esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems
###### Abstract  
This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.  
uncrewed aerial vehicle; uncrewed ground vehicle; heterogeneous multi-agent system; actuator failures; consensus; Lyapunov stability theory; optimal method +
Footnote †: journal: Applied sciences
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 5 Machine Learning and Deep Learning Models to Predict Diabetes in Children and Adolescents
### Deep Neural Network  
A deep neural network (DNN) is an artificial neural network (ANN) with deep layers. Deep layers means that the network consists of multiple layers: an input layer, a hidden layer, and an output layer. The number of hidden layers is two or more, and they are connected to enable data processing and learning. Based on this, an enhanced DNN diabetes prediction model is developed [104]. The main advantage of the DNN is found in the possibility of it being based on a classification model, which is already in application, and for it to be used to predict the risk [105]. In [106], a case study of DNN leveraging the Pima dataset for diabetes prediction is considered. The supervised DNN for processing, classifying, and analyzing large volumes of data allows for the input of raw data that are specific to the disease. In study [107], the new DL method that utilizes a multilayer perception (MLP) algorithm based on DNN is introduced. The proposed model is built by implementing ten hidden layers and a large number of epochs, taking into account 18 significant features. Various validation metrics were used to ensure the reliability of the results, including cross-validation methods and statistical measures, such as accuracy, F-score, sensitivity, specificity, and the Dice similarity coefficient. The method achieved a very high diabetes prediction accuracy, reaching up to 99.8% [107].  
Finally, in review [108], it is concluded that the potential of recent ML and DL models has demonstrated significant promise in predicting diabetes effectively. However, further validation using larger datasets is essential to confirm their reliability and scalability. A key advantage of these models is their potential integration into mobile applications and diabetes management devices, offering improved health monitoring for patients while reducing the risk of complications. Additionally, these models can help identify critical risk factors, supporting early intervention in healthy individuals to prevent the onset of diabetes. Published studies highlight their ability to enhance accuracy and efficiency in managing blood glucose levels, but refinement and testing remain crucial. As ML techniques advance, they are expected to play a central role in disease prognosis, personalized treatment strategies, and preventative care, transforming the landscape of diabetes management and healthcare.  
Paper [86] provides an overview of patient and healthcare staff perceptions regarding ML-based predictive models. Generally, perceptions were positive, although there is some uncertainty and skepticism about the accuracy of the models, likely stemming from the lack of sufficiently large datasets for system modeling or the insufficient comprehensiveness of factors. For this reason, further research is necessary.
## 3 Consistent Path Congestion Model
### A Demo of the PSI Calculation Process  
A step-by-step color-coded demonstration of how to get the proposed indicator PSI is given in Figure 1. In this demo, there are 21 data records, which is a fragment of the real-life traffic dataset. The parameters used in this demo are shown in Table 2. From Figure 1 it is obvious that the period when PSI \(I_{\Delta t_{n}}=1\) (the red zone) starts as soon as the \(S_{W,+}\) is 1 (the yellow zone), and ends when the \(S_{W,-}\) first turns to 1 (the green zone).  
Figure 1: A demo of the calculation process of each of the indicators proposed, on a sample of 21 data records obtained from an experiment with real-life Abilene Backbone network traffic data.
## 3 Results
### Plant Traits Influencing VIs and Their Linkage to GPP  
Global sensitivity analyses with PROSAIL were used to investigate the importance of ecosystem properties on spectral reflectance (Figure 5a) and Vegetation Indices (Figure 5b). Red reflectance is strongly driven by LAI and chlorophyll concentration (C\({}_{\text{ab}}\)), while NIR reflectance is influenced by LMA and to a lesser extent LAI. NDVI is particularly sensitive to LAI and C\({}_{\text{ab}}\) (driving 65 and 23% of overall NDVI variation, respectively), and mirroring the importance of these properties in driving red reflectance. NDVI is largely insensitive to LMA, apparently because the calculation of NDVI under typical conditions deemphasizes changes in NIR reflectance. EVI and NIR\({}_{\text{v}}\) were sensitive to LAI and LMA, with both properties showing a similar importance. These two VIs showed increased sensitivity to NIR reflectance relative to NDVI, a pattern that has been discussed previously [6; 12]. Other parameters, including sun and sensor view angles and leaf angle, have a smaller effect on these VIs. The VIs predicted using PROSAIL driven with site-specific data captured the observed variation of MODIS VI at the study sites (Supplementary Material 4).  
We further explored how LAI, C\({}_{\text{ab}}\), and LMA and their interactions control the value of VIs (Figure 6). NDVI showed the sharpest saturation to LAI, reaching 80% of maximum LAI response at \(\sim\)1.7 m\({}^{2}\) m\({}^{-2}\). NDVI also showed a sharp saturation to C\({}_{\text{ab}}\), reaching 80% of maximum response at a C\({}_{\text{ab}}\) of \(\sim\)17 \(\mu\)g cm\({}^{-2}\), which is within the lower range of values observed at the sites (Supplementary Material 5). NDVI's sharp saturation at low C\({}_{\text{ab}}\) and its low sensitivity to LMA prevents NDVI from fully capturing an optical signal of a leaf trait that is related to A\({}_{\text{leaf}}\) (Figure 6a,b).  
EVI and NIR\({}_{\text{v}}\) had a comparatively less sharp saturation to LAI than NDVI, with its saturation to LAI varying with LMA up to 3 m\({}^{2}\) m\({}^{-2}\). Furthermore, the value of EVI and NIR\({}_{\text{v}}\) saturated modestly with increasing LMA across the range of values observed at the sites. This implies that decreasing LMA from the lowest value (similar to that of the Pinyon-juniper woodland) to the highest value (similar to that of the grassland), causes a relatively large increase in EVI and NIR\({}_{\text{v}}\). EVI and NIR\({}_{\text{v}}\)'s balanced sensitivity to LAI and LMA, low saturation to the signal of a leaf trait related to A\({}_{\text{leaf}}\), and multiplicative sensitivity to LMA and LAI, allowed both VIs to largely mimic the response of GPP to LAI and A\({}_{\text{leaf}}\) (Figures 3b and 6c,d).  
Figure 5: Global sensitivity analysis of PROSAIL radiative transfer model for top-of canopy reflectance (**a**) from 400–1000 nm and (**b**) for red and Near InfraRed Vegetation (NIR) bands and vegetation indices. The spectral ranges used for the broad band reflectance and vegetation indices is shown on (a) for reference, and match those used by MODIS sensors.
## 4 GRAPH4 Architecture
### Workflow  
To have a more in-depth view of how GRAPH4 works, we can summarize the workflow in six main steps that are shown in Figure 3. The first four steps are executed at the startup of a network:  
1. The AG Generator is fed with information about the topology of the network (hosts, active services, connections, etc.), which is used to generate the AG that identifies the vulnerable paths of the network.  
Figure 2: How the interaction between the control plane and the data plane works in GRAPH4. The hosts in red are the ones deemed vulnerable by the AG: the control plane instruments the switches to gather metrics on the switches close to those hosts.  
2. The control plane, with that knowledge, can identify the vulnerable hosts that need to be monitored.
3. The control plane identifies the switches that are involved in forwarding traffic to or from said hosts and prepares the rules to be installed.
4. The Match Action Tables (MATs), which implement the low-level detection of attacks, are installed in the relevant switches. Then, two more steps are repeated in a loop at network runtime:
5. The data plane carries out network monitoring by inspecting the traffic and, if the metrics indicate that an attack is detected, it notifies the control plane.
6. When the control plane is notified of an attack, it can deploy countermeasures, e.g., by installing additional MATs on the switches.  
This process could be generalized to leverage the use of any metrics capable of providing indicators on attacks of different kinds. In fact, GRAPH4 is a control-data plane collaboration scheme revolving around two core features: (i) vulnerability assessment via AG and (ii) minimal but reliable distributed in-line attack detection. This idea shows an example of how the integration of model-based and measure-based metrics allows for the optimization and distribution of resource allocation--and ensuing energy usage--across the network, which is pivotal for in-line detection of attacks.
## 3 Materials and Methods
### Applications and Performance
#### 3.2.2 Application in Complex Behavior  
Ethoflow was also applied to learn the detection of trophallaxis, the complex social behavior of food exchange among nestmates, in _M. quadrifasciata._ Thus, 1270 labeled images were generated (724 for non-trophallaxis and 546 for trophallaxis) (Figure 6). In this dataset, 70% of the data was used for training, while 20% was used for validation. Another sample dataset (10%) was used to assess the classifier's performance based on the global accuracy from the confusion matrix, Kappa index, and Z-test (5%).  
Figure 5: Schematic representation of the heuristic used to generate labeled images for the IS model automatically. The segmented objects (indicated by green masks) on a homogeneous background (**A**) are glued to random backgrounds (**B**).  
Figure 6: Examples of images that were automatically labeled with our heuristic to train the CNN model to recognize trophallaxis in the stingless bee _Melipona quadrifasciata._ The images with green outlines (**top**) are examples of trophallaxis. The images with dashed purple outlines (**bottom**) are examples of non-trophallaxis.  
The labeled images used to train the CNN model for recognizing trophallaxis were also generated through a heuristic automatically. When bees perform trophallaxis, they position themselves in front of each other and exchange food. Based on this predictable positioning, the heuristic was based on the individuals' area and body length. Initially, the program estimates the median (\(M\)) and standard deviation (\(sd\)) of the body area (\(a\)) and length (\(l\)) in frames where there is no crossing (no meeting between individuals). Subsequently, the software obtains the images (\(b\)) from the video and labels them as trophallaxis _if_:  
\[\left\{\begin{array}{l}area(b)\geqslant 2*(M(a)-sd(a))\text{ and }\\ area(b)\leqslant 2*(M(a)+sd(a))\text{ and }\\ length(b)\geqslant 2*(M(l)-sd(l))\text{ and }\\ length(b)\leqslant 2*(M(l)+sd(l))\end{array}\right\},\textit{Else}:b\text{ is not trophallaxis}. \tag{27}\]
###### Abstract  
The gradual increase in the maturity of sensor electronics has resulted in the increasing demand for wireless sensor networks for many industrial applications. One of the industrial platforms for efficient usage and deployment of sensor networks is smart grids. The critical network traffic in smart grids includes both delay-sensitive and delay-tolerant data for real-time and non-real-time usage. To facilitate these traffic requirements, the asynchronous working-sleeping cycle of sensor nodes can be used as an opportunity to create a node connection. Efficient use of wireless sensor network in smart grids depends on various parameters like working-sleeping cycle, energy consumption, network lifetime, routing protocol, and delay constraints. In this paper, we propose an energy-efficient multi-disjoint path opportunistic node connection routing protocol (abbreviated as EMOR) for sensor nodes deployed in neighborhood area network. EMOR utilizes residual energy, availability of sensor node's buffer size, working-sleeping cycle of the sensor node and link quality factor to calculate optimum path connectivity after opportunistic connection random graph and spanning tree formation. The multi-disjoint path selection in EMOR based on service differentiation of real-time and non-real-time traffic leads to an improvement in packet delivery rate, network lifetime, end-end delay and total energy consumption.  
energy-efficient wireless sensor networks; multipath opportunistic node connection; smart grids; neighborhood area network; asynchronous working-sleeping cycle strategy; energy-efficient routing protocol; opportunistic connection random graph; service differentiation in smart grids +
Footnote †: journal:  
0000-0001-9991-4500][PERSON]. [PERSON]  
1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 12 Satellite Control Facility (SCF-L) directorate, SE wing, Space and Upper Atmosphere Research Commission, Lahore 54000, Pakistan 2
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 2 Background and Related Work
### Feature Engineering Techniques in Apache Spark MLlib  
Emerging research shows that feature engineering approaches widely used in Apache Spark MLlib enhance the efficacy of various fields. For example, a study on heart disease prediction proposed the Principal Component Heart Failure (PCHF) approach which improved the detection rate by concentrating on the most important characteristics [50]. In the domain of cybersecurity, for instance, MLlib provided within Apache Spark was used for feature extraction using K-Means Clustering for IDS in detecting the abnormality of data and demonstrating the capability of the algorithm in the security domain [51]. Furthermore, diabetes prediction used Spark MLlib to evaluate the performance of different machine learning algorithms and demonstrated that the Random Forest algorithm yields high accuracy, underlining the penetration of the library in analytics of health data.  
Further, the feature engineering capacity of Spark MLlib has been used to process large-scale text data with a considerable increase in run time compared to SparkSQL using the RDD API [52]. Such improvements also highlight Spark MLlib's Effective Feature Engineering tools and show how big data with applications in healthcare, cybersecurity, and all sectors can be analyzed.
# Routing Protocols for Delay Tolerant Networks:
## 1 Introduction
### The Need for a Quantitative Evaluation
The selection of the 10 protocols is based on the techniques employed: we attempted to selected as many different techniques as possible in order to establish a broader understanding of the implications of each technique on the the delivery ratio, the delay of message delivery and the overhead imposed. We strongly believe that studying such a broad range of techniques and protocols allows one to get a well-rounded understanding of the challenges, weaknesses and benefits of existing DTN routing protocols, while at the same time highlighting different approaches that can be explored by future research. We note that in the past, several attempts have been made to quantitatively evaluate the performance of DTN protocols; however, very few studies incorporated a large and diverse set of protocols, with the most notable one being [28]. The rest of the article is structured as follows. In the following section, we present the high-level architecture and basic functionality of the main components of our reference design. The section also includes the implementation aspects of 16 main-stream DTN protocols in relation to the proposed reference architecture. In Section 3, we present the quantitative experimental evaluation conducted on 10 representative DTN protocols. The section provides a detailed description of the benchmarking scenario, both in terms of synthetic traces and traces acquired from real-world experiments. An extensive discussion of the results acquired is included along with a comparative study of the performance of the protocols. In Section 4, we provide technical recommendations in terms of the mechanisms introduced in the literature and how they can be applied in practical scenarios. Finally, in Section 5, we summarize our findings and provide pointers for future research.
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 1 Introduction
We propose a deterministic sharding protocol, _cross-reserve_, to address the above scalability issues by exploiting the characteristics of the ordering-free execute-validate (EV) blockchain architecture. We also implement a blockchain prototype called NeuChain+, which leverages deterministic transaction execution to process cross-shard transactions. Since the execution result is deterministic after batching transactions into a block, there is no need to use a commit protocol such as 2 PC to determine whether to commit or abort, eliminating the need for multiple rounds of cross-shard coordination. Moreover, remote reads accessing other shards cannot be processed locally when executing cross-shard transactions because each shard only has a partial of the ledger. Although NeuChain+ eliminates the costly cross-shard coordination, the execution of cross-shard transactions cannot start until the cross-shard read sets transmission is complete, causing synchronization waits. To this end, NeuChain+ pipelines the read sets transmission. Transaction execution and read sets transmission can proceed concurrently, further reducing latency. The main contributions of this paper are as follows:  
Figure 1: NeuChain’s scalability and storage pressure experimental results. (**a**) NeuChain LAN scalability experimental results. (**b**) Single-node storage size during NeuChain running for 60 s.  
* We propose a cross-shard transaction processing protocol, _cross-reserve_, which utilizes deterministic concurrency control to avoid costly commit protocols. * We pipeline and parallelize the read sets transmission during transaction execution to further reduce cross-shard commit latency. * We propose the sharding--EV architecture, which enhances horizontal scalability while achieving high throughput by eliminating explicit ordering. * We implement a sharding blockchain prototype, NeuChain+, based on our team's previous work, NeuChain. Experimental results show that the throughput of NeuChain+ is approximately 2.9\(\times\) that of NeuChain and outperforms other state-of-the-art blockchain systems with 1.7-75.3\(\times\) throughput under the SmallBank workload.
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 3 Results
### Cycling Scenario  
Since the RSSI of all received packets is averaged for each power level of each frequency band, this metric has a similar performance in this dynamic scenario as in the static ones. The PRR curves have also the expected tendency, as shown in Figure 9.  
In dynamic scenarios, the most interesting result is the RSSI variation with time. Given that the sender node on the subject's ankle moves in a quasi-uniform circular motion, it gets closer and farther from the receiver node in a periodic manner, which is reflected in the RSSI metric. The RSSI variation with time in the cycling scenario for several of the available transmit power levels of the 433 MHz band can be seen in Figure 10.  
The periodicity is very clear in these graphs, where the shape of each complete pedal turn is very clearly depicted for all power levels. Not only could this periodicity be exploited for TPC or other energy efficient algorithms, but the very shape of the curves could be used by the WBAN to intelligently recognize the human activity taking place. The periodic peaks of the curve maintain their shape with every power level for the 433 MHz band, differing only in the RSSI values around which they are centered.  
Figure 8: PRR in the sitting scenario.  
Figure 9: Performance metrics in the cycling scenario: (**a**) Average RSSI, (**b**) PRR.  
The RSSI variation with time for the 868 MHz and 2.4 GHz band and a transmit power level of 0 dBm is presented in Figure 11. Particularly for the 2.4 GHz band, its lower RSSI and PRR values are reflected on the periodic shape, where the pedaling motion is not as easily recognizable. The distinctive peak shape with two contiguous maxima that is present in both sub-1 GHz frequencies, is not visible for this band. However, the maxima and minima corresponding to each pedal turn are easily observable, and the periodicity of the curve is still clear and can be exploited.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 4 Discussion
### Data Acquisition and Fusion  
Site 1 presented multiple challenges for UAS-surveying and fusion. These challenges included the size of the site, limited access to surveyed reference points, and a large vertical shift caused by an ellipsoid/datum difference between the UAS (i.e., Phantom 4 Pro) and USGS NED DEM. Additionally, the DTM generated with SfM in WebODM was not a true DTM because it still contained remnants of tree canopies and buildings, which needed to be removed in preparation for fusion. However, even with these less than ideal circumstances, the fused DEM provided a more accurate representation of the topography than the outdated NED DEM as confirmed by comparison with a limited number of available surveyed points. While GCPs are helpful for validation, these results are in agreement with other studies indicating that they are not essential for accurate topographic modeling [45].  
However, the fused DEMs accuracy is lower than the accuracy required for the source data of the USGS NED DEM (RMSE \(<\) 0.25 m) [63; 64]. Regardless, our rapid low-cost approach still had a lower RMSE than the USGS NED DEM (3.36 m), indicating that the fused DEM successfully captured the changes identified by the priority queue. The large RMSE of the USGS NED DEM computed using the surveyed stormwater inlets located within the newly constructed landscape reflects significant topographic modifications caused by the site grading. The areas with large edge elevation differences between the UAS DEM and USGS NED DEM are the most likely to contain distorted elevations from the fusion smoothing process. In future work parameter optimization of the fusion module will help minimize morphological distortions.