esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Free Localization Techniques  
Range-based localization techniques first utilize the signal measurements (time, angle, or channel-based) for estimating the distance from various reference nodes, followed by the various localization algorithms described in Section 2.2 for estimating the nodes' position. On the other hand, the range-free methods utilize the topological information of the sensor nodes (i.e., the number of hops) [7] for directly estimating the position. Although the range-based methods provide higher accuracy, they require additional hardware and, thus, higher cost and deployment complexity. On the other hand, the range-free methods require no additional hardware, which makes them more cost effective at the expense of lower accuracy levels [50]. In this section, we provide an overview of several popular range-free localization algorithms, including proximity detection, centroid, distance vector (DV)-hop, APIT, and SNAP.
## 4 Experiments
### Performance Analysis
#### 4.4.2 Performance for Dog Tracking
(**a**) Image 163; (**b**) Image 164; (**c**) Image 165. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Number of Dogs** & **Total Image Number** & **Number of Dogs Tracked** & **FN** & **FF** & **IDSW** & **MOTA** \\ \hline Model 1 & & 357 & 351 & 6 & 0 & 0 & 98.32\% \\
1 & Model 2 & 357 & 351 & 6 & 0 & 0 & 98.32\% \\ Model 3 & 357 & 351 & 6 & 0 & 0 & 98.32\% \\ \hline \multirow{3}{*}{2} & Model 1 & 231 & 159 & 70 & 1 & 1 & 68.83\% \\  & Model 2 & 231 & 160 & 70 & 0 & 1 & 69.26\% \\ Model 3 & 231 & 160 & 70 & 0 & 1 & 69.26\% \\ \hline \multirow{3}{*}{3} & Model 1 & 48 & 31 & 6 & 11 & 0 & 64.58\% \\  & Model 2 & 48 & 42 & 6 & 0 & 0 & 87.50\% \\ Model 3 & 48 & 42 & 6 & 0 & 0 & 87.50\% \\ \hline \multirow{3}{*}{4} & Model 1 & 80 & 66 & 14 & 0 & 0 & 82.50\% \\  & Model 2 & 80 & 66 & 14 & 0 & 1 & 81.25\% \\ Model 3 & 80 & 66 & 14 & 0 & 0 & 82.50\% \\ \hline \end{tabular}
\end{table} 
Table 14: Results of dog tracking experiments conducted using IMG_0014 data-set.
_Appl. Sci._**2014**, \(4\), 444-467; doi:10.3390/app4030444  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Tunable Multicolored Femtosecond Pulse Generation Using Cascaded Four-Wave Mixing in Bulk Materials**  
**[PERSON] 1,2, [PERSON] 3 and [PERSON] 1,2,4,5,* ***  
Advanced Ultrafast Laser Research Center, University of Electro-Communications,  
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan; E-Mail: [EMAIL_ADDRESS]  
Japan Science and Technology Agency (JST), Core Research for Evolutional Science and  
Technology (CREST), 5 Sanbancho, Chiyoda-ku, Tokyo 102-0075, Japan  
State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and  
Fine Mechanics, Chinese Academy of Science, 390 Qinghe Rd., Jiading, Shanghai 201800, China; E-Mail: [EMAIL_ADDRESS]  
Department of Electrophysics, National Chiao-Tung University, 1001 Ta Hsinchu Rd.,  
Hsinchu 300, Taiwan  
Institute of Laser Engineering, Osaka University, 2-6 Yamada-oka, Suita, Osaka 565-0971, Japan  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-42-443-5845; Fax: +81-42-443-5825.  
_Received: 4 June 2014; in revised form: 22 July 2014 / Accepted: 12 August 2014 / Published: 22 September 2014_  
**Abstract:** This paper introduces and discusses the main aspects of multicolored femtosecond pulse generation using cascaded four-wave mixing (CFWM) in transparent bulk materials. Theoretical analysis and semi-quantitative calculations, based on the phase-matching condition of the four-wave mixing process, explain the phenomena well. Experimental studies, based on our experiments, have shown the main characteristics of the multicolored pulses, namely, broadband spectra with wide tunability, high stability, short pulse duration and relatively high pulse energy. Two-dimensional multicolored array generation in various materials are also introduced and discussed.  
**Keywords:** multicolored laser pulse; tunable laser pulse; cascaded four-wave mixing
## 6 Conclusions  
MANET routing protocols are a prominent topic, and numerous approaches have been made to monitor and manage topological information, facilitate network scalability, and extend network lifetime. Routing protocols assist communication in the MANET environment. How well these routing protocols operate is in influenced by various factors. The paper highlighted and formulated a refined OCI-OLSR protocol by varying all the control intervals in OLSR and using an optimal value for all intervals that are influential for offering distinguished network performance. The clustering aspect was consolidated into the proposed enhanced OLSR strategy, which is a notable technique to improve the performance of routing because it brings down flooding and further selects a cluster head based on an equipped factor, diminishing overall network costs. The computation of the proposed OCI-OLSR scheme is carried out on the NS-3 simulator. The findings revealed that the optimized OLSR variant delivers better execution results than the standard OLSR and AODV mechanism over various parameters thereby reshaping the network's overall performance and trustworthiness.  
Conceptualization, J.S. and G.S.; methodology, J.S.; software, D.G. and G.M.; validation, J.S., G.S. and D.G.; formal analysis, J.S.; investigation, J.S. and G.S.; resources, G.M. and A.N.; writing--original draft preparation, J.S., G.S. and D.G.; writing--review and editing, G.M. and A.N.; visualization, J.S. and G.S.; supervision, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
The work is funded by the Researchers Supporting Project number (RSP2023R34), King Saud University, Riyadh, Saudi Arabia.  
Data availability StatementData is available upon request.  
The authors acknowledge the Researchers Supporting Project number (RSP2023R34), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Search Strategy  
The search for publications in scientific databases, such as PubMed and Web of Science Core Collection, was meticulously conducted in English. The following databases have been carefully chosen: MEDLINE, PsychINFO, EMBASE, CENTRAL, ISRCTN, and ICTRP from the WOS platform for their extensive coverage of medical specialties, particularly rehabilitation, as previously reviewed by [PERSON] et al. [27] and [PERSON] et al. [28]. The search was conducted from 22 nd May to 3 rd June 2024, followed by a thorough study refinement process from 4 th June to 15 th June 2024.  
A comprehensive search strategy was employed, incorporating a combination of MeSH terms such as \"stroke\" AND \"FES\" AND \"VR\"--23 results, \"stroke\" AND \"functional electrical stimulation\" AND \"VR\"--63 results, \"stroke\" AND \"FES\" AND \"virtual reality\"--41 results, \"stroke\" AND \"functional electrical stimulation\" AND \"virtual reality\"--140 results. Studies were included regardless of publication year to ensure a comprehensive and up-to-date perspective.  
The eligibility criteria identified (1) the population, (2) interventions, (3) comparisons, (4) outcomes, and (5) study designs. The PICOS strategy [29] was followed during the preliminary search. Criterion (1) included patients who have suffered a stroke, both men and women, with a focus on those experiencing upper limb impairments. Criterion (2) interventions were studies examining the use of VR combined with FES as a rehabilitation tion intervention for upper extremity function. Criterion (3) comparison consisted of the intervention on VR and FES intervention against conventional therapy, other forms of rehabilitation, or no intervention at all. Criterion (4) outcome was related to improving upper limb coordination, functional mobility, and the ability to perform daily activities, including enhanced fine motor skills. Criterion (5) study design consisted of randomized clinical trials (RCTs) (without group limitation) that used VR (immersive and non-immersive), and FES were included. Review and meta-analysis papers, case reports, pilot, and validation studies were excluded.  
Irrelevant to the research were studies not conducted on human subjects or adults, investigations employing multiple types of electrostimulation simultaneously without distinct patient groups or lacking virtual reality exercise rehabilitation, studies exclusively focused on patient evaluation without implementing a rehabilitation program or intervention, studies with fewer than ten rehabilitation sessions, studies comparing healthy individuals to those with post-stroke lesions, research unrelated to the subject matter, and studies with fewer than eight participants. Finally, studies that did not have the text published in English were excluded.
# Eye Tracking and Human Influence Factors' Impact on Quality of Experience of Mobile Gaming
## 2 Background and Methodological Overview
### Pupil Labs Core  
This paper employs the Pupil Labs Core device for gaze estimation along with its accompanying software. The device utilized in this study features two cameras: a world-view camera capable of recording 720p videos at 60 Hz, focusing on the user's front view, and an eye camera focusing on the user's gaze at 200 Hz. The world-view camera can be equipped with either a wide-angle or narrow-angle lens; we opted for the narrow-angle lens due to the limited calibration area available on the mobile viewing device. While the device can operate with multiple eye-tracking cameras targeting both eyes, we used a monochrome setup for the eye camera to ensure that the video viewing experience was not compromised, as illustrated in Figure 1. Detailed technical specifications of the Core device are available in this paper and on the manufacturer's website [26; 27].  
The Core software package includes three components: Pupil Capture, Pupil Service, and Pupil Player [43], with version 3.5.1 being used in this study. Pupil Capture is utilized to calibrate both cameras to enhance the confidence of gaze estimation and to establish the required field of view for the world camera. It also enables various plugins and records the sessions. Pupil Player is employed for post-processing, visualizing data, exports, and replaying recordings, as depicted in Figure 2a.  
Pupil and Gaze Positions  
It is crucial to understand that the confidence level in pupil detection does not indicate the position where the subject looked at the calibrated area. Instead, it reflects the accuracy of the algorithm in identifying the pupil from eye camera images, thereby indicating the reliability of baseline data (pupil position). A confidence value of 0 means the pupil was not detected at all, rendering the data unreliable, while a value of 1 indicates complete certainty in the pupil's position. For meaningful analysis, values above 0.6 are generally considered to be reliable [44].
# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 4 Experiments and Results
### Evaluation Metrics  
The problem solved in this paper is a binary classification problem. To evaluate the performance of the classification model in practical scenarios, typical classification evaluation indicators are usually used for evaluation, including Sensitivity (\(SEN\)), Specificity (\(SPE\)), Precision, F1 score, accuracy (\(ACC\)), and area under the receiver operating characteristic (ROC) curve (\(AUC\)). Here, the area under the ROC curve is used to assess the model's ability to predict positive and negative instances across various thresholds. When the data category distribution is unbalanced, the use of AUC and ACC can more comprehensively evaluate the comprehensive performance of the model. Different classification models are evaluated by the above six evaluation indicators:  
\[SEN=\frac{TP}{TP+FN} \tag{19}\]  
\[SPE=\frac{TN}{TN+FP} \tag{20}\]  
\[Precision=\frac{TP}{TP+FP} \tag{21}\]  
\[F1\;score=\frac{2\times Precision\times Recall}{Precision\times Recall} \tag{22}\]  
\[ACC=\frac{TP+TN}{TP+TN+FP+TN} \tag{23}\]  
\[AUC=\int_{0}^{1}SENd\bigg{(}\frac{FP}{FP+TN}\bigg{)} \tag{24}\]  
In the formulas, \(TP\) represents true positives, \(TN\) represents true negatives, \(FP\) represents false positives, and \(FN\) represents false negatives. Recall is calculated in the same way as Sensitivity (\(SEN\)). By computing these six indicators on the actual dataset, we can compare them with those of currently popular models to demonstrate the effectiveness of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Classes**} & \multicolumn{2}{c}{**Slices**} & \multicolumn{2}{c}{**Bags**} \\ \cline{3-5}  & & **Train and Val** & **Test** & **Train and Val** & **Test** \\ \hline \multirow{4}{*}{TCIA} & A & 9045 & 2683 & 167 & 49 \\  & B & 1712 & 386 & 34 & 7 \\ \cline{1-1}  & G & 1629 & 741 & 29 & 14 \\ \cline{1-1}  & Normal & 15,318 & 4517 & 230 & 70 \\ \cline{1-1}  & Total & 27,704 & 8327 & 460 & 140 \\ \hline \multirow{4}{*}{CC-CCII} & NCP & 16,232 & 5142 & 230 & 70 \\ \cline{1-1}  & CP & 12,151 & 3425 & 230 & 70 \\ \cline{1-1}  & Total & 28,383 & 8567 & 460 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the experimental setup for TCIA and CC-CCII.
## 2 Numerical Simulation of Armature Electromagnetic Force
### Electromagnetic Force with Different Conductivity  
Electrical conductivity is one of the factors that affect \(P_{e}\), and it also affects the stability of the numerical solution. Additionally, deep learning requires the armature electromagnetic force sample data under different conductivities; under a low conductivity, the electromagnetic railgun model has low computational costs and high computational efficiencies, and we find that the armature electromagnetic force at room temperature conductivity is closer to reality by comparing the calculation results under different conductivities. Therefore, taking the rail conductivity (\(3.45\times 10^{7}\) S/m) and armature conductivity (\(2.20\times 10^{7}\) S/m) at room temperature as the standards for numerical simulation, the dynamic characteristics of the armature electromagnetic force at 100%, 80%, 60%, and 40% standard conductivity are analyzed.  
When the armature conductivity is always the standard conductivity and only the rail conductivity changes, the armature electromagnetic force is calculated, as shown in Figure 6. The \"pseudo-oscillation\" of the numerical solution appears under a different rail conductivity, and the occurrence time is basically the same. The reason for this is that the rail region is stationary, and its governing equation is the diffusion equation without convection term. \(P_{e}\) exists in the convection-diffusion equation, so the change in the rail conductivity will not affect the stability of the numerical solution.  
Figure 6: Armature electromagnetic force calculations when only the rail conductivity changes.  
When the rail conductivity is always the standard conductivity and only the armature conductivity changes, the armature electromagnetic force is calculated, as shown in Figure 7. The governing equation of the moving armature region is the convection-diffusion equation. In the process of increasing the velocity, the lower the armature conductivity, the slower \(P_{e}\) of the element increases, and the later the \"pseudo-oscillation\" of the numerical solution appears. In the case of a 40% standard armature conductivity, the stable numerical simulation of the whole launch process can be realized.  
The critical velocity of the numerical simulation can be stabilized under a different armature conductivity, as shown in Table 3. Under the standard armature conductivity, the critical velocity that can stabilize the numerical simulation is 503.86 m/s. The lower the conductivity, the greater the critical velocity of the stable numerical simulation. At a 40% standard armature conductivity, the critical velocity of the stable numerical simulation increases to 956.49 m/s.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 5 Data Collection IV: Micro-Doppler
### Experimental Operating Parameters  
We perform the single-blade collection at a radial distance of \(R_{b}=1.52\) m (Collection IVa) and the double-blade collection at a radial distance of \(R_{b}=3.51\) (Collection IVb), respectively. The one-blade apparatus has a blade length \(L_{1b}=0.165\) m, and the two-blade apparatus has two blades with equal lengths \(L_{2b}=0.145\) m. The sampling rate for this collection is \(f_{s}=1\) MHz with a collection time of 10 seconds (\(10^{6}\) total samples). During both collections, the quasi-monostatic transmit/receive setup and blade mount are both stationary; the only movement occurring is/are the rotating blade(s). Additionally, we assume that \(\theta\approx 0\) since \(R_{b}\) is much larger than the spacing between the transmit array and receive antenna. A photograph of the transmit-receive setup is shown in Figure 17.  
To generate a larger micro-Doppler shift, we increase the carrier frequency from \(f_{0}=2\) GHz to \(f_{0}=5.90\) GHz, which is near the upper limit (6 GHz) of the TwinRX operational frequency range. Due to long data streaming times, host PC limitations cause a substitution of CW signal generators as the transmit hardware. The X310 with a TwinRX daughterboard is used to sample the signal from the receive antenna.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike FPA System  
To address the aforementioned issues with the digital FPA system, we conceived an FPA artificial vision system based on spike encoding, which more precisely simulates the biological \"scan-gaze\" visual system. Figure 2 depicts a block diagram of this FPA system.  
The system's working mode is divided into an accelerated mode (correspondingly, scan mode) and an accurate mode (correspondingly, gaze mode), which are switched via an externally input control signal.
## 3 Results  
OIT corresponding to each climatic zone that was analysed in these two constructive variants; the cold and frozen storages are presented in Figures 4 and 5.  
The diagrams show the variation curves of investment costs, depending on the insulation thickness, the variation curves of the electricity costs (determined by the heat transfer through building parts with thickness of the insulation), as well as the curves resulting from the sum between the investment and operating costs (i.e., electricity cost). The minimum points represent the optimal value of the OIT. The insulation cost is increasing linearly with the thickness of insulation, while the electricity cost is decreasing.  
By analysing Figures 4 and 5, it can be noticed that, for cold storage, OIT is 150 mm regardless of the climatic zone, and for frozen storage OIT is similarly 180 mm. Therefore, OIT versus each thickness insulation at different storage temperatures of 0 and \(-23\,^{\circ}\)C is inversely proportional to EER. These results were in concordance with [18; 25; 26].  
Figure 4: OIT for cold storage.  
Although the maximum temperature difference is 9 \({}^{\circ}\)C between the 5 climatic zones from Romania, given the fact that the energy efficiency and the electricity costs are different, this leads to the same OIT value.  
In the evaluation of the energy consumption for storage spaces, specific indicators were used, such as SEC (Specific Energy Consumption). This term was determined according to the energy consumption/m\({}^{3}\) cooled and year for each type of storage space. The values of this indicator are represented in Figure 6, which shows a downward trend of the SEC value for cold storage, depending on the climatic zone from Romania. For frozen storage, the deviation is between 0.4... 3.6% compared to the maximum value obtained in Bucharest, which is 75.93 kWh\(\cdot\)m\({}^{3}\)/yr. For Romania, the average values of the SEC indicator resulting from the calculation are 54.83 kWh\(\cdot\)m\({}^{3}\)/yr for cold storage and 74.55 kWh\(\cdot\)m\({}^{3}\)/yr for frozen storage. The average values obtained in this paper were compared with those presented by [PERSON] [7; 27] and resulted in a deviation of about 1.58% for cold storage and 4.1% for frozen storage, respectively.  
Figure 5: OIT for frozen storage.  
Figure 6: SEC values for the 5 climatic zones from Romania.  
The correspondence between the cold storage energy consumption and the storage capacity was also investigated. The energy consumption per unit volume for a cold space at 150 mm thickness insulation was found between 0.0058... 0.0075 kW/mc and for frozen storage at 180 mm thickness insulation between 0.01... 0.0104 kW/mc respectively.
## 4 Validation of Model of Interior Joint  
The experimental findings for model calibration are equated with the analytic model calculations to evaluate the correctness of the suggested design method. The comparison of the analytical model for principal compressive stress with varying direction is given in Table 2. A good correlation is noted when the test results of the considered specimens are compared to those obtained using the proposed Equation (12) of the effective FRP strain (\(\varepsilon_{f,e}\)) with \(R^{2}\) = 0.80, using the varying direction of the principal compressive stress. As a result, the average ratio between the experimental and analytical (determined using the proposed model) joint shear strengths of the considered specimens is 0.98. This ensures the accuracy of the suggested model to assess the shear strength of the FRP-strengthened under-designed interior joint. Furthermore, the low values of coefficient of variation \"CoV\" (0.24) and standard deviation \"SD\" (0.24) also support the accuracy of the proposed equation. Ultimately, the accuracy of the proposed equation of the effective FRP strain is also verified. Therefore, it can be concluded that the derived equation can effectively be used to determine the effective FRP strain of the FRP-strengthened interior BCJs. The comparison of the experimental joint stress and that computed with the analytical model using the constant direction of the principal compressive stress is given in Table 3. Similar to the principal compressive stress with varying direction, a decent relationship is noted among the test results of the considered specimens and results of the proposed analytical model for the principal compressive stress with constant direction. The average ratio of experimental joint shear forces to analytical ones (calculated using the suggested model) for the investigated specimens is 0.92. This assures the suggested model's correctness for evaluating the shear force of the FRP-strengthened under-designed interior joints. In addition, the small coefficient values of variation \"CoV\" (0.22) and standard deviation \"SD\" (0.20) also confirm the precise nature of the suggested equation. Finally, the precision of the suggested FRP strain equation is checked. The resulting equation may be efficiently utilized to identify the effective FRP strain of the reinforced FRP internal BCJs.