esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
This trade-off between error correction capability and bandwidth expansion necessitates a careful balance between these competing factors, particularly in channels prone to burst errors. The presented work uses a block coding FEC mechanism to enhance the visual quality of videos by recovering transmission errors. Methods for error recovery using convolution codes can be designed in the future. The proposed algorithm assists in preserving the integrity and confidentiality of transmitted videos by incorporating H.264 syntax-based encryption with the proposed FEC framework and can be tested in the future with other advanced encryption standards to evaluate its impact on encrypted videos. The performance of this work can be further examined on the basis of other quality assessment metrics. Compression efficiency and speed in H.264/AVC has reached the point where it can no longer be further enhanced. Therefore, several new video codecs have emerged to meet the increasing demands of multimedia technology, such as H.265, HEVC/H.265, AV1, and VVC. The effectiveness of this approach can further be evaluated on higher video resolutions (2K, 4K, 8K, etc.) [46]. As our work has been implemented using the H.264/AVC CABAC entropy coder, it can be implemented in modern coding standard H.265 and HEVC for future multimedia transmission, which provides twice the compression as the H.264 coding standard [47,48]. The data reduction efficiency in H.265 is much higher in compressing videos and makes transmission of ultra-high-definition 4K or 8K videos easier [49]. To provide better compression efficiency, reduce complexity, and improve scalability, scalable video coding (SVC) techniques, such as H.264/SVC and VP9, can be optimized to enable efficient video transmission over heterogeneous networks [50]. It can be expanded to a scalable coding environment where the number of parity bits added is dependent on scalable bitstreams to offer variable quality demands for devices with varying resolutions. The cross-layer design and optimization of error correction mechanisms can be explored, where the application layer's FEC strategy is informed by and coordinates with error correction mechanisms at lower layers. This would potentially lead to more efficient use of resources and improved overall system performance.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 3 Bayesian Filtering MBPU Method
### Multi-Baseline UIFPU Algorithm
We can use Figure 1 to represent the two-dimensional Bayesian filtering PU method. In the figure, the red dots represent the pixel to be unwrapped. First, we find the eight pixels adjacent to the pixel to be unwrapped and find the unwrapped phase. The green rectangle in Figure 1 represents the unwrapped pixels. Then, we obtain the error map according to the coherence coefficient, and obtain the weight values according to the error of the corresponding pixels. Finally, we weight the red dots pixel with green rectangular pixels to obtain the final unwrapped pixels. The red rectangle in Figure 1 represents the final unwrapped pixels.
## 2 Route Generation Methodology
### Higher-Level Path Generation: Genetic Algorithm (GA)
#### 2.3.5 Objective Function  
The higher-level path generation uses the pre-computed paths calculated by the lower-level path generation algorithm. Therefore, only one of the start points for each area is selected. However, the GA can select the optimal starting location such that the total vehicle distance is minimized.  
Figure 6: Chromosome blocks used to group every three genes together, and the corresponding candidate crossover points.  
Figure 7: A visual depiction of the mutation schemes. No mutation is applied to the first chromosome block, staring point mutation is applied to the second chromosome block, and vehicle assignment mutation is applied to the third chromosome block.  
The objective function for the higher-level graph generation can be seen in Equations (3) and (4). In Equation (3), \(f\) is the objective function, which is to minimize the sum of distances traveled by each vehicle \(v_{i}\). The vehicle distance can be calculated in Equation (4), where \(d_{k}\) is the distance from the vehicle's current location to the next sub-area's starting location. When \(k=0\), \(d_{k}\) is the distance from the vehicle starting location to the starting location of the first sub-area assigned to vehicle \(i\). When \(k=l\), \(d_{k}\) is the distance from the ending location of sub-area \(l\) to the vehicle ending location.  
\[f=\min\sum_{i=0}^{j}v_{i} \tag{3}\]  
\[v_{i}=\sum_{k=0}^{l}d_{k} \tag{4}\]  
The objective function can be modified according to the problem requirements. In this case, the total distance (sum of all vehicle distances) was to be minimized. However, some challenges in the UGV surveillance domain are related to selecting an appropriate objective function to achieve the desired outcome. Several other objectives can be achieved, such as minimizing the overall travel time (maximum vehicle travel time), or balancing the workload across all UGVs. Selecting an appropriate objective function also requires communication between research partners such that the objectives are mathematically and physically feasible.
## 4 Discussion  
### Discussion on Colorimetric Results: Comparison Between Treated and Untreated Samples  
The results of this study indicate that plasma treatments and mordants cause significant changes in colorimetric parameters. The lightness (L) of the fibers decreases significantly with plasma treatment, from 89.96 \(\pm\) 0.29 in the white fiber (LB) to 46.32 \(\pm\) 1.26 in the mordanted sample (LM0), 60.17 \(\pm\) 0.80 in the plasma-treated sample (LP5), and 52.10 \(\pm\) 0.82 with plasma treatment (LP15), indicating a notable darkening. The a* parameter increases, highlighting reddish tones in the LP15 sample (22.91 \(\pm\) 1.18). Conversely, the b* value shifts towards blue tones in LP10 (\(-\)0.97 \(\pm\) 0.39) and towards yellow tones in LP15 (15.81 \(\pm\) 0.45), reflecting significant chromatic variations induced by the plasma treatment. Furthermore, there is higher color saturation (C*) in LP15 and an increase in color intensity (K/S) with plasma exposure time, with greater intensity observed when mordants are utilized in the dyeing process.  
The results of this study are consistent with those of previous research, such as that conducted by [PERSON] et al. [43], which showed that plasma application decreased dye concentration as a function of treatment time, resulting in lower color intensity and a change in the lightness of the treated fibers. These findings support the hypothesis proposed at the beginning of the research that the APPJ has the ability to alter colorimetric parameters. Progressive darkening of wool was observed as the plasma exposure time increased, as reflected in the decrease in the L* value. Additionally, the APPJ generated various shades in the cochineal-dyed wool, producing reddish tones (positive a*) and yellow tones (negative b*). These results confirmed the ability of the APPJ to modify the colorimetric parameters of cochineal-dyed wool.  
These findings are highly relevant to the textile industry, as plasma treatment offers a more sustainable alternative to traditional dyeing methods that often use metallic mordants. Reducing the use of metallic mordants is a crucial goal for the textile industry because these compounds can be toxic to the environment and human health. Future studies are recommended to evaluate the impact of the APPJ on the colorimetry of other types of fibers and dyes, as well as to test different plasma combinations. Additionally, it would be interesting to investigate the effect of the APPJ on color uniformity and to explore methods to optimize the plasma treatment process to achieve greater uniformity.
## 1 Introduction
### State of the Art
#### 1.1.3 Wind Hunter Project  
Another concept [6] being developed in a corporate-academic partnership is the Wind Hunter Project. The aim is to make zero-emission commercial shipping possible using hydrogen fuel and wind power. Energy is harvested using sails and is stored as hydrogen, which will be used for powering a propeller when the wind speed is low (cf. Figure 3 for further details). The concept's feasibility was demonstrated on a sailing yacht, and the subsystems involved were investigated [7].
# A High-Fidelity Energy Efficient Path Planner for Unmanned Airships
## 6 Numerical Simulations
### Test Environment  
The DEMs (terrain) for the test environments were generated based on statistical data from the real DEM of Vancouver, BC, Canada, which is publicly available on GeoBase [52] as it features significant changes in elevation. One hundred DEMs were then generated by randomly adding blocks of buildings to the terrain according to the seed variables listed in Table 1.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Mean altitude & 600 m \\ Altitude standard deviation & 600 m \\ Water table altitude & 0 m \\ Mean grade & 18.4\% \\ Max grade & 306\% \\ Average number of blocks & 20 \\ Min block height & 15 m \\ Max block height & 200 m \\ Block length & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seed values for terrain generation based on typical conditions in Vancouver, BC, Canada.  
For each environment, a 3D spatial WVF was generated with the help of the CFD software WindStation using a single wind vector seed to populate the entire control volume based on the interaction with the terrain and the building topography. The wind seed direction was a uniformly random integer between \(0^{\circ}\) and \(359^{\circ}\). The wind seed magnitudes were produced based on the approximation that wind magnitude follows a Weibull probability density function [53] with a shape value of two and a scale of \(\mu_{w}/0.89\), where the mean wind velocity \(\mu_{w}\), for Vancouver is 3.61 m/s (averaged annually) [54]. The WVF resolution was set equal to the terrain's nodal resolution of 50 m to negate any computational time required for interpolation between nodes. The WVF was generated using the k-epsilon turbulence model with surface roughness set to 2 m and the max residual set to \(10^{-5}\).
# Use of Data-Driven Simulation Modeling and Visual Computing Methods for Workplace Evaluation
## 4 Results
### Ergonomics Analyses Results  
The manufacturing workplace with manual product assembly and automated controls operations presented in Section 3.1 was examined with ergonomic analyses OWAS and RULA. Since the workstation represents a half of the entire assembly process (the workstation is mirrored on the \(y\)-axis, where the work is performed by another worker), only results for one workplace will be presented. Figure 9 shows a three-dimensional workplace model, which was captured with a spherical camera. The model was first computed visually and transferred to the Autodesk Inventor software environment. With the model used, we can perform the ergonomic analysis faster, easier and more realistically, while the three-dimensional model represents a real-world workplace environment. The model can be adapted according to the workplace evaluation proposals and specific ergonomic analyses.  
Results of the performed OWAS analysis show that the majority of performed worker's movements is safe, except for two positions where harmful effects are possible. During the reach out for the assembly part a bend is present in the lumbar spine, estimated as potentially harmful for the musculoskeletal system (Figure 9, left window), and the same warning (with a bit different code) appeared during the phase when putting a product into the right testing position. The problem could be solved easily with an adjustable height worktable.  
As we expected, the results of the performed RULA analysis were slightly worse than the results gained with OWAS. The worker repeats movements with the upper limbs constantly, and the majority of movements is made with the wrist. There is also a constant bend in the neck for suitability of parts' assembly. The RULA score during the working process is constantly 3-4. This is the value of first warnings, and it means that the problem should be investigated further, as shown in Figure 9 (right window). The solution of this problem is questionable, because changes in the assembly area (different positions of hands and wrists) can cause changes in neck positions. Probably the most effective solution for this problem would be process automatization, as proposed in the S\({}_{2}\) evaluation, the introduction of a collaborative workplace.  
Figure 9: Worker’s position during work with OWAS and rapid upper limb assessment (RULA) analyses results.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 4 Results
### Route 3--Moving to Another Floor  
When a route requires changing floor, a user expects a single direction indicating the final floor for the manoeuvre, for example, 'Go down with the elevator/stairs to the \(-2\) level'. If the route requires a change of more than one floor, the directions in stage 3 of the algorithm are generated for each floor separately. There are a few cases to consider:  
* Elevators: These are represented on each floor separately and connected in one graph. A route that uses an elevator to traverse a few floors contains a series of _Path Nodes_ for each floor. Such a sequence is easy to detect and is replaced with a single navigation direction;
* Well-defined staircase with stairs connecting consecutive floors: There are a few possible configurations of stairs. In Figure 13a, the stairs reach the next floor (or mezzanine) after two turns, but all these manoeuvres are irrelevant from a navigational point of view. The algorithm generalises the navigational direction using the attributes of the _Interior Space_, indicating whether they are parts of the staircase and analysing distances between consecutive stairs;
* A different situation is presented in Figure 13b. The possible connections between the ground floor and floor \(-2\) fork to opposite sides of the stairs. When the user goes down, there is no alternative path, so directions should be generalised. However, in the case of going up, it is important to take the correct exit from the station. Hence, the information on whether to take a left or right turn is essential;
* Subsequent stairs: Although some stairs are connected to a single navigational direction, in some cases, the algorithm should consider the consecutive stairs to be separate objects, which is an expected outcome when they are of different types (stairs, escalators, moving walkways) or if the distance between them is big enough. An example of such a situation is shown in Figure 14. The tests led to the decision to implement the rules to generalise the directions if the distance is less than 12 m, regardless of whether they are of the same type or not.  
Figure 12: Map with the path network for the ticket zone of the Politechnika metro station; the map presents _Interior Spaces_ (grey), _Path Edges_ (green and red for parts of the route), and _Path Nodes_ (circle); the red arrow indicates the decision point where the ‘turn right’ direction should be added.
## 4 Results and Discussion
### 12-Bus Test System
Voltage profile of 12-bus distribution test system is shown in Figure 13, the \"Without DG\" curve shows the voltage profile for the original test system and this curve identify that the minimum voltage occurs at the end of the feeder. In addition, it is observed that the voltages at all buses are within the acceptable voltage range. It is clear that the bus voltages will enhance when DGs are optimally connected in the electrical distribution system.
## 3 The Proposed Healthcare System: BSNCare+
### The Authentication Phase
\(\odot\) Are the received value \(V_{1}\) and the computed value \(H(M_{A_{1}}\mid 1\ T_{seq}\mid 1\ ID_{j}\mid 1\ N_{j}\mid K_{js})\) equal? \(\odot\) Are the received value \(AID_{i}\) and the computed value \(H(M_{1}\mid GPS_{i}\mid 1\ ID_{j}\mid 1\ GPS_{j}\mid 1\ T_{seq})\) equal? * Condition (2): If the _Server_ cannot find any \(Tr_{seq}\) in the request, i.e., \(M_{A_{2}}\) and \(M_{A_{1}}\), the _Server_ then examines the freshness and validity of \(AID_{i}=sid_{h}\). If the _Server_ cannot identify the \(sid_{h}\) from the backend database, the server terminates the connection and requests \(BS_{i}\) to try with another valid shadow identity \(sid_{h}\). If one of the two examinations is passed, the _Server_ then generates two random numbers \(N_{s1}\) and \(N_{s2}\), and sets \(N_{s1}\) as the new track sequence number \(Tr_{seq}\), i.e., \(Tr_{seq_{max}}=N_{s1}\). Subsequently, the _Server_ computes \(Tr=H(K_{is}\mid 1\ N_{s2})\oplus Tr_{seq_{max}}\), \(V_{3}=H(Tr\mid K_{is})\), \(V_{2}=H(ID_{j}\mid 1\ N_{s2}\mid K_{js})\), \(SK_{is}=H(GPS_{i}\mid 1\ K_{is}\mid 1\ T_{i})\) and \(SK_{js}=H(K_{js}\mid 1\ ID_{j}\mid 1\ N_{s2}\mid 1\ N_{j})\). Note that \(SK_{is}\) is the session key, which will be utilized for the next secure communication between \(BS_{i}\) and the _Server_, where \(SK_{js}\) is the session key agreed by the \(MG_{j}\) and the _Server_. After that, \(S\) sends \(M_{A_{3}}\): {\(N_{s2}\), \(Tr\), \(V_{3}\), \(V_{2}\)} to the \(MG_{j}\) as a response. **Step 4, \(MG_{j}\to BS_{i}\): \(M_{A_{4}}=\{N_{s2}\), \(Tr\), \(V_{3}\)}**  
After receiving \(M_{A_{3}}=\{N_{s2}\), \(Tr\), \(V_{3}\), \(V_{2}\}\), the \(MG_{j}\) first computes \(H(ID_{j}\mid 1\ N_{j}\mid 1\ K_{js})\) and checks if the received value \(V_{2}\) is equal to the computed value \(H(ID_{j}\mid 1\ N_{s2}\mid K_{js})\). If it holds, the mutual authentication between the \(MG_{j}\) and the _Server_ is achieved. A session key \(SK_{js}\) is securely agreed by the \(MG_{j}\) and the _Server_. Finally, the \(MG_{j}\) forwards \(M_{A_{4}}=\{N_{s2}\), \(Tr\), \(V_{3}\}\) to \(BS_{i}\). Upon obtaining \(M_{A_{4}}\), \(BS_{i}\) calculates \(H(Tr\mid K_{is})\) and compares it with the received value \(V_{3}\). If these two values are the same, \(BS_{i}\) derives \(Tr_{seq_{max}}=H(K_{is}\mid 1\ N_{s2})\oplus Tr\) and sets \(Tr_{seq}=Tr_{seq_{max}}\), which will be used for the next new authentication session. Finally, \(BS_{i}\) computes a session key \(SK_{is}=H(GPS_{i}\mid K_{is}\mid 1\ Tr)\), which is shared with the _Server_. Obviously, mutual authentication between \(BS_{i}\) and the _Server_ is guaranteed as well.
## 1 Introduction
In the proposed model, the time-domain dilated residual blocks are constructed for down-sampling and up-sampling, they are used to focus on speech contextual information and detailed features during convolution. They are similar to the residual units applied in the time-frequency domain in [11]. Shuffle Attention is used to suppress irrelevant information after down-sampling to improve the enhancement effect. In addition, in the training stage, we are inspired by [22] to define a CS loss function using the measurement of speech, which can further remove noise in noisy speech. In the experiment, we analyze the effectiveness of CS loss and compare the influence of different loss functions on the enhancement results through the ablation experiment. Experimental results show that the CS loss is helpful to improve enhancement performance. Furthermore, the proposed model can achieve higher speech quality and intelligibility with fewer parameters than existing U-Net-based models. Our model has only 32.99% of the parameters of the latest Sinc-SEGAN [9]. Compared with classical Attention-Wave-U-Net [14], the proposed model has improved 11.07%, 9.97%, 4.18%, and 10.70% on PESQ, CSIG, CBAK, and COVL, respectively. Furthermore, compared with causal DEMUCS [15], our model has higher SSNR, SAR and SDR scores, so its speech distortion is less. By removing the types of noise that are different from the open dataset, it is found that the proposed model can still achieve a good enhancement effect, indicating that the proposed model has good generalization performance.
## 2 Materials and Methods  
### Patient Bone Fracture Model and Plate Design  
In order to reconstruct the patient's data to 3D digitized model and design a custom-fit distal tibia fixation plate, a \(\sim\)70 kg female human patient with a spiral distal fractured right tibia (fracture classified 43A1.1) was scanned by computer tomography, as shown in Figure 1[33]. The patient's bone was dislocated and fractured into two fragments. CT raw data is archived in standard Digital Imaging and Communications in Medicine (DICOM) format. The DICOM format files are then transferred and processed in MIMICS software (Materialise, Leuven, Belgium) for segmentation and obtaining the 3D digital models of the spiral fractured tibia. The 3D digital model was exported as \"STL\" format and imported and converted as a solid through Autodesk Inventor 2019 (Autodesk, San Francisco, CA, USA) software to be employed in the finite element bone-plate construct model.  
Figure 1: _Cont._  
The initial custom-fit fixation plate design 'INT-BP' was created in Autodesk Inventor 2019 (Autodesk, San Francisco, CA, USA) based on the 3D digital tibia bone anatomical data and considered a distal tibia locking compression fixation system to treat a spiral distal tibia fracture with a length of 194 mm, of width 13.5 mm and thickness of 4.6 mm, as shown in Figure 1. Synthes distal tibia locking compression plate (X39.912) was used as a design reference [34].
# Perceptual Characteristics of Voice Identification in Noisy Environments
## 3 Perceptual Characteristics for Different Frequency Distributions
### Data Analysis and Results of Experiment 2  
Figure 6 shows the results of voice identity perception in four speech noise environments with different frequency bands removed. The ANOVA analysis was carried out on two impact factors. One is frequency distribution, and the other is SNR. The significance level was set as 0.05. The results are shown in Table 3. The results show no significant difference between the perception data under two different SNR conditions (\(p\) = 0.065), while there is a significant difference in the impact of frequency distributions (\(p\) = 0.02).  
Figure 6: The results of the voice identity perception in four speech noise environments with different frequency bands removed.  
Although the perceptual data under the two SNRs are not significantly different, the difference in accuracy between the four types of speech noises decreases as the SNR increases. When the SNR is \(-8\) dB, the difference in accuracy is 0\(\sim\)0.06. In addition, the difference in accuracy is 0\(\sim\)0.04 when the SNR is \(-4\) dB. As the SNR increases from \(-8\) dB to \(-4\) dB, the influence of 300\(\sim\)640 Hz and 1370\(\sim\)2930 Hz speech noise on voice identity perception is almost unchanged, while the influence of 640\(\sim\)1370 Hz and 2930\(\sim\)6250 Hz speech noise is more obvious.  
There are significant differences in the impact of the four frequency distributions of speech noise. We conducted a least significant difference (LSD) analysis on the perceptual data, and the results are presented in Table 4. It can be seen that the effect of 2930\(\sim\)6250 Hz speech noise on voice identity perception is significantly different from that of the other three frequency bands. However, there is no significant difference between 300\(\sim\)640 Hz, 640\(\sim\)1370 Hz, and 1370\(\sim\)2930 Hz speech noise. For comparison with the perceptual data in the unprocessed speech noise, we added the results obtained in the unprocessed speech noise at \(-4\) dB and \(-8\) dB to Figure 6. The result shows that the influence of 300\(\sim\)640 Hz, 640\(\sim\)1370 Hz, and 1370\(\sim\)2930 Hz speech noise is similar. The removal of the corresponding frequency bands of speech noise improved perceptional accuracy. The 2930\(\sim\)6250 Hz speech noise has a relatively small impact on perception. After removing this frequency band, the perceptual result is closer to the unprocessed speech noise. The two values tend to be consistent when the SNR is \(-4\) dB.