esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Clinical Applications of AI to CHF Management
### Natural Language Processing  
Natural language processing (NLP) refers to the branch of computer science--specifically, the branch of artificial intelligence or AI--concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics--rule-based modelling of human language--with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in text or voice data and 'understand' its full meaning, complete with the speaker or writer's intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and rapidly summarise large volumes of text--even in real-time. Some prominent platforms are the linguamatics' NLP software [[https://www.linguamatics.com/products/linguamatics-nlp-platform](https://www.linguamatics.com/products/linguamatics-nlp-platform)]([https://www.linguamatics.com/products/linguamatics-nlp-platform](https://www.linguamatics.com/products/linguamatics-nlp-platform)) accessed on 5 April 2023, which extracts data from electronic health records to provide insights on heart failure patients; Nuance Communications' Dragon Medical One platform [[https://www.nuance.com/healthcare/campaign/ppc/dragon-medical-one-solution](https://www.nuance.com/healthcare/campaign/ppc/dragon-medical-one-solution)]([https://www.nuance.com/healthcare/campaign/ppc/dragon-medical-one-solution](https://www.nuance.com/healthcare/campaign/ppc/dragon-medical-one-solution)) accessed on 5 April 2023, that enables healthcare providers to document patient information using voice recognition and convert it into structured data. Among the others, the EHR analytics platform from MedAware [[https://www.medaware.com/](https://www.medaware.com/)]([https://www.medaware.com/](https://www.medaware.com/)) accessed on 5 April 2023, can analyse electronic health records to support the diagnosis and management of heart failure [55]. NLP-based tools might become special tools, particularly for home-based monitoring of CHF patients, for early recognition of clinical worsening. In this context, the patient represents her/his control, and the tool scans the patient's discourse searching for new features that might reflect a reduced functional capacity or a worse clinical condition.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 3 Simulation Setup and Performance Metrics
### Performance Metrics
#### 3.2.1 Throughput (bits/s)  
The average data rate of the successful data packets or message delivery from the source flying node to the destination flying node over a communication link in a particular unit of time is called throughput. Mathematically, throughput can be determined using the following Equation (1):  
\[\text{Throughput}=\frac{\text{N}\,\times\,\text{S}\,\times\,8}{\text{T}} \tag{1}\]  
where N is assumed to be the number of successful packets transferred, S is the size of the packet, and time duration is the existing higher throughput, which is a requirement and characteristic of any network.
## 3 Results and Discussion
### Stage III
#### 3.3.3 Water Activity  
The transformed quadratic model facilitated the significant prediction (\(p\)-value \(<\) 0.05) of \(a_{w}\) of tested MD model solutions. A very well-fitting model to the data obtained from the experiment was noted, which was confirmed by the high value of the determination coefficient (R\({}^{2}\) = 0.8840) and the insignificant result of the lack of fit test (\(p\)-value \(>\) 0.05) (Table A2). As the estimated parameter values (Equation (5)) and response surface plots (Figure 9) showed, which was similar to MC, it can be observed that increasing the IAT and FC had a negative effect on \(a_{w}\), while increasing IAH had a positive influence on \(a_{w}\). The effect of IAT was larger than IAH and FC. As ANOVA showed, IAT influenced significantly the \(a_{w}\) of model MD powders (both linear and quadratic effects), but at the same time, IAH and FC terms were not significant (\(p\)-value \(<\) 0.05).  
MD model powders had \(a_{w}\) varying from 0.064 \(\pm\) 0.002 to 0.375 \(\pm\) 0.001 (Table A1). As aforementioned, powders of \(a_{w}\) below 0.2 can be considered as shelf-stable, thus most of variants fall within the safe range for \(a_{w}\). It should be underlined that \(a_{w}\) parameter should be taken into consideration in parallel with MC, as MC itself is not sufficient to evaluate the product's stability. Strong positive correlation was noted according to PCA (Figure A4), confirming this statement. \(A_{w}\) describes the water availability, thus when powder has a high MC, but at the same time a low \(a_{w}\), it would indicate that the water present is bound structurally. The variants that were distinguished with the highest \(a_{w}\) above 0.3, and MC higher than 5% were all center points, obtained from 20% MD solution at 100 \({}^{\circ}\)C using medium IAH, and were labeled as not shelf-stable (Table A1). It would seem that either increasing IAT or lowering IAH would drastically enhance the product's shelf-stability by increasing the water evaporation rate.  
\[\begin{split}\sqrt{a_{w}}=0.6173-0.0603\cdot IAT+0.0145\cdot IAH- 0.0054\cdot FC+0.0186\cdot IAT\cdot IAH+0.0019\cdot IAT\cdot FC-\\ 0.026\cdot IAH\cdot FC-0.225\cdot IAT^{2}\end{split} \tag{5}\]  
Figure 9: Water activity (\(a_{w}\)) response surface plots for paired factors: (**A**) inlet air temperature—IAT and inlet air humidity (IAH); (**B**) inlet air temperature and feed solution concentration (FC); (**C**) feed solution concentration (FC) and inlet air humidity (IAH) when the third factor was adjusted to its midpoint.
## 3 ELIMAIA Dosimetry Description
### Detectors for Relative and Absolute Dosimetry
#### Secondary Electron Monitor  
The SEM detector is a tantalum foil, 12.5 \(\upmu\)m-thick, and with a diameter of 130 mm, provided with an electrically insulated PMMA circular frame (Figure 8).  
Its working principle is based on the secondary electron emission; when charged particles pass through an interface of a solid material, very low energy electrons can be emitted from the surface [33]. The main parameter describing the secondary electron emission is the secondary emission yield, which is the average number of electrons emitted when an incident projectile passes through a surface and it is proportional to the energy loss of the particle in the surface layer [34]. The low energy secondary electron spectrum can be described by an exponential decay law. The number of secondary electrons generated increases with the energy released by the primary particles along the SEM thickness [35]. Therefore, the higher the incident beam energy, the lower the number of emitted electrons. According to Monte Carlo simulation results, energy losses in the Ta thickness between few hundreds of KeV up to about 1 MeV are predicted for beam energies from 60 MeV down to 5 MeV. The SEM is basically a beam current monitor, but it can be used as a device to measure the dose, once cross-calibrated against a reference dosimeter; indeed, a relationship between SEM current and the absorbed dose rate at a specified point in the radiation field can be found. The SEM detector will be mounted in a vacuum chamber, placed at the end of the in vacuum beam line section, and will be mainly used to monitor the beam flux in real time.  
Figure 8: Picture of the SEM detector developed for the ELIMAIA beamline.  
It will be provided with an actuator that will allow it to remove the detector from the beam axis when it is necessary.
## 6 Results and Discussion
### Convergence Rate  
As mentioned earlier, one of the outstanding advantages of the SSA is its very fast convergence property. It is, therefore, necessary to examine the convergence profile of the proposed algorithm. In this work, we compared the convergence rate of our proposed system to the convergence rates of other well-known algorithms, such as PSO and GA. Figure 4 shows a comparison of the convergence rate of the PQD classification system using different algorithms for optimal feature selection, while the detail of accuracy and required  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Classification Accuracy (\%)**} \\ \cline{2-7}
**Type of PQD** & **1 Type** & **1 Type** & **1 Type** & **(Energy, & **Energy,** & **All** & **Adaptive** \\  & **(Energy)** & **(Entropy)** & **(Kurtosis & Entropy, & Entropy, S.D, & **Types** & **SSA** \\  & & & & & S.D.) & **Mean, Kurtosis)** & & \\ \hline Harmonic & 97 & 100 & 99 & 100 & 100 & 100 & 100 \\ Sag with harmonic & 59 & 66 & 72 & 81 & 89 & 78 & 93 \\ Well with harmonic & 86 & 94 & 68 & 95 & 98 & 96 & 97 \\ Interruption with harmonic & 67 & 93 & 97 & 93 & 99 & 93 & 100 \\ Flicker & 72 & 84 & 99 & 92 & 99 & 100 & 100 \\ Oscillatory transient & 75 & 92 & 97 & 97 & 100 & 100 & 100 \\ Impulsive transient & 74 & 85 & 100 & 90 & 100 & 100 & 100 \\ Periodic notch & 100 & 53 & 100 & 100 & 100 & 77 & 100 \\ Spike & 47 & 73 & 26 & 61 & 77 & 87 & 100 \\ Overall accuracy & 77.92 & 84.69 & 80.85 & 91.23 & 96.61 & 94.38 & 98.77 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Magnitude**} & \multicolumn{5}{c}{**Classification Accuracy (\%)**} \\ \cline{2-5}  & **All Features** & **PSO** & **GA** & **SSA** & **Adaptive SSA** \\ \hline Without noise & 94.38 & 98.46 & 97.85 & 96.85 & 98.77 \\
10 dB & 55.26 & 72.31 & 69.38 & 60.55 & 72.86 \\
20 dB & 74.89 & 88.43 & 77.85 & 81.17 & 89.11 \\
30 dB & 87.20 & 93.60 & 92.38 & 89.66 & 93.91 \\
40 dB & 90.03 & 94.46 & 89.85 & 90.52 & 94.95 \\
50 dB & 91.45 & 95.94 & 96.31 & 91.63 & 96.37 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of PQD classification under a noisy environment.  
iterations is summarized in Table 8. It was found that the system based on the adaptive SSA had the highest convergence rate to reach the classification accuracy of 98.77% at iteration 79. The conventional SSA can provide 96.85% accuracy at iteration 105. Meanwhile, the accuracy of 98.46% was reached by the PSO at iteration 80 and of 98.15% was reached by the GA at iteration 106. Then, the classification system using the adaptive SSA is not only able to provide very high accuracy, but it also has a rapid convergence rate.
## 4 Operation of the System Implemented
### Storing Information in the Computer  
The _Monitoring Center_ will carry out a polling similar to the one described above. After fulfilling the monitoring, storing and whatever other tasks that might be established for the system, a request from the computer will be sent to the _Monitoring Center_ so that all the information available is transferred to the PC.  
Up to five possible streams have been defined for the communication between the _Monitoring Center_ and the main computer. All of them have a structure consisting of a header plus a data section (when necessary). The information will be transmitted using ASCII characters. The five stream types are:  
* NACK. Indicates that the last stream was not correctly received.
* ACK. Indicates that the last stream was correctly received.
* DAT. This is a data stream.
* NDAT. Stream used by the overall coordinator to let the computer know there are no more available data.
* ENABLED. Used by the computer to request data from the overall coordinator.
## 4 Application to Rolling Bearings Fault Diagnosis
### Comparisons and Discussions
To further evaluate the average performance of the classifiers, the recognition rates under four motor loads are averaged and shown in Figure 12.  
From Figure 12, similar conclusions can be drawn: the NN classifier obtains the lowest classifications. Generally, the reduced recognition rates are also with fault classes 1-3 (i.e., ball fault mode). The classification accuracy is the highest with ESVM, which further highlights the performance of the proposed method.
## 1 Introduction
### Organization  
The paper is organized as follows: Previous work is reviewed in Section 2. The network model and parameters for the best CH election are briefly discussed in Sections 3 and 4.  
The proposed BEA-SSA model is shown in Section 5. The results and conclusion are further illustrated in Sections 6 and 7.
## 5 Conclusions  
The outcomes of this study indicate the importance of factors such as rainfall, temperature, soil, land use, and geology, in the modeling and estimation of sediment yield and maximum discharge in each of the prototype river basins selected. The proliferation of the erosion phenomena in Cluj County, Romania, can be attributed to a diverse range of factors. The sources encompass a combination of natural and anthropogenic influences. The attributes of the river basins within the studied region exerted a substantial influence on the extent of soil erosion. The aforementioned elements encompassed the spatial arrangement of the land, the study of water in the region, and the physical features of the landscape. Furthermore, the geological composition and permeability of the rocks, the diverse range of land use and land cover, and the magnitude of the strong precipitation events were all significant factors to be taken into account. The observed soil erosion rate in the Chinteni river basin was 99.71 m\({}^{3}\) km\({}^{-2}\) yr\({}^{-1}\), twice as high as that in Batin, despite the similarities in their characteristics. This finding indicates that the Chinteni region is experiencing significant degradation, and emphasizes the urgent need for the deployment of soil conservation measures in the area. Calculating the soil loss rate and maximum outflow using the IntErO model compares favorably to difficult alternatives regarding field-based methods of estimating sediment yield, due to these methods requiring significant work and a significant amount of time to provide comparable findings. Following the model's calibration and validation processes, it will be possible to use it to analyze similar river basins in Romania and other regions of Europe. The findings of this study can provide policymakers with useful information for developing improved guidelines for soil and water conservation, which will conserve the soils of the river basins, as well as the future implementation of the IntErO model, in order to analyze the impact of climate change, land use and land cover changes.  
All authors have contributed equally to the work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UASMV Bucharest, through the project \"PROINVENT\", Contract no. 62487/03.06.2022 POCU/993/6/13--cod SMIS: 153299 financed by The Human Capital Operational Programme 2014-2020 (POCU), Romania.  
Not applicable.  
This work of [PERSON] was supported by the project \"PROINVENT\", Contract no. 62487/03.06.2022 POCU/993/6/13--cod SMIS: 153299 financed by The Human Capital Operational Programme 2014-2020 (POCU), Romania.  
The authors declare no conflict of interest.
## 4 OpenFlow Flow Table Memory Management
### Timeout and Eviction Mechanisms
Similarly to preserved useful entries in the flow table, the proposal in [53] designed a flow cache architecture with a two-stage timeout mechanism to better identify and keep important flow entries. Initially, flow entries are stored in the primary table with timeout calculated by the controller using knowledge of flow entry. Thereafter, an inactive time with no packet expected, instead of completely removing entries, but are moved to a secondary table for a second chance. It is noted that the approach preserved the active flows, thereby evicting short-lived flows to avoid waste of flowtable resources. However, it required knowledge of flow before deciding on a suitable timeout. Hence, it is not feasible to obtain old flow installation, evict, install, and packet-in in real-time [46]. In addition, moving an entry to secondary storage for the second chance, the process may cause a packet to incur additional processing delay, which may not be good for delay-sensitive application [55]. Therefore, setting proper idle timeout is significant to efficiently manage the precious flow table space. Additionally, it is also important to consider the deterministic life span of the flow entry (hard timeout) to preserve long-duration flows with a large number of packets within a short inter-arrival. In contrast to the idle timeout features, [PERSON] et al. in [57] consider a dynamic allocation of hard timeout for predictable and unpredictable flows to maximize the utilization of flowtable. The aim is to keep an unpredictable flow for a short time while preserving predictable flows. This is achieved by analyzing packet trace to study the nature of packet arrival for each, thereafter adjusting hard timeout accordingly. When flow table utilization reaches a certain capacity, LRU is used to evict an entry with a max timeout. Even though the scheme has achieved in preserving predictable flows, still inherent is the problem of poor entry eviction scheme. LRU is not an appropriate eviction algorithm in SDN because it is a packet driven algorithm, which can only be implemented in theory, but in practice it may not be compatible with OpenFlow, and worse, it violates one of the SDN principles that deleted all control functions to the controller [33]. IHTA in [54] considers the dynamic idle value to short live flows and adjusts hard timeout value to long live flows with short packet inter-arrival time. This has significantly reduced the issuance of the packet-in event to the controller, which improves the communication overhead. The shortcoming of LRU is overcome by leveraging on OpenFlow built-in data collection. Flows with a small number of packet count are considered to be victims and therefore, removing such flows improved the efficiency of the limited storage. It is effective to conclude that to some extent IHTA has reduced communication overhead and improved the limited flowtable usage without changing the architecture of SDN.
# Experimental Study of the Volumetric Error Effect on the Resulting Working Accuracy--Roundness
###### Abstract  
Currently, various types of software compensations are applied to machine tools. Their aim is to increase the working accuracy of the tools. The improvement in working accuracy is then further assessed according to the increase in the dimensional and shape accuracy or the surface quality of the workpiece. This publication describes the effects of the volumetric accuracy of a machine tool on the working accuracy of a workpiece, where total roundness (RONt) is evaluated in multiple cuts. In the experiment, two test workpieces are manufactured on a three-axis milling machining centre. The first is made using a standard machine setup while the second with activated volumetric compensation. The LaserTRACER self-tracking laser interferometer is used to compensate for volumetric accuracy. In the second part, verification measurements are performed with a Ballbar, where roundness error is evaluated according to ISO 230-4. Then two test workpieces are machined, and, in the last part, measurement is performed on Talyrond 595S roundness measuring equipment. Finally, the results are analysed and the dependence between the volumetric accuracy, the circularity error of the machine and the working accuracy of the CNC machine tool is established, represented by the RONt of the workpiece. This paper presents new and unpublished relations between the volumetric accuracy of the machine tool and the RONt of the workpiece.  
machine tool error; workpiece quality; machining accuracy; volumetric error; circularity; roundness Article
## 3 Research Method
### Search Process  
The search process as per the mapping protocol was conducted in the first week of the April 2020. The systematic mapping protocol include the following steps:  
Figure 1: Mapping between research questions and classification schemes.  
* Formulation of the search string and selection of bibliographic databases (Section 3.3).
* Triggering of the bibliographic database against the formulated search string, resulting in the initial list of studies.
* The initial list of extracted studies from four bibliographic databases was screened through multiple stages against the inclusion and exclusion criteria to limit the number to \"most suitable\" studies.
* The multiple stages of screening include the following:
* Screening the studies on the basis of title and then the abstract, resulting in \(n\) studies (two stage screening).
* These \(n\) studies are then subjected to a third stage of screening. In this stage, their Google citations are extracted, which are then screened by reading their title and abstract (forward snowballing approach as proposed in [9]. This results in \(m\) publications (two stage screening).
* The total number of studies subjected for analysis to meet the objectives of the mapping study are then \(n+m\).  
The search process included all the authors' efforts to ensure the quality of the study searching process and selection process [10,11]. The well-known papers related to requirement engineering in software startup context as published in leading venues are checked if they are included in the list of 461 papers extracted after execution of search strong over bibliographic databases. The objectivity of the inclusion & exclusion criteria is ensured by testing it over the same bibliographic databases, using the same search string but only for year 2020. The resulting papers selected by the researchers are then compared to see the level of agreements between the selections. The small disagreements were resolved using consensus meetings and some inclusion & exclusion criteria are refined to make them more objective.
## 3 Methodology
### Overall Architecture  
The overall architecture of PLGTransformer is illustrated in Figure 1. The model combines the strengths of CNNs for local structural perception, the Swin transformer for global dependency modeling, and GCNs for structured spatial representation. This synergy enables accurate segmentation of multi-scale and diverse land cover types in remote sensing imagery. The PLGTransformer comprises three main functional modules.  
Figure 1: The overall structure of the PLGTransformer we propose consists of four components: the local feature extractor, the global feature extractor, the pyramid self-fusion module, and the graph fusion module.  
First, three heterogeneous local feature extractors are employed in parallel to extract multi-scale local representations, enhancing the model's ability to capture texture and boundary information. Second, a hierarchical Swin transformer backbone is used to model global context through shifted window-based multi-head self-attention, efficiently capturing long-range dependencies. Third, pyramid pooling modules are embedded into both local and global branches to broaden semantic perception and enhance contextual awareness via multi-scale pooling. After local and global features are extracted, a learnable weighted fusion mechanism is designed to explicitly integrate these complementary sources, optimizing the combination of heterogeneous features. Furthermore, a graph-based fusion module is introduced to enhance spatial structure awareness. This module leverages GCNs to model topological relationships within the fused features, strengthening spatial adjacency and boundary continuity.  
Finally, a convolutional prediction head and an upsampling module map the refined feature maps back to the original input resolution, yielding pixel-wise semantic segmentation outputs. In summary, PLGTransformer adopts a three-level synergy of local-global-structural design, forming an end-to-end segmentation framework with both multi-scale understanding and spatial modeling capabilities for remote sensing applications.