esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Position Tracking System Based on Composite Sensors
### UWB Positioning Algorithm
\tag{16}\]  
The optimal intersection points \(\{(x_{ij}^{*},y_{ij}^{*}),(x_{ik}^{*},y_{ik}^{*}),(x_{jk}^{*},y_{jk}^{*})\}\) for 3 anchors are obtained by the above method, then the position of target tag node is calculated by the centroid-based trilateration. \[\left\{ \begin{array}{l}\hat{x}_{t}=\frac{1}{3}\Big{(}x_{ij}^{*}+x_{ik}^{*}+ x_{jk}^{*}\Big{)}\\ \hat{y}_{t}=\frac{1}{3}\Big{(}y_{ij}^{*}+y_{ik}^{*}+y_{jk}^{*}\Big{)}\end{array} \right. \tag{17}\]  
By changing the position of 3 anchors, the position of the target tag node can be computed by the above method.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 3 Experiment and Result Analysis
### Preprocessing Result  
The proposed preprocessing technique is compared with other brightness enhancement methods such as histogram equalization (HE) [41], adaptive gamma correction using weighting distribution (AGCWD) [28], brightness preserving dynamic fuzzy histogram equalization (BPDFHE) [42], and non-parametric modified histogram equalization (NMHE) [43]. The results of all algorithms, including the proposed gradient-based adaptive gamma correction method (GAGC), are provided in Table 3 and Figure 8.  
From Figure 8, we observe that HE introduces over-saturation at the smooth areas (because of probability distribution uniformly on all intensities), producing a black and white smooth region. Meanwhile, AGCWD performs better on the dark smooth regions; however, bright smooth regions are also enhanced. Because the bright smooth regions contribute to the cumulative distribution function (CDF) of the image, an over-saturation procedure is introduced. The BPDFHE modifies the image histogram using fuzzy algorithms and does not produce over-saturation. However, dark shade cannot be brightened through this method. NMHE performs well for intensity variations and mutates histograms by a weighting factor that is computed by a local variance of pixels with the same intensity values. A lesser weight is introduced to the smooth regions and higher weights in varying regions. The proposed preprocessing method (GAGC) does not have a problem with over-saturation and performs well in dark regions. The smooth areas (intensity peaks in the histogram) do not contribute too much to the gradient of the image. Overall, the proposed preprocessing algorithm and AGCWD perform better than other methods.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 3 Collaborative Disaster-Monitoring System
Therefore, at the base station, the system reconstructs seismic data and detects seismic events in accordance with our belief propagation fusion method [45]. This system transmits earthquake data, events and alerts to the server through a satellite link. The task of the disaster-monitoring system is to maintain the surveillance of earthquake activities, and most importantly, transmit alerts for dangerous vibrations as quickly as possible. After earthquake alerts are received and broadcast by the system server, people around the earthquake epicenter can take prompt action to avoid and mitigate damage.
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 2 Characteristics of Arabic Handwritten Calligraphy and Its Complexity  
Arabic consists of 17 main character forms. With the addition of dots placed above or below certain of them, it provides a total of 28 letters. As shown in (Figure 2) above, the same letter shape can form a \"b\" or \"baa\" sound when one dot is placed below ((\(\lnot\))), a \"t\" or \"taa\" sound when two dots are placed above ((\(\lnot\))), or a \"th\" or \"thaa\" sound when three dots are added above ((\(\lnot\))). Short vowels are not included in the alphabet but instead indicated by signs (diacritics) placed above or below the consonant or long vowel that they follow. The Arabic writing system is a Right to Left (RTL), which means words are written and read in RTL way as in Hebrew, Persian, and Urdu. Texts are written in a cursive way where letters must be interconnected unlike other languages, such as English or French,  
Figure 2: Components of the Arabic language. (**a**) The alphabet consisting of 28 characters and (**b**) the different forms of the same character based on the position in the word.  
where connecting letters is a matter of choice rather than obligation. One must also know that a letter in Arabic can be written in three different forms based on its location or syllable in the word (beginning, middle, or at the end) as shown in Figure 2.
# Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet +
###### Abstract
[22] to present a novel hybrid intelligent fault diagnostic frame that is superior to some conventional fault diagnosis techniques and exhibits high precision for rolling bearings. Time domain signals are converted to two-dimensional (2D) images in several studies of fault diagnosis, utilizing 2D forms of signals [23; 24]. Although 2D image-based bearing fault diagnosis has achieved excellent accuracy, its performance is still mostly dependent on handmade feature extraction. Currently, the pre-trained model is the most widely used convolutional neural network and has become one of the hotspots in bearing fault diagnosis. Bearing fault signals are complex due to the high variance, nonlinear, and nonstationary characteristics of vibration signals [12]. As a result, the input distributions of the layers of the pre-trained model differ from each other. This can make achieving high accuracy in parameter training very challenging and time-consuming, which requires proper setup [25]. In a traditional AlexNet, the fully connected (FC) layer is located in the final three layers. An FC layer consists of multiple layers that are all connected [26]. It can be defined as a function from \(Rm\) to \(Rn\). Each input parameter of each layer influences each output parameter. However, the FC layer of the AlexNet has too many training parameters, which makes the model training and testing time longer, and causes overfitting. To address the aforementioned problems, this work develops a novel technique for the intelligent identification of bearing faults in rotating machinery. It effectively reduces training parameters, solves the traditional AlexNet model's overfitting problem, and also increases the classification accuracy. In the proposed technique the best pre-trained CNN model is firstly selected for the bearing fault diagnosis. In the next step, the selected CNN model is improved by replacing the FC layer with a global average pooling (GAP) layer adding some batch normalization (BN) layers to prevent this internal covariate shifting, which effectively decreases the parameter quantity, overfitting, and calculating time of the CNN model. The last step constructs a hybrid model concept to achieve the best performance.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 8 Performance Analysis
### Experiment 1: Total Trust Value Assessment at the Vehicle Node Layer
The experimental results demonstrate that the method for determining anomalous nodes based on hybrid trust values is suitable for multi-vehicle collaboration scenarios in vehicular networks and can effectively detect anomalous behavior in highly dynamic environments.
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### The Effect of Double-Gratings on MTF  
Because the plane grating used in this article does not have focal power, changing the traditional single-plane grating to a dual-plane grating has no effect on the system aberrations. The biggest impact of the dual-gratings on the image quality of the optical system should be the impact on the diffraction limit. The diffraction limit of an optical system is shown in Equation (11).  
\[A=1.22f\frac{\lambda}{D} \tag{11}\]  
where \(f\) is the focal length of the system, \(D\) is the diameter of the entrance pupil of the system, and \(\lambda\) is the wavelength. Due to the use of dual-gratings, only half of the aperture in each band is involved in imaging, that is, \(D\) is half of the single grating. Therefore, its diffraction limit will be doubled, resulting in the MTF cut-off frequency being reduced to half, which will cause The reduction of MTF. The amount of decrease of the upper limit of the MTF can be derived.  
Then the upper limit of MTF can be calculated as a formula, where \(x\) is the spatial frequency, and \(y\) is the upper limit of MTF:  
\[y=-1.22f\frac{\lambda}{D}\times x+1 \tag{12}\]  
The upper limit of MTF for reducing the pore size can be calculated as:  
\[y=-1.22\times\sqrt{2}\times f\frac{\lambda}{D}\times x+1 \tag{13}\]  
Therefore, the MTF reduction varies with spatial frequency as:  
\[\delta y=1.22\times(\sqrt{2}-1)f\frac{\lambda}{D}\times x \tag{14}\]
## 2 Materials and Methods  
### The Strain Isolation and Identification  
The strain was isolated using the standard dilution microbiological method (ISO, International Organization for Standardization) from 53 raw goat milk samples supplied by farmers in the central Slovakia region. Milk samples diluted in Ringer solution (pH 7, Merck, Darmstadt, Germany, 1:9 solution) were plated on M17 agar (pH 6.9, Difco-Becton Dickinson company, Sparks, MD, USA). The plates were incubated at 37\({}^{\circ}\) C for 48 h, to grow _Lactococci_. The picked colony was checked for purity by its inoculation on Brain heart infusion/agar (BHA, Difco, Sparks, MD, USA) enriched with sheep blood. The pure strain was stored using the Microbank system (Pro-Lab Diagnostics, Richmond Hill, ON, Canada) for further testing.  
The taxonomy of the strain was identified using MALDI-TOF mass spectrometry, according to [PERSON] [16], as previously described by [PERSON] et al. [17]. The strain evaluation revealed a score which responded to probable genus identification (1.700-1.999, [16]).  
Moreover, its taxonomy was confirmed using sequencing analysis, as previously described by [PERSON] et al. [18]. DNA from the pure colony was extracted by DNA-zol direct (Molecular Research Centre, Inc. Cincinnati, OH, USA), according to the manufacturer's instruction. The 16S rRNA gene from the colony was amplified by PCR with the universal primers 27F (5\({}^{\prime}\)-AGAGTTTGATCCCGGCTCAG-3\({}^{\prime}\)) and 1492R (5\({}^{\prime}\)-CGGTTACCTTGTTACGACT-3\({}^{\prime}\)) originally presented by [PERSON] [19], as reported by [PERSON] et al. [20]. OneTaq 2X Master Mix with the Standard buffer (New England Biolabs, Foster City, CA, USA) was used in this analysis. The PCR cycling conditions comprised an initial denaturation phase of 5 min at 94 \({}^{\circ}\)C, then 30 cycles of denaturation at 94 \({}^{\circ}\)C for one minute (min), annealing at 55 \({}^{\circ}\)C for one min, and primer extension at 72 \({}^{\circ}\)C for 3 min. The final primer extension step was performed at 72 \({}^{\circ}\)C for 10 min. The PCR was conducted in a thermal cycler (TP professional Basic, Biometra GmbH, Goettingen, Germany). The product was visualized with GelRed (Biotin Inc., Hayward, CA, USA) on 3% (\(w/v\)) agarose gel electrophoresis in Tris-acetate-EDTA buffer (pH 7.8).  
The amplified products were sent for Sanger sequencing (Microsynth, Austria GmbH, Wienna, Austria). The obtained chromatogram of sequences was analyzed using Geneious 8.0.5 (Biomatters, Auckland, New Zealand). The strain was identified based on data searches comparing 16S rRNA sequences obtained from reading with sequences available in the GenBank, using BLASTn-Basic Local Alingement Search Tools nucleotide (http/www.ncbi.nlm.nih.gov/BLAST, 31 April 2022) from the National Centre for Biotechnology Information. The selected nucleotide sequence was deposited in GenBank to receive an accession number.
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
###### Abstract  
This review gives an overview of passive constant flow regulators dedicated to microfluidic applications. Without external control and energy consumption, these devices deliver a constant flow rate regardless of pressure variations, making them very attractive for various microfluidic applications, including drug delivery, flow chemistry, point-of-care tests, or microdialysis. This technical review examines progress over the last three decades in the development of these flow regulators and focuses on the working principle, fabrication methods, performance, and potential applications.  
passive flow regulator; flow control valve; microfluidics; microsystems; microvalves; MEMS; lab-on-a-chip; diagnosis; hydrocephalus shunt; drug delivery
## 4 Discussion
### The Efficiency of the IM  
In terms of energy, the efficiency is also essential along with the PF, so that its variations concerning the two variables, k and \(\gamma_{\rm Vk}\), are shown in Figure 8.  
As in the case of the PF, the low levels of the voltage harmonics, \(\gamma_{\rm Vk}\) \(\in\) {5%, 10%}, are felt to be imperceptible or, to a minimal extent, at the IM efficiency level. Thus, for the level, \(\gamma_{\rm Vk}\) = 5%, the decrease of the efficiency is \(\Delta\eta\) \(\leq\) 0.02%, and for \(\gamma_{\rm UN}\) = 10%, this is \(\Delta\eta\) \(\leq\) 0.24% (practically imperceptible, regardless of the order of harmonics). It can also be stated that for orders k \(\geq\) 11, even at a level of the voltage harmonics \(\gamma_{\rm Vk}\) = 30%, the IM efficiency does not decrease by more than 0.5%, compared to the rated value (Table 3 and Figure 8).  
More significant decreases of the IM efficiency due to the harmonics are recorded in the case of the 5 th-order harmonic: \(\Delta\eta\) = 1.77%, when \(\gamma_{\rm Vk}\) = 25%; and \(\Delta\eta\) = 2.55%, when \(\gamma_{\rm Vk}\) = 30%. In the case of the 7 th-order harmonic, the decreases are slightly smaller: \(\Delta\eta\) = 0.87%, when \(\gamma_{\rm Vk}\) = 25; \(\Delta\eta\) = 1.27%, when \(\gamma_{\rm Vk}\) = 30%.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 5 Rf Stray Currents Simulations
### Frequency Domain Simulations  
For the frequency domain simulations, a simplified approach was used: we assume that when CVD II is installed, the RF stray currents are mostly confined to the secondary side of the RF transformer, referred to HVD through the capacitive divider. Thus, only the secondary can be represented using an ideal voltage source. Inevitably, these simulations can be carried out only for the case of CVD installed at the secondary. Performing the simulations in an extended frequency range points out the presence of resonance peaks for the RF stray currents. Figure 7 shows the resonances of the stray currents flowing through all the conductors of the transmission line and through ISEG filter. Two transmission line lengths are considered, one representing the current SPIDER configuration (43 m) and one resembling MITICA transmission line [15] (100 m).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & \(V_{\text{OUT}}\) [V] & \(I_{\text{OUT}}\) [A] & \(f\) [MHz] & \(I_{ISEG-F}\) [A] \\ \hline \multirow{2}{*}{CVDII} & EXP & 2500 & 25 & 0.933 & 2.92 \\  & SIM & 2490 & 31 & 0.94 & 3.07 \\ \multirow{2}{*}{CVDI} & EXP & 2250 & 27 & 0.935 & 1.16 \\  & SIM & 2200 & 29.3 & 0.94 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between measurements performed in SPIDER (EXP) and circuital simulation (SIM) in the original (CVD II) and modified (CVD I) configurations. The figure of merit for the RF stray currents is the current measured at the ISEG output filter branch (\(I_{ISEG-F}\)). All the voltage and current values are RMS.  
The relative position of the resonance peaks from the working frequency range determines the entity of unwanted RF currents flowing in the electrical system. If a resonance is brought within the operating frequencies, potentially dangerous working conditions can arise, leading to overloads in electrical equipment not intended to operate with RF power.  
Being able to preemptively identify and avoid these resonances in SPIDER also permit applying this analysis process to the case of MITICA, which presents several differences with respect to SPIDER (transmission line length, layout of conductors and components inside the beam source).
## 3 Experimental Results and Discussion
### Tensile Experimental Results
#### 3.1.2 Woven 3D TRC  
Six specimens of the woven 3D TRC were tested in tension along the x- and y-direction. Figure 12 compares the experimental curves for both orientations and provides their respective averaged curves.  
Table 5 summarizes all of the tensile parameters in both orientations for the woven 3D layup. In the first stage, both orientations showed a similar uncracked stiffness E\({}_{1}\) and matrix cracking stress. This was expected since the first stage is dominated by the properties of the matrix. In the third stage however, a clear difference in post-cracking stiffness was observed: 761 MPa in the x-direction compared to 446 MPa in the y-direction. This was expected since for the woven 3D textiles, the wert yarns along the x-axis were straightened, while the warp yarns along the y-axis were not. Both orientations failed by means of fiber pull-out at similar stress values (respectively 4.96 MPa and 4.82 MPa along x and y). The larger failure strain in the y-direction, without an increase in failure stress, further indicated that the fibers were originally not straightened in this direction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Knitted 3D AVG** & **Woven 3D AVG x** & **Woven 3D AVG y** & **2D-EQ AVG** \\ \hline E\({}_{1}\) (MPa) & 10,973 & 10,903 & 10,957 & 10,736 \\ E\({}_{2}\) (MPa) & 273 & 365 & 229 & 318 \\ E\({}_{3}\) (MPa) & 724 & 761 & 446 & 710 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Averaged stiffness in three stages in tension for all layups.  
Figure 11: Tensile comparison of averaged curves of all layups.  
Figure 12: All tensile experimental curves in x- and y-direction for the woven 3D TRC layup, as well as averaged curves.  
Comparing the knitted 3D and woven 3D layups (Tables 3 and 5), the following conclusions were drawn. Firstly, as expected, the first stage stiffness E\({}_{1}\) and matrix cracking stress \(\sigma_{\rm c}\) were similar for both layups. Secondly, the post-cracking stiffness along the x-direction of the woven 3D layup was also similar to the results in both directions of the knitted 3D layup. This is in accordance with [21], which concluded that no contribution could be expected from knitted transversal connections on the tensile mechanical behavior of TRC. This was also confirmed in Figure 11 and Table 4, which showed a similar stiffness for the woven 3D TRC and the equivalent 2D layup.  
Additionally, the lower pull-out stress of the woven 3D compared to the knitted 3D (4.96 MPa and 4.82 MPa compared to 7.02 MPa) can be attributed to the lack of SBR (styrene-butadiene) coating of the woven 3D layup, as concluded in [20; 23; 24; 25].
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Radio Resource Management
#### 5.2.2 Interference Mitigation  
Interference prediction, estimation, cancellation, and coordination techniques for NB-IoT become a challenge. This is because of the sharing of spectrum resources between NB-IoT and legacy LTE. Similarly, with NB-IoT being deployed in a small cell or macrocell scenarios in heterogeneous networks, interference becomes a concern. Several works have tried to address this by means of resource blanking, power control, or better uplink and downlink scheduling schemes and frequency and timing synchronization, etc. However, it is still challenging to incorporate the NB-IoT features such as repetition, low complexity (which affects channel estimation quality), and mobility in deploying the already existing LTE interference management techniques. As seen in the possible NB-IoT deployment scenarios above, there is still a need for deploying effective schemes that will ensure better NB-IoT performance without degrading the LTE performance [91; 92].
## 4 Results and Discussions
### Effect of Initial Impact Velocity  
Figure 9 shows the comparison of normalized target resistance and penetration depth with initial impact velocity at \(r_{\rm t0}/R_{\rm P}\) values of 5, 10, and 20. Figure 10 shows the comparison of penetration velocity and tail velocity with penetration depth at initial impact velocities of 1000 m/s, 1500 m/s, and 2000 m/s. From Figures 9 and 10, the following can be seen:  
* The penetration depth increases with increasing initial impact velocity and gradually approaches the fluid dynamic limit. In the case of \(r_{\rm t0}/R_{\rm P}=5\), when the initial impact velocity is greater than 2500 m/s, the penetration depth will be slightly greater than the fluid dynamic limit. This is because when \(v_{0}>2500\) m/s at \(r_{\rm t0}/R_{\rm P}=5\), \(R_{\rm T}\) is less than \(Y_{\rm P}\), and rigid penetration occurs during the penetration process;
* \(R_{\rm t}/R_{\rm t}{{}^{*}}\) decreases approximately linearly as the initial impact velocity increases. This is because \(r_{\rm cf}\) increases as the initial impact velocity increases, leading to an increase in the lateral boundary effect;
* Both the initial penetration velocity and penetration depth increase significantly as the initial impact velocity increases.  
Figure 8: Decay functions in this article and [17] vs. \(r_{\rm t0}/r_{\rm c}\).