esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[13] analyzed the factors affecting MOOC learners' turnover from three aspects: learners' own factors, curriculum related factors and technical factors, and proposed specific strategies to stimulate and maintain MOOC learners' learning motivation combining with the ten principles of online learning motivation and stimulation proposed by relevant scholars [14]. [PERSON] et al. [15] explored the impact of forum posting and social network behavior on school dropout from the perspective of social network analysis. The above work focused on the theoretical analysis of the influencing factors of MOOC dropout, but lacked quantitative analysis of each factor. In general, although there are many studies on the dropout rate, there is no standardized research method recognized by the academic community, and most of the studies are still in the process of exploration [5]. In this research, we select the 2013 Spring curriculum data on the HarvardX platform, use statistical methods to conduct quantitative analysis on the factors that may affect MOOC learners' dropout behavior, and make feature selection based on the statistical analysis results. Then, we establish prediction models for MOOC learners' dropout behavior using three traditional prediction models in machine learning: logical regression, K-nearest neighbor algorithm and random forest algorithm(a decision tree model based on bagging framework). The model with high prediction performance is selected through experimental comparison to predict the dropout behavior. This research can enable the teaching staff and managers to discover the learners' dropout behavior tendency in advance and make appropriate intervention as soon as possible, and make learners better adapt to the learning process, thereby reducing the dropout rate, improving the quality of teaching, and achieving better teaching results [16]. The main contributions of this study are: (1) dividing the factors that affect learners' dropout behavior into two categories: learners' own characteristics and learners' learning behaviors for statistical analysis; (2) conducting feature selection based on statistical analysis of factors affecting dropout behavior; (3) establishing learner dropout behavior models using logistic regression, random forest and K-nearest neighbor models, respectively;(4) carrying out extensive experiments on the data set of the Harvard X platform, train and test the three prediction models, and drawing the conclusion that the random forest prediction effect is better than the other two models. The remainder of this paper is organized as follows: The second section introduces the dataset and methodology. The third section presents the results of the study. The fourth part fulfills the discussion, followed by the conclusion in the last section.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Time Synchronization  
The IEEE 802.1 AS TSN sub-standard includes a version of the precision time protocol (PTP), which enables the distribution of a single reference clock across network devices in a master/slave basis. The availability of a common clock is likewise a key requirement for future wireless TSN-enabled technologies like WiFi 7, as it would permit to successfully schedule MU transmissions in both uplink and downlink, as well as to establish coordination mechanisms among APs [35; 36].  
Indeed, IEEE 802.1 AS can already be operated over IEEE 802.11 by means of the timing measurement (TM) procedure defined in IEEE 802.11v, which takes wireless link asymmetric delay into consideration. Time is propagated in private action frames between a master (i.e., the AP) and a slave (i.e., the STA), with the latter being able to compute the clock offset and adjust its own time accordingly.  
Furthermore, the last revision of the IEEE 802.1 AS standard (IEEE 802.1 AS-2020 [37]) contains the fine timing measurement (FTM) procedure, a novel synchronization method already included in IEEE 802.11 mc. FTM can be considered an extension of the TM scheme,  
Figure 2: Centralized network management model for wired-wireless TSN networks.  
but with a finest timestamp resolution (0.1 ns vs. 10 ns) and some other minor changes that do not hamper backwards compatibility [38].
## 4 Numerical Simulation
### The Discussion about the Number of Modes Used  
In this paper, the first six frequencies and first three mode shapes are adopted to identify the damage. The influence of the number of the considered modes on the damage identification results has been verified by performing identification based on fewer natural frequencies and mode shapes. The maximum differences between the identified damage factor and the theoretical value using different numbers of modes and the improved and traditional method are shown in Tables 8 and 9, respectively.  
It can be seen from Tables 8 and 9 that the identification errors obviously increase when the number of the considered modes decreases. Especially, if only the first mode shape was used, there would be an obvious misjudgment, which indicates the information is insufficient.  
In practice, modal information is obtained from measurements always with a certain error. The error of higher mode shapes and frequencies is larger, so the first six frequencies and the first three mode shapes are selected in this paper. In order to ensure the accuracy of damage identification, there must be enough modal information. From the numerical simulation results in Tables 8 and 9, one can infer that the selection of the first six frequencies and the first three mode shapes is sufficient, which indicates that the selection in this paper is reasonable.
## 5 Discussion
### Comparison of Prediction Accuracy of NNG and ALASSO over Initial Models  
We compared the prediction accuracies of the NNG and ALASSO models with those of the models used to generate the initial estimates (OLS, ridge, and lasso). Results showed that NNG and ALASSO consistently outperformed OLS models, which is not surprising given that OLS models estimate all parameters without shrinkage, increasing the risk of overfitting and leading to higher prediction errors on new data [3; 10]. These findings are consistent with those of [PERSON] and [PERSON] [17], who demonstrated that NNG is effective in improving on the initial estimator in terms of both variable selection and estimation accuracy.  
However, when ridge and lasso estimates were used as initial estimates, NNG and ALASSO often produced worse predictions than those from ridge and lasso initial models in scenarios with high correlation as well as in small sample sizes with low-to-moderate SNR (SNR < 1). In such settings, distinguishing between relevant and irrelevant variables is challenging [3], and NNG and ALASSO may select models with too few variables, resulting in underfitting and poor generalization [10; 17]. Conversely, in scenarios with sufficient information, where minimal shrinkage is required [6], NNG and ALASSO outperformed the ridge and lasso initial models. In these settings, ridge or lasso shrinkage can be excessive, leading to suboptimal predictions [15; 40].  
Overall, our findings suggest that, while NNG and ALASSO consistently outperformed OLS models, they do not always outperform ridge and lasso initial models in data with small sample sizes, high correlation, and low SNR. In such cases, any modeling strategy may be limited, and a more appropriate course of action may be to describe the data and recommend conducting a more informative study. However, when sufficient information is available, NNG and ALASSO enhance both the variable selection and prediction accuracy of initial models.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 1 Introduction
Comparative analysis against BDT demonstrates that FlexBFT exhibits superior performance, particularly excelling in scenarios characterized by continuous network fluctuations.
## 4 Experiment and Results
### Performance Metrics  
The performance metrics used for the experiments include precision, recall, average precision (AP), and mean average precision (mAP). Their equations and descriptions are as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
Precision is represented mathematically as shown in Equation (7), serving as an indicator of the accuracy of predictions. Specifically, it quantifies the proportion of accurately identified instances among all predictions made, making it crucial for assessing the reliability of detection results, particularly the true defect identification.  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{8}\]  
Recall is represented mathematically as shown in Equation (8), indicating the proportion of actual positive instances successfully identified by the model. Essentially, recall measures the model's ability to accurately detect all relevant instances that should have been identified.  
AP quantifies how consistently a model maintains high precision at varying recall levels. Defined as the area under the precision-recall curve, with recall on the x-axis and precision on the y-axis, a larger area suggests superior model performance by demonstrating the model's capability to sustain high precision even with increasing recall. In essence, a high AP value implies that the model effectively identifies true positives without accruing false positives while trying to capture all relevant instances.  
\[\text{mAP}=\frac{1}{N}\sum_{i=1}^{N}\text{AP}_{i} \tag{9}\]  
The mAP represents the average AP values calculated for each class. \(N\) denotes the total number of classes, and \(AP_{i}\) refers to the Average Precision value for the \(i\)-th class. A higher mAP value indicates effective model performance across various categories, achieving high precision and recall for each class.
## 6 Complexity Analysis
### Complexity of Clustering Algorithms
#### 6.3.1 \(K\)-means  
For \(K\)-means clustering, the number of operations in the worst-case-scenario if the Lloyd's algorithm is used is given by \(O(nkdi)\), where \(n\) is the number of data to be clustered, \(k\) is the number of clusters (\(2^{M}\)), \(d\) is the dimensionality (in our case, 2), and \(i=2^{O(\sqrt{n})}\) is the number of the required iterations.
## 5 Methods for Identification and Quantification of Coumarins  
A range of analytical methods are commonly employed to identify and quantify coumarins, as well as other compounds found in spirit beverages. Different techniques can be applied, depending on criteria such as the characteristics of the sample, the desired level of sensitivity, and the equipment available.  
The identification of natural coumarins is mostly based on their fluorescence, as they produce a blue hue when exposed to UV light, and it is precisely because of this property that they are frequently characterised by spectrometric techniques. Table 2 presents a concise overview of the excitation and emission wavelengths for prevalent coumarins that occur in wood and agedspirit beverages. This feature provides the basis for the development of identification and quantification techniques [161]. On the other hand, coumarins are photosensitive and can change with natural light, so these characteristics are used for their isolation and analysis, as well [117; 161].  
The most common methods for the separation, identification and/or quantification of coumarins include the following: thin-layer chromatography (TLC); gas chromatography (GC); high-performance liquid chromatography (HPLC); ultra-performance liquid chromatography (UPLC); liquid chromatography-mass spectrometry (LC-MS); capillary electrophoresis (CE); UV-Vis spectrophotometry; and fluorescence spectroscopy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **\(\lambda\)excitation (nm)** & **\(\lambda\)emission (nm)** \\ \hline
4-methyllumbelliferone & 218 & 454 \\ coumarin & 280 & 393 \\ aesculin & 335–337 & 409 \\ umbelliferone & 340, 380 & 467 \\ scopoletin & 345–347 & 430, 460 \\ aesculetin & 347–353, 340 & 467 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Excitation and emission wavelengths of prevalent coumarins in wood and agedspirul beverages.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 4 Security Analysis
### User Untraceability  
Another frame of privacy attack relates to trying to obtain the physical location of a person by tracing his/her device (in this case, the personal mobile device carried by the patient). If the personal mobile device transmits the same information continuously, then its location can be traced by a malicious attacker. In the proposed system, the session key \(SEK_{AP}\) and \(SEK_{BP}\) is changed for every communication round in order to prevent location tracking. Therefore, the proposed system achieves user untraceability and protects location privacy.
## 4 Conclusions  
In this study, the structural characteristics and trajectory of TLV were systematically studied and analyzed. The detailed conclusions are as follows:  
1. With the exception of the main leakage vortex, the secondary leakage vortex or even tertiary leakage vortex near the impeller outlet is formed under the high-discharge condition. The vortex structure under the low-discharge condition is more complex than under the high-discharge condition. The indicator of a leakage vortex under the low-discharge condition is a leading-edge overflow phenomenon, which causes a reverse-flow vortex.
2. The absolute flow angle reflects the flow pattern in the blade inlet. With decreasing discharge, the absolute flow angle increases, and the initial position of the leakage vortex moves forward to the leading edge. An obvious reverse flow is formed under the low-discharge condition, leading to an absolute flow angle close to 180\({}^{\circ}\).
3. Unstable flow can be characterized by entropy production theory. A region with high EPR is caused by a high-velocity gradient, as well as mixing and rolling up between the tip leakage flow and main flow. With decreased discharge, the peak of the EPR distribution curve moves upstream; this is consistent with the direction of the initial position movement of the TLV. The blade load is reduced, and the distribution law is changed near the tip clearance.
4. The TLV trajectory is consistent with that of low pressure and the highest-entropy gradient troughs near the shroud. With decreased discharge, the TLV initial position and the interface between the main flow and the leakage flow gradually move upstream. The tip leakage angle increases significantly.  
Conceptualization, B.Q. and Y.C.; methodology, Y.C.; software, L.W.; validation, B.Q., Y.C. and Q.D.; formal analysis, W.S. and L.W.; investigation, W.S.; resources, B.Q.; data curation, B.Q.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z.; visualization, Q.D.; supervision, D.Z.; project administration, W.S.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52179089.  
Not applicable.  
The authors declare no conflict of interest.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 4 Data Aggregation Using Compressed Sensing Theory
### Compressed Sensing Theory
#### 4.2.3 Observation Matrix Representation  
The DODAG root collects the aggregated data from each path i = 1,2,...,M. The DODAG root collects data in path 1, represented in Equation (2).  
\[y_{1}=\sum_{i=1}^{k}r_{1i}\times d_{i} \tag{2}\]  
Similarly, the DODAG root collects the aggregated data from the M paths, represented in Equation (3).  
\[y=\sum_{i=1}^{M}y_{i} \tag{3}\]  
where \(y_{i}\) indicates the aggregated data in a particular path and y indicates the aggregated data from the M paths.
## 3 Results and Discussion
### Effect of QS Signaling on Biofilm Formation on Microplastic Surfaces  
#### 3.4.1 Injection of Exogenous QS Signaling Molecules  
AHL, a QS signal molecule, is known to play an important role in PAO1 biofilm formation. Therefore, to investigate the effect of QS on microplastic surface biofilm formation, an additional experiment was conducted by injecting various concentrations of exogenous AHLs into the PAO1 culture during biofilm formation. As shown in Figure 5, when AHL (the cocktail of C4-HSL and 3-oxo-C12-HSL) was injected at final concentrations of 5, 10, and 20 uM, respectively, no difference was detected in the amount of biofilm when compared to the control group (w/o AHL injection). It was reported that the deletion of genes that synthesize C4-HSL and 3-oxo-C12-HSL in the PAO1 resulted in a reduction in biofilm formation [35]. However, no research has been done to see if the addition of exogenous AHLs to PAO1 promotes biofilm formation. As shown in this experiment, the biofilm formation of PAO1 was not significantly affected by exogenous AHLs, which were additionally injected at high concentrations.  
Figure 5: Effect of AHL on the biofilm formation on PE microplastic surface. Error bars were defined as standard deviations (n = 12, biological replicates). One-way ANOVA was performed and  
the Tukey post hoc test was conducted to compare the AHL treatment to the negative control, where significant differences are indicated as follows: NS, not significant \(p>0.05\).
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
## 2 Literature Review
### Evaluation Aspects of RAG  
In the evaluation of RAG models, there are three primary quality metrics to assess the two main targets retrieval and generation. These quality metrics, including context relevance (retrieval metric), answer faithfulness (generation metric), and answer relevance (generation metric), serve to gauge the effectiveness of the RAG system [17; 18].  
Context relevance scrutinizes the precision and specificity of the retrieved context, ensuring relevance while minimizing the processing costs associated with irrelevant content. Answer faithfulness ensures the fidelity of the generated responses to the retrieved context, thereby maintaining consistency and avoiding contradictions and \"hallucinations\". Answer relevance requires that the generated responses directly address the posed questions, effectively tackling the main inquiry [17; 18].  
RAG evaluation methods can be categorized into two types: reference-required and reference-free evaluation. In the reference-free approach, the quality of outputs is determined based on intrinsic text characteristics, bypassing the need for human-annotated ground truth labels in the evaluation dataset. Conversely, the reference-required approach compares outputs to a ground truth annotated by humans [16].  
Reference-free evaluation frameworks leverage LLMs to assess quality scores, such as context relevance, answer faithfulness, and answer relevance, by analyzing the alignment of the generated text with the retrieved context. However, this method's reliability can be compromised if the retrieved external information is of low quality [16]. Frameworks such as RAGAS (Retrieval-Augmented Generation Assessment) by [PERSON] et al. [17; 19], ARES (Automated RAG Evaluation System) by [PERSON] et al. [18], or RAG Triad by [PERSON] [20] are classified as reference-free frameworks according to [16]. Nonetheless, both RAGAS and ARES require a form of \"ground truth\" for evaluation besides using synthetic training data for assessing the quality of individual RAG components. Let us note that various other approaches related to reference-free evaluation methods are available [21; 22; 23; 24; 25], which, for instance, may require the manual evaluation of RAG's output.  
In contrast to reference-free evaluation methods, reference-required benchmarks involve comparing outputs to a reference or ground truth annotated by humans. These benchmarks provide quantitative metrics to measure the performance of the RAG model and deepen the understanding of its capabilities across various evaluation aspects. For in stance, the Retrieval-Augmented Generation Benchmark (RGB) [8] evaluates four essential abilities for RAG: noise robustness, negative rejection, information integration, and counterfactual robustness. However, this approach focuses on question-and-answering tasks to measure RAG performance. Other approaches, such as those discussed by [PERSON] et al. [16], support a wider range of RAG applications but are more challenging to implement.
# Modeling the Energy Performance of LoRaWAN
## 1 Introduction  
Low-Power Wide Area Networks (LPWAN) define a category of wireless communication technologies that have recently gained significant momentum. Industry, academia and standards development organizations have devoted significant efforts to LPWAN in the last years [1; 2]. Such technologies typically offer a link range of one or more kilometers, while a single infrastructure element (often called a gateway) is capable of supporting hundreds of thousands of devices such as sensors and actuators [3; 4]. Therefore, LPWAN technologies enable Internet of Things (IoT) applications, such as smart cities, by means of low cost and low complexity infrastructure.  
Among LPWAN technologies, LoRaWAN (and its related modulation, LoRa), is perhaps the technology that has attracted the attention of most academic work as of the writing of this paper [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. This can be explained by the public availability of its specifications [27], the availability of certified hardware [28] and the fact that LoRaWAN communication can be enabled without the need to establish a relationship with an operator.  
Many LoRaWAN devices, such as sensors or actuators, will typically be battery-operated. Therefore, it is crucial to investigate the characteristics of LoRaWAN energy consumption. However, published works only focus on this topic to a limited extent, providing only rough estimates on parameters related with LoRaWAN energy performance, while not considering realistic behavior of LoRaWAN device hardware, as well as the impact of the main LoRaWAN parameters and mechanism settings [13; 22; 25; 26].  
In this paper, we present analytical models that characterize device current consumption, device lifetime and energy cost of data delivery with LoRaWAN. For a realistic analysis, the models are derived based on measurements performed on a currently prevalent LoRaWAN platform. The models developed allow studying the impact of relevant LoRaWAN parameters and mechanisms such as data rates, acknowledged transmission and payload size, as well as Bit Error Rate (BER) and collisions, on LoRaWAN energy performance. Evaluation results illustrate energy performance and trade-offs of LoRaWAN. Among others, we have found that using an appropriate configuration a LoRaWAN end-device platform powered by a 2400 mAh battery can achieve a 1-year lifetime while sending one message every 5 min, and an asymptotic theoretical lifetime of 6 years as communication becomes infrequent.  
The remainder of the paper is organized as follows. Section 2 reviews related work. Section 3 overviews LoRaWAN, describing its general architecture and focusing on its physical and MAC layers. Section 4 models LoRaWAN end-device current consumption, end-device lifetime, and energy cost of data delivery, whereas Section 5 provides evaluation results obtained by using the models presented. Section 6 concludes the paper.