esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work investigated the characteristics and performance of a long-distance microscope imaging system optimized for quantification of droplets and particle fields in high-speed flows. We developed and thoroughly characterized two ultrafast illumination systems based on light-emitting diode (LED) and vertical-cavity surface-emitting lasers (VCSEL). The illumination systems were designed to generate short light pulses with energetic and spatial distribution properties optimized for line-of-sight, long-distance microscope imaging. We selected a state-of-the-art long-distance microscope lens to be mounted onto a high-resolution (12 Mpix), high-throughput (2.37 Gpix/s) machine-vision camera, for a final digital resolution of 2.5 \(\upmu\)m/pix. The relatively simple optical setup on the illumination side was aided by ray tracing simulations, enabling object plane energy densities sufficient to use short (50 ns) light pulses to illuminated the scene. Energy transmission measurements demonstrated the high energy losses with the LED system, whereas the laser-based illumination maintained fairly constant energy density as function of distance. We provided estimates regarding the energy delivered by the illumination systems to the object and image planes, and correlations between illumination intensities and recorded digital levels, as measured by the camera system. The laboratory experiments to characterize the imaging system with both illuminations devices showed that the system was near diffraction-limited when VCSEL illumination was used, although it was likely limited by digital resolution, and possibly the diffuse aspect of the illumination when using LED lighting. Both illumination solutions provided comparable performance with respect to resolution, detectability, and sizing accuracy, as well as depth-of-field. Droplet sizing measurements performed on a sample droplet field generated by an airbrush confirmed these findings, with very similar droplet size distributions measured when the droplet field was illuminated with the LED or VCSEL illumination systems.  
Conceptualization; methodology; validation; formal analysis; investigation; data curation; writing--original draft preparation; visualization; supervision; project administration; funding acquisition by J.M. Writing--review and editing; supervision; project administration; funding acquisition by W.D.B. All authors have read and agree to the published version of the manuscript. This research was funded by NASA Shared Services Center (NSSC) contract numbers 80 NSSC19C0019 and 80 NSSC19C0032. The authors are grateful for the financial support by NASA Glenn Research Center through several research projects and contracts, including NASA Shared Services Center (NSSC) SBIR Phase III contracts. The authors declare no conflict of interest.
## 4 Balanced Energy-Efficient Clustering (BEE) and Multihop BEE (BEEM)
### Multihop Bee--Beem  
When the network size scales, the sensors that are far away from the BS may die much earlier than the sensors close to the BS when using direct inter-cluster transmission. If the sensors do not support long distance communication, the connectivity of the network is hard to guarantee. For the above reasons, it is necessary to support multihop inter-cluster communication. Besides, multihop inter-cluster routing can further balance the power usage on each individual sensor throughout the network.  
HEED declared that many multihop communication protocols could be used to guarantee a connected inter-cluster overlay topology. It provided a proof on a uniformly distributed network. However, it failed to provide the detailed implementation. Here we propose a multihop inter-cluster communication approach specialised for WSNs.  
In BEEM, we adopt the same idea that is used in CCS and S-WEB in order to organise the sensors into layers as shown in Figure 4. The higher-layer CHs can transmit data to the 1-lower layer CHs. The BS will broadcast a beacon signal to all the sensors in the network at the initial stage. Based on the received single strength, the distance between a sensor and the BS can be estimated from the Log Distance Path Loss Model [33]. Based on the distance between a sensor and the BS, every sensor assigns itself a layer index. Supposedly the cluster range is \(Tr\), and then the maximum value for the distance from a CH to its nearest CH will be \(2 Tr\). Considering the BS as the centre point, the difference of the radius between two adjoining layers should be \(2 Tr\). If a CH wants to transfer data back to the BS, it will transfer the data to one of the lower layer CHs first. The CH transmission range should be at least \(2 Tr\) to guarantee that it can communicate with its nearest CH. If \(R=2 Tr\), the CHs at layer \(n\) can transfer data to \(n-1\) layer. If \(R=m\times 2 Tr\), the CHs at layer \(n\) can transfer data to \(n-m\) layer. In BEEM, we set \(R=2 Tr\).
# Respiration Monitoring for Premature Neonates in NICU
###### Abstract  
In this paper, we investigate an automated pipeline to estimate respiration signals from videos for premature infants in neonatal intensive care units (NICUs). Two flow estimation methods, namely the conventional optical flow- and deep learning-based flow estimation methods, were employed and compared to estimate pixel motion vectors between adjacent video frames. The respiratory signal is further extracted via motion factorization. The proposed methods were evaluated by comparing our automated extracted respiration signals to that extracted from chest impedance on videos of five premature infants. The overall average cross-correlation coefficients are 0.70 for the optical flow-based method and 0.74 for the deep flow-based method. The average root mean-squared errors are 6.10 and 4.55 for the optical flow- and the deep flow-based methods, respectively. The experimental results are promising for further investigation and clinical application of the video-based respiration monitoring method for infants in NICUs.  
Keywords: respiration; respiration rate; video processing; remote sensing; biomedical monitoring +
Footnote †: journal: Medical Imaging
# TEDL: A Text Encryption Method Based on Deep Learning
## 9 Security Analysis  
According to the [PERSON] guidelines, a good encryption method should have a large enough key space. The key space of TEDL is \(2^{X}\). Considering the example with arXiv address, set \(X_{1}=30\), \(X_{2}=2\), \(X_{3}=8\), \(X_{4}=256\). In theory, \(X_{4}\) can be infinite, but given that the space of hash is \(2^{256}\), we assign that \(X_{4}=256\). Otherwise, referring to the _drawer principle_, there must be two different hashes colliding. See Table 2 for a comparison of the key space.  
For the same key space, the time required to complete encryption for each key differs. There may be doubts here: for the attacker, the longer time for trying each key, the more time will be spent on the crack, but in turn, does the time required for the communication parties to normally transmit information increase dramatically? It is not the case for TEDL, owing to the two-stage structure. The brute-force crack is mainly performed at the first stage while the communication between the two parties is mainly carried out at the second stage. Therefore, it improves safety while ensuring efficiency.  
In addition, TEDL does not directly use the key in the encryption. Instead, the key is the instructor during the encryption and decryption process. Therefore, techniques that involve ciphertexts analysis, such as Differential Cryptanalysis [43], Linear Cryptanalysis [44], Truncated Differentials [45], Boomerang Attacks [46], Impossible Differentials [47] and others [48; 49], are not effective since these ciphertexts involve limited knowledge about keys, making it infeasible for attackers to predict keys.  
Besides, TEDL bases the security on the difficulty in parameter interpretation in deep learning, which is another hard problem. Not only the parameters themselves are uninterpretable, but the trend of their variation is also unexplained, which is core challenge, as described in [50].  
Furthermore, the application of SHA-256 makes TEDL more secure. In cryptography, the avalanche effect refers to an ideal property: when the input makes the slightest change (for example, inverting a binary bit), an indistinguishable change in the output occurs (there is a 50% probability that each binary bit in the output is inverted). The ideal state of nonlinear diffusivity is the avalanche effect. Ref. [51] shows that SHA-256 has excellent nonlinear diffusivity. Therefore, even if word vectors are similar, the outputs are quite different. Moreover, because of the irreversibility of the secure hash function, the relation between ciphertext and plaintext is extremely weak and intractable. It is impossible for an attacker to decrypt. Finally, other related security analyses will be illustrated with experimental results.
# Evaluation of Radio Communication Links of 4G Systems
## 4 Results and Discussion
### Signal to Interference andNoise Ratio (SINR)  
SINR is a tool for measuring the quality of the UE network when the signal energy decreases with distance, i.e., the path loss of environmental parameters (for example, background noise, and the interference force of another simultaneous transmission).  
The signal-to-noise ratio SINR is given by the following equation:  
\[\text{SINR}=\text{S}/(\text{I}+\text{N}) \tag{13}\]  
S: Power of the received signal.  
I: Interference power.  
N: Noise power.  
The SINR distribution before and after treatment is given in Figure 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Range** & \multicolumn{2}{c}{**RSRQ (dB)**} \\ \hline  & **Before Treatment** & **After Treatment** \\ \hline
[min, \(-\)19.5] & 323 (00.14\%) & 1 (00.02\%) \\
[\(-\)19.5, \(-\)14] & 25,171 (11.13\%) & 6 (00.10\%) \\
[\(-\)14, \(-\)9] & 144,806 (64.01\%) & 1879 (31.41\%) \\
[\(-\)9, \(-\)3] & 55,162 (24.38\%) & 4049 (67.69\%) \\
[\(-\)3, Max] & 768 (00.34\%) & 47 (00.79\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RSRQ values before and after treatment.  
Figure 10: Histogram’s distribution of RSRQ (before and after treatment).  
In the case of SINR, the values in the normal state are (15, 30) dB. In good condition, the values are higher than 30 dB. When the value of SINR is less than 15 dB, an action of optimization is necessary. These actions are possible: boost the power transmission, handover management; increase the height of the transmitter, optimization of antenna tilt parameters, and the implementation of new eNB if necessary.  
The obtained values of SINR before and after treatment are given in Table 3:  
The histogram's distribution of SINR before and after treatment is presented in Figure 12.  
The aim is to reduce the value of the SINR. According to Figure 12 and Table 3, there is great amelioration in all ranges of the SINR. The best amelioration is in the range of [15, 30] dB. In this range, the reduction of 46,223 dB is introduced. In the ranges [\(<\)0] dB, [\(0,5\)] dB, [\(5\), \(10\)] dB and [\(10\), \(15\)] dB, the reductions are 13,298 dB, 30,557 dB, 42,001 dB, and 46,223 dB, respectively. The weak reduction is in the range of [\(>\)30] dB (reduction of 2340 dB).
[23] utilized VMD for seismic signal analysis, demonstrating higher spectral and spatial resolutions in the instantaneous spectra compared with EMD. However, in the aforementioned studies, the selection of mode number \(k\) in VMD was often determined empirically and conveniently. For example, [PERSON] and [PERSON] [18] applied VMD to analyze simulation and blasting signals, without providing a basis for choosing \(k\). [PERSON] et al. [20] determined \(k\) based on the number of decomposition layers in EMD, which is an empirical approach. [PERSON] et al. [24] used correlation coefficients and energy ratios to determine \(k\), but this method is not suitable to AE signals and lacks physical meaning. The accurate determination of \(k\) in VMD is crucial as it greatly impacts the decomposition results and can lead to poor outcomes. Therefore, selecting an appropriate \(k\) holds significant importance in VMD. Several methods have been proposed for selecting mode number \(k\), including correlation coefficient [25; 26], kurtosis index [27; 28], and entropy [29]. In this study, we combine the advantages of AE signal frequency characteristics and correlation coefficient to construct a weighted frequency index as the objective for optimizing VMD parameters. Additionally, we employ a novel optimization algorithm called beetle antennae search algorithm (BAS) [30] to optimize VMD parameters. BAS is a simple method that has the advantages of a simple-principle, parameter-efficient search algorithm that significantly reduces computational requirements compared with the particle swarm optimization algorithm [31]. To address the difficulty in filtering out noise from AE signals, this paper proposes an improved VMD method for AE signal analysis and noise reduction. The remaining sections of this paper are organized as follows: Section 2 introduces the fundamental theory of VMD and fuzzy entropy; Section 3 provides a brief overview of the improved VMD-relatedalgorithms; Section 4 verifies the proposed method using simulation signals; Section 5 applies the proposed algorithm to reduce AE signal noise and analyzes the multifractal characteristics of AE signals.
# Design of a Device Based on an LED Matrix for Water Sterilization
## 3 Results
### LED 2 Exhibits Efficacy in Eradicating Both Vegetative Microorganisms and Resistant Cellular Forms  
The effect of LED 2 was tested on suspensions of different pathogenic bacteria in water, including vegetative cells of _K. pneumoniae_, _E. faecalis_, _S. enterica_, and _C. perfringens_, as well as spores of _C. perfringens_. When tests were conducted under static conditions, viable vegetative cells were completely eliminated in 40 min and spores in 120 min (Figure 3A-E). Stirring increased the efficiency of the procedure, achieving 0% viability with 1 min of treatment in _K. pneumoniae_ and _E. faecalis_, with 2 min in _S. enterica_, and with 1 and 5 min for the vegetative and resistant forms of _C. perfringens_ respectively (Figure 3A-E). In the case of yeasts, it took 90 min and 120 min to kill _S. cerevisiae_ and _C. albicans_ under static conditions, but less than 40 and 5 min, respectively, under stirring (Figure 3F,G).
# Crack Location and Degree Detection Method Based on YOLOX Model
###### Abstract  
Damage detection and evaluation are concerns in structural health monitoring. Traditional damage detection techniques are inefficient because of the need for damage detection before evaluation. To address these problems, a novel crack location and degree detector based on YOLOX is proposed, which directly realizes damage detection and evaluation. Moreover, the detector presents a superior detection effect and speed to other advanced deep learning models. Additionally, rather than at the pixel level, the detection results are determined in actual scales according to resolution. The results demonstrate that the proposed model can detect and evaluate damage accurately and automatically.  
deep learning; crack location; object detection; crack assessment; YOLOX Article
## 2 Geodetic Data Sets and Processing  
The use of geodetic observation data is efficient for the study of earthquakes because of their high precision and their complementarity. In this study, several types have been selected--GPS, ERS-SAR and SPOT-optical images, levelling and benchmarks. GPS data have a high temporal resolution and a precision less than 1 mm/year useful to study slow deformation such as the post-seismic phase but it can also be used to study fast deformation such as the co-seismic phase; however their spatial distribution is sparse. Levelling and benchmarks have also an high spatial precision (few cm or higher) but rely on field measurement campaigns. InSAR data have a high spatial density and sub-cm precision but a low temporal sampling rate depending on the satellite (for instance, 6 days for Sentinel satellites but 35 days for ERS). Finally, SPOT images have a similar spatial density to SAR images, but can often provide useful measurements despite vegetation where SAR images lose coherence and at higher density than a pixel offset map estimated from the SAR amplitude would give. Even though the study area is partially covered by dense vegetation making InSAR output non-coherent, the joint study of InSAR with correlation of optical images and terrestrial measurements is an efficient and precise way to understand earthquakes. In central western Taiwan, from InSAR outputs, information about the footwall of the fault is obtained and SPOT offsets cover the full study area more densely than GPS. Levelling and benchmarks give precision about vertical movements and about the curved part at the north of the fault as benchmarks were measured only in this area (Figure 1B,C).
# Robust Bilinear Probabilistic Principal Component Analysis
## 3 Robust BPPCA
### The Model
\begin{cases}X=C\gamma^{r}+W+Y^{\prime}_{\epsilon},\\ Y^{\prime}=ZR^{\mathrm{T}}+\mathcal{E}_{r},\\ Y^{\prime}_{\epsilon}=\mathcal{E}_{c}R^{\mathrm{T}}+\mathcal{E},\end{cases}  \tag{14}\]  
where \(Y^{\prime}\) and \(Y^{\prime}_{\epsilon}\) are the row projected intermediate and residual matrices, respectively. By (11), we have the conditional distributions  
\[Y^{\prime}|\mu\sim\mathcal{N}_{d_{c},d_{r}}(0_{d_{c}\times d_{r }},\mu_{c}I_{q_{r}},\mu_{r}\Sigma_{r}), \tag{15a}\] \[Y^{\prime}_{\epsilon}|\mu\sim\mathcal{N}_{d_{c},d_{r}}(0_{d_{c} \times d_{r}},\mu_{c}\sigma_{c}^{2}I_{d_{c}},\mu_{r}\Sigma_{r}),\] (15b) \[Y^{\prime}|Z,\mu\sim\mathcal{N}_{q_{c},d_{r}}(ZR^{\mathrm{T}}, \mu_{c}I_{q_{r}},\mu_{r}\sigma_{r}^{2}I_{d_{r}}),\] (15c) \[X|Y^{\prime},\mu\sim\mathcal{N}_{d_{c},d_{r}}(CY^{\prime}+W,\mu_ {c}\sigma_{c}^{2}I_{d_{c}},\mu_{r}\Sigma_{r}), \tag{15d}\]  
where \(\Sigma_{r}\) is given by (12). In addition, by using (15c) and the Bayes' rule, the conditional distributions \(Z|Y^{\prime},\mu\) and \(Y^{\prime}|X,\mu\) can be calculated as  
\[Z|Y^{\prime},\mu \sim\mathcal{N}_{q_{c},d_{r}}\Big{(}Y^{\prime}R\Phi_{r}^{-1}, \mu_{c}I_{q_{r}},\mu_{r}\sigma_{r}^{2}\Phi_{r}^{-1}\Big{)}, \tag{16a}\] \[Y^{\prime}|X,\mu \sim\mathcal{N}_{q_{c},d_{r}}\Big{(}\Phi_{c}^{-1}C^{\mathrm{T}}(X -W),\mu_{c}\sigma_{c}^{2}\Phi_{c}^{-1},\mu_{r}\Sigma_{r}\Big{)}, \tag{16b}\]  
where  
\[\Phi_{c}=C^{\mathrm{T}}C+\sigma_{c}^{2}I_{q_{c}}\quad\text{and}\quad\Phi_{r}= R^{\mathrm{T}}R+\sigma_{r}^{2}I_{q_{r}}. \tag{17}\]  
In (14), the bilinear projection in the RBPPCA model is split into two stages by first projecting the latent matrix \(Z\) in the row direction to obtain \(Y^{\prime}\), then \(Y^{\prime}\) being projected in the column direction to finally generate \(X\). Similarly, we can also consider the decomposition of the bilinear projection by first projecting column and then row directions to rewrite (11) as  
\[ \begin{cases}X=Y^{c}R^{\text{T}}+W+Y^{c}_{e},\\ Y^{c}=CZ+\mathcal{E}_{c},\\ Y^{c}_{e}=C\mathcal{E}_{r}+\mathcal{E},\end{cases}  \tag{18}\]  
where \(Y^{c}\in\mathbb{R}^{d_{c}\times q_{r}}\) and \(Y^{c}_{e}\in\mathbb{R}^{d_{c}\times d_{r}}\) with \(Y^{c}|\mu\sim\mathcal{N}_{d_{c},d_{r}}(0_{d_{c}\times q_{r}},\mu_{c}\Sigma_{c },\mu_{r}I_{q_{r}})\) and \(Y^{c}_{e}|\mu\sim\mathcal{N}_{d_{c},d_{r}}(0_{d_{c}\times d_{r}},\mu_{c}\Sigma_ {c},\mu_{r}\sigma_{r}^{2}I_{q_{r}})\). Furthermore,  
\[Y^{c}|Z,\mu \sim\mathcal{N}_{d_{c},d_{r}}(CZ,\mu_{c}\sigma_{r}^{2}I_{d_{c}}, \mu_{r}I_{q_{r}}), \tag{19a}\] \[X|Y^{c},\mu \sim\mathcal{N}_{d_{c},d_{r}}(Y^{c}R^{\text{T}}+W,\mu_{c}\Sigma_ {c},\mu_{r}\sigma_{r}^{2}I_{d_{r}}),\] (19b) \[Z|Y^{c},\mu \sim\mathcal{N}_{q_{c},d_{r}}\left(\phi_{c}^{-1}C^{\text{T}}Y^{ c},\mu_{c}\sigma_{c}^{2}\Phi_{c}^{-1},\mu_{r}I_{q_{r}}\right),\] (19c) \[Y^{c}|X,\mu \sim\mathcal{N}_{d_{c},d_{r}}\Big{(}(X-W)R\phi_{r}^{-1},\mu_{c} \Sigma_{c},\mu_{r}\sigma_{r}^{2}\Phi_{r}^{-1}\Big{)}. \tag{19d}\]
## 4 Conclusions  
The effects of local properties, hardness and microstructure for four laser welded butt joints in thin HSLA steel were considered to explain fatigue crack growth behavior. Estimates and predictions of fatigue lives were also made. The main conclusions drawn are:  
* Although the differences were small, each welded series presented at the crack start and propagation sites (CGHAZ and FZ) had higher hardness than the BM. The hardness range for the welded series was 335-370 HV and the hardness for the BM was 295 HV;
* The welded series presented similar FCGR rates in the HAZFZ, but lower than in the BM. This was due to lower hardness being present in the BM because it was verified as a bainitic-ferritic microstructure, while, in the welded series it was observed as a bainitic-martensitic microstructure in the CGHAZ and the FZ. Crack closure was observed only in the BM and in the S3 series, which can be attributed mainly to the crack tip plasticity for the BM and the effect of compressive residual stresses for the S3 series. The welding parameters did not influence in the FCGR rates;
* At higher stress levels, fatigue life is strongly influenced by the quantity, size and location of the imperfections. The presence of crack-like imperfections located in critical positions, such as near to the lateral edge or several along the width of the specimen, favor the initiation and coalescence of cracks so that the fatigue initiation period can be neglected;
* It has been shown that estimations and predictions of welded series' fatigue life are very sensitive to the adjustment of the initial crack size and the crack growth rates, and to an adequate identification of imperfections and assumptions of a type of superficial crack.  
Author Contributions: Conceptualization, P.G.R. and J.A.M.F.; methodology, P.G.R., C.C. and J.A.M.F.; acquisition, analysis and interpretation of data, P.G.R., C.C. and J.A.M.F.; writing--original draft preparation, P.G.R.; writing--review and editing, P.G.R. and J.d.J. All authors have read and agreed to the published version of the manuscript.  
This research is sponsored by national funds through FCT--Fundacao para a Ciencia e a Tecnologia, under the project UIDB/00285/2020.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors acknowledge the support provided by the Universidad de las Fuerzas Armadas-ESPE with the Ph.D. scholarship and to [PERSON] and [PERSON] of the Orthopedia Medica company where the laser welding was performed.  
**Conflicts of Interest:** The authors declare no conflict of interest.