esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Audio-Visual Assisting
#### 3.5.1 Audio Features Assisting Visual Recognition
To address the challenge of insufficient visual information related to lip movements, and the insufficiency of visual information, a cross-modal memory enhancement method known as Visual-Audio Memory (VAM) was proposed [122]. VAM is based on a key-value storage structure, functioning as a cross-modal memory and involving two main components, i.e., the lip-video key memory, which stores visual cues related to lip shapes, and the audio value memory, which captures the associated audio characteristics. This architecture enables the audio value memory to annotate audio features while simultaneously guiding the lip-video key memory to retain the locations of these annotated audio signals. Consequently, this innovative approach alleviates the limitations posed by the inherent lack of visual data to an extent. Leveraging audio information to supplement and enhance the visual features of lip movements enriches the lip-reading process and enhancing overall recognition accuracy. Addressing the challenges posed by insufficient visual information and the presence of homophones, [PERSON] [125] introduced multi-head visual-audio memory (MVM) by modeling the interrelationships between paired audiovisual representations for the effective retention of audio features. The architecture comprises multi-head key memories that safeguard visual features alongside a single value memory dedicated to audio knowledge, allowing MVM to draw forth potential candidate audio features during the recognition phase. During inference, visual inputs can retrieve and store audio representations from memory by examining the learned interrelationships. This synergy empowers the recognition model to enrich visual data with these audio representations, thereby significantly improving its proficiency in discerning homophones. Enhancing audiovisual memory necessitates supplementary storage for both visual and auditory features. This involves querying the storage system for data matching that generates corresponding auditory features for the stored visual characteristics, requiring additional storage capacity from the system. Furthermore, the efficacy of this data matching is contingent upon the quantity of stored features, which significantly influences the model's overall recognition performance.
## 4 Verification and Simulation
### Comparison of Different Levels of Automation  
The simulation scenario considered in this section is a simple traffic intersection. Apart from the driver model, all other parameters were the same for the three simulations. Three induction loops were placed on the three lanes of one edge/road in the scenario, recording the amount of vehicles passing by every 60 s. Since the total number of vehicles in the three simulations was the same, the departure time of each vehicle was the same, and the simulation scenario was also the same. The only difference was the level of automation of vehicles. Therefore, by comparing the number of vehicles passing through the induction loops per unit time, the traffic flow of the three scenarios can be compared. Figure 6 shows the results of the simulation with the driver models of three different degrees of automation: Level 0, Level 2 and Level 5. The horizontal and vertical axes of the bar graph have been set the same in order to compare the differences between the three simulations in a clearer way. The departure time of all the vehicles ended before 660 s (simulation time). From the figure, it can be seen that the simulation with the Level 0 automation driver model takes the longest time of the three simulations. The simulation results in a duration of 1920 s. The second situation (simulated with the Level 2 automation driver model) is better in the total time with 1745 s, but the maximum traffic volume per minute remains the same as with Level 0. The third simulation with the fully autonomous driver model only took 1540 s, and the maximum traffic volume per minute has a significant increase.  
Figure 5: (**a**–**d**) Behavior of fuzzy control model compared with subjects’ driving behavior.
## 4 Discussion
In Appendix A, the axial and lateral resolution and contrast-to-noise-ratios (CNR) of the beam-formed data by the different beam-forming, weighting and apodization methods were evaluated. The relation between lateral resolution and displacement estimation accuracy was also described previously by [PERSON] et al. [16]. Since lateral and axial displacement estimation was coupled, the accuracy of lateral displacement estimates also affected the accuracy of the axial estimates. For the 12L4 VF transducer, the opposite effect can be noticed in the results with and without the top left artifact (Figures 7b, 8b and 9). The increase in performance with apodization or angular weighting may be partially explained because weighting or apodization reduced artifacts in beam-formed data caused by broken elements in the left part of the transducer footprint. As can be observed in Figure 9, the improvement in displacement estimation accuracy by weighting was less when neglecting the artifact. Another explanation might be related to the fact that side lobes are stronger for the 12L4 VF compared to the other transducers given its increased element width-to-wavelength ratio. Weighting suppresses side lobes which might also explain the improved displacement estimates despite the worse lateral resolution. In this study, a homogenous phantom was used so the effect of improved contrast (Appendix A) by weighting might be limited and has to be further investigated in an inhomogeneous phantom or in ex vivo tissue which can also be rotated. Furthermore, Lu's-fk performed equally or outperformed DaS with optimal line density and for all transducers. This implied that the computationally more efficient Lu's-fk method can be used as alternative for the widely applied DaS. Furthermore, Lu's-fk often outperformed Stolt's-fk method probably because Lu's-fk resulted in an improved lateral resolution in the beam-formed data (Appendix A).
# Effects of Core Stabilization Exercise Programs on Changes in
## 1 Introduction
Moreover, few studies have applied systematic trunk exercise interventions for alleviating deteriorations in trunk muscle functions due to a sedentary lifestyle, with a concomitant assessment of the mechanical and neuromuscular properties of trunk muscles and the isokinetic muscle functions of the lumbar spine. Given the increasing amount of time spent sitting by modern-day people, there is a need for studies on appropriate exercise interventions for trunk stabilization that analyze the isokinetic muscle function of the lumbar spine and the mechanical and neuromuscular properties of trunk muscles. Based on previous studies, muscle fatigue tends to decrease the time it takes to contract 10 to 90% of the maximum contractile displacement (contraction time (Tc)) [28]. With the strengthening of the trunk muscles through exercise, Tc is expected to increase due to the reduction in muscle fatigue. The results of a previous study analyzing the effect of the 3D moving platform exercise for 8 weeks did not show statistical significance, but based on the study results showing an increase in the maximum radial displacement (Dm) of the muscle, Dm is expected to increase through exercise [29]. In addition, based on previous studies, core stabilization exercise increases neuromuscular control by improving the sensory receptors and motor control of the core muscle [23]. Therefore, Vc90 is expected to increase through core stabilization exercise. Additionally, based on a previous study where the isokinetic muscle function of the lumbar region improved after 12 weeks of lumbar stabilization exercise [30], the isokinetic muscle function of the trunk will be improved through core stabilization exercise. Therefore, the central purpose of this study is to propose basic data for facilitating the development of an effective core stabilization exercise program for preventing musculoskeletal disorders caused by a lengthy sedentary lifestyle. Specifically, we aimed to determine the effects of a 7-week core stabilization exercise program on the mechanical and neuromuscular properties of the erector spinae, including muscle stiffness, contraction velocity, contractile response, maximum displacement, and the isokinetic muscle function of the trunk in adult females with sedentary work patterns.
# Fire Safety Risk Analysis of Conventional Submarines
## 1 Introduction  
Fire dynamics are well understood in many industries, including the maritime industry, where various regulations, guidelines, technologies and engineering expertise have served to reduce the risk associated with fires. Fire incidents on submarines present unique challenges, however, and continue to pose a serious risk to submarine operations and submariners. The UK Ministry of Defence (MoD) reported 266 fire incidents on its nuclear submarines in the last 25 years, 20 of which required significant on-board resources to contain [1]. In a space of five years between 2011 and 2015, the Indian Navy have suffered four serious fire or fire safety system related incidents on their conventional submarines, claiming a total of 41 lives [2, 3, 4].  
The current geo-political and maritime security environment dictate that conventional, diesel-electric submarines are still a popular choice for many nations such as Australia. Conventional submarines can provide improved stealth compared to nuclear submarines once submerged. Submarines may be required to travel large distances and avoid detection to maintain security of national interests such as shipping lanes. This requires long underwater range and endurance which is currently generally provided by lead-acid battery storage that, when depleted, require submarines to surface to charge with diesel generators.  
During battery charging there is a greater likelihood of a submarine being detected. Emerging energy generation and storage technologies have the potential to significantly increase energy supply for underwater operation for the conventional, diesel-electric design. As new submarine designs emerge, they are likely to feature some emerging energy generation and storage technologies in the manned submarine domain. From the application of these technologies, fire risks previously not considered in submarine design and operation will be encountered which will necessitate research, development, testing and evaluation of potential controls. The primary purpose of this study is to review a range of possible technologies that may be integrated into the future submarines and provide a risk-based assessment of fire safety and mitigation options.  
The paper consists of various sections starting with a methodology which also outlines the limitations of this study. An overview of systems/technologies and risks are described from a historical perspective, including available mitigation measures. Then, the fire safety risk is considered for various potential technologies including future submarine context, functional description, fuel and ignition source and technology maturity. It is followed by a semi-quantitative fire risk assessment and suggestions for potential mitigation measure. The concluding section summarizes the findings and proposes future works.
# Discrete Data Rate Adaptation for Wireless Body Area Networks
## 3 Optimization Problem Model
Output:
* Selected data variation for each sensor: \(d_{i}\in D_{i}\cup\{\varnothing\}\) for \(v_{i}\in S\); Maximize
* the total utility of the selected data variations: \[\sum\limits_{v_{i}\in S}\mathrm{u}(d_{i})\] Subject to* The capacity constraint for each network node \(v_{j}\in V\): \[\sum_{v_{k}\in F_{j}}\tau(d_{k})\leq C_{j}\]
* The capacity constraint for each network edge used for routing \(e_{ij}\in E_{r}\): \[\sum_{v_{k}\in Q_{ij}}\tau(d_{k})\leq C_{ij}\]
## 3 Methods
### Effect of Vegetation  
#### 3.2.1 Vegetation Backscattering Model  
The IEM was originally developed to describe the scattering from bare soil surfaces only; therefore, vegetation backscattering effects are not explicitly incorporated in the model [11]. In this study, the WCM [26] was used to reduce the effect of vegetation. The WCM, introduced by [PERSON] and [PERSON] [26], was developed for predicting the backscattering from a vegetated area. In this study, the basic structure of the WCM was adopted for characterizing the backscattering in our study area.  
The WCM, which is a first-order approximation of the radiation transferred from vegetation canopies using empirical coefficients and parameters, assumes that vegetation consists of a collection of spherical water droplets that are structurally held in place by dry matter [11]. In the WCM, the total backscattering (\(\sigma^{o}\)) is represented as the incoherent sum of the contribution of the vegetation (\(\sigma^{o}_{veg}\)) and the contribution of the underlying soil (\(\sigma^{o}_{soil}\)), which is attenuated by the vegetation layer. Thus, the WCM can be represented by the following equation:  
\[\sigma^{o}=\sigma^{o}_{vgg}+\tau^{2}\sigma^{o}_{soil} \tag{6}\]  
where  
\[\sigma^{o}_{vgg}=AV_{1}\cos(1-\tau^{2}) \tag{7}\]  
\[\tau^{2}=\exp(-2\,\mathrm{B}\,V_{2}\,/\cos 0) \tag{8}\]  
In these equations, \(\tau^{2}\) is the two-way attenuation through the canopy, \(V_{1}\) is a description of the canopy, \(\theta\) is an incident angle, and \(V_{2}\) is a second description of the canopy. Because an important part of the scattering and attenuation is controlled by the leaves, many studies [17, 39, 40] propose using the LAI as the canopy descriptor. In this study, the canopy descriptors \(V_{1}\) and \(V_{2}\) were associated with the LAI, _i.e._, \(V_{1}\) = \(V_{2}\) = _LAI_. The LAI was estimated from the optical imagery using PROSAIL model [41]. \(A\) and \(B\) are empirical coefficients that depend on the canopy type [40, 42]. To estimate the vegetation parameter \(A\), Michigan Microwave Canopy Scattering model (MIMICS) [43] was used to calculate vegetation backscattering coefficients \(\sigma^{o}_{veg}\) at locations where ground-based measurements ofsoil and other ancillary data are available. Then, the value of \(A\) was derived using the least squares method. The attenuation factor (\(\tau\)) in Equation (6) was calculated using the method described by [PERSON] [19]. Then, the value of \(B\) was obtained using the least squares method. The IEM model was used to replace \(\sigma_{soil}^{o}\) in the WCM model. This replacement allows for a more realistic soil moisture contribution in the total backscattering of Equation (6) [2].
## 3 Results
### Evaluation of SIF Simulations
#### 3.3.1 Evaluation of SIF Simulations with Fixed FQE
Specifically, at the erecting period, the \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) values were largely overestimated with positive RE values, but at the booting or flowering period, the \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) values were largely underestimated with negative RE values. In addition, with fixed FQE, the SCOPE model cannot credibly reproduce the seasonal cycles of \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\). As the wheat grows, the measured \(\overline{\mathrm{SIF_{A}}}\)/\(\overline{\mathrm{SIF_{B}}}\) increased from the erecting to the flowering period with a wide range from 1.370 to 2.470, while the simulated \(\overline{\mathrm{SIF_{A}}}\)/\(\overline{\mathrm{SIF_{B}}}\) remained in the range from 1.690 to 1.916, without clear seasonal changes. These results indicated that the fixed FQE was not suitable for unbiased \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) simulations at all growth stages. Considering that the \(\mathrm{fqe1}\)/\(\mathrm{fqe2}\) value was directly proportional to the simulated \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\), the unregulated \(\mathrm{fqe1}\)/\(\mathrm{fqe2}\) may be the major factor limiting the seasonal variation of simulated \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\).
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 4 SDN Based Architecture
### Programming the Topology
#### 4.4.3 Scripts and Commands for RQ#3  
Finally, we investigated the flow setup delay in a tree topology that has a higher degree of complexity. To generate the tree structure depicted in Figure 5, we crafted a script in the programming language Python. We carried out a number of iterations of the script, gradually raising the number of switches in the hierarchy from one to twelve and recording the flow setup latency for each of these iterations. The number of access points and the number of stations were both kept constant during each execution.  
**Custom tree topology:** We investigate the flow setup delay in a topology based on a bespoke tree. To generate the tree structure depicted in Figure 5, we crafted a script in the programming language Python. As can be seen in Figure 5, the generated topology is composed of three layers: one controller, one OpenFlow Switch, three access points, and ten stations.  
The following command was used to generate ping traffic between stations Sta1 and Sta10:  
_sta1 ping -c1 sta10_  
The stations that are located at the source and the destination are not connected to the same access point. As a result, their routes must pass through a minimum of one core switch and two access points; the flow path lengths involve a total of four hops. For the purpose of determining the flow setup latency of tree topology, the round-trip time of ping requests was measured.  
Selecting relatively simple topologies when using Ryu to build a Wi-Fi network is easier to configure and manage with a limited number of switches and access points. Network administrators avoid potential configuration errors or issues, making the network more reliable and easier to troubleshoot. It is cost-effective and more scalable. Network administrators can add new switches and access points as needed without having to reconfigure the entire network. It is more efficient, as with fewer switches and access points, network traffic can be streamlined, reducing the likelihood of network congestion or performance issues. There may be situations where a more complex topology is necessary; however, selecting a simple topology can provide several benefits, including ease of management, cost-effectiveness, scalability, and efficiency when building a Wi-Fi network using Ryu.
## A Hop-Count Analysis Scheme for Avoiding Wormhole Attacks in MANET  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2* * 1** Department of Electrical Engineering, National Cheng Kung University / EE Building 92975R, No.1, Da-Shuei Rd., Tainan City 701, Taiwan; E-Mails: [EMAIL_ADDRESS] (S.-M.J.); [EMAIL_ADDRESS] (C.-S.L.)
* 2 Department of Computer Science & Information Engineering, National Formosa University / No.64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwan
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-5-631-5572; Fax: +886-5-633-0456  
_Received: 22 April 2009; in revised form: 20 May 2009 / Accepted: 22 June 2009 / Published: 24 June 2009_  
**Abstract:** MANET, due to the nature of wireless transmission, has more security issues compared to wired environments. A specific type of attack, the _Wormhole attack_ does not require exploiting any nodes in the network and can interfere with the route establishment process. Instead of detecting wormholes from the role of administrators as in previous methods, we implement a new protocol, MHA, using a _hop-count analysis_ from the viewpoint of users without any special environment assumptions. We also discuss previous works which require the role of administrator and their reliance on impractical assumptions, thus showing the advantages of MHA.  
**Keywords:**_ad hoc_ network; hop-count analysis; MHA; network security; wormhole attack
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 4 Showcasing: From GbE-Based to UHD Multi-Stream Video  
In order to make the testbed appealing to the general public, a video base demonstrator was put together, as it is depicted Figure 22. Such showcase was a joint-collaboration with the Multimedia group in IT Leira that developed a 180\({}^{\circ}\) field of view system as in [36].  
The UHD multi-stream showcase has been presented at several key forums, creating great impact with both scientific community and industrialist. Such video content was generated independently with the 180\({}^{\circ}\) field of view camera setup [36], which is composed of 9 UHD cameras responsible for 20\({}^{\circ}\) video acquisition each. Due to the agglomerate high data throughput, a GigabitEthernet connection is required to route all 9 UHD cameras data from the encoded stream outputted from the Raspberry Pi to the transmitting FPGA. That said, a GbE switch merges all 9 Raspberry Pi ethernet connections into the single GbE available at TX baseband unit. At RX side, a high performance laptop with GbE interface runs VLC for real-time decoding and video displaying, the decoded video content from the RX baseband unit.  
This has proven to be eye-catching and intuitive, since the visitors could directly interact with the system, e.g., by blocking the direct radio path and observe the immediate impact in both received signal constellations and received video quality. Such user interaction coupled to the fact that 9 UHD video streams were being displayed in real-time on the computer, provided the required validation of a real-time over-the-air user experience. On the other hand, for those with a technical background, there is a Graphical User Interface (GUI) displaying in real-time the received signal constellation, as well as the estimated BER values.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 6 Results and Analysis
### Battery SOC Estimation
 \\ \hline UDDS & EKF & 0.9984 & \(2.2\times 10^{-4}\) & \(8.6\times 10^{-3}\) & 0.9967 & \(3.0\times 10^{-4}\) & \(4.9\times 10^{-3}\) \\  & UKF & 0.9996 & \(4.7\times 10^{-5}\) & \(3.3\times 10^{-3}\) & 0  
can be seen that both EFK and PHM present the best results, confirming that the results are valid to support the superiority of the data-driven PHM for the datasets used.