esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience Analysis Grid-Rasch Rating Scale Model for Measuring Organizational Resilience Potential
## 2 Materials and Methods
### RAG and the Four Pillars of Resilience  
The Resilience Assessment Grid (RAG) is based on four pillar abilities that permit an organization to perform resiliently: responding, monitoring, learning, and anticipating.  
* **Responding**, i.e., _knowing what to do_, is concerned with the ability to take prompt and appropriate action in response to a given situation. This pertains to an organization's ability to discern the optimal response to circumstances of change or disruption, whether through the implementation of pre-established actions, the adaptation of existing operations, or the invention of novel operational methods. It is of the utmost importance for organizations to be able to react to both anticipated and unexpected events in a timely and effective manner. This enables them to maintain readiness and adaptability, thereby avoiding or effectively dealing with crises.
* **Monitoring**, i.e., _knowing what to look for_, is the process of observing and assessing the status of a system or process over time to identify potential issues or changes that may require attention. Monitoring entails monitoring one's own performance and external operating conditions, thereby enabling the identification of potential threats or opportunities in the near term.
* **Learning**, i.e., _knowing what has happened_, is a crucial aspect of organizational capability. This ability entails an awareness of past occurrences and the ability to learn from them. The ability to learn from experience enables organizations to enhance their responsiveness and adapt their objectives in accordance with evolving needs.
* _Knowing what to expect_, i.e., **Anticipating** potential future developments that could represent changes or difficulties, such as new regulations or alterations in operating conditions, is a crucial ability for organizations seeking to maintain a competitive advantage. By paying attention to scenarios that could impact the organization, this ability allows for the preparation of strategies to navigate environmental changes.
# A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
## 3 System Modules
### Pre-Processing Engine
A buffer is used in the pre-processing engine to temporarily store input ECG data, which includes a 16-bit \(\times\) 1 k SRAM and a write address generator; the sampling data points of at least three heart beats can be stored in it. When ECG data are received from the SPI receiver, the write address will be incremented by 1. Once the write address reaches its maximum value, it will restart from 0, so the previous data will be overwritten. It should be noted that the write address can be considered as the index of the current input data in the buffer, which is further used to calculate the index of the R-peak point; this will be discussed later.
## 4 Simulation Setup
### Performance Metrics  
The benchmarks considered in this paper to evaluate the effectiveness of the proposed algorithms are as follows:  
* **Accuracy:** This reflects the overall effectiveness of the entire signal detection system. \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{N}}\] (7) where TP (true positive) is the correctly identified spoofed signals. TN (true negative) is the valid signals that are correctly classified. N is the total predictions.
* **Precision:** This reflects how correctly the system is able to identify spoofed signals among the predicted spoofed signals. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (8) where TP (true positive) is the correctly identified spoofed signals. FP (false positive) refers to signals that are not spoofed but are classified as spoofed.
* **Recall:** This reflects the capability of the system to capture the spoofed signals. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (9) where TP (true positive) is the correctly identified spoofed signals. FN (false negative) is the spoofed signals wrongly identified as original signals.
* **F1 Score:** This is the combination of recall and precision. It gives a balanced view of the two metrics. \[F1\ Score=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (10)
* **Loss:** This is the error in the model's predictions and the real classifications. \[L(Y,\hat{Y})=-\frac{1}{N}\sum_{i=1}^{N}\log(\hat{Y}_{i})\] (11) where N is the number of samples in the dataset. \(\hat{\text{Y}}_{i}\) is the predicted probability for the correct class of samples.
###### Abstract
Despite widespread adoption of the aforementioned negative sampling methodologies, two fundamental challenges persist [10]: (1) the absence of a robust quantitative metric for evaluating the credibility of selected nonlandslide samples and (2) inadequate investigation into the mechanistic effect of credibility thresholds on the accuracy of susceptibility evaluation models. Addressing these challenges is critical for fully exploiting the predictive capability and generalizability of ML approaches. Given these considerations, this study selected the Liangshan Yi Autonomous Prefecture in Sichuan Province, China, which is a region characterized by the densest tectonic structures, most intense seismic activity, and highest geohazard susceptibility, as its research area [11]. This study focuses on the following two primary objectives. (1) To address the lack of quantitative evaluation criteria for negative samples, this study proposes a fuzzy membership-based sampling method to compute the credibility of negative samples (defined as the confidence level of stable nonlandslide instances). The proposed method quantifies the \"dissimilarity\" between candidate nonlandslide locations and known landslide environments based on fuzzy membership theory and generates spatially continuous credibility distribution maps (with a value range of 0-1), where higher scores indicate more reliable nonlandslide attributes. (2) To investigate the mechanistic influence of negative sample credibility thresholds on landslide susceptibility model accuracy, we rigorously evaluated the effects of varying credibility thresholds on model performance and landslide susceptibility zoning using various metrics, including F1-score, the area under the receiver operating characteristic curve (AUC), and landslide ratio. This analysis aims to decipher the mechanisms by which credibility thresholds govern model precision. This study aims to (1) propose a sampling method capable of quantitatively evaluating negative sample credibility to generate high-precision landslide susceptibility maps, thereby providing guidance for landslide risk management in the Liangshan Prefecture; (2) elucidate the quantitative relationship between negative sample credibility thresholds and model accuracy to identify the optimal selection threshold for negative samples in landslide susceptibility assessment in the Liangshan Prefecture; and (3) establish a transferable negative sample sampling framework to provide a practical paradigm for geohazard-prone regions facing similar challenges globally.
## 2 Study Area and Datasets
### Satellite Data  
#### 2.3.1 Sentinel-1 C-Band SAR Satellite Data  
Sentinel-1 is a C-band radar imaging mission (wavelength \(\lambda\) 5.6 cm) composed of two satellites designed to study land and oceans. Sentinel-1A was launched on 3 April 2014 and Sentinel-1B on 25 April 2016. The Sentinel-1 constellation provides multiple SAR image acquisition strategies, with the TOPS (Terrain Observation by Progressive Scans) Interferometric Wide Swath (IWS) mode supporting wide-area InSAR applications [37]. Ground sampling distances of Sentinel-1 are in the order of 2.3 m in range and 13.6 m in azimuth directions, respectively.  
Acquisitions are regularly available over all polar areas every 6 to 12 days [38]. This is, however, modified regionally with respect to general user requirements. Good temporal sampling by the Sentinel-1 mission became available over central Yamal in 2016 after the launch of Sentinel-1B. Table 1 and Figure 2 give an overview of the available data pairs, regularly available every 12 days.  
All IWS images for central Yamal include VV polarization which has been shown applicable for InSAR seasonal subsidence in tundra environments [14]. All scenes were acquired between 7 and 8 a.m. local time from descending orbit. As the normal mode is right looking, the area is viewed from the SE. This means that the slope of the CALM grid is facing the sensor (see Figure 3). Due to the limitation to one orbit the incidence angle has been the same at the study site in all cases with approximately 34.5\({}^{\circ}\)Figure 3: Overviewof Circumpolar Active-Layer Monitoring (CALM) grid and location of multi-annual subsidence measurements (red points with IDs) and new sites 2018 (black dots). Digital elevation model (in m) from [PERSON] et al. (2018) and detailed land cover at active-layer thickness probing locations from [PERSON] et al. (2019). For location of the CALM grid, see Figure 1. The photo shows an example for a plywood plate with rod ([PERSON], 3 September 2017).
# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 1 Introduction
Lightweight AI algorithms, including decision trees, K-means clustering, federated learning, and TinyML, can be implemented over fog nodes, and thus, enable novel services [11]. The decision tree is suitable for quick and interpretable classification tasks, and K-means clustering can be used in resource-constrained environments for anomaly detection. Federated learning ensures data privacy, while training models collaboratively across distributed IoT devices. Also, TinyML enables machine learning deployment directly on microcontrollers, which can empower the future of IoT. The second form of distributed edge computing is multiple access edge computing (MEC), which enables resources at the network edge to be located farther than fog. MEC nodes have higher computing capabilitiesthan fog nodes. MEC servers can process higher tasks and implement more complicated algorithms [12]. The current work is motivated by the recent advances in distributed edge computing schemes and their role in IoT networks. Thus, the current study seeks to develop a robust IoT based on distributed edge computing to meet the demands of the next generation of IoT networks. Edge computing was introduced mainly to increase IoT network scalability, availability, and reliability. Moreover, it reduces communication latency to enable ultra-reliable low-latency (uRLL) applications [13]. The ultimate objective of this work is to build a reliable IoT network based on distributed edge computing technology. The main contributions of the work can be summarized as follows:  
1. Developing a reliable network structure for heterogeneous IoT applications using distributed edge computing technology. 2. Developing a two-level edge computing scheme that deploys MEC and fog computing. 3. Modeling the developed network. 4. Developing a communication model for distributed end devices. 5. Developing an energy model for distributed end devices. 6. Proposing an optimized resource allocation strategy for the proposed model to reduce energy consumption and optimally use the network computing resources. 7. Performance evaluation of the proposed network. The rest of this paper is organized as follows. Section 2 presents the background and related work. Section 3 presents the proposed edge-based IoT network structure. Section 4 presents the proposed network communication, energy, and computation models. Sections 5 and 6 provide optimized energy and resource allocation models. Section 7 provides the simulation setup and introduces the obtained results. Section 8 gives a discussion of the obtained results, and finally, a conclusion is given in Section 9.
# A Review of Neural Network-Based Emulation of Guitar Amplifiers
## 3 Neural Network-Based Methods
### Loss Functions
In order to train the recurrent networks presented in the works of [PERSON] et al., a different pre-emphasis filter was applied to the ESR with the transfer function \(H(z)=1-0.85z^{-1}\) along with a term to compensate for a DC offset present in the prediction:  
\[\mathcal{L}_{\text{DC}}=\frac{\left(\frac{1}{N}\sum_{i=0}^{N-1}(y[i]-\hat{y}[i ])\right)^{2}}{\frac{1}{N}\sum_{i=0}^{N-1}y[i]^{2}}. \tag{17}\]  
In a further work [53], various pre-emphasis filters are studied and compared, along with weighting functions in order to gauge which combination better reflects perceptual accuracy. The filters with the following transfer functions were tested:  
First-Order High-Pass: \[H_{\text{HP}}(z)=1-0.85z^{-1}\] (18) Folded Differentiator: \[H_{\text{FD}}(z)=1-0.85z^{-2}\] (19) First-Order Low-Pass: \[H_{\text{LP}}(z)=1+0.85z^{-1}\] (20)  
The low-pass filter is preceded by A-weighting in order to decrease emphasis in regions where little energy is present as this weighting aims to mimic the equal loudness curves of the human ear. Listening tests carried out in this study showed that pre-emphasis filtering enabled better accuracy during the modelling task, with the A-weighted low-pass filtering achieving the best performance. The Mean Absolute Error (MAE) is also present in the state-of-the-art in both a purely time-domain formulation and a variant in which spectral content is also taken into account. The MAE is defined as  
\[\mathcal{L}_{\text{MAE}}=\frac{1}{N}\sum_{i=0}^{N-1}|\hat{y}[i]-y[i]| \tag{21}\]  
and was used to train all of the autoencoders presented in a previous section [37; 38; 39]. [PERSON] and [PERSON] [28] use a combination loss comprising both time-domain and spectral features. For the time-domain, the MAE was used, and for the spectral magnitude, the Short-Term Fourier Transform (STFT) loss from [54] is used, leading to the following cost function: MAE + STFT where:\[\mathcal{L}_{\text{STFT}}=\mathcal{L}_{\text{SC}}+\mathcal{L}_{\text{SM}}\text{ where} \tag{22}\] \[\mathcal{L}_{\text{RMSE}}=\sqrt{\frac{\sum_{i=0}^{N-1}(y[i]-\hat{y}[i])^{2}}{N}} \tag{14}\]0 (23) \[\mathcal{L}_{\text{RMSE}}=\sqrt{\frac{\sum_{i=0}^{N-1}(y[i]-\hat{y}[i])^{2}}{N}} \tag{14}\]1  
\(\|.\|_{\text{F}}\) is the Frobenius norm. An overview of the loss functions used in this field is presented in \[\mathcal{L}_{\text{RMSE}}=\sqrt{\frac{\sum_{i=0}^{N-1}(y[i]-\hat{y}[i])^{2}}{N}} \tag{14}\]2  
The loss functions used for training can also be applied to the evaluation process for an objective measure of performance during testing. However, other methods also exist to provide a more comprehensive assessment of the overall quality.
## 3 Software and Methodology  
### Software  
COMSOL Multiphysics is a powerful multiphysics simulation software developed by COMSOL AB, widely used in various fields such as engineering, scientific research, and education. It supports the simulation of coupled multiphysics phenomena, including but not limited to fluid dynamics, heat transfer, and electromagnetism, as well as more advanced custom equations, such as the coupling of Groundwater Contaminant Transport with Bacterial Chemotaxis movement in this study. This coupling simulation allows researchers to analyze the transport behavior of contaminants in groundwater and the response of bacteria to these contaminants. COMSOL provides a graphical user interface, advanced solvers, parameterized modeling, rich post-processing tools, and support for APIs and scripting. Its modular design and intuitive interface further enhance the software's flexibility and user-friendliness. These features enable engineers and researchers to efficiently design and optimize complex systems at the design stage, predict product performance, and save time and cost in the research and development process.
## 5 Proposed Reliable, Energy Optimized and Void Aware Routing Protocol
### Information Dissemination  
Since the ASNs are uninformed about the status of other neighboring ASNs; therefore, they initiate a Hello_Message containing their ID, residual energy, layer number, and depth information. The ASNs having built-in depth measuring gauges help in determining their depth values. Let us assume that n represents the total number of horizontal layers, beginning from the surface of the ocean. \(\varpi_{\mathrm{N}}\) represents the width of the \(\aleph\) layer, while Ln  
Figure 3: Proposed model for the ASN deployment in each layer.  
represents the total depth of the nth layer. Each ASN can compute its layer identification using the following equation [47].  
\[\mathrm{L_{n}}=\sum_{i=1}^{n}\mathrm{\sigma_{N}} \tag{16}\]  
The layer identification at a certain depth dt can be determined by each ASN using the expression given below [47].  
\[\mathrm{L_{n}}\leq\mathrm{d_{n}}<\mathrm{L_{n+1}} \tag{17}\]  
The Received Signal Strength Indicator (RSSI) of Hello_Message serves as an indicator of separation between neighboring ASNs. Each ASN compiles a list of other ASNs in the immediate vicinity after receiving the Hello_Message. To avoid excessive delays, the timeout duration is kept constant, which is equivalent to the maximum delay (dm) in transmitting data between two adjacent ASNs.  
\[\mathrm{dm}=\frac{\mathrm{R_{t}}}{\mathrm{v}} \tag{18}\]  
where \(\mathrm{R_{t}}\) and \(\mathrm{v}\) are the maximum transmission limits of ASN and the speed of sound underwater, which is 1500 m/s [17; 38; 48].
## 3 Results and Discussion  
### Effect of Illumination on Carotenoid and Chlorophyll Content  
Dark-grown chia microgreens showed an etiolated, pale phenotype with increased hypocotyl length. After exposure of the etiolated chia microgreens to light, a change of color from pale to green was evident after 24 h, indicating the beginning of chlorophyll synthesis and subsequent development of the photosynthetic apparatus. Similar findings were also reported by [PERSON] et al. [27], where the authors investigated chia germination and seedling development under different light regimes. These authorsfound that when chia was germinated under constant-dark conditions, seedlings had the longest shoots, while under constant-light, the shoots were shorter.  
The concentrations of total chlorophyll and carotenoid content differed significantly between dark-grown microgreens and microgreens exposed to light (Figure 1). Compared to dark-grown microgreens, the increase was observable 24 h after the treatment. Additionally, there was an evident increase in the concentration of both total chlorophyll (Figure 1a) and carotenoid content (Figure 1b) in chia microgreens 48 h after the treatment.  
The increase of photosynthetic pigments in treated chia microgreens indicates the association between chlorophyll and carotenoid synthesis and the length of exposure to light.  
As reviewed in [PERSON] [PERSON] and [PERSON] (2018), chlorophylls are good antioxidants because of their effective scavenging activity of reactive oxygen species (ROS) and inhibition of lipid peroxidation. Chlorophylls are most commonly used as natural food colorants, but, recently, their bioactive properties ([PERSON], 2018) have become more in focus as research has shown their emerging role as potential prebiotics in rebalancing gut microbiota in mice ([PERSON], 2018).  
Dietary intake of carotenoids is associated with a reduced risk of degenerative diseases ([PERSON], 2018; [PERSON] and [PERSON], 2018) and stroke prevention ([PERSON], 2018), and this suggests their anti/pro-oxidant roles ([PERSON], 2018). In our study, light caused an increase of carotenoid content in chia seedlings and therefore increased their antioxidative properties. Lower light intensity in a range from 100 to 300 umol m\({}^{-2}\) s\({}^{-1}\) had a positive effect on photosynthetic processes ([PERSON], 2018), increased chlorophyll content ([PERSON], 2018), and more effective light use ([PERSON], 2018) in plants. In this study, we used the light intensity of 100 umol photons m\({}^{-2}\) s\({}^{-1}\), which generally has a beneficiary effect on the synthesis of different investigated bioactive compounds and enhances antioxidative properties in chia microgreens.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Channel Setup  
#### 4.2.1 Watermark Channel Setup  
The watermark channel [42] setup encompasses a series of experiments conducted in diverse underwater environments, each selected to test the system's resilience to varying propagation conditions. The test locations span foods, harbors, and shelves, with operational ranges from 540 m to over 3000 m and water depths ranging from 10 m to 80 m as given in Table 5. The transmitter is positioned in different configurations, from bottom-mounted to suspended, influencing the multipath propagation characteristics and the receiver's signal quality.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline UWA modulation scheme & OFDM \\ Sub-carriers, N & 1024 \\ Pilots & N/4 \\ Pilot insertion & Comb \\ Guard interval & CP \\ CP size & N/4 \\ Noise model & AWGN \\ SNR & \(-10\):5:15 dB \\ Sampling frequency \(f_{s}\) & 48–96 kHz \\ Carrier frequency & 14 kHz \\ Frequency spacing & 4.88 Hz \\ OFDM symbol period & 0.204 s \\ Modulation scheme & BPSK \\ UWA channel & Watermark \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of system parameters for UWA-OFDM.  
The frequency range varies from 4 kHz to 37.5 kHz, allowing comprehensive testing across different acoustic spectra. Sounding durations range from 32.9 s to 59.4 s, ensuring robust data collection over extended periods. The system accommodates delay spreads of up to 128 ms and Doppler shifts up to 9.8 Hz, crucial for maintaining signal integrity under motion-induced frequency variations. SISO and SIMO receiver configurations are tested, with element spacings ranging from 1 m to 3.75 m, enhancing spatial diversity for improved reception. Total experiment durations vary, with some lasting up to 33 min, enabling a thorough assessment of the system's performance across diverse real-world conditions. The configuration of Watermark channels are shown in Figure 7a-c.
## 4 Proposed Approach
### Design Methodology
#### 4.2.9 Controlled Linkability  
User metadata, such as usage or access patterns, can constitute sensitive information. A privacy-preserving identity management solution should allow users to control which activities are linkable and which remain provably unlinkable. In our solution, this is achieved through minimal information disclosure, the use of privacy-preserving techniques, and by ensuring unlinkability when the Identity Provider (IdP) interacts with third-party services.
# Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
## 4 Discussion
### Limitations  
There were limitations associated with the study, notably, the heterogeneity amongst findings and methodologies between studies, making them unfeasible for meta-analysis. This review is limited to emerging technologies that are currently being used--or have already been tested in a sporting context--in a sporting population. This excludes studies that have predictive models, tools and methods to identify injuries that may potentially impact the industry. The search criteria used in the process of this review were narrow and may have limited what areas could be explored in the review, namely with the methodology of reviews, and the search criteria have a potential language exclusion bias, concentrating on English language-only studies. This process has highlighted that various studies initially included may have the potential to identify injuries or sicknesses in sports settings. However, the study design meant they were excluded for numerous reasons, such as retrospective design, improper validation of devices/tools, or the participant cohort. The findings are limited in generalizability to female athletes, as under 5% of participants included across the studies were female.