esa-sceva/satcom-chunk-collection huggingface dataset:
# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
## 3 Results
### Soil Variables Versus CUE and WUE
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **GPP Algorithm** & **References** \\ \hline \multirow{3}{*}{MODIS 250 m} & Light use efficiency model using MODIS 250 m & \multirow{3}{*}{[40]} \\  & derived leaf area index and the fraction of & \\  & photosynthetically active radiation & \\ \hline \multirow{3}{*}{Landsat 30 m} & Light use efficiency model using Landsat 30 m & \multirow{3}{*}{[40]} \\  & derived leaf area index and the fraction of & \\  & photosynthetically active radiation & \\ \hline \multirow{3}{*}{GOSIF GPP} & OCO-2 SIF vs. GPP universal relationships & [66; 67] \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 4: Summary of the GPP algorithm for the remote sensing data set used in this study. Figure 8: Pruned regression tree for (**A**) CUE\({}_{\text{SIF}}\) predicted as a function of three significant variables. The number in boxes 1–4 represents the mean CUE\({}_{\text{SIF}}\) of the node, \(n\) value represents the terminal node size, soil variables names under the boxes represent the splitting criterion, and the numbers in boxes 5–9 represent the mean estimated CUE. Note that the position of “yes” and “no” at each split represents the decision split at each node. (**B**) Boxplots showing the distribution of CUE in each terminal branch in the regression tree. The model has r\({}^{2}\) of 0.45 and MAE of 0.1.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 7 Algorithms for Synthesizing the Optimal Data Fusion Configuration  
The proposed approach in this article consists of two phases:  
1. Forming a model of a geographical area with a set of data sources attached.
2. Calculating the effectiveness, cost, and timing properties of the model.  
In the previous sections, forming a model is exemplified as a set of manual actions carried out by the analyst. Although manual steps may be feasible in small scales, it may become intractable if the model becomes too large. This article proposes two algorithms for automated synthesis: _Best-fit_ and _Optimal-fit_[41].
## 2 GWO's Contribution in Reducing Active Power Losses in the Generation Stage
#### GWO and Reduction in Active Energy Loss by Determining the Best Allocation of DGs
With less time and better accuracy, a distribution network's active power loss may be reduced via the best allocation of distributed generating units (DGs). The novel approach has the advantages of being quicker to implement and more precise. This is a non-linear constrained optimization problem whose goal is to reduce power consumption while considering a variety of limitations. The one-by-one search method (OBOSA) is suggested in this work to find the DG sites that would result in the least power loss achievable. To examine and validate the efficacy of the suggested technology, an IEEE 33 and 69 radial distribution test bus system was employed. OBOSA's performance is assessed when compared to GWO and ALO-based techniques [40; 41]. An alternative strategy to solve this non-convex, discrete issue is to use the hybrid grey wolf optimizer, a unique metaheuristic tool. There is a considerable decrease in power loss when this technique is used with IEEE 33-, IEEE 69-, and Indian 85-bus radial distribution systems. Based on this information, power loss and the voltage profile of the buses are shown to have decreased dramatically throughout the network. Distribution systems with DG and capacitor banks (CBs) might benefit from using a multi-objective optimization approach to better allocate their capacity (RDS). Due to this, the advantages of CB and DG injections in the RDS are highly reliant on both the appropriate amount and volume of these drugs, along with the selection of an appropriate injection site. As a realistic alternative, renewable energy has received a lot of attention in recent years because of its numerous benefits. It has been a few years since the GWO and the cuckoo search, two new hybrid metaheuristic techniques, were created. Searching for the best possible solution is the goal of the HGWOCS algorithm used to solve DGs. The loss sensitivity factor may be used to restrict the search area (LSF). The active power losses will serve as the study's goal function, as specified. The IEEE 69-bus is used to demonstrate this concept when the demand is minimal [42]. Figure 2 shows the basic topology of the simple power distribution network with a distributed generation (DG) source. Specifically, a relay (R) is used to protect the main feeder, whereas fuses are used to protect the laterals that provide electricity to the loads. Most of the time, relays have mechanisms that can tell if they are receiving too much inverse time overcurrent in most cases.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 4 Mathematical Analysis
### Throughput Calculation  
In this MAC protocol, we consider the data traffic is symmetric. Thus, the uplink data length \(L_{UP}\) and downlink data length (\(L_{DW}\)) are the same size during the data transmission. Let, \(L_{UP}=L_{DW}=L\). To calculate the throughput, we use the following formula:  
\[Throughput=\frac{Amount\ of\ Data}{Transmission\ Time}\]Presently, the throughput is calculated for BFD, TNFD and HD communications, using the following equations,  
\[Th_{BFD} =\frac{L_{\text{{L}IP}}+L_{\text{{D}W}}}{T_{BFD}}=\frac{2L}{T_{BFD}} \tag{6}\] \[Th_{S-TNFD} =\frac{L_{\text{{L}IP}}+L_{\text{{D}W}}}{T_{S-TNFD}}=\frac{2L}{T_{S-TNFD}}\] (7) \[Th_{D-TNFD} =\frac{L_{\text{{L}IP}}+L_{\text{{D}W}}}{T_{D-TNFD}}=\frac{2L}{T_{D- TNFD}}\] (8) \[Th_{HD} =\frac{L_{\text{{L}IP}}\,\text{or}\,\,L_{\text{{D}W}}}{T_{HD}}= \frac{L}{T_{HD}} \tag{9}\]  
where,  
\[T_{BFD}=T_{RTS}+T_{CTS-AI}+3T_{S}+T_{D}+T_{ACK},\]  
\[T_{S-TNFD}=T_{RTS}+T_{CTS-AI}+T_{NF}+T_{RT}+3T_{S}+T_{D}+T_{ACK},\]  
\[T_{D-TNFD}=T_{RTS}+T_{CTS-SRA}+T_{CTS}+4T_{S}+T_{D}+T_{ACK},\]  
\[T_{HD}=T_{RTS}+T_{CTS-AI}+3T_{S}+T_{D}+T_{ACK}\]
## 4 Classification Based on Taxonomy and Design Drivers
### Data Aggregation  
In [38], the authors propose four application profiles where two are aggregation profiles called _ConsAggr_ and _ProdAggr_. Data aggregation is proposed using replication nodes proposed in QAR, see Section 3.7. In _ConsAggr_ the profile consumer queries dominate production queries. In this profile, events produced in a given area are aggregated using the replication node for that area. In contrast,with the _ProdAggr_ profile the production traffic dominates consumption traffic. Replicas aggregate events received from producers to achieve an effective overall traffic reduction. In Resilient Data Centric Storage (RDCS) [36], three types of event query called _List_, _Summary_ and _Attribute_-based are proposed for the DCS scheme. A query request for all stored data for a particular event type is referred to as a _List_ while a query request for aggregated or summarized data for a particular event type is called a _Summary_ query. On the other hand, an _Attribute_-based query requests data for all events that match certain constraints based on attribute values. However, it is not mentioned how the monitor node will aggregate data for an event type to give a response to the summary query. TinyDB [9], maximise [39] and TAG [40] are some of the aggregation mechanisms proposed mainly for WSN databases, which can be adapted to DCS networks as well.
###### Abstract  
This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN's superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.  
Keywords:expansive soils; sand content; machine learning innovation; predictive modeling; soil property optimization; artificial neural networks +
Footnote †: journal: _Sciencedence_
## 3 Methods
### Entity Classification Module
#### 3.2.2 Lexical Feature Enhanced Entity Classification  
The span embedding representation utilized in the entity extraction stage of the proposed model is composed of the raw span embedding, a sentence embedding, a width embedding, coarse-grained POS embeddings, and fine-grained POS embeddings. Coarse-grained and fine-grained POS embeddings are used to enhance and highlight the part-of-speech features in the span. The enhanced span feature is shown in Figure 3. For example, for a span \"Hakawati Theatre\", the POS of span \"NNP\" is obtained using NLTP toolbox, an embedding layer \(E_{c}\) is used to convert the POS of span into coarse-grained POS embedding \(e^{c}\). Then, the POS tags of words in spans are obtained, \"NNP\" for \"Hakawati\" and \"NNP\" for \"Theatre\". The POS embedding of each POS of word token \(e^{t}\) is converted by embedding layer \(E_{f}\). Next, the fine-grained POS embedding is obtained from \(e^{t}\) using Equation (5). The enhanced embedding of span is composed of \(e^{f}\), \(e^{c}\), the raw span embedding, a sentence embedding and a width embedding.  
In entity classification, the width of the span was embedded by Equation (8):  
\[\mathrm{e_{i}^{m}}=\mathrm{E_{m}}(\mathrm{m_{i}}),\mathrm{e_{i}^{m}}\in\mathbb{R} ^{\mathrm{N_{m}}} \tag{8}\]  
\(\mathrm{e_{i}^{m}}\) is the width embedding. \(\mathrm{E_{m}}\) is the embedding function for width. \(\mathrm{m_{i}}\) is the width of span \(\mathrm{s_{i}}\), which is the number of words in the span \(\mathrm{s_{i}}\). \(\mathrm{N_{m}}\) is the dimension of width embedding which is a hyperparameter. All the embeddings of a span were concatenated and form the span vector \(\mathrm{a_{i}^{s}}\) for classification by Equation (9):  
\[\mathrm{a_{i}^{s}}=\mathrm{concat}\Big{(}\mathrm{e_{i}^{s}},\mathrm{e_{i}^{m}},\mathrm{C},\mathrm{e_{i}^{c}},\mathrm{e_{i}^{f}}\Big{)},\mathrm{a_{i}^{s}}\in \mathbb{R}^{\mathrm{N_{s}}} \tag{9}\]  
\(\mathrm{a_{i}^{s}}\) is the span embedding enhanced by the lexical feature used for named entity recognition, which contains the raw span embedding \(\mathrm{e_{i}^{s}}\), the width embedding \(\mathrm{e_{i}^{m}}\), the sentence embedding \(\mathrm{C}\), and two POS embeddings (\(\mathrm{e_{i}^{c}}\) and \(\mathrm{e_{i}^{f}}\)). \(\mathrm{N_{s}}=\mathrm{N_{d}}*2+\mathrm{N_{m}}+\mathrm{N_{c}}+\mathrm{N_{f}}\).  
Finally, a fully connected layer with softmax function is used to calculate the probability of entity for a span using Equation (10):  
\[\hat{y}_{1}^{e}=\mathrm{softmax}(\mathrm{W}\mathrm{a_{i}^{s}}+\mathrm{b}) \tag{10}\]  
\(\hat{y}_{1}^{e}\) is the probability of predicting to be an entity for span \(\mathrm{s_{i}}\). W and b are the trainable weight and bias in the fully connected layer.  
After the entity classification, spans that were predicted not to be entities were filtered and did not enter the following relation classification.
## 4 Design of Proposed Scheme: FAPR
### Reactive Part
#### 4.3.5 Backup Path Allocation Module  
The backup path allocation module is the final step in the reactive part of the FAPR scheme for link failure recovery, and is responsible for allocating backup paths and installing the corresponding flow table entries into the switches for flows affected by link failures. Firstly, we need to obtain the network performance metrics of backup paths. For example, we can utilize the data collection module to obtain the information of each link, in order to calculate the remaining bandwidth and delay. Secondly, we can allocate the most appropriate backup path for each flow according to its application type and the network performance metrics of alternative backup paths. Thirdly, we need to compare the original primary path and the chosen backup path for each flow to find out whether two paths compose a forwarding loop. If so, the backup path must be optimized to remove the circle to avoid broadcast storm. The corresponding algorithm will be explained in Section 4.5.3. Finally, the flow table entries are installed into the switches, and then packets can forward along the backup path to the destination that satisfies the QoS required by the flow.
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 4 ReBLOM-Based Crowdsourcing for EVs Routing Problem with Recharging Station
### Potential Threats
The data packets transmitted by the channel are all encrypted, and excluding the corresponding key, cannot be read. Due to the characteristics of the blockchain-distributed ledger, information/data cannot be forged or tampered with unless it reaches 51% of the system's computing power. Likewise, a decentralized network structure can minimize this kind of attack. Malicious Trust Center attack [30]: Malicious nodes act as relay nodes and are also responsible for forwarding information/packs, but in the wrong direction. That is, malicious nodes masquerade as vehicle relay nodes that maintain a high reputation value and honest historical behavior. Similar to a black hole attack, the masquerading relay node sends a packet to a malicious node, where it is then dropped. In the traditional centralized reputation mechanism, the reputation values of vehicle miners are stored in a centralized trust center. Therefore, a malicious trust center may obtain benefits by tampering with or forging the reputation value of vehicle participants. In our proposed system, transactions are stored on the blockchain, and not on a trusted center. This ensures that the reputation value will not be tampered with by malicious nodes, and solves the potential threat brought about by malicious trust centers. Moreover, the calculation of local reputation and comprehensive reputation depends on subjective and objective reputation evaluation and cross-chain historical reputation. The information recorded in the previous block is difficult to forge or tamper with through disguise or attack.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 3 Results and Discussion
### Influence of the Satellite PIFCB  
In this section, static experiments using single GPS and BDS data with the TF-C, TF-F, and TF-CF models to carry out a comparison with the dual-frequency IF combination PPP model (DF) of the first and second frequency are described. The main validations are the reliability of the models, the influence of the GPS satellite PIFCB and the influence of the BDS-2 PIFCB in BDS.
## 4 Conclusions  
A series of NOx removal experiments were carried out to study the influence of KCL solution as the ground electrode in a dielectric barrier non-thermal plasma reactor. The results indicate that the use of KCL solution as the ground electrode of the DBD reactor provides a promising strategy to improve the NOx removal efficiency. The conclusions are as follows.  
1. The discharge stability and uniformity of the liquid ground electrode are much better than that of the metal mesh electrode.
2. The NOx removal efficiency varies with the power supply frequency. Under the conditions defined in this experiment, the removal effect is the best when the power frequency is 8 kHz and the maximum NOx removal efficiency reaches 85.82%.
3. The NO/NOx removal efficiency increases with the rise of solution conductivity of the ground electrode. Additionally, when the conductivity exceeds 10.00 ms/cm, the influence of conductivity on NOx removal effect is significantly reduced.
4. When the solution temperature is 30\(-\)60 \({}^{\circ}\)C, the NOx removal efficiency increases with the reaction gas temperature, and this phenomenon is particularly significant when the KCL solution concentration is lower than 0.1 mol/L. A 100% NO removal efficiency is achieved at the solution concentration of 0.1 mol/L regardless of the solution temperature.
5. In the N\({}_{2}\), NO, and NO\({}_{2}\) system, the second positive band spectrum of N\({}_{2}\) occupies the dominant position. After O\({}_{2}\) is added and the power supply voltage is increased to 150 V, the NO spectrum disappears and the NO removal efficiency reaches 100%.  
Figure 9: Schematic diagram of emission spectrum distribution: (**a**) Spectrum of N\({}_{2}\); (**b**) Spectrum of N\({}_{2}\)/NO/NO\({}_{2}\); (**c**) Spectrum of N\({}_{2}\)/O\({}_{2}\)/NO/NO\({}_{2}\), and (**d**) Spectrum of N\({}_{2}\)/O\({}_{2}\)/NO/NO\({}_{2}\) at different voltages.  
**Author Contributions:** Conceptualization, Z.W. and J.Z.; Data curation, Y.G. and W.Z.; Formal analysis, X.X. and W.Z.; Investigation, Y.G. and W.Z.; Resources, Z.W., J.Z. and Y.J.; Software, C.C.; Writing--original draft, X.X. and Y.G.; Writing--review and editing, Z.W. and Y.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by China Postdoctoral Science Foundation (2021M690496), The Fundamental Research Funds for the Central Universities of China (3132019331), National Natural Science Foundation of China (51876019), and National Key R&D Program of China (SQ2019 YFE011597), Natural Science Foundation of Liaoning Province(2020-HYLH-36).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Comparing with Existing Tools for the Kgly Site Prediction  
The prediction performance of PTG-PLM is compared with the existing tools for Kgly site prediction on the Kgly independent dataset which is obtained from PredGly and is BERT-Kgly. We compare PTG-PLM with four existing tools for Kgly site prediction including BERT-Kgly, NetGlycate, Gly-PseAAC, and BPB- GlySite on the same Kgly independent dataset. These tools are also described briefly in Section 1.2. On the Kgly independent dataset, PTG-PLM based on embedding extracted from ProtBert-BFD PLM has the best performance as was shown in Table 8. So, we compare the performance of PTG-PLM based on ProtBert-BFD embedding against the four Kgly existing tools in terms of accuracy, recall, precision, F1, AUC, and MCC as shown in Table 10 and Figure 8. It is shown that PTG-PLM outperforms the existing tools for Kgly site prediction with a 0.64 accuracy, 0.67 recall, 0.64 precision, 0.65 F1, and 0.28 MCC. Regarding the AUC metric, the BERT-Kgly is better than our model.  
Figure 7: Chart for the performance of PTG-PLM and the existing tools for Ngly site prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**MCC**} \\ \cline{1-1} \cline{5-7}
**Tool** & & & & & & & \\ \hline GPP [11] & 0.7333 & 1 & 0.434 & 0.605 & 0.8324 & 0.5371 \\ GlycoPP [12] & 0.6578 & 0.891 & 0.363 & 0.516 & 0.7445 & 0.3945 \\ GlycoEP [7] & 0.7956 & 0.957 & 0.5 & 0.657 & 0.8554 & 0.5874 \\ GlycoMine [13] & 0.4489 & 0.4565 & 0.175 & 0.253 & 0.4517 & \(-0.0781\) \\ GlycoMine,PU[10] & 0.9378 & 1 & 0.767 & 0.868 & 0.9609 & 0.8407 \\ SPRINT-Gly [15] & 0.9422 & 1 & 0.78 & 0.877 & 0.9637 & 0.8503 \\ PUStackNGly [18] & 0.9511 & 1 & 0.807 & 0.893 & 0.9693 & 0.8703 \\ PTG-PLM & **0.965** & **1** & **0.852** & **0.920** & **0.978** & **0.902** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of performance of PTG-PLM against the existing tool for Ngly site prediction.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 5 Single Scanner-Based DoA Estimation
### Space-Time Data Processing and Fusion Method
The measurement data from two locations (i.e., the locations \(\mathrm{P}_{1}\) and \(\mathrm{P}_{2}\) as shown in Figure 3) are collected in sequence at different times. Therefore, to combine the data samples from two measurement locations, the timestamp data are removed from the dataset after denoising over time; that is, an individual data sample is transformed from \(\{t_{n},\,\hat{P}_{r}[n],\,\hat{\theta}[n]\}\) to \(\{\hat{P}_{r}[n],\,\hat{\theta}[n]\}\). Parameter \(n\) is then treated as a general indexing parameter instead of the discrete time index. With the removal of timestamps, the denoised measurement data samples \(\{\hat{P}_{r}[n],\,\hat{\theta}[n]\}\) can now be viewed as the RSS measurements parameterized by the heading direction angle, i.e., \(\hat{P}_{r}(\theta)\), for \(\theta=\hat{\theta}[0],\,\hat{\theta}[1],\,\ldots,\,\hat{\theta}[N_{t}-1]\). In the spatial data binding process, the denoised data samples \(\hat{P}_{r}(\theta)\) from two locations are first simply combined into one single dataset. Then, the heading direction angles between \(0^{\circ}\) and \(360^{\circ}\) are segmented into a total of \(N_{\theta}\) consecutive angle bins of an equal angular width \(\delta_{\theta}\). The data within each angle bin are combined using the median operation, i.e.,  
\[\bar{P}_{r}(\theta_{k})=\mathrm{med}\{\hat{P}_{r}(\theta)\mid\theta\in[\theta_ {k},\,\theta_{k+1})\}, \tag{6}\]  
for \(0\leq k<N_{\theta}\), where \(\theta_{k}\) and \(\theta_{k+1}\) are the boundaries of the \(k\)th heading direction angle bin. Similar to the temporal data binding, here we use the median instead of the average to combine multiple data samples within each angle bin for noise reduction because it is well known that the median is statistically more robust to outliers than the average. After spatial data binding, the RSS profile over heading direction angle \(\hat{P}_{r}(\theta_{k})\), \(0\leq k<N_{\theta}\), is further processed for smoothing using a moving average filter with a window size of \(2L+1\), i.e.,  
Figure 3.0  
for \(0\leq k<N_{\theta}\), where \(l^{\prime}=\mathrm{mod}(l,\,N_{\theta})\), and the mod is the modulo operator. The modulo operator is required here since the heading direction angle is defined in the range between 0 and \(360^{\circ}\) covering a complete circle. Finally, the RSS profile over heading direction, \(\bar{P}_{r}(\theta)\), is used to estimate the DOA of the BLE beacon signals, i.e.,  
Figure 3.1