esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 High-Frequency TMD-Based Vibration Control Methodology for PSPPs
Therefore, the high frequency of the structure that is affected by the resonance is controlled using the TMD, and the mode whose structure is affected by the high-frequency vibration is considered as a subsystem to be analyzed separately; the specific technical route is shown in Figure 2. First, the plant structure mode is identified in order to accurately obtain the modal and excitation load parameters and determine the main modal parameter information to be controlled, and the optimal multiple-degree-of-freedom structure is used to control the optimal relationship expression (Equations (6) and (7)) to further determine the TMD parameters. Then, according to the design formula of the TMD (Equation (8)), the geometric and material parameters of the TMD equipment are determined. The geometric parameters should be determined according to the size of the project's space to satisfy the space requirements as much as possible without affecting the normal operation of the hydropower station. The material parameters should be determined in coordination with the geometric parameters; it is advisable to use materials with satisfactory performance, fatigue resistance, and low cost. The TMD device is fabricated and updated using the geometric and material parameters that are specified for each condition, and whether it satisfies the design requirements is determined. If it does not satisfy the design requirements, the geometric and material parameters must be readjusted. If it does satisfy the requirements, it is installed in the controlled position of the powerhouse structure, and the vibrational response to the plant structure is measured under normal operation of the unit to evaluate the control performance of the TMD device. At the same time, the influence of different control strategies and layout positions on the damping performance is also very important. Therefore, in the following experiment and numerical simulation, the influence of different control strategies and placement on high-frequency vibration of the structure was further analyzed. Figure 1: Concept map of the tuned mass damper (TMD) device.
## 3 Experimental Study  
### Basic Parameters  
The radius of the rotor applied in this study is 200 mm with a fixed chord length, which is manufactured by the Beijing Satellite Factory in China with carbon fiber and special manufacturing process. The specific parameters of rotor and the chord length variation are shown in Table 1 and Figure 2. Additionally, the rotational speed is between 1500 r/min and 2300 r/min, and the Reynolds number was ranged from \(0.75\times 10^{5}\) to \(1.25\times 10^{5}\).
## 3 Results
### Comparison of Experiments
By conducting multiple repetitions of the experiment at the same test point, random errors are minimized, and the quantity and quality of the data are ensured:  
\[\
abla f_{D}=\frac{2 Vf}{c}=\frac{2}{\lambda}, \tag{7}\]  
In this context, \(f_{D}\) is the frequency shift after the laser is reflected; \(f\) is the laser frequency; \(V\) is the velocity; \(c\) is the speed of light; and \(\lambda\) is the laser wavelength. The support plates with widths of 11.5 mm and 7 mm were replaced in turn. A force hammer was used to strike the 27 selected measurement points, with each point struck three times to reduce random errors. The first six natural frequencies of the rotor were obtained using Quick SA 4.0 software. The experimental results were compared with the simulated results, and the relative error was calculated. The details are provided in Tables 4 and 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode Order**} & \multicolumn{2}{c}{**Numerical Natural**} & \multicolumn{2}{c}{**Measured Natural**} \\  & **Frequency/Hz** & **Frequency/Hz** & **Percentage Error/\%** \\ \hline
1 & 61.3 & 63.73 & 3.96 \\
2 & 127.73 & 127.48 & \(-\)0.20 \\
3 & 278.67 & 283.36 & 1.68 \\
4 & 327.83 & 321.59 & \(-\)1.90 \\
5 & 354.97 & 371.89 & 4.77 \\
6 & 377.55 & 415.97 & 10.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of simulated and experimental modes at a plate width of 11.5 mm. Figure 8: Diagram of experimental equipment layout and connections. From Tables 4 and 5, it can be observed that the relative error between the frequencies obtained from the experimental modal analysis and those from the finite element software simulation is mostly within 10%. The error between the simulation and experimental results meets the required standard, ensuring the authenticity of the experimental data. This indicates that the finite element simulation is reasonable and can serve as a practical reference.
## 2 Literature Review
### MWSN Architecture
#### 2.4.1 Distributed MWSN Architecture
An on-demand passive clustering mechanism [31] overcomes the two limitations of the self-organizing routing mechanism, that is, the limited scalability and low adaptability of high-density sensor networks. In addition to routing protocols and network architecture, researchers have extensively studied other aspects of sensor networks. For example, collaborative signal processing uses distributed data compression and transmission [32]. This paper presents special research on an important task in sensor networks, the target detection. There have been many papers and monographs on this subject. We propose optimal fusion rules under the assumption of conditional independence, and study the decision fusion in detail. Specifically, this study presents a solution for optimal sensor node selection under the condition of many communication constraints. We assume that the performance of local sensing nodes is known. However, it is very difficult to estimate the performance of local sensor nodes through the experiment for a dynamic target and passive capture information sensor nodes. The performance of the target changes in real time with movement, even if the local sensor nodes can estimate their detection performance to a certain extent in real-time, but the cost of transmitting to the fusion center is very large. For most inexpensive sensor networks based on delay tolerance, limited communication and saving energy and bandwidth are very important. In this paper, we propose an information fusion algorithm based on a multi mobile agent framework and statistical decision method to solve the problems above.
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 2 Orthogonal Time Frequency Space Communication
### Delay-Doppler Resolution and Grid Mismatch  
The granularity of the delay-Doppler grid with respect to real parameters of a wireless communication channel determines the OTFS communication performance in various scenarios and environments. Its setting is also crucial for sensing applications, as the delay granularity influences the ability to determine the distance to the objects, while the Doppler granularity is crucial to correctly estimating the speed of moving objects. The granularity in the delay domain \(\Delta_{d}\) can be expressed as:  
\[\Delta_{d}=\frac{1}{M\Delta f}, \tag{3}\]  
and the granularity in the Doppler domain \(\Delta_{D}\) equals  
\[\Delta_{D}=\frac{1}{NT_{sym}}, \tag{4}\]  
where the symbol time \(T_{sym}\) is equal to the reciprocal value of subcarrier spacing \(\Delta f\), and \(N\), \(M\) are dimensions of the delay-Doppler grid.  
OTFS equalizers generally perform well in the case of the actual Doppler shift being an integer multiple of the Doppler-domain granularity, while in the non-integer case, special countermeasures must be taken [25]. The non-integer ratio of the actual delay and Doppler with respect to the grid resolution manifests in leakage of the multipath component over several neighboring delay-Doppler elements, as illustrated in Figure 2. In [14], we evaluated the bit error rate (BER) of OTFS in the Doppler grid mismatched case, with such mismatch defined as the remainder after dividing the actual Doppler shift by the Doppler-domain granularity \(\frac{1}{NT_{sym}}\). Similar effects are also observed in the case of a non-integer multiple of the actual delay and the OTFS delay grid resolution. Note that the effect of grid mismatch has not been sufficiently considered throughout the state-of-the-art work on OTFS-based radars [16; 17].
## 2 Various Types of Flexible Sensors (Temperature, Pressure, Humidity and Chemical)
### Temperature Sensors  
Temperature is an important indicator in many industries including food storage, air conditioning control and aviation. In some cases, slight deviations in temperature can mean something significant, therefore the sensitivity, response times, accuracy and reliability of the sensor needs to be at high standards. Temperature measurements can be used to indicate the critical status in a manufacturing plant, the stability of a car engine or the health of a human being.  
Flexible temperature sensors are required in body temperature monitoring devices. These are useful especially for health monitoring and gained much interest during the COVID-19 pandemic whereby smart masks and skin temperature sensors have been deployed for early detection of the virus [32]. The accuracy and sensitivity of the temperature sensor is imperative, especially in medical applications.  
The human body often indicates sickness by a deviation in the normal temperature of 36-37.5 \({}^{\circ}\)C. Temperature measurement has been used by doctors for centuries as a means to detect illness. During the COVID-19 pandemic, the use of temperature sensors has increased rapidly because one of the major symptoms of disease is an increase in body temperature to above normal levels. During the pandemic, in many countries, people must pass a temperature test before entering a shop or a bus. Some workplaces in the Republic of Ireland have a temperature sensor on the entrance that can alert you if your temperature is above normal levels, which could be a sign for the COVID-19 disease. Many schools worldwide are measuring the temperature of the students daily via inferred temperature sensors. Inferred sensors are being used to allow social distancing as these sensors can detect the temperature from a distance. An alternative and more efficient way to provide real time temperature data is the use of flexible temperature sensors attached to the skin or embedded into the clothing or face mask. The main challenges in fabricating these types of sensors are achieving comfort for the user, bendability/durability of the sensor after bending cycles, interference with water (washability), achieving good flexibility, achieving required electrical properties and printing related issues. In the following sections, various examples from the literature are examined, in which researchers are tackling the aforementioned issues in various innovative ways.
_Appl. Sci._**2012**, \(2\), 315-326; doi:10.3390/app2020315  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Anomalous Magnetization Enhancement and Frustration in the Internal Magnetic Order on (Fe\({}_{0.69}\)Co\({}_{0.31}\))B\({}_{0.4}\) Nanoparticles**  
**[PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{1,\ast}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Centro Atomico Bariloche, CNEA-CONICET, 8400 S.C. de Bariloche, RN, Argentina;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Facultad de Ciencias, Departamento de Fisica, Universidad de Los Andes, Merida, 5101, Venezuela; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) Present address: Institut National de l'Energie Solaire-Commissariat a l'Energie Atomique, 50 avenue du lac Leman, 73375 Le Bourget du Lac, France  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +54-294-444-5158; Fax: +54-294-444-5299.  
_Received: 14 February 2012; in revised form: 19 March 2012 / Accepted: 23 March 2012 / Published: 2 April 2012_  
**Abstract:** We have studied the internal magnetic order of 3-nm (Fe\({}_{0.69}\)Co\({}_{0.31}\))\({}_{0.6}\)B\({}_{0.4}\) amorphous nanoparticles. These nanoparticles were dispersed in a non-magnetic matrix (non-interacting nanoparticles) to contrast them with the powder samples, where strong interparticle interactions are present. In similar fashion to the bulk alloy, this system exhibits a saturation magnetization maximum as a function of Fe composition near 69 at% Fe for the powder and dispersed samples at all temperatures. The saturation magnetization (\(M_{S}\)) of the dispersed sample shows anomalous behavior, revealing frustration in the internal magnetic order of the particles. Unexpectedly, the \(M_{S}\) of the non-interacting sample at low temperatures is larger than the corresponding bulk alloy or the calculated value of \(M_{S}\) for the same Fe-Co composition. By contrast, the powder sample has low \(M_{S}\) values and it is approximately constant in temperature.  
**Keywords:** fine-particle systems; amorphous magnetic materials; Fe-Co-B alloys; magnetic anisotropy; magnetization enhancement
## 4 Model Evaluation
### Results of the Model
#### 4.2.1 First Scenario  
For this scenario, the model was trained to categorize 13 labels belonging to the dataset, equivalent to those shown in Table 1. The model evaluation for this scenario resulted in the following values for the metrics of accuracy, precision, recall, and F1 score presented in Table 5.  
The confusion matrix resulting from the test is displayed in Figure 3. This matrix provides more detailed information on the labels, for which the model had a precision less than the average reflected in Table 5.  
In this scenario, the model exhibits confusion for the DrDoS DNS-DrDoS LDAP labels. This confusion lies in the nature of the attacks and their similarity, as the information captured by the network monitoring instrument is similar. The values reflected in the dataset attributes will also have this similarity, thereby negatively affecting the metrics of the model. The same behavior is observed for the Portmap, UDP, SSDP, and NetBios labels.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Performance Evaluation Metrics  
The performance of proposed EDADA-RPL is compared with the familiar existing protocols RPL and LA-RPL.  
**Packet delivery ratio:** the proportion of data packets received successfully to the total sent.  
**Delay:** time taken for transmitting the data from source to DODAG root.  
**Energy consumption**: the number of miliwatts spent on transmitting data packets in the network from source to DODAG root.  
**Packet overhead:** the amount of control packets generated in the transmission of data packets.
## 2 Background and Related Work
### Controlled Vocabulary  
A controlled vocabulary is an organized collection of units of significance, i.e., terms that have a determined and well-known meaning, without duplicates (synonyms) that can cause ambiguities or misunderstandings. Their purpose is to organize information (data pieces) in a structured manner, provide consistency, indicate semantic relations, making it easy to classify, query, and retrieve data [2; 3; 4]. The most frequent examples of CVs can be categorized as ontologies, taxonomies, thesauri and, lately, folksonomies. Other examples of CVs include header-subject lists, classification schemes or specialized glossaries and dictionaries [26]. However, all these CV variations can be considered as included in the previous categories. Support tools to CV's applications make extensive use of NLP (natural language processing) and knowledge management techniques [23; 27].  
Ontologies are formal representations of concepts, ideas or objects, pertaining to a specific domain, as well as all the relationships between those concepts [28]. The ultimate goal of ontologies is to facilitate automated reasoning and, by doing that, provide a deeper understanding of the domain.  
A taxonomy is a classification of things or concepts, often with an explicitly imposed hierarchy and a set of principles that underlie such classification [28]. Search engines exploit taxonomies to increase the precision or relevance of query results, as well as to speed up the search itself.  
A thesaurus groups together words with similar (synonyms) and opposite (antonyms) meanings [29]. Thesauri are reference tools that provide users with a choice of the most adequate terms (words) to associate with an idea or concept. The combination of these \"most adequate terms\" leads to precise, fit and apt definitions of an idea or concept and avoids misunderstandings.  
A folksonomy is a system of classification of terms or concepts created from a collaborative labelling work. Folksonomy is also known as social tagging, collaborative tagging, social rating and social bookmarking. The fundamental difference with respect to a taxonomy is that in the latter the terms and their meanings are pre-established, whereas in folksonomies they constantly evolve and change in the classification scheme based on the contributions of user groups. The most frequent applications of folksonomies are in the areas of social networks and websites, to locate and recover multimedia content [24].  
Controlled vocabularies make extensive use of the above concepts and are widely used in the requirements engineering domain [18; 25].
# Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition
## 1 Introduction
### Literature Review
However, due to overly diverse feature information, the final network layer for decisions tended to ignore small parts of the features. The self-mutual distillation learning-based system [23] focuses on both short-term and long-term information to enhance the discriminative power for better sign language recognition. 3D ConvNet with the bidirectional long short-term memory system [24] improved sign language recognition performance through data extraction and time-series information. Excellent recognition performance for continuous gestures is achieved by a long short-term memory (LSTM) technique [25] with four different sequences. However, the above methods do not perform well for datasets in which most letters are not continuous gestures. Transfer deep-learning methods [26; 27] have been used to recognize ASL. The advantage of these methods is their ability to fine-tune the weights of the advanced deep neural networks for image recognition to reduce the waste of computational resources while performing well. However, these methods have the slight limitation of incomplete gesture feature extraction. A 2D-CNN with the joint encoding [28] technique performed excellently but had high hardware requirements. A two-stream CNN [29] method used the addition and concatenation operations to extend the feature maps and thus help the CNN better recognize gestures. This method is slightly insufficient in environments with complex backgrounds. Some researchers are studying ensemble models [30] to improve image recognition performance. A trainable ensemble [31] takes the outputs of individual models to the final decision and demonstrates the possibility of the ensemble being capable of improving sign language recognition performance in learning the correlation of independent model prediction results. Another ensemble-learning method [32] uses various learning algorithms to generate recognition results based on multiple features. Better recognition performance is achieved through a voting scheme. By voting on different recognition results, the models complement their drawbacks, allowing different CNN models to maximize their performance advantages. This allows the CNNs to obtain various features in the feature extraction and reduce the possibility of losing information in the decision portion. Thus, ensemble learning brings new opportunities for better ASL recognition performance.
# Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
## 3 Methodology  
This paper presents an innovative concept of an IoT Air quality monitoring station with a low cost to detect CO (carbon monoxide), CO\({}_{2}\) (carbon dioxide), temperature, and humidity. This system is designed to detect air quality and send it directly to the cloud-based platform ThingSpeak. Also, we conducted a simulation of the blockchain platform using Ganache v2.7.0 software and Truffle v5.11.4 software. A block diagram of the proposed system is shown in Figure 2.  
Figure 1: Generic view of IoT air monitoring solution [11].  
Figure 2: Block diagram of proposed system.
# Sensitivity Analysis for Microscopic Crowd Simulation
## 3 Results and Discussion
### Impact of the Parameter Range  
So far, all of the measures have shown that varying the personal space strength parameter \(h_{p}\) over the interval \((30,70)\) has the largest impact on the maximum Voronoi density. However, the range of the personal space strength parameter is difficult to determine. First, it is a non-physical parameter, which means that we cannot measure it directly. Second, we know that personal space also depends on the society and on cultural effects [56]. Third, other factors, such as groups and social identities, will have an impact on the need for personal space [57].  
In view of this complexity, we decided to run our analysis for a second, much smaller parameter interval. The changed parameter limits are listed in Table 2, all other parameter ranges are set as before, according to Table 1.  
The result of the analysis is shown in Figure 13. We observe three effects: first, there is a larger variation between different runs--even for a high number of samples (for \(M=1000\) or \(\alpha=20\), respectively). Appendix B contains the results with both methods. Second, the indices differ significantly from the indices obtained with the original parameter ranges, as shown in Figure 12, especially for the personal space strength parameter. Third, while the relative ranking of the parameters is still similar, there are larger differences between indices among the methods than for the original interval (Figure 12).  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Index** & **Parameter** & **Range** & **Type** \\ \hline
6 & Personal space strength \(h_{p}\) & \(\mathcal{U}(40,60)\) & float \\ \hline \hline \end{tabular}
\end{table}
Table 2: Alternative range for the personal space strength parameter.  
Figure 13: Normalized activity scores, and Sobol’ indices for the bottleneck scenario with the new range for the personal space strength (\([40,60]\)). The parameter index is mapped as follows: 1—control parameter, 2—free-flow speed mean, 3—free-flow speed standard deviation, 4—number of agents, 5—bottleneck width, 6—personal space strength.  
We conclude that a trustworthy sensitivity study of the personal space strength parameter requires a pre-calibration of the parameter to empirical data. However, while controlled experiments for the bottleneck exist, e.g., [41], they do not deal with psychological parameters. We argue that, in this case, the method helps to indicate a direction for future research. An approach to pre-calibration is discussed in Appendix B.3.