esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### DL Methodology
#### 3.3.2 Optimizer  
The way to optimize the DL models' learnable weights through backpropagation is another important control knob for classification performance. Figure 4b shows the trend of optimization techniques mentioned in the selected studies within years. A total of 50% (184 out of 368) studies did not explicitly report their optimization method. Three most frequently used optimizes are adaptive moment estimation (Adam) (66.8%, 123 out of 184). Stochastic gradient descent (SGD)/ SGD with momentum (SGDM) (12%, 22 out of 184), and root mean square propagation (RMSProp) (3.8%, 7 out of 184).
## 8 Future Directions
### Edge AI  
One of the most transformative trends in AI deployment within communication networks is Edge AI. By bringing computational intelligence closer to data sources, Edge AI enables real-time decision-making, reduces latency, and alleviates bandwidth constraints associated with cloud computing. This approach is particularly beneficial for applications requiring low latency, such as network monitoring and security in IoT ecosystems [147].  
Edge AI can also help address data privacy concerns by processing data locally rather than transmitting it to centralized servers. However, achieving efficient AI models at the edge requires advancements in model compression, hardware acceleration, and energy-efficient algorithms. Table 15 compares the benefits and limitations of Edge AI versus cloud-based AI for network applications. It provides a quantitative comparison between Edge AI and cloud-based AI for communication networks across several key parameters. Edge AI typically offers lower latency due to its local processing capabilities, with latencies ranging from 1 to 10 milliseconds, whereas cloud-based AI systems generally experience higher latency (50-150 ms) due to the need for data transmission to centralized cloud servers. In terms of data privacy, Edge AI ensures a higher level of privacy as data is processed locally, reducing the exposure to external security risks, while cloud-based AI carries moderate privacy risks due to reliance on external infrastructures. Energy efficiency is another strength of Edge AI, as it consumes significantly less power (10-50 watts) compared  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Interpretability** & **Use Case** \\ \hline Deep Learning & 97 & Low & Complex anomaly detection \\ \hline Decision Tree & 85 & High & Operator decision support \\ \hline Reinforcement Learning & 90 & Medium & Real-time traffic management \\ \hline \hline \end{tabular}
\end{table}
Table 14: Trade-off between interpretability and performance in AI models.  
to power-hungry cloud systems (100-500 watts). However, cloud-based AI outperforms Edge AI in computational power, handling much higher workloads (10\({}^{12}\) FLOPs) compared to Edge AI's limitations (10\({}^{9}\) FLOPs). Edge AI also benefits from higher processing speed for simpler tasks (1-10 million operations per second), while cloud-based AI is capable of handling complex workloads with greater speed (100 million to 1 billion operations per second). In terms of deployment complexity, Edge AI systems are moderately complex to deploy due to the need for integration with local devices, while cloud-based AI systems have higher deployment complexity due to the large-scale infrastructure and integration challenges. Lastly, cloud-based AI systems offer superior scalability, as they can scale up easily by adding more servers, while Edge AI faces scalability limitations due to the constraints of local hardware.
## 1 Introduction  
The global population is aging, leading to increased attention to dementia and other mental health disorders. These conditions are common among older adults and have become a significant public health issue [1]. Dementia, which primarily affects older adults, hinders daily activities and social interactions, significantly impacting well-being [2]. Itmainly affects individuals over 65 years old, causing cognitive dysfunction, hyperactivity, sleep disturbances, aggression, and depression [3]. The primary treatment for people with dementia is medication. However, adherence to medication is often suboptimal because managing medication and adhering to a regular schedule can be extremely challenging due to the symptoms of dementia. As a result, a combination of medications and non-pharmacological treatments is often recommended [4]. Robotherapy is an innovative non-pharmacological approach for treating dementia, utilizing various technologies based on robots and artificial intelligence [5].  
In line with the growth of technology, innovative technologies such as social robots have been studied in the older adult population, demonstrating promising emotional support and companionship for seniors [6]. A social robot is one that interacts with its audience based on social and cultural behavior norms and can be autonomous or semi-autonomous [7]. Robotic pets are a type of social robot that mimic the appearance and behavior of real pets, helping to reduce anxiety and aggression in individuals with dementia [8]. Animal robots are often the preferred designs for roobtherapy, and older adults with dementia tend to favor robotic pets with familiar and realistic designs, such as robot cats or dogs [9].  
Joy For All (JFA) robotic pets are equipped with sensors that can be activated by touch and light, allowing the robots to provide autonomous responses through verbal inputs and movements for interaction [10]. JFA robots lack advanced functions and programming capabilities, but older adults with dementia find them more interactive than more sophisticated robots [11]. Additionally, the affordability of JFA robots enables specialized centers to acquire and provide them to their clients [12]. This study aimed to explore the interactions of older adults with mild dementia using JFA cat-type robots and the potential benefits of roobotherapy. In this study, the potential therapeutic effects of roobtherapy were assumed to include positive emotional stimulation, reduction of anxiety, induction of calm or relaxation, and the promotion of emotional bonding through interaction with the robot. These assumptions were based on previous evidence showing that robotic pets can enhance emotional well-being, reduce behavioral symptoms, and improve the quality of life in older adults with dementia [13; 14].
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 4 Experiment
### Experimental Data
#### 4.2.2 Coastal Zone Change Detection Dataset (BCZ_CD)  
We acquired multi-temporal remote sensing data from September to December for the coastal zones of Tianjin, Liaoning, Shandong, and Hebei provinces using Gaofen series satellites (GF1, GF1B, GF1C, GF1D, GF2, GF6) and resource series satellites (ZY302, ZY1E). The selected provinces cover diverse natural environments, ranging from estuarine wetlands to sandy coasts, coastal plains, and mudflat saline-alkaline lands, and have been significantly impacted by human activities such as reclamation, port construction, marine aquaculture, and industrial expansion. This ensures the dataset's representativeness in both spatial and ecological diversity. The image data, which were collected during the autumn and winter seasons, capture seasonal features such as vegetation withering, tidal changes, and shoreline morphology adjustments, reflecting the complex dynamics of the coastal zone under the combined effects of climate change and human activities.  
Based on expert-provided change patch data, the images were divided into 77 pairs of pre- and post-change remote sensing images. The images underwent ortho-correction, radiometric calibration, atmospheric correction, and resampling, resulting in 128 cropped pairs of images. These preprocessing steps eliminate geometric and radiometric errors, improve image contrast and consistency, and provide a high-quality dataset for subsequent change detection.  
To meet the model's data volume requirements and GPU memory limitations, the dataset was augmented through rotation and flipping. Each image was then cropped into 16 non-overlapping patches, as shown in Figure 9. This data augmentation method not only expands the dataset but also increases its diversity, enabling the model to better adapt to different change patterns and noise interference, thus enhancing its generalization ability. Ultimately, 6048 sample pairs were created and split into training and test sets (5536/512). The coastal zone change detection dataset (BCZ_CD) is shown in Figure 10.
###### Abstract  
This paper proposes a composite control method that integrates an active fault-tolerant predictive control scheme and an event-triggered mechanism for networked multi-agent systems. The approach considers random communication constraints in the forward and feedback channels as well as actuator faults. At each time instant, the event trigger determines whether to send system outputs based on the current system state. A Kalman filter is then utilized to estimate both the system state and potential faults by incorporating system output information transmitted through the feedback channel. Concurrently, iterative predictions are performed according to the established system model. Furthermore, a predictive sequence of control inputs is generated through the designed control protocol. Leveraging timestamping technology, the system precisely applies the appropriate control commands to the actuator at designated moments. As a result, the proposed control method compensates for both random communication constraints and actuator faults while effectively reducing data transmission over the communication network. Finally, the proposed method is validated through numerical simulations.  
networked multi-agent systems; event-triggered method; fault-tolerant predictive control; random communication constraints; actuator fault +
Footnote †: journal: Applied sciences
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods  
### Dataset  
#### 2.1.1 EuroSAT Dataset  
[PERSON] et al. [31] utilized multi-spectral labeled image data from the Sentinel-2A satellite for LULC classification. Sentinel-2A and Sentinel-2B are two comparable land monitoring satellites that constitute a two-satellite constellation. The satellites were efficiently deployed in June 2015 (Sentinel-2A) and March 2017 (Sentinel-2B) [31]. A collection of 27,000 images, each 64 by 64 pixels in size, was manually labeled and grouped into 10 categories, each with 2000-3000 images [31]. [PERSON] et al. specified ten LULC classes that are distinguishable at a resolution of 10 m/pixel and are regularly covered by the European Urban Atlas [31; 33]. The categorized EuroSAT dataset is already accessible to the general public [32]. The RGB variant of the EuroSAT collection was utilized for training, validation, and testing purposes. Figure 1 illustrates a subset sample of EuroSAT collection from the EuroSAT collection [31; 33].  
Figure 1: A sample of images from the EuroSAT dataset. There are 20 sample images per row from the following categories (from top to bottom), respectively: Annual Crop, Forest, Herbaceous Vegetation, Highway, Pasture, Industrial, Permanent Crop, Residential, River, and Sea/Lake.
# Preoperative Planning for Superior Messetric Artery Aneurysm
## 4 Conclusions  
By using our custom-made imaging and planning platform, CardioCTnav, we were able to provide our vascular surgeon with a clear 3D reconstruction and precise preoperative planning tool that helped him to determine exactly what procedure was most suitable, to evaluate the type of stent technology needed for repair, and, most importantly, to reduce the time necessary to perform the procedure.  
Our particular case presented an SMA with an aneurysm in its entire length and multiple branches coming off of the aneurysm segment, which made the repair more difficult. Excision and interposition grafting could have resulted in segmental bowel ischemia because of multiple patent side branches. Therefore, the vascular surgeon considered two possibilities to repair this aneurysm. The first method was similar to the reimplantation techniques of thoracoabdominal aneurysms, and involved the simulated reimplantation of multiple side branches into two polyester interposition grafts, with known good and durable results for 2 years after the operation. The polyester grafts that were selected theoretically (two 7.0 \(\times\) 58 mm Jostent peripheral stent grafts made by Jomed, Helsingborg, Sweden) had a 7-mm size that matched the dilated proximal and distal native SMA at the area of the anastomosis and made reimplantation of the side branches also easier than using an autologous vein graft. The second simulated option was to use bare-metal renal stent grafts (for example, Herculink Elite(r) stents made by Abbott Vascular, Santa Clara, CA, USA, with dimensions of 7.0 \(\times\) 15 mm, 135 mm).  
In the future, we plan to further extend our planning module with new options like multiple stent simulations offering different sizes, blood flow calculations, etc.  
Conceptualization, C.I.D., C.C., R.T.P. and D.D.; methodology, D.D., R.T.P., C.I.D. and S.M.C.; software, C.C., L.D.P. and L.F.B.; validation: C.G.T. and D.M.C.; formal analysis, R.S.; investigation, M.F.T.; resources, D.D.; data curation, R.S.; writing--original draft preparation, C.C., S.M., R.T.P. and D.D.; writing, D.D. and C.G.T.; visualization: S.M.C. and M.F.T.; supervision, C.I.D. and D.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations
# The Impact of Blenderized Tube Feeding on Gastrointestinal Symptoms, a Scoping Review
The nutrients and food isolates (e.g., milk protein) are extracted from foods in these tube feedings but are provided without the natural food matrix [5]. On the contrary, BTF, also referred to as \"blenderized formula\" or \"homemade blended formula\" and \"pureed by g-tube,\" consists of whole foods provided through a feeding tube [6; 7]. Products based on real foods, such as milk, meat, and vegetables, are also commercially available for enteral tube feeding (ETF) [8]. The number of patients receiving long-term enteral nutrition along with the use of BTF has surged over the last decade. As per the Oley Foundation members' survey in 2017, most of the 216 participants, specifically pediatric (89%) and adult (66%) patients, were consuming BTF partly or totally for their nutritional needs [9]. Although guidelines currently do not recommend BTF as a first choice for tube feeding [1], many families still choose BTF over commercial formulas for several reasons, including severe gastrointestinal (GI) symptoms, intolerance to polymeric enteral formulas, or food allergies and intolerances [10]. Parenteral nutrition can play a role in relieving gastrointestinal signs/symptoms especially in children with severe neurological impairment [11]. However, given its risks and its potential to become inappropriately life sustaining, clinicians need to consider changes in conventional enteral nutrition, including blenderized formula. This study aims to describe and quantify the impact of BTF on GI symptoms of patients without any age or diagnosis limitations.
## 4 Enabling Technologies for FSO Communication
### Error Correction Codes and Modulated Signal Optimization
#### 4.7.2 Coded Modulation and Error Correction Codes
Polarcoded MIMO-FSO systems with spatially correlated fading using OOK modulations are introduced in [285], where polar codes outperform LDPC codes under the same conditions. However, OOK modulations cannot satisfy the capacity demands of future optical communication. Unipolar M-ary PAM (M-PAM) is a candidate for achieving near capacity in FSO systems [311], and the non-uniform distributions of M-PAM can significantly increase the capacity of FSO systems [297; 312]. Furthermore, efficient FSO communications can be realized by integrating higher-order modulation schemes with channel coding, a method referred to in the literature as CM [217; 313]. This approach improves system reliability by combining high-order modulations with FEC codes and offers flexible data rates through adjustments in modulation schemes or code rates [164; 308].
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 2 Materials and Methods
### Methodological Framework  
#### 2.4.1 RUSLE Model Application  
Recent advancements in GIS and remote sensing technologies have made it relatively easy to estimate and monitor soil loss, both at local and global scales (with varying climates), using the empirical RUSLE soil erosion model. As an updated version of the Universal Soil Loss Equation (USLE), the RUSLE equation forecasts soil erosion risk using five environmental and anthropogenic variables: rainfall erosivity, topography, vegetation cover and land support practices. The average annual soil loss (\(A\)) in the RUSLE model is expressed by Equation (2) [64]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Institute** & **Country** & **Resolution** \\ \hline CCCMA\_CANESM2 & Canadian Centre for Climate Modeling and Analysis & Canada & 2.8\({}^{\circ}\)\(\times\) 2.8\({}^{\circ}\) \\ CESM1-CAM5 & Community Earth System Model Contributors & USA & 1.25\({}^{\circ}\)\(\times\) 0.9\({}^{\circ}\) \\ CSIRO\_MK3.6.0 & Commonwealth Scientific and Industrial Research Organization & Australia & 1.875\({}^{\circ}\)\(\times\) 1.875\({}^{\circ}\) \\ MIROC-5 & Model for Interdisciplinary Research On Climate & Japan & 1.4\({}^{\circ}\)\(\times\) 1.4\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the four GCMs applied in this study.  
Figure 6: Integration of the climate change scenarios with the \(R\) factor used in this study.  
\[A=R*K*LS*C*P \tag{1}\]  
where:  
\(A\) is expressed in MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\), \(R\) is the rainfall erosivity factor (MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\)), \(K\) is the soil erodibility factor (t ha h h\({}^{-1}\) MJ\({}^{-1}\) mm\({}^{-1}\)), \(LS\) is the slope length and steepness factor (dimensionless), \(C\) is the cover management factor (dimensionless, ranges from zero to one) and \(P\) is the conservation practice factor (dimensionless)  
The various geospatial datasets, with their respective sources used in this study, are as summarized in Table 2. The RUSLE model was run to estimate the current soil erosion rate based on baseline rainfall erosivity, while future soil losses were forecasted based on respective projected rainfall erosivity scenarios.
## 3 Four-Port MIMO Antenna
### Performance Comparison  
Table 1 provides a comparison of the proposed ultra-wideband antenna operating over 26.5-43.7 GHz with the antenna already published in literature. The comparison in terms of size, number of ports, operational bandwidth, peak gain, ECC, and minimum/maximum isolation is performed. It can be seen from the table that antenna has compact size, offers wideband and high gain, along with good and acceptable values of ECC and isolation. This comparison also makes the proposed antenna a good applicant for future 5G devices having a compact size and operating over wideband and high gain.
## 2 Materials and Methods
### Information Sources  
A literature search was conducted in online scientific databases from December 2021 to February 2022. The databases that were included in this review were PubMed, Scopus, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), Crossref and Google Scholar. In addition, reference lists from included studies and literature reviews were also manually searched for by spotting any other relevant works that could potentially be included. Since there were several literature reviews with similar objectives, they were used as reference points for building the current research but were mainly served as additional sources to identify more articles. Although they did provide relative information, nevertheless, all the included papers were studied again from the beginning.
## 1 Introduction
(2015) analyzed the dynamic load response characteristics and impact failure processes of roadway anchorage-bearing structures under the combined effects of dynamic and static loads using the FLAC\({}^{\text{3D}}\) dynamic module. Their findings indicated that the failure of roadway anchorage-bearing structures during pressure bumping was primarily influenced by the mechanisms of energy accumulation, overload, and release. Furthermore, the stability of the anchorage structure was closely linked to dynamic load energy, bearing capacity, and deformation characteristics. [PERSON] et al. (2016) applied cosine waves with varying impact energies to study the propagation process of vibration waves and the response characteristics of the roadway surrounding the rock support system. They found that the attenuation of vibration wave energy and propagation distance follows a power relationship. The vibration waves employed in the aforementioned studies predominantly utilize synthetic waves or simplify the actual vibration waves in order to analyze the dynamic response characteristics of roadway-anchored surrounding rock. However, these approaches do not adequately account for the complexity of real vibration waves and their effects on the surrounding rock in roadway anchoring contexts. [PERSON] et al. (2017) noted that the field-measured vibration wave can more accurately reflect the failure characteristics of the roadway. The vibrational waves generated by rock bursts primarily consist of longitudinal waves (P-waves) and transverse waves (S-waves). These two types of waves exhibit significant differences in their effects and the mechanisms of instability they induce in the mechanical response of the surrounding anchored rock. To accurately represent the response behavior of P-waves and S-waves to the anchored surrounding rock within the context of FLAC\({}^{\text{3D}}\) numerical simulations, it is essential to isolate and calibrate the recorded vibration waves. This process ensures that the signals corresponding to P-waves and S-waves can be precisely extracted and subsequently integratedinto the numerical model in accordance with their distinct propagation characteristics. This study investigates the roadway impact accident that occurred at the (4-5) 06w working face of a mine in Xinjiang, providing the engineering context for the analysis. This study utilizes the synchronous compression transformation method of the Barworth filter to improve the separation of vibration waves. A comprehensive analysis of the dynamic response characteristics of P-waves and S-waves in relation to the surrounding rock of roadway anchorage in the near field is conducted. This provides a scientific foundation for the design of roadway anti-scour supports.