esa-sceva/satcom-chunk-collection huggingface dataset:
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 4 DTN Routing Protocols in Disjointed Satellite Networks
### DTN Flooding-Based Techniques
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline Protocols & **Category** & **Decision Criteria** & **Advantages** & **Limitations** & **Delivery Ratio** & **Average Delay** & **Overhead** & **Complexity** \\ \hline Epidemic [6] & None & - & Simple no prior knowledge required & - & High drop ratio & & & & \\ \hline \multirow{2}{*}{PROBERT [19]} & \multirow{2}{*}{History} & - & Unbrand and based on the delivery probability & - & High drop ratio & & & \\ \cline{3-8}  & & & - & High drop ratio & & & & \\ \hline [PERSON] et al. [20] & \multirow{2}{*}{Finding} & \multirow{2}{*}{History} & - & Unbrand and based on the delivery probability & - & Arch like Epidemic & & & \\ \cline{3-8}  & & & - & Lower delivery probability & & & & \\ \hline [PERSON] et al. [20] & \multirow{2}{*}{Finding} & \multirow{2}{*}{History} & - & Ungering policies. & - & Incer highly traffic load & & & \\ \cline{3-8}  & & - & Condiers included buffer size & - & Condiers unlimited bandwidth & & & \\ \hline [PERSON] et al [20] & \multirow{2}{*}{History} & - & Redabating the number & - & High-drop ratio with limited resources & High-d resources unlimited & Low of resources unlimited & High & \\ \cline{3-8}  & & & - & Incerry process to deter & & & & \\ \cline{3-8}  & & & - & Incerry process without unnecessary messages. & - & High-drop ratio with limited resources & High-d resources unlimited & Low of resources unlimited & High & \\ \cline{3-8}  & & - & No prior knowledge required & - & High-d power consumption & High-d resources unlimited & Low of resources unlimited & High & \\ \hline NNCAR [5] & \multirow{2}{*}{History} & - & Less messages traffic & - & High-drop ratio with limited resources & & & & \\ \cline{3-8}  & & & - & High-d power consumption & High-d resources unlimited & Low of resources unlimited & High & \\ \hline PRESN [5] & \multirow{2}{*}{History} & - & Reduced noise & - & High-drop ratio of resources and limited & & & & \\ \cline{3-8}  & & - & - & High-d power consumption & High-d resources unlimited & Low of resources unlimited & High & \\ \hline SUPER [5] & \multirow{2}{*}{History} & - & Reducing the number of messages. & - & Average drop ratio of resources & & & & \\ \cline{3-8}  & & - & Power-efficient & are limited & & High-d resources unlimited & Low of resources unlimited & High & \\ \hline \hline \multirow{2}{*}{Mobbypaper [5]} & \multirow{2}{*}{History} & - & Less messages traffic & - & High-drop ratio & & & & \\ \cline{3-8}  & & - & High-d power consumption & High-d resources unlimited & Low of resources unlimited & High & \\ \hline MED [6] & \multirow{2}{*}{History} & - & Less messages traffic & - & High-d high ratio of resources & & & \\ \cline{3-8}  & & - & Incerry process without & & High-d resources unlimited & Low of resources unlimited & High & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Delay tolerant network routing algorithms.
## 2 Materials and Methods
### Performance Metrics
```
input: 2 DArray target_seg(target segmentation), 2 DArray inferred_seg(inferred_segmentation) output:ccc (Centroid Correlation Coefficient) \(\in(-\infty,1]\)  target_c \(\leftarrow\) find_centroids(target_seg)  pred_c \(\leftarrow\) find_centroids(inferred_seg)  mean_dist \(\leftarrow\) 0 if len(target_c) = len(inferred_c) then  // Pair blobs such that total distance is minimized  dist_matrix \(\leftarrow\) distance_matrix(target_c, inferred_c)  (row_ind, col_ind) \(\leftarrow\) linear_sum_assignment(dist_matrix)  mean_dist \(\leftarrow\) mean(dist_matrix(row_ind, col_ind)) else then  // Associate each blob with its closest counterpart  short, long \(\leftarrow\) sorted([target_c, pred_c], key=lambda x : len(x))  total_dist \(\leftarrow\) sum([min([dist(p, q) foreach q in short]) foreach p in long])  mean_dist \(\leftarrow\) total_dist / len(short) end if // Normalize the mean distance with its diagonal  diag \(\leftarrow\) sqrt(height(target_seg)\({}^{2}\) + width(target_seg)\({}^{2}\))  normalized_dist \(\leftarrow\) mean_dist / diag // sqrt is used to dilate the usable range near CCC values of 1  ccc \(\leftarrow\) 1 \(-\) sqrt(normalized_dist) return CCC
```  
**Algorithm 1** Centroid correlation coefficient pseudocode implementation.
## 8 Conclusions  
This article has introduced the concept, requirements and architecture of UANs, and has examined the main current and emerging UAN classes. From the study, we conclude that LR-WPAN UANs enable the widest range of Smart City applications, at the expense of incurring a high economic cost. Whereas WLAN UANs are promising, they exhibit coverage and flexibility limitations due to the reuse of priorly deployed infrastructure. On the other hand, some SO UAN variants and DTN UANs efficiently support applications that involve sporadic transmissions. The latter require adequate data mule route planning and, on the other hand, constitute the only UAN class not suitable for event-based applications. Finally, MNO UANs offer good sensor node location flexibility, but should only be used for infrequent transmission applications, and show a high long-term economic cost.
# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
###### Abstract  
In the context of Industry 4.0 (I4.0), the Asset Administration Shell (AAS) has been gaining significant attention in recent years. The AAS serves as a standardized digital representation of an asset, encapsulating all relevant information about the asset throughout its lifecycle. Since its introduction in 2015, the past decade has seen considerable progress in developing traditional AAS solutions, namely AAS Type 1 and Type 2. As this initial phase reaches maturity, it becomes essential to shift focus toward AAS Type 3 (proactive), a specific category that extends traditional AAS functionalities by incorporating higher levels of autonomy, intelligence, and collaborative capabilities. However, AAS Type 3 is still in its early stages, lacking formal specifications and comprehensive implementation guidelines. In this context, Multi-Agent Systems (MAS) have been investigated as a means to enhance traditional AAS solutions toward the realization of AAS Type 3, particularly by embedding autonomous, intelligent, and collaborative behaviors. Building on this perspective, this paper explores the role of MAS in realizing AAS Type 3 through a comprehensive analysis of existing agent-based AAS approaches in the literature. Furthermore, this paper proposes a reference model based on common patterns found in the literature to support the development of AAS Type 3 solutions, contributing to the discussion on the formalization of specifications and providing greater clarity on this emerging topic. Finally, to better demonstrate key aspects of the model, some illustrative examples are presented to guide its application and facilitate understanding.  
Asset Administration Shells; Industry 4.0; Multi-Agent Systems Article
## 1 Introduction
Specifically, pure tones are comprised of a single frequency, whereas the voice has a complex harmonic structure, containing a fundamental frequency as well as multiples of that frequency (i.e., harmonics). To date, only a few studies have examined children's abilities to discriminate pitch stimuli with more complex stimuli, with findings indicating variable results as to when children become adult-like [40; 41]. As such, continued and well-controlled work is necessary to further understand whether discrimination of complex stimuli, such as the voice, changes throughout maturation. Previous work has discussed the optimal methods of testing auditory processing across the lifespan, with researchers noting that an ideal task is one that requires minimal memory and attentional demands, as those can vary greatly in certain age and/or clinical populations [42]. Additionally, the language used to elicit a response can impact the results: Complex relational language, such as 'higher' or 'lower' has been shown to result in poorer discrimination as compared to less complex language, such as'same' and 'different' [43]. Additionally, attentional abilities of the participant may impact discrimination abilities [35], especially in procedures that have increased attentional demands [36; 41; 44]. The potential effects of attention on auditory discrimination abilities are especially important to consider when evaluating CwVN, given their personality factors and characteristics discussed in the literature. Specifically, CwVN may also have differences in awareness and attention [45; 46]. [PERSON] and colleagues [47] conducted a systematic review to examine personality traits, psychological factors, and behavioral factors that may be related to CwVN. Differing results were found regarding inattentive behaviors;some studies noted that children with voice disorders had higher scores of distractibility [45] and higher parental and teaching ratings of inattentiveness [46]; other studies showed no differences from matched controls [48; 49]. Thus, when examining auditory discrimination abilities in children, the experimental methods must be carefully chosen so that potential discrimination differences are not overshadowed by attentional differences. The current study examined the ability to discriminate vocal pitch using an experimental paradigm with low memory, attention, and language demands. Vocal pitch was selected because it is a salient feature of voice that does not require extensive or complex training to understand. The first hypothesis was that school-age CwVN would have poorer discrimination abilities compared to age- and sex-matched children with typical voices (CwTV), suggestive of a perceptual deficit in CwVN. The second hypothesis was that children under eight years of age would have poorer auditory discrimination abilities compared to older children and adults, similar to trends noted in previous work performed with pure tone stimuli [34; 35; 36; 37; 38; 39].
## 2 Materials and Methods
### Data Processing  
Data processing: Microsoft Excel software (ver. 2019; Microsoft Corp., Redmond, WA, USA) was used for basic data collation and calculation. Winnmster software (ver. X; Airsense Analytics) was used to generate electronic-nose data. Origin software (ver. 2021; OriginLab Corporation, Northampton, MA, USA) was used for principal component analysis (PCA) of electronic-nose response values and radar plotting of sensory evaluation results. GC-MS partial least squares discriminant analysis plots were all produced using SIMCA software (ver. 14.1; Sartorius, Gottingen, Germany). For sensory data, statistical analysis was performed using SPSS 26.0: intra-panel consistency was verified by Cronbach's \(\alpha\) coefficient (\(>\)0.8) to ensure reliability of evaluators' judgments, and differences in sensory attributes between groups were analyzed via one-way ANOVA followed by [PERSON]'s HSD post-hoc test (\(p<\) 0.05) to determine significant variations.
###### Abstract  
Hydrogen permeation reduces a material's properties and increases the risk of brittle fracture, which causes a potential safety hazard. A workpiece's hydrogen permeation resistance could be improved by improving its surface integrity through surface processing. This paper studies low-alloy steel's surface integrity and its hydrogen permeation resistance in a hydrogen production reactor, using the electrochemical cathodic hydrogen-charging method to carry out electrochemical hydrogen-charging experiments. After the specimens were pretreated using different surface-grinding methods and shot peening pressure strengthening, they were hydrogen-charged on a self-designed and built electrolytic hydrogen charging platform. Before and after hydrogen charging, the specimens' section hardness and tensile strength were tested, and the fracture morphology of the specimens was observed. The influence laws of surface roughness and surface residual compressive stress on the distribution of material hardness along the depth, the variation in material hardness, the fracture morphology, and the decline in the tensile properties of the low-alloy steel specimens after 5 h of hydrogen charging were analyzed. The reasons for the influence of surface integrity indexes on the hydrogen permeation resistance of the specimens were also analyzed. Based on the experimental results, a series of mechanical processing parameters were proposed to improve the material's permeation resistance, which provides a theoretical and practical basis for the processing of materials with high surface integrity and hydrogen permeation resistance. Through the experiments, it was found that the hydrogen permeation resistance of the Ra 0.17 \(\upmu\)m surface roughness specimen was the best of all specimens with different surface roughness values, and its hydrogen embrittlement sensitivity index was 20.96%. The specimen had the best hydrogen permeation resistance under 336 MPa surface residual compress stress, and its hydrogen embrittlement sensitivity index was 16.45%.  
hydrogen production reactor; surface integrity; fracture morphology; tensile strength; hydrogen embrittlement sensitivity index
## 5 Analysis of Composite Joint Test Results
### Load-Displacement Curves  
Figure 11 shows that the displacement was augmented linearly with the increase in load. The trends of the curves show that the load-to-displacement ratio of CJA was larger than that of CJB. No obvious turning point appeared in CJA and CJB2. Hence, the load-displacement curves of CJA and CJB2 were in the linear elastic stage. The slope of the curve decreased after 3800 kN in CJB1 on account of the initial crack observed at 3800 kN. The displacement values of the concrete chord were 13.83 mm, 15.94 mm, and 16.17 mm in CJA, CJB1, and CJB2, respectively, when the load procedure was finished. Obviously, the bearing capacities of CJA, CJB1, and CJB2 were higher than 6000 kN. Furthermore, the bearing capacity was at least 2.93 times the design load. Overall, almost all members were in the elastic stage and had not only good deformability but also great potential to carry a greater load.  
The joint stiffness was defined as the ratio of the yielding load to the corresponding displacement [12]. In view of the fact that the three composite joints were almost in the elastic stage, in this study the joint stiffness was defined as the ultimate load over the corresponding displacement. The joint stiffness values of CJA, CJB1, and CJB2 were 43.38 \(\times\)\(10^{4}\) kN/m, \(37.61\times 10^{4}\) kN/m, and \(37.11\times 10^{4}\) kN/m, respectively. Obviously, these results suggest that there was an association between the enhancement of the joint stiffness and the increase in the embedded depth of the PBL shear connectors.
## 5 Conclusions and Recommendations
\begin{tabular}{l l} FID & Flame Ionisation Detection \\ FP & Fine Particle \\ FTP & Federal Test Procedure \\ GAINS & Greenhouse Gas-Air pollution Interactions and Synergies (IIASA model) \\ GC-MS & Gas Chromatography-Mass Spectrometry \\ GDI & Gasoline Direct Injection \\ GFED & Global Fire Emissions Database \\ GHG & Greenhouse Gas \\ GPF & Gasoline Particle Filter \\ HC & Hydrocarbons \\ HDV & Heavy-Duty Vehicle \\ HOM & Highly Oxidised Matter \\ HRTEM & High-Resolution Transmission Electron Microscopy \\ HULIS & Humic-Like Substance \\ HVO & Hydrotreated Vegetable Oil \\ ICCT & International Council on Clean Transportation \\ IVOCs & Intermediary Volatility Organic Compounds \\ IR & InfraRed \\ JRC & Joint Research Centre \\ LCV & Light Commercial Vehicle \\ LDV & Light-Duty Vehicle \\ LEV & Low Emission Vehicles \\ LLE & Loss of Life Expectancy \\ LPG & Liquefied Petroleum Gas \\ MCE & Modified Combustion Efficiency \\ MPI & Multi-Point Injection \\ NDC & Nationally Determined Contributions \\ NEDC & New European Driving Cycle \\ NEE & Non-Exhaust Emissions \\ NFR & Nomenclature For Reporting \\ NMVOCs & Non-Methane Volatile Organic Compounds \\ NOx & Nitrogen Oxides \\ NP & Nanoparticles \\ OC & Organic Carbon \\ OM & Organic Matter \\ PAHs & Polycyclic Aromatic Hydrocarbons \\ PAM & Potential Aerosol Mass \\ PC & Passenger Car \\ PFI & Port Fuel Injection \\ PM & Particulate Matter \\ PMx & Particulate Matter for particle diameter below x \(\upmu\)m (x = 0.1, 1, 2.5, 10) \\ PN & Particle Number \\ POA & Primary Organic Aerosols \\ PODE & Polyoxymethylene Dimethyl Ethers \\ POP & Persistent Organic Pollutant \\ PUF & Polyurethane Foam \\ RDE & Real Driving Emissions \\ rBC & Refractory BC \\ SCR & Selective Catalyst Reduction \\ SDG & Sustainable Development Goal \\ SLCP & Short-Lived Climate Pollutants \\ SMPS & Scanning Mobility Particle Sizer \\ SNAP & Selected Nomenclature for Air Pollution \\ SOA & Secondary Organic Aerosol \\ SOx & Sulphur Oxides \\ SVOCs & Semi-Volatile Organic Compounds \\ \end{tabular}
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 3 Energy Management for an Energy-Harvesting Sensor Node
### EBC Design
A conservative policy is to set the energy budget to the minimum required, hence maximizing the lifetime at the cost of a low quality of service. On the other hand, setting the energy budget to an arbitrary high value leads to high quality of service at the cost of short lifetime. Similarly to what has been done for the charging state, a customizable energy management, which can be tuned according to the need of an application, is proposed. The energy budget correction \(\delta e_{B}\) is set at a value proportional to the residual energy variation \(\Delta e_{R}\), and the proportionality factor is a function of \(e_{R}\) denoted by \(\mu_{D}\left(e_{R}\right)\)[30]:  
\[\mu_{D}\left(e_{R}\right)=M_{D}\left(1-\frac{e_{R}-E_{ENI}^{down}}{E_{ENI}^{ down}-E_{R}^{fail}}\right)^{K_{D}}. \tag{4}\]  
where \(M_{D}\) and \(K_{D}\) are positive parameters allowing the tuning of the discharging strategy. \(\mu_{D}\) decreases with \(e_{R}\), as the less energy is stored, the more conservative we must be, and the impacts of \(K_{D}\) and \(M_{D}\) on the discharging strategy are similar to the ones of \(K_{C}\) and \(M_{C}\) on the charging strategy.
## 3 Magnetic Field Intensity Measurement
### The Amplification Correction  
Obtaining a more accurate picture of the EMF structure, in terms of both intensity and direction, involved the similar measurement processes for each of the three sensor coils (OSCs).  
Since the field values were very low, it was necessary to amplify the induced EMVs by different factors to compensate for the coil surface differences. On the other hand, it was useful to share as many elements as possible from the measurement chain.  
The solution was the distinct and adjustable amplification AX, AY, and AZ for each input coil signal, followed by analog multiplexing, as shown in Figure 4.  
To obtain similar responses to similar fields for the three coils X, Y, and Z, the emitting coil was placed successively for each coil so that the center of the emitting coil was in the center of the sensor coil, and the normal axis of the emitting coil surface corresponded to the normal of the sensor coil, according to the previous point.  
Because the input excitation from the emitting coil was the same in all three situations, this configuration ensured the same field for each coil. The AX, AY, and AZ amplifications were adjusted so that the numerical output value was the same. Considering that, due to  
Figure 3: Measurement of the deviation of the sensor coil axis corresponding to the Z direction in the Y direction.  
random factors (noise), the numerical values were not identical for each measurement, the average of several successive values was used for these settings.  
The adjustments applied according to the method presented above provided the advantage that the common elements in the measuring chain acted in a similar way for the three EMVs, so that the ratio between the measured numerical values was proportional to the ratio between the field strengths.
## 4 Results and Discussion
### Predictive Capability of the Models
#### 4.3.2 Prediction of Relative Humidity \(RH_{out}\) at the End of the Roadway  
The predicted humidity data on the testing set were compared with the actual humidity data, as shown in Figure 10. Among the 182 pieces of data, the proportions of the training and test sets were the same as those in Section 4.3.1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Number of Units in** \\ **LSTM Layer** \\ \end{tabular} & \multirow{2}{*}{**Batch Size**} & \
\begin{tabular}{c} **Optimizer** \\ **Learning Rate** \\ \end{tabular} &
\begin{tabular}{c} **Dropout Layer** \\ **Parameter** \\ \end{tabular} \\ \hline LSTM-GA & 78 & 11 & 0.0068195 & 0.283 \\ \hline PCA-LSTM-GA & 34 & 20 & 0.001759 & 0.151 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal hyperparameters of the models for temperature prediction.  
Figure 9: Comparison between predicted and actual temperatures \(T_{f_{out}}\) on the testing set among the three models. (**a**) LSTM. (**b**) LSTM-GA. (**c**) PCA-LSTM-GA.  
The hyperparameters of the LSTM-GA and PCA-LSTM models are listed in Table 3.  
As displayed in Figure 10, the values of MSE and \(R^{2}\) of the LSTM model are 0.0069 and \(-\)0.1504, respectively; those of the LSTM-GA model are 4.1360 \(\times\) 10\({}^{-4}\) and 0.9314; and those of the PCA-LSTM-GA model are 3.2413 \(\times\) 10\({}^{-4}\) and 0.9462. Furthermore, it can be observed that the predicted curve of the PCA-LSTM-GA model is the most consistent with the actual humidity curve.