esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Route Discovery Time
#### 4.3.1 Under MP-AODV  
MP-AODV uses a four-way handshake process to discover and establish node-disjoint paths. MP-AODV first discovers and establishes the main route by sending the first RREQ and RREP, and then performs the second route discovery the second RREQ and RREP, as shown in Figure 1b.  
The latency of delivering the first RREQ from the source to the destination, denoted by \(t_{RREQ1}^{MP-AODV}\), can be calculated this way:  
\[t_{RREQ1}^{MP-AODV}=n_{1}^{MP-AODV}\times t_{prop.}+(n_{1}^{MP-AODV}+1)\times t _{node}, \tag{15}\]  
where \(n_{1}^{MP-AODV}\) is the hop count of the first path.  
The latency of delivering the first RREP from the destination back to the source, denoted by \(t_{RREP1}^{MP-AODV}\), can be determined thus:  
\[t_{RREP1}^{MP-AODV}=n_{1}^{MP-AODV}\times t_{prop.}+(n_{1}^{MP-AODV}+1)\times t _{node}. \tag{16}\]  
Similarly, the latency of delivering the second RREQ and RREP, denoted by \(t_{RREQ2}^{MP-AODV}\) and \(t_{RREP2}^{MP-AODV}\), respectively, are as follows:  
\[t_{RREQ2}^{MP-AODV}=n_{2}^{MP-AODV}\times t_{prop.}+(n_{2}^{MP-AODV}+1)\times t _{node}, \tag{17}\]  
\[t_{RREP2}^{MP-AODV}=n_{2}^{MP-AODV}\times t_{prop.}+(n_{2}^{MP-AODV}+1)\times t _{node}, \tag{18}\]  
where \(n_{2}^{MP-AODV}\) is the hop count of the second path.  
The total time used for route discovery under MP-AODV, denoted by \(t^{MP-AODV}\), can be determined as follows:  
\[t^{MP-AODV}=t_{RREQ1}^{MP-AODV}+t_{RREP1}^{MP-AODV}+t_{RREQ2}^{MP-AODV}+t_{ RREP2}^{MP-AODV}. \tag{19}\]  
Therefore:  
\[t^{MP-AODV}=2(n_{1}^{MP-AODV}+n_{2}^{MP-AODV})\big{(}t_{prop.}+t_{node}\big{)} +4t_{node}. \tag{20}\]
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 3 Ground Procedures  
### Project Description  
This project introduced a proof-of-concept mixed reality application to improve ground procedures for service technicians at NASA Kennedy Space Center (KSC). Future crew members in LDEMs will perform complex hardware maneuvering, handling, and maintenance without real-time communications support from MCC. In this project, a ground procedure for handling and transport of delicate and complex flight hardware was addressed within a mixed reality interface. Ground procedures are performed at KSC for NASA by the Test and Operations Support Contract (TOSC), which supports the handling and transport of flight hardware from original equipment manufacturers to the launchpad. Technician procedure execution occurs by way of a Work Authorization Document (WAD), which is required to perform specific and complex ground processing tasks and work steps. WAD procedures are distributed in a PDF format and printed on paper to be brought to the site where tasks are performed. The printed PDF procedures tend to be long, repetitive, and can potentially lead to missteps and mistakes. Our research indicated that there is substantial precedent for emerging technology solutions such as AR within other industrial manufacturing domains, and an emerging technology solution may improve collaborative task execution for stakeholders. The primary research aims of this project were to improve the efficiency and reliability of technician procedure execution and enhance situational awareness for collaborative task execution of ground procedures. A mixed reality application was proposed to enable a hands-free interface for technician work execution, introduce a checklist format improving buyoff of work-steps from all collaborators, provide improved foresight to later parts of the procedure document, and integrate supplementary resources or information within a single interface.  
Future LDEMs will require astronauts to perform complex collaborative procedures with highly sensitive hardware. Different astronauts may possess different certifications for certain repair or transport tasks, and procedures must support efficient and intuitive collaborative execution. The prototype for this project focused on a task requiring personnel at NASA KSC to safely transport flight-ready hardware from one facility to another. The task presents highly targeted work steps demanding buyoff from a single ground service technician, with collaborative buyoff required from other ground service personnel. While our research indicated the collaborative nature of ground service and processing is a significant aspect of procedure execution, due to time constraints the prototype was designed for a single service technician. Additionally, while a proof-of-concept was derived for a heads-up display, a functional prototype was instead tested with multiple users on an iPad.  
Figure 3: User interface demonstrating detected volume and providing a procedure execution step.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 2 Results and Discussion
### Aroma-Active Compounds by GC-MS-O  
The extracts obtained by SPME were analyzed to assess the aroma-active compounds of the cubeb fruits using GC-O. Table 2 listed the identified aroma-active compounds of the _L. cubeba_ and _P. cubeba_. A total of 12 compounds were tentatively found to be the aroma-active compounds at olfactometry port for odor description in GC-O analysis, including eight terpen, one ketone, two alcohols, and one aldehyde. The odor descriptions of all the aroma-active compounds identified in the volatiles of _L. cubeba_ and _P. cubeba_ were basically similar to the reported of other fruits, such as blackberry [27], bayberry [17], strawberry [28], orange [29], and gooseberry [30].  
According to the evaluation of the odor and the odor description of the reported, it can be concluded that the flavor of turpentine-like might be caused by \(\alpha\)-pinene (A1), \(\beta\)-pinene (A4),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Codes**} & \multirow{2}{*}{**Odor**} & \multicolumn{2}{c}{**DF a**} \\ \cline{3-4}  & & _Litesa cubeba_ & _Piper cubeba_ \\ \hline A1 & pine, turpentine & 4 & 2 \\ A4 & pine, resin, turpentine & 4 & 6 \\ A6 & turpentine, mint, spice & 0 & 2 \\ A10 & lemon, citrus, mint & 6 & 6 \\ A12 & herb & 4 & 6 \\ A13 & turpentine & 4 & 4 \\ A18 & herb & 0 & 4 \\ A23 & wood, herb & 2 & 2 \\ B2 & camphor wood & 2 & 6 \\ C2 & flower, lavender & 2 & 8 \\ C8 & turpentine, nutneg & 4 & 4 \\ D3 & lemon, fruit & 6 & 4 \\ \hline \hline \end{tabular}  
* Sum of times detected by four assessors.  
\end{table}
Table 2: Principal aroma-active components of cubeb berries determined by SPME-GC-MS-O using DFA.  
\(\alpha\)-phellandrene (A6), \(\gamma\)-terpinene (A13), and terpinen-4-ol (C7); the fruity and flower flavor might be due to the presence of D-limonene (A10), citral (D3) with higher level in _L. cubbea_, and linalool (C2) with higher level in _P. cubbea_; the herbal flavor might come from \(\beta\)-ocimene (A12), \(\alpha\)-cubebene (A18) identified only in _P. cubbea_, and carophyllene (A23); camphoraceous flavor might be caused by camphor (C2), which is stronger in _P. cubbea_ with higher content than in _L. cubbea_. It is interesting to note that \(\alpha\)-cubebene was in very low proportions, also had high detection frequencies in all the extracts of _P. cubbea_. \(\alpha\)-cubebene had been detected in other study for cubbe fruit [7].
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 3 Results and Discussion
### Photon Detection Probability  
The sensitivity of SPADs are characterized by photon detection probability (PDP). The PDP is calculated in this work by dividing the number of counts (without the dark counts) by the number of photons incident. Figure 5d shows the PDP as a function of V\({}_{\text{ex}}\). The PDP is the product of the probability of avalanche by an electron or a hole and the quantum efficiency of the device. The quantum efficiency of this CASPAD is calculated to be 47%, at a wavelength of 785 nm, from the measured responsivity (\(\sim\)0.3 A/W) from our previous work [14]. The low probability of avalanche breakdown by an electron (\(\sim\)12.2% max) for the CASPAD is primarily attributed to the square geometry of the p-n junction, the breakdown occurs at the sharp corners due to high concentration of electric field at sharp edges in a similar way to premature edge breakdown [17,18]. Further investigation by light emission tests confirms that the breakdown occurs at the sharp corners (shown in Figure 6). Light emission tests show that the light emission primarily stems from 3 out 4 corners proving that the breakdown does not occur in the entire p-n junction but only at the sharpest corners where the electric field is concentrated. Breakdown occurs much less frequently in one of the corners likely due to non-uniformity of the CMOS process. The PDP could be improved by having a circular geometry. A summary of performance parameters is listed in Table 1 and comparison is made with a few conventional SPADs fabricated in 350 nm CMOS.  
Figure 6: Photomicrograph of the CASPAD device emitting light at (**a**) V\({}_{\text{ex}}\) = 2.1 V and (**b**) V\({}_{\text{ex}}\) = 2.8 V.
# An SSI-Based Solution to Support Lawful Interception
## 5 Design of the SSI Stack
### Stack Overview  
This four-layer architecture standardizes interactions between LEA, LEMF, and EA without relying on a centralized authority. Without loss of generality, the EA can authenticate itself through a verifiable credential issued by the LEA, which follows the W3C format of Layer 3 and is released using the DIDComm protocol of Layer 2. Additionally, at Layer 1, the Sovrin blockchain acts as a decentralized registry that keeps track of the credential definitions and revocation statuses. This allows different countries or law enforcement agencies to verify digital credentials independently by checking the blockchain, thus ensuring global interoperability. Finally, the Governance Framework defined in Layer 4 ensures that LEA, LEMF, and EA are compliant with legal and security regulations, especially when working across different countries. When different law enforcement agencies or external agencies from different countries need to share or access intercepted data, they must follow the same legal and security guidelines. For example, in a cross-border cybercrime investigation, a European LEA could gain access to the intercepted data from a U.S.-based LEMF by authenticating itself through a verifiable credential, issued under the W3C standard and verified via blockchain. The U.S.-based LEMF can validate the request without manual verification, ensuring secure and interoperable access. Moreover, the adoption of the SSI stack ensures the interoperability between our system and other SSI systems. In practice, this approach allows us to incorporate the verification and exchange of verifiable credentials from other SSI systems adopting the same standard defined in the stack (e.g., law enforcement badges or digital warrants issued as verifiable credentials) into our access control process.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 2 Materials and Methods
### Assays for the Inhibition of biofilms
#### 2.6.1 Quantitative Inhibition of Biofilms by ZnO-NPs  
The quantitative assessment of the biofilms development by ZnO-NPs was studied with crystal violet dye in 96-well polystyrene plates [20]. Two-fold dilutions of varying sub-MICs of ZnO-NPs were made in the wells of polystyrene plate in sterile LB broth. The bacteria were cultivated until log phase and diluted in Luria-Bertani broth for inoculum preparation. In the control group, no treatment with ZnO-NPs was given. The microtiter plate was incubated for 24 h in static incubator at 37 \({}^{\circ}\)C. Following incubation, wells of polystyrene plate were emptied and then gently washed three times with phosphate buffer (sterile) to remove the planktonic and loosely bound bacterial cells. Wells were then stained with crystal violet solution (0.1%) for 20 min. Excess amount of crystal violet was washed gently with sterile phosphate buffer followed by air-drying at room temperature for 20 min.  
The crystal violet-stained wells solubilized in ethanol (90%) and OD of wells were recorded using microplate reader at 620 nm. The % inhibition was computed by comparing with respective control groups.
## 3 Results
### Evaluation  
Before starting with the interpretation of the results, a plausibility check has to be conducted, e.g., using Minitab, in order to detect any outliers, transposed digits or typos. No irregularities are to be mentioned. As mentioned before, Minitab is the statistics software used in this work for analyzing the results. In this kind of studies, it is common to make use of Pareto diagrams of standardized effects as well as of main effect diagrams in order to identify and evaluate the significance of the impact factors and their interactions on the response. While the fiber-filled material showed no significant effects within the chosen factor range and thus has not been investigated further, the glass spheres filled material revealed a strong relation between surface quality and tool temperature. Higher tool  
Figure 6: **(a)** measurement field for warpage detection. **(b)** Measured values of the warpage of glass sphere filled material with a standard deviation of 3.56 \(\upmu\)m. **(c)** Measured values of the warpage of fiber filled material with a standard deviation of 2.22 \(\upmu\)m.  
Figure 7: Shrinkage measurement of length and width in comparison to the original dimensions. **(a)** Measurements of shrinkage of glass sphere filled material with a standard deviation of 0.036%. **(b)** Measurements of shrinkage of fiber filled material with a standard deviation of 0.05%.  
temperatures lead to smoother surfaces (Figure 8; higher slopes in the main effect diagram). However, variations of the direction of injection, holding time and pressure did not lead to any significant improvements. Warpage was mostly influenced by the direction of injection. Particularly, injection orthogonally to the reflective area leads to a geometrically balanced mold flow situation. No adverse effects on surface quality could be observed. Furthermore, an increased tool temperature improved warpage effects significantly by more than 50% (Figure 9). Evaluation for shrinkage showed barely a significant impact on the direction of injection. Other correlations for shrinkage could not be detected.  
As seen in prior works [30], regarding filled thermoplastics, the geometry of the fillers as well as their aspect-ratio play a pivotal role in defining the filler orientation and, hence, the isotropy of the material and even in creating a filler-free molding skin at the surface. Fibers are subjected to rotational forces caused by flow velocities and, thus, more susceptible to reach the surface and affect its quality. Similar effects could emerge during thermos  
Figure 8: Evaluation of surface roughness of spherical glass-filled thermoset. (**Top**) Pareto diagram with the threshold for significance. (**Bottom**) main effect analysis.
# Air Quality Assessment of a School in an Industrialized Area of Southern Italy
###### Abstract  
In this study, simultaneous monitoring of indoor and outdoor Volatile Organic Compounds (VOCs) was carried out in a school, by both Radiello(r) cartridges and real-time monitors (Corvus, IonScience Ltd., Fowlmere UK). Moreover, an outdoor air quality assessment was performed with data from an air quality monitoring station (ARPA Puglia) located close to the school. In particular, VOCs, Benzene, Toluene, Ethylbenzene, and Xilenes concentrations, obtained by using Radiello(r) diffusive samplers, were monitored in two classrooms, two bathrooms, and outside of the school building for three weeks during winter 2019. Simultaneously, the Total VOC (TVOC) concentrations were measured by means of real-time monitors inside and outside the classroom in order to individuate the activation of sources during the sampling campaign days. The results evidence that indoor TVOC concentrations were bigger than those outdoors; this suggests the presence of indoor pollutant sources, both in the classrooms and the bathrooms. The results of our study can help the school management by the following recommendations: increasing ventilation in classrooms during school activities and after cleaning; involving students in discussions regarding the use of acrylic paints, permanent markers, perfumes and deodorants, and the limitation of smoking activities both in bathrooms and outdoor spaces.  
volatile organic compound (VOC); indoor air quality; outdoor air quality; air quality monitoring station +
Footnote †: journal: Medical  
allergy symptoms. In [PERSON] et al. [4], elevated levels of benzene in classrooms were significantly associated with the upper airways, lower airways, and systemic symptoms in schoolchildren. Moreover, the adverse effects of limonene were observed in schoolchildren. The health effects linked to limonene are irritation of the nose, eyes, throat, and skin [5]. However, another study performed in a population-based sample of approximately 3000 Canadians suggested that household exposure to limonene may increase the spread of the exhibition of asthma symptoms in children [6]. [PERSON] et al. [4] demonstrated a stronger linear-positive association between the VOC score and systemic symptoms, as well as upper and lower airway symptoms.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 2 Materials and Methods
### Experimental Setup  
In this work, we have designed several experiments to validate the performance of our proposed CSI-based HAR approach. Particularly, in each experiment, the subjects were asked to perform a set of pre-explained tasks in the area between a transmit-receive pair of antennas. During the experiments, a beep sound was used to guide the subjects and inform them on when to start a specific task. The transmitter and the receiver were both retrofitted with \"Intel Ultimate N Wi-Fi Link 5300\" to extract the CSI values from the transmitted packets using the CSI tool described in [40]. The transmitter and the receiver were configured according to the settings provided in Table 1. As shown in Table 1, we have a one-to-many architecture in which only one antenna is transmitting the packets, and three antennas are receiving the packets.
## 3 HJ-1B images simulation
### Fire characteristics  
In general, the temperatures of active fires vary from 800 K to 1,200 K, and that of smoldering fires range from 600 K to 800 K [28]. Extremely large fires with temperatures higher than 1,400 K may occur in forested areas, but with a low probability, and consequently, they are not considered in the general research. Fire areas from 10 m\({}^{2}\) to a maximum of 10,000 m\({}^{2}\) were simulated in the study, corresponding to the fractions from 0.01% to 10% of a 300 m resolution of HJ-1B pixel. Fire emissivities were assumed to be an average value of 0.95 in both MIR and TIR channels.
## 2 Materials and Methods
### Evaluation of Model Performance  
The predicted GPPs were evaluated against the EC estimates (GPP_tower) at an eight-day interval during the growing season. The alpine shrub meadow (GCT) and alpine swamp meadow (SD) sites were analyzed separately due to their highly contrasting soil conditions. The model performances in the two temperate steppse (NM and XL) were evaluated together due to (1) their similar environmental conditions and (2) the limited EC observations in the NM site. The correlation coefficient (\(R^{2}\)), root mean squared error (RMSE, %) [38], percent bias (PBIAS, %) [91], and corrected Akaike Information Criterion (AICc) [80; 92] were used to evaluate the model performance. The higher the \(R^{2}\), and the lower the RMSE and AICc, the better the model performance. The RMSE, PBIAS, and AICc are calculated as follows:  
\[RMSE = \frac{100}{\mathcal{O}}\times\sqrt{\frac{\sum\left(M_{i}-O_{i} \right)^{2}}{N}} \tag{12}\] \[PBIAS = \frac{\sum\left(M_{i}-O_{i}\right)\times 100}{\sum O_{i}} \tag{13}\]  
Figure 3: Light-response curve function on 16–22 July 2011, at the XL site.  
\[AIC_{C}\ =\ N\times\ln(\frac{RSS}{N})+2K+\frac{2K\times(K+1)}{N-K-1} \tag{14}\]  
where \(\overline{O}\) is the average of the observed values, \(O_{\rm i}\) and \(M_{\rm i}\) represent the observed and modeled GPP, respectively, and \(N\) indicates the total number of estimates at the study site. The optimal value of PBIAS is 0, while positive and negative values suggest an underestimation and an overestimation of GPP, respectively. \(K\) is the number of parameters fit by the regression plus 1, and \(RSS\) is the residual sum of squares of the model. Since the AICc is an estimator of the relative quality of statistical models for a given set of data, it was only applied to examine the inter-model differences in this study.  
We also compared the modeled and observed GPP at an annual scale to evaluate the capability of these models to capture the interannual patterns of GPP. Given that a single environmental scalar may not fully capture the environmental stresses over the temperate steppe and alpine meadow, stepwise multiple linear regressions were developed between the observed GPP and all the variables used by these satellite-based models (i.e., EVI, Ta, LSWI, PAR, LST, and VPD) to calculate the independent and total explanation powers of them with the GPP's variability.