esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
## 2 Materials and Methods
### Device Characteristics  
Two devices were directly compared with each other. The first was a commercially available BrainDriver v2.1 tDCS device (TheBrainDriver, Chicago, IL, USA). The second device was an Axis am-tPCS device (U: The Mind Company, Columbus, OH, USA). The stimulation parameters in the BrainDriver were user-selected, ranging from 0.5 to 2 mA in current and with a stimulation duration between 20 and 30 min [41]. The BrainDriver had a constant DC voltage of 9 V. The device had a single anode and single cathode, each with a sponge electrode 2 cm in diameter, contained in an appropriately sized rubber sheath. Parameters for neuromodulation were based on prior work, with a current of 2 mA applied for 20 min [9; 42]. The sponges were soaked in saline solution prior to active use.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Agar Gel w/Salt** & **PBS** & **Beef** & **This** \\ \hline Reference & Owda (2021) [12] & Guido (2019) [10] & Guido (2020) [11] & N/A \\ \hline Type & -Electric Stim & -DBS Testing & -RF Heating & -Electric Stim \\ \hline Time (h) & 24 & 1 & \textless{}1 & 1-24 \\ \hline Cost (USD) & USD 25 & USD 60 & USD 10 & USD 10–25 \\ \hline Measure & Voltage (V) & Voltage (V) & Thermal, Temp & Voltage (V), EF \\ \hline Limits & -Long prep time & -Costly & -Different domain & -Purpose-built \\ \hline Benefits & -Customizable & -Chemically close & -Cheap, fast setup & -Cheap, fast setup \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between prior phantom models and the current work.  
The Axis device's two electrode pads were designed differently [29]. Each electrode pad was a 3D printed plastic block with one side covered by a 16.1 cm\({}^{2}\) pad of copper tacks facing inwards. A scattered array was used to preclude current saturation. A constant voltage of 5 V was applied with a current of 4 mA. Each pulse comprised 0.9 ms on and 0.1 ms off. The anode and cathode were altered every 2.5 s to entrain local connections. The stimulation program ran for a fixed duration of 15 min. The manufacturer reported that the Axis device uses less total power than comparable devices [29]. Electrodes from the Axis device were coated in conductive saline solution. For both devices, electrodes were positioned in fixed locations, secured with electrical tape if necessary. Although the stimulation time and applied power from each device differed, the phantoms were tested with a complete, manufacturer-recommended dosage.
# Mdpj _sciences_
## 4 Experiment Result and Analysis
### Implementation Details  
The principal objective of the experiment was to predict the (\(T+1\))-th maintenance projects using the previous \(T\) records. The model parameters were initialized randomly, and hyperparameters, along with activation functions, were calibrated based on the verification set as before. The batch size was maintained at 32 for consistency. During training, the model underwent 100 epochs using Adam optimization algorithms, with the learning rate set at 0.001. The computational tasks were performed using Python 3.12.3 and PyTorch version 2.3.1+cu121. The hardware devices was equipped with 64 GB of memory and NVIDIA GeForce RTX 3090 GPUs (Nvidia, Santa Clara, CA, USA). To maintain robustness and replicability, the experiment was repeated five times, each with a different random seed.  
The NVIDIA GeForce RTX 3090 GPU used for the experiments is for model training purposes. In the actual deployment and application of the model, the on-board ECU typically does not possess such high-performance computing resources. Instead, the model inference can be accomplished using lighter embedded computing resources, such as the Cortex-M4/M7 architecture. Techniques such as model quantization, pruning, and edge cloud computing deployment strategies will be explored to ensure the feasibility and efficiency of the model in real vehicle applications. Model quantization reduces the model size and computational load by decreasing the precision of the weights. Pruning involves removing redundant parameters, which helps in reducing the model complexity. Edge cloud computing can offload some computational tasks to nearby cloud servers, balancing the load between the vehicle's ECU and the cloud.
# A Predictive Model Using Long Short-Time Memory (LSTM) Technique for Power System Voltage Stability
###### Abstract  
The stability of the operation of the power system is essential to ensure a continuous supply of electricity to meet the load of the system. In the operational process, voltage stability (VS) should be recognized and predicted as a basic requirement. In electrical systems, deep learning and machine learning algorithms have found widespread applications. These algorithms can learn from previous data to detect and predict future scenarios of potential instability. This study introduces long short-term memory (LSTM) technology to predict the stability of the nominal voltage of the power system. Based on the results, the recommended LSTM technology achieved the highest accuracy target of 99.5%. In addition, the LSTM model outperforms other machine learning (ML) and deep learning techniques, i.e., support vector machines (SVMs), Naive Bayes (NB), and convolutional neural networks (CNNs), when comparing the accuracy of the VS forecast. The results show that the LSTM method is useful to predict the voltage of an electrical system. The IEEE 33-bus system indicates that the recommended approach can rapidly and precisely verify the system stability category. Furthermore, the proposed method outperforms conventional assessment methods that rely on shallow learning.  
machine learning (ML); deep learning; long short-term memory (LSTM); electrical power system; convolutional neural network (CNN) model; voltage stability +
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 6 Future Prospects
The fusion technique fuses the spaces, and then the fused feature space is transformed into the image space to produce the fused image. An infrared and visible image fusion technique based on a converter and generative adversarial network was proposed by [PERSON] [114] et al. The converter fusion module in the generator is made up of the spatial converter and the channel converter merged. The transformer is also used in other phases of the VIF approach, among other approaches. For example, to fuse the features of infrared and visible images together, [PERSON] [115] et al. suggestedthe YDTR approach, which integrates CNN and transformer in the encoding and decoding branches. There are various benefits to using converter-based techniques for fusing images from the visible and infrared spectrums. For instance, the converter can more quickly learn the mapping relationship between the two separate picture fusion modes. Additionally, the algorithm is adaptable, and the converter's settings can be changed by various tasks to enhance the quality of the fusion. However, there are few infrared and visible image fusion methods based solely on converters, which are all applied to fusion tasks by combining converters with other networks. Transformer-based techniques are still in their infancy as an application in the fusion of infrared and visible images, but they will undoubtedly advance in the next few years.
# Spare Parts Made by Additive Manufacturing to Improve Preventive Maintenance
## 1 Introduction
More than 200 existing industrial examples are manually collected and labelled as training data; in the meantime, multiple regression algorithms are tested against each potential AM to find better predictive performance [21]. In the study of AM implementation methodologies for spare parts, categorisation with respect to economic and technological criteria is important. In this field, [PERSON] et al. used the Delphi method to determine and validate the relevant criteria for the classification of spare parts. This generic framework benefits professionals in identifying technically compatible spare parts for AM that enhance the company's competitiveness [22]. [PERSON] et al. identify distrust in quality, insufficient knowledge of materials and design among stakeholders, as well as poor availability of spare part design documentation as the main barriers when adopting AM in spare part production. These three key barriers are interconnected. They conclude that training engineers in design and material issues, as well as in the development of high-quality design documentation, will significantly increase the implementation of AM in aftermarket parts [23]. Spare parts redesign has been studied by [PERSON] et al., providing design considerations based on agile hardware development practices to improve the quality of spare parts and reduce delivery times. This methodology is derived from case studies in the military over two years. The study presents three different use cases that follow this methodology, and provides process weaknesses and recommendations for production engineers and designers [24]. The study of a structured approach to the configuration of the spare parts supply chain should determine whether to centralise or decentralise stock management, also considering hybrid configurations. Furthermore, such a method should define whether there is a need to switch the production of spare parts from conventional manufacturing (CM) to AM technologies [25]. Up to now, little consideration has been given to the impact of AM on spare parts inventory management, both from a technical and an economic point of view. For this reason, several studies have contributed to clarifying when the use of AM for spare parts inventory management would provide benefits over CM techniques from a technological point of view [26; 27; 28]. The main objective of this work is to determine the possible spare parts of an automotive manufacturing company in order to print them and study their usability, getting to know if they are susceptible to be used as regular parts and if they are affordable in preventive maintenance with the goal of improving the maintenance domain with 3D printing. The novelty of this research work lies in the use of spare parts made by additive manufacturing in an industrial automotive factory. This means that it is applied, not to the product, but to the manufacturing process itself. It can also be noted that a methodology for the selection of process spare parts is developed.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
### The Role of Big Data and IoT in Mental Health Management  
In this context, the Internet of Things (IoT) has emerged as a transformative technology capable of addressing mental health monitoring gaps. IoT devices equipped with environmental sensors can continuously collect vast amounts of real-time data, creating new opportunities for monitoring the interplay between environmental conditions and mental health. The application of IoT in health monitoring is increasingly prevalent, with several studies exploring its role in managing chronic conditions, sleep disorders, and stress levels [8, 9, 10]. When combined with big data analytics, fuzzy logic, and machine learning (ML) techniques, IoT systems can process and analyse these datasets to uncover meaningful patterns, provide actionable insights, and deliver personalised recommendations. With the proliferation of IoT devices, environmental data can now be collected continuously at an unprecedented scale. Big data analytics offers a powerful framework to process, analyse, and extract actionable insights from these data.  
This paper proposes a novel IoT-based system for mental health monitoring that integrates these technologies to address critical shortcomings in traditional and emerging methods. The system aims to offer real-time classification, visualisation, and predictive analytics tailored to individual needs by capturing and analysing ambient environmental data alongside personal sensitivity profiles. The proposed approach enhances the accuracy and relevance of mental health monitoring. It fosters a shift from reactive to proactive care by enabling early detection of adverse conditions and their impact on emotional well-being.  
This study aims to address the following questions:  
1. How can big data and IoT technologies improve environmental monitoring for mental health management?
2. What predictive capabilities can be introduced to anticipate adverse conditions?
3. How can these predictions be personalised to account for individual sensitivities?  
The remainder of this paper is organised as follows: Section 2 reviews related work in IoT-based mental health monitoring and the role of environmental factors. Section 3 outlines the design and architecture of the proposed system, detailing the integration of IoT devices, big data analytics, fuzzy logic, and machine learning. Section 4 presents initial experimental results, highlighting the system's performance in terms of accuracy, scalability, and real-world applicability. Section 5 discusses the implications of the findings, including their potential to empower mental healthcare providers and individuals. Finally, Section 6 concludes the paper, summarising contributions and outlining directions for future research.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 4 Digestate Processing
### Nutrient Removal and Recovery
Nitrogen and/or phosphorous can also be removed by struvite (Mg(NH)\({}_{4}\)PO\({}_{4}\)*6H\({}_{2}\)O) precipitation, which is induced at elevated pH values in the presence of NH\({}_{4}\), magnesium (Mg), and phosphate (PO\({}_{4}\)\({}^{3-}\)). For this purpose, magnesium oxide and phosphate ions need to be added in excess and the pH value is increased by the addition of alkali. Chemical demand is the main disadvantage of struvite precipitation. In contrast, an advantage is that struvite can be used as slow-release fertilizer and that there are several established processes for struvite recovery and products [17; 46; 60]. In addition, removing solids before the precipitation might be advantageous. Ion exchange is another option to remove NH\({}_{4}\)-N, which is hardly used since the liquid needs to be free of particles [17]. It is operated in batch-mode because the ion exchange resins need to be regenerated, causing also a certain demand for chemicals (NaCl) [17]. In most processes, the amount of nutrients that are removed depends on the processing time or cycles and on the use of chemicals--the longer digestate is processed or the more cycles are applied, the more NH\({}_{4}\)-N or phosphorus will be removed.
# A Survey on Hardware Accelerators for Large Language Models
## 6 In-Memory Hardware Accelerators  
### Att  
In 2020, [PERSON] et al. presented another approach for the acceleration of attention-based accelerators, called ATT [32], based on resistive RAM. ATT is based on crossbar-based resistive RAM that can eliminate weight movement between memory and processing units; it has a dedicated pipeline design for attention-based neural networks. The proposed scheme consists of several modules.  
The Q-K-V engine employs a crossbar-based ReRAM to perform the matrix-vector multiplications. The mask issuer computes masks according to the matrix fetched by the Q-K-V engine. Two different masks are output from this module. One is forwarded to the attention engine, and the other is stored in the mask cache to filter the inputs and outputs of the fully connected engine.  
Finally, the attention engine performs matrix-to-matrix multiplication in three stages. The first stage computes the inner products between q vectors and the transposed k vectors. The second stage applies softmax to the results of these inner products. The third stage involves multiplying the softmax results by the v vectors  
The proposed scheme was simulated using CACTI 7.0 at 32 nm to model the power and area of the SRAM buffer and the mask cache. Based on the performance evaluation, ATT can achieve a speedup of 202\(\times\) compared to the NVIDIA GTX 1080 Ti GPU.
# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 3 System Model and Definitions
### Price Function for Parking Vehicle  
With the support of the crowdsourcing platform, the parking vehicles will cooperate to complete the task offloaded to the ECP. The utility function of the parking vehicle is defined as the received reward minus its energy consumption for task processing, which is denoted as:  
\[U_{P_{j}}=\sum_{i=1}^{n}\left(p_{new}q_{ij}-\phi\frac{\alpha q_{ij}}{c_{je}} \right). \tag{13}\]  
The meanings of the above parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Meaning** \\ \hline \(Q_{i}\) & Total task quantity \\ \(q_{iv}\) & Amount of tasks offloaded to VEC severs \\ \(q_{ie}\) & Amount of tasks offloaded to ECP \\ \(q_{ij}\) & Amount of tasks offloaded to parking vehicle \(P_{j}\) \\ \(r_{v}\) & Rate of task transmission from the requesting vehicles to the VEC servers \\ \(r_{e}\) & Rate of task transmission from the requesting vehicles to the ECP \\ \(d\) & Task transfer rate of ECP \\ \(C_{v}\) & Computing capability of VEC servers \\ \(C_{je}\) & Computing capability of parking vehicles \\ \(p_{iv}\) & Price per unit resource for VEC servers \\ \(p_{ie}\) & Price per unit resource for ECP \\ \(p_{new}\) & Price per unit resource for parking vehicles \\ \(\alpha\) & Number of CPU cycles required to process a one-byte task \\ \(\sigma\) & Energy consumption factor for task assignment of ECP \\ \(\rho\) & Transmission energy factor for requesting vehicles \\ \(\varphi\) & Energy consumption factor of edge nodes \\ \(\mu\) & Time processing factor of edge nodes \\ \(\omega\) & Satisfaction factor of requesting vehicle \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main notation used in this paper.
## 5 Features and Data Preparation
### Variable's Information and Features Analysis
#### 5.2.2 Electric Power Variables  
Energy consumption profiles are of a great interest for assessing the vehicle's battery range and energy efficiency. Instantaneous energy data also provides valuable information of many aspects of the driving, including the driving strategy. In addition, some particularities of the current driving environment can be inferred from this data. The electrical current between the battery and the electric motor is directly related with the mechanical energy required to accelerate/decelerate the vehicle. When complemented with additional information from other sensors, it is possible to create a comprehensive model of the instantaneous situation of the vehicle and use it to optimize energy use through driving strategy adjustment.  
In this work, the electric energy was measured at the vehicle's motor controller, which manages the power delivery and regeneration between the motor and the battery pack. The variables measured were current and voltage, which are directly related with the electric motor torque and speed. An instantaneous high current demand can cause momentary battery voltage drops. This information can help in characterizing both the electric power demand profiles and the battery response to them.
# Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing
## 5 Conclusions  
Ground-coupled heat transfer calculation is an important and difficult issue in building simulation and air-conditioning design. A new semi-analytical calculation method was presented, in the current study, based on a two-dimensional transient heat transfer equation. Compared with the long-time solutions in previous studies, the new method could also calculate the initial transient heat transfer. Prior to this, numerical simulation methods are almost the only way to solve the initial transient ground-coupled issue. The current method is significant for temporary housing that is built rapidly and is of a short life cycle. At the same time, the method is more efficient than common numerical methods due to no iterative process.  
The large influence of soil initial temperature conditions occurs in hot and cold climates when the indoor environment is exactly a concern. A more accurate calculation would be provided by the current method, while long-time solutions might even misestimate heat flux directions. It was concluded that larger ground thermal capacity would have more effects on the initial heat transfer of ground-coupled floor. That is, thermophysical parameters of ground, such as the density, the heat conductivity, and the specific heat capacity, are of significance. Moreover, insulation could effectively reduce the heat exchange between indoor and outdoor environments and slow down the thermal release of ground.  
The same as other analytical solutions, the current method is not able to deal with complex floor configuration problems. In addition, the coupled calculation of floor and building envelope heat transfer would be furtherly studied in future work, and helpful guidance could be provided for the air-conditioning design and energy demand prediction in temporary housing.  
Conceptualization, E.L.; Methodology, P.D.; Writing--original draft, P.D.; Writing--review & editing, J.L., M.X., Z.C. and E.L.; Visualization, J.L. All authors have read and agreed to the published version of the manuscript.  
This project is funded by the National Natural Science Foundation of China (no. 52078314), China.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: Effects of floor insulation on the face and edge heat fluxes of floor surface in temporary housing built **(a)** on 1 January; **(b)** on 1 July.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping
### Literature Review According to Analytic Methods and Approaches
#### 3.2.2 Fuzzy Theory  
[PERSON] et al. [27] applied certainty factor analysis and fuzzy theory to mining subsidence susceptibility estimations. Certainty factor analysis was used to estimate the relative weight of eight major factors influencing ground subsidence. The relative weight of each factor was then converted into a fuzzy membership value (0-1) and integrated as SSI using fuzzy combination operators to generate several coal mine subsidence susceptibility maps. Consequently, the fuzzy \(\gamma\)-operator with a low \(\gamma\) value and fuzzy algebraic product operator were specifically useful for ground subsidence prediction. This is a meaningful case study as an example of applying fuzzy membership functions (FMRs) and various fuzzy operators (i.e., fuzzy AND, fuzzy OR, fuzzy algebraic product, fuzzy algebraic sum, and fuzzy gamma operations) to mining subsidence studies. However, the previously mentioned use of the term'subsidence hazard map' instead of the'subsidence susceptibility map' in this study will need to be corrected.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 6 Technical/Critical Analysis and Recommendations for Deployment  
In this section, we provide a comprehensive analysis of our research study, highlighting both its technical aspects and critical insights gained through the development process. We also offer recommendations for the deployment and usage of the Q-RPL protocol drawing from the challenges encountered during our study.