esa-sceva/satcom-chunk-collection huggingface dataset:
# A Web-Based Application for Smart City Data Analysis and Visualization
## 4 Experimental Protocol: System Implementation
### Dataset Management and Description  
The data streams employed in this study are from publicly available open repositories maintained by the city of Basel, Switzerland ([[https://data.bs.ch/explore/?refine.tags=smarte+strasse](https://data.bs.ch/explore/?refine.tags=smarte+strasse)]([https://data.bs.ch/explore/?refine.tags=smarte+strasse](https://data.bs.ch/explore/?refine.tags=smarte+strasse)), accessed on 25 August 2023). These comprise CSV-format files containing information on air quality, traffic flow, noise levels, parking occupancy, and EV charging infrastructure. For the proposed pipeline, the datasets were preferred to be stored locally for comprehensive analysis. The reasons behind this decision are related to several practical and research-driven factors. First, all involved sensors (see Figure 3), except those monitoring EV charging stations, have been taken out of active operation, making real-time data collection no longer feasible. Beyond this necessity, local data storage ensures comprehensive control over data integrity and availability, allowing consistent preprocessing and repeatable experimentation without dependence on external service uptime or network stability. Moreover, it avoids issues such as API rate limits and schema changes which may introduce inconsistencies into longitudinal studies. From a research perspective, maintaining locally stored datasets enhances reproducibility, a key principle in empirical machine learning and scientific research [35; 59]. Lastly, this strategy supports a controlled and sandboxed environment ideal for testing, development, and iterative model tuning without the unpredictability of live data streams.
## 2 Materials and Methods
### TLS and Photogrammetry--Field Work  
Laser scanning was performed at 25 positions (Figure 3). The density of scanning and the geometry of the network of positions were chosen so that individual scans could be automatically registered during processing. A total of 1.207 billion points were acquired.  
Due to the extremely narrow space of the gap between the chapel walls and the surrounding terrain of the square (only 1 m), it was also necessary to use a fisheye lens (Figure 4 on the left). In areas with poor lighting conditions, it was also necessary to use a flash. A total of 789 images were taken (600 with f = 20 mm and 189 with fisheye f = 16 mm). The exterior of the chapel and the underground ossuary are connected through an entrance with stone steps (Figure 4--opening no. 1) and another 2 openings on the opposite side of the building (Figure 4--openings no. 2 and 3).  
\begin{table}
\begin{tabular}{c c} \hline
**Max. scan speed** & 976,000 pts/s \\
**Range** & 0.6–120 m \\
**Field of view (vertical/horizontal)** & 300\({}^{\circ}\)/360\({}^{\circ}\) \\
**Ranging error** & \(\pm\)2 mm at 25 m \\
**Ranging noise** & 0.95 mm at 25 m and 90\% reflectivity \\ \hline \end{tabular}
\end{table}
Table 1: Trimble TX5 Laser Scanner Parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Sensor size** & 36 \(\times\) 24 mm \\
**Image size** & 7360 \(\times\) 4912 pixels \\
**Lens 1** & AF-S Nikkor 20 mm f/1.8 G ED \\
**Lens 2** & Nikkor 16 mm f2.8 D AF Fisheye \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the Nikon D800E DSLR.  
Figure 2: Floor plan of the chapel (red—upper floor, blue—ossuary in the basement) (**left**) and cross section (**right**).  
The photogrammetric connection of the additional openings 2 and 3 was realized using image strips with overlaps that made it possible to join the image blocks in the exterior and in the ossuary (Figures 5 and 6) and thus to increase the overall robustness of the camera network. However, geometrically correct recording of images of such narrow openings is not easy.
## 5 Comparison and Discussion
### Comparison of the Proposed Method, the AP Method, and the PRISM Method  
There are limited failure mode analyses within the RFID field, primarily focusing on either the tags [43] or the readers [54]. However, in a specific study [52], the authors delved into examining 12 failure modes associated with the tag, reader, and link layers. It is important to note that in these referenced studies, the use of FMEA was not aimed at ranking the failures but rather at identifying them. Consequently, the data provided could not be utilized for comparison purposes.  
The findings from our study will be employed to utilize various methods outlined in the existing literature. These include the conventional FMEA, the partial risk map (PRISM) detailed in [18], and the application priority method referenced in [19] and elucidated in [60]. Figure 9 illustrates the results pertaining to the ranking and classification of the six methods.  
From Figure 9, it is evident that the outcomes of the three distinct functions within the PRISM method exhibit close similarities but notably differ from both the conventional FMEA and the application priority (AP) method. Specifically, around 73% of failures were given higher priority in the PRISM method compared to conventional FMEA. Moreover, the AP categorizations differ from both the PRISM and conventional methods for approximately 80% of the failures.  
The newly proposed FMEA-DEA method showcases disparities with the conventional FMEA in 80% of the failure modes (FMs) and diverges entirely from PRISM in all failure modes. In particular, approximately 73% of the failures were assigned a lower priority in the FMEA-DEA method compared to other methods. Additionally, the AP classification notably differs from the proposed FMEA-DEA method by approximately 67%.  
In summary, it is evident that different methods yield distinct results. This was anticipated due to their individual mechanisms for evaluating and prioritizing risk across failure modes. Table 6 offers a summary of the percentage similarities between the outcomes of the compared methods.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 5 Evaluation
### Under Sybil Attacks  
In this section, we study the performance of these defense mechanisms under Sybil attacks. Assume that the attacker can randomly present as nodes 3 and 6. It broadcasts invalid timestamps every \(3T\) periods. In Figure 7a, clearly, FTSP cannot converge under a Sybil attack. Since timestamps with ID 3 or 6 include valid and erroneous time information, these timestamps are more irregular in comparison with manipulation attacks. Nodes 4 and 5 may synchronize to a wrong clock, as could nodes 7, 8, 9, and 10. The safe nodes 3 and 6 disguised by the attacker would be mistaken for malicious nodes in threshold detection and be broken up with the network, as shown in Figure 7b. In contrast, Figure 7c shows that RSTS is robust against a Sybil attack since it does not disrupt the communication with the suspicious nodes and exploits the correct difference of receiving timestamps for synchronization.  
Figure 6: Performance of FTSP, FTSP with threshold detection, and RSTS under manipulation attacks. The representation of the lines is the same in (**a–c**). (**a**) FTSP; (**b**) FTSP with threshold detection; and (**c**) RSTS.
# Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition
### Research Questions  
In this study, we take the stance of the interactive approach and propose a novel robot conversation strategy that exploits the entrainment phenomenon and clarifies the human's indication without explicit requests from the robots: the robot uses confirmation behavior to implicitly align with the people's reference behaviors by eliciting clear referencing behaviors from a user. In addition, we compare the proposed strategy with the explicit request strategy, which is the existing strategy of the interactive approach proposed by some past research works. This research answers the following two questions:  
1. Does the implicit entrainment strategy that exploits entrainment phenomenon improve the performance of indicated object recognition? (Experiment 1)
2. Robots can employ either a conversational strategy that explicitly requests additional information or one that encourages implicit human entrainment. Which strategy results in better indicated object recognition, and which strategy do humans prefer the robot use? (Experiment 2)  
This paper is an extended version of previous works [21; 22; 23; 24]. Refs. [21; 22] reported the concept and the results of preliminary experiments. In addition, parts of this work were published in [23; 24], written in Japanese. To clearly present the novelty and novel knowledge which the paper includes, we have added additional results, references, and more discussions.
# Analysis of the Chip Geometry in Dry Machining of Aeronautical Aluminum Alloys
## 3 Results and Discussion
### Parametric Relationships  
The experimental results above suggest that it is possible to search for relationships which relate the different chip geometric parameters with \(f\). These relationships have been obtained for those variables which have shown a marked dependence on \(f\) (\(h_{c}\), \(h_{v}\), \(S\), and \(t\)). For this purpose, different models have been tested. Among them, a potential relationship illustrated the best fitting, Equation (9).  
\[CGP=\ K\cdot f^{\kappa}, \tag{9}\]  
In Equation (9), \(CGP\) is the mean value of the chip geometric parameter analyzed (\(h_{c}\), \(h_{v}\), \(S\), or \(t\)), and K and x are constants. Table 5 provides the results obtained from the fit of this potential relationship, to the experimental data. It should be noted that the experimental values of \(t\) have been obtained by Equation (6).  
It can be seen from the data in Table 5, that the value of the exponent (x) is close to linearity for the UNS A92024 alloy, particularly for \(S\) and \(t\), whereas its value is lower for UNS A97075, for all parameters analyzed. Therefore, it can be deduced that the chip geometric parameters analyzed have shown a slightly lower dependence on feed for the UNS A97075 alloy than the UNS A92024 alloy. These results are consistent with all of the observations previously mentioned.  
The potential proposed relationships have shown a good fit for all of the parameters analyzed (\(R^{2}\geq 0.9\)). Notwithstanding, it should be pointed out that these potential relationships have been obtained from the mean values of each geometric parameter, so only show their general trend. Standard deviations reach values around 25%-30% in some cases (e.g., for \(h_{c}\) and \(h_{v}\)), so R\({}^{2}\) is far lower, and its value is not representative of the process dispersion.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Visual Quality of the Point Clouds  
The visual quality of the point clouds will be presented on the appearance of the spherical targets scanned by individual scanners (Figure 8), including the result of the reference scanner Leica ScanStation P40 (Figure 8f). It clearly shows the low level of noise produced by static scanners (Trimble X7, Leica RTC 360--Figure 8a,b), with the cloud acquired with the Leica RTC 360 being smoother than Trimble X7. On the other hand, the high level of noise produced by SLAM scanners is also clearly visible, although the noise level of the newer ones (Emesent Hovermap ST-X, Figure 8c, and FARO Orbis, Figure 8e) is lower than that of the older generation SLAM scanner (GeoSLAM ZEB Horizon RT, Figure 8d). Even this simple visual comparison clearly demonstrates the technological advancement in the performance of newer SLAM scanners.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scanner** & **Trimble X7** & **Leica RTC 360** & \
\begin{tabular}{c} **Emesent** \\ **Hovermap ST-X** \\ \end{tabular} &
\begin{tabular}{c} **GeoSLAM ZEB** \\ **Horizon RT** \\ \end{tabular} & **FARO Orbis** \\ \hline \(\text{RMSE}_{\text{SFHER}}\) [mm] for & & & & & \\ individual & 2.0 & 1.5 & 4.4/5.5/4.8 & 17.5/15.9/17.9 & 6.6/8.0/3.9 \\ measurements & & & & & \\ \(\text{RMSE}_{\text{SFHER}\&}\) [mm] & 2.0 & 1.5 & 4.9 (0.6) & 17.1 (1.1) & 6.4 (2.1) \\ (StDev) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(\text{RMSE}_{\text{SFHER}}\) values after initial transformation based on the four spherical targets used for georeferencing of the clouds and standard deviation (StDev in the brackets).
# AI-Driven Waste Management in Innovating Space Exploration
## 5 Challenges and Considerations
### Economic Implications and Environmental Impacts  
Evaluating innovative waste management technologies in space indicates that long-term savings from resource recovery and reduced resupply needs can outweigh initial high investment costs. While these systems require substantial upfront investments in development, deployment, and crew training, their return on investment improves as they lessen dependency on Earth for resources, providing a break-even point for space agencies and private companies. International partnerships are vital for funding and advancing these technologies, with successful collaborations reducing individual cost burdens and enhancing capabilities. The commercial potential for private companies in the space sector is significant in space waste management and in developing spin-off technologies for Earth. Effective waste management contributes to environmental sustainability by minimising space debris and conserving raw materials. Technologies like plasma arc gasification support closed-loop systems for long-duration missions. Integrating waste management with existing life support systems, such as air filtration and water recovery, is crucial for crew health and mission success. Systems must efficiently recycle waste while ensuring safety and thoroughly sterilising biological waste to prevent contamination [78; 79]. The Controlled Ecological Life Support System (CELSS) exemplifies this integration, regenerating essential life-support materials for crew sustainability (Figure 6).
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 4 Ccrlb
### CCRLB for Satellite  
In the presence of errors in the satellite position, the TDOA-based log-likelihood function can be expressed as  
\[\ln(p\{\mathbf{r}^{o}|\mathbf{\xi}_{2}\})=\lambda_{t}-\frac{1}{2}(\mathbf{r}-\mathbf{r}^{o})^ {\rm T}\mathbf{Q}_{\mathbf{r}}^{-1}(\mathbf{r}-\mathbf{r}^{o})-\frac{1}{2}(\mathbf{s}_{t}-\mathbf{s}_ {t}^{o})^{\rm T}\mathbf{Q}_{s}^{-1}(\mathbf{s}_{t}-\mathbf{s}_{t}^{o}) \tag{42}\]  
\(\lambda_{t}\) is also a constant and \(\mathbf{\xi}_{2}=\left[\mathbf{u}^{\rm T},(\mathbf{s}_{t}^{o})^{\rm T}\right]^{\rm T}\).  
The FIM can be given as  
\[\mathbf{FIM}_{TDOA}=\begin{bmatrix}\mathbf{F}_{1}^{TDOA}&\mathbf{F}_{2}^{TDOA}\\ \left(\mathbf{F}_{2}^{TDOA}\right)^{\rm T}&\mathbf{F}_{3}^{TDOA}\end{bmatrix} \tag{43}\]  
where  
\[\left\{\begin{array}{l}\mathbf{F}_{1}^{TDOA}=\left(\partial\mathbf{r}^{o}/\partial u ^{\rm T}\right)^{\rm T}\mathbf{Q}_{\mathbf{r}}^{-1}(\partial\mathbf{r}^{o}/\partial u^{ \rm T})\\ \mathbf{F}_{2}^{TDOA}=\left(\partial\mathbf{r}^{o}/\partial u^{\rm T}\right)^{\rm T}\mathbf{ Q}_{\mathbf{r}}^{-1}\left(\partial\mathbf{r}^{o}/\partial(\mathbf{s}_{t}^{o})^{\rm T} \right)\\ \mathbf{F}_{3}^{TDOA}=\left(\partial\mathbf{r}^{o}/\partial(\mathbf{s}_{t}^{o})^{\rm T} \right)^{\rm T}\mathbf{Q}_{\mathbf{r}}^{-1}\left(\partial\mathbf{r}^{o}/\partial(\mathbf{s}_{ t}^{o})^{\rm T}\right)+\mathbf{Q}_{s}^{-1}\end{array}\right. \tag{44}\]  
The CRLB for TDOA-based localization is obtained as  
\[\mathbf{CRLB}_{TDOA}=\left(\mathbf{FIM}_{TDOA}\right)^{-1}=\begin{bmatrix}\mathbf{X}_{1}^ {TDOA}&\mathbf{X}_{2}^{TDOA}\\ \left(\mathbf{X}_{2}^{TDOA}\right)^{\rm T}&\mathbf{X}_{3}^{TDOA}\end{bmatrix} \tag{45}\]  
where \(\mathbf{X}_{1}^{TDOA}\in\mathbb{R}^{3\times 3}\).  
And, the CCRLB can be given as  
\[\mathbf{CCRB}_{TDOA}=\mathbf{CRLB}_{TDOA}-\mathbf{CRLB}_{TDOA}\mathbf{\tilde{\Lambda}}\left( \mathbf{\tilde{\Lambda}}^{\rm T}\mathbf{CRLB}_{TDOA}\mathbf{\tilde{\Lambda}}\right)^{-1}\bm {\tilde{\Lambda}}^{\rm T}\mathbf{CRLB}_{TDOA} \tag{46}\]
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model
### Monte Carlo Algorithm
#### 2.1.2 Photon Propagation  
The photon path length is the distance the photons travel in the homogeneous medium between interactions, which can be absorption, scattering, or both. The probability of a photon traveling over an optical scattering path of a length \(l\) is given by [10]  
\[P_{l}(l)=1-e^{-l}. \tag{8}\]  
A typical method to obtain a sample from a given probability distribution function is the inverse transform sampling [23]. Since Equation (8) is an invertible function, to obtain a sample, one simply needs to invert it:  
\[l=-\ln(1-P_{l}(l)). \tag{9}\]  
Then, we add a random, uniformly distributed number between \([0,1]\), represented here as \(P_{l}(l)\). Hence, the optical path length is then defined as  
\[l=c\cdot r, \tag{10}\]  
where \(c\) is the attenuation coefficient and \(r\) is the geometric distance between optical events.  
Finally, it comes naturally that \(r\) yields  
\[r=-\frac{1}{c}\ln(1-P_{l}(l)). \tag{11}\]  
Regarding the scattering, each successive scattering event causes the trajectory to rotate its local coordinate frame by \((\theta,\phi)\). Given a volume scattering function \(\hat{\beta}\), the new angle \(\theta^{\prime}\) is given by solving the following equation:  
\[q=\int_{0}^{\theta^{\prime}}\hat{\beta}(\theta)\sin(\theta)d\theta, \tag{12}\]  
where \(q\) is another random, uniformly distributed number between \([0,1]\). The new azimuthal angle \(\phi^{\prime}\) is independent of any probability density function and therefore is sampled uniformly between \([0,2\pi]\).  
Figure 3: Scheme of direction cosines. The blue vector depicts the photon’s direction vector, and the red vectors (\(\mu_{x}\), \(\mu_{y}\), \(\mu_{z}\)) are the projections of the photon’s direction vector onto the \(x\), \(y\), and \(z\) axes.
## 3 Results  
### Chemical Identification and Semi-Quantification  
#### 3.1.1 Comparison of Headspace Methods  
To investigate how the use of various odor collection methods can affect our representation of odor profiles, we identified and compared the chemical composition of headspace volatile collection between the Tenax(r) filter and SPME fiber (Table 1; Figures S1 and S2). Variations in the overall chemical composition of the tested samples were assessed using NMDS and ANOSIM, which revealed a clear association of certain compounds to each of the two methods (Table 2). Altogether, Tenax(r) headspace extracts collected a wider range of molecules compared to the SPME technique, which may be explained by a longer time of collection. In contrast, the SPME technique detected shorter hydrocarbon-chain volatiles such as 2-butanone, isobutyl-alcohol, acetin, and other compounds likely concealed by the solvent peak in Tenax(r) extracts. With both headspace collection methods, several pyrazines were detected in all bacteria, as well as from the TSB media (Table 1). Two compounds (i.e., isoamyl alcohol and 2-phenylethylanol), excluding the pyrazines, were produced by all bacteria and absent from the TSB media, and were detected with the SPME technique. Isoamyl alcohol was the major compound (i.e., highest relative proportions) detected in all the bacteria via SPME.  
Several species-specific compounds were also detected solely by one method. For instance, 2-phenylethylanol was detected only from CF via Tenax(r) extracts, whereas this compound was identified in all SPME samples (Table 1). Similarly, phenol was found only in CF with SPME. In contrast, indole was detected from all bacteria and TSB except CF, with both methods. Furthermore, all bacteria, apart from CF, produced 2-nonanone, detected using SPME. Some esters, such as ethyl octanoate and hexyl hexanoate, were detected via Tenax(r) headspace extracts only, and they were unique to EC. Unlike the SPME fiber, the Tenax(r) filters facilitated the detection of volatile compounds such as 2-coumaranone and o-cymene in CF and KO, respectively. Reciprocally, both isobutyl-alcohol and 3-hydroxy-2-butanone were detected in small quantities from PA by SPME only.  
\begin{table}
\begin{tabular}{c c c c} \multicolumn{3}{c}{**Group-SMIT**} \\ \hline \multirow{2}{*}{**Compound**} & **Functional** & **ANOSIM Statistics** & **Group-SMIT-
## 3 Ambient Parameter Monitoring and Sensor Technology  
From the spoilage mechanisms discussed before, it becomes obvious that ambient parameters strongly influence the quality and shelf life of fruit and vegetables. Purposeful and continuous monitoring is required to provide safe, fresh and nutritious products. Traditional ambient parameter tracking in fruit and vegetable supply chains relies on non-connected, partly even non-digitalized practices [3]. New approaches are under investigation to complement or replace those monitoring methods. The lack of real-time information is a particular problem that new solutions try to overcome to improve food safety and reduce waste [38; 39]. Moreover, the availability of real-time information on ambient conditions is a critical requirement for predictive shelf life calculations [7; 40]. One approach to provide this information is to use IoT-based sensors with wireless communication technologies.
## 4 Results and Discussion
### Crash Analysis  
As part of the evaluation of the three different tunnel safety scenarios, accident data were analyzed based on the researchers' observations from simulation replays of each participant under the various tunnel lighting conditions. To examine potential safety differences between the scenarios, they were compared using a 'Safety Value (SV)' formula, as provided in Equation (2).  
\[SVs_{i}=1-\frac{TAs_{i}}{\sum_{i=1}^{n}TAs_{i}} \tag{2}\]  
where:  
\(SVs_{i}\) Safety value for the scenario \(i\).  
\(TAs_{i}\) Total accident number for the scenario \(i\).  
\(\sum_{i=1}^{n}TAs_{i}\) Total accident number for all scenarios.  
Table 4 indicates the safety ratings for each tunnel safety scenario. The table shows that S-3 has the highest safety value (SV = 0.6), indicating that it is safer than S-2. S-1 is not included in the safety value analysis because there are no vehicles waiting or stopping in the tunnel to create a hazardous situation. The blue sky lighting in S-1 is designed to provide psychological relief for drivers travelling through the long tunnel, and since no risky situations are present in this scenario, a safety assessment was not deemed necessary.  
Figure 7: The comparisons of vehicle speeds in the tunnel entrance, inside and exit locations for each scenario.