esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modulation Effects and Dynamics Symptom of Planet Gear Fault
### Dynamic Model of Planetary Gear
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nr** & **96** & **Np** & **34** \\ \hline Ns & 28 & NP & 4 \\ \(m_{\text{s}}\) & 0.29 kg & \(I_{\text{s}}\) & \(2.1\times 10^{-4}\) kg\(\cdot\)m\({}^{2}\) \\ \(m_{\text{P}}\) & 0.34 kg & \(I_{\text{P}}\) & \(2.9\times 10^{-4}\) kg\(\cdot\)m\({}^{2}\) \\ \(m_{\text{r}}\) & 3.64 kg & \(I_{\text{r}}\) & \(5.05\times 10^{-4}\) kg\(\cdot\)m\({}^{2}\) \\ \(m_{\text{c}}\) & 2 kg & \(I_{\text{c}}\) & \(4.9\times 10^{-3}\) kg\(\cdot\)m\({}^{2}\) \\ \(k_{\text{s}}\) & \(1\times 10^{8}\) N/m & \(c_{\text{s}}\) & \(1\times 10^{5}\) N/m \\ \(k_{\text{st}}\) & \(1\times 10^{8}\) N/m & \(c_{\text{st}}\) & \(1\times 10^{5}\) N/m \\ \(k_{\text{r}}\) & \(1\times 10^{9}\) N/m & \(c_{\text{r}}\) & \(8\times 10^{4}\) N/m \\ \(k_{\text{rt}}\) & \(1\times 10^{10}\) N/m & \(c_{\text{rt}}\) & \(1\times 10^{7}\) N/m \\ \(k_{\text{P}}\) & \(1\times 10^{8}\) N/m & \(c_{\text{p}}\) & \(1\times 10^{5}\) N/m \\ \(k_{\text{pt}}\) & \(1\times 10^{8}\) N/m & \(c_{\text{pt}}\) & \(1\times 10^{5}\) N/m \\ \(k_{\text{c}}\) & \(1\times 10^{8}\) N/m & \(c_{\text{c}}\) & \(1\times 10^{5}\) N/m \\ \(k_{\text{ct}}\) & \(1\times 10^{8}\) N/m & \(c_{\text{ct}}\) & \(1\times 10^{5}\) N/m \\ \(T_{c}\) & 1 Nm & \(T_{s}\) & 0.2258 Nm \\ Module & 3 & Tooth width & 20 mm \\ \(\text{PT}_{\text{HR}}\) & 17 & Sampling rate & 25,600 Hz \\ Fault tooth & P177 * & & \\ \hline \hline \end{tabular}  
* P177 means tooth 7 on planet gear 1.  
\end{table} 
Table 1: Dynamic simulation parameters. Figure 6: Dynamic simulation signal: (**a**) fault condition and (**b**) health condition. gearbox runs. Its period is 0.2 s as well, which indicates that the vibration signal is modulated by the planetary movements. The dynamic simulation signal reveals that:  
1. under health condition, planetary gearbox generates vibrations since meshing stiffness is time varying. 2. damage on planet gear mainly influences meshing stiffness when planet gear meshes with ring gear or sun gear. 3. planetary gearbox vibration signal is modulated by the planetary movements. 4. vibration signal under fault condition contains more impulsive signal components that are caused by the damage on planet gear, but the regular vibrations are still significant and dominant.
## 4 Performance Evaluation
### Empirical Analysis of WR-Store  
It is important to understand the characteristics of the Windows registry because the performance of WR-Store depends on the internals of the Windows registry. However, the internals of the Windows registry have not been known or reported much. In this section, we perform the empirical analysis of WR-Store to understand the characteristics of its performance. As explained in Section 3.2, we have two parameters affecting the performance of WR-Store: (1) the depth of the registry, \(WR_{D}\), and (2) the length of each subkey of the registry, \(WR_{L}\). We represent each setting of WR-Store as \(xD\_yL\) where \(x\) is a value for \(WR_{D}\) and \(y\) is for \(WR_{L}\). Here, we use _KVData2_ as the data set and measure OPS (operations per second) while the number of continuous operations is fixed as 1 thousand.  
Figure 5 shows the performance of WR-Store as \(WR_{D}\) is increased while \(WR_{L}\) is fixed as 1; Figure 6 shows the performance of WR-Store as \(WR_{L}\) is increased while \(WR_{D}\) is fixed as 1. In both experiments, when \(WR_{L}\times WR_{D}\) is 2, 3, and 4, the performance of WR-Store is relatively better than the other cases. \(WR_{L}\times WR_{D}\) determines the number of values under a registry key, and we conclude that it is more important factor to the performance of WR-Store than each of \(WR_{L}\) and \(WR_{D}\). Therefore, we need to find the most efficient value for \(WR_{L}\times WR_{D}\), and then, we need to select proper values for \(WR_{L}\) and \(WR_{D}\) for a determined value for \(WR_{L}\times WR_{D}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Key-Value Stores** & **Size (KB)** & **Ratio** \\ \hline WR-Store & 26 & 1.00 \\ RocksDB & 3997 & 153.73 \\ BerkeleyDB & 1890 & 72.69 \\ LevelDB & 462 & 17.77 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The size of the executable file.  
Figure 5: Performance of WR-Store as \(WR_{D}\) is varied. (**a**); Read throughput (**b**); Write throughput; (**c**) Delete throughput.
## 4 Experimental Result and Discussion
### Case Study and Analysis
(2) Shanghai and Xi'an  
From Figures 5 and 6, a mobile pollution source is prominent in Shanghai, which promotes NO\({}_{2}\) and further helps PM\({}_{2.5}\) occupy the majority of the particle pollution. Excessive emission of Volatile Organic Compounds (VOCs) and a high temperature leads to the dominance of O\({}_{3}\) during the year, except in winter. The \"Weekend effect\" and \"holiday effect\" for daily concentrations (Figure 5) and \"Peak Type\" for hourly concentrations (Figure 6A-D) appear in Shanghai too, which is common in a metropolis. Two methods have similar performances in terms of their daily reports (Figure 5), and their CRs (T) are 85.48% and 48.77% (0.62) for the chief pollutant and level. From Figure 6A-D, exaggerated hazard of particulate matter by the AQI often leads to a questionable chief pollutant (mainly PM\({}_{2.5}\) and PM\({}_{10}\)) and level. Even though when particulate matter is the actual chief pollutant, the level of the AQI is seriously overvalued too. On the contrary, based on the actual pollutant concentration, results of the AQFCE in hourly report are more convincing. Excluding the influence of particulate matter, the CR has a sharp rise about 40% of both the chief pollutant (47.59%-84.76%) and level (58.74%-96.99%). Figure 6: Hourly pollutant concentration variation and air quality evaluation result of chief pollutant and level of Shanghai. It is clear from Figure 7 that ozone pollution is also a tough problem in the summer of Xi'an. Perennial pollution transport from the northeast and local pollution emission as well as sand dust transport from the northwest cause severe particle pollution in autumn, winter (mainly PM\({}_{2.5}\)) and spring (mainly PM\({}_{10}\)). From Figure 7, comparison results of daily and hourly reports are similar to Beijing and Shanghai. The AQFCE and AQI have an approximate performance in chief pollutant (83.84%) and level (49.9%/0.78) for daily reports (Figure 7), while huge divergences on the chief pollutant in summer (Figure 8D) and level in winter (Figure 8B) stem from magnified particle pollution by the AQI for hourly reports. Table 5 presents the comparison of evaluation results between the AQI and AQFCE by consistency and similarity.
## 3 Results
### Effects of Temperature and pH on Activity and Stability  
In order to characterize the recombinant keratinases, the effects of temperature and pH on the recombinant keratinases was determined in this study. We used 1% soluble keratin to measure the enzyme activity. As shown in Figure 4, the optimal temperature of DgeKer and DgeKer-C were 70 \({}^{\circ}\)C. Moreover, the thermostability of DgeKer and DgeKer-C was also characterized. The residual activity of DgeKer after being incubated at 60 \({}^{\circ}\)C for 90 min was approximately 54%, and that of DgeKer-C was 69%, indicating that both of them have thermal stability. As shown in Table 2, the half-life (t\({}_{1/2}\)) of DgeKer at 60 \({}^{\circ}\)C was 103.45 min, which was longer than that of KerA (26.46 min) but shorter than that of DgeKer-C (169.10 min). At 70 \({}^{\circ}\)C, DgeKer-C also exhibited better temperature stability than DgeKer. Unfortunately, KerA completely lost its enzymatic activity after being incubated at 70 \({}^{\circ}\)C for 20 min (Figure 4c). In terms of pH, the effects of pH to DgeKer and DgeKer-C was about the same (Figure 4b). DgeKer and DgeKer-C showed maximum activity at around pH 9-DgeKer and DgeKer-C were sensitive in the range of pH 3-7, while they were stable in range of 8-DgeKer and DgeKer-C had approximately the whole activity after being treated at pH 11 for 60 min. The date appears to suggest that DgeKer and DgeKer-C have potential industry application, due to their outstanding thermostability and pH stability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Keratinases** & **t\({}_{1/2}\) (min)** \\ \hline DgeKer & 103.45 \(\pm\) 1.26 \({}^{\text{b}}\) \\ DgeKer-C & 169.10 \(\pm\) 8.20 \({}^{\text{c}}\) \\ KerA & 26.46 \(\pm\) 0.22 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} Superscript letter \({}^{\text{a},b,c}\) down the column were used to indicate significant difference at \(p<0.05\).  
\end{table}
Table 2: The half-life of DgeKer and DgeKer-C at 60 \({}^{\circ}\)C.  
Figure 4: Effects of temperature and pH on keratinase activity of DgeKer, DgeKer-C and KerA. (**a**) The effects of temperature for DgeKer, DgeKer-C and KerA. (**b**) The effects of pH for DgeKer, DgeKer-C and KerA. (**c**) The thermostability for DgeKer, DgeKer-C and KerA. (**d**) The pH stability for DgeKer, DgeKer-C and KerA. The filled triangle represents DgeKer, and the filled square represents DgeKer-C. Error bars represent standard deviations.  
As shown as in Table 3, recombinant keratinases DgeKer and DgeKer-C showed the same capabilities to hydrolyze soluble keratin and feather powder. With soluble keratin as substrate, DgeKer and DgeKer-C showed highest specific activity of 40,192.30 \(\pm\) 230.48 U/mg and 40,408.89 \(\pm\) 342.73 U/mg, respectively. DgeKer and DgeKer-C had almost the same enzyme activity on feather powder, approximately 1450 U/mg.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 7 Open Challenges
### Security Management  
The existence of a logically centralized controller and cluster heads in the SDN-based architecture for WSNs calls for efficient management of security. This includes investigation and development of support for securing the controller in both distributed and centralized architectures in addition to counter measures whenever a cluster head or controller is attacked. There is also an open challenge in development of SDN-based key management systems and other encryption techniques for WSNs.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 4 Globally-Referenced Electro-Optical SLAM (GEOSLAM)
### GNSS Aiding
#### 4.2.2 Initialization in GNSS-Aided SLAM  
When initializing, GEOSLAM performs visual-only SLAM for the first \(N_{i}\) keyframes in the \(\mathcal{S}\) frame, and stores the GNSS position measurements of the antenna provided in the \(\mathcal{G}\) frame. Subsequently, GEOSLAM finds the least-squares Euclidean transformation to obtain the optimal rotation matrix \(R_{\mathcal{G}}^{\mathcal{S}}\) and translation vector \(\mathbf{t}_{\mathcal{G}}^{\mathcal{S}}\) between the two coordinate systems from the set of vector observations. Note that a full similarity transformation is not required since the known stereo baseline renders the \(\mathcal{S}\) frame with correct scaling. The estimated Euclidean transformation minimizes the squared difference between the transformed GNSS measurements in \(\mathcal{S}\) and the visual SLAM predicted trajectory of the GNSS antenna, also in \(\mathcal{S}\). The specific method used to estimate the transformation is based on SVD decomposition as discussed in [62].  
\[\left(R_{\mathcal{G}}^{\mathcal{S}},\mathbf{t}_{\mathcal{G}}^{\mathcal{S}} \right)=\operatorname*{arg\,min}_{R\in\text{SO}(3),\mathbf{t}\in\mathbb{R}^{3 }}\sum_{n=1}^{N_{i}}\left\|\left(R\mathbf{z}_{\mathcal{A}_{n}}^{\mathcal{G}}+ \mathbf{t}\right)-\left(\mathbf{x}_{\mathcal{C}_{n}}^{\mathcal{S}}+R\left( \mathbf{\theta}_{\mathcal{C}_{n}}^{\mathcal{S}}\right)\mathbf{t}_{\mathcal{A} _{n}}^{\mathcal{C}_{n}}\right)\right\|^{2} \tag{5}\]  
It must be noted that this transformation need only be approximately correct such that the GNSS estimates, used as measurements, will not diverge with respect to the visually-derived trajectory. Because the jointly estimated trajectory in \(\mathcal{S}\) gets transformed back to \(\mathcal{G}\) using the same approximate transformation, any errors in the transformation are cancelled.
# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 4 Practical Implications
### Match Outcome  
The research conducted on predictive modeling of tennis match outcomes provides several key practical implications for the field [37; 39; 44; 54; 56; 58].  
[PERSON]'s et al. [54] study underscored the advantage of decision tree classifiers over other models, such as learning vector quantization (LVQ) and support vector machine (SVM). These findings suggest that decision tree classifiers should be prioritized in predictive models to enhance the reliability of match outcome predictions. Coaches and analysts can leverage these models to make more informed decisions based on historical match data, thereby improving training and match preparation strategies.  
The identification of serve strength as a critical predictor of match outcomes emphasizes the need for focused technical training. Players and coaches should prioritize training regimes that enhance serve effectiveness, including aspects such as serve angle, speed, and placement. The detailed analysis of factors influencing serve strength, such as trunk rotation and shoulder angle, provides actionable insights for optimizing training protocols to improve match performance.  
[PERSON]'s et al. [44] research on the use of the challenge system in tennis revealed important insights into decision-making processes and system vulnerabilities. The identification of factors influencing the likelihood and success of challenges, such as shot speed and point importance, suggests that players can improve their challenge success rates by better understanding these variables. Additionally, [PERSON] et al.'s study [49] on serve characteristics in doubles matches indicated that variables such as serve angle and placement are crucial for success. These findings can be extended to develop predictive models for doubles matches, providing coaches with specific tactical insights. Furthermore, the methodological approach used in this study, involving tracking data and ML techniques, can be applied to other sports and contexts. This broader application can enhance predictive modeling across various sports, leading to more tailored and effective training and strategy development.  
Furthermore, [PERSON]'s et al. [37] research emphasized the significance of incorporating time as a factor in performance prediction models. The superior performance of the neural network auto-regressive (NNAR) model over the other models suggests that time-series analysis should be integrated into predictive models. This approach allows for more accurate forecasts of player performance over time, aiding in long-term planning and performance improvement. Coaches and sports analysts can utilize these models to track and predict performance trends, making data-driven decisions for future matches.
## 4 Experiments and Discussion  
### Experimental Data  
In this paper, the traffic flow data of several road sections in Changsha City from 17 September to 6 October 2019 are used as experimental data, as shown in Figure 4, including the flow data of 13 observation points, denoted by A to M, where G is the road section to be predicted, and the data collection period is 5 min. Then, 288 data are collected daily by a single detector, for a total of 5760 data, and 115,200 data for all detectors. Considering that the data collected by a single detector will have a certain probability of missing values due to its defects and the influence of external environmental factors, the average value of the traffic flow data before and after the missing data is taken to fill it and remove the abnormal values; the traffic flow data of observation points E and F are analyzed to know that there is a large gap with the actual data, so the data of observation points E and F are eliminated, the traffic flow data of 5 and 6 October are used as the test set, and the remaining data are used as the training set.  
The required external environmental data were obtained from the Changsha weather detection station, whose latitude and longitude were (112.7833, 28.1166); the collected environmental data were wind speed, temperature, relative humidity, rainfall, and visibility, with a collection interval of 1 h. Their total number was 2400, and some of the data are shown in Table 1.  
The time interval of the collected road traffic flow is 5 min, while the interval of the external environmental features collected is 1 h. To match with the traffic flow data, we consider the continuity of environmental changes, so we perform a linear interpolation operation on the environmental data and adjust the time interval to 5 min. To prevent  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time** & **Wind Speed (m/s)** & **Temperature (°C)** & **Relative Humidity (\%)** & **Rainfall (mm)** & **Visibility (km)** \\ \hline
9/17/0:0 & 2.7 & 25 & 84 & 0 & 8 \\
9/17/1:0:0 & 2.7 & 24.3 & 85 & 0 & 2 \\
9/17/2:0:0 & 2.7 & 23.2 & 86 & 0 & 10 \\
9/17/3:0:0 & 2.7 & 22.7 & 86 & 0 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: The data in the external environment section indicates.  
Figure 4: Diagram of the location of the observation point.  
the influence of the magnitude between different features in the prediction, the data are normalized and mapped to the range of [0, 1] uniformly. The formula is as follows.  
\[x_{i}^{g}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{14}\]  
where \(x_{i}^{g}\) is the normalized data, \(x_{i}\) is the original traffic flow data, and \(x_{\text{max}}\) and \(x_{\text{min}}\) are the maximum and minimum values in the traffic flow, respectively.
## 5 Conclusions  
Delay/Disruption Tolerant Networks are a promising technology for rural internet connectivity applications. They can connect people in remote places using non-real-time applications at low costs. Since DTNs can use local transportation infrastructure, they can reach most regions where no networking or even communications infrastructure exists. For our application, we have shown that DTNs are a feasible way to retrieve sensor data from an isolated spot if we can have a vehicle or a set of vehicles that eventually or periodically will reach that spot.  
Simulation results show that our Distributed Forwarding Algorithm can deliver most messages (around 99%) using delivery probabilities learned from neighboring nodes, contact history information and replication, and a buffer of 10 MB or more. In the most demanding sub-scenarios, those where 2 MB messages were generated every 1 to 2 min, all protocols performed similarly. However, when we increased the connectivity in the network, giving sensor nodes the opportunity to directly contact the MAPs or the ICS, our proposed algorithm performed better on delivery rate and delay than all other protocols for buffer values over 100 MB.  
Due to our proposed DFA implementation of replication and its slow updating of transient delivery probabilities, it uses more hops trying to get the message to its destination and it generates more overhead than Spray and Wait and PROPHETv2, in most cases.  
Our algorithm can be used in applications like GOLDFISH where data is taken from a remote site and decision making takes place in another location. We are looking at applications where those decisions do not have to be taken instantly with a single event, but over a lapse of time with historical data--applications like pollution monitoring.  
Future work includes researching tuning parameters for the algorithm and a better way to use the contact information to schedule transmission or look for alternative routes. Also, we look forward to performing real tests with the necessary hardware and mobile nodes in a real scenario.  
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 269985. The author [PERSON] received funding by the Colombian \"Departamento Administrativo de Ciencia, Tecnologia e Innovacion - Colciencias Conv. 528/2011\".  
[PERSON] developed this project as part of his research. [PERSON] supervised the research and participated in the revision processes.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 40: Results for scenario 2.4. Average overhead with different Buffer Sizes, generating 2 MB messages every 1 to 2 h, **(a)** MAPs moving randomly on the path; **(b)** MAPs following the path in only one direction. DFA: Distributed Forwarding Algorithm.
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 2 Data and Methods
### Aerosol Lookup Table  
For retrieving the AOD from HJ-1 A/B CCD data, a model simulated aerosol lookup table was established in this study. We first assumed the surface and the atmosphere to be uniform and Lambertian. Then, the apparent reflectance \(\rho^{*}\) received by the sensor of the satellite can be expressed as follows [24]:  
\[\rho^{*}(\theta_{s},\theta_{v},\varphi)=\rho_{a}(\theta_{s},\theta_{v},\varphi )+\frac{\rho_{s}}{1-\rho_{s}S}T(\theta_{s})T(\theta_{v}), \tag{4}\]  
where \(\theta_{s}\) and \(\theta_{v}\) denote the solar and view zenith angle, respectively; \(\varphi\) denotes the solar and view azimuth angle; \(\rho_{a}\) is the atmospheric path reflectance (the reflected contribution of the atmosphere); \(\rho_{s}\) is the surface reflectance; \(S\) is the atmospheric downward reflectance; and \(T(\theta_{s})\) and \(T(\theta_{v})\) are the downward and upward atmospheric transmittance, respectively.  
Let \(T=T(\theta_{s})T(\theta_{v})\), Equation (4), be simplified as  
\[\rho^{*}=\rho_{a}+\frac{\rho_{s}T}{1-\rho_{s}S}. \tag{5}\]  
Suppose there were three real values of surface reflectance (0.0, 0.5, and 0.8). Three corresponding apparent reflectances (\(\rho_{1}^{*}\), \(\rho_{2}^{*}\), and \(\rho_{3}^{*}\)) can be obtained by the second simulation of the satellite signal in the solar spectrum model (the 6S model). According to Equation (5), the apparent reflectances \(\rho_{1}^{*}\), \(\rho_{2}^{*}\), and \(\rho_{3}^{*}\) can also be expressed as  
\[\rho_{1}^{*}=\rho_{a}+\frac{0.0T}{1-0.05}=\rho_{a}, \tag{6}\]  
\[\rho_{2}^{*}=\rho_{a}+\frac{0.5T}{1-0.5S}, \tag{7}\]  
\[\rho_{3}^{*}=\rho_{a}+\frac{0.8T}{1-0.8S}. \tag{8}\]  
Figure 2: The discontinuity points of interband regressions for the HJ-1 A/B CCD data. (**a**) Solar zenith angle is 30\({}^{\circ}\), view zenith angle is 30\({}^{\circ}\), and relative azimuth angle is 45; (**b**) solar zenith angle is 0\({}^{\circ}\), view zenith angle is 0\({}^{\circ}\), and relative azimuth angle is 0\({}^{\circ}\).  
By solving the simultaneous Equations (6)-(8), \(\rho_{a}\), \(T\), and \(S\) can be derived as  
\[\rho_{a}=\rho_{1}^{*}, \tag{9}\]  
\[T=2(\rho_{2}^{*}-\rho_{1}^{*})(1-0.55), \tag{10}\]  
\[S=\frac{0.8\rho_{2}^{*}-0.5\rho_{3}^{*}-0.3\rho_{1}^{*}}{0.4(\rho_{2}^{*}-\rho_ {3}^{*})}. \tag{11}\]  
Considering the computational accuracy and efficiency of the computer, the solar zenith, view zenith, and relative azimuth angles were divided at \(6^{\circ}\), \(3^{\circ}\), and \(12^{\circ}\) as the intervals, respectively. The 65 model [24] was used to repeat the above process to simulate \(\rho_{a}\), \(S\), and \(T\), corresponding to different solar/view angles and AOD conditions (with an interval of 0.05), and a four-dimensional (solar zenith angle, view zenith angle, relative azimuth angle, and AOD) aerosol lookup table was established. Table 3 shows the input parameters for the 6S model (in this study, we took a mid-latitude summer atmosphere and continental aerosol type as an example).