esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Results  
The parameter combination \((K=4,\alpha=93)\) was obtained by the SSA optimization of VMD. The original load data from Place 1 were decomposed using the optimized VMD parameter combination, and the decomposition effect is shown in Figure 7. The first bar in Figure 7 shows the original load data with strong random fluctuations. After the decomposition, four IMFs and Res are obtained, which realize the noise reduction of the original sequence, and the fluctuations of several components show a certain regularity. According to the oscillation frequency of each component curve used to distinguish the low-frequency component and high-frequency component, IMF1 is the low-frequency component (using MLR for forecasting), and the remaining several components and Res are the high-frequency components (using LSTM for forecasting), thus constructing a hybrid forecasting model to complete the first stage of forecasting.  
This paper uses the training set to train the parameters of the first stage in the learning model. In order to avoid information leakage and overfitting problems caused by using the training set data again in the second stage of learning, the forecasting values of each component are obtained in the second stage using the validation set for prediction, and the component forecasting values are combined with the influencing factors selected by PCC and MIC and the loadings at the corresponding moment selected by the ACF function to construct the feature vector. We input the feature vector into the fully connected layer for the second stage of forecasting to obtain the final forecasting values. In this paper, the load values for the 24 moments on 10 January 2015 are predicted by the iterative forecasting approach.  
Figure 7: The decomposition results of the SSA–VMD algorithm of Place 1.
## 2 Materials and Methods
### RF Stage Design
This figure shows how AD9364 is connected to the FMC connector, as well as the FPGA and RF Output to the power amplifier stage (not shown at this work). Figure 5: Block diagram of the IC AD9364. In this figure, the AD9364 manufacturer describes how it connects internally. to the manufacturer's specifications [12]. However, a frequency of 8 MHz was used for testing purposes in this work to ensure a safe transfer of information to registers. Regarding the operating and configuration modes of the registers, there is a relatively large number of reserved and/or empty positions; thus, in order not to cause improper operation of the system, it is advisable not to refer to them. To reduce transients while changing the transmission frequency setting on the device, the power to the matching stage on the transmission lines is directly connected to a 1.3 V voltage source. The capacitors, shown in Figure 6, are intended for removing noise and transients at the transmitter output. It is worth mentioning that all the designs, measurements, and tests for the resulting devices were carried out in a test laboratory with a controlled environment (temperature, voltage levels, electromagnetic noise, and electric shocks, etc.) in such a way that the results obtained are completely similar to those obtained by simulations tools. Design rules concerning the CubeSat standard [14] and PCB design [15] were applied to the electronic design process. The final result was a 6-layer PCB of 10 \(\times\) 10 cm (see \begin{table}
\begin{tabular}{c c c} \hline \hline
**Nodes** & **Initial** & **Final** \\ \hline Initialization & 0x000 &... & 0x017 \\ Setting & 0x018 &... & 0x05F \\ Transmitter & 0x060 &... & 0x0D7 \\ Configuration & 0x0F0 &... & 0x1 FC \\ Rx Analog Registers & 0x230 &... & 0x3F6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Functions of the AD9364 registers. Figure 6: The RF matching stage, designed to avoid interference for 2–3 GHz. Figure 7); on the other hand, the manufacturer suggests a fixing or soldering area on the PCB with the intention of achieving high performance and achieving adequate operation of the IC [13]. In Table 3, the thickness of the 6 PCB layers is shown. Within the PCB, it is intended to integrate devices with the ability that they can be selected (RF switch) and digitally tuned [16] to take advantage of the available space in it and to be able to use different devices such as antennas, amplifiers, and filters in frequency bands that are too far apart from the main one. The PCB for the radio transmitter was designed using Altium(r), a high-end PCB design software package. The simulation of signal behavior on the PCB was carried out using Keysigth(r) EMPro(r) and Genesys(r) tools. The purpose of the simulation was both to validate low levels of electromagnetic interference (EMI) between interconnecting tracks and to avoid radiation to other components included in the board before PCB fabrication.
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 2 Related Work
### Secure Routing in Networks  
The authors in [28] present a framework called intrusion prevention for mobile IoT devices to provide reliable data routing based on a blockchain. The proposed framework is categorized into two phases. Firstly, every node stores its neighbors' information in the routing table. Using the uncertainty principle, the cluster heads are selected. Secondly, the authors present a security model that improves the network reliability based on the blockchain. Furthermore, the authors in [29] propose a blockchain based encryption and localized routing scheme to discover a route. Moreover, blockchain technology is used for data security that separates the data into blocks. Furthermore, a blockchain based contractual routing protocol is introduced that establishes trust between IoT vendors and cooperators during data transmission. The proposed system comprises a multi-hop network where the sets of source, intermediary, destination and gateway devices are present. In the blockchain contractual routing, each source node uses a smart contract to request a route from gateways or destination nodes within a specific time [30]. Moreover, the authors in [31] propose a routing scheme using blockchain and reinforcement learning to enhance both the routing efficiency and the security of WSNs. The proposed scheme consists of two parts that are the blockchain and routing network. Moreover, there are three types of nodes: server, terminal and routing. The routing nodes are connected with a terminal node that receives the packet from other nodes. The source terminal sends the packet to the target terminal with the help of intermediary nodes, called routing nodes. Furthermore, the server nodes aggregate the data packets. Blockchain technology ensures fairness and tractability of the transactions. A consensus mechanism, PoA, is selected that efficiently processes the transactions performed in the network.
## 2 Materials and Methods  
### Materials  
HPLC-grade acetonitrile, formic acid, tetrahydrofuran, isopropanol, and sodium phosphate were purchased from Sigma-Aldrich (Milan, Italy). Betamethasone 17-valerate (BM 17-V), beclomethasone (BC), beclomethasone dipropionate (BCDP), methylprednisolone (MP), budesonide (BD), flunisolide (FN), flumenthosone (FM), and dexamethasone (DM) were purchased from Sigma-Aldrich (Milan, Italy). Purified water from the Milli-RX system (Millipore, Milford, MA, USA) was used to prepare mobile phase and standard solutions. Nitrocellulose syringe filters (0.20 mm) were purchased from Phenomenex (Torrance, CA, USA). Commercial cosmetic products, namely, cream A, cream B, and serum, were available on the market; the ingredient lists are reported in Table S1 of the Supplementary Material. An ELISA kit was purchased from Neogen (Lansing, MI, USA). It contained the EIA buffer (phosphate-buffered saline solution with bovine serum and a preservative), the washing solution (phosphate-buffered saline solution with a surfactant), the substrate (stabilized 3,3',5,7'-tetramethylbenzidine (TMB) plus hydrogen peroxide), the drug-enzyme conjugate (drug horseradish peroxidase concentrate), the antibody-coated plate, and the stop solution (non-acidic reagent).
# Digital Health Technologies to Support At-Home Recovery of People with Stroke: A Scoping Review
###### Abstract  
(1) Background: Digital health technologies (DHTs) are increasingly being utilized to facilitate receiving rehabilitation services remotely, offering innovative solutions to enhance recovery outcomes. This scoping review examines the role of DHT in home-based stroke rehabilitation, focusing on its applications, effectiveness, and limitations. It identifies key advancements and future directions for improving stroke recovery through technological innovations. (2) Methods: Using [PERSON] and O'Malley's framework, a systematic search was conducted across multiple databases to identify studies involving DHT for home-based stroke rehabilitation. Eligible studies incorporated technologies for monitoring and evaluation. Data extraction followed PRISMA-ScR guidelines, synthesizing findings across various research designs. (3) Results: Ten studies were reviewed, categorizing technologies into wearable devices, smartphones, and sensor-based solutions. These tools primarily assessed mobility, upper extremity function, cognitive function, daily living activities, and continuous physiological monitoring. High feasibility and usability were reported, though challenges included small sample sizes and user-centered design limitations. (4) Conclusions: Most DHTs used for evaluating and monitoring home-based stroke rehabilitation are wearable and sensor-based, mainly focusing on mobility and upper extremity function. Their application is effective, but limitations remain. Future research should address these gaps to enhance usability and coverage.  
digital health technology; stroke rehabilitation; home monitoring; evaluation +
Footnote †: journal: Applied sciences
## 5 Privacy Risk Assessment: Advancements within the PRUDEnce Framework
### Relationship between Entropy, Feature Frequency, and Re-Identification Risk  
When analyzing data with entropy-applied trajectory feature frequency, such as location or time, it becomes clear that features with lower entropy values also have lower frequencies and pose a higher risk of re-identification for the individuals. This relationship between entropy and frequency highlights several key factors contributing to the increased risk.  
Less frequent features tend to have a lesser impact on the overall uniqueness of the data. With fewer instances, each occurrence becomes more significant in distinguishing individuals within the dataset. Consequently, the aggregated data offer less anonymity, making it easier for adversaries to differentiate between individuals.  
Regarding entropy, low-frequency features contribute less to the overall uncertainty in the dataset because their probability of occurrence is low. In entropy calculations, probabilities of rare events (e.g., visits to low-frequency locations) have less impact on the overall entropy than probabilities of more common events. Therefore, low-frequency features tend to have lower entropy, indicating less uncertainty in the distribution of visits. While low-frequency features may be easier to predict due to their less variable and more predictable nature, this predictability can increase the risk of re-identification.  
Applying entropy to features highlights the heightened risk associated with low-frequency data. These features, characterized by their low entropy, offer less anonymity and increased predictability, making them more susceptible to re-identification.
## 3 Materials and Methods
### Method for Generating the Sonic Watermark
#### 3.2.3 The Signal Processing Blocks  
* The SNR improvement block--Consists of a filter bank with nine bandstop filters used to increase the chirp-to-tick sound SNR. It was detailed in [23] that noise in the same bandwidth with the chirp signals can determine false chirp detections if a matched filter and threshold comparator is used as the detector. To minimize the probability of false chirp detections, the ticking sounds are filtered using 9 bandstop filters to remove their spectral content existent at frequencies occupied by the chirp signals. Because the stop bands of those filters are very narrow compared to the corresponding critical bands, the filtering effect is inaudible. The magnitude response of this signal processing block is illustrated in Figure 8.
* The controlled delay line--Is used to encode auxiliary information by deviating the temporal distance between the ticking sounds by a small amount. The auxiliary information that is encoded is the room identification number. The input data of this block are the bits of the PRBS generator with its seed equal to the room ID. The bits of the binary sequence switch on or off the controlled delay line (\"1\" \(\rightarrow\) delay line active, \"0\" \(\rightarrow\) delay line bypassed). When active, the block delays the input signal with \(\Delta t=20\) ms. The signal at the input of this block is a mixture made of the filtered ticking sound and the chirp signals corresponding to the current ENF value. Changing the state of the delay line should be done between ticks, to avoid the occurrence of audible artifacts. An example of the delay line functioning is shown in Figure 9 along with the bits of the PRBS generator that are used to control it.  
Figure 8: The magnitude response of the SNR improvement signal processing block.
## 4 Experiment and Results
### Experimental Result
#### 4.4.2 False Positive Error Analysis Experiment of Characteristics  
In order to further verify the LFE module effect, the diagnosing methods for false positive (_FP_) errors in object detection proposed by [PERSON] et al. [62] were used. Figure 5 was one of the methods that showed the evolution of the proportion of four false positive errors with the increase in the total number of false positives. In Figure 5, the network with five LFE blocks inserted (Figure 5b compared with the original YOLOv4 Figure 5a) in terms of the error distribution on the PASCAL VOC 07+12 datasets. The \(x\)-axis represents the increasing number of false positive labels, and the \(y\)-axis represents the proportion of each error under a certain number of false positive labels, expressed in area. Through a change in the area occupied by the four errors, it can be seen that the diminution of the area occupied by the location errors was the most obvious, thus proving the effectiveness of our method for location feature extraction.  
According to the change in the number of various errors, the proportions of location errors, similarity errors, background errors, and other errors (see Figure 6 for the results) were analyzed. We found that location errors accounted for the largest proportion in the original YOLOv4, they decreased most after the LFE modules insertion, and their proportion of the total errors also decreased most. It proved the effectiveness of our LFE module in reducing location errors and extracting location features.  
Figure 5: Error distribution and diagnosis diagram. The first row is the diagnosis result of the original YOLOv4, and the second row is the diagnosis result of the YOLOv4 added with the location feature extraction structure. Loc: position error; Sim: similarity error; BG: background error; Oth: other error.
# Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
## 4 Experiment Results
### Dataset and Experimental Setting  
A limited set of distraction classes were included in earlier datasets and most datasets were not public. The dataset of StateFarm on Kaggle [38] was the first publicly available dataset containing 10 distraction classes, used for competition purposes. The AUC Distracted Driver dataset [24], shown in Figure 5, is similar to the StateFarm dataset with the same 10 distraction classes. However, the sampling rate is low, and the motion information between consecutive frames is unstable. Hence, in this study, we referred to the StateFarm and AUC Distracted Driver datasets to compile our own dataset as shown in Figure 6. Ten driver distraction classes were also defined [24; 38], including safe driving, text right, phone right, text left, phone left, adjusting radio, drinking, reaching behind, hair or makeup, and talking to passenger. We had 20 participants and the videos were shot in the same car. The videos were divided into 198 films, 28,779 RGB images, and 57,558 optical flow images of horizontal and vertical components. All images were resized to \(344\times 356\) pixels before the training and test process.  
Figure 4: Configuration of fusion network.  
Figure 5: AUC Distracted Driver dataset [24].  
Due to the limited amount of data in our dataset, the pre-trained model on UCF-101 [53] was used for network initialization, and then all the layers of spatial and temporal ConvNet streams were fine-tuned. In order to reduce the training time, videos of the 10 actions with large hand movements were selected from the UCF-101 dataset. All experiments were performed on a personal computer (PC) with an NVIDIA GeForce GTX TITAN X graphics processing unit (GPU) having 3072 CUDA cores with 12 GB of random-access memory (RAM), using an Intel i7-6700K central processing unit CPU and 16 GB of RAM; the frameworks of Keras and Tensorflow were used.
# A Validated Thermal Computational Fluid Dynamics Model of Wine Warming in a Glass
## 3 Simulation Model
### Material Properties  
The wine is modeled as an aqueous ethanol solution. Further compounds make up a much smaller proportion of the wine than ethanol and are, therefore, neglected [21]. The amount of sugar and acid in the investigated red wine is more than ten times lower compared to ethanol. Some sweet wines could probably contain a non-negligible amount of sugar but are not considered here; see discussion in Section 1. Ethanol's volume fraction \(\phi_{\text{eth}}=13.5\%\) of the utilized red wine equals a mass fraction of \(\omega_{\text{eth}}=0.1086\). [PERSON] [26] provides temperature-dependent transport properties of an aqueous ethanol solution with \(\omega_{\text{eth}}=0.1\), see Table 1, which were used for all simulations of red wine hereafter. Temperature-dependent transport properties of water were taken from [27]. While the visible solar radiation gets transmitted through water, low-frequency radiation (\(\lambda>0.95\,\upmu\)m) is absorbed within a layer of approximately \(2\,\text{mm}\)[28]. Consequently, the liquid surface is treated as opaque for thermal radiation, having an emissivity of \(\epsilon_{\text{Iq}}=0.96\)[29].  
The surrounding room air is considered an ideal gas with a variable density of \(\rho_{\text{air}}=\frac{pM}{RT}\), where \(M\) is the molecular weight of air, and \(R\) is the universal gas constant. The residual temperature-dependent transport coefficients of dry air (\(c_{\text{p}},\kappa,\mu\)) are taken from [27]. The influence of humidity on the transport properties can be neglected for the considered temperature range, see [30] for a comparison of dry and humid air. Air is considered an optically thin medium in this study. Only CO\({}_{2}\) and H\({}_{2}\)O interact significantly with thermal radiation [28]. Those air components exhibit relatively low partial pressures at room temperatures under standard atmospheric pressures and low relative humidity. Consequently, their contribution to radiative heat transfer can be neglected.  
Transport properties of the glass and the table were not easily found in a single, coherent and, hence, had to be combined from various sources; see Table 2. The thermal radiation of the surrounding white walls (\(\epsilon_{\text{wall}}\approx 1\)) lies in the infrared band. While the visible solar radiation gets transmitted through glass, it absorbs a large share of infrared radiation [28]. This legitimates the modeling of glass as an opaque body (\(\epsilon_{\text{glass}}=0.84\) for soda-lime glass) within this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(T/\epsilon^{\text{c}}\) & \(\rho/\kappa\text{g}\,\text{m}^{-3}\) & \(\epsilon_{\text{p}}/\text{kg}^{-1}\,\text{K}^{-1}\) & \(\kappa/\text{W}\,\text{m}^{-1}\,\text{K}^{-1}\) & \(\mu/\text{Pa}\,\text{s}\) \\ \hline
0 & 984.9 & 4360 & 0.503 & 0.00325 \\
10 & 983.8 & 4310 & 0.516 & 0.00217 \\
20 & 982.0 & 4280 & 0.529 & 0.00153 \\
30 & 978.7 & 4270 & 0.542 & 0.00116 \\ \hline \end{tabular}
\end{table}
Table 1: Transport properties of a \(\omega_{\text{eth}}=0.1\) aqueous ethanol solution [26].
## 4 Experiments And Results
### False Positive Classification Results  
According to the results from Table 2 through Table 4, it is obvious that, after introduction of the False Positive Classification (FPC) module, Render 3D U-Net decreases the false positive phenomena and improves performance on medical volume segmentation. In the liver segmentation task, the FPC module helped Render 3D U-Net obtain a better performance on mIoU and dice. In the kidney segmentation task, Render 3D U-Net with the FPC module obtained a higher mIoU and dice. In the heart segmentation task, Render 3D U-Net with the FPC module obtained a higher mIoU and Dice. Another comparison between Render 3D U-Net with FPC and Render 3D U-Net without FPC also confirmed the effectiveness of proposed FPC module in solving false positive phenomena and in improving performance. These comparisons strongly prove again that the improvement is attributed to the proposed point-sampling method and false positive classification module.
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 4 Results and Discussion  
The proposed methodology is evaluated using two primary criteria: (1) assessment of the FSOs communication system design, and (2) analysis of the blockchain network performance metrics.
# Radiation Damage of Polydimethylsiloxane and Polyimide by X-ray Free-Electron Laser
###### Abstract  
A crystal delivery system is essential in serial femtosecond crystallography experiments using an X-ray free-electron laser (XFEL). Investigating the XFEL-induced radiation damage to materials potentially applicable to sample delivery devices is vital for developing a sample delivery system. In this study, we investigated the radiation damage caused by an XFEL to polydimethylsiloxane (PDMS) and polyimide (PI), which are widely used as sample delivery materials in synchrotron X-rays. Upon XFEL exposure, the PDMS film produced irregularly shaped and sized holes, whereas the PI film produced relatively regular shaped and sized holes. When XFELs were exposed to the channel of the PDMS-based microfluidic device, holes were generated on the film by the radiation damage and the microfluidic device and the internal channel region were structurally destroyed. The PI-based microfluidic device experienced no structural destruction, except for the holes generated by the XFEL. However, as the XFELs were continuously exposed, bubbles generated from the solution due to radiation damage; the accumulation of these bubbles interfered with the path of the inner channel of the microfluidic device. Our results will not only help understand the phenomenon of radiation damage of PDMS and PI films by XFEL, but also provide insight into the directions to pursue in developing applications of PDMS and PI films in XFEL studies.  
X-ray free-electron laser; radiation damage; polydimethylsiloxane; polyimide; serial femtosecond crystallography; sample delivery; microfluidics +
Footnote †: journal: Applied Sciences