esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SD-GPSR Routing Method
### Design of the Control Layer
#### 3.2.2 Location Disruption  
When the connection between a node and the controller is disrupted, the node cannot use the controller to obtain the location information of the destination node. In this case, the disconnected node sends a request message to the nearest node, which will be forwarded to the controller by the node's neighboring nodes. When the controller receives the request message, it identifies the disconnected node based on the source IP in the message and establishes a secure channel with the source node again.  
When a data packet is being forwarded and approaches the vicinity of the destination node's location, but the destination node is not within the one-hop neighbor range of the forwarding node, it is essential to establish a suitable interrupt response to prevent packet dropping caused by the inability to locate the destination node. In such cases, the forwarding node C uses a Packet-in message to report the data packet to the controller. Upon receiving the data packet, the controller uses a centralized routing algorithm to calculate a route path and installs flow tables using Flow-mod messages. The controller also includes the data packet in a Packet-out message and sends it to the reporting node. After processing the data packet, considering that the location of the destination node has changed, subsequent nodes that continue to forward data based on the destination location stored in the source node may also need to request the controller again when the data reach node C. To prevent this situation, when the controller receives the request message, it includes the destination node's location information in a Packet-out message and sends it back to the source node, preventing subsequent data packets from using incorrect location information for forwarding.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 3 Objective Evaluation
### Generalizability  
To demonstrate the robust applicability of AIO, additional simulations were run using both different loudspeaker configurations and a different HRIR dataset. In both sets of simulations, the effect of AIO was assessed as in Section 3.2, with PSD calculations comparing Ambisonic renders to the original HRIRs for all available measurement locations according to (17) and single values of \(\overline{\text{PSD}}\) then calculated from (18).  
The first looked at different loudspeaker configurations. For all other aspects of this paper, AIO has been applied to Lebedev loudspeaker configurations. Here, another common Ambisonic loudspeaker configuration was used: spherical T-designs [45]. For each Ambisonic order \(\{M=1,M=2,...,M=5\}\), T-designs corresponding to \(\{L=8,L=12,L=24,L=48,L=70\}\) were used respectively, fulfilling the orthonormality requirement \(T\geq 2M+1\)[46]. The \(\overline{\text{PSD}}\) results, calculated from 16,020 locations on the sphere, are shown in Figure 10. This illustrates how AIO produces an overall improvement in PSD, regardless of the type of loudspeaker configuration.  
Figure 8: Perceptual spectral difference between HRIRs and binaural Ambisonic rendering (Ambisonic orders \(\{M=1,M=2,...,M=5\}\)) for all directions over the left hemisphere, without and with AIO (mean of left and right PSD values).  
Figure 10: \(\overline{\text{PSD}}\) for Ambisonic orders \(\{M=1,M=2,...,M=5\}\), without and with AIO computed using T-design loudspeaker configurations.  
Figure 9: \(\overline{\text{PSD}}\) for Ambisonic orders \(\{M=1,M=2,...,M=5\}\), without and with AIO.  
Secondly, to assess how AIO works when using other HRIR datasets, renders were made for \(\{M=1,M=2,M=3,M=5\}\) using individualized HRIRs from human subject H20 of the SADIE II database [47], using the corresponding Lebedev loudspeaker configurations as before. The omission of \(M=4\) was due to a lack of necessary measurements. The \(\overline{\text{PSD}}\) results, calculated from 2114 locations on the sphere, are shown in Figure 11. This illustrates how, again, AIO produces an overall improvement in PSD for all tested orders of Ambisonics, regardless of the HRIR database or subject used.  
The tests on generalizability show that AIO improves the spectral reproduction of binaural Ambisonic rendering for all virtual loudspeaker configurations, regardless of Ambisonic order or HRIR dataset. However, the magnitude of improvement has been shown to vary with loudspeaker configuration, as shown by the differences between Figures 9 and 10. A trend appears to exist between loudspeaker configurations regardless of HRIR dataset, as shown by the similarities between Figures 9 and 11.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Power Reduction Techniques
#### 3.1.2 Radio Optimization
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Power Reduction Scheme** & **Reference** & **Wireless** & **Power** & **Communication** & **Consumption/Citation** & **Application** & **Limitations** \\  & **Example** & **Protocol/Device** & **Saving/Ritely** & **Distance/Field** & **Size** & & & & \\  & & & & & & & & \\ \hline \multirow{4}{*}{**Radio optimization scheme**} & [53]/2/2012 & CC1110 module & \(\approx\)10\% & 50 \(\times\) 50 m\({}^{2}\) & Agricultural environment sensors & Precision agriculture & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} \\  & TPC & [53]/2/2012 & ZigBee CC2420 & 8.5\% & 180, 66, and 60 m & Agricultural environments sensors & Agriculture & \
\begin{tabular}{} \end{tabular} \\  & & & & & & & \\ \cline{1-1}  & Cognitive radio & [19]/2012 & ZigBee-Pro & 14 years & Long & Temperature, light intensity, humidity/humidifier, heater, and ventilation & Greenhouse & Limited to one topology (i.e., star topology) to save power \\ \hline \multirow{2}{*}{**Radio optimization scheme**} & [58]/2/2016 & Simulation &
\begin{tabular}{} \end{tabular} & 10.30, 55\% (\cdot\) 408K & 10.30, and 100 m & Different applications including agricultural sensor network & Suggested to use in agriculture application & Power consumption increases with communication range \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Radio optimization schemes for previous research in agricultural applications.
## 2 Advanced Metering Infrastructure (AMI) Features
### Framework of AMI System  
As shown in Figure 1, the AMI system consists of various technologies and applications that combine to function as a single entity. These technologies are SMs, user gateways (UGs), wide-area communication infrastructure, and Meter Data Management Systems (MDMSs). Brief descriptions of these technologies are mentioned below:* **SMs:** SM is a programmable solid-state device that can perform a variety of functions, including Bi-directional metering, including the real-time monitoring of power consumption, peak demand, voltage, current, frequency, and power factor. They can support net metering, transmit notifications of power outages or restoration, monitor power quality, permit remote turn-on or turn-off operations, and assist with time-based pricing. They can also give consumption statistics for both consumers and utilities. Additionally, they can help with (DR) goals [15], which enable greater energy efficiency because information feedback has been demonstrated to decrease customer consumption [16].
* **UGs:** The Universal Gateway (UG) is responsible for performing protocol switching and facilitating communication between two different networks, such as the wide area network and the in-home network. This function is typically conducted by other devices, like SMs or personal computers (PCs).
* **HANs:** A Home Area Network (HAN) is a specific type of local area network that connects to Distributed Energy Resources (DERs), local control devices, SMs, and the (UG) [16; 17].
* **Wide-area communications infrastructure:** They support continuous contact between the utility, customer, and the controlled electrical load. It has stringent privacy restrictions while using open, two-way communication protocols. One popular architectural [18] feature is the use of local aggregators to compile data from collections of meters SMs and send them to a central server. Radiofrequency, optical fiber, the power line carrier, the Internet...etc., may all be considered for use in providing any or this entire infrastructure.
* **Meter Data Management System (MDMS):** MDMS is a database of meter data that includes analytical capabilities and is connected to the user side through an AMI head end. It communicates with other electric power utility systems, such as customer information systems, outage management systems, distribution management systems, and so on, through enterprise buses.
###### Abstract  
Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE) models and process-based models have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP, especially for croplands. Recently, remote measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new perspective to assess actual levels of plant photosynthesis. In the presented study, we evaluated the performance of three approaches, including the LUE-based multi-source data synergized quantitative (MuSyQ) GPP algorithm, the process-based boreal ecosystem productivity simulator (BEPS) model, and the SIF-based statistical model, in estimating the diurnal courses of GPP at a maize site in Zhangye, China. A field campaign was conducted to acquire synchronous far-red SIF (SIF\({}_{760}\)) observations and flux tower-based GPP measurements. Our results showed that both SIF\({}_{760}\) and GPP were linearly correlated with APAR, and the SIF\({}_{760}\)-GPP relationship was adequately characterized using a linear function. The evaluation of the modeled GPP against the GPP measured from the tower demonstrated that all three approaches provided reasonable estimates, with R\({}^{2}\) values of 0.702, 0.867, and 0.667 and RMSE values of 0.247, 0.153, and 0.236 mg m\({}^{-2}\) s\({}^{-1}\) for the MuSyQ-GPP, BEPS and SIF models, respectively. This study indicated that the BEPS model simulated the GPP best due to its efficiency in describing the underlying physiological processes of sunlit and shaded leaves. The MuSyQ-GPP model was limited by its simplification of some critical ecological processes and its weakness in characterizing the contribution of shaded leaves. The SIF\({}_{760}\)-based model demonstrated a relatively limited accuracy but showed its potential in modeling GPP without dependency on climate inputs in short-term studies.  
GPP; SIF; MuSyQ-GPP algorithm; BEPS +
Footnote †: journal: Remote Sensing, 2017
## 4 Conclusions  
In this study, the improved YOLOv5s-ShuffleNet-CBAM model could greatly reduce the model size, fully meet the lightweight requirements, and lay a software foundation for the development and deployment of future mobile devices for the detection of maize seed surface mildew [27]. The improved YOLOv5s-ShuffleNet-CBAM model has the potential to markedly accelerate computational speed and reduce model size. In terms of evaluation indicators, compared with YOLOv5s, the improved YOLOv5s-ShuffleNet-CBAM model could not only have no loss in detection in _Precision_ but also show improvement to a certain extent, with a _Precision_ rate of 90.2%, a _recall_ rate of 88.0%, and a _mAP50_ of 95.5%. Importantly, the model size was reduced from 13.8 MB to 2.4 MB with a decrease of 82.6%. The model performs similarly to the enhanced YOLOv8s model but with only 18.3% of the size. Additionally, the K-fold cross-validation proved the model stability, and Grad-CAM validated the model effectiveness. The improved model not only preserves its high degree of accuracy in identifying moldy maize seeds, but its overall complexity is also significantly reduced, thereby facilitating its deployment on a diverse range of platforms, including embedded devices, mobile devices, and online platforms within the specified application domain. In future work, a further refinement of the improved YOLOv5s-ShuffleNet-CBAM model will focus on ensuring a lightweight architecture while maintaining high precision, with the aim of enhancing practical applications and streamlining deployment in real-world scenarios.  
Conceptualization, Y.X. and A.S.; methodology, A.S.; software, T.C.; validation, A.S.; formal analysis, W.L.; investigation, J.K.; resources, Y.X.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, Y.X.; visualization, W.T.; supervision, W.T.; project administration, J.K.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62203285), the Young Talent Fund of Xi'an Association for Science and Technology (959202413041), the Xi'an Science and Technology Plan Project (23 NYGG0070), and the Natural Science Basic Research Program of Shaanxi (2022)Q-181).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no conflicts of interest related to this article.
## 2 Materials and Methods
### Windthrow and Landslide Detection
#### 2.3.1 Normalized Difference Vegetation Index Filtering Method  
The NDVI is an effective index used to measure the type and amount of vegetation by measuring the difference between near-infrared and red bands, with values ranging between -1 and 1. This index was first presented by [PERSON] et al. [28].  
The NDVI filtering method for landslide detection shown by [PERSON] [22] identifies landslides through the NDVI thresholds \(T_{1}\) and \(T_{2}\) using the NDVI filtering rule (Equation (1)).  
\[NDVI_{before}-NDVI_{after}>\ =\ T_{1}^{*}NDVI_{after}<=\ T_{2} \tag{1}\]  
where:  
\(NDVI_{before}\) = NDVI values obtained from the pretyhoon mosaic;  
\(NDVI_{after}\) = NDVI values obtained from the posttyphoon mosaic;  
\(T_{1}\) and \(T_{2}\) = Threshold value.  
Figure 2: Flowchart of the windthrow and landslide classification and assessment procedure. Input features are identified in the gray boxes.  
To perform the NDVI filtering method, we used the Map Algebra Function of the Spatial Analyst Package in ArcGIS 10.7, where threshold values were defined based on the NDVI values calculated from the pre and posttyphoon images. Histograms of known damaged areas were created to support the choice of threshold values along with the visual interpretation of the classified result. The threshold values for _Setana North_ were defined as \(T_{1}=-1\) and \(T_{2}=0.65\), and those for _Setana South_ were defined as \(T_{1}=0.1\) and \(T_{2}=0.6\) (Figure 3).
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 2 Introduction to GNSS Spread Spectrum Codes
### Spread Spectrum Code Generation Methods Based on LFSR
#### 2.2.2 Gold Code  
The Gold code is a modulo-2 sum generated by two N-length shift registers with distinct feedback polynomials and initial values. Notably, GPS L1C/A, GPS L5, BDS B1I, GLONASS L1 OC, Galileo E5a, E5b, etc., employ the Gold code. In the case of the GPS L1C/A code generator [13], it comprises two registers of identical length (as depicted in Figure 4). The feedback taps are selected from different positions within the two registers. G1 represents the modulo-2 sum of the 3 rd and 10 th bits, which feeds back into Register 1 with a feedback polynomial of \(f(x)=1+x^{3}+x^{10}\). Similarly, G2 is the modulo-2 sum of  
Figure 1: Schematic Diagram of the Glonass L1 OF/L2 OF Code Generator.  
Figure 3: Structure of the GPS L2C Ranging Code Generator in Fibonacci Configuration.  
Figure 2: Structure of the GPS L2C Ranging Code Generator in Galois Configuration.  
the 2 nd, 3 rd, 6 th, 8 th, 9 th, and 10 th bits, also feeding back into Register 1 with a feedback polynomial of \(f(x)=1+x^{2}+x^{3}+x^{6}+x^{8}+x^{9}+x^{10}\). The output sequence is obtained by performing a modulo-2 sum of the sequences stored in Registers G1 and G2.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 8 Lipase-Catalyzed Synthesis of Sustainable Polyesters and Polyamides from Biobased Monomers
### Biobased Unsaturated Aliphatic Polyesters
By replacing the unactivated dicarboxylic acids with alkyl diesters, a series of PBSIs with various molar compositions and significant higher molecular weights were obtained, with \(\overline{M_{\mathrm{w}}}\)'s of up to 28,300 g/mol. In addition, we found that: (1) the most suitable approach is azeotropic polymerization using the solvent mixture of cyclohexane and toluene, which results in PBSIs with high molecular weights and desirable chemical compositions; (2) high molecular weight PBSIs with <30 mol % of itaconate can be prepared by using the two-stage enzymatic polymerization in diphenyl ether; and (3) the two-stage enzymatic melt polymerization gives PBSIs with controllable chemical compositions but low molecular weights. Moreover, the \({}^{13}\)C-NMR study revealed that different microstructures are present in PBSIs obtained from different polymerization methods. The formation of I-B-I-3 microstructures is crucial for synthesizing high molecular weight PBSIs with desired chemical compositions; and more I-B-I-3 microstructures can be produced by CALB in the solvent mixture of cyclohexane and toluene under an azeotropic condition. However, by replacing diethyl succinate (C2) with the other diacid ethyl esters with relatively longer chain length (C3-C10), the two-stage enzymatic polymerization in diphenyl ether resulted in series of unsaturated aliphatic polyesters with desired molar compositions and high \(\overline{M_{\mathrm{w}}}\)'s of up to 57,900 g/mol (Scheme 32) [257]. The molar percentage of itaconate in the unsaturated polyesters can be tailored from 0% to 35% by adjusting the feed ratio of itaconate; and all C=C bonds were well preserved in the resulting polyesters. We found that products with relatively lower molecular weights were generally obtained from the enzymatic polymerization at a higher feed ratio of itaconate; however, with diethyl dodecanedioate having the longest chain length (C10) among the tested diacid ethyl esters, higher molecular weight products were obtained at higher feed ratios of itaconate. Moreover, the obtained itaconate-based polyesters can be thermally cross-linked or photo-cured. By adjusting the diacid chain length and itaconate composition, the thermal and mechanical properties of the cured polyesters can be tuned.
# A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors
## 5 DDoS Classifier Optimization
### GBDT Algorithm Parameters  
After the feature selection, in order to obtain higher accuracy, it is necessary to adjust the parameters of the GBDT algorithm. The optimization of the GBDT algorithm's parameters ensures that the GBDT algorithm achieves better detection accuracy and better generalization ability. There are four main parameters of the GBDT algorithm, namely, the number of the largest weak learner (CART tree), the subsampling, the condition of the subtree's continual division, and the maximum depth of the subtree. The number of the largest weak learners is represented by n_estimators, such that when n_estimators are too small, it is easy to underfit; in contrast, when n_estimators are too large, it is easy to overfit. The sampling uses the subsample to indicate that the GBDT algorithm does not put back the sample. If the value is less than 1, only a part of the sample will be used for the decision tree fitting of GBDT. The condition in which the tree continues to be divided is represented by min_samples_split, which limits the conditions under which the subtree continues to be partitioned. The maximum depth of the tree is represented by max_depth, which can easily cause overfitting. These four parameters are important for creating a robust GBDT model.  
This article starts with the number of weak learners (n_estimators). The result is shown in Figure 6. When n_estimators = 400, the training accuracy is optimal. The accuracy of the number of iterations drops rapidly after 400. Figure 7 shows that the maximum depth max_depth is not as large as possible, and the minimum division set min_samples_split is not as small as possible. When the maximum depth max_depth is small, the effect decreases as the minimum division set increases. When max_depth = 16, min_samples_split = 150, and the precision is 0.9997. Figure 8 shows that the subsample value 0.25 is optimal. This paper uses the parameters n_estimators = 400, max_depth = 16, min_samples_split = 150, subsample = 0.25 as the optimized model.  
Figure 6: n_estimators and accuracy.
## 4 Results and Discussion  
### Comparisons between MOD05 and In Situ Measurements of Water Vapor Content  
The determination of atmospheric WVC is of vital importance in the retrieval of LST from satellite data. Although the usage of _in situ_ WVC measurements can provide more accurate results than other satellite-based WVC data, the _in situ_ WVC measurements are acquired for many reasons, such as human resources, time and other irresistible things. Therefore, the satellite-based WVC data are more economic and flexible. In this paper, the performance of the WVC from MODIS (MOD05), which are used for the HJ-1B/IRS atmospheric correction, is first assessed with the WVC from the _in situ_ automatic sun-tracking photometer CE318 (referred to CE318-WVC).  
The results from the cloud-free observations (Figure 3a) demonstrate that the values of the RMSE, MRE, and the determinant coefficient R\({}^{2}\) between the MOD05-WVC and CE318-WVC are 0.34 cm, 18%, and 0.93, respectively. Figure 3b clearly shows that the MOD05-WVC is slightly overestimated compared with the _in situ_ CE318-WVC. When the WVC is approximately less than 1.5 cm (which usually occurs in semi-arid area), the WVC differences between the MOD05 product and the CE318  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **MOD05-WVC** & **CE318-WVC** & **MOD05-Based-** & **CE318-Based-** & _In Situ_-LST** \\
**(Month/Day)** & **(cm)** & **(cm)** & **LST (K)** & **LST (K)** & **(K)** \\ \hline
06/19 & 1.86 & 1.48 & 306.4 & 305.56 & 301.44 \\
06/30 & 2.45 & 2.29 & 306.1 & 305.31 & 298.64 \\
07/08 & 2.18 & 1  
data are lower than 0.4 cm, which is acceptable in the LST retrievals. This result makes MOD05-WVC data reasonable for LST estimation in semi-arid area of the Heihe River Basin.  
Among all the CE318 sites, Wuxing Village (Figure 2) provides the relatively complete observations: both CE318 data and _in situ_ LST data are obtained here, and more cloud-free days occur. In this study, Table 2 presents the retrieved LST using the two WVC datasets for a maize site (AMS 5) in Wuxing Village. The largest LST difference between the MOD05-based LST and CE318-based LST appeared on June 19 as 0.84 K (with a WVC difference of 0.38 cm). The large error may be caused by the different atmospheric conditions at Building 7. The RMSE and R\({}^{2}\) between the MOD05-WVC and CE318-WVC in Wuxing Village are 0.12 cm and 0.98, respectively. Meanwhile, the RMSE and R\({}^{2}\) between the MOD05-based LST and _in situ_ LST are 1.27 K and 0.53, respectively, and those between the CE318-based LST and _in situ_ LST are 1.35 K and 0.71, respectively. The results demonstrate that use of the MOD05 product ensures a LST retrieval accuracy that is comparable to that produced when using the CE318 data.