esa-sceva/satcom-chunk-collection huggingface dataset:
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 4 Transference of Programmed Quantum States Using Double Teleportation
### General Notation for the Control of Post-Selection Problem
By defining \(c_{\omega}^{-}=(-1)^{j+m}\cos\Delta_{\omega}^{-}\), we note that \(P_{j}\) depends from \(p_{0},\beta_{0}\), and \(c_{\omega}^{-}\) in general, as Figure 3 shows. Figure 3a,b show the contours on which \(P_{j}\) become constant in agreement with the color scale on the right (for \(j=0,1\) on the left and right respectively). Below, Figure 3c,d show the three dimensional version of Figure 2c,d with their \(K\) values shown in black in their top. In addition, each contour was additionally coloured in agreement with their \(P_{j}\) value in each point of the space (from the reddest for \(P_{j}\approx 0\) to the bluest for \(P_{j}\approx 1\), also in agreement with the color bar besides and with the previous plots). In fact, the solutions are first found by selecting \(K\) and then intersecting each lower plot with its corresponding upper plot (for the same \(j\) value). Despite, \(c_{\omega}^{-}\) is not an independent parameter as the last intersection shows. Figure 3: **(a,b)** Contour plots of \(P_{j}\) for \(j=0,1\) respectively as function of \(p_{0},\beta_{0}\), and \(c_{\omega}^{-}\). **(c,d)** Three-dimensional version of plots in Figure 2c,d, now including \(c_{\omega}^{-}\) and coloured from red (\(P_{j}=0\)) to blue (\(P_{j}=1\)) in agreement with \(P_{j}\) values through them for \(j=0,1\) respectively.
## 3 Results and Discussion
### Monosaccharide Composition Analysis  
High-performance capillary electrophoresis was used to perform monosaccharide analysis of rose root polysaccharides. This technique has many advantages e.g., high resolution or low consumption of reagents and requires relatively limited sample pre-treatment [27].  
The carbohydrate analysis of the hydrolys CPL-Rx revealed the presence of 7 different carbohydrate peaks (Figure 1).  
Their percent ratio calculated based on the corresponding peak area is shown in Table 2. Glucose was identified as the major monosaccharide. Galactose, fructose, and arabinose were determined in smaller amounts. Moreover, signals from 3 other unidentified carbohydrates were observed (marked as Unknown I, II and III). These carbohydrates constitute a relatively small part of the analysed sample. Based on their migration times as well as our previous observations and experiments, these peaks may represent oligo- or disaccharides. It was also reported by other authors that oligo- or disaccharides migrate before monosaccharides during HPCE analysis [16].  
To the best of our knowledge, the composition of root polysaccharides of any rose species has not been studied to date. CPL-Rx was found to have a different monosaccharide profile from those previously reported for _Rosa raxpurghii_ Tratt. or _Rosa laevigata_ Michx. fruits [28,29]. A study of Kushui Rose (_Rosa setate x Rosa rugosa_) petal wastes has, however, indicated that glucose and galactose constituted the major components of its neutral polysaccharide fraction [30].
## 4 Test Results and Interpretation
### Major SWIR Spectral Characteristics of Altered Minerals
After measuring the characteristic absorption peaks, absorption depths, and crystallinity of the extracted samples, we created scatter plots to perform systematic and statistical analysis on the parameters and determined the variation range of chlorite-type Pos2250 in this mining area to be 2247.87-2260.05 nm, with an average value of 2253.96. The range of CSM values was 0.212-9.082, with an average value of 2.37.
## 4 ARCHES LIBS Module as LRU2 Payload
This feature, designed together with the initial version of the docking interface, was successfully tested for the first time during the ARCHES project and enabled the efficient usage of the LIBS instrument. The LIBS module is equipped with the Payload Box Infrastructure Management System (PBIMS), an electronic board responsible for power management and data communication with the host system. The PBIMS provides regulated power to the payload system with up to seven different voltage buses. In the case of the LIBS instrument, only buses with 5 V and 48 V are used. On its power input, the PBIMS can automatically select from up to three different non-regulated power sources ranging between 24 V and 30 V. Telemetry data transmission to the host system is enabled by various wired and wireless communication interfaces on the board. Additionally, onboard sensors such as an inertial measurement unit (IMU) and environmental gauges are included. Once communication is established with the PBIMS onboard controller, telemetry data and power switching control become available. Moreover, the controller runs automated powering schemes for the proper operation of the LIBS instrument, especially during power-up. Figure 7: Docking interface ENVICON, showing the retracted grasping springs as well as the male, co-axial connector (**left**). The JACO robotic manipulator arm on a trajectory to the pre-docking position and the grasping springs opened to form a funnel-like structure (**right**).
## 4 Results
### Comparison before and after Integration  
To examine the integration effect with object segmentation, the corn area before and after integration in the prefecture-level city was compared with the census area obtained from the _HEBEI Economic Yearbook 2017_. The corn area was estimated by subtracting the summer harvest grain area from the cereal area, because the corn area for each city is unavailable, and the cereal area primarily contains the corn and winter wheat that are harvested in summer. The scatter figure comparing the corn area predicted in this research with the census data at each prefecture-level city is shown in Figure 11. After obtaining the integration segmentation results and pixel-based classification results, during which the misclassified pixel and small-scale noise were removed, the corn area results were closer to the census data. The results showed that the corn area estimated from the optical and SAR images correlated well with the census data, with an R\({}^{2}\) = 0.91 and a root mean square error (RMSE) of 470.90 km\({}^{2}\). Additionally, due to the integration, the corn area consistently decreases regardless of degree, which may be a result of an unbalanced distribution of classified pixels within objects whose interiors are heterogeneous.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 4 Results
### Simulation Environment  
Veins (Vehicles in Network Simulation) were used for the experiments. The software is an open source framework for OMNeT ++ and SUMO-based vehicular network simulation [38]. For this experiment, the intersection required for the experiment was constructed using SUMO [39].  
Experiments were carried out by setting up a complete grid-shaped virtual zone rather than using actual roads to obtain formalized data. The shape of this area is shown in Figure 18.  
Figure 17: PCDP Cluster Detect Message.  
Figure 18: Simulation Area Built by SUMO.  
Table 2 shows the variables used in the experiment. Roads 1000 m in length are arranged as 6 \(\times\) 6 grids, and the simulation time is approximately 36,000 seconds using the whole route. Vehicles are designed to cross the number of paths that can come from the grid. In each case, five vehicles pass through, and there are 924 total routes.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 3 Results
### E2E Loss Rate  
Figure 7 reports the E2E loss rate achieved by our AC agent, the optimal policy and a round-robin scheduling scheme that does not use redundancy and transmits using only a single path in a round-robin fashion without considering the channel conditions of each path. In our simulations, the E2E loss event \(\kappa\) occurred when all the transmitted bit(s) were lost; that is, they could not be recovered on any of the paths. The E2E loss rate in terms of the Bit Error Rate (BER) was calculated as follows:  
\[\psi=\frac{\sum_{i=1}^{m}\kappa}{\sum_{i=1}^{m}\eta} \tag{18}\]  
where \(\psi\) is the E2E loss rate in terms of the \(BER\), \(\kappa\) is the loss event at a given iteration (\(\kappa=1\) if all sent bits are lost and \(\kappa=0\) otherwise), \(m\) is the number of iterations in a window (episode) and \(\eta\) is the number of bits transmitted in each iteration, which is equal to the number of links used in each iteration because each bit was transmitted over a unique link. The numerical values of the average E2E loss rates are reported in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **E2E Loss Rate (BER)** & **Bandwidth (Mbps)** \\ \hline Actor-critic & 5.12\% & 2.1 \\ Optimal policy & 4\% & 2.1 \\ Round-robin & 8\% & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: E2E loss rate and bandwidth utilization.  
Figure 6: Categorical distribution for multi-link scheduling with AC vs. optimal scheduling at different elevation angle **PLRs! (PLRs!)** (good = 0, bad = 1).
## 3 Methodology and Materials
### Applying with Attention Mechanisms
#### 3.2.1 Applying Self-Attention with a Positional Layer
The element in the \(i\) th row and the \((2j)^{th}\) (Equation (3)) or the \((2j+1)^{th}\) (Equation (4)) column is as follows:  
\[p_{i,2j}=\sin\left(\frac{i}{10,000^{2j/d}}\right) \tag{3}\]  
\[p_{i,2j+1}=cos\left(\frac{i}{10,000^{2j/d}}\right) \tag{4}\]  
This process ensures that the model understands the content of each element and its position within the sequence. Algorithm 2 outlines a function that takes the _sequence_length_ and the number of dimensions as input. It initializes a matrix _pos_enc_ of size _sequence_length_\(\times\)_dimensions_ filled with zeros. The function then iterates over each position in the sequence and each dimension in the embedding space. For each pair of positions and dimensions, a value is computed using either the sine or cosine function, and these values are assigned to the corresponding elements in the _pos_enc_ matrix. Finally, the function returns the _pos_enc_ matrix, which represents the positional encodings. However, it is crucial to recognize the potential pitfalls associated with the intricate dynamics of positional self-attention. Its comprehensive consideration of all sequence attributes can present challenges, mainly when dealing with time series data. Many researchers have focused on addressing these issues. By incorporating self-attention mechanisms, we address several limitations of traditional autoencoders in a time series analysis. Self-attention improves the model's ability to capture long-term dependencies by dynamically computing the relationships between all elements in a sequence, regardless of their distance from one another. This allows the model to focus on the most relevant time steps, enhancing interpretability by highlighting which data points contribute most to the prediction. Additionally, self-attention provides greater flexibility in handling variable-length sequences, avoiding the need for fixed-size inputs and allowing for better generalization across different datasets.
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 2 Materials and Methods
### Establishment of the Basic Coordinates  
Four basic coordinate frames are used in the geo-location algorithm, including the imaging coordinate frame \(V(X_{v}Y_{v}Z_{v})\), the aircraft coordinate frame \(B(X_{b}Y_{b}Z_{b})\), the navigation coordinate frame \(P\big{(}X_{p}Y_{p}Z_{p}\big{)}\) and the geodetic coordinate frame \(G\big{(}X_{g}Y_{g}Z_{g}\big{)}\).
# Economic Analysis of a Zero-Water Solar Power Plant for Energy Security
## 2 Methodology  
### Design Proposal of the Earth-CAT System  
The Earth-CAT system analyzed here has been designed for a 20-MW solar power plant with thermal storage [16]. Figure 1 shows the flow conditions assumed for the cooling air (\(\dot{m}_{Air}\)) and the steam entering the air condenser of the plant from the turbine (\(\dot{m}_{Steam}\)) and exiting the condenser after it has been condensed (\(\dot{m}_{Water}\)). \(\dot{m}_{Steam}\) is the same mass flow rate as \(\dot{m}_{Water}\). The ambient air is cooled from 35 \({}^{\circ}\)C to 25 \({}^{\circ}\)C in the Earth-CAT system, and it then enters the condenser. It is heated back to 35 \({}^{\circ}\)C in the condenser of the plant. For the considered operational conditions, a total air mass flow of 3956 kg/s of air is estimated. Compared to a wet-cooling system, the air mass flow required is much larger due to the higher heat capacity and density of water. This is the principal reason why water cooling is preferred in energy applications, when possible.  
Depending on the pipe geometry and the conditions of the air flow, the mass flow is divided into parallel pipes. The design that achieves the objective of cooling the air to 25 \({}^{\circ}\)C involves 10,965 PVC pipes with a diameter of 0.5 m, a length of 300 m, a thickness of 10 mm, and a flow velocity of 1.5 m/s [16]. The air pressure at the inlet of the Earth-CAT system is approximately 1.1 bar to compensate for the losses in the pipes and the air condenser. This is a conservative value since the pressure losses are considered to be approximately 226 Pa in both the condenser and the cooling system [16].  
Pipes are arranged in parallel with a distance of 1 m between them and are buried at a depth from 4 m to 10.5 m, with five rows of pipes arranged in the vertical direction. A schematic of the design is shown in Figure 2. A total land surface of 99 hectares is required around the plant, equal to almost half of the land surface required by the 20-MW CSP plant (195 hectares) [33].
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 2 Proposed Methodology
### Discrete Teager Energy (DTE)  
The DTE operator is basically an AM-FM modulation technique for estimating the energy of a processed signal. It is a valuable processing tool for analyzing time-, frequency- and time-frequency-based signals [9; 15]. To understand the mathematical background of this approach, let us assume a signal \(S_{S}\left(t\right)\). The DTE operator applied on the \(S_{S}\left(t\right)\) results in  
\[\psi[S_{s}(t)]=\dot{S}_{s}(t)-S_{s}(t)\ \ \ddot{S}_{s}(t) \tag{7}\]  
where \(\dot{S}_{s}(t)\) is the first derivative and \(\ddot{S}_{s}(t)\) is the second derivative of \(S_{s}(t)\). The DTE for a sinusoidal signal can be written as  
\[\psi[A_{m}\cos\omega t]=\ A^{2}\omega^{2} \tag{8}\]  
where \(A\) represents the magnitude of the signal, The real-valued AM signal can also be defined as  
\[S_{s}(t)=a\left(t\right)\cos\left(\omega t\right) \tag{9}\]  
where \(a\left(t\right)\) denotes the amplitude of the signal and \(\omega\) is the carrier frequency of the signal  
\[\psi[a(t)\cos\omega t]=\ a^{2}(t)\omega^{2}+\cos^{2}(\omega t)\psi[a(t)] \tag{10}\]  
Here, \(\psi\) is used to estimate the contained envelope. For a discrete signal, the DTE is written as  
\[\psi[S_{s}(t)]=\ S_{s}(t)^{2}+S_{s}(t-1)S_{s}(t+1) \tag{11}\]where \(S_{s}(t)\) is the discrete signal and \(t\) is the sampling time. The expression of the DTE from the output obtained from the SST is presented as follows:  
\[\psi_{i}=DTE_{K}^{\text{SST}}=\sum_{i=0}^{k+1}DTEO_{m}^{\text{approx}}(i)=\sum_{i =1}^{k+1}\left[A_{m,i}\right]^{2}\omega_{m,i}^{2} \tag{12}\]  
where \(k+1\) represents significant modes obtained by applying the SST on the target signal.  
The implementation of these algorithms is explained with the help of the flowchart shown in Figure 1.  
The target signal is processed as mentioned in the flowchart. EMD is applied first to obtain the suitable IMF, which is further processed with the SST operation to find the DTE index, which acts as a fault detection indicator. Whenever the index crosses the threshold, the fault can be detected by the proposed method. The relaying algorithm then sends a trip signal to the circuit breaker. Selecting an appropriate threshold will always decide the security of a protection function. A standard threshold-setting mechanism is followed for choosing the optimal threshold value [14]. The steps are mentioned below:  
1. Extract the local maxima from one full cycle of data of \(\phi_{\text{m}}\) for each cycle as follows: \[\phi_{\text{max}}=\phi_{\text{m}}=\max(\phi_{0},\phi_{1},\ldots\ldots\ldots \phi_{n-1})\] (13) where, \(i=0,1,\ldots\ldots,n-1\) and \(n\) is the number of samples per cycle.
2. Set the threshold \(\phi_{\text{th}}\) using Equation (13): \[\phi_{\text{th}}=\varepsilon\phi_{\text{m}}\] (14) The variable \(\varepsilon\) depends on the fault scenario considered for the fault case.  
With the implementation of the algorithm, fault analysis is performed, and the obtained results are discussed in the next section.
# Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
## 2 System Model  
The proposed MIMO VLC system is constructed with \(N_{\rm t}\) emitters and \(N_{\rm r}\) receivers, as shown in Figure 1. Generally, the modulation designed for VLC is compatible with spatial modulation (SM) and spatial multiplexing (SMP). Moreover, the modulation can be improved by optimizing power allocation for precoding and equalization. Compared with SM, SMP can increase the channel capacity or improve the transmission efficiency of communication systems. Although this scheme may suffer from the risk of inter-channel interference (ICI), the equalizer in the MIMO VLC system with spatial diversity [18] can overcome this issue and avoid a decrease in transmission efficiency. Moreover, the SMP can easily fit the bit allocation strategy designed with precoding and equalization in our system to maintain the transmission quality in each channel and improve the system throughput.
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods
### Statistical Tests  
We used the linear regression method to calculate the GPP's mean change rate over the entire temporal domain. A two-tailed t-test and the Mann-Kendall test were used to examine the significance of the trend, while the Kolmogorov-Smirnov test was used to establish the optimal marginal distribution function of GPP (\(p<0.05\)). In addition, we adopted several goodness-of-fit measures to evaluate different copula models' performance, including the squared Euclidean distance (SED) between the theoretical copula and the empirical copula, the root mean squared error (RMSE) and the Akaike information criterion (AIC).
## 4 Results and Discussion
### Soil Moisture Estimation
The presence of dew drops in the morning may act to increase the dielectric constant of vegetation [54]. It may have directly introduced errors into the soil moisture retrieval. However, these error sources are not severe. Figure 5: Scatterplot illustrating the relationship between the measured and predicted soil moisture when using the WCM to reduce the effect of vegetation. Figure 6: Scatterplot illustrating the relationship between the measured and predicted soil moisture when using the modified WCM to reduce the effect of vegetation.
## 3 Results and Discussions
### Atom Economy (AE) or Atom Efficiency  
The molecular weights of biodiesel product, oil, catalyst, and alcohol were used to calculate the _AE_ for ethanol and methanol, which were 86% and 88% respectively. Even though atom economy is one of the most commonly used green metrics [21], a conclusion cannot be made from this result since it is only based on molecular weight.  
Figure 3 shows the _atom efficiency_ results based on biodiesel yields, and it was observed that methanol is more atom efficient than ethanol. This could be due to the fact that ethanol has a higher molecular weight and higher boiling point, and it requires higher temperature (energy) than methanol to react. The efficiency increases as the reaction time increases due to higher reaction temperatures and reaction exposure. However, it is not favorable when using low catalyst concentrations due to lower yield and lower atom economy.  
Figure 2: Environmental factor results for the simultaneous effect of microwave and ultrasound (MW/US) in the waste cooking oil (WCO) transesterification. Effect of (**a**) different reaction time; (**b**) alcohol to oil molar ratio; (**c**) catalyst concentration and (**d**) sample (oil) volume.
## 2 Challenges and Methodology
### Problems by Data Analysis
For instance, using the low fixed threshold could result in misclassifying some data of normal states into data of abnormal states, which trigger a false alarm related to the false positive error. On the contrary the high fixed threshold could lead to many false negative errors. Furthermore, for the purpose of disrupting the system, if a malicious adversary injects the malicious sensing data that enters as normal into the system through the intermediate network, the malicious data cannot be distinguished from the normal data. Consequently, the system has false negative errors. The threshold adaptive to the dynamic environment is required to accurately detect the anomalies but should be sensitive to the changes of residuals generated. Otherwise, the false errors including FP and FN errors increases. The dynamic environment involves the environment uncertainty. The changes of residuals are mainly caused by both model uncertainties.
## 2 Background  
### Total Cost of Operation of Autonomous Trucks  
A small body of literature has examined the economic impact of ATs on TCO, although a multitude of research studies have been conducted to investigate the effects of ATs in the context of heavy-duty and long-haul truck transportation [6]. Overall, previous literature has confirmed overall TCO reduction from the perspective of freight transport operators. Specifically, the grey literature of [PERSON] et al. [7] argued that costs associated with trucking logistics will decrease by 47%, primarily due to a reduction in labor. [PERSON] [8] analyzed TCO for three different types of ATs and found that TCO will reduce by 15-20% overall. A study by [PERSON] et al. [9] revealed significant cost reductions for freight transport operators; for instance, the operation costs for 16-ton trucks, 24-ton trucks, 40-ton trucks, and 60-ton trucks will reduce by 45%, 37%, 33%, and 29%, respectively. [PERSON] and [PERSON] [10] suggested that ATs will be able to travel 24/7 without requiring driver rest time, and in comparison to driving as it is conducted today, they could achieve overall cost reductions of 40% per kilometer. [PERSON] and [PERSON] [11] estimated the economic impact of ATs in the United States to be USD 1.2 trillion, as they will reduce the demand for auto maintenance, traffic police, medical, insurance, and legal services.  
In detail, ATs would be of enormous advantage to freight transportation and trucking business by eliminating or reducing the wages of truck drivers and allowing the firmsto address the scarcity of truck drivers; yet, the employment opportunities available to millions of truck drivers will decline as a result [11]. [PERSON] and [PERSON] [4] also argued that ATs will result in lower costs for drivers. Moreover, [PERSON] and [PERSON] [3] found that improvements in fuel efficiency will result in lower TCO for truck owners over the long term. [PERSON] et al. [12] suggested that since ATs will be designed to have a lower fuel consumption, they will reduce fuel expenses. In addition, vehicles have the potential to improve their hours of service because they can travel around the clock without requiring drivers to take breaks. [PERSON] et al. [6] found that ATs will be cost-competitive since they will eliminate mandatory break and rest times and reduce energy consumption.