esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Probabilistic Fusion (PF-SSUn)  
Psychological research shows that human judgment and decision making is a combination of a variety of information and gives results in a probabilistic way. For intelligent vehicles, obstacle detection is also a judgment and decision-making process. The UOD-SS and UOD-Un methods proposed in this paper detect unexpected obstacles from the perspectives of semantic information and uncertainty information, respectively. The two methods are independent of each other, so the fusion of the obtained results can bring many new advantages. The Bayesian framework is suitable for decision inference based on multi-factor probabilistic methods, which is more in line with the human brain's judgment and decision-making process than the methods of obstacle detection using single informa  
Figure 6: Uncertainty degree distribution histogram (due to the high proportion of known pixels, the leftmost part of the histogram is omitted to achieve a better display effect).  
tion. In this paper, it is used to fuse the results obtained by the above two independent methods, and the probability that a pixel is an unexpected obstacle can be expressed as:  
\[p(o)=\frac{p_{pr}\cdot p_{SS}\cdot p_{Un}}{\big{(}p_{pr}\cdot p_{SS}\cdot p_{Un} \big{)}+\big{(}1-p_{pr}\big{)}(1-p_{SS})(1-p_{Un})}. \tag{3}\]  
Among them, \(p_{pr}\) represents the prior probability; \(p_{SS}\) and \(p_{Un}\) represent the probability that the pixel is judged as an unexpected obstacle by UOD-SS and UOD-Un, respectively. Finally, the judgment of whether the pixel is an unexpected obstacle depends on the threshold of \(p(o)\).  
\(p_{SS}\) can be obtained directly from the Softmax layer of the network, and the Softmax function can be expressed as:  
\[Softmax\big{(}z_{j}\big{)}=\frac{e^{z_{j}}}{\sum_{c=1}^{N}e^{z_{c}}}. \tag{4}\]  
Among them, \(z_{j}\) represents the output of the previous stage unit of the class \(j\); \(c\) is the class index; \(N\) is the total number of classes. Therefore, \(p_{SS}\) can be expressed as:  
\[p_{SS}=\frac{e^{z_{3}}}{e^{z_{1}}+e^{z_{2}}+e^{z_{3}}}, \tag{5}\]  
where \(z_{1}\), \(z_{2}\), and \(z_{3}\) represent the output of the previous stage unit of background class, free space class and unexpected obstacles class, respectively.  
\(p_{Un}\) can be obtained according to the degree of deviation between the uncertainty degree of each pixel and the threshold. Assuming that the probability at the threshold is 50%, it can be expressed as:  
\[p_{Un}=\frac{1}{1+exp(u_{t}-u_{i})}. \tag{6}\]  
Among them, \(u_{t}\) represents the value of the uncertainty degree at the threshold; \(u_{i}\) represents the uncertainty degree of pixel \(i\).
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 4 Channel Estimation for the Core Network: Link eNB-to-Relay Node
### Interpolation Methods  
Once the frequency response of the pilot symbols has been obtained, in the second stage, the channel response of data symbols can be derived by interpolation employing the adjacent pilot symbols. In this paper, we consider three two-dimensional interpolation methods: BLI, BCI, and BNNI. We obtain these 2D interpolation methods by performing two successive 1D interpolations. Therefore, in the first step, the associated 1D interpolation methods (i.e., Linear, Cubic, and Nearest-Neighbors, respectively) are performed in the frequency domain, then in the time domain [27; 28]. To obtain a better result in channel estimation, we performed interpolation using multiple subframes. It is worth highlighting that, after data symbol estimation, we have the conditions to estimate the channel matrix \(\mathbf{H}\), as presented in Figure 3.
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 3 Experiments and Results
### Kinematic Data Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multicolumn{2}{c}{**The Mean of**} & \multicolumn{2}{c}{**The STD of**} & \multicolumn{2}{c}{**The Mean of**} & \multicolumn{2}{c}{**The STD of**} \\  & **Smartphone C/N\({}_{0}\)** & **Smartphone C/N\({}_{0}\)** & **Receiver C/N\({}_{0}\)** & **Receiver C/N\({}_{0}\)** \\ \hline G14 & 36.07 & 3.77 & 46.53 & 0.59 \\ G16 & 37.02 & 4.69 & 44.05 & 1.04 \\ G26 & 39.29 & 2.62 & 51.41 & 0.52 \\ G29 & 39.32 & 4.00 & 48.94 & 1.21 \\ G31 & 39.46 & 2.14 & 52.06 & 0.56 \\ G32 & 35.32 & 4.74 & 43.69 & 1.99 \\ E02 & 36.41 & 3.17 & 48.39 & 0.49 \\ E07 & 36.09 & 4.45 & 47.87 & 0.62 \\ E08 & 35.63 & 4.80 & 47.46 & 0.75 \\ E30 & 36.93 & 3.59 & 50.82 & 0.55 \\ R03 & 37.38 & 4.24 & 46.67 & 0.68 \\ R12 & 35.72 & 3.21 & 45.81 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean and standard deviation of \(C/N_{0}\) of GPS, Galileo and GLONASS [dB-Hz]. Figure 8: Horizontal position errors of four data sets in kinematic mode.
## 4 Results of Mission Analysis  
### Orbital Analysis  
The analysis for the orbit requirements for MeznSat took into consideration the following factors:  
1. Orbit Lifetime--The minimum mission Lifetime required by the sponsor is three years.
2. Coverage time--Communication opportunity with the satellite using the mission's main ground station located at Khalifa University in Abu Dhabi.
3. Revisit--To calculate the MeznSat revisit over UAE in one day.
4. Payload operation opportunity--The opportunity for imaging over UAE.  
All orbital analysis was done using Systems Took Kit (STK) [25]. Figure 14 shows that the altitude ranges between 45 km and 600 km satisfy the mission lifetime requirements (indicated by the orange ellipse on the figure), given the minimum mission requirements of three years and that the satellite is required to decay in less than 25 years as per the Inter-Agency Space Debris Coordination Committee (IADC) guidelines [26].
## 2 Experimental Section  
Figure 1 shows the setup of the regenerative amplifier using either Yb:YAG or Yb:CaF\({}_{2}\) as gain medium. In our experiments we used single crystalline Yb:YAG (produced by FEE Ltd., Idar-Oberstein, Germany) with an Yb\({}^{3+}\)-concentration of 10% and a thickness of 1 mm as well as Yb:CaF\({}_{2}\) (produced by CIMAP, Universite de Caen, Caen, France) with an Yb\({}^{3+}\)-concentration of 4.5% and a thickness of 2.5 mm. The laser disks with a diameter of 12.5 mm, anti-reflection (AR) coating on the front-side and  
high-reflection (HR) coating on the rear side were glued on a water-cooled heat sink. Without saturation effects about 90% of the pump light is absorbed in the Yb:YAG crystal and 94% in the Yb:CaF\({}_{2}\) crystal.  
Two 11 W fiber-coupled laser diodes with a fiber core diameter of 105 \(\upmu\)m and an NA of 0.15 were used for pumping at either at 940 nm (for Yb:YAG) or 978 nm (for Yb:CaF\({}_{2}\)). The pump light was collimated and re-focused into the gain media by a spherical lens telescope having a magnification of 1.5 (L1 and L2), while pump and laser beams were separated in angular domain. A concave mirror (M1) with 300 mm radius of curvature, a distance of 165 mm between M1 and the active mirror (Yb:YAG or Yb:CaF\({}_{2}\)) and a cavity length of 1.3 m result into a mode-size diameter of 130 \(\upmu\)m (1/e\({}^{2}\)) at the gain media.  
As the seed pulse source, an Yb:KGW fs-oscillator (Mikan, Amplitude Systems Ltd., Pessac, France) was used providing an average output power of 1 W at a repetition rate of 78 MHz. Mode matching between oscillator and regenerative amplifier especially the beam divergence was obtained by aligning the lens separation of the telescope (T). We used a water-cooled double-crystal BBO Pockels cell with a size of 3 mm \(\times\) 3 mm \(\times\) 20 mm for each crystal (produced by Leysop Ltd., Basildon, UK) for pulse picking as well as cavity-dumping. The optical propagation is along the c-axis of the crystal whilst the electric field is along the a-axis. Non-picked pulses are also amplified by one pass here. Therefore, the seed power was reduced to 50 mW after isolation (FI) by adjusting the half-wave plate HWP1.  
Furthermore, a directly water-cooled Pockels cell driver in bridge configuration with a maximum repetition rate of approximately 5 MHz at a quarter-wave voltage of 2.2 kV was developed by Bergmann Messgeraete Entwicklung KG [17]. An electrical average power of 1 kW is supplied for a repetition rate of 5 MHz at 2.2 kV. The cavity length of the laser was adapted to the switching times (5 ns) of the Pockels cell driver.  
Figure 1: Experimental setup: OSC, fs mode-locked Yb:KGW oscillator; FI, Faraday isolator; FR, Faraday rotator; TFP, thin-film polarizer (angle of incidence: 62\({}^{\circ}\)); QWP, HWP1 and HWP2, quarter/half-wave plates; T, spherical lens telescope (magnification: 1); PC, BBO-Pockels cell; LD, 11 W fiber coupled laser diode; L1, L2, spherical lenses with 25 mm diameter (\(f1\) = 50 mm, \(f2\) = 75 mm); M1, dielectric concave mirror with radius of curvature: 300 mm; M2, M3, dielectric flat mirrors (HR 0\({}^{\circ}\), 1010–1060 nm).
## 4 Results and Discussion
### EDX Background Subtraction  
The methodology used for background-free EELS can also be applied to EDX spectra. Although the presence of a background signal in EDX is quite limited, there is an issue when due to the poor energy resolution, different peaks start overlapping. The standard method to disentangle these peaks is to use a deconvolution method [21; 31; 32]. However, with this method caution should be taken since artefacts can arise [32]. The coincidence method gives the ability to subtract other X-ray lines by selecting a particular EEL energy window and creating the X-ray spectrum using X-rays inside and outside the time coincidence window. In Figure 5a, the conventional EEL spectrum from a GaAs semiconductor is shown where the different edges are indicated. In Figure 5b, the background-subtracted EDX spectra are shown where the electron energies are indicated on (a). The red spectrum only uses electrons arising from the Ga L\({}_{23}\) edge which makes that only the X-rays originating from Ga are visible. When taking electrons with energies inside the blue region, both X-ray lines are seen. This is because the EEL edges are delocalized which makes that in this energy-loss region electrons arising from both the interaction with Ga and As are located. A method to disentangle the As L\({}_{\alpha}\) from the Ga L\({}_{\alpha}\) spectrum is to normalize both spectra from Figure 5 in the region where only the Ga L\({}_{\alpha}\) X-rays are present. Then the coincidence X-ray spectrum, with both electrons coming from Ga and As, is subtracted with the coincidence X-ray spectrum where only the Ga electrons are present resulting in an X-ray spectrum containing only X-rays from the As L\({}_{\alpha}\) line. Another advantage of the  
Figure 3: **(a)** Conventional EEL spectrum obtained from eight separate acquisitions in consecutive energy windows. The black line indicates the fitted background a using power law where the fitted area is indicated with the grey area. **(b)** The correlated EEL spectrum when using only electrons which have an Si X-ray detected within the time correlation window. In the inset figure, the X-ray spectrum is shown where the blue area indicates the energy of the X-rays selected which correspond to the X-rays from Si. The black line is the background signal obtained from using the electrons outside the time coincidence window. **(c)** The coincidence and conventional background-subtracted EEL spectra from which it is seen that the background subtraction method using the correlation with X-rays can reproduce the same result as using the conventional power-law subtraction method albeit at the expense of a lower count rate. Please note that for the correlated result, no assumptions on background shape and no pre-edge fitting region was required.  
coincidence method compared to conventional EDX is that it removes the phosphorescent X-rays since the energy loss of the correlated electron does not correspond to the expected edge. This improves the accuracy of the quantification and further investigation needs to be done to confirm this hypothesis.
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
## 2 Fault Characteristics of the Distribution Networks with PV and BESS
### Reactive Current Distribution in Fault Network Under Symmetrical Faults  
It is not practical to determine the fault direction merely using negative sequence quantities during symmetric faults, and the PV, BESS, and the synchronous power source all output reactive current during faults. Therefore, the flow direction of reactive current can be utilized to identify the fault direction.  
As shown in Figure 1, when a forward fault occurs, in most cases, the reactive current will flow from the bus C to the fault point, and the reactive current detected at PR4 will be larger than zero. In a few cases, when the fault location is far from bus C and the fault resistance is large, the reactive current from the PV and BESS is low, and at this time, the reactive current detected by PR4 will be less than zero.  
When a reverse fault occurs, in most cases, the reactive current flows from line AC to bus C. At this time, the reactive current detected by PR4 is less than zero. In a few cases, when the fault point is located in line CD close to bus C, the voltage drop of bus C is large, and the reactive current emitted by the PV and BESS is large while the reactive current consumed downstream of bus C is low, then the reactive current detected by PR4 will be greater than zero.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Ground Station  
A TT&C ground station was installed at NCU for the purpose of supporting the IDEASSat mission as well as future small satellite missions. The UHF and S-band components of the ground station will be introduced in the following subsections.
## 5 Issues Relating to the Fundamental Design Aspects
### Energy Management  
Constraints in the energy supply of IoT devices pose technical challenges, making it important to design reliable power supply and data communication mechanisms to achieve efficient IoT device communication. UAVs have been promising in addressing this challenge [104]. Additionally, UAVs have energy constraints that affect scheduling and flight time. From Section 4, it was observed that energy consumption plays a significant role in the planning, deployment, and operation of UAV-assisted data gathering [1], since proper network energy management helps to prolong the operation of both UAVs and sensor nodes. As observed in Section 4, not all papers consider UAV energy consumption in the design of UAV age-optimal trajectories, despite power and energy have a significant impact on AoI [86]. Considering that the energy consumed by both UAVs and sensor nodes is vital, this section is dedicated to discussing this subject.
## 3 Satellite Characteristic Fundamentals
### MSS Systems  
MSS systems constitute an important solution to providing communication services to mobile users in different conditions and scenarios, such as in under-populated regions, in emergency areas, and on planes, trains, or ships; see Figure 1. The main characteristics that are possible to exploit for these types of systems are represented by robustness communication, wide area coverage, and broadcast/multicast capabilities. However, these systems can be in the condition of non-line-of-sight (NLoS) propagation caused by the presence of obstacles in the path between a satellite and mobile terminals [17].  
The first MSS system was realized by the International Maritime Satellite Organization (Immarsat) in 1982, when it started to offer a global voice, data, and telex service. This system was designed for maritime mobile satellite communication applications, but the land mobile market was identified, after various studies and analyses, as the most important element [15].  
The MSS systems are classified in three different categories [18]:  
* Land mobile satellite (LMS) service;
* Maritime mobile satellite service;
* Aeronautical mobile satellite service.
## 3 Results and Discussion
### Correlation between Phenolic Compounds and Antioxidant Potential
#### 3.4.1 Phenolic Acids  
Three hydroxycinnamic acids (_p_-coumaroyl tartaric acid, 3-caffeoylquinic acid, and 5-5'-dehydrodiferulic acid) were characterized in the fermented and unfermented coffee samples. _p_-coumaroyl tartaric acid and 5-5'-dehydrodiferulic acid can only be traced in fermented coffee samples, and 3-caffeoylquinic acid can be traced in both the unfermented and fermented coffee samples. The fermentation of coffee could lead to the degradation of hydroxycinnamoyl esters, for example, the caffeoyl-O-methylquinic acid, and release quinic acid and the corresponding hydroxycinnamic acid [43].  
Hydroxcinnamic Acids  
Compound **1** (RT = 9.185 min) with [M-H]- ion \(m/z\) at 295.0470 was detected only in the fermented Geisha coffee bean extract and characterized as _p_-coumaroyl tartaric acid. The compound 3,5-5'-dehydrodiferulic acid, with a retention time of 27.889 min, can be detected in the positive mode with [M+H]+ ion at \(m/z\) 387.1073 and was present in both fermented Geisha and Bourbon phenolic extracts. Compound **2** (RT = 25.788 min) characterized as 3-caffeoylquinic acid (3-CQA) was detected in both unfermented coffee bean and fermented coffee bean, performed product ions in both positive and negative mode at \(m/z\) 353.0871. The identification of 3-CQA has confirmed fragments at \(m/z\) 253, \(m/z\) 190, and \(m/z\) 144, which indicated the loss of HCOOH-3H2O (100 Da), C\({}_{6}\)H5O\({}_{2}\)-3H2O (163 Da), and C\({}_{7}\)H1\({}_{10}\)G\({}_{6}\)-H2O (209 Da) from the parent ion [11; 13]. 3-CQA is the most abundant isomer of the caffeoylquinic acid that belongs to one of the major subclasses of chlorogenic acids in coffee beans. It is naturally found in green coffee beans and has essential biological antioxidant activity among several chronic diseases like cardiovascular disease and obesity [44]. 3-CQA was also detected in several cultivars of coffee beans in both roasted and green conditions by [PERSON] et al. [45] and [PERSON] et al. [46].
## 3 Proposed Methods and Model Architecture
### SPPF Module  
Drawing inspiration from the He Kaiming SPPNet [34], the SPP module processes features across distinct levels through parallel multiple max pooling layers. Employing pooling kernels with a consistent step size but distinct sizes (specifically, a pooling layer of \(13\times 13\), \(9\times 9\), and \(5\times 5\)) to process features. The output information layer of each pooling operation maintains the same scale. Following this, the feature maps generated by the four branches are concatenated and spliced according to the channel direction. The SPPF module extends the functionality of the SPP module while improving the efficiency under the same calculation results. Its structure is indicated in Figure 3, which adopts serial mode to connect three pooling layers with \(5\times 5\) pooling cores. Through this sequential pooling operation, the receptive field of each pooling layer can be expanded, enabling the network can grasp the broader scope of global context features. In addition, this approach helps mitigate the loss of spatial information, ultimately enhancing the model's robustness.
## 6 Conclusions and Future Work  
In this paper, we proposed a two-tier Particle Swarm Optimization (PSO) algorithm in the clustering and routing stages for distributed multi-hop compressed sensing in 3-D UWSN. We divided the whole network into different segments according to the vertical distances of the nodes. On top of that, we also divided the whole network into different layers according to the horizontal distances from the nodes to the sink and the PSO algorithm we apply in the network is firmly associated with the layers. Dividing the layers help to define the energy consumption in different layers to measure energy-consuming balance and to initialize the particle hierarchically so as to accelerate the particle convergence speed. And a factor to forecast the signal recovery performance is added on the fitness function of the PSO algorithm for routing to control the MSE of the reconstructed signal while maintaining a relatively long lifetime. Our proposed algorithm is compared with recently proposed algorithms for distributed multi-hop compressive sensing routing problem using real marine temperature data rendered by NASA. The results validate that our proposed algorithm has a longer life time with lower measurement error and postpones the round that has the first dead node.  
Figure 12: The comparison results in terms of data recovery qualities.  
Figure 13: The comparison results in terms of MSE in different measurement ratio.  
For the future work, we will focus on the synthesized consideration of the clustering and routing stage while doing optimization. In the current study, the two stages are considered separately. On the further study, we will conduct the two steps consistently to improve the performance a step further.  
Conceptualization, X.C. and W.X.; methodology, X.C.; software, W.X.; validation, X.C. and W.X.; formal analysis, S.C.; investigation, X.C. and W.X.; resources, X.C.; data curation, W.X.; writing--original draft preparation, W.X.; writing--review and editing, X.C.; visualization, W.X.; supervision, X.C.; project administration, X.C. and S.C.; funding acquisition, X.C. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant 61301181, U1530120 and Scientific Research Starting Foundation of Central South University, and the APC was funded by the National Natural Science Foundation of China under No. U1530120.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  
The authors declare no conflict of interest.
# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 2 Jet Flavour Tagging
### The Deeplet Tagger  
Recently, a new network architecture was developed: the Deeplet tagger [21]. Different from CSVv2 and DeepCSV taggers, this architecture examines all jet constituents simultaneously. The Deeplet algorithm uses a large number of input variables that can be categorised into four groups: global variables (jet kinematics, the number of tracks in the jet, etc.), charged and neutral PF candidates, and variables of the SVs related to the jet. For the same reasons seen in Section 2.1, the jets \(\mathrm{p_{T}}\) and \(\eta\) are reweighted during data preprocessing to avoid discrimination closely related to the kinematic domain used during training.  
The basic idea in the Deeplet architecture is to use low-level information from all subjet features. In order to process an input variable space of such dimensions, the architecture needs an appropriate training procedure. Four separate branches are used in the first step: all four of the groups listed above except the global variables are filtered through a \(1\times 1\) convolutional layer. Each of the three outputs is then processed into a recurrent layer of the Long Short-Term Memory (LSTM) type [22]. The three LSTM outputs are collected with the global variables and then input in a fully connected layers. In order to discriminate between b-tagging, c-tagging, and quark/gluon tagging, the six output nodes of the previous layers are integrated into a multi-classifier.  
Training is performed using the Adam optimiser with a learning rate of \(3\times 10^{-4}\) for 65 epochs and categorical cross entropy loss. The learning rate is halved if the validation sample loss stagnates for more than 10 epochs. In Figure 4, the Receiver-Operative Characteristic (ROC) curves for two different \(\mathrm{p_{T}}\) ranges for the same dataset are reported and compared to the performance of the DeepCSV tagger. Such curves display the background misidentification efficiency versus the signal efficiency measured from Monte Carlo simulation.