esa-sceva/satcom-chunk-collection huggingface dataset:
# Residual Stress Relaxation of DRWDs in OSDs under Constant/Variable Amplitude Cyclic Loading
###### Abstract  
An orthotropic steel deck (OSD) has a complicated structure, and its fatigue life is mainly determined by various welding details. Fatigue assessment of deck-to-rib welding details (DRWDs) under long-term train loads is an important concern for engineers. Properly assessing the initial residual stress and the mechanism of stress relaxation in DRWDs under long-term external loading is a prerequisite for predicting the fatigue damage and service life of OSDs. In this paper, a finite element analysis method is proposed to calculate the residual stress relaxation in DRWDs of OSDs under constant/variable amplitude cyclic loading. First, experiments on full-size OSD specimens were carried out using the hole drilling strain-gauge method, and the multi-axial distribution characteristics of residual stress on the sub-surface of the deck were obtained. On this basis, a refined residual stress analysis model of DRWDs using thermal-structural sequence coupling analysis and life and death unit technology is established, and the accuracy of the model is verified by the test data. Second, a coupling stress analysis model that considers the welding residual stress and mechanical stress using cyclic plastic constitutive model is established. The combined influence of number of cycles, stress amplitude, and stress ratio on multi-axial residual stress relaxation effect under constant/variable amplitude cyclic loading is investigated. Finally, a release formula of welding residual stress relaxation coefficient is proposed based on the external loading stress amplitude, stress ratio, and material yield stress. The results show that (1) with the increase in the number of loading cycles, the stress decreases until it is stabilized, while the global distribution of welding residual stress remains unchanged. Most of the welding residual stress release (about 95%) occurs in the first cycle; (2) the residual stress relaxation decreases with the increase in stress amplitude and increases linearly with the stress ratio; (3) the residual stress release is controlled by the maximum amplitude stress in the variable amplitude cyclic loading. After the residual stress is released, the stress will not continue to be released if the DRWDs have the same or smaller amplitude loading.  
orthotropic steel decks (OSDs); deck-to-rib welding details (DRWDs); welding residual stress; stress relaxation effect; variable amplitude cyclic loading
## 4 Simulation and Evaluation  
The methods being compared in this study are all based on the basic downlink SCMA system model, with \(J=6\), \(K=4\), \(M=4\), and perfect CSI. The components of the channel vector \(\mathbf{h}_{j}\) are modeled as independently and identically distributed complex Gaussian random variables with zero mean and unit variance. And each method uses the same codebook, provided by [20]. The whole received signal set has 2 million random samples. In order to minimize the loss function in Equation (7), we adopt an adaptive motion estimation (ADAM) optimizer [21], in which the learning rate is set as 0.002. The batch size of \(K\)-Polygon diagrams supplied into the classification network per cycle is set as 64.
###### Abstract  
Underwater ionic current signal sensing shows great potential for electric-field-sensing-based target detection, information transmission and communication. Nevertheless, it is still a challenging task. Herein, a self-powered underwater ionic current signal sensing system using contact-induced charge transfer is presented. The system mainly consists of a working electrode, a metal sheet and a sensing electrode that is immersed in electrolyte solution. Upon touching the working electrode with a metal sheet with a different work function, charge transfer occurs on the interface, and a corresponding ionic current is induced. The generated ionic current can be detected with the sensing electrode far away from the working electrode. It was found that the magnitude and direction of the generated ionic current are determined by the contact potential difference (CPD) between the working electrode and the contacting metal sheet. Additionally, the effects of water temperature, the ionic concentration of the electrolyte solution, electrode surface area and hydrostatic pressure are systematically investigated. The detected signal magnitude decreased with an increase in the distance between the working electrode and the sensing electrode. A proof-of-concept demonstration of underwater information transmission via Morse code with this method was successfully achieved.  
contact-induced; ionic current; contact potential difference; charge transfer; sensing Article
## 2 Methods for AMR Detection  
Recently, analytical techniques related to AMR detection have been elaborately reviewed [23]. In brief, traditionally, AMR in bacteria and fungi has been detected using culture-based techniques such as broth dilution assay [24; 25], agar dilution assay [25; 26], disk diffusion tests and E-strip tests [27; 28; 29]. The minimum inhibitory concentration (MIC) of antibiotics is generally used to measure the drug susceptibility of microbial isolates, which also provides information on AMR. Despite their accuracy, these methods are limited only to cultivable bacteria, which represent less than 3% of the environmental microbiome. In addition, these methods are highly time-consuming and require skilled workers [30]. To compensate for the shortcomings of the culture-based AMR detection techniques, molecular methods such as polymerase chain reaction (PCR) and microarrays came into play for AMR assessment in microbes. In PCR-based AMR detection, the presence of AMR genes has been detected by the amplification of AMR genes using specific primers designed to amplify AMR genes [31; 32]. On the other hand, microarray technology detects  
Figure 2: Sankey plot showing the list of ARGs present in HWW and MWW.  
and quantifies AMR genes and its expression patterns by hybridizing probe DNA/RNA sequences to the complementary target sequences on a microarray plate [33; 34]. This method is particularly useful for the multiplexed detection of various ARGs simultaneously [35]. While these technologies have improved the efficiency of AMR detection, these methods still depend on the designed probes and primers, which restrain their detection to only known gene sequences. Conversely, metagenomic sequencing techniques offer more extensive details by directly sequencing the entire genomic content of environmental samples using next-generation sequencing techniques (NGS). Such methods allow for the identification of the entire composition (microbial and ARG) of samples including novel genes and organisms providing a comprehensive understanding of the microbial diversity and AMR [36; 37; 38; 39]. Apart from these techniques, mass spectrometry (MS) techniques such as liquid chromatography-mass spectrometry (LC-MS) [40; 41] and gas chromatography-based mass spectrometry (GC-MS) [42; 43], Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF-MS) [44] has also emerged as vital tools for the detection of AMR. The mass-to-charge ratio of molecules is detected by MS enabling the precise and rapid identification of molecules that take part in the microbial AMR process. These methods have proven to be useful in clinical studies and continue to play an important role in advancing our understanding of AMR [23].
## 6 Numerical Results Discussion and Optimization Analysis
### Optimization Analysis
#### 6.3.1 Multi-Objective Genetic Algorithm  
Multi-objective genetic algorithms are based on the principles of natural selection and genetics and draw on biological evolution processes to solve multi-objective optimization problems. Multi-objective genetic algorithms process populations of potential solutions, applying genetic operators such as selection, crossover, and mutation to allow the population to evolve generation after generation to obtain better solutions. Unlike single-objective optimization problems, where a clear goal guides the search for the best solution, multi-objective problems require a balance between competing goals. This equilibrium is often represented by a set of solutions called the Pareto front. The Pareto front contains non-dominated solutions, meaning that no other solution in the group is better on all objectives simultaneously, reflecting a trade-off between objectives.  
The key processes of multi-objective algorithm are depicted below.  
1. Initialization 1. Create an initial population. 1. Set the crossover rate, mutation rate, and maximal generation time.
2. Since the stopping conditions do not apply, do the following. 2. Create an offspring population by mutation and crossover. 2. Evaluate the offspring population. 2. Combine the current population and the offspring population. 2. Compute the rank and crowding distance for all the individuals in the population obtained from (2.3). 2. Generate a new population. 2. Go to (2.1).
## 7 Conclusions  
Reverberation time and other room acoustic parameters are influenced by air absorption, especially at frequencies above 1 kHz. For pure tones, the air absorption is a complicated but known function of the ambient temperature and relative humidity. However, room acoustic measurements are normally made in octave bands, and the air absorption of sound in octave bands is not a linear function of the distance of sound propagation. The air attenuation increases rapidly with increasing frequency, and it is very high in the 8 kHz octave band.  
The summation method is known from the literature, and it is found to be a realistic possibility for correction of the impulse response, thus normalizing the room acoustic parameters derived from that. It is quite laborious but can provide accurate results. It assumes that the sound source has a flat spectrum within each octave band.  
For the reverberation time measurements, an alternative approximate method is proposed. The attenuation as a function of distance can be approximated by two linear attenuation curves, meeting in a knee point. The knee point is defined as the distance of sound propagation where the pure-tone attenuation at the center frequency of the octave band reaches 14 dB at 1 kHz and higher frequencies. At longer distances after the knee point, the course of the air attenuation follows the pure-tone attenuation at a frequency one-third octave below the center frequency of the octave band.  
Both for the summation method and the simplified linearization method, it should be noted that, in principle, the distance between the sound source and microphone should be known. Currently, it is not a part of the usual practice to report this distance, so this yields a minor complication in practical measurements. In most practical cases, it may be sufficiently accurate to apply a default distance of 10 m.  
It is shown that with the summation method, it is possible to normalize room acoustic measurements to the air attenuation of a standard atmosphere, at least in octave bands up to 4 kHz. At 8 kHz, the results become less reliable because of the influence of a non-flat spectrum of the sound source.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The measurements in the Oslo Opera House were made by [PERSON], in Multiconsult, Oslo, and he has contributed valuable information about the measurements. [PERSON] of Odeon AS, has contributed fruitful discussions and implementation of the summation method for air attenuation in the ODEON room acoustic software.  
Author [PERSON] was employed by the company Odeon A/S.
## 3 Background
### V-Blast  
V-BLAST is a spatial multiplexing technique to achieve spectral efficiency for a given bit rate and transmission power. It can boost channel capacity to improve the single-sensor data rate, or increase the number of supported sensors in the system. Its spectral efficiency ranges from 20-40 bps/Hz [13] while efficiency of traditional wireless communication techniques ranges from 1-5 bps/Hz (mobile cellular) to around 10-12 bps/Hz (point to point fixed microwave system). In V-BLAST a single user's data stream is split into multiple sub-streams or multiple users can transmit their data simultaneously. An array of transmitter antennas is used to transmit all sub-streams simultaneously in the same frequency band, hence the spectrum is used very efficiently. Since the user's data is being sent in parallel over multiple antennas, the effective transmission rate is increased approximately in proportion to the number of transmit antennas used. In this system, the number of receivers is greater than or equal to the number of transmitters. The transmitted sub-streams are independent of one another. Individual transmitter power is scaled by _1/N\({}_{t}\)_. Thus, the total power remains constant independent of the number of transmitters (_N\({}_{t}\)_).
## 3 Results
### Cytotoxicity Assessment by MTT and LDH  
We selected colorimetric assays for cytotoxicity assessment. The MTT assay is a very popular and widely used colorimetric assay for the evaluation of cytotoxicity [62] as it provides beneficial aspects such as rapidity, reliability and significant knowledge regarding the metabolic activity of cells. The LDH assay provides information about cellular damage (lysis of cell's membranes) after cell treatment, and complements the MTT assay in drawing conclusions about the potential mechanism of action.  
Our samples were analyzed from a starting concentration of 500 mg/mL, as the scientific literature indicates [63; 64; 65; 66; 67; 68]. The results obtained show that this concentration provokes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Total Aerobic** & \multirow{2}{*}{_E. coli_ **CFU/g**} & _Enterobacteriaceae_ & **Yeast and Molds** & **Water Activity** \\  & **Count CFU/g** & & **CFU/g** & **CFU/g** & **Value** \\ \hline Yeast powder & \(<\)10 & \(<\)10 & \(<\)10 & \(<\)10 & 0.355 \(\pm\) 0.1 \\ \hline \multirow{2}{*}{Pea powder} & 7.0 \(\times\) 10\({}^{3}\) & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{0.408 \(\pm\) 0.17} \\  & 3.85 log \(\pm\) 0.07 & & & & \\ \hline Almond powder & \(<\)10 & \(<\)10 & \(<\)10 & \(<\)10 & 0.424 \(\pm\) 0.20 \\ \hline \multirow{2}{*}{Spirulina powder} & 3.1 \(\times\) 10\({}^{3}\) & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{\(<\)10} & \multirow{2}{*}{0.363 \(\pm\) 0.18} \\  & 3.49 log \(\pm\) 0.31 & & & & \\ \hline \multirow{2}{*}{Pleurotus powder} & 4.6 \(\times\) 10\({}^{5}\) & \multirow{2}{*}{\(<\)10} & 5.2 \(\times\) 10\({}^{3}\) & 2.7 \(\times\) 10\({}^{3}\) & \\  & 5.66 log \(\pm\) 0.1 & & 3.72 log \(\pm\) 0.27 & 3.43 log \(\pm\) 0.18 & 0.550 \(\pm\) 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for microbiological and stability indicators.  
a significant cell viability reduction in the case of _Pleurotus_ spp. flour (up to 77% viability loss), followed by yeast powder (up to 69% viability loss). The subsequent concentrations (250 mg/mL, 62.5 mg/mL, 7.813 mg/mL and 0.651 mg/mL) tend to follow the same pattern (as seen in Figure 2A). Pea powder and almond powder present similar results, with viability reduction rates of up to 30%. Spirulina powder has great biocompatibility results (except for a concentration of 500 mg/mL--which is higher than the recommended concentration for most similar supplements). In the case of yeast powder, concentrations lower that 7 mg/mL have beneficial effects and act as nutritive substrates for cells.  
Results obtained by the LDH assay (Figure 2B) conferee great evidence regarding cellular membrane integrity. All samples analyzed showcased membrane damage over 7.81 mg/mL. This result demonstrates that tested ingredients have an impact on the metabolic activity of cells--as MTT assay results indicate, and affect the cells integrity, provoking leakage of DNA.
# Applying BERT for Early-Stage Recognition of Persistence in Chat-Based Social Engineering Attacks
## 2 Background  
### Natural Language Processing  
In Natural Language Processing (NLP) terms, persistence recognition can be cast as a Natural Language Understanding (NLU) task. Furthermore, there are a plethora of relative NLU tasks that can be utilized to describe persistence recognition, such as Semantic Textual Similarity, Natural Language Inference, and paraphrase Recognition. All these NLU tasks are closely related, but they also differ in several aspects:  
* Semantic Textual Similarity (STS) [5,6] is the semantic task of inferring the relation between different text data units. It is usually measured as a numerical score in the range of [0, 1] and quantifies the semantic similarity between different text data units. In earlier days, techniques such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) were used to represent text as vectors to aid the calculation of semantic similarity. These techniques were designed to identify if two text units contained the same words or a similar group of characters. However, sentences can have different meanings while containing the exact, same words or can contain different words while representing semantically similar concepts.
* Natural Language Inference, sometimes also called Textual Entailment [7, 8, 9], is the classification task of determining, given a text premise and a text hypothesis, whether the hypothesis is entailed by, contradictory to, or independent of the premise. The most popular categories used for textual entailment relationships are entailment, contradiction, and neutral.
* Paraphrase Recognition [10] can be described as another text classification task that determines whether two text data units have a similar meaning (or not) in a specific context. Semantic textual similarity identifies the semantic equivalence between text data units as a continuous value, while textual entailment and paraphrase detection produce a categorical output. However, it is possible to quickly transfer, e.g., from the STS measure area to the Paraphrase Detection task, by introducing a paraphrase detection threshold. For example, one can set a certain value of the STS measurement above which two different text data units can be considered as related. The aforementioned NLU tasks can also broadly be classified based on the approach used to achieve the objective, as follows:
* knowledge-based, where ontologies, databases, or dictionaries are used (e.g., WordNet [11])
* corpus-based, where information retrieved from large corpora is used (e.g., word2 vec [12])
* deep neural network-based, where recent developments of neural networks are utilized (e.g., CNNs [13], Transformers [3], etc.), and
* hybrid-based, where characteristics from all the above-mentioned methods are combined [14]. For each of these NLU tasks, there are several popular datasets used for the performance evaluation of the corresponding algorithms. These datasets are composed of sentences or pairs of sentences with associated classification labels.
## 3 ML Application in O-RAN
### Session Management
While user access management in O-RAN refers to the process of controlling and securing access to network resources and services by users and applications, it includes the management of user identities, authentication, authorization, and accounting (AAA). The goal of user access management is to ensure that only authorized users and applications can access the network, and that they can do so in a secure and controlled manner. User access management in O-RAN typically involves the use of access control policies, authentication mechanisms, and auditing tools to manage user access and monitor network activity. It is a critical component of network security and privacy, as unauthorized access to network resources can lead to data breaches, network disruptions, and other security incidents. Effective user access management is essential for ensuring the secure and reliable operation of O-RAN with session management. In [50], the authors address the anticipated handover-rate and load balancing issues if O-RAN is deployed under the conventional user access control schemes typically based on signal strength or capacity measurements. This is a result of the openness nature of O-RAN, where the BS functions are decomposed and virtualized into CUs, DUs, and RUs, where they are massively deployed throughout the network. Therefore, typical access control procedures would make this a practically intractable process due to the massive signaling overhead and system complexity. This can be considerably mitigated if each UE autonomously selects proper BSs (or CUs/DUs/RUs). In this direction, a federated DRL-based scheme to address user access control in the O-RAN is proposed to establish intelligent user-centric access control mechanism to optimize the overall throughput and avoid frequent handovers. This is achieved by enabling the UE to train two deep Q-networks (DQNs) using its own observations, and making access decisions based on its DQNs outputs. Then, the DQN parameters are forwarded to a global model server installed in the RIC. This server can select just a group of UEs in each instance to further mitigate the signaling overhead. Afterward, the global model is updated by aggregating DQN parameters obtained from the selected UEs. The DQN global parameters are then disseminated to each UE to further improve its access decision. The achieved results show that the proposed scheme can reduce the frequency of handover in a O-RAN environment up to 53% when compared to a conventional access control based on signal strength UE measurements.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 4 Discussion of Wave-Induced Pile-Seabed Response
### Pile-Diameter Effect
The third-order Stokes wave suggested by [PERSON] [34] was used to calculate wave load \(F_{\text{w}}\). The interaction between the pile and seabed was considered using the \(p\)-\(y\) curves suggested by API [12] for silt and by [PERSON] et al. [4] for fine sand. Substituting the relative parameters into Equation (8) and combining the pile displacement analysis method, the calculated pile displacement \(y_{\text{cal}}\) is shown in Figure 14, where \(y\) means the measured pile-head displacement. Figure 14 indicates that the calculated and measured values of pile displacement increased with the decrease in pile diameter. By comparing the two, we could see that the calculated value agrees well with the measured value for the pile with a diameter of 5 cm. More-significant differences were found for the small-diameter pile. Larger pile responses were induced with the small-diameter pile and resulted in a strong compression effect on surrounding soil, leading to higher residual pore pressure and degenerating the lateral stiffness of the pile. However, these interactions could not be considered by the methods using the \(p\)-\(y\) curve, which led to errors. In other words, the effect of pore pressure induced  
Figure 14: The calculated and measured pile displacement. Figure 13: Displacement components in Sample 2 seabed experiments: (**a**) permanent component; (**b**) cyclic component. by pile response was more significant than that induced by the increase in drainage path because of pile diameter; the traditional evaluation method of pile displacement is more applicable for piles with a large diameter or small pile response.