esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definition and Methods of Analysis
### SA-AS-TRP Algorithm
Therefore, the calculation of the reference steps for the evaluation of the TRP of spurious emissions must be based on the entire device size, including the mechanical structure. The structure of the DUT in Figure 1 includes the depth (d), width (w), and height (h); then,  
\[D=\sqrt{d^{2}+w^{2}+h^{2}} \tag{9}\] \[D_{\text{cyl}}=\sqrt{d^{2}+w^{2}}\]  
The regular size of a DUT is \(10\times 80\times 160\) mm (\(d\times w\times h\), as shown in Figure 1). According to Equations (8) and (9), the reference step becomes  
\[\Delta\theta_{\text{ref}}\approx 0.5^{\circ},\Delta\phi_{\text{ref}}\approx 1 ^{\circ} \tag{10}\]  
where \(\lambda\) in Equation (8) is 0.0015 m, i.e., \(f=200\) GHz. This is because the FCC test requirements cover a frequency range from 9 kHz to 200 GHz for spurious emissions [11]. In this study, we discuss angular sampling under the harshest conditions, so we took the highest frequency to make all test situations applicable. A correlation factor \(\Delta\text{TRP}\) is defined to describe the deviation between the measured TRP and the target TRP. \[\Delta\text{TRP}=\text{TRP}_{\text{measured}}-\text{TRP}_{\text{target}} \tag{11}\]  
\(\text{TRP}_{\text{target}}\) is calculated by using a dense full spherical grid method, where the sampling steps are \(\Delta\theta_{\text{ref}}\) and \(\Delta\phi_{\text{ref}}\). \(\text{TRP}_{\text{measured}}\) can be calculated with the spherical grid method, the two-cut method, and the three-cut method sampled at any angle. Furthermore, \(\text{TRP}_{\text{target}}\) is considered to be an accurate TRP, which is used as a benchmark to measure the accuracy of \(\text{TRP}_{\text{measured}}\). In particular, when \(\text{TRP}_{\text{measured}}\) is calculated using the spherical grid method and the sampling step is exactly equal to \(\Delta\theta_{\text{ref}}\) and \(\Delta\phi_{\text{ref}}\), \(\Delta\text{TRP}=0\). Subsequently, the standard deviation (STD) of 10,000 calculation results was used to characterize the measurement uncertainty caused by angular sampling in the TRP test. \[\text{STD}=\sqrt{\frac{1}{M-1}\sum_{m=1}^{M}(\Delta\text{TRP}_{m}-\overline{ \Delta\text{TRP}})^{2}} \tag{12}\]where \(M=\) 10,000, and the bar denotes the average value. As detailed in Algorithm 1, the SA-AS-TRP algorithm randomly generates beams with different HPBWs as the sources. Subsequently, the \(\Delta\)TRP of the three TRP measurement methods is calculated using different sampling steps. The aforementioned steps are repeated 10,000 times, and the STD of each sampling step is computed. Finally, the measurement uncertainty introduced by angular sampling can be determined based on the STD.
###### Abstract  
For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath-Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.  
intelligent fault diagnosis; rolling element bearing; ensemble empirical mode decomposition; permutation entropy; linear discriminant analysis; clustering
## 3 Results and Discussion
### Characteristic of White LED Spectra  
For better understanding of the characteristic of pc-WLED on mesopic luminance and gamut volume, the optimal spectra \(\text{Opt}^{\#}1\) with the variance of peak wavelength shifts and the different power density ratios (i.e., the rate of the maximum powers of blue, green, and red components) of three components were investigated under 10 and 0.1 cd/m\({}^{2}\) luminance levels, respectively. Note that the luminance level is referred to as the luminance of an equal energy white. The test SPDs were normalized to have the equivalent total radiance to the equal energy white. As shown in Figure 5; Figure 6, the results of test spectra where the peak wavelength is shifted from 450 to 480 nm for  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **10 cd/m\({}^{2}\)** & **1 cd/m\({}^{2}\)** & **0.3 cd/m\({}^{2}\)** & **0.1 cd/m\({}^{2}\)** \\ \hline \(\text{LER}_{\text{Opt}}\)\({}^{\#}1\) & 395 & 394 & 387 & 450 \\ \(\text{LER}_{\text{Opt}}\)\({}^{\#}2\) & 444 & 446 & 450 & 480 \\ \(\text{LER}_{\text{Opt}}\)\({}^{\#}3\) & 311 & 343 & 319 & 436 \\ \(\text{CR}_{\text{Opt}}\)\({}^{\#}1\) & 74 & 79 & 63 & 42 \\ \(\text{CR}_{\text{Opt}}\)\({}^{\#}2\) & 41 & 62 & 62 & 39 \\ \(\text{CR}_{\text{Opt}}\)\({}^{\#}3\) & 63 & 63 & -8 & 27 \\ \(\text{R}_{\text{P/Opt}}\)\({}^{\#}1\) & 66 & 76 & 71 & 54 \\ \(\text{R}_{\text{P/Opt}}\)\({}^{\#}2\) & 44 & 60 & 60 & 44 \\ \(\text{R}_{\text{P/Opt}}\)\({}^{\#}3\) & 71 & 71 & 24 & 47 \\ \(\text{R}_{\text{S/Opt}}\)\({}^{\#}1\) & 99 & 111 & 119 & 117 \\ \(\text{R}_{\text{S/Opt}}\)\({}^{\#}2\) & 76 & 90 & 80 & 86 \\ \(\text{R}_{\text{S/Opt}}\)\({}^{\#}3 blue-chip, 500 to 550 nm for green phosphor, and 605 to 645 for red phosphor with 5 nm interval, and power density ratios ranging from 0.1 to 1.0, are depicted.  
According to the simulation results, it indicates that the mesopic luminance will increase with the shifts of the blue peak from a shorter wavelength to the longer ones, but the color gamut volume shows the opposite trend under these two luminance levels. Besides, the two lighting levels present the same tendency except for the wavelength shift of the green peak, which is due to the conversion of luminous efficacy. For the density ratio, the mesopic luminance increases with a higher rate of green. Moreover, the higher power of blue and red components contributes to the extension of the color gamut volume.  
Figure 5: Characteristic of luminance and gamut volume of peak wavelength shifts (left to right: blue, green, and red).  
Figure 6: Characteristic of luminance and gamut volume of different power density ratios (left to right: blue, green, and red).
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Methods
Therefore, it can be expected that distance to the ground might correlate with the achieved accuracy. The ranging accuracy decreases as distance to the ground increases, also, with increasing distance, the laser beam passes through more low vegetation before it hits the ground. The horizontal distance from each checkpoint to the nearest point of the PLS\({}_{\text{hh}}\) trajectory and the TLS station was calculated using QGIS (version 3.16.2) software. In addition to the influence of the distance to the ground, i.e., checkpoint, we assessed the influence of interpolation in areas with no classified ground points on the accuracy of DTM reconstruction. Around each checkpoint, a 30 cm buffer had been created, and all checkpoints that did not have any classified ground point within 30 cm were labelled as interpolated based on nearby data. The analysis was conducted based on the statistics (Equations (1)-(7)) of all non-interpolated and interpolated points.
## 5 Discussion
This implies that the cross-effectsare unpredictable depending on the tasks needed for each application, which complicates setting homogeneous guidelines for application developers that use the OS. Therefore, these challenges must be faced at all levels of development. At the hardware level, an increase in computational resources in low-power devices could reduce the negative effects in communications when the CPU load is high. In fact, there are already low-power microcontrollers that include a second CPU, which may be exclusively dedicated to communications [30]. On the other hand, at the software level, it would be necessary to design communication protocols that take into account this high processing load. In addition, new process management methods or software strategies could also be developed that allow the OS to jointly manage the communication protocols and the scheduling of the processing tasks in order to comply with the restrictions of both. Finally, hybrid strategies could also be designed to make the boundary between hardware and software more flexible and, in this way, implement the tasks with stricter time constraints at a hardware level, thus allowing the most critical actions to be parallelized.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 3 Routing Protocols for Wireless Sensor Networks
### Optimal Clustering  
The number of cluster heads has a great impact on network performance. According to [14], the optimal number of cluster heads is shown in Equation (18).  
\[k_{opt}=\frac{\sqrt{N_{A}\cdot E_{fs}}}{\sqrt{2\pi\cdot E_{mp}}}\frac{M}{d_{ {}_{eBS}}^{2}}. \tag{18}\]  
In Equation (19), \(N_{A}\) is the number of nodes in set \(A\), \(M\) is the area side length, and \(d_{{}_{eBS}}\) is the distance from the node to the base station.  
The probability of a node being elected as a cluster head is shown in Equation (19).  
\[p=\frac{k_{opt}}{N_{rounds}}. \tag{19}\]
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 3 Methodology
### System Architecture  
This study aims to develop a conversational AI for quickly answering soldiers' questions in the military training mission and supporting multiple conversation rounds. The architecture of our conversational system is shown in Figure 1. The user's query is first parsed by NLP, followed by a slot-filling module, which identifies important entities, and then the intent type is detected by the intent detection module. The system performs retrieval-based answer generation through the extracted entities and intents. The retrieval-based question-answering system ranked and selected the optimal responses. If the user confirms the answer is clear, take action; otherwise, the system will generate a new query to verify the user's intent. Slot filling and intent detection are the NLU stages for understanding, and retrieval-based answering and query generation are the NLG stages for responding. In the NLU stage, we use the CRF and SVM models to train slot-filling and intent-detection modules, respectively, which are practical and easy to implement due to the limited training information set. In addition, we make some summaries of the use of these models in related research. In the NLG stage, we use a learned ranking method to obtain retrieval-based answers. There are two basic types of generating sentences: extracted and abstract. This method is determined according to the greater probability of finding and querying within the existing corpus. The advantages of this method are that the grammar is relatively smooth and easy to understand and does not require a large number of training datasets--the main reason for responding to build mods. We use a template-based strategy in the query generation module. This template-based query generation method may propose a new query for the missing intent or entity and user to be confirmed with the user, that is, for the intent and entity to continue the dialogue, with the intention to avoid generating a new query and diverging context.
## 3 Selected Urban Areas and Data Sources  
Several definitions from different sources help to distinguish urban or agglomerated areas from rural areas, which is a crucial point for the analysis presented in this article. Demographia defines urban areas as: \"A continuously built up land mass of urban development that is within a labor market (_i.e._, metropolitan area or metropolitan region), without regard for administrative boundaries (_i.e._, municipality, city or commune)\" [36]. The identification of these areas is usually based on maps and satellite images that estimate the continuous urbanized area [36]. It is also important to distinguish between urban areas and metropolitan areas in which: \"A metropolitan area is an urban area plus the satellite cities around the urban area and the agricultural land in between.\" Since these factors could potentially influence the results of the analysis conducted, it was decided to use urban areas instead of metropolitan areas to avoid forests, agricultural and other uninhabited areas in the selected regions.  
A variety of online sources allow Internet users to retrieve freely available urban area information. However, oftentimes sources show inconsistencies in their provided information due to different geographical definitions of urban areas [37]. Since none of the available sources, such as Natural Earth Data or CORINE, provided the information needed for a comprehensive comparison of worldwide urban areas, it was decided to trace the urban area boundaries based on Bing satellite imagery. The center of each polygon was primarily based on the location of the city name feature in the standard OSM map. The urban area sizes that were implemented during the polygon generation and their corresponding population information were retrieved from Demographia [36]. Figure 1 shows a world map highlighting the selected regions for which urban area polygons were generated.  
During the urban area selection for the analysis it was decided to choose at least one large, well-known urban area (city) for each continent to provide world wide information. In total, 12 urban areas and their related area extent, absolute and population density information were chosen as shown in Table 1.  
After the areas of interest were defined, generated and included all desired information for analysis, an OSM history dump file was retrieved from the OSM project [38]. This particular file includes the entire history (versions) of all geodata that is included in the worldwide OSM database until October 19, 2012. Doing so enabled us to analyze the potential development of the datasets for each urban area for the past few years by clipping the information from the worldwide dataset and applying Java based tools that were specifically developed for this research project.
## 4 Optimization Mechanism Design  
### Research Motivation  
1. In the DiffServ networks without a method for service guarantee, there is a gap between service guarantee rates of different service requirements (shown in Figure 5). With the increase of \(v(Q_{i})\), the service requirement becomes loose. The service guarantee rate is high when the service requirement is loose. That is, the rate increases with the increase of the value of service requirement. Therefore, it is necessary to reduce the transmission delay of data with a small value of service requirement to improve the service guarantee rate of these kinds of data.
2. Compared with the data generated near the sink, the data packets generated by sensing nodes far from the sink spend more hops to arrive at the sink. This process contributes most of the delay. The delay can be effectively reduced by reducing the delay of these packets, and service guarantee rate can be improved as well.
3. The aggregators far from the sink transmit fewer data packets than aggregators near the sink. The data aggregation and transmission are the primary consumption methods of energy. In the model, all the aggregators are homogenous in energy. To enhance the transmission frequency of these distant nodes, the energy efficiency can be improved while the lifetime doesn't extensively deteriorate.
## 2 Materials and Methods
### DSG Proximate Composition Analysis
#### 2.2.4 Protein Content Determination  
The protein content of the dried distiller's spent grain (DSG) sample was determined using the Kjeldahl method, as described by the Association of Official Analytical Chemists (AOAC) [32], with some modifications. Briefly, 1.5 g of milled raw dried DSG was mixed with 15 mL of 95-98% sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) and 3 mL of 35% hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and two copper catalyst tablets were added. The mixture was allowed to react for approximately 10 min before being placed in a KjeldATHERM digestion block unit (Gerhardt, Konigswinter, Germany) at 410 \({}^{\circ}\)C for 3 h. A blank, containing all reagents but without the sample, was also prepared.  
After the digestion process (indicated by a clear hydrolysate), the hydrolysates were transferred to a distillation unit (VAPODEST), where they were distilled into 25 mL of 4% boric acid containing two drops of Tashiro indicator. The distillate was titrated against 0.1 N HCl until the endpoint, indicated by a pink color, was achieved. The volume of acid used during titration was recorded. A blank titration was also performed using the blank hydrolysates. The percentage of Kjeldahl nitrogen and the crude protein content were calculated using Equations (4a) and (4b).  
\[\text{Kjeldahl Nitrogen}=((\text{Vs}-\text{VB})\times\text{M}\times 14.01)/( \text{DSG Dry weight})\times 100. \tag{4a}\]  
\[\text{Crude Protein \%}=\text{\% Kjeldahl Nitrogen}\times 6.25. \tag{4b}\]  
where Vs is the volume of acid used for the sample.  
Vs is the volume of acid used for the blank.  
M is the molarity of HCl.
## 3 Results
### Driving Factors of Carbon Fluxes
#### 3.3.2 Driving Factors of Diurnal GPP, NEE, and \(\mathrm{R_{e}}\)  
All variables considered in the correlation analysis, except for \(\mathrm{CCO_{2}}\) and RH, exhibited significantly positive impacts on monthly average diurnal GPP, NEE, and \(\mathrm{R_{e}}\) during the two growing seasons (Figure 4a,b). Among them, PAR and VPD were identified as the predominant drivers of both diurnal GPP and NEE, with a Pearson correlation coefficient of approximately 0.90 and 0.84 (\(p<0.01\)). RH had significantly negative effects on GPP, \(\mathrm{R_{e}}\), and NEE, with correlation coefficients of \(-0.75\), \(-0.60\), and \(-0.74\) (\(p<0.01\)) during the two growing seasons, respectively. \(\mathrm{CCO_{2}}\) also exerted the negative influences on GPP, \(\mathrm{R_{e}}\), and NEE, with correlation coefficients of \(-0.68\), \(-0.69\), and \(-0.61\) (\(p<0.01\)). Although \(\mathrm{T_{a}}\) was significantly correlated with GPP and NEE, it acted as the primary determinant of diurnal \(\mathrm{R_{e}}\), with a correlation coefficient of 0.94 (\(p<0.01\)). \(\mathrm{T_{s}}\) and \(\mathrm{CH_{2}O}\) had little influence on GPP and NEE, yet their effects on \(\mathrm{R_{e}}\) were remarkable (Figure 4).  
The path analysis showed that the variance explained by the environmental factors on monthly average diurnal GPP, \(\mathrm{R_{e}}\), and NEE were 89%, 94%, and 85% in 2018, and 90%, 94%, and 86% in 2019, respectively (Figure 6a-f). Diurnal GPP and NEE were positively affected directly by PAR, with direct path coefficients of 0.75 and 0.88 (\(p<0.01\)), respectively. The direct effect on diurnal \(\mathrm{R_{e}}\) primarily stemmed from the positive influence of VPD and \(\mathrm{CH_{2}O}\), with direct path coefficients of 0.64 and 0.40 (\(p<0.01\)). \(\mathrm{T_{a}}\) exerted an indirect positive influence on GPP and NEE mainly through PAR, whereas RH had an indirect negative impact through PAR. Furthermore, \(\mathrm{T_{a}}\) also exerted an indirectly positive effect on \(\mathrm{R_{e}}\), with an indirect path coefficient of 0.83, and mediated through VPD. The effect of \(\mathrm{CCO_{2}}\) on \(\mathrm{R_{e}}\) was primarily indirect and negative, with VPD accounting for more than half of its influence.  
Figure 5: Pearson correlation coefficient between biophysical factors and carbon fluxes (GPP, gross primary productivity; \(\mathrm{R_{e}}\), ecosystem respiration; NEE, net ecosystem exchange) during the (**a**) vigorous growth period and (**b**) non-vigorous growth period. Biophysical factors are vapor pressure deficit (VPD), concentration of air carbon dioxide (\(\mathrm{CCO_{2}}\)), concentration of air water vapor concentration (\(\mathrm{CH_{2}O}\)), air temperature (\(\mathrm{T_{a}}\)), air relative humidity (RH), photosynthetically active radiation (PAR), soil temperature (\(\mathrm{T_{S}}\)) at 5 cm depth, soil water content (SWC) at 20 cm depth, and the leaf area index (LAL). \({}^{*}\) and \({}^{**}\) represent a significance level of \(p<0.05\) and \(p<0.01\), respectively.
## 3 Methodology
### Goal and Scope Definition
#### 3.1.2 System Boundary  
Figure 1 illustrates the system boundary diagram for the cradle-to-retailer life cycle of dry lentils in their natural state. The main processes include the following steps:  
1. _Conventional cultivation_ of lentils in both examined areas.
2. _Pre-treatment of harvested seeds_ to prevent insect proliferation. Dry lentils are particularly susceptible to infestation by various beetles, such as the lentil bruchid, and moths, the latter often necessitating fumigation with phosphine.
3. _Cleaning of fumigated lentils_, during which the cleaning waste is further processed to remove dust and herbaceous materials and, finally, pelletized for use as cattle feed.
4. _Temporary storage of cleaned lentils_ in polyethylene (PE) super-sacks at room temperature.
5. _Packaging of lentils_ in 25 kg food-grade paper bags, which are thermally sealed to extend the shelf life of dry lentils to approximately one year when stored at room temperature.
6. _Transportation of bagged lentils_ to a network of retailers over distances ranging from 50 to 300 km, using commercial light vehicles.  
Figure 1 also shows a block diagram related to the production of malted and dehulled lentils from cleaned and selected lentils (step iii), involving the following steps:  
1. _Dry lentil steering_ in water at 25 \({}^{\circ}\)C for 5 h [(10; 11)].
2. _Soaked seed germination_ at 25 \({}^{\circ}\)C under continuous humidification for 72 h to reduce phytic acid and oligosaccharidesacharide contents [(10; 11)].
3. _Drying (kilning) of germinated lentils_ to a moisture content of 10 \(\pm\) 2% (\(w/w\)) by setting the maximum temperature at 75 \({}^{\circ}\)C for 7-8 h.
4. _Malted lentil decorication and splitting_: A D-250 stone roller dehuller (Zanotti srl, Casalvolone, Italy), with a 250 kg/h capacity, allowed cuticles and rootlets to be gently removed without damaging the inner cotyledons. Waste materials were aspirated, yielding clean, split yellow cotyledons ready for final packaging.  
The subsequent processing steps coincide with those described for dried lentils, namely, temporary storage in PE super-sacks at ambient temperature (step iv), packaging and room storage (step v), and distribution (step vi).