esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### BERT-Readability and BERT-Technicality  
We developed BERT-Readability and BERT-Technicality regression models to predict ease of reading and technicality levels in scientific research abstracts on a prediction scale between 0 and 100. These models were trained using the curated dataset, which included annotations for ease of reading and technicality scores for each abstract. We evaluated the performance of BERT-Readability and BERT-Technicality using the RMSE metric. The results demonstrated the models' effectiveness in predicting ease of reading and technicality scores.  
As shown in Table 2, the RMSE for ease of reading is 10.61 for English abstracts and 11.80 for German abstracts. For technicality, the RMSE is 9.42 for English abstracts and 10.07 for German abstracts.  
Furthermore, we conducted a comparative assessment, pitting our models against widely adopted and well-established readability formulas commonly utilized in healthcare literature [45; 46], as shown in Table 2. The results consistently demonstrated that our models outperformed these traditional formulas in terms of prediction accuracy. This underscores the critical advantage of employing specialized models tailored explicitly for medical experts.  
The existing readability formulas assessed in our comparison rely on a set of general features, such as sentence length, syllable count, and word complexity, which are designed for assessing text readability across various domains. In contrast, our models have been meticulously fine-tuned to account for the specific needs and nuances of medical research abstracts, offering a more precise and effective solution for this specialized context.  
Overall, the dataset and models presented in this study offer valuable resources for assessing the ease of reading and technicality of scientific research abstracts in both English and German. These models serve as valuable tools for personalized search engines, whether by enabling the use of filtering-based techniques or contributing to the ranking algorithm used in the retrieval process. This enables medical practitioners to access relevant research findings that align with their language proficiency and expertise.
## 4 Effects of Cannabinoids on Gastrointestinal Motility
### Endocannabinoids
#### 4.1.2 Effects of Endocannabinoid-like Compounds on Gastrointestinal Motility  
Naturally occurring endocannabinoid-like compounds palmitoylethanolamide (PEA), oleoylethanolamide (OEA), and oleamide (OA) were demonstrated to have an inhibitory effect on the upper GIT motility of mice. PEA inhibited upper GIT motility in healthy mice [67] and in a model of chronic GI inflammation induced by croton oil administration,where this inhibition was independent of CB1, CB2, opioid, and alpha-2 adrenergic receptors, and was also not blocked by the NO synthase inhibitor L-NAME. In the same study, PEA levels in the small intestine of croton oil-treated mice were significantly lower than in control animals [68]. OEA inhibited small intestinal transit in mice [67; 69] but did not slow whole gut transit or colonic propulsion. OEA worked independently of CB1, CB2, PPAR\(\alpha\), and TRPV1 and was not blocked by the GLP-1 antagonist exexem-3(9-39) amide [69]. The same study found that OEA blocked in vivo stress-induced hypermotility at dosages that had no effect on physiological transit [69]. OEA, but not PEA, slowed gastric emptying independently of CB1, CB2, and PPAR\(\alpha\) receptors and was present at higher levels in the stomachs of mice fed a high-fat diet [70]. OA slowed small intestinal transit in mice [67].
## 4 Results
### User Results
#### 4.2.2 Dual-Frequency  
In the previous chapter, the performance of multi-frequency and multi-GNSS PPP-RTK with no regional atmospheric corrections was accessed. Many other contributions, like those mentioned in the Introduction [3; 4; 6], are based on dual-frequency observations. Especially the common approach to PPP-RTK, where ionospheric-free code and phase observations are processed in combination with the Melbourne-Wubbena linear combination [34; 35], which makes use of only dual-frequency observations. To gain further insight into the benefits of a multi-frequency approach over a dual-frequency approach, user processing is repeated and limited to two frequencies. In this chapter, we take a closer look at how dual-frequency affects the convergence time of the kinematic user coordinate solution when compared to a multi-frequency approach. For this, the static phase bias PPP-RTK products are used again to compute kinematic coordinate solutions with dual-frequency observations. The two frequencies used are the first two frequencies of each GNSS listed in Table 1.  
In Figure 8, the performance of the presented methods is visualized for kinematic coordinate solutions. When compared to the results of the multi-frequency processing (see Figure 6), it is clear that the time until an accurate ambiguity-fixed solution is available significantly increases. While a fixed solution was achievable in three or fewer epochs in most cases with multi-frequency observations, this increased to seven or fewer epochs. Specifically, a fixed solution was achieved in seven epochs or fewer in 71% of all cases. In 10 and 20 epochs or less, a fixed solution was found in 94% and 97% of all cases, respectively.  
With the presented methods of wide-lane ambiguity resolution, it was not possible in any case to fix the wide-lane ambiguities in the first epoch. The single epoch positioning results are thus not differentiated in Figure 9. This leads to worse single epoch positioning performance when compared to the wide-lane fixed multi-frequency counterpart. When compared to the no wide-lane fix multi-frequency results, the dual-frequency results are still worse. The rms of the single epoch's north, east, and vertical residuals are given as 39 cm, 31 cm, and 156 cm, respectively.  
Figure 8: Visualizing the temporal characteristics of the user solution. Similar to Figure 6, except that the results for dual-frequency processing are shown.
## 1 Introduction  
Orthotropic steel decks (OSDs), characterized by their composite structure of deck plates, longitudinal stiffeners, and transversal diaphragms, are extensively employed in long-span steel bridge construction due to their advantageous strength-to-weight ratios [1, 2]. Nevertheless, these structural systems have been demonstrated to be particularly vulnerable to fatigue damage under repetitive vehicular loading, with crack initiation being exacerbated by welding imperfections, residual stresses, and component interaction constraints [3, 4]. Field investigations have revealed that diaphragm-to-U-rib welded connections (DUs) consistently emerge as critical fatigue-prone components in operational OSD systems [5].  
Strategic perforations in diaphragms are conventionally designed to permit uninterrupted longitudinal rib continuity, though these geometric discontinuities have been shown to induce constraint attenuation at connection interfaces. Recent analytical studies have further confirmed that such aperture configurations in the diaphragm plate significantly compromise structural integrity by modifying stiffness distribution patterns and promoting localized buckling phenomena, ultimately leading to stress intensification near notch boundaries [6, 7, 8]. Notably, permanent diaphragm deformations at notch regionshave been documented in multiple in-service bridges through advanced monitoring campaigns [9; 10]. While parametric investigations focusing on notch geometry optimization (e.g., radius dimensions and diaphragm thickness variations) and assembly tolerances have been systematically conducted [11; 12; 13], the precise implications of diaphragm buckling on fatigue performance evolution and its associated mechanical response modifications remain insufficiently characterized.  
A comprehensive investigation was conducted through integrated field measurements and numerical analysis to elucidate the deformation-induced stress redistribution mechanisms. Stress monitoring data from instrumented bridges were correlated with finite element modeling results to analyze diaphragm deformation impacts on both the stress characteristics and fatigue performance. Based on the mechanistically derived stress evolution patterns, a novel treatment scheme was proposed to overcome the adverse fatigue performance caused by diaphragm buckling, which improved the maintenance efficiency and provided a reference for similar projects.
## 3 Results
### Plant Water Status and PSII Functionality  
The water potential measured at predawn (\(\Psi\)pd) showed that trees in the unaffected stands (NA-trees) had had better water status conditions, i.e., higher (less negative) water potential values, than the non-symptomatic individuals in the decaying stands (NS-trees, Figure 7). This finding reflects that the pines in the healthy stands had better water status conditions (Table 2). The comparison between NS-trees and S-trees within declining stands showed no significant differences, and the differences were only found at stand level (C1 compared to C2) (Table 3).  
Figure 6: Proportion of dry needles versus normal needles per branch (%), measured in each pine stand and disease degree. The striped dark gray bars show the values for the NA-trees; the smooth light gray bars show the values for the NS-trees; the smooth dark gray bars show the values for the S-trees. Values are the mean \(\pm\) SE for \(n=6\) pines per stand and disease degree.  
Figure 7: Panel (**a**) Water potentials measured at predawn (\(\Psi\)pd) measured in each pine stand and disease degree, for both surveys T1 and T2. The striped dark gray bars show the values for the NA-trees; the smooth light gray bars show the values for the NS-trees; the smooth dark gray bars show the values for the S-trees. Values are the mean \(\pm\) SE for \(n=6\) pines per stand and disease degree.  
The maximum photochemical PSII efficiency (Fv/Fm) followed the same trend. The non-symptomatic individuals in healthy stands (NA-trees) obtained higher values for this variable (around 0.8) than the non-symptomatic individuals in decaying stands (NS-trees) (Figure 7, Table 2). Within declining stands, NS and S-trees did not show significant differences between disease degrees (Table 3). The results indicated that C2 had lower Fv/Fm values than C1 and P1 during the first survey (T1), with no differences in T2 (Table 3). Regarding the sampling date, we observed only a slight increase in declining stands.
## 6 Financial Aspects  
Disease-related malnutrition has relevant consequences on patient outcome, especially in the most severely ill patients treated on ICUs and IMC-Us. However, for several reasons it remains difficult to demonstrate the economic benefit related to improved outcome rooted in MNT. The comparisonof different clinical practices in hospitals and countries, variable costs for hospital staff and MNT products, and heterogeneous accounting systems complicate any reliable comparison of cost and benefit. Furthermore, the acquisition of nationwide and international data is limited and explains why study data are scarce [128] and why cost-economic analyses show discrepancies in this important field of interest.  
Relevant data for the critically ill exist concerning the potentially cost-saving introduction of the pre-mixed multi-chamber bag compared to compound PN. In a real-world analysis of PN use, potential savings of up to $1545 USD per patient using a multi-chamber bag could be achieved according to US data from the Premier Perspective(tm) database [129]. Patients on PN also benefit from a nutritional care team in order to assure adequate indication, application, and adaptation of PN, resulting in savings of 245,000 \(\epsilon\) EUR in one year per 100 treated patients in a Swiss university hospital [130]. A meta-analysis involving six clinical trials evaluating early EN in the ICU setting found a statistically significant reduction in mortality compared to standard timing of EN [131]. A cost-effectiveness analysis of these data showed a $14,462 USD reduction in total costs per patient for acute hospital care of patients receiving EN within 24 h after admission. These results were robust also when including sensitivity analysis with European cost data [132].  
The Swiss SPN study added PN on days 4-8, whenever \(>\)60% of the caloric needs where not covered by EN on day 3. Based on the Swiss accounting system (Swiss DRG), a simulated calculation revealed a risk reduction for infection of 10%, resulting in savings of 63,048 CHF per infection [7; 133]. The cost-analysis of the EPaNIC trial showed increased expenses with early-PN, mainly due to pharmacy-related costs and higher expenditures for PN and anti-infectious agents when compared to late PN patients who only received PN when EN remained insufficient after the first week of ICU treatment. Withholding early PN resulted in a net cost saving of 1210 Euro per patient [134].  
The pronounced loss of lean body tissue, lack of mobilization, and increased protein energy needs all lead to higher complication rates with increased morbidity and mortality in this most fragile group of critically ill patients. Further studies, such as the recently published EFFORT study [135], should describe the effect of MNT not only in terms of clinical benefit but also with regard to monetary equivalent. Such data are of utmost interest to support and spread the relevance of MNT and linked-care teams.
## 4 SkyDRL for Multi-Cloud Service Composition Problem
### SkyDRL
```
0: discounted factor \(\gamma\), learning rate \(\alpha\), minibatch size \(b\), initial exploration and final exploration \(\epsilon\), sampling weight \(\beta\)
1: Initialize replay memory D with capacity \(N\) and initial priority \(p_{1}=1\)
2: Initialize action-value function Q with Q-network parameters \(\theta\)
3: Initialize target action-value function Q' with target-network parameters \(\theta^{\prime}\)
4: Initialize \(SK=\phi//\text{skyline list}\)
5: Insert all cloud services of the root R into the heap
6:for each episode do
7:for each t do
8: Acquire skyline services \(SK\) using the Branch and Bound skyline algorithm
9: With probability \(\epsilon\) select a random action \(a_{t}\) in \(SK\)
10: Otherwise select \(a_{t}=argmax_{a}Q(s_{t},a_{t};\theta)\) in \(SK\)
11: Execute action \(a_{t}\), observe reward \(r_{t}\) and next state \(s_{t+1}\), \(s_{t}=s_{t+1}\)
12: Store transition \((s_{t},a_{t},r_{t},s_{t+1})\) in D with maximal priority \(p_{t}=max_{i}<tp_{i}\)
13: Sample mini-batch \(b\) of transition \(\left(s_{j},a_{j},r_{j},s_{j+1}\right)\), for each sample \(j\sim P(j)=p_{j}/\sum_{i}p_{i}\)
14: Compute importance-sampling weight \(w_{j}=\left(N\times P(j)\right)^{-\beta}/max_{i}(w_{i})\)
15:if episode terminates at step \(j+1\)then
16: set \(y_{j}=r_{j}\)
17:else
18:\(y_{j}=r_{j}+\gamma Q(s_{t+1},argmax_{a}Q(s_{t+1},a;\theta),\theta^{\prime})\)
19:endif
20: Update Q-network parameters \(\theta\) with a loss function of \(\frac{1}{b}\sum_{j=1}^{b}w_{j}\left(y_{j}-Q(s_{t},a_{t};\theta)\right)^{2}\)
21: Compute TD-error \(\delta_{j}=y_{j}-Q(s_{t},a_{t};\theta)\)
22: Update transition priority \(P_{j}\leftarrow\left|\delta_{j}\right|\)
23: Every C steps reset \(\theta^{\prime}\leftarrow\theta\)
24:endfor
25:endfor
```  
**Algorithm 2**Skyline-Enhanced Deep Reinforcement Learning Approach
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 2 Related Work
It can be considered a cornerstone article in the edge intelligence field to tackle questions of challenges, frameworks and application domains. In this scenario, the contribution of the current manuscript to the existing surveys and review papers, when compared to the research published in 2020, is mainly in terms of the groups of techniques and strategies. Table 1 shows the comparison between our work and the other surveys mentioned in this section. In summary, the main gaps of the analyzed works are focused on aspects such as \"Techniques and Strategies\" on the edge. The table also shows the aspects of \"Challenges\"and \"Different Application Domains\", where edge intelligence can be used. To show the difference of our work compared to the related work we found, we used the six challenges, eight groups of techniques and strategies and six application domains that emerged from our SLR. Considering the six challenges we describe in this work, we observed that [13] does not even mention any challenge questions, [14; 16] only address one of the challenges we describe, [10] addresses all the challenges we also discuss and [15] addresses two of these challenges. On the other hand, when considering the groups of techniques, [10] describes only three of the eight groups we present and [13] presents two of them, [14; 15], four of them, and [16] does not cite any of them. Finally, considering the six application domains we discuss, [10; 15; 16] do not discuss any of them, [13] discusses only one and [14] discusses four of them. Table 1 shows these numbers. In this way, our SLR is more complete than all these related works. Regarding the framework, our paper presents several frameworks that were developed to implement techniques and strategies from different groups that we identified and as a source for other EI works. Our work does not propose any framework. We just organize and compile information from a considerable numbers of frameworks from the researched \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Scope**} \\ \cline{2-4}
**Paper** & **Challenges** & \
\begin{tabular}{c} **Group** \\ **of Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Different Application** \\ **Domains** \\ \end{tabular} \\ \hline [PERSON] et al. [13] & 0/6 & 2/8 & 1/6 \\ [PERSON] et al. [14] & 1/6 & 4/8 & 4/6 \\ [PERSON] et al. [16] & 1/6 & 0/8 & 0/6 \\ [PERSON] et al. [15] & 2/6 & 4/8 & 0/6 \\ [PERSON] et al. [10] & 6/6 & 3/8 & 0/6 \\ Our work & 6/6 & 8/8 & 6/6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing surveys. literature that has contributed to this field of research. We intend to elaborate an aggregated base so that new works can use this research as a reference source. It is a survey paper, not a research paper, and our goal is to report the state of the art of the field.
## 2 Advances in Multiple Access Techniques for 6G Mobile Wireless Networks
### Spatial Division Multiple Access  
In wireless communication systems, achieving high spectral efficiency requires serving multiple users through effective MA technologies. In MIMO systems, the key MA technology is SDMA. From an information-theoretic perspective, SDMA can be seen as employing an interference management strategy that treats interference as noise. SDMA provides near-optimal spectral efficiency when interference between users is minimal. However, in practical wireless systems, channel feedback errors make it difficult to guarantee perfect CSI in the downlink. As a result, the performance of SDMA is often far from optimal.
## 3 Proposed Approach
### Segmentation
#### 3.1.3 K-Means Algorithm  
In the first iteration, \(k\) cluster centres \(v=\{v_{1},\ldots\ v_{k}\}\) are initialised randomly [70]. The distance between all data points \(X=\{x_{1},x_{2},\ldots\ x_{n}\}\) and each cluster centre \(v_{j}\) are calculated according to the following equation:  
\[d_{ji}^{(t-1)}\big{(}x_{i},v_{j}\big{)}=\|x_{i}-v_{j}^{(t-1)}\|,\ i=1,2\ldots n,j=1,2\ldots k \tag{1}\]  
where \(x_{i}\) represents the \(i\)th data point, \(v_{j}\) shows the \(j\)th cluster centre, and \(n\) is the number of data points. Then each sample is assigned to the nearest cluster. Class means, or cluster centres, can be updated by the following equation:  
\[v_{j}^{(t)}=\frac{\sum_{x\in j}x_{i}}{n_{j}} \tag{2}\]  
where \(n_{j}\) is the number of data points belonging to the \(j\)th cluster at the iteration \(t\). K-means converges when the cluster centres do not change anymore based on the following equation:  
\[v_{j}^{(t+1)}=v_{j}^{(t)}\ \forall j=1,2\ldots k \tag{3}\]  
Figure 3: Clustering by K-means, (**a**) cluster 1, (**b**) cluster 2, (**c**) cluster 3, and (**d**) cluster 4.
## 2 Integrated Dynamics Model of the Helicopter Engine/Transmission System/Rotor  
### Simplified Generic Variable Speed Rotor Helicopter Dynamic Model  
A high-speed compound helicopter features a configuration that combines a coaxial counter-rotating rigid dual rotor with a tail propeller. This design was first realized on the Sikorsky XH-59 helicopter in the 1970s [22]. Figure 3 illustrates the layout of the power system, which primarily comprises a dual turboshaft engine, a transmission system, and a twin rotor. With the two sets of rotors rotating coaxially, one above the other, the rotor torque is balanced. Hence, the tail rotor evolves into a propulsive configuration.  
As explained in [11; 23], the analysis of the turboshaft engine, transmission system, and main rotor subsystem coupling is fundamental for accurately understanding the dynamic characteristics of a power system. A general power system model of a high-speed compound helicopter is developed based on the power system layout shown in Figure 3. To simplify the analysis, a two-stage reduction structure is used instead of the conventional three-stage reduction design, and a single engine is used to replace the dual engine, power \(P_{e}\), output speed \(\Omega_{e}\), and engine torque \(T_{e}\), enters the variable speed transmission system  
Figure 3: Layout of a coaxial rigid rotor in a high-speed helicopter power system.  
through the one-way clutch; the engine speed undergoes a deceleration ratio \(i_{\rm{p}}\). Following a secondary deceleration via the parallel wheel train, with a transmission ratio \(i_{\rm{p}}\) it produces an output torque \(T_{out}\) that drives the rotor to rotate. This results in torsion torque \(T_{MR}\) and lift \(L_{MR}\). A block diagram illustrating the principle of this power system is shown in Figure 4.