esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Slicing Concepts
* The Slice Manager runs the placement process to determine where to instantiate each network service and creates the network graph for the slice. * Following the placement decisions, the Slice Manager communicates with the VIM, WIM, and EMS in order to provide resources (sub-network slices). * VIM creates a new tenant for the slice. * WIM creates virtual links or/and flows on SDN switches with specific resource-QoS requirements, as declared in the NEST, to activate appropriate traffic steering. * EMS provides the required resources and configurations (e.g., associate traffic or user IDs to APNs, spectrum frequency allocation, bandwidth, etc.). * The Slice Manager communicates with the NFVO to make the deployment and instantiation of the network services included in the communication service. * The Slice Manager returns a slice ID to the operator, for further management and monitoring purposes. Based on the on-boarded NEST, the Slice Manager must make the mapping between the available data plane resources and the described slice requirements. At this stage, Slice Manager calculates and decides on the placement of each network service and its components, based on numerous parameters, such as slice requirements, available resources, geographic location, duration of the slice, network policies, etc. The tentative Slice Manager architecture is illustrated in Figure 3, and its implementation is open source [9]. The Slice Manager is based on a highly-modular microservice architecture. Each microservice is running on a container, which supports various virtualization technologies. The aim of this technology decision is based upon the general containerization advantages which include, among others: (i) application development and maintenance simplification, (ii) service elasticity, and (iii) scalability capabilities ad hoc for each case of use, upon request. Figure 3: Slice Manager architecture. As seen in Figure 3, the Slice Manager is split into multiple component-specific functions. Below, we briefly describe the core components.
# Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks
## 1 Introduction
[PERSON] et al. [21] address high-speed object positioning and tracking in a 5G network. The authors obtain a positioning accuracy of less than 3 m for real data collected on a high-speed train route between Shanghai and Beijing. Authors use an extended Kalman filter to predict the positions. The objective of this paper is to present an innovative method to exploit the 5G radio network to assist GPS in Geo-localization. The evaluation was carried out on a simulated signal on air and compared to recently published works. Parameters, such as Synchronization Signal-Reference Signal Received Power (SS-RSRP), distance between two COs were estimated. The method was validated together with the suitable distribution law to inform about the accuracy of the precision. In fact, distance is the only exact metric that we can use as a basis for experimental validation of our method. Whether it is the distance between two COs or the distance between two positions of a single COs, this measurement can be obtained easily and with the desired level of accuracy. Knowing \(D_{ij}\) this will be a key solution to many problems in industry 4.0, e.g., (inter-car distance detection, inter-robot distance detection and even inter-drone etc...). The distance \(D_{ij}\) will become fully known without the deployment of any sensors or GPS equipment. In 5G concept any single device will be covered immediately by the nearest 5G antenna (MC). Hence the coverage is guaranteed everywhere, certainly in outdoors or even in indoors. Another important application, knowing \(D_{ij}\), will give full knowledge of a realtime constellation of Connected Objects (COs) in move. In this work, following are the four global achievements:  
* We demonstrated analytically and by simulation that the proposed algorithm gives good accuracy when approaching the 0.55 m barrier. This barrier has never been reached in previous works. * We have full knowledge of the error distribution, as it follows the Rayleigh law. This knowledge will help to tune the solution to other positioning scenarios and applications. * The CRLB is derived to showcase the robustness of the proposed estimator at low and high SNR. Theoretical analysis and simulation both confirm the validity of the proposed model. * A comparative study of complexity is also performed with two recent works [PERSON] in [22] and [PERSON] in [23].
## 3 Results
### Actuation System  
#### 3.2.1 Actuation Mechanism  
To achieve the desired linear motion of the gripper handles for the purpose of grasping and releasing objects, several actuation mechanisms were evaluated, including four-bar linkages, pneumatic actuators, and linear actuators. However, the adoption of linkage mechanisms was deemed impractical due to their requirement of significant space and complex design to generate the required motion. Similarly, utilizing two linear actuators simultaneously was found to be problematic because of their need for extra space for the linear actuators' housing. Although pneumatic actuators are commonly used in robotic grippers, they were unsuitable for this gripper's application. This is due to their inherent high operating speeds, which are not conducive to the delicate and soft objects that necessitate a slower grasping process. Additionally, pneumatic systems have other problems, such as air leaking and the heavy weight of the vacuum compressor.  
During the design process, worm gears and spur gears were evaluated for the rack and pinion mechanism. Ultimately, spur teeth were selected as the optimal choice for the rack and pinion mechanism due to their simplicity and efficiency. As for actuation, a stepper motor was selected based on its precision and ease of control. To enhance the motor's torque, a planetary gearbox was incorporated. This mechanism facilitates the conversion of rotational motion from the motor into the linear movement of the gripper arms. By utilizing an open-loop control system, this gripper is capable of reliably grasping and releasing various objects. The performance of the gripper system relies solely on the pressure exerted by the flexible component on the objects. If the motor is sufficiently strong to compress the flexible element, the mesh design will be able to surround and grasp any object within the specified range. Several factors, including the size, shape, and orientation of the object, influence the required grasping force, which needs to be provided by the motor. However, the most crucial factor for grasping force is directly related to the thickness of the flexible element. To reduce the motor's workload, the wall thickness of the flexible component was determined through experimental and analytical methods as the controlling factor. A thinner wall allows for increased flexibility of the gripper end-effector and requires less power from the motor for grasping the object. The wall thickness of each component within the flexible element can be adjusted to suit the desired application.  
Figure 5 depicts the pinion gear utilized in the design. Additionally, a keyhole was incorporated into the gear design to allow for the insertion of the key between the gear and the motor shaft.
## 4 Risk Analysis for Current Protocols
### OneNet
#### 4.5.4 Replay/Man-in-the-Middle  
We have further identified that, because the key exchange process in Anonymous Diffie-Hellman does not perform any key authentication [40], the protocol is susceptible to Man-in-the-Middle attacks, especially if a spoofed HID is involved to intercept the connection. This attack can be successfully especially at the initialization of \"Secure Mode\".  
Our investigation has not identified any potential room for replay attacks on the protocol. An unsigned 32-bit integer called Sequence Number (SQN) is used to prevent replay attacks. The SQN number starts at zero when a message using a secure method called Data Security Security Association (SA). SQN then increments with every message. SQN values for a Data Security SA shall not be reused. If an SQN reaches its maximum value, then the Data Security SA is no longer valid, and a new one must be created.
## 3 Results and Discussion
### Porosimetric Analysis  
The pore size distributions of samples are shown in Figure 5, while Table 3 summarizes the basic descriptive statistics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**SCI**} & \multicolumn{3}{c}{**SCE**} \\ \cline{2-7}  & **L** & **a** & **b** & **L** & **a** & **b** \\ \hline Sample 1 & 38.83 \(\pm\) 0.01 & \(-0.92\pm\) 0.01 & \(-2.33\pm\) 0.01 & 39.61 \(\pm\) 0.01 & \(-0.89\pm\) 0.02 & \(-2.36\pm\) 0.01 \\ Sample 2 & 81.97 \(\pm\) 0.01 & 0.77 \(\pm\) 0.01 & 6.34 \(\pm\) 0.01 & 81.49 \(\pm\) 0.01 & 0.80 \(\pm\) 0.01 & 6.22 \(\pm\) 0.01 \\ Sample 3 & 86.32 \(\pm\) 0.01 & 0.16 \(\pm\) 0.01 & 3.13 \(\pm\) 0.01 & 85.86 \(\pm\) 0.01 & 0.21 \(\pm\) 0.01 & 3.03 \(\pm\) 0.02 \\ Sample 4 & 86.01 \(\pm\) 0.01 & 0.21 \(\pm\) 0.01 & 3.41 \(\pm\) 0.01 & 85.82 \(\pm\) 0.01 & 0.26 \(\pm\) 0.10 & 3.31 \(\pm\) 0.01 \\ Sample 5 & 46.69 \(\pm\) 0.39 & 0.63 \(\pm\) 0.02 & 0.76 \(\pm\) 0.24 & 46.45 \(\pm\) 0.39 & 0.59 \(\pm\) 0.02 & 0.83 \(\pm\) 0.24 \\ Sample 6 & 47.46 \(\pm\) 0.52 & 0.81 \(\pm\) 0.02 & 1.46 \(\pm\) 0.06 & 47.21 \(\pm\) 0.51 & 0.78 \(\pm\) 0.01 & 1.53 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Colorimetric analysis of all samples. Data found including (SCI) and excluding (SCE) specular component.  
Figure 3: XRD patterns of samples 3, 4, 5 and 6.  
Figure 4: Optical microscopy images of samples sythetized with silica fume: sample 1 (**a**), 5 (**e**) and 6 (**f**) and bottom ash: sample 2 (**b**), sample 3 (**c**) and 4 (**d**).  
Figure 5: Pore size distributions of all six samples quantified by mercury porosimetry analysis.  
The accessible porosity by intrusion is always above 41%, with a maximum of 63% for the sample 2. The anomalously low value for sample 3 (about 3% accessible porosity), which is clearly at odds with the morphological observations, indicates sample deformation and porosity collapse during the test. The sample 3 is therefore not considered in the mercury porosimetry results.  
The pore sizes of the samples 1, 5 and 6, all of which were prepared with SF, follow a unimodal distribution with a pore radius mode of 0.08-0.1 \(\upmu\)m. The sample 4 shows a bimodal pore size distribution, with a main peak at about 0.65 \(\upmu\)m and a secondary peak related to larger pores with a radius of about 7-8 \(\upmu\)m. Finally, the pore size distribution of sample 2 spans about an order of magnitude, with a mode of 1.79 \(\upmu\)m.
# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
### Phase Synthesis  
The phase synthesis of this configuration can obviously be generalized to cylindrical and even conical arrays by incorporating a linear array to form the elevation pattern. Such configurations are interesting for producing beams naturally pointing at intermediate or low elevations, thus avoiding losses due to pointing errors associated with more conventional planar arrays. When operating at zero elevation, the elemental antennas typically have their maximum radiation oriented along the radial axis.  
Phase synthesis allows for the realization of directive lobes with a \"moderately controllable\" level of secondary lobes. With this technique, we can control the received level in the direction of both useful and interfering radiation.  
For phase synthesis, the array factor is generally described by the following Equation (1):  
\[AF(\theta,\phi)=2\sum_{n=1}^{N}\cos(\beta a(\sin(\theta)\cos(\phi-\phi_{n})- \sin(\theta_{0})\cos(\phi_{0}-\phi_{n}))+\phi_{n}) \tag{1}\]  
The array factor is described by the following Equation (2):  
\[AF(\theta,\phi)=2\sum_{n=1}^{12}\cos(\beta a(\sin(\theta)\cos(\phi-\phi_{n})- \sin(\theta_{0})\cos(\phi_{0}-\phi_{n}))+\phi_{n}) \tag{2}\]  
The objective function (fitness) is chosen based on the optimization goal:  
\[fitness=AF(\theta,\phi)-AF_{d}(\theta,\phi) \tag{3}\]  
where: \(AF_{d}(\theta,\phi)=AF(\theta_{0},\phi_{0})\)  
Subject to the constraints \(\theta\in[\theta_{0}-\Delta\theta,\theta_{0}+\Delta\theta]\) and \(\phi\in[\phi_{0}-\Delta\phi,\phi_{0}+\Delta\phi]\)  
To apply the Taguchi method, the following steps must be followed:  
The number of parameters is set to \((k=12)\). The number of levels is established at \((s=3)\). The strength is defined as \((t=2)\). The experimental design chosen is OA \((27,12,3,2)\). The reduced function is determined as \((rr=0.9)\). The convergence value is set to 0.001.  
Figure 1 shows the electronic scanning of the space with a secondary lobe level of \(-8\) dB for a circular antenna array of 24 elements.
# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 4 Experiments and Results
### Dataset  
The dataset used in this study comprises 6383 entries annotated for misogynistic and non-misogynistic content, collected from the online platform Reddit [44]. Each entry includes the following features:  
* **Entry ID, Link ID, and Parent ID**: Unique identifiers for the data entry, its associated post, and its parent entry (if applicable).
* **Timestamp**: The entry_utc field records the posting time as a Unix timestamp.
* **Subreddit and Author**: The subreddit source, the author username, and the post or comment author.
* **Body**: The main textual content of the entry, which is central to the classification task.
* **Image**: Metadata indicating whether the post includes an image (present in 475 entries).
* **Annotation Details**: Includes level_1, level_2, and level_3 annotations to categorize content as misogynistic or non-misogynistic, with subcategories capturing nuances such as derogation or implicit abuse.
* **Highlight**: Specific text fragments marked as significant by annotators for content interpretation (available in 1972 entries).  
* **Strength of Abuse**: A qualitative measure indicating the nature and severity of abuse, provided for 743 entries.
* **Annotator ID and Label Date**: Tracks the annotator who labeled the entry and the date of annotation for traceability.  
The dataset contains 13.5% entries annotated at the most detailed level (level_3), providing granularity in content categorization. However, some limitations of the dataset include the imbalance between categories and the sparsity of certain fields, such as strength and highlight, which were populated in a limited subset of the data.
# The Leaky-Wave Perspective for Array-Fed Fabry-Perot Cavity and Bull's-Eye Antennas
## 2 Fabry-Perot Cavity Antennas
### Fabry-Perot Cavities for Beam Scanning in Elevation and Azimuth
The circular symmetry of the feeding array impresses full azimuthal stability to the value of the gain; interestingly, the same is true for the first sidelobe level, which remains  
Figure 7:1 Simulated values for reflection coefficient magnitude, considering different numbers of via-rings. Inserting more concentric rings the reflection coefficient dip shifts to higher frequencies. The horizontal dashed black line corresponds to the \(-10\) dB threshold. Reproduced from [47]. Figure 7:2 Schematic 2-D sketch of the FPCA structure, with stratified media inside the cavity and homogenized PRS. Multiple coaxial cables are shown as antenna feeders. Reproduced from [47]. stable above a certain number of array elements, maintaining, as the main beam, a perfect symmetry as shown in Figure 7:3 Additionally, by increasing the number of elements in the source ring, the sidelobe level shows little or no sensitivity after 8 elements are reached. The feeding-network system of a prototype realized with \(N=6\) coaxial cables is shown in Figure 16, where two concentric rings of via holes are inserted to ensure broadband impedance matching.
## 3 Performance Analysis
### BER Analysis  
BER is an important metric for transmission performance. Based on the PDF of SINR in (19), the BER expression of Bob can be written as [39]  
\[P_{e,b}=\int_{0}^{+\infty}\frac{1}{2}\mathrm{erfc}(\sqrt{\gamma})p_{\mathrm{ SINR}_{b}}(\gamma)d\gamma \tag{31}\]  
The closed-form BER expression has been derived in [39] when the parameters \(\alpha_{b}\) and \(\alpha_{b,\mathrm{AN}}\) are integers. Otherwise, the integration \(P_{e,b}\) cannot be expressed in closed-form. In this paper, we use GL quadrature sum [43] to approximate the value of \(P_{e,b}\). GL quadrature sum approximation can be expressed as  
\[\int\limits_{0}^{+\infty}e^{-t}f(t)dt\approx\sum\limits_{n=1}^{N_{GL}}w_{n}f(t_ {n}) \tag{32}\]where \(t_{n}\) and \(w_{n}\) are the abscissas and weight factors for the GL integration, which can be tabulated ([44], eq. (25.4.45)) or can be generated efficiently in software such as MATLAB R2020b. The accuracy of the GL quadrature sum increases with the number of terms \(N_{GL}\)[44].  
Hence, with a change of variable \(\gamma=\frac{t}{\beta_{b}}\), the integration \(P_{e,b}\) can be expressed as  
\[\begin{split} P_{e,b}&=\frac{1}{2\Gamma(\alpha_{b} )}\beta_{b}^{\alpha_{b}}\beta_{b,\text{AN}}^{\alpha_{b,\text{AN}}}\int_{0}^{+ \infty}\text{erfc}(\sqrt{\gamma})\gamma^{\alpha_{b}-1}e^{-\beta_{b}\gamma}U( \alpha_{b,\text{AN}};\alpha_{b}+\alpha_{b,\text{AN}}+1;\beta_{b}\gamma+\beta_{ b,\text{AN}})d\gamma\\ &=\frac{1}{2\Gamma(\alpha_{b})}\beta_{b,\text{AN}}^{\alpha_{b, \text{AN}}}\int_{0}^{+\infty}e^{-t}t^{\alpha_{b}-1}\text{erfc}\left(\sqrt{ \frac{t}{\beta_{b}}}\right)U(\alpha_{b,\text{AN}};\alpha_{b}+\alpha_{b,\text{ AN}}+1;t+\beta_{b,\text{AN}})dt\end{split} \tag{33}\]  
The integration \(P_{e,b}\) can be approximated by \(P_{e,b,GL}\), which can be expressed as  
\[P_{e,b,GL}=\frac{1}{2\Gamma(\alpha_{b})}\beta_{b,\text{AN}}^{\alpha_{b,\text{ AN}}}\sum_{n=1}^{N_{CL}}w_{n}t_{n}^{\alpha_{b}-1}\text{erfc}\left(\sqrt{\frac{t_{n}}{ \beta_{b}}}\right)U(\alpha_{b,\text{AN}};\alpha_{b}+\alpha_{b,\text{AN}}+1;t_ {n}+\beta_{b,\text{AN}}). \tag{34}\]  
It is shown in Section 5 that the approximation curves perfectly match numerical results for \(N_{GL}=300\).  
For the case \(g_{\text{AN}}=0\), the BER expression can be derived using [45], such that  
\[\begin{split} P_{e,b}&=\frac{\beta_{b}^{\alpha_{b} }}{2\Gamma(\alpha_{b})}\int_{0}^{+\infty}\gamma^{\alpha_{b}-1}e^{-\beta_{b} \gamma}\text{erfc}(\sqrt{\gamma})d\gamma\\ &=\left(1+\frac{1}{\beta_{b}}\right)^{-\alpha_{b}}\frac{\Gamma \left(\alpha_{b}+\frac{1}{2}\right)}{2\sqrt{\pi\Gamma(\alpha_{b}+1)}}{}_{2} F_{1}\left(\alpha_{b},\frac{1}{2};\alpha_{b}+1;\frac{1}{1+\frac{1}{\beta_{b}}} \right)\end{split} \tag{35}\]  
where \({}_{2}F_{1}(a,b;c;x)\) is the hypergeometric function ([41], Ch. 9.1).
# Shading on Photovoltaic Collectors on Rooftops
## 3 Fence Shading  
Figure 7 shows an inclined collector with an angle \(\beta\) facing south and a vertical fenced wall \(A\) oriented with an azimuth \(\gamma_{w}\) with respect to E-W direction. The shadow cast by the fence on the collector is developed in [4], Appendix B, providing expressions for the shadow angle \(\psi\) and the length \(OD\). For a vertical wall with an angle \(\gamma_{w}=90^{\circ}\) with respect to the E-W collector direction, the shadow angle \(\psi\) and the distance \(OD\) respectively become:  
\[\tan\psi=\sin\gamma_{\rm s}\sin\beta/\tan\alpha, \tag{13}\]  
\[OD=A\sin\gamma_{\rm s}/\tan\alpha, \tag{14}\]  
Figure 6: The distance from wall \(A\) to the first collector for inclined planes \(\varepsilon(^{0})\), see Figure 3.  
Figure 7: The shading by fence \(A\) on an inclined collector.  
Therefore, the shaded area is:  
\[S=\frac{A^{2}\sin\gamma_{\rm s}}{2\sin\beta\tan\alpha}. \tag{15}\]  
To reduce the shading on the collectors, the collectors are shifted away from the fence by a distance \(D\). The shadow length becomes (see Equation (14)),  
\[OD=(A\sin\gamma_{\rm s}/\tan\alpha)-D. \tag{16}\]  
_Numerical Results--Distance From Fence_  
The distance from the north-south vertical fence to the collector row is determined by the shaded area (see Equation (16)), i.e., by the shading losses. Figure 8 depicts the annual percentage energy shading losses with the distance \(D\) from the fence for several heights \(A\) (see Figure 7), for collector width \(H=1.882\,m\), collector length \(L=33\,m\), latitude \(\phi=32^{0}N\), and inclination angle \(\beta=20^{0}\). Figure 8 pertains to a PV system bounded by two north-south vertical fences, one to the east and the second to the west of the collectors. If, for example, one agrees to accept a loss of 0.3% in direct beam radiation, one may place the collectors \(D=1.0\,m\) away from a fence of height \(A=1.0\,m\).  
The shading losses decrease with an increase in the distance from obscuring objects; however, higher obscuring objects increase the shading losses.
## 3 Resource Allocation under Total Power Constraint
### Power Allocation
 \tag{11}\]  
\[p_{r}^{(1)}= \begin{cases}\frac{a\left(b-g\right)P_{T}}{\sqrt{ab-ag+bg}\left(b+ \sqrt{ab-ag+bg}\right)}&\text{if }b>g\\ 0&\text{otherwise}\end{cases}  \tag{12}\]  
if \(Q_{1}/c\geq p_{s}^{(1)}\) and \(Q_{2}/d\geq p_{r}^{(1)}\) hold. Note the superscript indicates the first case and the subcarrier index subscript is omitted for brevity due to \(N=1\). (2) If the interference threshold of the primary network is less than the power constraint of the secondary network, then the power allocation is determined entirely by the interference threshold. In this case, the power allocation is given by \(p_{s}^{(2)}=Q_{1}/c\) and \(p_{r}^{(2)}=Q_{2}/d\) if \(Q_{1}/c+Q_{2}/d<P_{T}\) holds. (3) If the interference threshold is too small for the secondary source while too large for the secondary relay, then the power allocation is constrained by the source's interference threshold. Under this circumstance, the power allocation is given by \(p_{s}^{(3)}=Q_{1}/c\) and \(p_{r}^{(3)}=P_{T}-Q_{1}/c\) if \(Q_{1}/c+Q_{2}/d>P_{T}\) and \(Q_{1}/c<p_{s}^{(1)}\) hold. (4) If the interference threshold is too large for the secondary source while too small for the secondary relay, then the power allocation is constrained by the relay's interference threshold. Under this circumstance, the power allocation is given by \(p_{s}^{(4)}=P_{T}-Q_{2}/d\), and \(p_{r}^{(4)}=Q_{2}/d\) if \(Q_{1}/c+Q_{2}/d>P_{T}\) and \(Q_{2}/d<p_{r}^{(1)}\) hold.