esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Procedures  
### Participants  
The experiment was conducted with 86 students enrolled in an assistive technology course who voluntarily consented to participate and maintained consistent attendance throughout the study. The participants were randomly assigned to groups using an Excel (Microsoft Excel, Microsoft 365 Apps for Business, Version 2021, Microsoft Corporation, Redmond, WA, USA)-based randomization macro, executed three times to ensure unbiased allocation. Of the initial 132 students, the following were excluded from the study due to their ineligibility in accordance with the research criteria: 26 who did not provide voluntary consent, 12 who withdrew from the course midway, and 8 who missed more than two class sessions (Figure 1). The study was conducted by dividing the participants into two groups: the Traditional Learning Group (TLG), which studied using printed paper-based materials; and the AI Learning Group (ALG), which utilized AI textbook-based learning. All the processes were conducted under the oversight and strict supervision of the Office of Academic Affairs.  
A homogeneity test was conducted to evaluate the comparability of the AI Learning Group (ALG) and the Traditional Learning Group (TLG) based on general characteristics such as age, GPA, gender distribution, year level, and course type. The results demonstrated no significant differences between the two groups. For age, the mean in the ALG was \(26.26\pm 7.20\) years, while the TLG mean was \(24.79\pm 3.47\) years. A \(t\)-test revealed a t-value of \(-1.20\) and a \(p\)-value of \(0.2341\), indicating no statistically significant difference. Regarding GPA, the ALG had a mean of \(4.06\pm 0.41\) compared with the TLG's mean of \(3.97\pm 0.44\). The \(t\)-test for GPA resulted in a t-value of \(-1.03\) and a \(p\)-value of \(0.3081\), confirming no significant difference in academic performance. In terms of gender, the ALG consisted of \(26\) females and \(17\) males, while the TLG comprised \(27\) females and \(16\) males. A chi-squared test was performed, yielding a chi-squared value of \(0.123\) and a \(p\)-value of \(0.726\), which showed that there was no statistically significant difference in gender distribution between the groups. Both groups were composed entirely of third-year undergraduate students enrolled in the same course type, ensuring consistency in academic level and program structure. This result suggests that random segmentation was successful. The two groups were similar in terms of gender distribution, meaning that comparisons between the groups may be fair in future analyses (Table 1).
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 4 Proposed Approach for Lower Limb Activity Detection
### Preprocessing Phase  
EMG and IMU signals acquire noise while traveling through different tissues. Some of these artifacts can be eliminated by means of high-pass filtering (rejecting certain frequency regions), such as inherent noise in electronic equipment and inherent instability due to the firing of motor units. On the other hand, some artifacts require detailed signal decomposition to separate noisy and unwanted components from the significant signal features and cannot be removed by filtering. Wavelet denoising is one of the most powerful tools to separate noisy wavelet coefficients from detailed coefficients representing features of the signal of interest. A common example of artifacts requiring denoising is motion artifacts.
## 1 Introduction
This study not only seeks to enhance the efficiency of industrial processes but also demonstrates how emerging technologies can contribute to sustainability, expanding the possibilities for adaptation and implementation across various industries. The proposed model offers significant value for small and medium-sized enterprises, such as Alpha, which face similar challenges related to unplanned downtime and high maintenance costs. The structure of this article is as follows: Section 2 provides a comprehensive review of the state of the art, addressing the concepts of predictive maintenance, RPA, and ML and exploring the integration of these technologies in failure prediction and operational efficiency improvement. Section 3 describes the methodology adopted, justifying the choice of the PICO approach and explaining the hypothesis formulation process. Section 4 presents the proposed model and its advantages over existing models. Section 5 illustrates the practical application of the model through a case study. In Section 6, the results are analyzed, discussing the implications of RPA and ML integration in failure prediction and operational efficiency, with a focus on sustainability aspects. Finally, Section 7 concludes the article, summarizing the main findings and suggesting directions for future research. In summary, this article aims to deepen the understanding of the integration between RPA and ML for failure prediction and the optimization of operational efficiency in predictive maintenance, highlighting the social, economic, and environmental benefits of applying these emerging technologies.
## 2 Models and Methods  
### Physical Model  
This study used a refrigerated room built at the Harbin University of Commerce [23]. The coordinate system origin is in the center of the inner surface of the floor of the refrigerated room in Figure 1a. The interior dimensions of this refrigerated room were 8.7 m length \(\times\) 5.0 m width \(\times\) 2.7 m height. The refrigerated room walls were 0.15 m thick and made of polyurethane foam. The room was equipped with a couple of refrigerating units whose dimensions were 1.0 m length \(\times\) 0.4 m width \(\times\) 0.5 m height. The latter were placed in the middle of the top front of the refrigerated room. The distances between the refrigerating units and the adjacent walls were 0.5 m, while the distance between the two refrigerating units was 2.23 m. The room is depicted in Figure 1a, while the air delivery is illustrated in Figure 1b. The air velocity was 13 m/s. The temperature of airflow outgoing from the two refrigerating units was 255.15 K, thereby providing a standard temperature of frozen food storage. The temperature outside the refrigerated room was 293.15 K [23].  
The dimensions of product packages were 1.3 m length \(\times\) 1.1 m width \(\times\) 0.7 m height. Eight stacking modes are demonstrated in Figure 2: (i) two pyramidal stacking modes whose upper package was on the center of four lower packages (UPF-PSMs), (ii) two pyramidal stacking modes whose upper package was in the center of two lower packages (UPT-PSMs), and (iii) their four corresponding NSMs. The lengths L1-16 in Figure 2 were: L1 = L6 = 0.45 m, L2 = L5 = L11 = 0.4 m, L3 = 0.325 m, L4 = L7 = L8 = L9 = 0.25 m, L10 = 0.2 m, L12 = L13 = L14 = L15 = L16 = 0.1 m. The initial product temperature was 261.15 K. The geometric description of refrigerated room, refrigerating units and product packages (stacked as shown in Figure 2) was performed by means of the SolidWorks 2014 software [24].  
Three stacking approaches were used in this study. The first was to ensure that the heights of package piles for PSMs and corresponding NSMs were the same; the second ap  
Figure 1: Experimental refrigerated room: (**a**) schematic, and (**b**) air circulation.  
Figure 2: Stacking modes: (**a**) NSM with one-pile, (**b**) UPF-PSM with one-pile, (**c**) NSM with two-piles, (**d**) UPF-PSM with two-piles, (**e**) NSM with three-piles, (**f**) UPT-PSM with three-piles, (**g**) NSM with six-piles, (**h**) UPT-PSM with six-piles, (**i**) top view of (**a,b**), (**j**) top view of (**c,d**), (**k**) top view of (**e,f**), (**m**) top view of (**g,h**).  
proach was to provide the same bottom areas of package piles for PSMs and corresponding NSMs; the third approach aimed that as many as possible packages with different stacking modes are placed in the refrigerated room.  
Unstructured tetrahedral meshes of the models were created by Workbench 18.0 [25]. The skewness for all models was below 0.9.
## 6 Simulation Results
### Performance Evaluation
In the simulations, each CM randomly sends temperature values to the CHs. From all graphs, it is clear that our protocol performs better compared to typical protocols, such as LEACH and HEED.
## 3 Results and Discussion  
### Examination of Jacobians  
In this study, the water contents of two soil layers (superficial (WG1) and root-zones (WG2)) and LAI were applied as the control vectors in the EKF. The observation terms are satellite SSM and LAI. The Jacobian matrix is the following:  
\[\textbf{H}=\left(\begin{array}{cc}\frac{\partial SSM}{\partial LAI}&\frac{ \partial SSM}{\partial W\partial Y}&\frac{\partial SSM}{\partial W\partial Y} \\ \frac{\partial LAI}{\partial LAI}&\frac{\partial LAI}{\partial W\partial \  
witing point (w\({}_{\text{fc}}\)-w\({}_{\text{wilt}}\)) [24]. Observation error for GEOV1 LAI and the background error for the modeled LAI were assumed to be equal to 0.2 m\({}^{2}\) m\({}^{-2}\)[8].  
The Jacobian elements are important in order to understand the efficiency of the data assimilation. Figure 4 shows that the dLAI/dWG2 Jacobian term was very variable from year to year. It had generally positive values, but negative values were also found. Small positive perturbation of the root-zone water content directly affected the development and the growing of the plants [7]. Large Jacobians corresponded to the water stress, when WG2 reached the wilting point, a small increase in the WG2 produced a large increase in biomass production. Furthermore, largely positive and negative values might indicate the nonlinearity of the Jacobians. Zero Jacobians occurred during the winter season. Small negative values were generated because the soil water content exceeded the field capacity in these cases.
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 5 Experiments
### Real World Data  
To evaluate various graph clustering algorithms, we employ three publicly available real-world networks. Complete descriptions and data for these networks are accessible through the UCI Network Data Repository ([[https://networkdata.ics.uci.edu/index](https://networkdata.ics.uci.edu/index)]([https://networkdata.ics.uci.edu/index](https://networkdata.ics.uci.edu/index)) (accessed on 25 July 2024)) and the Stanford Large Network Dataset Collection ([[https://snap.stanford.edu/data/index.html](https://snap.stanford.edu/data/index.html)]([https://snap.stanford.edu/data/index.html](https://snap.stanford.edu/data/index.html)) (accessed on 25 July 2024)).  
**Zachary's Karate Club Network:** The Karate Club dataset is a well-known social network dataset. Collected by [PERSON] in the 1970s, it was derived from observing the social interactions among members of a karate club at an American university. The dataset includes 34 members and 78 edges representing social interactions among them. This network became particularly famous due to a case study where internal conflict led to the club's split. [PERSON] accurately predicted the members' alignment during the split, showcasing the potential of social network analysis in understanding dynamics of social structures. This dataset is frequently utilized to test the effectiveness of social network analysis and graph clustering algorithms.  
**Books About US Politics:** The network dataset consists of books related to US politics, which were published around the 2004 presidential election. It represents books as nodes, sourced from sales on Amazon.com. The connections or edges between nodes indicate that books were often bought together by the same customers. This network includes 105 nodes and 441 edges. According to [PERSON], these books are categorized into three groups: \"liberal,\" \"neutral,\" and \"conservative.\"  
**Dolphin social network:** This dataset offers a comprehensive analysis of the behavioral patterns of 62 bottlenose dolphins in New Zealand, resulting in the creation of a social network diagram that illustrates their interactions. In this network, each of the 62 nodes represents an individual dolphin, with edges between nodes indicating frequent interactions between dolphin pairs. The Dolphin network is frequently cited as a classic example for community detection and segmentation in complex network studies, illustrating the social connections among the dolphins, which are categorized into two distinct groups.
# Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks
## 4 Proposed Method
### Adaptive Search in DCS-SOMP Approach for 3D Parameter Estimation
\tag{20}\]  
Thus, using \(\mathbf{\ddot{\phi}}^{(k)}\), \(\mathbf{\dot{\phi}}^{(k)}\), \(\mathbf{\ddot{\theta}}^{(k)}\), and \(\dot{\mathbf{\theta}}^{(k)}\) we determine the new sensing matrix as follows:  
\[\mathbf{U}^{(k)}_{t}=[\mathbf{a}_{t}(\mathbf{\ddot{\phi}}^{(k)},\mathbf{\dot{\phi}}^{ k})], \tag{21}\]  
\[\mathbf{U}^{(k)}_{r}=[\mathbf{a}_{t}(\mathbf{\ddot{\theta}}^{(k)},\dot{\mathbf{\theta} }^{(k)})], \tag{22}\]  
\[\mathbf{\omega}^{(k)}[n]=(\mathbf{U}^{(k)}_{t}\mathbf{x}^{(g)}[n])^{T}\otimes \mathbf{U}^{(k)}_{t}. \tag{23}\]  
Using \(\mathbf{R}_{l}\) and \(\mathbf{\omega}^{(k)}\), we determine new values for \((\phi^{\prime zz}_{l},\phi^{cl}_{l})\), \((\theta^{\prime zz}_{l},\theta^{cl}_{l})\), and \(\tau^{cl}_{l}\) from (10), (12), and (16), respectively, which initiates a new iteration. Although the adaptive step enhances the sensing matrix for 2D-AoD and 2D-AoA estimation, it also yields improvements in ToA estimation. The continual improvement of the maximum correlation with each iteration in the adaptive process positively impacts the ToA estimation accuracy. Therefore, our adaptive approach involves using a smaller number for \(q_{az}\) and \(q_{el}\), enabling us to achieve a high number of samples in the overall search space while consuming less memory. This is due to the reduced size of the matrix \(\mathbf{\omega}\) in terms of its total number of elements. Additionally, our approach requires less time than the simple method while maintaining greater accuracy. Figure 1(a),b clearly demonstrates that when using the DCS-SOMP method, a small sample space is accommodated, should memory space or execution time be constrained. The simulations reveal that when using over 18 samples in the sensing matrix, the DCS-SOMP method becomes impractical in terms of execution time and memory. On the other hand, the adaptive method consumes less memory, exhibits shorter execution times, and is capable of accommodating larger sample spaces. Furthermore, the sample space accommodated by the adaptive method consistently remains close to the actual value, enhancing its precision. We calculate the number of elements (\(q_{\omega}\)) in \(\mathbf{\omega}\) using the formula: \(q_{\omega}=((N_{t}M_{t})(q_{az}q_{el})^{2})K\), where \(K=1\) for DCS-SOMP. To generate Figure 1(a), we arbitrarily set the number of antennas at the transmitter to \(N_{t}=32\) and the number of transmitted symbols to \(M_{t}=20\), similar results were obtained with other values. Algorithm 1 outlines our proposal. The adaptive search step is called at line 17. Algorithm 2 provides a summary of the adaptive search step.
# Recent Advances of the BIRALET System about Space Debris Detection
## 3 Results and Discussion
The passage of the objects within the beam of SRT is clearly recognizable thanks to its sweep in time and frequency. The thick line in the middle of the diagram (centered at 410.85 MHz) represents the carrier signal. Its presence is due to the relative proximity of the receiver with the transmitter of the BIRALET (the baseline of the bistatic radar is about 40 km). Actually, this peculiarity simplifies the interpretation of the measured Doppler frequency, which can be calculated from the frequency shift between the object echo and the carrier at the center of the visibility interval (which corresponds to 15:36:20 UTC in the given example). The measured Doppler frequency was retrieved for all of the objects and compared with the estimated one, showing a good agreement between the observed and predicted Doppler frequency. The maximum difference is in the order of magnitude of 1 kHz, the mean value of the difference is about 0.34 kHz, and the standard deviation is about 0.27 kHz. This comparison is reported in Table 2, for every object in the list, identifiable by its catalogue number (NORAD ID).
## 5 Evaluation
### Performance on the Random Selection Gold Standard  
This evaluation assesses the classifiers' performance on the random selection gold standard introduced in Section 5.1.1 using a k-fold cross-validation with four folds. The test data were divided into 80% training data and 20% evaluation data in each run. Once all runs were executed, the results were summarized.  
This procedure was not applied to the GPT/heuristic classifier because it only uses few-shot training with examples that were not part of the evaluation dataset. As it can easily be seen in Tables 12 and 13, the DistilBERT classifier is the best performer. The Transformer model's effective use of self-attention and pre-training mechanisms was key to extracting meaningful semantics for classification, even from input data that are limited to bipartite skill labels.  
SVM results were as expected as they did not perform well in the presence of limited training data. The low performance of the GPT-based heuristic is not entirely surprising as it lacked domain knowledge and was not optimized for classifying human resources tasks.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Predicate** & **Topic** & **Is Offshorable** & **Example** \\ \hline clean & * & no & clean house \\ drive & * & no & drive truck \\ program & * & yes & program python \\ write & * & yes & write article \\ \hline \hline \end{tabular}
\end{table}
Table 10: Example rules for identifying offshorable and non-offshorable skills. The asterisk in the topic column indicates that the rule works across all skill topics.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Predicate** & **Topic** & **Automatable** & **Example** \\ \hline moderate & * & no & moderate meeting \\ negotiate & * & no & negotiate contract \\ calculate & * & yes & calculate production time \\ monitor & * & yes & monitor process \\ \hline \hline \end{tabular}
\end{table}
Table 11: Example rules for identifying automatable and non-automatable skills. The asterisk in the topic column indicates that the rule works across all skill topics.  
Although the ensemble model is considerably more complex than DistilBERT, it still performs similarly to this Transformer model. This result indicates that the additional generalization capabilities provided by this more sophisticated model did not translate into better performance since the model has been constrained by the scarcity of input data, which was limited to bipartite skill descriptions.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 4 Method
### Prompting Strategies and Temperature Levels  
Prompt Techniques. In this study, we employed various prompting strategies to instruct the LLMs to convert information from clinical reports into the FHIR format. The specific prompts used are provided in Appendix B. In total, we evaluated the LLMs in six different setups, combining each of the three prompt strategies (basic, two-step reasoning, and chain-of-thought) with both zero-shot and few-shot examples.  
The basic strategy directly asks the LLMs to convert the clinical report to FHIR. For the two-step reasoning strategy, following the approach from [PERSON] et al. [34], we first asked the LLMs to group the information that might be interesting into an FHIR bundle. In a subsequent step, the LLMs were tasked with converting this structured information into FHIR. Based on recent advancements in LLMs' reasoning capabilities, we implemented the iterative self-correcting chain-of-thought (CoT) strategy [26; 36]. This involved first asking the LLMs to perform the conversion and then, in a second call, comparing and correcting the generated FHIR with the clinical report. Details about the prompts and design choice are provided in Appendix B.1.2  
The zero-shot technique involved presenting the LLMs with the text of an unstructured document and asking them to convert it to FHIR following the desired structure. This approach assessed the LLMs' ability to perform the task without any prior examples, relying solely on their pre-trained knowledge.  
The few-shot technique involved providing the LLMs with a detailed example of an unstructured document and its corresponding FHIR document before asking them to convert a new clinical report. To ensure a robust and representative few-shot learning approach, we iteratively designed a detailed example that covered every resource information. This example is described in Appendix B.1.1 and was chosen for its representativeness, complexity, and relevance to the study's objectives.  
Temperature. Temperature is a crucial hyperparameter that regulates the randomness of the LLMs' generated outputs. A temperature of 0 produces a deterministic output, whereas a higher temperature value yields more diverse outputs. In this study, we experimented with low (0) and high (2) temperature levels. This decision was informed by a previous study [37], which suggested that temperature variations within the range of 0 to 1 do not significantly impact the LLMs' performance in problem-solving tasks. By examining the effects of these two temperature levels, we aimed to gain insights into the LLMs' performance and the influence of temperature on the conversion to FHIR based on clinical cases.
## 3 Integration of Multi-Domain MEC Environments
### Business Implications
#### 4.1.3 Pricing Schemes  
Pricing MEC services is an open topic even for single domain deployments. A multi-domain approach increases the complexity of pricing, due to the diversity of scenarios that can emerge in the integration of MEC providers for delivering a single MEC service spanning more than one administrative domain.  
The pricing schemes must be designed to work both in a single- and multi-domain fashion, involving either independent or combined MEC providers, for third party demands. Moreover, even for simple pricing formulas, the values of the parameters will be also dynamically adapted, e.g., according to situational demand or resource/capability availability, or defined by market mechanisms such as spot markets (such as those of Amazon Web Services for EC2 service), or negotiated bilaterally.  
For dynamic service offerings, it can be envisioned pay-as-you-go models as applied for cloud services, where the price for each resource or service is proportional to the time for which is utilized. When more complex capabilities are involved, it can be considered an additional service set up price, reflecting coordination costs, while the contained resources and services are priced as defined above. Finally, some connectivity service (i.e., bandwidth capacity among providers or towards Internet) with assured quality (in line with the third party application needs) could be charged proportional to either the nominal capacity or the 95 th percentile, similar to today's Internet eXchange Points (IXPs) and bilateral peering and transit pricing agreements.