esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Climatic Factors at the Global Scale  
The global analysis of monthly climatology relationships reveals significant \(\text{SIF}_{\text{P}}\) correlations with VPD, Tmin, and SM over approximately 60, 59, and 35 percent of the global domain, respectively (Figure 5). In many areas, \(\text{SIF}_{\text{P}}\) was significantly correlated with more than one climate variable. In most northern temperate, boreal, and Arctic ecosystems, Tmin had a dominant influence on productivity consistent with a shorter growing season and colder mean annual temperatures at higher latitudes. VPD had a secondary and positive influence on \(\text{SIF}_{\text{P}}\) in these areas due to the large seasonal temperature cycle at higher latitudes and close relationship between temperature and VPD. VPD and Tmin had primary and secondary influences on \(\text{SIF}_{\text{P}}\) in wet tropical areas, with a general tendency toward greater VPD control in wetter tropical areas of Southeast Asia relative to the African Congo and Amazonia, which have relatively longer dry cycles [46]. The larger VPD influence on \(\text{SIF}_{\text{P}}\) in wet tropical areas is consistent with the direct role of the atmospheric moisture deficit in driving water movement through the soil-plant-atmosphere system via evapotranspiration, and by consequence canopy-atmosphere \(\text{CO}_{2}\) exchange, when water supply is non-limiting. However, the correlations were relatively weak due to the characteristic smaller seasonal climate cycle in the tropics.  
Soil moisture had a dominant influence on \(\text{SIF}_{\text{P}}\) over arid climate zones and other areas with marked seasonal dry cycles, including the southwest US, east Africa, southern Eurasia, the Indian subcontinent, and central Australia. In Australia, soil moisture was the dominant control on \(\text{SIF}_{\text{P}}\). In Africa, soil moisture was a dominant control on productivity over 15.7% more area than VPD. The global patterns in relative influence of the different climate factors on \(\text{SIF}_{\text{P}}\) are also consistent with the tower site (Figure 2) and aggregated regional summaries (Figure 3). These results confirm the importance of soil moisture related water supply controls on productivity in addition to atmospheric moisture demand constraints represented by VPD, particularly in semi-arid and arid climate zones extending over more than 30 percent of the global domain.
## 2 The Proposed LPC-NN Method
### Proposed LPC-NN Method  
In this section, we present the LPC-NN algorithm proposed in this paper for classifying vibration signals from bearings.  
At the first stage, LPC coefficients are extracted from the signals using the LPC algorithm described in Section 2.1. These coefficients are features that are then fed to the input of the neural network. To increase the sample size of the training dataset, the signal is divided into short overlapping time frames (see Section 3.1). The set of 50 LPC coefficients calculated for each frame provides the training dataset for the classifying NN.  
At the second stage, the 50 features are used as input for an NN with one hidden layer. Since the feature extraction process is simple, the NN has a simple structure. The input layer size is \(50\times 1\), which is consistent with the dimension of the features extracted. This is followed by a layer of 50 neurons using the activation function \"tanh\", and then a SoftMax layer calculates the probability of each of the possible outcomes. The number of neurons in the output layer is equal to the number of classes in the problem being considered. For the CWRU dataset, the output layer has 10 neurons, while for the SUSU dataset, it has 5 neurons.  
Conventional NN techniques (categorical cross-entropy function, Adam optimizer, Keras EarlyStopping, and ModelCheckpoint callbacks) are applied [44]. When training the NN, we set aside 10% of the data for validation and monitor the accuracy on the validation set, and if there is no improvement within 50 epochs, training is stopped. Next, the model with the best accuracy on the validation set is selected. Figure 11 shows the full scheme of the method proposed.  
Figure 10: t-SNE visualization for the CWRU (**left**) and SUSU (**right**) data after feature extraction.  
For each dataset, feature extraction is used to generate 50 LPC coefficients, which are then supplied to the NN. This generates the probabilities that the original signal belongs to each of the 10 classes. In Section 4, we apply this algorithm to the CWRU and the SUSU datasets.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 2 Literature Review
### RPL Version Number Attack
This mechanism detects and monitors the operations of the node in the RPL network based on the monitored nodes (regular nodes) and the monitoring nodes, in which detection operations are performed. The evaluation results showed that the mechanism had a satisfying performance. The literature review clarified that the RPL security has been widely studied due to the huge number of threats in the IoT. Researchers [31; 32; 33] developed many solutions for RPL rank attacks and version number attacks. These attacks need to be handled because of the trade-off between providing a safeguard against them and maintaining an efficient performance of the network in the IoT environment. The studies are effective in detecting these attacks, but they suffer from many flaws that have to be addressed. Based on the analysis in [12], we can observe that there are several parameters that can have an impact on the network performance and detection accuracy, such as (1) the RPL network topology type; (2) the number of nodes; and (3) the malicious node locations. Therefore, to support these observations, we propose a SRPL-RP to detect, mitigate, and isolate these attacks in the RPL network. In addition, this system supports different types of attacks with multiple types of RPL network topologies.
# Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation
## 4 Experiment and Analysis
### Parameter Setting  
According to Section 3, there are three parameters, i.e., \(\alpha\), \(\beta\) and \(\gamma\), that need to be determined in the objective function of the proposed SSRR-AGLP. The best values of these parameters are tuned by searching the grid {0.0001, 0.001, 0.01, 0.1, 1, 10, and 100} in an alternate manner. We first fix parameter \(\gamma\) as 0.01, and the influences of the parameters \(\alpha\) and \(\beta\) on different datasets are illustrated in Figure 3. From these results, the tendencies of \(\alpha\) and \(\beta\) on the accuracy rates increase first and then decrease for all the databases, which indicates that locality constraint term and label propagation can improve the performance of SSRR-AGLP. Moreover, when the values of parameters are set between 1 and 10, the performance of our SSRR-AGLP is insensitive to the parameter values. Then, we fix parameters \(\alpha\) and \(\beta\) with best values, and the results of our algorithm under various values of parameter \(\gamma\) on the different databases are listed in Table 3. From this table, we can find the proposed SSRR-AGLP reaches the best performance when the parameter \(\gamma\) is set as 0.01 for all the databases. Moreover, when the parameters are set between 0.001 and 0.1, the performance of our SSRR-AGLP is insensitive to the parameter value of \(\gamma\). Finally, we can find the best parameter values for our model are {\(\alpha\) = 1, \(\beta\) = 10 and \(\gamma\) = 0.01} on Yale database, {\(\alpha\) = 1, \(\beta\) = 1 and \(\gamma\) = 0.01} on ORL database, {\(\alpha\) = 10, \(\beta\) = 1 and \(\gamma\) = 0.01} on Extended YaleB database, {\(\alpha\) = 1, \(\beta\) = 1 and \(\gamma\) = 0.01} on AR database and {\(\alpha\) = 10, \(\beta\) = 1 and \(\gamma\) = 0.01} on CMU PIE database. Therefore, according to the experimental results, we can set the parameters \(\alpha\) and \(\beta\) as relatively larger values (1 and 10), and set the parameter \(\gamma\) as a small value (from 0.001 to 0.1) for the application tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Training Sample Size**} & \multirow{2}{*}{**Test Sample Size**} \\ \cline{2-3} \cline{2-3}  & **Labeled Samples** & & **Unlabeled Samples** \\ \hline Yale & 3 & 3 & 5 \\ ORL & 3 & 2 & 5 \\ Extended YaleB & 18 & 14 & 32 \\ AR & 4 & 3 & 7 \\ CMU PIE & 6 & 6 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of sample selection for different databases.  
Figure 3: The influence of the parameters on different databases.
## 3 Results
### Spatial Representation of Data Distribution  
The approaches of both Bivariate LISA (i.e., BiLISA) and [PERSON]'s \(I\) were employed to conceive any potential correspondence in the variation patterns from the spatially distributed farms in terms of cultivated areas against the spatial distribution of the total number of palm trees, as well as the spatial distribution of the selected date cultivars. The BiLISA test was applied on the obtained parameters, while the [PERSON]'s \(I\) test was used to examine just two parameters (i.e., farms' cultivated areas and total number of palm trees), in which the vertical axis indicates the neighboring values of farms' cultivated areas, and the horizontal axis indicates the total number of palm trees/farm, given as farms' average values. The BiLISA clustering map presented in Figure 6a discloses the distinguished significance, particularly between the oasis' total cultivated area and the total date palm trees. The farms in the northern part of the oasis, however, showed noticeable spatial correspondence (Figure 6b), specifically between the high number of palm trees and the bigger cultivated areas, with a high significance rate (\(p\)-value 0.001). In addition, the obtained bivariate [PERSON]'s \(I\) was assessed to be 0.16 with 999 permutations and 0.001 pseudo \(p\)-value (Figure 6c). These results verify the strong relationships between the farms' cultivated area and the number of trees per farm, which is consistent with the previously obtained outcomes of this investigation.  
In addition, the BiLISA clustering maps between the oasis cultivated area and the selected date cultivars show a significant correlation between the spatial distribution of cultivated areas and the number of sole cultivar trees (Figure 7). Thus, a noticeable number of farms of the Khlas cultivar in the northern subregion showed a relatively high agreement between the high number of cultivar trees with the bigger assigned cultivated areas (Figure 7a). The same status applies for the Shishi cultivar in the northern subregion (high-high class); yet the most significant correspondence observed between the low trees number and the smaller cultivated areas for the studied cultivars (i.e., Khlas, Ruziz, and Shishi) was in the eastern subregion of the study area (low-low class). This implies the plausible connections between the cultivated areas with the number of trees planted in these areas in the oasis.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multicolumn{3}{c}{**Categories**} \\ \cline{2-4}  & **Khlas** & **Ruziz** & **Shishi** \\ \hline
**Interval by Interval** & **Pearson’s R** & 0.810 & 0.453 & 0.424 \\ \hline
**Ordinal by Ordinal** & **Spearman Correlation** & 0.806 & 0.388 & 0.397 \\ \hline
**Approximate Significance** & 0.000 a & 0.000 a & 0.000 a \\ \hline
**N of Valid Cases** & 288 & 288 & 288 \\ \hline \hline \end{tabular} a. Based on normal approximation.  
\end{table}
Table 4: Pearson’s R (Interval by Interval) and Spearman (Ordinal by Ordinal) tests for cultivated area versus cultivars’ area correlations.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Random Forest Classifier
#### 4.2.3 Ndvi-Rf  
The RF using NDVI data was first developed with the 17 PC values obtained from the application of the PCA on the NDVI temporal series. The accuracy obtained when validating the model is reported in Table 5. Then, the analysis of the variables' importance allows us to fix a threshold value of 4% for the increase in mean square error. This threshold reduced the number of variables from 17 PC values to seven. The important PC variables obtained includes the first five, the eighth and 11 th PC dimensions. The regeneration of the RF classifier using the seven important PC dimensions of NDVI produced approximately similar accuracy to that using all the PC variables. The overall accuracy decreased by only 1% while the kappa and F-score decreased by 2% and 3% respectively.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Architectures
#### 2.1.2 Metaverse  
The Metaverse comprises seven tiers that form the value chain of its marketplace, which can be broadly classified into two groups, as shown in Figure 4. The first group, comprising layers 1 to 6, concentrates on its foundational infrastructure, while the second group, consisting solely of layer 7, is dedicated to social activities within the Metaverse. Each layer will be expounded upon in greater detail in the following.  
Figure 3: Layer architecture of Mixed Reality adapted from [5].  
**Layer 1: Infrastructure**  
This particular layer comprises the requisite technological infrastructure that enables the seamless operation of 5G, WiFi, Cloud, and other layers.  
**Layer 2: Human Interface**  
The second layer focuses on the hardware utilized to facilitate spatial computing, encompassing a range of devices such as mobile phones, smartwatches, smart glasses, and wearables. This integration of technology provides swift and accurate data collection concerning our environment.  
**Layer 3: Decentralization**  
The decentralization of the Metaverse is facilitated through the implementation of edge computing, AI agents, microservices, and blockchain, which allow user participation without the need for permission. This approach allows for a more democratic and inclusive environment that empowers users to engage with the Metaverse on their terms. With this technology, individuals have greater control over their experiences and can contribute to the development of the Metaverse in a more meaningful way. These decentralized technologies represent a significant step towards creating a more equitable and accessible virtual world.  
**Layer 4: Spatial Computing**  
Spatial computing combines digital and augmented realities, allowing us to digitize objects, enable sensors to detect moving objects, and map the physical world in 3D engines, VR, AR, and multitasking UI.  
**Layer 5: Creator Economy**  
This particular layer of the Metaverse is dedicated to the financial sector, where designers and creators utilize design tools and asset markets to establish the economy of Web 3.0. This sector is currently experiencing exponential growth and is poised to play a significant role in shaping the future of online commerce.  
**Layer 6: Discovery**  
Within the Metaverse ecosystem, two distinct discovery systems exist: inbound and outbound. Inbound discovery is characterized by individuals intentionally seeking out information, while outbound discovery involves proactively pushing messages to individuals, regardless of their request.  
**Layer 7: Experience**  
This layer defines user experience in the Metaverse, which includes video games, social interaction, shopping, sports, and many others.  
Figure 4: Layer architecture of the Metaverse adapted from [7].
# Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan
## 3 Results
### 3-Month and 12-Month SPEI Time Series  
Having identified six spatially contiguous zones showing similar behavior of drought, SPEI time series were developed for each zone. Figures 5-10 display the 3-month and 12-month SPEI time series for individual zones for the period of 1902-2015. The 3-month SPEI provides us with a seasonal picture of drought, reflecting short duration meteorological droughs, whereas the 12-month SPEI sheds light on long-duration, persistent hydrological droughs in the region. The blue bars represent the positive values of SPEI, whereas, the negative values of SPEI is shown by the red bars. The least-squares regression line has also been plotted, and it is worth noting that it has a negative slope for all the zones. This suggests that the average SPEI has decreased over the study period. The distinction between wet and dry periods is much more distinct in the 12-month SPEI time series as compared with the 3-month time series.
## 6 Conclusions  
An agricultural drought monitoring model was constructed based on the Euclidean distance method with the effects of multiple drought-causing factors in Tibet. The effects of soil moisture on agricultural drought monitoring models were included when generating the TVMPDI. Then, this model was constructed to monitor, analyze, evaluate and verify drought levels in Tibet, and the following main conclusions were obtained:  
1. The relation coefficients between the TVMPDI and SPEI were all above 0.5, and the correlation between the TVMPDI and other remote sensing drought indices performed well. In addition, the TVMPDI monitored the drought conditions in summer and autumn from May to July 2009 and 2010, which were basically consistent with the actual drought conditions, and reflect better the drought distribution and drought conditions in Tibet.
2. The TVPDI based on atmospheric indices performs perfectly in delineating meteorological drought, but failed to recognize water availability in soil systems, which is critical for crop growth and agricultural drought delineation. The TVMPDI with soil moisture information was more suitable for agricultural drought monitoring than the TVPDI.
3. In the 16-year span from 2003 to 2018, the southeastern region of Tibet experienced a gradual wetting process. The gradual humidification in summer was not significant.
4. Various drought indices divided the study into arid and semi-arid areas in the west and humid areas in the east. In the western arid and semi-arid regions, soil wetting caused by precipitation promoted vegetation growth, while in the eastern humid regions, precipitation inhibited vegetation growth.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), [PERSON] and [PERSON]; formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), [PERSON] and [PERSON]; visualization, [PERSON] ([PERSON]); supervision, [PERSON]; project administration, [PERSON] ([PERSON]) and [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Key R & D project of Sichuan Science and Technology Department (Grant No. 2021 YFQ0042), The Science and Technology Project of Xiang Autonomous Region (Grant No. XZ201901-GA-07), Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20030302), National Key R&D Program of China (2020 YFD1100701).  
Not applicable.  
The authors declare no conflict of interest.
# Combinatorial Olfactory Signaling in Short-Distance Determines Host Plant Recognition in Locust
###### Abstract  
Selecting palatable plants matters for insect herbivores' survival, especially for food-restricted oligophagous and monophagous species. However, the definite selection strategy to distinguish host plants from nonhost plants, as well as the underlying sensory basis, remains controversial. Here, we investigated the olfactory recognition of host plants in oligophagous migratory locusts. By establishing one novel behavioral paradigm that allowed the free-moving locusts to make olfactory choices in short-distance, we demonstrated that palps were required to differentiate host plants apart from nonhost counterparts sensitively. Specifically, the characteristic odors between the host plant and nonhost plant defined the behavioral differentiation of food sources, and this process required intact palps. Further, single nonhost odor suppressed the behavioral potency to host plant extraction, while single host odor attenuated the behavioral repulsion to nonhost plant. We also identified the palps odorant receptors (ORs) repertoire that modulated the short-range recognition of key volatiles from host plants and nonhost and demonstrated that combinatorial olfactory signaling controls food choice. Our results support a \"pull-push\" model in which olfactory signaling on locust palps acts as a key tuning modulator in host plant recognition, expanding the knowledge of insect chemosensation.  
host plant selection; locust; olfaction; palps; odorant receptor; oligophagous
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Study of FL-LoRaMAC
#### 6.2.4 Performance with Pruning  
While performing model pruning, a sparsification parameter specifies the percentage of the model parameters that are pruned. The performance of the model was evaluated for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comm.** & **Training Time** & **Recall** & **Precision** & **Accuracy** \\
**Rounds** & **(s)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
2 & 11,229.9 & 86.13 & 93.9 & 92 \\ \hline
3 & 16,794.85 & 99.27 & 95.77 & 97.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training time and model performance for different numbers of communication rounds.  
varying sparsification percentages and plotted in Figure 13. These results were obtained by training the model 100 times and the plots show the average and standard deviation for the performance metrics.  
It is observed that the model performance degrades as the parameter matrix gets more and more sparse. Sparsity is a hyper-parameter and it can be observed that, for the representative application, the model performance is not substantially affected for  
Figure 13: Performance with various levels of sparsification: (**a**) recall, (**b**) precision, and (**c**) accuracy with the varying sparsification percentages.  
up to 50% sparsity. Hence, if 50% of the model parameters are pruned, the model still performs well. If the framework chooses not to send those pruned parameters, this will result in at least 50% savings in bandwidth with minimal compromise to the performance of the system.