esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Adaptive Multipath Managing:
## 4 Performance Evaluation  
### Simulation Topology  
The performance evaluation was carried out on Network Simulator 2 Version 2.35 (NS-2) [25] with the MPTCP module [26]. The experiments considered a typical MPTCP based device-to-device communication scenario presented in Figure 7. As the figure shows, an MPTCP sender and an MPTCP receiver are connecting with each other via three independent transmission paths (denoted A, B, and C). Path A's bottleneck has a 10 Mbps bandwidth and 10-20 ms propagation delay. Path B's bottleneck has a 11 Mbps bandwidth and 10-20 ms propagation delay. Path C's bottleneck has a 2 Mbps bandwidth and 10-60 ms propagation delay. In the simulation, Path C will experience a short term failure from 10 to 30 s and a complete failure after 50 s of simulation time (we simulated Path C failure by bringing down the bottleneck link between routers \(R_{3,1}\) and \(R_{3,2}\)). The parameters shown in Table 3 were used for configuring multiple paths. The other parameters of the MPTCP used the default values specified in NS-2.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(p_{\text{list}}\) & The collection of all the available paths in the MPTCP connection \\ \(p_{\text{A\_list}}\) & The collection of paths activated for multipathing \\ \(p_{\text{R\_list}}\) & The collection of paths removed from multipathing \\ \(p_{\text{list}(i)}\) & The \(i^{\text{th}}\) path within \(p_{\text{list}}\) \\ \(p_{\text{A\_list}(j)}\) & The \(j^{\text{th}}\) path within \(p_{\text{A\_list}}\) \\ \(p_{\text{R\_list}(k)}\) & The \(k^{\text{th}}\) path within \(p_{\text{R\_list}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations used in the MPTCP-LM\({}^{3}\) based multipath management algorithm.  
In the experiment, all the wireless access links were attached to a Uniform Path-Loss (UPL) model and a Two-state Markov Loss (TML) model in order to simulate the highly frequent and bursty frame loss in the data link layer. The UPL and TML models were used to represent the distributed loss caused by wireless noise interference and the infrequent continuous loss caused by wireless signal fading, respectively [32]. In addition, in order to simulate the complex behaviors of Internet traffic [33], we attached each of the three paths to a VBR (Variable Bit-Rate) traffic generator to send the VBR cross-traffic to its corresponding sink. The packet size of the VBR cross-traffic utilized in the simulation was chosen as follows [34]: 49% were 44 bytes in length, 1.2% 576 bytes, 2.1% 628 bytes, 1.7% 1300 bytes, and the other 46% 1500 bytes in length, in which 90% of the VBR cross-traffic was transmitted by TCP technology and the remaining 10% by UDP technology. The VBR cross-traffic on each of the three paths consumed 0-50% of the access link bandwidth. The simulation time was set to 100 s with infinite FTP traffic.
## 3 Methodology
### Space Syntax Monitoring Scene Analysis
#### 3.2.3 Axial Space Analysis  
Roadways are critical settings for video surveillance, and to optimize camera placement, this study employs space syntax to build an axial line model of road areas, analyzing spatial characteristics as a basis for surveillance optimization. In space syntax, an axial line is defined as the longest line that can be drawn from any point within the spatial configuration. Axial lines are primarily used to describe linear spaces such as roads, simplifying space into lines, which correspond to linear movement through the space. Moving along an axial line is typically the most economical and efficient way of traveling and aligns with natural human movement, giving axial lines dual significance for both visual sightlines and movement. Space syntax further specifies that if axial lines cover a given space, they should be as long as possible, non-overlapping, and minimized in number.  
There are various methods for generating axial lines, primarily involving two steps: line generation and line simplification. Once axial lines are generated, their intersections form an axial line topological graph, as shown in Figure 4, where nodes represent axial lines and edges represent their intersections. After constructing the axial graph, spatial attributes such as connectivity, control value, depth, and average depth value are computed and integrated similarly to those in a convex space graph. In Figure 4c,d, the red means higher integrated importance value and green means lower.  
Figure 4: Example of axial analysis. (**a**) Original map; (**b**) detected axial nodes; (**c**) topology relation graph of axial nodes; (**d**) importance of each axial line.  
Figure 3: Example of visibility analysis.  
After obtaining the attribute values for each axial node, these values are assigned to the corresponding area, with the highest value taken if an area contains multiple axial lines. Since the axial analysis is limited to road spaces in this study, the relative importance of road space within the surveillance system is increased. Spatial attributes are also rasterized to support various familiar forms of quick computation and integration.  
Thus, through space syntax analysis, this study has established multiple attribute raster maps of the surveillance space, encompassing the main types of space syntax--convex space, visibility, and axial lines--each containing multiple sub-attribute maps. Besides the spatial attribute values calculated from space syntax, semantic information also plays a crucial role in camera installation; for instance, doors, windows, and sensitive areas require comprehensive surveillance coverage. Therefore, a semantic raster map is also created, defining areas that need surveillance coverage based on semantic information, as an essential supplement to spatial analysis.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 2 Direct Visual Odometry  
Given a reference image \(\mathcal{I}_{t-1}:\Omega\rightarrow\mathbb{R}\) acquired at time \(t-1\) and an input image \(\mathcal{I}_{t}:\Omega\rightarrow\mathbb{R}\) acquired at time \(t\), we seek to estimate the 3D egomotion of the camera between the frames. Estimating the camera's motion is performed by solving for the parameters of a _warp_, \(\xi_{t-1:t}\), which relates the pixels in \(\mathcal{I}_{t-1}\) to the pixels in \(\mathcal{I}_{t}\). For brevity, we drop the time subscript on the warping parameters and denote it by \(\xi\), and denote the reference image by \(\mathcal{I}\) and the input image by \(\mathcal{I}^{\prime}\). The intensity of a pixel in the reference image is given by \(\mathcal{I}(\mathbf{p})\), where \(\mathbf{p}=\left(u,v\right)^{\top}\in\Omega\). Similarly, the intensity of a pixel in the input image is given by \(\mathcal{I}^{\prime}(\mathbf{p}^{\prime})\), where the position vector \(\mathbf{p}^{\prime}\) is the result after warping \(\mathbf{p}\) according to the parameters \(\xi\),  
\[\mathbf{p}^{\prime}=\mathcal{W}(\mathbf{p};\xi). \tag{1}\]Leaving the warping function undefined for the moment, the images are registered, or aligned, by minimizing the photometric error according to the following objective:  
\[\min_{\xi}\;\sum_{\mathbf{p}\in\Phi}\left\|\mathcal{I}(\mathbf{p})-\mathcal{I}^{ \prime}\big{(}\mathcal{W}(\mathbf{p};\xi)\big{)}\right\|^{2}, \tag{2}\]  
where \(\Phi\subset\Omega\) is a set of selected pixels from the image domain. Solving this expression is non-linear regardless of the warping function, as pixel intensities are unrelated to their coordinates. A common method for minimizing the objective is the Gauss-Newton (GN) algorithm. In general, the expression  
\[\sum_{\mathbf{p}\in\Phi}\left\|\mathcal{I}(\mathbf{p})-\mathcal{I}^{\prime} \big{(}\mathcal{W}(\mathbf{p};\xi+\Delta\xi)\big{)}\right\|^{2}, \tag{3}\]  
is linearized w.r.t. some small change in the parameters, \(\Delta\xi\), and then the parameters are updated by  
\[\xi_{\kappa+1}=\xi_{\kappa}+\Delta\xi\,, \tag{4}\]  
until \(\xi\) and \(\Delta\xi\) converge.  
This formulation, as well as a solution procedure, was first given by [PERSON] and [PERSON] [PERSON] and [PERSON] (2007) in their seminal work, on which parametric image alignment, and subsequently direct VO, is founded. For a detailed presentation of optimization algorithms and alternative formulations, see [PERSON] and [PERSON] [PERSON] and [PERSON] (1999). We will discuss our optimization technique in a latter section after introducing the warping function.
## 2 Experimental Methodology
### Optical Techniques and Methodology
#### 2.3.2 2-Color Pyrometry Technique  
The in-cylinder soot concentration evolution (KL factor) can be analyzed using the 2-Color pyrometry technique by collecting the soot thermal radiation at two different wavelengths. More details about the method and the radiation analysis can be found in [37].  
The setup comprises two identical high-speed cameras (Photron NOVA S9) with different bandpass filters (see Figure 3). One of the cameras is equipped with a filter centered at 660 nm, while the other is centered at 560 nm. Both filters have a 10 nm FWHM. Most of the dynamic range of both cameras was ensured by adjusting the exposure time of them, depending on the operating condition: 0.5 us (660 nm camera) and 5 us (560 nm camera) at 21% O\({}_{2}\), and 0.7 us (660 nm camera) and 20 us (560 nm camera) at 15% O\({}_{2}\). Furthermore, both cameras were coupled with a Karl-Zeiss Makroplanar lens with a 100 mm focal length and a maximum f-aperture of 2. The frame rate was fixed at 25 kfps, corresponding to the same frame rate used for the OH* chemiluminescence imaging, with a resolution of 4.6 pixels/mm.  
For accurate implementation of the 2C algorithm, it is essential to ensure temporal and spatial matching between the signal of two wavelengths. As mentioned earlier, temporal matching is achieved by synchronizing the acquisition of both cameras. Regarding spatial  
Figure 3: A sketch of the optical setup used to apply both OH* chemiluminescence and 2-color pyrometry techniques.  
alignment, it can be accomplished by employing a preprocessing routine that utilizes the reference wavelength to transform the signal at the other wavelength. This routine involves a transformation matrix encompassing translation, rotation, and scaling. Applying a scale-invariant method [38], a set of feature points is extracted from the images of both wavelengths. Subsequently, the matching between the feature points from both lists is calculated to derive the spatial transformation matrix. Finally, this transformation matrix is applied to one of the wavelengths to achieve spatial alignment. It is important to note that the spatial matrix transformation is computed for each iteration.  
Once the transformation matrix has been applied, the 2C algorithm obtains the spatial distribution of KL inside the combustion chamber. It is calculated for each repetition and averaged pixel by pixel (10 repetitions) to avoid the combustion stochastic behavior hindering the analysis. This procedure is applied for the whole combustion event to obtain the temporal evolution of the spatial KL distribution inside the combustion chamber. Furthermore, average time evolution curves are obtained by calculating the ensemble average of KL along the whole field of view. A circular mask (a matrix composed of ones and zeros) is applied to the raw images to consider only the radiation inside the combustion chamber and reject radiation from outside (see Figure 4). The KL distributions reported in this paper are represented using the same intensity range and a 16-color Look Up Table.
## Appendix B Gain and QBER
### Estimation procedure  
The secret key rate formula given by Equation (13) can be lower bounded by  
\[R^{i}\geq qp_{\mathrm{acc}}\big{\{}-Q^{i}f(E^{i})H(E^{i})+(p_{1}^{i}Y_{1}+p_{0 }^{i}Y_{0})[1-H(e_{1}^{U})]\big{\}}, \tag{35}\]  
where \(e_{1}^{U}\) denotes an upper bound on the single-photon error rate \(e_{1}\). Hence, for our purposes, it is enough to obtain a lower bound on the quantities \(p_{1}^{i}Y_{1}+p_{0}^{i}Y_{0}\) for all \(i\in\{s,d\}\), together with \(e_{1}^{U}\). For that, we can directly use the results obtained in [38], which we include in this Appendix for completeness. The probabilities \(p_{n}^{i}\) given by Equation (10) need to satisfy certain conditions that we confirm numerically. In particular, we have that  
\[p_{1}^{i}Y_{1}+p_{0}^{i}Y_{0}\geq\text{max}\Bigg{\{}\frac{p_{1}^{i}(p_{2}^{d} Q^{s}-p_{2}^{s}Q^{d})}{p_{2}^{d}p_{1}^{s}-p_{2}^{s}p_{1}^{d}}+\Bigg{[}p_{0}^{i}-p_ {1}^{i}\frac{p_{2}^{d}p_{0}^{s}-p_{2}^{s}p_{0}^{d}}{p_{2}^{d}p_{1}^{s}-p_{2}^{ s}p_{1}^{d}}\Bigg{]}Y_{0}^{U},0\Bigg{\}}, \tag{36}\]  
where \(Y_{0}^{U}\) denotes an upper bound on the background rate \(Y_{0}\) given by  
\[Y_{0}\leq Y_{0}^{U}=\text{min}\Bigg{\{}\frac{E^{d}Q^{d}}{p_{0}^{d}e_{0}}, \frac{E^{s}Q^{s}}{p_{0}^{s}e_{0}},1\Bigg{\}}, \tag{37}\]  
with \(e_{0}=1/2\). The single-photon error rate \(e_{1}\) can be upper bounded as  
\[e_{1}\leq e_{1}^{U}=\text{min}\Bigg{\{}\frac{E^{d}Q^{d}-p_{0}^{d}Y_{0}^{L}e_{ 0}}{p_{1}^{d}Y_{1}^{L}},\frac{E^{s}Q^{s}-p_{0}^{s}Y_{0}^{L}e_{0}}{p_{1}^{s}Y_ {1}^{L}},\frac{p_{0}^{s}E^{d}Q^{d}-p_{0}^{d}E^{s}Q^{s}}{(p_{1}^{d}p_{0}^{s}-p_ {1}^{s}p_{0}^{d})Y_{1}^{L}}\Bigg{\}}, \tag{38}\]where \(Y_{1}^{L}\) and \(Y_{0}^{L}\) represent, respectively, a lower bound on the yield \(Y_{1}\) and the background rate \(Y_{0}\). These quantities are given by  
\[Y_{1}\geq Y_{1}^{L}=\text{max}\Bigg{\{}\frac{p_{2}^{d}Q^{s}-p_{2}^{s}Q^{d}-(p_{ 2}^{d}p_{0}^{s}-p_{2}^{s}p_{0}^{d})Y_{0}^{U}}{p_{2}^{d}p_{1}^{s}-p_{2}^{s}p_{1}^ {d}},0\Bigg{\}}, \tag{39}\]  
and  
\[Y_{0}\geq Y_{0}^{L}=\text{max}\Bigg{\{}\frac{p_{1}^{d}Q^{s}-p_{1}^{s}Q^{d}}{p_{ 1}^{d}p_{0}^{s}-p_{1}^{s}p_{0}^{d}},0\Bigg{\}}. \tag{40}\]  
To evaluate these expressions we need the statistics \(p_{n}^{i}\) for \(n=0,1,2\). Using Equation (10), and assuming again \(\theta_{\Lambda}=\pi/2\), we obtain  
\[p_{0}^{s} = A_{-}(\zeta), \tag{41}\] \[p_{1}^{s} = \zeta[A_{-}(\zeta)-e^{-\zeta}(I_{1,\zeta}-L_{-1,\zeta})],\] \[p_{2}^{s} = \frac{\zeta}{2}\Big{\{}\zeta A_{-}(\zeta)+e^{-\zeta}\Big{[}\frac {2}{\pi}\Big{(}1-\frac{\zeta^{2}}{3}\Big{)}+(1-2\zeta)(I_{1,\zeta}-L_{-1, \zeta})+\zeta(I_{2,\zeta}-L_{2,\zeta})\Big{]}\Big{\}},\]  
and  
\[p_{0}^{d} = A_{+}(\zeta), \tag{42}\] \[p_{1}^{d} = \zeta[A_{+}(\zeta)-e^{-\zeta}(I_{1,\zeta}+L_{-1,\zeta})],\] \[p_{2}^{d} = \frac{\zeta}{2}\Big{\{}\zeta A_{+}(\zeta)+e^{-\zeta}\Big{[}- \frac{2}{\pi}\Big{(}1-\frac{\zeta^{2}}{3}\Big{)}+(1-2\zeta)(I_{1,\zeta}+L_{- 1,\zeta})+\zeta(I_{2,\zeta}+L_{2,\zeta})\Big{]}\Big{\}}.\]
_Appl. Sci._**2015**, \(5\), 77-87; doi:10.3390/app5020077  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Excess Phase Noise Characterization in Multifrequency Remote Clock Distribution Based on Femtosecond Frequency Combs  
**[PERSON] 1,2,\({}^{\dagger}\), [PERSON] 3,\({}^{\ddagger}\), [PERSON] 1 and [PERSON] 1,\({}^{\star}\)**  
1 Department of Physics, University of Alabama in Huntsville, Huntsville, AL 35899, USA;  
E-Mail: [EMAIL_ADDRESS]  
2 Research Institute of Telecommunications Transmission, Beijing 100191 China;  
E-Mail: [EMAIL_ADDRESS]  
3 Department of Electrical and Computer Engineering, University of South Alabama, Mobile, AL 36688, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-256-824-2138; Fax: +1-256-824-6873.  
Academic Editor: [PERSON]  
_Received: 7 March 2015 / Accepted: 28 April 2015 / Published: 7 May 2015_  
**Abstract:** Remote distribution of optical frequency references, based on multifrequency sources such as femtosecond frequency combs, holds many advantages over its single-frequency counterpart. However, characterizing the excess noise caused by the transmission links or external perturbations in a multifrequency scheme posts new challenges. We have experimentally demonstrated direct measurement of excess phase noise spectrum in both free-space and fiber-optic transfer of a frequency comb using a multiheterodyne technique. In fiber-optic distribution, we focused on the excess phase noise under a single-tone acoustic perturbation. Increased overall noise power and a change of phase noise spectrum have been observed. In free-space distribution, a fractional instability of \(3\times 10^{-14}\) at 1 s was observed for a 60 m outdoor atmospheric transmission, and large phase modulation due to air fluctuations causes a sizable line broadening.  
**Keywords:** clock distribution; optical frequency comb; multiheterodyne; phase noise; fiber optics; free-space communication
## 3 Results and Discussion
### Roughness  
The results from the DektakXT profilometer used to perform a Hills and Valleys profile on both treated and untreated specimens is displayed in Figure 10. The untreated surface is 3.2 times rougher than the treated surface. A control factor for fatigue strength in specimens with high surface roughness is crack propagation, whereas for specimens with low surface roughness it is crack initiation [86]. Additional stress raisers are introduced as a result of rougher surfaces; increasing the number of potential crack initiation sites. Stress raisers as a result of rougher surfaces will reduce crack initiation life and the fatigue limit. Therefore, smooth surfaces can be considered as a contributing factor to the improved fatigue life, as seen by the fatigue performance of the treated samples with improved surface roughness.  
The effects of UIT on the AM titanium surface are also observed using a two-dimensional map scan as shown in Figure 11. The peaks and valley analysis clearly illustrate the effects on both surface roughness and surface porosity. The untreated surface has a much higher density of hills and valleys unlike the treated surface, which has a much more uniform surface and less pronounced differences between peaks and valleys. The highly irregular surface of the DMLS titanium is attributed to the manufacturing process and incomplete fusion. The final layer of metal powder during printing is not completely melted and produces a very rough surface. The high impact forces of the UIT helps diminish these effects and improves the surface quality and fatigue performance.  
Figure 11: Roughness map scans between the (**a**) untreated and (**b**) treated surfaces.  
Figure 10: The average roughness of treated and untreated specimens measured by DektakXT.
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 6 Challenges and Future Recommendations
### Video Camera Related Challenges  
* Postures may be partially occluded by objects or other body parts [72; 118].
* Real-time processing is more confusing than offline applications, especially in healthcare, sports, and military applications [60; 126].
* Environment conditions might affect the accuracy of the recognition, such as light, distance, camera quality and angle, and outside weather conditions [90; 116].
* Data collection and privacy is another critical point to consider. Private data usage concerns affect this research works [87; 124].
* Training and testing data availability is another challenge. Usually in machine learning algorithms we need to collect large datasets, which is time-consuming and expensive [84; 121; 128].
* There are several wearable devices, and cameras are used in posture recognition. It might be difficult to accept by users [74; 88].
* It is difficult to make a general model because there are several types of postures and clothes [116].
* Ambiguity is another challenge. Postures might close each other, and our models cannot distinguish between them [66; 98].
## 8 Simulation Results and Discussion
The calculated factor of safety and the corresponding slip surface are presented in Figure 14.  
The obtained factor of safety (FS \(<<\) 1.0) for Sector 6 indicates the presence of unstable rock blocks, which, under the assumed joint network configuration, are susceptible to sliding, thus explaining the very low FS value. The potential slip surface is in the upper portion of the slope. Based on the analysis results, it can be concluded that Sector 6 is characterised by unstable rock blocks that are likely to initiate slope instability processes, including sliding and rockfall. The identified slip surface is presented in Figure 15.  
The stability analysis for Sector 7 was conducted under two scenarios:  
* Scenario 1: The slope above the rock wall is composed of debris material (according to [52]):
* Scenario 2: The slope is composed of weathered material, consistent with the assumptions in the other cross-sections. Figure 13: The slip surface for Sector 4 identified via the total displacement distribution: FS \(<<\) 1.0. Figure 14: The slip surface for Sector 5 identified via the total displacement distribution; FS = 1.17. Figure 15: The slip surface for Sector 6 identified via the total displacement distribution; FS \(<<\) 1.0. When the slope above the rock wall is composed of debris, the calculated safety factor of 1.60 indicates a very low probability of landslide occurrence. However, when the slope is composed of weathered material, the resulting factor of safety of 1.20 suggests probable landslide occurrence in Sector 7. In both cases, the potential slip surface is located within the weathered/debris layers; however, in the debris scenario, individual blocks exhibiting localised slip surfaces were also identified. The lithology and shear strength parameters of the layers overlying the rock wall should be verified through detailed field investigations and laboratory testing. The stability analysis for Sector 8 (similarly to Sector 7) was performed under two scenarios: (i) the slope located above the rock wall composed of debris material (as per [52]), and (ii) the slope composed of weathered material, consistent with the assumptions used in the other cross-sections. The calculated factor of safety for both scenarios involving the weathered layer and the debris layer exceeds a value of 2.0, indicating that the occurrence of landslides in Sector 8 is highly unlikely. However, the possibility of local rockfalls and minor slides cannot be entirely excluded. The potential slip surface is within the weathered material and/or debris layers.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.1 Cyclic Redundancy Check (CRC)  
The CRC is an error detection technique in which a checksum is generated and appended to the payload. In NB-IoT, the checksum (CRC bits) is 24 bits, generated by 24 registers and XORs according to the following polynomial [10]:  
\[CRC24(D)=D^{24}+D^{23}+D^{18}+D^{17}+D^{14}+D^{11}+D^{10}+D^{7}+D^{6}+D^{5}+D^{4 }+D^{3}+D+1 \tag{1}\]  
As shown in Figure 2, the CRC block is implemented as a shift register with the length of the CRC polynomial with the input data stream connected to the least significant flip-flop. The flip-flops that correspond by location to the ones in the CRC polynomial are connected to XORs to be XOR-ed with ones. In the meantime, multiplexers are placed to decide whether the register content or the XOR output will be passed to the next block [10; 11].