esa-sceva/satcom-chunk-collection huggingface dataset:
# Photon Counting Statistics of a Microwave Cavity Coupled with Double Quantum Dots
## 2 Model and Theoretical Formalism
### Photon Emission Counting Statistics
#### 2.3.1 Waiting Time Distributions and Large-Deviation Statistics for Photon Emission  
Using the method of characteristics [41], one can find the moment generating function (MGF) \(\mathcal{M}(\mathrm{s},\mathrm{t})=\mathrm{Tr}\ \mu(\mathrm{s},\mathrm{t})\) as follows:  
\[\mathcal{M}(\mathrm{s},\mathrm{t})=\frac{2\xi e^{(\gamma+W)t/2}}{2\xi\cosh \Big{[}\frac{\xi(\gamma+W)t}{2}\Big{]}+(1+\xi^{2})\sinh\Big{[}\frac{\xi( \gamma+W)t}{2}\Big{]}}, \tag{20}\]  
where \(\xi=\sqrt{1-4\overline{n}_{c}(1+\overline{n})(e^{s}-1)/(1+W/\gamma)}\), \(W=A_{-}-A_{+}\). \(\overline{n}_{c}=(\gamma\overline{n}+A_{+})/(\gamma+W)\) is the average photon number of the cavity in a steady state. The waiting time distributions for waiting time \(\tau\) between each two photon emissions can be calculated with [42]:  
\[\mathcal{W}(\tau)=\partial_{\tau}^{2}\Pi(\tau)/p, \tag{21}\]  
where \(\Pi(\tau)=\mathcal{M}(-\infty,\tau)\), \(p=-\partial_{\tau}\Pi(\tau)\big{|}_{\tau=0}\).  
Thus, we can obtain  
\[\mathcal{W}(\tau)=\overline{\Gamma}(\gamma+W)\overline{\gamma}\frac{\gamma+W+ 6\overline{\Gamma}+\Big{(}\gamma+W+2\overline{\Gamma}\Big{)}\cosh[\overline{ \gamma}t]+\overline{\gamma}\sinh[\overline{\gamma}t]}{\overline{\gamma}\cosh \Big{[}\frac{\overline{\gamma}t}{2}\Big{]}+\Big{(}\gamma+W+2\overline{ \Gamma}\Big{)}\sinh\Big{[}\frac{\overline{\gamma}t}{2}\Big{]}}e^{(\gamma+W)t/ 2}, \tag{22}\]  
where \(\overline{\gamma}=(\gamma+W)\sqrt{1+4\overline{n}_{c}(1+\overline{n})/(1+W/ \gamma)}\), \(\overline{\Gamma}=\gamma(1+\overline{n})\overline{n}_{c}\).  
For the long time limit, the cumulant generating function \(\Theta(\mathrm{s})\) for the photon emission current is  
\[\Theta(\mathrm{s})=\lim_{t\to\infty}\frac{\ln\mathcal{M}(s,t)}{t}=\frac{\gamma+ W}{2}\Bigg{\{}1-\sqrt{1-4\frac{\gamma}{\left(\gamma+W\right)^{2}}k(s)} \Bigg{\}}, \tag{23}\]  
where \(k(\mathrm{s})=(A_{+}+\gamma\overline{n})(1+\overline{n})(e^{s}-1)\).  
The probability \(P(J,t)\) can be extracted as a Fourier coefficient of the MGF [30]:  
\[P(J,t)=\frac{1}{2\pi i}\int_{-\pi}^{\pi}\mathcal{M}(s,\tau)e^{-ms}ds=\frac{1}{2 \pi i}\int_{-i\pi}^{i\pi}e^{t[\Theta(s)-Js]}ds, \tag{24}\]  
where \(J=\) m/t. For the long time limit, this integral can be solved using the saddle-point approximation. Let \(s_{0}\) be the solution to the saddle-point equation \(\Theta^{\prime}(s_{0})=J\). The integral in Equation (24) can be performed explicitly, meaning that we can get  
\[\frac{\ln[P(J,t)]}{t}\approx\Theta(s_{0})-Js_{0}, \tag{25}\]  
\[\begin{array}{c}\frac{\ln[P(J,t)]}{t}\approx\frac{\gamma+W}{2}+J-\frac{ \sqrt{4^{2}+(\gamma+W)^{2}+4\gamma(\gamma+W)\overline{n}_{c}(1+\overline{n})} }{2}\\ +J\,\ln\frac{\gamma(\gamma+W)\overline{n}_{c}(1+\overline{n})}{\gamma\left[ \sqrt{4^{2}+(\gamma+W)^{2}+4\gamma(\gamma+W)\overline{n}_{c}(1+\overline{n}) }-2J\right]}.\end{array} \tag{26}\]
## 4 The Impacts of Microalgae/Cyanobacteria and Their Extracts on Crops' Production
### Crops' Protection
#### 4.2.1 Protection against Biotic Factors
In this study, the authors concluded that: (i) polysaccharides extracted from C. _valgaris_ and C. _sorokiniana_ promoted an increase in \(\beta\)-1,3-endoglucanase activity of tomato plants; (ii) polysaccharides extracted from C. _sorokiniana_ induced an increased activity of phenylalanine ammonia lyase; and (iii) polysaccharides extracted from C. _valgaris_ and C. _reinhardtii_ induced an increased activity of peroxidase and ascorbate peroxidase. Although the majority of plant defence mechanisms may be promoted by microalgal/cyanobacterial polysaccharides, several metabolites presenting antioxidant, antimicrobial and cytotoxic properties are commonly found in microalgal/cyanobacterial biomass [119]. For example, the fatty acids' contents, especially the type and amount of myristic, palmitic, oleic and eicosapentaenoic acids, are the major contributors to the antimicrobial properties commonly associated to microalgae and cyanobacteria [122; 123]. Due to their antioxidant nature, carotenoids may also have antibacterial activity [112]. Phenolic compounds, in turn, are responsible for the degradation of pathogenic organisms' cell walls [119]. Therefore, the use of microalgae and/or cyanobacteria in food crops can induce plants' defence mechanisms not only through the activity of their polysaccharides, but also through the activity of these biologically-active compounds, as it has already been documented in the literature. [PERSON], et al. [103] demonstrated that a methanolic extract from the brown seaweed _Sargassum wightii_ exhibited activity against _Xanthomonas oryzae_, which is a common bacterial blight of rice. More recently, [PERSON], et al. [108] reported a significant suppressive effect of the seaweeds _Spatoglosum variable_, _Stokeyia indica_ and _Melanothamus afalmusainii_ against eggplant and watermelon pathogens (e.g., the root rotting fungi, _Fusarium solani, Fusarium oxysporum_ and _Macrophomina phaseolina_, and the root knot nematode, _Meloidogyne_ spp.). Besides the inhibitory effect against these organisms, the authors reported an improved plant growth (increased fresh weight of shoots, increased length of wines in watermelon and increased shoot lengths in eggplant).
# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 4 Experiments  
The TAKEN system uses the Carla Simulator to simulate the autonomous vehicle and its environment. It is an open-source tool, transparent and closely related to the real world. It supports the simulation of different sensors such as LIDAR, RADAR, GNSS system, odometer, depth cameras, segmentation cameras and RGB cameras. People can build environments using the Unreal engine and associate rules and regulations. Carla simulator provides free access to its various digital assets such as vehicles, pedestrians, buildings and other entities present in the scene. The simulator is characterised by scalable client-server architecture. It also enables the programmers to attach more than one player to the same server. In addition, it facilitates traffic management, recording and tracing, ROS bridging and Autoware implementation, scenario runners and public contributions.  
The Carla simulator provides us with a variety of towns/maps in which we could test our autonomous models. The working environment registers a set of actions taken by the player and associates with it the corresponding responses. The player on the other hand perceives its environment through various inbuilt simulated sensors and uses it as the source/input for the next action that ought to be taken, this is represented in the Figure 9. The environment is also associated with non-players such as pedestrians and other vehicles. The TAKEN system seeds such instances and simulates their movement/actions so as to align them with the behavioural models.  
The TAKEN system implements a simulated car which is capable of driving on its own in the Carla simulator's environment. The system uses several functionalities and add-ons provided by Carla. The proposed model falls under level three autonomy. Different situations and scenarios such as the sudden appearance of a pedestrian or a vehicle in front of the car, roadblocks, overtaking, and roundabouts which we generally come across while driving, are addressed in this work using various concepts namely, visual perception, state estimation, planning and controllers. The simulated player can manoeuvre correctly by adhering to traffic rules and regulations in medium traffic conditions. In addition to these, the paper elaborates on strategies through which we could account for sensor failures, navigation systems specific, and the task of knowledge sharing using the centralised/decentralised communication architecture. Table 1 delineates the uniqueness and the general qualitative comparison of the TAKEN system with other existing works.  
Figure 9: Environment-agent interaction (here, the environment is the Carla Simulator Server and the agent is a client side python script modelling SDC).
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
## 2 Sampled Phase-Only Hologram (SPOH)
### Generating a Sampled Phase-Only Hologram
For a small down-sampling factor of \(\tau=8\), the intensity distribution is non-uniform with obvious patches that are brighter in appearance. The intensity becomes more uniform with larger down-sampling factor, however the number of sampled points is decreased, leading to larger amount of empty voids and dimmer appearance. In general, a good compromise between the uniformity of intensity distribution, and the number of sampled points is attained with down-sampling factor \(\tau=16\). In the next section, we shall illustrate the changes in the frequency spectrum caused by down-sampling, and how its effect can be reduced.
## 3 Materials and Data Recording Details
### Data Preprocessing  
At the first step of data analysis, all obtained fEPSP signals were preprocessed to remove artifacts [50]. Since the stimulation signal in all experiments had the same duration and were applied at the same moment, we removed the related artifacts from our data by excluding 20 time points corresponding to the stimulus.  
Then, noise was filtered by a Gaussian filter with \(\sigma=35\) and radius \(r=4\sigma\). After that, we normalized the data by converting them into a dataset with zero mean and unit variance. The data was then separated into inputs and outputs for the models.  
In order to obtain signals for training, we divided all signals into six groups with different values of the stimulus amplitude. All signals inside each group were then averaged, except one used as a test signal. This allowed us to exclude the signal dependence on the experimental conditions, such as the inclination and exact location of the electrode insertion, the exact insertion depth, etc.  
The data used in the model as input and output signals consisted of 1500 time samples recorded, respectively, from the CA3 and CA1 regions. The optimal number of time samples in the input data was determined experimentally during the optimization process.
## 2 Materials and Methods  
### Materials  
The RAW264.7 macrophage cell line was purchased from the Korean Cell Line Bank (KCL, Seoul, Korea). Dulbecco's modified Eagle's medium (DMEM) and penicillin/streptomycin were purchased from Welgene (Daegu, Korea), and fetal bovine serum (FBS) was obtained from the American Type Culture Collection (ATCC, Manassas, Virginia, USA). For the cytotoxicity assay, we purchased LPS from Sigma Aldrich (Burlington, MI, USA), the EZ-Cytox analysis kit from DoGEN (Seoul, Korea), and ELISA kits (IL-6 and TNF-\(\alpha\)) from BD Biosciences (Flankin Lakes, NJ, USA). Radioimmunopeptidisation assay (RIPA) buffer and polyvinylidene diffluoride (PVDF) membranes were obtained from Millipore (Burlington, MA, USA) and the protease inhibitor cocktail and Enhanced Chemiluminescence (ECL) solution were purchased from Roche Diagnostics Corporation (SIN, USA) and Thermo Fisher Scientific (Waltham, MI, USA), respectively. Antibodies against p65 (C-20), p38 (C-20), ERK1 (C-16), JNK (FL), and \(\beta\)-actin (I-19) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Antibodies against phospho-NF-kB p65 (Ser-536) (93H1), phospho-p44/42 MAPK ERK1/2 (Thr202/Tyr204) (20G11), phospho-SAPK/JNK (Thr-183/Tyr-185) (81E11), phospho-p38 MAPK (Thr-180/Tyr-182) (D3F9) were purchased from Cell Signaling Technology, Inc. (Danvers, MA, USA). Ultrapure LPS from _Escherichia coli_ (0111:B4) was obtained from Invitrogen (San Diego, CA, USA). Anti-rabbit IgG horse radish peroxidase-conjugated antibody was purchased from Santa Cruz Biotechnology Inc. (Santa Cruz, CA, USA). For real-time reverse transcription PCR, we purchased the RNeasy mini kit from Qiagen (Hilden, Germany) and the SYBR Green PCR Master Mix from Applied Biosystems (Waltham, MA, USA).
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
The former is used to implement the function converting the input of fuzzy linguistic variables into the system  
Figure 1: Fuzzy logic system which is composed of fuzzification interface, inference system, knowledge base and defuzzification interface. output of crisp values. The latter has two components: the database, which contains the membership function of the fuzzy partitions associated to the input linguistic variables, and the rule base, which contains the necessary if-then rules in the fuzzy analysis. Most fuzzy systems use inference method proposed by [PERSON] [10] in which the both premise and consequent parameters are defined by fuzzy sets. Mamdani-method lays the theoretical foundation of fuzzy logic and its fuzzy rules have the form shown as follows:  
\[ \begin{array}{l}If\ \ x_{1}\ \ \textit{is}\ A_{1j}(m_{1j},\sigma_{1j})\ \textit{and}\ \ \ x_{2}\ \textit{is}\ A_{2j}(m_{2j},\sigma_{2j})\ \ \cdots\ \textit{and}\ \ \ x_{n}\ \textit{is}\ A_{nj}(m_{nj},\sigma_{nj})\\ \ \textit{Then}\ \ \textit{y}\ \textit{is}\ B_{j}(m_{j},\sigma_{j})\end{array} \]  
where \(m_{nj}\) and \(\sigma_{nj}\) represent a Gaussian membership function with mean and deviation, respectively, with the \(n\)th dimension and the \(j\)th rule [11]. It translates the consequence parameter of the if-then rules into a fuzzy set as output variable. In addition, [PERSON], [PERSON] and [PERSON] introduced a modified inference scheme named TSK fuzzy model [12], and its first two steps, fuzzifying the inputs and performing the fuzzy inference, are the same as the original Mamdani model. However, this kind of TSK fuzzy model defines a linear combination of the crisp inputs instead of fuzzy sets to be used as the consequent parameters. The standard of fuzzy rules of TSK fuzzy model is shown as follows:  
\[ \begin{array}{l}If\ \ x_{1}\ \ \textit{is}\ A_{1j}(m_{1j},\sigma_{1j})\ \textit{and}\ \ x_{2}\ \textit{is}\ A_{2j}(m_{2j},\sigma_{2j})\ \ \cdots\ \textit{and}\ \ \ x_{n}\ \textit{is}\ A_{nj}(m_{nj},\sigma_{nj})\\ \ \textit{Then}\ \ \textit{y}=p_{0j}+p_{1j}x_{1}+\ \cdots\ +p_{nj}x_{n}\end{array} \]  
where \(p_{nj}\) represents the influence degree of each input parameter in the system. Since the consequent of a rule is crisp, the defuzzification step becomes obsolete in the TSK inference scheme. Instead, the model output is computed as the weighted average of the crisp rule outputs. This computation is more succinct than the defuzzification of the original centroid method. TSK fuzzy model is more conducive to the quantitative study for the whole system compared with Mamdani method. Therefore, this paper proposes a distributed clustering algorithm EEDCF based on TSK fuzzy model for WSNs to cope with the problems mentioned above. For each node, we consider node's residual energy, node's degree and residual energy of node's neighbor nodes as the input parameters to calculate the probability of being CH by TSK fuzzy model in a distributed way when CH election occurs.
## 2 Urban Emergency Responses  
Medium-sized and large emergencies affecting urban areas (e.g., train derailments, fires affecting buildings, and accidental or intentional explosions) usually represent a challenging situation for first responders, because of the lack of event early detection systems, mechanisms to perform a quick diagnosis of the situation, and supporting information to make decisions in a distributed way. The complexity of the urban scenarios usually increases this challenge.  
Immediately after an incident occurs, a 911 service receives the emergency calls. The operators then have to determine the veracity of such an incident, and perform a first diagnose of it (type, size and complexity) using the information provided by the people [11]. Based on this diagnosis, the operators dispatch resources to perform the first response process. The diagnosis and dispatching processes usually take some minutes.  
These resources have to arrive quickly to the emergency place since the reaction time and the efficiency in the first response are crucial to reducing the number of victims in an emergency [11, 12]. In [13] it has been proven that reducing the first response time by one minute increases a six percent the number of lives saved in car crashes. These numbers are probably representative of other urban incidents.  
The coordination of the emergency response activities represents a major challenge to first responders for several reasons: (1) civilians usually go to the affected area to see what is happening,obstructing thus the transportation of resources and the response process; (2) services in the area (e.g., mainly communication and transportation) tend to collapse or they are temporarily suspended for safety reasons (e.g., energy, gas and water); (3) the available radio channels used by first responders are not enough to coordinate the response activities; and (4) there is little or no supporting information to make decisions and coordinate the efforts among the participants. Therefore, improvisation is the regular pattern in these situations [14, 15].  
Clearly, supporting these emergency response activities is a huge challenge, and there is probably no system able to deal with it. However, we can address part of it using an IoT-based solution; particularly the communication support and the information flow can be improved using this type of system. The following section presents different approaches and proposals that can be used or adapted to deal with such a challenge.
## 4 Experiments and Results
### Results of Autoencoder-Based Denoising
#### 4.4.3 Results and Discussion for Signal Denoising  
Besides accuracy, we assessed the reconstruction accuracy of our CDAE with different types of metrics for all channel scenarios and SNR levels.  
Our CDAE achieves an average accuracy of above 77% within 500 epochs for all datasets under light to strong noise conditions, while SNR value varies from 0 to 20 dB across all channel scenarios. The lowest achieved accuracy is 76.79% for spectrograms under SNR 0 dB for _scenario 5_ and _scenario 4_. The highest denoising accuracy for the CDAE is 79.96% for the dataset with SNR 20 dB and _scenario 4_. The radio signals with SNR 10 dB  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Channel Scenario**} & **Channel Condition for** & \multirow{2}{*}{**Channel Condition for IEEE 802.11**} \\  & **Unlicensed LTE** & \\ \hline Scenario 1 & LTE moving & \begin{tabular}{c} Delay spread (model B) with \\ \(d=3\) m + No pathloss \\ \end{tabular} \\ \hline Scenario 2 & LTE moving & \
\begin{tabular}{c} Delay spread (model B) with \\ \(d=10\) m + pathloss \\ \end{tabular} \\ \hline Scenario 3 & LTE fading channel &
\begin{tabular}{c} Delay spread (model B) with \\ \(d=30\) m + pathloss \\ \end{tabular} \\ \hline Scenario 4 & LTE moving with no fading & Doppler \\ \hline Scenario 5 & AWGN & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel scenario based on LTE and IEEE 802.11 channel models.  
and _scenario 2_ reaches 79.96% of testing accuracy with 295 epochs only. It was observed that the datasets of SNR 0 dB under the channel propagation model of _scenario 2_ requires only 171 epochs to reach a 78% of accurate denoising.  
With 77% average denoising accuracy, our CDAE successfully recovers the essential features for all IEEE 802.11 family (802.11a, IEEE 802.11n, IEEE 802.11 ac, and IEEE 802.11 ax) and unlicensed LTE signals. This encoding accuracy provides a robust foundation for protocol identification, as we discuss in more detail in the following subsection. The performance of our CDAE is uniform for all channel scenarios and SNR; this supports the notion of using a library of CDAEs to reconstruct the original \(T(x)_{ij}\) regardless of the level of noise or the channel condition, or the type of the radio signal.  
Figure 6 shows reconstructed spectrograms from different protocols in _scenario 1_ with SNR 0 dB.  
We remark that reconstructed spectrograms in Figure 6 are almost the same obtained across all channel scenarios for all the 25 datasets. Reconstructed spectrogram images clearly preserve the preamble for the WLAN IEEE 802.11 protocol and the subframes of the LTE signal. This could be related to the advantage of using convolutional layers, which preserves the spatial locality for input images. Our results emphasize the robustness of the CDAE architecture to reconstruct corrupted radio spectrogram, whatever the noise level or the channel propagation effect in the field.
## 1 Introduction
In this paper, Dynamic-DSO is proposed. We focus on reducing the impact of dynamic objects in direct visual odometry by combining semantic segmentation networks with DSO [11], which are the state of the art of current direct methods. We applied convolutional neural networks to the input image to generate the pixel-wise semantic information of dynamic objects. Then, we filtered out the dynamic candidate points in the process of keyframes candidate points extraction based on semantic information of dynamic objects. In pyramid motion tracking model, the photometric error calculated by the projection points in dynamic region of subsequent frames are removed from the whole photometric error. In order to obtain the precise camera pose, the sliding window optimization which neglects the photometric error calculated in dynamic region of each keyframe is applied. The proposed Dynamic-DSO exploits the semantic information in the image, significantly improves the positioning accuracy and robustness of direct method in dynamic environments. The contributions of this work are as follows:  
* The proposed system is entirely based on the direct method. We exploited the semantic information of dynamic objects and eliminated the dynamic candidate points from keyframes, which ensured that only static candidate points were reserved in the tracking and optimization process. * In the pyramid motion tracking model and sliding window optimization, the photometric error calculated by the projection points in the image dynamic region are removed from the overall residuals, resulting in more accurate pose estimation and optimization. * We evaluated the proposed system on the public TUM dynamic dataset and the modified Euroc dataset, and achieved robust and accurate results. The rest of this paper is organized as follows. Section 2 introduces the related work. Section 3 introduces the framework of Dynamic-DSO. In Section 4, the main work is described and demonstrated in detail. In Section 5, a series of comparative experiments are reported to analyze the performance of the proposed method in dynamic environments. Finally, conclusions and future work are discussed in Section 6.