esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Energy Output from Grid to Streetlights  
Public lighting able to power millions of streetlights is responsible for 19% of global electricity usage, 30-50% of a typical city's energy demand, and the already excessive levels of CO\({}_{2}\) emissions [43; 44]. Thus, this represents a priority issue for cities in their strategy towards sustainability. In this framework, the electrical energy produced by the PV system has been estimated in terms of the number of streetlights served in the city of Rome. Considering the lack of contemporaneity between the PV energy output and the public energy demand, the energy generated by the PV system is fed into the grid, and an equivalent amount will subsequently be drawn to fulfill a portion of the energy demand designated for public lighting. The data, concerning the public lighting network of the city of Rome, are from 2021 and have been provided by the Agency for the Oversight and Quality Assurance of Local Public Services of the Capital City of Rome (ACOS) [45]. According to the ACOS 2021 report [46], the public lighting network covers approximately 6370 km of roads and over 650 illuminated monuments, powering 226,728 lamps (averaging one lamp per 12 inhabitants and one light point every 28 m of road). The installed LEDs constitute approximately 92% of the total public lighting fixtures. Detailed average data are reported in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Monthly** & **Consumption** & **Consumption per Individual User (kWh)** \\ \hline Domestic electrical & 1,321,992 & 233,737,722 & 177 \\ consumption & 298,920 & 510,686,443 & 1708 \\ Non-domestic electrical & & & \\ consumption & 298,920 & 744,424,165 & 459 \\ \hline Total & 1,620,912 & 744,424,165 & 459 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Domestic and non-domestic electrical consumption in Rome according to [41].
## 3 Proposed Methodology
### Network Training and Evaluation Metrics  
A valuable tool for assessing the recognition performance of DL models is the confusion matrix, which provides a clear and visual representation of their performance. The multi-class confusion matrix can be mathematically represented: the rows represent the instances in the predicted class, while the columns represent the instances in the actual class.  
\[C=\begin{bmatrix}c_{11}&c_{12}&c_{13}&\dots&c_{1n}\\ c_{21}&c_{22}&c_{23}&\dots&c_{2n}\\ c_{31}&c_{32}&c_{33}&\dots&c_{3n}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ c_{n1}&c_{n2}&c_{n3}&\dots&c_{nn}\\ \end{bmatrix}\]  
The confusion elements for each class are given by:  
* True positive: \(TP(C_{i})=C_{ii}\);
* False positive: \(FP(C_{i})=\sum_{t=1}^{n}c_{ii}-TP(C_{i})\);
* False negative: \(FN(C_{i})=\sum_{t=1}^{n}c_{ii}-TP(C_{i})\);
* True negative: \(TN(C_{i})=\sum_{t=1}^{n}\sum_{k=1}^{n}c_{lk}-TP(C_{i})-FP(C_{i})-FN(C_{i})\).  
The evaluation of the DL models utilized in this study involved analyzing a confusion matrix and calculating four commonly used metrics: accuracy, precision, recall, and F1-score.  
Accuracy is a measure of systematic error and is calculated by dividing the sum of true positive and true negative by the total number of records. Precision is determined by computing the ratio of examples that are correctly classified as belonging to a specific smartwatch user's class to all examples that are classified as belonging to that class. Recall is evaluated as the ratio of examples that are classified as belonging to a specific smartwatchuser's class to all examples that actually belong to that class. Lastly, the F1-score is a metric that blends precision and recall using the harmonic mean.  
The mathematical expressions for these evaluation metrics were written as:  
\[Accuracy=\frac{1}{|Class|}\times\sum_{i=1}^{|Class|}\frac{TP_{i}+TN_{i}}{TP_{i} +FP_{i}+TN_{i}+FN_{i}} \tag{8}\]  
\[Precision=\frac{1}{|Class|}\times\sum_{i=1}^{|Class|}\frac{TP_{i}}{TP_{i}+FP_{ i}} \tag{9}\]  
\[Recall=\frac{1}{|Class|}\times\sum_{i=1}^{|Class|}\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{10}\]  
\[F1\text{-}score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{11}\]
# Analysis on the Isostatic Bipod Mounts for the HERA Mission LIDAR
###### Abstract  
The Light Detection and Ranging (LIDAR) is a time-of-flight altimeter instrument being developed for the HERA mission, designated as Planetary ALTimeter (PALT). PALT is positioned in the center of the top face of the HERA probe, and therefore, it cannot use radiators to stabilize its internal temperature. The contribution of this paper is the design of isostatic bipod mounts for the LIDAR primary mirror. The performance of PALT must be maintained over a wide operational range, from \(-60\)\({}^{\circ}\)C to \(80\)\({}^{\circ}\)C. These temperature requirements imply that a careful isostatic mount structure design is critical to maintaining performance in all operational scenarios. The purpose of the instrument is to perform range measurements from 500 m to 14 km. The instrument will contribute to the detailed characterization of the asteroid's topography, assist the probe navigation in operations such as fly-bys (including on the dark side of the asteroid) or landing. PALT has an emitter system that generates 2 ns, 100 \(\mu\)J, 1535 nm laser pulses and a receiver system that collects the backscattered signal from the asteroid. The receiver system is composed of a 70 mm diameter Cassegrain telescope and a refractive system that focuses the signal on the sensor.  
isostatic bipod mounts; LIDAR; PALT; HERA mission; a-thermalization; optical performance +
Footnote †: journal: Physics Letters B
## 3 Results
### Overlying Stratum Collapse Law and Porosity Simulation
The immediate roof above the gob behind the working face completely collapses and the porosity of the area is stable at 0.45, 3.5 times that of the unmined area. The area shows yellow-green color in the figure. The simulation results of overlying stratum collapse and porosity evolution suggest that, after the third caving step is completed, that is, when the working face advances to ~160 m, the first gob block enters the load-affected zone of the horizontal \"three-zone\". Although the overlying stratum has collapsed, the porosity here is still relatively large. This is because the gravity of the overlying stratum cannot fully act on the residual coals and gangue in the gob due to the supporting effect of the hinged rock beams, and thus cannot well compact the residual coals and gangue, leaving a certain number of pores. A certain amount of air flow can leak into the gob through these pores. Yet the wind flow speed is relatively low, which cannot bring the heat generated by the oxidation of residual coal away, resulting in more heat generation than heat loss. If the advancement of the working face is slow and the heat accumulation is long enough, the spontaneous combustion of residual coal will occur. The porosity of this area decreases significantly after the working face advances to the fourth block because it enters the compaction zone of the horizontal \"three-zone\" of gob. The residual coal and gangue are completely compressed by the overlying stratum that has lost the hinge function. The reduced porosity and air leakage result in an area that is unfavorable for spontaneous combustion. Based on the PFC3D simulation of the overlying stratum collapse and porosity evolution, it can be concluded that the gob behind the working face enters the spontaneous combustion risk zone at ~160 m. The simulation results of the overlying stratum collapse and porosity provide a basis for the study on the 3D spatiotemporal distribution of the flow field in the gob.
## 2 Graphene-Based Circular Patch-Shaped Yagi-like MIMO Antenna  
The MIMO antenna with Yagi-like structure using graphene patch geometries is shown in Figure 1. Figure 1a shows the 4-port 2 \(\times\) 2 MIMO antenna 3D view, with circular-patchedgeometry arranged in a Yagi-like structure. The antenna geometry has been formed using graphene material with a thickness of 0.03 \(\upmu\)m. The conductivity of the graphene sheet was set as 10\({}^{8}\) S/m, with a loss tangent of 0.004. Because of its exceptional electromagnetic, mechanical, and tunable qualities, graphene material is widely employed in a wide range of Terahertz (THz) applications [18; 19]. However, various articles have also observed that the multilayered graphene ink/powder does not provide great tunability. Hence, the fixed conductivity constant is considered for the overall structure. The overall size of the antenna is set as L \(\times\) W = 620 \(\times\) 800 \(\upmu\)m\({}^{2}\). The design equation for the proposed antenna structure is given in Appendix B. The polyamide substrate thickness is 16 \(\upmu\)m, with a loss tangent of 0.004 and a dielectric constant of 4.3 [20; 21]. The dimensions of the structure are shown in Table 1. The dimensions shown in the structure were calculated by considering the wavelength of 1 THz signal (\(\sim\)300 \(\upmu\)m for a single radiating element). The other dimensions were calculated by placing the periodic Yagi-like structure at an equal distance. The radius of the circular patch is set from small to large to couple all the small and large frequencies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**a1** & **a2** & **a3** & **a4** & **r1** & **r2** & **r3** & **r4** & **h** & **l** & **t** & **g1** & **g2** \\ \hline
50 & 140 & 180 & 180 & 15 & 25 & 35 & 45 & 40 & 350 & 70 & 100 & 350 \\ \end{tabular}
\end{table}
Table 1: Dimensions of the 2 \(\times\) 2 MIMO antenna structure (all dimensions in \(\upmu\)m).  
Figure 1: Schematic of the circular-patched Yagi-like THz MIMO antenna structures. (**a**) Three-dimensional view of the antenna, with graphene as the conducting material with Port notation. (**b**) Front side and backside view of the antenna. (**c**) A comprehensive view of the patched Yagi-like structure, with dimensions. (**d**) Schematic of the single antenna with dimensions.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 4 Analysis of Performance Results
### Theoretical Mathematical Model Results
#### 4.1.1 L and DR for 802.11p and 802.11 bd Protocols  
Table 5 shows the results of the 802.11p and 802.11 bd protocols for the following configuration: different MCS options (corresponding to various modulation options), a payload size of 1500 bytes, an SCS of 156.25 kHz, and 10 MHz bandwidth.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **802.11p/bd** & **LTE-V2X/NR-V2X** \\ \hline Bandwidth & 10 MHz & 10 MHz/20 MHz \\ \hline Payload size & 375, 1500, and up to 4000 bytes & 375, 1500, and up to 4000 bytes \\ \hline Modulation and coding schemes (MCS) & MCS4, 5 (16-QAM), MCS6, 7 (64-QAM) & MCS0, 2, 5, 7 (QPSK), MCS11, 13, 16 (16-QAM) and MCS21, 24, 27 (64-QAM) \\ \hline Subcarrier spacing & 156.25 kHz & 15 kHz, 30 kHz, and 60 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
For both protocols, as the value of the MCS options increases, L decreases and DR increases. The L values in 802.11 bd are always lower when compared to 802.11p. In both protocols, the data rates are very close for MCS0, MCS1, and MCS2 modulations. From the MCS3 modulation, 802.11p outperforms 802.11 bd. This could be attributed to the fact that the 802.11p protocol was implemented specifically to perform reliably against channel interference in V2V communications, while 802.11 bd prioritized latency reduction.  
In the analysis of different MCS options, it was observed that 802.11 bd presented lower L compared to 802.11p. This is a crucial characteristic of vehicular communications.  
Figure 2 compares the performances of 802.11p and 802.11 bd by analyzing DR and L metrics for various MCS options with payloads from 1 to 4000 bytes and 10 MHz channel bandwidth. As shown in Figure 2A, the DR is always higher in 802.11 bd when compared to 802.11p since the data rate increases with increasing the modulation order.  
Figure 2B shows that the L values for 802.11 bd are always lower, which may be due to the fact that 802.11 bd has four more data carriers available for transmission. Therefore, it can be concluded that 802.11 bd has better performance than 802.11p, presenting lower L and a higher DR. These are important features that benefit the URLLC services used in the implementation of various autonomous driving use cases.
# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 2 Research Methods
### Machine Learning Models  
#### 2.3.1 Multivariate Ridge Regression  
Multivariate Ridge Regression [28], a regularization method based on linear regression, is primarily designed to address multicollinearity issues and enhance the generalization ability of the model. In standard linear regression, the model estimates regression coefficients by minimizing the Mean Squared Error (MSE). However, when the input features exhibit high correlations (i.e., multicollinearity), the coefficient estimates in standard linear regression can become unstable, potentially leading to overfitting in the training data. Ridge regression introduces an L2L_2L2 regularization term (i.e., the sum of the squared regression coefficients) into the loss function, effectively constraining the magnitude of the coefficients and thereby reducing the model complexity. The objective function of ridge regression can be expressed as follows:  
\[\text{minimizeMSE}+\lambda\sum_{j=1}^{p}w_{j}^{2} \tag{3}\]  
where \(\lambda\) is the regularization parameter that controls the strength of the regularization. By adjusting \(\lambda\), ridge regression achieves a balance between model complexity and training error. When \(\lambda\) is small, the model behaves similarly to ordinary linear regression; when \(\lambda\) is large, the model imposes stricter constraints on the regression coefficients, thereby reducing the risk of overfitting.  
A distinctive feature of ridge regression is that it assigns non-zero coefficients to all features (a non-sparse solution). Compared to Lasso regression, ridge regression emphasizes the balanced treatment of features rather than feature selection.  
Ridge regression performs exceptionally well in high-dimensional data scenarios, particularly when the number of features exceeds the number of samples. For instance, in applications such as gene expression analysis, economic forecasting, and engineering modeling, datasets often contain a large number of highly correlated features. Ridge regression effectively mitigates the instability in coefficient estimates caused by feature multicollinearity in standard linear regression. Additionally, ridge regression has relatively low computational complexity. Its closed-form solution enables faster model training, especially on small datasets, providing convenience for practical applications.  
However, ridge regression also has certain limitations. For example, it cannot perform feature selection like Lasso regression, and its ability to interpret highly correlated features is relatively weak.
## 3 Results
### Volatile Organic Compounds (VOCs) Released from Beef Patties  
The SPME-GC/MS analysis of beef patties revealed the total presence of 24 compounds (Table S1) across all treatments and storage times (T0, T1, and T2). Specifically, the untreated control was characterised by typical compounds of meat products such as acids (hexanoic acid and pentanoic acid), alcohols (ethanol, 1-pentanol, 2-ethyl-1-hexanol, 2-hexen-1-ol, and 3-methyl-1-pentanol), aldehydes (2-ethylbutanal and 2,3,6-trichlorobenzaldehyde), esters (ethyl acetate), hydrocarbons (1,3-dimethylbenzene), ketones (2-butanone and 3-hydroxy-2-butanone), and others, such as 2,4-bis(1-methylethyl)-phenol. The VOC profiles of the CH-treated samples were similar to those of the controls, with slight differences in the quantities but not the types of compounds detected. Specifically, some statistically significant differences were observed in 2-hexen-1-ol (higher in CH at T0), hexanoic acid (higher in CH at T2), 2-ethyl-1-hexanol (higher in control at T1), and pentanoic acid, 2-butanone, and ethyl acetate (all higher in control at T1 and T2). Interestingly, the fatty acid ester ethyl octanoate was detected exclusively in the control and plain CH samples and significantly increased from T0 to T2 (by 138.37 and 306.98%, respectively). Ethanol was the most abundant compound, especially after 4 days (from 3.05 at T0 to 26.44 at T2 in the control and from 0.78 to 21.71 when applying CH). Traces of typical aromatic hydrocarbons, phenols, monoterpenes, and monoterpenoids of EOs were also revealed, probably due to contamination.  
In the _L. nobilis_ EO-enriched CH-treated samples, significant amounts of 1,8-cineole (from 302.31 at T0 to 184.06 at T2), linalool (from 200.13 to 100.97), p-menthene (from 108.02 to 85.25), and m-cymene (from 98.06 to 20.79), together with smaller amounts of \(\beta\)-phellandrene, 1-pentanol, and \(\gamma\)-terpinene were found. The compound 1,4-hexadiene was found only in the formulation containing the _L. nobilis_ EO. Regarding the _P. nigrum_ EO-enriched CH-treated beef, we noticed a substantial presence of p-menthene (from 678.76 at T0 to 210.44 at T2), followed by \(\delta\)-2-carene (from 297.49 to 93.76), \(\gamma\)-terpinene (from 98.13 to 21.31), \(\beta\)-phellandrene (from 79.14 to 22.46), and smaller amounts of linalool.  
For both the EOs, a reduction in the abundance of specific compounds was observed over time (from T0 to T2). For instance, over 4 days, \(\gamma\)-terpinene decreased by 83.48 and 78.27% in the _L. nobilis_ and _P. nigrum_ CH formulations, \(\beta\)-phellandrene by 82.31 and 71.62%, m-cymene by 78.80 and 76.75%, linalool by 49.55 and 79.95%, 1,8-cineole in the former by 39.12%, and \(\delta\)-2-carene and p-menthene in the latter by 68.49 and 69.00%, respectively.
## Materials and Methods  
### Experimental Set-Up  
#### 2.1.1 Overview  
The presented measuring device is based on the concept of stereo triangulation. The projection of a laser beam array on the superior surface of the vocal folds during phonation enables their 3D reconstruction when captured with a high-speed camera. A detailed explanation of the underlying principle and measurements concerning the accuracy can be found in [22]. In order to realize this concept, the miniaturized set-up as shown in Figure 2 consists of two basic elements, namely the imaging unit and the projection unit.  
The imaging unit includes a standard, rigid laryngoscope connected to a 300-Watt Xenon light source, a zoom coupler and a high-speed camera. The critical diameter at the tip of the laryngoscope (SOPRO-COMEG GmbH, Tuttlingen, Germany) is only 8.5 mm. The high-speed camera is connected to the laryngoscope by means of a Precision Optics Corporation zoom coupler enabling variable zoom settings. The FASTCAM MC2 high-speed camera (Photron, San Diego, CA, USA) is recording 512 \(\times\) 256 px at 4000 fps. Higher frame rates are accessible at respectively lower spatial resolutions.  
The projection unit comprises a laser light source, the endoscopic splitting laser projection unit (LPU) and the connecting glass fiber. A technical elaboration on the exact design of the laser projection is given in Section 2.1.2.  
The laryngeal imaging endoscope and the LPU are aligned parallel to each other and are both equipped with a 70\({}^{\circ}\)-optic at the tip. The relative angle between the optical axis of the laryngoscope and the laser projection is fixed at 7.1\({}^{\circ}\), as illustrated in Figure 2 on the lower left. In a working distance of 50-80 mm beneath the tip, the lateral width of the laser projection covers a sufficient portion of the camera's field of view (FOV). The use of a custom-made mounting allows an effective handling of the measuring device, guaranteeing reproducible fixation and avoiding unnecessary calibration measurements. The resulting total width of 13.5 mm is well tolerable for the majority of our subjects.
## 2 Test Introduction
### Test Data Acquisition  
To monitor the temperature and strain distributions inside the concrete pavement during the test, four layers of data acquisition points were set up with a total of 24 temperature and strain measurement locations.  
The pavement temperature was measured using K-type thermocouples. Twenty-four temperature measurement points were arranged in four layers at depths of 2 cm, 5 cm, 10 cm, and 15 cm, respectively, as shown in Figure 3. In the first layer (2 cm depth), for example, points A\({}_{1\text{-}1}\), A\({}_{1\text{-}2}\), and A\({}_{1\text{-}3}\) were in the vertical direction of the carbon fiber heating wire, while points B\({}_{1\text{-}1}\), B\({}_{1\text{-}2}\), and B\({}_{1\text{-}3}\) were in the vertical direction of the midpoint between two neighboring heating wires, as illustrated in Figure 3.The K-type thermocouple had a measurement range of \(-60\) to \(250\)\({}^{\circ}\)C with an accuracy of \(0.5\)\({}^{\circ}\)C and a sampling interval of 30 min. The temperature data logger was manufactured by Keysight.  
Figure 1: 3D model of carbon fiber-heated pavement.  
Figure 2: Carbon fiber-heated pavement model construction process.  
Similarly, 24 strain measurement points were arranged in four layers at the same depths to monitor the thermal strain evolution. In each layer, six strain gauges were installed, including three (A1-1, A1-2, A1-3) in the heating wire direction and three (B1-1, B1-2, B1-3) in the middle of the two wires, following the same numbering rule as the temperature points shown in Figure 3. The strain was measured using a combination of buried fiber Bragg grating (FBG) sensors (horizontal arrangement) and 120-10 AA weldless steel strain gauges (vertical arrangement).  
The test was carried out in a constant temperature and humidity laboratory, where the environmental parameters such as ambient temperature and wind speed could be controlled and monitored in real time.
# Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
## 7 Conclusions  
In this paper, we proposed two distributed EKF techniques for the 3-D UAV Jammer localization: the DEKF and the DEKF-DR. Moreover, the existing centralized EKF (EKF-Center) based method is also extended for the 3-D localization scenario. The DEKF and EKF-Center are only based on received power from the boundary nodes. However, the DEKF-DR utilized an additional edge node received power to formulate the problem in terms of Distance Ratio. Specifically, the DEKF-DR is based on the distance ratio estimated at the boundary node to estimate the distance between the jammer and the boundary node. The performance of the proposed distributed techniques was compared with the conventional centralized EKF based solution. Unlike the performance of the EKF-Centr, which depends on the number of cooperative nodes, the performance of the proposed DEKF and DEKF-DR are insensitive to the number of nodes, as they only rely on a single node. Moreover, the localization accuracy of the DEKF-DR is found to be the highest and it is not affected by the jammer location or its transmission power. Last but not the least, the DEKF-DR has a lesser computational load in contrast to the EKF-Center due to its distributed design.  
Conceptualization; methodology; implementation, W.A., M.M. and A.J.A.; writing-original draft preparation, W.A.; review and editing, U.M.A.-S.; visualization, A.J.A.; supervision, M.M.; project administration, U.M.A.-S.; funding acquisition, U.M.A.-S. All authors have read and agreed to the published version of the manuscript. This Project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (KEP-6-135-41). The authors, therefore, acknowledge with thanks the DSR for technical and financial support. The authors declare no conflict of interest.