esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fifth-Generation Telecommunications Technology and Architecture
### Reliability and Integrity of 5G Services  
5G networks are expected to enable and support an unprecedentedly wide range of applications, from autonomous vehicles and telemedicine to smart cities and industrial automation. Many of these applications involve mission-critical services where even minor service interruptions or data alterations can have potentially catastrophic consequences [36]. Therefore, the reliability and integrity of services delivered over 5G networks are of paramount importance. Reliability in the context of 5G refers to the network's ability to deliver a consistent level of service, without interruptions or significant fluctuations in performance, even under challenging conditions. This is crucial for applications such as telemedicine and autonomous driving, where a high level of service availability and low latency are required. Network reliability also extends to the network's resilience against malicious attacks or system failures, with the ability to rapidly recover and maintain core functions [37]. Integrity, on the other hand, pertains to the assurance that data transmitted over the network remain unaltered during transmission, whether by accidental errors or de  
Figure 3: Threat landscape of 5G networks [35].  
liberate tampering [38]. Given the sensitive nature of the data handled by 5G applications, ensuring data integrity is fundamental. An example is in financial transactions, where even a small discrepancy can lead to substantial monetary loss, or in healthcare applications, where incorrect patient data can result in erroneous diagnoses or treatments. To ensure reliability and integrity, 5G networks should adopt robust security measures, including advanced encryption techniques, reliable error detection and correction mechanisms, secure routing protocols, and resilient network architectures. Techniques such as redundancy, fault tolerance, and real-time anomaly detection can enhance the reliability and integrity of 5G services. Moreover, leveraging AI and machine learning for proactive threat detection and response can further strengthen network resilience [39]. The interconnected world that 5G networks facilitate brings immense opportunities, but it also requires unwavering attention to the reliability and integrity of services, cementing these elements as key pillars in the development and operation of 5G networks.
# Active Tendon Control of Stay Cable by a Giant Magnetotrstive Actuator Considering Time-Delay
## 1 Introduction
To eliminate the adverse effect of time-delay, many control algorithms considering the time-delay compensation have been proposed, including the Smith predictive control [25], the extended Kalman filter to obtain an optimal filtered estimation of structural responses [26], the Neural network predictor [27], the Neural Network Based Hysteresis Compensation [28], the polynomial modulating function compensation [29], the time-delay compensation controller based on regional pole-assignment method [30], the phase shift compensation method [31], etc. In this paper, theoretical and experimental studies on active control of a cable model with time-delay effect are carried out. Firstly, the time-delay control equation of the small-sag cable was established and the related control algorithm was proposed. Secondly, a giant magnetostrictive actuator (GMA) for the active control of a stay cable was developed and the relationship between input voltage and the output force of GMA is determined. Thirdly, a multi-modes time-delay compensation method for the active cable control is proposed with help of the phase shift method. Finally, the control performance of the proposed system is validated by numerical simulations and experiments under different excitation conditions.
## 4 Simulations
### Evaluation of EDETA-e Protocol Underwater
#### 4.1.1 Simulation and Scenario Parameters  
The protocol was tested in three different deployment areas. The first one with \(100\times 100\)\(m^{2}\), the second one with \(150\times 150\)\(m^{2}\) and the last one with \(200\times 200\)\(m^{2}\). In all of them 100 and 200 nodes where randomly deployed, which gave 6 different scenarios to test. Each scenario was simulated several times and the mean value of all data was calculated. All the simulations were seeded using the number 1290674301 and each repetition was done advancing the run number [5].  
In these experiments, the transmission speed was set to 500 bps. This speed could be increased, but we wanted to account for the speed reduction given by the use of CDMA spreading codes.  
Since the protocol used was EDETA-e, the number of CHs and their placement was chosen _ad-hoc_ for each scenario. Only one sink was deployed at the centre of every scenario. The configuration phase of the EDETA-e protocol was set to last 18,000 s and the nodes were configured to send their data periodically every 250 s. Each node woke up at its defined time interval and sent one byte with its collected data to its CH. After that, each CH had to aggregate these data and send it to its parent in the tree structure until the data reached the sink.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 5 Experimental Verification
### Experimental Results for the DC/AC Converter  
In this section, the results for the DC/AC converter are shown, taking into consideration the results obtained for the DC/AC converter since the output values for the boost converter are the input values for the inverter. In this case, the first two intervals are considered and the results are shown. Then, four signals are presented to validate the operation of the DC/AC converter. The signals are the DC/DC converter input voltage, \(V_{in}\), the boost converter input current, \(i_{in}\), the DC/AC converter input voltage, \(V_{o}\), and the grid current, \(i_{grid}\). For 820 W/m\({}^{2}\), the voltage at the inverter input, \(V_{o}\), is 53.8 V, and the current provided by the DC/DC converter is 0.87 A, with transferring power of 46.8 W. The DC/DC converter transfers power of 51.01 W, then the inverter efficiency is 92%. For an irradiance of 500 W/m\({}^{2}\), the voltage at the inverter input is 52.7 V and the current is 0.6 A, whereas the power provided by the DC/DC converter is 31.62 W and the power transferred to the power grid is 29.9 W, obtaining, in this case, an efficiency of 94.5%. Table 3 presents the values for the inverter input current, the DC/AC converter input and output voltage, and the inverter output current.  
\begin{table}
\begin{tabular}{c} **(A)** \\ \end{tabular} \\ \hline
[0–0.2) & 0.87 & 2.93 & 0.62 & 17.41 & 0.98 & 53.8 & 1 & 1.19 \\
[0.2–0.45] & 0.49 & 1.66 & 0.64 & 18.01 & 0.96 & 52.7 & 0.69 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of DC/DC converter input voltage and current, DC/AC converter input voltage, and grid current for different irradiances, i.e., 820 W/m\({}^{2}\) for the first interval and 500 W/m\({}^{2}\) for the second.  
Figure 18: DC/DC converter control signal.  
Figure 19 shows the DC/DC converter input voltage, \(V_{in}\), and current, \(i_{in}\), the inverter input voltage, \(V_{o}\), and the inverter output current as a sinusoidal waveform. When the irradiance changes, there is a change in the parameter values, with the DC/DC converter output voltage and current dropping, while \(V_{in}\) increases its value to reach the MPP. Moreover, the inverter output current decreases its value with the irradiance. The inverter injects low harmonic distortion, with a total harmonic distortion (THD) of 2%.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 2 Materials and Methods
### Cooperative Transmission of Spatial Information Network
#### 2.2.2 Resource Allocation Optimization of Space Compression
Row\(\frac{d_{total}}{2}-2M+d_{i,j}\) of matrix \(\mathbf{P}\) is located in \(\left[\mathbf{H}^{[i,r]}\quad-\mathbf{H}^{[j,r]}\right]^{T}\) null space. Therefore, the null space dimension of \(\left[\mathbf{P}\mathbf{H}^{[i,r]}\quad-\mathbf{P}\mathbf{H}^{[j,r]}\right]\) is not less than \(d_{i,j}\). The precoding matrices \(\mathbf{V}^{[i,j]}\) and \(\mathbf{V}^{[j,i]}\) can always be found such that Equation (13) holds. This condition also makes \(rank(\mathbf{P})\geq\frac{d_{total}}{2}\) true. That is, \(\mathbf{P}\) is full rank.
# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 4 Simulation Experiments  
We analyze the performance, advantages, and disadvantages of the method in this paper by comparing many simulation experiments with the baseline method. The experimental simulation data mainly include synthetic data based on actual satellite-related data and simulation data generated based on satellite and mission characteristics. The results  
Figure 7: The matrix representation of the tasks on the satellite, the horizontal axis represents the tasks that increase in order, the vertical axis represents the time, and the value in the element is the satellite number.  
show that our proposed algorithm has the advantages of high efficiency and high task revenue in accomplishing the scheduling tasks. As shown in Table 1, the following table describes the operating environment of the experiment.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 2 Mobile Communications Security Issues
### 3G-CDMA Security  
In 3G network, a radio link of a particular base station (Node B) connects a mobile station to a visited network. Multiple base stations of the network are connected to a Radio Network Controller (RNC) and multiple RNCs are controlled by a GPRS2 support node (GSN) in the packet-switched case  
Figure 1: Operation and security processes in mobile architecture.  
or a mobile switching center (MSC) in the circuit-switched case (see Figure 1). The tracking of all mobile stations that are currently connected to the network are kept by the visitor location register (VLR) and the SGSN, and the identifier of every subscriber, which is permanent, is his IMSI. While the locally valid temporary mobile subscriber identities (TMSI) identifies a subscriber whenever possible, the IMSI is sent over the air interface as frequently as possible in order to protect against profiling attacks. There is a long term secret key Ki shared by both every UMTS subscriber and his dedicated home network. The current location of all subscribers of the home network is tracked by his home location register (HLR). The current SGSN or the MSC/VLR respectively, help carry out the Mutual authentication between a mobile station and a visited network. UMTS supports encryption of the radio interface as well as integrity protection of the signaling messages [8; 9]. For a detailed description we refer to [8; 9]. Moreover, in order to protect against man-in-the-middle, UMTS-AKA procedure was designed; to protect against network impersonation, UMTS applies both the validity of an authentication token and the integrity protection of the signaling messages. Protecting against replay of authentication data, the freshness and the origin (home network) of the authentication challenge is guaranteed by the authentication token. The integrity protections prevent the possibility that a man-in-the-middle can simply forward correct with timely authentications messages and fool both the mobile station and the base station into not using encryption for subsequent communication [10].
# Recent Advances in Optical Detection of Aminoglycosodies
## 4 Other Optical Assays
### Electrochemiluminescence  
Electrogenerated chemiluminescence (ECL) analysis is a trace analytical technology combining electrochemical technology with chemiluminescence technology. Luminol is one of the ECL substrates and H\({}_{2}\)O\({}_{2}\) can act as a co-reactant in the luminol ECL reaction [72]. Platinum electrodes adsorbed with Ag NPs on the surface showed a significantly enhanced luminol-H\({}_{2}\)O\({}_{2}\) ECL signal compared with the naked platinum electrode. Through the Ag-S or Pt-S bond, the SH-aptamer was immobilized on the electrode surface. The recognition of kanamycin by the aptamer caused an obvious decrease in the ECL signal. The label-free ECL sensing platform displayed a wide linear range from 0.8-170 nM, with a limit of detection of 0.1 nM [73]. Higher sensitivity (0.16 pM) was obtained based on ECL-resonance energy transfer between CDs and Au-GO composite [74].
# Mass Processing of Sentinel-1 Images for
## 3 Methods
### Integrated Maritime Surveillance Platform: Blue Hub  
The Blue Hub is JRC's maritime surveillance R&D platform [37]. Among other functionalities, this platform receives and stores ship reporting data (such as AIS) and ship detection data from SUMO, and fuses these data. During the fusion process, the reporting data are interpolated or extrapolated to the SAR image acquisition time. A shift in the image azimuth direction is applied to the interpolated reported positions based on their reported speed and the imaging geometry, to account for the speed-induced shift that the ship will experience in the image during the SAR image formation process [8]. This is necessary for an accurate collocation of the SAR detection and the interpolated reported position. A SAR detection and an interpolated reported position are deemed to be correlated if they are geographically closer than 500 m, and may still be correlated if the distance between them is under 5000 m. The fusion process outputs the list of interpolated reported positions, the list of SAR detections that are correlated to interpolated reported positions, the list of uncorrelated SAR detections, and the list of uncorrelated interpolated reported positions.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
However, this paper presents, for the first time, a complete study, which contains the antenna electrical parameters and the colorimetric parameters of the OLED before and after antenna integration. The remainder of this article is organized as follows: Section 2 describes the material characteristics used in the design. To justify the choice of the transparent antenna, Section 3 describes a comparison between this antenna and a non-transparent one. The experimental results are discussed and compared with the simulation results in Section 4. The method for antenna integration into an OLED light source and the effect of this integration on the radio-electrical parameters of the antenna and on the OLED colorimetric parameters are studied in Section 5. Conclusions and perspectives are presented in Section 6.
## 2 Materials and Methods
### Surgical Procedure  
Prior to the surgery, 60 mL of peripheral blood was collected for PRP preparation using the Arthrex Angel System(tm). The arthroscopy was conducted in a standard procedure with the use of anteromedial and anterolateral incisions and 30-degree optic. Arthroscopic debridement of loose bodies and the torn meniscus was performed; chondral defects were visualized and graded according to Outerbridge classification [32]. After identification of chondral defect, the surface of the defect was cleansed from the calcified subchondral bone (Figure 1), the edges of the defect were cut to create even edges, and microfractures were done with the use of angulated awls. The depth of the microfractures was estimated by observing the blood or fat outflow from the microfracture. After the microfractures were created, the joint was drained from all the residing liquid. PRP was injected directly over the cartilage defect using the dry arthroscopy technique (Figure 2). The PRP injected into the lesion was in direct contact with exposed collagen fibers; therefore, endogenous activation of PRP and fibrin network production started at the lesion site [33]. No suction drains were used after the surgical procedures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **PRP Group N = 16** & **Control Group N = 13** & _p-Value_ \\ \hline Age, year (mean \(\pm\) SD) & 63 \(\pm\) 3.8 & 59 \(\pm\) 6.8 & n. s (\(p\) = 0.069) \\ Female gender, n (\%) & 11 (68.7\%) & 8 (61.5\%) & n. s (\(p\) = 0.685) \\ BMI (mean \(\pm\) SD) & 30.6 \(\pm\) 2.85 & 31 \(\pm\) 4.1 & n. s (\(p\) = 0.763) \\ Duration of symptoms, months & 53.2 \(\pm\) 66.9 & 78 \(\pm\) 81.7 & n. s (\(p\) = 0.539) \\ History of trauma, n (\%) & 3 (18.75\%) & 1 (7.7\%) & n. s (\(p\) = 0.391) \\ Involvement of the dominant extremity, n (\%) & 5 (31.25\%) & 6 (46.15\%) & n. s (\(p\) = 0.411) \\ Opposite extremity involvement, n (\%) & 6 (37.5\%) & 10 (77\%) & \(p\) = 0.034 \\ Prior to surgery physiotherapy, n (\%) & 12 (75\%) & 11 (84.6\%) & n. s (\(p\) = 0.525) \\ Medial meniscus lesions, n (\%) & 10 (62.5\%) & 8 (61.5\%) & n. s (\(p\) = 0.958) \\ Lateral meniscus lesions, n (\%) & 5 (31.25\%) & 3 (23\%) & n. s (\(p\) = 0.624) \\ Bilateral meniscus lesions, n (\%) & 3 (18.75\%) & 3 (23\%) & n. s (\(p\) = 0.775) \\ MFC cartilage lesions grade III-IV, n (\%) & 11 (68.75\%) & 6 (46.15\%) & n. s (\(p\) = 0.219) \\ LFC cartilage lesions grade III-IV, n (\%) & 3 (18.75\%) & 1 (7.6\%) & n. s (\(p\) = 0.391) \\ MTC cartilage lesions grade III-IV, n (\%) & 7 (43.75\%) & 9 (69.2\%) & n. s (\(p\) = 0.170) \\ LTC cartilage lesions grade III-IV, n (\%) & 2 (12.5\%) & 0 (0\%) & n. s (\(p\) = 0.187) \\ PFJ cartilage lesions grade III-IV, n (\%) & 8 (50\%) & 2 (15\%) & n. s (\(p\) = 0.051) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical and baseline characteristics.  
Figure 1: Preparation of microfracturing zone.
## 2 Literature Review
This paper illustrates a planar four-port and four-element flexible MIMO antenna resonating at WLAN and sub-6 GHz 5G bands. Having features such as flexibility, a bi/omnidirectional radiation pattern, planar structure, wide bandwidth covering Sub-6 GHz and WLAN bands, high inter-elemental isolation, and suitable MIMO diversity parameters under normal and bending conditions makes the proposed antenna appropriate for smartphone applications. Each radiator has a single feeding port connected to the central common radiating plane. The antenna achieves the required isolation as the four antenna elements are positioned at the diagonally opposite corners of the ground plane, thus achieving the required spatial diversity. SAR analysis and the flexibility of the antenna prove the practical usage of the antenna for slim and foldable smartphone devices.
## 3 Results
### The Real-Time Monitoring Experiment  
The synthesis of silica nanoparticles using a Stober reaction was monitored by the UNPS for 60 min, performing a measurement every minute.  
Figure 7 shows the measured speed of sound and attenuation, and their estimated fit at 30 min after the start of the reaction. In the beginning of the experiment the speed of sound was the same for all frequencies, and the attenuation peaked at 14,400 dB/m for 175 MHz. After 30 min we saw changes in speed of sound and attenuation. These are a consequence of the particles growing in size (blue dots in Figure 8a) and weight concentration (Figure 8b). The changes in the first minute occurred simply too fast to be followed by the UNPS. The rapid change in the first 20 min, however, was nicely captured by the UNPS. This was also true for the more gentle change in the next 40 min.  
Figure 7: (**a**) Measured and estimated fit of the speed of sound and (**b**) attenuation, 30 min after the start of the Stober reaction.  
It is reassuring to see that the particle growth behavior measured by the UNPS, DLS (yellow dots in Figure 8a) and TEM (orange dots in Figure 8a) were to a large extent in agreement: rapid growth in the beginning followed by a more gentle growth. Of course the DLS and TEM were not directly measuring inside the reactor, and needed dilution and respectively drying of samples to measure the diameter. These processes might have influenced the mean diameter.
## 3 Methods
### Estimation of Gross Primary Productivity (GPP) Using Solar-Induced Chlorophyll Fluorescence (SIF)  
GPP is calculated using the Light Use Efficiency (LUE) model, which expresses GPP as the product of the absorbed photosynthetically active radiation (_APAR_) by vegetation and the light use efficiency (\(\epsilon_{p}\)). The relationship can be represented as follows:  
\[\text{GPP}=APAR\epsilon_{P} \tag{3}\]  
SIF is influenced by both _APAR_ and the light use efficiency (\(\epsilon_{p}\)) of vegetation [43]. This relationship can be expressed as:  
\[\text{SIF}=APAR\epsilon_{F}(\lambda)f_{esc}(\lambda), \tag{4}\]  
where \(\epsilon_{F}(\lambda)\) represents the fraction of photosynthetically active radiation absorbed by the plant that is re-emitted as fluorescence at wavelength \(\lambda\), and \(f_{esc}\) (\(\lambda\)) denotes the proportion of chlorophyll fluorescence at wavelength \(\lambda\) that escapes the canopy. By combining Equations (3) and (4), the following expression can be derived:  
\[\text{GPP}=\text{SIF}\frac{\epsilon_{P}}{\epsilon_{F}(\lambda)}\frac{1}{f_{ esc}(\lambda)}, \tag{5}\]  
Furthermore, given that the canopy structure of the vegetation within the satellite's coverage area remains relatively stable over a specific time period, it can be assumed that\(f_{\text{esc}}\) (\(\lambda\)) is constant [43]. Consequently, the relationship between GPP and SIF primarily hinges on the ratio of \(\varepsilon_{P}\) to \(\varepsilon_{F}\), expressed as:  
\[\text{S}=\frac{\varepsilon_{P}}{\varepsilon_{F}(\lambda)f_{esc}(\lambda)}\frac {\text{GPP}}{\text{SIF}}, \tag{6}\]  
In this equation, S represents the fitting coefficient that quantifies the relationship between GPP and SIF. Once S is determined, SIF can be utilized to estimate GPP, as expressed by the following equation:  
\[\text{GPP}=\text{SIF}\times\text{S}, \tag{7}\]
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 5 Results and Discussion  
In this section, the execution performance of conflict detection based on our proposed GPCDA and EPCDA and a single CPU (Host) are evaluated. The filter databases required for the experiment were obtained from Class-Bench [40], which provided 12 parameter files obtained from three types of practical applications, including access control lists, firewalls, and IP chains. For each parameter file, we generated six filter databases with sizes 5K, 10K, 15K, 20K, 30K, and 100K. The performance evaluation indicator was defined as the average time required for each filter to execute conflict detection, counted in microseconds. (_nThread_, _nBlock_) represents the allocation of _nThread_\(\times\)_nBlock_ threads for parallel execution. Algorithms for simulation experiments were implemented in C++, whereas the GPCDA and the EPCDA were implemented with an additional version 7.5 CUDA toolkit for parallel programs. The test environment was set up using an Intel Core i5-4570 3.2 GHz PC with 12 GB memory. The GPU configuration was NVIDIA GeForce 970X (computation ability:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input**: & \\ filter database _filter_[] & \\
**Output**: & \\ conflict results & \\
**1** & _threadID_ = _blockIdx.x_ \(*\) _blockDim.x_ \(+\) _threadIdx.x_; \\
**2** & _threadSize_ = _blockDim.x_ \(*\) _gridDim.x_; \\
**3** & _base_ = _threadSize_ \(*\) 2; \\
**4** & _start_ = _(base_ \(-1\)) \(-\) _threadID_; \\
**5** & \(i\) = _threadID_; & // dispatch direction \(\rightarrow\). \\
**6** & **do** \\
**7** & **for**\(j\gets i+1\) to _filter.size_() \(-1\) **do** \\
**8** & _detection(filter[], filter[\(i\)])_; \\
**9** & **end** \\
**10** & \(i\) = _base_; \\
**11** & **while**\(i<\)_filter.size_(); \\
**12** & \(i\) = _start_; & // dispatch direction \(\leftarrow\). \\
**13** & **do** \\
**14** & **for**\(j\gets i+1\) to _filter.size_() \(-1\) **do** \\
**15** & _detection(filter[], filter[\(i\)])_; \\
**16** & **end** \\
**17** & \(i\) = _base_; \\
**18** & **while**\(\)_start_ \(<\)_filter.size_(); \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of notation.  
5.2) [41], which provided 13 SMs to support parallel computing. Each SM was composed of 128 SPs, 4G device memory, 98 KB shared memory, and a 48 KB L1 cache.