esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Tuned Spring on Vibration Control Performance of Modified Liquid Column Ball Damper
## 3 Parameter Optimization
### Optimization of Tuning Frequency of Liquid Column Ball System  
The equations of motion of the TLCBD system shown in Figure 3 were solved numerically using state-space representation at a length ratio (\(\alpha\)) of 0.6, 0.7, and 0.8. All three arrangements were investigated in the tuning frequency ratio (\(\eta\)) range of 0.6 to 1.2 under harmonic excitation (\(\frac{\omega_{cut}}{\omega_{s}}=1\)). It can be seen in Figure 3 that by increasing the length ratio the response got better at all the tuning frequency ratios. In all the selected length ratios of the TLCBD, the response improvement percentage became almost constant in the tuning  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mass ratio, \(\mu=m_{f}/m_{s}\) & 0.05 \\ Primary structure fundamental frequency (Hz) & 0.93 \\ The primary structure damping ratio (\(\zeta_{s}\)) & 0.05 \\ Ball–tube diameter ratio & 0.80 \\ Damping ratio (\(\zeta_{t}\)) & 0.049 \\ Kinematic viscosity (\(v\)) (Nm/s) & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal design parameters for the tuned liquid column damper.  
frequency range of 0.96 to 1.04, as shown in Table 2. It can also be seen in Figure 4 that the slope of the curve became constant in all three arrangements in the aforementioned tuning frequency range. The maximum response reduction improvement (58%) was achieved at a length ratio = 0.8. Thus, maximum response reduction can be obtained at \(\alpha\) = 0.8 and \(\eta\) range (0.96 to 1.04).
# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 2 Related Works
However, the authors of [34] classified the channel conditions into three types, which were insufficient to reflect indoor channel conditions. The use of deep learning has been attempted in order to overcome the limitations of rule-based methods. The authors of [38; 39; 40] estimated positions by training a neural network with RSS vectors from multiple APs. These were types of fingerprint-based methods that derived the location coordinates output from the neural network, which was trained with RSS patterns. Therefore, similarly to other fingerprint-based methods, there was a disadvantage in that they could only operate in a pre-surveyed environment. Previous studies about indoor localization using Wi-Fi RSS with deep learning techniques were mainly based on fingerprinting. The fingerprint-based methods can achieve high positioning accuracy, but require heavy site surveys and are vulnerable to minor environmental changes, such as changes in APs' locations or obstructions. For these reasons, the goal of this study is to present a range-based localization method that is robust to changes in APs and that minimizes the need for site survey. The range-based method using only RSS has challenges because of the ranging error in the indoor environment with many walls. The model based ranging had a limitation in accurate ranging in the complex indoor environment. Therefore, we intended to improve the RSS ranging accuracy by applying data-driven deep learning to only the ranging part of the range-based localization algorithm, and evaluated the effect of the method on positioning accuracy in indoor environment.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 5 Flutter Energy Harvesting
### Bio-Inspired Flutter FEHs
Nevertheless, flutter-based TEEHs have more advantages of providing a higher voltage and especially a smaller harvester's volume. Rapid voltage generation and current pulses suitable for triggering alternating-polarity voltage pulses in ferroelectric materials were observed in [121]. This design offers a very high output, small volume and high level of electrical polarization which is necessary for generating the voltage needed for certain applications, such as small-scale high-voltage polarization equipment. Moreover, the effective modulation of output pulses under varying wind speeds showcases its adaptability and control in polarization processes. New materials for TE energy harvesting can assist in natural micro- to nanostructures with high outputs [123], promising environmentally friendly energy harvesting solutions on a large scale. The work carried out in [126] effectively shows a comprehensive approach to capturing wind energy and sensing wind direction, suggesting the promising potential of multi-directional wind sensing to monitor  
vibrations generated in transmission lines with considerable outputs to ensure sustainable power sources in designing IoT systems for environmental monitoring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fluttering Mechanism**} & \multirow{2}{*}{\begin{tabular}{c} **Cut-in Velocity** \\ **(m/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Optimum** \\ **Resistance** \\ **(kf))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Velocity at** \\ **Max Power** \\ **(m/s)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Output Power** \\ **(mW)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **References** \\ **References** \\ \end{tabular} } \\ \hline \multicolumn{6}{c}{**PE-FEHs**} \\ \hline Flag flutter & - & 66.6 & 25 & 1.12 & [PERSON] et al. [110] \\ \hline Additional wake & 6.6 & 39 & 10.6 & 0.00681 & [PERSON] et al. [111] \\ \hline Flag flutter & 5 & 500 & 20 & 0.746 & [PERSON] et al. [112] \\ \hline Leaf flutter & - & 1000 & 7 & 0.157 & [PERSON] et al. [132] \\ \hline - & & 680 & 7.5 & 0.54 & [PERSON] et al. [133] \\ \hline Leaf flutter & 2 & 220 & 3 & 0.00076 & [PERSON] et al. [135] \\ \hline \multicolumn{6}{c}{**EM-FEHs**} \\ \hline \multicolumn{6}{c}{Cantilever spring} & 2.5 & 4.7 & 5 & 1.6 & [PERSON] et al. [113] \\ \hline - & 4 & - & 8 & 1.1 & [PERSON] et al. [114] \\ \hline Wind belt-type & 3 & - & 7 & 50 & [PERSON] et al. [115] \\ \hline Wind belt-type & 3 & 0.6 & 10 & 0.705 & [PERSON] et al. [117] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of flutter-based FEHs. Figure 10: Bio-inspired FEHs: (**a**) wing-stimulated TEEH [130]; (**b**) PEH with MFC layers reproduced with permission from [133] Elsevier, 2020.
## 1 Introduction
### Routing Protocols: A Brief Overview
#### 1.3.1. Ad Hoc On-Demand Distance Vector (AODV)  
AODV is a reactive routing protocol that is one of the best routing protocols to adapt to changes in topology. Generally, it performs its complete operation with the help of three different types of messages, i.e., RREQ (route request), which performs route discovery for destination by broadcasting the same to its neighbor in the network, RREP (route reply) is a reply message which is generated by neighbor nodes to provide the information about destination node or of next node to which data are to be forwarded and if there is any kind of failure in the network it will be broadcasted by RRER (route error) [9; 10].
## Sensor Data Security Level Estimation Scheme for Wireless Sensor Networks  
**[PERSON] * and [PERSON]**  
Graduate Program in Applied Computer Science, University of Fortaleza, Av. [PERSON] 1321, Fortaleza 60.811-905, Brazil; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +55-85-9692-9209.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 18 November 2014 / Accepted: 23 December 2014 / Published: 19 January 2015_  
**Abstract:** Due to their increasing dissemination, wireless sensor networks (WSNs) have become the target of more and more sophisticated attacks, even capable of circumventing both attack detection and prevention mechanisms. This may cause WSN users, who totally trust these security mechanisms, to think that a sensor reading is secure, even when an adversary has corrupted it. For that reason, a scheme capable of estimating the security level (SL) that these mechanisms provide to sensor data is needed, so that users can be aware of the actual security state of this data and can make better decisions on its use. However, existing security estimation schemes proposed for WSNs fully ignore detection mechanisms and analyze solely the security provided by prevention mechanisms. In this context, this work presents the sensor data security estimator (SDSE), a new comprehensive security estimation scheme for WSNs. SDSE is designed for estimating the sensor data security level based on security metrics that analyze both attack prevention and detection mechanisms. In order to validate our proposed scheme, we have carried out extensive simulations that show the high accuracy of SDSE estimates.  
**Keywords:** wireless sensor networks; network security; security level; security metrics; cryptography; key management; intrusion detection systems
## 4 Conclusions  
In this work, a fuzzy logic controller performing the thermal load following of different user profiles was developed to improve the energy use and overall conversion efficiency of a micro-solar CHP plant based on an LFR solar field. First, an ANN was developed by the authors to recognize the kind of user based on the related thermal load, and then different sets of linguistic rules were included in the fuzzy logic controller to better adjust the plant operation with the request of the specific user.  
Hence, the performances of the plant in the case of a fuzzy logic controller were assessed and compared with those in the case of a baseline controller. In general, the proposed fuzzy controller shows good capability in performing the load following by increasing the contribution of the TES unit in supplying the ORC unit and reducing the number of switches between the different OMs. When coupled with a school building load profile, the plant works in OM3 for a significantly longer period by storing the collected thermal energy from the solar field into the TES unit (when thermal demand is absent or low) and providing this amount of thermal energy to the ORC unit in the early morning after operating in OM5 and OM6. In this way, although the overall electrical and thermal production of the ORC unit is lower, the plant is able to cover more the thermal energy demand of the user. When coupled with a residential user load profile, instead, the electrical and thermal production of the plant even increases by means of a fuzzy logic controller but  
Figure 11: Daily trend of TES temperature and exchanged power when coupled with a residential user load profile: (a) sunny winter day in the case of a baseline controller and (b) sunny winter day in the case of a fuzzy logic controller.  
Figure 10: Daily trend of plant performance coupled with a residential user load profile: (a) sunny winter day in the case of a baseline controller and (b) sunny winter day in the case of a fuzzy logic controller.  
the absence of an additional TES unit between the ORC system and the user to store the thermal energy output from the condenser of the ORC unit limits the ability of the plant in satisfying the thermal energy demand all the time.  
In any case, the analysis has proven the capability of the proposed fuzzy logic controller in performing thermal load following, and the proposed control strategy could also be adopted in the case of other integrated systems based on CSP technologies and latent heat TES units.  
Conceptualization, L.C. and M.P.; methodology, S.D.G., M.P., and A.F.; software, S.D.G. and R.T.; validation, S.D.G. and R.T.; investigation, S.D.G. and R.T.; data curation, S.D.G. and R.T.; writing--original draft preparation, L.C. and A.F.; writing--review and editing, L.C. and A.F.; supervision, M.P. and A.F.; project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in the framework of the European Union's Horizon 2020 Research and Innovation Programme (grant agreement no. 723596).  
The authors declare
## 6 Real Use Case Analysis for Industrial Workers  
The experiments in Sections 4 and 5 were performed in a controlled laboratory environment. However, because there might be a difference between the controlled environment and the actual manufacturing environment, an additional qualitative evaluation was performed on industrial workers of a small and medium-sized company that assembles air conditioners and heaters, as shown in Figure 23.  
For this real use case analysis, seven industrial workers (average age: 29.7 years old, SD: 3.59) participated in the experiment, and none of them had any prior knowledge of smart glasses. First, we explained the purpose of the experiment to the workers and presented the concept, methods, and functionality of devices, such as smart glasses, visual context, and interaction methods. Then, the workers experienced the task assistance manual required for the actual assembly through smart glasses. Besides, we showed related AR manufacturing services through videos and animations.  
After the experiment, the workers were asked to fill out the questionnaire. We analyzed the cognitive load in performing manufacturing tasks since the real industrial field is different from a controlled environment, and wearable AR should not provide additional cognitive burden. We conducted another qualitative evaluation based on the validated questions from NASA task load index (NASA-TLX) (1: very low, 7: very high) [48]. Questions in the NASA-TLX questionnaire include (1) required-mental and perceptual activity, (2) required-physical activity, (3) time pressure felt, (4) hard to work, (5) successful in accomplishing goals, and (6) stressed. We also used these questions without any modifications. Details are shown in Reference [48]. According to the questionnaire analysis (Figure 24), subjects mentioned that they could perform given tasks with less cognitive load with the help of wearable AR. They were satisfied with the visualization and interaction of manufacturing information using AR smart glasses. Some of them commented that wearable AR was very effective when used for training or task assistance with beginners.  
However, although AR smart glasses were useful for task assistance, the workers also mentioned that currently available AR smart glasses were heavy to wear, and the screen was not visible in bright places. Also, for the visual context, they preferred the stereo rendering and direct augmentation rather than the non-stereo rendering and indirect augmentation as there was no mismatch between the physical and virtual objects. However, it was suggested that improvement should be made to the fact that much information is not visible at any given time. For the interaction method, they commented that it would be easier to perform the touch interaction in the process of performing simple tasks. However, they mentioned that using two hands was effective in the actual manufacturing environment.  
Figure 23: Industrial workers were experiencing task assistance through AR smart glasses.
## 2 Related Work
### State-Space Model  
The State-space model is also a rapid and flexible generalized model for handling a wide range of spatio-temporal dynamic problems. In the state-space, a state transition or state correlation model is constructed using stochastic theory, and then the transition or correlation model is used to estimate the value of the unobserved spatio-temporal position.  
The state-space model based on Kriging can effectively use the characteristics of both. As often happens in geostatistics, the Kriging state-space model also constructs the random field into time-dependent trend terms and residual fields, where the trend term is used to describe large-scale variation characteristics and the residual field is used to compensate for local variation. The universal Kriging state-space model [3] has been used for the spatial interpolation of medical images, and the error is corrected according to a Kalman filter of time observations. The results of this model provide an optimal predictor for this process decomposition and provide a basis for extending recursive filters to the spatial domain of Kriging theory. [PERSON] [31] constructs Kriging state-space model using six-parameter quadric in polynomial trend surface analysis. [PERSON] [32] construct the Kriging state model of the power trend using Euclidean distance, thus realizing the estimation of arbitrary point sources at different times.  
However, this model does not take further advantage of the characteristics of the universal Kriging method, which also requires the consistency of the observed data. At the same time, due to the limitations of its algorithm, it cannot carry out prediction in the absence of state inputs from the system. However, compared with the KKF algorithm, the universal Kriging state-space model requires less prior knowledge.
# Energy-Efficient BWP Configuration for Multi-Slice Users
###### Abstract  
Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service.  
5G; energy efficiency; QoS; multi-slice connectivity; BWP +
Footnote †: journal: _Sensors_
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 4 Performance of In-Network Links
### Deployment and Experiment Designs
In the Jiufeng experiment, we investigate all of the link information recorded and find that the total patterns of the PRR, the RSSI, the LQI and the symmetry of these links are similar to their counterparts in the single link experiment, merely with lower measurements. We hence do not describe the performance of each in-network link of the Jiufeng experiment. Instead, we focus on deeply investigating the time-varying characteristics of the in-network low-power wireless links in a real-world forest environment.
## 4 Conclusions  
Although plant-based flours are referred to as a generic term, not all products under this name have a similar composition and therefore do not make a similar contribution from a nutritional point of view. All the flours analyzed in this study (chickpea, pea, soybean and hemp) have carbohydrates as their main component and provide a similar amount of energy (Kcal/100 g). However, pea and chickpea flours contain large amounts of protein and fiber, while soybean and hemp flours provide significant amounts of fat. Regarding the mineral composition, potassium and phosphorus as macro-elements and iron as a microelement are the major components in all flours. The phenolic composition, antioxidant activity and fatty acid profile of these meals vary greatly depending on the plant species. In addition to these differences, plant-based flours have been shown to have highly variable compositions between different commercial brands, which makes it necessary to educate consumers to check the nutritional labels of this type of food in order to ensure healthy diets. Surprisingly, the presence of high amounts of lead has also been detected in some of these flours, so health authorities should be aware of this fact in order to implement control mechanisms for plant-based flours.  
Figure 1: Projection plot of the flour samples in the space defined by the two firsts PCs. TPC: total phenolic content, TEC: total flavonoid content, F/DF: flavanones and dihydroflavonols, TEAC: total equivalent antioxidant activity (nmol Trolox/100 g).  
**Author Contributions:** Conceptualization and methodology, A.M.V.-Q. and I.R.; formal analysis, Y.A.; data curation, M.H.-J.; writing--original draft preparation, M.H.-J. and Y.A.; writing--review and editing, A.M.V.-Q. and I.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** [PERSON] acknowledges the Algerian government for the grant long-term residential doctoral program abroad and [PERSON] acknowledges the Own Program III: Grants for Pre-doctoral Contracts of the University of Salamanca co-funded by Banco Santander.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Text Detection and Recognition for X-ray Weld Seam Images
## 2 X-ray Weld Seam Image Text Recognition Model Framework
### A Text Recognition Model Based on MATRN
#### 2.2.3 The HFA Module  
The U-Net structure in the Position Attention module performs multiple encoding and decoding operations on the visual features obtained through the backbone. Encoding operations progressively downsample the input features through multiple convolutional layers, reducing the size of the feature maps. This focuses on the higher-level features, providing a larger receptive field that helps the model capture global information more effectively. It aids in efficiently representing the overall structure and key features of the image. Decoding operations, achieved through upsampling, restore the encoded features to their original spatial dimensions, preserving more spatial location information. This contributes to accurate character localization.  
However, our dataset contains numerous long text lines, for which the transformer architecture proves more effective. Therefore, we introduce the Hybrid Feature Aggregation (HFA) structure (Figure 6) into the U-Net architecture. The HFA structure comprises positional encoding and transformer units, embedded after the encoding layers into the U-Net structure. Initially, positional encoding is applied to the input features. Subsequently, the features with positional encoding are fed into the transformer encoding layer. Through self-attention mechanisms, this layer captures the correlations between different positions, allowing the features at one position to interact with the features at all other positions, not confined to just neighboring positions. This capability aids in capturing long-range relationships, particularly pertinent to the inter-character relationships within a text line and the contextual information between characters. It facilitates the extraction of more abstract features, thereby better expressing the structure of the text lines.  
of and address the specific challenges associated with each task, ultimately providing more comprehensive and reliable solutions for integrated tasks in practical applications.