esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Attribution of Climatic Factors to Global NEP IAV  
The spatial pattern of the contributions of climate variables to global NEP IAV is shown in Figure 8. The result indicated that the temperature contributed more than precipitation and solar radiation at the global scale. In southeast North America and northern South America, the contributions of the temperature were larger than 0.006%. In Europe and some areas of Asia, the contribution values were smaller than \(-0.002\%\) (Figure 9a). Compared to temperature, precipitation (Figure 9b) and solar radiation (Figure 9c) played negative roles in global NEP IAV in most areas of global.  
Figure 6: Contribution to global NEP IAV counted by (**a**) latitudes and (**b**) different vegetation types. Positive latitudes represent the Northern Hemisphere and negative latitudes represent the Southern Hemisphere.  
Figure 7: The spatial pattern of the contribution to IAV NEP in different seasons.  
We counted the contributions by climate factors (Figure 10a). The results indicate that temperature contributed the most to the global IAV of NEP, with the contribution being 45.5%, followed by precipitation (4.8%) and solar radiation (2.4%). These results indicated that temperature played a major role in regulating the global IAV of NEP.  
We also counted the contributions by climate factors at different seasons (Figure 10b). The temperature contributions to IAV of NEP were 28.5%, 66.2%, 28.3%, and 69.4% in MAM, JJA, SON, and DJF, respectively. The contributions of precipitation to IAV of NEP were 13.5%, 8.4%, 7.2%, and 2.3% in MAM, JJA, SON, and DJF, respectively. Finally, the contributions of solar radiation to IAV of NEP were 3.8%, -0.3%, 3.4%, and 7.9% in MAM, JJA, SON, and DJF, respectively. These results suggested that temperature contributed the most to NEP IAV in any season.  
The climatic control on the IAV of NEP is substantially different across regions and seasons (Figure 11). Temperature played a negative role in the global IAV of NEP. There were 21.3%, 33.0%, 30.5%, and 30.0% of pixels that showed significant negative correlations with temperature for MAM, JJA, SON, and DJF, respectively. Precipitation played significant positive roles in NEP IAV over globe. There were 12.4%, 14.3%, 10.9%, and 8.0% of pixels that showed significant positive correlations with precipitation for MAM, JJA, SON, and DJF, respectively. In a comparison of the temperature and precipitation, most areas of global showed less correlation with solar radiation. There were only 6.6%, 7.6%, 8.2%, and 7.1% of pixels in the globe which expressed significant correlations with solar radiation for MAM, JJA, SON, and DJF, respectively. These results indicate that global warming is not conducive to the formation of a global carbon sink, and abundant precipitation could facilitate the formation of a carbon sink in global terrestrial ecosystems.  
Figure 8: Seasonal contributions to global IAV of NEP.  
Figure 9: The contributions of (**a**) temperature, (**b**) precipitation, and (**c**) solar radiation to global IAV of NEP.
# Implementation of Pavement Defect Detection System on Edge Computing Platform
###### Abstract
Finally, we conducted a quantitative analysis and comparison of DL object models with real-time object recognition, which includes YOLOv3 [3] used in this paper as well as YOLOv4 (YOLO, version 4) [4], MobileNet-YOLO [5], TF-YOLO (tiny fast YOLO) [6], and RetinaNet [7]. We ultimately chose YOLOv3 and implemented it with a field-programmable gate array (FPGA)-based hardware accelerator as an embedded system; the results show that YOLOv3 can still fulfill the criteria for real-time operation while maintaining the accuracy of the original system model. The main contributions of the proposed pavement defect detection system are as follows:  
* Two different FOV lenses and tracking-via-detection strategy are used to increase the detection accuracy rate, decrease the misjudgment rate, and lengthen the recognition distance efficiently. * A lightweight YOLOv3 model is developed by the model compression technology and then implemented on an FPGA-based hardware accelerator edge computing platform, which not only satisfies the real-time computing requirements while maintaining the accuracy of the original system model but also becomes a competitive product in the automotive electronics market. * To prove the effectiveness of the proposed pavement defect detection strategy, the first dataset for Taiwan pavement defects is established, which is called the Taiwan pavement defect image dataset (TPDID). * In order to verify the robustness of the proposed road defect detection system, an extensive video recorded on the Pacific Northwest highway [8] is used. The experimental results show that the proposed system can effectively recognize potholes under severe conditions, such as water surface reflection interference or potholes with complex structures.
# Reduction of Satellite Signature Effect in High-Accuracy Satellite Laser Ranging to Etalon
###### Abstract  
Etalon is considered to be one of the most promising satellites for studying crustal motions, Earth rotation, and other scientific applications. Unfortunately, its outsized shape and signature result in a measurement range bias of several millimeters. On the basis of simulations of the echo signals, we analyze the center of mass corrections (CoM) for Etalon due to variations in satellite signature effect at different incidence angles. To minimize range bias caused by satellite signature effects, a center of mass corrections filter has been proposed for the processing of standard SLR data. According to the relationship between RMS of CoM and the upper limits of the rejection criteria, the measurements with the lowest variability of CoM are selected for normal points. Statistics indicate that the center of mass corrections filter can improve the stability of the collected data by 79%, and reduce the mean RMS of normal points from 163.7 \(\times\) 41.8 ps to 118.2 \(\times\) 8.94 ps. Additionally, the new algorithm is applicable to Etalon-2. In particular, this paper enriches and provides a useful reference for minimizing the effects of satellite signatures on the production of SLR data by providing a theoretical model that incorporates systematic errors in SLR.  
satellite laser ranging; satellite signature effect; pro-processing algorithm; center of mass corrections; Etalon 2023131106. [[https://doi.org/10.3390/appl31911106](https://doi.org/10.3390/appl31911106)]([https://doi.org/10.3390/appl31911106](https://doi.org/10.3390/appl31911106))
## 3 Proposed Methodology  
This study proposes a DL-based approach for RFI recognition and AMC by benefiting from the transfer learning strategy. The general framework is based on the hierarchical classification in which the first and second levels determine the type of the received signal that is either SoI or a combination of SoI with any of the jamming signals and the modulation type of SoI, respectively [6]. To this end, in the first classification level, a classifier is trained to determine the type of received signals. Further, a classifier is trained per each type of received signal to recognize the modulation type of the combined SoI. Figure 1 demonstrates the proposed methodology, which follows four steps: (1) data acquisition, (2) scalogram computation, (3) Feature extraction using pretrained CNN, and (4) classification. Each step will be fully elaborated in the rest of this section.
# Adjuvant Effects of _Lavandula angustifolia_ Oil in Experimental Carrageenanan-Induced Thrombosis
## 3 Results
### Levels of Oxidative Stress Parameters  
The oxidative stress markers assessed reflected differences between the groups, with statistically significant improvements in the LO pretreated groups compared to the thrombosis group (Table 1 and Figure 2). The TNCL2 group showed significantly lower values for all oxidative stress parameters than the NC group.  
The results showed no statistically significant differences between the T and NC groups in plasma levels of total plasma antioxidant capacity (TAC) and thiol. Groups pretreated with LO showed no differences compared to the control group (Table 2 and Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **MDA, nmol/L** & **NOx, \(\mu\)mol/L** & **TOS, \(\mu\)mol H\({}_{2}\)O\({}_{2}\) equiv/L** \\ \hline C & 4 [3.77 to 4.34] & 27.18 [24.23 to 29.32] & 12.23 [11.13 to 12.72] \\  & [3.42 to 4.73] & [21.17 to 30.91] & 110.39 to 13.68] \\ T & 4.81 [4.56 to 5.3] & 33.18 [30.8 to 35.28] & 14.66 [13.88 to 16.09] \\  & [4.18 to 5.88] & [28.54 to 38.12] & [12.73 to 17.23] \\ NC & 3.95 [3.80 to 4.08] & 27.63 [26.05 to 30.00] & 9.64 [9.23 to 10.23] \\  & [3.44 to 4.38] & [24.52 to 35.61] & [8.5 to 10.98] \\ TNCL1 & 1.94 [1.91 to 2.18] & 26.41 [24.67 to 27.73] & 7.87 [7.51 to 8.18] \\  & [1.86 to 2.38] & [23.91 to 29.50] & [6.56 to 8.35] \\  & [0.64 [0.61 to 0.76]] & 21.64 [20.80 to 22.42] & 5.43 [5.03 to 5.64] \\ TNCL2 & [0.52 to 0.82] & [19.11 to 23.84] & [1.80 to 5.97] \\ * stat. (\(p\)-value) & 43.4 (\(<\)0.0001) & 32.8 (\(<\)0.0001) & 46.3 (\(<\)0.0001) \\ \hline \end{tabular} Data are summarized as median [Q1 to Q3] and min to max, where Q1 and Q3 are the first and third quartiles, min is the minimum, and max is the maximum value. * Kruskal–Wallis test; C = control group; T = thrombosis group; NC = subcutaneous hadroparin calcium group; TNCL1 = lavender oil 100 mg/kg b.w. and nadroparin calcium; TNCL2 = lavender oil 200 mg/kg b.w. and nadroparin calcium; MDA = malondialdehydedehyde; NOx = nitric oxide; TOS = total oxidative stress.  
\end{table}
Table 1: Levels of oxidative stress markers in plasma by groups.  
Figure 2: Plasma levels of oxidative stress markers by groups. (C = control group; T = thrombosis group; NC = subcutaneous radroparin calcium group; TNCL1 = lavender oil 100 mg/kg b.w. and nadroparin calcium; TNCL2 = lavender oil 200 mg/kg b.w. and nadroparin calcium).
## 3 Computed Tomography and Mitral Valve
### CT Assessment of the Mitral Apparatus for Transcatheter Procedures
#### 3.3.5 Vascular Structures  
Preprocedural planning for TMVI includes careful evaluation of surrounding vascular structures using CT. Therefore, a fundamental part of the evaluation concerns the course and patency of the circumflex artery (Cx) due to its proximity to the posterior mitral annulus. When this distance is too short, some transcatheter procedures are contraindicated due to the risk of vessel closure during the fixation of the device. The distance between these two structures should be measured on multiple views to avoid the risk of dissection, closure, or perforation of the coronary sinus (CS) during or after the procedure [91; 92]. In particular,the relationship between the distance and angle between these two structures and the procedural success of patients undergoing transcatheter annuloplasty with the Carillon device has been demonstrated [93]. Consistently, a distance of CS to Cx of <8.6 mm in the distal device landing zone has been shown to be predictive of complications on the circumflex artery [94].
## 3 Methodology of the Proposed HOC
### ML Partial Classifier  
The second classifier machine learning (ML) (statistical classifier) works parallel with the first classifier. Based on offline training and testing datasets, some classification rules were built. Based on these rules, the statistical classifier (algorithm) makes its online decisions to identify the captured flows. The ML classifier, plays an essential role in the HOC system. Unlike port, the statistical classifier makes a classification decision about the traffic flow in most cases. This means the port classifier will ignore the flow if there are no well-known port numbers or the traffic is encrypted, whereas the statistical classifier will automatically make the classification decision without constraint. The statistical classifier in an HOC system considers the following factors, which can help improve the ML classification quality.  
The statistical algorithm imports the statistical rules from the offline training stage and uses these rules to check which class this traffic flow belongs to. In the training stage, more than 10 Weka algorithms were tested. These included: class selection, features selection, algorithms selection and building the classification rules. The statistical classifier in the HOC system is a trade-off between the rules generated by one of the three algorithms, i.e., rules.PART, Tree.J48 and RandomTree. The rules generated by the algorithm in the offline training stage are used for the classification (offline and online). After wide analysis, the rules of the PART algorithm were used (this algorithm is also used in [33]). This algorithm generates four times fewer rules than the other algorithms. In addition, the accuracy gained by the rules of this algorithm is high.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 3 Design of _uw_-Cellular Network
### Protocol Design
#### 3.3.5 Application Layer Protocol Design  
The protocols in this layer provide the rules and formats that govern how data is treated. Specific application protocol defines the processes and methodology to deal with the specific type of data including formatting, packing, sending and receiving.  
According to the application requirements of the monitoring network, we provide dedicated protocols to format and parsed the sensor data, such as CTD, Current Meter and navigation data from AUV. In addition to that, we also proposed protocols to deal with the underwater optical and acoustic images. Protocols for processing instruction data have been also designed including checking, setting and control.
_Applied_  
_sciences_  
_Identification of Previously Unrecorded _Bacillus_, _Serratia_, and _Mucor_ Strains Isolated from Yogurt  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 1**  
1 Department of Biology, College of Sciences, Princess Nourah Bint Abdulrahman University,  
P.O. Box 84428, Riyadh 11671, Saudi Arabia; [EMAIL_ADDRESS]  
2 Plant Protection Department, College of Food and Agriculture Sciences, King Saud University,  
P.O. Box 2460, Riyadh 11451, Saudi Arabia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract** (1) Background: Foodborne illnesses are recognized as a significant threat to public health and the economy in both developed and developing nations. The safety of foods containing microorganisms has consequently become a major worldwide concern. One of the most frequent causes of food deterioration in the world is microbial contamination. (2) Methods: Yogurt containers that were bought commercially in Riyadh, Saudi Arabia during their validity period contained four different species of bacteria and one type of fungus. Using nutritional agar and Czapek-Dox agar medium, the bacteria and fungi were isolated. The isolates of the fungi and bacteria were identified using a scanning microscope. The isolates were further identified and classified for molecular evolutionary analyses using the 16S rRNA and ITS sequences from the bacteria and fungi, respectively, in conjunction with the universal primers 27F, 1492R, ITS1F, and ITS4R. (3) Results: A total of 131 separate strains were identified from 12 yogurt samples based on their phenotypic characteristics. In total, 79 isolates (60.3%) consisted of _Serratia marcescens_, _Bacillus subtilis_, and _Mucor circinelloides_, with 52 isolates (39.7%) being _Bacillus cereus_. While the cells of _Bacillus_ and _Serratia_ are shaped like rods, the sporangia of _Mucor_ are large, round, and black. Each strain was identified by its accession number, which were as follows: MK590996.1: _B. cereus_; MK591144.1: _B. subtilis_; MK591002.1 and MK591014.1: _S. marcescens_; and MK559692.1: _M. circinelloides_. The maximum identification was found to be between 98.64 and 100% when BLAST was used to compare the sequences to the NCBI GeneBank database. (4) Conclusions: Genus and species identification was performed using the similarity score values. Yogurt products containing high concentrations (39.7%) of _Bacillus cereus_ isolates carry a significant risk of health hazards due to the potential for spreading pathogenic bacteria to humans.  
**Keywords:** yogurt; isolation; identification; SEM; _Bacillus; Serratia; Mucor_; KSA
## 2 Related Work
Besides these, there are also many works [27; 29; 33] that use an additional encoder to capture the query-aware contextual representation of passage after attention. More recently, BERT-like models [5; 6; 19] have been applied in the MRC task and some even surpass human performance on multiple datasets [1; 2]. Most models take BERT as a strong encoder and propose some task-specific modules upon it. DCMN [9] and DUMA [34] add an interaction attention layer to model the relationship among passage, question and options. NumNet [35] and RAIN [36] introduce number relation into the BERT-based MRC network for addressing the arithmetic question. However, most of these studies take PLM as a black box and only use the output hidden states at the top layer [3; 9; 10; 34], wasting the rich information at other layers. As far as we know, there is still no work to leverage the different level representations of PLM to address MRC tasks. Multi-Decision Network. Most of the previous work utilizes diverse information by building multiple decision modules. These modules are either located in the same layer of the neural network (parallel) [37], or located in different layers (serial) [38; 39; 40]. All these models are trained from a random initialization state, so that they take much pains to differentiate the input representation of each decision module. This goal is usually achieved by assigning different training samples to each decision module. Some works [31; 38; 39] employ a sample-specific network architecture with reinforcement learning to spontaneously determine which decision module to train. This method is sometimes unstable and each sample is only assigned to one decision module which reduces the size of the training corpus for each module. [PERSON] et al. [40] proposes a soft way with a weighted loss function to adjust the distribution of training samples on different decision modules. BERT-based multi-decision models are the most similar work to ours. They add classifiers to the output of each transformer layer and aim to reduce the inference time of BERT in sentence classification task. FastBERT [41] adopts a self-distillation mechanism that distills the predicted probability distribution of the top-layer classifier (as a teacher) to other classifiers at the lower layers (as students). The students simply imitate the teacher, but neglect learning the cases that only the lower layers could solve. This method damages the information diversity of the original BERT model. In contrast, DeeBERT [42] completely stops the gradient propagation from the classifiers at the lower layers to the transformer network. This keeps the information diversity but loses the performance at the lower layers.
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 4 Improved PSSS-Based Time Synchronization Algorithm
### Fine Synchronization  
The \(\hat{\theta}_{1}\) has an error of \(\pm N\) samples, so fine synchronization is required to obtain an accurate frame start position. First, the baseband signal \(R(\theta)\) is intercepted near the  
Figure 8: Flow chart of Half-symbol-based algorithm.  
coarse synchronization point. The range is a complete PSBCH physical channel. Similar to the coarse synchronization step, it performs a block cross-correlation algorithm and peak detection to obtain a fine synchronization peak. The process can be expressed as follows:  
\[C_{2}(\theta)=\sum\limits_{j=0}^{I-1}\left|\sum\limits_{n=0}^{L-1}R^{*}(\theta+n +jL)\hat{p}(n+jL)\right|^{2} \tag{20}\]  
\[T_{21}(\theta)=\beta\text{max}(C_{2i}(\theta)) \tag{21}\]  
\[S_{21}(\theta)=\underset{\theta}{\arg}\{C_{1i}(\theta)-T_{11}(\theta)>0\} \tag{22}\]  
where \(C_{2}(\theta)\) is the result of block cross-correlation and \(T_{21}(\theta)\) is the threshold of it.  
The values in \(S_{21}(\theta)\) equal to the relative distance between the two PSSSs are selected and added to obtain the selective summing peak. The second decision is performed with a larger threshold \(T_{22}(\theta)\) to obtain the secondary fine synchronization peak \(S_{22}(\theta)\), that is, the fine synchronization point. The above can be expressed as follows:  
\[S_{21}^{sum}(\theta)=sum_{21}(\theta)+sum_{21}(\theta\pm d) \tag{23}\]  
\[T_{22}(\theta)=\beta\text{max}(S_{21}^{sum}(\theta)) \tag{24}\]  
\[S_{22}(\theta)=\underset{\theta}{\arg}\{S_{21}^{sum}(\theta)-T_{22}(\theta)>0\} \tag{25}\]  
Finally, the fine estimate point can be expressed as  
\[\left\{\begin{array}{l}\theta_{21}=\text{argmax}(S_{22}(\theta))\\ \theta_{22}=\text{argmax}(S_{22}(\theta)),\theta\
otin(\theta-N/2,\theta+N/2) \\ \theta_{2}=\text{min}(\theta_{21},\theta_{22})\end{array}\right. \tag{26}\]
## 6 Conclusions  
This work presented the design and construction of a high precision dual-axis pedestal type solar tracker with a TRL of 7-8. The STS is capable of moving both manually and automatically. Videos of the system's functions and its range of motion were presented. A closed-loop on-off control was implemented in a low-cost embedded system based on Arduino. The Arduino-based control system proved to be a technically and economically feasible alternative to be implemented in STS for CSP and CPV systems where high accuracy is required. If higher reliability is desired, however, it is recommended to use a controller with a higher processing capacity and a more advanced control algorithm.  
The tracking accuracy was experimentally evaluated using test procedures adapted from the IEC 62817 standard. The total mean ISTEs with the DSS-MEMS and the PPS on a sunny day were 0.37\({}^{\circ}\) and 0.52\({}^{\circ}\), respectively. The total mean RSTA measured with the DSS-MEMS on a sunny day was 0.390\({}^{\circ}\), and 0.536\({}^{\circ}\) on a partially cloudy day. The energy analysis showed that the developed STS can increase the generation of a PV system up to 37.5% in relation to a fixed PV system. In addition, through an optical-energetic analysis, it was validated that the proposed tracker allows for maintaining energy conversion efficiencies greater than 90% in a CPV system. With the tracking precision achieved, it is possible to use the developed STS in solar energy harvesting systems with an acceptance angle greater than 0.6\({}^{\circ}\).  
Finally, a cost analysis of the developed system was presented: the approximate cost of the control and monitoring panel was US $459 and US $2746 USD for the solar tracker. Despite employing low-cost electronic elements in the construction of the control panel and an on-off control algorithm, the solar tracking precision achieved was high enough for the system to be implemented with CPV modules.  
Conceptualization, A.D.-P. and I.S.-T.; methodology, M.A.-C.; software, M.A.-C. and C.P.-O.; formal analysis, S.K.; investigation, M.A.-C. and C.P.-O.; writing--original draft preparation, M.A.-C.; writing--review and editing, S.K. and A.D.-P.; supervision, I.T.-Z.; project administration, A.D.-P.; funding acquisition, I.T.-Z. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Universidad Tecnologica de San Juan del Rio and Centro de Investigaciones en Optica, A.C.  
Not applicable.  
The authors thank [PERSON] and [PERSON] for their support in the design and construction of the control panel.  
The authors declare no conflict of interest.