esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 3 Results
### Open-Loop Pointing Accuracy Test  
The feedforward controller was designed using the hysteresis model, and an open-loop feedforward compensation experiment was conducted for the PZT FSM. Three control methods were compared, which are direct control, feedforward control based on the PI model, and feedforward control based on the PI rate-dependent model. The signal generator generates sinusoidal input signals, and the output signals are collected by an angle sensor inside the PZT FSM. The sampling frequency is 1 kHz. Since the coarse tracking turntable's tracking errors are about \(\pm 400\)\(\upmu\)rad, the fine tracking system is required to track signals with amplitudes below 400 \(\upmu\)rad. Therefore, the input signal amplitude for the open-loop tracking experiment is set to 400 \(\upmu\)rad.  
The results of the open-loop tracking experiment are shown in Figure 14. The experimental results show that the feedforward control methods can enhance the tracking accuracy of PZT FSM, and when the tracking signals are in the middle- and high-frequency ranges, the feedforward control based on the P-I rate-dependent model exhibits even higher tracking accuracy. The root mean square (RMS) and peak-to-peak (P-P) values of the tracking errors are shown in Tables 1 and 2.  
Figure 13: Hysteresis curves of model and measured at middle and high frequencies. **(a)** 10 Hz, **(b)** 20 Hz, **(c)** 40 Hz, **(d)** 50 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Frequency & PZT & PI & (R-D)PI \\ (H2) & RMS (\(\mu\)rad) & RMS (\(\mu\)rad) & RMS (\(\mu\)rad) \\ \hline
1 & 28.69 & 14.58 & 17.64 \\
10 & 45.74 & 27.43 & 27.27 \\
20 & 64.59 & 43.23 & 34.74 \\
30 & 83.11 & 60.03 & 38.28 \\
40 & 104.48 & 80.19 & 45.29 \\
50 & 125.98 & 100.17 & 55.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS values of tracking error for three control methods.  
Figure 14: Sinusoidal tracking and tracking errors. (**a**) 1 Hz tracking, (**b**) 1 Hz tracking errors, (**c**) 30 Hz tracking, (**d**) 30 Hz tracking errors, (**e**) 50 Hz tracking, (**f**) 50 Hz tracking errors.
## 5 Graph Simulations and Results
### Results
#### 5.2.1 Uniform Link Metrics  
The simulation of the shortest-path mode of route discovery in networks with uniform link cost (hop count) shows that window jitter is able to significantly reduce the number of collisions caused by RREQ flooding, when compared to \"standard\" jitter. Figure 9 depicts that the collision reduction becomes more relevant as the network density grows.  
This reduction is due to the fact that the use of window jitter, when compared to \"standard\" jitter, increases the probability that the first RREQ received by an intermediate router (or a destination) has traversed the shortest path (according to the metric in use) available, and therefore, no additional RREQ retransmissions need be performed (and no additional route replies need be sent after the first) over a path with better quality than the one previously advertised. The better the quality of the first advertised path, the less RREP control packets involved in a single route discovery process; and the less likely the packet collisions.  
Figure 10 depicts the optimality index of window jitter and \"standard\" jitter, as a function of network density, when using shortest-delay RREQ flooding. When routers are only allowed to forward the first RREQ received from a given source towards a given destination, the use of window jitter improves significantly the quality of the routes identified through RREQ flooding. This confirms the results from the theoretical analysis of Section 4.1 about the probability of delay inversion in \"standard\" jitter and window jitter.  
As mentioned in Section 3.2, the objective for window jitter is to ensure that the first RREQ that is received, also is the RREQ that has traversed the fewest number of hops and, therefore, represents the better path. Window jitter implicitly assumes a constant link metric, and it is able to provide a significant improvement in the route discovery performance when no more information about link quality is available.  
Figure 10: Optimality index, shortest-delay RREQ flooding.  
Figure 9: Number of collisions, shortest-path RREQ (Route Request) flooding.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process
### Solid-State (ss) and Wet-State Anaerobic Digestion
#### 2.4.2 Solid-State AD (ss-AD)
It was found that VFA generation and feedstock degradation were faster in a thermophilic digester than in a mesophilic digester, and that 95% of the methane yield potential was achieved in 11 and 27 days under thermophilic and mesophilic conditions, respectively [50]. The fermentative microbial activities are enhanced under thermophilic ss-AD; therefore, more care must be taken, as this behavior may cause accumulation of VFAs and result in inhibitory effects on the AD process [51]. High energy input for heating under thermophilic ss-AD should be considered, as it may offset the higher methane production yields and rates [7; 52]. Accordingly, net energy production and the performance of ss-AD under different temperatures should be evaluated and optimized for feasible process operation. In this regard, even though thermophilic ss-AD of switch grass showed greater methane yield than mesophilic ss-AD, the latter had higher net energy production than thermophilic conditions because of reduced initial heating requirements after inoculation [52]. Throughout the literature, the feasibility of two-stage reactors has been confirmed regarding energy balance. For instance, in a two-stage AD system treating OFMSW, better energy balance was obtained, where higher gross energetic potential was related to the higher performance in the methanogenic reactor rather than the hydrogen production from the first stage [1]. Furthermore, in a similar reactor treating thin stillage, optimizing the process was able to increase the energy balance by 18.5% [53; 54], and better energy balance with a surplus of 2.17 kJ/day was also documented in a two-stage reactor treating sewage sludge as compared to a single-stage reactor [27].
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 1 Introduction
In the spatial encoding module, we utilize dilated convolutions to rapidly increase the receptive field, capture deep semantic information, and circumvent spatial information loss [26] and up-sampling caused by down-sampling. Within the temporal encoding module, we employ the Transformer encoder to extract temporal features for each pixel in the feature map time series and perform pixel-wise classification based on the spatio-temporal features. Finally, we validate the effectiveness of our proposed model on the PASTIS dataset. The main contributions of this paper are as follows:  
1. We propose a novel backbone network for semantic segmentation tailored for pixel-level classification tasks sensitive to spatial information. This network abandons down-sampling, which may lead to unrecoverable spatial information loss, and instead combines dilated convolutions and dense connections to rapidly expand the receptive field and obtain multi-scale features. 2. For the first time, we utilize a Transformer encoder to extract temporal features for each pixel. To emphasize the annual cyclic patterns of crops, position encoding is calculated based on the position of the acquisition date within that year. 3. We provide an open-source implementation of the model based on PyTorch on GitHub, which can be found at the following URL: [[https://github.com/ThinkPak/stenn-pytorch](https://github.com/ThinkPak/stenn-pytorch)]([https://github.com/ThinkPak/stenn-pytorch](https://github.com/ThinkPak/stenn-pytorch)) (accessed on 25 October 2023). The subsequent sections of this paper are organized as follows: In Section 2, we will provide a detailed introduction to the PASTIS dataset, including information about the study area and the organizational structure of the dataset. In Section 3, we will outline the research background of the proposed model and provide detailed explanations of the model's two encoders: the spatial encoder and the temporal encoder. Section 4 will cover the evaluation metrics used, experimental details, and the results obtained. In Section 5, we will extensively discuss the experimental results, focusing on key observations and insights gained from analyzing the SITS data. Finally, in Section 6, we will summarize the main contributions of this paper, address its limitations, and provide recommendations for future research.
## 3 Methodology
### Obtaining the Exotic ET, \(LE_{exo}\)  
Referring to \(LE_{exo}\), it requires ground-based observations of latent heat flux to determine its value. For each EC station inputted to the method, we know its local ET, which can be estimated according to Section 3.1 and its actual ET, which is measured by EC stations; then the exotic ET can be determined by Equation (2). However, the attained exotic ET, \(LE_{exo}\), is ground-based and needs to be extended to a regional scale from a point scale. In the study, we used a spatial interpolation method, the inverse distance weighting (IDW), to interpolate the ground-based \(LE_{exo}\) to acquire the spatial distribution results. We assume that \(LE_{exo}\) is affected by the intensity of horizontal advection, which moves to other places through diffusion. Since the IDW interpolates according to the distance between two inputted objectives, it is appropriate to use it to acquire the spatial distribution of \(LE_{exo}\). The process of interpolation is described as follows.  
If the pixel covering the inputted EC station is expressed as Pixel \(i\) with \(LE_{exo(i)}\), the exotic ET of other pixels (Pixel \(x\), \(LE_{exo(x)}\)) around Pixel \(i\) can be calculated by the IDW. Formulas can be written as:\[LE_{exo(x)} = \sum_{i=1}^{n}w_{(i)}LE_{exo(i)} \tag{13}\] \[w_{(i)} = \frac{1/{d_{i}}^{k}}{\sum_{i=1}^{n}1/{d_{i}}^{k}} \tag{14}\]  
where \(LE_{exo(x)}\) (W/m\({}^{2}\)) is the exotic ET of any pixel to be calculated, \(i\) is the number of the inputted EC stations, \(w_{(i)}\) is the weight, \(d_{i}\) (m) is the distance between the Pixel \(i\) and Pixel \(x\), and \(k\) is the specified power exponent.
## 3 Experiments
### Experimental Results on 3D Grating
In the same way, the regression analysis was performed to extract the three unknown profile parameters, and the non-linear LM algorithm was also applied to fit the measured signatures with the modeled ones. The extracted profile parameter values for the fits and the SEM-measured values are shown in Table 2. The uncertainties appended to the SEM-measured and scatterometry-measured values in Table 2 were estimated in a similar manner to those in Table 1. Figure 5 presents the fitting result of the model-calculated best-fit \(N\), \(C\), and \(S\) parameters and the measured spectrum (black dots) containing whole range (blue lines) and only 36 optimal points (red circles), respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measuring Mode**} & \multicolumn{3}{c}{**Dimensions (nm)**} & \multirow{2}{*}{**RMSE**} & **Time** \\ \cline{2-2} \cline{4-5}  & \(D\)/nm & \(H_{1}\)/nm & \(H_{2}\)/nm & \\ \hline SEM & 226.7 \(\pm\) & 355.1 \(\pm\) 2.05 & 104.3 \(\pm\) 1.30 & — & — \\  & 28.32 & 34.3 \(\pm\) 1.44 & 107.6 \(\pm\) 0.62 & 28.77 & 14 \\  & 250.4 \(\pm\) 2.21 & 343.9 \(\pm\) 1.03 & 106.4 \(\pm\) 0.51 & **17.45** & **4.5** \\  & Random (36) & 262.4 \(\pm\) 2.38 & 338.7 \(\pm\) 2.42 & 108.1 \(\pm\) 0.89 & 26.50 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of fitting results of the 3D photoresist grating extracted from different spectrum and SEM measurements. Figure 5: Fitting results of the calculated and the ellipsometer-measured \(N\), \(C\), and \(S\) parameters at the incidence angle \(\theta=65^{\circ}\) and azimuthal angle \(\varphi=0^{\circ}\). From Table 2, the three average values of the 3D grating dimensions extracted using 36 optimal points are very close to the values obtained using the full range spectrum, but the extracted parameter \(D\) has about a 25 nm deviation from the SEM-measured value, which is probably caused by the obvious non-uniformity of the grating surface depicted in Figure 4, and the SEM-measured values include estimation errors in manually measuring the SEM micrographs. Table 2 also shows that the proposed method results in the lowest value of RMSE, the highest measurement precision, and the least time cost. It is observed from Figure 5 that the best-fit \(N\), \(C\), and \(S\) parameters using the optimized spectrum are in better agreement with the measured signatures compared to those of the full range. Consequently, we may conclude that the proposed dependence-analysis-based data-refinement method can be applied to determine several optimal measurement points without any loss in the accuracies for profile reconstruction in optical scatterometry.
## 3 Model Validation
### Ambient Temperature Effects
Figure 4 shows the simulation results that are compatible with the experimental results both during injection and after the EOI. The initial injection is important to consider, as it provides the momentum to change the conditions in the combustion chamber caused by the onset of fuel injection, thereby increasing the injection rate, temperature, and pressure. As shown in Figure 5, the initial spray penetration (0.0-0.3 ms) reveals that both cases cannot properly capture the initial ramp for both vapor and liquid penetration. Due to the very short time error of the simulation results (less than 0.1 ms), the injection rate input data from the virtual injection rate generator may not be as accurate as those of the actual injection rate. Figure 2: Comparison of the spray shapes and equivalence ratio histories of the experimental result [23] and the simulation result under an ambient temperature of 440 K.  
Figure 4: Comparison of the spray penetration of the experimental result [23] and simulation result at different ambient temperatures. (**a**) Maximum vapor penetration, (**b**) liquid spray penetration and (**c**) injection mass flow rate. Figure 3: Comparison of the spray shapes and equivalence ratio histories of the experimental result [23] and the simulation result for an ambient temperature of 900 K.  
Figure 6 shows a comparison of the spray angle (left graph) and the spray cone angle (right graph) with varying ambient temperatures. The measurement results from experimental data show that the ambient temperature influences the spray angle but not on the spray cone angle. The trend of the measurement results of the spray cone angle is the same for both cases, which means that the spray cone angle is independent of the ambient temperature and ambient pressure. In addition, the simulated spray cone angle results show the same trend as the measured experimental results. These results show that the constructed model effectively predicted spray behavior under varying conditions of ambient temperature and ambient pressure.
## 3 Results and Discussions  
### Qcm  
QCM can estimate the affinity of membrane-bound peptide with immobilized membrane [41; 42]. To investigate the affinity of phenylseptin peptides with DMPC bilayers, we performed QCM measurements. Table 1 summarizes the values of binding constants _Ka_ of L-Phes and D-Phes. The order (\(10^{6}\) M\({}^{-1}\)) suggested that the peptides have a certain affinity for the DMPC membrane. The binding constant values also showed that D-Phes has a slightly stronger affinity to DMPC membrane. These results indicated that the differences in the N-terminal structure might be manifest as differences in the affinity and antimicrobial activity. Additionally, L-Phes and D-Phes differed in their hydrophobicity, as evident from the different HPLC retention times of L-Phes and D-Phes (Figure 1). This is consistent with the result of [PERSON] and colleagues [18]. The higher hydrophobicity of D-Phes could be also related to the conformations adopted by the N-terminal region, which in turn, could affect the interactions of the phenylalanine residues with the model membrane.
# Automatic Roadway Features Detection with Oriented Object Detection
## 2 Background  
### Roadway Features in Relation to Safety  
The cost of road crashes is huge, both economically and socially. Significant research around the world has focused on investigating the most significant features contributing to road safety. In this subsection, different roadway features are reviewed to quantify their contribution to road safety. Factors affecting crashes differ based on the driving environments. For example, in North America research efforts focused on geometric features and roadway elements, and found that horizontal alignment, lane, shoulder and traffic volumes significantly affect road safety; while in Africa features such as signs, marking, barriers, shoulder and right of way significantly affect road safety. In Asia, horizontal alignment and right of way were the most significant features affecting road safety. In Europe and New Zealand, horizontal alignment, vertical profile and lanes were the most significant features. The most notable roadway features that affect safety on roads can be summarized as follows (categorized by geography):  
* Canada [7]: traffic volume, lane width, access-point density, and segment length.
* US [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]: number of horizontal curves, curves radius, lane width, shoulder width, tangent length, annual average daily traffic (AADT), speed limit, median presence, hazardous elements on the roadside, vertical curvature, and road classification.
* Italy [18]: segment length, AADT, lane width, curvature indicator, and vertical grade.
* In England and Wales [19] and in New Zealand [20]: curvilinear alignments and road curvature.
* India [21; 22]: roadside clearance, lighting, shoulder maintenance, and signs.
* Malaysia [23]: horizontal curvature, shoulder width, roadside activity, and median presence.
* China [24; 25]: presence of heavy vehicles and non-motor vehicles, road density, trip frequencies, and land use types.
* South Korea [26]: presence of sidewalks and lighting and sufficient sight distance.
* Egypt [27; 28; 29; 30]: shoulder width and type, roadside activities, annual average daily traffic (AADT), presence of motorcycles or heavy vehicles, signs, marking, lane width, median, barrier, lighting, and pavement condition.
* In Nigeria [31; 32]: signs, marking, roadside fixed objects, shoulders, and pavement condition.
## 4 Conclusions  
A total of 160 VOCs were detected in eight pear varieties, and the VOCs with the highest number and content of total VOC were esters, alcohols and aldehydes, which interacted with each other and collectively affected the feature fragrance of pears. The results of OPLS-DA and HCA were used to classify the eight pear varieties into six groups. Based on VIP \(>\) 1 and \(p\) \(<\) 0.05, the content of 31 VOCs was further screened to reach a significant level of difference among samples. In the PCA of pears based on differential VOCs, the cumulative variance contribution of the first two PCs reached 66.7%, and the -pear samples were further differentiated using clustering heat map analysis. The content of VOCs and aroma flavor in crispy pears did not change much with the extension of shelf life, while the changes were more obvious in soft pears. Aroma flavor was much stronger and the number and content of volatile substances detected in _P. ussuriensis_ cultivars were higher than those of _P. communis_ cultivars and crispy pears. With the extension of shelf life, the changes in SSC in different pear varieties differed, and SSC was positively correlated with the content of esters and hydrocarbons in a highly significant way. In conclusion, this study revealed the main volatile components of different pear varieties, clarified the material basis for aroma flavor differences of pear varieties with different flesh textures, and provided a reference for determining the optimal shelf life of different pear varieties concerning aroma evaluation and studying the mechanism of differences in VOCs in the future.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12234224/s1](https://www.mdpi.com/article/10.3390/foods12234224/s1)]([https://www.mdpi.com/article/10.3390/foods12234224/s1](https://www.mdpi.com/article/10.3390/foods12234224/s1)). Table S1. VOCs content, VIP value and P value of eight pear varieties at different shelf life.  
Conceptualization, Y.X., G.G. and L.T.; data curation, formal analysis, software, writing--original draft, Y.X.; funding acquisition, supervision, writing--review, L.T.; methodology, Y.X., G.G. and X.D.; validation, Y.X., G.G., X.D. and Y.Z.; project administration, Y.C.; writing--review, L.T., Y.C., D.Q. and J.X.; editing, Y.C., D.Q. and J.X.; investigation, H.H; resources, H.H. and C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-RIP), the Earmarked Fund for the China Agriculture Research System (CARS-28-01), and the National Science and Technology Resource Sharing Service Platform Project (NHGRC2022-NH02-1).  
Data will be made available on request from the corresponding author.  
This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. The authors confirm that they have no conflict of interest with respect to the work described in this manuscript.
###### Abstract  
Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near-far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD's opportunities. Numerous studies have been conducted to address the prevalent near-far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes.  
long range (LoRa); spreading factor (SF); lower spreading factor zone (LSFZ); higher spreading factor zone (HSFZ) +
Footnote †: journal: _Sensors_