esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model of DCK-LEACH
### Energy Consumption Model  
In this paper, the network energy model uses the same radio energy model as the one defined in LEACH [29; 30]. When the communication distance between two communicated nodes is greater than the threshold \(d_{0}\), the communication energy model uses the multipath fading channel model. When the communication distance between two communicated nodes is less than the threshold \(d_{0}\), the communication energy model uses the free-space channel model, where the threshold \(d_{0}\) is shown in Equation (1).  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{1}\]  
where \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are denoted as the amplification factor parameters of the free-space channel model and the multi-path fading model, respectively.  
The energy consumption \(E_{tx}\) of a node for sending \(m\) bits of data to a node at distance \(d\) is as follows:  
\[E_{tx}(m,d)=\left\{\begin{array}{ll}mE_{elec}+m\varepsilon_{fs}d^{2},d<d_{0} \\ m\varepsilon_{elec}+m\varepsilon_{mp}d^{4},d\geq d_{0}\end{array}\right. \tag{2}\]  
where \(E_{elec}\) denotes the energy consumption required by a node to send or receive a packet, and d denotes the distance between the transmitting node and the receiving node.  
The energy consumed by a sensor node for receiving data is independent of the distance between the transmitting and receiving nodes. The energy consumed by the node to receive m bits of data is as follows:  
\[E_{rx}=mE_{elec} \tag{3}\]  
where \(E_{rx}\) indicates the energy consumed by the node to accept the packet.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
###### Abstract  
Delta-Differential One-Way Ranging (DeltaDOR) is widely used in deep spacecraft navigation, and cross support could enhance navigation accuracy with more interferometry baselines and longer baseline. In China Mars mission Tianwen-1, formal joint cross-support interferometry tracking between China Satellite Launch and TT&C General (CLTC) and European Space Operations Center (ESOC) under commercial contract was conducted around the critical stages of the mission, such as Mars orbit insertion. Cross-support interferometry is a new challenge to CLTC, as the correlator for routine DeltaDOR measurements do not fit for cross support, because of observable definition, blind station clock searching, and so on. This paper discusses the new method and algorithm adopted in joint cross support, especially for spacecraft tone signal processing and clock estimation when correlating with the data of two stations from different agencies. Results of the cross-support interferometry tracking activities are also analyzed. Observables from CLTC and ESOC are consistent with each other, and the difference in observables is in the order of tens of ps. All the baselines are induced to evaluate the accuracy of the spacecraft orbit determined and predicted by CLTC, and the DeltaDOR residuals have a root-mean-square (RMS) better than 0.5 ns (the goal is 1 ns), which could enhance the confidence of the orbit accuracy and the effectiveness of control parameters during critical orbit operation.  
Keywords:delta-differential one-way ranging; cross-support interferometry; China Mars mission; Tianwen-1; joint interferometry tracking +
Footnote †: journal:
# Pre-Composting and Vermicomposting of Pineapple (_Ananas Comosus_) and Vegetable Waste
## 3 Results and Discussion
### Pre-Composting Process
#### 3.1.8 Sensory Characteristics of Pre-Composting  
Figure 4 shows three images of pineapple waste during the pre-composting process, the original substrate was yellow to light brown in color, as time went by it turned to a dark brown color. These characteristics are in accordance with [PERSON] and [PERSON] [41] who have indicated that the end of the process leads to a dark colored organic matter originated by heat absorption due to its degradation. At the beginning of the experiment, pineapple waste gave off penetrating odors, with time these smells dissipated, leading to an odor free product at the end of the pre-composting process, it also presented a porous texture as in FAO [8].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Initial Value** & **Final Value** \\ \hline Organic matter (\%) & 85.1 \(\pm\) 1.87 & 68.88 \(\pm\) 1.40 \\ Total organic carbon (\%) & 49.36 \(\pm\) 0.69 & 39.95 \(\pm\) 1.0 \\ Total nitrogen (\%) & 3.1 \(\pm\) 0.05 & 1.5 \(\pm\) 0.03 \\ C/N relation & 15.92 & 26.63 \\ \hline \hline \end{tabular} Mean values (mean \(\pm\) standard deviation (SD), n = 3).  
\end{table}
Table 1: Initial and final characterization of the pineapple waste in the pre-composting process.
## 6 Result of Case Studies
### Model 2--LNG Cargo Containment Boxes Only
The median value of difference in the peak shear stress levels using the TIRF and DIR was found to be a negative value, as shown in Figure 15. Therefore, most cases using the TIRF approach with the linear boundary condition lead to conservative results. The maximum differences in the peak shear stress and impulse with the nonlinear boundary condition using the TIRF and DIR were 1.0% and 0.0%, respectively. These differences were less than those in the analyses with the linear boundary conditions. The maximum differences in the peak bending stress and impulse with linear boundary condition using the TIRF and DIR were 8.9% and 10.2%, respectively, which were found to be considerably higher than those in the shear cases. However, the maximum difference in the peak bending stresses using the TIRF and DIR is far away from its median value (approximately 5%), as shown in Figure 16.  
As the FE simulations using the DIR considering the geometry and boundary nonlinearities are expected to provide the closest results to the actual dynamic structural behaviors for the dynamic impact load, peak shear stress, and bending stress values, the impulse values were compared with the simulation results using the TIRF and considering the nonlinear geometry and linear boundary condition. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Size** \\ **Time** \\ **[ms]** \\ \end{tabular}  } & \multirow{2}{*}{**Avg**} & \multirow{2}{*}{**Peak (Quinness)** [Num]**} & \multicolumn{2}{c}{**Peak (Quinness)**} & \multicolumn{2}{c}{**Inplane (Linear)**} & \multicolumn{2}{c}{**Inplane (Unsionionion)**} & \multicolumn{2}{c}{**Peak DIR**} & \multicolumn{2}{c}{**Inplane (DIRF)**} \\  & & & **[ms]** & & **[ms]** & & **[ms]** & & **[ms]** & & **[ms]** & & **[ms]** & & **[ms]** \\ \hline \multirow{8}{*}{20} & 0.13 & 601.5 & 702.0 & 467.2 & 401.9 & 402  
The maximum differences in the peak shear stress and impulse values using the TIRF with the linear boundary condition and the DIR with the nonlinear boundary condition were 5.2% and 2.4%, respectively, as shown in Table 6. The maximum differences in the peak bending stress and impulse values using the TIRF with the linear boundary condition and the DIR with the nonlinear boundary condition were 11.4% and 12.0%, respectively. These differences were higher than those in the shear response cases. However, the TIRF approach was found to be practically applicable in comparison with the DIR.
# Study on the Common Rail Type Injector Nozzle Design Based on the Nozzle Flow Model
## 3 Results and Discussion
### Discharge Coefficient and Effective Nozzle Exit Diameter  
Figure 9 shows discharge coefficient (\(C_{d}\)) during the injection period with maximum injection pressure of 160 (MPa) for diesel and 50 (MPa) for DME. Typically, the injection rate increases as injection pressure rises. Due to the reduction of friction in the nozzle, the discharge coefficient gradually increases. However, when the pressure at the vena contracta is lower than the vapour pressure of fuels, the cavitation occurs. It reduces the discharge coefficient during injection. Since the injection pressure increases, the cavitation appears for both diesel and DME. The maximum discharge coefficient of DME is \(C_{d}=0.91633\) and higher than that of diesel. Because the kinematic viscosity (\(\
u\)) of DME is lower than that of diesel, the flow efficiency of DME in the nozzle is higher. The maximum discharge coefficients also increase according to increase of the diameter of the nozzle hole for DME fuels. As shown in Table 2, the vapour pressure of DME is 550 (kPa), higher than that of diesel, \(P_{\text{vup}}=10\) (kPa). Thus, cavitation in DME starts to occur at a lower inlet injection pressure, compared to that of diesel. However, because the maximum injection pressure in DME is lower than that in diesel, the period of cavitation in DME is narrower. Meanwhile, the cavitation period slightly expands due to increase of  
Figure 8: Area ratio according to needle lift.  
Figure 7: Peak injection rate according to nozzle hole diameter for the base injector.  
the nozzle diameter for DME, as shown in Figure 9. This is because the effect of the increased flow rate in cavitation is greater than suppression of the cavitation due to the increased diameter of the nozzle. Cavitation conditions such as inlet injection pressure and period of cavitation region for diesel and DME are summarized in Table 3.  
Figure 10 shows the ratio of the effective nozzle exit diameter \((D_{eff})\) under injection period with maximum injection pressure of 160 (MPa) for diesel and 50 (MPa) for DME. The effective nozzle exit diameter is an important design parameter that determines the initial SMD (sauter mean diameter) in combustion analysis with the diameter of the fuel being injected through the nozzle [5]. The ratio of the effective nozzle exit diameter is initially kept at 1.0 and then gradually decreased when cavitation occurred inside the nozzle. The ratio of the effective nozzle exit diameter is 0.905 for diesel, compared to 0.920 in DME at maximum injection pressure condition of \(t=0.5\;(\mathrm{ms})\). Since the maximum injection pressure of diesel is higher than that of DME, the ratio of the effective nozzle exit diameter reduces for the wider range of injection duration. In the case of DMEs, as the diameter of the nozzle increases, the point of reduction in the ratio of the effective nozzle exit diameter occurs slightly earlier, as is shown in Figure 9.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 6 Conclusions  
This paper reviews the advances in high-speed, high-power photodiodes from fundamentals to applications. (1) For the overall design and consideration of photodiodes, this paper discusses the principles of PIN-PD and UTC-PD, while reporting the detailed calculation of the 3-dB bandwidth for the two typical photodiodes and summarizes the energy band diagrams for several kinds of photodiodes. Given the equivalent circuits, this paper shows the general ones and RF small-signal ones together, and also considers the different designs for the RC-delay time part and the external circuit parts, including the air bridge, CPW pad, and load. Additionally, the DC saturation and RF output power are also analyzed in detail and a table for comparison is also included. (2) The solutions of bandwidth-responsivity trade-off for photodiodes are discussed in detail, which range from the use of high-reflected mirrors to the making of microholes and extending the reflected mirrors for group-IV photodiodes. (3) The photodiode photonic-integrated applications are presented, including the integration with short stubs and various planar antennas for high output power at high-speed conditions, and showing the parameter comparisons in tables, which provides a reference for the practice use of photonics-based devices operating at the system-level with the radiation power up to the mW-level and beyond in the THz band.  
Conceptualization, Q.C.; methodology, software, Q.C.; validation, Q.C.; formal analysis, Q.C.; investigation, Q.C.; resources, Q.C.; data curation, Q.C.; writing--original draft preparation, Q.C.; writing--review and editing, Q.C. and X.Z.; visualization, Q.C.; supervision, X.Z., M.S.S. and R.K.; project administration, M.S.S. and R.K.; funding acquisition, M.S.S. and R.K. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Author [PERSON] was employed by the company Blue Origin LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 ACDM-Based VLP System by Using OPRC
### Simulation and Experimental Results
In the simulation environment, the system shows impressive positioning accuracy, with a maximum PE of approximately 1.10 cm and an average error of just 0.51 cm. In contrast, the experimental environment shows a slight decline in performance, with the maximum PE increasing to around 1.60 cm and the average PE rising to 0.72 cm. Notably, in both simulation and experimental tests, the highest positioning accuracy is observed near the origin of the \(X\)-axis (X = 0). This is attributed to the maximum received light intensity in this area, which minimizes the impact of noise on positioning accuracy. However, in the experimental tests, the weaker light output from \(Tx_{2}\) compared to \(Tx_{1}\) results in more significant positioning errors in the positive \(X\)-axis direction (away from \(Tx_{2}\)) compared to the negative \(X\)-axis direction. Additionally, environmental noise and measurement errors lead to fluctuations in positioning accuracy during the experiment. Figure 4: Estimated RSS of the ACDM-VLP system from: (**a**) simulation; and (**b**) experimental tests. Figure 5: Distribution of VLP accuracy based on OPRC: (**a**) STDE in simulation; (**b**) STDE in experimental tests; and (**c**) positioning error. For instance, the positioning error at the \(-30\) cm position is approximately \(0.2\) cm greater than at the \(-35\) cm position. Despite these challenges, our positioning system demonstrates strong robustness, with an average PE less than \(1\) cm. This confirms the effectiveness and accuracy of our proposed method, especially in asynchronous systems.
## 3 Results
### Comparison of the Reaction Time between the MSM and RP  
The reaction time of the DiC for detecting ERD by the MSM and RP were compared. The time between the start of the EEG playback and the start of elbow flexion by the DiC was measured and compared with the reaction time (t) (Table 2). Because data normality was not confirmed when the Shapiro-Wilk test was performed regarding the MSM and RP reaction times (MSM, \(W\) = 0.79, \(p\) = 0.014; RP, \(W\) = 0.84, \(p\) = 0.044), [PERSON]'s signed rank sum test was used for this analysis. The MSM reaction time (13.02 \(\pm\) 0.16 s) was significantly shorter than that of the RP (19.95 \(\pm\) 7.45 s) (\(W\) = 9, \(p\) = 0.0037; Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **FMA-UE** & **MMSE** & **MI Quality (VAS)** & \multicolumn{2}{c}{**Reaction Time**} \\  & & & & **MSM** & **RP** \\ \hline
1 & 8 & 29 & 10 & 12.3 & \(-\) \\
2 & 6 & 28 & 9 & 12.9 & 52.6 \\
3 & 11 & 26 & 6 & 13.2 & 18.2 \\
4 & 9 & 24 & 8 & 12.9 & 22.5 \\
5 & 8 & 30 & 10 & 13.2 & 35.9 \\
6 & 4 & 28 & 7 & 12.8 & 16.7 \\
7 & 7 & 27 & 6 & 12.9 & 91.1 \\
8 & 7 & 29 & 10 & 13.2 & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reaction time between the reception of the electroencephalogram and activation of the DiC and patients’ subjective quality of MI.  
Figure 6: EEG data of patient 6: lesion, right radial coronary infarction; FMA-UE score, 8 points; MMSE score, 30 points; age, 77 years. The patient passed 109 days from the onset of stroke. ERD, downward convex waveform surrounded by red lines; ERS, upward convex waveform surrounded by blue lines.
## 2 Proposed Methodology
### Two-Stage Sliding Window Strategy  
In the data pre-processing part, the samples in the dataset were first assigned to the training set and validation set. In the proposed two-stage sliding window strategy, the initial execution is performed on the elements of both training and validation set. The elements is split into fixed-length sequences using a time window of length (\(\Delta\)) with sliding step of (\(\delta\)) as shown in Figure 1a. The process continues until the last value of the series is segmented. The split-sequence is matched to the predefined level of unfolding to transform \(x\) into a two-dimensional vector. The values for \(\delta\) and \(\Delta\) were selected considering the sampling capacity of the data acquisition module, as well as the printing speed. This network parameters are applicable on the first and second case studies conducted on the performance evaluation. The second stage of the sliding window technique shown in Figure 1b is performed on the training set split sequenced to define the forecasting interval \(h\) which is calculated through the difference of the shift between \(m\) and target series. This technique achieves less error approximation and achieves better prediction performance as concluded in [36].
## 2 Antenna System Configuration  
This section describes the design of an antenna system suitable for both creating an aerial communication network and simulating the techniques presented in this paper.
## 3 Discussion  
The above section results show that, overall, the proposed self-optimizing HO parameter scheme with dual MRNs is superior to the conventional static scheme with fixed parameter values. This is because a fixed parameter setting is adequate for general cases and UEs, but in a high-speed environment, a more flexible scheme is necessary. The proposed self-optimization scheme automatically tunes parameters based on performance and velocity; therefore, the HH and CIO can be set to more appropriate values that are in line with changes in data communication.  
In the implementation of our dual MRNs, the communication interruption time is reduced. This result can be explained by the following processes: (i) the passengers of the train are served by the sMRN during the HO procedure of the main mobile relay node (mMRN); (ii) if the HO procedure of the mMRN has failed, only mMRN starts the new HO procedure; and (iii) the dual MRNs execute the HO procedures by the powerful outside antennas of the train. In this scenario, outside antennas can use the MIMO that can provide the QoS of the backhaul link to the DeNBs.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 4 Experiment
### Experimental Results and Analysis  
#### 4.4.1 Aspect-Based Text Sentiment Analysis  
We first conduct the comparative experiment with MEMCapNet and three baseline methods on four textual evaluation datasets, Lap14, Rest14, Rest15, and Rest16. In the experiment using MEMCapNet for single-text aspect-based sentiment classification, the Vision2 Text attention module in Figure 1 is removed. The experimental results are shown in Table 4, where we can observe that the MEMCapNet proposed in our method performs better than the other models in the terms of accuracy and F1-score. Obviously, we can find the reason from the model structures of these models. Compared with ATAE-LSTM, MemCapsNet has a convolutional neural network and a memory module, which can better extract local features of the text. Although MemNet uses the memory network, it only models the global context with long-distance dependence, which cannot find the structural information of local phrases and clauses. IACapsNet uses a capsule network for extracting the local features of text, but it does not get a better effect due to the lack the auxiliary information on the global context.  
Then we further conduct the comparative experiment only using the text features on two multimodal datasets, and rows 1-3 of Table 5 show the results of MemCapNet and the reproduced results of the baseline models. It can be observed that although the results on all models are degraded, the MemCapNet proposed in our method still outperforms other baseline models. Experiments on two kinds of datasets both verify the effectiveness of MemCapNet on single-text aspect-based sentiment analysis. Compared with the results in Table 4, the performance in Table 5 is lower due to the degraded quality of online crawled review datasets relative to the well-defined evaluation datasets, and the lack of visual support in the same text-image review for sentiment labels.
# Antioxidant and Antimicrobial Properties of Selected Red Seaweeds from Central Portugal
## 4 Discussion  
In the present work, we assessed the antioxidant and antimicrobial activity of twelve seaweed species that regularly occur across the central coast of Portugal. Our target species, _Porphyra umbilicalis_, _Ceranium cililatum_, _Osmundea pinnatifida_, _Chondrus crispus_, _Sphaerococcus coronopifolius_, _Placanium carilagineum_, _Cornulina elongata_, _Ellisolandia elongata_, _Amphiroa rigida_, _Jania rubens_, _Mesophyllum lichenoides_ and _Liagora viscosita_, were generally available for gathering, depending on the season. The results regarding the antioxidant and antimicrobial activity of the twelve aqueous seaweed extracts analyzed showed distinct and interesting results across taxa, both for ABTS and TPC analysis, as well as the MIC evaluation. It is, however, of utmost importance to keep in mind that, since each species was collected in one specific season and sample area only (following their availability), and since species can show natural variability shaped by seasonal patterns, environmental gradients [55], and even life-cycle [70], in addition taxonomic classification itself, extrapolation endeavors and comparisons across studies must be sensibly conducted.
## 4 Discussion
This information is interesting because it is in agreement with the experimental log \(P\) of the main repellents currently available on the market. Indeed, IR3535, picaridin (icaridin), and DEET have experimental log \(P\) values of 1.7 [54], 2.11 [55], and 2.4 [56], respectively. Otherwise, among the repellents always predicted as such by the MH models, 19 have their MHlogP ranging between 3 and 4, and one, \(n\)-capric acid, has a value of 4.02 (#538 in Table S2). In contrast, only three repellents have their MlogP values \(\geq\) 3, namely 3 (#2085; 2-(\(m\)-tolyl)cyclohexanol (trans), 3.12 (#1163; 1-ethyl-1,2,3,4-tetrahydroo-2-naphth)), and 3.12 (#2058; beta-(1,2,3,4-tetrahydroo-5-naphth) ethanol). More generally, although MHlogP, which is based on the atom/fragment contribution method of [PERSON] and [PERSON] [30], leads to the calculation of descriptors that are more discriminative than MlogP, which is calculated according to the regression equation of [PERSON] et al. [28; 29], it is more prone to giving unrealistic log \(P\) values in molecules including long aliphatic carbon chains. Fortunately, these molecules are non-repellents and their activity remains correctly predicted (Table S2). Analysis of the literature shows that lipophilicity is a physicochemical property that is found to be more or less important [14; 24; 57; 58; 59; 60] for describing and modeling the biting deterrence or repellent activity of molecules against mosquitoes. This difference is easily explainable because these studies always involve a very small number of molecules showing specific structural or functional characteristics. Our current study and the previous one [25] based on a large and highly structurally diverse dataset clearly show that lipophilicity, encoded by log \(P\), plays a key role in distinguishing the molecules that are repellents from those that do not repel mosquitoes. Our comparative study also demonstrates that molecules with a repellent activity are restricted to a range of log \(P\) values. It is noteworthy that [PERSON] et al. [31] also showed the prominent role of log \(P\) in the prediction of repellent molecules for clothing application. Obviously, it is important to stress that although log \(P\) appears important for modeling the repellent activity of molecules, it is not the only parameter involved.