esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 4 Monitoring Results and Discussion
### Impedance Trend According to Seasonal Change  
Figure 12a shows the impedance data of the zelkova trees measured from September to November and photos of the target trees on specific dates. Figure 12b shows an image of a zelkova tree taken on 24 September, and it was observed that some of the tree's leaves were starting to change color, as shown in the partially enlarged photo. When the color of the leaf begins to change, it means that the exchange of substances with the leaf stops and stem resistance increases. In the impedance graph in Figure 12a, a change in the impedance trend is also visible, as shown at point AA. As summer gradually turns into fall, the leaves of the trees change from green to brown, as shown in Figure 12c on 11 October. Figure 12d shows a photo of a zelkova tree taken on November 5, showing that all of its leaves have fallen. This can also be interpreted in relation to the sharp increase in the impedance levels in early November, at the time indicated by BB in Figure 12a. Therefore, overall, the change in the electrical impedance of the trees can be seen to be linked to changes in the leaves on the trees depending on the season.
## 3 Materials and Methods
### Proposed Lumpiness Classification  
The lumpiness factor provides a measure of the relative variability between the stochastic distributions of demand transactions and the intervals between transactions. Three statistical aspects of the historical demand data are  
* Intervals between the demands;
* Demand size;
* Relationship between intervals and sizes.  
The proposed lumpiness classification is based on the coefficient of variation (CV) concept, which is a useful approach when variability between point estimates is compared [44; 45].
# Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport
###### Abstract
In this work, an energy-efficient framework adapted to a wearable artificial intelligence-of-things (AIoT) embedded system for arrhythmia detection during training sessions is proposed. The integration of an EH circuit with a Dynamic Power Management Strategy (DPMS) for a battery extension operation is developed. The DPMS is programmed into an ultra-low-power microcontroller unit adjusting the ECG signal sampling and transmission to the corresponding intensity training period, therefore reducing the energy consumption of the system. A Convolutional Neural Networks (CNN) is proposed to analyze ECG signals of different heart rates and automatically detect arrhythmias in real time with high accuracy. Moreover, a visual interface is implemented in a mobile device to analyze the signals and generate a list with the sequence of arrhythmias in real time. This interface enables clinicians to identify during training sessions which part of the ECG signal is significantly associated with a cardiac event. Figure 1 illustrates the conceptualization of the proposed self-powered and wearable AIoT cardiac arrhythmia detection system. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy. Moreover, the CNN-based arrhythmia detection algorithm achieves a precision of 98.6% using 55,222 ECG samples for training the system from different databases, such as MIT-BIH, ECG-ID, Long Term ST, QT, among others, and 1320 ECG samples were acquired and used to validate the system. The rest of this paper is organized as follows. The design of the wearable ECG prototype and the dynamic power management strategy are presented in Section 2. The AIoT architecture based on CNN is described in Section 3, and the database and experimental results with test-case scenarios for high-performance athletes are assessed in Section 4. Finally, discussion and conclusion are drawn in Sections 5 and 6, respectively.
## 5 Experiments and Discussion
### Comparison of Compensation Methods  
A set of typical imaging parameters was used to assess the performance of the proposed backward-squint image motion compensation method, following the configuration used in a previous study [23]: focal length \(f\) = 100 mm, flight altitude \(H\) = 2000 m, platform velocity \(v\) = 150 m/s, and a field of view of 33.4\({}^{\circ}\)\(\times\) 33.4\({}^{\circ}\). In that study, the roll scanning angle was 30\({}^{\circ}\) and the pitch obliquity angle was 20\({}^{\circ}\), and image motion compensation was implemented using a three-axis stabilized platform. The reported maximum residual image-plane velocity after compensation was 2.6 mm/s.  
Under the same conditions, the aircraft's position and its ground projection are illustrated in Figure 21.  
Figure 22 shows the position variation of the four boundary points of the ground projection over time after compensation using the proposed method.  
The corresponding coordinates of the four ground boundary projection points over time in Figure 22 are listed in Table 4.  
The root mean square (RMS) deviation of the four boundary points in Table 4 is calculated at 5 ms intervals, as shown in Table 5. The maximum RMS deviation among the four ground projection boundary points is 0.6047 m, and the minimum is 0.0923 m. By projecting the displacement at each time step onto the line-of-sight vector, the image motion angle deviation corresponding to every 5 ms ranges from 0.0371 mrad to 0.1305 mrad. After conversion to the imaging plane, the image motion on the focal plane every 5 ms is between 3.4 \(\upmu\)m and 12.0 \(\upmu\)m, with a maximum image motion compensation deviation velocity of 2.4 mm/s.  
As summarized in Table 6, the proposed image motion compensation method achieves a compensation accuracy of 2.4 mm/s, which represents an improvement of approximately 7.7% compared to conventional methods under the same conditions. In addition, it employs a two-axis frame with internal mirror-based compensation, making the design structurally more lightweight and offering better portability.  
Figure 21: Aircraft position and ground projection for backward-squint imaging with a roll angle of 30\({}^{\circ}\) and a pitch angle of 20\({}^{\circ}\).  
Figure 22: Position variation of ground boundary projection points over time: (**a**) upper; (**b**) right; (**c**) left; (**d**) lower.
# GANBA: Generative Adversarial Network for
## 2 Background
### Anti-Spoofing or Presentation Attack Detection (PAD)  
The goal of anti-spoofing is to differentiate between _bonafide_ and _spoofing_ speech. Two hypotheses are computed for each test utterance: (i) it is _bonafide_ speech, or (ii) it is a spoofing attack.  
In the last ASVspoof challenges [8; 9; 10], DNN-based models have been the most effective approach to differentiate between _bonafide_ and _spoofing_ speech. A wide range of features have been proposed for training these models, such as linear frequency cepstral coefficients (LFCC) [32], spectrograms [33], constant Q cepstral coefficients (CQCC) [34], and raw speech samples [35].  
Anti-spoofing systems are typically evaluated using the \(\text{ERR}_{\text{PAD}}\) metric, where false acceptance happens when a _spoofing_ utterance is detected as a _bonafide_ utterance while false rejection occurs when a _bonafide_ utterance is detected as a _spoofing_ attack. Moreover, in order to take costs and priors of the different hypotheses into account, the ASV-constrained minimum tandem detection cost function (min-tDCF) metric [36] has been recently proposed to evaluate anti-spoofing systems. This has been the primary metric of the ASVspoof 2019 and 2021 challenges [9; 10].
## 1 Introduction
However, these approaches target only the \"current cell temperature\", making it difficult to predict temperature variations over time. Efforts have also been made to predict time-dependent cell temperature changes based on self-heating theory. [PERSON] et al. [50] assumed a high-rate discharge scenario, positing that total heat generation could be represented as a linear sum of the heat from each component, and derived an analytical solution by separating the spatial distribution and temporal change of temperature in one dimension. [PERSON] et al. [51] derived an analytical solution for the one-dimensional reaction progress rate within the cell based on the conservation of energy and mass of chemical species and compared it with numerical solutions for verification. Nevertheless, even with these theories, it remains challenging to express the temporal change in cell temperature purely through analytical solutions, and numerical solutions are still required in part. In this study, the complete analytical solutions for the temperature change of the battery cells under external heating conditions have been derived. The cell temperature is represented at a single point, and the energy balance considers heating from an external heater, heat generation from thermal decomposition reactions, and heat dissipation to the environment. First, the activation energy and the pre-exponential factors for the decomposition reaction rate are identified from heat generation profiles obtained through DSC (differential scanning calorimetry) measurements. Subsequently, the Arrhenius-type reaction rate for thermal decomposition of electrode materials is approximated by a linear function of temperature. Furthermore, we model the propagation of thermal runaway to adjacent cells and assess the applicability of this approach. The derivation of such an analytical solution is expected to provide a simple and generally predictable temperature profile that does not require specialized software or programs. Furthermore, it could be used as a validation of numerical simulations as a baseline condition.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 4 System Model
### Networking Routing Protocol
#### 4.2.2 Network Routing Protocol in Satellite Broadband Internet System  
The digital twin technology [18] discussed in this study primarily focuses on the application scenarios of network routing protocols, specifically in the context of the low Earth orbit (LEO) satellite network. The routing protocols in the LEO satellite network are applicable to both broadband and mobile satellite communication architectures [19; 20]. However, since the routing in the broadband architecture is based on full-mesh routing, the research scope of the digital twin for the LEO satellite network proposed in this study is specifically targeted at the broadband routing protocols.  
The deployment and application of the routing protocols in the satellite broadband internet architecture are illustrated in Figure 2. In the broadband regime, the routing is characterized by full-set routing, while, in the mobile regime, subscriber satellite-Earth routing is absent. In the case of a satellite broadband internet system [21; 22] that employs full-mesh routing, it includes inter-satellite routing between the LEO satellite nodes [23], feeder-side satellite-to-ground feeder routing between the LEO satellites and gateway stations, and user-side satellite-to-ground user routing between user terminals.
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
###### Abstract  
Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point cloud, which affects the accuracy of point cloud classification and segmentation. To address this issue, this paper proposes a local domain multi-level feature fusion point cloud deep learning network. First, dynamic graph convolutional operation is utilized to obtain the local neighborhood feature of the point cloud. Then, relation-shape convolution is used to extract a deeper-level edge feature of the point cloud, and max pooling is adopted to aggregate the edge features. Finally, point cloud classification and segmentation are realized based on global features and local features. We use the ModelNet40 and ShapeNet datasets to conduct the comparison experiment, which is a large-scale 3D CAD model dataset and a richly annotated, large-scale dataset of 3D shapes. For ModelNet40, the overall accuracy (OA) of the proposed method is similar to DGCNN, RS-CNN, PointConv and GAPNet, all exceeding 92%. Compared to PointNet, PointNet++, SO-Net and MSHANet, the OA of the proposed method is improved by 5%, 2%, 3% and 2.6%, respectively. For the ShapeNet dataset, the mean Intersection over Union (mIoU) of the part segmentation achieved by the proposed method is 86.3%, which is 2.9%, 1.4%, 1.7%, 1.7%, 1.2%, 0.1% and 1.0% higher than PointNet, RS-Net, SCN, SPLATNet, DGCNN, RS-CNN and LRC-NET, respectively.  
point cloud; convolutional network; classification; segmentation 20231310804. [[https://doi.org/10.3390/app131910804](https://doi.org/10.3390/app131910804)]([https://doi.org/10.3390/app131910804](https://doi.org/10.3390/app131910804))
## 3 Perceived Risks of CRCBPs
In these cases, where projects deal with a significant set of variables and there is a need to prioritize decision-making parameters based on their relative importance, using several techniques is a good tool for prioritizing and making more accurate scientific decisions. In most previous research, risk ranking has been done by applying different methods such as the Analytic Hierarchy Process (AHP) [42; 43], the Analytic Network Process (ANP) [11; 24], the Choosing by Advantages (CBA) method [44], and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [10; 45]. Furthermore, more sophisticated techniques have been advanced (e.g., Mata et al. [46]). For example, Liu et al. [47] proposed a model that integrates the Internet of Things (IoT), Building Information Modeling (BIM), a security risk analysis method, the Apriori algorithm, and a complex network. In terms of produced results, with a focus on construction project delay, Cheng and Darsa [27] found that the most important risk factors in the Ethiopian context are 'change order', 'corruption/bribery', and 'delay in payment'. Yet, recently, Chattapadhyay et al. [29] while investigating risks in megaprojects--by collecting and prioritizing risks as stated by 70 Indian megaprojects experts--found that the most severe risks are a delay in obtaining traffic regulation orders, inappropriate equipment, political and legal issues, political instability, government intervention, regulatory confirmation and regulation order delays, and wrong engineering designs. Comu et al. [17], instead, prioritized these risks for CRCBPs in the Turkish context by using the ANP model; they found that 'Exchange rate and inflation rate fluctuations' 'Political instability', and 'Location selection' were risk factors for CRCBPs projects in developing countries. In terms of categories that can result from the application of the above tools, Draji Jahromi et al. 's [48] study identified twelve criteria for assessing risks, which are vulnerability, threat, consequence, uniqueness, risk uncertainty, proximity of risk, interaction of each risk, risk identification, response to risk occurrence, risk manageability, risk occurrence, and risk forecasting. In another study, Mohammadi Talvar and Panahi [49] also introduced various criteria for risk assessment, including technical dimensions, experimental, management, good track record and credibility, competence, and proposed price. Failure to manage risks in CRCBPs usually leads to excessive costs and prolongation of the project [50] and considering the implementation of a risk management process in CRCBPs is necessary in order to try to countervail these failures.
Sensors_**2008**, \(8\), 7259-7274; DOI: 10.3390/s8117259  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Mobile Sensor Network System for Monitoring of Unfriendly Environments**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, P.R. China;  
E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed. E-Mail: [EMAIL_ADDRESS]  
_Received: 15 October 2008; in revised form: 7 November 2008 / Accepted: 13 November 2008 / Published: 14 November 2008_  
**Abstract:** Observing microclimate changes is one of the most popular applications of wireless sensor networks. However, some target environments are often too dangerous or inaccessible to humans or large robots and there are many challenges for deploying and maintaining wireless sensor networks in those unfriendly environments. This paper presents a mobile sensor network system for solving this problem. The system architecture, the mobile node design, the basic behaviors and advanced network capabilities have been investigated respectively. A wheel-based robotic node architecture is proposed here that can add controlled mobility to wireless sensor networks. A testbed including some prototype nodes has also been created for validating the basic functions of the proposed mobile sensor network system. Motion performance tests have been done to get the positioning errors and power consumption model of the mobile nodes. Results of the autonomous deployment experiment show that the mobile nodes can be distributed evenly into the previously unknown environments. It provides powerful support for network deployment and maintenance and can ensure that the sensor network will work properly in unfriendly environments.  
**Keywords:** Wireless sensor network; Mobile sensor network; Mobile node; Localization; Deployment
# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 5 Validation and Discussion about Skewness-Aware Online Edge Learning
### A Redundancy Mechanism for Skewness-Aware Online Edge Learning  
When it comes to the skewness awareness after the initial training stage, inspired by using log transformation to deal with high skewness, we propose maintaining two online learning models respectively over the observed data and over the transformed data at runtime. As such, given a predefined threshold, the online learning system can keep monitoring skewness and accordingly switch between the two models to output predicted values, as illustrated in Figure 3. As for the threshold, according to the suggestions from  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Number of Hidden Neurons** & **Original OS\_ELM** & **Skewness-Aware OS\_ELM** \\ \hline
23 & NRMSE: 70.06\% & NRMSE: 53.88\% \\
50 & NRMSE: 50.85\% & NRMSE: 48.14\% \\
100 & NRMSE: 41.41\% & NRMSE: 38.77\% \\
200 & NRMSE: 36.33\% & NRMSE: 33.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction Accuracy Comparison between Original OS_ELM and Skewness-aware OS_ELM.  
Figure 2: Time-series prediction of the online-sequential extreme learning machine (OS_ELM) over the nyc_taxi dataset.  
the literature [51], we distinguish between balanced data and skewed data by checking whether or not the absolute skewness value is less than three.  
Such a redundancy mechanism may be particularly suitable for the partially skewed scenario where the online learning logic involves a time/observation window or a forgetting mechanism. Correspondingly, RSS or EWSS can be employed to monitor skewness in this case. It should be noted that this redundancy inevitably requires double computing resources for a single online learning task. Therefore, the cost-benefit analysis should play a prerequisite role in applying the redundancy mechanism to real-world edge environments. In addition, the log transformation can be replaced with other suitable techniques of curing skewed data when implementing this redundancy mechanism.  
At the time of writing this paper, we are still exploring suitable (partially skewed) time series data to validate the proposed redundancy mechanism. As discussed in Section 2, it seems that few skewed time series are employed and shared in the community. The lack of reusable skewed data confirms the widely known fact of publication bias, i.e., the existing research may (have to) have intentionally selected balanced datasets to study and publish, because researchers can barely obtain positive results from the online learning techniques over skewed data [6].  
Considering the ever-evolving nature of the discipline, we hope to use this proposed redundancy mechanism to inspire more collaboration in skewness-aware online edge learning.
# Deep Subspace Clustering with Block Diagonal Constraint
###### Abstract  
The deep subspace clustering method, which adopts deep neural networks to learn a representation matrix for subspace clustering, has shown good performance. However, this representation matrix ignores the structural constraint when it is applied to subspace clustering. It is known that samples from different classes can be taken as embedding in independent subspaces. Thus, the representation matrix should have a block diagonal structure. This paper presents the Deep Subspace Clustering with Block Diagonal Constraint (DSC-BDC), a model which constrains the representation matrix with block diagonal structure and gives a block diagonal regularizer for learning a suitable representation. Furthermore, to enhance the representation capacity, DSC-BDC reforms the block-diagonal structure constraint by performing a separation strategy on the representation matrix. Specifically, the separation strategy ensures that the most compact samples are selected to the represent data. An alternative optimization algorithm is designed for our model. Extensive experiments on four public and real-world databases demonstrate the effectiveness and superiority of our proposed model.  
deep subspace clustering; representation matrix; Block Diagonal Constraint; separation strategy +
Footnote †: journal: Applied Sciences