esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Farm-to-Retailer Environmental Profile of Dry Lentils
This uncertainty primarily stems from the significant variability in yields of harvested and cleaned ML grains. The cleaning waste for ML grains can range from 40% to 60% of the lentil seeds delivered to \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**IC\({}_{\text{i}}\)** & **ML** & & **OL** & **Unit** \\ \hline CC & 3.22 & \(\pm\) & 0.48 & 2.14 & \(\pm\) & 0.18 & kg CO\({}_{\text{2e}}\) \\ OD & \(6.36\times 10^{-8}\) & \(\pm\) & \(1.18\times 10^{-8}\) & \(3.11\times 10^{-8}\) & \(\pm\) & \(1.97\times 10^{-9}\) & kg CFC11\({}_{\text{e}}\) \\ IR & \(8.63\times 10^{-2}\) & \(\pm\) & \(1.42\times 10^{-2}\) & \(7.51\times 10^{-2}\) & \(\pm\) & \(2.39\times 10^{-3}\) & kBq \({}^{235}\)U\({}_{\text{e}}\) \\ PhOF & \(1.52\times 10^{-2}\) & \(\pm\) & \(2.29\times 10^{-3}\) & \(1.28\times 10^{-2}\) & \(\pm\) & \(1.16\times 10^{-3}\) & kg NMVO\({}_{\text{e}}\) \\ PM & \(1.82\times 10^{-7}\) & \(\pm\) & \(3.31\times 10^{-8}\) & \(1.71\times 10^{-7}\) & \(\pm\) & \(1.62\times 10^{-8}\) & disease inc. \\ A & \(1.79\times 10^{-2}\) & \(\pm\) & \(2.30\times 10^{-3}\) & \(9.71\times 10^{-3}\) & \(\pm\) & \(8.53\times 10^{-4}\) & mol H\({}^{+}\) \\ FWE & \(5.03\times 10^{-4}\) & \(\pm\) & \(4.60\times 10^{-5}\) & \(1.68\times 10^{-4}\) & \(\pm\) & \(6.99\times 10^{-6}\) & kg P\({}_{\text{e}}\) \\ ME & \(4.46\times 10^{-3}\) & \(\pm\) & \(7.04\times 10^{-4}\) & \(3.78\times 10^{-3}\) & \(\pm\) & \(3.84\times 10^{-4}\) & kg N\({}_{\text{e}}\) \\ TE & \(5.14\times 10^{-2}\) & \(\pm\) & \(8.42\times 10^{-3}\) & \(4.16\times 10^{-2}\) & \(\pm\) & \(4.24\times 10^{-3}\) & mol N\({}_{\text{e}}\) \\ ETFW & \(128.00\) & \(\pm\) & \(2.95\) & \(7.16\) & \(0\) & \(9.96\) & CTU\({}_{\text{e}}\) \\ C-HT & \(1.86\times 10^{-9}\) & \(\pm\) & \(2.23\times 10^{-10}\) & \(1.47\times 10^{-9}\) & \(\pm\) & \(8.14\times 10^{-11}\) & CTU\({}_{\text{h}}\) \\ NC-HT & \(3.64\times 10^{-8}\) & \(\pm\) & \(5.89\times 10^{-9}\) & \(2.16\times 10^{-8}\) & \(\pm\) & \(1.97\times 10^{-9}\) & CTU\({}_{\text{h}}\) \\ LU & \(2.34\times 10^{-3}\) & \(\pm\) & \(5.53\times 10^{-2}\) & \(1.09\times 10^{-3}\) & \(\pm\) & \(1.40\times 10^{-2}\) & Pt \\ WU & \(1.32\times 10^{-1}\) & \(\pm\) & \(1.61\times 10^{-2}\) & \(1.89\times 10^{-1}\) & \(\pm\) & \(4.49\times 10^{-3}\) & m\({}^{3}\) depri. \\ RUF & \(4.33\) & \(\pm\) & \(1.13\) & & \(5.45\) & \(0.76\) & MJ \\ RUM & \(1.45\times 10^{-5}\) & \(\pm\) & \(2.33\times 10^{-6}\) & \(7.95\times 10^{-6}\) & \(\pm\) & \(4.29\times 10^{-7}\) & kg Sb\({}_{\text{e}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Environmental profile for 1 kg of dried ML and OL grains in a farm-to-retailer perspective using the PEF standard method: mean values and standard deviations across mid-point impact categories (IC\({}_{\text{j}}\)).
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 1 Introduction
Their study showed that a 90% increase in the pile diameter results in about a 70% increase in the scour depth and an 85% decrease in the scour hole volume. Many other researchers [13; 14; 15; 16; 17; 18; 19; 20] have worked on the size and spacing of the piles. To find an efficient pile that can reduce the scour due to the erosive action of flowing water, [PERSON] [21] employed perforated piles having holes with different orientations. The author has reported an 80% reduction in scouring when the hole orientation is 45\({}^{\circ}\) and a 47% reduction in scouring when the hole is oriented at 90\({}^{\circ}\). However, the presence of holes in piles can lead to loss of strength and possible structural failure. Apart from this, soil improvement measures can be applied around bridge piers to prevent the possibility of excessive souring. For example, corrugated aprons [22] can be used to counter the formation of deep scour holes. All these experimental investigations on piles identify variables of interest that set up the basis for empirical and numerical approaches for estimating the depth of scour holes [23; 24]. [PERSON] et al. [25] offered an empirical method for predicting the scour depth based on the pile cap. However, it is essential to use caution in using the empirical relations of piers and pile caps for piles, because they can lead to ambiguous results. A review of the literature shows that various shapes of bridge piers and pile caps are studied extensively with a sense of purpose to minimize local scour [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. However, the effect of pile shapes in a group has not been studied in great detail. [PERSON] et al. [39] have carried out experimental investigations concerning scour around a complex pier with a lenticular or elliptical pile cap. It is shown that the elliptical pile cap can effectively reduce the scour. (However, field observation has revealed that most of the time the filing of the earth is up to piles and pile cap shape does not play any role). It is clear from the review of the above literature that there are few studies examining scour around bridge piles. Thus, it is the purpose to provide a bridge to this subject and to investigate the effect of different shapes of piles on the degree of scouring. The study extends the pile geometry to three different shapes of pile groups and examines these piles experimentally under constant conditions. It is found that the scouring around the lenticular piles is least as compared to the square and circular piles. Moreover, the frontal piles experience the maximum scouring as compared to the end piles. Finally, the experimental scouring values are further compared with the commonly used pier empirical models, which suggests amending these empirical formulas for predicting the scour-hole depth.
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.10. Interference Mitigation Factor  
In [34], the interference mitigation factor (IMF) is a quantitative measure of adaptive mechanism for mitigating interference among multiple WBASNs. The SINR value in a WBASN is used to maintain link quality and adapt different schemes such as modulation, data rate, and duty cycle. In this mechanism, the IMF determines the reduced transmit power level in the adaptive transmission status. Then, each WBASN selects its modulation scheme, data rate, and duty cycle value based on the IMF.  
For adaptive modulation, the set of modulation schemes is BPSK, QPSK, and 8 PSK. If the link quality is good or the SINR is higher than the threshold SINR, WBASN chooses 8 PSK modulation for transmission. Otherwise, BPSK modulation is chosen, indicating that the link quality is not good. The data rate and the duty cycle can also be adaptively set, and they have the maximum values at normal operating mode. In addition, the IMF is calculated on a case-by-case basis: (1) adaptive modulation, in which the IMF is the function of the SINR and channel condition; (2) adaptive data rate, in which the IMF is the proportional function of data rate and transmitted power; and (3) adaptive duty cycle, in which the IMF is the function of the duty cycle in normal state and current state.
# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 4 Security Analysis
### A MiTM Adversary
**Resistance to MiTM attacks:** Once the identification of the validator is unknown to the legitimate candidate, avoiding a MiTM attack is difficult. Impersonation is feasible without a way to verify the intended verifier. At this point, we want to underline the technical skill necessary to carry out such a sophisticated assault. The opponent intercepts the request from a legitimate applicant who attempts to enter a platoon advantageously without knowing that a verifier represents the platoon's identification. There is no way to avoid this attack using the CAVVPM protocol, but there are a few viable solutions [26; 27]. Using highly-focused antennas on both the applicant and the verifier might be a solution (e.g., at mmWave frequencies) by directing the candidate's transmitter forward when the verifier's antenna is behind; an attacker has a short window of time to carry out the MiTM. Using a single-receiver transmission localization device to locate the transmitter's position is another option worth considering (e.g., [27]). It is accurate but used in static situations rather than high-speed mobility environments. The Doppler move between the applicant and the validator might be used to identify messages supplied by the attacker as an alternate approach. This assault can only be used in spontaneous platooning settings, leaving these avenues open for further research [28].
# A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm
## 4 The Proposed Multi-Objective Five-Elements Cycle Optimization Algorithm
### Update of Individuals
We randomly select an individual as \(X\_best(k)\) among the individuals with \(Rank\) value of 1 after the fast non-dominated sorting and crowded distance calculation method in current population. In Equation (13) \(XC_{ij}(k+1)\) represent the updated individual. It can be seen from Equations (12) and (13) that \(X_{ij}(k)\) is updated around the best element of the \(j\)-th cycle \(X\_local_{j}(k)\) under the probability \(P_{s}\). Otherwise, it is updated around the current best optimal solution \(X\_best(k)\). From Equation (12), we can see that large \(P_{s}\) is inclined to exploitation, while small \(P_{s}\) is inclined to exploration. In order to enhance the convergence and flexibility of the proposed algorithm, the value of the probability \(P_{s}\) is not fixed but changes dynamically according to the current iteration number. The variation pattern of \(P_{s}\) with the iteration number is shown in Figure 4. In the initial stage of population evolution, since it is expected that the individuals can converge to the optimal solutions quickly, we assign a small value to \(P_{s}\) so that individuals tend to find updated solutions in the global scope. As the number of iteration increases, we hope to find optimal solutions which are more diverse and distributed, so the value of \(P_{s}\) gradually increases, making individuals tend to perform local search. In this way, the search range of the solutions is dynamically selected according to the phase of evolution, so that the individuals can quickly and effectively approach the best optimal solution set. We define the calculation of \(P_{s}\) as follows:  
\[P_{s}=1-(P_{s_{\min}}+(P_{s_{\max}}-P_{s_{\min}})\times exp(-20\times(k/T)^{6}) \tag{14}\]  
where \(P_{s_{\min}}\) and \(P_{s_{\max}}\) represent the values of minimum and maximum probability respectively; \(k\) is the current number of iterations and \(T\) is the maximum iteration.
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 2 Related Work
### Scalable Video Coding (SVC)  
Scalable Video Coding (SVC) is a video coding method created by the Joint Video Team (JVT) of the ISO/IEC. The SVC method [16] is able to maintain the reconstruction quality associated with the rate of partial bit streams by enabling the transmission and decoding of such partial bit streams, as illustrated in Figure 2. In particular, the SVC method builds up multiple layers into a single video file. For example, assume that a single video file is encoded into three layers of high quality, medium quality, and low quality through the SVC method, denoted by layer 3, layer 2, and layer 1, respectively. In contrast to when the user chooses layer 1 (where only low-quality layers will be transmitted), when the user selects layer 3 all of layers 3, 2, and 1 are sent at once. This allows the user to be provided with all three quality levels of videos. For that reason, layer 3 is an enhancement layer and layer 1 is a base layer. In other words, SVC enables the transmission of video with various resolution and image quality while retaining bit rate, so that the user can be provided One Source Multi-Use (OSMU) [17] service through one encoding. Therefore, as in previous studies, the SVC method has been studied in real-time video streaming [18; 19]. In particular, datasets through SVC methods have been used for Dynamic Adaptive Streaming over HTTP (DASH) technology.
## 2 Materials and Methods
### Hybrid Densenet-Resnet Architecture  
The proposed CNN architecture (Figure 2) is based on the V-Net and, more precisely, on the architecture proposed by [16], which combines a densenet [17] encoder with a resnet [13] decoder. All design decisions were guided by validation results.  
The full architecture is sufficiently described in Figure 2. Therefore, only a few interesting design choices will be discussed here. Firstly, the proposed Dense block includes a residual connection, which empirically helped the CNN converge faster. Secondly, every Dense block contains between 12 and 24 \"standard\" convolutions (kernel of size (3, 3, 3)), as well as several \"bottleneck\" convolutions (kernel of size (1, 1, 1)), for a total of 72 \"standard\" convolutions, which is a huge number compared to similar architectures such as V-net (with only 12 convolutions in its resnet-based encoder) or BOWDA-net [23] (with 28 convolutions in its densenet-based encoder). This makes the encoder better capable of learning more complex representations of the input data. Comparatively, the decoder can have a simpler resnet architecture, since the heavy lifting (which is feature extraction) has already been done by the encoder. Thirdly, channel-wise PReLU was employed as activation function [24], as it provides a slightly better performance at a negligible additional computational cost. A channel-wise PReLU function is similar to a ReLU (Rectified Linear Unit) [25] function, but with a learnable slope \(\alpha\) for the negative inputs (instead of being just zero); \(\alpha\) is shared among all activations in a channel, but is  
Figure 2: Architecture of the CNN. The encoder is composed of four Dense Blocks connected by Downsampling blocks. The decoder uses three Residual Blocks connected by transpose convolutions. Several skip connections transfer information from the encoder to the decoder. Furthermore, to the right of the decoder, intermediate outputs are used to perform Deep Supervision.  
different for every channel. Fourthly, transpose convolutions were used in the decoder, since they were found to provide a better performance when compared to upsampling followed by a convolution.  
Due to the huge memory requirements intrinsic to the densenet architecture, very small batch sizes had to be employed (4 for the MR dataset, and 2 for the US dataset), as well as a technique known as Gradient Checkpointing [26], which allows to reduce the Graphics Processing Unit (GPU) memory requirements at the cost of increased computation times. It works by keeping a fraction of the CNN activations in memory at any given time (instead of all of them), and recomputing the rest when they are needed.
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 3 Results
### Spillover Ratio (SOR)  
The SOR (Equation (3)) was estimated as mean of uniform area to the mean of water or air area (Figure 4). The SOR in water (0.205 \(\pm\) 0.021) was significantly higher than that in air (Figure 4A) (0.119 \(\pm\) 0.018) in the reconstruction methods without Gaussian post-filtering (\(p<\) 0.001). In the iterative methods, the SOR slowly decreased with increasing iteration numbers. The SOR rapidly increased with increasing Gaussian filter size (Figure 4B). The SOR values of the 1, 2, and 3 mm Gaussian filtering compared with nonfiltering  
Figure 3: The uniformity of images was compared by %SD in uniformity area of the NEMA NU-4 mouse image quality phantom. (**A**) The uniformity of 3 DRP and OSEM3D was higher than that of FBP. The uniformity of OSEM2D was lower than that of FBP. In iterative reconstruction methods, image uniformity decreased with increasing number of iterations. (**B**) In the iterative reconstruction methods (OSEM2D and OSEM3D), the image uniformities were compared by post-Gaussian filter effect. The image uniformity increased with the use of a Gaussian filter of 1 or 2 mm. However, the image uniformity decreased with the use of a Gaussian filter of 4 mm.  
Figure 2: The RC values were obtained for five hot rods of the NEMA NU-4 mouse image quality phantom. (**A**) The RC values were overcompensated in iterative reconstruction methods (black lines). (**B**) The RC curve trend in the iterative reconstruction methods with 2 mm Gaussian filtering was similar to that in the FBP reconstruction (dotted lines).  
increased approximately 1.04, 1.19, and 1.74 times in the OSEM2D reconstruction method and about 1.04, 1.21, and 1.80 times in the OSEM3D reconstruction method.  
PSNR was calculated on the hot rod of 5 mm diameter. The higher PSNR was 22.44 dB in OSEM3D_18 and the lowest PSNR was 19.32 dB in OSEM2D_18. The PSNR values of other methods (3 DRP, 3 DRP_Hann, FBP, OSEM2D_14, OSEM2D_16, OSEM3D_14, and OSEM3D_16) were 19.68, 19.78, 20.52, 19.79, 19.49, 19.77, and 21.48 dB, respectively.
# Highlight Removal Emphasizing Detail Restoration
## 3 Method of This Article  
The flowchart of the proposed method is shown in Figure 1, and it mainly consists of the following steps: First, apply the Retinex model to the input image, separating the image into an illumination space and reflection space. Next, process the illumination image based on the sum of the mean and standard deviation of the current image pixels, separate the highlight positions of the current image, and generate the corresponding mask. Then, based on this mask, separate the specular reflection in this area. The advantage of this approach is that it only processes the highlight region separately, avoiding color distortion due to global processing. Given that there are noticeable processing artifacts between the highlight and non-highlight regions after highlight removal, this paper introduces a compensation function to reduce the post-processing discontinuity. Finally, due to the significant loss of details at the original highlight positions after highlight removal, this paper uses an approach of the adaptive Laplacian operator and gradient fusion to enhance the image, resulting in the final output of the image after highlight removal.  
This paper utilizes the illumination component separated based on the Retinex model and employs the mean and standard deviation method to isolate the highlight mask. Building upon the obtained highlight mask, separation of specular reflection is performed. The advantage of this approach lies in improving the traditional method of globally processing highlight images, thus avoiding color bias issues after separation. Additionally, to enhance the detail information after removing highlights from the image, this paper introduces gradient fusion and the Laplacian local enhancement algorithm. The processing logic in Figure 1 can be expressed using the following formulas:  
\[V=I-S(G(R(I)))+L(I) \tag{1}\]  
In the above formula, \(V\) represents the final output image, \(I\) represents the input highlight image, \(S(\cdot)\) represents the specular reflection component extraction function, \(G(\cdot)\) represents the smoothing function used primarily for a smooth transition of the edges after processing the masked region, \(R(\cdot)\) represents the highlight extraction function proposed in this paper based on the Retinex model, and \(L(\cdot)\) represents the gradient information of the image.
## 5 Analysis and Discussion
For a specifically designated area, it is possible to use local GNSS receiver real-time RINEX data to estimate the TEC values in near-real-time latency; thus, producing (every hour) the previous 23- or 47-h TEC values and testing them via a post-mission training set to statistically determine whether an event would occur or not.
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 5 Experimental Results
Although the efficiency is improved slightly, the circuit complexity and cost are increased significantly. Besides, two dual-switch forward converters are parallel connected in the inverter of Reference [21], and each secondary side of the transformer is center tap, resulting in high circuit complexity. The proposed inverter uses only a single active-clamp forward converter and does not need to add a third winding, so its circuit architecture is much simpler than other forward inverters.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 2 Theoretical Background  
Although this study is primarily focused on experimental evaluation within a controlled indoor environment, the inclusion of classical fog attenuation models provides a crucial theoretical framework for understanding wavelength-dependent performance in free-space optical (FSO) systems under realistic atmospheric conditions. These models--rooted in Mie scattering theory--offer insight into how fog, haze, and airborne particulates affect optical signal propagation, while also highlighting the limitations of the visibility-based approximations commonly used in FSO link design.  
While such atmospheric conditions were not simulated in the current setup, a thorough understanding of these attenuation mechanisms is essential for the design of FSO systems that remain robust under environmental degradation. These models serve as a basis for predicting wavelength-dependent behavior and inform the planning of future experiments involving simulated fog, aerosol dispersion, and extended-range testing in both indoor and outdoor environments.  
This theoretical discussion complements the technical focus of our work by situating wavelength selection within a broader atmospheric and photonic context. It also enables the interpretation of our laboratory measurements as baseline performance indicators under clear-air conditions, providing a foundation for future validation of model predictions in dynamic or adverse environments.  
Moreover, the performance of any FSO system depends not only on atmospheric factors but also on component-level parameters such as beam attenuation, photon-to-electron conversion efficiency, laser diode characteristics, and modulation behavior. A comprehensive understanding of these interdependent elements is essential for optimizing visible-light FSO links, particularly in emerging applications such as indoor networking, IoT connectivity, and vehicular communication. The subsections that follow present the necessary theoretical constructs for interpreting the experimental results in Section 4.