esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Primary Part: Individual Patient-Level Data for Patients Initially Diagnosed with Stage I-III NSCLC
#### 3.1.3 Initial Treatments

Table 2: _Cont._Stage I (7.0%; 8/115) and Stage II patients (37.8%; 37/98). Conversely, ST rate only partially reflects adjuvant ST in Stage III patients (17.0%; 18/106) since 54.7% of Stage III patients were unresected and treated with ST \(\pm\) RT. More than a third of Stage III patients (35.8%) were treated with RT (\(\pm\)ST/surgery), which was CRT in 81.6% (31/38) of cases (Table 2). Among treated patients in the Stage I, II and III subpopulations, 7.0% (8/114), 44.2% (42/95), 61.2% (60/98) were managed with a multimodal approach. Most common management strategies (frequency \(\geq\) 10.0%) were:'surgery-alone' in 75.4% (86/114) and 'RT-alone' in 16.7% (19/114) of the treated Stage I;'surgery-alone' in 43.2% (41/95), and'surgery + ST' in 38.9% (37/95) of treated Stage II; 'ST + RT' in 30.6% (30/98), 'ST-alone' in 28.6% (28/98), and'surgery + ST' in 24.5% (24/98) of treated Stage III patients. Treatment sequences, including ST pharmacologic categories and drug classes, for each disease stage are depicted in Figure 2.  
Platinum-based ChT was the most frequent ST administered across stages, while ICI and TT was reported for 28.1% and 0.7%, respectively, the latter two mainly driven by the Stage III subpopulation (Table 2). Unresected Stage III patients were treated with  
Figure 2: Results based on individual patient-level data for patients initially diagnosed with stage I-III NSCLC: combinations of treatments received as part of initial therapeutic strategy for early-stage NSCLC, per disease stage at initial diagnosis. For each NSCLC stage, inner pie chart shows modality (i.e., higher level) and outer pie chart shows pharmacologic category and drug class (i.e., lower level). \({}^{\dagger}\) Recorded as neoadjuvant ST but did not undergo surgery. \({}^{\ddagger}\) One Stage III patient received ChT spanning both the pre- and post-operative periods, thus ST was included as adjuvant therapy as well. Abbreviations: ALKi, Anaplastic Lymphoma Kinase inhibitor; aPD-1, anti-Programmed Cell Death 1; aPD-L1, anti-Programmed Death Ligand 1; ChT, Chemotherapy; ICI, Immune Checkpoint Inhibitor; N, number of patients with available data; NSCLC, Non-Small Cell Lung Cancer; PDC, Platinum Double Chemotherapy; RT, Radiotherapy; SA, single-agent; ST, Systemic Therapy, SUR, Surgery. ChT + RT (43.3%; 26/60), ICI \(\pm\) ChT (\(\pm\)RT; 35.0%; 21/60), ChT-alone (18.3%; 11/60), or RT-alone (3.3%; 2/60); ChT + RT was followed by ICI (anti-PD-L1) in 16.7% (10/60). ST among resected Stage III patients comprised ChT in 57.9% (22/38), ICI \(\pm\) ChT in 15.8% (6/38), and TT in one patient (2.6%) (Figure 2). Further details on surgical therapies, reasons for not performing surgery and not administering ST as well as timing of therapy are provided in Table 2.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## 4 Case Studies
### Deformation Prediction for the CNC Milling Machine Bed  
A CNC milling machine is mainly composed of a bed, column, slider, and toolbox among other components. The column and slider, under static conditions, exert a large force on the bed, which is expressed by the red arrows in Figure 7. When the milling machine is being operated, the bed is also affected by the milling impact from the toolbox. Since the deformation has a great influence on the milling precision, the design of the milling machine bed needs good resistance to the deformation; thus, it is very important to accurately predict the deformation during design.  
As the milling force is small, we only considered the column and slider weights applied to the milling machine bed, and we predicted the static deformation. The simplified structure of the bed is mainly controlled by eight variables, which are shown in Figure 8. The variables' design space is set as \(x_{1}\in\) [40,60], \(x_{2}\in\) [40,60], \(x_{3}\in\) [50,80], \(x_{4}\in\) [40,60], \(x_{5}\in\) [20,40], \(x_{6}\in\) [20,45], \(x_{7}\in\) [15,30], \(x_{8}\in\) [50,80], and \(x_{9}\in\) [40,60]; all the variables are in millimeters. The force of the beam is 56.5 kN and that of the slider is 23.68 kN. The slider is positioned at the initial position of the bed. FEA simulations were  
Figure 5: Boxplots of R\({}^{2}\) for the six numerical examples.  
Figure 6: Comparison of computational time of ensemble models.  
From Table 4, RLS-EM has the best RMSE and R\({}^{2}\) values; further, it has the second-best AAE value. The performance of BP is better than that of KRG, SVR, and other ensembles in AAE; however, the performances of RMSEs of OWS, NPWS, and OWS are better than those of the individual surrogate models. The results reveal that because the linear or nonlinear relationships inside are unknown, when encountering a black-box engineering problem, using an individual surrogate model to approximate the relationship between the design variables and the responses may yield inaccurate results. However, the inaccuracies of each individual surrogate model do not considerably affect the approximate accuracy of RLS-EM; the RLS-EM performs well for the deformation prediction of the milling machine bed. When all the samples are obtained by a time-consuming FEA analysis process, there is no significant increase in the amount of computation caused by the search of the regularization parameter compared to the time-consuming error metrics used in other ensemble methods. Therefore, RLS-EM is an effective engineering problem modeling method that effectively improves the computational efficiency while keeping the modeling accuracy.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on Improved CNN
#### 3.1.4 CNN Parameter Adjustment and Optimization  
Despite the general ability of CNNs to self-learn and share weights whilst adopting sparse connections, there exist certain shortcomings, such as long training time, low accuracy, low generalization ability, and high overfitting tendency, which must be addressed. To this end, further adjustment and optimization of CNN parameters were performed in this study. The proposed optimized network was trained using the ship dataset to ensure better classification performance of the proposed CNN network. The following points concerning the proposed CNN must be noted.  
Weight decay: Addition of a regularization term after the cross entropy loss function used in this paper served to reduce overfitting of the CNN model to some extent. The said regularization term contains a weight-decay coefficient, the value of which was set as 0.0005.  
The learning rate affects the convergence speed and network-training performance. The learning rate was set as 0.001 in accordance with the stochastic gradient descent algorithm (SGD) [35], and the momentum parameter was set as 0.9.  
The batch size was set to 32 with the maximum number of iterations within the self-built and VAIS datasets equal to 15,500 and 3700, respectively.
###### Abstract  
This work presents in vitro and in vivo aroma release analysis of three espressso coffees carried out by PTR-ToF-MS headspace and nosepace analysis, respectively. The products were _C. arabica_ coffees prepared using an espressso coffee machine: a low-caffeine _C. arabica_ var. _laurina_ light roast, a low-caffeine _C. arabica_ var. _laurina_ dark roast, and a single-origin coffee from Ethiopia which were roasted to a medium roast degree. Headspace analysis allowed for discrimination between coffees with a prediction accuracy of 92% or higher. Relevant discriminating compounds were related to the roasting degree and varietal compounds. Coffee nosepace consisted of 35 mass peaks overall. Despite this relatively low number of detected peaks, coffee discrimination was still possible with \(\geq\)93% accuracy. The compounds most relevant to the discrimination were those related to the roasting degree. Major differences--both qualitative and quantitative--were found between headspace and nosepace profiles.  
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Footnote †: journal:  
of inter-individual differences in coffee aroma release [9], and the correlation between time-resolved aroma release and retronasal perception [10].  
Untargeted analysis by means of PTR-ToF-MS has been carried out successfully with the aim of differentiating coffees based on their headspace composition. For example, it was possible to discriminate between Arabica and Robusta [11] or among different Robusta blends based on their geographical origin [12; 13].  
The aim of this study was to verify whether it was possible to discriminate espresso coffees based on their nosespace profiles. The same products were also analyzed by means of headspace analysis. This allowed performing a thorough comparison between in vivo and in vitro aroma release. Two Arabica espresso coffees, naturally low in caffeine, and a single-origin Ethiopian coffee were analyzed. PTR-ToF-MS was capable of characterizing both in vivo and in vitro release, highlighting key differences between the three espresso coffees. However, the key differentiating aroma compounds and the composition of the respective release profiles significantly differ between in vitro and in vivo.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 4 Mechanism of Grouting Properties of Alluvial Filler Soil  
The alluvial filler soil is composed of loose sediments deposited by flowing water. Hence, the main mechanism of grouting properties of alluvial filler soil involves:  
(1) Filling voids: Alluvial filler soil often contains voids and gaps between the particles, making it less stable and prone to settlement. Grouting involves injecting a cementitious or chemical grout into these voids, which then fills the gaps and displaces the water present, effectively increasing the overall density and reducing permeability.  
(2) Binding particles: The injected grout acts as a binding agent that bonds the soil particles together. As the grout cures and hardens, it forms a solid matrix, reinforcing the soil and increasing its strength.  
(3) Increasing strength: The grout not only fills the voids but also contributes to the overall strength of the alluvial filler soil. The grout forms a cohesive mass that provides additional load-bearing capacity to the soil, reducing the risk of settlement or collapse.  
(4) Improving permeability: Alluvial filler soil typically has high permeability, which can lead to seepage or instability issues. Grouting can reduce the permeability of the soil. by filling voids and creating a more compact structure.  
(5) Stabilizing soil: By binding the particles together and increasing strength, grouting stabilizes the alluvial filler soil. This stabilization prevents soil erosion, slope failures, and other geotechnical problems, ensuring the overall safety and durability of the construction.  
(6) Reducing settlement: Alluvial filler soil can experience settlement when subjected to heavy loads or vibrations. Grouting mitigates settlement issues by improving the soil's load-bearing capacity and reducing compressibility.  
Overall, grouting can significantly enhance the properties of alluvial filler soil, providing numerous benefits for various engineering applications.  
Figure 6: Curves of second grouting pressure and grouting volume with time.
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 3 Results
### Validation of SAM Segmentation Accuracy
#### 3.3.5 Hojiblanca--Ellipse-Fitting Error Analysis (Automatic vs. Manual Ground Truth)  
The results for the Hojiblanca variety are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Mean \(\pm\) SD** & **Interpretation** \\ \hline IoU\({}_{\rm ellipse}\) & 0.938(11) & Excellent spatial overlap \\ MAE\({}_{\rm a}\) (px) & 2.580 & \(\sim\)0.3\% error in major semi-axis \\ MAE\({}_{\rm b}\) (px) & 2.970 & \(\sim\)0.4\% error in minor semi-axis \\ MAE\({}_{\rm AR}\) & 0.034 & Aspect-ratio bias \(<\) 4\% \\ RMSE\({}_{\rm 0^{\circ}}\) & 5.150 & Small angular deviation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation metrics for the Gordal dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Mean \(\pm\) SD** & **Median (IQR)** & **Interpretation** \\ \hline IoU & 0.904 \(\pm\) 0.004 & 0.904 (0.0047) & Excellent spatial overlap \\ Dice & 0.950 \(\pm\) 0.002 & 0.950 (0.0026) & Confirms IoU result \\ Precision & 0.904 \(\pm\) 0.004 & 0.904 (0.0047) & \(\sim\)9\% over-segmentation \\ Recall & 1.000 \(\pm\) 0.000 & 1.000 (0.0000) & No false negatives \\ \hline MAE (px\({}^{2}\)) & 246.5 & – & Small absolute size error \\ MAPE (\%) & 7.58\% & – & Size bias \(<\) 10\% \\ RMSE (px\({}^{2}\)) & 246.7 & – & Consistent with MAE \\ Pearson \(r\) (area) & \(>\)0.98 & – & Preserved rank/order \\ \(R_{y=x}^{2}\) & 0.039 & – & Shows uniform +7\% bias \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation metrics for the Hojiblanca subset (n = 9150).  
The automatically generated ellipse reproduces the manual geometry almost perfectly: sub-pixel errors on both semi-axes (\(\leq\)1 px) and an IoU of approximately 0.95 confirm the accuracy of the fitting procedure.
## 3 Empirical Results and Analysis
### Establishment of Prediction Equations of Peak Ground Motion
#### 3.1.3 Comparison between PGA and EPA  
EPA represents the average value of ground motion in a frequency range, while PGA only represents the value at a specific frequency point, which is the maximum absolute value of the ground acceleration time history. Owing to the difference in the physical interpretation of PGA and EPA, their attenuation characteristics also differ. Figure 4a displays the distribution of the PGA-to-EPA ratio, and Figure 4 compares the attenuation curves of PGA and EPA. From Figure 4a, it can be observed that the majority of station records have higher PGA values compared to EPA values. The average ratio of horizontal PGA to horizontal EPA was approximately 1.476, with a range of 0.76 to 3.22. The average ratio of vertical PGA to vertical EPA was roughly 1.543, with a range of 0.88 to 4.268. Figure 4b reveals that PGA has a slightly faster attenuation rate compared to EPA, which is evident in both horizontal and vertical directions. Near the fault (with a fault distance smaller than 10 km), the horizontal EPA and vertical PGA values were very close. As the fault distance exceeded 100 km, the attenuation curves of the vertical PGA and vertical EPA became increasingly similar.
## 3 Results and Discussion  
### Infrared Analysis  
A sharp (C=N) band of the azomethine group was observed 1602 cm\({}^{-1}\) in the free ligand [12]. After complexation, the vibration band of the azomethine group was decreased in intensity and shifted toward a lower frequency about 16-60 cm\({}^{-1}\) in the metal complexes, except the Cu(II) complex that has low intensity band and shifted toward higher frequency about 2 cm\({}^{-1}\). This shift or disappearance of the peaks support complex formations [26]. The observed sharp band of the pyrazine ring (C=N) in the ligand at 1561 cm\({}^{-1}\) was shifted more than 5 cm\({}^{-1}\) in the metal complexes, except the Cu(II), Table S1. This indicates that the pyrazine ring is involved in coordinating to the metal ions [12]. New broadband in three metal complexes Cu(II), Mn(II) and Ni(II) at 3334, 3315 and 3457 cm\({}^{-1}\) could assign to \(\
u\)(H\({}_{2}\)O) [22].  
A new band of \(\
u\)(M-O) band have appeared in the wavenumber range 522-575 cm\({}^{-1}\) and \(\
u\)(M-N) band in range 424-444 cm\({}^{-1}\)[22]. Figure S1 showed the comparison of the IR spectra of the free ligand and the metal complexes.  
\({}^{1}\)H-NMR Spectra for Ligand and Zn(II) Complex  
The \({}^{1}\)HNNMR spectrum of the Schiff base has discussed in detail in our previous work [12]. The signal at 12.38 ppm in the free ligand indicated the presence of phenolic-OH proton has disappeared in the Zn(II) complex spectrum and confirmed the coordination by phenolic oxygen to the zinc ion. In addition, the Schiff base shows another signal at 9.52 ppm which can be ascribed to the azomethine proton (-CH=N). This signal was shifted to 10.22 ppm after complexing to Zn(II) ion and reveals the involvement of azomethine nitrogen in the coordination of the zinc complex [12]. The \({}^{1}\)H NMR data for the ligand and Zn(II) are shown in Figure S2 and summarized in Table S2.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Factors Influencing Rubber Toughening
#### 2.2.2 Rubber Particle Size  
Within a certain size range, smaller rubber particles often exhibit better dispersion properties and are more effective at initiating silver lines and shear yielding, leading to improved toughening performance. However, smaller particles are not always better. [PERSON] et al. [31] investigated the effect of particle size on the impact toughness of piperidine-cured epoxy resins. Their study found that the particle size around the rubber layer needs to be greater than 0.35 \(\upmu\)m; reducing the particle size around the rubber layer results in decreased fracture toughness.  
[PERSON] et al. [32] incorporated rubber particles of various sizes and hollow latex particles into DGEBA epoxy resins. Their research indicated that within the range of 0.2 to 40 \(\upmu\)m, smaller particles generally result in higher fracture toughness. [PERSON] et al. [33] suggested that the optimal rubber particle size for toughening depends on the properties of the epoxy resin but typically falls within the range of 0.1 to 5 \(\upmu\)m. Particles smaller than 0.1 \(\upmu\)m are too small to effectively cavitate and thus do not contribute to the toughening process. Particles larger than 5 \(\upmu\)m primarily act as bridging particles, as they do not interact with the stress field at the crack tip.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 5 Simulation Results
### Average Sum Rate According to the Number of Transmit Antennas  
Figures 13 and 14 show the average sum rate according to the number of transmit antennas comparing the conventional hybrid beamforming system and the proposed hybrid beamforming system. Also, Figures 13 and 14 show the average sum rate of hybrid beamforming system forming 4 and 16 beam patterns. Additionally, Figure 13 shows the simulation results at SNR of 8 dB and Figure 14 shows the simulation results at SNR of 20 dB. As the results in Figures 13 and 14, the average sum rate of the conventional hybrid beamforming system is high when the number of transmit antennas increases. However, the increase rate of average sum rate is very low according to the number of transmit antennas. On the other hand, the average sum rate of proposed hybrid beamforming system is high when the number of transmit antennas increases and the increase rate of average sum rate is high according to the number of transmit antennas. It means that the conventional hybrid beamforming system has low antenna gain when the number of transmit antennas increases. However, the proposed hybrid beamforming system has high antenna gain when the number of transmit antennas increases. In addition, the simulation results can be seen that proposed hybrid beamforming has better average  
Figure 11: The average sum rate of the hybrid beamforming schemes, \(N_{t}=16\).  
Figure 12: The average sum rate of the hybrid beamforming schemes, \(N_{t}=32\).  
sum rate than the conventional hybrid beamforming system when transmitter sends the data stream using the same number of beam patterns.
## 6 Results
### Experimental Results
#### 6.2.3 Comparison of VANET Communication Link  
After completing the establishment of the cluster and the election of the CH, the communication link is established on the cluster obtained in Section 5.1 using Algorithm 2. For comparison, the VANET link of TDCR was also established. Table 5 shows a comparison of 100 groups of data obtained by the test.  
Notes: In each test, each CH generates a VANET communication link to the RSU. The total number of hops in a single test is the sum of the hops of all links in a single test. The maximum number of hops in a single test refers to the number of hops of the longest link in a single test. The average number of hops in a single test is the total number of hops in a single test divided by the number of CHs.  
Since the number of clusters in CTDHR is smaller than TDCR, its total hop count is also smaller than TDCR in the test. In Algorithm 2, when the VANET link capacity cannot meet the node's demand, the node will look for other VANET paths. The newly identified path is no longer the shortest, so CTDHR is larger than TDCR in terms of the average hop count. However, due to the small number of clusters in CTDHR, the maximum number of hops of a single link in CTDHR is still smaller than that in TDCR. Thus, the maximum number of 'forwarding' in CTDHR is less than TDHR for packets sent via VANETs when  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Framework** & **Average Value** & **Max Value** & **Min Value** & **Median Value** \\ \hline Total number of & CTDHR & 30.75 & 40.00 & 23.00 & 30.00 \\ hops in a single test & TDCR & 47.88 & 61.00 & 40.00 & 47.00 \\ \hline Maximum number of & CTDHR & 3.80 & 5.00 & 3.00 & 4.00 \\ hops in a single test & TDCR & 4.36 & 6.00 & 3.00 & 4.00 \\ \hline Average number of & CTDHR & 2.21 & 2.92 & 1.87 & 2.17 \\ hops in a single test & TDCR & 1.54 & 2.00 & 1.15 & 1.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics on the stability of clusters and the number of clusters for CTDHR and TDCR.  
another condition is the same. Therefore, CTDHR generates less forwarding delay and can better satisfy the QoS of data packets.
## 3 Losnus
### Basic Principle of LOSNUS  
The basic system construction is shown in Figure 1. Multiple active transmitting beacons are fixed at well-defined room positions.  
The locating process of _LOSNUS_ is started by a well-defined sequence of US signal frames shown in Figure 2a. An activation unit sequentially activates each transmitter and emits at frame consisting of an optional lead-in, a linear frequency modulated chirp and a transmitter coding field shown in Figure 2b. An optional starting sequence can be used for low power systems allowing remote wakeup of receiver units. Transmitting delays are chosen such that LOS signals of consecutive transmitters do not overlap at the receivers. In case of over-lapping of LOS and NLOS signals, perfect decoding of the larger LOS signal is common [19]. Binary cross-correlation [20, 21] of the received frame with the reference signal delivers high resolution ToA measurements. Once the start of the frame is known, the transmitter coding field can be easily analyzed. Transmitters are encoded using a fixed-frequency coding scheme where the number of transmitters is scalable by the number of frequency hops and slots used. Therefore, the concept is comparable to an N-FSK. Both signal processing and transmitter identifications are explained in more detail in Sections 3.3 and 3.4.  
The process described above delivers numerous pairs of ToA and transmitter IDs where always the first pair is selected corresponding to the LoS signal. The coordinate positions are then calculated using multilateration.  
Figure 1: Basic localization of sensor nodes by Ultra Sound (_LOSNUS_) system architecture.