esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reverse-Transcription-Polymerase Chain Reaction (RT-PCR) Analysis  
Total cellular RNA from conditioned RAW264.7 macrophage cells was isolated using TRIzol(r) reagent (Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer's protocol and RT PCR was performed. Briefly, equal amounts of total RNA (5 ug) were reverse transcribed in a 40 uL reaction mixture containing 8 uL Molony Murine Leu-kemia Virus Reverse Transcriptase (M MLV RT) 5\(\times\) buffer, 3 uL 10 mM dNTPs, 0.45 uL 40 U/uL RNase inhibitor, 0.3 uL 200 U/uL M MLV RT (Promega Corporation) and 3.75 uL 20 uM oligo dT (Biometer Corporation, Oakland, CA, USA). Single-stranded cDNA was amplified by PCR using 4 uL 5\(\times\) Green Go Taq(r) Flexi reaction buffer, 0.4 uM 10 mM dNTPs, 0.1 uL 5 U/uL Taq polymerase, 1.2 uL 25 mM MgCl2 (Promega Corporation), and 0.4 uL primer (20 pM/uL). The following primer pairs were used: COX-2 sense, 5\({}^{\prime}\) TTGAAGACCAGGAGTACAGC 3\({}^{\prime}\) and antisense, 5\({}^{\prime}\) GGTACAGTCCATGACATCG 3\({}^{\prime}\); iNOS sense, 5\({}^{\prime}\) GACAAGCTGCATGTGACATC 3\({}^{\prime}\) and antisense, 5\({}^{\prime}\) GCTGGTAGTTCCGTTGTT 3\({}^{\prime}\); TNF-\(\alpha\) sense, 5\({}^{\prime}\) GGCAGGTCTACTTGGAGCTATGC 3\({}^{\prime}\) antisense, 5\({}^{\prime}\) ACATTCGAGGTCCAGTGAATTCG 3\({}^{\prime}\); IL-6 sense, 5\({}^{\prime}\) GTCGGAGGCTTAATTACACATGTTC 3\({}^{\prime}\) and antisense, 5\({}^{\prime}\) ACTCCTTTGACACGCTTAC 3\({}^{\prime}\); IL-1\(\beta\) sense, 5\({}^{\prime}\) TGCAAGTTCCCCAACTGGTAATC 3\({}^{\prime}\) and antisense, 5\({}^{\prime}\) GTGCTGCCAATGTCCCTTGAAT 3\({}^{\prime}\); actin sense, 5\({}^{\prime}\) TCATGATGGTGACATCCG 3\({}^{\prime}\) and antisense, 5\({}^{\prime}\) CCTGAAGCATTTCCGGGTGCACGATG 3\({}^{\prime}\). The PCR conditions were as follows: For COX-2:30 cycles of denaturation at 95 \({}^{\circ}\)C for 1 min, annealing at 63 \({}^{\circ}\)C for 1 min and extension at 72 \({}^{\circ}\)C for 1 min; for iNOS: 30 cycles of denaturation at 95 \({}^{\circ}\)C for 1 min, annealing at 50 \({}^{\circ}\)C for 1 min and extension at 72 \({}^{\circ}\)C for 1 min; for TNF-\(\alpha\): 30 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 58 \({}^{\circ}\)C for 1 min and extension at 72 \({}^{\circ}\)C for 30 s; for IL-6: 30 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 59 \({}^{\circ}\)C for 1 min and extension at 72 \({}^{\circ}\)C for 1 min; for IL-1\(\beta\): 30 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 60 \({}^{\circ}\)C for 30 s and extension at 72 \({}^{\circ}\)C for 30 s; \(\beta\)-actin: 25 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 63 \({}^{\circ}\)C for 30 s and extension at 72 \({}^{\circ}\)C for 1 min. \(\beta\)-actin was used as an internal control to evaluate the relative expression of iNOS, TNF-\(\alpha\), IL-6, and IL-1\(\beta\).
## 3 Results and Discussion
### In Vitro Evaluation of Urease Inhibition  
The capacity of synthesized derivatives of \(N\),\(N\)-dimethylpyrimidine tritone (**3a**-**i** and **4**) and \(N\),\(N\)-diethyl-thio pyrimidine dione (**3j**-**p** and **5**) to inhibit urease in vitro was examined and compared with that of acetohydroaxamic acid **(AHA)** (IC\({}_{50}\) = 20\(\pm\) 0.4 \(\upmu\)M), as a standard compound (Table 1).  
Of note, the tested compounds showed potent urease inhibition activity, twelve of them (**3a**, **3d**, **3e**, **3f**, **3h**, **3j**, **3k**, **3l**, **3m**, **3n**, **3o**, and **4**) exerting greater activity than the reference acetohydroaxic acid. The most active member between this series is compound **3h** which the structure attributed the pyridine moiety. Separation the pyridine ring from the enaminone functionality in compound **3j** with CH\({}_{2}\) group, the activity has been decreased compared to the most active member **3h**. On the other hands, compounds **3k**, and **3o** showed superior activity to their counterparts **3b** and **3g**. Indeed, **3l** and **3o** showed activity similar to that of **3d**, and **3f**. The chloro atom in _para_-position in compound **3m**, the activity has been decreased compared to the _ortho_ position of compounds **3f**, and **3n**. Compounds **3c**, **3i** and **3p** with sulfonic, cyclic amide or sulfonamide functionalities shown less activity compared to standard drug. The cyclohexyl group in compound **3e** increases the activity with 1.8 folds than the control. it was observed that in case of compounds **4** and **5** the presence of the \(N\),\(N^{\prime}\)-diethyl group and the S atom in the thiobarbituric derivative **5** make them more voluminous compared to its analogs \(N\),\(N^{\prime}\)-dimethyl and the \"O\" atom in the barbituric derivatives **4**. Finally, compounds **3b**, and **3g** were not active.  
However, there are some discrepancies regarding whether AHA is or is not a competitive inhibitor to the active site of the urease enzyme [51; 52]. Therefore, some limitations could be applied, and we think that comparisons are important.
# PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything
## 1 Introduction
### Motivation and Contributions  
According to various findings and studies, the main real-time network vulnerability that must be addressed and eliminated to allow secure end-to-end IoE communication, apart from data leakage, data alteration, and eavesdropping, is the non-cooperation of the nodes in the network. As such, it is crucial to identify misbehaving network nodes by employing incentive-based or reward-based mechanisms to promote the participation of honest nodes in the network as much as possible, with the intent of achieving network veracity. When numerous active and passive network threats are combined with the capabilities of quantum computers, the network becomes unstable, putting secrecy and  
Figure 2: Security in Internet of Everything.  
integrity at risk. To offer resilience against network gaps discovered during end-to-end IoE communication, a technique is required to identify dishonest entities while maintaining post-quantum end-to-end reliability. Furthermore, to the best of our knowledge no studies in the existing literature have addresses this particular issue in the Internet of Everything context using post-quantum geo-encrypted end-to-end data communication. Taking the above into account, the _PINE_ protocol is proposed to solve the aforementioned security concerns through the following contributions:  
* In order to maintain data confidentiality, integrity, and availability in a communication network, the protocol integrates a future-safe end-to-end lattice geo-encryption technique in a bi-directional multi-hop mobile relay IoE network to deliver a confidential message from the sender(s) to the receiver(s) and vice versa.
* The protocol demonstrates a robust attack model which reports non-cooperative relay nodes to the sender node and mitigates such nodes with the assistance of the direct and overheard network information (NetInfo), which contains the details of the Node-Id, EnergyStatus, PacketForwardCount, PacketDropCount, Timestamp, and Incentive accumulated by the relay node and the relay node's neighbour (known as neighbour-relay nodes).
* Decisively, the sender node is liable for generating incentives (in the form of currency) for the cooperative or non-cooperative nodes by comparing the direct and overheard NetInfo received from both the relay and neighbour-relay nodes.
* The sender maintains an Incentive Table (InTab) to facilitate further judgement in choosing the nodes and flooding the resulting incentive value to the respective nodes based on both direct and overheard NetInfo. By employing such a strategy, the sender and forwarding nodes can make intelligent decisions to select the next worthy hop.
* The protocol further addresses and defends against non-cooperation network attacks namely, Type-III selfish attacks and blackhole attacks.
## 3 Proposed UDS-Based SSI
### Speech Synthesis  
As the final step of the UDS-based SSI system, an audible speech signal is synthesized using the speech parameters predicted from the UDS features. An approach based on a linear prediction (LP) model was adopted in the previous studies, where a voice was generated by filtering the excitation source through an all-pole filter that reflects the vocal tract transfer function [32]. Typical feature variables that represent the vocal tract transfer function are MCC [21, 22, 24, 25, 27, 30] and LPC [18, 28]. In the LP-based synthesis approach, it is necessary to build the prediction rules for an excitation source, which is represented either by the periodic impulse train (for voiced speech) or by white Guassian noise (for unvoiced speech).  
Since the magnitude spectra of the speech signals were predicted in this study, an approach using short-time Fourier transform (STFT)-based synthesis was adopted. Continuous waveforms were obtained by concatenating the windowed short-time speech signals obtained by inverse Fourier transform. Since the phase spectrum was unavailable, it was necessary to determine how to produce the phase spectrum. There were two possible ways to generate the phase spectra; using a random phase and a method of the least square error estimation of modified short time Fourier transform magnitude (LSEE-MSTFTM) [43]. The latter was achieved by iteratively minimizing the squared error between the STFT of the continuous reproduced signal and the predicted magnitude spectrum. We compared results from the two approaches, based on the quality of the reproduced speech signals. The findings indicated that the performance of the LSE-based phase estimation was highly affected by the prediction errors of the magnitude spectra. This means that the quality of the LSE-based phase estimation method was remarkably superior to the use of a random phase when the DNN produced the magnitude spectra with small prediction errors. In the opposite case, however, a phase spectrum was obtained to fit the incorrectly predicted magnitude spectrum, which resulted in highly distorted voices. As a result, the average quality in terms of PESQ was almost equal in both approaches. In the present study, the use of a random phase with advantages in computational complexity was adopted.
## 5 Discussion
### Influence of Resolution  
This study relied heavily on high-resolution images, which play the most important role in recognizing cotton plants correctly; hence, we discuss the manner in which the resolution influences the result. As shown in Figure 7, the cotton field image was obtained at a resolution of approximately 3 mm/pixel in the study field, and this image was resampled to a resolution of 6 mm/pixel, 9 mm/pixel, 1.2 cm/pixel, 1.5 cm/pixel, and 1.9 cm/pixel to further compare. As shown in Figure 7, there was no large difference between the images 3-and 6-mm/pixel resolution images; visual inspection can identify the cotton easily. However, as the resolution decreased, the images became blurrier. When the resolution was less than 1.2 cm/pixel, the cotton plants are especially difficult to recognize, and with resolutions of 1.5 and 1.8 cm/pixel, small cotton plant cannot be identified in the images. However, the resolution of the flight image is the key parameter, which not only influences the monitoring accuracy but also the cost. To achieve higher resolution with a UAV system, the flight altitude must be lower and the imaging area smaller. Higher resolution means higher time consumption and an increase in monitoring cost. Considering the recognition accuracy and the cost, we suggest using a resolution of no less than 1.2 cm/pixel in practice for image collection.  
Figure 6: The budding rates of the validation samples obtained from the three methods.
## 2 Status of Research and Objectives in Radiant Tube Lifetime Modeling
### Microscopic Damage Analysis  
On the one hand, microscopic damage analyses on radiant tubes were carried out with sample process temperatures and total process time as their primary parameters. [PERSON] et al. [8] performed failure analysis of U-type radiant tubes, made of centrifugally cast Ni-Cr steels, in a CGL. Three root causes of failures were identified: high-temperature oxidation due to overheating of the tube (>1090 \({}^{\circ}\)C), hot spots created by supports, and possible uncontrolled combustion/explosion due to fluctuations in gas compositions. The failure of radiant tubes by high-temperature oxidization or carburization attacks were also confirmed by [9; 10; 11; 12; 13]. Another key finding of the microscopic analyses was radiant tube failures caused by high-temperature creep. [PERSON] et al. [14] studied  
Figure 3: Example of temperature changes in a double P-type radiant tube in a CGL furnace: Region 1 representing the classic case of tube temperatures under burner on/off firing, Region 2 showing an instance of burner full load firing, and Region 3 illustrating tube temperature changes due to strip parameter variation.  
metallographically the damage of radiant tubes in a continuous annealing line (CAL) and found signs of the advanced stage of creep deformation. It was also suggested that severe creep deformation might have contributed towards the cracking of protective oxide scales and thus caused an accelerated corrosion attack. [PERSON], et al. [15] confirmed that the root cause of failures in a CAL furnace was a high-temperature creep and added the importance of considering the effect of gravitational forces (dead weight of tube) into account. [PERSON] et al. [16] supported this view and also pointed towards the role played by the tube temperature gradients during operation, such as during process changes, plant shutdown, or startups. The macroscopic thermomechanical behavior of the tube during the operation was not the subject of these investigations. Therefore, these studies--even though informative--are of limited value towards assessing the lifetime of radiant tubes in a CGL.
## 2 Methods
### Cost Volume Construction with Plane Scanning  
The cost volume is a structure formed by the fusion of feature bodies from different perspectives, which mainly plays a role in measuring the similarity between the feature maps obtained by feature extraction and determining whether they match or not and the commonly used construction method is the plane scanning method. The process of constructing the cost volume is mainly divided into three steps:  
Figure 4: DRE-Net network architecture improved by self-attention mechanisms.  
1. Determine the discrete hypothetical depth planes, in which when the spatial resolution of the cost volume is fixed, the larger the number of hypothetical planes, the more accurate the result is accordingly, but it will increase the GPU memory and the running time.
2. Transform the feature maps extracted from each view into the hypothetical planes and construct the cost volume by using the microscale monoclinic transform, in which, at the depth d, the source image of the ith view is the same as the reference image. The reference image with monoclinicity \(Hi(d)\) can be expressed as: \[H_{i}(d)=K_{i}\cdot R_{i}\cdot(I-\frac{(t_{0}-t_{i})\cdot n_{0}^{T}}{d})\cdot R _{0}^{T}\cdot K_{0}^{T}\] (4) where \(K_{i}\), \(R_{i}\), and \(t_{i}\) denote the internal reference, rotation, and translation of the source image camera of the \(i\)th viewpoint, \(n_{0}\) denotes the principal axis of the reference camera, and \(I\) denotes the hypothetical planar interval, respectively.
3. The feature bodies of different viewpoints are formed into the feature body Fi by the univariate responsive change, and then all the feature bodies are aggregated into a single cost volume by the variance-based cost metric Fi, which is used to judge the similarity between N viewpoints and can be expressed as: \[C=\frac{\sum_{i=1}^{N}\bigl{(}F_{i}-\overline{F}_{i}\bigr{)}^{2}}{N}\] (5)
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 4 Numerically Verification
### The Effect of Vehicle Velocity  
A higher velocity of the vehicle is considered in this case, i.e., \(v=10\) m/s. The bridge damage remains set at 13 m from the left end with a damage level of 40%. The proposed approach is used to detect bridge damage and its location. The corresponding results are illustrated in Figure 14. As shown, the different wavelet energy between the damaged and undamaged bridge conditions is obvious and even greater than that of the lower vehicle velocity in Figure 10. In other words, the higher velocity of the vehicle can improve the feasibility of damage detection using the proposed approach. However, a higher vehicle means a shorter length of measured data. The proposed approach requires dividing the data into parts. Therefore, short data brings about less robustness for detecting the damage location. In this case, the proposed approach identifies the bridge damage location at No. 8 as shown in Figure 14b, i.e., at the location of 14-16 m from the left support. The damage location identification result is not very accurate.  
The results show that higher speeds can improve the likelihood of successful damage detection, but damage location identification may be less accurate. To address this issue, it is recommended to record vehicle responses for undamaged bridges with lower and higher vehicle speeds as references. For routine inspections, the bridge is scanned using a higher velocity and the corresponding damage index (change in wavelet energy) is calculated. If no significant changes were found, the bridge was assessed as \"good\". Otherwise, one can inspect the bridge again using a lower speed and then calculate the corresponding damage index to localize the damage. In this way, the effectiveness of damage detection and the accuracy of damage location can be ensured at the same time.
## 5 Conclusions  
This paper establishes a fluid-solid-thermal coupling model in the fracture closure process and solves it using the DDFM. The influence of factors such as proppant concentration, proppant placement, proppant mechanical properties and proppant diameter are analyzed by analyzing the change law of shale gas production. The simulation results show that:  
1. The fracture closure width calculated by the real fracture model is smaller than that of the discontinuous discrete fracture model. However, the applicability of the model to ultra-deep shale gas reservoirs needs further verification, and the impact of extreme geological conditions (such as high-stress anisotropy) is not considered.
2. The higher the proppant concentration, the wider the fractures formed, but it did not have a significant impact on shale gas production. When the initial fracture width increased from 5 mm to 1 cm, the cumulative gas production over 900 days increased by only 3.65%.
3. Poor proppant placement means that the proppant is unevenly placed in the fracture. At this time, the proppant cannot effectively support the fracture surface, resulting in a decrease in conductivity and effective drainage area and a significant decrease in shale gas production.  
Figure 19: Effect of the proppant diameter on shale gas production.  
Figure 18: Effect of the proppant diameter on the dimensionless fracture surface crossflow coefficient.  
When the elastic modulus of the proppant increases, the fracture width and cumulative gas production increase accordingly, but the proppant with a larger elastic modulus increases the embedding amount, resulting in a poor fracture transformation effect. Although the increase in the Poisson's ratio of the proppant increases the leakage area, it reduces the crossflow capacity, so the Poisson's ratio of the proppant has little effect on shale gas production.
5. Small-sized proppants have a lower proppant laying concentration, which brings lower construction costs. Small-sized proppants are easier to pump to the far end of the fracture network, and the actual construction effect will be better than large-sized proppants. Therefore, small-sized proppants are more suitable for deep shale gas hydraulic fracturing construction.  
Conceptualization, S.C. and S.W.; methodology, Y.J. and S.W.; validation, Y.X.; investigation, S.C.; resources, Y.X.; writing--original draft preparation, S.C.; writing--review and editing, S.C., Y.J. and S.W.; supervision, Y.J.; project administration, Y.X.; funding acquisition, Y.J. and S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42407252, No. 52334001 and No. U24B2029) and China University of Petroleum, Beijing (No. 2462023 BJRC007).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to some basic research involving confidentiality.  
The authors declare no conflicts of interest.
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 3 IPL Fading Channel Model Application
### Ergodic Channel Capacity Analysis  
The ergodic channel capacity is defined as the instantaneous capacity averaged over the stochastic channel realizations:  
\[\overline{\mathrm{C}}=\int_{0}^{\infty}\log_{2}(1+\gamma)f_{\gamma}(\gamma) \mathrm{d}\gamma, \tag{32}\]  
**Theorem 5**.: _The ergodic capacity of the IPL channel \(\overline{\mathrm{C}}\) and its high-SNR asymptotics are given by_  
\[\begin{cases}\overline{\mathrm{C}}=\frac{1}{\Gamma(\alpha)\ln 2}\mathrm{H}_{2,2}^{2 }\!\left(\begin{array}{c}\dot{\Omega}_{\alpha,\beta}\\ \dot{\gamma}^{\beta}\end{array}\right)\left(\begin{array}{c}(1,1),(1,\beta) \\ (\alpha,1),(1,\beta)\end{array}\right),\\ \overline{\mathrm{C}}\bigm{|}_{\mathcal{T}\to\infty}\simeq\log_{2}\mathcal{ \bar{\gamma}}+\frac{1}{\beta\ln 2}\Big{(}\mathcal{G}_{e}+\psi^{(0)}(\alpha)-\ln\dot{\Omega}_{ \alpha,\beta}\Big{)},\end{cases} \tag{33}\]  
_where \(\mathcal{G}_{e}\approx 0.577\) is the Euler-Mascheroni constant and \(\psi^{(0)}(\cdot)\) is the digamma function [46]._  
which means that the obtained result does not depend on \(\bar{\gamma}\).  
To overcome that issue, one can resort to the well-known relation (see, for example, [54]):  
\[\overline{\mathbb{C}}\left.\right|_{\bar{\gamma}\to\infty}\backsimeq\log_{2}\bar {\gamma}+\frac{1}{\ln 2}\frac{\mathrm{d}}{\mathrm{d}r}\bigg{(}\frac{\mathbb{E}\{ \gamma^{\prime}\}}{\bar{\gamma}^{r}}\bigg{)}\right|_{r=0}. \tag{42}\]  
Since the raw moments were derived in the proof of Lemma 1,  
\[\frac{\mathbb{E}\{\gamma^{\prime}\}}{\bar{\gamma}^{r}}=\frac{\Gamma\Big{(}1- \frac{r}{\bar{\gamma}}\Big{)}\Gamma\Big{(}\alpha+\frac{r}{\bar{\gamma}}\Big{)} }{\Gamma(\alpha)\bar{\upsilon}_{\alpha,\beta}^{\frac{r}{\bar{\gamma}}}}. \tag{43}\]  
Finally, differentiating (43) and taking a limit as \(r\to 0\) yields  
\[\overline{\mathbb{C}}\left.\right|_{\bar{\gamma}\to\infty}\backsimeq\log_{2} \bar{\gamma}+\frac{1}{\beta\ln 2}\Big{(}G_{e}+\psi^{(0)}(\alpha)-\ln\mathcal{U}_{ \alpha,\beta}\Big{)}. \tag{44}\]