esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Maximum Pressure Rise Rate  
Figure 7 depicts the maximum pressure rise rate (MRPR) recorded in several engine configurations, namely, diesel (D100), DP20, DP20H5 (with 5% hydrogen), DP20H10 (with 10% hydrogen), and DP20H15 (with 15% hydrogen). Based on the graphical depiction, it is evident that the MRPR (Mean Relative Power Ratio) of the DP20H5, DP20H10, and DP20H15 engines exceeds that of the D100 engine across all tested configurations. The officially specified lowest MRPR for DP20H15 fuel is recorded as 2.87 bar per degree. However, the D100 sample has a higher MRPR value, reaching a maximum of 3.25 bar per degrees at 23.5\({}^{\circ}\) b TDC (before top dead centre). The experimental findings suggest that the diesel fuel blend containing 15% hydrogen had the lowest Maximum Rate of Pressure Rise (MRPR) while running at maximum capacity, with a recorded value of around 11.69%. This observation may be attributed to the cet  
Figure 6: Cylinder pressure with injection timing for hydrogen blends.  
This research investigates the influence of hydrogen supplementation on the variability in cylinder pressure throughout the process of combustion, in contrast to the utilisation of D100 fuel. Significant modifications are seen in diesel fuel containing 10% and 15% hydrogen. The MRPR demonstrates a decline at various engine configurations with the introduction of hydrogen propellant [32; 33]. The heating value of hydrogen is recorded at 120 MJ/k, representing a 64.5% increase compared to the heating value of diesel fuel, which is measured at 42.5 MJ/kg. Table 1 displays the cetane number values for hydrogen and diesel fuel, which range from 5 to 10 and 48 to 52, respectively.
## 2 Literature Review
### Readability Formulas  
Since the early 20 th century, researchers in this field have developed a variety of readability formulas aimed at laypeople. Many of these formulas are still widely used today [25]. Among the most commonly used formulas are Simple Measure of Gobbledygook (SMOG), the Dale-Chall Readability formula, the Flesch Reading Ease formula, the Fog Index, and the Fry Readability Graph. More details about readability methods can be found in [26].  
These formulas typically analyze syntactic complexity and semantic difficulty. Syntactic complexity is often evaluated by examining sentence length, while semantic difficulty is measured using factors such as syllable count or word frequency lists. Other factors that have been found to influence readability include the presence of prepositional phrases, the use of personal pronouns, and the number of indeterminate clauses. However, it is important to note that readability formulas specifically targeting medical practitioners are currently lacking. Further research is needed to develop readability formulas tailored to the unique needs and expertise of medical professionals.
## 3 Multiple Nodes Cooperative Transmission
### Co-Carrier-Based Selection Combination  
To fully utilize the multiple signals from the \(M\) satellites, we propose SC with co-carrier transmission method in this section. In SC, UTs are able to receive \(M\) superposed signals in \(M\) time slots, and select one path with the best channel condition. Under this mechanism, signals at the \(k\)-th UT can be expressed as  
\[y_{k,1}^{{}^{\prime}}=\mathbf{h}_{k,1}^{\mathrm{H}}\mathbf{w}_{1}(s_{k}+x_{1 })+n,\]  
\[\ldots,\]  
\[y_{k,M}^{{}^{\prime}}=\mathbf{h}_{k,M}^{\mathrm{H}}\mathbf{w}_{M}(s_{k}+x_{M}) +n, \tag{7}\]  
where \(y_{k,m}^{{}^{\prime}}\) denotes the signal of the \(m\)-th satellite arrived at UT-\(k\).  
According to the co-carrier transmission scheme, SINRs at UT \(k\) can be calculated as  
\[SINR_{1,k}^{{}^{\prime}}=\frac{|\mathbf{h}_{k,1}^{\mathrm{H}}\mathbf{w}_{1}| ^{2}P_{k}}{|\mathbf{h}_{k,1}^{\mathrm{H}}\mathbf{w}_{1}|^{2}P_{1}+\sigma^{2}},\]  
\[\ldots,\]  
\[SINR_{M,k}^{{}^{\prime}}=\frac{|\mathbf{h}_{k,M}^{\mathrm{H}}\mathbf{w}_{M}|^{2 }P_{k}}{|\mathbf{h}_{k,M}^{\mathrm{H}}\mathbf{w}_{M}|^{2}P_{M}+\sigma^{2}}. \tag{8}\]To obtain a better system performance, we select the strongest signal path for transmission. In this case, we have \(SINR_{k}^{\prime}=\max\{SINR_{1,k}^{\prime},\ldots,SINR_{M,k}^{\prime}\}\). Assume the \(m\)-th satellite provides the best SINR. After SIC, SNR of the beam-center UT under the \(m\)-th satellite is the same with (6). In this paper, we assume \(P_{1}=\ldots=P_{M}=P\).
## 1 Introduction
### Machine Learning and Deep Learning  
State-of-the-art forecasting systems today are mostly based on numerical models, and these are unable to accurately describe the physical processes leading to earthquake events. The physics and complexities of fault motion in different tectonic environments include many characteristics related to the structure of the Earth's crust, from plate tectonics to the microscopic processes involving friction, electric charge generation, and various chemical reactions [7; 24]. Thus, the lack of sufficiently detailed real-time measurements prevents accurate forecasting of earthquake events. Recent advances in cloud-based big-data technologies now make data-driven solutions feasible for increasing the numbers of scientific computing applications [26; 27]. One data-driven solution is machine learning (ML), which brings patterns in large data sets to the surface by finding complex mathematical relationships within the data [28]. Thus, researchers are gradually harnessing multivariate data analysis techniques from ML disciplines to predict occurrences of natural hazard events from past distribution patterns [6; 29]. Algorithms that are commonly used to tackle classification and regression problems include linear regression [30], decision trees and random forest (RF) [31], and support vector machines (SVMs) [32]. Additionally, there are deep learning (DL) models that are based on artificial neural networks (ANNs), inspired by the biological neural network of the brain [33]. The state-of-the-art DL neural network architectures include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and LSTM (long short-term memory).
## 2 Tunnel Description and Geological Conditions  
The Central Yunnan Water Diversion Project is located in the mountainous region of the Central Yunnan Plateau in Yunnan Province on the southeastern edge of the Tibetan Plateau, with high mountains and deep valleys along the route and developed and complex geological formations. The project starts from Shigu Town of Lijiang City and ends at Xinpobei Village of Honghe Prefecture. The main water diversion channel is about 664.24 km long, and there are 118 main water diversion structures, including 58 tunnels with a length of 612.00 km, accounting for 92.13%. Adverse geological structures are developed along the whole route of the project, and there are 43 major fracture zones directly intersecting with the main water diversion channel, including 16 engineering active fracture zones.  
The Xianglushan tunnel is located at the junction of the Hengduan Mountains and the central Yunnan Plateau, and it crosses the watershed of the Jinsha River and the Lancang River, with complex regional tectonic and geological conditions. The location of the Xianglushan tunnel is illustrated in Figure 1a. The total length of the tunnel is 62.60 km, with a depth of 600-900 m and a maximum depth of 1450 m, which is a typical super-long tunnel with a large depth. The rock masses of the Xianglushan tunnel revealed by the excavation are dark grey thinly bedded epidote schist and chlorite schist in the lower part of the Middle Triassic. The tectonic rocks are cataclasite, breccia, granulitic rock, powdery rock, and fault clay. The continued excavation would encounter the cataclasite zone, granulitic rock zone, powdery rock zone, and breccia zone, respectively. There is a risk of water inrush under high groundwater head after the excavation of the relatively poorly water-bearing strata (granulitic rock zone and powdery rock zone), as revealed in Figure 1c-e. The inappropriate treatment of water inrush may drain the groundwater system of the Baihanchang reservoir and even have a greater impact on the Baihanchang reservoir and the const.  
DEM-Based Analysis of the Catastrophic Process of Water Inrush at the Face of the Tunnel Adjacent to the Water-Rich Fault
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 4 1-2 Random Oblivious Transfer
### Definition  
As in Figure 4, like the WSE protocol, the 1-2 random oblivious transfer (ROT) protocol is also a basic two-party cryptographic protocol and is a random version of the 1-2 oblivious transfer (OT). Based on the 1-2 ROT protocol, we can easily implement the 1-2 OT protocol and the bit commitment (BC) protocol. In the 1-2 ROT protocol, instead of inputting two information strings \(m_{0},m_{1}\in\{0,1\}^{l}\), [PERSON] obtains two random key strings \(S_{0},S_{1}\in\{0,1\}^{l}\). At the same time, [PERSON] obtains the random key string \(S_{c}\) according to its input \(c\). If we want to implement the 1-2 OT protocol, just after running the 1-2 ROT protocol, [PERSON] encrypts the information strings \(m_{0}\) and \(m_{1}\) with the two strings of keys \(S_{0}\) and \(S_{1}\) obtained by ROT protocol. [PERSON] can use \(S_{c}\) for decryption to obtain \(m_{c}\).  
In the security definition of the 1-2 ROT protocol, [PERSON] cannot obtain [PERSON]'s input \(c\), and [PERSON] cannot obtain another string of keys \(S_{1-c}\) except \(S_{c}\). The specific definition of security is as follows:  
**Definition 4**.: _An \(\varepsilon\) - secure 1-2 ROT is a protocol between [PERSON] and [PERSON], where [PERSON] has input \(c\in\{0,1\}\), and [PERSON] has no input, satisfying:_1. _Correctness: If [PERSON] and [PERSON] are honest, then for any distribution of [PERSON]'s input_ \(c\) _which is unknown to [PERSON], [PERSON] gets outputs_ \(S_{0},S_{1}\in\{0,1\}^{l}\) _which are_ \(\varepsilon\)_-close to randomness and independent of_ \(c\)_, and [PERSON] obtains_ \(Y=S_{c}\) _with probability_ \(\varepsilon\)_;_
2. _Security for [PERSON]: If [PERSON] is honest, then for any cheating strategy of [PERSON] resulting in his state_ \(\rho_{B}\)_, there exists a random variable_ \(D\in\{0,1\}\)_, and_ \(\lambda>0\) _such that:_ \[\mathrm{H}_{\min}(S_{1-D}|B^{\prime})\geq\lambda,\] _and_ \[\mathrm{d}(S_{1-D}|B^{\prime})\leq\varepsilon;\]
3. _Security for [PERSON]: If [PERSON] is honest and obtains output_ \(Y\)_, then for any cheating strategy of [PERSON] resulting in her state_ \(\rho_{A}\)_, there exists a random variable_ \(D\in\{0,1\}\)_, such that:_ \[\mathrm{H}_{\min}(D|A^{\prime})\geq 1-\varepsilon,\] _and_ \[\mathrm{Pr}(Y=S_{c}^{\prime})\leq\varepsilon.\]
## 3 Results and Discussion
### Anaerobic Digested Wastewater Performance
#### 3.1.2 Biogas and COD Removal Performance  
The biodegradation of the organic content of the wastewater in the generation of biogas was monitored. The efficiency of COD removal was used to ascertain the efficiency of the methanogenesis phase of the bioreactors at different nanoparticle and biochar loads. It was deduced that the degradation of the organic substrate by the microorganism increased the percentage of COD removal and biogas production [30, 39, 40]. The COD removal observed was between the range of 65-78%, whereas the biogas was also within a range of 3-25 mL/day. However, the biogas production was variable over time and never reached a true steady state. This was due to the variation in the organic content (COD) and enzymatic reactions in the bioreactor. The low biogas production and COD removal recorded in some of the runs (Figure 2) might be due to the lag phase of microbial growth in the batch-conditioned reactor. Further, the high biogas production and COD removal recorded may be attributed to the exponential growth of the methanogens with respect to an acclimatized time rate. This indicates the high levels of organic matter (COD) being consumed by the methanogenic microorganism for growth, resulting in the conversion into biogas production [41, 42, 43].
# Co-Operatively Increasing Smoothing and Mapping Based on Switching Function
## 4 Simulations
### Experiment 1  
No anomalies or wild values were introduced. In this study, simulation experiments were conducted without any anomalies or wild values to verify the localization effect of this paper's method in an ideal situation. The UAV movement time was set to 60 s and the UAV was allowed to move randomly in space. Both IMU+ camera data and UWB data were added with normal levels of noise. An evaluation of the localization results of the various methods is shown in Table 2.  
It can be seen from Table 2 that the method proposed in this paper can achieve a high positioning accuracy and precision in the absence of anomalies and outliers. This shows that the method proposed in this paper is effective and superior in ideal situations.
## 4 Experiments  
### Experimental Setup  
The segmentation model was experimentally evaluated with 20 different configurations. Deeplabv3+ is used with four distinct loss functions (Cross-Entropy, Dice, Tversky, and Focal loss) and five different backbones: ResNet-18, ResNet-5, MobileNetV2, Xception, and InceptionResNetV2. The models are improved and trained in the MATLAB environment, installed on a server equipped with an NVIDIA GeForce RTX 3090 GPU and the Fedora operating system at the IT-Lisbon facility.  
Image of resolution of 512 \(\times\) 512 pixels is chosen with mini-batch size of 22 to obtain the most significant information at the lowest computing cost. Stochastic gradient descent (SGDM) optimizer with a momentum of 0.9 and a piecewise schedule learning rate method is used for training the model. The learning rate drop period is set to 10 with the dropping factor and initial learning rate assigned as 0.3 and 0.01, respectively. The maximum number of training epochs was set to 100, with the L2 regularization factor as 0.0005. These chosen parameter choices were assumed to have a rapid learning rate while converging to an optimal solution when the learning rate drops. The experiments are parameterized to avoid overfitting by terminating training sooner when the validation curve converges.  
Every convolutional layer in the trained network is followed by a Batch Normalization layer, which improves training stability, speeds up network convergence, and improves performance even when the batch size is small. The parameters a and b are set to 0.3 and 0.7 for the Tversky loss function and 0.25 and 2 for the focal loss function, respectively.  
The final dataset was randomly divided into 60% for training, 20% for validation, and 20% for testing for five-fold cross-validation. As a result, 2410 samples were used for training, 803 for validation, and 803 for testing.  
Besides, the present work performed experiments with data augmentation to increase network accuracy during training by randomly transforming the original data. As augmentation techniques, random horizontal/vertical reflection, random left/right reflection, random X/Y translation of \(\pm\) ten pixels, and random rotation were employed. The results were the same as the original, with no remarkable enhancement for augmented data, so the present work refers only to show the original results.
## 2 Engineering Overview and Numerical Modeling
### Modeling of Construction Procedure in Flac3d  
The excavation and support stages of the foundation pit in FLAC3D are in accordance with the actual construction stages, which are shown in Table 1. Specifically, the initial stress field is obtained by applying the \(K_{0}\) gravity field in the FLAC3D model through the elastic solution method, followed by assigning the PH model to the soil body, and then solving the model in order to obtain the stress field under the real soil mechanical parameter conditions. The realization of the pile body is described in detail in Section 2.5. When the soil is dug out, the NULL model is applied in FLAC3D [22]. The prestressing in the anchor is applied by applying the command \"apply tension value\" to the free section of the anchor. The convergence criterion in the excavation phase is based on the system unbalance force ratio of less than 1\(\times\) 10\({}^{-5}\).
# Magneto-Optical Traps for Cold Atomic Gravimetry: Research Status and Development Trends
## 3 Classification and Application of MOTs
### Two-Dimensional MOT for Precooling  
Inspired by pyramidal atomic funnels, [PERSON] of the Netherlands Institute used a two-dimensional MOT(2D-MOT) in 1998. There was a through-hole in the reflector to obtain a cold atomic beam of 8 m/s from a rubidium vapor chamber. The flux of the cold atomic beam was \(9\times 10^{9}\) atoms/s [78]. The experimental setup is shown in Figure 9. In the figure, I is the current in the coil; the arrow direction indicates the current direction.  
In 2019, [PERSON] et al. used a 2D-MOT to generate cold atomic beams, resulting in a loading rate of 2.8 \(\times\)\(10^{9}\) atoms/s for a three-dimensional MOT(3D-MOT) [79]. In 2022, [PERSON] et al. designed a CAG for on-board dynamic measurement of absolute gravity in the external field. They used a 2D-MOT pre-cooling, and a six-beam MOT accessed through a differential tube to finally trap about \(10^{8}\) atoms in 300 ms with a cooling temperature of 6 \(\upmu\)k [80]. In 2023, [PERSON] at the Institute of Physics in Heidelberg replaced the conventional Zeeman reducer with a 2D-MOT to produce a high-flux slow atomic beam. This led to a 3.6-fold increase in the loading rate of a 3D-MOT, with a loading rate of \(10^{8}\) atoms/s, trapping about \(3\times 10^{8}\) atoms. The cold atom temperature was 15 \(\upmu\)k [81].  
For the pre-cooling of atoms, 2D-MOTs are often used in atomic gravimetry due to their special structure. The cold atomic beam enters a 3D-MOT through a differential tube,  
Figure 9: Two-dimensional MOT setup.  
which has the advantage of eliminating background gas interference and improving the loading efficiency of the 3D-MOT [82].
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 6 Discussion
### The Limitations of the BOMSDM  
In this study, the BOMSDM obtained a set of Pareto optimal solutions with dual objectives. However, we did not design a method for selecting an implementation solution from the whole set because, in reality, the weighting of the two objectives heavily depends on the decision-makers' highly uncertain preferences.  
In addition, this study focused on social equity and efficiency in service provision, with particular attention paid to the uncertainty of the facility number and inter-community competition. However, these objectives and constraints do not fully capture the complexity of real-world CFLPs. Future research could optimize the model in several ways: First, new objectives could be introduced to enhance its comprehensiveness, such as incorporating facility investment costs, accessibility, and environmental impacts as optimization goals [80; 81; 82]. Second, uncertain parameters could be added to improve the model's adaptability, such as varying facility capacity, uncertain demand or demand patterns, and fluctuating facility service areas [43; 83; 84]. Third, the model could be further validated in extreme scenarios, such as the community allocation of medical or disaster relief resources in emergencies or the impact of increased online services on existing facility systems. Overall, although the BOMSDM underwent preliminary testing in this study, it is still not a mature model. Future work will require further validation in more diverse scenarios to confirm its robustness and applicability.  
Another limitation of the BOMSDM is the utilization of the NSGA-II algorithm with constraints. Although it performs well in handling bi-objective models, after adding constraints, the integrated algorithm requires a considerable number of iterations to achieve convergent results when facing large-scale instances. Furthermore, our analysis reveals that models employing a non-incremental strategy yield inferior target values compared to those utilizing incremental strategies. This discrepancy may be attributed to the greater computational burden and the tendency of genetic algorithms to become ensnared in local optimization traps. Therefore, we look forward to future research that can develop or introduce more efficient algorithms.
###### Abstract  
In order to study the influence of freeze-thaw cycles on the stability of cutting slopes in high-latitude and low-altitude permafrost regions, we selected a cutting slope (the K105+700-800 section of National Highway 332) in the Elunchun Autonomous Banner in Inner Mongolia as the research object. Located in the Greater Xing'an Mountains, the permafrost in the Elunchun Autonomous Banner is a high-latitude and low-altitude permafrost. The area is also dominated by island-shaped permafrost, which increases the difficulty of dealing with cutting slopes, due to its morphological complexity. Surface collapse, caused by freeze-thaw erosion in this area, is the main reason for the instability of the cutting slope. Indoor freeze-thaw tests, field monitoring, and an ABAQUS numerical simulation model were conducted so as to quantify the decrease in rock strength and slope stability under freeze-thaw conditions. The following conclusions were drawn. (1) As the number of freeze-thaw cycles increased, the compressive strength of the rock specimens obtained from this slope gradually decreased. After 50 freeze-thaw cycles, the uniaxial compressive strength measured by the test decreased from 40 MPa to 12 MPa, a decrease of 37%. The elastic modulus value was reduced by 47%. (2) The safety factor of the slope--calculated by the strength reduction method under the dynamic analysis of coupled heat, moisture, and stress--gradually decreased. After 50 freeze-thaw cycles, the safety factor of the slope was only 0.74. (3) Reasonably reducing the number of freeze-thaw cycles, reducing the water content of the slope, slowing down the slope, and increasing the number of grading steps can effectively improve the stability of the slope. The results of this study can provide a reference for the design and stability analysis of slopes in permafrost regions of the Greater Xing'an Mountains.  
high-latitude and low-altitude permafrost; compressive strength; coupled heat-moisture-stress; strength reduction method; slope safety factor +
Footnote †: journal:
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 5 Implementation Based on Hyperledger Fabric
### Remote DB Server  
The remote DB server is implemented based on leveldb. It comprises three databases: blockDB, txIndexDB, and merkleTreeDB. When a block is received by the remote DB server, first, the redundancy of the block is checked through its hash; secondly, if the received block has not already been saved, the blockData are saved to the blockDB, the transactions in the block body are traversed to save the indices of the transactions to the txIndexDB,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Key** & **Before Update** & **After Update** \\ \hline blkHash & block file, block offset & VC for the block, hash of the entire block a  \\ \hline blkNum & block file, block offset & blkHash \\ \hline txID, blkNum,txNum & block file, block offset, tx offset,tx validation code & tx validation code \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contents of indexDB before and after updating.  
and the Merkle Tree is calculated for the block and saved to the merkleTreeDB. Table 2 lists the contents of these databases.  
The remote DB server provides four APIs for the peers to invoke:  
* [noitemsep,topsep=0 pt]
* AddBlock: uploads a block to remote DB server;
* GetBlockByHash: retrieves a block by blockHash and returns the block;
* GetBlockByTxId: retrieves a block by transaction ID and returns the block;
* GetTransactionByTxId: retrieves a transaction-by-transaction ID and returns the transaction and its proof.