esa-sceva/satcom-chunk-collection huggingface dataset:
# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
## 2 Related Work
The main contributions of the article are:  
* proposing a GPU-based hardware acceleration for the 3 GPP 3D channel model, which is a highly parameterized and realistic channel model for 5G NR networks;
* application of various CUDA-based optimization techniques to efficiently utilize GPU resources and increase the overall performance of the channel model simulator;
* evaluation of the performance and accuracy of the GPU accelerator using benchmark parameters and comparison with both a CPU-based C++ model and a previous design on an FPGA based on the same 16 nm technology node as the GPU;
* showing that the GPU accelerator can achieve an overall speedup of about 240\(\times\) compared to the CPU model and 33.3% higher single-precision performance than a comparable FPGA design, while maintaining high accuracy and flexibility.
## 3 Results
### Diurnal Variations of ChlorophyllI Fluorescence on Different Days during the Season  
The canopy SIF simulated by the SCOPE model showed that canopy SIF\({}_{687}\) and SIF\({}_{760}\) had similar seasonal and diurnal variations (Figure 5a,b). They both reached their peaks in the summer across all these days and their daily maximum appeared near noon (12:00). The variations of canopy SIF closely correlated to the PAR incident above the canopy (SIF\({}_{687}\): R\({}^{2}\) = 0.67, \(p\) < 0.001; SIF\({}_{760}\): R\({}^{2}\) = 0.89, \(p\) < 0.001). Noticing that an extremely high PAR occurred on July 22 th at both SIF\({}_{687}\) and SIF\({}_{760}\). However, the GPP on that day did not show significant high values. We inferred that the excess light was emitted in the form of fluorescence to avert damage to the photosynthetic apparatus of the leaves. For the leaf level, the results illustrated that the value of F\({}_{\rm s}\) remained stable throughout one given daylight, although it held seasonal variations with lower in spring and higher in autumn. The variations in leaf PAR and photosynthesis rate (PAR\({}_{\rm leaf}\) and P\({}_{\rm apparent}\)) were consistent with these of PAR and GPP in the canopy (Figures 5 and 6). The P\({}_{\rm apparent}\) also happened to be at the lowest level on 22 July with the high incident PAR (Figure 6c).  
Figure 4: Comparison between measured GPP by eddy covariance system (GPP\({}_{\rm EC}\)) and simulated GPP by SCOPE model with retrieved vegetation parameters (GPP\({}_{\rm SCOPE}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Date** & 21 April & 22 May & 1 July & 22 July & 31 August & 24 September & 23 October & 25 November \\ \hline
**Cca** & 5.02 & 6.23 & 8.59 & 10.13 & 9.65 & 13.14 & 7.20 & 2.86 \\
**Cdm** & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 \\
**Cw** & 0.050 & 0.05 & 0.049 & 0.050 & 0.050 & 0.049 & 0.050 & 0.050 \\
**Cs** & 0.72 & 0.34 & 0.64 & 0.25 & 0.26 & 0.24 & 0.33 & 0.69 \\
**N** & 3.00 & 3.00 & 3.00 & 2.17 & 3.00 & 2.96 & 3.00 & 3.00 \\ LIDFa & 0.91 & \(-0.45\) & \(-0.51\) & \(-0.75\) & \(-0.89\) & \(-1.00\) & \(-0.97\) & 0.00 \\
**LIDPb** & \(-0.40\) & 1.00 & 0.78 & \(-0.09\) & 1.00 & 0.65 & 1.00
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Separation-Based Technologies
#### 4.1.6 Adsorption
The data shown are all from adsorbents activated by one of the methods already referred to, since due to the specific nature of nitrate ions, raw adsorbents do not show remarkable adsorption capacity. Disparity in values is observed, ranging from more than 225 mg/g (amine functionalized cellulose) to as low as 3 mg/g (polydopamine modified zeolite). Regeneration and recycling of charged adsorbents are essential for an eco-friendly operation and for minimizing the need for adsorbents, especially when they have required a time-consuming and reagent-intensive activation process. The energy required in this operation must also be taken into account. To this end, desorption and recovery of adsorbents in adsorption-desorption cycles could help to circularize the process and achieve waste reduction. The use of recovered nitrates is not a problem, since they can be added to agricultural lands as a nitrogen fertilizer [123]. Figure 4: Schematic illustration of surface functionalization of sorbents.
## 3 BER Considering Leaky Feeder Transmitter
### Calculation of BER of a QPSK Modulation Technique
For the LF cable, the signal that will be transmitted form an arbitrary slot is shown in (6); this equation can be rewritten in a phaser form as  
Figure 2.0  
If the slots are close enough so that the relative phase rotation and time delay can be ignored(\(e^{\frac{\left|2\,\pi D\,B\right|}{\lambda}}\cong 1\)) (but not the fading), the sum of the signals will be given by the real part of  
Figure 2.1  
In this case, the new amplitude of the sum of the signals will be  
Figure 2.2 Figure 2.3  
The bit energy of the received signal is given by  
Figure 2.4  
and the SNR of the \(\ l^{th}\) path under Rayleigh fading is given by  
Figure 2.5  
The total SNR from all paths \(\ \ \gamma_{t}\) will be given by [13]  
Figure 2.6  
Substituting Equation (15) in Equation (16), the \(\gamma_{t}\) can be in the form of  
Figure 2.7  
By substituting (13) in (17), the total SNR \(\gamma_{t}\) is given by  
Figure 2.8 Figure 2.9  
Define the conditional probability of error of multipath as [16],  
Figure 3.0  
According to (17), the average SNR \(\overline{\gamma}\) is given by  
Figure 3.1  
By substituting (19) and (20) in (2), for QPSK when \(M=4\), the BER of the LF will be given as  
Figure 3.2  
where the closed form of (22) will be [13],  
Figure 3.3  
Here, \(\mu=\sqrt{\left(\frac{\overline{\gamma}}{1+\overline{\gamma}}\right)}\). To show the effect of the LF parameters on the BER, substitute (21) in (23):  
Figure 3.4  
where \(I_{1}\) and \(I_{2}\) are given by (25) and (26), respectively, and \(\overline{\gamma_{t}}\) is given by (21):  
Figure 3.5  
Figure 3.6  
If \(\overline{\gamma_{t}}>>1\), then (23) can be simplified to  
Figure 3.7  
Equation (27) shows clearly that the BER of the LF depends on inter slot distance \(D\), the attenuation of the cable \(k\) and on the number of slots \(N\) in addition to the path fading gain \(\alpha_{l}\).
## 1 Introduction
### Related Works
Another tool to develop direct VO systems consists of optical flow methods, which describe the motion between two frames as the apparent movement of intensity patterns in an image. In [1; 11], this type of technique was used with downward-facing cameras to estimate velocity in mobile robots and multicopters, respectively. This algorithm achieved a great approximation to the motion field under certain lighting conditions (no photometric distortion and with constant brightness in all directions of an image). However, this calculation involves computing the inverse of a matrix of local brightness gradients, which can be impossible if this matrix is singular for that region. To address this issue, the calculation can be performed by considering regions around significant points in the image obtained with a feature-detection method. This method is the one followed by the Lucas-Kanade (L-K) algorithm [17], which was used in [2; 11]. Other methods combine the advantages of both feature-based and direct algorithms, like the hybrid, semidirect approach used in [15]. This technique extracts features only from selected keyframes, employing direct techniques to solve pose matching and an L-K optical flow algorithm to obtain sub-pixel accuracy. This method significantly reduces the processing required for feature matching and improves the robustness of the algorithm [18]. Although these previous approaches still dominate the field of VO [14], recent years have seen a significant increase in the importance of deep learning techniques. These methods have shown great performance both in the development of VO systems [8; 14; 19; 20; 21] or just in improving the specific task of geometry-based algorithms, such as aiding with depth estimation in monocular cameras [22] or using the concept of aleatory uncertainty to calculate photometric and depth uncertainties, leading to greater accuracy [23]. However, there are still many challenges to solve in order to achieve a robust and accurate VO using these techniques [24]. The remainder of this paper is organized as follows: Section 2 provides a detailed explanation of the algorithm process, while Section 3 describes the setup developed to validate the VO method and its calibration. Section 4 presents the results of the experiments and an analysis of these efforts, and in Section 5, a discussion of the proposed method is provided. Finally, Section 6 concludes this paper.
# A Multi-Channel Data Simulator Based on the Time Unification System
## 3 The Design of the Time Unification
### The Time Unification Design in Storage Mode  
The design of the time unification for the simulator in the storage working mode is shown in Figure 3. It mainly comprises a PPS control module, SATA solid-state storage module, SATA DMA module, DDR data buffer module, Data DMA module, Data First In First Out (FIFO) module, and output interface module. After completing the import of the simulated data into the SATA solid-state storage, the SATA DMA module finds the storage location of the simulated data based on the data index and transfers the simulated data to the DDR data buffer module in blocks when sending the simulated data to the target system. The SATA DMA module controls the Data DMA module to start the transfer between the DDR data buffer module and the Data FIFO module after completing the transfer of one data block. The Data FIFO module controls the sending of the data block according to the control of the PPS control module, following the configuration parameters with a fixed size, period, and delay. The Data FIFO module also sends a ready signal to the Data DMA module and SATA DMA module, thereby controlling the read speed of the Data DMA module and SATA DMA module.  
The simulator can unify the timing of the output simulated data based on the PPS input in the storage mode and output the PPS to the target system, achieving the time  
Figure 3: The simulator’s time-unified structure in the storage mode.  
unification system. The SATA solid-state storage can store a large amount of simulation data in a non-volatile manner, eliminating the need to reload the simulation data for each simulation in the same simulation scenario. The simulator is independent of the host computer's operating system and achieves a stable simulated data output in the storage mode.  
The simulator also has the function of storing the target system result data. It can store the high-speed result data output by the target system and enable subsequent simulation result analysis. The simulator communicates with the host computer through the PCIe interface for data transfer and control. The PCIe interface can realize high-speed data import and export.
# Leveraging Large Language Models for Sensor Data Retrieval
## 5 Evaluation  
As described in Section 3, the system comprises two primary components: data gathering and data retrieval. Concerning the data gathering component, prior work by the authors [49] evaluated various LLMs, including ChatGPT and Llama 2, to assess their performance in transforming raw sensor data into a structured format. The highest precision and recall achieved were 93.51% and 85.33%, respectively, demonstrating their suitability for the task.  
Therefore, this study focuses solely on evaluating the second component, data retrieval, utilising different LLMs to generate representative word embeddings for each table. Specifically, given an input table containing sensor data, this evaluation measures the precision and Mean Reciprocal Rank (MRR) [50] of the retrieval system in delivering relevant indexed sensor datasets that could be combined with the query data content.  
The primary objective of this experimentation is to assess the effectiveness and efficiency of the proposed LLMs in a specific, well-defined context rather than to exhaustively explore all potential variables and scenarios. The experimental setup has been designed to provide a clear and focused investigation into the models' capabilities, particularly in relation to execution time and retrieval accuracy. These metrics were chosen because they are critical to the practical application of these models in real-world settings, where performance and speed are often key considerations for deployment.  
Applying this approach to a specific use case will allow assessing the feasibility of applying it more broadly to other contexts. Future work may extend this foundation, exploring additional dimensions and scenarios to build upon the insights presented herein.  
The remainder of this section presents the models utilised, the datasets collected, and the outcomes of each proposed experiment. Furthermore, a final section is included, discussing the limitations of this approach and the privacy and security issues that may arise from the use of LLMs in the context of sensor data and IoT.
# Vulnerability Analysis of Column-Supported Reinforced Concrete Silo Structures
## 3 Determination of Silo Damage Index and Seismic Demand Parameters  
### Determination of Silo Damage Index and Seismic Demand Parameters  
In this section, the damage state is classified, and the seismic vulnerability of the column-supported reinforced concrete silo is analyzed through the IDA method. In practice, the lower structure of a column-supported reinforced concrete silo is composed of support columns, and the upper part is the reinforced concrete silo wall, both of which are connected by a ring beam. There is no uniform standard to measure its deformation under seismic action [17; 27]. In fact, the lower supporting column is the weak part, and the strength and deformation of the supporting column play a key role in the bearing capacity of the structure. Test results [29] have shown that there is less attenuation in the stiffness of the silo wall under earthquake action, and the damage is mainly due to the lower supporting column and its point of connection. Therefore, the top displacement of the supporting column was selected as the EDP, and the peak ground acceleration (PGA) was selected as the intensity measure (IM).  
Referring to the \"Code for seismic design of buildings\" [30] along with the structure site conditions [31], 12 natural seismic waves were selected from the Pacific Earthquake Engineering Center (PEER) database, as listed in Table 2. Notably, the site fortification intensity of the standard response spectrum was 7 degrees, and the site category was IV. The selected seismic waves should match the spectrum characteristics corresponding to the basic period of the silo structure and maintain the natural variability of the seismic excitations and include strong ground motions. Therefore, it is required that: (1) the magnitude of the seismic waves is greater than 6.0; (2) the epicenter distance of the selected earthquake is greater than 10 km; (3) the duration of the earthquake is longer than 15 s and (4) the selected motions are recorded from a very soft soil profile that corresponds to the \"Code for seismic design of buildings\". The acceleration time series of the ground motion are scaled with a scale factor of 1.0, and a small part of seismic waves that differ from the above principles mainly consider the spectral characteristics of the structure. Figure 6 shows the selected acceleration response spectrum and the standard design response spectrum.  
Figure 5: Absolute peak acceleration of silo wall under El-centro wave (0.281 g).
# A Spectrum Management Platform Architecture to Enable a Sharing Economy in 6G
## 5 Use Case: Spectrum Access in Massive IoT
This is indeed a very simple trading paradigm, but the following figures show that even such an uncomplicated scheme can produce a significant improvement in resource utilisation. Figure 4 shows the performance results in terms of SINR for different numbers of IoT devices distributed in the deployed area. The upper and lower edges of the plotted boxes are the 25 th and 75 th percentile of the values. The median values are indicated by the central red lines. The whiskers extend the SINR values to the most extreme points still not considered an outlier, while the values which we considered as outliers are indicated by red dots. The figure shows that our approach results in better performance in terms of SINR compared with regular 5G operation regardless of how many IoT devices are connected to the network. Specifically, the figure illustrates how: (1) the median value is always higher for our approach; and (2) although the standard method has an extensive range of SINR, most of these values are below the median value achieved by the proposed approach. Figure 4: \(Cont.\)  
Figure 5 illustrates how this overall increase in SINR leads to improved overall connectivity. It shows that the probability of denying connectivity decreases by 76%, and 16%, and 13%, for 1000, 2000, and 3000 IoT devices, respectively, when HODNET is applied compared with a standard 5G connectivity scenario. However, while with HODNET the blocking probability is always lower than in a standard 5G scenario regardless of the number of devices, it is also clear from Figure 5 that the network is reaching over-saturation when going well above 1000 devices. There is, of course, an absolute maximum to the available capacity and HODNET does not provide extra capacity but instead optimises the effectiveness with which these resources are used by the devices. As such, while this problem may only be solved by the development of more efficient RATs and the addition of usable frequency bands, HODNET can help to maximise the available resources.
## 3 Mathematical Modeling and Design Methodology
### Modeling of Hydropower Facilities  
There can be different topologies for a PHS system. However, the volume of the reservoir and the head elevation are the essential design parameters [40]. Other parameters of the PHS, such as the penstock and the selection of pumps and turbines, are more important in the mechanical design [41]. In this paper, the Al-Wehda impoundment was assumed to be the UR of the PHS energy system in this study. This dam is near Harta city, in northern Jordan. The actual head of this dam is 110 m. In this design, the volume of the lower reservoir (LR) of the PHS is one of the design variables to be optimally determined in order to truly size the hydro power plant facility. The two main operating modes to be considered in the energy management strategy and the mathematical modeling of the PHS system are the generating (discharging) and the pumping (charging) modes.
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 3 Testbed Design and Test Cases
### Emulated Attack Scenarios  
To evaluate the developed testbed and obtain a deeper insight into the security of LoRaWAN we have trialed the two different types of MitM attacks, namely:  
* **The replay attack.** This trial is carried out by an attacker intercepting the transmission between ED and GW. Specifically, the attacker eavesdrops a message from the ED and sends it via its own malicious device to the GW. The success of this attack depends on whether the frame counter on NS is activated or not. In case the frame counter is activated, the first step is to jam the ED before it can deliver the eavesdropped message to the GW [21] and then proceed with the attack. When the frame counter is not activated (which is common in many commercial networks to enable ABP devices re-joining the network after a power-down or reboot), the attack can be carried out without any jamming. The time sequence of this attack can be seen in Figure 6.
* **The bit-flipping attack.** The attacker intercepts the message and decrypts it, modifies it, encrypts it again and sends it to the GW. This attack allows an attacker to change all the information in the message. The time sequence of this attack can be seen in Figure 7.
## 2 Materials and Methods
### Analytical Methods  
The mineralogical composition of the raw materials was measured by PXRD (Panalytical X'PERT\({}^{3}\) Powder with Pixcel 1D detector). The PXRD measurements were carried out with 45 kV and 40 mA. As apertures 0.04 rad soller slit and fixed slits (0.125\({}^{\circ}\) and 0.25\({}^{\circ}\)) were used. Diffractograms were recorded from 5\({}^{\circ}\) to 100\({}^{\circ}\) 2\(\Theta\) with 49.725 s per step and stepsize: 0.013\({}^{\circ}\) 2\(\Theta\). For qualitative investigation the Highscore Plus Suite [35] and for quantitative phase analysis the Rietveld method [36] with Profex-BGMN [37; 38] was used. To determine the amorphous content, 10 wt % rutile (Kronos 2900-TiO\({}_{2}\); Rutile, supplied by KRONOS TITAN GmbH, Leverkusen, Germany) was added as internal standard. Chemical composition was analyzed with wave length dispersive X-ray fluorescence (SRS 3000; Siemens, Karlsruhe, Germany; Rh-tube; vacuum: 200 mbar; measurement, 30 s for each line). Therefore, melting tablets with 1 g sample material and 8 g Di-Lithium Tetraborate (FX-X100-2) supplied by FLUXANA (Bedburg-Hau, Germany) were prepared.  
To investigate the degree of carbonation depending on pressure (p) and temperature (T), the contents of wollastonite accompanied by the increase of calcium carbonate and increase of the amorphous phase contents different approaches were applied to identify and quantify educts and products. For quantitative phase analysis, PXRD with the Rietveld method [36] and differential scanning calorimetry with thermogravimetry (DSC-TG; STA 449 F3 Jupiter, Netzsch, Selb, Germany) coupled with mass spectrometer (MS; QMS 403D Aeolos Netzsch; atom mass units 1 to 50 u were recorded) and infrared spectroscopy (IR; Tensor II with TGA-IR extension compartment from Bruker, Karlsruhe, Germany) were used. The thermoanalytical measurements were carried out from ambient temperature (296 K) to 1273 K (1000 \({}^{\circ}\)C) with a heating rate of 10 K per minute. For the DSC-TG measurements ~15 to ~25 mg sample material per measurement were used.  
With DSC-TG/IR/MS the amount of X-ray amorphous content of CaCO\({}_{3}\) in the product could be analyzed as well.  
\begin{table}
\begin{tabular}{c c} \hline
**Element (Oxides)** & **Content (wt \%; Normalized)** \\ \hline SiO\({}_{2}\) & 50.55 \\ CaO & 45.95 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.52 \\ Al\({}_{2}\)O\({}_{3}\) & 0.52 \\ MgO & 0.17 \\ MnO & 0.13 \\ TiO\({}_{2}\) & 0.05 \\ K\({}_{2}\)O & 0.03 \\ ZrO & 0.01 \\ LOI \({}^{1}\) & 2.09 \\ Total & 100.02 \\ \hline \end{tabular} \({}^{1}\) LOI: loss of ignition at 1323 K.  
\end{table}
Table 2: Chemical composition of the used raw material determined by Siemens SRS 3000.  
The content of carbonate was calculated from the mass loss associated with CO\({}_{2}\):  
\[\text{CaCO}_{3}[\text{wt }\%]=\frac{\text{mass loss CO}_{2}\ [\text{wt }\%]}{(\text{mass CO}_{2}\ [\text{g/mol}] )} \tag{2}\]  
Microstructure of educts and products were characterized by SEM (JEOL JSM 6300 combined with X-Flash 5010 detector from Bruker; secondary electron pictures were taken with 15 keV; samples were sputtered with gold).