esa-sceva/satcom-chunk-collection huggingface dataset:
# Video over DSL with LDGM Codes for Interactive Applications
## 6 Conclusions  
This study has shown study that industry-standard 2D parity codes underperform in terms of combined latency and error recovery. RS codes are attractive in terms of error recovery but are not so attractive for low-latency applications of video streaming. Whenever there is interactivity RS codes at the application layer potentially result in a lack of synchronization between two communicating parties. This problem arises owing to their computational complexity and the need to apply them in an interleaving mode, which increases the latency budget. As an alternative, this paper proposes that LDGM codes with small block lengths represent a natural candidate for low-latency interactive video streaming and, as results quoted in this paper indicate, can lead to up to a 4 dB reduction in video distortion for active sports sequences. The coding overhead is just a 9% increase in datarate. Furthermore, by rearranging columns of the parity-check matrix with the least error-recovery properties it is possible to improve the average response to error bursts. Given that rateless codes may have patents applied, LDGM codes offer a further commercial advantage, because licensing fees no longer apply. The latter advantage makes LDGM codes suitable for applications such as telemedicine in which video streaming does not generate a compensating revenue stream.  
Future work will check the performance of these codes against video content other than the sports sequences so far investigated. The HEVC codec is aimed at high definition (HD) video and, hence, the HD video should also be investigated, confirming the real-time performance. In general, as the number of packets in HD video communication is much larger than for standard definition (SD) video the impact of any one packet loss in terms of error propagation is expected [51] to be less than that for SD video. Consequently, a pure FEC technique such as in this paper may be even more effective for HD video. In general, the real-time transmission and FEC coding time will scale linearly according to the number of packets in an HD video frame relative to an SD frame. Thus in [44], there were 31 and 68 rows of macroblocks for SD and HD respectively, with coding at rates of 4 and 16 Mbps respectively using H.264/AVC. HEVC [1] has approximately 50% more coding efficiency than H.264/AVC, which clearly saves bandwidth. However, paradoxically, increased coding efficiency, reduces the error protection arising from LDGM FEC (at the same rate as applied to H.264/AVC streams) because greater coding efficiency implies greater dependencies between the bits in the video bitstream.  
[PERSON] and [PERSON] were chiefly responsible for the experimental contributions in this paper, whereas [PERSON] and [PERSON] contributed to theoretical development. [PERSON] was also responsible for the presentation of this paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Prototype Development System  
#### 3.1.1 Eccentric Correction  
To determine the eccentricity value between the rotation platform and samples, the measurement results must be first converted to X\({}_{0}\) relative offset values.  
The X\({}_{0}\) relative offset value (\(\delta\)) is obtained by deducting all the measured values from the first measured value. In the expression \(\delta(i)=X_{i}-X_{0}\), \(i=0\)-N, where N is the total number of measurement points for the entire circle, and \(\delta(0)=0\) is the first offset value.  
Figure 4a shows the origin translated by subtracting the starting X\({}_{0}\) value, meaning that the first measurement value \(\delta(0)\) is transferred to the 0 value as \(\delta(0)=0\).  
In the standard ring gauge measurement, the conversion result of the X\({}_{0}\) relative offset shows the effect of eccentricity. The maximum and minimum values of \(\delta\) equal X max and X min in Equation (11); Equation (11) is used to calculate the eccentricity value \(\mathbf{r^{\prime}}\), and \(\mathbf{r^{\prime}}\) is substituted into Equation (8), to determine the eccentric correction curve.  
In the eccentric correction of the prototype system, only the ring gauge eccentricity value was used.  
Figure 7 shows the measurement results of the ring gauge that have been converted into X\({}_{0}\) relative offset values. In addition to the sinusoidal oscillation trend, this X\({}_{0}\) relative offset curve also includes the high-frequency error caused by the reducer vibration. The prototype system did not filter the measurement results.  
All results are compared with the relative offset converted to X\({}_{0}\), as shown in Figure 8. As shown in Figure 8a, after calculation of the eccentricity value \(\mathbf{r^{\prime}}\), using Equation (11), the \(\mathbf{r^{\prime}}\) value is used to obtain the eccentric correction curve, using Equation (8). Figure 8b shows the result of subtracting the eccentric correction curve. If the curve is a horizontal line and all values are 0 mm, this means that all the values are the same as the first set of data; values closer to 0 imply that the error is small.  
Figure 8: \(Cont\).  
Figure 7: Ring gauge measurement results translated to X\({}_{0}\) relative offset data (0.25 rpm with RS-232).  
Figure 8b shows that there are 80 periods of the wavelet phenomenon, and the number of small noise peaks is 80, which is same as the reducer ratio; this indicates the occurrence of loading deformation caused by the meshing of the flexible reducer and the fixed wheel on the harmonic reducer [22]. Because the speed of the prototype system is only 0.25 rpm and the RS-232 has almost no signal transmission noise, the reducer ratio of 1:80 can be determined clearly.
# A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph
## 4 Cycle Detection and Removal with Vertex Priority
### Graph Simplification  
In Algorithm 1, line 1 simplifies the given graph \(\mathbf{G}\) by deleting the vertices and arcs that do not belong to any cycle. This step aims to decrease the scale of the graph, especially when \(\mathbf{G}\) is sparse. In fact, many graphs derived from real world are sparse [19]. Table 1 shows seven digraphs taken from SNAP (Stanford Network Analysis Platform) [20]. We can see that more than 50% of vertices having zero in- or out-degree. The debt graph is sparser than these graphs, as shown in Section 5.3.  
**Proposition 1**.: _Given a digraph \(\mathbf{G}=\left\{\boldsymbol{V},\boldsymbol{E},\boldsymbol{W},\boldsymbol{P}\right\}\), a vertex \(v\in\boldsymbol{V}\) does not belong to any cycle if its out-degree or in-degree is 0._  
According the above proposition, Algorithm 2 shows the simplification process. It first gets the in- and out-degrees of vertices (lines 2-5). The _while_ loop (lines 7-18) repeats deleting vertices of \(d_{i}^{in}=0\) or \(d_{i}^{out}=0\), and their corresponding arcs. Lines 8-14 find the first vertex \(v_{k}\) satisfying Proposition 1. If such a vertex exists (line 15), it calls Algorithm 3 to delete this vertex. Algorithm 3 uses two _for_ loops to delete the arcs associated with \(u\) (lines 2-5 and 6-9) and \(u\) itself (line 10).  
```
1 FunctionSimplify(\(\mathbf{G}\))
2 forall\(v_{i}\in\boldsymbol{V}\)do
3\(d_{i}^{in}\leftarrow\) in degree of \(v_{i}\)
4\(d_{i}^{out}\leftarrow\) out degree of \(v_{i}\)
5 endfor
6\(k\gets 1\)
7 while\(k>0\)do
8\(k\gets 0\)
9 for\(i\gets 1\) to \(|\boldsymbol{V}|\)do
10 if\(v_{i}\in\boldsymbol{V}\ \wedge\ (d_{i}^{in}=0\ \lor\ d_{i}^{out}=0)\) then
11\(k\gets i\)
12 break
13 endif
14 endfor
15 if\(k>0\)then
16 Delete(\(\mathbf{G},v_{k}\))
17 endif
18 endwhile
19 return\(\mathbf{G}\)
20 endfunction
```  
**Algorithm 2**. **Simplify a Weighted Digraph**  
\begin{table}
\begin{tabular}{c|c c c c c c c} \hline \hline
**Graph Name** & **p2-Gnutella104** & **p2-Gnutella25** & **p2-Gnutella30** & **p2-Gnutella31** & **Email-EuAll** & **Web-NotreDame** & **Wiki-Talk** \\ \hline \(|\mathbf{V}|\) & 10,876 & 22,687 & 36,682 & 62,586 & 265,214 & 325,729 & 2,394,385 \\ \(|\mathbf{E}|\) & 39,994 & 54,705 & 88,328 & 147,892 & 420,045 & 1,497,134 & 5,021,410 \\ ratio\({}^{1}\) & 54,805 & 74,065 & 74,12\% & 74,29\% & 95.79\% & 57.65\% & 94.88\% \\ \hline \end{tabular}  
\({}^{1}\) “ratio” is the ratio of vertices of out- or in-degree 0 to \(|\mathbf{V}|\).  
\end{table}
Table 1: The sparse characteristic of Stanford network analysis platform (SNAP) graphs.
## 5 Discussion
### Comparison Results with State-of-the-Art Models
#### 5.4.1 Comparison Results from UCI-HAR Dataset  
To confirm the effectiveness of our ConvResBiGRU-SE model, we compare its performance with several state-of-the-art methods on the UCI-HAR dataset. Table 8 shows the comparative analysis, which includes various deep learning architectures. These methods encompass different strategies for HAR, from traditional convolutional and recurrent architectures to more advanced hybrid models.  
Table 8 compares the proposed ConvResBiGRU-SE model with various prominent HAR methodologies utilizing the UCI-HAR dataset. The results underscore the remarkable efficacy of our model, attaining an accuracy of 99.18%, substantially surpassing current methodologies.  
Historically, earlier methods, such as [PERSON] et al.'s Residual BiLSTM [42] and [PERSON] et al.'s CNN [45], achieved accuracies of 93.6% and 91.98%, respectively. More recent advancements, including [PERSON] et al.'s iSPILnception [46] and [PERSON] et al.'s EnsHAR [47], showed improved accuracies of 95.09% and 95.05%, respectively. This steady increase in accuracy over the years indicates continuous progress in HAR model architectures.  
When evaluating different architectural strategies, simpler models like CNN and Hierarchical Deep LSTM had relatively modest accuracies of 91.98% and 91.65%, respectively. Hybrid architectures, such as [PERSON] et al.'s LSTM-CNN model [43], performed better with an accuracy of 95.78%, suggesting the advantages of combining various neural network components. Nonetheless, our proposed ConvResBiGRU-SE model surpasses these hybrid approaches by over 3%.
## 3 Proposed AR HUD System
### AR HUD Interface Position  
To provide an unobtrusive vision to the external scene, the projection of the three symbols is consistent with the visual elements' recommendations of SAE J2831 [37].  
Furthermore, the projected AR interface appears approximately 2.5 m from the windshield to avoid the effect of \"double accommodation\" of the driver's gaze an issue that forces the user to focus either on the external environment (far view) or in the close-proximity projection (short view) [23; 28]. Although the projection is within the driver's field of view (FOV), the interface appears in the lower section of the windscreen for two main reasons. The first is that the particular section, according to previous studies is ideal for the non-urgent type of information, which register within the driver's perspective yet have minimum-attention requirements [38; 39].  
Previous studies investigating the optimal position of HUD-based messages suggested that the ideal location for a specific application depends on the relative importance attributed to this by interface designers and previous performance metrics collected [23; 24; 40]. Hence, depending on the type of information (i.e., crucial/urgent or supplementary/non-urgent) that will be superimposed onto the windshield, it is feasible to \"highlight\" or \"shadow\" its readability by changing its position. Where the middle sections are reserved for crucial information (i.e., collision warnings) whilst higher and lower, closer to the edges of the windshield, is ideal space for supplementary information (i.e., infotainment).  
Secondly, the selected projection position, closer to the dashboard, was deemed ideal because the driver can access and interact with the AR interface with a small pointing gesture without taking the hands off-the-steering wheel.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 4 Attitude Point-to-Point Generator
The attitude guidance is represented by the quaternion time-history, calculated by using the differential equation of quaternion kinematics, \(\dot{\mathbf{q}}=1/2\,\Omega(t)\mathbf{q}_{0}\), where \(\mathbf{q}_{0}\) is the initial attitude of the satellite. The solution is retrieved by integrating over the time interval \([0;t_{f_{1}}]\). Therefore, if the satellite follows the attitude guidance of step 1, represented by the quaternion time history, the rotation would be stopped and the attitude reaches the final condition \(\mathbf{q}_{f_{1}}\).
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.4 Clustering  
Multi-objective optimization based clustering schemes are being preferred over the single objective optimization based clustering techniques. Multi-objective optimization facilitates to consider multiple optimization criteria while formulating the clustering as an optimization problem. For example in [59], an automatic clustering technique is proposed which is based on the hybrid evolutionary algorithm immunized PSO. A multi-objective optimization based clustering algorithm has been proposed in [60] which simultaneously optimizes network life time, energy consumption, dead sensor nodes and delivery of total data packets to the base station. The authors argued that the proposed clustering algorithm based on particle swarm optimization gave better results as compared to the other existing methods.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 5 Conclusions  
This paper proposed a tunnel-free mobile IPv6 protocol using a DMM approach. In addition, it analyzed and compared the existing IPv6 mobility management protocols in terms of the handover latency, handover blocking probability, and packet loss. From the results of the analysis conducted, it was confirmed the proposed tunnel-free approach with MBB concept allowed an MN to prepare for the handover beforehand, and anchored the transmission from the current BMA instead of tunneling it from the previous BMA, avoiding a reduced handover delay, consequently reducing the handover blocking probability and data packet loss.
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
## 3 Energy Harvesting Application
### Experiment with Metglas 2605 SA1
Therefore, another important figure of merit lies in the ratio of \(\Delta B\) over \(\Delta\sigma\), denoting the magnetostrictive coupling. In [43], the \(\Delta B/\Delta\sigma\) of the devices using Terfenol-D could reach 0.024 T/MPa. In [44], another energy harvesting device using Galfenol has a ratio of 0.034 T/MPa. This value of flux density variation of stress variation for Metglas 2605 SA1 is presented in Figure 12. It shows that the optimal condition is achieved around 100 A/m with a ratio reaching nearly 0.015 T/MPa, about 63% of that of Terfenol-D. This figure demonstrates the interest in using Metglas 2605 SA1 in a low magnetic excitation zone while Terfenol and Galfenol usually require bias fields higher than 20 kA/m, as shown in Table 1, and therefore bulky magnets bringing a large reluctance to the magnetic circuit. Metglas 2605 SA1 is most active at much lower magnetic excitation field, which is consistent with a realistic implementation of magnetostrictive microgenerators in addition to cost-effectiveness. Figure 11: Estimated harvestable energy density of Metglas 2605 SA1 in a unipolar cycle with the model in the range of 0–4 kA/m. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **m/cm\({}^{3}\)** & **mJ/cm\({}^{3}\)** & **mJ/cm\({}^{3}\)** & **mJ/cm\({}^{3}\)** & **mJ/cm\({}^{3}\)** & **mJ/cm\({}^{3}\)** \\  & **(\(-\)30 MPa)** & **(\(-\)18 MPa)** & **(\(-\)6 MPa)** & **(6 MPa)** & **(18 MPa)** & **(30 MPa)** \\ \hline Experimental & 1.18 & 0.64 & 0.19 & 0.04 & 0.26 & 0.29 \\ \hline Model & 1.36 & 0.58 & 0.13 & 0.09 & 0.19 & 0.23 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental validation of the estimated energy density of one unipolar cycle under 4 kA/m.
## 3 Sub-Zoning Pressure Prediction Results for the Supports in the Ultra-Long Mining Face  
### Prediction of Support Resistance  
Based on the resistance monitoring data for the supports in an ultra-long fully mechanized mining face in Shaanxi, a roof-weighting criterion value was generated according to the historical data collected during the advancement of the mining face. Then, the AGCRN model was applied to predict the changes in the working resistance of the supports in the #202 mining face. When the predicted resistance value of the support exceeded the threshold, the ultra-long mining face was considered to be under roof weighting. The prediction results serve as valuable guidance for whether production dispatchers should promptly execute an emergency response plan.  
Due to space limitations, only support #60 in the lower measurement area is discussed to illustrate the results.  
Figure 8 shows the predicted working resistance of support #60 in the lower measurement area of mining face #202. The green line represents the measured end-of-loop resistance of the support, the red line represents the predicted value of the end-of-loop resistance of the support based on the AGCRN model, and the yellow line represents the value of the roof-weighting criterion of the ultra-long fully mechanized mining face. The errors in the predicted working resistance and roof-weighting interval were both within 5%, suggesting that the AGCRN model can achieve the effective prediction of the resistance of the support and the roof weighting of the mining face.  
Figure 7: Comparison of the performances of the AGCRN model and four reference models, they should be listed as: (**a**) LSTM; (**b**) GRU; (**c**) DCRNN; (**d**) AGCRN.
# Thermal Relaxation Spectra for Evaluating Luminescence Quantum Efficiency of CASN:Eu2+
## 2 Materials and Methods
### Method 1  
A cube beam splitter (BS) and a mirror were put before the Sagnac interferometer, as illustrated in Figure 1. The probe laser beam from a He-Ne laser (R-32734, Newport, Irvine, CA, USA) was divided with the beam splitter into the two equal-intensity beams used as the signal and reference beams. The signal beam is transmitted through the refractive index gradient while the reference beam is bypassing it. The interference of both probe and reference laser beams was detected at the same time with two photodiodes after the Sagnac interferometer on the two conditions with/without the excitation light. The signal beam contains information of both the photothermal effect of the sample and the noise factor of the probe laser beam. The reference beam carries only the noise factor common to the probe laser beam. The two photodiodes were connected in the opposite directions to subtract the photocurrent of the reference beam from that of the signal beam. Thus, the residual output signal from the two combined photodiodes carries only the photothermal signal without the probe beam noise. The photocurrent generated from the two photodiodes was input to the lock-in amplifier (7265, Signal Recovery, Edinburgh, UK). Subtraction of the common noise from the probe laser beam was thus successfully performed to achieve the improvement in the S/N of the photothermal deflection detection.  
The interference light intensities of the signal and reference beams were adjusted to the same intensity to subtract the noise of the probe laser beam. These two probe laser beams were propagating in parallel via adjusted mirror angle before the BS.  
The focal length of the plano-convex lens at the input of the interferometer was 300 mm in both method 1 and 2. The beam waist at the sample was calculated to be 166 \(\upmu\)m from the diameter 1.4 mm at the lens, the lens focal length, and the wavelength of He-Ne laser. The modulation frequency of the excitation light was 20.0 Hz.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 3 Our Perceptual Hash Algorithm Design
### Integrity Authentication  
At the receiver, the authentication process is implemented via the comparison between the reconstructed perceptual hash value and the original one: the higher the perceptual hash values' difference, the greater the corresponding images' difference. Although the hamming distance is frequently used to evaluate the difference between two sequences, it is not suitable for this purpose, because the length of the hash value may vary along with the change of algorithm parameters. We have adopted the followed \"Normalized Hamming Distance\" [25] to evaluate the difference between two hash values:  
\[Dis=\left(\sum_{i=1}^{L}\lvert h1(i)-h2(i)\rvert\right)/L \tag{10}\]  
where \(h_{1}\) and \(h_{2}\) are perceptual hash values with \(L\) length. It is observed that the normalized hamming distance \(Dis\) is a float between 0 and 1. If the \(Dis\) of two perceptual hash values of the same area is lower than the threshold \(T_{h}\), it means that the corresponding area is content-preserving; otherwise, it means that the content of the corresponding area has been tampered with.  
Furthermore, the tampering can be located in the corresponding geographic regions by comparing each hash value of each grid. The higher the resolution of the grid division, the more fine-grained the authentication granularity can be. To obtain higher resolutions, we need to divide the image into more grids, compute more unit perceptual hash values, and compare more hash values.
###### Abstract  
The ionospheric response and the associated mechanisms to geomagnetic storms are very complex, particularly during the February 2014 multiphase geomagnetic storm. In this paper, the low-latitude ionosphere responses and their coupling mechanisms, during the February 2014 multiphase geomagnetic storm, are investigated from ground-based magnetometers and global navigation satellite system (GNSS), and space weather data. The residual disturbances between the total electron content (TEC) of the International GNSS Service (IGS) global ionospheric maps (GIMs) and empirical models are used to investigate the storm-time ionospheric responses. Three clear sudden storm commence (SSCs) on 15, 20, and 23 February are detected, and one high speed solar wind (HSSW) event on 19 February is found with the absence of classical SSC features due to a prevalent magnetospheric convection. The IRI-2012 shows insufficient performance, with no distinction between the events and overestimating approximately 20 TEC units (TECU) with respect to the actual quiet-time TEC. Furthermore, the median average of the IGS GIMs TEC during February 2014 shows enhanced values in the southern hemisphere, whereas the IRI-2012 lacks this asymmetry. Three low-latitude profiles extracted from the IGS GIM data revealed up to 20 TECU enhancements in the differential TEC. From these profiles, longer-lasting TEC enhancements are observed at the dip equator profiles than in the profiles of the equatorial ionospheric anomaly (EIA) crests. Moreover, a gradual increase in the global electron content (GEC) shows approximately 1 GEC unit of differential intensification starting from the HSSW event, while the IGS GIM profiles lack this increasing gradient, probably located at higher latitudes. The prompt penetration electric field (PPEF) and equatorial electrojet (EEI) indices estimated from magnetometer data show strong variability after all four events, except the EEI's Asian sector. The low-latitude ionosphere coupling is mainly driven by the variable PPEF, DDEF (disturbance dynamo electric fields), and Joule heating. The auroral electrojet causing eastward PPEF may control the EIA expansion in the Asian sector through the dynamo mechanism, which is also reflected in the solar-quiet current intensity variability.  
**Keywords:** ionosphere; geomagnetic storm; total electron content (TEC); global navigation satellite systems (GNSS); space weather