esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 2 Theoretical Background
### Laser Diode Efficiency  
Laser diodes are favored in FSO systems for their compact size, high modulation capability, and optical efficiency. Their performance is commonly described by two parameters:  
* Slope Efficiency \(\text{n}_{\text{slope}}\): Indicates how effectively the diode converts electrical current into optical power beyond the threshold current. It is given by the following [19]: \[\text{n}_{\text{slope}}=\left(\frac{\Delta\text{P}_{\text{opt}}}{\Delta \text{I}}\right)_{\text{above threshold}}\approx\frac{\text{P}_{\text{opt}}}{ \text{I}-\text{I}_{\text{th}}}\] (10) where \(\text{P}_{\text{opt}}\) is the optical output power (in mW);  
I is the input current (in mA);  
\(\text{I}_{\text{th}}\) is the threshold current (in mA).  
* External Differential Quantum Efficiency (EDQE) \(\text{n}_{\text{EDQE}}\): Relates the number of emitted photons to the number of injected electrons and is expressed as follows [20]: \[\text{n}_{\text{EDQE}}=\frac{\Delta\text{P}_{\text{opt}}/\text{hv}}{\Delta \text{I}/\text{e}}=\text{n}_{\text{slope}}\frac{\text{e}}{\text{hv}}\approx \left(\frac{\text{e}}{\text{E}_{\text{g}}}\right)\frac{\text{P}_{\text{opt}} }{\text{I}-\text{I}_{\text{th}}}\] (11)where  
\(\mathrm{e}{=1.6\times 10^{-19}C}\) is the elementary charge;  
\(\mathrm{h}=6.626\times 10^{-34}\) Joule-hertz\({}^{-1}\) is [PERSON]'s constant;  
\(\mathrm{v}\) is the frequency;  
\(\mathrm{E}=\frac{\mathrm{hc}}{\lambda}\) is the photon energy.  
These parameters allow for comparison of laser diodes based on their wavelength-specific energy conversion efficiency and are used in Section 4 to evaluate diode performance.
## 5 Performance Evaluation
### Reliability of Automated Size Extraction  
The function was analyzed based on the degree of data deviations between automatically extracted sizes and actual sizes. Figure 15 shows the deviation graphs. The deviations were calculated for the three items such as b, waist (W), and hip (H) lengths among the six items because these are the primary items which determine whether the clothes would fit or not. When grading a * wear, the universal latitude deviation is 4 cm so that the range within four (manifesting domain) will be considered as an ideally judged domain. Women's measurements were closer to approximated values compared to men's measurements. The B, W, and H are the deviations between actual measurements and automatically extracted sizes. As shown in the picture, all of them stayed within the range of 4 cm. However, the body types of those who were in a higher age group or with different body types could exceed the scope of the red domain.  
Therefore, it is recommended that data should be entered after taking the measurements directly with a tapeline. Moreover, if one wishes to automatically extract the measurements of smaller deviations with just height and weight, the selection options, such as nationality, age, and body type should be added. Thus, the measurement data from several countries are required in addition to a dataset categorized by ages and body types. To achieve this, data of body measurements are necessary and the applications that allow a convenient scanning of body sizes with smart devices to accumulate the data will be expected to be useful to the size-recommending clothes shopping malt systems soon.
## 4 A Method for Configuring VWSNs to Use the Properties of Brain Networks
### VWSN Topology Construction Method
The degree of an \(N\)-th-tier module is the number of neighboring \(N\)-th-tier modules connected with the module by \(N\)-th-tier virtual links. Strategy function \(G^{intra}\) is a function for embedding a new link preferentially according to the degrees of the endpoint modules. The strategy \(intra\) for adding a new link can be any of \"high-high (hh)\", \"low-low (ll)\" and \"high-low (hl)\". When \(intra=\text{hh}\), a pair of higher degree modules is selected preferentially for a new link, and when \(intra=\text{ll}\), a pair of lower degree modules is selected for a new link. When \(intra=\text{hl}\), a higher degree module and another lower degree module are selected preferentially and connected. The definitions of \(G^{intra}\) for each possibility are as follows:  
\[G^{hh}(k_{i},k_{j}) = k_{i}\cdot k_{j}\] \[G^{ll}(k_{i},k_{j}) = k_{i}^{-1}\cdot k_{j}^{-1}\] \[G^{ll}(k_{i},k_{j}) = \text{max}(k_{i},k_{j})\cdot|k_{i}-k_{j}|\]  
The number of added \(N\)-th-tier virtual links is \(\lceil C^{N}_{\text{intra}}|E^{N}_{0}|\rceil\), where \(|E^{N}_{0}|\) is the number of links embedded in the \(N\)-th-tier initial virtual topology and \(C^{N}_{\text{intra}}\) is a constant satisfying \(0<C^{N}_{\text{intra}}\leq 1\). For the second problem, we describe a method for mapping the endpoints of an \(N\)-th-tier virtual link to sensor nodes. In this method, we recursively select the endpoints of an \(N\)-th-tier virtual link from its submodules until the endpoint nodes are determined. We define the probability \(p_{\text{inter}}^{N}(M_{i}^{N-1},M_{j}^{N-1})\) of mapping an \(N\)-th-tier virtual link to an \((N-1)\)-th-tier virtual link between \(M_{i}^{N-1}\) and \(M_{j}^{N-1}\) in the same way as Equation (3). The strategy _inter_ for mapping can be one of \"High-High (HH)\", \"Low-Low (LL)\" and \"High-Low (HL)\", with the same meanings as \"hh\", \"ll\" and \"hl\", respectively, but applied to links between modules. The number of virtual links for each mapping is \(\lceil C_{\text{inter}}^{N}(E^{M_{x}^{N}}+E^{M_{y}^{N}})\rceil\), where \(E^{M_{x}^{N}}\) denotes the number of \((N-1)\)-th-tier virtual links in the \(N\)-th-tier VWSN topology of \(M_{x}^{N}\), and \(C_{\text{inter}}^{N}\) is a constant value satisfying \(0<C_{\text{inter}}^{N}\leq 1\). We call an endpoint node of a virtual inter-module link a connected node. Next, we assign a virtual link to one of physical shortest paths, and the routing tables are constructed by the method mentioned in the following section. From here, we suppose that any node can be reassigned to a relay node of a physical path of any virtual link in the event of node failure. Therefore, we assume that the user requires the maximum degree of reliability. The route recovery method is also described in the following section.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
###### Abstract  
In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with higher channel gain and sufficient residual energy help a sender relay its data packets to its recipient is employed to achieve this objective. We first propose a transmission power optimization algorithm to prolong network lifetime by optimizing the transmission powers of the sender and its cooperative nodes to maximize their minimum residual energy after their data packet transmissions. Based on it, we propose a corresponding power-optimized cooperative MAC protocol. A cooperative node contention mechanism is designed to ensure that the sender can effectively select a group of cooperative nodes with the lowest energy consumption and the best channel quality for cooperative transmissions, thus further improving the energy efficiency. Simulation results show that compared to typical MAC protocol with direct transmissions and energy-efficient cooperative MAC protocol, the proposed cooperative MAC protocol can efficiently improve the energy efficiency and extend the network lifetime.  
wireless sensor networks; medium access control (MAC); cooperative MAC protocol; transmission power optimization; cooperative node selection; network lifetime; energy efficiency Article
## 2 Related Work
### Photogrammetry  
Photogrammetry is one of the oldest techniques for digitising objects in 3D graphics. The process of data handling involves using software to reconstruct the 3D geometric shape of an object from various images. Recent advancements in automated reconstruction techniques now allow for quicker creation of 3D models from a collection of photos.  
[PERSON] et al. developed a VR application based on photogrammetric models, specifically applied to the iconostasis of the Serbian Orthodox Cathedral Church of Saint Nicholas [14]. The research outlined a comprehensive workflow, including the generation of an accurate 3D reconstruction of the iconostasis using photogrammetry, model optimisation, retopology, control and analysis, followed by the creation of the VR experience using a game engine. The result is an interactive walkthrough of the church. This method showed the significant role of photogrammetry in creating realistic 3D models for VR applications, particularly in cultural heritage preservation.  
The work of [15] also provided insights into cultural heritage promotion. They proposed a VR solution using photogrammetry techniques to enhance heritage promotion in a tourist centre in Trujillo. The research identified deficiencies in heritage promotion, particularly in its diffusion and perception by the population. The authors tested with 30 participants and confirmed that the proposed VR solution significantly improves the promotion process. In addition, they underscored the role of realistic 3D models created through photogrammetry in delivering a more engaging and authentic experience to tourists.  
In another application domain, [PERSON] et al. researched the use of photogrammetry to generate realistic 3D models of physical structures for use in VR, particularly in engineering surveys [16]. The study explores how digital twins (digital replicas of physical objects) created through photogrammetry can serve as effective substitutes for real-world engineering inspections. The authors explored the effectiveness of bimanual gestural input, augmented with gaze-tracking, in interacting with these 3D models in VR. A key outcome of the study was the demonstration that such an interaction mechanism is an effective modality for domain experts in VR, facilitating efficient manipulation and accurate measurement of elements within the photogrammetric 3D models. The qualitative feedback from the six domain experts involved in the case study underscores the potential of this method in professional settings.
## 4 Simulation Result and Discussion
### Analysis of Case Studies
#### 4.3.1 Case I: Base Case
Similarly, for the New England 39 Bus test system, the proposed model gives a better result with precision of 97.01%, recall of 96.95%, and F1-score 96.97% compared to other classifiers. The results show that the performances of TempCNN and LSTM classifiers are comparable with the proposed model having a marginal difference of accuracy of less than 1%, while the SVM and DT classifiers performed poorly with accuracy less than 93%. The results show that the DT classifier requires less computational time for both the training and testing data set compared to other classifiers for both the test systems under study. Even though the training time required for the proposed model is high, the data processing time for the test samples is quick enough to meet the criterion for online application. This approach requires the manual labeling of a large number of data samples, which is time consuming in practice and may give a biased result with unknown topology change conditions. \begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline Optimizer & Adam \\ Exponential decay rate (\(\beta_{1}\)) & 0.9 \\ Exponential decay rate (\(\beta_{2}\)) & 0.999 \\ Constant (\(\epsilon\)) & \(10^{-7}\) \\ Learning rate & 0.001 \\ Batch size & 32 \\ Number of epochs & 200 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameter settings of the proposed TempCNN-LSTM model on test systems. Figure 11: Architecture of the proposed TempCNN-LSTM model employed on test systems.
# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
## 2 Materials and Methods
### Preprocessing  
The data underwent a thorough preprocessing phase. Initially, they were cleaned to remove unnecessary text while retaining relevant information. Since our dataset consisted of question-answer pairs between a user and an assistant, we identified certain textual elements that could be removed. In the dataset in autotrain-data-1w6s-u4 vt-i7 yo, we removed strings such as \"Human-\", \"Assistant:\", \"n\", \"t\", and replaced hyphens (\"-\") between words with a single space. In the dataset in s200862/medical_qa_meds, we eliminated tags like \"
\", \"
\", \"
\", \"
\", along with newline (\"\
\") and tab (\"\t\") characters. Next, all the punctuation was removed from the text. Finally, the data were converted to lowercase for consistency. As stated above, the rows of the datasets are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Feature Name** & **Data Type** & **Split** & **Number of Examples** & **Size (Bytes)** \\ \hline autotrain-data-1w6s-u4 vt-i7 yo & autotrain\_text & String & Train & 23,437 & 19,109,937 \\ medical\_qa\_meds & text & String & Train & 5942 & 1,638,400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets.  
Figure 1: Overview of the pipeline flow.  
medical texts, made of multiple sentences. We split all the texts so as to have one sentence in each row; thus, the number of examples rose from around 29,000 to 261,936 sentences.
## 3 Materials and Methods
### Comparative Analysis of Prediction Results and Selection of the Final Model  
The comparative analysis of the prediction results and the selection of the final model represents the last step in the research process. Based on the prediction performance expressed through the relative error criterion, one of the most accurate models is selected for each of the three observed approaches to optimizing the set of input variables. The main goal of this procedure is to test the statistical significance of the differences in prediction results for three ML models, i.e., for three approaches to predictor set optimization. Given that the same data set is used for testing in all three cases, the prediction results are compared using statistical methods, specifically by the ANOVA test with Repeated Measures and the Friedman test.  
In addition to relative error, as one of the key indicators of prediction performance, special attention is paid to its complexity and interpretability when selecting an ML model [5]. According to the conducted studies, models with more complex ML algorithms are more demanding for interpretation. The dimensionality of the space of input variables, and the complexity of the functions that the models need to learn, in addition to the algorithms, can affect the complexity of the model [5]. Figure 7 shows the methodological steps, from the optimization of a set of independent variables in each of the three investigated predictive models to the comparative analysis and selection of the final ML model.  
According to the results of numerous studies, priority is given to simpler, more interpretable solutions, although complex predictive models usually provide better performance [38; 39; 40]. In paper [39], several definitions of the concept of interpretability are listed, among which the following stands out: \"interpretability in ML is a degree to which a human can understand the cause of a decision from an ML model\". For this reason, in recent years, a relatively new field, Interpretable Machine Learning (IML), has appeared. Within it, methods are investigated to transform ML models, the so-called black boxes, into white box models [5; 39; 41]. Figure 8 shows common models ranked according to accuracy and interpretability in relatively recently published research papers [42; 43; 44; 45]. In the figure,  
Figure 7: Steps of the methodological procedure from the optimization of the set of predictors to the selection of the final model.  
the accuracy from the lowest to the highest value is given in a down-up orientation, while the interpretability with a growing trend is oriented in the Top-down direction.
# Scalable Dew Computing
## 1 Introduction  
Classical cloud computing [1; 2] focuses on providing servers to process complex computations, and does not include Internet of Things (IoT) devices [3; 4]. Post-cloud systems [5; 6] upgrade the classical cloud computing systems to bring the computation closer to the user, locating smaller servers nearby the user such asdata consumers at the network edge [7]. These architecture models appear in different forms, such as cloudlets [8], fog computing [9], edge computing [10], mobile edge computing [11], and dew computing [12].  
Edge computing [13] improves the response times and saves bandwidth in a distributed computing platform, while the dew computing [12; 14; 15] extends it by adding independence and collaboration features and providing the autonomous processing [16], where instead of being on the edge, the devices can perform autonomously out of the edge.  
Serverless computing is the approach when the users are relieved to take care of the resources, transferring the problem to the cloud providers. Instead of requesting the resources, the end-users are just specifying the functions to be executed, and the cloud provider ensures relevant resources. It simplifies the way the users look at the cloud computing [17; 18] by enabling a platform [19] where the user does not need to request more or fewer servers, transferring this issue to the provider. Since dew computing targets autonomous processing on the device itself, these architectures are not able to provide scalability.  
The deviceless computing approach [20], also recognized as a serverless approach for IoT [21], was applied for streaming IoT applications [22] focusing on the \"traditional\" edge server solution, and in this paper, the emphasis is on the dew computing and providing the scalability.  
The rest of the paper has the following structure. Related work is elaborated in Section 2. Section 3 presents the architecture model, analyzing it for dew computing systems, and Section 4 presents the new implementations of scalability and hardwareless computing. A use case andanalysis of challenges and limitations are discussed in Section 5. Finally, Section 6 presents relevant conclusions and directions for future work.
## 3 Results and Discussion
### Behavior of Alpha Tocopherol during Storage  
Alpha-tocopherol is the main fat-soluble antioxidant present in olive oil. The initial \(\alpha\)-tocopherol content in the Arbequina variety EVOO was 180 mg/kg (Table 2), similar to those published by [PERSON] et al. [28]. Figure 1 shows the degradation of \(\alpha\)-tocopherol for all conditions during 12 months of storage and highlights the dramatic drop of \(\alpha\)-tocopherol in the C2 condition throughout the storage period, reaching 80 mg/kg after 12 months, equivalent to a retention percentage of 40%. These results were similar to those reported by [PERSON] et al. [5] in olive oil stored at 22 \({}^{\circ}\)C with light and without oxygen in which they reported a rapid decline of \(\alpha\)-tocopherol during the first 6 months of storage, losing a total of 50% of the initial content after 36 months of storage.  
Figure 1: Evolution of \(\alpha\)-tocopherol, during 12 months of storage for different storage conditions.  
Conditions C1, C3, C4, and C5 presented smaller losses of \(\alpha\)-tocopherol with retention percentages of 80, 71, 69, and 65%, respectively. The greatest degradation of \(\alpha\)-tocopherol at room temperature with light (C2) is likely attributed to the effect of light on the chlorophylls and its degradation products of EVOO. As previously discussed, these degradation products would act as photosensizers, activating oxygen at a fundamental state to form singlet oxygen, accelerating the oxidation of the oil by photooxidation. Alpha-tocopherol would act by trapping singlet oxygen via a charge transfer attenuation mechanism [34], resulting in the production 8- hydroperoxy-tocopherol, which is easily split by mild acidic conditions to decopherylquinone, causing the loss of \(\alpha\)-tocopherol [35].  
Temperature also appears to strongly influence the degradation of \(\alpha\)-tocopherol as storage at 40 \({}^{\circ}\)C (C5) resulted in significant degradation (\(p<0.05\)) relative to the initial level, despite being stored in darkness. Under this condition, \(\alpha\)-tocopherol would interrupt the propagation step in lipid autoxidation based on the redox system of decopherol-tocopheryl semiquinone [34; 35]. The large loss of \(\alpha\)-tocopherol in C2 can be attributed to the greater efficiency of photooxidation on lipid oxidation.
## 3 Results
### Changes in Volatile Compounds  
Table 2 shows the changes of volatile compounds in the R-B mixed juice before and after fermentation with \(L\). _plantarum_ TUST-232. Totals of 53 and 61 volatile compounds in the R-B mixed juice were detected before and after fermentation, respectively, including 16 aldehydes, two olefins, 10 ketones, eight alcohols, three esters, 11 acids, and three other volatile compounds before fermentation, as well as 16 aldehydes, four olefins, eight ketones, 13 alcohols, six esters, 11 acids, and three other volatile compounds after fermentation. After fermentation, the R-B mixed juice showed a significant increase (\(p<0.05\)) in the relative contents of some ketones, alcohols, and acids, such as 3-hydroxy-4-hexanone, 1-octen-3-one, heptanoic acid, nonyl alcohol, and myristic acid, in contrast to a significant decrease (\(p<0.05\)) in the relative contents of some aldehydes, such as (2Z)-2-heptenal and heptanal. Furthermore, 16 flavor substances disappeared, and 24 flavor substances were newly produced. Figure 4: Changes in the content of organic acids before and after fermentation of the R–B mixed juice fermented with \(L\). _plantarum_ TUST-232. Organic acids, including oxalic acid, citric acid, malic acid, succinic acid, lactic acid, and adipic acid, were measured. The error bars indicate means \(\pm\) SD (\(n=3\)); the differences between groups were analyzed by a two-tailed paired Student’s \(t\)-test. * indicates significant differences at \(p<0.05\).