esa-sceva/satcom-chunk-collection huggingface dataset:
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 3 Mechanotransduction  
### Osteocytes  
Exposure of osteocytes to shear stress generated in blood vessels within the Volkmann and Haversian canals and interstitial fluid in the lacunar-canalicular network and in spaces between the crystallites of the mineral hydroxyapatite and collagen fibers distorts their shape and activates the Wnt signaling pathway via translocation of \(\beta\)-catenin to the nucleus [8]. Wnt signaling is critical for bone mass accrual, bone remodeling, and fracture repair, and loss of function in Wnt or in mutations of its co-receptor LRP5 result in low bone mass and an increase in fragility fractures [8; 9; 10]. Cell shape distortion involves cytoskeletal rearrangement of actin fibers and microtubules, both of which are known to be integral players in mechanotransduction. Osteocyte cilia, dendritic extensions, and integrins in the extracellular matrix also act as mechanosensors [10; 11]. [PERSON] and [PERSON] propose that the bone loss observed in disuse and microgravity is due to a lack or decreased interstitial fluid flow [12].  
[PERSON] and associates have shown that osteocytes also respond to changes in hydraulic pressure. Cultures of osteocyte-like MLO-Y4 cells sensed cyclic changes in hydraulic pressure and responded by increasing intracellular calcium concentrations, COX-2 levels, and RANKL/osteoprotegerin mRNA ratios, and by altering cytoskeleton organization of actin and microtubules [13]. Mobilization of intracellular calcium activates downstream signaling of protein kinase A (PKA), MAPK, c-Fos, and prompts nuclear translocation of NF-kB. Blocking calcium release attenuates the mechanically induced upregulation of osteogenic gene expression in vitro and abrogates load-induced bone formation in vivo. COX-2 is the gene that encodes cyclooxygenase-2, a mediator of prostaglandamtions that play a role in bone homeostasis [13; 14].  
Figure 2: A photomicrograph of an osteocyte embedded in its lacuna. Evolved from osteoblasts entrapped in bone matrix, osteocytes form a multitude of dendritic extensions that communicate with osteoblasts, osteoclasts, and other osteocytes through a network of channels cut into the bone matrix. These dendritic extensions are sensitive to pressure changes associated with mechanical loading and unloading and allow osteocytes to regulate the balance between bone absorption and bone formation. Osteocytes undergo apoptosis in zero gravity, losing their control over bone homeostasis, and secrete sclerostin, a mechanosensitive protein that inhibits bone formation and enhances bone resorption (photograph by [PERSON]).
###### Abstract  
HTTP/2 video streaming has gotten a lot of attention in the development of multimedia technologies over the last few years. In HTTP/2, the server push mechanism allows the server to deliver more video segments to the client within a single request in order to deal with the requests explosion problem. As a result, recent research efforts have been focusing on utilizing such a feature to enhance the streaming experience while reducing the request-related overhead. However, current works only optimize the performance of a single client, without necessary concerns of possible influences on other clients in the same network. When multiple streaming clients compete for a shared bandwidth in HTTP/1.1, they are likely to suffer from unfairness, which is defined as the inequality in their bitrate selections. For HTTP/1.1, existing works have proven that the network-assisted solutions are effective in solving the unfairness problem. However, the feasibility of utilizing such an approach for the HTTP/2 server push has not been investigated. Therefore, in this paper, a novel proxy-based framework is proposed to overcome the unfairness problem in adaptive streaming over HTTP/2 with the server push. Experimental results confirm the outperformance of the proposed framework in ensuring the fairness, assisting the clients with avoiding rebuffering events and lowering bitrate degradation amplitude, while maintaining the mechanism of the server push feature.  
a _Article_  
FAURAS: A Proxy-Based Framework for Ensuring the Fairness of Adaptive Video Streaming over HTTP/2 Server Push
# Noise Reduction Mechanisms of an Airfoil with Trailing Edge Serrations at Low Mach Number
## 6 Effect of Serrations on the Sound Field
The OASPL of the baseline airfoil is compared with that of the serrated airfoil, resulting in a reduced OASPL for the serrated airfoil. More specifically, in the frequency band range from 146 to 10,000 Hz, the OASPL of the baseline NACA0012 and serrated airfoils are 81 dB and 73 dB, respectively. Thus, the airfoil with trailing edge serrations indicates that both the local peak SPL near 1800 Hz and the overall SPL are reduced. From the above observations, it is shown that the airfoil with trailing edge serrations decreases the distribution of the sound source near the trailing edge and also reduces the overall SPL as well as the local peak value of SPL in a specific frequency range. In particular, in the flow around NACA0012 airfoil, the location where the strong sound source distribution begins to appear and the location where the separated boundary layer reattaches is in good agreement. Therefore, it can be said that applying serrations on upstream of the reattachment point for an airfoil is effective in terms of noise reduction. Figure 15: Averaged friction coefficients on the suction side of NACA0012 airfoil.
## 3 Results and Analyses
### Algorithm Implementation
As shown in Figure 7, the objective function value of the photoacoustic image reaches saturation after 30 iterations. Figure 8 illustrates the optimal clustering result, optimal sound speed field and photoacoustic image obtained by the proposed method. As shown in Figure 8b, the sound speed distribution of various tissues in the final obtained inhomogeneous sound speed field is very close to the real sound speed field. The photoacoustic image obtained based on this sound speed field is compared with the image at the homogeneous sound speed in Figure 4. The artifacts of the photoacoustic image in Figure 8c are significantly reduced, and the boundaries of various tissues are clearer. To fully evaluate the effectiveness of the proposed method, the k-means sound speed estimation method is used to estimate the sound speed field of the digital breast model [11]. The result obtained is set as a control group for the proposed method. The k-means sound speed estimation method first performs Hilbert transformation on the photoacoustic image obtained at the homogeneous sound speed. This image is transformed into a unipolar photoacoustic image. After that, k-means is used to cluster the photoacoustic pressure values of each pixel in the unipolar photoacoustic image to obtain the regional distribution of each tissue. Finally, the sound speed of each tissue in the sound speed field is assigned according to the corresponding relationship between clinical data and clustering result. The photoacoustic image is obtained based on the updated sound speed field. Figure 9 shows the results obtained by the k-means sound speed estimation method. Among them, the sound speed field shown in Figure 9b is obtained from the real sound speed values in Table 1. Since the regional distribution of each tissue in the sound speed field is quite different from the real distribution, the quality of the photoacoustic image obtained based on this sound speed field is not effectively improved. Figure 7: The fitting plot of the objective function value and the number of iterations.
# A Time Bank System Design on the Basis of Hyperledger Fabric Blockchain
## 3 System Overview
### System Architecture Overview  
As shown in Figure 2, the proposed time bank system can be divided into multiple layers. Firstly, the user-layer consists of the enrolled SPs and SRs, who can access each client-site through an interface configured in the application-layer so as to connect to the blockchain. Notice that peers bridge between users and the blockchain network; therefore, a user can go through one of the selected peers to interact with the network for completing well-defined business logic functions by invoking specific Chaincodes.  
Figure 1: Basic processing steps of the proposed time bank system.  
Figure 2: Architecture of our Fabric-based time bank system.  
Users can not only put their data on blockchain but also request data from the blockchain-based ledgers. In addition, every organization has a specific entity, called fabric-CA, which is responsible for the registration of identities and the issuance of certificates.  
The Fabric network in our system is composed of three different channels. The first is called the service channel (SC), which deals with all the service-related networking issues. The second is called the token channel (TC), which is responsible for collecting all wallet data from the ledger. The third is called the grading channel (GC), which allows the enrolled members to grade each other. Each channel is attached with Chaincodes running on peers and provides a platform for program execution to transfer transactions. In a Fabric framework, all the transactions are evaluated and reach consensus in the ordering phase. Finally, all the transactions are sent to a special entity called the \"orderer\". The orderer puts all the transactions in execution order and updates them on the ledgers, for both endorsing and non-endorsing peers, with respect to their original channels.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 2 SCTN-Based Resonator
### Frequency Detection Using SCTN-Based Resonator  
The following section introduces the resonator circuit, designed as a frequency detector which utilizes the fundamental phase-shifting capability of the spiking neuron. We propose employing an SNN architecture to identify the sub-band frequencies embedded in an EEG signal by exploiting the inherent phase-shifting feature.  
Figure 1a shows the resonator circuit composed of the SCTN-based network. This circuit consists of four SCTNs, for which each neuron introduces a phase shift of \(\frac{\pi}{4}\) degrees. The first neuron's phase shift is denoted as \(\chi\). Figure 1b illustrates the output of each of the four neurons in the proposed architecture (for \(\chi=30^{\circ}\)). The amplitude expresses the spike rate within a predefined time window. The spike rate at the output of each SCTN in a predefined time window represents the phase shift and therefore the signal amplitude, as depicted in Figure 1. For a one-second normalized time window, the spike rate is given in Hz. The fourth neuron's output is fed back to the first neuron, resulting in a feedback  
path with a phase shift of \(\chi+\frac{3\pi}{4}\). Thus, the first neuron receives two input signals: (a) the original input signal and (b) a shifted signal (\(\chi+\frac{3\pi}{4}\)).  
The resonator is designed to identify a wide range of frequencies within a time series signal by leveraging the neuron's intrinsic phase-shifting capability. The leakage factor and the leakage period are adjusted to target a specific frequency. The output neuron emits a train of pulses whenever a frequency within the range of \(f_{0}\pm\Delta f_{0}\) is detected. We employed a supervised STDP algorithm to adjust the network parameters for the desired frequency [43].  
A modified supervised STDP learning rule is used for training the SCTN-based resonator circuits. To achieve the desired frequency response, the SCTN's weights (\(W_{ij}\)) and biases (\(\Theta_{i}\)) are adjusted during the training phase.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Convolutional Neural Network
#### 4.3.2 CNN Using NDVI Temporal Series  
Using the optical branch of the developed CNN, the approach was also applied on the NDVI temporal series composed of 17 different NDVI values. The data was also divided into 50% for training, 40% for validation and 10% for testing. The accuracy obtained from the 10 iterations is reported in Table 8. The overall accuracy reaches 91.6% while the kappa and F-score values reach 0.81 and 0.91 respectively. The irrigated class also shows a lower F-score value (0.87) than the non-irrigated class (0.94).
## 4 Results
### Surface Water Storage
With the current configuration, storage changes of all lakes larger than 80 km\({}^{2}\) can be monitored, whereas Jason still misses about 15 km\({}^{3}\) for lakes larger than 200 km\({}^{2}\). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Scenario** & **Number of Targets** & **Water Volume Variation in km\({}^{3}\)** & **Mean Variations in km\({}^{3}\)** & **Mean Size in km\({}^{2}\)** \\ \hline Jason only & 29 (22.8\%) & 90.6 (50.4\%) & 3.13 & 224.2 \\ Sentinel-3A/B & 97 (76.4\%) & 161.9 (90.1\%) & 1.67 & 129.7 \\ Past configuration & 71 (55.9\%) & 137.1 (76.3\%) & 1.93 & 158.5 \\ Current configuration & 116 (91.3\%) & 176.5 (98.1\%) & 1.52 & 123.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Water storage changes from WGHM that are mapped by different altimetry constellations. The values in parentheses are the percentages of the total number and total volume variations from WGHM, respectively. It should be noted that all numbers presented here are based on information about the satellites' orbit configurations; i.e., whether a lake is crossed by any of the satellites' tracks. Additional lakes will be missed when no reliable height information can be determined, which is especially conceivable for small lakes with a steep surrounding topography (see Section 5.4 for more details).
# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 2 Materials and Methods
### Experimental Setup  
In the MIAS database, the most common image size was 4320 pixels \(\times\) 2600 pixels. Thus, in this study, the dimensions 4320 pixels \(\times\) 2600 pixels were selected for breast tumor screening [33; 34]. The vertical and horizontal resolutions of each image were identical at 600 dpi, with a bit depth of 24 bits. A total of 156 mammography images (78 subjects), including 62 images with malignant (M) or benign (B) tumors and 94 images without tumors, were obtained. Given a specific bounding box measuring 100 pixels \(\times\) 100 pixels, feature patterns were screenshots from the 156 images. In total, 932 feature patterns, including 564 tumors and 368 tumor-free screenshots, were obtained. In each classifier's training stage, 282 tumor and 184 tumor-free screenshots (50% feature patterns) were randomly selected to train the multilayer CNN classifier. The remaining 50% of the feature patterns were used to evaluate the classifier's performance for each cross-validation. This study used the relevant data, as shown in Table 1, to establish a multilayer CNN-based classifier. We designed a fractional-order convolutional layer, two general convolutional layers, and two MP processing layers for feature enhancement and extraction. The convolutional layer had 16 kernel convolutional windows. In the kernel window, the sliding window moved the number of columns and rows in steps of 1 (stride = 1) at each point of the convolution operation. The padding parameter was set to 1 to maintain the feature pattern after the convolutional operation. During the pooling process, the MP window moved with a stride of 2 (stride = 2) each time. During each feature enhancement, and extraction process, the possible tumor features and contours were gradually enhanced by the convolutional-pooling processes; hence, it can be observed that the multilayer CNN-based classifier can improve the accuracy of pattern recognition based on these enhanced features. Tenfold cross-validation was performed using precision (%), recall (%), F1 score, and accuracy (%) as indicators [35; 36; 48] to evaluate the prediction performance of the proposed classifier. Figure 5 shows the visualization of the confusion matrix; for example, the classifier used 466 images for rapid screening, and the results show 178 TPs, 13 FPs, 269 TNs, and 6 FNs. The precision (%), recall (%), F1 score, and accuracy (%) can be calculated from the confusion matrix.  
Figure 5: Output confusion matrix of the multilayer CNN-based classifier.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Internet of Agricultural Things  
We can see IoT implementation everywhere with the ongoing advances in this technology. One of the hottest implementations of IoT is in agriculture. From basic smart home gardening systems to smart farms, the IoAT offers intelligent solutions to different agricultural applications, such as precision farming, livestock monitoring, greenhouse monitoring, and agricultural drones. Smart farms often use a variety of sensors to collect real-time data on the farms status to monitor both livestock and crops, for example, with sensors that monitor oil richness, temperature, humidity, gas, air pressure, water pressure, and crop disease for field monitoring; and temperature, heart rate, and digestion for livestock [11]. One of the many innovations designed and implemented is SeeTree, an \"Intelligence Platform for Trees\" that allows growers to monitor the productivity and health of individual trees. It can scan and analyze hundreds of millions of trees using rich information sources, including drones, satellites, IoT sensors, climate data, and more [12; 13].
# Acoustic Simulation for Performance Evaluation of Ultrasonic Ranging Systems
## 3 Numerical Results  
In Figure 3, it is possible to see the range estimation error along the section A shown in Figure 1 for decreasing SNR levels from 30 dB down to \(-20\) dB.  
The range \(R\) is estimated using the usual technique based on finding the position of the cross-correlation peak (\(\tau_{MAX}\)) [17; 28]. The adopted technique, in favorable conditions, produces a good estimation of the \(TOA\) and, from this, the estimated range \(R\), i.e.,  
\[R=\left(\frac{\tau_{MAX}}{f_{S}}-TOE\right)c_{air}-R_{cal} \tag{1}\]  
where \(\tau_{MAX}\) is the lag of the maximum peak of the cross-correlation between the received signal and its copy stored at the receiver (proportional to \(TOA\)), \(TOE\) is the time of emission of the ultrasonic signal, and \(R_{cal}\) is a calibration constant including all the fixed delays of the system.  
In particular, \(R_{cal}\) is range-independent and, together with the \(TOE\), it is assumed to be known through some calibration operations. Finally, \(c_{air}\) (m/s) is the speed of sound in the air, which is related to the ambient temperature \(T\) (\({}^{\circ}\)C) according to:  
\[c_{air}=331.5\sqrt{1+\frac{T}{273.15}} \tag{2}\]  
Figure 2: Emitting transducer: circular and planar piston transducer with a diameter of 8.5 mm divided into 0.125 \(\times\) 0.125 mm\({}^{2}\) square mathematical elements.  
The ranging error was evaluated on a rectangular grid of points of section A, referring to Figure 1. The grid pitch is 5 cm in the \(x\) and \(z\) directions. For each point, the lag of the correlation peak (\(\tau_{MAX}\)) and, from these, the distance estimations from the emitter through (1) were obtained. Finally, the ranging errors were computed by subtracting the ground-truth values at each point of the simulation grid from the values just obtained. A typical source of errors in calculating the range is given by the discretization of the \(\tau_{MAX}\) value, with an uncertainty of \(\pm T_{S}/2\) according to the time sampling of the system.  
In Figure 4a, the overall ranging error at any grid point from Figure 3 is displayed as a cumulative distribution function (CDF) or cumulative error distribution, which is the percentage of readings with error less than the value of a given abscissa. Figure 4b shows a zoomed portion of the CDF in the range 0-5 mm in order to see the behavior details of the ranging mechanism for SNR levels from 30 dB down to \(-20\) dB.  
Figure 3: Numerical results at different SNR levels using a linear chirp 30–50 kHz. A linearized air absorption around 40 kHz has been assumed. Spatial error distribution along the section A of Figure 1: (**a**) SNR = 30 dB, (**b**) SNR = 20 dB, (**c**) SNR = 10 dB, (**d**) SNR = 0 dB, (**e**) SNR = \(-10\) dB, (**f**) SNR = \(-20\) dB. Note the color bar error different ranges. For decreasing SNR from 30 dB down to 0 dB, the error is equal or less than 0.343 mm that is the spatial quantum. For lower SNR error rapidly increases up to more than 500 mm.
## 4 Results and Discussions  
### Antenna Gain and VSWR  
Figure 8 shows the array gain at a 44 mm far-field distance with and without the decoupling structure. When compared to an array without a decoupling structure, the MIMO antenna's average gain has increased by 12 dB as a result of the MIMO structure and the decoupling structure. In Figure 9, the VSWR of the antenna with and without MEBG structure is presented. As can be observed, the VSWR is less than 2.0 within the frequency band for both scenarios. This indicates that there has not been a major change in the VSWR of the antenna since the decoupling structure was introduced, and thus the performance is not degraded.  
Figure 7: S–parameter of decoupling structure of the MIMO antenna with variations in \(d\).
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 4 Experiment Description and Result Analysis  
Calibration experiments were designed using simulated and real datasets. All the experiments were performed by the following process. First, the point cloud for each scan location was captured by defining the \"frame\", as MBL completes one rotation and covers 360\({}^{\circ}\) of the horizontal field of view. Next, plane fitting using Maximum Likelihood Estimation SAmple Consensus (MLESAC) for all point clouds was processed [46]. Each point lying on its surface has a plane number and parameters. Common planes that are mutually detected in all point clouds are manually matched to the reference scan. Least squares adjustment and accuracy assessment follow.