esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake
## 2 Materials and Methods  
### Data Collection  
The dataset used in this work is retrieved from an on-line accessible experimental dataset from the Minnesota starvation experiment, described by [30]. In this study, the energy intake of 32 male participants (age between 20 and 33 years old) is reduced to resemble the weight loss in undernourished prisoners of war. Prior to the 24 weeks of energy restriction (S1-S24), a 12-week control phase (C1-C12) is used to bring all participants as close to the group norm as possible. Information on daily energy intake for each participant during the control phase is available for only the last three weeks (C10-C12). For each participant, an average of daily energy intake for each week of the starvation period is specified. The starvation phase is followed with a recovery phase of 20 weeks (R1-R20). However, no information on daily energy intake is obtained during this last phase of the Minnesota starvation experiment. Physical activity is mandatory during both, control and starvation phases, and includes at least 35 km of walking per week. Total energy expenditure (TEE) is not directly measured but estimated based on resting energy expenditure (REE) and energy expended during physical activity (EEPA). REE is calculated the week prior to the starvation phase and at the end of the starvation phase. Therefore, no weekly estimation on TEE is provided. During the entire study, each participant is weighed once every week. Therefore, the dataset is comprised by the averages of body weight and daily energy intake from the last 3 weeks of the control phase and the entire starvation phase for the 32 test participants. The average height of the participants present in the dataset is \(H=(1.79\pm 0.06)\) m. The average initial and final bodyweights are \(BW_{i}=(69.50\pm 0.10)\) kg and \(BW_{f}=(53\pm 4)\) kg. Finally, the average energy intake during the last three weeks of the control phase is \(El_{c}=(3.5\pm 0.3)\cdot 10^{3}\) kcal day\({}^{-1}\), and the average energy intake during the starvation experiment is \(El_{s}=(1.6\pm 0.2)\cdot 10^{3}\) kcal day\({}^{-1}\).
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 4 Evaluation  
### Implementation  
#### 4.1.1 Hardware Implementation  
The transmitter of Wi-Senser is a TP-Link commercial wireless router model TL-WDR5620, which is set to operate in the 2.4 GHz frequency band and uses the IEEE 802.11n protocol standard. The receiver of Wi-Senser is a Lenovo laptop computer with an Intel5300 wireless network card (3.3 GHz Intel(R) Core (TM) i5 CPU, 8 GB RAM, 512 GB solid-state drive), which has three antenna interfaces that we externally connect to enhance the received signal strength. The laptop computer is installed with Ubuntu 12.04 LTS and operates in client mode. The laptop computer sends ping commands to the wireless router at a data transmission rate of 20 pkts/s, and the wireless router also transmits 2.4 GHz WiFi signals to the laptop computer at the same data transmission rate. We modified the firmware and driver of the network card using tools provided in reference [14] to obtain CSI measurements of 30 subcarriers in the 2.4 GHz WiFi signal. MATLAB R2018b is used to process and analyze the collected CSI data. Wi-Senser uses a high-definition camera to record actual human head motion. The experimental deployment is shown in Figure 2.
###### Abstract  
The temperature and moisture content inside a grain pile are two important indicators for judging the safety of grain storage. To accurately predict the temperature and moisture content inside a grain pile, a numerical simulation was carried out of the drying process of a mesoscale wheat grain soil based on a thin-layer drying method, considering non-uniform porosity. The effectiveness of this method for wheat piles was verified through a comparison with the experimental data. The influence of different ventilation cage heights and ventilation temperatures on heat and moisture transfer in the wheat grain pile were also studied. The results show the following points. The numerical simulation method in this paper can effectively predict the temperature and moisture content of a wheat grain pile. The non-uniform porosity distribution model can better reproduce the state of ventilation during storage of wheat grain piles than the uniform porosity distribution model. The distribution patterns of flow lines in silos with different ventilation cage heights have certain similarities, but the high-speed airflow area will decrease as the height of the ventilation cage increases. Different ventilation temperatures will significantly affect the areas of high temperature and the rewetting inside a wheat grain pile.  
mesoscale wheat grain pile; thin-layer drying; porosity; numerical simulation; heat and moisture transfer +
Footnote †: journal:  
applied sciences
## 2 Skin and Proximity Effect Losses
### Definition of the Skin Depth  
The skin depth (\(\delta\)) is a critical parameter for analyzing skin effect losses in conductors. It quantifies the depth below the conductor surface at which the current density decreases to approximately 37% of its value at the surface. In another word, (\(\delta\)) is the depth at which the current density decays to \(1/e\) of its surface value. Figure 3 illustrates the exponential decay of current density within a conductor. The skin depth is given by [13]  
\[\delta=\sqrt{\frac{2}{\omega\mu\sigma}}, \tag{12}\]  
where  
* \(\omega=2\pi f\) is the angular frequency (rad/s);
* \(\mu\) is the magnetic permeability (H/m);
* \(\sigma\) is the electrical conductivity (S/m).  
For copper, with \(\sigma\approx 5.8\times 10^{7}\,\mathrm{S/m}\) and \(\mu\approx\mu_{0}=4\pi\times 10^{-7}\,\mathrm{H/m}\), the skin depth at 50 Hz can be approximately calculated by using (12):  
\[\delta=\sqrt{\frac{2}{2\pi(50)(4\pi\times 10^{-7})(5.8\times 10^{7})}}\approx 9.3 \,\mathrm{mm}. \tag{13}\]  
Figure 3: Current density versus distance from the surface of an AC-carrying conductor [15].  
This indicates that at 50 Hz, the current is primarily confined to a thin outer layer of the conductor, and as the frequency increases, the skin depth decreases, intensifying the skin effect. This area is shown by the green circles on the 2D schematics in Figure 4.
## 3 LPBF Sensing Techniques
### Optical Techniques
#### 3.1.3 Techniques Using a Visible Light Source
**Digital image correlation (DIC)** is a technique that records 2D strain and deformation changes on the surface of the investigated specimen. DIC is an optical technique combining image registration and tracking methods to measure changes in 2D images[126] accurately. DIC is accomplished using two charge-coupled device camera measurements, identifying 3D surface contour and surface strain distributions by measuring displacement in the depth direction. Via digital camera imagery, the surface characteristics can be allocated to image pixels, and the comparison between the current digital image and the next one can be used to calculate the deformation of the object[127]. The advantage of DIC is that it can provide high-precision strain measurements. The disadvantage of DIC is that it cannot account for rigid-body rotation.
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques
### Frequency Scaling
#### 3.3.2 Nonratio-Based Frequency Scaling
Considering the same case as for linear-equation-based estimation, the estimated attenuation is formed in a different way as in [47]:  
\[\hat{A}_{3}=k_{3}\Big{[}A_{1}/k_{1}\Big{]}^{\
icefrac{{{({a_{3}}-{a_{2}}}/{{a_{ 1}}-{a_{2}})}}{{\left[{{A_{2}}/{{k_{2}}}}\right]}^{\
icefrac{{{({a_{1}}-{a_{3}} }/{{a_{1}}-{a_{2}})}}}{{\left({dB}\right)}}}}}} \tag{32}\]  
where \(k_{i}\) and \(\alpha_{i}\) are the parameters related to the frequencies \(f_{i}\), and it was assumed that the pathlengths in the considered links could be approximated to be the same in a condition where the Earth antennas' station elevation angle was the same for synchronized satellite links;  
II. Instantaneous:  
The instantaneous rain attenuation is performed using the spatial rain rate distribution developed through the SAM rain rate distribution. The hysteresis attenuation behavior is noticed between different frequency bands, especially the downlink and uplink operating frequency of the satellite microwave link from the Earth station facility. The rain attenuation at two frequencies shows a hysteresis behavior, which was revealed in [41,48]. Moreover, the impact of the hysteresis for less severe episodes was not seen (such as widespread stratiform rain where the peak attenuation is not exceeded). In [49], it was noticed that the isopleths of the attenuation of a pair of frequencies tend to follow medium-to-high attenuation levels, such that the attenuation process can be viewed as jointly log-normal (a log-normal model to describe the statistical behavior of rain attenuation). Consequently, the mean, standard deviation, and covariance parameters of the two frequencies were determined using the cumulative distribution function of the rain attenuation graph (Gauss integral/log). The resulted covariance parameter indicates the correlation of the frequencies. The authors recommended a modified statistical parameter \"standard deviation\" to control uplink power through a modification as:  
\[A_{2}=A_{1}\Big{[}\
icefrac{{{f_{3}}}}{{{f_{1}}}}\Big{]}^{1.72}\cdot\exp[ \Delta a(\xi)] \tag{33}\]  
where \(\Delta a\) is a log-normal variable with zero mean (\(\mu=0\)) and standard deviation (STD) \(\sigma_{\text{depend}}\); \(\sigma_{\text{depend}}\) is the STD of a links' attenuation depending on any other radio links. The STD for the independent case be written as \(\sigma_{\text{independ}}\). These two STD can be related to the reduction coefficient (\(\xi\)) as [50]:  
\[A_{2}=A_{1}(\Phi_{2}/\Phi_{1})^{1-H(\Phi_{1},\Phi_{2},A_{1})} \tag{25}\]0  
where we can compute \(f_{2}\) for the pair of frequencies for which we know \(f_{1}\) and vice versa. This model supports frequency ranges up to 1-100 GHz [49]. It also relies on rain inhomogeneity and considers the significant association between the peak precipitation and attenuation values. At a lower base frequency, the attenuation levels are close to that of the CCIR relation of (13).
# Development of an Artificial 3D Liver Phantom for Analysis of Radiotherapeutic Effects In Vitro
## 3 Results
### In Vitro Response to Photon Irradiation in Normoxic and Hypoxic Conditions  
Conventionally, most irradiation studies utilize monolayer \"normoxic\" in vitro approaches. Therefore, we wanted to observe how cancer cells grown in both hypoxic and normoxic conditions respond to irradiation treatment. As shown in Figure 3, HepG2 cells grown under normoxic conditions demonstrated a dose-dependent increase in DNA damage upon radiation with 5, 10, and 20 Gy, as indicated by \(\gamma\)H2 AX-positive staining (Figure 3A-D). Indeed, the \(\gamma\)H2 AX-positive cell fraction 30 min post 5 Gy treatment increased to 45.93% \(\pm\) 7.15% (_p_! 0.0001) when compared to non-irradiated controls, with higher radiation doses further increasing \(\gamma\)H2 AX expression in samples irradiated with 10 Gy and 20 Gy up to 70.99% \(\pm\) 8.50% (_p_! 0.0001) and 79.64% \(\pm\) 7.97% (_p_! 0.0001), respectively (Figure 3E-G). Although \(\gamma\)H2 AX response was prolonged up to one hour after irradiation with 77.54% \(\pm\) 4.13% (_p_! 0.0001) and 96.11% \(\pm\) 10.82% (_p_! 0.0001) \(\gamma\)H2 AX-positive cells in 10 Gy and 20 Gy samples, respectively (Figure 3F,G), 5 Gy irradiated cells grown in well plates had a maximum response at 30 min post irradiation (Figure 3E). Therefore, fixation at 30 min post irradiation was set for MCs. Within these, a dose-dependent effect was also observed. For 5, 10, and 20 Gy, the \(\gamma\)H2 AX-positive cell rate reached 19.75% \(\pm\) 5.47% (_p_! 0.01), 28.98% \(\pm\) 5.73% (_p_! 0.0001) and 55.55% \(\pm\) 16.47% (_p_! 0.0001), respectively (Figure 3H). However, \(\gamma\)H2 AX response was significantly reduced in the hypoxic MC environment by 57.00% (_p_! 0.0001), 59.18% (_p_! 0.0001), and 30.24% (_p_! 0.0001) for 5, 10, and 20 Gy, respectively, compared to normoxic conditions (Figure 3H). Our findings here fit well with other literature reports that hypoxia can result in radiotherapy resistance. Therefore, taken together, our data offer an alternative and more appropriate in vitro approach for the study of HCC, and a potential model to study radiotherapy resistance--a significant hurdle in the clinic.  
Figure 2: Fluorescence images of Hfi1\(\alpha\) (red) and DAPI (blue) stained cells in either normoxic (**A**) or hypoxic (**B**) conditions. Statistical comparison of fluorescence intensity of Hfi1\(\alpha\) in the nucleus between normoxic (control) and hypoxic conditions (4 h in a MC) (**C**). Fluorescence images were acquired with a 40\(\times\) objective; the white scale bar corresponds to 20 μm. Statistics were performed with a two-tailed _t_-test using Bonferroni for post hoc test. \(n\) = 4, *_p_! 0.05.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
## 5 Experiment
### Implementation Details  
We use the proposed architecture for Tensorflow [51] training and testing. Table 1 summarizes the detailed parameters of the proposed architecture at each layer. The adopted back-propagation optimization algorithm is the ADAM optimizer [52], the initial learning rate is 0.0001, and the batch size is 8. Connectionist Temporal Classification (CTC) is used as the objective function. We trained the proposed architecture for 100 epochs on the public GRID dataset and reached a stable convergence point.  
There are 33,000 sentence sample videos including 33 speakers on the GRID dataset [29]. This experiment randomly uses 31 speakers with 31,000 samples as the training set and two speakers with 2000 samples as the evaluation set. we should calculate the training loss, evaluation loss, the training and evaluation of Word Error Rate (WER), and Character Error Rate (CER) for every epoch.  
For the GRID dataset, the proposed approach is compared with [22; 23; 24; 30], which are referred to as 'LipNet', 'WLAS', 'LCANet', and '3D-2D-CNN-BLSTM', respectively. In addition, in order to reflect the impact of key modules on the architecture, we separate the various modules of the architecture for comparison experiments.
## 2 Materials and Methods
### Sliding-Window Segmentation Methods
#### 2.3.1 Conventional Sliding Window  
The segmentation of signal data plays a critical role in signal recognition and prediction. A sliding-window approach segments the continuous time series dataset into short segments. Many researchers [15; 16; 36] use the sliding-window approach to engineer the feature of the fall datasets. There are two methods of sliding-window segmentation for fall detection: fixed-size non-overlapping sliding windows and fixed-size overlapping sliding windows. We use fixed-size overlapping sliding windows because it enables us to generate more data by overlapping them. The fixed-size overlapping sliding window technique processes the signal data along the temporal axis based on the window size and stride size (step size). Here, we segment the sample signal into a 0.2 s sample signal for each label using a fixed-size overlapping sliding window. The size of window that overlaps is 0.1 s. If we obtain a single row of falls in this 0.2 s window signal, this window is labeled a fall.
## 4 IoT Adoption Benefits
### Direct Benefits
#### 4.1.6 Improved Supply Chains and Transportation  
IoT systems can enable unparalleled visibility along the supply chain by connecting supply chains with the organization and effectively tracking export and import activities [33, 45, 76, 83]. With this feature, production activities can be scheduled and planned through the supplied data in response to demand and other market dynamics [7, 65, 67], bringing transparency to the processes [22], retrieving demand and supply [37, 38], and feedback information in real-time for all concerned parties [10]. Concurrently, IoT technology can aid organizations in fleet management [41]. Sensor networks can monitor field personnel, field vehicles, mobile equipment, and product transportation systems (e.g., planes, ships, trucks, and trains) in real-time [4, 41]. However, manually coordinating an entire fleet is challenging for one fleet management operator [4]. Therefore, using IoT geo-positioning sensors' location data, automated fleet management can be implemented using data-driven systems to track equipment operator and driver performance, identify hassle-free delivery routes, assess field personnel, and assist facility managers and supervisors in optimizing fleet deployments [4, 49]. Amazon is a good example of improving fleet management by applying IoT technology. By geo-positioning sensors, Amazon can track their vehicles along with the drivers assigned to them at any time. This feature allowed them to ensure safety compliance with the drivers, track the shipments locations vehicles' health, and enhance the overall visibility of the supply chains [89].
# A Continuous Music Recommendation Method Considering Emotional Change
## 6 Experiments and Evaluation
### Experimental Results and Evaluation
#### 6.3.1 Evaluation of Music Emotion Analysis Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Music** & **Happy** & **Angry** & **Sad** & **Peaceful** \\ \hline
1 & M001 & 0.1986 & 0.4710 & 0.0644 & 0.0801 \\
2 & M002 & 0.6330 & 0.2273 & 0.0393 & 0.0992 \\
3 & M003 & 0.6706 & 0.2518 & 0.0190 & 0.0559 \\
4 & M004 & 0.1711 & 0.0735 & 0.1513 & 0.6027 \\
5 & M005 & 0.1702 & 0.0767 & 0.1537 & 0.5984 \\
6 & M006 & 0.2010 & 0.4892 & 0.0919 & 0.0942 \\
7 & M007 & 0.6549 & 0.2273 & 0.0291 & 0.0776 \\
8 & M008 & 0.2010 & 0.4892 & 0.0919 & 0.0942 \\
9 & M009 & 0.7108 & 0.2666 & 0.0014 & 0.0127 \\
10 & M010 & 0.1727 & 0.0751 & 0.1531 & 0.5995 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
570 & M570 & 0.1388 & 0.0863 & 0.1525 & 0.6006 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Example of the results from the calculation of the emotional values of music. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotion** & **MSE** & **RMSE** & **MAE** & **NRMSE** \\ \hline Happy & 0.0132 & 0.1149 & 0.0809 & 11.49\% \\ Angry & 0.0231 & 0.1520 & 0.1029 & 15.20\% \\ Sad & 0.0085 & 0.0924 & 0.0559 & 11.09\% \\ Peaceful & 0.0136 & 0.1168 & 0.0744 & 13.18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average error and error rate of the calculation results for each emotion. users and the calculated value. Increasing the number of user evaluations or comparing them with exact values can improve the accuracy of these results.
###### Abstract
[PERSON] examined and parametrically analyzed the characteristics of icing on a cylinder rotating around a horizontal axis using the icing wind tunnel method [29]. In these studies, the research object was still based on the HAWT. In contrast, research on the characteristics of icing on straight-bladed vertical axis wind turbines (SB-VAWT) is scarce. Apart from the difference in the direction of the rotation axis, there are also other differences in characteristics between the HAWT and the SB-VAWT. For the HAWT, the linear velocity increases along the blade span from the hub to the tip. For the SB-VAWT, the linear velocity along the blade span is constant. In addition, the angle of attack (AOA) of the HAWT blade is constant under given working conditions. However, that of the  
Figure 1: Icing on wind turbines. (**a**) Icing on a HAWT; (**b**) icing on a VAWT. SB-VAWT blade changes all the time under given working conditions. Therefore, it is necessary to study the characteristics of icing on SB-VAWT's. [PERSON] used a moving reference frame (MRF) to predict the ice type and the ice accretion shapes on a static VAWT using FENSAP-ICE [30]. [PERSON] combined a multiple reference frame (MRF) and the sliding mesh technique (SMT) to predict the ice shape on a rotating VAWT using FENSAP-ICE [31]. The simulation results showed good agreement with the experimental results. [PERSON] researched the characteristics of icing the blade with airfoil NACA0018 rotating around a vertical axis by a wind tunnel test and initially explored the distribution of icing on the blade surface [32]. Similarly, there are also many kinds of airfoil blades for SB-VAWT's, and the ice accretion mainly occurs on the leading edge. It is difficult to propose a norm for assessing the characteristics of icing on all types of wind turbine blades. For this reason, the blade also can be simplified to a cylinder, as in the research method for HAW's, as shown in Figure 2.  
In this study, in order to conduct basic research on the characteristics of icing on VAWT's, the rotating cylinder was selected as the research object, instead of a wind turbine blade. An icing wind tunnel system was built based on a conventional tunnel that could be used at low natural winter temperatures, and the icing tests were carried out in this experimental system. The characteristics of icing on rotating cylinders with different diameters were explored under different rotational speeds and icing times, and the changing laws of icing were parametrically analyzed. The experimental results were compared with those for a cylinder rotating around the horizontal axis in previous work, and the factors leading to the differences in the two rotation modes were analyzed. The research findings provide a foundation for exploring the icing mechanism of a rotating object.