esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 OOD-Mask Detection Framework
### Experiments & Results
#### 6.4.2 Experiment on Sigmoid Scores  
In the second experiment, in order to extract the OOD data within the class of incorrect mask-wearing, the sigmoid confidence scores in the original network were used to correspond to the different distributions of the three classes (wearing mask, without mask, and incorrect mask-wearing). We used the test dataset of three-class to evaluate the performance while keeping the performance of ID data as constant as possible. It was shown that the data peaks of the three classes were all stacked around a sigmoid score of 0.8, but there was still a large part of the OOD data scattered in the range of less than 0.7. If 0.7 was used as the threshold, value If less than 0.7 could be regarded as OOD, and values greater than this were regarded as ID. However, this demarcation method reduced the performance of wearing mask and without mask greatly. After changing the score range many times, we chose the data with a sigmoid score of between 0.5 and 0.67 as the OOD by observing three sigmoid score distributions. The evaluation results of the second experiment are  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Train**} & \multirow{2}{*}{**Test**} & **OOD-Mask** & \multicolumn{2}{c}{**Wearing\_MASK (\%)**} & \multicolumn{3}{c}{**Without\_MASK (\%)**} & \multicolumn{3}{c}{**Incorrect\_MASK-Wearing (\%)**} \\ \cline{3-11}  & & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline
1 & 2-cls & 2-cls & ✗ & 74.69 & 82.06 & 78.21 & 72.20 & 84.33 & 77.80 & & - \\ \hline
2 & 2-cls & 3-cls & ✗ & 81.41 & 72.65 & 76.96 & 78.65 & 78.36 & 78.50 & 40.40 & 25.48 & 31.25 \\ \hline
3 & 2-cls & 3-cls & ✗ & 83.08 & 72.65 & 77.51 & 82.04 & 75.00 & 78.36 & 53.18 & 53.60 & 55.76 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiment Results.  
Figure 7: Examples within the dataset.  
presented in Table 5. While the performance of wearing-mask and without-mask remained unchanged, the best F1-score for incorrect mask-wearing was only 31.25%, which meant the extraction was not satisfactory.
## 2 Problem Description  
This study generates observation schemes for static tasks over an execution period. The tasks observe point targets according to user demands that include the latitude and longitude of the point target, observation duration, required observation time, etc. All user demands in the past period are collected and sent to the operation center. Then, considering the objectives and constraints, the operation center exports an observation scheme based on optimization algorithms. Subsequently, the scheme codes are sent to satellites, which execute tasks according to the uploaded scheme codes. After the current execution period ends, the observation system proceeds to the next execution period. In practical management, generating a schedule can be considerably complicated because many details must be considered, such as regulations and user requirements. Following previous research [6; 30; 56; 57], we simplified the problem by considering several assumptions. 1. A solar panel can provide sufficient energy for each satellite. 2. The download management of the acquired images is not considered because this issue falls outside the scope of this study. 3. There is, at most, one observation sensor running on a satellite at any time. 4. The maneuver time for pitch and roll maneuvers is considered when calculating the attitude transition time between two continuous tasks. 5. Observation tasks are point targets and targets preprocessed to be covered in a single observation. 6. Each task can be conducted once at most and does not need to be repeated. 7. If a task is successfully scheduled, it can be executed successfully without being affected by other factors. 8. The task and its execution demands are defined before scheduling starts; no new tasks or variations in demands occur. Given the above assumptions, the MAEOSP in this study can be described as follows. The task set containing a total of \(M\) tasks is defined as \(I=\{1,2,\cdots,\ M\}\). For task \(i\in I\), the observation time required by the user is \([rs_{i},re_{i}]\), the observation duration is \(d_{i}\), the observation profit is \(P_{i}\), and the storage occupied by \(i\) is \(s_{i}\). The satellite set is \(J=\{1,2,\cdots,\ N\}\). Each satellite, \(j\in J\), has a memory capacity of \(C_{j}\). The number of VTWs for task \(i\) in satellite \(j\) is \(n_{tuji}\). The set of VTWs for task \(i\) in satellite \(j\) is \(TW_{ij}=\left\{tw_{ijk}\middle|k=1,2,\cdots,n_{tuji}\right\}\), where \(tw_{ijk}=\left[tw_{ijk},tw_{ijk}\right]\). \(\left[ts_{ij},te_{ij}\right]\) represents the observation time for task \(i\), where \(te_{ij}=ts_{ij}+d_{i}\). The symbols and notations are shown in Table 1.
## 5 Conclusions  
In summary, various bow-tie microstrip antennas were designed and verified experimentally to support the optical antenna in the range of 4-8 GHz for wideband (UWB) applications. The finite integration technique (FIT)-based high-frequency electromagnetic solver, CST Microwave Studio, was used for numerical analysis. The designed antennas were fabricated and tested for different materials and substrate thicknesses. It has been observed that all antennas are well matched and accurate results are achieved. In order to overcome the bandwidth limitation of these antennas, broadband slot bow-tie antenna has been introduced. The slot bow-tie antenna was modelled, designed and fabricated. The obtained percentage bandwidth is 20% with a return loss of \(-25\) dB. However, the measured result shows that the bow-tie slot antenna is not perfectly matched, as was expected. Therefore, the slot bow-tie antenna was fabricated again with the lowest available relative permittivity of 1.96 and substrate thickness of 0.508 mm. As expected, the measured result indicated that the slot bow-tie antenna is well matched, and the obtained percentage bandwidth is about 22% with a return loss of \(-25\) dB. As briefly explained here, the designed slotted bow-tie antenna, which is shown in Figures 3 and 5, and experimentally verified as shown in Figure 15, is one of the novel aspects of the proposed study due to its unique dimensions and shape.  
The given inaccuracy of adaptation may be that it transitions from an unbalanced to a balanced medium may inversely affect the pattern of the proposed antenna. To address this problem, it would be helpful to use a balanced-unbalanced transformer at the end of the cable, which can provide the necessary isolation to prevent common-mode currents or other methods as given in [26]. For future activity, we plan to implement one or more of these solutions to the proposed antenna in order to supply more benefits to UWB applications, specifically in medicine.  
Conceptualization, H.N.A. and Y.I.A.; methodology, H.N.A., Y.I.A.; software, H.N.A.; validation, M.K., L.D. and Y.I.A.; formal analysis, H.N.A., Y.I.A. and F.F.M.; investigation, H.N.A.; resources, M.B.; data curation, H.N.A.; writing--original draft preparation, H.N.A. and Y.I.A.; writing--review and editing, M.B., E.U. and F.F.M.; visualization H.N.A.; supervision, M.K. and L.D.; project administration, M.K. and H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant no. 2017 YFA0204600), the National Natural Science Foundation of China (Grant no. 51802352) and the Fundamental Research Funds for the Central Universities of Central South University (Grant no. 2018 ZZTS355). The author would like to thanks Central South University for technical support. The authors declare no conflict of interest.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
###### Abstract  
Simultaneous localization and mapping (SLAM) is fundamental for intelligent mobile units to perform diverse tasks. Recent work has shown that integrating neural rendering and SLAM showed promising results in photorealistic environment reconstruction. However, existing methods estimate pose by minimizing the error between rendered and input images, which is time-consuming and cannot be run in real-time, deviating from the original intention of SLAM. In this paper, we propose a dense RGB-D SLAM based on 3D Gaussian splatting (3 DGS) while employing generalized iterative closest point (G-ICP) for pose estimation. We actively utilize 3D point cloud information to improve the tracking accuracy and operating speed of the system. At the same time, we propose a dual keyframe selection strategy and its corresponding densification method, which can effectively reconstruct new observation scenes and improve the quality of previously constructed maps. In addition, we introduce regularization loss to address the scale explosion of the 3D Gaussians and over-elongate in the camera viewing direction. Experiments on the Replica, TUM-RGBD, and ScanNet datasets show that our method achieves state-of-the-art tracking accuracy and runtime while being competitive in rendering quality.  
dense visual SLAM; scene reconstruction; 3D gaussian splatting; keyframe selection +
Footnote †: journal: Medical Engineering, Hebei University of Technology, Tianjin 300401, China  
1
Footnote 2: email: {[EMAIL_ADDRESS]}
## 2 Forward Model Development
### ZTT Model Summary  
Equation (18) is the final model for ocean color reflectance defined as \(L_{u}/E_{d}\), called the ZTT model hereafter. In Equation (18) the \(\overline{\mu}_{d}\) term is approximated by Equation (14), the \(\beta(\psi)/b_{b}\) term approximated by Equation (10), the \(\Psi_{K_{Lx}}\) term described by Equations (4) and (6), the \(\overline{\mu}_{\infty}\) term described by Equation (8), and \(\widetilde{b}_{b}\) represented by Equation (11). The ultimate assignment of the \(f_{L}\) term is addressed in the Results. The geometry variables, \(E_{d}\)(0\({}^{+}\),\(\theta_{s}\)'), \(V\), and molecular water scattering parameters in the above can be considered knowns. The model is fully spectral. Key unknowns are \(b_{b}\) and \(a\) (or \(b_{b}/a\) and \(\eta_{bb}\)), and since pure seawater absorption \(a_{w}\) in the visible is considered known with good accuracy [61] the effective unknowns are \(b_{bp}\) and absorption by non-water constituents \(a_{pg}\). The two additional unknowns are \(P_{bb}(\psi)\) and \(\widetilde{b}_{bp}\). In the forward implementation here, these four parameters must be provided from direct measurements or through some assumptions. In the inversion implementation, these are the parameters that may be solved through techniques to minimize errors in the expression if there are enough spectral bands (i.e., degrees of freedom) in \(r_{rs}\), although \(a\)_priori_ assumptions may be required for \(P_{bb}(\psi)\) and \(\widetilde{b}_{bp}\).  
For ocean color reflectance defined as \(L_{u}/E_{od}\), the simpler relationship in Equation (7) can be used. Water Raman effects can still be considered by applying an algorithm, such as in Reference [60].
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Wavelength and Propagation Direction Calculation of \(n\)th Ocean Waves
#### 2.5.2 Propagation Direction Calculation of \(n\)th Ocean Wave  
In order to facilitate the subsequent 180\({}^{\circ}\) ambiguity removal of wave propagation direction, the propagation direction \(\phi^{n}\) of \(n\)th ocean waves is defined as the angle between wave propagation direction and range direction. It is calculated as  
\[\phi^{n}=\left\{\begin{array}{ll}-\tan^{-1}\frac{k_{n}^{n}}{k_{r0}^{n}},&k_{r 0}^{n}>0,k_{a0}^{n}<0\\ 180^{\circ}-\tan^{-1}\frac{k_{n}^{n}}{k_{r0}^{n}},&k_{r0}^{n}<0\\ 360^{\circ}-\tan^{-1}\frac{k_{n}^{n}}{k_{r0}^{n}},&k_{r0}^{n}>0,k_{a0}^{n}>0 \end{array}\right. \tag{11}\]  
The propagation direction of \(n\)th ocean waves calculated by Equation (11) exists 180\({}^{\circ}\) ambiguity. In order to avoid the influence of 180\({}^{\circ}\) ambiguity on estimating significant wave height, it is necessary to de-blur. The following will introduce the 180\({}^{\circ}\) ambiguity removal method.
## 2 Literature Review and Hypotheses Formulation
### Cost-Benefit Consideration  
Security measures are additional and usually non-automated actions that employees must intentionally perform in order to protect their mobile devices. We posited that these actions are conducted with the expectation that the actions will bring positive expected outcomes (benefits) within reasonable costs to the perpetrator. Expected outcome refers to the anticipated results or consequences of an action [28] and has been posited to determine behavior [21; 29; 30; 31; 32]. We believe that employees will take the necessary precaution to protect their mobile devices whenever they expect that such action will bring about positive outcomes, and the relevant hypothesis was constructed as below:  
**Hypothesis 1 (H1).**_Expected outcome significantly affects mobile security practices_.  
Past studies have shown that security measures are often viewed as inconvenient, effortful, and interrupting the achievement of assigned tasks. For example, in [33], the interviewed employees believed that security measures made it difficult for them to carry out their work. Security-related measures are generally viewed as bothersome and obstructive [34], constraining and inconvenient [35], and negatively impacting productivity [36], especially in the context of mobile devices. People tend to forsake their efforts when inconveniences are encountered [37; 38; 39; 40]. The majority of respondents in [8] chose not to apply security measures to their mobile devices due to the complexity of the measures. Therefore, it was speculated that security inconvenience forms the cost of mobile security practices. The second hypothesis was formulated as follows:  
**Hypothesis 2 (H2).**_Security inconvenience significantly affects mobile security practices_.  
With regard to the use of mobile devices, we believe that the influence of inconveniences due to security measures is even more significant. This is because mobile devices are used for work purposes due to the convenience and utility provided. Nonetheless, the implementation of mobile security measures would instigate additional efforts on the part of the employees and reduce the expected utility. For example, a good security practice forbids the use of public networks. This limits the use of mobile devices when the employee does not have access to  
Figure 1: Hypothesized path model.  
a secured network. Password-protected devices require employees to key in the password each time he or she needs to use the device, increasing the expected time to complete a task. Using a mobile device is no longer handy or useful if the employee has to grapple with various inconveniences of the security measures. Security inconvenience is thus believed to affect the expected outcome. This proposition is hypothesized below:  
**Hypothesis 3 (H3)**.: _Security inconvenience significantly affects expected outcome._
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 5 Discussion
### Blockchain for IoT: Potential Applications for Smart Cities
#### 5.2.5 Decentralized Infrastructure and Connectivity  
In traditional IoT systems, centralized infrastructure and communication networks can be vulnerable to single points of failure or cyber attacks. Blockchain technology can enable decentralized infrastructure and connectivity by leveraging peer-to-peer networks. Devices in a blockchain-based IoT system can communicate directly with each other, eliminating the need for intermediaries and enhancing the system's resilience [51; 52]. This decentralized architecture improves the overall reliability, security, and efficiency of IoT systems, making them more suitable for smart city applications.
# Strength Analysis of Real-Life Passwords Using Markov Models
## 6 Discussion
### Practical Use of the Approach  
Our approach can be used in a typical Web-based password-protected service. A practical example of our approach follows:  
The Markov model is represented by a Markov matrix that contains the conditional transitional probabilities (see Section 3) needed for calculating the password score. This matrix is stored and available on the server-side, while the actual password checker is available on the website (i.e., the client-side). On the client-side, the user enters the password in the password field. The password entered in plain text is then sent through a secure connection (HTTPS) to the server-side. On the server-side, the Markov matrix is used to calculate the score of the password. If the password is assessed as strong or very strong, the password is hashed, salted and stored in the database. Otherwise, a warning is presented to the user, stating that the password is too weak to be accepted by the system and it requires a change. Additional instructions on how to create a secure and strong password are displayed. The levels of password strength are defined on the basis of the scores of all the passwords in all training datasets. Basically, the solution can be used as an additional step at the server-side and there is no need for additional modification of the existing user interface (i.e., the website).
## 2 Materials and Methods
### Measurements during the Race (Polar Team Pro)  
Heart rate (HR) and movement data were recorded using the Polar Team Pro(r) system (Polar Electro, Kempele, Finland). The system consists of a chest belt containing a sensor unit (Polar H7 Bluetooth 4.0 smart chest strap) with built-in ECG electrodes, a 10 Hz integrated GPS, and a 200 Hz micro-electromechanical system motion sensor. After the monitored session is over, all wearable sensors are attached to the dock for data synchronization to the iPad. From the iPad, the data are transferred wirelessly to the Polar Team Pro web service via the iPad's built-in Wi-Fi or cellular connection. The Polar Team Pro system provides coaches with extensive data on the following areas: HR, location map, distance, velocity, acceleration, and power. The accompanying wearable sensors are equipped with Bluetooth technology that enables continuous data transfer with a range of up to 200 m [23].  
During the races, we recorded the average (HR\({}_{\text{mean\%}}\)), minimum (HR\({}_{\text{min\%}}\)), and maximum (HR\({}_{\text{max\%}}\)) heart rate about the hundredth percentile, the number of sprints (No. sprint), and the maximum speed (Speed\({}_{\text{max}}\)), and calculated the training load (TL).  
The heart rate (HR) and movement data measured during the competition are shown in Figures 2 and 3. These figures illustrate the three riders' heart rates and speed patterns at different stages.  
In the first third of the race, all three riders worked at over 90% of their heart rate. This dropped steadily over the next third of the distance. The biggest decline was in the third period of the distance for rider (c), whose heart rate decreased to 90-80% (Figure 2).  
There were several significant reductions in speed in the first three laps of the race, and then this pattern shows a balanced speed behavior in the following stages. In the middle part of the race, the speed decreased several times again, and then leveled off in the last part, reaching its maximum value (<100 km/h) (Figure 3).
## 3 EM Analytical Model
### EM Models for Non-Segmented PMS Machines
#### 3.1.1 Magnetic Flux Density Due to PM  
\(B_{/R}\) wich is the flux density located in the air gap can be calculated analytically for an equivalent slotless machine \(B_{/R-sl}\). In this case the resolution of Poisson equation by variable separation allows to obtain the classical expression of this flux density as a Fourier Series which is given in this reference paper [9]. A permeance function \(\lambda(\theta)\) is used to take into account the variation of magnetic flux density in the slot openings (1). Then the flux density generated by rotor permanent magnets in the air gap (at a \(r\) radius) is given by (2).  
\[\lambda(\theta)=\frac{h_{g}+\frac{h_{m}}{h_{r}}}{h_{g}+\frac{h_{m}}{h_{r}}+r_{ fl}(\theta)\frac{\pi}{2}} \tag{1}\]  
\[B_{/R}=B_{/R-sl}(r,\theta)\times\lambda(\theta) \tag{2}\]  
In (1), \(h_{g}\), \(h_{m}\) and \(\mu_{r}\) are the air gap and magnet thicknesses and magnet magnetic permeability, respectively. \(r_{fl}\frac{\pi}{2}\) is the flux line additional length located inside the slot opening area (as shown in Figure 4). The considered flux lines used are considered as circular arcs in the slot openings areas.  
Figure 5 presents the relative permeance function for the non-segmented machine used in the validation section (Section 4.1.1) for an angular width corresponding to one pair of poles (this machine has 414 slots and 138 pole pairs with non-closed slots and a ratio between slots and slot period equal to \(\beta_{s}\) = 0.46).  
Figure 4: Flux lines in a slot opening for a radial magnetization.
## 1 Introduction
The main works that have dealt with the retrospective assessment of time-varying acoustical signals often focused on loudness perception, on very controlled stimuli (pure tones, white noise,  
Figure 1: Modeling framework. (**A**): relations between sound-level time series and perceived overall pleasantness; (**B**) relations between sound-level time series and perceived continuous pleasantness; (**C**): relations between perceived continuous and overall pleasantness (**C**). specific sound sources, etc. ), or on short sound sequences. Evaluating retrospective global judgments, such as the pleasantness of the sonic environment during urban walks, requires new experimental set-ups and stimuli, closer to the in situ experience. Recently, virtual reality and auralization tools have been proposed by some authors, in order to fulfill this requirement [34; 35], and more immersive experiments could help in highlighting these temporal effects over longer sound sequences. An in-situ experiment by [PERSON] et al. revealed that recency or trend effects significantly influence the global judgment for very short paths (inferior to 1 min), but not for larger paths (>15 min) [36]. These results need to be compared with other experiments. The relation between the continuous instantaneous judgement during a time-varying sound environment, and the physical properties of the stimuli, are also of particular interest. They enable one to estimate the integration, relaxation, and reaction times that link sound levels to momentary evaluations: a null reaction time and an integration time of about 2.5 s were, for example, found relevant in [37]. In addition, the links between overall pleasantness evaluations and sound levels time-series must be furthered investigated. The present paper aims at investigating how the sound pleasantness of an urban walking trip can be estimated through measurements of the sound pressure level along walking paths in an urban environment. Two experiments are built. For both, the participants had to assess the continuous and overall sound pleasantness of sound sequences:  
* A first experiment is based on different arrangements of two audio files, and aims to determine how the global temporal structure of a sound sequence affects its continuous and overall sound pleasantness appreciation. The sound sequences are built with the goal of assessing the effect of the temporal structure of the \"background\" sound environment. Therefore, strong markers of the soundscape or peaks in the sound levels were specifically avoided. * A second experiment is based on the same principle, but with real sound sequences, played conjointly with video content, in order to investigate the same questions with natural sequences and a higher ecological validity.