esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Firmness and SSC Prediction Results
#### 3.1.2 Feature Extraction  
Figure 2 shows the trend of the average spectrum of the best models selected for predicting soluble solids content (SSC) and firmness (FF) in kiwifruits. The curves represent the normalized average reflectance of the considered spectra. The selected wavelengths used in the predictive models for SSC and firmness are summarized in Table 3. The main selected wavelengths (around 958, 978, 1085, 1107, 1452, and 1465 nm) are strongly consistent with those reported in the literature. Several authors have identified characteristic peaks in kiwi spectra around 974 nm, 1200 nm, 1460 nm, and 1780 nm, confirming their relevance to O-H and C-H bonds, associated with sugar content, water, and the cellular structure of kiwis [13; 19; 20; 42].The first broad peak, centered around 974 nm, is attributed to the combined absorption of water and carbohydrates [37], while [43] identified the bands at 980.39 and 1470.59 nm as corresponding to the first and second overtone of the O-H bond in water, respectively. Additionally, the bands around 1197.60 nm and 1785.71 nm are related to the overtones of the CH group, suggesting a connection with sugars, cellulose, and cellular water [13].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Model** & **Parameters** & **Selected Wavelengths** \\ \hline \multirow{3}{*}{Raw-PLS} & & 901.00, 929.90, 952.41, 1084.58, 1107.19, 1129.81, 1145.96, 1220.28, \\  & SSC & 1326.78, 1381.52, 1420.09, 1452.17, 1503.38, 1528.92, 1551.23, 1570.33, 1621.09, 1624.25, 1627.42, 1659.01, 1668.46, 1687.35 \\ \multirow{3}{*}{SNV-PLS} & & 913.84, 926.69, 955.63, 978.16, 997.48, 1020.04, 1107.19, 1149.19, 1171.81, 1204.12, 1330.00, 1464.99, 1548.05, 1583.04, 1681.06, 1684.20 \\ \hline \end{tabular}
\end{table}
Table 3: Selected wavelengths for soluble solids content (SSC) and firmness (FF) prediction using the best PLS models.  
Figure 2: Normalized mean spectrum of the wavelengths selected for the prediction of firmness (16 WL) and SSC (22 WL), highlighting representative peaks (within \(\pm\)0.1% of the interpolated spectral peak positions) and shared wavelengths.  
Vis/NIR spectroscopy can be effectively used to determine the SSC content in kiwifruit [20]. However, one of the main concerns regarding the performance and robustness of NIR models for fruits and vegetables is the influence of the \"richness\" of variation within the calibration sample, a topic that has been widely discussed [41]. This concern is further highlighted by the performance observed in our firmness prediction.
## 2 An Overview of the HJC Model
### Strength Equation  
The strength equation in the HJC model is shown in Figure 1, and its strength function is as follows:  
\[\sigma^{*} =[A(1-D)+BP^{*N}][1+\text{CIn}\ \overset{\bullet^{*}}{\varepsilon}] \tag{1}\]  
The normalized dimensionless equivalent stress is expressed as a function of damage, pressure and strain rate. In this equation, \(\sigma^{*}\) is defined as \(\sigma^{*}=\sigma/\!f_{c}^{*}\) (\(\sigma\) is the actual equivalent stress, \(f_{c}^{*}\) is quasi-static uniaxial compressive strength); \(P^{*}\) is defined as \(P^{*}=P/\!f_{c}^{*}\) (\(P\) is actual pressure); \(\overset{\bullet^{*}}{\varepsilon}\) defined as \(\overset{\bullet^{*}}{\varepsilon}=\overset{\bullet}{\varepsilon}/\varepsilon_ {0}\) (\(\overset{\bullet}{\varepsilon}\) is actual strain rate, \(\varepsilon_{0}\) is reference strain rate, \(\varepsilon_{0}\)\(=1.0\) s\({}^{-1}\)); \(A\) is the normalized cohesive strength; \(B\) is the normalized pressure hardening coefficient; \(C\) is the strain rate coefficient; and \(N\) is the pressure hardening exponent. Since \(\sigma^{*}\) increases as \(P^{*}\) increases, there is a limit value for \(\sigma^{*}\), defined as \(S_{\text{max}}\), which is the normalized maximum intensity.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Gradient Boosting Regressor  
GBRs are based on the boosting meta-algorithm, which yields an estimation model in the form of an ensemble of weak prediction models usually using decision trees [29]. GBRs construct an additive model in a stage-wise fashion, and it allows the optimization of arbitrary differentiable loss functions. To formulate the GBRs a tree ensemble model uses \(M\) additive functions to estimate the output.  
\[\hat{y}_{i}(\mathbf{x})=\sum_{m=1}^{M}f_{m}(\mathbf{x}_{i}),\;\;f_{m}\in \mathcal{F} \tag{4}\]  
where \(\mathcal{F}\) denotes the function space which includes the whole regression trees \(\mathcal{F}=\left\{f(\mathbf{x})=w_{q(\mathbf{x})},\;w\in\mathbb{R}^{T},\;\;q: \;\mathbb{R}^{d}\to T\right\}\). \(q\) denotes the structure of each tree that maps the corresponding leaf index. \(T\) denotes the number of leaves in the tree. Each \(f_{m}\) corresponds to an independent tree structure \(q\) and leaf weight \(w\). Unlike DTRs, each regression tree contains a continuous score on each leaf, and here, \(w_{j}\) represents the score on the \(j^{th}\) leaf. The leaf weight is calculated by minimizing the loss function:  
\[\mathcal{L}=\sum_{i}l(\hat{y}_{i},y_{i})+\frac{1}{2}\lambda\sum_{j=1}^{T}w_{j} ^{2}, \tag{5}\]  
where, \(l\) represents a differentiable loss function that measures the difference between the prediction \(\hat{y}_{i}\) and the target \(y_{i}\). \(\lambda\) denotes a regularization constant value to penalizes the complexity of the model, and the optimal \(w_{j}\) can be obtained using a second-order Taylor series approximation of Equation (6) [30].  
\[w_{j}=\frac{\sum_{i\in I}\frac{\partial l(y_{i},y_{i})}{\partial(\hat{y}_{i}- 0)}}{\sum_{i\in I}\left(\frac{\partial^{2}l(y_{i},y_{i})}{\partial(\hat{y}_{i} =0)^{2}}\right)+\lambda}, \tag{6}\]  
where \(I_{j}\) is a dataset contained at a leaf \(j\).  
In scikit-learning implementation, a GBR also has the same main hyperparameters as a DTR with the addition of _n_estimors_ and _learning_rate_ which may help the model shrink the contribution of each tree.
## 3 BER Considering Leaky Feeder Transmitter
### Calculation of BER of a QPSK Modulation Technique  
The attenuated and phase-rotated MPSK signal emanating from the \(n^{th}\) slot can be described as in (6) [9]. \[S\left(t\right)=\sqrt{\left(\frac{2\,E_{b}}{T}\right)}\,\cos\left(2\,\pi\,f_{c} \,t+\frac{\left(2\,m-1\right)\,\pi}{4}\right)10^{\frac{-Dnk}{\Delta\eta}}\,e^{ \frac{\left[2\,\pi\,\beta D\,\pi\,\right]}{\lambda}}, \tag{6}\]  
where \(m=1,2,3,\ldots M\) is the constellation number of the M-phase shift keying; \(\lambda\) is the wavelength and \(E_{b}\) is the energy per bit. For a single slot, \(E_{b}=\frac{A^{2}T_{b}}{2}\), where \(A\), \(T_{b}\) are the amplitude of the carrier and bit duration, respectively. Since the signal emitted from each slot has noise added and travels along a Rayleigh fading channel, it can be modeled as a MISO system as shown in Figure 2.  
Since the received signal in a multipath channel consists of a series of attenuated, time-delayed, and phase-shifted replicas of the transmitted signal, the channel can be modeled as shown in Figure 3. which is a complex impulse response in the time domain. Figure 1: Basic configuration of the leaky coaxial cable. Figure 2: Leaky feeder channel model. The base band impulse response can be written as [15]  
\[h\left(t,\tau\right)=\sum_{l=1}^{L}a_{l}\left(t\right)\,e^{j\theta_{l}\left(t \right)}\,\delta\left(\tau-\tau_{l}\left(t\right)\right), \tag{7}\]  
where \(a_{l}\left(t\right)\), \(\tau_{l}\left(t\right)\), and \(\theta_{l}\left(t\right)\) are random variables expressing indicate the amplitude, excess delay, and phase shift of multiple paths, respective to the reference path \(l\). In addition, \(L\) is the number of multipath components at the measurement instant. \(\delta(. )\) represents the Dirac delta function. The received signal is given by  
\[y\left(t\right)=\sum_{n=1}^{N}\sum_{l=1}^{L}S^{*}\left(t\right)a_{l}\left(t \right)\,e^{j\theta_{l}\left(t\right)}\,\delta\left(t-\tau_{l}\left(t\right) \right), \tag{8}\]  
where \(N\) is the number of equally spaced slots and \(*\) is the convolution sign. The theoretical BER of the QPSK modulation under an AWGN channel can be given by the following standard expression:  
\[P_{b}=Q\left(\sqrt{\left(\frac{2\,E_{b}}{N_{0}}\right)}\right). \tag{9}\]  
Similarly, the theoretical BER of the QPSK modulation over a Rayleigh fading channel for a single antenna is given by [12]  
\[P\left(E\right)=\frac{3}{4}\,\bigg{\{}\left(\left(1-\sqrt{\left(\frac{0.5\, \overline{\gamma_{t}}}{1+0.5\,\overline{\gamma_{t}}}\right)}\right)\,\left( \frac{4}{3\,\pi}\right)\right)\,\left(\left(\frac{\pi}{2}\right)+\arctan \left(\sqrt{\left(\frac{0.5\,\overline{\gamma_{t}}}{1+0.5\,\overline{\gamma_{t }}}\right)}\,\cot\left(\frac{\pi}{4}\right)\right)\right)\,\bigg{\}}. \tag{10}\]  
Figure 3: Simulation for a mathematical model of the multipath impulse response.
# Novel Polymer Sorbents with Imprinted Task-Specific Ionic Liquids for Metal Removal
## 2 Materials and Methods
### Apparatus  
The VBC synthesis, substitution reaction, and functionalization with the pyridine derivatives were confirmed via Raman and FTIR spectroscopy. A Raman spectroscopy analysis was carried out with the use of Renishaw's inVia Raman System. The Raman spectra of all materials were collected in the spectral range from 3200 to 200 cm\({}^{-1}\) with lasers emitting 633 nm and 785 nm. Raman map showing the distribution of VBC modified with Ox3.10 was recorded on a 200 \(\upmu\)m \(\times\) 200 \(\upmu\)m surface with a 10 \(\upmu\)m step size measurement. FT-IR analyses were carried out on a Vertex 70 Spectrometer (Bruker Optics FT-IR) in the range of IR 500-4000 cm\({}^{-1}\) with a resolution of 2 cm\({}^{-1}\). The elemental composition of fabricated sorbents was determined using the elemental analyzer FLASH EA 1112 Series of Thermo Electron Corporation.  
Thermogravimetric investigations were performed on a TG 209 F3 Tarsus analyzer (NETZSCH-Geratebau GmbH, Selb, Germany) in the temperature range of 30-600 \({}^{\circ}\)C. Approximately 10 mg of sample was placed in Al\({}_{2}\)O\({}_{3}\) crucible and was analyzed with a heating rate of 10 \({}^{\circ}\)C/min under nitrogen atmosphere (flow of purge gas 20 mL/min and protective gas 10 mL/min). A DSC analysis of 5-10 mg of the prepared material was performed on DSC1 (Mettler Toledo, Switzerland) in the temperature range 25-200 \({}^{\circ}\)C with a heating rate of 20 \({}^{\circ}\)C/min under argon atmosphere (20 mL/min). The heat of evaporation of residual solvents used in the synthesis was determined from the first run of DSC measurement.  
The atomic absorption spectrometer (AAS--ContrAA 300, Analytik Jena, Jena, Germany) was used for the measurement of metal ion concentrations in aqueous samples. Zeta potential measurements were conducted using a ZetaSizer Nano ZS (Malvern, UK) to study the surface charge changes of the final materials under different pH. The morphology of the sorbents was studied with an FEI Quanta 250 FEG scanning electron microscope. Images were taken for secondary electrons in the low vacuum mode at a pressure of 70 Pa. An accelerating voltage of 20 kV was used. The XPS analyses were used to confirm which moiety located on the sorbents surface was responsible for the metal adsorption. The analyses were carried out in a PHI VersaProbeII Scanning XPS system using monochromatic Al K\(\alpha\) (1486.6 eV) X-rays focused on a 100 \(\upmu\)m spot. The photoelectron take-off angle was 45\({}^{\circ}\), and the pass energy in the analyzer was set to 117.50 eV for survey scans and 46.95 eV to obtain high energy resolution spectra for the C 1s, N 1s, Cl 2p, Br 2p, and O 1s regions. A dual beam charge compensation with 7 eV Ar\({}^{+}\) ions and 1 eV electrons were used to maintain a constant sample surface potential regardless of the sample conductivity.
## 3 Results and Discussions
### End-to-End Delay Comparison  
The EMBLR routing scheme performs much better in EED as compared with other routing approaches, as shown in Figure 6. The EED includes the propagation delay between the source and the destination device, together with queuing delay for every intermediate device. The other two routing schemes forward the data packets through the long and congested route, which induces more EED. On the other hand, the EMBLR exploits the Backpressure algorithm with the queue length size of the intermediate device. It diverts the data packets flow from a high to a low congested device that leads to network load balance, and thereby minimizes the data transmission time between source and destination devices. Furthermore, the EMBLR selects the highly-reliable path, which keeps the route stable and reduces EED for data transmission by exploiting the HETX metric. Overall, it can be observed that EMBLR reduces the EED by approximately 18.78% and 12.83% as compared with the MP-OLSRv2 and MEQSA-OLSRv2 routing approaches, respectively, at 60 m/s device speed.  
Figure 5: Throughput vs. various device speeds.  
Figure 6: End-to-end delay vs. various device speeds.
## 3 Results
### Analysis of Pairwise HRV Synchronization between Group Members
The California group members' heart rhythms were significantly more synchronized with each other during the HLI period than before (\(p<0.05\)) and after (\(p<0.001\)) the HLI period, and the same pattern was found in the UK group, (\(p<0.01\) before), (\(p<0.001\) after), and the Saudi Arabia group (\(p<0.001\) before), (\(p<0.01\) after). In the Lithuania group, the members HRV rhythms were significantly more synchronized before (\(p<0.001\)) and during the HLI as compared to the period following it (\(p<0.001\)). In the New Zealand group, the heart rhythms among the group members was more synchronized during (\(p<0.001\)) and after the HLI (\(p<0.001\)) periods. Figure 5: The results of a repeated measures (within-subjects) ANOVA test assessing differences in the pairwise heart rhythm synchronization for all five groups during 15-min periods before (1), during (2) and after (3) using the Heart lock-In technique (* corresponds to \(p<0.05\), ** corresponds to \(p<0.001\), *** corresponds to \(p<0.0001\)). Note that in order to assess the pairwise heart rhythm synchronization between participants in one group, the Euclidean distance from the optimal time lag vectors of each participant to all other participants in the group was computed and mean Euclidean distance was calculated. The lower mean Euclidean distances correspond to the higher level of pairwise synchronization. It can be seen, for example, that the California’s group members’ heart rhythms were significantly more synchronized with each other during the HLI period than before and after the HLI period, etc.
## 3 Results and Discussion
### Adsorption Efficiency of LPD Biomass
#### 3.2.4 Effect of Initial Pb(II) Ion Concentrations  
The effects of initial Pb(II) concentrations on the bioadsorption are represented in Figure 4b. When raising the initial Pb(II) ion concentration from 10 to 550 mg/L, and keeping the other parameters constant (0.2 g/50 mL adsorbent dose, pH 6, temperature = 25 \({}^{\circ}\)C, and 150 rpm agitation speed), the adsorption capacity increased. This higher metal uptake may be attributed to a higher collision probability of the metal ions with the plant sorbent material. At the highest metal ion concentration (550 mg/L), the adsorption capacity became almost constant, indicating a saturated state due to the limited number of active adsorption sites on the sorbent. The observed result is in line with previous studies using mango kernel [35], _Eichhornia crassipes_[23], and _Olaander_ plant [36].
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
### Motivation  
Fine tuning involves adjusting the weights (model parameters) using a much smaller dataset, a set relevant to the specific domain. Although the dataset is comparatively smaller, fine tuning updates all the parameters across all the layers. Because large language models are enormous, updating all the weights becomes expensive and computationally intensive, and the model size grows. This requires a non-trivial amount of GPU memory, and, hence, the fine-tuning process incurs significant overhead. For example, consider the LLaMA3-8B model, Large Language Model Meta AI with 8 billion parameters [11]. During backpropagation, it learns the \(\Delta W\) matrix, which contains information on how much the original weight matrix (\(W\)) needs to be updated to minimize the loss function during training. The \(\Delta W\) matrix will have the same number of parameters (8 billion) as the original \(W\) matrix. As a result, computing the \(\Delta W\) matrix becomes memory intensive and computationally expensive.  
Also, as an entirely new model is required for every task, fine tuning becomes parameter inefficient [12]. Furthermore, fine-tuning LLMs also results in high CO\({}_{2}\) emissions (carbon footprints) [13]. With an increasing focus on environmental sustainability, using LLMs effectively and efficiently has become paramount. Several parametric-efficient fine-tuning (PEFT) methods have been proposed. These methods freeze the pretrained weights and train only a low number of additional parameters, called adapters. Such PEFT techniques allow the reuse of pretrained LLMs with reduced energy consumption, leaving behind minimal computational, resource, and carbon footprints. Additionally, they are helpful in scenarios where larger models must be fine tuned on devices with comparatively smaller GPU memories because of budget constraints and resource limitations. PEFT techniques, such as LoRA [14] and QLoRA [15], have been proposed that address these issues by significantly reducing the GPU memory requirements needed during fine tuning. These techniques present a promising alternative to the ICL and fine tuning. They adapt to and store only a low number of task-specific parameters, considerably boosting operational efficiency when deployed.
## 4 KGs Evaluation
### Extrinsic Evaluation  
#### 4.2.1 What Is an Extrinsic Evaluation?  
Extrinsic evaluation goes beyond assessing a model's internal consistency or predictive accuracy by quantifying its practical value in real-world application environments. This evaluation framework examines how effectively a knowledge graph (KG) model performs in operational settings, considering factors such as processing speed, usability of the outputs, and performance improvements over existing systems when integrated into tasks like question answering, recommendation, ranking, legal judgment prediction, medical diagnosis, code mapping, and more.  
For example, in the public transportation domain, extrinsic evaluation may involve comparing a CSV-based mapping tool with systems like Morph-KGC and SPARQL Anything using GTFS data, to verify whether they produce identical results. In question-answering tasks, the integration of large-scale language models with KGs has been shown to enhance metrics such as Mean Average Precision and Mean Reciprocal Rank, demonstrating the model's ability to rank and retrieve correct answers effectively.  
In medical applications, studies leveraging datasets such as MIMIC-III and MIMIC-IV have shown that patient similarity search models can achieve high accuracy and F1-Scores, while in multilingual entity alignment, improvements are quantified using datasets like DBP15K. Similarly, in the legal domain, judgment prediction models based on datasetslike ECHR and CAIL2018 have attained superior Accuracy and AUC-ROC compared to traditional legal AI systems. Other studies in biomedical, agricultural, and railway defect detection applications have demonstrated tangible performance benefits--such as improved link prediction accuracy and reduced query response times--when KG models are applied in real-world scenarios.  
Overall, extrinsic evaluation provides an objective measure of how well a KG model performs in practical settings, highlighting its benefits in operational environments and its advantages over conventional methods through measurable improvements in various application-specific metrics.  
Knowledge graphs constructed or refined using LLM-based systems can exhibit hallucinated relationships that degrade the overall performance. For instance, a clinical decision-support application may rely on KG-based inferences only to discover that certain predictive links between symptoms and diagnoses are fabricated by the generative model. Extrinsic evaluation thus benefits from dedicated error-detection strategies--such as outlier analysis or correlation checks with trusted medical databases--to ensure that the discovered relationships are both accurate and clinically valid. By incorporating such validation layers, practitioners can reduce erroneous recommendations and maintain higher reliability in domains where trust is essential.
###### Abstract  
To investigate the ground deformation induced by the variation of excess/insufficient support pressure in water-rich sandy soil, a series of centrifuge tests in tunnels located at cover over diameter (C/D) ratios equal to 0.5, 1.0 and 1.5 were carried out. The soil pressure, pore pressure, maximum surface displacement and the ground movement induced by reduced/increased support pressure were investigated. The results show that when the burial depth is large (\(\geq\)1.5 D), the ground deformation caused by the support pressure is difficult to extend to the surface. When the burial depth is shallow (<1.5 D), the ground disturbance range induced by excess support pressure is approximately fishtail-shaped while it is approximately chimney-shaped induced by insufficient support pressure. For the former, the surface disturbance range can extend horizontally to about twice the buried depth beyond the front of the face and the maximum heave occurred at a distance of approximately buried depth beyond the front of the excavation face. For the latter, the surface disturbance range can extend horizontally to about buried depth beyond the front of the face which was larger than that in dry sandy soil. Based on the centrifuge test, the ground deformation mechanism was analyzed compared with previous studies and a supporting pressure control coefficient was proposed to conduct quantitative analysis of surface displacement induced by support pressure. Finally, the control of support pressure based on the surface displacement was discussed to provide some advice for practical engineering.  
Keywords:excess/insufficient support pressure; water-rich sand; centrifugal model tests; ground deformation +
Footnote †: journal: Applied Sciences