esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Task Offloading  
Due to proximity to the users and the potentially high number of stations for 5G networks, these small cell stations can be used to offload tasks that are too computationally intensive or battery-consuming for most users' mobile devices. DL systems can be trained in mobile edge computing systems to minimize the cost of offloading tasks in vehicular networks [70] and small wireless devices [60] by using both immediate and long-term rewards during the training stage. These ask offloading systems respond to changes in real-time demand for computation and supporting resources at the mobile edge computing nodes. As the scale of task offloading initiatives increases until each edge node is simultaneously receiving and running computational requests, the scheduling objective becomes almost intractable without machine learning techniques. Additional intelligent systems have been designed to simultaneously minimize costs of energy, computation, and delay by exploiting DQNs to schedule AR/VR offloading tasks [71] and then rely on additional DL techniques to reallocate resources at the mobile edge in real time. Beyond cost minimization, task offloading schemes can also be trained to minimize delay or maximize users' quality of experience /service (QoE/QoS) [70].
## 2 Methodology  
In general terms, the methodology applied in this study was based on the framework defined in the FEMA P695 document, which allows for the systematic and objective determination of seismic performance factors for various structural typologies and/or materials used in modern seismic-resistant designs of buildings. Although its original application does not explicitly consider hillside structures, its analytical framework is sufficiently flexible to be employed in such contexts. In this work, the methodology was implemented with an emphasis on assessing how seismic design factors vary with different slope inclinations, thereby exploring their validity and applicability under topographic conditions that are rarely addressed in traditional design codes. The overall FEMA P695 methodology is summarized in Figure 1.  
This study began with the development of a set of structural archetypes that represented the specific structural typology under investigation, aiming to validate seismic design parameters and propose improvements where necessary. If the applicable design code lacks defined seismic parameters, the methodology also serves as a solid foundation for their objective determination based on a comprehensive set of analyses.  
The methodology primarily relies on two types of nonlinear analyses: pushover analysis and incremental dynamic analysis (IDA). Based on the results of these analyses, and within a framework that implicitly accounts for the uncertainties related to both the structural system and the seismic input, a set of parameters is derived. These parameters enable the validation of existing design coefficients and the identification of additional coefficients that support the structural analysis and design process. The overall objective is to prevent structural systems from exceeding the collapse limit state by employing a collapse margin ratio (CMR).  
The main goal of this research was to determine and/or validate the seismic design parameters typically used in linear design procedures prescribed by major international seismic codes, such as the response reduction factor (\(R\)), the displacement amplification factor (\(C_{d}\)), and the overstrength factor (\(\Omega_{0}\)). These coefficients represent, from the perspective of the structural designer, the material properties and the detailing level of the structural system. In particular, this study evaluated how these factors are affected by the presence of sloping terrain, with the aim of providing technical criteria for their adjustment or validation in complex topographic conditions. The following sections present the relevant methodological details.
## Appendix B Details of Numerical Simulations  
Under the passive frequency-shift attack, the parameters obtained by statistics can be shown in the following. The number of the right raw bits can be simulated as  
\[\begin{split} n_{\text{sig}}=&\sum_{k,m\in M} \left[p_{zv,km}\left[\overline{p}_{d}e^{-\frac{P_{zz,km}^{A}}{2}}-\overline{p }_{d}^{2}e^{-\mu_{zz,km}^{A}}\right]\right.\\ &+p_{vz,km}\left[\overline{p}_{d}e^{-\frac{p_{zz,km}^{B}}{2}}- \overline{p}_{d}^{2}e^{-\mu_{zz,km}^{B}}\right]\right]\times 2 Np_{z}^{2}p_{z0}p_{z1}, \end{split} \tag{14}\]  
and the wrong raw bits as  
\[\begin{split} n_{\text{err}}=& 2 Np_{z}^{2}\left[p_{z 1}^{2}\sum_{k,m\in M}p_{zz,km}\left[-\overline{p}_{d}^{2}e^{-(\mu_{zz,km}^{A}+ \underline{p}_{zz,km}^{B})}\right.\right.\\ &+\overline{p}_{d}e^{-\frac{P_{zz,km}^{A}+\underline{p}_{zz,km}^ {B}}{2}}\left.I_{0}(\sqrt{\mu_{zz,km}^{A}\underline{p}_{zz,km}^{B}})\right]+p_ {z0}^{2}p_{d}\overline{p}_{d}\right].\end{split} \tag{15}\]  
The number of the effective events when [PERSON] sends the states with intensity \(\mu_{a}\) and [PERSON] sends vacuum states can be shown as  
\[n_{av}=2N_{av}\sum_{k,m\in M}p_{av,km}(\overline{p}_{d}e^{-\mu_{av,km}^{A}/2}- \overline{p}_{d}^{2}e^{-\mu_{av,km}^{A}}). \tag{16}\]Similarly, we can obtain the parameters \(n_{va}\), \(n_{vb}\), and \(n_{bv}\). The number of the effective events when both [PERSON] and [PERSON] send the vacuum states is  
\[n_{vv}=2N_{vv}p_{d}\overline{p}_{d}. \tag{13}\]  
Above, \(p_{d}\) is the dark count rate and \(\overline{p}_{d}=1-p_{d}\), \(\mu^{A}_{av,km}=\mu_{a}\eta^{A}_{av,km}\), \(\mu^{B}_{ua,km}=\mu_{a}\eta^{B}_{va,km}\), \(\mu^{A}_{bv,km}=\mu_{b}\eta^{A}_{bv,km}\), and \(\mu^{A}_{vb,km}=\mu_{b}\eta^{A}_{vb,km}\). Note that the intensities of state \(|e^{i\theta_{A}}\sqrt{\mu_{a}\eta^{A}_{aa,km}}\rangle\) and \(|e^{i\theta_{B}}\sqrt{\mu_{a}\eta^{B}_{aa,km}}\rangle\) from [PERSON] and [PERSON] in \(X_{a}\) windows may be different, but this does not mean it could not cause right detection. After the interference, the intensity of the left and right detectors will be  
\[\begin{split}\mu^{I}_{aa,km}=&\frac{\mu^{A}_{aa, km}+\mu^{B}_{aa,km}}{2}+\sqrt{\mu^{A}_{aa,km}\mu^{B}_{aa,km}}\text{cos}\delta,\\ \mu^{\prime}_{aa,km}=&\frac{\mu^{A}_{aa,km}+\mu^{B }_{aa,km}}{2}-\sqrt{\mu^{A}_{aa,km}\mu^{B}_{aa,km}}\text{cos}\delta,\end{split} \tag{14}\]  
where \(\delta=\theta_{B}-\theta_{A}\). The number of the error events in \(C_{\Delta^{\pm}}\) can be shown as  
\[n^{R}_{\Delta^{+}}=N_{\Delta^{+}}\sum_{k,m\in W}p_{aa,km}\Big{[}-\overline{p} _{d}^{2}e^{-\mu^{A}_{aa,km}-\mu^{B}_{aa,km}}+\overline{p}_{d}\int_{-\Delta/2}^ {\Delta/2}e_{d}e^{-\mu^{A}_{aa,km}}+\overline{e}_{d}e^{-\mu^{I}_{aa,km}}d \frac{\delta}{\Delta}\Big{]}, \tag{15}\]  
where \(e_{d}\) is the misalignment-error probability and \(\overline{e}_{d}=1-e_{d}\). Similarly, we can obtain the parameter \(n^{L}_{\Delta^{-}}\).
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 2 Materials and Methods
### Training and Evaluation Procedure
#### 2.2.2. Increasing the Learning Difficulty  
The results obtained with the earlier implementation of this framework on NSL-KDD, ISCXIDS2012, CIC-IDS2017 and CSE-CIC-IDS2018, as documented in [11], showed great  
Figure 1: A visual overview of the experiment’s architecture [11].  
classification results. The consistency with which these results occurred in tandem with manual inspection of the intermediate cross-validation results allowed us to conclude that these results are stable and valid. That work already included measures to increase the learning difficulty in an effort to try to invalidate or reinforce the conclusions from a first examination of CICIDS2017 [34].  
Data-reduced models are a central component in this work. That reduction was carried out along two axes. The most straightforward of the two is vertical data reduction. This entails reducing the number of samples to learn from through stratified sampling inside a train-test-validation splitter. The models were trained at different points of training volume ranging from (0.1% sample usage to 50% sample usage). The data were first split into a training and test set, with a further split happening on the training set into actual training and validation. Instead of using a fixed portion to test, the complement of the initial split was always taken (e.g., 5% training, 95% test, training further split into training and validation). The second axis is that of horizontal data reduction (i.e., feature reduction). Instead of applying this to strip out the inconsequential features, the opposite was carried out. A list was compiled of the features on which splits were chosen most often in the trees which classified the entire dataset. These top features of CIC-IDS2017 are shown in Table 2. Some features that would obviously contaminate the classification results were removed from the data prior to any training. For CIC-IDS2017/8, these include _Flow ID_, _Source IP_, _Source Port_ and _Destination IP_. On a total of 79 remaining features, the 20 most discriminative features were removed before training. This procedure happened in blocks of 5, starting with the best 5 features first, then expanding to remove the 5 next best and so on. Previous findings showed the remarkable resilience of most methods to both horizontal and vertical data reduction [11].
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 2 Materials and Methods
### Acquisition of Multispectral Data  
Yield estimation models using multispectral images of high spatial resolution are widely applied in studies for short-cycle crops because they enable visualization of the spatial distribution of the more and less productive areas. The images that comprise the purely spectral models are captured using an unmanned aircraft [19; 20] and satellites with spectral bands sensitive to agricultural variables [17; 18].  
To define the prediction variables of our estimation models, we used high-resolution multispectral images from the Planet satellite because our experiments were in small areas and there was a high availability of images referring to the crop development period.  
Regarding the Planet satellite constellation, images were captured using a CCD camera equipped with a Bayer Mask filter. The sensor functions via photon filtering, and converts the photons into electrons. In this manner, the digital values of each pixel in each band are obtained through electron amplification. The Planet images have a spatial resolution of 3 m and a radiometric resolution that varies between 12 bits for the \"analytic\" images. The spectral resolution of the satellite consists of the following 4 bands: * spectral ranges (average wavelength) of blue (\(\rho\)485), green (\(\rho\)545), red (\(\rho\)630), and near-infrared (\(\rho\)820). Bands are captured with the Planet Scope 0, Planet Scope 1, and Planet Scope 2 optical instruments [21].
## 3 Model Description
### Offset Modeling  
The introduction of an offset angle not only affects the port timing but also causes a significant change in the kinematics of the overall system, while the kinematics of each single crank mechanism remain unaffected. In an OP engine, the variation of the cylinder volume depends on the instantaneous positions of both pistons. As a result, introducing an offset angle alters the volume swept by the pistons and, consequently, the engine's effective displacement. To properly address this phenomenon, it is necessary to explain how this parameter has been modeled.  
In a configuration without offset, both pistons reach the top dead center (TDC) simultaneously. In contrast, with an offset angle, when the exhaust piston reaches TDC, the intake  
Figure 6: Pressure profile.  
piston has not completed its stroke yet. Similarly, when the intake piston reaches TDC, the exhaust piston has already begun its expansion stroke. As the offset value increases, the dead volume also grows. Consequently, in an OP engine with offset, the minimum cylinder volume no longer coincides with the simultaneous TDC of the two involved crank mechanisms as in the case of zero offset. It is reached at an angular distance of \(\alpha/2\) from the TDC of one of the crankshafts, when the pistons are closest to each other. To handle this, a specific convention for the angular reference system was adopted in the model. In this study, the reference has been defined relative to the intake piston, with the crank angle \(\theta=0^{\circ}\) corresponding to the TDC of cylinder 1 of the intake crankshaft, see Figure 5. As the offset varies, the position of the exhaust piston is updated accordingly, relative to the intake piston. For instance, with an offset of \(10^{\circ}\), at a crank angle of \(\theta=0^{\circ}\), the intake piston is at TDC, while the exhaust piston is at the position it would occupy at 10 crank degrees relative to TDC. It is important to note that this convention is adopted solely to define a reference crankshaft for plotting the graphs. However, the pressure profile does not remain constant relative to this crankshaft; instead, it is updated based on the offset angle.  
Another critical aspect influenced by the offset angle is the spark advance, which is typically defined as the angular distance (in crank degrees) before TDC, at which combustion begins. As previously mentioned, with an offset angle, the minimum cylinder volume no longer aligns with TDC. If ignition timing were fixed relative to the TDC of one crankshaft, the spark advance would vary as a function of the offset angle. This variability could lead to significant changes in combustion characteristics, as earlier spark advance tends to produce higher peak pressures and temperatures, which occur at earlier crank angles. To eliminate this dependency, the start of combustion was related to the point of minimum volume rather than TDC. This approach ensures that combustion timing remains independent of the offset angle, allowing for a more consistent and unambiguous comparison between different configurations.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 3 Proposed 2-Stage Pipelined Architecture for SIMECK32/64
### 2-Stage Pipelined Key Expansion (or) Key Schedule Block  
As we described in Section 2, the key schedule block of SIMECK generates a distinct round key (i.e., \(k_{i}\)). Therefore, the master key (\(K\)) is split into four words and each word is needed to be stored in buffers, i.e., \(b_{2}\), \(b_{1}\), \(b_{0}\), \(k_{0}\). The lowest word of \(K\) is loaded into \(k_{0}\) while the highest word is needed to be stored in \(b_{2}\). However, our proposed 2-stage pipelined key expansion block of a SIMECK is illustrated in Figure 3. The pipeline registers are highlighted with blue color. The objective of the key expansion block is to generate an initial key for the initial round and to generate round keys for SIMECK round operations using Equation (3). More insight details are in the text that follows.  
_Initial round key._ A key with a size of 64 bit is split into four smaller segments of lengths \(n\) to create _key_in 0_, _key_in 1_, _key_in 2_, and _key_in 3_, where \(n\) determines the size of smaller 16-bit key lengths. After splitting the key, the smaller segments are needed to load in parallel in the key expansion function. It takes one clock cycle to load smaller 16-bit keys. Moreover, four \(2\times 1\) routing multiplexers (shown with MUX) are incorporated in the key expansion block, as shown in Figure 3. The output of each multiplexer is kept in a buffer, i.e., RA, RB, RC, and RD. The length of each buffer is similar to the size of each segment of the key. The output of each buffer is used for two purposes, i.e., (i) to generate a 64-bit key for the initial round and (ii) 64-bit keys for the next SIMECK rounds. As shown in Figure 3, the _key_out 0_, _key_out 1_, _key_out 2_, and _key_out 3_ signals are coming from the RA, RB, RC, and RD buffers. These signals determine a 64-bit key for the initial round.  
_Keys for other rounds._ For generating round keys, the outputs from RA, RB, RC, and RD buffers are connected as inputs to the circular blocks (i.e., \(circ\_round\) 0, \(circ\_round\) 1, and \(circ\_round\) 2). The \(circ\_round\) 0, \(circ\_round\) 1, and \(circ\_round\) 2 blocks are mandated to generate the round keys. The internal architecture of these circular blocks is highlighted with orange color in Figure 3. Each block contains inputs from two buffers to generate 64 bit of the resultant round key. Moreover, the circular blocks require operations such as the data expansion block to generate the round keys for SIMECK. The terms \(\ll 5\) and \(\ll 1\) in Figure 3 mean that a 5-bit and 1-bit circularly shift towards left. The # of 0's determines the register value with all zeros. The multiplexers in an orange portion of Figure 3 with \(2\times 1\) size select the right signals before or after shifting. Finally, the _key_out0_, _key_out1_, _key_out2_, and _key_out3_ signals contain the round key for the SIMECK block cipher algorithm and these smaller keys are connected to the data block for encryption/decryption operations.
###### Abstract
In this work, we focus on the optimization of parallel Rate-1 node decoding by incorporating flipping combinations related to the order of input paths and make the following contributions:  
* We present a new algorithm for the candidate generation in Rate-1 nodes of SCL decoders based on a partial order of the candidates. With this algorithm, the number of candidates is significantly reduced. We show that the presented algorithm preserves the error-correction performance of conventional SCL decoding. * We introduce an extended threshold-based candidate selection scheme for practical SCL decoder implementations, which further reduces the number of considered decoding paths with negligible error-correction performance degradation. Compared to the state-of-the-art, this scheme can reduce the number of considered candidates and the sorter complexity in terms of the number of comparators by up to 56.3% and 81.7%, respectively. * We present new architectures for Rate-1 nodes, which are based on the presented candidate generation algorithms. Using these architectures in unrolled Fast-SSCL decoder implementations significantly reduces the implementation costs. We present a set of decoder implementations in a 28 nm Fully Depleted Silicon on Insulator (FDSOI) Complementary Metal-Oxide-Semiconductor (CMOS) technology for a Polar code with \(N=128\) to compare the implementation costs in terms of area efficiency and energy efficiency with state-of-the-art decoders. Improvements of up to 158.8% and 62.5% in the aforementioned metrics are observed, respectively. The remainder of this paper is structured in four sections. We describe the background of Polar code decoding in Section 2. Within four steps, we develop an algorithm to generate the required candidate paths in the parallel decoding of Rate-1 nodes in Section 3. An additional threshold is introduced to further reduce the number of candidates. In Section 4, we analyze the impact of the candidate reduction on the error-correction performance and present estimations on the complexity reduction, together with a comparison of hardware implementation results of decoders with different list sizes. Section 5 concludes this paper.
## 3 Proposed Solution
### Forward Error Correction
It is calculated as \(\text{PSNR}=10\times\log_{10}\frac{(2^{n}-1)^{2}}{\text{MSE}}\), where \((2^{n}-1)\) is the maximum pixel value of the image (e.g., 255 for 8-bit representation) and MSE is the Mean Squared Error value (described in Subsection 4.1.3). A higher PSNR value (in decibels (dB)) indicates a better quality video. Generally, a PSNR that is between 20 and 25 dB is considered to be acceptable for wireless transmission quality loss [66]. The PSNR values given in Table 1 show an increase in PNSR values in all Y, U, and V color components in the recovered videos when decoded with the proposed addition of FEC compared to the PSNR of the decoded videos without error correction, which suffer from noise. Moreover, results were evaluated for five different QPs (8, 12, 24, 36, and 48) to further determine the FEC's effect. The PSNR of the decrypted video sequences at different QPs, with and without FEC, is summarized in Table 2. The results show that at a QP value of 8, the visual quality in terms of the chrominance components is better. An apparent increase in Y-PSNR (Y indicates the luminance component) is observed at QP 24. This shows that the overall increase in visual quality is best at QP 24 (Figure 6f), as the human visual system is more sensitive to luminance than chrominance (recorded in the U and V components). #### 4.1.2 Sism

SSIM calculates the similarity of the two images using structural distortions to evaluate the perceptual distortion based on the difference in the luminance values as given in Equation (16). It ranges from 0 to 1. It can be observed in Figure 7 and Table 2 that the SSIM plots of decrypted videos with FEC are better than the respective videos decrypted and decoded without FEC at their corresponding QPs, and it is lower for the QP value of 48,

Figure 6: Effect of FEC at QP 12, 24, 36, and 48 on the Four People (HD) video. even after applying FEC. Table 2 depicts that the SSIM values are not always consistent between two videos of similar resolution. \[\text{SSIM}=\frac{\left(2\mu_{x}\mu_{y}+C1\right)\left(2\sigma_{xy}+C2\right)}{ \left(\mu_{x}^{2}+\mu_{y}^{2}+\text{C1}\right)\left(\sigma_{x}+\sigma_{y}+ \text{C2}\right)} \tag{16}\]

where \(\mu\) (mu) represents the mean luminance contrast, whereas \(\sigma\) denotes the standard deviation for variance contrast of two \(x\) and \(y\) frames being compared. The C1 and C2 are the constant to ensure function stability when the denominator becomes zero.