esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Towards Autonomous SC with Tenderfone
### An Agent-Oriented Language for SC
#### 3.1.5 End User API  
Users can interact with Tenderfone smart contracts pretty much as they do with state-of-the-art smart contracts in mainstream BCT. In particular, Tenderfone exposes three API primitives aimed at letting off-chain users manipulate smart contracts.  
* deploy(@StaticKB, +Args, -GeneratedID) creates a smart contract whose static KB is set to StaticKB and triggers its callback init/1 with arguments Args. If execution of the callback is successful, the smart contract is actually deployed, and a fresh and unique identifier is automatically assigned to it by the Tenderfone Interpreter. Furthermore, the generated identifier is bound to variable GeneratedID and thus returned to the user.
* invoke(+Goal, @SmartContractID) triggers the receive(Goal, UserID) callback, where UserID is the identifier of the calling user, on the smart contract whose identifier is SmartContractID. Execution fails if receive(Goal, UserID) fails, and in that case, all side effects occurred are discarded; hence, invoke/2 has all-or-nothing semantics.
* observe(+Query, @SmartContractID, -Result) triggers the answer(Query, User, Result) callback, where variable User is bound to the identifier of the invoking user, on the smart contract whose identifier is SmartContractID. Execution fails if and only if the triggered answer/2 callback fails. In any case, all side effects possibly occurred are dropped, as observe/3 is intended with a read-only semantics to let users inspect the state of the blockchain and of SC.
# MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification
## 1 Introduction
To address the aforementioned challenges and drawing inspiration from the work of [14], we adopt a two-stage training strategy. In the first stage, we employ a pre-trained model to obtain multiple loss variation values for each epoch of all samples and fuse these values with attention features, which are semantic features extracted by multi-head self-attention layers, to enhance the discrimination among samples. We observe that the variation pattern of the pre-collected average training loss values (as illustrated in Figure 1) is similar to the findings of [14]. In the second stage, leveraging the high generalization capability of meta-learning, MLRNet calculates weights based on the fused sample features and analyzes the loss variation for each sample, assigning more reasonable weights and obtaining the final weighted loss values through a bi-level optimization problem with the classifier. In summary, our contributions are as follows:  
* We propose a Meta-Loss Reweighting Network (MLRNet) based on the concept of meta-learning with attention features, which performs well on complex biased situations. * We embed the labels of samples, concatenate them with the features extracted by self-attention layers, and fuse it with pre-collected sample multi-loss variation values. This fused representation is referred to as the discriminative feature (or DF). Through MLRNet analysis of discriminative features, we identify the bias type of each sample and automatically assign appropriate weights to biased data. * We conduct experiments on several English and Chinese benchmark datasets, including artificially generated and real-world biased datasets. The results demonstrate that our approach achieves better and more generalizable performance than prior works. The structure of the paper is organized as follows: Section 2 discusses related work, Section 3 presents our proposed method, Section 4 demonstrates the experimental results and analysis, and Section 5 concludes the paper.
# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **F1-Score** & **Recall** \\ \hline Pathological & 95.41 & 94.22 & 96.13 \\ \hline Healthy & 94.59 & 93.78 & 95.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The classification report of the testing phase. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epoch** & **Train Loss** & **Valid Loss** & **Accuracy** & **Time (s) Per Epoch** \\ \hline
0 & 0.44897 & 0.21589 & 0.9514 & 02:54 \\
1 & 0.42963 & 0.198756 & 0.9510 & 02:55 \\
2 & 0.30987 & 0.15890 & 0.9631 & 02:54 \\
3 & 0.24964 & 0.136987 & 0.9601 & 02:54 \\
4 & 0.243253 & 0.129874 & 0.9628 & 02:54 \\
5 & 0.19250 & 0.112589 & 0.9536 & 02:55 \\
6 & 0.20147 & 0.023698 & 0.9574 & 02:55 \\
7 & 0.12369 & 0.029647 & 0.9611 & 02:55 \\
8 & 0.024898 & 0.002369 & 0.9594 & 02:54 \\
9 & 0.025369 & 0.004782 & 0.95.47 & 02:54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The test results of the Voice Pathology Detection system. Figure 3: The curve of the log scale of the learning rate against the loss. Some of the SVD voice pathology features can clinically identify pathological cases without the need for advanced analytical systems. However, voice pathology features changes with the changes in the stages of the disease development which affects the accuracy of the voice pathology detection results. In the early stages of the disease, the voice of the patient will not be significantly affected while when the disease becomes severe there will be a high irregularity in the voice of the patient. Hence, this work proposes a Voice Pathology Detection system that integrates a CNN model to perform voice pathology detection. The deep learning CNN of the Voice Pathology Detection system achieves high prediction accuracy results of up to 96.28% in distinguishing healthy from pathological cases. The main contribution of this work lays on modelling and tuning a deep learning CNN with ResNet34 layers to provide accurate voice pathology detection. Additionally, most of the related works use conventional dysphonic voice pattern analysis features which are easy to predict and even clinically interpretable while this dataset considered complex and challenging [1,12]. On the other hand, the limitations of this work are mainly due to the SVD testing dataset used and can be summarized as: (i) the small size of the tested cases, (ii) the absence of gender separation in the cases and (iii) ignoring the severity of the pathology in the features.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 3 Experimental Setup and Dataset
### Collected Dataset
That is, assuming that a channel composed of several paths was collected, the corresponding sample in the dataset contains: (a) the time instants at which the paths composing the PDP were received, (b) the power levels (in dBm) on each path, and (c) the total power received on the channel (note that this value maps onto the RSRP value discussed above, apart for measurement approximations). As mentioned above, the analysis presented in this paper focuses on the channel samples for the PCIs/SSBs detected with the highest power (one per operator); however, aiming at allowing further investigation and analysis by the research community, the open-sourced dataset includes channel samples for all the PCIs/SSBs pairs detected during each campaign [56]. Figure 4: Received power of the detected SSBs for Op1 (multiple SSBs) and Op2 (single SSB) over time at Location 1.
## 1 Introduction  
Deep and small holes with diameters in the range of 1-10 mm and large depth-to-diameter ratios are more and more widely used in the aviation, aerospace, and automotive industries [1]. The depth-to-diameter ratio of these precisely manufactured small holes often reaches a value up to several tens, even several hundred [2, 3]. These deep and small holes present great challenges to the measurable depth-to-diameter ratio and the precision of existing measurement methods.  
A contact probe can go deep into small holes with a long stylus or extension rod. However, stylus bending and contact deformation of the probing head as well as the part being measured significantly limit the measurable depth and precision. Some research has aimed tried to reduce the probing force to several mN [4, 5], but high measurable depth and high precision cannot be achieved at the same time. Noncontact probing methods are promising solutions to this challenge [6, 7, 8]. Of all the noncontact probing methods, optical methods are more promising to obtain higher resolution and precision [9; 10; 11; 12]. Based on various \"optical needle\" principles, nanometer resolution can be easily achieved. However, applying optical probes in deep and small hole measurement is difficult because optical probes usually have only 1D nanometer resolution probing ability and are, thus, more suitable for 2D and 2 \(\frac{1}{2}\) D structure measurement, such as for the shape measurement of a shallow optical surface. A variety of micro and nano probes with nanometer resolution and high stylus aspect ratio have been developed [13; 14; 15]. However, these probes are relatively small and more suitable for the measurement of micro/nano structures. Additionally, their stylus length and measurable depth is limited to several millimeters.  
Aside from the demand for new probing methods, other difficulties need to be solved to realize ultraprecision measurement of deep and small holes, such as multi-degree-of-freedom adjustment of hole attitude and macro displacement measurement of the probe.  
In this paper, a patented ultraprecision measurement method [16] based on spherical scattering electrical-field probing (SSEP) is proposed, and a hole diameter measuring machine (HDMM) is specially designed and developed. Key techniques such as multi-degree-of-freedom adjustment of hole attitude, laser interferometry for macro displacement measurement of the probe, and hole diameter measurement process planning are described. Finally, experiments are carried out with the HDMM and a probe that is specifically developed for method verification.
## 4 Experimental Results and Analysis  
### Dataset Production  
Existing object detection public datasets, such as COCO and PASCAL VOC, are used to detect specific categories, and there is no public data information for classroom students' behavioral states. Therefore, this paper manually constructed a classroom behavior detection dataset, based on real classroom videos, which contains four types of behaviors: listening, looking down, lying down, and standing. As shown in Figure 12, students who looked at the blackboard or read books carefully were labeled as listening state. Students who lowered their heads or played with cellphones were labeled as looking down state. Students who lay on their sides or buried their heads were labeled as lying down state. Students who stood up and answered questions were labeled as standing state. Since the head features of listening and looking down states were more obvious, only the head region was marked in this paper, while lying down and standing need to be identified by body posture, so the whole-body region was marked.  
Dataset images were captured at frame intervals from recorded class videos. In order to ensure good generalization performance and robustness, each image contains multiple student objects and multiple action poses. The images were screened out with insignificant actions and small changes before and after, and the LabelImg tool was used to manually label each image. The xml file generated by each image includes the label category (listening, looking down, lying down, standing) and the coordinate information of the real frame. A total of 10,826 labels were marked, and the training set and test set were divided by 8:2. The number of labels for each label is shown in Table 1.  
Figure 12: Some samples of this dataset.  
Unbalanced training data will cause the network model to pay more attention to a large number of objects during the training process, and it is easy to cause the model to overfit. In the dataset of this paper, the number of standing samples is relatively small, and in real life, sample collection is not easy. Therefore, we used data augmentation to expand the number of samples of standing poses to improve network performance. In this paper, a selection of images was intercepted of the student objects in the standing posture and data enhancement was carried out. Horizontal flip, brightness enhancement, and Gaussian noise were added to the cropped image, and the enhancement effect is shown in Figure 13.
# An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart
## 4 Results
### Results Validation  
Theoretical calculations of the parameters of this chart have not been identified. Therefore, a simpler code was developed to simulate the \(ARL\)s of the B-ATTRIVAR SS \(S^{2}\) chart aiming to validate the application's results and ensure the consistency of simulations and control limit calculations. Control limits are set in it, and it calculates \(ARL\)s for different values of \(\lambda\).  
Figure 4: Editable table on the interface.  
Hence, to validate the application results, the control limits generated by the application were utilized as input parameters for the code. Subsequently, the results obtained from both methods were compared in terms of \(\%S^{2}\), \(ARL_{0}\), and \(ARL_{1}\). The reasons for selecting these parameters are as follows:  
* Analyzing whether \(\%S^{2}\) verifies if the chart submits the expected percentage of samples in the stage of variable inspection;
* Analyzing whether \(ARL_{0}\) verifies if the chart commits to the type I error after the expected number of tested samples is calculated;
* And analyzing whether \(ARL_{1}\) verifies if the chart detects a signal at the expected speed, or after the expected number of tested samples is calculated.  
As shown in Table 2, several scenarios were tested, all being \(\mu_{0}=0\) and \(\sigma_{0}=1\), and the columns labeled as \"reference\" correspond to the values obtained through the simpler code.  
The first five rows on the table represent the execution of simulations with default values set on the application interface, as in Figure 2. In the next five rows, the application was executed only by changing the \(\%S^{2}\)\({}_{max}\) parameter from \(15\%\) to \(10\%\). The next nine rows contained changes not only in \(\%S^{2}\)\({}_{max}\) but also in \(n\) and \(\lambda\). This approach enables the analysis of whether alterations in one or more parameters affect the validity of its results.  
Its results demonstrate a strong consistency with those obtained through the reference code, i.e., \(\%S^{2}\), \(ARL_{0}\), and \(ARL_{1}\) show notably similar values. For instance, the largest percentage differences observed in the \(\Delta\)\(ARL_{0}\) and \(\Delta\)\(ARL_{1}\) columns were \(5.66\%\) and \(2.77\%\), respectively.
## 2 Theoretical Foundations of CFD
### Typical Stages of CFD Simulation: Pre-Processing, Solving, Post-Processing  
CFD simulations typically encompass three fundamental stages: pre-processing, solving, and post-processing, each of which plays a critical role in the overall success and accuracy of the simulation [63]. The pre-processing stage involves several crucial tasks, beginning with the definition of the computational domain and the creation of the geometry that represents the physical system under investigation. This is followed by mesh generation, where the domain is divided into discrete elements or cells, a process that significantly influences the accuracy and computational efficiency of the simulation. A finer mesh can yield more accurate results, particularly in regions of high gradient, such as near solid boundaries. In addition, the boundary and initial conditions must be specified, detailing the physical parameters, such as velocity, temperature, and pressure, that define the system's behavior. The careful consideration of these conditions is essential, as inaccuracies can lead to misleading results [43]. The solving stage involves the application of numerical algorithms to solve the discretized governing equations. This process may include iterative methods that converge to a solution over multiple time steps. During this phase, the choice of solver, as well as the turbulence and heat transfer models, are critical. Various solvers can be employed depending on the flow regime--steady or unsteady--and the complexity of the model. However, achieving convergence can sometimes be challenging, requiring adjustments to the mesh, solver settings, or relaxation factors to ensure numerical stability [64]. Post-processing is the final stage, where the raw simulation data are transformed into meaningful visualizations and analyses. This typically includes the generation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
of contour plots, vector fields, and surface renderings to illustrate the velocity, temperature distributions, and pressure fields within the domain. In addition, a quantitative analysis, such as extracting specific data points or calculating flow rates and heat transfer coefficients, is performed to evaluate performance metrics and validate the results against experimental or theoretical benchmarks. By interpreting the outcomes of the simulation, engineers can gain critical insights into the fluid dynamics and thermal processes, guiding further optimization and design efforts in food technology applications [65].
# _applied sciences_
## 3 Results and Discussion
### Biological Evaluation
#### 3.2.2 Influence on LPS-Stimulated Macrophages  
Considering the pivotal role of immunometabolic disorders in type 2 diabetes, target compounds were also profiled using a phenotypic screening approach [38]. Primary peritoneal murine macrophages were stimulated with LPS to induce a pro-inflammatory state. Target compounds were tested for the suppression of LPS-induced nitric oxide (NO) synthesis at a screening concentration of 10 \(\upmu\)M. According to the MTT test, seven compounds (compounds **11**, **18**, **20**, **22**, **24**, **25**, and **27**) inhibit the formation of NO by LPS-activated peritoneal macrophages in mice. Of these, compound **24** with an IC\({}_{50}\) value of 7.8 \(\upmu\)M was the most active, but this compound has no activity against a-glucosidase. Compound **20** is also non-cytotoxic, suppresses the synthesis of NO with an IC\({}_{50}\) 39.11 \(\upmu\)M, and inhibits a-glucosidase with an IC\({}_{50}\) of 18.88 \(\upmu\)M.  
Compound **15**, the most potent glucosidase inhibitor, also actively suppressed NO synthesis (IC\({}_{50}\) 4.8 \(\upmu\)M), but according to the MTT test, it also reduced cell metabolic activity. Final conclusions about cytotoxicity can be made after the involvement of additional research methods. The MTT test can both overestimate and underestimate the actual cell viability. In particular, such artifacts have been described for triterpene acids [39; 40; 41]. In general, 2,3-indolo-oleanolic acid derivatives appear to be more active than A-ring modified oleanolic acid derivatives. The anti-inflammatory activity was improved upon the introduction of the \(N\)-alkyl-piperazine moiety at C28 carboxyl, as exemplified by compounds **15** and **24**.  
We observed a lack of correlation between \(\upalpha\)-glucosidase and NO inhibition. Similar results were reported for betulinic acid derivatives [42]. Compound **15** appeared to be dual active and the most potent one, hence, it was nominated as a lead for further studies. There are a considerable number of studies on the role of intracellular glucosidase enzymes in pancreatic islet metabolism and macrophage immune response [43; 44], also see [45], however the precise mechanistic basis of intracellular endoplasmic reticulum glucosidase involvement in NO synthesis remains to be elucidated. Also, multiple other protein targets can be engaged by triterpenoids, and the factor of the various cellular permeabilities of the studied compounds cannot be ruled out.
## 6 Conclusions and Future Work  
In this study, we offer several solutions for optimization problems, including multi-hop in CH selection and orientation in clustering. For this purpose, we propose two algorithms in this work. First, we present a bacterial interaction and foraging-based CH selection method. The main purpose of this protocol is to create the best set of CHs, when considering energy balance, node degree and energy level, distance, and cost function parameters with an integrated manner. Thanks to the designed CH selection algorithm, MS can collect data by single-hop as soon as it approaches within the transmission boundary range of CH. Hence, we get rid of the multi-hop challenge by this algorithm. Secondly, we propose a routing algorithm that can work dynamically with energy and transmission boundary range awareness, using the \([CH_{1}\,\ \text{CH}_{2}\,\ldots,CH_{n}]\) set that was obtained as a result of the first algorithm. Thus, MS learns the best trajectory that was obtained by visiting the all of the CHs in the most advantageous way. The MS detects CHs in the coverage areas, called intersection point, representing the path in our algorithm, and it adds this path to the route savings. The proposed scheme was simulated and compared with the IACO, PSO, nCRO-UCRA, BI-DFAMDT, and starfish algorithm by varying number of nodes, network sizes, and MS speeds. This study shows superiority over the competitive studies with respect to energy efficiency, packet loss rate, number of alive nodes in the network, total remaining energy of the network, number of packets received by BS, trajectory length travelled by MS, packet delivery delay, throughput, packet delivery rate, advance energy usage rate, and functional overhead performace metrics; this claim can be observed from the simulation results that were obtained from the Matlab 2016b software. It can be said that our study is more adaptive and applicable in real WSN scenarios. However, future work will be focused on fault tolerance and detection or time complexity issues that are not addressed in the designed network. We plan to develop new methods to overcome these problems.  
Conceptualization, E.E.; Methodology and Software, S.Y.; Validation, S.Y. and E.E.; Formal Analysis, S.Y. and E.E.; Investigation, E.E.; Resources, S.Y. and E.E.; Data Curation, S.Y.; Writing-Original Draft Preperation, S.Y. and E.E.; Writing and Review&Editing, E.E.; Visualization and Supervision, E.E.  
This research received no external funding.  
The authors declare no conflict of interest.