esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 2 Basic Theory
### Quasi-Bell State  
The quasi-Bell state [12] is an entangled state that is constructed using nonorthogonal quantum states but has maximum entanglement. The two modes of the quasi-Bell state can be labeled S and A. In this paper, we use two coherent states as the nonorthogonal quantum states, where the coherent states with amplitudes \(\alpha\) and \(\beta\) are denoted by \(|\alpha\rangle\) and \(|\beta\rangle\). The quasi-Bell states are represented as the quantum states of the composite system SA as follows:  
\[|\Psi_{1}\rangle_{\mathrm{SA}} = h_{1}(|\alpha\rangle_{\mathrm{S}}|\beta\rangle_{\mathrm{A}}+|- \alpha\rangle_{\mathrm{S}}|-\beta\rangle_{\mathrm{A}}), \tag{5}\] \[|\Psi_{2}\rangle_{\mathrm{SA}} = h_{2}(|\alpha\rangle_{\mathrm{S}}|\beta\rangle_{\mathrm{A}}-|- \alpha\rangle_{\mathrm{S}}|-\beta\rangle_{\mathrm{A}}),\] (6) \[|\Psi_{3}\rangle_{\mathrm{SA}} = h_{3}(|\alpha\rangle_{\mathrm{S}}|-\beta\rangle_{\mathrm{A}}+|- \alpha\rangle_{\mathrm{S}}|\beta\rangle_{\mathrm{A}}),\] (7) \[|\Psi_{4}\rangle_{\mathrm{SA}} = h_{4}(|\alpha\rangle_{\mathrm{S}}|-\beta\rangle_{\mathrm{A}}-|- \alpha\rangle_{\mathrm{S}}|\beta\rangle_{\mathrm{A}}), \tag{8}\]  
where  
\[h_{1} = h_{3}=\frac{1}{\sqrt{2(1+\kappa_{\mathrm{S}}\kappa_{\mathrm{A}})}}, \tag{9}\] \[h_{2} = h_{4}=\frac{1}{\sqrt{2(1-\kappa_{\mathrm{S}}\kappa_{\mathrm{A}})}},\] (10) \[\kappa_{\mathrm{S}} = \langle\alpha|-\alpha\rangle=\langle-\alpha|\alpha\rangle=\mathrm{ e}^{-2|\alpha|^{2}},\] (11) \[\kappa_{\mathrm{A}} = \langle\beta|-\beta\rangle=\langle-\beta|\beta\rangle=\mathrm{e}^{ -2|\beta|^{2}}, \tag{12}\]  
and \(\alpha\) and \(\beta\) are nonnegative real numbers.  
\(|\Psi_{2}\rangle_{\mathrm{SA}}\) has the maximum entanglement, and the amount of entanglement is 1 ebit when \(\alpha=\beta\). Therefore, we treat \(|\Psi_{2}\rangle_{\mathrm{SA}}\) with \(\alpha=\beta\) as the maximum quasi-Bell state in this paper. The average number of photons in mode S is \(\langle n\rangle_{\mathrm{Max}}=\left|\alpha\right|^{2}\coth(2|\alpha|^{2})\), and the minimum average number of photons is 0.5 because \(\langle n\rangle_{\mathrm{Max}}\to 0.5\) when \(\alpha\to 0\). Additionally, we treat \(|\Psi_{1}\rangle_{\mathrm{SA}}\) with \(\alpha=\beta\) as the non-maximum quasi-Bell state in this paper. Note that the amount of entanglement in this case is smaller than 1 ebit. The average number of photons in mode S is \(\langle n\rangle_{\mathrm{NonMax}}=|\alpha|^{2}\tanh(2|\alpha|^{2})\), and the minimum average number of photons is 0 because \(\langle n\rangle_{\mathrm{NonMax}}\to 0\) when \(\alpha\to 0\).
## 5 Results and Analysis
### Key Performance Indicators
#### 5.5.2 Power Indicator  
The peak demands on the days in four seasons were 5.81 kW, 6.10 kW, 9.63 kW and 8.91 kW, respectively. The fuel cell module performed as an energy buff by providing the peak power of 5.71 kW, 5.82 kW, 8.68 kW, and 8.29 kW on the corresponding days while the maximum wind power was only 4 kW. The fed-in power to the grid reached the highest value on the summer day (3.75 kW). In this study, the peak demand and peak wind power had quite different characteristics while the fuel cells could balance the discrepancy. The fuel cells therefore reduced a need for any frequent and significant power exchange between the load and the grid. Therefore, the proposed system improves the resilience of power grids whilst maximizing the utilization of local renewables.
# Development of a CALPHAD Thermodynamic Database for Pu-U-Fe-Ga Alloys
## 4 Crystal Structure
### Fe-Ga Assessment  
This is the first published CALPHAD assessment of the Fe-Ga system (Figure 4), based on a preliminary internal report by coauthors [7]. Since ordering of the iron-rich side is not pertinent to the scope of this investigation and given the uncertainties reported in experimental studies [65; 66; 67], it is therefore not considered at this time. However, the overall shape of the phase diagram is reproduced in the present work, relevant invariant reactions and stoichiometric compounds are included, and improvements to the preliminary work of [PERSON] et al. [7] have been made so that some of the invariant reactions are better represented. This includes adjustments to the peritectic reaction of Fe\({}_{3}\)Ga\({}_{4}\), where the reaction temperature is improved by 30 \({}^{\circ}\)C compared to [7] and the composition is in better agreement with experiments by [65; 66; 67]. Similarly, reaction temperatures and composition ranges are improved for reactions 3 and 5 in Table 5, however the best fit of experimental data resulted in a eutectic instead of a peritectic reaction for reaction 3. Major modifications to the liquidus and solidus ranges result in a more acceptable fit compared to the preliminary assessment. Since the \(\alpha\)-Fe (BCC\({}_{-}\)A2) phase is considered without any possible ordering, some disagreement on the Fe-rich side is to be expected. While the literature review (presented above) includes adequate descriptions of the ordering of \(\alpha\)-Fe, \(\alpha\)-Fe and \(\alpha\)''-Fe (Fe\({}_{3}\)Ga composition dependence), it is to be noted that experiments proved difficulty in achieving equilibrium conditions in this phase space, which supports the current decision to exclude ordering on Fe-rich side. Similarly, the focus is on small amounts of Fe-impurities, therefore, only stoichiometric compositions were presented for each of the compounds and parameters for any ordered iron phase were neglected. This provided some difficulty when optimizing this region of the  
Figure 3: U-Fe phase diagram based on the present CALPHAD assessment compared with selected experimental data [56; 58; 59; 60; 61; 62; 64].  
phase diagram and further investigation would be useful. The liquid phase is modeled using three interaction parameters, two of them temperature dependent. Even with multiple parameters, the liquidus curve on the Ga-rich side is difficult to reproduce due to the sharp decrease close to 100 at.% Ga. As additional terms or parameters for the liquid phase do not lead to significant improvement, the authors believe that including ordering for the BCC phase is necessary to have a better representation on the liquidus and solidus line across the phase diagram. Otherwise, this work shows rather acceptable improvements over the preliminary assessment of [7] and is a satisfactory representation of the Fe-Ga phase equilibria as seen experimentally.
# A 2.4 GHz 20 W 8-Channel RF Source Module with Improved Channel Output Balance
## 1 Introduction  
RF source modules for plasma generators have been used as a magnetron type in the sub-GHz frequency band, but recently this type of module has been developed by using a solid-state power amplifier (SSPA) in the 2.4 GHz industrial-scientific-medical (ISM) band [1, 2, 3, 4, 5, 6]. To produce high-quality wafers in the semiconductor process, area coverage, uniformity and reliability during plasma generation should be considered [7, 8]. Until now, plasma RF sources have had low area coverage, because they consisted of one channel with high output power [9, 10]. In addition, there was a problem in that the density of the center and that of the periphery are not uniform, which degrades reliability. Furthermore, it was difficult to improve the drain efficiency due to the high-level output power. Although various attempts have been made to improve plasma density by configuring multi-channels, there was a problem in that the size, weight, and cost of the entire system increased because individual RF sources were required as much as the number of plasma sources [4, 5]. For this reason, an 8-channel RF source module was previously presented that focused on area coverage, uniformity, and drain efficiency [11]. Although improvement of the drain efficiency was confirmed in the previous module, the uniformity problem could not be completely solved due to the difference in transistor performance as a result of a 2-stage power amplifier (PA) being applied to each channel. Moreover, due to there being manydevices, such as voltage gain amplifiers (VGAs) and relays to control the transistors, the size of the module including the DC control part was inevitably increased.  
Therefore, in this paper, a 2.4 GHz 20 W 8-channel RF source module with improved channel output balance is proposed to solve the aforementioned problems. The power amplification part is simply constructed to a single 2-stage PA that has a high-level output power to improve the channel output balance. The output terminal consists of 8-channel as previously mentioned, but it is implemented with a 3-section of 2-way Wilkinson divider, not a commercial 2-way divider, to minimize the magnitude and phase difference between channels. Additionally, the channel spacing was reduced to 3.2 cm to improve the uniformity. In addition, to alleviate performance degradation due to high-level output power characteristic, a water jacket is optimized to improve the drain efficiency and the module size. The remainder of this paper is organized as follows. Section 2 describes the proposed RF source module, which is classified into three parts. The measurement results of the proposed RF source module are presented in Section 3. Finally, concluding remarks are provided in Section 4.
## 1 Introduction
### Wireless Powered Communication Network  
A wireless powered communication network integrates wireless energy transfer (WET) and wireless information transfer (WIT) in a wireless communications network, which provides a solution to limited battery resources in mobile devices. In the WPCN, an energy provider transmits energy to user terminals on downlink, which then use this energy to transmit information on uplink.  
Figure 1: Deep neural network: A fully connected multi-layer neural network that consists of one input layer, two hidden layers and one output layer. Here, \(\mathbb{R}^{n}\) shows an n-dimensional real number vector.  
It is assumed that the transmitter has a fixed power supply (e.g., a battery), whereas the receiver has no fixed power supply and thus needs to replenish energy via wireless energy harvesting from the received interference and/or signal sent by the transmitter.  
When we use the time division duplex (TDD) mode in the WPCN, the total coherence interval is divided into two phases (the downlink energy transfer phase and the uplink information transfer phase) while assuming that the exact channel state information is known. For energy and throughput efficiency, it is necessary to schedule uplink and downlink allocation time so that the required QoS of the WPCN can be achieved. In [5], the author mentioned that throughput and energy are functions of time allocation parameters, and QoS can be achieved only by properly designing these parameters. Moreover, for the efficient usage of available resources by the energy provider and to confirm the QoS of the WPCN, it is also necessary to optimize the transmit power of the energy provider. In [12], [PERSON] et al., provided a study on the joint optimization of transmit power control, of information and energy transfer scheduling and of receiver mode switching for wireless information transfer with energy harvesting. In [13], [PERSON] et al., jointly optimized power and time allocation for each user in order to achieve proportional fairness while controlling the energy consumption offset for the network at a low level.
## 3 Results and Discussion
Figure 10 shows the negative bias stress (NBS) and positive bias stress (PBS) of Sample B. The V\({}_{\rm G}\) was applied at \(-\)20 V and 20 V for 0 s, 100 s, 600 s, 1200 s, 2400 s, and 3600 s. As Figure 10 shows, Sample B shifts 0.5 V to the negative direction after NBS was applied for 3600 s while it shifted 9.6 V to the positive direction after PBS was applied for 3600 s. \begin{table}
\begin{tabular}{l c c c c c} \hline  & **Sample A** & **Sample B** & **Sample C** & **Sample D** & **Sample E** \\ \hline \(\mu_{\rm sat}\) (cm\({}^{2}\)/(V-S)) & \(7.1\pm 0.1\) & \(8.0\pm 0.6\) & \(7.6\pm 0.6\) & \(7.3\pm 0.3\) & \(5.2\pm 0.6\) \\ Ion/Ioff & \((1.86\pm 0.20)\times 10^{6}\) & \((2.01\pm 0.34)\times 10^{6}\) & \((1.66\pm 0.16)\times 10^{6}\) & \((1.45\pm 0.27)\times 10^{6}\) & \((8.89\pm 2.77)\times 10^{5}\) \\ SS (V/dce) & \(0.23\pm 0.02\) & \(0.18\pm 0.03\) & \(0.25\pm 0.05\) & \(0.29\pm 0.05\) & \(0.27\pm 0.02\) \\ Von (V) & \(-0.22\pm 0.12\) & \(1.08\pm 0.20\) & \(2.41\pm 0.14\) & \(3.04\pm 0.18\) & \(5.45\pm 0.11\) \\ \hline \end{tabular}
\end{table} 
Table 4: The characteristics of Zr-AZTO TFT devices annealed at 350 \({}^{\circ}\)C.  
Figure 9: The electrical characteristics of Zr-AZTO thin film transistors the devices annealed at 350 \({}^{\circ}\)C. (**a–e**) The output curves of Sample A–E; (**f**) The transfer curve of Sample A–E.
## 5 Discussion
The simulation of existing transport modes with AVs showed a reduction in the average daily travel time, compared with the existing condition, of 26.7%, including the total average waiting time. This shows the benefit of using AVs instead of conventional private cars, where fewer cars are on the road network, and no parking process is needed. Furthermore, the simulation of the existing transport modes with the P&R system showed an increase in the average total daily travel time by 51.2%, and this increase was caused by the use of a slower transport mode (public transport, instead of private cars), traveling a farther distance (for some travelers) to use P&R facilities, and the walking distance at the end to reach their destination. It is worth noting that this increase cannot be compared with scenario (1), because the benefit can be realized by travelers in terms of a cost reduction or an environmental impact reduction, such as noise, and emissions, and congestion. Travelers who travel long distances, where public transport is not affordable, are more likely to use their private cars to reach the public transport stop closest to their destinations. In that regard, AVs were used as a replacement for cars, and the P&R system was integrated into the daily activity plans of travelers. AVs were used as a feeder system to public transport, and the result was a reduction in the travel time of 38.4%, compared with the existing transport mode with P&R scenario. Thus, the P&R system increases travel time, decreases the number of vehicles inside a city center, decreases congestion resulting from the parking search process, allows travelers to avoid using expensive parking inside the city center, and generally increases the daily utility of travelers. In addition, using AVs would provide more benefits to travelers, such as travel time minimization, travel cost minimization, onboard activities, and the ability to utilize land spaces for purposes other than parking spaces.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 3 Evaluation Methods  
### Image Segmentation Evaluation Metrics  
A combination of Intersection over Union (IoU) loss and Dice loss is used to improve segmentation accuracy and handle class imbalance:  
\[\text{Loss}=\
u\text{BCE Loss}+\mu\text{IoU Loss}+\lambda\text{Dice Loss} \tag{3}\]  
where BCE loss is used to compute the cross-entropy error between the predicted results and the ground truth labels.  
Dice loss and IoU loss are employed to enhance sensitivity to small target regions.  
The weight coefficients \(\lambda\), \(\mu\), and \(\
u\) are set to 0.5, 0.25, and 0.25, respectively.  
The model performance is evaluated using these two metrics:  
1. IoU (Intersection over Union): This metric measures the overlap between the predicted and ground truth regions. It is defined as follows: \[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}\] (4)
2. Dice Coefficient: This metric measures the similarity between the predicted segmentation and the ground truth, and is defined as follows: \[\text{Dice}=\frac{2\times\text{TP}}{2\times\text{TP}+\text{FP}+\text{FN}}\] (5)  
The confusion matrix is also used to evaluate the performance of an image segmentation model. In segmentation, the classification result for each pixel fills the confusion matrix.  
The basic form of the confusion matrix for image segmentation has four elements:  
True Positive (TP): The number of pixels correctly predicted as the target class.  
False Positive (FP): The number of pixels incorrectly predicted as the target class (background predicted as the target class).  
False Negative (FN): The number of pixels incorrectly predicted as background (target class predicted as background).  
True Negative (TN): The number of pixels correctly predicted as the background class.
## 2 HAS Message Format and Decoding Performance Metrics
In particular, it was assumed that Galileo HAS will broadcast two types of message, denoted as:  
* MT1 Full Corrections (MT1 FC): including satellite masks, orbit and clock corrections, and code and phase biases [3; 6],
* MT1 Clock Correction Subset (MT1 CCS): including a subset of clock corrections [3; 6]. The code \"MT1\" indicates \"Message Type 1\", which is the only message type currently defined. It has a flexible structure and supports several configurations. A MT1 FC message provides a full set of corrections and it is updated every 120 s. It comprises \(K=15\) pages and is broadcast in blocks of eight consecutive epochs. The MT1 CCS message comprises \(K=2\) pages and is broadcast in blocks of two consecutive epochs following the transmission of the MT1 FC message. Clock corrections are updated every ten seconds. These parameters imply that page dissemination is performed as follows: for eight consecutive seconds all satellites transmit HAS corrections as MT1 FC pages; after that, MT1 CCS corrections are broadcast for two seconds. This ten second pattern is repeated indefinitely with an update of the clock corrections each ten seconds. After 120 s, the MT1 FC message is also updated. Additional details on the dissemination pattern adopted here can be found in [3; 7]. The TTRD is evaluated as the time required to recover a MT1 FC and a MT1 CCS message, and taking into account possible resets in the Reed-Solomon decoding process due to the update of the corrections. The final parameters of the HAS transmission scheme are not defined yet and experimentation is ongoing to determine the settings that maximize the dissemination performance. Thus, the parameters adopted for the analysis and described above may be different from those that will be finally adopted for the Galileo HAS. Despite this fact, the parameters adopted are considered to be representative of the future HAS service and no significant deviations are expected.
# A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging
###### Abstract  
Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence's current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round's loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model.  
Chinese grammatical error correction; sequence tagging; iterative training; conditional random field; focal loss
## 3 Application to a Case Study
### Analysis of the Factors Affecting the Health of an Offshore Platform Structure
#### 3.5.3 Marine Life Growth  
Over time, an increasing number of marine organisms will attach themselves to the structure and grow. This increases the weight of the underwater structure and reduces its overall natural vibration frequency. A simplified linear mass-increase model can be described by the expression [41]:  
\[m(t)=m_{i}(t-t_{0}), \tag{11}\]  
where \(m(t)\) is the mass of marine life at time \(t\), \(m_{i}\) is the annual growth rate of marine life on the structure, and \(t_{0}\) is the time at which the growth of marine life began.  
As noted, the growth of marine life is realized through added mass. It is assumed that the growth rate of marine life on underwater components is 0.4 cm per year and that the added mass density is 1400 kg/m\({}^{3}\)[41].  
The diagram of time-varying effect on frequency under marine life growth is shown in Figure 13. It illustrates that the platform frequency generally decreases with the increasing of service time. Within the service life, the effects of marine organisms on the frequency are 0-8%, so the impact of marine organisms is mainly on the higher order modes.
# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 3 Data Characteristics
### Data Shape  
Each PLOAMd message is 13 bytes long, resulting in a learning vector of 13 elements (each field contains separate elements, and 10 bytes of the data fields are expanded). The header is shown in Figure 1. As mentioned above, the protocol analysis is inspired by natural language processing, which analyzes a sequence of words. In this scenario, words are replaced by vectors of 13 elements and shaped into a sequence of 30 vectors. The sequence length is empirically evaluated as the optimal length considering the ability to locate a possible non-defined sequence and to distinguish sequences. Final learning samples are generated by using a sliding time window over captured messages and returning sequences of correct shape (30 messages \(\times\) 13 features).
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 4 Detecting the \(\mathit{IRFS}\) Attack
### Faking Double Detection Events
Here, we show the six choices for \((\ket{0_{Z}},\ket{0_{Z}})\) and \((\ket{1_{Z}},\ket{0_{Z}})\) biqubits. Figure 4: The error rate of double detection events caused by the \(IRFS\) attack is \(\frac{1}{6}\). If it is compared against the \(QBER\) of the quantum channel, the maximum secure distance to detect the \(IRFS\) attack is 176 km. In the presence of the \(IRFS\) attack, perfect visibility and zero dark counts are assumed in the link between [PERSON] and [PERSON] and from her to [PERSON]. We discussed \(pc\) (\(pe\)) for double detection events in Appendix C. Therefore, \(pc=\frac{p_{e}^{2}}{p_{e}^{2}+p_{e}^{2}}\) and \(pe=\frac{p_{e}^{2}}{p_{e}^{2}+p_{e}^{2}}\), and we derived the \(QBER\) of the parallel and orthogonal states as \(QBER=\frac{(1-V)^{2}}{(1-V)^{2}+(1+V)^{2}}\). If we compare the \(QBER\) of double detection events produced by the quantum channel against the \(\frac{1}{6}\) error rate caused by the eavesdropper, we find that the maximum secure distance for detecting the \(IRFS\) attack when the eavesdropper fakes double detection events is 176 km, which is within the range of the \(BB84\) key rate, as is shown in Figure 4.