esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 3 Problem Formulation and Method Overview
### Notation and Problem Formulation
For the pose estimation problem, we temporarily take these two misalignments as priors. Our final goal in the estimation problem is to determine the antenna's downtilt and azimuth angles in the Earth frame, which together represent the rotation of the antenna relative to _EF_, denoted by \(\,{}^{E}_{G}\!R\). The symbol of \(O\) represents the object coordinate frame of the antenna. We summarize these quantities and their relationships in the graph model shown in Figure 2. A straightforward interpretation of the graph model provides us with a formulation of an estimation problem with a conditional cost function given all priors and observations, which is:  
\[f\left(\,{}^{E}_{G}\!R\,|\,{}^{A}_{M}\!R,\,{}^{A}_{CR}\!R,K,\mathcal{M},S_{1},S _{2},\ldots,S_{P},\Phi_{1},\Phi_{2},\ldots,\Phi_{P}\right), \tag{1}\]  
where \({}^{E}_{G}\!R\) is the pose to be estimated. The two main sources of input are the camera projection process and the IMU sensing process, so we re-express Equation (1) as follows:  
\[f =\sum_{i=1}^{P}\left(g_{1}\big{(}{}^{C}_{G}\!R_{i}|K,\mathcal{M}, \Phi_{i}\big{)}+g_{2}\big{(}{}^{A}_{E}\!R_{i}|S_{i},\,{}^{A}_{M}\!R\big{)}\right)\] \[=\sum_{i=1}^{P}g_{1}\big{(}{}^{C}_{G}\!R_{i}|K,\mathcal{M},\[PERSON]_{ i}\big{)}+\sum_{i=1}^{P}g_{2}\big{(}{}^{A}_{E}\!R_{i}|S_{i},\,{}^{A}_{M}\!R \big{)},\] (2a) s.t. \[{}^{E}_{G}\!R={}^{E}_{A}\!R_{i}^{A}\!C_{G}^{A}\!R_{i}, \tag{2b}\]  
Figure 2: The antenna pose estimation problem represented as a graph model. Filled blue circles represent unknown quantities; filled gray circles represent known quantities; solid dots represent priors; the green-boxed region represents the capture of \(P\) groups of data; and the arrows indicate the dependencies between the quantities. where \(g_{1}\) is the projection-related error and \(g_{2}\) is the sensing-related error; the constraint Equation (2b) models the relation between \(CF\) and \(IF\) and thus relates two error terms. Note that Equation (2a) is a generic formulation of our pose estimation problem in the Earth frame, and different solutions may arise depending on the choices of \(g_{1}\) and \(g_{2}\).
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.1 Disaster, Emergency and Risk Management
Every device equipped with RAMBLE generated information, used up information that it showed interest in to its neighbors, and acted as other devices' cache in an opportunistic manner. A real-world experiment of RAMBLE was carried out at Porto's Botanical Garden and included a crowdsourced information collecting scenario with participants having Android smartphones, as well as deployed cludlets. The result of the experiment revealed that mobile devices equipped with RAMBLE and modest cloudlets were effective in intelligence gathering. [PERSON] [96] designed a theoretical integrated framework that can assist disaster relief staff in improving their coordinator by utilizing relevant information generated from a complete crowdsourcing framework. The author adopted the case research decision tree of [97] in their study and recognized that training and development, as well as leadership styles, make a substantial impact on the volume, variety, velocity, validity and value of data. They further proposed that IoT can be most effectively used if trust can be built between IoT and humans. Their analysis also showed that reducing behavioral ambiguity among partners will aid the development of successful coordination. However, the integrated framework (CS-10) was not empirically tested. [PERSON] and [PERSON] [98] proposed SAFER, a monitoring system for disaster situations based on crowdsourcing and software defined fog computing. Their aim was to reduce service delay and increase quality of service (QOS), which could be achieved by lowering transmission and computational delays in disaster situations. SAFER therefore employed SDN to provide dynamic network path to reduce data transmission delay and also utilized optimized fog computation to reduce computation delay. Simulation evaluation of SAFER architecture using crowdsourced heterogeneous data demonstrated that data were processed in less time, allowing for the early discovery of disaster situations. This result could allow first responders to pull spontaneous actions, which would be impossible with traditional networks owing to delay. [PERSON] [99] designed a crowdsourcing and IoT integration model using crowdsourced data collected from social media and RFID devices, to improve disaster response. In addition, the authors adopted the SIR model to further build their model.
## 4 The Assessment of Lean Maintenance Effectiveness Concept in Enterprises
### Use of the OEE Indicator in Enterprises
However, its use increased significantly in micro companies (stage I: 10.26%, stage II: 5.88%). In addition, the fact this indicator is not most often used in the enterprises with unit production (stage I: 29.49%, stage II: 29.41%) as well as with medium-batch production (stage I: 23.08%, stage II: 29.41%) was identified. Its use increased significantly in mass production (stage I: 8.97%, stage II: 2.94%). Furthermore, the indicator is most often not used in the enterprises of aviation, metal processing, and automotive industries. However, all the enterprises of the food industry that took part in the second stage of the studies declared its application. A crucial issue during the conducted studies was to obtain the information on the rate of calculating the OEE indicator. The rate of obtaining such information is essential, because the OEE indicator values inform us on an ongoing basis about productivity of the possessed machines. If the information is collected too seldom, a prompt reaction will not be possible in cases when the use of machines decreases. Figure 5: Is the overall equipment effectiveness (OEE) indicator calculated?—study results. The conducted studies show that in many enterprises the OEE indicator is calculated once a month (stage I: 26.09%, stage II: 25.00%). However, more and more often, the OEE indicator is calculated once per shift--the increase of over 17%, less often once a day--the decrease of over 16%. The obtained results show that large enterprises calculate the OEE value once per shift most often, medium enterprises once a month, small and micro enterprises once a week. The rate of calculating the indicator does not differ significantly in case of conventional and numerical machines (most often once per shift). Comparing the study results from the stages I and II, the rate of calculating changed from once a month to once a week for the machines described as \"other\". Another element of the realized studies was collecting the information, which considered the value of the OEE indicator. Its value is important, because it allows us to conduct initially a general analysis of the effectiveness of the possessed machines. The value over 85% is considered as the world level value of this indicator [49]. Analyzing the obtained results, it was stated that the number of companies that declared an average value of the OEE indicator at the level of 50-70% (stage I: 18.18%, stage II: 33.33%) and at the level of 30-50% (stage I: 9.09%, stage II: 25.00%) increased significantly. In stage II of the studies, none of the analyzed companies declared the OEE values below 30%. The highest OEE indicator values of over 70% are obtained in large enterprises for numerical machines, in the aviation and automotive industry with major foreign capital. The lowest OEE indicator values, below 30%, are obtained in small enterprises for the machines described as \"other\", in the metal processing industry with Polish capital.
## 4 Discussion
### Vegetation Cover Types  
The vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information was evergreen needleleaf forests. In this biome, radiation limitation was found to have the greatest effect on productivity (16%) [19]. Evergreen ecosystems tend to have greater radiation constraints [11] and, after temperature, radiation is the most limiting climatic variable in this type of forest [43]. The greater sensitivity of  
Figure 4: Variation in absolute bias between the ground-measured GPP and the satellite-estimated GPP after adding biome CI information to the NIR and PPFD model (GPPnirppfdci vs. GPPnirppfd) for the different vegetation cover types.  
ENF to PPFD variations could be related to the facts that confer canopies are particularly effective in distributing light throughout the canopy due to the clumping of needles around stems [44] and that the leaf area index (LAI) in needleleaf canopies is higher than in other vegetation cover types [45; 46]. The total leaf area supported by a given crown is the most basic structural property affecting the amount of absorbed solar radiation and plant gas exchange [47; 48; 49], although it is also important to take into account how leaves are distributed. Of all vegetation types, evergreen needleleaf forests have the lowest CI [24]. They are highly organized, with structures composed of shoots of needles, branches, whorls, tree crowns and tree stands [50]. This organization allows more radiation to penetrate into the canopy and, thus, permits a greater use of light. The structure of these forests increases both canopy photosynthesis--since more light can reach the lower levels of the canopy--and the contribution of non-light-saturated shaded leaves to whole canopy rates, thereby resulting in a higher GPP than the GPP estimated considering only the green biomass. Thus, including CI in the model corrected for this effect, and values for ENF fitted the general model better.  
Our study area was limited to northern latitudes, and no tropical areas were included. Radiation also plays an important role in tropical forests since it is the main factor limiting CO\({}_{2}\) uptake during the rainy season. Seasonal and interannual variation in light availability may also limit CO\({}_{2}\) uptake [51]. Light may significantly regulate net ecosystem exchange and carbon storage in this biome, and possible changes in the distribution of cloud cover associated with climate change could cause significant future changes in carbon storage [52]. Tropical forests account for 32-36% of global terrestrial net primary production [53]. Unfortunately, given the frequent cloud cover in tropical areas, it is often difficult to obtain high-quality remote sensing data from tropical biomes in the spectral domain involved in NIRv computation and in the optical spectra in general.  
We did not analyse water stress effects on GPP, so caution should be taken when using the corrected NIRv in drought-prone areas.
# Peak-Frequency Histogram Similarity of Bowel Sounds for the Evaluation of Intestinal Conditions
## 3 Results
### Bin Width and Selection of the Number of BS Episodes for PFHS of BS  
PFHS depends on the BW and number of BS episodes used when generating the PFH. Therefore, we first investigated how these parameters affect PFHS when evaluating intestinal conditions before and after liquid intake.  
The recorded data were divided into the following intervals: before carbonated water intake (BSI), after carbonated water intake (ASI), before water intake (BWI), and after water intake (AWI). The N BS episodes detected in each recording interval were sampled without replacement to create the PFH within the BW. We calculated the PFHS between BSI-BWI, BSI-ASI, and BWI-AWI based on this PFH. The PFHS was obtained through sampling without replacement in each subject. This approach was repeated 30 times, and the mean PFHS was then computed. Figure 3 shows the relationship between the mean PFHS values obtained as functions of N and BW.  
Figure 1: PFH before and after carbonated water intake.  
Figure 2: PFH before and after cold water intake.  
It is confirmed from Figure 3a that the PFHS between BSI-BWI tended to be relatively low, regardless of the liquid intake. This suggests that PFHS was affected by the difference in intestinal conditions on different experimental days. Figure 3b,c show that the PFHS between BSI-ASI was generally smaller than that between BWI-AWI, but it can be confirmed that it tended to be low regardless of N, especially in the range where BW was relatively small. To evaluate the difference between these two systems, a significance test was conducted for the PFHSs between BSI-ASI and BWI-ASI, and the significance level \(p\) was calculated. The \(p\)-value was obtained through sampling without replacement in each subject. This approach was repeated 30 times, and the mean \(p\)-value was then computed. Figure 4 shows the relationships between the mean \(p\)-value and BW as a function of \(N\).  
It is confirmed from Figure 4 that there are regions with significance levels < 0.05 and regions with significance levels <0.1. These results showed that the PFH changes were larger when carbonated water was ingested than those obtained when cold water was ingested. Therefore, the effect of carbon dioxide on intestinal conditions was reflected in the PFH.  
In this study, we identified a trend toward lower PFHS values when comparing PFH before liquid intake on different experimental days. Therefore, in the next section, we will investigate whether the calculation of PFHS before and after liquid intake was affected by the PFH generated before liquid intake on the day of experiment.  
Figure 4: Relationship of \(p\) of PFHS in BWI-AWI and BSI-ASI when the number of BS episodes and BW were varied during the creation of PFH. The colors represent significance levels; a yellow color indicates a significance level of \(\geq\)0.1.  
Figure 3: PFHS plots between **(a)** BSI-BWI, **(b)** BSI-ASI, and **(c)** BWI-AWI when the number of BS episodes and BW were varied during the creation of the PFH.
## 4 Results
### Data Augmentation  
In reality, since the bootstrap algorithm created independently the pseudo features. Finally, as shown in Figures 4-6, while each feature vector which is created by the proposed algorithm represented properly the reference BP, the results of the conventional algorithm did not represent correlation between the feature vector and reference BP well. Finally, our experimental results were summarized in Tables 2 and 3. The proposed algorithm showed better performance than the bootstrap algorithms in terms of the value of ME \(\pm\) SD and r-value. While the average performance in terms of SDE was respectively improved by 23% and 11% for SBP and DBP when using the proposed algorithm, the average performance of the conventional algorithm was respectively decreased by 5% and 13% for SBP and DBP. It can be considered that the r-value has decreased because the data generated from the conventional algorithm had an adverse effect as a result. In addition, while the average r-value of the proposed algorithm was respectively increased by 18% and 16% for SBP and DBP, the average r-value of the conventional algorithm was respectively decreased by 2% and 11% for SBP and DBP.  
Figure 4: Data augmentation results for DiaT, PTTf and reference DBP (unit: mmHg). (**a**,**c**): the bootstrap algorithm, (**b**,**d**): the proposed algorithm.  
Figure 5: Data augmentation results for PTTb, PTTf and reference DBP (unit: mmHg). (**a**,**c**): the bootstrap algorithm, (**b**,**d**): the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**BP**} & \multicolumn{4}{c}{**Baseline (without DA)**} & \multicolumn{4}{c}{**Bootstrap (5 Times)**} & \multicolumn{4}{c}{**Bootstrap (20 Times)**} & \multicolumn{4}{c}{**Proposed (50 Subjects*8)**} & \multicolumn{4}{c}{**Proposed (100 Subjects*8)**} \\ \cline{3-14}  & & **ME** &
# Key Technologies in the Context of Future Networks: Operational and Management Requirements
## 3 Research Overview
For instance, the Berkeley Wireless Research Center (BWRC) involves university, industry, government and other research stakeholders focused on exploring innovations in wireless communication systems based on radio frequency and millimeter wave technologies, which are its main challenge to develop reconfigurable radio architectures. Likewise, the Broadband Wireless Access and Applications Center (BWAC) involves around fifty research centers with the aim to collaborate with the industry in the creation of innovative and scalable wireless networks.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### Resonant Constellation Configuration Analysis for Distinct Regions  
For each constellation group, orbital propagation was performed based on the constellation's period. Assuming the period was discretized into \(n\) time points, a time sequence \(\mathbf{T}=t_{0},t_{1},\ldots,t_{N}\) was generated. Following the method outlined in Section 2.4, \(m\) grid points were derived, and a DOP matrix \(\mathbf{D}\in R^{n\times m}\) was constructed, yielding:  
\[\mathbf{D}=\left(d_{ij}\right)_{n\times m}=\left[\begin{array}{cccc}d_{11}&d_{12 }&\ldots&d_{1m}\\ d_{21}&d_{22}&\ldots&d_{2m}\\ \ldots&\ldots&\ldots&\ldots\\ d_{n1}&d_{n2}&\ldots&d_{nm}\end{array}\right] \tag{31}\]  
Here, \(d_{ij}\) represents the DOP value at the \(j\)-th grid point during the \(i\)-th time instance. Each row of matrix \(\mathbf{D}\) records the instantaneous DOP values across all grid points. By computing the average DOP value for each row of \(\mathbf{D}\), a spatial average sequence of DOP values was obtained.  
\[\mathbf{D}_{at}=\left[\begin{array}{cccc}d_{1}&d_{2}&\ldots&d_{n}\end{array} \right]^{T} \tag{32}\]  
Taking the average of this sequence yielded the spatiotemporal average DOP value, which served as the metric for evaluating the current constellation's performance.  
\[\bar{D}=\frac{\sum\limits_{i=1}^{n}\bar{d}_{i}}{n} \tag{33}\]  
For the 1229 constellation configurations derived in Section 3, coverage assessment and DOP value calculations were performed across different regions. A quadruple coverage constraint was imposed: constellation configurations with a fourfold coverage below 90% were discarded. This metric was derived by calculating the spatial percentage of regions with four simultaneously visible satellites at each epoch, then taking the temporal average. The remaining configurations were ranked based on \(D\) (sorted from smallest to largest), identifying optimal and suboptimal resonant navigation constellations for specific target regions.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
## 3 Results and Discussion
### Changes in Soil Arsenic Content and Fractionation
The soils treated with Pteris vitt extraction (T1, T3, T5) exhibited a significant decrease in both the acid-soluble and reducible fractions, while an increase was observed in the oxidizable and residual fractions and a decrease in the migration and bioavailability of soil arsenic.
## 3 Results and Discussions
### Sorbent Capacity  
In order to evaluate the adsorption capacity of NaGO, which is an important factor for its performance, the method that was recommended by [PERSON] and [PERSON] was employed [52]. For this purpose, the experiments were carried out under the optimum d-SPE conditions. The analytes were detected in the effluents by ICP-AES. The adsorption capacities of NaGO are presented at Table 1, as well as the adsorption capacity of other reported materials. As can be seen, the adsorption capacity for most metals is considered satisfactory for the analysis of food samples regarding the levels on real samples. Higher adsorption capacity was reported for Zn, Cu and Pb. The good adsorption capacity of the proposed adsorbent can be attributed to its high surface area and to the efficient interaction between the material and the examined elements.  
For zinc, the capacity was higher than in ref. [56] and for copper the capacity was higher than in ref. [53; 57]. For nickel, the capacity was higher than in ref. [54] but lower than the one reported in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multicolumn{6}{c}{**Salorption Capacity (mg g\({}^{-1}\))**} \\ \cline{2-9}  & \multirow{2}{*}{**NaGO (This Work)**} & \multirow{2}{*}{**GO-Silica [33]**} & \multirow{2}{*}{**AL\({}_{0}\)Q\({}_{5}\)[54]**} & \multirow{2}{*}{**3-Mercaptopopytrlinebreakboxlinebreakboxline**} & \multirow{2}{*}{**Zn-Molecularity**} & \multirow{2}{*}{**Zn-Molecularity**} & \multirow{2}{*}{**GO/SiO\({}_{2}\)[57]**} & \multirow{2}{*}{**GO [58]**} \\  & & & & & & & & \\ \hline Cu & 40.3 & 5.5 & - & - & - & 6.0 & - \\ Zn & 228.1 & - & - & - & 68.6 & - & - \\ Ba & 4.8 & - & - & - & - & - & - \\ Cd & 4.3 & 16 & 1.90 & - & - & - & - \\ Ni & 3.9 & 7.4 & 1.19 & - & - & - & 7.0 \\ Pb & 24.9 & 25 & - & 1.19 & - & 13.6 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the adsorption capacities of NaGO and other sorbents reported in the literature.  
ref. [53; 58]. For cadmium, the capacity was higher than in ref. [54] but lower than the one reported in ref. [53]. Finally, for lead, the capacity was higher than in ref. [55; 57] and similar to the one reported in ref. [53].
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 3 Background
### Hyperledger Fabric
#### 3.1.2 Transaction-Processing Architecture  
For most of the blockchain systems, a consensus of the order of transactions in a block must be reached before calculating the results of the transactions. Since each node will calculate the results independently to verify the results, the order of the transactions must be determined to maintain consistency among nodes. This architecture renders the computational resources of the blockchain system un-scalable. To solve this problem, HLF adopts a different transaction-processing architecture called \"execute-order-validate\" [4], in which transactions are calculated concurrently by multiple endorsers then ordered by OSN and later validated by peers.