esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
###### Abstract  
Primary batteries are extensively employed as power sources in Internet of Things (IoT) devices for remote metering. However, primary batteries maintain a relatively consistent discharge voltage curve over a long period before experiencing a full discharge, making it challenging to predict the battery's life. In this study, we introduce a battery life prediction method to ensure the robust operation of IoT devices in remote metering applications. The robust battery life prediction process is divided into two stages. The first stage involves predicting the state of charge (SOC) to enable real-time remote monitoring of the battery status of metering devices. In the second stage, IoT devices implement a hardware-based alerting mechanism to provide warnings prior to complete discharge, leveraging a custom-designed Multi-Stage Discharge battery architecture. In the first stage, we developed the CNN-Series Decomposition Transformer (C-SDFormer) model, which is capable of accurately predicting the SOC of primary batteries. This model was specifically designed to support the real-time monitoring of battery status in large-scale IoT deployments, enabling proactive maintenance and enhancing system reliability. To validate the performance of the C-SDFormer model, data were collected from smart remote meters installed in households. The model was trained using the collected data and evaluated through a series of experiments. The performance of the C-SDFormer model was compared with existing methods for SOC prediction. The results indicate that the C-SDFormer model outperformed the traditional methods. Specifically, the SOC prediction achieved a mean absolute error (MAE) of less than 4.1%, a root mean square error (RMSE) of less than 5.2%, a symmetric mean absolute percentage error (SMAPE) of less than 7.0%, and a coefficient of determination (R\({}^{2}\)) exceeding 0.96. These results demonstrate the effectiveness of the C-SDFormer model in accurately predicting the SOC of primary batteries. For the second stage, a Multi-Stage Discharge (MSD) primary battery was developed to ensure a hardware-based low battery alert before the battery is fully discharged. This battery was designed to ensure the reliable operation of IoT devices, especially those whose batteries are not proactively managed through real-time monitoring in the first stage. By providing a low battery alert, the MSD battery reduces the risk of unexpected device shutdowns. This feature enhances the overall reliability of IoT devices, ensuring their continuous operation in remote metering applications.  
machine learning; state of charge (SOC); CNN-Series Decomposition Transformer (C-SDFormer); Multi-Stage Discharge (MSD) primary battery
## 5 Simulation and Discussion
### Intraday MPC Rolling Optimization Simulation Analysis  
#### 5.3.1 Forecast Data Source  
Similar to day-ahead scheduling, the main sources of uncertainty are the RES output and the fluctuations in various energy loads. Currently, forecasting methods for renewable energy output and load are well-established, and short-term forecast errors have significantly decreased. Given that the focus of this study is to validate the effectiveness of the DHB strategy, a random error of 5% was added to the day-ahead forecast data to simulate intraday RES and load forecasts. Considering the influence of different timescales, the disturbances of various energy types and loads were adjusted based on their respective timescales. Figure 8 shows the day-ahead forecast data and the intraday ultra-short-term simulation forecast data, with the latter exhibiting higher volatility due to its shorter timescale.
## General Near-Field Synthesis Based on the Optimization of Relevant Figures of Merit
### Direct Layout Optimization Algorithm
#### 2.2.1 Inputs of the Algorithm  
The algorithm has several inputs that are required to correctly perform the optimization: a starting layout of the reflectarray with the sizes of the geometrical features of the chosen unit cell (for instance, the dimensions of rectangular patches, the lengths of dipoles, etc.), the FoM specifications, the actual FoM calculated from the NF, and the number of degrees of freedom (DoF) which are going to be optimized.  
We consider a unit cell with \(L\) geometrical features. Since the reflectarray has \(N\) elements, the total number of available DoF for optimization is \(P=LN\). Thus, we can consider the vector \(\vec{\xi}=\{\xi_{1},\xi_{2},\ldots,\xi_{p},\ldots,\xi_{p}\}\) as representing the reflectarray layout, where \(\xi_{p}\) represents a DoF. In general, the total number of available DoF may be relatively large. For instance, some multi-resonant unit cells may have up to eight [39] DoF per element. If the reflectarray has several thousands of elements [40], then, the total number of DoF can be in the range of several tens of thousands of DoF. Some specific applications, such as high resolution SAR, may require optimization of more than 160,000 variables [41]. This may create difficulties in achieving successful optimization since the number of local minima grow exponentially with the number of DoF. To tackle this issue, one common approach is to start the optimization with a reduced number of DoF and loose requirements and gradually increase the number of DoF, while tightening the restrictions until convergence is achieved, as illustrated in the flowchart in Figure 2.  
Another input is the FoM calculated from the NF. In the present investigation, and for the purposes of optimizing a reflectarray to work as a PWG, we consider the magnitude and phase ripple as defined in (5) and (6), respectively. Thus, the total number of FoM to consider in the synthesis is two per frequency and polarization. Since there are \(N_{f}\) frequencies and the reflectarray will work in dual-linear polarization, there will be \(4N_{f}\) FoM to optimize. Similarly, the specifications that are also an input to the algorithm will impose the desired values that the FoM need to achieve.  
Figure 2: Schematic flowchart of the proposed DLO for a general NF synthesis based on optimization of the relevant NF FoM.
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 2 Methods
### The Transfer Learning Framework for RUL Prediction  
The transfer learning framework is illustrated in Figure 2. The framework is composed of two Long Short-Term Memory (LSTM) networks. The network on the top was trained on the large amount data of the source task. Then the learned model was fine-tuned by further training with the small amount of data from the target task, which is usually a different but related task. In our experiments, it represents the degradation failure under different working conditions.  
Figure 2: Transfer learning model architecture.
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
###### Abstract  
Given the ubiquity of mobile devices, understanding the context of human activity with non-intrusive solutions is of great value. A novel deep neural network model is proposed, which combines feature extraction and convolutional layers, able to recognize human physical activity in real-time from tri-axial accelerometer data when run on a mobile device. It uses a two-layer convolutional neural network to extract local features, which are combined with 40 statistical features and are fed to a fully-connected layer. It improves the classification performance, while it takes up 5-8 times less storage space and outputs more than double the throughput of the current state-of-the-art user-independent implementation on the Wireless Sensor Data Mining (WISDM) dataset. It achieves 94.18% classification accuracy on a 10-fold user-independent cross-validation of the WISDM dataset. The model is further tested on the Actitracker dataset, achieving 79.12% accuracy, while the size and throughput of the model are evaluated on a mobile device.  
activity recognition; convolutional neural networks; deep learning; human activity recognition; mobile inference; time series classification; feature extraction; accelerometer data +
Footnote †: journal: Applied Sciences
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 5 Case Study
### ESCR Is Low and 20% Changes in Load (ESCR = 1.7)  
Case 4 simulates the situation where 20% of load decrease with the weak onshore AC system. Figure 8 shows the response of the system after the load decreases when the onshore power system is weak. Figure 8a shows the changes of DC voltage during the 20% load decreases. The value of HMMC directly affects the variation of DC voltage. With the increase of \(H_{MMC}\), the variation of DC voltage also increases. Figure 8b can clearly find that onshore SG has temporarily increased the power output during the load decreases. At the same time, the onshore MMC temporarily reduces the active power output to deal with the power mismatch of the onshore system in Figure 8e. Improved inertia control still helps the system's active power return to normal levels through its own rapid adjustment capabilities. It means that improved inertia control can also support the main frequency stability of the system under more complicated conditions. Figure 8c shows that as the \(H_{MMC}\) value increases, the rotor speed of the SG is effectively damped. It means that the inertia constant provided by the improved inertia control effectively improves the stability of the system frequency. By observing Figure 8d, the improved inertia control strategy effectively gives the system frequency support. When \(H_{MMC}=0.4\) s, the system device response is greatly improved than before, and it is better than the system under the traditional control strategy.  
In addition, by observing Figure 8f,g, the two figures show the changes of offshore side MMC active power when the load is reduced by 10% and 20% under weak onshore AC system conditions. When the load is decreased by 10%, it affects the system which is close to the onshore side, but it does not affect the offshore side under the control of the improved inertia control strategy. When the load is decreased by 20%, the system is more affected, which also causes the active power on the offshore side to be affected, but it is still restored to a normal level under the control of improved inertia control. Although it has been more affected than the previous loading conditions, the improved inertia control strategy still effectively supports the system with a good blocking capability. When the onshore system is weak, it still has an impact on the offshore side MMC converter. But this impact is quickly corrected, then the normal operation of the system is guaranteed. Case 5 and case 6 systems have become stronger. Under the control of improved inertia control, neither a 10% load reduction nor a 20% load reduction can affect the offshore side. Therefore, case 5 and case 6 will no longer represent the active power graphics on the offshore side.
# Human Cognitive and Motor Abilities in the Aging Workforce: An Information-Based Model
## 3 A Review of Experimental Tests of Motor-Cognitive Abilities from Information Theory Perspective
### Tests on Cognitive Abilities
#### 3.1.1 Digit Symbol Substitution Test (DSST)  
DSST consists of digit-symbol pairs followed by a list of digits. Different versions of DSST test can be found in [49]. The DSST version adopted in this paper is described in [50; 51]. Under each digit, the subject should write down the corresponding symbol as fast as possible in the allowed time of 90 s. The number of correct associations of digit-symbol pairs within the allowed time is the score. The DSST indirectly measures the working memory because it requires the subject to remember the corresponding symbols and numbers memorized and to write down the symbol corresponding to each given number. The Information Processing Rate (IPR) of the subject is the amount of information processed in the time unit [bit/s]; it can be calculated as follows:  
\[\mathrm{IPR}_{\mathrm{DSST}}=\frac{2\times\mathrm{D}\times\log_{2}(2\times \mathrm{D})+\mathrm{n}\times\log_{2}2}{90}\left[\frac{\mathrm{bit}}{\mathrm{s }}\right] \tag{2}\]  
where D is the total number of digit-symbol pairs (for each digit there is a corresponding symbol) and n are the identified pairs in 90 s. The quantity \(\log_{2}(2\times\mathrm{D})\) is the amount of information to be processed to recognize one symbol or one digit, \(2\times\mathrm{D}\times\log_{2}(2\times\mathrm{D})\) the amount of information needed to recognize all symbols and digits (\(2\times\mathrm{D}\)), \(\log_{2}(2)\) the amount of information to be elaborated to identify a single pair, and \(\mathrm{n}\times\log_{2}(2)\) the overall amount of information elaborated to identify n digit-symbol pairs in 90 s.
[PERSON] et al. studied electrodeposition and the corrosion behaviour of copper in a choline chloride-ethylene glycol DES. In the electrodeposition process, a rough surface was obtained with the addition of ethylene diamine; the corrosion decreased and a relatively uniform surface was gained [5]. Many research groups have tried to deal with the physical properties of ethaline (choline chloride-ethylene glycol). it was found that the mass transport could be increased with an increasing temperature; when the temperature was increased to 50 \({}^{\circ}\)C, the viscosity decreased by 65% [7]. Concerning the physical properties of the electrolyte, such as conductivity and viscosity, it was found that it is insensitive to metal-salt addition but strongly temperature dependent [9]. The speciation of copper is well-defined in choline-based DESs. It was determined to be [CuCl\({}_{3}\)]\({}^{2-}\) and [CuCl\({}_{4}\)]\({}^{2-}\) for Cu(I) and Cu(II), respectively [20]. The dissolution of bulk copper was studied previously in these media, in the present work attempt was directed to deal with copper in more detail [21]. Deep eutectic solvents (DESs) are attracting significant research interest as electrochemical media, especially for electrodeposition, as they share many of the useful properties of ILs, but are typically greener and cheaper [22]. The influence of water and the changing potential on the interfacial region between a platinum electrode and a DES were studied. It was found that the nanostructural feature of interface decreased in the presence of small amount of water and was also strongly potential dependent [23].  
The aim of this work is to investigate the dissolution mechanism of copper in a DES and its comparable IL and compare it to the aqueous system. This is important for counter-electrode processes during electrodeposition, for electropolishing, and also to understand whether metallic copper corrodes in the DESs.
## 1 Introduction
[24] proposed a fatigue crack propagation model based on cohesive zone by combining Park-Paulino-Roesler (PPR) traction-separation relationship and conducted computational simulations on the proposed fatigue crack propagation model. Although research on cohesive force fatigue damage elements is relatively abundant, there is still no research experience specifically addressing fatigue damage at cold joint interfaces. This research gap undoubtedly poses significant challenges to the assessment of the safety status and prediction of the timing of cracking in concrete linings containing cold joints. In the context of the above research, this paper conducts the following four parts of work based on the fatigue fracture experiment of concrete components with cold joints under low stress levels and low stress amplitudes fatigue loads. Part 1: Derive the calculation formula for the fatigue damage of cohesive units under cyclic loads, and describe the implementation method of the cohesive fatigue damage unit constitutive model in numerical calculation software. Part 2: Constructing a numerical calculation model for three-point bending fracture experiments. Part 3: Based on the values of parameters in the fatigue damage constitutive model, multiple sets of data are constructed and substituted into the numerical calculation model to calculate the dataset required for the inversion of cohesive fatigue damage unit parameters. Then, a BP neural network for the inversion of fatigue damage parameters is constructed, and the fatigue damage parameters corresponding to different pouring intervals of cold joints are calculated using experimental data. Part 4: Compare the numerical simulation results with experimental data to verify the correctness of the cohesive fatigue damage element and BP neural network. Finally, analyze the damage and cracking process of concrete components with cold joints under fatigue loads.
## 4 Experiments
### Datasets and Evaluation Metrics
#### 4.1.3 Implementation Details  
Our models were trained and tested using the NVIDIA GEFORCE RTX2080 Ti GPU with 11 GB memory. The data augmentation part uses the default initialization parameters of YOLOv8, and the training period for all experiments was set to 100 epochs. In addition, due to the limited physical memory on the GPU, we set the batch size to 8 for training. The same training and testing configurations were used for all datasets. In addition, validation was conducted after each training phase to select the model with the best performance on the validation set. Finally, the best model was evaluated on the test set.
# PVTReID: A Quick Person Reidentification-Based Pyramid Vision Transformer
## 1 Introduction
CNN-based methods alleviate the impact of these factors on person features in various ways, among which local feature methods [17] and side information [2] have been demonstrated to be effectual means of strengthening the robustness of features. At the same time, the features extracted by CNN-based methods are different from those extracted by the PVT-based method, and their semantic information differs. Furthermore, considering side information such as cameras can reduce the impact of nonvisual factors on the robustness of person features. If complex information constructed via CNN is directly used for PVT, the encoding ability and pyramidal structure of PVT cannot be fully utilized. To successfully solve these problems, it is necessary to design modules specifically for PVT. For these reasons, we present a novel ReID method called PVTReID for obtaining robust person features. First, we build a strong basic network based on PVT with a few crucial adaptations. Second, in view of solving the problem of local person features that are indistinguishable from each other, we propose a local feature clustering module (LFC) by calculating the most discrete local features for further feature learning. LFC is used in the final stage of the framework to obtain person features together with the global features. Third, to further improve the robustness of the person features, we employ side information embeddings (SIE) to embed side information into the four encoding stages of the PVT. The contributions of this paper are summarized as follows:  
* We recommend a strong basic network that utilizes PVT for ReID with performance that is comparable to CNN-based methods. With the help of PVT, by using progressively smaller feature pyramids the PVT-based method reduces the computation of large feature maps, thereby alleviating the problem of slow inference for ReID person images. * We introduce a local feature clustering (LFC) module to compute the most discrete of the local features and cluster them. With LFC, we further separate the feature representations of different people in the feature space to make the person features more robust. * We perform side information embeddings (SIE) on the camera information and send this information to the feature extraction network for use in training, which reduces interference from nonvisual information in the resulting features and improves their robustness. In addition, we verify the effects of using camera information in training at different PVT encoding stages. * The final PVTReID framework achieves 87.8%, 63.2%, and 80.5% mAP accuracy on Market-1501, MSMT17, and DukeMTMC-reID, respectively, and has faster inference speed compared to CNN-based methods.
## 2 Device Description  
The HEMT, based on aluminum gallium nitride/gallium nitride (AlGaN/GaN), is a highly developed electronic component due to its structure and materials. One of this materials is Gallium nitride (GaN); it is characterized by high mobility, high electrical breakdown field, and high thermal conductivity. The HEMT appears as an evolution of the MESFET; the difference is that the HEMT uses a heterojunction, that is to say, a junction between materials having different energy bands, to make the electrons constituting the drain-source current pass through a non-doped semiconductor, to decrease the transit time and thus increase the frequency performance. The electrons' speed is all the greater when the doping of the semiconductor is weak, because the dispersion of ionized impurities is reduced [20].  
The field effect transistor based on the GaN heterostructure uses a 2-dimensional electron gas (2 DEG) as a conductive channel, where electrons circulate in a lightly doped material and result from the occupation of the energy levels of the potential well that is characteristic of the heterojunction. Three electrodes, commonly called source, gate and drain allow controlling the current and the voltage of the transistor operation. The source and drain contacts are Ohmic type, the gate contact is Schottky type. For power amplified applications, the common source arrangement is used and the source electrode is connected to the ground. The gate allows the control of the current density circulating in the transistor by acting electro-statically on the electron gas; it is the low power control electrode. The drain allows the control of the operating voltage; it is the high power control electrode [3; 21].  
The HEMT has a particular structure that consists of many layers with different materials (Figure 1). A silicon carbide (SiC) is used as a substrate on which the component is grown. The nucleation layer or the thermal boundary resistance (TBR) serves to realize a mesh tuning for the SiC and GaN layer; its other function is to reduce mechanical stress and defects in the GaN layer. The GaN layer contains the 2 DEG at the upper part, and the AlGaN layer is used to create a heterojunction with the GaN layer; there are many layers that provide several functions. After a bibliographic study, we found that the HEMT has several structures. It was also noticed that there is a difference between these structures regarding the topology and materials used; all this leads us to conclude that the HEMT is a technology that is still developing and improving in order to find the best structure that increases its performance and reliability [4,14].
## 6 Discussion
### Live Analysis  
To achieve our ultimate goal of live, real-time analysis, further work is required to fully automate the conversion of flows to bitmaps suitable for input into the model. This is done separately in our current tests, but can be easily automated and streamlined to allow for real-time conversion.  
Real-time analysis would also require us to develop a means of reliably extracting at least the first packet from each flow. In order to do this, we intend to use the features of Software-Defined Networking (SDN) [24] which allow packets to be copied and forwarded to different sources based on configurable flow rules.  
Additionally, though we only test IPv4 in this work, it would be advantageous for live implementation to also include support for IPv6. The model itself could be easily extended to IPv6, as the structure of IPv4 and IPv6 packets is similar in many respects.
## 3 Comparison of the Statistical Features between the Gaussian Model and SMART 1 Records
### Amplitude Variation of the Normalized Periodograms
Consequently, the analysis above shows that the Gaussian model underestimates the periodograms' amplitude variation of the actual ground motions.