esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis
#### 3.1.2 HS-SPME-GC-MS Analysis of Volatile Components in Kiwifruit Samples  
This research used HS-SPME-GC-MS for qualitative and quantitative analysis of volatile components in 23 kiwifruit samples. A total of 172 volatile compounds were detected by HS-SPME-GC-MS (see Table 2 for details). They were divided into nine categories according to functional groups, including 37 esters, 17 alcohols, 13 aldehydes, 16 ketones, 4 acids, 20 alkanes, 40 alkenes, 16 aromatic hydrocarbons and their homologues, and 9 other components. Firstly, the volatiles contained in 20 or more kiwifruit samples were defined as the main volatiles in kiwifruit. There were 16 primary volatiles in total, namely, ethyl acetate, n-propyl acetate, ethyl butanoate, hexyl formate, ethyl hexanoate, diisobutyl phthalate, ethanol, (E)-2-hexen-1-ol, (E)-2-hexenal, styrene, D-limonene, 3-isopropylide-6-methyl-1-cyclohexene, o-xylene, 3,5-bis(1,1-dimethylethyl)-phenol, ammonium carbamate, and methoxy-phenyl-oxime. Esters are the most abundant volatiles, with six kinds. Secondly, volatiles commonly contained in samples of less than 20 cultivars and more than 12 cultivars of kiwifruit were defined as secondary volatiles. There were 21 secondary volatiles, including 2 esters, 3 alcohols, 4 aldehydes, 1 ketone, 2 alkanes, 4 alkenes, 2 aromatic hydrocarbons and their homologs, and 3 other compounds.
## 3 Theory
### Estimation of the Inherent Parameters \(T_{ng}\), \(T_{g}\) and \(C\)
#### 3.2.3 Estimation of \(\alpha_{ng}\) and \(\Delta N_{ng}\)
For a room with dimensions 7.56 m \(\times\) 7.30 m \(\times\) 3.50 m and with an absorbent ceiling corresponding to case 1 in Table 2, the number of included modes in the non-grazing group given by the empirical and the theoretical approaches are compared in Figure 6. At lower frequencies (125 Hz and 250 Hz), the correspondence is good. At higher frequencies, the theoretical estimation gives significantly higher values compared to the empirical one. The consequence of this discrepancy will be further discussed in Section 5. The empirical and theoretical approaches described above are used for frequencies of 500 Hz and above. At 125 Hz and 250 Hz, the non-grazing absorption coefficient is estimated in the same way as the grazing one at low frequencies, i.e.,  
\[\alpha_{ng,ceiling}=\pi\rho cA_{xl}^{\prime} \tag{32}\]  
where \(A_{xl}^{\prime}\) is the real part of the admittance for the ceiling absorber, see Equation (27). By knowing the number of modes in each sector, i.e., \(\Delta N_{g}\) and \(\Delta N_{ng}\), and the representative absorption coefficients \(\alpha_{g}\) and \(\alpha_{ng}\), we can go on and estimate the corresponding reverberation times \(T_{g}\) and \(T_{ng}\).
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 5 Results
### User Traffic Prediction Module Results  
The user traffic prediction dataset is processed into a time series of user traffic on the network per 1 time step. In this way, features and trends in time can be extracted through ML and make future predictions.
# A Novel Method for Spectrometry Based on Imaging Systems
## 1 Introduction
In the last five years, Crytur Company has released this new scintillator, made by mixed rare-earth silicate of unknown compounds [11]. Despite CRY018 showing a light yield similar to that of NaI:Tl, and a poorer energy resolution with respect to LaBr\({}_{3}\):Ce, it is comparable with the latter in terms of density (i.e., detection efficiency) and time response. Therefore CRY018 represents a good candidate for \(\gamma\) spectrometry showing halfway characteristics between the previous scintillation crystals. In this work, the efficiency of a simple DoI estimator has been studied and verified using a round shape and white painted surfaces CRY018 monolithic crystal coupled to a MAPMT. The full reflective optical treatment enhances the spectrometric properties, as known in the literature [12; 13] and affects the shape of the scintillation light distribution. In monolithic detectors, the DoI information is achieved by modelling the scintillation light distribution with analytical functions [14; 15; 16]. For this analysis a continuous DoI determination method previously proposed in [17; 18; 19] was chosen. First, the DoI estimator has been evaluated studying charge distributions. Then, the dependency of spectrometric properties on the DoI has been investigated and qualitatively analyzed. The proposed system allows to produce several spectra of the same sample (i.e., as a function of the DoI) offering the possibility to perform a better identification of the gamma emissions. In environmental radioactivity measurements, in fact, the background spectrum mainly due to the presence of the \({}^{238}\)U and \({}^{232}\)Th decay chain \(\gamma\) emissions, could be challenging to discriminate. The DoI discrimination of events allows reducing the background component, providing different ratios between a full energy peak and a Compton distribution as the selection goes deeper in the crystal. In general, the background affects the full energy peak counts, increasing the error on the activity evaluation of the source. As a consequence, the minimum detectable activity (MDA) would increase worsening the detection efficiency. A further undesired background could be the presence of the self-absorption radiation emitted by the crystal itself. The imaging capabilities of the system could overcome this effect, enabling the use of crystals with self-activity. These scintillators have a greater Z\({}_{vff}\) (effective atomic number) providing an improved detection efficiency. Hence, the knowledge of the spectrometry as a function of the DoI, enabled by choosing a position-sensitive device, could provide a better understanding of the spectrum and a more reliable identification of the source.
## 1 Introduction  
The demand for electrical energy is rising quickly because of our growing dependence on electrical and electronic devices [1]. Clean and renewable energy sources--such as sound, vibration, wind, sea waves, and kinetic energy [2]-can help meet this demand [3]. By relying on these sustainable and freely available sources, we can reduce our use of traditional energy [4]. In everyday environments, human footsteps are an untapped source of energy that often goes to waste [1,3]. Because of the global shortage of energy resources, finding new energy sources, such as human footsteps, is now a necessity [5].  
Piezoelectric transduction is a promising technology for gathering ambient energy, with successful applications in areas such as structural engineering, transportation, wireless electronics, the Internet of Things (IoT), and wearable or implantable biomedical devices [3]. Piezoelectric materials change kinetic energy or mechanical vibrations from the environment into electrical energy [6]. Pedestrian energy harvesting applies this technology to convert the energy from walking, jogging, or jumping into electricity. This electricity can then power devices such as small sensors or street lighting and may even charge electric vehicles in the future. Proposals by researchers such as [PERSON] et al. [6] show that a useful amount of electricity can be generated. However, these models should be hybrid (footsteps plus solar) when foot traffic is high (e.g., a football pitch) or at Al-Haram Mosque (our focus). Last, the growing need for sustainable energy in high-traffic public spaces points out the drawbacks of traditional manufacturing methods for energy devices, which are often expensive and time-consuming [7].  
This review looks at existing energy-harvesting studies to help develop the best solutions for high-foot-traffic sites. Al-Haram Mosque in Saudi Arabia will be used as the case study. The review will include an analysis of the materials, designs, power output, and overall suitability for use at Al-Haram Mosque. Before that, a brief introduction to Al-Haram Mosque and its power needs will be presented.
## 2 Methods
### Computational Details for Dynamics
#### 2.2.2 Ethylene  
In ethylene (H\({}_{2}\)C=CH\({}_{2}\)), we consider vertical excitation into the \(S_{1}\) (\(\pi\pi^{*}\)) state. The excited molecule undergoes cis-trans isomerisation around the C=C bond, and decays via nonradiative decay through a twisted or pyramidalised conical intersection, or via H-atom migration to form ethylidene (CH\({}_{3}\)CH), which then decays through a different conical intersection. The basic dynamics is sketched  
Figure 1: Overview of the molecules and dynamics discussed in this article. **(a)** D\({}_{2}\): Potential energy curves for the ground \(X^{1}\Sigma_{\pm}^{+}\) and excited \(B^{1}\Sigma_{\pm}^{+}\) state potential energy curves. The vibrational wavepacket is excited onto the \(B^{1}\Sigma_{\pm}^{+}\) state centered on vibrational state \(\
u=38\) by a 14.3 eV pump pulse. The wavepacket (dashed line) is shown at the outer turning point; **(b)** Ethylene: After excitation to the \(S_{1}\) state, the dynamics is dominated by C–C bond twist, pyramidalization, and changes in C–C bond length; **(c)** 1,3-cyclohexadiene (CHD): Upon excitation from the 1A ground state onto the 1B bright state in the Franck–Condon region, the molecule decays through a sequence of conical intersections back to the ground state in either the ring-open or ring-closed form (based on illustration in Ref. [54]).  
out in Figure 1b. The population on \(S_{1}\) remains fairly constant for the first 30 fs, at which point the molecule begins to decay exponentially to \(S_{0}\) with an approximate lifetime of \(\approx\)112 fs.  
The simulations employ the AI-MCE method [55], with electronic potential energies, gradients, and nonadiabatic couplings calculated _on-the-fly_ using the MOLPRO electronic structure package [56]. A total of 1000 Ehrenfest trajectories are initiated in the Franck-Condon region using a Wigner distribution [57], and are propagated for 150 fs. The electronic structure is calculated at the state-averaged SA3-CAS(2,2)-SCF/cc-pVDZ level (two electrons in \(\pi\) and \(\pi^{*}\) orbitals) including the \(S_{0}\), the \(S_{1}\)\(\pi\)-\(\pi^{*}\), and the \(S_{2}\)\(\pi^{*2}\) states. The calculated lifetime from the simulations (112 fs) agrees well with the 110 fs obtained with AIMS dynamics using the same CAS(2,2) active space [58]. Including more dynamic correlation via CASPT2 drops the lifetime to 89 fs [58] while including Rydberg states (predominantly the \(\pi\)-3s state) lowers the lifetime further to approximately 60 fs [59; 60]. However, the influence of Rydberg states on the dynamics simulations is relatively small and experimental evidence for the role of Rydberg states remains under debate [61; 62]. Importantly, the small active space in the present study allows us to converge the simulations [49], which is the key aspect for the present discussion of X-ray scattering signals.
## 6 Conclusions  
In this study, a self-developed finite element program was employed to evaluate the upper-bound ultimate bearing capacity of shallow foundations in cohesive and C-phi soils. The method incorporates multi-segment zero-thickness interface elements to simulate complex failure mechanisms, based on the Mohr-Coulomb failure criterion, within a semi-infinite soil domain. Key findings include:  
1. Failure Mechanisms: For purely cohesive soils, the optimal failure mechanism comprises two triangular zones and a circular sector. For C-phi soils, it consists of two triangular zones and a logarithmic spiral sector.
2. Verification: Numerical results for both soil types show an excellent agreement with the upper-bound solutions by [PERSON] (1981) [PERSON] (1981) [PERSON] (1981), validating the modeling approach.
3. Transition Zone Accuracy: When the transition zone is discretized into 18 blocks, the computed bearing capacity factors closely match [PERSON]'s (1973) Vesic (1973) Vesic (1973) theoretical values, with errors ranging from 0.1% to 0.19%.
4. Frictional Soils: For friction angles between 10\({}^{\circ}\) and 40\({}^{\circ}\), the computed \(N_{c}\) and \(N_{q}\) values deviate by only 0.04-0.19% and 0.12-2.43%, respectively, from analytical solutions.  
The proposed method, while based on assumed mechanism geometry, provides an efficient and accurate tool for evaluating foundation behavior when engineering judgment suggests plausible failure configurations. Unlike discontinuity layout optimization or adaptive limit analysis, our approach emphasizes computational efficiency and transparency in the failure mechanism, which is advantageous for practical design contexts. Nonetheless, we acknowledge that extending the framework to include automatic mechanism detection is a valuable direction for future work.  
The primary contribution of this study is the formulation of a structured finite element model using multi-segment zero-thickness interface elements in the transition zone, enabling the high-precision evaluation of upper-bound capacity with minimal deviation from classical solutions. This method achieves excellent agreement (errors < 0.2%) with benchmark solutions while remaining computationally lightweight, offering an efficient alternative to full optimization-based limit analysis.  
Supervision, Methodology, and Writing--original draft, Y.-L.L.; Formula derivation and Software programming, C.-M.L.; Computation, Y.-T.H.; Verification, T.-H.H. and M.-L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
The authors declare no conflicts of interest.
## 3 Modeling Techniques
### Techniques Evaluation  
Statistical indicators alongside the graphical techniques were used to assess the performance and robustness of the proposed models. The statistical indicators include root mean square error (RMSE) and the coefficient of determination (R\({}^{2}\)), which is defined mathematically as follows:  
\[RMSE\ =\ \sqrt{\frac{1}{n}\sum_{i\ =\ 1}^{n}\left(x_{i\ exp}-x_{i\ pred}\right)^{2}} \tag{7}\]  
\[R^{2}\ =\ 1-\frac{\sum_{i\ =\ 1}^{n}\left(x_{i\ exp}-x_{i\ pred}\right)^{2}}{ \sum_{i\ =\ 1}^{n}\left(x_{i\ exp}-\overline{x}_{i\ pred}\right)^{2}} \tag{8}\]  
Figure 1: Flowchart of gene expression programming (GEP) algorithm procedure.  
The credibility of the developed models was tested using the leverage statistical approach. In this approach, the standardized residuals, which refer to the difference between the predicted results and the experimental points, were presented versus the statistical Hat matrix leverage values; this graph is called the Williams plot. The Hat can be calculated using the Hat matrix of the following form [56; 57]:  
\[H\ =\ X\Big{(}X^{t}X\Big{)}^{-1}X^{t} \tag{9}\]  
where \(X\) is an (k x d) matrix, k and d represent the dimension and the data points number, respectively, and \(X^{t}\) denotes the \(X\) transpose matrix. The limit leverage value (H*) depicted on the Williams plot with a purple line is a constant calculated as 3(d + 1)/k. The selection of data points is in the range of \(\pm\) 3 of the standard deviation from the mean, where the cut-off value of 3 ensures covering 99% of normally distributed data. The validity of the developed model and the credibility of the predictions were conditioned by the values of data points, which must be situated in the range of 0 \(\leq\) H \(\leq\) H* and \(-3\leq\) R \(\leq\) 3.
## 2 Related Work
### Challenges of Applying LLMs in the Legal Domain  
The application of LLMs in the legal domain faces unique challenges due to the complexity and specificity of legal language, with specialized terminology, intricate phrasing, and ambiguity that often requires contextual interpretation [2]. General-purpose LLMs, trained on broader datasets, struggle to capture these nuances. This leads to potential misinterpretations and inconsistencies in the treatment of statutory language, legal precedents, and case law [16; 19]. Another problem in environments where factual precision is essential is hallucination. LLMs tend to generate plausible but incorrect information. This is often due to data limitations, as legal texts are underrepresented in LLM training datasets, which prevents these models from acquiring the domain-specific knowledge necessary for accurate legal reasoning [2]. In addition, legal systems vary widely across jurisdictions, and legal terminology can vary by language and context. This requires extensive jurisdiction-specific data and fine-tuning to avoid inaccurate interpretations. However, this fine-tuning is resource-intensive, as legal prompts and documents are typically long. As processing requirements increase with input length, customization becomes costly and scalability across multiple jurisdictions is limited [2]. According to [PERSON] et al. [2], a major limitation to the integration of LLMs in the legal domain is the static nature of LLM knowledge. LLMs trained on static datasets lack awareness of legal changes, which can lead to regulatory and ethical challenges. For example, in jurisdictions governed by privacy regulations like the GDPR, models must ensure compliance with the most up-to-date version of the law. Moreover, the use of LLMs in the legal domain raises ethical concerns around data protection, bias in decision-making, and accountability [2].  
In summary, although great progress has been made regarding the application of LLMs to legal tasks, substantial gaps remain in their evaluation with regard to compliance-specific applications. No existing tool or benchmark provides structured, regulatory-compliant output catering to the specific requirements of GDPR documentation. This work addresses the shortcomings as mentioned above by introducing GDPR-specific benchmarks and experiments that evaluate open-source and closed-source models on a representative compliance task. This involves checking their ability to produce correct, formatted output and follow dynamic legal standards. The next section outlines the methodology for testing these capabilities.
## 3 Results and Discussion
### Optimization of QuEChERS Extraction Conditions
#### 3.2.2 Multivariable Method
The following second-order polynomial equation describes the proposed model:  
\[\text{Y}=104.8\ (\pm 1.82)-5.8\text{x}_{1}\ (\pm 0.86)+6.8\text{x}_{2}\ (\pm 0.86)-6.2\text{x}_{1} {}^{2}\ (\pm 0.94)-5.2\text{x}_{2}{}^{2}\ (\pm 0.94)-5.5\text{x}_{3}{}^{2}\ (\pm 0.94)-6.2\text{x}_{1} {}\text{x}_{2}\ (\pm 1.1) \tag{1}\]  
where, x\({}_{1}\) the amount of NaCl (g), x\({}_{2}\) the amount of C18 (mg), x\({}_{3}\) the % amount of CH3 COOH and x\({}_{1}\)\({}^{2}\), x\({}_{2}\)\({}^{2}\) and x\({}_{3}\)\({}^{2}\) and x\({}_{1}\)x\({}_{2}\) the interactions between the corresponding factors x\({}_{1}\), x\({}_{2}\) and x\({}_{3}\). The NaCl and C18 amounts' interaction proved to be statistically significant, exhibiting a negative sign which asserts the antagonistic interaction among these factors. Figure 3 (response surface plots) is indicative of the fact that the combination of both high and low values of the above-mentioned factors leads to decreased QuECHERS performance (R \(<\) 56%). In general, NaCl is added to control selectivity by reducing polar interference and water in acetonitrile-phase. On the other hand, C18 addition removes long chain fatty compounds, sterols and other non-polar interference. According to the desirability profile (Figure S8), the optimized parameter values were determined at the optimum value of the mean recovery being 113%. In conclusion, the overall optimum values of the QuECHERS parameters were: 10 g of milk sample, 10 mL of extraction solvent (ACN), 6 g MgSO\({}_{4}\) and 1.04 g NaCl (extraction salts), 150 mg MgSO\({}_{4}\), 50 mg PSA and 52.3 mg C18 (cleanup salts) and 1.1% CH\({}_{3}\)COOH in the extraction solvent. The adequacy of the predicted optimization model was also checked by performing analysis with the optimum conditions and was found to be well-fitted to real experimental conditions with the analytical method exhibiting excellent performance.
## 4 Experiment and Analysis
### Baseline Algorithm  
To verify the effectiveness of the proposed model, it is compared with the following methods. The baseline method is described as follows.  
1. Item-KNN [5] uses cosine similarity to define the degree of similarity between sessions and recommends items that are similar to previous clicks.
2. FPMC [7] is a sequence recommendation method based on Markov chain and matrices factorization.
3. GRU4 REC [9] uses RNN to model user sequences for session-based proposals.
4. NARM [10] uses an RNN with an attention mechanism to capture the main purpose and sequential behavior of users, adding an attention mechanism to capture the user's general interest and current interest in the current session.
5. CSRM [11] consists of an internal and an external memory encoder to model the preferences of the current session and neighborhood sessions in order to better predict the intent of the current session.
6. SR-GNN [4] uses GNN to capture complex item information transformation and uses an attention mechanism to capture long-term and short-term interests.
7. FGNN [14] learns item vectors through a weighted attention layer and learns session features through a session graph feature extraction layer.
8. STAMP [17] uses an attention network to capture the user's general interest and current interest in the current session.
9. CoSAN [42] uses a self-attention network to learn the current session embedding and collaborative feature information of items from neighborhood sessions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Yochoose1/64** & **Diginetica** & **Tmall** \\ \hline Clicks & 557,248 & 982,961 & 818,479 \\ Train & 369,859 & 719,470 & 351,268 \\ Test & 55,898 & 60,858 & 25,898 \\ Items & 16,766 & 43,097 & 40,728 \\ Average length & 6.16 & 5.12 & 6.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics data for the data sets.