esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Hydrothermal Carbonization on Catalytic Fast Pyrolysis of Agricultural Biomass
###### Abstract  
Fast pyrolysis has been a subject of intensive research thanks to its ability to produce high yields of liquid products, known as pyrolysis oil. This is an important renewable intermediate which could be used for the subsequent production of fuels and chemicals. For fossil-based materials, pyrolysis oil can provide circular building blocks. Furthermore, direct use of pyrolysis oil in gas turbines has also been proven feasible. However, a relatively high oxygen content in raw biomass has detrimental effects on the quality of such oil. This work proposes hydrothermal carbonization as a vaporization technique, beneficial from the point of view of subsequent fast pyrolysis. Within the scope of this work, the influence of the parameters of hydrothermal carbonization (HTC) on the kinetics of fast pyrolysis of agricultural biomass (miskanthus), as well as the influence of in situ use of a CaO catalyst, is investigated. Kinetics is investigated using a novel type of thermogravimetric analyzer (TGA) called Cyclonic TGA, which is able to achieve heating rates similar to a real fast pyrolysis process. Moreover, the influence of HTC on the removal of part of its inorganic constituents is determined within the scope of this work.  
HTC; fast pyrolysis; miscanthus Article
# Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach
## 3 Results and Validation
### Synthetic Test Model 2
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Standard Separate Inversion** & **Robust Joint Inversion** \\ \hline \(\alpha^{(1)}\) & \(1\times 10^{4}\) & \(1\times 10^{4}\) \\ \(\alpha^{(2)}\) & \(1\times 10^{4}\) & \(1\times 10^{4}\) \\ \(\beta^{(1)}\) & \(1\times 10^{1}\) & \(1\times 10^{1}\) \\ \(\beta^{(2)}\) & \(1\times 10^{1}\) & \(1\times 10^{5}\) \\ \(\gamma^{(1)}\) & 0 & \(3\times 10^{5}\) \\ \(\gamma^{(2)}\) & 0 & \(3\times 10^{5}\) \\ \(\eta^{(1)}\) & 0 & \(1\times 10^{0}\) \\ \(\eta^{(2)}\) & 0 & \(1\times 10^{0}\) \\ \(\mu^{(1)}\) & 0 & \(1\times 10^{7}\) \\ \(\mu^{(2)}\) & 0 & \(1\times 10^{2}\) \\ Strike & 0 & 0 \\ Dip & 0 & \(-45\) \\ \(Std\_Dev_{1}^{(1)}\) & 0.2 top z cell and \(1\times 10^{8}\) rest of domain & 0.2 top z cell and \(1\times 10^{8}\) rest of domain \\ \(Std\_Dev_{m}^{(2)}\) & 0.2 top z cell and \(1\times 10^{8}\) rest of domain & 0.2 top z cell and \(1\times 10^{8}\) rest of domain \\ \(Std\_Dev_{1}^{(1)}\) & 2\% 1 & 2\% 1 \\ \(Std\_Dev_{2}^{(2)}\) & 2\% 1 & 2\% 1 \\ \hline \hline \end{tabular} Difference between max(anomaly)–min(anomaly).  
\end{table} 
Table 3: Inversion parameters for separate and joint inversion. Figure 9: Profile comparison of separate vs. joint inversion results of test data 2. Density distribution models (g/cm\({}^{3}\)) and Magnetization distribution (A/m) sliced at (**a**) 300 m in \(y\)-axis and (**b**) 750 m in \(y\)-axis to better show position of four buried bodies. Rose and blue markers represent the original position of buried bodies.
## 3 Results and Discussion  
### THz Time-Domain and Frequency-Domain Spectra of PGRs  
The THz time-domain and frequency-domain spectra of the reference and the three PGRs are depicted in Figure 1. As can be seen from Figure 1A, the time-domain spectra contained both the amplitude information and the phase information. Compared with the corresponding reference spectrum of polyethylene, the time-domain spectra of the PGRs had amplitude decay and time delay. The amplitude attenuation was caused by the sample absorption of THz radiation. The time delay existed because of a higher refraction index in sample than in nitrogen. Furthermore, scattering was caused when the THz wave passed through the air pores between the PGR particles and the polyethylene particles inside the pellet [33]. Figure 1B shows the logarithmic spectra of the frequency-domain spectra (in dB scale). The reference signal of polyethylene had no significant absorption to THz. However, 2,4-D had three absorption peaks at 1.35, 1.57 and 2.67 THz, respectively. The characteristic absorption peaks of CPPU were located at 1.77 and 2.44 THz. IAA showed one absorption peak at 2.5 THz. As the polyethylene mixed in the PGR samples generally had no absorption of THz radiation, the absorption dips in Figure 1B could be designated as the fingerprints of the corresponding PGRs.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
###### Abstract  
Based on density evolution analysis of the existing belief propagation (BP) algorithm, the Turbo Decoding Message Passing (TDMP) algorithm was analyzed from the perspective of density evolution and Gaussian approximation, and the theoretical analysis process of TDMP algorithm was given. When calculating the prior message of each layer of the TDMP algorithm, the check message of the previous iteration should be subtracted. Therefore, the result will not be convergent, if the TDMP algorithm is directly analyzed based on density evolution and Gaussian approximation. We researched the TDMP algorithm based on the symmetry conditions to obtain the convergent result. When using density evolution (DE) and Gaussian approximation to analyze the decoding convergence of the TDMP algorithm, we can provide a theoretical basis for proving the superiority of the algorithm. Then, based on the DE theory, we calculated the probability density function (PDF) of the check-to-variable information of TDMP and its simplified algorithm, and then gave it a calculation based on the process of the normalization factor. Simulation results show that the decoding convergence speed of the TDMP algorithm was faster and the iterations were smaller compared to the BP algorithm under the same conditions.  
density evolution; Gaussian approximation; BP algorithm; TDMP algorithm; convergence speed +
Footnote †: journal: Energy Procedia
## 3 Experimental and Verification  
The Type 1 module consists of a front chamber, microspeker unit, and back chamber, as shown in Figure 8a. A B&K measurement system is used to obtain the SPL results, which included a monitor, computer host, a pulse multi-analyzer (B & K 3560C), a power amplifier (B & K 2718), a microphone (B & K 4191), and baffles, as shown in Figure 8b. B & K is the shorthand for the company's name (Bruel & Kjaer, Naerum, Denmark). The microphone is placed inside the anechoic chamber, and other devices are placed outside the chamber. The sound signal recorded by the microphone is transformed into SPL in frequency domain using fast Fourier-transform (FFT).  
The microspeker module is fixed to the small baffle and inserted on the big baffle using two clamps. The baffles prevent sound coming from the backside of the baffle from affecting the SPL measurement. The microphone is placed at a distance of 10 cm from the central axis of the microspeker module. The input voltage is 3.1 Vrms with sinusoidal signal sweeping from 100 Hz to 20 kHz. The octave band is 1/12 oct.  
The SPL comparison of type 1 module between the analysis and experimental results is shown in Figure 8c, which shows a good match.  
A comparison of the peak frequencies between the analysis and experimental results for the type 1 module is shown in Table 2. The peak frequencies predicted by method #1 and method #2 are in good agreement with the experimental results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & \(f_{HREC}\) **(Method \#1/Method \#2/Expt.)** & \(f_{TEC}\) **(Method \#1/Method \#2/Expt.)** \\ \hline Type 1 & 3099/3150/3000 & 14,929/15,000/15,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak frequencies comparison of type 1 module.  
Figure 7: Electromagnetic–mechanical–acoustic coupling model.  
5. The tube height is set as a constant value of 1.10 mm with the consideration of the real situation in smartphones. In type 6 and type 7, the tube width and tube height have been changed to remain the same tube area as 6.60 mm\({}^{2}\). A comparison of modules with different side-firing front chambers is shown in Figure 9.  
Type 1, Type 2, and Type 3 are in the first group that is used to investigate the tube length effect on mid-high frequency peaks.  
Type 1, Type 4, and Type 5 are in the second group with different tube widths to investigate the tube width (tube area) effect on mid-high frequency peaks.  
Furthermore, the last group includes Type 1, Type 6, and Type 7, maintaining the same tube length and tube area but different combination of tube width and tube height.
###### Abstract  
In general, the use of angle-diversity receivers makes it possible to reduce the impact of ambient light noise, path loss and multipath distortion, in part by exploiting the fact that they often receive the desired signal from different directions. Angle-diversity detection can be performed using a composite receiver with multiple detector elements looking in different directions. These are called non-imaging angle-diversity receivers. In this paper, a comparison of three non-imaging angle-diversity receivers as input sensors of nodes for an indoor infrared (IR) wireless sensor network is presented. The receivers considered are the conventional angle-diversity receiver (CDR), the sectored angle-diversity receiver (SDR), and the self-orienting receiver (SOR), which have been proposed or studied by research groups in Spain. To this end, the effective signal-collection area of the three receivers is modelled and a Monte-Carlo-based ray-tracing algorithm is implemented which allows us to investigate the effect on the signal to noise ratio and main IR channel parameters, such as path loss and rms delay spread, of using the three receivers in conjunction with different combination techniques in IR links operating at low bit rates. Based on the results of the simulations, we show that the use of a conventional angle-diversity receiver in conjunction with the equal-gain combining technique provides the solution with the best signal to noise ratio, the lowest computational capacity and the lowest transmitted power requirements, which comprise the main limitations for sensor nodes in an indoor infrared wireless sensor network.  
Keywords:angle-diversity; sensor network; infrared channel; simulation; signal to noise ratio +
Footnote †: journal: Sensors
## 2 Related Work
### Markov Decision Process  
In the Markov decision process (MDP), the dynamic change of the environment is completely determined by its current state. The difference between MDP and the partially observable Markov decision process (POMDP) is that the former defaults that agents can observe the global state of the environment, while the latter considers that each agent can only observe a partial state. Considering that multi-UAV can only observe the state of adjacent parts during operation, the POMDP is used. The POMDP consists of a seven-tuple \(\langle S,A,T,R,\Omega,O,\gamma\rangle\), where \(S\) is the state space, the \(A\) is the action space, \(T\) is the state transfer probability, \(R\) is the reward value, \(\Omega\) is the observation space, \(O\) is the observation probability, and \(\gamma\) is the discount factor.  
Figure 1 shows the flow of a POMDP at runtime. The initial state of the system is \(s_{0}\in S\), which includes the observation \(o_{0}\in\Omega\), with an observation probability of \(O(o_{0}|s_{0},a_{0})\), the agent chooses the action \(a_{0}\in A\) according to the policy. The policy \(\pi(a|o)\) is the rule that determines how the agent interacts with the environment when it is in the current observation \(o\). A policy can be expressed as a probability distribution or a deterministic function. The agent injects the action into the environment. The environment returns the next state \(s_{1}\) according to a transfer probability \(T(s_{1}|s_{0},a_{0})\), and so on. In this process, the agent gains the reward \((r_{0},r_{1},\cdots)\) based on the current state and injected action. In the POMDP iteration process, the agent hopes to maximize the reward gained by updating the policy. That is, if the policy is updated with the optimal policy \(\pi^{*}\), Equation (1) is established.  
\[E_{\pi(a|o)}\left[\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}\right]=E_{\pi^{*}} \left[\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}\right], \tag{1}\]
## 3 Results
### Biologicals in Asthma and Allergic Rhinitis
#### 3.3.5 Novel Biologic Therapies  
Nowadays, other biologists are being studied with the aim of treating non-allergic asthma.  
Tezepelumab is an IgG\({}_{2}\) monoclonal antibody that targets TSLP, which is a protein produced by epithelial cells in response to both allergens, viruses and other toxins involved in the triggering of asthma. This biological has recently been approved to treat severe asthma in patients aged 12 years and older [228], supported by previous clinical trials that have shown promising results in the treatment of allergic and non-allergic asthma with this biological [229]. In this randomized, double-blind, placebo-controlled trial, 1061 patients were randomized to receive tezepelumab or placebo subcutaneously for 52 weeks, once a month. Results showed that patients that received tezepelumab had lower rates of asthma exacerbations, lower blood eosinophil count, and an improvement in lung function parameters.  
On the other hand, another biological still under development isategolimab, a monoclonal IgG\({}_{2}\) antibody that binds to the IL-33 receptor (anti-ST2) [230]. The ZENYATTA study comprised 502 severe asthmatic individuals distributed into several groups: patients that received placebo or 70 mg, 210 mg, or 490 mg doses of astegolimab subcutaneously every 4 weeks [230]. Patients that received the highest and the lowest dose of astegolimab showed higher rates of reduction of asthma exacerbations in both types of patients with uncontrolled asthma: eosinophil--high (\(\geq\)300 cells/uL) and eosinophil--low (\(<\)300 cells/uL).
## 3 Results
### Influence of Different Cropland Use Transitions on GPP
#### 3.2.2 Loss of GPP Due to the Cropland Abandonment
Conversely, the eastern part of Guangdong was surrounded by mountains on three sides and by the sea on one side. Most of the cropland was distributed in the plains of Shantou and Shanwei. The favorable topographic conditions were conducive to reducing the occurrence of the phenomenon of cropland abandonment. As a result, this area experienced the least impact from cropland abandonment, with GPP losses accounting for only 7.39% of the total, despite cropland abandonment covering 13.29% of the total area. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Districts**} & \multicolumn{3}{c}{**Area (km\({}^{2}\))**} & \multicolumn{3}{c}{**GPP (10\({}^{4}\) 0)**} & \multicolumn{3}{c}{**Area (km\({}^{2}\))**} & \multicolumn{3}{c}{**GPP (10\({}^{4}\) 0)**} \\ \cline{2-9} \multicolumn{1}{c}{} & **1991–2000** & **2001–2010** & **2011–2020** & **1991–2000** & **2001–2010** & **2011–2020** & **1991–2020** & **1991–2020** \\ \hline \multirow{9}{*}{PRD} & Dongguan & 251.34 & 155.68 & 252.20 & 5.62 & 3.86 & 6.40 & \multirow{9}{*}{22,891.76} & \multirow{9}{*}{511.33} \\  & Foshan & 312.87 & 284.48 & 473.18 & 7.79 & 7.56 & 13.22 & & \\  & Guangzhou & 1271.63 & 1065.69 & 1108.78 & 25.95 & 25.44 & 29.55 & & \\  & Shenzhen & 237.85 & 156.49 & 179.22 & 4.22 & 2.73 & 3.39 & & \\  & Zhongshan & 87.45 & 71.61 & 142.81 & 1.76 & 1.40 & 3.09 & 22,891.76 & 511.33 \\  & Zhuhai & 119.51 & 114.91 & 161.45 & 0.74 & 1.02 & 1.29 & & \\  & Huizhou & 2233.60 & 1886.40 & 2254.87 & 42.57 & 48.17 & 59.62 & & \\  & Jiangmen & 1961.82 & 1901.88 & 1889.00 & 39.99 & 44.66 & 47.31 & & \\  & Zhaoqing & 1667.43 & 1145.09 & 1504.50 & 30.87 & 20.98 & 32.10 & & \\ \hline \multirow{9}{*}{EG} & Shantou & 566.59 & 449.93 & 662.83 & 9.74 & 10.94 & 15.23 & \multirow{9}{*}{12,326.04} & \multirow{9}{*}{158.57} \\  & Shanwei & 1304.80 & 1062.78 & 1406.83 & 24.53 & 23.08 & 33.66 & \\  & Jieyang & 2085.24 & 1387.45 & 1502.56 & 45.94 & 35.55 & 42.81 & \\  & Chaozhou & 876.09 & 477.51 & 543.43 & 16.61 & 11.84 & 12.93 & & \\ \hline \multirow{9}{*}{WG} & Zhanjiang & 5201.53 & 5912.45 & 5052.79 & 156.31 & 186.23 & 206.78 & \multirow{9}{*}{35,124.60} & \multirow{9}{*}{1005.33} \\  & Yangjiang & 2856.83 & 2411.39 & 1848.11 & 57.62 & 54.46 & 45.06 & \\ \cline{1-1}  & Maoming & 4455.34 & 4498.46 & 2887.70 & 104.24 & 113.01 & 81.63 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics on the area and GPP of cropland abandonment. Figure 7: Abandonment of cropland on GPP in Guangdong province from 1991 to 2020.
Also, they have been shown to inhibit transcription factors such as HIF-1\(\alpha\), NF-kB, and AP-1 but activate Nrf [25; 28]. One of the most promising MnPs is _ortho_ Mn(III) _meso_-tetrakis(\(N\)-n-Hexylpyridinium-2-yl)porphyrin (MnTnHex-2-PyP\({}^{5+}\), MnTnHex, Figure 1). This redox-active compound is the most lipophilic MnP, having high bioavailability and distribution while at the same time displaying low toxicity in healthy cells and tissues [25]. MnTnHex has been studied in different types of cancer, e.g., breast, renal cancer, and glioblastoma [24; 29; 30; 31]. It was also found to reduce the viability and migration of renal metastatic cancer cells [29]. In addition, when combined with doxorubicin (dox),MnTnHex reduced the migration of breast cancer cells [24]. In mice, MnTnHex was able to significantly delay brain tumor growth by 3 to 34 days, depending on the xenograft [31]. In a comparative study regarding the radioprotective effect of several antioxidant manganese compounds in ataxia-telangiectasia lymphoblastoid cells and wildtype (WT) cells derived from healthy individuals, MnTnHex was the most effective in protecting WT cells from radiation-induced apoptosis and DNA damage at nM levels [32]. This compound was also studied in V79 lung fibroblasts and did not exhibit cytotoxicity [33]. Other studies have addressed the radioprotective effect of MnTnHex in the lungs. [PERSON] et al. [34] demonstrated that MnTnHex could delay radiation-induced lung lesions in non-human primates. Also, at the concentration of 0.05 mg/kg, MnTnHex did not show any evidence of lung cytotoxicity. Regarding lung cancer cells, there is, however, a lack of information considering the cytotoxic effects of MnPs. The present study intends to fill this gap. In this report, the impact of MnTnHex, either as a single drug or combined with cisplatin, was assessed in NSCLC cell lines (A549 and H1975). Complementary endpoints related to cell viability, migration, and cell cycle distribution, were studied for the first time. Furthermore, this report provides molecular clues regarding the impact of MnTnHex and cisplatin on the extracellular metabolic profile of the two NSCLC cell lines using metabolomics as an innovative tool. This tool measures the levels of low molecular weight molecules participating in cellular biochemical processes, providing novel insights into the cellular metabolic response to MnTnHex and cisplatin.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 4 Results
### Classification Results of Our Proposed Method
#### 4.1.2 AP Waveform Recognition Results  
Figure 7a shows the filtered signal after attenuating noise. The detected AP waveform position is marked in Figure 7a which contains the start position, peak position, and end position. The whole waveform contains stimulus artifact and AP signals in Figure 7a. It is reasonable for the stimulus artifact often occurs at the start of AP signals. Figure 7b is the derivative signal. We used the adaptive noise threshold to eliminate noise peaks in signal. Then we can search the left peaks and troughs which marked with triangles in Figure 7a. The zero-cross points between the peaks and troughs are corresponding to peaks or troughs in raw signals. After careful processing, we got all possible peaks of waveforms similar to AP from these zero-cross points. Then we search the start and end positions of the peaks.  
Although we extracted 329 waveforms like AP from raw data, there were still existing 96 artifacts signals or non-standard APs among them. Therefore, we need a more accurate classification method to recognize standard AP.  
Figure 6: AP of cucumber (**a**) Raw signal; (**b**) Frequency spectrum of raw signal; (**c**) Frequency spectrum of raw signals in log scale; (**d**) Signal after filtering.
## 2 Airborne Instruments and Interface with the ATR-42
### Antenna and Cable Losses  
Each attenuator not only attenuates the incoming signal, but also introduces an associated thermal noise [1]. In the aircraft, each antenna was insulated to minimize temperature variations within its structure. Indeed, it was our aim to maintain identical temperatures at each port. The temperature of each OMT and horn was continuously monitored by means of a set of thermistors. Because coaxial cable losses are the main sources of signal error, a significant effort was made to very accurately determine its influence. Four 4.5 m long cables are needed to connect each antenna to the receiver unit, as shown in Figure 2. These cables transport signals (corresponding to the vertical and horizontal polarizations), which require predictable insertion phase and insertion loss characteristics. For the present application, it was decided to use SUCOFEED 1% coaxial cable from HuberSuhner, which has very low losses (0.19 at 30 \({}^{\circ}\)C) and a very low drift coefficient (0.055 K/C). Because the temperature in the aircraft cabin is not stabilized, the coaxial cables are insulated and equipped with five temperature sensors mounted inside their insulating cladding. Insertion phase is also a very important characteristic for RF cables. Although phase matched cables are not mandatory for radiometric applications, phase-stabilized cables must be used. The relative phase matching requirement is satisfied by matching the phase, as opposed to the absolute length, of the two cables linked to each antenna relative to each other.
## 6 Conclusions  
This paper presents a novel SLAM system based on 3D Gaussian splatting (3 DGS), called RK-SLAM, which utilizes 3 DGS as the core for tracking and mapping. The system incorporates two key technologies: the sub-Gaussian method and the related frame selection algorithm. The sub-Gaussian approach binds Gaussians to keyframes, allowing them to fine-tune in sync with the keyframe poses. This effectively mitigates the impact of tracking errors on mapping. The related frame selection algorithm calculates inter-frame relation based on co-visibility and spatial distance between frames, achieving a loop closure-like effect. This method overcomes the limitations of existing approaches, which struggle to relate frames that are far apart in terms of sequence order. As a result, RK-SLAM performs better in indoor SLAM sequences with cyclic structures, such as the Replica dataset and TUM-RGBD dataset. Our proposed SLAM system offers a potential solution for dense visual SLAM. Meanwhile, the sub-Gaussian method and related keyframe selection algorithm provide promising directions for future advancements in 3 DGS-based SLAM systems.  
Conceptualization, X.M. and Y.J.; methodology, X.M. and C.S.; software, X.M.; validation, Y.J., C.S., and S.Z.; writing--original draft preparation, X.M.; writing--review and editing, Y.J., C.S., and S.Z; supervision, S.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the STI 2030--Major Projects 2021 ZD0200400, National Natural Science Foundation of China (Grant No. 62203443), Major program of the National Natural Science Foundation of China (Grant No. T2293720/3724/3725), and the Excellent Youth Program of State Key Laboratory of Multimodal Artificial Intelligence Systems (E4 SP100101).  
Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.