esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Exopolysaccharide (EPS) Production  
The secretion of exopolysaccharides (EPS) by bacteria is a physiological process that is activated under a multitude of different environmental circumstances. The roles of these biopolymers includes basic functions such as maintaining the structural integrity of the cell envelope and enabling interactions within bacterial communities and between bacteria and eukaryotes [83]. The secretion of EPS by bacteria is also one of the most common mechanisms to protect the cell from the impact of adverse/harsh environmental conditions, such as starvation, pH, temperature, dehydration, or toxicity of heavy metals [84; 85; 86]. It is worth emphasizing that the genome of the ZCR6 strain carried the operons _bcs_ and _alg_, involved in the biosynthesis of cellulose and alginate, respectively (Table 6). Due to the presence of various reactive groups on the polymer chains, biopolymers (including alginate and cellulose) or polymer-producing bacteria are considered to be the most attractive bioadsorbents, as they possess high selectivity and reactivity toward heavy metal ions and are recommended for the clean-up of heavy-metal-contaminated environments [87]. For example, a significant absorption capacity for Cd\({}^{2+}\) or for both Cd\({}^{2+}\) and Cu\({}^{2+}\) was observed for _Pseudomonas_ sp. strains 375 and DC-B3, respectively [88; 89], indicating their potential to be used as low-cost, eco-friendly, and effective treatments for the bioremediation of heavy-metal-contaminated environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **Accession Number** & **KO Numbers** & **Gene Product** & **Activity** \\ \hline _bcsA_ & MBG8561468.1 & K00694 & Cellulose synthase (UDP-forming) (EC:2.4.1.12) & Exopolysaccharides \\ _bcsB_ & MBG8561469.1 & K20541 & Cellulose synthase operon protein B & biosynthesis \\ _bcsZ_ & MBG8561470.1 & K20542 & Endoplasmic (EC:3.2.1.4) & \\ _bcsC_ & MBG8561471.1 & K20543 & Cellulose synthase operon protein C & \\ _algF_ & MBG8561473.1 & K19296 & Alginate O-acetyltransferase complex protein algf & \\ _algI_ & MBG8561474.1 & K19294 & Alginate O-acetyltransferase complex protein algi & \\ _algI_ & MBG8561475.1 & K19295 & Alginate O-acetyltransferase complex protein algj & \\ _algB_ & MBG8562662.1 & K11384 & Two-component system. NTC family. Response & \\  & & & regulator alg & \\ _algA_ & MBG8559808.1 & K16011 & Mannose-1-phosphate & \\ _algL_ & MBG8560007.1 & K01729 & Poly(beta-D-mannuronate) lyase (EC:4.2.2.3) & \\ _algX_ & MBG8560008.1 & K19293 & Alginate biosynthesis protein algx & \\ _algG_ & MBG8560009.1 & K01795 & Mannuronan 5-epimerase (EC:5.1.3.37) & \\ _algE_ & MBG8560010.1 & K16081 & Alginate production protein & \\ _algK_ & MBG8560011.1 & K19292 & Alginate biosynthesis protein algk & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Genes involved in exopolysaccharides production.
## 6 Discussion and Future Research Directions
### Quality of Data and Datasets for Optimal ML Applications  
For the effective integration of ML into the future wireless network ecosystem, critical attention must be paid to the quality of data and datasets that are frequently deployed [227; 228]. Critical factors such as data size, source, and the split of the datasets between training, validation, and evaluation sets significantly impact the outcome of ML models [227]. The future 6G network is envisioned to be characterized by ubiquitous communication, multi-modal sensing and positioning, diverse quality-of-service (QoS) requirements, and a plethora of heterogeneity of IoT devices and services [227; 229], Hence, ML algorithm must rely heavily on the availability of suitable datasets for testing, verification, and simulations.  
However, most researchers do not provide datasets in many wireless communications applications [227]. The resultant effect of this dearth in datasets is the mismatch in the reported performance of these applications from the practical real-life applications [227]. A major cause of such discrepancy is that the data quality and datasets deployed by most researchers do not give a practical representation of real-world scenarios, as real-world datasets are almost impossible to perfectly model and capture.  
Datasets are critical to research as they allow other stakeholders in the industry to benchmark and compare different algorithms based on a common dataset [227; 230]. Researchers are beginning to develop both synthetic and real-world datasets for wireless network applications. Some of the available datasets in wireless networks include the ViWi dataset [230], the DeepMIMO dataset [231], the Massive Multi-Input Multi-Output (MaMIMO) dataset [232], and the DeepSense 6G dataset [229]. However, synthetic datasets are limited in their ability to reflect realistic insights into network performance after integration with machine learning algorithms in real-world deployments. A typical dataset consists of a sequence of m-tuples, and its input variables can include RGB, depth, LiDAR, Radar, channel, and locations, among others [230]. When real-world datasets are deployed, the results have demonstrated significant potential for real-world applications [62; 229].  
Data split ratio is another important criterion during ML integration to effectively model development for ML in wireless networks. Often, data are split on a 70% training and 30% validation set ratio [229; 233].
# An Integrated Metabolomics Study of Glucosinolate Metabolism in Different Brassicaceae Genera
## 4 Materials and Methods  
### Plant Material  
In this study, we used 12 most commonly consumed Brassicaceae vegetables in the Asian region: _Brassica olercea var. gemmifera_ (Brussels sprouts), _B. rapa subsp. chinensis_ (pak choi), _B. rapa subsp. pekinensis_ (Chinese cabbage), _B. olercea var. botrytis_ (cualiflower), _B. olercea var. capitata_ (cabbage), _B. rapa var. parachinensis_ (choy sum), _B. oleracea_ var. italica (broccoli), _B. olearacea var. alboglabra_ (kai lan), _Raphanus sativus_ (daikon radish), _R. raplanistrum subsp. sativus_ (red cherry radish), _Eruca sativa_ (rocket salad), and _Nasturtium officinal_ (watercress). Plant material was obtained from the local supermarkets on the day they appeared on market. The sources of the vegetables are listed in Table S1. It usually takes 5 days for vegetables from harvest to market under standardized transport and storage conditions. Only the edible part was used for analysis: Leaves (cabbage, Chinese cabbage, Brussels sprouts, pak choi, rocket salad), leaves and stems (choy sum, kai lan), florets (broccoli, cauliflower), roots (radish, cherry radish). For non-volatile metabolites analysis, they were immediately washed, wiped, snap-frozen in liquid nitrogen, and freeze-dried after being transported to our laboratory. The resulting dry powder was immediately stored at \(-80\)\({}^{\circ}\)C. For volatile metabolites analysis, fresh vegetables were processed and analyzed daily, immediately after being collected from the local supermarkets. The extraction methods and processing of samples for each different type of analysis were done as previously described [30; 48; 59]. Three samples from the same supermarket were purchased on different days and prepared for each of the 12 vegetables for both untargeted and targeted analysis. Our previous studies on phenolic compounds and glucosinolates of these same 12 vegetables did not differ much, suggesting that they are likely to be from the same sources cultivating with similar methods. Confirmation data was collected on five of the vegetables for five samples each, including the three used as examples of distinguished metabolite profiles in this study.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Retrieval Process
#### 4.3.3 Data Retrieval  
Upon receiving the user's query request \(Q=\{prf_{k}(\omega),E,C\}\), the fog node \(g_{o}\) first scans the column \(prf_{k}(W)\) of the lookup table \(T(g_{o})\) and finds the corresponding data items that contain \(prf_{k}(\omega)\). According to the one-to-n lookup table, n data items can be found. For example, since Table 3 is a one-to-two lookup table, then two data items \(id_{i}\) and \(id_{y}\) can be found, where the column \(prf_{k}(W)\) of \(id_{i}\) is \(\{prf_{k}(w_{i}),prf_{k}(w_{x})\}\) and the column\(prf_{k}(W)\) of \(id_{y}\) is \(\{prf_{k}(w_{y}),prf_{k}(w_{i})\}\), where \(prf_{k}(\omega)=prf_{k}(w_{i})\). For each found data item, the fog node performs the computation as follows:  
\[\begin{split} C^{*}(prf_{k}(W))&=[\prod_{d=1}^{n}c _{d}^{prf_{k}(W)}modN^{2}]\\ &=[(1+N)^{\sum_{d=1}^{n}f_{d}\cdot prf_{k}(w_{d})}\\ &\cdot(\prod_{d=1}^{n}r^{prf_{k}(w_{d})})^{N}modN^{2}]\\ &=[(1+N)^{1\cdot prf_{k}(w_{i})}\cdot(\prod_{d=1}^{n}r_{d})^{N}modN ^{2}]\\ &=[(1+N)^{prf_{k}(w_{i})}\cdot(\prod_{d=1}^{n}r_{d})^{N}modN^{2}] \quad,\end{split} \tag{7}\]  
where \(prf_{k}(w_{d})\in prf_{k}(W)\).  
Then, the fog node \(g_{o}\) can find the target data item \(id_{t}\) by checking whether \(C^{*}(prf_{k}(W))\) is equal to \(E\), i.e., when \(C^{*}(prf_{k}(W))=E\), the corresponding data item is the target data item \(id_{t}\). After finding the target data item, the fog node \(g_{o}\) further computes:  
\[C^{*}(C(DS)_{w\in W})=[\prod_{d=1}^{n}c_{d}^{C(DS)_{w\in W}}modN^{2}]\quad, \tag{8}\]  
\[C^{*}(C(\Lambda))=[\prod_{d=1}^{n}c_{d}^{C(\Lambda)}modN^{2}]\quad, \tag{9}\]  
\[C^{*}(VO_{G})=[\prod_{d=1}^{n}c_{d}^{VO_{G}}modN^{2}]\quad, \tag{10}\]  
\[C^{*}(VO_{W})=[\prod_{d=1}^{n}c_{d}^{VO_{W}}modN^{2}]\quad. \tag{11}\]  
After completing the above calculations, the fog node \(g_{o}\) returns \(C^{*}(C(DS)_{w\in W})\), \(C^{*}(C(\Lambda))\), \(C^{*}(VO_{G})\), and \(C^{*}(VO_{W})\) back to the user. Moreover, the fog uses \(h_{v}\) sent from the LBSP to hash \(C^{*}(C(DS)_{w\in W})\) and sends \(h^{g_{o}}=h_{v}(C^{*}(C(DS)_{w\in W}))\) to the blockchain. Upon receiving the returned data, the user can obtain \(c(ds)_{\omega}\), \(c(\lambda_{\omega})\), \(VO_{g_{o}}\), and \(VO_{\omega}\) with the private key \(sk\) and further obtain the available result set \(ds_{\omega}\) and the number of genuine description files \(\lambda_{\omega}\) with the shared key \(k_{AES}\).
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 3 Proposed Algorithm for Mobile 3D Body Circumference Measurement
### Ablation Study: Performance Comparison of Each Component  
In this study, to verify how each component of the proposed model affects body circumference measurement performance, we compared the following three settings:  
* Model A: Keypoint detection (only)
* Model B: Keypoint detection + LiDAR sensor
* Model C: Keypoint detection + correction algorithm + LiDAR sensor (proposed model)  
Table 2 summarizes the relative error (%) and runtime (seconds) for each setting.  
Model A (keypoint detection only): Since it relies solely on a single 2D image (keypoints), it is impossible to measure actual length due to the absence of depth information, leading to unmeasurable results.  
Model B (keypoint + LiDAR): By including LiDAR sensor data, it becomes possible to measure actual dimensions, unlike Model A. However, since the measurement process uses only keypoints detected from 2D RGB images, the relative error reached 19.2%.  
Model C (keypoint + correction algorithm + LiDAR, proposed model): By adding a correction algorithm on top of keypoints and LiDAR sensor data, the relative error was reduced to 5.8%. This indicates that the depth-map-based correction algorithm significantly contributes to improving the accuracy of body circumference measurements.  
Consequently, the proposed Model C, which employs a correction algorithm to more precisely utilize the 3D information from LiDAR, achieves both accuracy and practicality. This ablation study provides a quantitative verification of how each component such as keypoint detection, LiDAR sensor, and correction algorithm, contributes to performance improvement.
## 4 Conclusions  
In this current study, the two squill varieties (red and white) were investigated using a metabolomic approach (PCA and OPLS-DA) coupled with different chromatographic (SPME-GC/MS and UPLC/MS) and spectroscopic techniques (1D and 2D-NMR) for the first time, where metabolites diversity was identified in the two varieties. Volatiles assessment (SPME-GC/MS) resulted in identifying 27 volatiles in both red and white squills. Red squill was enriched with monoterenes hydrocarbons (47.82%) than white one (25.90%), loading to more aroma profiling. The 1D and 2D-NMR spectroscopic technique was utilized to identify 16 major metabolites. The phenolic compounds were abundant in the red squill, whereas butadienolides and fatty acids showed more intense peaks in the white one. Secondary metabolites identification (UHPLC/MS) revealed 130 metabolites representing a myriad of classes. Butadienolides class was the major one in white squill, whereas flavonoids were the major one in the red variety. The 1D and 2D-NMR spectroscopic technique was utilized to further identify 16 major metabolites. The phenolic compounds were abundant in the red squill, whereas butadienolides and fatty acids showed more intense peaks in the white one. Multivariate data analysis differentiated between both varieties and confirmed the abundance of flavonoids in red squill exemplified in dihydrohaempferol-O-hexoside, its aglycon, and taxifolin derivative, whereas fatty acids (oleic and linoleic acids) in addition to butadienolides, _viz._ hydroxyscilliglaucosidin-O-rhannoside, desacetylscilliroadin-O-rhannoside, and butotalidin-O-hexoside are more abundant in the white one. A cytotoxicity screening was implemented on both squills against different cell lines revealed the effectiveness of white squill over red one due to its enrichment with butadienolides class, which has yet to be confirmed using isolated butadienolides to be conclusive.
## 6 Results and Discussion
### Limitations of the Study  
The Minimum Redundancy and Maximum Relevance (MRMR) algorithms were used for the feature selection procedure [46]. Each feature was ranked based on minimum redundancy and maximum relevance and assigned an importance score [47]. Therefore, a feature with a high score is more important than a less-score feature. In addition, a large drop in the rank between features will ease the feature selection. However, a small drop will make the feature selection more challenging. Thus, this research discarded the sub-category feature because, after performing the MRMR algorithm on the sub-category feature, we observed that the drop in score between the 11 th and the 72 nd was relatively small, as shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research** & **Detection/Classification** & **ML Model** & **Accuracy** \\ \hline [PERSON] et al. [18] & Detection & Random Forest & 98\% \\ [PERSON] et al. [18] & Classification & Random Forest & 83\% \\ [PERSON] et al. [19] & Classification & Auto-Encoders & 94.50\% \\ [PERSON] et al. [20] & Classification & Convolutional Neural Networks + Long Short-Term Memory & 98.40\% \\ [PERSON] et al. [21] & Detection & LightGBM + Optimized Adaptive Sliding Windowing & 99.9\% \\ [PERSON] et al. [22] & Classification & Convolutional Neural Networks & 98.2\% \\ [PERSON] et al. [45] & Classification & XGBoost & 99.7\% \\ Proposed Method & Classification & Shallow Neural Networks & 100\% \\ Proposed Method & Detection & Shallow Neural Networks & 100\% \\ Proposed Method & Classification & Decision Trees & 99.9\% \\ Proposed Method & Detection & Decision Trees & 100\% \\ Proposed Method & Classification & Bagged Trees & 99.9\% \\ Proposed Method & Detection & Bagged Trees & 100\% \\ Proposed Method & Classification & Support Vector Machines & 99.80\% \\ Proposed Method & Detection & Support Vector Machines & 100\% \\ Proposed Method & Classification & K-Nearest Neighbor (KNN) & 99.40\% \\ Proposed Method & Detection & K-Nearest Neighbor (KNN) & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparing our ML models’ accuracy with existing ML models’ accuracy.
## 5 Result and Discussion  
A great deal of research has been conducted on methods of estimating depth, but not much research has been conducted to compare methods by applying them to actual environments, especially underground utility tunnels. Therefore, we compared two methods of estimating depth with a single camera.  
In the DenseDepth transfer learning method, an error of 1 m occurred at a distance of 10 m, and an error of 4 m occurred at a distance of 20 m. However, the coordinate system transformation method through floor plane projection produced an error of 23 to 27 cm at a distance of 5 m 60 cm and an error of 30 to 112 cm at a distance of 21 m 40 cm, resulting in significantly better accuracy than that of the DenseDepth transfer learning method. Therefore, the coordinate system conversion method through floor plane projection can be applied to estimate the position of an object when the position of the object undergoes change, when viewed through an installed CCTV comprising a single camera in an underground utility tunnel.  
However, based on the research results, it can be confirmed that the coordinate system conversion method has a large error range, even at the same distance. In order to determine this reason for this, it will be necessary to measure the distance of more points and to conduct further research.
## 4 Discussion
### Usability Factors  
Statistical analysis across cohorts showed no statistical significance at this sampling volume. The effect size was small (0.078), indicating that there was a weak correlation between participant sentiments regarding usability and cohort type. This is also seen with the \(p\)-value as it was insignificant for between cohort differences as well. This implies that participants, regardless of method of in-game navigation, received a similar user experience while playing _Limbitless Journey_. As such, integration of the eye-tracking interface into the menu was found to be equivalent in quality to that of traditional mouse-and-keyboard. The researchers believe this is an important step to ensuring that the hands-free calibration and actuation of the game is a cohesive training experience, as the true population for the training will require those features. Without the implementation of the eye-tracking interface, patient populations with low upper extremity control would not be able to play the game completely autonomously. Early testing and validation with this sampling population plays an important role in progressing the platform forward for streamlining testing with a more vulnerable population.  
Cohort 3, which received calibration and other in-game selection through researcher assistance, reported a decreased level of negative sentiments. Such a finding may have been due to the shifting of responsibility to the researcher rather than the participant. This third party input while troubleshooting may have changed user perception to lean more positive.  
An issue that consistently persisted was difficulty playing the game while complying with the think-aloud protocol. The temporalis muscle would activate when the participant was talking, which led to some frustrations. For instance, one participant stated:  
_\"Some of the flexes were a little bit difficult 'cause I think I was like accidentally flexing and then I would get stuck\"._  
However, the cumulative feedback was that the flex controller became easier to operate over time. One participant stated:  
_\"It was definitely challenging at times, but I think it helped me get used to the controller more\"._  
In an effort to improve usability, the game now uses dry electrodes instead of wet gel electrodes. Dry electrodes are frequently associated with increased usability due to their mobility [43]. Another feature that has been implemented in other games includes auto-calibration. Auto-calibration can customize the flex meter to best fit the user. This will be implemented in future iterations of the game to improve usability.  
The population utilized in this study should also be considered in the interpretation of these results, while all participants were able-bodied, this was done intentionally to ensure that an optimized version of the game was made prior to dispersal to the ALS population.
## 2 Materials and Methods
### Irradiation and Film Analyses  
The EBT-XD films were irradiated with 160 kV X-rays generated in a commercial X-ray irradiator (\"CP-160\", Faxitron, Wheeling, IL, USA) at 10 levels of absorbed doses: 1, 2, 4, 7, 10, 15, 20, 25, 30 and 40 Gy. The dose rate of the static X-ray beam was adjusted by changing the distance between the source and the film so that the irradiation time would be more than 2 min in every case. The field diameter was more than 5 cm even at the shortest distance. The color intensities of the irradiated EBT-XD films placed on a white heavy  
Figure 1: The materials used for this study (**a**): Gafchromic film “EBT-XD” and a portable colorimeter “nix pro 2” (abbreviated to “Nix” here) and a cross-section drawing of Nix (**b**). The black frame structure of Nix blocks well the ambient light; two LED sources (orange part) cast light beams and a color sensor (blue part) detects the reflected light; the data of color intensities measured as RBE and CMYK components are transferred to a smartphone via Bluetooth.  
paper were measured with Nix within a few hours and the data were recorded by using an exclusive application on the smartphone as RGB color components: RGB, red, green and blue. In reading the films, a thick white paper for drawing (Kent paper) was placed under the EBT-XD film (Figure 2a).  
There are artifacts associated with the color digitization of Gafchromic films scanned with flatbed scanners, which are known as 'orientation effect' and 'lateral response artifact' [1; 21; 25; 36; 41; 42; 43; 44]. This orientation effect is characterized by a change in response as a function of the orientation of the film on the scanner bed. Thus, the dependence of color intensity measurements on the angle in measurement was also examined for Nix by rotating with 15\({}^{\circ}\) intervals in a clockwise direction (Figure 2b).  
Since the radiochromic films are measured by using a flatbed document scanner [20; 25; 36], the irradiated films were scanned also with a commercial flatbed scanner (EPSON GT-X900, Seiko Epson Corp., Nagano, Japan) at the same timings as the Nix readings. A 48-bit color image of each film was read in reflection mode with a resolution of 600 dpi using built-in scanner software for Windows. The same-type scanners of the EPSON GT series have been used for scanning Gafchromic films in some preceding studies [13; 52; 53]. For making accurate comparisons of the color intensities measured with Nix, the EBT-XD films were covered with the same white heavy paper while being scanned with the flatbed scanner.  
Statistical analyses of the measured data and productions of plot graphs including regression curves were made using commercial data analysis software (IGOR Pro, WaveMetrics, Inc., Portland, OR, USA).
## 3 The Time Source Techniques
### Pulsar Star
#### 3.3.4 Reliability and Security  
Compared to the GPS timing system, the pulsar-based timing system offers enhanced resilience against system failures, space weather, and cyberattacks [67].  
* System Failures: The likelihood of system failures is significantly reduced since radio telescopes are typically installed in open, unobstructed environments.
* Resilience to Space Weather: The working frequency of a radio telescope receiver is generally broader than that of a GPS receiver. This wider frequency range allows some signals to penetrate the atmosphere, even under adverse weather conditions.  
Figure 6: Block diagram of pulsar data processing.  
* Protection Against Spoofing: GPS spoofing can be avoided because radio telescopes can observe multiple pulsars simultaneously. Unlike GPS receivers, which lock onto the strongest signal, radio telescopes identify specific pulsar signals, adding an extra layer of security.