esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Towards a Simplified Optical Transceiver
### SCOH System Classification
#### 7.3.1 Direct-Detection-Based Single-Ended photodetector  
In a conventional direct detection scheme, the IM is normally employed at the transmitter. Furthermore, the modulator is usually driven through a real-valued RF signal. This results in a Hermitian symmetric optical spectrum. However, it should be noted that half of the optical spectrum efficiency is wasted by this scheme. Another notable drawback of the direct detection scheme is the associated chromatic dispersion-induced power fading, which eventually gives rise to the transmission distance limitation [9].  
The chromatic dispersion-related challenges can be attended to by using the DCF as the compensating element. The DCF is designed to offer a negative dispersion coefficient that helps the suppression of the accumulated CD (compensate for the transmission fiber dispersion). However, this approach is limited by various factors such as higher-order dispersion, nonlinearities, and the lack of tunability. These problems can be overcome by adopting a tunable dispersion compensation approach. This approach is attractive due to the offered reduction in the inventory of the required compensation modules. It is also a promising solution in a reconfigurable network to adapt to routing path changes owing to its ability to track dynamic changes in dispersion. There are a number of approaches such as the linearly chirped fiber Bragg grating (FBG) with nonuniform heating, the nonlinearlychirped FBG with a simple mechanical stretcher, the electronic tap delay filter with weights, the virtually imaged phased array, and the multiple stage all-pass filter, which have been presented to achieve tunable dispersion compensation. Although these solutions are promising, they also present some limitations. For instance, the FBG can cause wavelength-dependent group delay and subsequently chromatic dispersion [136; 137]. It can also be addressed by exploiting the means that prevented the reception of the two interfering sidebands. In this context, an SSB field modulation is a promising candidate that can easily be employed [108]. Single-ended PD-based direct detection structures such as the offset SSB, VSSB, and blockwise phase switching-direct detection (BPS-DD) schemes are good implementation examples of this classification.
## 4 Discussion  
The results of the study indicate that the damage threshold of In-doped GaSe single crystal (sample no. 2) and undoped single crystal (sample no. 1) had different dependences on the pulse repetition rate of the testing laser radiation. A more significant decrease in LIDT with an increase in the pulse repetition rate was observed in the In-doped sample, as compared to the undoped one. In terms of the fluence, the damage threshold of sample no. 1 did not actually change as the pulse repetition rate increased, but the fluence threshold decreased with an increase in the repetition rate of radiation pulses. From the results presented in Figures 6 and 7, as well as in Table 2, it can be seen that there was no correlation between LIDT and the intrinsic absorption of the sample. At the same time, the results obtained using digital holography methods (Figures 3 and 4), as well as the results presented in Figures 6 and 7 and Table 2 indicate that the presence of volumetric defects in the GaSe:In-doped single crystal enhanced the accumulative effects compared to the undoped crystal (in which the digital holography did not detect any volumetric defects).  
Thus, the obtained results made it possible to conclude that the value of GaSe damage threshold at a test radiation wavelength of 2.091 \(\upmu\)m was influenced by accumulation effects (the damage threshold decreased as the pulse repetition rate and the pulse duration increased). The accumulation effects were more significant in the In-doped sample, since an increase in the frequency led to a more significant decrease in the damage threshold in terms of fluence and peak power, as compared to the undoped sample (for which the damage threshold in the fluence did not actually change with increasing frequency). The results obtained during the study indicate that doping with In enhanced the accumulation effect, which manifested itself in a decrease in the damage threshold with increasing frequency. The experimental results indicated that GaSe LIDT depends on both the fluence and the peak power of the test laser radiation.  
The results of this work confirm the conclusions obtained in Reference [12] on the significant influence of thermal effects on GaSe LIDT when exposed to pulsed nanosecond laser radiation, the role of which increases with the increase of pulse duration and laser pulse repetition rate.
# Analysis of the Impact of the New Two-Lane Shield Tunnel Underpass on the Existing Tunnels
## 2 Theoretical Analysis of Vertical Settlement in Underpass Tunnels
### Two-Lane Tunnel Excavation Prediction of Settlement Calculation for Existing Tunnels  
After calculating the free-soil displacements induced by the underpass tunnel, a simplified but effective analytical framework for investigating the effects of the proximity shield construction on the existing tunnel was developed by considering the tunnel as an elastic foundation beam, assuming the soil as a continuous homogeneous elastic medium, and simulating the interactions between the tunnel and the soil with the use of a spring model. A control equation describing the effect of additional soil deformation on the existing tunnel was obtained from the two-stage displacement method [22]:  
\[El\frac{d^{4}W(x)}{dx^{4}}+K[W(x)-S(x)]=0 \tag{6}\]  
where \(W(x)\) is the vertical displacement of the existing tunnel caused by shield excavation, mm; \(S(x)\) is the free vertical displacement of the soil body caused by excavation, mm, calculated by Equation (5); \(K=D\), \(k\) is the foundation bed coefficient, obtained from the calculation, \(k=\frac{0.65E_{s}}{1-p^{2}}\sqrt{\frac{E_{s}D^{2}}{El}}\), \(El\) is the equivalent bending stiffness of the existing tunnel, N\(\cdot\)m\({}^{2}\); \(E_{s}\) is the modulus of elasticity of the soil, MPa; \(v\) is the Poisson's ratio of the soil body; and \(D\) is the outer diameter of the tunnel of the shield, m.  
According to the finite difference principle, the solution of the above differential equation is:  
\[W_{z}(x)=A\sinh(\lambda x)\sin(\lambda x)+B\sinh(\lambda x)\bullet\cos(\lambda x )+C\cosh(\lambda x)\sin(\lambda x)+D\cosh(\lambda x)\cos(\lambda x)+W_{z}^{*}(x) \tag{7}\]  
where \(A\), \(B\), \(C\), and \(D\) are integration constants, which can be obtained from the boundary conditions of the existing tunnel, and \(W_{z}^{*}(x)\) is a special solution to meet the requirements.
## 4 Discussion
### 165 rRNA Gene Sequence Analyses of Growth-Inhibiting Bacteria against A. catenella (Group I)  
The taxonomic groups of _A. catenella_ (Group I) growth-inhibiting bacteria isolated in this study were the CFB group (42.6%), class \(\gamma\)-proteobacteria (33.3%), class \(\alpha\)-proteobacteria (18.5%), class \(\beta\)-proteobacteria (1.9%), and Actinobacteria and Firmicutes (3.7%). Regarding the results of the 165 rRNA gene analyses of algicidal and growth-inhibiting bacteria isolated from coastal water, most of those bacteria reportedly belonged to the class \(\alpha\)-proteobacteria, the class \(\gamma\)-proteobacteria, and the CFB group [9]. Total 718 heterotrophic bacterial strains were collected from Akkeshi Bay and were identified to be _Pseudomonas_ spp. in the class \(\gamma\)-proteobacteria, _Achromobacter_ spp. in the class \(\beta\)-proteobacteria, and _Flavouratum_ spp. in the CFB group, and the results noted that these three genera accounted for 80% of the total bacterial strains identified [30].  
Concerning the class \(\beta\)-proteobacteria, one strain of AK23 (Stn. 2, FLB) was isolated, and it is in a taxonomic group with many reports of its existence in freshwater. The most closely related strain was the \(\beta\)-proteobacterium strain TEG17 (97.68% homology), which is the strain of freshwater origin. The strain AK23 of \(\beta\)-proteobacterium was isolated on 18 May. This date is within the period with much snowmelt water flowing into Akkeshi-ko Estuary through Bekanbeushi River. The presence of the strain of growth-inhibiting bacteria of freshwater origin at the station in the north of Akkeshi Bay during the snowmelt period suggests that there may be a supply of growth-inhibiting bacteria against microalgae including the toxic dinoflagellate _A. catenella_ not only from the seagrass bed but also from the Bekanbeushi River.
## 2 Mathematical Model
### Construction of Uncertainty Model
The expression is as follows:  
\[Z(x)=\sum\limits_{i=1}^{n}W_{i}Z(x_{i}) \tag{16}\]  
where \(Z(x)\) represents the estimated value at the point to be interpolated, \(Z(x_{i})\) represents the value at the \(i\)-th interpolation point, \(n\) is the number of interpolation points involved, and \(W_{i}\) represents the weight of the \(i\)-th interpolation point. The calculation of the weight \(W_{i}\) is as follows:  
\[W_{i}=\frac{d_{(x,x_{i})}^{-a}}{\sum\limits_{i=1}^{n}d_{(x,x_{i})}^{-a}} \tag{17}\]where \(a\) is the distance exponent, \(d_{(x,x_{i})}\) is the distance from the \(i\)-th interpolation point \(x_{i}\) to the point to be estimated \(x\), and the weight \(W_{i}\) decreases as the distance increases. Additionally, the larger the distance exponent \(a\), the greater the weight assigned to interpolation points closer to the target point, making the interpolated value closer to the value of the nearest interpolation point. Conversely, a smaller \(a\) result in the interpolated value is closer to the average of the surrounding interpolation points. Spline interpolation simulates the shape of \(a\) given curve by inserting spline curves between known points, resulting in a smoother and more accurate outcome. Let the interpolation be performed on the interval \([a,b]\), where \(a=x_{1}<x_{2}<\dots<x_{n}=b\), and each interpolation point have a corresponding function value, denoted as F(\(x_{i}\)) = \(y_{i}\) ( = 1, 2, \(\dots\), \(n\)). If the function \(F(x)\) is a polynomial of degree at most 3 on the interval \([a,b]\), and \(F(x)\) has a second derivative on the interval \([a,b]\), then \(F(x)\) is called a cubic spline interpolation function. Specifically, \(F(x)\) exists as a cubic polynomial on each subinterval \([x_{i},x_{i}+1]\), which can be expressed as \(F_{i}(x)=a_{i}x^{3}+b_{i}x^{2}+c_{i}x+d_{i}\), where \(F(x_{i})=y_{i}\), \(F(x_{i}-0)\) = \(F(x_{i}+0)\), \(F^{\prime}(x_{i}-0)\) = \(F^{\prime}(x_{i}+0)\), \(F^{\prime\prime}(x_{i}-0)\) = \(F^{\prime\prime}(x_{i}+0)\), (\(i\) = 1, 2, \(\dots\), \(n-1\)). By combining the two methods, a multi-scale interpolation approach is adopted. Since the data scale range provided by the RUC model is too large, with the minimum spatial resolution being 13 km, it is not suitable for long-endurance hydrogen hybrid UAVs. First, IDW is used to reduce the data to a 1 km scale, and then cubic spline interpolation is applied to further reduce the IDW-processed data to a 5 m horizontal resolution, ensuring the capture of small variations in airflow.
## 6 Experimental Results
### Performance Evaluation
#### 6.3.2 [PERSON]'s Modification of Andrew Protocol  
For [PERSON]'s modification of the Andrew Protocol (Figure 4), we assumed the following delay values: \(D_{1}=2\), \(D_{2}=3\), and \(D_{3}=4\). With these delay settings, we identified two paths that indicate a Man-in-the-Middle behaviour (where locations 74 or 94 were reachable), two paths representing an honest execution (where locations 4 or 9 were reachable), and eight paths representing executions involving the Intruder (where locations 14, 24, 34, 44, 54, 64, 84, or 104 were reachable). The minimum lifetime values required to permit only honest executions are \(L_{1}=L_{2}=7\). The minimum values required to enable a Man-in-the-Middle behaviour (reachability of locations 74 and 94) are \(L_{1}=L_{2}=11\).
## 4 Experimental Analysis
### Analysis of Suppression Performance  
After segmentation, we compare the corresponding regions of the near-testing and the far-testing images to judge the belongings of each signal. As shown in Figure 10, there are four segments of signals. By comparing the signal bandwidth and energy differences, we find that for the signal in the red box, the energy difference between the near testing and the far testing is huge, which indicates that it is more likely to be a target signal rather than an external interference signal. In contrast, the signal in the black box has a small energy difference between the near and far tests and should therefore be considered external interference and be properly suppressed. This result is shown in Figure 10c.  
As shown in Figure 11, there are two time-varying signals generated during experiments. By judging the difference between near and far testing SLTVSF curves using JS divergence, KL divergence, cosine distance, and linear similarity, we state that the left-side signals have different time-varying features and should be judged as internal interference. In contrast, the right-side signals have similar time-varying features and should be judged as external interference.  
We use accuracy to assess the effectiveness of segmentation, which refers to the percentage of correctly classified samples in the total samples and is defined as follows:  
\[ACC=\frac{n_{\text{correct}}}{n_{\text{total}}}, \tag{10}\]  
where \(ACC\) denotes the accuracy of segmentation, \(n_{\text{correct}}\) denotes the number of correctly classified samples, and \(n_{\text{total}}\) denotes the total number of samples.  
The internal interference recognition accuracy is counted based on the experiment sets. As shown in Table 1, we find that the \(E^{2}ISA\) accuracy is 96.2%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal Class** & **Single-Frequency** & **Narrow-Band** & **Incomplete** & **Total** \\ \hline Number & 4 & 45 & 4 & 53 \\ Correct & 4 & 43 & 4 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different types of internal interference recognition results.  
Figure 10: Comparison of near–far SA waterfalls and suppression results: (**a**) near-site SA waterfall; (**b**) far-site SA waterfall; (**c**) suppression results.  
Whole-band internal interference testing curves with and without \(E^{2}ISA\) are shown in Figure 12. We find that \(E^{2}ISA\) can suppress the majority of the external interference and obtain clean EMC testing results.  
Figure 11: Signals with time-varying features.  
Figure 12: Whole\(-\)band internal interference testing curves with and without \(E^{2}ISA\) (there are five internal interference signals and massive environmental interference, which are all suppressed clearly): (**a**) whole\(-\)band internal interference testing curves without suppression; (**b**) whole\(-\)band internal interference testing curves with suppression.
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 3 Preliminary
### Hierarchical Prediction Model  
The model ran on a single device, and the model inference time only included the computation time of the model. When multiple devices are used for collaborative inference, the time required for data transfer between devices also needs to be considered to achieve collaborative inference, and a suitable model allocation strategy needs to be chosen to take the time to get the model prediction results shorter. Therefore, the computation time of the model needed to be modeled, while the output data volume was recorded to calculate the transmission time.  
As shown in Figure 2, the model inference time is mainly composed of three parts: end computation time, communication time, and edge computation time. We set 22 candidate cut points for ResNet18. The reasoning process on both sides of the cut point was performed on the terminal device and the edge device, respectively, and the communication time depended on the size of the feature map. Therefore, the model inference time varied withthe location of the cut point. We tested and counted the results of different split point selections on a Raspberry Pi and a laptop with Intel CPU chips.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Overall Aerodynamic Characteristics  
Based on the DSMC data for wall pressure and shear stress, integrations over the sphere yield the drag \(F_{x}\), the lift \(F_{y}\), and the moment about the \(z\)-axis \(M_{z}\). These aerodynamic forces and moments are expressed in terms of dimensionless coefficients:  
\[C_{D}=\frac{F_{x}}{(\rho_{\infty}V_{\infty}^{2}/2)(\pi R^{2})},\quad C_{L}= \frac{F_{y}}{(\rho_{\infty}V_{\infty}^{2}/2)(\pi R^{2})},\quad C_{M}=\frac{|M_ {z}|}{(\rho_{\infty}V_{\infty}^{2}/2)(\pi R^{2})R}. \tag{3}\]  
For the hypersonic rarefied flow over a rotating sphere, the coefficients \(C_{D}\), \(C_{L}\), and \(C_{M}\) generally depend on the Mach number \(M_{\infty}\), the Knudsen number \(Kn_{\infty}\), the accommodation coefficient \(\alpha\), the temperature ratio \(T_{\rm w}/T_{\infty}\), and the rotation parameter \(W\). The present numerical investigation only concerns the effect of rotation on the aerodynamic characteristics, so other influencing factors are fixed. The DSMC results of \(C_{D}\), \(C_{L}\), and \(C_{M}\) are plotted as functions of \(W\), and the corresponding theoretical free-molecular solutions are also presented for qualitative reference.
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 5 Discussion
Moreover, except for GNSS data, many other types of satellite tracking data are not considered in our study. Once the construction of the navigation augmentation LEO constellation is complete, the new ranging observations from LEO satellites to ground stations can be employed. These new types of satellite tracking data are expected to remove the potential systemic bias in the estimated orbits and further improve the orbit accuracy of both GNSS and LEO satellites.
## 4 The QACMOR Routing Method
### The QACMOR Algorithm
Exploring the unused nodes by using heuristic information, as Formula (4) shows, updating the local pheromone according to Formula (5) and (6) in Step 2, and updating the global pheromone with the quantum rotation gate will generate population diversity, preventing the algorithm from becoming trapped in local convergence or premature stagnation.
## 4 Applications
### Photoelectron Spectroscopy
In this framework, strong-field photoemission under an IR excitation can be addressed as a potential instruments for probing nano-localized field distributions and their ultrafast dynamical evolution. Strong-field photoemission was subsequently observed by [PERSON] et al. [PERSON] _et al._ (2016) also in plasmonic films under exposition to femtosecond, mid-infrared pulses provided by an OPCPA source at 3.1 \(\upmu\)m. Tunneling emission was here demonstrated at the considerably low intensity of \(\sim\)1 GW/cm\({}^{2}\), with the localized plasmon field being responsible for an extension of the photoelectron energy spectrum up to two orders of magnitude.
## 2 Related Work
### Proactive Approaches
used the bidirectional forwarding detection (BFD) protocol to detect each link for failure detection and compared the performance in milliseconds of different BFD detection intervals. On the other hand, [PERSON] [31] et al. focused on robust multicasting in SDN, considering fault tolerance. [PERSON] [19] et al. proposed a method called pro-VLAN, which recovers single link failures in SDN networks by calculating backup paths for each link in the network and assigning a unique VLAN ID. It has the advantage of high efficiency, strong scalability, and wide applicability. Similarly, in the design by [PERSON] [32] et al., VLAN tags were used to reduce backup path rules.
# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 4 Results
### Data Collection and Global Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Hashtags**} & \multirow{2}{*}{**Frecuency**} & \multicolumn{3}{c}{**Sentiment (\%)**} \\ \cline{3-5}  & & **Positive** & **Negative** & **Neutral** \\ \hline \#uber & 2207 & 18.12\% & 38.92\% & **42.95\%** \\ \#ubereats & 1114 & 16.78\% & **42.72\%** & 40.48\% \\ \#customerservice & 238 & 19.74\% & 37.81\% & **42.36\%** \\ \#ubersucks & 224 & 9.82\% & 37.5\% & **52.67\%** \\ \#boycottuber & 213 & 18.30\% & 37.08\% & **44.60\%** \\ \#covid19 & 190 & 32.10\% & 27.89\% & **40\%** \\ \#uberdriver & 174 & 14.36\% & **46.55\%** & 39.08\% \\ \#fraud & 167 & 22.15\% & 33.53\% & **44.31\%** \\ \#badcustomerservice & 157 & 26.75\% & 31.84\% & **41.40\%** \\ \#customerexperience & 148 & 14.86\% & 33.78\% & **51.35\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hashtags frequency and sentiment percentage. Figure 4: Global sentiment and emotion percentages. their placement on negative tweets and positive ones may encode a vocabulary of words that can describe a sentiment value in the context of both transport and customer services. To perform this task, we first made several pre-processing of the dataset. After that, we visualized in Figure 5 a scatter plot [41] containing the frequency of these words in both positive and negative tweets. This figure represents a distribution of words into a 2-dimensional space where their axis represents the frequency of the positive and negative categories for each word. In this way, the \(y\)-axis encodes the frequency in the positive category where words located on the top area frequently appear in positive tweets (e.g., \"quick response\" and \"worries\"). This means that these words will represent with high probability a positive sentiment in the context of transport and customer services. Similarly, the \(x\)-axis represents the negative category where the words located on the right area are highly frequent in negative tweets (e.g., \"want money\", and \"restaurant closed\"). However, there are several words such as \"thanksuber\" or \"driver\" which are located with high frequency in both categories. This means that these words are usually written on both negative and positive tweets. For example, the term \"thanksuber\" can be associated with both categories since users may post this word using an ironic expression providing a negative opinion towards the platform, or conversely, they are expressing their gratitude to the response provided by the service. In light of these results, it is observed that this first analysis does not provide deep information from the dataset since it only describes global statistical information. Hence, the following sections of this work will describe in detail the process to segregate the different tweets into their topic with the purpose of classifying the sentiment and emotion information of each of the latent topics.