esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Workflow
### Evaluation Metrics  
Performance metrics are essential for evaluating the effectiveness of clustering algorithms. To estimate the performance of the clustering algorithms, we employed precision, recall, and F1 score. These metrics use the following four input variables: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP occurs when a model correctly classifies traffic as belonging to a category, while TN occurs when the model correctly classifies the traffic as not belonging to a specific category. If the model incorrectly classifies a negative case as positive, we have FP, while the classification of a positive case as negative is FN. The metrics are computed as follows:  
\[\text{(P)recision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\, \tag{2}\]  
\[\text{(R)ecall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\, \tag{3}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}. \tag{4}\]  
The precision (2) evaluates true positives, asking what percentage of traces are correctly classified. The recall (3) is the ratio of positive instances correctly detected by the classifier. The F1 score (4) is a general measure of how often the classifier is correct across all classes.  
After clustering, we assigned labels to clusters based on the majority type of traces within each cluster. We could access the data labels because we knew which source each data comes from; see Section 3.1. The measures (2)-(4) are mainly used for supervised learning. However, we could apply them, as long as they served for comparison and their results were not fed back to the model for training. Compared to unsupervised learning metrics (e.g., silhouette score, Davies-Bouldin index etc.), they are simpler and more intuitive.
## 2 Results and Discussions  
### Analysis of Volatile Metabolites Using HS-SPME-GC-MS  
To optimize the sensitivity and selectivity of HS-SPME for volatile organic compounds (VOCs), various sample pretreatment parameters were evaluated. These included macroaction with liquid nitrogen, freezing at \(-80\)\({}^{\circ}\)C, lyophilization, salting-out using a 30% NaCl solution (\(w/v\)), and ultrasound treatment. Among these, macroaction with liquid nitrogen showed the most significant improvement in chromatographic peak area, with a 37% increase in relative peak area compared to other treatments (Figure S1a). Key VOCs such as linalool (127), ethyl hexanoate (89), 2-methylpentanoic anhydride (68), and 2-methyl-3-buten-2-oil (5) exhibited significant differences (\(p<0.05\)) across treatments, with the highest relative area recorded for liquid nitrogen maceration (Figure S1b). This improvement is attributed to the increased matrix surface area and the prevention of VOC loss or enzymatic degradation by freezing [27; 28].  
Contrary to expectations, lyophilization resulted in decreased sensitivity due to the loss of VOCs during water sublimation. Similarly, the salting-out effect demonstrated inconsistent sensitivity changes, likely influenced by the biological matrix and salt concentration [29]. In this study, the use of salting-out led to reduced chromatographic peak areas. Ultrasound treatment also caused a loss of VOCs, further diminishing the sensitivity of the analysis [30]. As a result, neither lyophilization nor salting-out was included in the sample preparation for VOC analyses. To enhance chromatographic sensitivity, different quantities of copoazu pulp macerated with liquid nitrogen were tested, ranging from 150 to 1200 mg. Figure S2 shows the relative areas of 11 representative chromatographic peaks as a function of sample quantity. Linalool displayed an increase in relative area up to 900 mg but declined at 1200 mg, while other compounds showed no significant variations. Based on these results, 750 mg was chosen as the optimal sample mass, as quantities above 900 mg suppressed VOC volatilization, reducing adsorption onto the SPME fiber--a behavior similarly reported in melon analyses [31]. Pre-equilibrium times of 6, 12, and 18 min were also compared, with no statistically significant differences observed. Consequently, 6 min was selected as the optimal pre-equilibrium time.
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
## 3 Results and Discussions
### OSNR Parameter Prediction  
Figure 3a illustrates the prediction accuracy of the OSNR parameter for the five different OSNR values. The narrow width of the boxplot indicates high prediction accuracy. The obtained R-squared and RMSE values were 1 and 0.09 dB, respectively, further confirming the high accuracy of the predictions. Figure 3b presents the application of the model to predict the OSNR parameter under the influence of 500 ps/nm CD. The results obtained are similar to those in Figure 3a, indicating that the model can still accurately predict the OSNR parameter with high precision. The achieved R-squared and RMSE values were 1 and 0.13 dB, respectively. Figure 3c depicts the performance of the model in predicting the OSNR parameter under the influence of OSNR and severe turbulence defined by \(10^{-13}\) m\({}^{-2/3}\). The model continues to accurately predict the OSNR parameter, as evidenced by the achieved R-squared and RMSE values of 1 and 0.16 dB, respectively. Finally, the combined effect of OSNR with 500 ps/nm CD and \(10^{-13}\) m\({}^{-2/3}\) turbulence is investigated, with the results presented in Figure 3d. The model is still able to accurately predict the low values of the OSNR parameter, although the accuracy slightly degrades for high values. The achieved R-squared and RMSE values were 0.96 and 0.54 dB, respectively, indicating high prediction accuracy. Overall, the model demonstrated high accuracy in predicting the various values of the OSNR parameter.
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
###### Abstract  
A three-dimensional (3D) wind profiling Lidar, based on the latest high power 1.5 \(\upmu\)m fiber laser development at Onera, has been successfully flown on-board a SAFIRE (Service des Avions Francais Instrumentes pour la Recherche en Environnement) ATR42 aircraft. The Lidar CELIVE (LIdar VETn) is designed to measure wind profiles from the aircraft down to ground level, with a horizontal resolution of 3 km, a vertical resolution of 100 m and a designed accuracy on each three wind vector components better than 0.5 m.s\({}^{-1}\). To achieve the required performance, LIVE Lidar emits 410 \(\upmu\) laser pulses repeating at 14 KHz with a duration of 700 ns and uses a conical scanner of 30\({}^{\circ}\) total opening angle and a full scan time of 17 s.  
lidar; fiber laser; 1.5 \(\upmu\)m; coherent; wind; aircraft Article
## 3 Results and Analysis  
### Effects of OIINS Irrigation on the Composition of Tomato Fruit Volatile Compounds  
The compounds, along with their chemical classes, retention indices, and relative content, are listed in Table 11 in Appendix A. A total of 66 volatile compounds were identified in the second truss of tomato fruits. They could be categorized into different classes based on their chemical structures, including 22 aldehydes, 17 alcohols, 7 esters, 7 ketones, 5 hydrocarbons, and 8 other volatile compounds. Moreover, 41 compounds were detected in the NNNNN treatment, whereas 36, 37, 34, and 36 compounds were identified in the OIINS-related treatments (Figure 2A). Irrigation with OIINS decreased the number of volatile fruits. Trans-2-hexneal had the highest relative content in the NNNNN treatment, followed by cis-3-hexneal, whereas the reverse was observed for the other four treatments.  
Figure 2: The number of volatile compounds in the different treatments. (**A**) The total number of volatile compounds detected during each treatment. (**B**) A Venn diagram showing that all treatments contained 22 common volatile compounds.  
Of these, a total of 22 volatile compounds were detected in each treatment, known as common volatile compounds, including eight aldehydes, five alcohols, four esters, three ketones, and two other types of compounds (Figure 2B). The total relative content of the common volatile compounds in the different treatments was 69.85%, 86.47%, 87.03%, 88.56%, and 74.23%, respectively, and they could be considered the main volatile substances in this variety of tomatoes (Table A1 in Appendix A). The OIINS-related treatments increased the relative content of the common volatile compounds in the fruits, but there were differences among the treatments.  
Effect of OIINS Irrigation on Quantity and Content of Chemical Classes of Tomato Fruit Volatile Compounds  
The quantity and relative content of the chemical classes of the tomato fruit volatile compounds under different treatments are shown in Figures 3 and 4, respectively. The most abundant class was aldehydes, followed by alcohols, esters, and ketones. However, the OIINS-related treatments decreased the number of aldehydes, alcohols (except ONNN and NNNO), and ketones (except for NONN). The highest relative content among the five treatments was for aldehydes, ranging from 72.63% to 78.90%, making them the main volatile compounds in the tomato fruits. The OIINS-related treatments increased the relative aldehyde content in the following order: N\(\mathrm{N}\mathrm{
## 3 Design of the Wirelessly Activated Node
### Energy Harvesting and Power Management
This pin is directly connected to the enable signal of the buck DC/DC converter, which tries to set its output to 3.3 V. Since the energy on C\({}_{\text{BUFFER}}\) is not enough to charge C\({}_{\text{STORE}}\) up to 3.3 V, the DC/DC will turn on very briefly, transferring charge from C\({}_{\text{BUFFER}}\) to C\({}_{\text{STORE}}\), whose voltage V\({}_{\text{ST}}\) (green curve in Figure 6) will slightly increase, till V\({}_{\text{BUF}}\) reduces to 2 V and the buck DC/DC converter gets turned off. The procedure then starts again without the harvester re-entering cold-start, thus reducing the time required to exit cold-start at the beginning of operation by a factor of 42. Power supply to the node is then provided through an LTC3106 (Analog Devices, Norwood, MA, USA) buck-boost DC/DC converter whose input is connected to the energy storage capacitors through a low leakage PMOS switch controlled by the RTC circuit. This converter can operate down to 700 mV input voltage, allowing the node to be operational with C\({}_{\text{STORE}}\) voltages between 0.7 and 3.3 V, corresponding to 4.8 mJ of available energy. A UVLO is also inserted to avoid any load to be connected to the C\({}_{\text{BUFFER}}\) and C\({}_{\text{STORE}}\) at power-up. In fact, RTC current consumption can be in the range of tens of uA when its supply voltage is below the minimum rated supply voltage of 1.5 V. Therefore, for low input power levels, deadlock conditions might occur where the harvester is not capable of overcoming this 1.5 V threshold and exiting the cold-start state. To avoid this occurrence,  
Figure 6: Mechanism to efficiently manage charging of a fully depleted storage capacitance. The reported values are referred to the real case scenario described in Figure 11.  
two additional PMOS load switches are inserted, one at the input of the LTC3106 (in series with the one controlled by the RTC) and one on the RTC power supply. Unfortunately, no fixed voltage is available on the node to determine an absolute threshold, after which RTC can be connected to C\({}_{\text{BUFFER}}\). As shown in Figure 7, a comparison between the voltage levels (V\({}_{\text{BUF}}\) and V\({}_{\text{ST}}\)) on C\({}_{\text{BUFFER}}\) and C\({}_{\text{STORE}}\) is performed, and power-on is enabled only when V\({}_{\text{ST}}\) reaches 95% of V\({}_{\text{BUF}}\). At power-up and during cold-start operation, V\({}_{\text{ST}}\) will always be lower than V\({}_{\text{BUF}}\), and the comparator output switches only when V\({}_{\text{ST}}\) reaches approximately 2 V. The ultra-low-power comparator is based on a TLV3691, consuming only 75 nA, featuring an internal UVLO circuit to prevent deadlock conditions. A large hysteresis is inserted to avoid the node from turning off again as soon as C\({}_{\text{STORE}}\) discharges during regular operation.
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 3 Design of Support Structure with Low Expansion Coefficient
### Design of Bearing Cylinder  
As the structure connecting the front and rear frames, the composite bearing cylinder has a great influence on the distance change of the optical elements in the \(Z\) direction. Therefore, it is necessary to minimize the thermal expansion coefficient of the bearing cyl-inder in the \(Z\) direction through the design of the ply angle.  
We chose the \(\pm 20^{\circ}/0^{\circ}/90^{\circ}\) laying method in the winding of the bearing cylinder, where the \(0^{\circ}\) direction is the same as that of +Z, as shown in Figure 7. The designed bearing cylinder can reduce the change in the distance between the secondary mirror and other mirrors when the temperature changes.  
So far, we have completed the thermal stability design of the off-axis camera support structure through the selection of composite layers. The obtained support structure has very low CTE in the Y and \(Z\) directions.
## 4 Experimental Evaluation
The graphs shown in Figures 11-14 prove that SDCS and optimal FOPID controllers can be used interchangeably in controlling the vertical and angular movements of the quadcopter. included in the coding steps before implementation so that they do not affect the success of the control phase. The assumptions underpinning this study involve conducting artificial implementations under idealized conditions, thereby neglecting potential environmental factors such as disturbances and noise. This framework aims to establish principles for systematically modeling the uncontrolled quadcopter and integrating the SDCS controller within a simulation environment. To enhance the precision of measurements, it is essential to account for environmental factors, including disturbances and noise, in the experimental process. This incorporation is crucial for achieving more accurate and reliable results. Another disadvantage of the SDCS method is that researchers have to use software such as ReaX and Simulink together. Despite the disadvantages and limitations mentioned, utilizing the SDCS method should be preferred instead of existing methods in control and path planning applications because it provides accurate and optimal results.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 4 Hamaliere Landslide Monitoring
### Localization Error and Reference Measurements
#### 4.5.1 Discussion on Antenna Position  
The above model (Section 4.2) is a tool for optimizing the antenna positions in a given terrain to minimize localization errors originating from both multipath and geometry. We performed calculations for several geometrical cases in a plane xOy geometry, searching for the lowest random deviation in the monitored zone. As a general rule, we conclude that surrounding the field with antennas yields the best accuracy (lowest localization random deviation). For example, if four antennas are spread around the Harmaliere field, the horizontal random localization error is expected to reduce to 1 mm.  
Such setups are not always possible in real-environment operational situations--the experimental setup obviously has to be designed taking into account the operational constraints and priorities. In cases where a portion of the field is inaccessible, for example, the distance between antennas (system aperture) should be maximized to obtain the lowest random deviation. This guideline has limitations, such as cable length or station cost, hence the final setup will generally be a compromise between precision and station/maintenance cost. Note that other localization methods, such as angle of arrival techniques [54; 58] rely on different system geometries and will not lead to the same optimal antenna disposition. The guidelines provided here only apply to a relative displacement scheme; absolute positioning is a different matter which we do not discuss here.
## 2 Materials and Methods
The FPGA board controlled the external clocking of the chains, the data input to the chains, as well as the error detection. The test chip was soldered onto a custom-made PCB board that allowed for access to all pins on the chip via a DIMM connection. During testing, at each positive clock edge, if the output of a chain did not match the input, then a SEU was recorded, and a respective register within the FPGA system was incremented. The data in each of the registers storing the number of upsets for each chain were then transmitted to a microcontroller where they were then logged and recorded in real time. The data were transferred using a serial protocol. During all tests, the chains were clocked at a frequency of 1 MHz, and the 'all 0' data pattern was used. Before the experiments were conducted on the test chip, each chain's functionality was validated by a series of tests. Input data of all 1 s, all 0 s, and alternative 0 and 1 patterns were inputted to the chain, and the outputs were observed to ensure that the proper data passed through the chains. In addition, the error capture system was validated by injecting voltage pulses onto the chains via the pins of the chip, and subsequent errors were noted on the test system. Additional checks were completed by injecting errors into the chains via the FPGA testing system. As such, all proposed chains functioned as expected, both before and during testing.
# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 4 Validation and Results
### End-to-End Integration Testing and Validation  
We performed end-to-end integration testing using the physical model we developed in the lab. We used a software planner to plan multiple flights over a month following a \(Z\) pattern. Our service drone completed its lab flight at a height of 3 m then uploaded the light, depth, and thermal videos to the cloud server. Uploaded videos were processed by a computing server powered by MATLAB where our proposed novel multimodal depth-RGB and thermal-RGB mosaicking algorithm was applied to produce orthomosaics of the inspected area. The output mosaics were then processed using YOLO and the structures in our physical model were detected using all three imaging models. The inspection report was then displayed on our user interface allowing the user to analyze the detected structures and visualize the changes in these structures over time. The temporal tracking ability facilitates the process of built cultural heritage preservation. Our Proposed user interface is shown in Figure 13.  
The user can select between the RGB, depth, and thermal views to analyze and visualize the thermal and depth anomalies of a detected structure. The interface also provides the user with an interactive 3D model of the structure and records the data to facilitate the process of temporal tracking and heritage preservation.
# Protein Dynamics in F-like Bacterial Conjugation
## 3 Structures Involved in Pilin Processing, Pilus Extension, and Retraction
### Proteins Responsible for Propilin Maturation
#### 3.1.1 The Pilin Protein TraA  
The propilin protein, TraA is a small protein \(\sim\)13 kDa that is processed by the leader peptidase LepB to a much smaller \(\sim\)7 kDa protein [58, 59]. The _traA_ gene is composed of 121 amino acids with a signal peptidase (SP) cleavage site present between Ala51 and Ala52, TraA is processed to a mature pilin of 70 amino acids represented by residues Ala52-Leu121 [62]. The propilin is then further processed by proteins TraQ and TraX. Structures of the assembled pili composed of mature TraA\({}_{\text{F}}\) and TraA\({}_{\text{pED208}}\) have been resolved by cryo-EM with the latter having higher resolution [16]. Both TraA proteins have four \(\alpha\)-helical domains which form similar all \(\alpha\)-helical structures [16, 37]. Both the NTD and the CTD are on the exterior of the assembly, with domain I exposed as well, domains II and IV are hydrophobic and span the IM prior to assembly with domain III extending into the cytoplasm to connect them, and a basic loop between domains II and III is exposed to the cytoplasm [11, 63]. These structural localizations agree with the proposed mechanism of phage attachment to TraA, which is predicted to be mediated through association with the NTD and CTD of TraA, and also promotes the hypothesis that DNA may pass through the pilus, as the pilus architecture allows for an inner lumen of 19 A that may allow for the transferosome to be chaperoned; the single-stranded DNA (ssDNA) would be stabilized by the positively charged loop created between domain II and III (Figure 3D) [16, 55].
## 2 Collaborative Filtering Algorithm Based on Personalized Privacy Protection
### Evaluation and Encoding of the Privacy Sensitivity of User Ratings  
For recommendation systems, malicious attackers often rely on users' rating data to infer and obtain user privacy. Numerous studies [14; 15; 16] have proved that there exists a certain correlation between the size of the ratings and user privacy, and too high and too low ratings tend to be more favorable for malicious attackers to infer users' private information, such as user preferences. On the other side, from the user's perspective, both users' rating habits and the degree of the sensitivity to privacy protection are different. Based on the above two factors, the classification rules of the privacy sensitivity towards users' ratings are proposed as follows:  
\[r_{ui}=\left\{\begin{array}{rl}\text{\emph{sensitive rating}},&\text{when }r_{ui}\in[r_{min},\overline{r_{u}}-\gamma_{u}]\cup[\overline{r_{u}}+\gamma_{ u},r_{max}];\\ \text{weakly}-\text{\emph{sensitive rating}},&\text{when }r_{ui}\in(\overline{r_{u}}- \gamma_{u},\overline{r_{u}}+\gamma_{u});\end{array}\right. \tag{1}\]  
where \(r_{min}\) and \(r_{max}\) represent the maximum and minimum values specified by the rating rules, respectively; \(\overline{r_{u}}\) denotes the average score of user \(u\); \(\gamma_{u}\) is an adjustable variable to adjust the range of intervals classified.  
Figure 1: Overview of Algorithmic architecture.  
Depending on the sensitivity type of ratings, the encoding function \(E\) is  
\[E(r_{ui})=\left\{\begin{array}{ll}b_{ui}^{s+},&\text{ when }\quad r_{ui}\in( \overline{r_{u}}+\gamma_{u},r_{max});\\ b_{ui}^{s-},&\text{ when }\quad r_{ui}\in(\overline{r_{u}}-\gamma_{u},\overline{r_{u}}+ \gamma_{u})\,;\\ b_{ui}^{s-},&\text{ when }\quad r_{ui}\in(r_{min},\overline{r_{u}}-\gamma_{u}); \end{array}\right. \tag{2}\]  
where \(b_{ui}^{s+},b_{ui}^{ns}\), and \(b_{ui}^{s-}\) are \(1,0\), and \(-1\), respectively, that is, the encoding function encodes the high-sensitivity score as \(1\), the low-sensitivity score as \(-1\), and the weakly sensitive score as \(0\).  
The rating of \(u\) constitutes the rating vector of \(R_{u.}=\{r_{u1},r_{u2},r_{u3},\ldots,r_{un}\}\). According to the function \(E\), \(R_{u.}\) is encoded to be the sensitive rating vector \(B_{u.}^{s}\) and weakly sensitive rating vector \(B_{u.}^{ns}\).  
\[B_{u.}^{s}=\left\{b_{ua_{1}}^{s},\ b_{ua_{2}}^{s},b_{ua_{3}}^{s},\ldots,b_{ua_ {x}}^{s}\right\}\]  
\[B_{u.}^{ns}=\left\{b_{uc_{1}}^{ns},\ b_{uc_{2}}^{ns},b_{uc_{3}}^{ns},\ldots,b_{ uc_{u-x}}^{ns}\right\}\]  
where \(a=\{a_{1},a_{2},a_{3},\ldots,a_{x}\}\) represents the collection of items corresponding to the sensitive rating, and \(c=\{c_{1},c_{2},c_{3},\ldots,c_{n-x}\}\) represents the collection of items corresponding to the weakly sensitive rating. Additionally, \(b_{ua_{i}}^{s}\in\left\{b_{ua_{i}}^{s+},b_{ua_{i}}^{s-}\right\}\).