esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 4 Model Setup and Analysis of the Project
### Upper Limit of Shield Slurry Support Pressure  
In the same formation, the slurry fracturing pressure of the shield is generally proportional to the burial depth of the location. In this project, the stratigraphic parameters of the reservoir area are relatively similar. The bottom of the reservoir is relatively flat. The difference in water depth inside the reservoir is small. In the simulation in this section, the two sections EK5 + 001 and EK5 + 915 are selected as the inflection points of the slope change of the shield tunnel line. Based on the upper limit of the support pressure of the four sections illustrated in Table 3, obtained from the simulation, the simplified upper limit of the support pressure of the entire tunnel crossing the reservoir can be obtained, as demonstrated in Figure 19.  
The upper limit of the shield slurry support provided in Figure 19 is a reference value only, considering the prevention of slurry fracturing. The purpose is to give advice on support pressure control during the construction of a shield tunnel crossing the reservoir. Since there are few methods to prevent and control the slurry fracturing of the shield, it is more important to accurately set the shield slurry support pressure. From the research in the previous section, it can be observed that increasing the viscosity of slurry can slow down the expansion of fractures. In engineering, the method of increasing the viscosity of slurry can be used to prevent slurry blow-out, improving the tunneling speed of the shield machine after the slurry fracturing occurs. The shield can also drive quickly to seal the fracture opening to prevent the further extension of the fracture. In summary, when the shield tunneling reaches the formation prone to slurry fracturing, it is necessary to adopt the various control methods mentioned above to avoid slurry blow-out. According to the improved XEFM, the shield support pressure upper limit to prevent slurry fracturing  
Figure 19: Predicted value of upper limit of shield slurry support pressure in tunnel crossing reservoir section.  
Figure 18: Variation of slurry pressure with slurry injection volume at different sections.  
can be obtained in the simulated case study, which can guide the construction personnel to control the shield support pressure value. When the upper limit of the shield support pressure is too small to make it difficult to be controlled during construction, it can be optimized from the perspective of shield tunnel site selection and buried depth design to avoid slurry fracturing.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 2 Research Methodology and Proposed System
### State-of-the-Art Research in Illegal Logging  
During this research, forest monitoring from illegal logging was conducted by taking imagery data through a satellite using various algorithms. The image from a bare forest can provide information that trees in the forest have been cut down [31; 32]. Unfortunately, this system cannot provide tracking and reporting information on illegal logging in real-time. Besides, this system needs higher and more advanced computation.  
Research using WSN has been conducted using integrated RFID, but the implementation of the tracking system is still separated [4]. A guardian forest system to detect the occurrence of illegal logging from the identification of chainsaw sound has been proposed. By utilizing the application program interface (API), the information was delivered through short message services (SMS) and web applications [33].  
This system was then replaced by using the ZigBee, that can identify and track illegal logging [34]. A combination of a vibration sensor and sound sensor was proposed by [35]. Those sensors were integrated with Fog-computing and Lightweight Software Devices Networking Controllers (FLCs); the communication data used ZigBee. Detection through chainsaw acoustics has been applied to detect the illegal logging combined with ZigBee communication. The sound was classified into three band frequencies [36]. So, to recognize the occurrence of the illegal logging, most of the proposed systems were utilizing sound and vibration sensors. In this research, the proposed system used a gyro sensor to detect the slope of the tree trunk and sensor sound to detect the chainsaw sound.  
However, ZigBee has a weakness in the coverage area communication and the infrastructure for wide area networks. The infrastructure of the network is affected by the network lifetime [17; 37]. The comparison of LoRaWAN, Wifi and ZigBee can be seen in Table 1. As stated in Table 1, LoRaWAN has a communication coverage range of kilometers, while ZigBee only has a range of 10-100 m. ZigBee mostly uses short-range radio technologies and is not suitable for the illegal logging scenario that requires a low-power wide area network to fulfil the characteristics of an IoT application. The characteristics that are required from IoT applications are long-range, low data rate, low energy consumption, and cost-effectiveness [38]. Since the illegal logging application is embedded with IoT technology, LoRa is applied in the proposed system.  
Based on the required infrastructure of Illegal logging application, the LoRaWAN infrastructure network is supporting the hierarchical multi-hop network protocol. Network topology STAR of STAR on LoRaWAN makes it possible to add a sensor node without changing the topology.
## 3 Results
### Mineralogical Compositions  
The X-ray diffraction patterns and semi-quantitative mineral-phase analyses of samples with 6-8% water content and varying water-solid ratios are presented in Figure 9. The semi-quantitative analytical procedure exclusively considers calcium oxides and represents a corrected outcome. The predominant constituents in the LFS feedstock were calcio-olivine and akermanite, denoted by the chemical formulas \(\gamma\)-C2S and Ca2 Mg(Si2O7), respectively. Ca2 Mg(Si2O7) is a mineral-phase variant of 3 CaO2 SiO2(C3S2), primarily formed due to the incorporation of Mg2+ ions into the C3S2 lattice, replacing some of the Ca2+ ions during the high-temperature melting process of LFS. It shares similar properties to C3S2. Previous studies have demonstrated the significant carbonation activity of \(\gamma\)-C2S and C3S2 [37]. Therefore, the primary sources of carbonation activity in the LFS were calcium silicate and akermanite. In contrast, fluorite does not react with CO2 and was included in the analysis solely for comparative purposes with other phases. Additionally, the presence of other trace substances in the LFS was not analyzed; thus, the results of this semi-quantitative analysis should be considered as a reference only. The primary product of the reaction between steel slag and CO2 is CaCO3 [38], and the three primary crystalline forms of laboratory-generated CaCO3 are calcite, aragonite, and vaterite [39]. \(\gamma\)-C2S carbonated at low temperatures produces CaCO3, primarily in the form of calcite [33]. Moreover, it can be discerned from Figure 9a that a prominent calcite diffraction peak appeared subsequent to carbonation, while no diffraction peaks of aragonite or vaterite were detected.  
Figure 8: Sample center profiles of LFS before and after carbonation.  
From Figure 9b, it can be discerned that an increase in the water-solid ratio results in a corresponding elevation of calcite generation following carbonation. The generation of calcite demonstrates an initial rise followed by a decline as water content increases, in line with the findings of carbon sequestration and thermogravimetry presented in Figure 7. Upon carbonation, the contents of calcium silicate and akermanite decrease as the degree of carbonation intensifies, thereby indicating that \(\gamma\)-C\({}_{2}\)S and Ca\({}_{2}\)Mg(Si\({}_{2}\)O\({}_{7}\)) serve as the primary active agents in the carbonation of LFS, which are transformed into stable calcite upon carbonation, concurrently fixing CO\({}_{2}\) within the calcite. However, calcium silicate and akermanite do not experience complete carbonation, likely due to the challenges associated with CO\({}_{2}\) penetration within the particles of LFS.
# DKsA Modulates Antimicrobial Susceptibility of _Acinetobacter baumanii_
###### Abstract  
The stringent response regulators, (p)ppGpp and DKsA, modulate various genes involved in physiological processes, virulence, and antimicrobial resistance in pathogenic bacteria. This study investigated the role of DKsA in the antimicrobial susceptibility of _Acinetobacter baumanni_. The \(\Delta\)_dKsA_ mutant (KM0248D) of _A. baumanni_ ATCC 17978 and its complemented strain (KM0248C) were used, in addition to the \(\Delta\)_dKsA_ mutant strain (NY0298D) of clinical 1656-2 strain. The microdilution assay was used to determine the minimum inhibitory concentrations (MICS) of antimicrobial agents. Quantitative real-time PCR was performed to analyze the expression of genes associated with efflux pumps. The KM0248D strain exhibited an increase of MICs to quinolones and tetracyclines, whereas KM0248D and NY0298D strains exhibited a decrease of MICs to aminoglycosoides. The expression of genes associated with efflux pumps, including _adeB_, _adelJ_, _abeM_, and/or _tetA_, was upregulated in both \(\Delta\)_dKsA_ mutant strains. The deletion of _dKsA_ altered bacterial morphology in the clinical 1656-2 strain. In conclusion, DKsA modulates the antimicrobial susceptibility of _A. baumanni_. The \(\Delta\)_dKsA_ mutant strains of _A. baumanni_ upregulate efflux pump gene expression, whereas (p)ppGpp-deficient mutants downregulate efflux pump gene expression. (p)ppGpp and DKsA conduct opposite roles in the antimicrobial susceptibility of _A. baumanni_ via efflux pump gene regulation.  
Acinetobacter baumanni; DKsA; (p)ppGpp; antimicrobial susceptibility; efflux pump gene +
Footnote †: journal: _Anticholicus_  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-000  
antimicrobial agents than the wild-type (WT) _Escherichia coli_ strain [17]. Additionally, the \(\Delta\)_dksA_ mutant was more susceptible to antimicrobial agents, including \(\beta\)-lactams, aminoglycosoides, quinolones, and tetracyclines, than the WT _E. coli_ strain [18]. These results indicate that DksA and (p)ppGpp coordinately regulate the transcription of genes involved in antimicrobial resistance. There was no difference in the minimum inhibitory concentrations (MIC) of ciprofloxacin and ofloxacin between the WT and \(\Delta\)_dksA_ mutant strains of _Pseudomonas aeruginosa_, but the minimum bactericidal concentrations of quinolones increased in the \(\Delta\)_dksA_ mutants [19]. We recently demonstrated that (p)ppGppdeficient (\(\Delta\)_A1S_0579) mutant was more susceptible to antimicrobial agents, including cephalospor, monobactam, carbapenem, fluoroquinolones, aminoglycos, colistin, tetracyclines, and trimethoprim, than the WT _A. baumannii_ ATCC 17978 strain via down-regulation of various efflux pump genes [20]. However, the role of DksA in antimicrobial susceptibility has not been characterized in _A. baumannii_. This study investigated the role of DksA in the antimicrobial susceptibility of _A. baumannii_ using WT _A. baumannii_, _AdksA_ mutant, and _dksA_-complemented strains.
## 5 Lattice Boltzmann Methods (LBMs)
### Single Relaxation Time (SRT) Collision Model  
Depending on the choice of the collision operator \(\Omega\), different lattice Boltzmann schemes can be constructed, the simplestof which is the single relaxation time (SRT) model, as already mentioned above (_also known as the lattice BGK model_) [151]. Without external force or source terms [114,152], the collision operator takes the following form:  
\[\underbrace{\Omega_{i}(f_{i})=-{\tau_{v}}^{-1}(f_{i}-f_{i}^{eq})}_{Dynamic \ field\ case},\ \underbrace{\Omega_{i}(g_{i,f/s})=-{\tau_{g,f/s}}^{-1}(g_{i,f/s}-g_{i,f/s}^{ eq})}_{Thermal\ field\ case} \tag{3}\]  
where the subscripts \(f\) and \(s\) represent the fluid and solid phases.\(f_{i}^{eq}/g_{i,f/s}^{eq}\) is the equilibrium distribution function and \(\tau_{v/g,f/s}\) is the single relaxation time, which are computed as:  
\[f_{i}^{eq}=\rho w_{i}\Big{(}1+e_{i}u/c_{s}^{2}+uu:\left(e_{i}e_{i}-c_{s}^{2}I \right)/2c_{s}^{4}e\Big{)},\ g_{f,i}^{eq}=w_{i}T_{f}\Big{(}1+e_{i}u/\left(c_{ s}^{2}\right)\Big{)},\ g_{s,i}^{eq}=w_{i}T_{s} \tag{4}\]  
\[\tau_{v}\ =3\
u+0.5,\ \ \tau_{T,f}=3\alpha_{e,f}/\left(\delta tc^{2}\right)+0.5,\ \ \ \tau_{T,s}=3\alpha_{e,s}/\left(\delta tc^{2}\right)+0.5 \tag{5}\]  
\(\alpha_{eff,f/s}\), \(c\ (=\delta x/\delta t)\), \(w_{i}\), \(\epsilon\) and \(c_{s}\ (=c/\sqrt{3})\) being the effective diffusivity, the lattice velocity, weighting factor, porosity and the sound speed, respectively.  
The force terms in Equation (2) can be calculated as:  
\[\underbrace{F_{ei}=F(e_{i}-u)^{2}f_{i}^{eq}/RT_{0}}_{Porous\ medium^{\prime}s\ effect},\ \ F=-\epsilon\left(\underbrace{v/K}_{Darcy\ force}+\underbrace{F_{\epsilon}/ \sqrt{K}\|u\|}_{Forchheimer\ force}\right)u\ \text{for the dynamic field} \tag{6}\]  
\[S_{i,f}=w_{i}\left(\underbrace{La/C_{p,f}(\left(f_{i}(x,t+\delta t)-f_{i}(x,t )\right)/\delta t)}_{Latant\ heat}+\underbrace{h_{sf}(T_{s}-T_{f})/\left( \epsilon(\rho C_{p})_{f}\right)}_{Solid/fluid\ interactions}\right)\text{for thermal field} \tag{7}\]  
\[S_{i,\,s}=w_{i}(\underbrace{h_{sf}(T_{s}-T_{f})/\left((1-\epsilon)(\rho C_{p })_{s}\right)}_{Solid/fluid\ interactions})\text{for thermal field} \tag{8}\]  
Figure 17: LBM general flowchart.  
where \(L_{a}\), \(T_{s/f}\), \(h\), \(K\) and \(F_{e}\) are the latent heat, solid/fluid temperature, interfacial heat transfer coefficient between solid and liquid phases, permeability and Forchheimer coefficient, respectively.  
The great success of the SRT-LBM comes from the simplicity of its numerical scheme, which can be split in two steps (_collision-streaming_): first, the collision mimicking the effects of inter-particle collisions, which is followed by a node-to-node streaming of discrete populations (\(f_{i}/g_{i}\)) on a Cartesian grid.  
Lastly, the mass density \(\rho\), the velocity \(u\), and temperature \(T\) are updated using the following relationships:  
\[\rho=\sum_{i}f_{i};\ u=\sum_{i}f_{i}e_{i}/\rho+\delta tF_{e_{i}}/2;\ T_{f/s}= \sum g_{i,f/s} \tag{9}\]
# Metal-to-Insulator Transition in Ultrathin Manganite Heterostructures
## 4 Mechanism of the MIT  
Since the existence of dead layers greatly degrade the performance of LSMO-based devices, tremendous efforts have been paid to investigate the origin of dead layers, leading to deep insight into the mechanism of the dead layer, from both experimental and theoretical points of view. A lot of detailed information on lattice distortion, charge distribution, and stoichiometry provide us many aspects of the LSMO dead layers. Several models have been proposed. The reconstruction of the Mn \(e_{\mathrm{g}}\) orbital [34; 35; 37; 52; 53; 54], reduced orbital hybridization (bandwidth) [40; 47; 51], the change of Mn valence [46; 47; 48; 53; 54; 55; 56] and structure imperfection (defects, disorder) [39; 44; 50; 57] are the four main proposed mechanisms in the past, which are summarized in Figure 7. Even for a single proposed mechanism, e.g., the orbital reconstruction, the origin associated with it can be quite different. The orbital reconstruction can arise from strain-induced octahedral deformation [34; 52], an interface interaction favoring an out-of-plane orbital [37] or a broken symmetry modified orbital configuration [35; 52]. Valence change can be driven by charge transfer due to polar mismatch [55; 56], or chemical reconstruction due to interdiffusion or Sr-segregation [46; 47; 48; 49]. The past research on one hand shows the complexity and diverse variation in LSMO, and on the other hand, it indicates that the dead layer is a result of cooperative effects from contributions such as strain, off-stoichiometry, and charge variation.
## 6 Discussion
### Confounding Factors  
Confounding factors include the fact that within the USTC16 dataset used, most of the benign TCP traffic was captured from connections established prior to the start of the capture files. Thus, we are unable to adequately compare benign TCP connection requests with malicious TCP connection requests. The model is likely able to differentiate easily between the malicious traffic, which consists entirely of connection requests, and the benign traffic, which contains almost no connection requests. It is therefore possible that its high accuracy would not be reproducible on new packet traces where both malicious and benign categories contain TCP connection requests, though the same can be said of every other prior result that relies on the benign data in this dataset [1; 3; 4; 5].  
However, after analyzing other datasets, many cases still show high accuracy. This may indicate that these datasets also include some as-yet-unknown feature that allows the model to overtrain for them as well. However, it may also indicate that our model is indeed very useful for detecting malicious flows.  
Our analysis has also thus far included packet payloads, which in our current dataset are unencrypted. We are unable to readily identify what features our model uses to classify flows due to the complexity of deep learning models. It is possible that one or more of the key features used by the model can be found within the payload of each packet, and encryption may obfuscate such features. Therefore, further testing is required to identify if the model could perform as well when considering encrypted traffic.
## 1 Introduction  
### _I.1. Research Background and Significance_  
Mangrove forests are wetland woody plant communities, primarily composed of evergreen trees and shrubs, that grow at the land-sea interface. These ecosystems play vital roles in maintaining ecological balance, serving both as marine carbon sinks and providing wind protection and wave attenuation functions. They are renowned as \"coastal guardians\"and \"marine lungs\" [1]. However, these critical ecosystems face multiple threats, including human activities such as aquaculture expansion [2] and river management projects [3], along with natural factors like biological invasion [4] and sea-level rise [5], leading to severe degradation of global mangrove forests.  
According to remote sensing data analysis, the global mangrove area decreased by more than 10,000 square kilometers between 1985 and 2020, resulting in significant deterioration of wetland ecological functions [6]. The Global Mangrove Alliance predicts this degradation trend will continue until 2030 [7], further exacerbating global climate change and triggering a vicious cycle of carbon storage loss. To address these challenges, international organizations and national governments have launched multiple initiatives. The International Union for Conservation of Nature and the United Nations Development Programme established the \"Mangroves for the Future\" program, while the Chinese government formulated the \"Special Action Plan for Mangrove Protection and Restoration (2020-2025)\". At the local level, the Beihai Municipal Government of Guangxi Zhuang Autonomous Region developed the \"Beihai City Mangrove Resource Protection Plan (2020-2030)\". These initiatives highlight the importance of effective mangrove monitoring in coastal ecological protection.
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
## 4 Simulation Results
### The Required Minimum Code Rate for Lossless Decoding  
For lossless decoding, the required minimum code rate (RMCR) of a DSC scheme can be used to present the coding efficiency of that DSC scheme. In this experiment, the RMCR of SPDCAC was compared with that of the traditional distributed arithmetic  
Figure 5: The BER performance of the proposed scheme using different posterior probability calculation methods.  
coding. However, due to a 'forbidden symbol' being added in the SPDCAC codec, we chose DJSCAC to compare with SPDCAC, and the results of arithmetic coding (AC) are provided for reference. We adjusted the value of \(P_{t}\) to obtain binary Markov sources with different internal correlation strengths. The length of each sequence is 1024. In a DSC scheme based on expanding probability intervals, in order to obtain additional compression, the probability intervals of the encoding distribution need to be enlarged. However, if the probability of the current symbol is already large enough, the extent to which the probability can be enlarged is very limited, because the symbol probability cannot exceed 1. Based on the above analysis, in order to make DJSCAC obtains the largest possible compression, and the maximum possible coding probability interval of DJSCAC needs to be set by choosing the largest factor \(\alpha(0\leq\alpha<1)\), which meets the inequality.  
\[(1+\alpha)p\leq(1-\mu) \tag{6}\]  
where \(p\) represents the larger probability in the binary conditional distribution used for the coding of the current symbol. It should be noted that in DJSCAC, the forbidden interval is located at the right end of probability intervals adjacent to the interval of symbol '1', its length \(\mu\) is set to 0.01. Similarly, the length of \(\mu\) is set to 0.01 for SPDCAC.  
Table 2 shows the RMCR of SPDCAC and DJSCAC coding different sources when \(H(X|Y)=0.1\). From the table, we can find that the code rate of the arithmetic coding when encoding a source with a larger \(P_{t}\) will be smaller than that when coding a source with a smaller \(P_{t}\). This shows that arithmetic coding can effectively utilize the internal correlation of the source to reduce the code rate. All DSC schemes based on arithmetic coding also have this property--that is, as the internal correlation strength of the source increases, lossless decoding can be realized at a smaller code rate. The RMCR of DJSCAC will be smaller than SPDCAC only when coding the source with a weaker internal correlation (\(P_{t}=0.6\)). However, in other cases, the RMCR of SPDCAC is significantly smaller than that of DJSCAC. In particular, when coding a source with \(P_{t}=0.9\), SPDCAC can save 50% of the code rate than AC, while DJSCAC can only save 29% of the code rate than AC. This phenomenon shows that the RMCR of SPDCAC is significantly better than that of DJSCAC when coding a source with a stronger internal correlation strength.
## 3 Proposed Methods
### Layer-Wise Reconstruction and Global Fine-Tuning  
After completing feature distillation for each layer, the parameters of the current layer are frozen to ensure that the training of subsequent layers does not interfere with the already reconstructed features. The process then continues with the feature distillation for the next layer until the feature maps of all layers have been reconstructed.  
Once feature distillation is completed for all residual blocks, all layers' parameters are unfrozen, and global fine-tuning is performed using the original age labels. The loss function during fine-tuning remains consistent with the original age regression module's loss function, further optimizing the model's feature representations and improving age estimation accuracy.  
Figure 3: Using ResNet-34 as the feature extraction network, the student’s feature maps after each residual block are trained to match those of the teacher network. Features are extracted from an occluded dataset, where the teacher network’s feature maps serve as a supervisory signal, guiding the student’s feature reconstruction layer by layer. After reconstructing the feature map at each residual block, all previous network layers are frozen before proceeding to the next layer’s training. In the fine-tuning stage, all network layers are unfrozen and fine-tuned using real age labels to remove residual noise features.