esa-sceva/satcom-chunk-collection huggingface dataset:
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 3 Results
### Experiment Setup  
Our proposed method is evaluated on two public loop closure detection datasets (New College and NYU). New College is an RGB SLAM dataset, which consists of 1073 image pairs with a resolution of 640 \(\times\) 480, recorded by a motion robot with two symmetrical cameras on the right and left side while moving through the outdoor environment, and all ground truth loops are annotated for evaluating the performance of RGB loop closure detection. NYU is an  
Figure 6: Combining RGB-D information by mid-level fusion pipeline.  
Figure 7: Combining RGB-D information by late fusion pipeline.  
RGB-D SLAM dataset that contains 1449 RGB-D image pairs with a resolution of 512 \(\times\) 424 including both color and depth images, captured by Microsoft Kinect.  
Here, to quantify the comparative experiment results, we use two evaluation criteria: precision rate and recall rate, which can be denoted as the following formulations:  
\[precision=\frac{N_{TruePositive}}{N_{TruePositive}+N_{FalsePositive}} \tag{6}\]  
\[recall=\frac{N_{TruePositive}}{N_{TruePositive}+N_{FalseNegative}} \tag{7}\]  
Precision rate refers to the probability that all loop closures extracted by the algorithm are true loops. Recall is the probability of being correctly detected in all true loop closures. The precision-recall curve is utilized to intuitively reflect the performance of different implementations in the next subsection.
# Late Reverberation Synthesis Using Filtered Velvet Noise +
## 3 Filtered Velvet Noise Reverberation Algorithm
### Schroeder Alpass Filters  
In order to further smooth the synthetic RIR, a cascade of Schroeder allpass (SAP) filters is used. This allows further reduction of the pulse density in VNC. Each SAP filter has the following transfer function [1]:  
\[A(z)=\frac{a+z^{-N}}{1+az^{-N}}, \tag{5}\]  
where \(-1<a<1\) is the allpass filter coefficient and \(N\) is the delay-line length. Figure 6 shows the structure of the FVN algorithm when the total sum of all branches is further processed with a cascade of filters, SAP\({}_{1}\) to SAP\({}_{K}\).  
Figure 5: Magnitude responses of coloration filters of order 10 for every second segment of the impulse response of the Pori concert hall. The same color codes as in Figure 4a are used such that the darker lines correspond to the beginning of the RIR and the color gets lighter towards the end of the RIR.  
Figure 7 shows the spectrogram of a velvet-noise sequence having only 44 non-zero samples per second and that of a SAP filter consisting of four cascaded filters. The delay line lengths of the SAP filters are 225, 341, 441, and 556 samples, and their filter coefficient is \(a=0.7\). The rightmost spectrogram is the result of convolving the velvet-noise sequence with the SAP filter's response, showing a wideband noise-like behavior. This example shows that the gaps in velvet noise can be filled by cascading SAP filters. The spectrograms in Figure 7 were generated using a 600-sample Hann window with 500 samples of overlap.  
By experimenting with different pulse densities and listening to the outcome, it was decided that \(N_{\text{d}}\) = 100 pulses/s is sufficient in the very beginning of the late reverberation, where segments are very short, whereas \(N_{\text{d}}\) = 40 pulses/s can be enough at the end where the bandwidth gets narrow. Between these extremes, the density is decreased linearly as a function of the segment index \(m\). The selected pulse density for each segment is shown in Figure 8.  
Figure 8: Pulse density and length of each segment for the Pori hall. The pulse density can be decreased towards the end of the RIR.  
Figure 6: FVN algorithm with Schroeder allpass filters (SAP) [16].  
Figure 7: Spectrograms of (**a**) a velvet-noise sequence, (**b**) the impulse response of four cascaded SAP filters, and (**c**) their convolution. White corresponds to 60 dB lower level than blue.
## 3 Intermediate Band States
Our results agree with previously reported experimental results [15]. In order to understand how defects in ZnTe alloys we calculate the formation energy and defect formation energy of ZnTe and \(TM^{2+}\)\({}_{0.25}\)Zn\({}_{0.75}\)Te (\(TM\) = V, Cr, Mn). The formation energy \(E_{f}\) can be written as  
\[E_{f}=E_{tot}-\Sigma~{}n_{i}\mu_{i}, \tag{1}\]  
where \(E_{tot}\) is the total energy of bulk ZnTe; \(n_{i}\) and \(\mu_{i}\) are the number of atoms and the chemical potential of the \(i\)th constituent of the bulk. The defect formation energy \(E_{d}\) can be determined by the difference of the formation energies between bulks ZnTe and \(TM^{2+}\)\({}_{0.25}\)Zn\({}_{0.75}\)Te (\(TM\) = V, Cr, Mn) and given by  
\[E_{d}=E_{tot\_defect}-E_{tot}-u_{d}~{}(\mu_{d}-\mu_{Zn}), \tag{2}\]  
where \(E_{tot\_defect}\) is the total energy of bulk \(TM^{2+}\)\({}_{0.25}\)Zn\({}_{0.75}\)Te (\(TM\) = V, Cr, Mn). \(n_{d}\) is the number of \(TM\) atoms. \(\mu_{d}\) and \(\mu_{Zn}\) are the chemical potential of \(TM\) and Zn, respectively. Table 2 lists the results of the formation energy and the defect formation energy for ZnTe and \(TM^{2+}\)\({}_{0.25}\)Zn\({}_{0.75}\)Te (\(TM\) = V, Cr, Mn). Figure 6: Left (Right) panel: HOMO and LUMO slab isosurfaces of 64-atom ZnTe (Cr\({}^{2+}\)\({}_{0.03}\)Zn\({}_{0.97}\)Te) cells arranged by energy and denoted by the shade of purple. The atoms are represented by spheres: Cr (green), Zn (gray, dark), and Te (yellow). We find that the V\({}^{2+}\) ion preferentially occupies the Zn site, leading to the minimum defect formation energy of 2.441 eV. In contrast, Cr\({}^{2+}\) ion occupies the Zn site and becomes more unstable due to the maximum defect formation energy of 3.158 eV.
## 4 Test and Evaluation Methods
### Service Performance Evaluation  
As shown in Figure 4, K0008+800 was taken as the dividing line for the comprehensive inspection of the bridge deck pavement conditions at K0008+700 to K0008+800 (right) and K0008+800 to K0008+900 (left). The three lanes in the Ningbo direction were named NB1, NB2, and NB3, and the three lanes in the Chuanshan direction were designated CS1, CS2, and CS3. The inspection included the SBDP condition index (SDPCI), SBDP riding quality index (SDRQI), SBDP ruting depth index (SDRDI), and SBDP wearing index (SDPWI). The comprehensive condition was assessed using the SBDP quality index (SDPQI), which was weighted by four sub-indices, SDRDI, SDRQI, SDPCI, and SDPWI, as shown in Equation (5).  
\[\text{SDPQI}=(0.35\text{SDPCI}+0.3\text{SDRQI}+0.15\text{SDPCI}+0.1\text{SDPCI})/0.9 \tag{5}\]
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 2 Materials and Methods  
### Data Source and Preprocessing  
The COSMIC dataset is available from the COSMIC Data Analysis and Archive Centre (CDAAC, [[http://www.cosmic.ucar.edu](http://www.cosmic.ucar.edu)]([http://www.cosmic.ucar.edu](http://www.cosmic.ucar.edu)), accessed on 12 June 2022). The data used in this study are second-level data provided by CDAAC-IONPRF files, which contain information on ionospheric electron density profiles. FS-3/COSMIC can provide 2000-2500 RO measurements per day, and more than 70% of the RO measurements can be successfully converted into electron density profiles, which are one of the most important products for space weather and ionospheric science. [PERSON] et al. describe the derivation of ionospheric electron density from RO measurements in detail [23]. The Abel inversion is often used to convert TEC data into electron density data. A generalised quadrature method has been proposed by [PERSON] et al. for the numerical solution of Abel-type singular integral equations, which simplifies the problem of solving singular integral equations [24]. It should be noted that a number of factors can affect the quality of the electron density data. The error of the electron density is derived from RO measurements, including the measurement errors and the error during the Abel inversion. [PERSON] et al. have found that the electron density derived by the Abel inversion method has a large error at low latitudes and a higher accuracy at midlatitudes and high latitudes [25]. A study of the source of error by [PERSON] et al. shows that measurement errors can hardly affect the accuracy of the electron density [26].  
Figure 1 shows the distribution of electron density data on 12 January 2010. Figure 1a shows that the electron density trajectories are almost uniformly distributed in the ionosphere, with less distribution in the polar regions as shown in Figure 2; Figure 1b shows the vertical distribution of electron density trajectories from 100 km to 800 km. The dataset used for this study spans from the 113 th day of 2006 to the last day of 2013. The dataset's  
negative values were eliminated in order to preserve the expected positive electron density values, which were then recorded as an NPY file for each year's electron density data.
## 5 Imbalance-Inducing Postprocessing
### Scalable Linear Boundary Optimization  
Recall the notion of \"windows\" defined at the beginning of Section 5. We now focus on a window \(W=W_{j}\). This is small enough so that all information on the vertices in the window (including all their edges) fit in the memory (of one Mapper or Reducer). The linear optimization postprocessing finds a new split point in each window, so that the total weight of edges crossing it is minimized.  
Let \(B\) and \(A\) denote the set of vertices appearing before and after \(W\), respectively, in the ordering \(\pi\). The edges going from \(B\) to \(A\) are irrelevant to the local optimization since those edges are necessarily cut no matter what split point we choose. The other edges have at least one endpoint in \(W\). Then, there is a simple algorithm of running time \(O(|V_{W}|\cdot|E_{W}|)\) to find the best split point in \(W\) where \(V_{W}\) and \(E_{W}\) denote the set of vertices and edges, respectively, corresponding to \(W\): look at each candidate split point and go over all relevant edges to determine the weight of the associated cut.  
This can be done more efficiently to run in time \(O(|V_{W}|+|E_{W}|)\), too. Let \(s_{v}\) for \(v\in W\) be the cut value (ignoring the effect of the edges between \(B\) and \(A\)). Let \(s_{0}=s_{\pi_{q_{j}-a^{\prime}}}\) (i.e., the cut value for the first split point in \(W\)), which is the total weight of edges between \(B\) and \(W\) and itself can be computed in time \(O(|V_{W}|+|E_{W}|)\) (without hurting the overall runtime guarantee). We scan the candidate split points from left to right and compute \(s_{v}-s_{0}\), however, the additive term \(-s_{0}\) does not matter in comparing the different split point candidates.  
For each vertex \(v\) following \(u\), start with \(s_{u}-s_{0}\), remove the edges between \(v\) and the vertices to its left (these already showed up in the cut value) and add up the edges between \(u\) and the vertices to its right; this gives the cut value \(s_{v}-s_{0}\).  
Since the windows are relatively small compared to the entire graph, and their corresponding subproblems can be solved independently, there is a very efficient MapReduce implementation for window-based postprocessing methods.
# Precision Agriculture: Assessment of Ergonomic Risks of Assisted Driving System
## 2 Materials and Methods
### Muscular Engagement Detection System--Electromyograph  
Our compact and lightweight EMG data recorders with built-in lithium-ion batteries (manufactured by OT Bioelectronics-OTB, based in Turin, Italy) were utilized. These devices were equipped with adhesive electrodes featuring a diameter of 24 mm. The sensor surface, made of Ag/AgCl, was 80 mm\({}^{2}\), surrounded by an adhesive area of 1808 mm\({}^{2}\) with a thickness of 1 mm [10].  
The experiments involved assessing muscle activity using surface EMG (sEMG) and determining the mean frequency (MNF) of the observed signals. The sEMG sensors were attached to the subject's body throughout the experiment and captured real-time data on the MNF from specific areas: right and left shoulders (SH_RX and SH_LX, respectively) and right and left lumbar regions (BK_RX and BK_LX, respectively) (see Figure 3). Muscle fatigue manifested through an increase in EMG signal amplitude and a shift in the power spectrum towards lower frequencies [12]. The increase in signal amplitude may be attributed to the recruitment of additional motor units or enhanced spatial and temporal synchronization of these units [12]. A concentration of the power spectrum at lower frequencies may suggest extended activation periods for each motor unit, potentially leading to slower nerve signal transmission.  
Data analysis utilized OT BioLab 1.7 software from Bioelectronics, incorporating band-pass filtering from 10 to 450 Hz to exclude background interference below 10 Hz and unreliable frequencies above 450 Hz. The resulting signal underwent full-wave rectification to convert negative amplitudes into positive ones. This process was vital for calculating various amplitude parameters, including mean and peak values, and the root mean squared (RMS). The amplitude of the EMG signal, indicating the number of active motor neurons and the nature of their activation, could be estimated through the average value of the rectified signal (ARV, i.e., average adjusted value).  
Figure 2: Experimental scheme of this study.  
The electromyographic sensors consist of disposable electrodes affixed to cleansed skin over the pertinent muscles, i.e., the left arm, right shoulder, left shoulder, and the lumbar area of the back.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 4 Forecasting Download and Upload Traffic
### Discussion  
This study aimed to examine the effectiveness of using SARIMA and Holt-Winters models for short-term forecasting of mobile network traffic. The findings of the study indicate that both models can yield valuable insights for predicting future traffic in mobile networks. The SARIMA model has been recognized for its capability to capture temporal patterns in time series data. It exhibited effectiveness in capturing short-term fluctuations and trends in mobile network traffic. Additionally, the Holt-Winters seasonal model, designed to account for the inherent seasonality in time series data, was also explored. By incorporating seasonal components such as trend and seasonality, the Holt-Winters model successfully captured cyclical patterns of mobile network traffic.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **SARIMA Model** & **Holt-Winters Model** \\ \hline MSE & **0.00004** & 0.00015 \\ RMSE & **0.006** & 0.0123 \\ MAE & **0.0046** & 0.0099 \\ MAPE & **4.17\%** & 9.9\% \\ MSLE & **0.00003** & 0.000118 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Metrics for upload traffic forecast evaluation.  
Figure 6: Upload traffic forecast using SARIMA model.  
Figure 7: Upload traffic forecast using Holt-Winters model.  
To assess the forecast results, we computed various evaluation metrics, including MSE, RMSE, MAE, MAPE, and MSLE. The results, presented in Tables 9 and 10, demonstrated the suitability of each model for different datasets. In an effort to facilitate comparison between the predicted and test data, distinct lines were plotted on Figure 8 with four lines representing the absolute error indicators for both models and the traffic directions.
## 3 Results and Discussion
### Topic 2: Healthcare Delivery  
#### 3.3.1 Emergency and Trauma Response  
Responding to emergencies and trauma situations is arguably the most critical aspect of healthcare delivery in the oil and gas industry, due to the potential consequences of these events. In an emergency or trauma situation, timely access to first-respondor care and emergency patient management may determine patient survival. This can be increasingly difficult in more remote environments, with data showing that the risk of major trauma death is almost double in rural and remote areas compared to metropolitan areas [22]. The first 60 min after a trauma situation, referred to as the golden hour, is often the most critical window in determining an outcome for the patient [23]. For this reason, strategies must be in place for timely patient care, including staff in remote environments being trained in to deliver cardiopulmonary resuscitation (CPR) and use automated external defibrillator (AED), and ensuring an increase in on-site staff able to provide medical care/first aid as remoteness increases [24].  
A number of frameworks exist which outline the minimum level of emergency care that must be available. For example, [PERSON] et al. [25] described the Medical Emergency Response Classification Instrument (MERCI), which was designed in the context of the oil and gas industry. The instrument takes into account the risk (administrative through to industrial), accessibility (remoteness), and population (number of workers) to provide an overall score. The score is then used to guide the determinations of times when support needs to be available (hours per day/at all times), staff required (doctors, nurses or a combination), and materials/equipment/medications which need to be available [25]. The IOGP have a similar framework for medical emergency response and primary care [26]. They propose that healthcare strategies be developed based on risk, healthcare needs, and locally available resources, but with the flexibility to be scaled up and down as needed. The emergency framework is premised on minimising harm and optimising recovery in the event of a medical emergency, and appropriate care is assessed by both quality and the speed at which it can be provided. The framework is separated into four tiers, ranging from first aid (including AED), which should be delivered in around 3-5 min (tier 1), through to referral to a specialist hospital as soon as possible (tier 4). The formentioned frameworks are similar in that they are context-specific and encourage flexibility in how healthcare is delivered during a medical emergency, and as such, it is clear that no single model of healthcare will suit all organisations and contexts.
## 5 Evaluation
### Further Improvement of Cracking Performance Using Proper Rules  
Throughout the password cracking experiments, the best64 rule was applied for all the datasets. The best64 rule is considered the most efficient ruleset because it has been updated through cracking competitions held by the Hashcat team. In the practical field, the best64 rule is used in the first cracking step. However, it was not the optimal rule set for REDPACK. Therefore, to further improve REDPACK's cracking performance, seven Hashcat rules (best64, dive, specific, generated, InsidePro-PasswordPro, Incisive-leetspeak, and T0X1 Cv1) were combined into a huge rule file. Then, the number of chances to contribute to password cracking based on each rule for Dataset 2 was noted. For the custom Hashcat rule set of REDPACK\({}_{\text{U4}}\), 100 rules(the same amount as best64) were chosen. REDPACK\({}_{\text{U4}}\) consists of Hashcat, PCFG, OMEN, and rPassGAN. REDPACK\({}_{\text{U3}}\) consists of Hashcat, PCFG, and rPassGAN. Our team evaluated the generality of this custom set of rules using the other datasets, 3, 4, 5, 6, 7 with REDPACK\({}_{\text{U3}}\). The candidates of REDPACK\({}_{\text{U3}}\) were distinct from those of REDPACK\({}_{\text{U4}}\). Furthermore, we created another dataset to establish the generality of the test. The dataset was based on LinkedIn passwords; the length of the dataset, which was composed of 4 class (symbol, digits, lower and upper letter) passwords, was from 8 to 10. The ID of this dataset was 1-1. In Table 5, the efficiency of this REDPACK rule was higher than that of best64 in the case of the REDPACK candidates. When the REDPACK rule was applied to PCFG, the REDPACK rule was as effective as best64. This custom set of rules can be improved progressively through various cracking experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset ID** & **Number of Generators** & **REDPACK** & **Union Set Being Cracked** \\ \hline
1 & 3 & 1,978,429 (\(-\)6.7\%) & 2,119,814 \\
1 & 4 & 1,995,126 (\(-\)6.4\%) & 2,132,173 \\
2 & 3 & 35,699 (\(-\)19.2\%) & 44,200 \\
2 & 4 & 35,344 (\(-\)24.9\%) & 46,495 \\
3 & 3 & 17,659 (\(-\)18.9\%) & 21,801 \\
3 & 4 & 17,378 (\(-\)26.4\%) & 22,655 \\
4 & 3 & 11,039 (\(-\)17.4\%) & 13,375 \\
4 & 4 & 10,464 (\(-\)25.2\%) & 13,859 \\
5 & 3 & 6022 (\(-\)17.4\%) & 7293 \\
5 & 4 & 5754 (\(-\)21.8\%) & 7505 \\
6 & 3 & 3277 (\(-\)16.6\%) & 3933 \\
6 & 4 & 3128 (\(-\)22.2\%) & 4024 \\
7 & 3 & 1846 (\(-\)12\%) & 2128 \\
4 & 4 & 1798 (\(-\)16.8\%) & 2163 \\ \hline \end{tabular}
\end{table}
Table 4: Performance loss of REDPACK was compared with the union set of unit generator models’ cracking results. When there were three unit models, REDPACK consisted of PCFG, Hashcat (dive, rockyou-30000), when there were four it consisted of rPassGAN, and PCFG, OMEN, Hashcat and rPassGAN.
## 5 Conclusions  
In this paper, an architectural framework to address the problem of digitally signing an encrypted image by embedding the signature as the payload of a lossless data hiding algorithm is discussed. Since encrypted images offer less room for embedding that the corresponding unencrypted images, either the embedding capacity should be increased, the size of the payload to de embedded should be reduced, or both. To reduce the size of the signature, and hence the amount of data that should be embedded, a recently proposed signature scheme, IBS-1, has been used. A nonuniform tessellation strategy allows to take advantage of local structure, which depends on the characteristics of the original image as well as on the encryption used. For example, a block cipher in ECB mode as in [17] would preserve much of the local structure of the original image. In ECB mode, however, encrypting a block of a fixed value always yields the same result, increasing the vulnerability to dictionary attacks.  
Directions for future work include the study of different usage scenarios, the integration of the proposed ideas in a Signcryption scheme [32,33], and the investigation of strategies aimed at improving the robustness of the proposed framework with respect to image transformations and manipulations.  
**Author Contributions**  
[PERSON] and [PERSON] have worked jointly to all phases of the realization of this paper.  
Figure 3: Example: (**a**) original image; (**b**) encrypted image; and (**c**) encrypted image with embedded short signature.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 5 Discussion
### Performance on Test Set  
MC-Net achieved promising results on corrupted axial test datasets with T1-contrast both in terms of SSIM and PSNR. In addition, the visual quality scores provided by two experienced imaging specialists demonstrate that MC-Net can attenuate motion artifacts and improve image quality.