esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
After obtaining the experiment's results, understanding and evaluating them is required to develop an approach to solve the problem.  
As we can see in Figure 8, the best approach was the agglomerative hierarchical clustering technique with TFIDF word embedder and PCA reducing to two dimensions. However, this approach reached the best value at two clusters, which we previously discarded; since the goal is to organize conversations, splitting them into meaningful groups, only two clusters would be too reductive. Therefore, the accepted approach to solve the objective of this work was the kmeans++ clustering technique with SBERT word embedder and UMAP reducing to two dimensions, which resulted in eight clusters.  
Analyzing these clusters manually and through word clouds (Figure 9A-H) made us accept this approach because of the variety and the excellent division between them, even though it has a silhouette score of 0.63.  
Figure 8: Best clustering results with two dimensions reduction on the baseball dataset.  
Validating this approach on the other dataset we had, the car brands dataset, yielded good results but not as good as the baseball dataset results, with 16 clusters and 0.59 silhouette score. The word clouds (Figures 10A-H and 11A-H) show some variety, but some clusters could have been better divided, since they refer to different topics.  
Figure 9: Word clouds with the clustering results of the baseball dataset.  
This discrepancy could be attributed to multiple factors, such as the annotation quality of the datasets, their size (since the baseball dataset is bigger than the cars dataset), the distribution of alternatives in each dataset (number of times they appear in messages), the quality of the discussion, and the arguments used. Nevertheless, we believe this approach can achieve the objective of dynamically organizing the conversation based on the arguments used. In a real setting, not fixating on the value of clusters and with even  
Figure 10: Word clouds with some clusters results of the car brands dataset.  
more data, this approach manages to pick the correct number of clusters for the input data and group that data into perceivable clusters that can then be utilized in intelligent reports for the decision makers.
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 2 Parameters Characterizing the Capacity of the Multi-Airport System
### Multi-Airport System Capacity
\(\Psi:\,\varsigma\rightarrow\mathbb{R}^{+}\) satisfies subadditivity and homogeneity, where subadditivity characterizes the nonlinear superposition of capacity due to airspace resource sharing: \(\Psi(\mathrm{C}_{1}+\mathrm{C}_{2})\leq\Psi(\mathrm{C}_{1})+\Psi(\mathrm{C}_{2})\). Homogeneity reflects a decreasing or constant effect of scale; that is, the increase in capacity of a single airport is greater than the increase in capacity of the entire airport complex: \(\Psi(\lambda\mathrm{C})=\lambda^{\alpha}\Psi(\mathrm{C}),\,\,\,\alpha\leq 1\). Thus, as shown in Figure 1, single-airport capacity can be represented by a curve defined by \(\Phi\), while multi-airport capacity can be represented by a frontier hypersurface defined by \(\Psi\). Figure 1 shows a schematic of the capacity curve. The horizontal coordinates in the left figure indicate the number of aircraft arrivals, and the vertical axis indicates the number of aircraft departures. The curve in the figure indicates the capacity frontier. Each coordinate in the right diagram indicates the capacity of each airport in the multi-airport system, the pink surface in the diagram indicates the capacity frontier hypersurface, and the green point is the available capacity on the frontier.
###### Abstract  
Microalgae are a rich renewable source of a wide variety of bioactive compounds. This study focuses on seven microalgae--_Limmogira_ sp., _Dunaliella_ sp., _Lobosphaera_ sp., _Numochloropsis_ sp., _Odontella_ sp., _Porphyridium_ sp., and _Tetraselmis_ sp.-- analyzing their nutritional compositions and the potential bioactivity of their hydroethanolic extracts obtained via ultrasound-assisted extraction. The total phenolic content (TPC) and antioxidant activity (ABTS, DPPH, and ORAC), as well as the antimicrobial activity of the extracts were determined. The protein content of the microalgae ranged from 22.9 \(\pm\) 0.1 to 59.8 \(\pm\) 1.6%, the fat content ranged from 5.3 \(\pm\) 0.0 to 36.6 \(\pm\) 0.0%, and the carbohydrates ranged from 24.0 \(\pm\) 3.0 to 46.1 \(\pm\) 1.0%. The highest contents of protein, fat, and carbohydrates were found in _Limmogira_ sp., _Numochloropsis_ sp., and _Lobosphaera_ sp., respectively. The TPCof the extracts varied between 0.53 \(\pm\) 0.09 and 3.18 \(\pm\) 0.53 mg GAE/100 mg DW. Antioxidant activity values ranged from 1.09 \(\pm\) 0.15 and 2.85 \(\pm\) 0.45 \(\upmu\)mol TE/100 mg DW for ABTS, 0.25 \(\pm\) 0.06 and 2.28 \(\pm\) 0.06 \(\upmu\)mol TE/100 mg DW for DPPH, and 2.37 \(\pm\) 0.58 and 18.32 \(\pm\) 1.00 \(\upmu\)mol TE/100 mg DW for ORAC. The extract from _Limnospira_ sp. exhibited the highest antioxidant activity and also showed notable antimicrobial effects. Meanwhile, the _Nannochloropsis_ sp. extract demonstrated the strongest antimicrobial activity against most tested bacterial strains. Lipids were successfully extracted from _Nannochloropsis_ sp. Furthermore, alginate and zein films incorporated with bioactive-rich extracts from _Limnospira_ sp. and _Nannochloropsis_ sp., along with the lipid-rich extract from _Nannochloropsis_ sp., were developed. These films showed significant antioxidant activity and effective antimicrobial activity against _Listeria monocytogenes_.  
microalgae; _Limnospira_; _Arthospira_; _Dunaliella_; _Lobosphaera_; _Nannochloropsis_; _Odontella_; _Porphyridium_; _Tetraselmis_; nutritional composition; antioxidant activity; antimicrobial activity; edible film
## State of the Art
### Security Management in SMEs  
A large number of information security processes, frameworks, and methods whose intention is to reduce the lacks shown in the Introduction and to lessen the losses that they cause have appeared, and the need for their implementation is being increasingly recognised by organisations. However, as shown previously, they are inefficient in the case of SMEs [35; 36].  
With regards to the most prominent standards, it is possible to state that the majority of security management models are based on the ISO/IEC27001 [37] and ISO/IEC27002 [38] international standards, and that the security management models that are proving to be most successful at large companies are ISO/IEC27001 [37], COBIT [39], and ISM3, but they are difficult to install and require an investment that is too high for the majority of SMEs to be able to assume [25; 35]. Although some very interesting new proposals oriented towards this type of companies are appearing, the way in which they confront the problems is incomplete.  
Numerous bibliographical sources detect and highlight how difficult it is for SMEs to use traditional security management maturity methodologies and models, since they have been conceived for large businesses [40; 41; 42; 43]. It has repeatedly been justified that the application of this type of maturity  
Figure 1: Application of Action Research during research.  
models and methodologies is difficult and costly for SMEs. Moreover, even large organisations tend to adopt related groups of processes as a set, rather than dealing with processes independently [44].  
The aforementioned security management methodologies and models have not proved to be valid in SMEs for three reasons [40; 41; 42; 43; 44]:  
* They were developed by bearing in mind organisations that have far more resources.
* They deal with only part of the security management system, and almost none of them confront the installation of these systems from a global perspective, which obliges companies to acquire, implement, manage, and maintain various methodologies, models, and tools in order to manage security. What is more, the few applications that have attempted to tackle all the aspects of security management are expensive to acquire and require a complex management and costly maintenance, signifying that they are not appropriate for SMEs.
* Finally, it is possible to conclude that, although several standards, regulations, guidelines to good practices, security management, and risk analysis methodologies and models exist, they are not integrated into a global model that can be applied to small and medium-sized companies with guarantees of success.  
Therefore, and as a conclusion to this section, it could be said that tackling the problem of developing a new methodology for security management and its maturity for SMEs' information systems is both pertinent and opportune, as is the development of a model that will validate its functioning and a tool that will support this model, based on the problems that this type of company confronts--which have led to continuous failures when these systems are installed in this type of company.
## 2 Lossless Transmission Circuit Equations with Terminal Conditions
### Global Behavior of 1-D Map  
Here, we provide Theorem 2 to guarantee that for any initial point, every orbit by the iteration of the 1-D map of \(\psi(\tau)=h(\psi(\tau-1))\) ultimately penetrates an invariant interval set.  
**Theorem 2**.: _If \(1>\frac{r}{Z}>\zeta>0\), \(1>\frac{R}{Z}\geq 0\) and Nagumo's 3 SPWL Resistor \((k=N)\): \(\eta>-1\), \((\xi+\frac{\frac{R}{Z}-\zeta}{Z})-\frac{E}{b_{p}}<0\), and \((\xi-\frac{\eta(\frac{R}{Z}-\zeta}{Z}))-\frac{E}{b_{p}}>0\) or TD 3 SPWL Resistor \((k=T)\): \(\eta>1\), \(0>-\frac{E}{b_{p}}+(1+\xi+\frac{\xi}{Z})+\frac{(\eta-1)(\frac{r}{Z}-\zeta)(1+ \frac{R}{Z})}{2Z}\), and \(0<-\frac{E}{b_{p}}+(\eta+\xi+\frac{(1-(\eta-1)\xi)\frac{R}{Z}}{Z})-\frac{( \eta-1)(r-Z)(1+\frac{R}{Z})}{2r}\), then the following is the case._  
1. _The 1-D map_ \(\psi(\tau)=h_{k}(\psi(\tau-1))\) _has a unique unstable fixed point_ \(\psi_{k*}\) _in the interval_ \([\psi_{k1},\psi_{k2}]\)_, where_ \(\psi_{N*}=\frac{(R-Z)\zeta b_{p}}{2(R-\tau)}+\frac{E(Z-\tau)}{2(R-\tau)}\)_,_ \(\psi_{T*}=\frac{(Z-\xi)(\zeta(1+\xi))b_{p}}{2(r-R_{\zeta}^{\prime})}+\frac{E (r-Z\zeta)}{2(r-R_{\zeta}^{\prime})}(>0)\)_._
2. _There exists an invariant interval_ \(l_{k\psi}=[h_{k}(\psi_{k2}),h_{k}(\psi_{k1})]\supset[\psi_{k1},\psi_{k2}]\) _such that_ \(h_{k}(l_{k\psi})\subset l_{k\psi}\) _and_ \(h_{k}(\psi_{k2})<\psi_{k1}<\psi_{k2}<h_{k}(\psi_{k1})\)_, where_ \(h_{N}(\psi_{N1})=\frac{(1-\frac{R}{Z})(\zeta+(1-\xi)\frac{r}{Z})bp}{2\frac{r}{ Z}(1+\frac{R}{Z})}+\frac{E}{1+\frac{R}{Z}}\)_,_ \(h_{N}(\psi_{N2})=\frac{(-1+\frac{R}{Z})(\eta\zeta+(\eta+\xi)\frac{r}{Z})bp}{2 \frac{r}{Z}(1+\frac{R}{Z})}+\frac{E}{1+\frac{R}{Z}}\)_,_ \(h_{T}(\psi_{T1})=\frac{(1-\frac{R}{Z})(1-Z(1+\xi))bp}{2\frac{r}{Z}(1+\frac{R}{Z })}+\frac{E}{1+\frac{R}{Z}}\)_,_ \(h_{T}(\psi_{T2})=\frac{(-1+\frac{R}{Z})((\eta-1)\zeta-1-(\eta+\xi)\frac{r}{Z})bp}{ 2\frac{r}{Z}(1+\frac{R}{Z})}+\frac{E}{1+\frac{R}{Z}}\)_._
3. _For any_ \(\psi_{0}\in(-\infty,\infty)\)_, there exists_ \(jth\) _iterate of_ \(h_{k}\) _such that_ \(h_{k}^{j}(\psi_{0})\in l_{k\psi},j(>0)\in\mathcal{Z}\)_, where_ \(h_{k}^{j}\) _denotes the_ \(jth\) _iterate of_ \(h_{k}\)_, i.e.,_ \(h_{k}^{j}\) _is the_ \(j\)_-fold composition of_ \(h_{k}\) _with itself._ \(\mathcal{Z}\) _is the set of non negative integers._  
Proof in Appendix A.
## 5 Discussion  
GC-PPP-RTK/INS/vision integration with a cascading filter can provide continuous positioning information with high precision. The improvements brought about by vision are more significant in challenging environments. Vision reduces the error divergence of PPP-RTK/INS when the GNSS signals are blocked. The positioning error of PPP-RTK/INS reaches 27.16 m after the GNSS signals were lost for 60 s. The inclusion of vision reduces the positioning error to 4.88 m. Vision also helps to improve the estimation accuracy of velocity and attitude. Although the improvements are significant, the positioning accuracy still needs to be improved for vehicle navigation.  
Dynamic objects can seriously affect the positioning performance of vision, so it is important to weaken the influence of dynamic objects. The dynamic object removal algorithm proposed in this paper can remove most fast-moving objects and can improve the positioning accuracy of vision. However, it is difficult to deal with slow-moving objects, which is worth further research.  
Only GPS and BDS-2 were used in our test so the inclusion of BDS-3 and other systems will be the subject of further research. The integration model of PPP-RTK/INS/vision proposed in this paper is realized by a cascading filter, which can still work when one subsystem is seriously disturbed. The tightly coupled integration of PPP-RTK/INS/vision is another integration method that is worth studying. Because the update frequency of vision is higher, the tightly coupled integration of PPP-RTK/INS/vision may face a heavier computing burden. Therefore, there are still many problems worthy of study in the integration of PPP-RTK/INS/vision.
## 3 System Design  
In this section, the system architecture and workflow of the Zhuang speech recognition and translation cloud platform are introduced. The system architecture is consisted of four components, including the Zhuang language database automatic construction system, the Zhuang language speech recognition model, the Zhuang-Other languages neural machine translation model, and the edge cloud platform acceleration, as shown in Figure 1.  
Applying deep learning to speech recognition requires collecting and annotating a large amount of audio data in the Zhuang language. Consequently, an automated system was created to create a database of the Zhuang language. This system is capable of extracting audio clips and video subtitles from the archives of Guangxi TV over the past ten years. This system not only obtains a minimum of 200 h of audio and labelled data but also drastically reduces the amount of manual effort needed. The next step was to develop a speech recognition network. SAformerNet, a more efficient and accurate transformer-based ASR network is achieved by inserting additional downsampling modules. SAformerNet has a faster training speed and a shorter decoding time than Conformer, but with similar recognition performance. The advantage of this development is that the network can be  
Figure 1: The system architecture of the Zhuang speech recognition and translation cloud platform.  
rapidly deployed. After that, NMT model for translating Zhuang into other languages is constructed by fine-tuning the BART model and corpus filtering strategy. The model is utilized to develop an encoder adaptor comprised of two FFNs and a decoder adaptor including a FFN, and an encoder-decoder attention layer integrated with BART. This is fine-tuned on a specific dataset to preserve computational resources and significantly diminish the duration of model training. Finally, the FPGA-accelerated edge computing approach is used to achieve fast response of network programs, and the optimization of the matrix product of GPU/CUDA and FPGA hardware devices is used to achieve edge-side acceleration. A small terminal platform is designed with the esp32-s3 development board as the main controller. The configuration of the edge server is NVIDIA GeForce GTX 1650, with 4 T storage. The model was trained on a single Nvidia RTX 3090 GPU. The architecture of the model was accelerated on a Xilinx XCZU9 EG FPGA.
## 5 Conclusions  
This paper presents a novel \"Both Ends Explanations for RL (BEERL)\" method that generates fine-grained explanations, which connects and compares the input and output explanations of a black-box model. In this manner, the contribution of every feature to every reward component is revealed explicitly, allowing each element to be evaluated individually. The explanations are generated at two different levels as a result of exposing reward priority. The first is at the Q-level, where all components generate values in the same range to minimize the risk of vanishing or exploding gradients, as exemplified in Appendix D.2. The second is at the RL-level where the reward priority is applied to explain the rationale behind choosing an action. Our method also produces a detailed contrastive explanation that explains why one action is chosen instead of another. Additionally, the contrastive explanation can be compressed to highlight the important element(s) that make it more comprehensible to humans. To identify the most important factor of the agent, the input feature and the reward component are compared by aggregating their contributions. The implemented agent can be modified by adjusting the reward priority to fulfill the desired properties, such as reducing the bias of the reward components. A further adjustment of the RL agent can also be performed, such as retraining the Q-function partially per component, removing non-contributing input feature(s), removing misbehaving or suboptimal reward component(s), and tuning reward prioritization without restarting the training process, etc.  
We implemented our method with respect to the remote electrical antenna tilt use case (as described in Section 4) and to two openAI gym environments (see Appendix D) to demonstrate the benefits of resultant explanations. A fine-grained explanation implies that the amount of information received by the user increases. Using the relevance table set by the user, the focus value summarizes the fulfillment of the desired properties. In the future, we intend to investigate a method to improve the focus value of the RL agent. This can be performed by incorporating the explanation during the training process. More experiments can be conducted on top of this work to mitigate bias between different actions.
###### Abstract  
(1) Background: The aim of this study is to determine the intra-rater and test-retest reliability of an upper extremity swap test performed in the one-handed plank position on a force plate and to establish if there are any between-limb differences in assessment with either open or closed eyes. (2) Methods: Fifteen males participated in this study (23.1 \(\pm\) 2.3 years; 71.2 \(\pm\) 7.4 kg; 175 \(\pm\) 4 cm). The swap test (ST) on the force plate was performed during two separate visits, with a one-week interval between tests performed with eyes open and closed, separately for the left and right upper limbs. (3) Results: Intra-rater reliability for measurements with eyes open and closed on both the left and right sides was good to excellent for almost all variables (ICC = 0.84-0.96). Moderate reliability was observed only for the center of pressure (COP) area. Test-retest reliability was somewhat lower. With eyes open and closed, good to excellent reliability was observed for COP length and velocity in the M-L direction and for the total COP length (ICC = 0.77-0.95). However, for COP length and velocity in the A-P direction, as well as for COP area, reliability was moderate (ICC = 0.58-0.70). No significant differences were found between the left and right sides in any variable. (4) Conclusions: The results of this study indicate that the upper limb sway test performed on a force plate in the one-handed plank position, both with eyes open and closed, is a reliable measurement option and has practical applications in clinical settings, including for baseline assessments, return-to-play protocols, and injury prevention strategies.  
**Keywords:** upper limb; sway test; stabilometry; platform; plank position; reliability
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 5 Performance Evaluation
### Impact of Backhaul Network  
In this section, we inspect the influence of the backhaul network bandwidth (i.e., \(b_{i,j}\)) on the performance of the sleep control mechanism. We set \(b_{i}=300\) Mbps and vary \(b_{i,j}\). In Figure 6, we show the total energy consumed in a MEC system for various \(b_{i,j}\)s (300, 600, and 1000 Mbps). We can observe in the figure that the effect of \(b_{i,j}\) on the total energy consumed in a MEC system is not so significant. The energy consumed by a MECS is subdivided into the energy consumed for processing tasks (\(e_{i}^{p}\)) and the energy to transfer tasks to other MECSs (\(e_{i}^{tr}\)). \(e_{i}^{tr}\) is influenced by the number of tasks transferred from a MECS to its neighbors, which does not depend on \(b_{i,j}\). In the case of \(e_{i}^{p}\), it increases with the utilization of a MECS and \(\mu_{i}\) is affected by \(a_{i}=a_{1,i}+a_{2,i}\). The tasks transferred among MECSs affect \(a_{2,i}\). However, even though tasks are distributed among the MECSs in an uneven manner, the total number of tasks imposed on a system does not change. Therefore, the total energy consumed in a MEC system is not affected by \(b_{i,j}\).  
Figure 7 shows the impact of \(b_{i,j}\) on the average task service delay. As can be seen in the figure, the average task service delay decreases as the bandwidth of a backhaul network increases. The tasks service delay incurred by a MECS \(i\) during a time slot \(t\) is \(d_{i}^{p}(t)=\left(Q_{i}(t)+a_{i}(t)\right)/b_{i}\). When a MECS \(i\) decides to sleep, it transfers the tasks in its task queue to its active neighbors, which causes a task transfer delay \(d_{i}^{tr}=\eta_{i,j}Q_{i}/b_{i,j}\). As \(b_{i,j}\) increases, \(d_{i}^{tr}\) decreases. Therefore, the average task service delay in a MEC system decreases as the communication bandwidth of a backhaul network increases.  
Figure 5: Distribution of the tasks queue length (\(|N|=10\), \(\rho=1.0\)). (**a**) REJO; (**b**) Proposed.  
Figure 6: Impact of the backhaul network bandwidth on the total energy consumption. (**a**) 10 MECSs; (**b**) 20 MECSs.
## 3 Results and Discussion
### Modelling of Z-ISO for In Silico Structure-Function Analysis
In the broad search regime, with the same sampling method used, Autodock Vina did not find the equivalent H266/H354 bis-His binding site in the maize enzyme model. This is possibly due to the rotamer position of H354 in the maize structure being more directly aligned with H266 (Figure S5), leading to a narrower binding cavity that is difficult to identify in the simulation. Given that heme binding would likely occur through axial ligation, a narrower sampling area (2744 A\({}^{3}\)) was used for the maize enzyme focusing on the H266 site. Under these conditions, the heme B group was reproducibly positioned with the central Fe positioned approximately between H266 and H354, albeit with a higher binding energy for all modes than the alternative H150 site (Figure S6). The lowest energy binding modes from each of the two putative heme binding sites are displayed for the maize structural model in Figure 6B,D,E. The model with the highest-affinity binding mode for the putative H266/H354 site was subsequently used to dock 9,15,9\({}^{\prime}\)-tri-_cis-_\(\zeta\)-carotene to investigate whether the putative cofactor binding site was close to the carotenoid substrate, thereby identifying a possible active site. The twenty binding modes identified in the simulation fell broadly into three groups of similar orientation, all with similar binding energies (Figure S7). One of these groups positions the substrate in proximity to both the predicted position of the heme B cofactor and the H150 residue and is displayed in Figure 6C. The proximity of H150 to the 15-_cis_ bond of 9,15,9\({}^{\prime}\)-tri-_cis-_\(\zeta\)-carotene and its known involvement in Z-ISO activity suggests that this residue may have a catalytic role in the _cis_-to-_trans_ isomerization. A conserved serine (S177 in maize) is located \(\sim\)3 A away from H150, suggesting these two residues may form a catalytic dyad (Figure S8B). Finally, D294 has also been implicated in Z-ISO activity [6]. The AlphaFold structures identified possible H-bond networks from this residue to residues on other secondary motifs, suggesting a role for this aspartate in protein folding (Figure S7A). Alanine substitutions of D294 and its predicted H-bond partners R252 and H253 gave increased free energy predictions relative to the WT protein (Figure S8A).