esa-sceva/satcom-chunk-collection huggingface dataset:
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Slice Type Allocation and Forecasting  
The factory is treated as the slice customer, with a list of device types; slice type allocation is performed for each device type, as presented in Table 2. Four standardized slice types are considered: eMBB, URLLC, mIoT, and V2X [3].  
The growth of devices is subsequently estimated in the period between 2021 and 2030, with a CAGR of 6% for a specified list of devices based on inputs from a real electronics assembly factory, while the growth of IoT devices is expected to be higher than the rest of the devices. Thus, for IoT devices, a higher growth rate of 10% is assumed for the selected time period [35]. The calculations are made for ten of such factories. The resulting device forecast is presented in Table 3.  
The device growth forecast and the slice allocation tables are used to estimate the slice size forecast, which is the number of devices to be supported by a slice type annually, as shown in Figure 4.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Device Type** & **eMBB** & **mIoT** & **URLLC** & **V2X** \\ \hline Production testers & - & x & x & - \\ \hline Surface mount technology machines & - & x & x & - \\ \hline Programmable logic controller machines & - & x & x & - \\ \hline IoT devices, sensors & - & x & - & - \\ \hline Computers & x & - & - & - \\ \hline Mobile robots & x & x & x & x \\ \hline Handheld devices & x & - & x & - \\ \hline Network cameras & x & - & x & - \\ \hline Office printers & x & - & - & - \\ \hline Telepresence (AR/VR) & x & - & x & - \\ \hline Wide-area logistics vehicles & - & x & - & x \\ \hline \hline \end{tabular}
\end{table}
Table 2: Slice Type Allocation for the Smart Factory case.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Device Type** & **2021** & **2022** & **2023** & **2024** & **2025** & **2026** & **2027** & **2028** & **2029** & **2030** \\ \hline Production testers & 2120 & 2247 & 2382 & 2525 & 2676 & 2837 & 3007 & 3188 & 3379 & 3582 \\ \hline Surface mount technology machines & 636 & 674 & 715 & 757 & 803 & 851 & 902 & 956 & 1014 & 1075 \\ \hline Programmable logic controller machines & 106 & 112 & 119 & 126 & 134 & 142 & 150 & 159 & 169 & 179 \\ \hline IoT devices, sensors & 3300 & 3630 & 3993 & 4392 & 4832 & 5315 & 5846 & 6431 & 7074 & 7781 \\ \hline Computers & 3180 & 3371 & 3573 & 3787 & 4015 & 4256 & 4511 & 4782 & 5068 & 5373 \\ \hline Mobile robots & 32 & 34 & 36 & 38 & 40 & 43 & 45 & 48 & 51 & 54 \\ \hline Handheld devices & 424 & 449 & 476 & 505 & 535 & 567 & 601 & 638 & 676 & 716 \\ \hline Network cameras & 106 & 112 & 119 & 126 & 134 & 142 & 150 & 159 & 169 & 179 \\ \hline Office printers & 424 & 449 & 476 & 505 & 535 & 567 & 601 & 638 & 676 & 716 \\ \hline Telepresence (AR/VR) & 21 & 22 & 24 & 25 & 27 & 28 & 30 & 32 & 34 & 36 \\ \hline Wide-area logistics vehicles & 53 & 56 & 60 & 63 & 67 & 71 & 75 & 80 & 84 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Devices forecast (2021–2030) for ten factories with a 6% CAGR (with 10% CAGR for IoT devices).
# Thermal Fatigue Properties of Ultrasonically Bonded Copper Joints
###### Abstract  
Thermal fatigue generally occurs in ultrasonically bonded copper joints in electronic devices as the bonding substrate is composed of plural materials, leading to differences in the coefficient of thermal expansion. In this study, we found that the thermal fatigue resistance of the ultrasonically bonded copper joints was influenced by the grain size and hardness of the bonding substrate through the evaluation of the thermal fatigue properties. Copper alloys C1020 and C1940 were used as substrate materials to investigate the influence of the initial properties of the bonding material on the thermal fatigue resistance. We evaluated the crack propagation due to thermal fatigue via thermal cycle tests. Microstructural observations of the region fractured because of thermal fatigue revealed that cracks resulting from thermal fatigue did not progress in the fine grain region formed at the bonded interface. It was inferred that grain boundaries were an obstacle to crack propagation. C1940 has higher hardness and finer grains than C1020, and showed a lower decreasing rate of the peel strength and bonding area after the thermal cycling test than C1020 joints. Thus, a hard copper material with fine grains is effective in suppressing thermal fatigue fracture of ultrasonically bonded copper joints.  
ultrasonic bonding; interfacial microstructure; thermal reliability +
Footnote †: journal: Applied Physics Letters
# Characteristics of Ordinary Portland Cement Using the New Colloidal Nano-Silica Mixing Method
###### Abstract  
This study applies a new method of mixing colloidal nano-silica (CNS). Previous studies have used powdered nano-silica or colloidal nano-silica and applied a binder weight substitution method. In this study, we tried to use ordinary Portland cement (OPC) as a binder and replace CNS with weight of mixing water. CNS was replaced by 10%, 20%, 30%, 40%, and 50% of the mixing water weight. The flow value, setting time, compressive strength, hydration reactant (X-ray diffractometer; XRD), pore structure (mercury intrusion porosimetry; MIP), thermal analysis, and scanning electron microscopy (SEM) analysis were performed. Experimental results show that the new substitution method improves the mechanical and microstructural properties through two effects. One is that the weight substitution of the mixing water shows a homogeneous dispersion effect of the nano-silica particles. The other is the effect of decreasing the w/b ratio when the CNS is substituted because the CNS is more dense than the mixing water. Therefore, we confirmed the applicability of mixing water weight replacement method as a new method of mixing CNS.  
colloidal nano-silica; mixing water; ordinary Portland cement; mechanical and microstructural properties +
Footnote †: journal: Applied Sciences
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## Improvement of YOLOv7 Object-Detection Model
### YOLOv7 Introduces Dynamic Convolution  
Dynamic convolution [27] means finding a suitable balance between the number of network layers and the computational cost, to better improve the expression ability of the model. The essence of dynamic convolution is not a single convolutional kernel, but twenty according to the actual situation of the convolutional kernels' dynamic adjustment, with the targeted selection of the appropriate parameters for feature extraction, so that the various small targets can have better feature extraction, thus avoiding much leakage and the many false detections of small targets. The general dynamic perceptron is shown in Figure 5.  
As can be seen from Figure 5, the output is as follows:  
\[y=g\big{(}\widetilde{W}^{\top}x+\widetilde{b}\big{)} \tag{7}\]  
\[\widetilde{W}=\sum_{k=1}^{K}\,\pi_{k}(x)\widetilde{W}_{k} \tag{8}\]  
\[\widetilde{b}=\sum_{k=1}^{K}\,\pi_{k}(x)\widetilde{b}_{k} \tag{9}\]  
\[\text{s.t. }0\leqslant\pi_{k}(x)\leqslant 1,\sum_{k=1}^{K}\,\pi_{k}(x)=1, \tag{10}\]  
where: W, b, and g denote the weight, bias, and activation function, respectively; \(\pi\)k denotes the attention weights, which are not fixed, but vary with the input. \(\pi\)k contains the attention weight calculation and dynamic weight fusion, i.e.,  
\[O\big{(}\widetilde{W}^{\top}x+\widetilde{b}\big{)}\gg O\big{(}\sum\pi_{k} \widetilde{b}_{k}\big{)}+O[\pi(x)] \tag{11}\]  
where: O (\(\cdot\)) denotes the amount of perceptron computation.  
From the above, the dynamic convolutional structure having the same processing as the dynamic perceptron is shown in Figure 6. After the processing of the dynamic convolutional layer before the BN and ReLU layers, the same K channels with the convolutional kernel are set in one of the random layers, which are further fused with the involvement of the self-attention weights to obtain the convolutional kernel parameters of the layer. Meanwhile, in order to obtain the global spatial features, global average pooling is performed first. As a result of this process, what was originally a fixed convolutional kernel is now changed to a convolutional kernel that can be dynamically selected based on the inputs, significantly improving the feature expression ability.  
Figure 5: Dynamic perceptron.  
On the basis of the aforementioned enhancements, Figure 7 depicts the framework of the YOLOv7-CA dynamic model proposed in this paper. The improved portion is depicted in the figure's red box.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 3 Modeling the Arrival Processes  
### Marginal Distribution of the Arrivals  
In order to check the impact of the tail of the marginal distribution of the arrival process on the performance of the system, we consider the following distributions to model the number of users joining the system in a given slot. Arrivals in different slots are assumed to be statistically independent, i.e., the arrival point process is memoryless. Specifically, we will consider two possible point processes for the arrivals: discrete versions of the Lognormal and the Pareto probability distributions, in addition to the classical Poisson process on the line.
## 4 Inversion of Fatigue Damage Parameters  
In the cohesive fatigue damage constitutive model, there are three material parameters that need to be provided to the UMAT subroutine, namely the initial normal strength \(\sigma_{max,0}\), the corresponding separation displacement \(\delta_{0}\) of the initial normal strength, and the fatigue limit \(\mathrm{C_{f}}\). The initial normal strength can be calculated using the following formula:  
\[\sigma_{max}=\frac{S}{2 BD^{2}}(3P_{max}+\frac{1}{2}mg) \tag{8}\]  
where: \(P_{max}\) represents the maximum load applied to the specimen (N); \(m\) is the mass of the specimen at midspan (kg); \(g\) is the acceleration due to gravity (m/s\({}^{2}\)); \(B\) is the width of the specimen (m); \(D\) is the height of the specimen (m); and \(S\) is the span length (m).  
Currently, there is no direct method to measure the corresponding separation distance for normal strength and the fatigue limit. Typically, an inverse calculation method is employed to determine the values of these two parameters. However, this method is inefficient and requires extensive trial and error for each case. In this study, inspired by neural network principles, we use numerical simulations to build a large dataset. Subsequently, we construct a BP neural network for the inversion of parameters of the cohesive fatigue damage unit. By utilizing this neural network, the fatigue damage parameters for different pouring intervals with cold joint defects can be obtained computationally, thus circumventing the need for extensive trial and error calculations.
# Reliability-Based Design of Driven Piles Considering Setup Effects
###### Abstract
According to the LRFD methodology, the development of the analysis model is introduced to estimate the soil setup after the end of driving (EOD), and then the resistance factor of the pile setup is determined [16]. [PERSON] et al. [23] put forward a methodology to separately calculate the resistance factors for ultimate base and shaft resistance for reliability-based design of driven piles considering setup effects based on the LRFD principle. This paper modifies the resistance factor and factor of the safety formula of driven piles by incorporating setup effects into the LRFD principle. In the modified formula, the increase factor \(M_{\text{setup}}=R_{\text{setup}}/R_{0}\), defined as the ratio of setup resistance to initial ultimate resistance, is used to express the increasing degree of the ultimate resistance with time. The influence of parameters (ratio of dead load to live load \(\rho=Q_{\text{D}}/Q_{\text{L}}\), target reliability index \(\beta_{\text{T}}\), \(M_{\text{setup}}=R_{\text{setup}}/R_{0}\), correlation coefficient between \(R_{0}\) and \(R_{\text{setup}}\), \(\rho_{\text{R0,Rsetup}}\)) on the resistance factor and factor of safety are analyzed.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 3 CFP Firmware Design Scheme
### MCU Design
#### 3.3.3 CFP Status Management Function  
The CFP status management function operates the module according to the CFP state transition described in Section 2. The state transition function operates according to the control command requested from the host and the control set at the host hardware pin.
## 5 Experimental results and discussion
### Performance evaluation of EE-TGA and \(E^{3}\)-FA on standard unconstrained benchmarks
#### 5.1.3 EE-TGA and \(E^{3}\)FA benchmark simulations' overall conclusion  
Based on the experimental output of the unconstrained functions' simulations, it could be concluded that in the cases of both enhanced implementations, the algorithms outperformed the original metaheuristics. In [31; 39], TGA and FA were compared to other metaheuristic algorithms, and comparing those algorithms to our proposed approaches EE-TGA and \(E^{3}\)-FA, it could be concluded that our algorithms outperformed most of the metaheuristics used for comparison purposes in [31; 39].  
In the case of EE-TGA, the global optimum was reached at seven functions. On the other hand, the TGA algorithm reached the global optimum only at five functions out of ten. The second enhanced algorithm, \(E^{3}\)-FA, reached the global optimum on four unconstrained functions, while the original FA managed to do the same for only three functions. Hence, EE-TGA had a 63.6% success rate at reaching the global optimum versus 45.5% fir TGA. \(E^{3}\)-FA reached the global optimum in 50% and the original FA in 38% test cases.  
Figure 4: \(E^{3}FA\) vs. FA convergence speed graph for the F5 benchmark.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{_f(\(\mathbf{x^{3}}\))_} & \multicolumn{3}{c}{**FA**} & \multicolumn{3}{c}{\(E^{3}\)-FA} \\ \cline{2-9}  & & \(Best\) & \(Mean\) & \(StdDev\) & \(Best\) & \(Mean\) & \(StdDev\) \\ \hline F1 & \(-1.8013\) & \(-1.8013\) & \(-1.8013\) & \(0.00\) & \(-1.8013\) & \(-1.8013\) & \(0.00\) \\ F2 & \(0\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) \\ F3 & \(0\) & \(1.24\times 10^{-4}\) & \(1.32\times 10^{-4}\) & \(3.87\times 10^{-5}\) & \(\mathbf{1.52\times 10^{-5}}\) & \(\mathbf{5.61\times 10^{-5}}\) & \(\mathbf{2.49\times 10^{-6}}\) \\ F4 & \(0\) & \(3.23\times 10^{-5}\) & \(5.14\times 10^{-5}\) & \(1.12\times 10^{-5}\) & \(\mathbf{1.97\times 10^{-5}}\) & \(\mathbf{3.48\times 10^{-5}}\) & \(\mathbf{1.07\times 10^{-6}}\) \\ F5 & \(0\) & \(4.48\times 10^{-11}\) & \(0.895\) & \(1.28\) & \(\mathbf{0.00}\) & \(\mathbf{1.82\times 10^{-3}}\) & \(\mathbf{5.25\times 10^{-2}}\) \\ F6 & \(-1\) & \(-1\) & \(-3.33\times 10^{-1}\) & \(0.50\) & \(-1\) & \(\mathbf{-1}\) & \(\mathbf{0.00}\) \\ F7 & \(0\) & \(1.71\times 10^{-8}\) & \(5.93\times 10^{-3}\) & \(4.23\times 10^{-3}\) & \(\mathbf{0.00}\) & \(\mathbf{5.98\times 10^{-6}}\) & \(\mathbf{1.62\times 10^{-4}}\) \\ F8 & \(-186.7309\) & \(-186.7309\) & \(-186.7309\) & \(0.00\) & \(-186.7309\) & \(-186.7309\) & \(0.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative analysis of the firefly algorithm (FA) and the enhanced firefly algorithm (\(E^{3}\)-FA).
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks  
The ability to efficiently allocate network, storage, and processing resources in virtualized network environments is key in service provider networks. This is even more so in NFV/SFC environments, where user traffic type and frequency can either be deterministic or nondeterministic, bringing the need for the underlying system to provide and allocate resources efficiently. The chaining of network functions to provide end-to-end services cannot be achieved without an efficient service orchestration scheme in place [68]. Efforts such as the work by [PERSON] et al. [69] propose algorithms to handle SFC orchestration, resource utilization, and optimization. The algorithm proposed by [69] for example, is based on the breadth-first search (BFS) algorithm, which reduces overall chain-wide latency and bandwidth consumption. This section presents frameworks that have been designed to achieve the goal of efficiently allocating resources in NFV/SFC and the orchestration of network services. We categorically focus on frameworks that handle resource allocation and service orchestration as the core contributions in this subsection of our work by presenting the technologies used and the implementation approach employed by the authors. This has also been summarised in Table 3.  
Figure 8: NFV/SFC frameworks taxonomy.
# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions
### Experimental Setting
#### 4.1.2 Evaluation Metrics  
According to [61], we utilized several common evaluation metrics: the Dice Similarity Coefficient, specificity, and precision. We also included three object detection metrics, structure measure [62], mean absolute error, and enhanced-alignment measure [63].  
These metrics compare the prediction map with Sigmoid activation to the ground-truth G of object-level annotations. The metrics measure similarity/dissimilarity between the ground truth and predicted segmentation. We quantified segmentation performance using popular medical metrics along with additional detection-based metrics. These evaluate alignment, error, and topological similarity between predictions and ground truth annotations.
# Variation of the Chemical Composition of Waste Cooking Oils upon Bentonite Filtration
## 3 Results and Discussion
### Principal Component Analysis  
The data related to the VOCs content were subjected to a principal component analysis in order to identify any discrimination between the filtered samples and the control (raw WCOs). The data were centered and autoscaled, and then PCA was performed. The cumulative explained variance by the two first principal components was about 78% and, as reported in the score plot (Figure 1), the three samples were perfectly clustered in three different groups meaning that the information contained in the variables was suitable to discriminate the samples.  
The main differentiation between the three groups (raw and filtered samples) occurs in the PC1, while on the PC2 a discrimination among the replicates is found. In the loading plot the variables are reported according to their contribution to the discrimination. The variables which contribute most to the discrimination along the first principal component are those located in the highest absolute values, namely: V20, 23, 26, 32, 33, 35, 36, 38, 39, 41, 43, 44, 46 (pyrazine, methyl-, 2-propanone, 1-hydroxy-, formamide, N,N-dimethyl-, acetic acid, furfural, 2,4-heptadien, propanoic acid, 12-furancarboxaldehyde, 5-methyl-, butanoic acid, furanmethanol, 2-undecenal, 2,4-decadienal, hexanoic acid), which, as expected, are representative of organic acids and molecules which contain double bonds. Both classes of compounds are retained, or partially retained, by bentonite.
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 3 Results  
### Image Acquisition and Saliva Secretion Measurement  
The number of enrolled patients was actually 123, with an additional 53 patients re-enrolled. More than two tongue images were acquired for each patient, and a high-quality image was selected. The blurred images were converted to a sharpness image using an image sharpness algorithm. In the Figure 5, the amount of saliva secretion was measured using 176 data and ranged from 11.7 to 33.6 (average, 27.599; standard deviation, 3.035). Among the enrolled patients, there were more normal data (119) than abnormal data (57).
## 5 Spatial Chromosome Organization Using SBFSEM  
To date, our understanding of the spatial organization of chromosomes remains sparse. SBFSEM has been helpful in providing insights into the 3D spatial organization of chromosomes within the nuclear space at different stages of the cell cycle. Atophase, all 46 lymphocyte chromosomes, segmented and reconstructed from the SBFSEM stack, were identified and analyzed for their radial positioning inside the nucleus. Chromosomes were shown to have gene-density-based correlation with the radius of the nucleus, where gene-poor chromosomes including chromosome y, 18 and a homolog of chromosome 13 were located towards the periphery of the nucleus, whereas gene-rich chromosomes including chromosomes 17, 22 and chromosome 19 occupied more central positions in the nucleus (Figure 4A(i)) [44]. SBFSEM also made possible the visual analysis of neighborhoods for individual chromosomes. Each chromosome and its spatial neighbors in closer proximity were identified. An example of a neighborhood cluster for chromosome 1b ('1b' is a representative of one of the two chromosome homologs) is shown in Figure 4A(ii) [44]. Radial organization of larger metaphase chromosomes from groups A and B and smaller metaphase chromosomes from groups E, F and G in an RPE cell was determined using SBFSEM. Larger chromosomes were present on the periphery, and smaller chromosomes were located towards the center of the nucleus (Figure 4B) [54]. These observations further attest to the importance of SBFSEM in providing information regarding the structure and the overall architecture of the chromosomes from different cell types and stages of the cell cycle.  
Figure 4: Spatial chromosome organization using SBFSEM. (**A(i)**) A 2D scatter diagram showing the radial organization of 46 chromosomes within a prophase nucleus. These chromosomes have a gene-density-based correlation with the radius of the nucleus. (**A(ii)**) A reconstructed 3D neighborhood cluster of prophase chromosomes (prepared from lymphocytes), showing 10 chromosomes that are in close proximity to chromosome 1 (1b) Scale bar—1 μm [44]. (**B**) A 2D scatter diagram showing the radial organization of largest (1–5) and smallest (19–22) metaphase chromosomes within an RPE cell. The chromosomes followed a volume-based correlation [54].