esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Confusion Matrix Algorithm  
The confusion matrix contains the information about the predicted and actual classification. The confusion matrix is usually in the form of a table and is utilized to evaluate the performance of the classifier on a set of testing data where the true value is established. Figure 12 illustrates the basic terms in the confusion matrix formed in the table. The terms are usually in a form of whole number:  
* True Positive (TP) is the cases that we predicted yes and they do have the cases.
* True Negative (TN) is the cases that we predicted no and they do not have the cases.
* False Positive (FP) is the cases that we predicted yes but they do not have the cases.
* False Negative (FN) is the cases that we predicted not but they do have the cases.  
Based on the confusion matrix basic terms, a list of rates that execute the classifier performance is computed. Table 4 indicates the list of rates with their definition used in this paper. There are five rates that have been used which are accuracy, sensitivity, specificity, precision, and F\({}_{1}\) score.  
Figure 12: Confusion matrix table.
## 3 Results  
### IOD of Arcs from LEO Objects  
First, the ground-based optical-electrical array observations from Changchun Observatory were used for TLE matching to identify the known objects. TLE data are public. The results are shown in Table 2, where about 85% of the arc segments were from known objects. The average arc length of the observed data is about 39 s.  
The errors are estimated from the TLE of the known debris. TLE can be regarded as a reference or true orbit for the IOD elements, and it is more precise than IOD elements. So, the IOD errors can be calculated by comparing the TLE orbit and the IOD elements at a common epoch for space debris. Then, the IOD elements are estimated, and the initial orbit results of the LEO objects are shown in Table 3, which contains the initial orbit-determination success rate and the SMA errors. A set of 7488 orbits was used, and the success rate of initial orbit determination was about 95.6%, higher than the previous success rate in [14]. Finally, the initial orbit correlation was performed, and the correct rate of initial orbit correlation was about 89%.
## 3 Results and Discussion
### Mechanical Properties of NR Composites Using Different Cross-Linking Agents
#### 3.3.1 Tensile Properties
On the other hand, moduli of the NR composites vulcanized with organic peroxides also show significant increments after SWCNTs incorporation; for example, the NR-T29 composites increase 83% and 41% at 100% and 300% strain, respectively, and the NR-T145 composites recorded 25% and 67% enhancement at the same conditions. However, when DCP was used as a cross-linker, a moderate increment in modulus value for SWCNTs is observed, showing a maximum increment of 16% when compared with the neat formulation; nonetheless, it should be noted that the values measured for modulus at 100 and 300% strain for the NR-DCP composites (see Table 5) are very close to those obtained for the NR-S composites. For NR composites containing CB as filler, increments in modulus for most of the samples are less significant than those observed in the NR composites with SWCNTs. Particularly, in the NR composites vulcanized with organic peroxides (T29 and DCP), the CB addition causes a detrimental effect in modulus at 100% strain, unlike the tendency observed in the NR-T145 composites, where the CB particles produce a notable increase in stiffness for the NR formulations. Figure 6: Relative increments in modulus at (**a**) 100 and (**b**) 300% strain for the NR composites vulcanized with different cross-linking agents and incorporating CB or SWCNTs as fillers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & **Tensile Strength** & **Strain at Breakage** & \multicolumn{2}{c}{**Modulus (MPa)**} \\  & **(MPa)** & **(\%)** & **100\%** & **300\%** \\ \hline NR-S & 14.77 & 800 & 0.87 & 2.50 \\ NR-S-CB & 10.39 & 600 & 0.95 & 2.84 \\ NR-S-SWCNTs & 25.23 & 790 & 1.10 & 4.05 \\ \hline NR-T29 & 4.52 & 651 & 0.36 & 1.14 \\ NR-T29-CB & 2.82 & 687 & 0.34 & 1.07 \\ NR-T29-SWCNTs & 7.90 & 564 & 0.66 & 1.61 \\ \hline NR-DCP & 7.25 & 600 & 0.86 & 2.56 \\ NR-DCP-CB & 8.77 & 516 & 0.83 & 2.78 \\ NR-DCP-SWCNTs & 10.14 & 495 & 0.88 & 2.98 \\ \hline NR-T145 & 5.52 & 592 & 0.52 & 1.50 \\ NR-T145-CB & 5.06 & 406 & 0.63 & 2.88 \\ NR-T145-SWCNTs & 3.44 & 372 & 0.65 & 2.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Values of tensile properties obtained for NR composites.
## 3 Results and Discussion
### Extent of Dust Accumulation and Soil Loss
#### 3.2.1 Extent of Dust Accumulation
This supports the conclusion that the MDCO method is highly effective in regions with significant wind activity. The higher dust collection observed from the SDC compared to the BSNE can be attributed to the SDC's larger dimensions and its proximity to the dust source. The differences in dust deposition were attributed to factors such as the source of dust; particle size; and topographical features, like slope and soil type. The proximity of the Surface Dust Collector (SDC) to the dust source played a significant role in the results. Sodic soils, known for their poor structure and high erosion susceptibility, showed higher dust accumulation. The SDC collected nearly twice as much dust as the BSNE, indicating its greater efficiency in capturing surface dust. The higher dust collection observed from the SDC compared to the BSNE also can be attributed to the SDC's larger dimensions and its proximity to the dust source.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques
#### 4.2.4 Proximity-Based Methods  
Proximity-based localization is simple but usually has low positioning accuracy. In VLC-based systems, LEDs are deployed as the reference beacons at known positions. A photodiode can detect whether the receiver module is located within the coverage range of a transmitting LED. For example, a proximity-based localization system using lighting LEDs is proposed in [68]. The identification data (ID) of LEDs are sent along with the illumination so that the receiver can identify the nearby LEDs (reference beacon positions) and thus estimate its approximate position.  
In [69], a VLC proximity-based localization system is built using conventional LED lamps. Each lamp periodically broadcasts its coordinates and identification data. A photodiode is used to receive this information and determine its location in proximity to the lamps. By a mapping of the received LED lamp ID and geographic area, a simple indoor navigation application is demonstrated. A similar application of the above method for tracking visitors and providing navigation in museums is shown in [70]. A VLC receiver can also receive digital contents corresponding to his/her location in the museum.  
To improve positioning accuracy, the VLC-based IPS in [71] embeds a six-axis geomagnetic sensor at the receiver for providing three axes of azimuth and three axes of tile angulation information for localization. Meanwhile, each LED light bulb broadcasts its coordinates as data through VLC technology. In addition, the impact of the maximum receivable signal incident angle (FOV) limit and optical receiver sensitivity limit to positioning accuracy is investigated. The experiment of an accurate IPS is demonstrated.  
Another example of a proximity-based IPS is the angular diversity localization scheme used in [72]. The system contains a luminous LED array, with each LED transmitting at a different frequency, and has a lens located in front of the LED array so as to yield different light intersection regions due to different light overlapping patterns of the LEDs. Each region is distinguished by the presence or absence of different LED lights. The location of the receiver can thus be estimated by identifying each region. The above design does not require accurate light intensity or orientation measurements. Clearly, a larger number of LEDs in each luminary may create a larger number of distinguishable regions for possible smaller localization error. In general, the accuracy of the system will depend on the number of lights and also the receiver's height.
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 3 Flat Luneburg Design and Implementation
### Feeding Patch Antenna  
To accomplish dual-polarized performance, a dual-polarized stacked microstrip patch antenna was used as the feeding source for the FLL, as shown in Figure 6. The used two substrate layers are both Rogers 4003C (\(\epsilon_{\mathrm{r}}\) = 3.38, tan\(\delta\) = 0.0027). The bottom patch is directly excited by two SMPM connectors using two orthogonally displaced tri-section stepped-impedance-microstrip-line transformers for dual polarizations, and the top patch is on the bottom of the superstrate. The antenna was designed at the targeted frequency band, and its performance is shown later.
## 2 Materials and Methods
### Operational Use Case  
A fully automated inspection flight along a pre-planned route passing through controlled airspace was defined as the test scenario (Figure 3). During the mission, the UAV is always connected to a UTM system and avoids cooperative traffic independently. Before entering the controlled airspace, the UAV contacts ATC to obtain clearance. While flying within controlled airspace, the UAV autonomously activates the Mode S transponder to be visible via ADS-B to manned traffic. All flight movements of the UAV within the control zone are coordinated with ATC via a collaborative interface. After leaving the controlled airspace, the UAV autonomously deactivates the transponder, as manned traffic is not to be expected at VLL airspace outside of a CTR. This way, the footprint on the 1030 MHz/1090 MHz spectrum can be minimized. During the complete flight, the telemetry and system states are monitored via the UTM system by the operator.  
Figure 1: The main components of the MMT.  
Figure 2: Experimental multicopter platforms with integrated MMT device.
## 4 CKC-Based Taxonomy on Security Threats to SF and PA
### The Taxonomy
#### 4.2.4 Threats Related to the Exploitation Stage  
Some state-of-the-art attacks on SF and PA exploit a vulnerability in the software or in the underlying Operating System (OS) to get the victim run their malicious code in the exploitation stage [37; 101]. Others use SQL injection techniques for this purpose [37; 102]. Accordingly, the cyber-threats related to the exploitation stage can be classified as follows.  
* **Exploiting Software and OS Vulnerabilities:** Zero-day exploits are good examples for this kind of threat [103]. They are not detectable by common software vulnerability protection mechanisms. They exploit unknown software vulnerabilities for which no patch or fix is available. Moreover, viruses, worms, Trojan horses, and backdoors are common threats related to OS vulnerabilities in smart devices and consequently in SF and PA [104; 105].
* **SQL injection:** In recent years, SQL injection has frequently hit data-driven applications using code injection techniques [37]. It can potentially hit SF and PA databases as well.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Estimation of the Channel Capacity
#### 3.5.3 Summary of the Results  
The mean values per site are depicted in the CENELEC A and FCC band (see Table 2).  
These theoretical capacity values per subcarrier both in the CENELEC A and FCC bands are very sufficient for the AMI application which requires 2.4 kbits/s in the G3-PLC communication.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mean Channel Capacity** & **Urban Dense 1** & **Urban Dense 2** & **Sub Rural** & **Rural** & **Urban** \\ \hline CENELEC A (kbits/s) & 238 & 170 & 216 & 102 & 136 \\ FCC (Mbits/s) & 1.58 & 1.73 & 2.21 & 1.31 & 1.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean channel capacity value per site.  
Figure 19: Estimation of the channel capacity in the CENELEC A band.  
Figure 20: Estimation of the channel capacity in the FCC band.  
Regarding the urban dense 1 site, the channel capacity depends on the location. The high capacity is particularly obtained in the FCC band.  
The capacity in the FCC band is much more interesting, because it varies from 1.3 Mbits/s in the rural site to 2.2 Mbits/s in the sub rural site. In conclusion, the FCC band represents an attractive band for the integration of IoT in new smart grids' applications.
# Integrated Digital-Twin-Based Decision Support System for
## 5 Integrated DT-Based Decision Support System for Relocatable Module Allocation Plan  
### Develop Data Structure and Data Acquisition  
The creation of an integrated building asset data management system necessitates the design of a comprehensive database schema that accurately represents the various lifecycle phases of the building units. Figure 5 illustrates the modules' database schema. This relational database schema is constructed using an entity-relationship conceptual data model, which includes three primary components: entity, relation, and attribute [61]. Relations are depicted in the following format, with the primary keys underlined: relation-name (attribute 1, attribute 2,..., attribute n). The integrated module unit asset management relational model encompasses two primary databases: static and dynamic.  
The static data encompass historical as-built information and result from past module assessments. This dataset includes information on RMS projects (project ID, project description, project location, project location, usage schedule, number of floors, and number of each module type), RMS storage (storage ID, storage location, and storage area), and modules (module ID and module type). Data from the design and construction phases can be stored in BIM models and retrieved from the DT platform, in which the project ID, storage ID, or module ID serve as unique identifiers for each respective entity. In addition, the project schedule, initially defined during the lease contract, can be extended upon the client's request.  
On the other hand, the dynamic database includes data that are processed during facility management and assessment over time. These data are retrieved from real-time monitoring and analysis conducted within the DT platform. It includes RMS storage details (the quantity of each module type and quantity of empty slots), module information (location, usage schedule, status, condition, maintenance record, and usage record), and condition ratings (condition rating ID, condition rating description, rating date, sensor data, POE results, and required actions). GPS sensor data reflect the current geographic location or coordinates of the modular unit, determining its status (in storage, transporting, onsite assembling, in use, or reserved). The module's status is a crucial criterion for deciding its availability for relocation. Additionally, based on sensor data and POE analysis from the DT platform, condition ratings are conducted to assess each module's condition anddetermine necessary actions. This information updates the module's status, indicating whether it is available for reuse or requires maintenance. Historical records of the modular unit's usage, including dates, locations, projects, and maintenance activities, are maintained. This includes details of the types of maintenance performed and any issues addressed. Additionally, the quantity of each module type and the number of empty slots in storage facilities indicate the number of available modular units and available storage space.
###### Abstract  
This paper investigates a low-Earth orbit (LEO) satellite downlink for high-speed data communication in interference situations. A choke ring horn type antenna is used as the data transmitting antenna with an isoflux pattern in the LEO satellite, which has a beam coverage of \(\pm 51.6^{\circ}\) and a bore-sight gain of 4.4 dBi at 8 GHz. The receiving antenna on the ground station is a parabolic type antenna with a diameter of 11.3 m, and it has a half-power beam width (HPBW) of 0.2\({}^{\circ}\) with a maximum gain of 59 dBi at 8 GHz. The jamming-to-signal ratio (J/S) is calculated assuming that the LEO satellite transmits signals to the ground station, and an elevation angle of the interference source varies from 0\({}^{\circ}\) to 90\({}^{\circ}\) at an altitude of 10 km. Applying antenna characteristics, such as HPBWs and side lobes, to the calculated space wave path loss makes it possible to predict the J/S results according to the location of the interference source and the satellite. The results show that it is necessary to consider the space environment to accurately analyze the LEO satellite downlink, especially at the low elevation angle of the satellite.  
LEO satellite; link budget; antenna radiation patterns; interference situation; wave propagation; space environment; unwanted interference source; J/S 2023
## 5 Results and Discussion
A comparison of the results obtained with the results of similar distillation systems according to thermal efficiency and water yield is presented in Table 2.  
Figure 8: Influence of water depth on heat transfer coefficient by convective. Figure 9: Energy efficiency with respect to time. Based on Equations (18)-(20), the hourly input and output exergy rates are depicted in Figure 10. It is depicted that the output energy rates increase when solar radiation increases. According to Figure 9, the output exergy rates at water depths of 2 and 3 cm decrease away from the output exergy rate obtained at water depths of 1 cm. The output exergy dropped by about 13% and 36%, respectively, compared to the still with 1 cm of water depth at 13:00 p.m. Figure 11 gives the exergy efficiency evolutions for the three considered cases. Generally, the exergy efficiency trend increases progressively after 11:00 a.m. and reaches its maximum value at 13:00. The maximum exergy efficiencies for the three considered cases are found to be about 32.3, 31.4, and 26.0. At about 16:00 p.m., the behaviour of the curves is reversed. It is obvious that the exergy efficiency with 3 cm of water depth was higher than others. This behaviour was due to the energy-storing ability of the water held in the basin, which led to an increase in the evaporation rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Water Depth in** & **Accumulated** & **Thermal** & **Climatic** \\  & **Basin (cm)** & **Productivity** & **(mL/m2)** & **Efficiency (\%)** & **Conditions** \\ \hline
**[PERSON] et al.**[6] & 2.5 & 1409 & 22.3 & Iraq \\
**[PERSON]**[28] & 1 & 1400 & 27 & India \\
**[PERSON]**[29] & 2 & 1485 & 30.96 & Egypt \\
**Current study** & 1 & 1448 & 23.62 & Iraq \\  & 2 & 1196 & 23.38 & Iraq \\  & 3 & 993 & 21.61 & Iraq \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with similar distillation systems. Figure 10: Hourly input and output exergy rates with respect to time. The accumulated variations in water productivity from solar still with a water depth ranging from 1 to 3 cm are plotted in Figure 12. As time progresses, water production from the solar still with 1 cm water depth increases until they achieve the maximum values (339 mL/m\({}^{2}\)) at 13:00 p.m. Then, the behaviour is reversed, and they gradually decrease until sunset. As a result, the accumulated productivity is found to be 1468.84 mL/m\({}^{2}\). Increasing the water depth from 1 cm to 2, and 3 cm caused a decrease in hourly water productivity of up to 20% (from 8:00 a.m. to 14:00 p.m.) and an increase of up to 17.2% percent (from 15:00 p.m. to 18:00 p.m.). While increasing the water depth to 3 cm caused a decrease in hourly water productivity up to 47.7%, (from 8:00 a.m. to 14:00 p.m.), and an increase of 33% (at or from 15:00 p.m. to 18:00 p.m.). At 2 and 3 cm, the accumulated productivity is found to be 1357.54 and 1223.82 mL/m\({}^{2}\), which is 8.2% and 20% less than the solar still at 1 cm. Figure 11: Exergy efficiency with respect to time.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 5 Conclusions  
In this paper, we proposed an iterative hybrid multi-user equalizer for the uplink of broadband mmWave mMIMO CE-OFDM systems. Due to hardware constraints, the analog part of the equalizer is fixed over the iterations and subcarriers, while the digital equalizers are iterative and computed on a per subcarrier basis. These ones were designed by minimizing the sum of the MSE of all subcarriers. The user terminals employ a low complex analog precoder based on AoD.  
The results showed that the proposed iterative multi-user equalizer is quite efficient to mitigate the ISI and the multi-user interference, achieving a BER performance closer to the fully digital counterpart. The results also show that by increasing the number of users (and of RF chains), the performance for iteration 4 also increases, which is a promising result for a realistic scenario with several users. Finally, when we increase the analog processing, keeping the same complexity for digital processing, the performance is improved, which proves that the analog part of proposed equalizer is very efficient to improve the overall system performance. Therefore, the proposed equalizer can be a good choice for practical broadband mmWave CE-OFDM systems employing mMIMO terminals.  
Investigation, R.M.; Supervision, D.C., A.S. and R.D.; Validation, A.G.; Writing--original draft, R.M.; Writing--review & editing, S.T., D.C. and A.S.  
This work was supported in part by the European Regional Development Fund (FEDER) through the Competitiveness and Internationalization Operational Program (COMPETE 2020) of the Portugal 2020 framework, Regional OP Centro (CENTRO 2020), Regional OP Lisboa (LISBOA 14-20) and by FCT/MEC through national  
Figure 8: Performance comparison of the proposed iterative hybrid equalizer with \(N_{rx}^{RF}=U=8\), for a different number of receive antennas.  
Figure 7: Performance comparison of the proposed iterative hybrid equalizer with \(N_{rx}=32\), for different number of users \(U=N_{rx}^{RF}\).  
funds, under Project MASSIVE5G (AAC n\({}^{\alpha}\) 02/SAICT/2017), project UID/EEA/50008/2019 and FCT grant for the first author (SFRH/BD/129395/2017).  
The authors declare no conflict of interest.
## 3 Methods
### Gaussian Tracking  
In Gaussian tracking, following [28], we adopt the proposed analytical Jacobian of the \(\mathbf{SE}(3)\) camera's extrinsic matrix with respect to the 3D Gaussians. For the loss function, we compute the losses for the RGB \(L_{rgb}\) and depth \(L_{depth}\). Notably, similar to [28], we further optimize affine brightness parameters for exposure and penalize non-edge or low-opacity pixels. Consequently, the loss is expressed as follows:  
\[\begin{split} L_{rgb}=&\parallel(exp(\alpha_{exp}) \hat{I}+\beta_{exp})-I\parallel_{1},\\ & L_{depth}=\parallel\hat{D}-D\parallel_{1},\end{split} \tag{10}\]  
where \(\hat{I}\) and \(\hat{D}\) represent the rendered RGB image and depth image, respectively, while \(I\) and \(D\) denote the ground truth RGB image and depth image. \(\alpha_{exp}\) and \(\beta_{exp}\) are exposure-related factors and are trainable parameters, typically initialized to 0. The final expression of the loss function is as follows:  
\[L_{track}=\gamma L_{rgb}+(1-\gamma)L_{depth}, \tag{11}\]  
where \(\gamma\) is a hyperparameter.  
After tracking, it is necessary to determine whether the current frame qualifies as a keyframe. Similar to [28], we measure the covisibility by measuring the intersection over the union of the observed Gaussians between the current frame and the last keyframe. If the covisibility drops below a threshold, or if the relative translation is large with respect to the median depth, the current frame is registered as a keyframe. The covisibility \(CoV_{ij}\) between frame \(i\) and frame \(j\) can be calculated as follows:  
\[CoV_{ij}=\frac{|V_{i}\bigcap V_{j}|}{\min(|V_{i}|,|V_{j}|)}, \tag{12}\]  
where \(V_{i}\) represents the observed Gaussians for frame \(i\).