esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### General Characteristics of Wetland Publications
#### 3.1.3 Journal and Conference Analysis  
The eligible publications in our systematic review appeared in 88 different journals and conferences, illustrating the diversity of disciplines interested in wetland studies. Among the selected papers, 307 and 37 were journal and conference papers, respectively (see Figure 6).  
Among peer-reviewed publishing journals, 51 have published one or two papers on the topic. The majority of reviewed sources come from 8 peer-reviewed journals (with a share of 52%); see Figure 7a. Only journals with at least three publications are included in this figure. As shown, the highest number (top five) of publications associated with wetland studies occurs in the _Remote Sensing (MDPI)_, _Remote Sensing of Environment (RSE)_, _International Journal of Applied Earth Observation and Geoinformation_, _IEEE Journal Selected Topics in Applied Earth Observation and Remote Sensing (IEEE-JSTARS)_, and _Canadian Journal of Remote Sensing (CJRS)_ journals.  
Figure 5: Word cloud shows the most frequent terms in wetland studies. The larger the text, the more frequently the word appeared in considered papers.  
Figure 6: The number of journal and conference publications included in the meta-analysis.  
As shown in Figure 7b, the order of the publisher centers, whose journals account for the majority of the published papers is as follows: _MDPI_, followed by _Elsevier_, and _IEEE_ (with an overall share of 70%). Moreover, 14% of the journal papers were published by _Taylor & Francis_, 9% by _Springer_, 5% by _SPIE_, and only 2% by _WILEY_. Among the conference proceedings, the _IEEE International Geoscience and Remote Sensing Symposium (IGARSS)_, with 63% of papers (\(n=23\) out of 37), is the first ranked conference for publishing papers with the focus on wetland studies.
## 3 Results  
The study was successfully completed by all participants without any adverse events. The temporal changes in the MCP, PIP, and DIP joints of the index finger during active MCP joint flexion and rPMS are shown in Figure 2. In the active movements, the PIP and DIP joints flexed slightly with MCP joint flexion (Figure 2a). Shortly after the initiation of rPMS, the MCP joint abducted and flexed, and the flexion angle increased as the abduction angle of the MCP joint decreased. The PIP and DIP joints extended during rPMS (Figure 2b).  
The maximum joint angles for each participant are shown in Table 1. They were normally distributed. The maximum active flexion of the MCP joint was 83 \(\pm\) 4\({}^{\circ}\), with the PIP joint extending by 6 \(\pm\) 14\({}^{\circ}\) and the DIP joint extending by 6 \(\pm\) 10\({}^{\circ}\). The maximum active abduction of the MCP joint was 16 \(\pm\) 6\({}^{\circ}\). Using rPMS, the maximum flexion of the MCP, PIP, and DIP joints, as well as the maximum abduction of the MCP joint, were 82 \(\pm\) 10\({}^{\circ}\), \(-\)12 \(\pm\) 5\({}^{\circ}\), \(-\)8 \(\pm\) 7\({}^{\circ}\), and 15 \(\pm\) 6\({}^{\circ}\), respectively. The maximum flexion and abduction of the MCP joint were not significantly different between active motion and rPMS. However, the PIP and DIP joints extended significantly more during rPMS (Figure 3). The median NRS for pain during rPMS was 2 (range: 0-4).  
Figure 2: Temporal changes in the MCP, PIP, and DIP joint angles of the index finger during active MCP joint flexion and rPMS. The solid lines represent the average changes in joint angles of the index finger, while the vertical lines represent the SD. (**a**) The PIP and DIP joints were slightly flexed with MCP joint flexion in active MCP joint flexion. (**b**) The MCP joint abducted and flexed shortly after the onset of rPMS, and the flexion angle increased as the abduction angle of the MCP joint decreased. The PIP and DIP joints extended.
# Supply Chain Data Analytics for Digital Twins:
## 4 Case Studies
### Validation  
The efficiency and the capabilities of the Digital Twin Analytics Framework were validated through its application and deployment to the industrial environments of the Greek and Spanish pilot factories. These deployments demonstrated how the framework addresses the limitations of the existing Digital Twin infrastructure by enabling interoperability, extensibility, and providing advanced data analytics and intelligence.  
The framework is not designed to replace, but to expand the functionalities of the already installed Digital Twins in an industry. Thus, the primary validation is to expand the existing Digital Twins and present the new services and capabilities that are enabled.  
Figure 5: Comparison results in the main dashboard after simulations using external services.  
More specifically, the framework's successful integration provided the following:  
* Enhanced data management capabilities: Beyond the basic data functionalities, the framework enabled more sophisticated data extraction, data transformation and secure data storage and data exchange.
* Intelligence: The framework integrates Machine Learning (ML) algorithms into industrial processes. This includes capabilities, such as real-time anomaly detection, data forecasting of operational parameters and future measurement's values and recommender systems.
* Insights and decision-making support: The framework provides user interfaces for factory engineers and people in charge. These interfaces visualize real-time operational data by using interactive charts and dashboards, create post-analysis reports for every tracked component that Digital Twins contain, and receive alerts when action is needed. Additionally, forms that utilize external services are provided to enable users to simulate and evaluate new parameters and take better data-driven decisions.  
The validation of the framework, therefore, is its proven ability to act as an intelligent layer. It transforms simple monitoring to advanced analytics and insights dashboards and interfaces. The successful deployment in both Greek and Spanish pilot environments serves as a proof-of-concept for the framework and its capabilities.  
Figure 6: Dashboard of the data generated from the recovered magnets in the Spanish pilot. As seen in the dashboard, manual data entry by operators can produce inconsistencies in the data.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 4 Analytical Model and Performance Analysis
### Virtual Collision Handling in EDCA
\tag{11}\]  
where \(R_{d}\), \(H_{MAC}\), \(H_{PHY}\) and \(\delta\) denote the transmission data rate on the CCH, the header length of MAC-layer, the header length of physical-layer and the propagation delay, respectively. Because there are two kinds of packets (safety-related packet with broadcast mode and WSA packet with unicast mode on the CCH), \(T_{vuln}\) can be calculated by [38]:  
\[T_{vuln}=2\cdot\max(T_{e},T_{s}) \tag{12}\]  
Therefore, the external collision probability \(P_{oc}\) is calculated by  
\[ \begin{array}{ll}P_{oc}=1-\sum_{k=0}^{\infty}{(1-\eta_{total})^{k}\frac{{( N_{cs}-1)}^{k}}{k!}e^{-(N_{cs}-1)}}\\ \hskip 14.226378 pt\times\left[\sum_{k=0}^{\infty}{(1-\eta_{total})^{k}\frac{N _{\text{int}}^{k}}{k!}e^{-N_{\text{int}}}}\right]^{T_{vuln}/T_{virt}}\\ =1-e^{-(N_{cs}-1)\eta_{total}}e^{-N_{\text{int}}\eta_{total}T_{vuln}/T_{virt}} \end{array}  \tag{13}\]  
where \(T_{virt}\) denotes the average duration of a virtual time slot [40], and the expression is given in Equation (37).
## 6 Results and Discussion
### Accuracy Analysis  
The accuracy of the developed HC+SU-CMO scheme in contrast with conventional approaches such as ALO, AO, BOA, CMBO, SSA, NN, RNN, Bi-GRU, SVM, and KNN for 60, 70, and 80 learning rates is demonstrated in Tables 2 and 3 for dataset 1 and dataset 2 respectively. HC+SU-CMO shows the highest accuracy for learning rate 70 in dataset 1 and dataset 2. The results have summarized that the adopted hybrid classifier + SU-CMO performance is enhanced over the existing models. The phase of feature extraction makes a significant contribution to improving the accuracy rate. A loss of accuracy is possible if features aren't extracted. Furthermore, hyperparameter tuning plays an essential part in ensuring the validity of the model. Tuning is a method of improving a model's performance by selecting appropriate parameters without overfitting or producing an excessive amount of variation in the data.
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Quadratic Discriminant Analysis (QDA) Classifier  
The QDA classifier [85] defines discriminant function(s) that separate(s) classes that are supposed to feature Gaussian (normal) probability density distributions characterized by mean values and covariance matrices. The discriminant function(s) determines points within the feature space where probability density estimates for each class are equal (i.e., the classification likelihood is 50%/50%). The Gaussian parameters for each class are derived from a training dataset using the maximum likelihood (ML) method, which is convenient for small, under-represented classes. A simplified version of the QDA called the Linear Discriminant Analysis (LDA) assumes that the covariance matrices for each class are equal, which results in linear discriminant functions. Nevertheless, both classifiers require that the number of samples is larger than the number of features. The advantages of QDA and LDA are related to their simplicity and accurate classification in the case of classes that feature Gaussian probability distributions. If this assumption is not met, then the classification results might be poor.
# FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
## 1 Introduction  
Machine reading comprehension (MRC) is a sub-field of natural language understanding (NLU) that aims to teach machines to read and understand human languages (text). A user can ask the machine to answer questions based on a given paragraph or text document. Generally, MRC requires modeling complex interactions between the context and the query in a specific domain. It could be used in many NLP applications such as dialogue systems and search engines as shown in Figure 1--a Google search engine with MRC techniques can directly return the correct answers to questions rather than a list of content and web pages. These kinds of techniques have been based on hand-crafted rules that need substantial human effort and resources. However, with the rise of artificial intelligence, there has been an explosion of various MRC benchmark datasets and models that contribute to a better understanding of the task and show their ability to exceed human performance. Despite this rapid progress on MRC datasets and models, most of the existing work has focused on algorithms for improving model performance.  
At present, several MRC models have already surpassed human performance on many of the MRC datasets [1], but there is still a limit in terms of data availability due to privacy concerns, collaborative training, resource consumption, and communication overhead due to data transfer. Hence, there is a need for extending existing MRC models in a way that keeps data private on its generated location and allows several participants to train a machine learning model by sharing only model parameters. This will let cross-silo(companies) or cross-device (phones, IoT devices) participate in the training process and let the model learn from large distributed datasets by sharing knowledge between different local models. To address these gaps, we proposed a privacy-aware approach based on federated learning to learn new global models in a geographically distributed manner using our FEDn framework [2], build more challenging MRC models by integrating with private data generation and labeling for local accurate training as well as an incremental learning approach to strengthening the model performances during collaborative training without compromising the data.  
The remainder of this paper is organized as follows. Section 2 surveys related work. Section 3 details the proposed approach and architecture of FedQAS, with an emphasis on its privacy and scalability properties. In Section 4, we demonstrate the frameworks potential in an evaluation based on the SQuAD dataset. Finally, Section 4 concludes the work and outlines future work.
## 2 Materials and Methods
### E-Nose Analysis  
#### 2.2.1 The Schematic of the Electronic Nose Architecture  
As indicated in Figure 1a, the electronic nose is an apparatus analogous to the biological olfactory system, primarily consisting of three components: the gas sensor array, signal preprocessing, and mode identification. The gas sensor array functions similarly to the olfactory sensory cells in the biological olfactory system, while the signal preprocessing component corresponds to the olfactory receptor cells in the organism, and the mode identification serves a role akin to that of the biological cerebral processes.  
During the procedure, upon receiving the gas information from the monitored sample, the gas sensor array converted this information into electronic signals, facilitating the efficient identification and classification of the samples by simulating the mechanisms of the human olfactory system. Following the reaction of various gases with the gas sensor materials, a specific feature-corresponding spectrum was generated, enabling qualitative analysis by these spectra to achieve accurate identification of the samples being measured.
# An Internet of Things-Based Low-Power Integrated Beekeeping Safety and Conditions Monitoring System
## 4 Proposed IBSMC System Experimentation
### Scenario I(b) LoRaWAN Protocols Experimentation  
In this scenario, the IBSMC system LoRaWAN BSDP and BEP protocols are examined, in terms of energy consumption with respect to 4G data transmissions and close to real-time data uplink capabilities. According to ETSI EU 868 ISM band regulation, the node transmission time is expressed with the unit of duty cycle and it is limited to 1% per node. The TTN Network Fair Access policy is even more strict, offering to node clients through their server a maximum 0.0347% duty cycle per node (average of 30 s of daily data transmission per node). The author's experimentation was initiated by trying to identify the relation between SF values and gateway distance from the central hive. The results of this experimentation are presented in Figure 5a, where the SF value of 7 is concentrated in LOS  
Figure 4: IBSMC BSDP protocol experimentation. (**a**) Mean RTT values over beehive distance from the central beehive. (**b**) Total number of RTR frames and lost frames over a number of 4000 received frames.  
communication distances of 1-2 Km, the SF = 8 in 3 Km, SF = 9 in 4-5 Km, SF = 10 in 6-7 Km, SF = 11 in 8-10 Km and above 10 Km SF = 12. Since no mobility of the central hive occurs, and LOS is guaranteed, coverage distances and BSDP frames time on air remain the same, as shown in Figure 5a.  
The ETSI 1% duty cycle limitation enforces a limit on the hourly number of LoRaWAN frame transmissions in respect to SF, as presented in Figure 5b. More specifically, for SF = 11 and SF = 12, the boundary is set to one frame transmission per hour. Such a limitation is bonding for close to real-time events captured by the BEP protocol and may force the IBSMC system to transmit more frames than allowed (especially for SF > 9).  
Focusing on energy consumption, a cross comparison between existing beekeeping monitoring systems that utilize 3G/4G communication technologies, IBSMC LoRaWAN low-energy transceivers are offering 2.5 times more useful back-to-back payload data transmissions (without protocol overhead)  
Figure 5: (**a**) Beehive central cell LoRaWAN BSDP and BEP frames time on air-over-gateway distance and number of transmitted framed. (**b**) Beehive central cell LoRaWAN BSDP and BEP max number of frames transmitted per hour in order to comply with the ISM band 1% duty cycle constraint.  
on predefined battery energy reserve, and up 10-15 times less connection setup time, as shown in Table 2. This makes the LoRaWAN capability of the IBSMC system capable to achieve a very low energy footprint with up to 5-6 times less energy consumption.
## 6 Conclusions  
This study highlights the potential of FBs-derived volatile compounds (VCs) as effective biostimulants for enhancing plant growth using non-contact exposure methods. Our findings demonstrate that 15 g of shitiake FBs VCs had a greater effect on promoting growth and biomass accumulation in rice seedlings compared to 15 g of enokitake FBs VCs. This is evidenced by a 67.4% (\(p<0.05\)) increase in the dry weight of rice seedlings exposed to shitiake FBs VCs, while only a 39.5% (\(p>0.05\)) increase was recorded in rice seedlings exposed to enokitake FBs VCs, compared to the control. Additionally, shitiake-FBs VCs promoted a 37.5% increase in plant height, whereas enokitake FBs VCs resulted in only an 18.5% increase, confirming their significant biostimulatory effects. Furthermore, the optimal doses of shitiake mushroom FBs for most pronounced growth promotion were observed at 30 g and 60 g, whereas lower doses (15 g) had minimal effects. Our results also showed that waste shitiake FBs promoted moderate but significant growth in rice seedlings, confirming that even post-harvest FBs retain bioactive volatile emissions. This validates the potential for repurposing waste mushroom FBs into agricultural biostimulants for plant growth. Experiments with synthetic volatiles revealed dose-dependent effects in 1-octen-3-one; low concentrations (1 uL) of 1-octen-3-one enhanced biomass, while higher concentrations of 1-octen-3-one suppressed growth. Interestingly, rice seedlings exposed to natural FBs VCs exhibited greater biomass accumulation and shoot elongation compared to those treated with individual synthetic volatiles. Overall, these findings underscore that natural FBs outperform synthetic individual volatiles in promoting plant growth, emphasizing the importance of whole volatile-emitting systems in sustainable agriculture. We conclude therefore that mushroom FBs show potential as a scalable, cost-effective, and environmentally friendly biostimulant technology that can be applied without direct contact, potentially reducing reliance on synthetic agricultural inputs.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/mps8020029/s1](https://www.mdpi.com/article/10.3390/mps8020029/s1)]([https://www.mdpi.com/article/10.3390/mps8020029/s1](https://www.mdpi.com/article/10.3390/mps8020029/s1)), Figure S1: Experimental container setup for non-contact exposure of rice seedlings to volatile compounds (VCs) emitted from mushroom FBs; Figure S2: Container setup for dose-response testing of synthetic individual volatile compounds (VCs) identified in waste shitiake FBs; Table S1: GC-TOF-MS conditions for analysis of VCs derived from waste shitiake FBs.
## 4 Energy Management Systems
### Optimization-Based Energy Management Systems
#### 4.2.3 Model Predictive Control  
The operational environment of FCHEVs is marked by its intrinsic complexity and continual variability. In response to these dynamic operating conditions, the EMS must possess the capability to swiftly adapt, ensuring sustained optimality, real-time responsiveness, and inherent adaptability. A critical aspect of achieving these objectives is the analysis and anticipation of driving condition data, which serves as a fundamental prerequisite for optimizing fuel efficiency and enhancing the longevity of the power system. Consequently, a significant portion of the scholarly community has turned to model predictive control (MPC) as a strategic approach for energy management within FCHEVs [112]. As elucidated in Figure 21, MPC excels in its capacity to optimize forthcoming control processes through a combination of rolling optimization and feedback adaptation, ultimately culminating in optimal control within the succeeding time intervals. The prediction technologies utilized encompass stochastic models grounded in Markov processes and machine learning techniques predicated on neural networks.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 5 Control Design
### Right Factorization  
Right factorization [16] of the model of the control target described above is performed based on operator theory. Define the heat input including interference in the heat spreader as \(z_{a_{n}}\) and the heat input of the tube as \(z_{w_{k}}\). The derived model is subjected to right factorization based on operator theory. First, from Equations (12)-(16), \(\omega_{a_{n}}\), \(\omega_{w_{k}}\) are shown.  
\[\omega_{a_{n}}(t)=\frac{z_{a_{n}}(t)}{m_{a}c_{a}}\qquad\qquad\qquad\qquad \omega_{w_{k}(t)}=\frac{z_{w_{k}}(t)}{m_{w}c_{w}} \tag{18}\]  
Defining \(z_{a_{n}}\) and \(z_{w_{k}}\) in Equation (18), Part A\({}_{n}\) and Part W\({}_{k}\) can be shown as in Equations (19) and (20).  
\[\text{Part}\,\text{A}_{n}:\dot{y}_{a_{n}}(t)=\frac{z_{a_{n}}(t)}{m_{a}c_{a}}+ \sum_{m=1}^{4}(-1)^{m}A_{a_{nm}}y_{a_{n}}^{m}(t) \tag{19}\]  
\[\text{Part}\,\text{W}_{k}:\dot{y}_{k}(t)=\frac{z_{w_{k}}(t)}{m_{w}c_{w}}-A_{w_ {k}}y_{k}(t) \tag{20}\]  
Here, \(y_{A_{n}}\) is the temperature of the heat spreader Part A\({}_{n}\). Also, \(y_{k}\) is the temperature of the tube Part W\({}_{k}\). \(y_{A_{n}}\) and \(y_{k}\) can be observed from the installed temperature sensors.  
Equations (21)-(23) are shown for the heat input \(z_{a_{n}}\) and \(z_{\omega_{n}}\) including the interference of each plant.  
\[z_{a_{1}}(t)=2u_{d_{1}}(t)+\frac{\lambda_{a}S_{3}}{dx}y_{a_{2}}(t )+\alpha_{w}S_{6}y_{1}(t) z_{w_{1}}=\alpha_{w}S_{6}y_{a_{1}}(t)+\frac{\lambda_{w}S _{5}}{dx}y_{2}(t) \tag{21}\] \[z_{a_{2}}(t)=\frac{\lambda_{a}S_{3}}{dx}y_{a_{1}}(t)+\frac{ \lambda_{a}S_{3}}{dx}y_{a_{2}}(t)+\alpha_{w}S_{6}y_{2}(t) z_{w_{2}}(t)=\alpha_{w}S_{6}y_{a_{2}}(t)+\frac{\lambda_{w}S _{5}}{dx}(y_{1}(t)+y_{2}(t))\] (22) \[z_{a_{3}}(t)=2u_{d_{3}}(t)+\frac{\lambda_{a}S_{3}}{dx}y_{a_{2}}(t )+\alpha_{w}S_{6}y_{3}(t) z_{w_{3}}(t)=\alpha_{w}S_{6}y_{a_{3}}(t)+\frac{\lambda_{w}S _{5}}{dx}y_{2}(t) \tag{23}\]  
Based on operator theory, the plant under control is right factorized into invertible operator \(D_{n}^{-1}\) and stable operator \(\tilde{N}_{a_{n}}\). The heat spreader Part A\({}_{n}\) is expressed as the following operator.  
\[D_{a_{n}}:\{z_{a_{n}}(t)=m_{a}c_{a}\omega_{a_{n}}(t) \tag{24}\] \[N_{a_{n}}:\left\{\begin{array}{l}\dot{x}_{a_{n}}(t)=\omega_{a _{n}}(t)+\sum_{m=1}^{4}(-1)^{m}A_{a_{nm}}y_{a_{n}}^{m}(t)\\ y_{a_{n}}(t)=x_{a_{n}}(t)\end{array}\right. \tag{25}\]