esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware Implementation and Experimental Results
### FIR3 (Farrow interpolator)
composed of a 16-bit constant \(\times\) 27-bit signal multiplier and also two 27-bit adders in series between two flip-flops, where the first flip-flop incurred a 0.283 ns delay, the multiplier gave rise to a 3.236 ns delay, and the adders reflected a delay of 1.245 ns + 0.996 ns, along with a delay of 2.298 ns from the remaining path, totaling a 8.06 ns critical path delay. Figure 24 shows synthesis results and required operating frequencies of FIR3. Each required operating frequency was determined by the input frequency of each case and the decimation factor of FIR1 (CIC filter) that is the prior stage. For instance, the input frequency of case 10 (20 MHz + 20 MHz LTE CA) was 312 MHz and FIR1 conducted decimation by 2, and hence FIR3 for case 10 should run at 156 MHz to say the least, which is the most stringent requirement. The graph in Figure 24 exhibits the 6.41 ns or 1/156 MHz clock period in the red line. It is shown that as the required speed increased, a small amount of area (GE) increased. The ASIC synthesis in 180 nm CMOS technology was carried out so as to obtain a critical path delay less than 6.41 ns. In view of the P&R routing delay of the ASIC, a delay margin of 2 ns was secured and a 4.22 ns-delay Farrow interpolator was designed and synthesized, corresponding to 236.97 MHz in frequency.
## 5 Energy Model and Transmission Reliability  
Wireless network devices perform many functionalities (i.e., sensing, processing, transmitting and receiving information). Among all those functionalities, wireless communication is the one, which depletes most of the energy [36]. Therefore, in this study, we do not consider the energy dissipated in the data processing. The energy consumption model for the proposed DC2 HC is adopted from the radio model used in [40], it assumes a simple communication model for the radio hardware battery consumption. In this model, the transmitter consumes an amount of power to run the radio electronics and the power amplifier. On the other side, the receiver consumes energy to detect and decode the radio signal as illustrated in Figure 9. Radio model parameters are illustrated in Table 4.  
Equation (6) computes the energy required to transmit a data packet with \(l\) bits over a distance \(d\), where \(E_{elec}\) is the power dissipation to run the transmitter circuitry for transmission or reception. \(\varepsilon_{amp}\) represents the power amplifier and depends on the distance to the receiver. \(\varepsilon_{FS}\) is the power consumption of the free space propagation, \(\varepsilon_{MFS}\) is the power consumption of multi-path propagation and \(d_{0}\) is a threshold distance. When the distance between the transmitter and the receiver is lower than \(d_{0}\), the free space model is used. Otherwise, the multi-path model is used. \(d_{0}\) is calculated as:  
\[d_{0}=\sqrt{\varepsilon_{FS}/\varepsilon_{MFS}}\]  
\[E_{Tran}(l,d)=E_{elec}\times l+\varepsilon_{amp}\times l \tag{6}\]  
where:  
\[\varepsilon_{amp}=\left\{\begin{array}{l}\varepsilon_{FS}\times d^{2}:d<d_{ 0}\\ \varepsilon_{MFS}\times d^{4}:d\geq d_{0}\end{array}\right.\]  
Equation (7) represents the dissipated energy by the receiver in receiving \(l^{\prime}\) bits of data.  
\[E_{Rec}=E_{elec}\times l \tag{7}\]  
Transmission reliability plays an essential criterion to improve the QoS of an application. The well-known available Link Quality Estimators (LQEs) are shown in Figure 10. Among all these estimators, Radio Signal Strength Indicator (RSSI), Link Quality Indicator (LQI) and Packets Received Rate (PRR) are the most common metrics used to estimate  
Figure 8: The cluster head joining process.  
Figure 9: Radio energy consumption model.  
transmission quality [36]. RSSI measures the power signal of the received packets. LQI indicates the correctness of the received packets based on the first eight bits of that packet. LQI is an efficient estimator, but the RSSI perform good results with a small number of measurements and converge faster than LQI. PRR is considered as the best indicator for link quality [55]. However, it requires more time and energy to perform an accurate quality value. For these reasons, RSSI is chosen as the link quality estimator in this study. Lower RSSI value implies a weaker signal. It is measured in decibel, the closer this value is to zero the better the signal is. For example, \(-50\) dBm is a good signal and \(-75\) dBm is fairly reasonable.
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Crack Growth and Linkage in Pre-Cracked Rock Disks  
To rigorously validate the predictive capabilities of the SW-DVIB framework for simulating fracture mechanics, a central straight-through-crack Brazilian disk (CSCBD) test was conducted. This test enables a systematic investigation of the crack initiation, propagation, and coalescence in brittle materials under controlled loading conditions. The geometric configuration and boundary conditions of the CSCBD specimens are shown in Figure 15, where the displacement-controlled loading was imposed along the vertical axis. The micromechanical input parameters were calibrated as follows: \(E=5.17\) GPa, \(\widetilde{e}_{y}=5.1\times 10^{-4}\), \(\widetilde{e}_{b}=8.67\times 10^{-4}\), \(G_{f}=20\) J \(\cdot\) m\({}^{-2}\), \(\alpha=0.6\), \(v=0.2\), and \(\rho=2700\)kg/m\({}^{3}\). This parameter selection ensures consistency with experimental benchmarks and theoretical fracture mechanics principles.  
The simulated load-displacement behavior of the pre-cracked specimens subjected to vertical compressive loading is presented in Figure 16a. The numerical results indicated that the failure load exhibited a strong dependency on the crack inclination owing to variations in the stress redistribution across the specimen. To quantify this effect, the normalized failure load was computed following the experimental formulation [48], which expresses the ratio between the failure load of the pre-cracked specimens and the corresponding failure load of the intact specimens. The computed failure load for the uncracked specimens was determined using 62.15 MPa, which served as the reference condition. A comparison  
Figure 14: Validation of fracture simulation through experimental benchmarks [67]: (**a**) \(\alpha=30^{\circ}\); (**b**) \(\alpha=45^{\circ}\); (**c**) \(\alpha=90^{\circ}\). (Simulation results are displayed on the left side, and experimental outcomes on the right).  
Figure 15: Model diagram of the CSCBD test; displacement loading is adopted.  
between the simulated normalized failure loads and the experimental observations is shown in Figure 16b. The numerical model accurately reproduced the experimentally observed trends, confirming that pre-existing cracks introduced structural weakening and reduced the load-bearing capacity of CSCBD specimens. Notably, the minimum failure load was observed at \(\beta=45^{\circ}\), confirming previous findings on the critical influence of crack orientation on fracture toughness.
## 3 Results and Analysis
### Model Performance Evaluation Metrics  
For assessing the enhanced model, six prevalent performance evaluation indices were adopted, namely mean average precision (mAP), average precision, Recall, Precision, GFLOPS, and parametric quantities of the model. The evaluation process is described in Equation (9):  
\[mAP=\frac{1}{n}{\sum_{i=0}^{n}{AP_{i}}} \tag{9}\]  
where n represents the number of categories, \(AP_{i}\) is the area included by the PR (precision-recall) curve for a category, and the formula for \(AP\) is shown in (10).  
\[AP=\int_{0}^{1}{PRdr} \tag{10}\]  
The m\(AP\) in this experiment reflects the average across four categories: healthy, spotted, decayed, and crusted.  
The accuracy rate, defined as the proportion of boxes accurately predicted by the model, measures model misdetection and is computed as per Equation (11).  
\[Precision=\frac{TP}{TP+FP}\times 100\% \tag{11}\]  
Recall, the ratio of correctly predicted frames to labeled frames by the model, measures model omission and is calculated according to Equation (12).  
\[\text{Recall}=\frac{TP}{TP+FN}\times 100\% \tag{12}\]  
where TP denotes the count of correct prediction results, FP represents incorrect prediction results, and FN refers to the unlabeled boxes that were not predicted.
## 3 Numerical Modeling
### Model Building  
Under the actual service conditions of underground water conveyance culverts, the cavity concrete not only undergoes mechanical damage caused by loads such as water pressure and earth pressure, but also produces chemical damage caused by dissolution, and its durability degradation is a complex chemical-mechanical damage evolution model [32]. The existing damage models mostly used quantitative analysis of the elastic-plastic damage process of concrete under a single mechanical load or considering only chemical-seepage coupling. In this paper, we consider chemical damage under the action of dynamic water dissolution [33] and establish a combined chemical-mechanical-seepage numerical model of concrete dissolution damage to study the deformation and force of the underground culvert and surrounding soil under concrete dissolution degradation.  
The finite element software ABAQUS was used to build the model, as shown in Figure 4, and the combined chemical-mechanical-percolation mesh-type consisted of C3D8P cells, which generated 456,050 mesh nodes and 416,468 mesh cells.  
Displacement boundary condition setting: The surface and channel surface are free. The four sides of the model are only allowed to move in-plane, and their normal displacement constraint is 0. The bottom of the model uses fixed constraints to limit the displacement to 0. Seepage boundary condition setting: It is considered that there is sufficient water recharge, and groundwater is always kept 20 m below the surface. The pore water pressure in the horizontal plane 20 m below the surface is always set to 0. The hydrostatic pore pressure boundary is on the side of the model. In the ground stress equilibrium stage, the surface leakage area of the inner wall of the culvert adopts the ABAQUS default state impermeable boundary. The water pressure and pore pressure of the inner wall of the culvert are set at 3.5 m. The setting of the interaction between the culvert and soil: The contact surface between the outer surface of the culvert and the soil is restrained by a tie, and the rotational freedom of the slave surface is limited to be the same as that of the main  
Figure 4: Three-dimensional finite element model.  
Figure 3: Geological profile of culvert engineering.  
surface. In this simulation, except for the medium-fine sand and sandy pebbles, which adopt the linear elastic principal structure, all the soils are simulated by the elastic-plastic principal structure relationship based on the Mohr-Coulomb yield criterion, and the C20 concrete adopts the linear elastic principal structure.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 6 Conclusions  
In this paper, we propose an automatic MCS level adaptation algorithm using SINRs of ACK frames in CSMA/CA wireless networks to accommodate the expected high density of IoT devices in CSMA/CA wireless networks. The proposed algorithm does not require any modification of the existing CSMA/CA mechanism because it is based on the information from normal ACK frames in IEEE 802.11 standards. Our simulation results show that the proposed algorithm utilizes resources of IEEE 802.11a wireless networks almost as effectively as the highest MCS level of a given MCS scheme for various combinations of PPP densities of access points and wireless devices.  
In addition, we show through simulations that the proposed algorithm results in low retry limit ratios in sent packets compared to those of the highest MCS level. Furthermore, the fairness among per user throughputs also exhibits better results than those of the highest MCS level. Since the proposed algorithm works in a purely distributed manner and is less aggressive than the highest MCS level, we expect other performance improvements to be further revealed in the near future.  
Figure 19: Fairness among user throughputs.  
Figure 18: Ratios of retry limits to sent packets.  
**Acknowledgments:** This work was supported by the Hongik University new faculty research support fund.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# Cell/Module Integration Technology with Wire-Embedded EVA Sheet
## 2 Materials and Methods  
Figure 1 shows a schematic of the module manufacturing process. First, the c-Si solar cell used in the module was a busbarless cell, unlike a cell with a conventional busbar. The front electrode of the wire-embedded EVA sheet module was designed using various simulations such as Gridler 2.5 and MathCAD, and the influence of various variables on the module output was analyzed. The size of the c-Si solar cell was 156.75 \(\times\) 156.75 mm\({}^{2}\), and a p-type PERC (passivated emitter and rear cell) structure solar cell was used. The  
wire was not in direct contact with the busbarless cell. The module was fabricated using a wire-embedded EVA sheet. The EVA sheet is used to establish contact between the wire and finger electrode. Because the solder coating on the wire is melted during the lamination process to form an interconnection between the wire and finger electrode on the front of the solar cell, a module was fabricated using a BiSnAg wire with a low melting point of 139 \({}^{\circ}\)C. To fabricate the unit module, the module is laid up in the order of glass, wire-embedded EVA sheet, solar cell, wire-embedded EVA sheet, and back sheet. The materials of the stacked module structure are bonded at low temperatures through a lamination process. Materials of 1 \(\times\) 1 (1 solar cell) and 2 \(\times\) 2 (4 solar cells) modules were bonded to a laminator equipment (LRLA505, ARON, Inc.). Here, 1 \(\times\) 1 means a module consisting of one 6-inch solar cell, and 2 \(\times\) 2 means a module consisting of four 6-inch solar cells connected in series. After laying the module material on a heated plate, pumping was carried out in a vacuum of 1 Torr or less, and the EVA sheet started melting. When pumping was completed, pressure was applied to the silicon rubber compressed in the upper chamber, and the back sheet surface of the module laid on the heated plate was pressed. The glass/solar cell/back sheet was bonded; concomitantly, air bubbles inside the module generated during the pumping process were removed by pressure. Finally, the interconnection between the wire-embedded EVA sheet and the busbarless cell was formed, and an electron collection path was formed in the cell through a wire instead of a busbar using a silver paste. The characteristics of the wire-embedded EVA sheet module were calculated on the basis of the I-V characteristics of the solar cell under standard sunlight conditions (AM1.5G and 100 mW/cm\({}^{2}\)), and were measured using the PLS-300 equipment of MC Science.
### Hybrid Non-Ribosomal Peptide/Polyketides  
Some SMs are the result of the joint action of PKS and NRPS [44]. Both enzymatic complexes can be encoded by independent genes participating in the same biosynthetic pathway. This is the case in the ochratoxin A biosynthetic clusters in _Aspergillus_ and _Penicillium_ sp. [25]. Other examples are the PKS and NRPS genes involved in the synthesis of the lipopeptides fusaristatin A in _Fusarium germinearum_ and W493 A and B in _Fusarium pseudograminearum_[45].  
The similarities between the modular structures of type I PKS and NRPS have facilitated the evolution of chimeric genes, which combine PKS and NRPS modules [46]. Examples are found with the two orientations in their amino-carboxyl distribution, PKS-NRPS and NRPS-PKS [47]. In these large multifunctional complexes, usually consisting of a single NRPS module with one or more PKS modules, the substrates are passed between the different modules to introduce the corresponding PK or NRP extension to the molecule. This new combinatorial possibility provides an additional source of diversity to the resulting compounds [48]. Examples of PKS-NRPS hybrid products are shown in Table 1 and Figure 1.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 3 Method
### Image Data Augmentation  
When training deep-learning models, large amounts of data are needed for training, and it is necessary to try to avoid overfitting during the training. Proper use of regularization strategies related to deep learning, such as L1/L2 regularization, dropout, batch normalization, early stopping, and data augmentation, is needed. Among these strategies, data augmentation is regarded as an effective method for training a generally applicable model using limited training data [25]. In data augmentation, after the image is rotated, resized, scaled, and flipped, or has its brightness or color temperature changed, the original image in the dataset is changed to create more images that will allow the model to continue learning. In order to make up for the lack of data, in this study, an augmented training method was included in the training. After using horizontal and vertical flipping processing, the training image was increased by small-scale translation and scaling in order to enhance the generalization ability of the model.
## 4 Experimental Results
### Model Verification
#### 4.1.3 Comparison with Other Existing Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **Sensors** & **Verification Against** & **RMSE (m/s)** & **Mean Absolute Error (m/s)** & **References** \\ \hline Feature-based K-nearest & SSM/1 & Best Track & 9.31-10.19 & 7.20-8.23 & [PERSON] and Tag [15] \\ \hline Warm Core Anomaly & AMSU & Best Track & 7.20 & 5.40 & [PERSON] et al. [43, 44] \\ \hline Multivariate Regression & IR & Within 3 aircraft & & & \\  & & reconnaissance-based best track & 8.59 & 6.79 & [PERSON] et al. [45] \\ \hline Advanced & IR & Within 1 h aircraft & & & \\  & & reconnaissance-based best track & 7.67 & 5.61 & [PERSON] and [PERSON] [6] \\ \hline Dvorak Technique (DT) & Visible/IR & Within 2 h aircraft & & & \\  & & reconnaissance-based best track & 3.09-7.20 (avg. –5.14) & 2.57-5.66 (avg. –4.12) & [PERSON] et al. [46] \\ \hline Dviation Angle & & & & & \\ Variance (DAV) & IR & Best Track & 6.17-7.72 & - & [PERSON] et al. [9, 10] \\ \hline Feature Analogs in & & Within 12 h aircraft & & & \\ Satellite Imagery (FAST) & IR & reconnaissance-based best track & 6.53 & 5.61 & [PERSON] et al. [47] \\ \hline Deep Convolutional & IR & Aircraft reconnaissance dataset & 4.63-8.23 (avg. –6.02) & - & [PERSON] et al. [48] \\ \hline PMW-IE Combined & & & & & \\ Model for t = 6 h & TMI & Best Track/Within 3 h aircraft & & & \\  & & reconnaissance-based best track & 6.17/6.48 & 4.63/4.94 & [PERSON] et al. [21] \\ \hline The proposed model & SSMIS/HV2A & Best Track & 5.94 & 4.62 & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the model-estimated TC intensities with previously developed techniques using the statistical values of RMSE and MAE.
## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
An overview of the research topic presented in Section 2 shows that centralizing NS control in one location allows for more efficient control compared to each NS element having its own control plane. This leads to reduced energy consumption for each NS element, which has a positive impact on the overall energy consumption of the entire NS. In addition, it is shown that the implementation of VNF technology in network slicing also has a positive impact on NS energy efficiency. This impact is reflected in the ability to implement multiple NFs on a single physical device through the virtualization of network resources, and thus achieve equivalent effects to the realization of NFs on separate physical devices. In this regard, the implementation of VNFs requires virtual links among NS resource elements, which have a lower energy footprint compared to physical links. In addition, cloud computing is another key enabler of network slicing, which can ensure even more energy-efficient NS resource utilization. In cloud computing, the more efficient use of processors, storage, and cooling systems reduces heat generation or overheating, which has a positive impact on NS energy efficiency. It is worth mentioning that data centers performing cloud computing often rely on renewable energy sources, ultimately reducing costs for MNOs and contributing to a reduction in energy consumption from non-renewable sources.
# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 3 Materials and Methods
### LDA for Topic Modeling  
Latent Dirichlet Allocation (LDA) [23] is a generative probabilistic model of a corpus. This algorithm considers that each document is described by a distribution of topics, and each topic can be reduced to a mixture of words based on the frequency to be assigned to that topic. This unsupervised machine learning method allows reducing the dimensionality of corpora to obtain relevant information. Moreover, LDA requires the user to define the number of T topics for distributing the words from the corpus.  
We have implemented LDA to obtain the underlying structure of _latent_ topics in our dataset. To do this, Python's Gensim library [24] was selected due to its excellent and intuitive implementation. Moreover, the Gensim library allows the execution of the algorithm with multi-threads, resulting in an efficient and fast analysis. In this project, the Gensim default \(\alpha\) and \(\beta\) hyperparameters from LDA were selected.
# Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design
## 3 System Model  
The design of an efficient quantum satellite backbone requires extremely accurate control, and the use of SDN technology could be fundamental to achieve this goal. In order to increase the generation rate of Bell pairs on the selected path, an appropriate path selection algorithm is required. These are the functions of the MCS depicted in Figure 2. Moreover, considering that the management of swapping procedures could increase the overhead, it is relevant to minimize this, especially for distributed operations [10]. This goal can be achieved by the efficient management of entanglement generation and swapping operations by the CP integrated into the constellation itself.  
The following sections describe in detail the architecture together with the protocol and the related exchanged messages.