esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reliability Calculation of a Single Slip Pathway
### Reliability Calculation of Elements
#### 3.2.1 Equivalent Linear Limit State Function of Elements  
The element limit state function \(Z=g(X_{1},\,X_{2},\,\ldots,\,X_{n})\) is expanded to the first order using the Taylor series at the design verification point \(x_{i}\)*. Since \(g(x_{1}\)*, \(x_{2}\)*, \(\ldots,\,x_{n}\)*) = 0, we have  
\[Z\approx\sum_{i=1}^{n}\frac{\partial g}{\partial X_{i}}\left|{}_{X_{i}=x_{i}^ {*}}\left(X_{i}-x_{i}^{*}\right)\right. \tag{4}\]  
By replacing the random variables \(X_{i}\) in the above equation with standard normal distributed random variables \(u_{i}\), we have  
\[X_{i}=u_{i}\sigma_{X_{i}}+\mu_{X_{i}} \tag{5}\]  
where \(\sigma_{Xi}\) and \(\mu_{Xi}\) are the standard deviation and mean of the random variable after equivalent normalization at the design verification point, respectively.  
Substituting Equation (5) into Equation (4) gives us  
\[Z\approx\sum_{i=1}^{n}\frac{\partial g}{\partial X_{i}}\left|{}_{X_{i}=x_{i}^ {*}}\left(u_{i}\sigma_{X_{i}}+\mu_{X_{i}}-x_{i}^{*}\right)\right. \tag{6}\]  
According to the definition of the reliability index, we have  
\[\beta=\frac{\mu_{Z}}{\sigma_{Z}}=\frac{\sum\limits_{i=1}^{n}\frac{\partial g}{ \partial X_{i}}\left|{}_{X_{i}=x_{i}^{*}}\left(\mu_{X_{i}}-x_{i}^{*}\right) \right.}{\sqrt{\sum\limits_{i=1}^{n}\left(\sigma_{X_{i}}\frac{\partial g}{ \partial X_{i}}\left|{}_{X_{i}=x_{i}^{*}}\right.\right)^{2}}} \tag{7}\]  
From the above equation, we have  
\[\sum\limits_{i=1}^{n}\frac{\partial g}{\partial X_{i}}\left|{}_{X_{i}=x_{i}^{ *}}\left(\mu_{X_{i}}-x_{i}^{*}\right)=\beta\sqrt{\sum\limits_{i=1}^{n}\left( \sigma_{X_{i}}\frac{\partial g}{\partial X_{i}}\left|{}_{X_{i}=x_{i}^{*}} \right.\right)^{2}}\right. \tag{8}\]Substituting Equation (8) into Equation (6), we obtain  
\[Z\approx\sqrt{\sum\limits_{i=1}^{n}\left(\sigma_{X_{i}}\frac{\partial g}{\partial X _{i}}\mid_{X_{i}=x_{i}^{*}}\right)^{2}}\left(\sum\limits_{i=1}^{n}\alpha_{i}u_ {i}+\beta\right) \tag{9}\]  
where  
\[\alpha_{i}=\frac{\sigma_{X_{i}}\frac{\partial g}{\partial X_{i}}\mid_{X_{i}=x_ {i}^{*}}}{\sqrt{\sum\limits_{i=1}^{n}\left(\sigma_{X_{i}}\frac{\partial g}{ \partial X_{i}}\mid_{X_{i}=x_{i}^{*}}\right)^{2}}} \tag{10}\]  
Because of the following:  
\[P\left(Z=\sqrt{\sum\limits_{i=1}^{n}\left(\sigma_{X_{i}}\frac{\partial g}{ \partial X_{i}}\mid_{X_{i}=x_{i}^{*}}\right)^{2}}\left(\sum\limits_{i=1}^{n} \alpha_{i}u_{i}+\beta\right)\leq 0\right)=P\left(f=\sum\limits_{i=1}^{n} \alpha_{i}u_{i}+\beta\leq 0\right) \tag{11}\]  
Thus, we obtain  
\[f=\sum\limits_{i=1}^{n}\alpha_{i}u_{i}+\beta \tag{12}\]  
where \(f\) is the equivalent linear limit state function of the element.
# The Impact of the Stringent Response on TRAF GTPases and Prokaryotic Ribosome Assembly
## 5 (p)ppGpp-Binding RA-GTPases Involved in 50S Assembly
### HflX
During conditions of relatively low nutritional availability, bacteria form 100S ribosomal complexes consisting of two 70S particles stabilised by HPF (Hibernation Promoting Factor) in Firmicutes, and HPF in conjunction with RMF (Ribosome Modulation Factor) in \(\gamma\)-Proteobacteria [115]. The former arranges in a side-to-side configuration, whereas the latter arranges in a head-to head configuration. In Firmicutes, 100S complexes are produced constitutively throughout exponential and stationary phase, whereas Proteobacteria and Cyanobacteria only produce the 100S complexes during stationary phase and, in the latter case, darkness [116; 117]. HPF occludes the PTC of the 50S ribosomal subunits, rendering the 100S translationally inactive. While IF3, EF-G and RRF have been implicated in 100S dissociation _in vivo_, the predominant splitting factor is yet to be identified. It has, however, been shown that HfIX can split the 100S ribosome into the 70S constituents in a GTPase-dependent manner, as HfIX-GTP showed strong splitting activity in stark contrast to HfIX-GMPPNP, which was incapable of splitting the 100S [115]. It is unlikely that HfIX is the unidentified primary 100S splitting factor, as it is mainly transcribed during heat shock conditions and has a much lower level of transcription during exponential growth [108].
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 4 Models
### 3D MultiRes U-Net Model  
3D MultiRes U-Net is based on the original U-Net and 3D U-Net version. However, the architecture is more complex. The model consists of nine resolution blocks and ten resolution paths.  
The resolution path consists of two 3D convolutions with eight filters each. The first is of size \(1\times 1\times 1\), the second has a size of \(3\times 3\times 3\) and is followed by the ReLU activation. Then, the result of both operations is added and sigmoid activation followed by batch normalization is performed. The resolution block consists of three connected 3D convolutions, each of size \(3\times 3\times 3\), followed by the ReLU and dropout with a rate of 20% to reduce overfitting. The first convolution has 32 filters, and the next two have 16. Then, the result of those three operations is concatenated and, after batch normalization, is added to the 3D convolution of \(1\times 1\times 1\) with 64 filters, which are performed on the original input of the resolution block. Finally, sigmoid activation followed by batch normalization is performed. The contracting path consists of four resolution blocks, each followed by max-pooling of size \(2\times 2\times 1\) and resolution paths. The expansion also consists of four resolution blocks, each followed by upsampling of size \(2\times 2\times 1\). The outputted feature maps by upsampling operation are concatenated with the corresponding results of the resolution path from the contracting one. Finally, the last resolution block is applied, followed by a convolution 3D of size \(1\times 1\times 1\). A sigmoid activation is used to produce final probability maps (zero padding).  
Similar to previous architectures, 3D MultiRes U-Net model as described above was also used in the full 3D and slab-based setup but needed to be adapted to the box-based setup. Particularly, the number of filters in a resolution block was reduced to a quarter from (32; 16; 16; 64) to (8; 4; 4; 16).
## 2 Literature Study
### Calculation Parameters
#### 2.1.2 Influence Length  
The second important calculation parameter is the length of the considered segments [24]. The influence length determines how many measurement points are included in the calculation of the TQI of one segment. Typical values for standard gauge railway lines are 100 m or 200 m [10]. High speed lines may be evaluated with longer segments while narrow gauge lines possibly benefit from shorter influence lengths because of frequently changing track parameters (radii, superstructure type, etc.) [12].  
Greater influence lengths blur short-track irregularities and show a broader picture of track quality. Shorter influence lengths highlight isolated defects and follow the original signal more closely [24]. Figure 2 visualizes the moving standard deviation of the same signal as before (longitudinal level of the test section, left rail), calculated once with a 50-m and once with a 200-m influence length.  
Constant calculation parameters are the premise for calculating the track quality indices in a time sequence-time series of individual TQIs for maintenance predictions, time series of a holistic TQI for  
Figure 1: Comparison of discrete and moving calculation of track quality indices at the example of the standard deviation.  
Figure 2: Moving calculation of track quality indices with different influence lengths at the example of standard deviations.  
network evaluations. Only if the computation type and the influence length are always identical and the positioning of the input data is exact [24], can correct conclusions be drawn.
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 1 Introduction  
Radio Frequency IDentification (RFID) has been widely used in a variety of applications, such as supply chain management [1, 2], warehouse inventory [3, 4], objects monitoring and tracking [5, 6]. RFID-based access control is one of these applications, which aims to help manage pedestrian access and block unauthorized staff at any entrance. The existing RFID-based access control systems consist of a high-frequency (HF) RFID reader and a blocking body, e.g., flap barriers, which require visitors to swipe their identity cards individually and wait for the opening of the blocking body, resulting in low-frequency pedestrian access and even congestion in places with large passenger flow.  
In this paper, we propose a barrier-free access control system (RF-Access) with UHF RFID technology. Compared with HF RFID, UHF RFID has a longer communication range and a higher reading rate, which makes it possible to remove the process of swiping the identity card. In addition, we remove flap barriers from the system, which provides non-intrusive access control: the visitors just go across the system without any stay at the entrance, and meanwhile RF-Access performs the user authentication, which greatly improves time efficiency and quality of service. To achieve this goal, RF-Access needs to address two key challenges: motion direction detection and illegal intrusion detection. In RF-Access, we firstly propose a dual-antenna system setup together with a time-slot-based model to monitor users' moving directions, which is robust to different environmental factors such as multi-path effects. Afterward, we use a tag array to detect illegal intrusion in case attackers do not carry any RFID tags. Experimental results show that the proposed system has good performance. The main contributions of this paper are three-fold.  
* We propose a novel barrier-free access control system (RF-Access) with UHF RFID technology. The main advantage of RF-Access is that it provides non-intrusive access control by removing flap barriers and operations of swiping the card, which greatly improves time efficiency and quality of service.
* RF-Access addresses two key issues of non-intrusive access control: motion direction detection and illegal intrusion detection, by using a dual-antenna tag-array system setup together with a time-slot-based model to monitor users' moving directions.
* We implement a prototype of RF-Access with commercial RFID devices. Extensive experiments show that our system can detect the moving direction with 99.83% accuracy and detect illegal intrusion with an accuracy of 96.67%.  
The rest of the paper is organized as follows. Section 2 introduces the related work. Section 3 details our access control system RF-Access. Section 4 implements the system and evaluates its performance. Finally, Section 5 concludes this paper.
## 5 Multiple Scatterer DRFM
### Multiple Scatterer DRFM Results  
In the example discussed here, the system modeled using the multi-scatterer DRFM contained a set of four sources, each with a progressive amplitude, time delay, and Doppler shift. The multi-scatterer waveform results are plotted in Figure 20, where in amplitude alone, the two waveforms are easily distinguished. The individual contributions of the scatterers can be seen in Figure 21.  
Figure 19: Simulink implementation of a multi-scatterer DRFM, with four signals synthesized.  
Figure 20: Example of a multi-scatterer DRFM signal generated as the sum of four scatterers compared to the received radar signal.  
The complexity of implementing a DRFM jammer using this architecture is not actually in the electronics but in gathering the extensive data to build the database for the look-up table. Specifically, it would require a range or anechoic chamber where the jammer platform could be measured across all variables: azimuth, elevation, frequency, etc.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 3 Structure of the ARIBC
### Services Offered by the ARIBC
#### 3.6.2 Confidentiality Service  
If the \(ID\) of a user is known (because the user her/himself chose to publish it), the user's public key is also known (or can be computed, by using the user ID and the public parameters of the authority). Thus, anyone can send an encrypted message to that user. The recipient can decrypt the message using the corresponding private key. As the use of asymmetric encryption is not efficient for long messages, the most common use of the confidentiality service is for sharing a symmetric key to be used for exchanging subsequent confidential messages. The confidentiality service is implemented by means of the Encryption operation and the Decryption operation. Hereafter, we present the adaptation to our proposal of the SK-IBE scheme algorithms as presented in section 3 of [44].  
The input to the Encryption operation consists of:  
1. The \(Plaintext\in\{0,1\}^{length}\), (where \(length=\) length of the data in bits) to be encrypted;
2. The ARIBC Public Parameters, ARIBC-PP \(=(G_{1},G_{2},G_{7},e,P_{G_{1}},P_{G_{2}},KPAK,e(P_{G_{1}},P_{G_{2}}),\phi,H_{ 1},H_{3},H_{4},H_{5})\)
3. A random integer \(\sigma\in\{0,1\}^{length}\), generated with the random number generator \(R\); and
4. The receiver's \(ID_{receiver}\in\{0,1\}^{*}\). The computations performed in the Encryption operation are:  
1. \(r=H_{4}(\sigma,Plaintext)\); where \(H_{4}\): \(\{0,1\}^{length}\times\{0,1\}^{length}\rightarrow\mathbb{Z}_{p}^{*}\)
2. \(g^{r}=e(P_{G_{1}},P_{G_{2}})^{r}\);
3. \(Q=(H_{1}(ID_{recipient})P_{G_{1}}+KPAK)\); where \(H_{1}\): \(\{0,1\}^{*}\rightarrow\mathbb{Z}_{p}^{*}\),
4. \(U=r(Q)\);
5. \(V=\sigma\oplus H_{3}(g^{r})\); where \(H_{3}\): \(G_{7}\rightarrow\{0,1\}^{length}\)
6. \(W=Plaintext\in H_{5}(\sigma)\); where \(H_{5}\): \(\{0,1\}^{length}\rightarrow\{0,1\}^{length}\)
7. \(c=(rQ,\sigma\oplus H_{3}(g^{r}),Plaintext\in H_{5}(\sigma))=(U,V,W)\in(G_{1} \times\{0,1\}^{length}\times\{0,1\}^{length})\). The output of the Encryption operation is the triplet (Ciphertext) \[c=(U,V,W)\in(G_{1}\times\{0,1\}^{length}\times\{0,1\}^{length})\] The input to the Decryption operation consists of:  
1. The ARIBC Public Parameters ARIBC-PP \(=(G_{1},G_{2},G_{7},e,P_{G_{1}},P_{G_{2}}KPAK,e(P_{G_{1}},P_{G_{2}}),\phi,H_{ 1},H_{3},H_{4},H_{5})\);
2. The Private Key (\(SSK\)) of the recipient \(SSK_{recipient}\in G_{2}\); and
3. The Ciphertext \((c)=(U,V,W)\). The computations performed in the Decryption operation are:  
1. \(g^{\prime}=e(U,SSK_{recipient})\);
2. \(\sigma^{\prime}=V\oplus H_{3}(g^{\prime})\);
3. \(m^{\prime}=W\oplus H_{5}(\sigma^{\prime})\);
4. \(r^{\prime}=H_{4}(\sigma^{\prime},m^{\prime})\);  
The output of the Decryption operation is \(m^{\prime}=Plaintext\), unless \(U\
eq r^{\prime}(H_{1}(ID_{recipient})P_{G_{1}}+KPAK)\), in which case the retrieved data (\(m^{\prime}\)) should be discarded, as they correspond to an invalid \(ID_{receiver}\). Both the encryption and the decryption processes described above are shown in graphical form in Figure 4.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Uncertainty of OSA Measurements  
The measurements provided by the OSA come with a certain degree of uncertainty, which sets the lower limit for model accuracy. Striving for a model that achieves a maximum error lower than that of the measurements is impractical, as it would mean accurately estimating an inherently inaccurate value. Instead, it is wise to estimate this uncertainty and aim for similar accuracy levels with the models.  
One contribution to the uncertainty is the sensitivity of the total input and output power measurements. OSAs represent those values with one decimal place, so the absolute uncertainty due to quantization of \(P_{IN}\) and \(P_{OUT}\) is:  
\[\delta P=\frac{0.1\,\mathrm{dBm}}{2}=0.05\,\mathrm{dBm} \tag{21}\]  
This variation in both \(P_{IN}\) and \(P_{OUT}\) results in a noise figure error of up to \(0.1\,\mathrm{dB}\), representing the absolute minimum uncertainty, assuming that all other quantities are obtained with ideal infinite accuracy. To establish a benchmark for the experimental error in measurements, four distinct datasets from identical devices are analyzed. The commercial devices under test are _EDFA-35_ from Cisco in the low-gain range [30]. Measurements are compared for identical values of total input power, gain, tilt, and channel index, and the maximum difference among them is evaluated. Figure 13 shows the cumulative distribution of measurement errors. In this graph, the \(y\)-axis represents the percentage of dataset measurements with errors within the value indicated on the \(x\)-axis. A curve that shifts more to the left indicates better performance, as it means a larger portion of the dataset has errors lower than those shown on the \(x\)-axis.  
From this figure, it is clear that 90% of the measurements for Cisco EDFA-35 devices working in the low-gain range condition have errors within \(0.149\,\mathrm{dBm}\), while an absolute error of \(0.247\,\mathrm{dBm}\) ensures that 99% of the measurement errors are safely included. These two error values will be referred to as the 90%-threshold and the 99%-threshold in later sections. Therefore, it is reasonable to select \(0.25\,\mathrm{dBm}\) as a plausible lower limit for model accuracy. However, this value may vary for different devices due to its experimental nature. This worst-case threshold for 99% of the measurements highlights the superior accuracy of our proposed polynomial approach with respect to other models in the literature. Specifically, we refer to [31], where a Gradient Boosting Regression (GBR) approach was used for THz applications, achieving a Mean Absolute Error (MAE) of \(0.3428\) in fitting the predicted noise figure values.
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
## 4 Radially Periodic Leaky-Wave Antennas  
This section deals with the analysis of radially periodic LWAs. Since the latter are constituted by two-dimensional, leaky-wave, radiating devices, the design rules in terms of directivity and fractional bandwidth as a function of the leaky phase and attenuation constants are the same as those presented in Section 3.1. The main difference lies in the evaluation method of \(\beta\) and \(\alpha\). In FPCAs, it is indeed possible to consider the PRS in the homogenization regime and, thus, to exploit the transverse resonance technique applied to the TEN of the device. In radially periodic LWAs, the periodicity of the annular metal strip grating printed on top of the GDS is instead comparable to the operating wavelength \(\lambda\) and the radiation is usually related to the first-order, fast, Floquet-Bloch mode in propagation [43, 87]. Here, we provide a simple technique for the Bloch analysis in Section 4.1 and validate it in Section 4.2 through a full-wave simulation of the entire structure.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 3 Orchestration in Intermittently Available Platforms
### Limited Lifetime of NFVI Nodes  
SUAVs are autonomous devices that require some power supply source to be operative. As these units spend most of their time flying, they usually incorporate an autonomous rechargeable battery to act as its power source. Naturally, performing different actions (e.g., routing, transmitting/receiving wireless traffic, executing software functions, etc.) will eventually deplete their battery lifetime: even when they are not executing any specific task, the mere act of staying in the air will reduce their charge until their eventual depletion. Hence, all VNFs running inside its payload must be migrated into another operative unit to avoid, or mitigate, operational cuts on the services being provided. Unfortunately, this action is not as trivial as it may seem at first: not only exists several limitations regarding the computing, storage and networking resources involved in the migration, but also its transition should be anticipated with enough time to ensure a seamless migration procedure, only possible when a VIM is able to monitor the battery lifetime of the aircraft. This VIM could use algorithms to indicate when the aircraft should leave/enter a recharging station such as the one seen in work [23] or [24], allowing it to also predict when the services would need migration before replacing the unit. However, at the time this paper is being written, most VIM implementations do not consider battery lifetime as a limited resource, making VNF migration in these environments suboptimal.  
Other measures to preserve battery lifetime could be perching some SUAVs on land, for instance in specific-purpose ground structures, or using different batteries for flight management and computational resources (making sure that the battery on the payload exceeds the flight time of the other one).
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 2 Burst Error, OFDM, and Interleaving
### Fft-Ofdm  
As can be seen in Figure 1, depicting the OFDM transmitter, the data generator produces \(\{d_{k}\}\) in a random binary form. Firstly, constellation mapping is implemented. The quadrature phase-shift keying (QPSK) modulator is used for this work to map the data to appropriate QPSK symbols \(X_{m}\). The serial-to-parallel converter then transforms the high-speed information symbols into \(N\) sub-channel parallel information. The parallel data symbols are fed into IFFT to generate the multicarrier OFDM signal as follows [18]:  
\[x_{k}=\frac{1}{\sqrt{N}}\sum_{m=0}^{N-1}x_{m}e^{j2\pi km\pi/N},\ 0\leq m\leq N-1 \tag{1}\]  
where \(\{x_{k}\ |\ 0\leq k\leq N-1\}\) is a sequence in the discrete time domain, and \(\{X_{m}\ |\ 0\leq m\leq N-1\}\) are complex numbers in the discrete frequency domain.  
To prevent the ISI that happens in multipath channels, guard intervals are placed between frames. These intervals are either added zeros or a cyclic prefix (CP). The most commonly used strategy is the addition of a CP [19; 20]. The CP is a copy of the IFFT last \(N\) samples at the start of the OFDM frame. The multipath versions of a symbol do not interfere with the next symbol by adding the CP to each OFDM symbol with a length longer than the channel length. Consequently, the impact of ISI is totally abolished, and at the same time, the channel appears to be circular, allowing a circular convolution to be implemented between the OFDM signal and the channel impulse response. This method is reversed at the receiver side to get the decoded information. The CP is detached, and the frequency domain equalizer (FDE) is used. The FDE relies on a CP guard interval between successive data blocks. The FDE can invert the channel completely. The benefit of FDE is that the complexity is comparatively low [21; 22].  
Equalizer coefficients are calculated by Minimum Mean Square Error (MMSE) or Zero Force (ZF) technique [23]. The MMSE equalizer requires the Signal-to-Noise-Ratio (SNR) estimation, which allows a trade-off between channel inversion and noise enhancement [24]. The FFT is applied at the receiver to reconstruct the signal as follows [18]:  
\[X_{m}=\sum_{k=0}^{N-1}x_{k}e^{-j2\pi km/N},\ 0\leq k\leq N-1 \tag{2}\]