification subsystem; de-fuzzification subsystem. Moreover, all components are described by an hierarchy of sub-components in order to fully specify an FLS. All such components are grouped into XML tags with an IP address of devices that compute them, thus enabling networked interoperability among sensors and devices.  
JFML library is an open source Java library that allows the design of FLS based on the IEEE Std 1855-2016 [60]. The use of a cited library allows for the implementation of all FLS types enclosed in the standard.
## 4 Conclusions  
When the symbol rate of the TR acoustic communication using SC is high, the large ISI span caused by multipath in underwater acoustic channels reduces the performance of the TR process and needs to be removed through long adaptive equalizers. To decrease the extent of ISI, a TR acoustic communication method using FMT modulation is proposed in the paper. The performance of the proposed method has been assessed through simulation and experiment, and compared with that of the  
Figure 10: Performance comparison between two communication methods in the experiment: (**a**,**b**) performance of the TR process and adaptive equalization in the proposed method; and (**c**,**d**) performance of the TR process and adaptive equalization in the TR acoustic communication using SC modulation.  
TR acoustic communication using SC modulation. Results indicate that the ISI span of the proposed method is smaller than that of the TR acoustic communication using SC modulation, and therefore the proposed method can improve the performance of the TR process and reduce the computational complexity of adaptive equalization for post-process in contrast with the TR acoustic communication using SC modulation.