esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Model Parameter Setups  
MergeKit [22] is an open-source model fusion toolkit that offers a comprehensive set of APIs and strategies for integrating pretrained language models. The toolkit supports a variety of fusion techniques, including, but not limited to, weighted averaging, voting mechanisms, and advanced parameter optimization strategies. With MergeKit, model fusion can be efficiently performed, and parameters can be automatically adjusted during the fusion process. This paper employs methods integrated into MergeKit, such as Slerp [30] and TIES [13], for model fusion. The GPUs employed in the experiment are two NVIDIA GeForce RTX 4090 units, and the deep learning framework used is PyTorch v2.3.2 [31].  
The base model consists of llama-3-chinese-8b-instruct-v3 and llama-3-8b-Instruct with the total number of training rounds set to 50, the initial learning rate of Q-learning is set to 0.01, the discount factor is set to 0.9, the exploration rate is set to 0.8, and the rate of exploration rate is set to decay by 5% every 5 epochs. For the initial fusion method, Slerp [30] was chosen, and the initial interpolation factors were set to [[0, 0.5, 0.3, 0.7, 1], [1, 0.5, 0.7, 0.3, 0]] These factors define the interpolation ratios between the two models at different layers, controlling how much each model contributes to the final fused model at various points in the interpolation process. For example, t = 0.5 means an equal contribution from both models, while t = 0.3 and t = 0.7 represent skewed contributions. Additionally, the initial model weights in the TIES [13] method were set to [0.5, 0.5], which define the contribution of each model to the final merged model. These weights ensure a balanced integration of the models during the fusion process.
## 2 Materials and Methods
### Chondrogenic Pellet Cultures  
For chondrogenic pellet cultures, MSC (\(n=2\); 23- and 31-year-old) were seeded at a density of 3000 cells/cm\({}^{2}\) in T-75 culture flasks (IWAKI, Japan) with \(\alpha\)MEM containing 20% FBS, 1% P/S. Upon 70-80% confluency, MSC were harvested and aliquoted at 2.5 \(\times\) 10\({}^{5}\) cells per 15 mL polypropylene falcon tubes (Corning Inc., Midland, NC, USA). Chondrogenic pellet culture was initiated by spinning down the tubes at 500 g for 5 min. The resulting cellular pellet was cultured for 1 day at 21% O\({}_{2}\) at 37 \({}^{\circ}\)C. The next day, the medium was replaced by differentiation media as previously described. Based on the alcian blue assay the growth factor combinations; GDF5/TGF-\(\beta\)1, TGF-\(\beta\)2/Wnt3a, TGF-\(\beta\)1/Wnt3a, TGF-\(\beta\)1/Wnt3a/GdP6, GDF6/Wnt3a were applied. As a negative control, unstimulated MSC were identically cultured but were not stimulated with growth factors. The pellets were incubated with media replenishment thrice a week, at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\) and hypoxic conditions of 2% O\({}_{2}\). Additionally, human NP cells obtained from surgical waste material were similarly cultured in differentiation media without growth factors (\(n=3\)).
## 3 Results and Discussion  
### Solvent Selection for Can Extraction  
Different solvents were tested with the aim to extract compounds with different polarities present in the coating of metal cans. Samples were extracted under different conditions, both methanol and acetonitrile for 24 h at 70 \({}^{\circ}\)C, hexane for 4 h at 60 \({}^{\circ}\)C, and a mixture of hexane and ethanol (3:1 \(v/v\)) for 24 h at 20 \({}^{\circ}\)C. In Figure 1, chromatograms obtained after extraction with different solvents are shown. Methanol was the solvent selected for extraction because more peaks were detected and identified. Table 2 lists the compounds detected after extraction with different solvents. As the analyzed samples were already in contact with the drink, some of the identified compounds may have their origin in food. On the other hand, it is interesting to note that flavorings are commonly used in these beverages, thus some of the detected compounds in the samples are authorized as food flavorings in the European Union [20]. These compounds are indicated in the table with their corresponding Flavis Number (FL No.). Some of them are, for example, benzoic acid methyl ester and caprylic acid methyl ester.  
Figure 1: Chromatograms of sample BC04 extracted with different solvents.  
Ester compounds were mainly identified in extraction with methanol as a solvent. Some studies show the migration of these types of compounds in cured varnishes used in food packaging [21]. In this work, samples were extracted with ethanol 95% (_v/v_). Adipic acid has been reported as a chemical intermediate used in the manufacturing of polyurethane resins [22].
# Study on the Detection Mechanism of Multi-Class Foreign Fiber under Semi-Supervised Learning
## 3 Results
### Comparative Experiment  
To further verify the detection performance of the improved model, Efficient YOLOv5-cotton, in foreign fiber detection, we conduct comparative experiments with YOLOv3 [28], YOLOv5 [8], YOLOv7 [29], Faster-RCNN [30], and DETR [31]. We also perform experiments under two sizes of the training set to better reflect the effectiveness of the semi-supervised foreign fiber detection algorithm in this paper: (1) The same 10% foreign fiber training set (336 labeled images) used in the Efficient YOLOv5-cotton experiment; (2) The entire foreign fiber training set (3360 labeled images). Similarly, we used multiple indicators for comparative evaluation, including [EMAIL_ADDRESS] (mean average precision), parameter quantity, precision, and recall. The results of the comparative experiment are shown in Table 4.  
It is not difficult to see that the model size of the Efficient YOLOv5-cotton trained in this paper is only 3.08 M, less than 8.6% of the model sizes of YOLOv5n trained on the 10% training set. There is a significant reduction compared to the model sizes of YOLOv3-tiny, YOLOv7, Faster RCNN, and DETR trained on the 10% training set. Moreover, the reduction in model size does not lead to a decrease in the mean average precision. Compared to the YOLOv3-tiny, YOLOv7, and Faster-RCNN models trained on the entire training set, the [EMAIL_ADDRESS] of Efficient YOLOv5-cotton increases by 7.7%, 0.6%, and 5.3%, respectively. Even on the entire dataset, Efficient YOLOv5-cotton achieves the same [EMAIL_ADDRESS] as DETR. However, it is slightly inferior to the [EMAIL_ADDRESS] of YOLOv5n on the entire training set, but with a 51% reduction in the model size. On the same 10% labeled training set, our proposed method could surpass YOLOv3-tiny, YOLOv5n, YOLOv7, Faster-RCNN, and DETR in terms of [EMAIL_ADDRESS] and model size. The model in this paper reduces the model size while taking into account the detection accuracy, and its detection accuracy is better than that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Network Model** & **[EMAIL_ADDRESS] (\%)** & **Params (M)** \\ \hline \multirow{4}{*}{Train on 336 labeled pictures} & YOLOv3-tiny & 79.4 & 16.63 \\  & YOLOv5n & 94.4 & 3.37 \\  & YOLOv7 & 76.4 & 71.34 \\  & Faster-RCNN & 77.6 & 108.12 \\  & DETR & 95.1 & 473.95 \\ \hline \multirow{4}{*}{Train on 336 labeled pictures} & YOLOv3-tiny & 89.3 & 16.63 \\  & YOLOv5n & 97.2 & 6.31 \\  & YOLOv7 & 96.4 & 71.34 \\  & Faster-RCNN & 91.3 & 108.12 \\  & DETR & 97.0 & 473.95 \\ \hline \multirow{4}{*}{Train on 336 labeled pictures and 3024 unlabeled images} & Efficient & 97.0 & 3.08 \\  & YOLOv5-cotton & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of comparative experiments.  
of YOLOv3-tiny, YOLOv7, Faster-RCNN, and DETR. With a model size of only 3.08 M, it will be effective when deployed on mobile or embedded devices. The overall performance has obvious advantages compared to YOLOv3-tiny, YOLOv5, YOLOv7, Faster-RCNN, and DETR.
## Results  
### Measurement Reliability of the Telemedicine System  
Before addressing our main goal, i.e., that of assessing the reliability of human DVP measurements in general, we determined the repeatability of measurements made by our telemedicine system. Measurement error was assessed by detecting stable artificial signals generated by a pulse oximeter simulator (Figure 3). The overall measurement error of the telemedical system may be produced by the data analyzing algorithm, the measurement error of a single pulse oximeter, or the variability due to using different pulse oximeter devices to detect the pulse signals. Firstly, in order to assess the combined contribution of the algorithm and the error of a single pulse oximeter to the overall measurement error, we detected the normal pulse signals of the simulator with a single, randomly chosen pulse oximeter and repeated it five times (Table 1; Normal condition, 1 st column). The results showed that the measurement was stable: the confidence intervals (CIs) were very close to the mean of the five measurements, and the coefficient of variation was below 1% for each calculated variable.  
Then, we randomly chose four other pulse oximeters of the same release, repeated the measurements as described above, and averaged the results of the 25 measurements. These showed that the output data had low variability as evidenced by narrow CIs and small (lower than 1%) CVs for each parameter (Table 1; Normal condition, 2 nd column).  
After proving that our system detects and analyzes normal pulse signals reliably, we repeated the measurements described above with signal presets of the simulator, which simulate abnormal conditions. For this purpose, we used the Abnormal 1 and the Abnormal 2 presets (Figure 3). The former preset of the simulator generates a signal with high heart rate (95/min). In this setting, the reliability of pulse detection and analysis was similar to that of the Normal condition except for the calculation of the aging index and d/a parameter, as the second derivative of this preset has no detectable c and d points (Table 1; Abnormal 1 condition).  
The Abnormal 2 signal preset mimics a condition where the signal is of low intensity (a typical source of error in DVP detection). Similar to what we observed with the Abnormal 1 signals, the results of these measurements also showed stable detection and analysis for most parameters, except for the aging index and the d/a ratio--for the same reasons as in Abnormal 1 (Table 1; Abnormal 2 condition).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**n = 5**} & \multicolumn{3}{c}{**Neemal**} & \multicolumn{3}{c}{**n = 5**} & \multicolumn{3}{c}{**Abnormal 1**} & \multicolumn{3}{c}{**n = 5**} \\  & **Mean [C]** & **CV/S** & **Mean [C]** & **CV/S** & **Mean [C]** & **CV/S** & **
# The Impact of Noise on Learning in Children and Adolescents: A Meta-Analysis
## 3 Results
### Qualitative Synthesis
#### 3.1.1 Impact of Classroom Noise  
The impact of classroom noise on students' cognitive and academic performance is significant and multifaceted. Studies indicate that classroom noise affects various cognitive functions and academic outcomes. For instance, ref. [19] found that classroom noise significantly impacts students' reading fluency. Similarly, ref. [20] observed that noise affects sentence comprehension, with variations depending on children's selective attention. Children with low selective attention exhibited a significant increase in response time under noisy conditions.  
The authors of [15] investigated the effects of classroom noise on writing, arithmetic, and reading skills. Their findings showed moderate effects favoring the experimental group exposed to noise in most assessments. In addition, ref. [14] found that noise had a significant impact on nonword repetition accuracy and verbal processing time. They observed reduced accuracy and increased processing times under noisy conditions.  
In their report, the authors of [16] highlighted improvements in listening comprehension, nonverbal processing, and spelling with classroom sound field systems, implying that well-managed classroom acoustics can mitigate some of the negative effects of noise. Similarly, [17] found high aircraft noise exposure resulted in small yet noteworthy variations in reasoning, vocabulary, and perception scores when compared to low-noise-exposure groups.  
The study [21] focused on speech perception and listening comprehension, finding significant differences under varying classroom noise conditions. In [22], it was demonstrated that adequate acoustic conditions improve verbal working memory and reduce errors such as omissions and intrusions.  
The study [19] revealed a noteworthy influence of classroom noise on students' reading fluency, with an effect size of 0.34. In [20], it was noted that sentence comprehension, both in terms of accuracy and response time, was variably impacted depending on children's selective attention. Children with high selective attention showed no significant differences in accuracy under different noise conditions, while those with low selective attention exhibited a significant increase in response time under noisy conditions.
## 4 Results and Discussion
Whereas, in binary classification, the entire dataset is separated into three groups and each group is classified separately and independently. In this case, the errors in one class do not affect the performance of the other class. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**One-Shot Classification**} & \multicolumn{2}{c}{**Binary Classification**} \\ \hline
**Groups** & **Accuracy (\%)** & **Groups** & **Accuracy (\%)** \\ \hline Benign & 60.0 & Benign & 92.5 \\ \hline Grade 3 & 55.0 & Grade 3 & 90.0 \\ \hline Grade 4 & 85.0 & Grade 4 & 90.0 \\ \hline Grade 5 & 50.0 & Grade 5 & 95.0 \\ \hline Total & 65.5 & Total & 92.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Support vector machine (SVM) classifier, comparison between one-shot and binary classification. Figure 8: Comparison between support vector machine (SVM) classifiers among the four Gleason grade groups. In the case of one-shot classification, the classifier could not accurately distinguish among the four groups. In the case of binary classification, the classifier was almost always accurate, with little variation. In Table 9, we compare the accuracy of different standard classification methods with our proposed method. The classification accuracy achieved for the class low vs. high grade using the proposed method is higher than other methods described in the literature. On cancer diagnosis, when classified Malignant vs. Benign, our result is better than [PERSON] et al. (2018) and [PERSON] et al. (2006), but not higher compared to [PERSON] et al. (2017), because they used different types of features that are extracted from the tissue image, namely color channel histogram, fractal dimension, fractal code, wavelet, and MAGIC. The authors of Reference [4] computed the features of epithelial nuclei objects in the tissue image, whereas, our method computed the features of all nuclei objects existing in the biopsy prostate tissue image. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**One-Shot Classification**} & \multicolumn{2}{c}{**Binary Classification**} \\ \hline
**Groups** & **Accuracy (\%)** & **Groups** & **Accuracy (\%)** \\ \hline Benign & 37.5 & Benign & 87.5 \\ \hline Grade 3 & 67.5 & Grade 3 & 90.0 \\ \hline Grade 4 & 45.0 & Grade 4 & 75.0 \\ \hline Grade 5 & 70.0 & Grade 5 & 77.5 \\ \hline Total & 55.5 & Total & 82.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multilayer perception (MLP) classifier, comparison between one-shot and binary classification.
# Swarming Behavior Emerging from the Uptake-Kinetics Feedback Control in a Plant-Root-Inspired Robot
## 2 Materials and Methods
### Clues from Plants
#### 2.1.3 Plant Intra-Communication and Local Storage  
The sharing of minerals and other substances between roots and shoot is possible in plants thanks to the internal vascular system, which is composed of two channels, called the xylem and phloem (Figure 1). The xylem is the central vessel that runs along the whole structure and represents a direct omnidirectional connection from roots to shoot. Here, water and nutrients are transported by bulk flow, exploiting water pressure, to the aerial parts of the plants. On the other hand, the phloem is a slower connection channel, with an osmotic mechanism, enabling substances to be diffused from source to sink. In this channel, elements move from an area with a high concentration (source) to an area with a lower concentration (sink). In fact, the direction of transport is defined by the nutritional requirements of the different plant organs or tissues, ensuring nutrient cycling and a fair redistribution between shoot and roots. It can thus be considered as an important communication channel of the internal nutritional status [31], e.g., if a required nutrient is not delivered to the sink, it is highly probable that that nutrient is not available.  
Once mineral nutrients in the soil reach the root surface and are absorbed by cells, they can (I) be immediately used for local processes (e.g., protein synthesis), (II) sent to the shoot through the xylem or (III) stored in vacuoles, which are cell components that work as pools for substances that need to be readily provided to cell processes [43]. When the request from the shoot is high, nutrients are rapidly pumped up (alternative II), and then redistributed towards the requesting sinks.  
Plants thus also have the following three properties:  
**Property 6**: _There is a fast and direct highway where nutrients are transported immediately from roots to shoot;_  
**Property 7**: _Nutrients are distributed among requesting organs according to the strength of their requests;_  
**Property 8**: _Root tissues and cell vacuoles are local memories storing information on nutrient status._  
In fact, regulation of the uptake rate has been correlated to the mineral nutrients stored in vacuoles [44].
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 2 Key-Enabled Technologies for Future Networks
### Self-Organized Networks  
The exponential increase of on-line services (e-bank, e-health, streaming) and the number of connected devices has brought new challenges to the network infrastructure in terms of security, performance and reliability. The management and rapid response to unexpected problems in the network (link failure, congestion, Distributed Denial of Service-DDoS, delay) is fundamental to guarantee QoS/QoE to users. Network intelligence mechanisms are needed in order to resolve/mitigate possible problems, to decrease the service recovery time and the operational costs [14]. Moreover, the use of advanced techniques such as artificial intelligence, data mining or pattern recognition enables proactive and reactive self-management actions capable of preventing potential problems and maintaining the subscribed network services. However, the implementation of  
Figure 2: NFV Reference Architecture.  
self-organized solutions [13] in current networks is limited by the rigidity of the traditional network architectures. The modification or customization of a flow in the network requires the individual configuration of each network device, and the deployment of new network services/protocols, from design to implementation, can take a long time. In this context, the smart integration of the novel technologies listed above has the potential to provide operators a smart network infrastructure capable of managing complex network scenarios and reducing operational costs, as shown in Figure 3.  
The combination of SDN with NFV aims to enhance the management and orchestration process in current networks. On one hand, SDN can control the network behavior and it may require specific network functions in order to fulfill the new requirements and ensure the QoS levels established by the service provider. On the other hand, NFV allows the rapid deployment of these functions, without constraints of location or hardware vendor. For its part, cloud computing and SON could facilitate the scalability of the services, the customization of network infrastructure and the smart control and management of the available resources. It is expected that machine learning and data mining can provide higher and more intelligent mechanisms related to monitoring and management tasks [14]. It is important to note that future mobile networks envisage to provide a system driven by software, relying on technologies such as SDN, NFV, cloud computing and SON. These concepts aim to cover flexibility needs in terms of resource allocation, smarter monitoring and the introduction of new network functions, while ensuring the quality of the services. Furthermore, these technologies have been applied in radio and network fields in order to tackle their requirements.
# A Novel Deep Learning Model to Predict Ultimate Strength of Ship Plates under Compression
## 2 Methodology
### Curved Plate Analytical Modeling Scenarios and Analysis Techniques  
The analytical model scenario to be used as the training data for the neural network was selected considering the three initial deflection amounts proposed by [PERSON] et al. [8] in Equation (1). Where \(w_{0 pl}\) refer to maximum values of representative initial deflections, \(\beta\) refers to plate slenderness ratio, and \(t\) refers to plate thickness.  
A total of 4050 scenarios were selected by combining geometric shapes, length, curvature, and thickness of the curved plate. The thickness of the plate was selected according to the yield strength of the material considering the curvature (1-9\({}^{\circ}\)) and plate slenderness range (1-5) in the curved plate.  
\[w_{0 pl}=\begin{cases}0.025\beta^{2}t&for\ slight\ level\\ 0.1\beta^{2}t&for\ average\ level\\ 0.3\beta^{2}t&for\ severe\ level\end{cases} \tag{1}\]  
The material properties and scenario of curved plate used in the analysis are listed in Tables 1 and 2, respectively. The arc-length method was applied to observe the nonlinear behavior, such as complex buckling deformation.  
A modal analysis was performed using ANSYS because the curved plate exhibited a shape different from the initial deflection shape of the flat plate. Figure 2 shows the shape of the 1 st-mode analysis, with the initial deflection of the curved plate applied. In the curved plate, b = 1000 mm, aspect ratio = 3, material yield stress = 235 MPa, curvature = 9\({}^{\circ}\), thickness = 13.5 mm and under transverse compression.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 5 Simulation Results and Discussion
### SIRA Scheme
As shown in Figure 9, the execution times are compared under a variety of physical infrastructure conditions as well as different NSRs. The algorithm execution times increase proportionally as the number of NSRs and the available physical infrastructure nodes increases. Under reduced NSRs and reduced PI, the three algorithms listed above complete their tasks in the shortest amount of time. When comparing the algorithms, SIRA's resource allocation algorithm takes the shortest amount of time compared to the other two under all working circumstances. Serving NSRs in the 5-35 range with a 100-node physical infrastructure takes less than 10 ms. Also, the response time for the same amount of NSRs used in 300 PI nodes is not higher than the 10 ms. The execution time of the VIKOR approach has not been included in the above figure for comparison because the execution times of the VIKOR and SIRA procedures are nearly identical. However, all the compared approaches solely deal with slice resource allocation, whereas the suggested SIRA approach also deals with slice formation via machine learning. Figure 8: Acceptance ratio of SIRA with other algorithms. Figure 7: Resource efficiency of SIRA with other algorithms.