esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Automated Processes
#### 2.2.2 Optimization Method  
Aside from the aforementioned method of computing boundary conditions, the same objective can be achieved by applying optimization algorithms that incorporate abundant search methods [32]. As shown in Figure 5, the only change compared to the dichotomy method is to replace the second loop in Figure 4 with the solving of an optimization workflow. Specifically, the independent variable corresponds to the range of stall margin, and solutions are pursued via optimization techniques integrated with diverse search algorithms. The optimization objective and constraint are defined by the following formulas.  
\[\text{Objective}:\min(\dot{m}_{in}) \tag{6}\]  
\[\text{Subject to}:\left\{\begin{aligned} \tilde{\xi}<10^{-4}\\ \left|\dot{m}_{out}-\dot{m}_{in}\right|/\dot{m}_{in}<0.1\%\\ \max(A(i))-\min(A(i))<0.1\%,i\in[N-\psi,N]\end{aligned}\right. \tag{7}\]  
After the stall margin searching loop, the [lower bound, upper bound] interval for boundary condition variation is defined. The subsequent process is then as follows:  
Figure 5: Automated solving flow chart employing optimization method.  
(a). A boundary condition value is prescribed based on the search method of the optimization algorithm.  
(b). Perform numerical simulation by updating the given value with the new boundary condition.  
(c). If the calculation converges, proceed to the next step (d); otherwise, return to step (a).  
(d). If the optimization objective has been satisfied, the boundary condition at the stall point is the value corresponding to the optimal point; otherwise, return to step (a).
# Online Distributed User Association for Heterogeneous Radio Access Network
## 5 Performance Analysis
#### Time Complexity Analysis  
In this section, we compute the time complexity of the different proposed algorithms. The time complexity of Algorithm 2 depends on the number of UEs and BSs. Each UE calculates the \(\eta_{i,j,k}\) and receives the \(\lambda_{i,k}\) information from the candidate BSs; afterwards, it goes for the user association procedure. Calculation of \(\eta_{i,j,k}\) and the probability of association for each BS depends on the factor that calculates how many BSs satisfy the SINR threshold. The number of BSs which satisfy the SINR threshold varies from UE to UE. We denote the average number of BSs accessed by these UEs to be C, where the time complexity for Algorithm 2 will then be of \(O(C)\), as the method is distributed in nature. Algorithm 3, on the other hand, works differently. Each UE selects a tier, \(k\), in the first level and a BS, \(j\), from that tier in the second level. Both selections of the tier and BS are performed on the number  
Figure 4: Similarity between UE’s utility and BS’s utility.  
of choices \(d1\) and \(d2\), respectively. In Level 1, for the selection of a tier, the algorithm tries to find a tier that has a minimum load out of the \(d1\) chosen tiers. This results in \(O(d1)\) for a single UE. In the second level of the algorithm, the UE finds a BS from the selected tier. The UE has a number of choices, but only \(d2\) numbers of BSs are eligible for the access procedure. Therefore, the time complexity for Algorithm 3 depends on both levels 1 and 2. The time complexity of this distributed scheme is thus the sum of the two choices in two different levels. Hence, the time complexity is \(O(d1+d2)\). Table 4 shows the comparison of different algorithms by considering association rule, time complexity and scheme employed.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed with Minitab Statistical Software (2023 Minitab, LLC, State College, PA, USA). The data were assessed for normality using the Kolmogorov-Smirnov test, which confirmed a normal distribution. As a result, variables were reported as mean values with their corresponding standard deviations.  
For the first objective of the study, measurements obtained using DG and the eyeTilt application were compared to those from the MoCap system during static trials. A paired _t_-test was performed to identify any statistically significant differences (_p_ < 0.05). To evaluate the relationship between measurements, Pearson's correlation coefficient (PCC) was calculated, describing the level of agreement between the compared systems (i.e., MoCap vs. DG and MoCap vs. eyeTilt). The strength of the correlation was classified as follows: \(\left|\,\mathrm{r}\right|\leq\) 0.4, weak; 0.4 < \(\left|\,\mathrm{r}\right|\leq\) 0.6, moderate; 0.6 < \(\left|\,\mathrm{r}\right|\leq\) 0.8, strong; and \(\left|\,\mathrm{r}\right|>\) 0.8, very strong [48]. Moreover, the intraclass correlation coefficient (ICC) was calculated to assess the reliability of measurements between the different systems. ICC values range from 0 to 1, where values closer to 1 indicate excellent agreement and values closer to 0 indicate poor agreement. The interpretation of ICC was classified as follows: ICC < 0.50, poor reliability; 0.50 \(\leq\) ICC < 0.75, moderate reliability; 0.75 \(\leq\) ICC < 0.90, good reliability; and ICC \(\geq\) 0.90, excellent reliability.  
Additionally, Bland-Altman plots were used to visually assess the level of agreement (LoA) between the paired measurements. The accuracy of the static angular measurements was then computed according to the following equation (Equation (1)):  
\[Accuracy=\frac{\left|Angle_{\text{MoCap}}-Angle_{\text{DG or eyeTilt}}\right|}{Angle_{ \text{MoCap}}}*100 \tag{1}\]  
Additionally, the root mean square error (RMSE) was computed according to the following equation (Equation (2)):  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(\hat{y}_{i}-y_{i}\right)^{2}}{n}}=\sqrt{ \sum_{i=1}^{n}\frac{\varepsilon_{i}^{2}}{n}} \tag{2}\]  
where \(\hat{y}_{1}\ldots\hat{y}_{n}\) represent the head rotation or inclination angles measured using the digital goniometer or the eyeTilt application, while \(\hat{y}_{1}\ldots\hat{y}_{n}\) correspond to the measurements obtained using the MoCap system. The differences between these values, \(\mathrm{e}_{1}\ldots\mathrm{e}_{n}\), denote the errors, and \(\mathrm{n}\) represents the number of observations (i.e., the number of participants). Low RMSE values, approaching zero, indicate a high level of agreement between the MoCap system and the other measurement tools.  
For the second aim of the study, potential limitations in head movements in individuals with AHP were analyzed during dynamic head movements. Unpaired _t_-tests were conducted to compare the ROM values for individuals with AHP with those of the control group. All statistical tests were performed with a significance level set at \(\alpha\) = 0.05.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 6 Conclusions  
We have presented a new PRC modeling scheme that can generate correction information when users cannot receive PRC data. Predicted PRC through our modeling showed a bias error of less than \(\pm\)1.0 m and an RMSE within 1.5 m, compared to actual PRC values from the reference station. Positioning accuracy using predicted PRC showed that horizontal RMSE for both DGPS and DBDS were 1.0 m, with vertical RMSE of 1.8 m and 3.0 m, indicating similar results from positioning with real PRC. From the above results, it was verified that modeling PRC values can be effectively used to maintain performance continuity in a DGNSS system, even in situations where correction data cannot be received. In particular, vertical accuracy was improved significantly with the prediction system.
## 3 Competence Identification Model for RO
### Competence-Based Assessment and Training in the STCW Convention  
International requirements on qualification and training of seafarers are regulated in the STCW Convention. The STCW Convention was first adopted in 1978 and entered into force in 1984 (STCW78). STCW78 described the minimum knowledge required for certifiating officers and ratings but did not include detailed criteria. After some tremendous shipping accidents, IMO started reviewing the Convention [38]. Finally, the amended Convention was adopted in 1995 and entered into force in 1997 (STCW95). The revised one primarily modified the requirements, especially about minimum knowledge. It introduced a competence-based assessment and training scheme, which is also named 'outcome-based', 'performance-based' and 'criterion-referenced/validated' [38]. The minimum required knowledge regulated in STCW78 was detailed and updated under the Knowledge, Understanding and Proficiency (KUP) column. Moreover, two columns, Methods for Demonstrating Competence and Criteria for Evaluating Competence, are newly added in STCW95. Then IMO has further amended and added some parts in the latest STCW Convention (STCW2010).  
One of the most important points on the introduction of competence-based training (CBT) in STCW95 is that candidates' training became 'outcome-based'. They should 'demonstrate their ability' to achieve the tasks that are required for the certification [38]. In other words, seafarers certified according to CBT are proved to take appropriate actions that are sought in their responsibilities onboard ship. Another point is that, in CBT system, training should be designed based on'measurable standards of performance', assessment should be placed as an 'essential part' and the training and assessment should be carried out with high level of quality assessment [39]. That means that CBT allows some flexibility on the design of the training course by maritime education institutes under explicit competence, and requires the quality assessment of the training scheme as an indispensable matter.  
Although there are some critics in the current competence requirements of STCW2010 that they have many concerns, including unclear and vague assessment criteria in the competence table [40], this system has recognised as a standard competence assessment and training tool in the world. [PERSON] et al. [41] suggest that current provision of regulations in the STCW Convention could be the starting point for discussing minimum training standards for autonomous systems. In fact, RSE in the IMO [6] utilises the STCW Convention when considering the competence of RO and crews. [PERSON] et al. [20] also review the competence for OOW in MASS by utilising Table A-II/1 in the STCW Convention.  
CBT in the STCW Convention requires the experience of seagoing service or onboard training for acquiring competence to be OOWs, chief mates and masters. For example, a cadet to be OOW on a seagoing ship of 500 GT or more shall receive an approved onboard training program of not less than 12 months or an approved seagoing service of not less than 36 months.
## 2 Materials and Methods
### Lipidomics Analysis  
Plasma samples of all patients, stored at -80 \({}^{\circ}\)C, were thawed at room temperature and immediately subjected to lipid extraction and analysis. Total lipid extraction from an aliquot of plasma was performed according to Folch procedure [14]. Calibration standard and plasma sample analysis was performed by liquid chromatography-electrospyrization-tandem mass spectrometry. Different volumes of calibration Standards (STDs)/plasma samples were injected: 0.5 \(\upmu\)L for STDs 1,2-phosphatidyl ethanolamine: PE(15:0)(15:0) and Ceramides: Cer(d18:1/17:0); 0.1 \(\upmu\)L for STDs cholesterol esters: CE(17:0), Triacyl glycerol: TG(17:0/17:0)/17:0), sphingomyelin: SM(d18:1/16:0), and 1,2-phosphatidyl! choline: PC(17:0)(17:0); 0.5 \(\upmu\)L for plasma samples, as such, and 0.1 \(\upmu\)L for plasma samples diluted 5-fold to avoid mass spectrometer saturation, due to the high concentration levels of these lipid species in plasma. Each injection was repeated in triplicate for each lipid standard to evaluate the reproducibility of the procedure. Lipid species absolute concentrations were calculated considering their area ratio (lipid peak area/internal Standards (ISTD) peak area) and interpolating it within the calibration curve of the corresponding external standard. MultiQuant 2.1 software (SCIEX, Concord, Canada) was used for lipid quantification. The lipids structures are presented in Annex 1.
# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 4 Experimental Results and Discussion  
### Data Specification  
As discussed in Section 2, the considered input variables and their encoded values are listed in Table 2. The traditional input variables labeled No. 1-11 were assembled with different weather variables labeled No. 12-15. These assembled input variables are defined as the input vector \([X_{t}]\). Hourly passenger flow (\(v_{PF}^{*}\)) is the target variable for forecasting (\(Y_{t}\)). Table 2 lists the descriptions of the input variables. \(v_{NH}\) has been encoded as a two-digit figure, shown in Table 3. The encoded values of input variables labeled No. 1-11 are described in [26].  
Figure 5: Dataflow in a deep long short-term memory neural network (LSTM_NN).  
In the experiment, we developed many LSTM NN-based models for hourly passenger flow forecasting with Keras in Python. All training data and testing data were normalized and shuffled before training and testing.  
Root mean square error (RMSE) and mean absolute percentage error (MAPE) are the two most commonly used criteria to evaluate the performance of a passenger flow forecasting model [26]. Thus, RMSE and MAPE in Equations (9) and (10) were used to evaluate the performance of the forecasting model.  
The lower the value in RMSE or MAPE, the better the performance.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{t=1}^{n}\abs{\left(Y_{r}\right)_{t}-\left(Y_{f} \right)_{t}}^{2}} \tag{9}\]  
\[MAPE=\frac{1}{n}\sum_{t=1}^{n}\frac{\abs{\left(Y_{r}\right)_{t}-\left(Y_{f} \right)_{t}}}{\left(Y_{r}\right)_{t}} \tag{10}\]
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Comparative Analysis of MAC Layer-Based UWAC Full-Duplex Protocols
#### 3.4.2 Delay Efficiency  
To decrease unexpected collisions and delays, FD channel was used in FD-CDMA-based protocols [46]. However, the FD channel is not the most successful way to tackle long propagation delays in UWAC networks. Future research in this field is still possible. The BD-FD-MAC [36] protocol, in which source nodes send ID-based RTS control packets to the destination node, increases the successful transmission time and effectively reduces delays. The FD-DA-CSMA [30] architecture effectively resolves collisions between the control to control and control to data packets through two different channels, resulting in low end-to-end delays. To prevent accidental collisions and significant propagation delays, the FS-MACA [37] uses the future scheduling (FS) technique to allow future agreements between the transmitter and receiver for successful transmission. However, this FS technique is incapable of effectively dealing with long propagation delays, and time synchronization remains a problem. To reduce the significant propagation delay between its neighbor nodes, FDCA-MAC [34] uses a full-duplex communication channel. The end-to-end delay between the UW nodes of the TFH-MAC protocol [69] increases drastically as the network load increases. The control and data packets in the E-FD-MAC [38] protocol are ID-based, which automatically prevents unexpected collisions and improves delay performance, while in the FDCP-MAC protocol [39], time stamp-based dispute resolution reduces the collision rate by a factor of 3 to 4.
## 2 Experimental Section
### Amplification of spPEP Transcript  
#### 2.3.1 Design of Degenerate Primers  
Degenerate primers shown in Table 1 were designed using COnsensus-DEgenerate Hybrid Oligonucleotide Primer (CODEHOP) [9] with the consensus sequence obtained from the ClustalW2 alignment [10] of the following insect PEP amino acid sequences: _Drosophila melanogaster_ (NP_610129), _Culex quinquefasciatus_ (XP_001843671), _Aedes aegypti_ (XP_001659779), _Nasonia vitripennis_ (XP_001603578) and _Apis mellifera_ (XP_395364).
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### LoRa PHY Layer
#### 2.2.2 Frequency Bands and Duty Cycle  
Depending on the geographical area in which LoRaWAN is deployed, the PHY level of LoRaWAN may operate in one of the three frequency regions, i.e., 433 MHz, 868 MHz, and 915 MHz. The payload size of each transmission can be up to 256 bytes, while the transmission rate is up to 27 Kbps. In Europe, LoRaWAN operates in the 863-870 MHz band, for which the standard defines five sub-bands with duty cycle (DC) restrictions per sub-band. For networks operating in unlicensed bands in the spectrum, the DC refers to the maximum percentage of time during which an end-device can occupy a channel [12]. Both end-devices and GWs comply with the restriction of DC. Therefore, the channel selection must apply pseudo-random channel switching to each transmission and comply with the maximum operating cycle. For example, DC in the EU 868 for end-devices is 1%. In particular, when a sub-band is used, it cannot be used again for the next off time (\(T_{\text{off}}\)), based on [12]  
\[T_{\text{off}}=\frac{ToA}{D_{c}}(\text{sec}) \tag{6}\]  
where \(D_{c}\) stands for the DC, while ToA is  
\[ToA=T_{\text{packet}}=T_{\text{pr}}+T_{\text{PHY}}(\text{sec}) \tag{7}\]  
and \(T_{\text{pr}}\) is the duration of preamble and can be obtained as  
\[T_{pr}=T_{s}\big{(}N_{\text{pre}}+4.25\big{)}(\text{sec}) \tag{8}\]  
where  
\[T_{s}=\frac{1}{R_{s}}(\text{sec}) \tag{9}\]  
and \(N_{\text{pre}}\) is the programmed number of symbols to be used by the radio frequency (RF) transceiver, for 868 MHz \(N_{\text{pre}}=8\) symbols.  
In (7), \(T_{\text{PHY}}\) is  
\[T_{\text{PHY}}=T_{s}N_{\text{PHY}}(\text{sec}) \tag{10}\]  
Moreover, \(N_{\text{PHY}}\) represents the number of symbols in PHY layer (without preamble) and can be evaluated as  
\[N_{\text{PHY}}=8+max\bigg{[}ceil\bigg{[}\frac{28+8 PL+16 CRC-4 SF}{4(SF-2 DE)} \bigg{]}(CR+4),0\bigg{]} \tag{11}\]  
where the ceil function returns the integer part rounded to the largest integer, PL is the PHY size of the payload, the \(CRC\) obtains the value 0 if the CRC field is not present in the packet, otherwise it obtains the value 1, the DE indicates the use of the low data rate optimization mechanism (LowDataRateOptimize) and takes the value 1 for SF12, SF11, and for BW 125 kHz and lower, and the value 0 for the remaining. Given the potentially long packet duration on high SFs the LowDataRateOptimize option can be adjusted to improve the robustness of the transmission to changes in frequency during packet transmission and reception, as it avoids issues regarding drifts of the crystal reference oscillator [30].
## 4 Performance Comparison
### Signaling Cost
#### Signaling cost for PMSS operation  
Equation (2) describes the signaling cost for the PMSS operation, while Table 6 shows the parameter setup.  
\[\begin{split}\text{SC}_{\text{PMSS}}=\text{C}_{\text{prodAR}}+ \text{C}_{\text{accessrouter}}+\text{C}_{\text{consumerAR}}+\text{C}_{\text{ accessrouter}}+\text{C}_{\text{accessrouter}}+\text{C}_{\text{ prodAR}}\\ =\text{S}_{\text{mobilityInterest}}\times(\text{a}+2\text{c})+ \text{S}_{\text{Interest}}\times(\text{a}+\text{c})\end{split} \tag{2}\]
## 4 Results  
### Luoyang Site Case Study  
#### 4.1.1 SAR Dataset  
In this study, 40 scenes of Stripmap with HIMAGE mode were used. The scenes have been acquired in ascending orbit with HH polarization in the observation period from 27 February to 17 October 2013 (see Table 1) and covered a swath of 40 km over Luoyang city, Henan Province in China (see Figures 3 and 4). The image pixel spacing is 2.264 m and 0.769 m in azimuth and range direction, respectively. Taking into account the 19.95\({}^{\circ}\) incidence angle, the calculated ground spatial resolution of SAR images is approximately 3 m.  
Figure 3: Location of Luoyang.
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 5 Conclusions  
The FBG hoop strain sensor, which is developed and encapsulated in our early research, brings the possibility of multi-point hoop strain measurements for a long-distance pipeline. In this paper, a BP neural network is employed as an important component in the pipeline leakage monitoring system. It is used to realize leak location recognition through hoop strain distributions.  
When choosing the appropriate number of hidden layer neurons, the established network makes the RMS error lie within the range of 1%. The small error of the test sample outputs as compared to the target values demonstrates the effectiveness of the proposed method. Furthermore, the influence of noises on the neural network's performance is discussed through simulation. The results show the good anti-noise ability of the network. To conclude, the neural network presented promising potential with good capability and robustness in pattern recognition for the pipeline leakage localization system.  
**Acknowledgments:** This work has been supported by the Fund of the National Natural Science Foundation of China (Grant Nos. 51608094, 51421064, 51327003 and 51678109) and the National Key Research and Development Program of China (Grant No. 2016 YFC0701107). These grants are greatly appreciated.  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments. [PERSON] analyzed the data. [PERSON] contributed materials/analysis tools. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Quantum key distribution (QKD) can protect the exchange process of confidential information between communicating parties. By using the basic principles of quantum mechanics and combined with \"one-time pad\" cipher encryption, information can be unconditionally secure. The BB84 protocol first describes the method of transmitting information by photon polarization state, and it expounds the transmission process of services between trusted relays. However, due to the defects of real experimental devices, there are security vulnerabilities in QKD in a real system. The birth of measurement-device-independent quantum key distribution (MDI-QKD) protocol solves the problem, providing immunity to hacker attacks at the end of the detector. It can enable both sides of the transmission service to establish a connection and generate secret keys through an untrusted relay node to ensure information security. However, the types and properties of link nodes in quantum key distribution network (QKDN) based on partially trusted relay are more complex, which can easily result in network fault. Therefore, how to prevent the impact of failure on QKDN has become an urgent problem. In this paper, we propose a protection scheme for QKDN with partially trusted relays. The method deals with trusted and untrusted relays differently and constructs the working and protection paths of the secret key for each service. It reduces resource conflict between the protection and working paths by establishing a key protection threshold, which realizes the resource trade-off between the two factors. Simulation results show that the scheme provides effective protection to the services, and it improves the stability and reliability of QKDN based on partially trusted relay.  
d +
Footnote †: journal: _Photonics_  
_2022_, 9, 239.