esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Influence of Groundwater Fluctuations on the Mechanical Behavior of Caisson Structure  
Based on meteorological and hydrological data, the project area receives an average annual precipitation of 677.7 mm. During construction, the groundwater level is situated 23.19 m below the ground and fluctuates seasonally above and below this level. Using the normal water level during construction as a reference, Figure 9 illustrates the horizontal displacement and the maximum principal stress of the upper caisson wall of the jacking pipes, focusing on the outer side of the left caisson wall of the settling well as the pathway. It can be seen that at the annual highest groundwater level (24.86 m), both the horizontal displacement of the upper caisson wall and the maximum principal stress increase. This phenomenon is attributed to the rise in groundwater level, leading to increased pore pressure in the soil and positive pressure at the bottom of the jacking pipes. The jacking pipes experiences upward buoyancy, exerting an upward pressure on the surrounding soil. This causes slight soil deformation, resulting in inward pressure on the upper caisson wall of the jacking pipes. The closer it is to the ground, the greater the displacement. Conversely,  
Figure 8: Mechanical behavior of the caisson structure during the construction phase: (**a**) the horizontal displacement and (**b**) the distribution curve of the maximum principal stress.  
when the groundwater level is at its annual lowest, the stress and the displacement of the caisson wall exhibit the opposite trend.  
Considering the seasonally fluctuations in the groundwater levels throughout the year, the changes in maximum horizontal displacement and stress of the caisson under seasonal fluctuations for three years were investigated, as shown in Figure 10. It can be observed that after long-term operation, the horizontal displacement changes relatively minimally with groundwater fluctuations, and the seasonal variation in stress remains within the concrete yield cracking range. This indicates that seasonal groundwater variations have minimal impact on the caisson.
# Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm
## 5 Conclusions  
To obtain the correct machining motion profile faster and make the machining quality closer to zero-defect manufacturing, this paper systematically investigated and researched the common optimized method of joints' trajectories for the common series manipulator. To solve the problems of the current optimization algorithm, such as convergence insufficiency and poor global search ability, this paper proposed the slime mould whale optimization algorithm (SMWOA). The optimized characteristics of the commonly used WOA and SMA were analysed in detail. The advantages of the two methods were combined. The dynamic parameter \(\mathbf{a}\) and dynamic weight \(\omega\) of SMA were introduced into WOA and the SMWOA proposed. The optimized experiments of 15 benchmark functions were carried out, and the experiments verified that the optimization performance of the proposed algorithm was clearly better than that of other classical optimization algorithms. Taking the shortest running time as the optimized goal, the SMWOA algorithm was applied to the optimization of the joint trajectories of a serial manipulator. The experimental results also verified the effectiveness of the algorithm. Although the optimization performance of the proposed algorithm is better than other classical algorithms in the experimental tests in this paper, the optimization ability for high-dimensional functions and complex problems is still unclear. It can still be further improved in terms of parameter adjustment and search strategy.  
Conceptualization, X.L., Q.Y. and X.Y.; methodology, X.Y., X.L. and H.W.; software, X.L., Q.Y. and Q.H.; validation, Q.Y., Q.H. and N.W.; formal analysis, H.W. and S.T.; investigation, Q.Y. and S.T.; resources, X.L. and Q.H.; data curation, X.L. and Q.H.; writing--original draft preparation, X.L. and N.W.; writing--review and editing, Q.H. and N.W.; visualization, H.W.; supervision, X.Y. and H.W.; project administration, Q.Y.; funding acquisition, X.Y. 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, grant number 52075306.  
Not applicable.  
The data that support the findings of this study are available within the paper.  
The authors declare no conflict of interest.
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
## 3 Methodology
### Out-of-Distribution Detection  
As shown in Figure 1, out-of-distribution detection is achieved by comparing the feature distance between the training dataset (in-distribution dataset) and the test samples. During the training stage, the whole network is optimized by minimizing the following loss function,  
\[\mathcal{L}=\lambda\times\mathcal{L}_{batch}+(1-\lambda)\times\mathcal{L}_{ae}, \tag{4}\]  
where the hyperparameter \(\lambda\) is a weight value to adjust the balance between two loss functions. During the test stage, the training datasets and the test samples are sent to the network to obtain feature representations, named \(\mathbf{x}_{in}\) and \(\mathbf{x}_{test}\), respectively. Then, a score is calculated based on the following equation,  
\[s=(\mathbf{x}_{test}-\mathbf{\mu}_{in})^{T}\mathbf{\Sigma}_{in}^{-1}(\mathbf{x}_{test}-\mathbf{ \mu}_{in}). \tag{5}\]  
This score is the Mahalanobis distance between two feature representations; \(\mathbf{\mu}_{in}\) and \(\mathbf{\Sigma}_{in}^{-1}\) are the mean and covariance matrix of \(\mathbf{x}_{in}\). The out-of-distribution decision is according to a rule that if the score \(s\) is larger than a pre-defined threshold, then the test sample is an outlier from the out-of-distribution dataset. Conversely, the test sample is from an in-distribution dataset.
# Electrical Property Measurement on Antistatic Coating During Aging Process
## 3 Results and Discussion
### Samples After Aging Process by Increased Temperature in Oven  
The increased temperature was applied to the samples on the Memmert UF55 plus oven (Memmert GmbH + Co. KG, Buchenbach, Germany). This oven is programmable, and it has forced air circulation in the chamber. For this measurement set, we wanted to compare the conductivity and capacitance changes after the aging process. The aging process lasted 60 days at a constant 70 \({}^{\circ}\)C temperature and constant hot air circulation. The sample parameter change was supposed to be caused by chemical structure changes of the antistatic paints. Thus, the paint degradation can cause the deterioration of the paint's capability to conduct the charge to earth. The measurement sets previously described were repeated with the same samples after the aging process with the same methods, devices, and frequency ranges.  
After the aging process, the conductive currents were lower. This is caused by the fact that the paint had dried and had lower humidity. The samples with the highest conductivity values before the aging process appeared to be the most influenced by aging. For e.g., PANI paint, which had an \(I_{G}=350\) uA conductive current value for the glass sheet case before aging, the value decreased to \(I_{G}=20\) uA after aging.  
The aging process also decreased the capacitance values of the samples on a glass sheet. Before aging, these values were in the range of 100-150 pF. After aging, the capacitance decreased to approximately 100 pF for the BISIL paint on a glass sheet and had the highest capacitance at 1 mHz: CBISIL \(\approx 110\) pF. The same effect could be observed with the sample on a steel plate: 350 pF before aging and 200 pF after aging for the BISIL paint sample.  
The loss factor _tg\(\delta\)_ also decreased after the aging process, especially in the case of the samples with conductive polymer PANI. This is caused by sample drying. For the TiO\({}_{2}\)/PANI sample on a steel plate, the maximum of _tg_\(\delta\) was 1.75 at 1 mHz.
to name a few. Introduction of air gaps and tapers were also attempted for SBS mitigation with scanty success nonetheless [34]. While application of temperature and strain gradients are prone to fluctuations and are cumbersome in terms of commercial deployments, phase modulation techniques pose a different challenge in PSA systems. In a PSA, the signal gain is sensitive to the relative phase difference between the two pumps and the degenerate signal (considering a dual-pump configuration) [35]. Therefore, phase modulation of the pumps induce different phases for the different modulation tones and hence degrades the PSA gain extinction ratio of the system [36]. For a dual pump configuration, synchronous phase modulation of the pumps can avoid such effects [37], however such active processes come with additional complexity and often is detrimental for the noise performance of the system. Another remedy towards the SBS problem runs in the lines of varying the properties of the fiber along the fiber length, dispersion being one of them [15; 24; 38]. Dispersion variation along the fiber length can be achieved by varying the radius of the fiber preform along the length while pulling it from a drawing tower. Here we consider a W-type refractive index of the preform as shown in Figure 1 as is standard for commercial highly nonlinear fibers (HNLF). Since the waveguide dispersion is a function of the core radius, the variation of the dispersion along the fiber length can potentially inhibit the build-up of the different acoustic modes that interact with the optical pump modes that generate the SBS process. Therefore another dimension of our investigation focuses on analyzing the SBS dynamics in a DOF using a steady-state numerical approach. We further investigate multiple longitudinally varying radius profiles and propose optimal designs for SBS mitigation, that in turn complements our goal of designing a high-gain dual pump PSA system. Furthermore, the SBS advantage of a DOF over a conventional highly nonlinear fiber (HNLF) was experimentally confirmed with an available DOF. We also numerically and experimentally compared the four wave mixing (FWM) conversion efficiency (CE), which is a key quantifier for any PSA system, of the available DOF and HNLF. This paper is organized as follows. In Section 2, we focus on the numerical calculation of the dispersion properties of the fiber. Sections 3 and 4 deal with investigating the PSA performance of dispersion tailored fibers, in single and dual pump configurations respectively. In Section 5 we numerically investigate the SBS threshold of DOFs and identify optimized profiles for further elevating the SBS threshold. Then in Section 6 we provide experimental results on SBS mitigation in a DOF and quantify the conversion efficiency (CE) of a DOF as a parametric amplifier. Henceforth in Section 7 we discuss the implications of the obtained numerical and experimental results. Finally in Section 8 we summarize the findings and conclude the key takeaways of this paper.
## 2 Results and Discussion
### Effect of Sample pH  
Nicotine exists in unprotonated, mono-protonated or di-protonated forms, depend on the acidity of the aqueous sample solution. Due to the basic properties, corresponding to the two N atoms in the structure of the unprotonated form, nicotine can obtain one or two protons in acidic solutions [25]. Unprotonated nicotine is volatile and able to enter the sample headspace, while protonated forms are not. The second nicotine acidic constant (pK\({}_{\text{a2}}\)) is eight, which is also the approximate dividing line between dominance by the unprotonated/protonated forms. Therefore, to obtain correct quantitative responses in the DI-DPME sampling of nicotine, the pH of the sample solution should be controlled precisely [19; 26]. For this purpose, the extraction efficiency was investigated over the pH range of 4.0-8.0. As shown in Figure 2, the best extraction efficiency was achieved at pH = 6 for both the DI  
Figure 1: Scanning electron microscope (SEM) images of the nanostructure polypyrrole (PPy) coating at two different magnifications: (**a**) 10 \(\upmu\)m and (**b**) 50 \(\upmu\)m.  
and EE-DI-SPME sampling methods. The extraction efficiency decreased by using lower pHs, due to a competition between nicotine and the proton to adsorb the fiber coating.
## 2 Baselines in the Literature
### Evaluation Criteria
#### 2.1.1 Accuracy  
Accuracy can be considered from two perspectives, i.e., precision and bias. The precision describes how close determined values are to each other, providing a measure for the statistical variability. The literature often focuses on precision, when talking about accuracy in general (e.g., [15; 17]). A baseline methodology is considered to have a bias when there is a systematic over- or underestimation, of a consistent magnitude. Bias is also known as trueness and is sometimes considered as a separate parameter, for example in [15]. Having a biased baseline is not necessarily a problem. In the work of [15], a positive bias (overestimating the baseline, benefiting the customer) has led to a higher customer participation, which in turn led to higher overall profit. Figure 1 further illustrates the difference between precision and bias, in the context of the concept of accuracy. A baseline is considered accurate, when it is both precise and unbiased.
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 1 Introduction  
Audio database searching can be facilitated by metadata relating to the characteristics of each audio excerpt. The ability to generate such metadata automatically can therefore be useful. A key characteristic of sound is its timbre, and brightness is one of the most commonly used timbral descriptors, e.g., it is in the top 3 most-searched timbral attributes on the Freesound audio sample repository [1].  
The timbre of a recorded sound will potentially be affected both by the sound source and by the choice of microphone used to record it. Previous work by the authors in [2] identified 31 perceptual attributes of recorded sound that can be affected by microphone choice. The largest impact of microphone choice was found to be on the brightness of the resulting recording. Models of brightness perception have been developed and tested previously, but not specifically in connection with microphone performance. The work documented in this paper aimed to develop a model sensitive to microphone-related, as well as source-related, brightness differences and to evaluate it against existing brightness models found to be useful in other application areas, e.g., differentiating between musical instruments [3, 4, 5], between variations of a single instrument [6], between musical performances [7], or between vocal articulations [8].  
This paper is split into four sections: Section 2 describes listening tests used to collect listener ratings of perceived brightness; Section 3 introduces previous research into the acoustic correlates of perceived brightness, outlines potential improvements upon these, and develops a linear regression model using the most suitable; Section 4 then presents an evaluation of the brightness model; and finally, in Sections 5 and 6, the model's performance is discussed and conclusions are drawn.
## 3 Methodologies
### Logistic Map
#### 3.1.3 Keystream Generation with the Logistic Map  
The keystream generation is crucial in determining the secrecy and security of this system; the calculation of the logistic map must be performed with high precision. This is to ensure that initial values with very small differences will yield significantly different extracted images, making them unrecognizable.  
To control this precision level, the decimal library, which provides the Decimal data type, is used. This data type has fast and much more precise rounding than the float data type. The precision level of the calculations can be set using the command getcontext().prec = n, where n is the desired precision level. With this, the precision level can be adjusted, such as to the available memory, required security level, and so on.  
The precision level is also directly related to the security level of the stego image. If the precision level used in the calculation is not high enough, then very small changes in the initial value will not reflect chaotic behavior. As a result, the system will be more vulnerable to brute force attacks. However, the high precision level must also not have significant drawbacks in terms of computation. This means that the precision level used should not excessively sacrifice computation time.  
Based on Figure 5, the increase in calculation time with increasing precision is something that can be predicted. However, the range of the calculation time can be considered small, with the difference between the precision levels of 16 digits and 60 digits being about 0.10 s. Thus, this method can adjust to the needs of security levels without a drastic difference in time.  
To conclude, as shown in Figure 6, the keystream generation process begins with converting \(x_{0}\) and \(r\) to decimal format for high-precision calculations. The initial value of \(x\) is set to \(x_{0}\). The logistic map computation is then performed for a specified number of iterations, represented by size. If the final iteration is reached, the process proceeds to generate the keystream output. During each iteration, the new value of \(x\) is computed based on the logistic map equation. This value of \(x\) is then modified to represent a pixel location or a non-negative integer. Instead of storing each computed value in an array, which would increase the memory usage and execution time, a generator is used to temporarily collect the values of \(x\) into memory. Once all iterations are completed, the function outputs a generator containing the keystream.  
Figure 5: Time graph of keystream calculation with precision levels from 16 digits to 60 digits for a secret image of size \(512\times 512\) pixels.
## 3 Results and Discussion
Limitations observed include that not all clinical notes were annotated by all evaluators; we observed a variability in the granularity of the concepts' (qualitative, quantitative, and temporal) annotation; sometimes, annotators included one word and on other occasions included two or more in the same entity; this variability was associated with fatigue or inconsistent use of the annotation guide. In the future, we suggest shorter annotation sessions and a series of retraining exercises before each session. Once the database increases in size, we anticipate that we will need to increase the number of annotators, which could increase variability. Therefore, establishing robust annotation protocols and ongoing training will be critical to maintaining high-quality annotations. When working with sensitive and private information, such as medical records, it is essential to ensure data security and privacy. Using local language models, such as BERTinstalled on a local machine, provides several key advantages in this regard. By processing data locally, we eliminate the need to transmit sensitive information over the Internet, significantly reducing the risk of data breaches or unauthorized access. Additionally, local installations allow for greater control over the data processing environment, enabling the implementation of stringent security measures and compliance with data protection regulations. This approach ensures that all processing occurs within a controlled and secure environment, safeguarding the confidentiality and integrity of private information.
# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 1 Introduction
### Scope of Data-Driven Building Energy Retrofitting  
This review covers data-driven retrofitting strategies across residential, commercial, and public buildings, each presenting distinct energy demands and retrofit challenges. In residential buildings, retrofitting typically focuses on cost-effective improvements to energy efficiency and occupant comfort. For example, [PERSON] et al. [10] demonstrated the use of machine learning models to predict energy performance in UK residential buildings, improving retrofit decision-making by accurately forecasting energy savings. Commercial buildings require more complex energy management systems, where retrofitting efforts must balance energy efficiency with operational needs. [PERSON] et al. [11] employed gradient-boosted decision trees to predict non-domestic building energy performance, enabling deep energy retrofits by identifying optimal energy-saving strategies. Public buildings, including schools and hospitals, present additional challenges such as regulatory compliance and sustainability goals. [PERSON] [12] applied digital twin technology in public buildings, optimizing retrofitting strategies by integrating real-time operational data, which improved both decision-making and energy savings. Across these sectors, the primary goals of retrofitting are to reduce energy consumption, minimize greenhouse gas emissions, and enhance occupant comfort and productivity.
## 5 Analysis of the Hybrid Propagation Channel Model  
The 3 DFPHPCM proposed in this study was established based on the three-dimensional vector PE and the three-dimensional RT. Only the line-of-sight, single scattering, and double scattering are considered in the theoretical study. When combined with the experimental measurements in urban microcellular environments, the variation in the path loss at each receiving point with the horizontal distance between the transmitting and receiving antennas was investigated for different numbers of scattered rays and different scattering angles.
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 4 Test Results and Discussion
### Grouting Compactness Detection Based on HHT Method  
The process of HHT is as follows: firstly, EMD method is used to decompose the preliminary processing signal into several IMF components based on the screening conditions of IMF; Then, Hilbert transform is performed for each IMF to obtain corresponding Hilbert spectrum. Therein, the Hilbert spectrum of all IMF is superimposed to obtain a Hilbert spectrum of the original signal. Finally, the amplitude square of the Hilbert spectrum is calculated as the Hilbert energy spectrum, which is used to detect the grouting compactness. The process diagram of the Hilbert-Huang conversion process is shown in Figure 7.