esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 3 Method for the Integration of Organizational Models
### General Process  
Let \(OM_{1}\), \(OM_{2}\),..., \(OM_{n}\) be \(n\) organizational models to be integrated. In broad lines, we understand by a _conceptual integration_ of \(OM_{1}\),..., \(OM_{n}\) the process of representing, correlating and joining the _conceptual structure_ (i.e., the modeling concepts and their interrelationships) of each \(OM_{i}\) obtaining as the final result an _integrated metamodel MM\({}^{int}\)_ whose conceptual structure subsumes the structure of all \(OM_{i}\). This idea of conceptual integration, as an iterative process, is depicted in Figure 1.  
More specifically, for \(n\) models we have \(n-1\) iterations. In the first one, three sequencial steps are performed for \(OM_{1}\) and \(OM_{2}\):  
1. Analysis and representation of the conceptual structure of \(OM_{1}\) and \(OM_{2}\) as _metamodels MM\({}_{1}\)_ and \(MM_{2}\), respectively. In the representation it is used a common _metamodeling language_.
2. Comparison of \(MM_{1}\) and \(MM_{2}\), and identification of _correspondences_ between the conceptual structure expressed in both metamodels. Such correspondences represent semantic overlapping areas between \(MM_{1}\) and \(MM_{2}\), i.e., modeling concepts and concept relationships that are assumed to have equivalent or similar interpretations. In general, the identified correspondences are explicitly expressed as _articulations art\((MM_{1},MM_{2})\)_ between \(MM_{1}\) and \(MM_{2}\), as described next.
3. From \(MM_{1}\), \(MM_{2}\) and \(art(MM_{1},MM_{2})\), we produce an integrated metamodel MM\({}^{int}_{\#1}\) that (\(i\)) is as expressive as both \(MM_{1}\) and \(MM_{2}\) (in the sense of retaining all concepts, relations and restrictions  
Figure 1: General conceptual integration method.  
found in both \(MM_{1}\) and \(MM_{2}\)); and (_ii_) avoids unnecessary replication of elements which were declared equivalent or similar by means of the correspondences between \(MM_{1}\) and \(MM_{2}\).  
From the second iteration onward, the same three steps are performed for each \(OM_{3\leq i\leq n}\) with the following differences: (1) Instead of \(MM_{1}\) and \(MM_{2}\) we have \(MM_{i}\) and \(MM_{\#(i-2)}^{int}\), respectively; \(MM_{i}\) is the metamodel representing \(OM_{i}\) and \(MM_{\#(i-2)}^{int}\) is the integrated metamodel from the previous iteration; (2) in the place of \(art(MM_{1},MM_{2})\) we have \(art(MM_{i},MM_{\#(i-2)}^{int})\) which is the articulation between \(MM_{i}\) and \(MM_{\#(i-2)}^{int}\); and (3) instead of \(MM_{\#1}^{int}\) we produce \(MM_{\#(i-1)}^{int}\), i.e., the integrated metamodel resulting from joining \(MM_{i}\) to \(MM_{\#(i-2)}^{int}\).
###### Abstract  
Ionic silver (Ag\({}^{+}\)) is being investigated as a residual biocide for use in NASA spacecraft potable water systems on future crewed missions. This water will be used to irrigate future spaceflight crop production systems. We have evaluated the impact of three concentrations (31 ppb, 125 ppb, and 500 ppb) of ionic silver biocide solutions on lettuce in an arcillite (calcinated clay particle substrate) and hydroponic (substrate-less) growth setup after 28 days. Lettuce plant growth was reduced in the hydroponic samples treated with 31 ppb silver and severely stunted for samples treated at 125 ppb and 500 ppb silver. No growth defects were observed in arcillite-grown lettuce. Silver was detectable in the hydroponic-grown lettuce leaves at each concentration but was not detected in the ice-grown lettuce leaves. Specifically, when 125 ppb silver water was applied to a hydroponics tray. Ag\({}^{+}\) was detected at an average amount of 7 ug/g (dry weight) in lettuce leaves. The increase in Ag\({}^{+}\) corresponded with a decrease in several essential elements in the lettuce tissue (Ca, K, P, S). In the arcillite growth setup, silver did not impact the plant root zone microbiome in terms of alpha diversity and relative abundance between treatments and control. However, with increasing silver concentration, the alpha diversity increased in lettuce root samples and in the water from the hydroponics tray samples. The genera in the hydroponic root and water samples were similar across the silver concentrations but displayed different relative abundances. This suggests that ionic silver was acting as a selective pressure for the microbes that colonize the hydroponic water. The surviving microbes likely utilized exudates from the stunted plant roots as a carbon source. Analysis of the root-associated microbiomes in response to silver showed enrichment of metagenomic pathways associated with alternate carbon source utilization, fatty-acid synthesis, and the ppGpp (guanosine 3\({}^{\prime}\)-diphosphate 5\({}^{\prime}\)-diphosphate) stringent response global regulatory system that operates under conditions of environmental stress. Nutrient solutions containing Ag\({}^{+}\) in concentrations greater than 31 ppb in hydroponic systems lacking cation-exchange capacity can severely impact crop production due to stunting of plant growth.  
Keywords:lettuce; silver; hydroponics; arcillite; substrate; metagenomics; essential elements; space crop _Article_
# On IRS-Assisted Covert Communication with a Friendly UAV
## 2 System Model
### Detection on CCS by [PERSON]  
In this subsection, [PERSON] determines the presence of covert communications by analyzing the received signals. To detect covert communications, [PERSON] employs the hypothesis testing method. [PERSON] first perceives the location of [PERSON], the UAV, and the IRS; then, according to (3), (4), and (6), the received signal at [PERSON]'s location can be obtained as follows:  
\[y_{W}[t]=\left\{\begin{array}{ll}\sqrt{P_{u}[t]}h_{UW}[t]x_{u}[t]+n_{W}[t],&H_{0}\\ \sqrt{P_{a}[t]}\big{(}h_{IW}^{H}[t]\Theta[t]h_{AI}[t]\big{)}x_{a}[t]+\sqrt{P_{ u}[t]}h_{UW}[t]x_{u}[t]+n_{W}[t],&H_{1},\end{array}\right. \tag{10}\]  
where \(H_{0}\) indicates that there is no covert message transmitted between the UAV and [PERSON], \(H_{1}\) indicates the opposite, and \(n_{W}[t]\) is the additive white Gaussian noise (AWGN) with mean 0 and variance \(\sigma_{w}^{2}\) at [PERSON]'s location, i.e., \(n_{W}[t]\sim CN(0,\sigma_{w}^{2})\). Because the data rate tends to be the same in each time slot, the received power at [PERSON]'s location can be expressed as  
\[\mathbb{R}[t]=\left\{\begin{array}{cc}P_{u}[t]|h_{\text{I}W}[t]|^{2}+\sigma_{w}^ {2},&H_{0}\\ P_{a}[t]|h_{\text{I}W}^{H}[t]\Theta[t]h_{AI}[t]|^{2}+P_{u}[t]|h_{\text{I}W}[t]|^{ 2}+\sigma_{w}^{2},&H_{1}.\end{array}\right. \tag{11}\]  
Assuming that [PERSON] employs a radiometer detector in each time slot to detect transmissions from [PERSON] to [PERSON], we can consider \(\tau[t]\) to be [PERSON]'s detection threshold. If \(\mathbb{R}[t]>\tau[t]\), then the decision state is \(D_{1}\), indicating that [PERSON] determines that covert communication exists between the UAV and [PERSON]. Otherwise, the decision state is \(D_{0}\), indicating that [PERSON] determines that no covert message is being transmitted. Thus, [PERSON]'s decision rule can be denoted as  
\[D[t]=\left\{\begin{array}{cc}D_{1},\mathbb{R}[t]>\tau[t]\\ D_{0},\mathbb{R}[t]<\tau[t].\end{array}\right. \tag{12}\]
## 2 Background
### Error-Prone Channel  
The RFID reader and tags frequently exchange messages within a frame. However, the signal strength of tags is typically very weak and significantly attenuated by distance comparing with the signal power of RFID reader. Therefore, the tags may be interfered from one or more readers [8; 9], and this phenomenon is reflected in the form of packet error rate (PER). For example, as shown in Figure 3, the signal from Tag A to Reader B may be interfered by the signal transmitted by Reader A. Reader B cannot identify this phenomenon is caused by the interference from Reader A. Therefore, Reader B would wrongly regard it as that two or more tags simultaneously send message in the same slot, even though there is only one tag within its interrogation range.  
Figure 1: Relationship between slots and frame.  
Figure 2: Collision in RFID (Radio Frequency Identification) system.  
The inability to distinguish the difference between collision and channel error would cause that the reader inaccurately estimates the number of tags within in its interrogation range. This is because that most tag estimation methods are based on the probability of collision and the reader cannot distinguish the difference between collision and interference. This means that the reader will overestimate the number of collision slots and underestimate number of readable slots, as shown in Figure 4. According to the overestimated collisions, the tag estimation algorithm would wrongly regard that too many tags transmit message in current frame which causes high collision probability, and then it increases the number of slots in the next frame in order to decrease the collisions. Therefore, the slots in the next frame are overestimated and the bandwidth usage inescapably decreases.
## 2 Materials and Methods
### Preparation of Plant Extracts  
**Ethanol extraction of anthocyanins:** Fresh fruits were blended in home blender. Samples with weight of 1000 g fruit were mixed with double (weight to volume) volume of 20-96% (volume to volume) ethanol-water solution acidified by 0-5% of hydrochloric, oxalic, citric or siccine acids for 0.5-1.5 h extraction with continuous mixing. Extracts were separated from material by filtration through a filter paper with vacuum suction using a Buchner funnel and water-flow pump. The material was mixed with fresh ethanol-water solution two more times for maximal extraction of anthocyanins. Filtrates were moved to boiling flask and ethanol was removed by rotary evaporator. Purification was carried out by mixing of 30-50 g of solid adsorbents (Amberlite XAD-7, Talcum and C18) which was activated with double volumes of ethanol and then with three volumes of acidified deionized water. Filtrate was mixed with 50 g of adsorbent and separated from filtrate by filtration through a filter paper. Adsorbent was flushed by two volumes of acidified water with 1% citric acid (to remove water soluble compounds--colorants, sugars, organic acids etc.) and that with two volumes of ethyl acetate (to remove polyphenols). Elite anthocyanin pigment was removed from solid adsorbent by extraction with acidified ethanol-water solution. Ethanol was removed by vacuum evaporation at 38 \({}^{\circ}\)C.  
**Acetone extraction of anthocyanins:** 1000 g of material was macerated with the same amount (weight to volume) of acetone. Filtrate was separated by vacuum suction using filter paper and Buchner funnel. Extraction of anthocyanins was carried out by maceration of fruit with 70% (\(v/v\)) aqueous acetone (70% of acetone and 30% of 1% citric acid water solution) for 30 min. Filtrate was separated by filter paper and Buchner funnel, moved to a separatory funnel and mixed with double volume of chloroform by turning funnel upside down a few times. Solution was stored overnight at 4 \({}^{\circ}\)C. Aqueous phase (upper portion) was separated by boiling flask. Admixtures of acetone and chloroform were removed by rotary evaporation in vacuum at 38 \({}^{\circ}\)C.  
The extracts encoded for MTT assay are as follows; RSNE: _Sambucus nigra_ ethanol, RSNA: _Sambucus nigra_ acetone; RVME: _Vaccinium myrtillus_ ethanol; RVMA: _Vaccinium myrtillus_ acetone; RACE: _Aronia melanocarpa_ ethanol, RACA: _Aronia melanocarpa_ acetone and RVCE: _Vaccinium copyosum_ ethanol respectively.
## 3 Pathogenesis of AD
### The Tau (\(\tau\)) Hypothesis  
The amyloid cascade concept has been the prevailing hypothesis in AD research, although it does not fully explain the etiopathogenesis of the disease. According to this hypothesis, the \(\tau\) protein appears as a secondary pathogenic event that leads to neurodegeneration [2; 82]. Tau is a microtubule-associated protein that is generated via the MAPT gene's alternative splicing [83]. It is mostly present in neuronal axons of the brain where it interacts with microtubules (MTs) [84]. Tau was first separated from plaques in the brains of AD patients by [PERSON] in 1988 and demonstrated for the first time that it may be the cause of dementia [58; 85]. It is a highly soluble protein related to microtubules, and its role is to stabilize microtubules [86]. These microtubules provide support for axonal transport, structural improvement, and neuronal development. In normal circumstances, Tau has a few phosphorylation sites and adversely regulates the attachment of \(\tau\) to microtubules. However, tau phosphorylation can become saturated under pathological conditions. Development of tau pathology is a complex multifactorial process, and dysfunctions arise in the phosphorylation mechanism of the \(\tau\) protein in AD, resulting in the enzyme's hyperphosphorylation. In the brains of AD patients, hyperphosphorylated tau induces configuration alterations and tubulin polymerization capacity reduction [87], culminating in microtubule dysfunction [88]. Increased cytosolic tau levels cause tau-tau interactions and polymerization, resulting in insoluble PHFs and straight filaments (SFs), which form intraneuronal fibrillary deposits called neurofibrillary tangles (NFTs) [87]. In this respect, AD is often distinguished by the inclusion of NFTs and hyperphosphorylation of the microtubule-associated \(\tau\) proteins that form these tangles [89]. NFTs are fragments of combined, as well as helical, protein filaments in neuron cytoplasm cells [63]. When the \(\tau\) protein interacts with kinases produced as a result of the increasing amount of A\(\beta\) in the system, it is hyperphosphorylated [86]. Due to the separation of the subunits of the tubules, the tubule becomes unstable, breaks apart, and then turns into large parts of the \(\tau\) filaments, which are further consolidated into NFTs [63]. Such NFTs are fibrillary, smooth, and highly insoluble patches in neuronal cytoplasms and systems, due to abnormal neuron connectivity, signal processing, and, finally, neuron apoptosis [58; 90]. Research findings indicated that cleavage and phosphorylation of \(\tau\) are regulated by soluble A\(\beta\) for the generation of NFT [66; 71]. In summary, the pathophysiology of pathogenic tau is still clearly unknown due to its intricacy and needs further study [91].
## 3 CGR-NER Architecture
### Representation Layer
#### 3.1.2 Glyph Representation  
From the perspective of image processing, we propose a hybrid neural network named CBHNN that combines 3 DCNN and BiLSTM to learn the semantic information contained in Chinese character glyphs and obtain the glyph representation of each character, as shown in Figure 2. The input of CBHNN is a sequence of glyph images \(P=\{p_{1},p_{2},\ldots,p_{n}\}\), for which the characters of the sentence S need to be converted into corresponding glyph images first. In order to enrich the visual features of Chinese character glyphs, three glyph images with different styles, Lishu, Xingkai and Fangsong, were used to concatenate the dimensions of the image channel. Therefore, the glyph image size of CBHNN input was 3 \(\times\) 24 \(\times\) 24. Then, three 3 DCNN blocks were used to capture the glyph feature contained in the glyph image. Each 3 DCNN block contains a 3 DCNN, ReLU function and 3D max-pooling operation. The number of output channels of each block was 64, 128, 256, and the output of the last block was batch-normalized and reshaped to obtain the glyph feature vector of 256 dimensions. Different from 2 DCNN, 3 DCNN can simultaneously extract the feature information of spatial and temporal dimensions, which means that CBHNN can not only extract the glyph visual feature in the Chinese character image, but also capture the glyph visual features of adjacent characters at the same time, so that the model can learn the potential word formation knowledge between glyphs. It makes the same Chinese character dynamically generate different glyph feature vectors in different contexts, which is beneficial to alleviate the problem of polysemy. Finally, CBHNN uses BiLSTM to extract contextual semantics and global dependency features from the glyph feature vector sequence, to obtain the glyph representation sequence \(E_{g}=\{g_{1},g_{2},\ldots,g_{n}\}\). The process of obtaining the glyph representation sequence corresponding to the sentence \(S\) can be denoted as  
\[E_{g}=CBHNN(P) \tag{2}\]  
where \(g_{i}\) is the glyph representation corresponding to the \(i\)th character of sentence \(S\). \(E_{g}\in\mathbb{R}^{n\times d_{g}}\), \(d_{g}\) are the dimensions of the glyph representation.
## 3 Results
### Objective Collected Data
#### 3.1.2 Games Scores  
In order to find any correlations between performance and the subgroups of patients, Kruskal-Wallis tests were performed. The game scores were calculated as averages across all trials performed by a person, for the three physical rehabilitation exercises: whack-a-mole, boxing, and collecting fruits. The same subgroups were analyzed as before, including technological experience, VR experience, and preferred level of difficulty for games.  
The results show that the distribution of the whack-a-mole score average is different across various subgroups based on the level of experience with technology of the users (\(p=0.04\))--Figure 9. Pairwise comparisons of the whack-a-mole average score by type of technological experience showed statistically significant differences between patients with low levels (T1) of experience and those with high levels (T3) of experience (\(p=0.023\)), borderline significance between low (T1) and medium (T2) levels of experience (\(p=0.057\)), and no significance between medium (T2) and high (T3) levels (\(p=0.28\)).  
Figure 8: Evolution of heart rate, oxygen level, and motion values of a patient during the entire testing procedure as shown in the ViHealth application: (**a**) charts; (**b**) ranges and distribution.  
Figure 9: Independent Samples Kruskal–Wallis Test: boxplot results for the whack-a-mole average score in the Kruskal–Wallis testing across various subgroups of patients based on their level of technology (1 = little experience T1; 2 = medium experience T2; 3 = very experienced T3).  
Regarding the correlation of the games' scores and other criteria for dividing patients (levels of experience with VR, preference for the difficulty of games)--Figure 10a,b--KW analysis displayed no significant differences, showing that performance in the physical rehabilitation games is not dependent on previous VR experience or gaming preferences.
## 4 Conclusions  
This study presented an experimental setup that enables high-speed temperature measurements within the prerunning melt pool in the vicinity of the keyhole during deep-penetration laser welding. The temperature measurements were supplemented by high-speed videography to identify specific phenomena inside the temperature profile. Based on the conducted experiments, the following conclusions can be drawn:  
* Direct in-process temperature measurements at a certain area inside the weld material up to the moment when the keyhole front wall reaches the point of measurement are possible by employing refractory tantalum probes. Up to now, the measuring method affects the process dynamics by causing blow-outs that are ascribed to air inclusions between the tantalum foil and the drill hole bottom. * The measurements performed with the small-band pyrometer exhibit temperature values lying significantly above the boiling point of the sample material. * The maximum temperatures in the approximate vicinity of the keyhole wall measured by the small-band pyrometer range between \(T_{\text{high}}=2640\) K and 2960 K, thus exceeding the boiling temperature of the base material at its upper boundary by \(\Delta T\approx 200\) K. Considering these results, an overheating of the keyhole wall, as predicted in the literature, can be confirmed directly. From the discussion, it is concluded that all recorded temperature values obtained within the relevant region only represent a lower temperature boundary at given points in time; thus, the real temperatures are expected to be even higher. * Comparative measurements conducted with color pyrometry showed maximum temperatures of up to \(T_{\rm high}\approx 3300\) K, supporting the assumption of an overheated keyhole wall, as well as an underestimation of the temperatures recorded by small-band pyrometry. **Author Contributions:** Conceptualization, P.W. and R.P. ; methodology, R.P. and P.W. ; data analysis, R.P. ; investigation, R.P. ; discussion of contents, R.P. and P.W. ; writing--original draft preparation, R.P. ; writing--review and editing, P.W. ; visualization, R.P. ; supervision, P.W. ; project administration, P.W. ; and funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the DFG (Deutsche Forschungsgemeinschaft, project number 331150978). **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviations**
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.6 Acetate Esters  
TM7-Y and TM10-Y wines generally had higher acetate ester concentrations than the other remaining young wines, with TM7-Y containing the highest concentrations of hexyl and 2-phenethyl acetate (Table 1). TPHT-Y wine had the lowest concentration of total acetates, mostly due to a significantly reduced isoamyl acetate level. Acetate ester concentrations result from a balance between acyl transferase enzymes, which promote their synthesis, and esterase enzymes, which facilitate hydrolysis [54]. In this study, prolonged maceration led to decreased acetate ester concentrations. The absence of mechanical actions reduces ester evaporation [45], while berry skins can also reduce acetate ester formation by extracting enzyme inhibitors or adsorbing the esters [51]. Another possible reason for the decrease is non-enzymatic acetate esters hydrolysis [50]. Acetate esters from acetyl-CoA and higher alcohols [49] and acetyl-CoA accumulation during fermentation affect acetate ester formation. Higher fermentation temperatures and oxygen availability also impact their synthesis. Heat accelerates chemical reactions, so acetate esters like isoamyl and hexyl acetate, which are stable at 0 \({}^{\circ}\)C, hydrolyze quickly above 30 \({}^{\circ}\)C [7]. This may explain a significant decrease in acetate esters in TPHT-Y treatment wine. Another reason is the possible negative effect of heating on acetyl-CoA formation by enzyme inhibition or precursor degradation. It has been found that pre-fermentation heating followed by traditional maceration does not significantly affect acetate ester concentrations, while pre-fermentation heating followed by pressing significantly increases their levels [19]. This was attributed to the absence of solids and high Yeast Assimilable Nitrogen (YAN) extraction during thermal treatment [31]. The inactivation of transferase and esterase enzymes during thermal treatment could have also had an impact on acetate ester production. Although previous research indicated that acetate ester concentrations decrease with barrel aging due to adsorption by wood and chemical hydrolysis [3; 33], in this study, no significant changes were observed in treatment TM7-A after aging. However, in the treatment TM10-A, the concentration decreased, while in treatments TM21-A and TPHT-A, the concentrations significantly increased with barrel aging (Table 1).