esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Purification of \(\beta\)-Glucosidase from M. guillermondii _NM218 and H. uvarum _Br345_  
The purification protocol for the \(\beta\)-glucosidase of _M. guillermondii _NM218 and _H. uvarum _Br345_ is shown in Table 1. Compared with the enzyme extract, the _M. guillermondii _NM218 enzyme was purified 12.2-fold with a specific enzyme activity of 1.95 U/mg. On the other hand, the _M. guillermondii _Br345_ enzyme was purified 12.4-fold with a specific enzyme activity of 2.11 U/mg. The extracellular extracts of strains _M. guillermondii _NM218 and _H. uvarum _Br345_ were precipitated by 80% ammonium sulfate solution, and the \(\beta\)-glucosidase obtained was further purified by chromatography columns. Throughout the elution process, a single peak with \(\beta\)-glucosidase activity was obtained on both DEAE-Sepharose-FF and Sephacryl1 S-200 columns (Figure 1), indicating the presence of a single \(\beta\)-glucosidase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Purification Steps** & **Total Activity/(U)** & **Total Protein/(mg)** & \multicolumn{2}{c}{**Specific**} & \multicolumn{1}{c}{**Purification/(Fold)**} \\  & & & **Activity/(U-mg\({}^{-1}\))** & & \\ \hline _M. guillermondii _NM218_ & & & & \\ \hline Enzymatic extract & 22.41 & 138.61 & 0.16 & 1 \\
80\% ammonium sulfate & 17.46 & 59.37 & 0.29 & 1.8 \\ DEAE-Sepharose-FF & 3.54 & 10.43 & 0.34 & 2.1 \\ Sephacryl1 S-200 & 1.77 & 0.91 & 1.95 & 12.2 \\ \hline _H. uvarum _Br345_ & & & & \\ \hline Enzymatic extract & 24.68 & 149.2 & 0.17 & 1 \\
80\% ammonium sulfate & 19.06 & 65.76 & 0.29 & 1.7 \\ DEAE-Sepharose-FF & 4.62 & 10.32 & 0.45 & 2.7 \\ Sephacryl1 S-200 & 1.86 & 0.88 & 2.11 & 12.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Purification protocol for the \(\beta\)-glucosidase from _M. guillermondii _NM218_ and _H. uvarum _Br345_.  
Figure 1: Purification of the \(\beta\)-glucosidase of _M. guillermondii _NM218_ and _H. uvarum _Br345_. Fractions after DEAE-Sepharose-FF column (**(a)** for NM218 and **(c)** for BF345) containing high \(\beta\)-glucosidase activity. Resulting peak fractions containing high \(\beta\)-glucosidase activity purified on Sephacryl S\(-\)200 column (**(b)** for NM218 and **(d)** for BF345). In both columns, the collected high \(\beta\)-glucosidase activity fractions are those having more than 5 mU/mL. The curve represents the enzyme activity, and the line represents the NaCl concentration.
## 3 Materials and Methods
### EMG Signal Preprocessing  
The EMG signal is in the form of a voltage value obtained by measuring microcurrent generated when a muscle contracts, and it contains considerable gesture information in a low-frequency band (500 Hz or below). The measured EMG signal contains various types of noise. The types of noise included in the EMG signal are baseline wander, power line noise, and external environmental noise [23; 24]. In this study, NF was used in the 60 Hz band and BPF was used in the 5-500 Hz band to remove noise. Figure 3 represents changes in EMG signals in the time-frequency domains before and after preprocessing, with blue signals representing EMG signals before preprocessing and red signals representing EMG signals after preprocessing. It can be seen in Figure 3 (in red box) that NF application removed the 60 Hz band and BPF application passed the 5-500 Hz band. The EMG signal from which noise had been removed was divided according to purpose after aligning the EMG signal using RMS prior to performing gesture recognition.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Information** & **Identification** & **DB** & **Channel** & **Feature** & **Classification** \\ \hline [PERSON] et al. [13] & EMG & Gesture, Biometrics & Acquired & 8 & CNN & CNN \\ \hline [PERSON] et al. [14] & EMG & Biometrics & Acquired & 11 & RMS, MAV, etc. & LDA \\ \hline [PERSON] et al. [15] & EMG & Biometrics & Acquired & 3 & CNN & CNN \\ \hline [PERSON] et al. [16] & EMG & Biometrics & Acquired & 8 & \begin{tabular}{c} DWT, CWT, \\ RMS, etc. \\ \end{tabular} & \begin{tabular}{c} CNN, \\ ExtraTrees \\ \end{tabular} \\ \hline [PERSON] et al. [17] & EMG & Biometrics & Acquired & 1 & \begin{tabular}{c} MAV, VAR, \\ RMS, etc. \\ \end{tabular} & OCSVM, LOF \\ \hline [PERSON], [PERSON] et al. [18] & EMG & Biometrics & Acquired & 2 & ZC, MF, etc. & SVM \\ \hline [PERSON], [PERSON] et al. [3] & ECG, EMG & Biometrics & Acquired & 1 & AC, FFT & OPF \\ \hline [PERSON] et al. [19] & \begin{tabular}{c} Face, Iris, \\ Finger Print \\ \end{tabular} & Biometrics & \begin{tabular}{c} Acquired \\ Publicly DB \\ \end{tabular} & - & \begin{tabular}{c} Gabor Wavelet, \\ Eigen Vector \\ \end{tabular} & PUBDNN \\ \hline [PERSON], [PERSON] et al. [20] & \begin{tabular}{c} Palm Print, \\ Hand \\ Geometry \\ \end{tabular} & Biometrics & \begin{tabular}{c} Acquired \\ Publicly DB \\ \end{tabular} & - & \begin{tabular}{c} Morphological \\ Information, \\ DCT \\ \end{tabular} & EN \\ \hline [PERSON] et al. [21] & EEG, Face & Biometrics & Publicly DB & 15 & SV, MAV, etc. & BP, KNN \\ \hline Proposed & EMG & \
\begin{tabular}{c} Gesture, \\ Biometrics \\ \end{tabular} & Publicly DB & 12 &
\begin{tabular}{c} LSTM, CNN, \\ MAV, VAR, etc. \\ \end{tabular} & LSTM, CNN \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 3 Results
### Evacuations in Historical District
#### 3.4.3 Detours  
In both scenarios, the average evacuation distance exceeds that of the static analysis, with peak values occurring primarily within the first 25 min following fire ignition. Thus, the average detour distance in the period is analyzed (Figure 7f).  
The first five minutes after ignition are found as the most rapid moments for average detour distance increasing, with values beginning to decline around the tenth minute. Although the proportion of evacuees taking detours is relatively low--resulting in a modest increase of less than 30 m in overall average evacuation distance compared to the static scenario--the variation in individual-level risk is substantial. Evacuees located near the ignition point may divert over 250 m to avoid high-temperature zones, nearly doubling their evacuation distance. For vulnerable populations, such as the elderly or mobility-impaired, this significantly elevates evacuation risk.  
After the 10 min mark, average detour distances decrease rapidly, approaching zero by the 30 min point. However, the two scenarios exhibit distinct patterns. In the  
Figure 8: Traffic load inside historical district: (**a,b**) Traffic load in the historical district, (**c,d**) Traffic load of static analysis in the historical district.  
16-600 scenario, where the ignition point is located near the district boundary, the affected area is limited, and detour distances diminish almost entirely within 10 min. In contrast, the 2-600 scenario features a centrally located ignition point adjacent to the W4 exit, the shortest evacuation route, thereby affecting a broader population and prolonging detour behavior considerably.  
These findings highlight that the spatial relationship between the ignition point and the shortest evacuation path plays a crucial role in determining the extent and duration of detour behavior. Evacuees situated near the fire source may experience significantly increased evacuation distances, underscoring the need for location-specific evacuation planning.
## 2 Materials and Methods  
### Materials  
Analytical grade chemicals chloroauric acid trihydrate (HAuCl\({}_{4}\):3H\({}_{2}\)O, 99.9%, Sigma-Aldrich, St. Louis, MO, USA), manganese sulfate monohydrate (MnSO\({}_{4}\):H\({}_{2}\)O, 99%, Sigma-Aldrich, St. Louis, MO, USA), potassium permanganate (KMnO\({}_{4}\), 99%, Sigma-Aldrich, St. Louis, MO, USA), ethylene glycol (EG, 99.8%, Sigma Aldrich, St. Louis, MO, USA), sodium borohydride (NaBH\({}_{4}\), 98%, Sigma-Aldrich, St. Louis, MO, USA), polyvinylpyrrolidone (PVP, Sigma-Aldrich, St. Louis, MO, USA, M.W. 55,00 g/mol), manganese oxide (MnO\({}_{2}\), ReagentPlus(r), St. Louis, MO, USA, \(\geq\)99%, Sigma-Aldrich--further noted MnO\({}_{2}\)-Comm), furfural (C\({}_{5}\)H\({}_{4}\)O\({}_{2}\), \(>\) 99%, Sigma-Aldrich, St. Louis, MO, USA) were used as received. All solutions were prepared using deionized water (18.2 M\(\Omega\)).  
The MnO\({}_{2}\) nanoflowers (MnO\({}_{2}\)-NF) and nanowires (MnO\({}_{2}\)-NW) were obtained by a hydrothermal approach, as described elsewhere [21]. In a typical procedure for the synthesis of nanoflowers, 0.4 g of MnO\({}_{4}\). H\({}_{2}\)O and 1.0 g of KMnO\({}_{4}\) were dissolved in 30 mL of deionized water. This solution was transferred to a 100 mL Teflon-lined stainless-steel autoclave. The autoclave was heated and stirred at 140 \({}^{\circ}\)C for 1 h and then allowed to cooled down to room temperature. For the synthesis of MnO\({}_{2}\) nanowires, the same procedure was employed, but the autoclave was heated and stirred at 140 \({}^{\circ}\)C for 18 h. The products were collected by centrifugation and washed three times with ethanol (15 mL) and three times with water (15 mL) by successive rounds of centrifugation and removal of the supernatant. As-prepared materials were then dried at 80 \({}^{\circ}\)C for 6 h in air.  
The supported gold nanoparticles were prepared as follow: Typically, 200 mg of MnO\({}_{2}\) support and 260 mg of PVP were added to 200 mL of EG. The obtained suspension was transferred to a 25 mLround-bottom flask and kept under vigorous stirring at 90 \({}^{\circ}\)C for 20 min. Then, 3.0 mL of a 120 mM NaBH\({}_{\rm{(aq)}}\) and 3 mL of a 24 mM AuCl\({}_{\rm{4}}\)\({}_{\rm{(aq)}}\) solutions were sequentially added to the reaction flask. This mixture was kept under vigorous stirring for another 1 h to produce Au/MnO\({}_{\rm{2}}\). After cooling down to room temperature, the Au/MnO\({}_{\rm{2}}\) materials were isolated by centrifugation and washed three times with acetic acid (15 mL), three times with ethanol (15 mL), and three times with water (15 mL) by successive rounds of centrifugation at 6000 rpm for 5 min and removal of the supernatant. After washing, the Au/MnO\({}_{\rm{2}}\) materials were dried at 120 \({}^{\circ}\)C for 6 h in air.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 3 Results  
### Mean Wind Speed and Direction  
In this paper, wind direction is measured clockwise, with due north having a wind direction of \(0^{\circ}\). Figure 3a,b show the 10 min averaged wind speed and direction against time curves at the east and west measuring points, respectively. The figures reveal that the two curves exhibit a close trend. The mean wind speed increased with time beginning at 22:00 on 9 August 2009 and reached a peak value at 14:00 on 10 August 2009. The maximum wind speeds were 11.75 and 10.99 m/s at the east and west measuring points, respectively. Afterward, the mean wind speed dropped over time. At roughly 14:00, the wind direction changed sharply. Figure 3a illustrates that the wind speed changed significantly at this time, indicating the gradual approach of the typhoon to the measuring point, and then the wind direction gradually changed to a large degree. Moreover, the wind speed of the two measuring points demonstrates that the mean wind speed at the east measuring point is slightly larger than that at the west measuring point, with a difference of 0.76 m/s at thepeak point. The wind direction at the east measuring point is greater in degrees than that at the west measuring point, with a maximum difference of 22.87\({}^{\circ}\) between them.
## 2 Related Work and Paper Contributions
### Paper Contribution  
To the best of our knowledge, this is the first work that investigates call failure prediction in IMS core network using SIP traces based on machine learning and that is able to obtain the failures' root causes occurring inside the IMS network. The contributions of the paper can be summarized as:  
1. Call failure use cases analysis: Based on a SIP traces (Call Data Records (CDRs)) dataset obtained from an Egyptian mobile network operator, we analyze three different call failure use cases (Section 3). In the aforementioned call failure use cases, we conclude that the failure in any protocol inside the IMS domain will lead to SIP protocol failure.
2. SIP features: We analyze for SIP traces, which is conducted to identify the features inside the SIP that classify the success and failure calls (Section 4).
3. Machine learning model: We propose an IMS call failure prediction model by building eight different ML prediction models to predict whether the ongoing call will succeed/fail based on the aforementioned SIP features (Section 6). We compare four different ML classifiers: decision tree, naive Bayes, K-Nearest Neighbor (KNN) and SVM with using two feature selection methods. The feature selection methods are; (i) the Filter Selection method using the Relieff algorithm and (ii) the Wrapper Model using the Slime Mould Algorithm (SMA) and Whale Optimization Algorithm (WOA). Predicting the failure enables the operator to prevent the failure by redirecting the call to another radio access technique by initiating the Circuit Switching fallback (CS-fallback) through the 380 SIP error response sent to the handset [12].
4. Call failures analysis: The advantage of the model is not limited to call failure prediction, but also to knowing the reason (root causes) behind the failure; more specifically, the multi-factorial reasons by using machine learning, which cannot be obtained using the traditional method (manual tracking of the traces). We perform an analysis for the prediction result (Section 7.3) for the KNN classifier using the Wrapper WOA feature selection algorithm, as it presents the highest prediction accuracy and best ROC performance. This enables us to obtain the call failures' root causes.
## 5 Proposed mMTC Device Grouping Algorithms  
Considering the traffic diversity of mMTC devices and the network's requirements, a sustainable and valuable grouping solution must consider all constraints during the grouping process.  
The problem is further formulated as a combinatorial optimization. It is an NP-hard problem. In our previous work, we proposed using a heuristic-based combinatorial method to group the device optimally. Consequently, the number of groups will be reduced while the successful connection of each device will be guaranteed. This is opposed to the popular idea of electing a cluster head for data transmission in each group [41]. In our previously proposed algorithm, the intra-group activity is more dynamic as devices take turns transmitting while others are in sleep mode. This will further reduce the total energy spent from a group perspective.
## 4 Experimental Results
### Measurement Statistical Analysis Experimental for Various Boxes in Different Scenarios  
In this experiment, eight different boxes in different scenarios were measured. Figure 18a shows a box with a red surface; Figure 18b shows a box with high reflective area on the surface, where the reflective area would affect the imaging of the laser stripes in the image; Figure 18c shows a box with an  
Figure 17: (**a**) Relationship between the average error and the box length, (**b**) Relationship between the measurement uncertainty and the box length.  
ideal state; Figure 18d shows a box with a complex pattern and appendages on the surface; Figure 18e shows a box with surface variation (not an ideal plane); Figure 18g,h show the measurement of the target box with several boxes positioned in one plane. Figure 18f,g show the measurement of the same box in different scenarios. The raw laser-box image, the edge map, and the key points measured by the system are listed in Table 7. The width W, length L, height H of the box, and the absolute errors are recorded in brackets. The experimental results were analyzed and the following results were obtained: box (c) is an ideal box, with excellent performance in measurement results. The measurement results of boxes (a) and (d) indicate that the color characteristics on the surface of the box and the complexity of the pattern have no effect on the dimensional measurement of the box. The measurement results of box (b) show that the system is slightly negatively affected by the optical quality of the surface, thereby affecting key point detection and length measurement. The absolute measurement error of the L, W, and H of the box (e) are 1.3, 9.3, and 11.3 mm, respectively. For the measurement results of the box (e), although the maximum error result is 11.3 mm, our algorithm has a good effect on the edge and key point detection of such a box with an uneven surface. Such measurement results are acceptable for most logistics operations. The measurements of the boxes (f) and (g) are almost identical, suggesting that our system works well in complex situations, where multiple boxes are in the same plane. The measurement results of the box (h) also verify the effectiveness of the measurement system in a complex environment. Experimental results show that the system is slightly negatively affected by the color, the pattern, and the optical quality of the surface. However, a considerable measurement error was detected when a box with an uneven surface was detected.  
Therefore, the experimental results show that the network designed in this paper can provide accurate positioning of the key point in the laser-box image even in the complex environment of multiple boxes. The measurements in Table 7 show that the errors of the side length of the box are between \(-2.2\)\(+3.8\) mm (excluding box (e) with irregular surfaces). This finding shows that the system we designed has a wide range of applicability.
## 2 Materials and Methods
### Data  
#### 2.2.1 GLASS GPP Dataset  
GPP data was provided by the Global Land Surface Satellite (GLASS) program [48]. This GLASS-GPP product was estimated by an improved EC-LUE model, which was derived from only four variables: normalized different vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible-to-latent heat flux. Given the invariant parameters (i.e., the potential LUE and optimal plant growth temperature) across the various land cover types [33], GPP can address the spatial and temporal dynamics of the carbon cycle. When compared with the MODIS-GPP product, the GLASS-GPP product exhibited a longer temporal cover, which includes data every eight days starting from 1982 to the present. Also, it can provide information of vegetation before and after 1987, when a disastrous fire occurred in the northern part of the GKM. In conclusion, the GLASS-GPP product demonstrated a high reliability with the revised EC-LUE model and had a long time series dataset [33], so it can be used to analyze the spatiotemporal distribution and variation of GPP in the GKM.  
A total of 1564 scenes from 1982 to 2015 with 46 scenes per year were analyzed. The original eight-day GPP product exhibited a spatial resolution of 0.05\({}^{\circ}\) with WGS1984 coordinate. We extracted the GPP data using the GKM's boundary and then projected the data to UTM. The Savitzky-Golay (S-G) filter [49] in the software TIMESAT [50] was used to smooth some outliers caused by the environment noise and system error of sensor in the original GPP time series. The S-G filter is widely used because it can remodel the raw time series dataset to closely capture the sudden change in original values. After the repeating test, we used a 4 \(\times\) 4 filter window to rebuild the GPP time series data that was required in the following step. We finally calculated the accumulated GPP (AG) over a certain period (i.e., total GPP during the growing season) within one year.
## 4 Improved Tight Combination Model
### Distance Threshold Setting  
The location information can be optimized by setting the threshold to eliminate the poor location data of UWB under NLOS. The TDOA positioning method and triangle three-side theorem [23] were used to set the threshold, as shown in Figure 3.  
The equation group corresponding to Figure 3 can be obtained as  
(1) In triangle ABC,  
\[\begin{array}{l}\Delta S+D_{i,t-1}-D_{i,t}>0\\ \Delta S+D_{i,t}-D_{i,t-1}>0\\ D_{i,t-1}+D_{i,t}-\Delta S>0\end{array} \tag{20}\]  
(2) In triangle ABD,  
\[\begin{array}{l}\Delta S+D_{n,t-1}-D_{n,t}>0\\ \Delta S+D_{n,t}-D_{n,t-1}>0\\ D_{n,t-1}+D_{n,t}-\Delta S>0\end{array} \tag{21}\]  
The combination results are shown in Equation (22):  
\[\begin{array}{l}2\Delta S+D_{i,t}-D_{n,t}+D_{n,t-1}-D_{i,t-1}=2\Delta S+ \Delta d_{in,t}-\Delta d_{in,t-1}>0\\ 2\Delta S+D_{n,t}-D_{i,t}+D_{i,t-1}-D_{n,t-1}=2\Delta S+\Delta d_{in,t-1}- \Delta d_{in,t}>0\end{array} \tag{22}\]  
From the state vector, the relative distance \(\Delta S\) between the current position and the starting position can be obtained by taking the measured value of the accelerometer for the second integral with time, and the distance threshold can be obtained as  
\[2\Delta S-||\Delta d_{in,t-1}-\Delta d_{in,t}||>0 \tag{23}\]  
Because an IMU has the advantage of short time and high-precision positioning, this threshold above can be used to filter the UWB poor data that are bigger than the distance threshold.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 3 Conceptual Design of a Ramjet Nozzle
### Nozzle Geometric Configurations
#### 3.3.3 Dual Bell and Contoured Nozzle  
The concept of the dual bell nozzle (patented in 1968 by Rocketdyne) offers a solution to the altitude expansion difficulties faced by conventional bell-shaped nozzles. The contour inflection ensures a controlled and symmetrical separation, therefore limiting the generation of high side loads of separated flow in conventional nozzles and increasing thrust (Figure 10, left, top). The area ratio of the smaller nozzle is comparatively much smaller than the reference main engine nozzle, resulting in a better thrust performance at lower altitudes. When ascending to higher altitudes, the separation point leaves the inflection point and moves down the exit plane where a higher area ratio of the nozzle extension provides thrust with minimal losses caused due to the change in altitude pressure (Figure 10, left, top). Dual bell nozzles are also classified as altitude compensating nozzles (ACN) (Figure 10, right) [11; 12].  
Figure 9: (**left**) Truncated idealised contoured (TIC) nozzle (**right**) and thrust-optimised contoured (TOC) nozzle.