esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 3 Methodology
### Model Validation  
In machine learning classification tasks, accuracy measures the overall correctness of the classifier, while precision and recall focus more on the classifier's performance on different categories. The _F1-Score_, which combines precision and recall, is especially useful for evaluating models in scenarios with different numbers of categories and unbalanced data, as referenced in Equations (2)-(5).  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{5}\]  
where true positive (_TP_) represents correctly predicted positive instances, true negative (_TN_) represents correctly predicted negative instances, false positive (_FP_) represents falsely predicted positive instances, and false negative (_FN_) represents falsely predicted negative instances. Additionally, the receiver operating characteristic (ROC) curve is used to demonstrate the relationship between the true positive rate (also known as the recall rate) and the false positive rate of the classifier at different thresholds. The area under the ROC curve, also known as the area under the curve (AUC) value, is a commonly used measure of a classifier's ability to distinguish between positive and negative samples.
## 2 Related Work
The Efficient Channel Attention (ECA) module [28] replaces dimensionality reduction with a 1D convolution, allowing efficient yet expressive channel modeling. Furthermore, the Temporal-Frequency Convolutional Neural Network (TFCNN) proposed by [PERSON] et al. [19] introduces a dual-branch attention module that independently models temporal and spectral importance. In parallel, [PERSON] et al. [20] proposed an attention-enhanced Convolutional Recurrent Neural Network (CRNN) architecture incorporating frame-level temporal attention to improve robustness under non-stationary acoustic conditions. In summary, these studies highlight the transformative potential of deep learning in advancing ESC toward high-precision classification and adaptive modeling under multi-source acoustic interference, offering methodological insights and technical foundations for urban soundscape analysis and intelligent acoustic systems.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 6 Discussion and Future Research Leads
### Out of the Scope Attacks  
Finally, other side-channels and SCA techniques, which out of the scope of this review, have been successfully applied against DNNs. Several endeavors have exploited timing SCA attacks against DNNs running on different implementation platforms, software and hardware. Timing measurements are usually accessible through the analysis of cache access patterns [47], which is a hardware resource shared between the attacker and victim application. However, the observation of other physical measurements including power or EM traces can also provide information on computation time [20]. These attacks have been widely studied against \(\times\)86 processor-based DNN implementations. [PERSON] et al. [48], for example, have successfully recovered the network parameters of an MLP model from a software implementation on a \(\times\)86 processor, assuming that the attacker knows the model layout (number of layers and neurons in each layer). Cache-based SCA techniques, such as the well known Flush+Reload [49], have also been used against DNN software implementations in order to recover model architectures [47; 50]. A close work [51] has recovered the network parameters on a CNN model implemented in a \(\times\)86 processor through persistent cache monitoring. Contrary to the vast majority of existing literature attacks, the target model is trained with real medical data. The observation and analysis of off-chip memory access patterns have also been exploited for deducing DNN architecture layouts and parameters [52]. Other works [53] have performed timing attacks using a timing model specifically adapted to micro-controllers in order to recover the network input data (data to be classified). Logical SCA attacks against DNNs as well as other attacks including trojan and fault injection and algorithmic-based attacks are also being studied [22].
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 4 PL-MMSE Kalman Filter with Nonlinear State Constraints  
Consider the nonlinear constraint on the system state is as  
\[h(x)=q \tag{39}\]  
where \(h(\cdot)\) is a nonlinear function. \(q\) is a scalar. Next, we address the nonlinear constraint using the linear approximation method and second-order approximation, respectively.
## 3 Materials and Methods
### Chemistry
#### 3.1.3 Synthesis of \(N\alpha\)-Fmoc-tyrosine-allyl ester (5)
\({}^{1}\)H-NMR (400 MHz, DMSO-d\({}_{6}\)) \(\delta\): 2.06 (s, 6 H, COCH\({}_{3}\)), 2.85 (m, 1 H, CH\({}_{2\text{a}}\)\(\beta\)), 2.94 (t, 2 H, \(J\) = 5.8 Hz, CH\({}_{2\text{C}}\)N), 3.06 (m, 1 H, CH\({}_{2\text{b}}\)\(\beta\)), 4.18 (m, 4 H, CH\({}_{2\text{F}}\H-5'), 5.34 (m, 1 H, H-3'), 5.44 (m, 1 H, H-2'), 5.67 (m, 1 H, H-5), 5.93 (d, 1 H, \(J\) = 5.6 Hz, H-1'), 7.11-7.92 (m, 13 H, Ar Fmoc y Tyr, H-6), 11.4 (bs, 1 H, NH). \({}^{31}\)P-NMR (162 MHz, DMSO-d\({}_{6}\)) \(\delta\): -11.10, -10.65. HRMS: [C\({}_{40}\)H\({}_{39}\)N\({}_{4}\)O\({}_{15}\)P]\({}^{+}\); calcd. 846.2149, found 846.2150. 1.7 Synthesis of 2-cyanoethyl-(\(Na\)-Fmoc-tyrosin-4-yl)-(2',3'-di-O-acetyl-5-fluorouracilidyl-5'-yl) phosphate (15)  
To a stirred solution of **13** (1.52 g, 1.68 mmol), coevaporated with MeCN (5 mL \(\times\) 2) in a 1:1 mixture of THF/DCM (10 mL, _v/v_), containing AcOH (0.42 mL, 8 mmol), Bu\({}_{3}\)SnH (0.91 mL, 3.36 mmol) and Pd(PPh\({}_{3}\))\({}_{4}\) (81 mg, 0.07 mmol) were added under an argon atmosphere. After two hours the reaction mixture was concentrated and purified by flash column chromatography (DCM:MeOH, 94:6) to yield **15** (0.97 g, 67%) as white amorphous solid. HPLC (system B): 7.80 min (95% analytical purity). \({}^{1}\)H-NMR (400 MHz, DMSO-d\({}_{6}\)) \(\delta\): 2.06 (s, 6 H, COCH\({}_{3}\)), 2.86 (dd, 1 H, \(J\) = 13.7, 2.8 Hz, CH\({}_{2a}\)\(\beta\)), 2.94 (t, 2 H, \(J\) = 5.8 Hz, CH\({}_{2}\)CN), 3.08 (dd, 1 H, \(J\) = 13.7, 4.0 Hz, CH\({}_{2b}\)\(\beta\)), 4.19 (m, 4 H, CH\({}_{2}\) Fmoc, CH Fmoc, CH \(\alpha\)), 4.29 (m, 3 H, CH\({}_{2}\)CH\({}_{2}\)CN, H-4'), 4.43 (m, 2 H, H-5'), 5.34 (m, 1 H, H-3'), 5.44 (m, 1 H, \(J\) = 5.93 (d, 1 H, \(J\) = 5.6 Hz, H-1'), 7.15 (d, 2 H, \(J\) = 8.3 Hz, Ar Tyr), 7.29-7.45 (m, 6 H, Ar Fmoc y Tyr), 7.65 (t, 2 H, \(J\) = 7.3 Hz, Ar Fmoc), 7.75 (bs, 1 H, NH), 7.88 (d, 2 H, \(J\) = 7.6 Hz, Ar Fmoc), 8.09 (t, 1 H, \(J\) = 6.6 Hz, H-6), 12.0 (bs, 1 H, NH), 13C-NMR (100 MHz, DMSO-d\({}_{6}\)) \(\delta\): 19.0, 19.1 (CH\({}_{2}\)CN), 20.3, 20.4 (COCH\({}_{3}\)), 35.6 (CH\({}_{2}\)\(\beta\)), 46.6 (CH Fmoc), 55.5 (CH \(\alpha\)), 63.3 (CH\({}_{2}\)CH\({}_{2}\)CN), 65.6 (CH\({}_{2}\) Fmoc), 67.0 (C-5'), 69.0 (C-3'), 71.5 (C-2'), 79.6 (C-4'), 87.4 (C-1'), 118.1 (CN), 119.7 (CH Ar, Tyr), 120.1 (CH Ar, Fmoc), 125.3 (CH Ar, Fmoc), 125.6 (C-6, \(J\) = 36 Hz), 127.1, 127.7 (CH Ar, Fmoc), 130.6 (CH Ar, Tyr), 135.4 (C Tyr), 139.6 (C-5, \(J\) = 234.7 Hz), 140.7 (C Fmoc), 143.8 (C Fmoc), 148.5 (C Tyr), 149.1 (C=O C-2), 156.0 (CONH), 157.0 (C=O C-4, \(J\) = 26.3 Hz), 169.4 (COCH\({}_{3}\)), 173.3 (C=O Tyr). \({}^{31}\)P-NMR (162 MHz, DMSO-d\({}_{6}\)) \(\delta\): -11.50, -11.40. HRMS: [C\({}_{40}\)H\({}_{38}\)FN\({}_{4}\)O\({}_{15}\)P]\({}^{+}\); calcd. 864.2047, found 864.2055.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 3 Encoding-Based Test Stimulus Compression Methods
### Variable-Length to Variable-Length  
The encoding method from variable-length to variable-length replaces sequences of continuous 0 s or 1 s, known as runs, with variable-length code words. Based on this, [PERSON] proposed Golomb encoding [24] and Frequency-Directed Run-length (FDR) encoding [33]. Golomb encoding groups 0 runs and 1 runs for encoding, with the code words having a prefix and a suffix. The prefix indicates the group of runs, and the suffix indicates the position of the run within the group. Once the group is determined, the suffix code length is constant, while the prefix code length varies, and the number of code words in each group is equal. The length of the suffix code is related to the size of the prefix and the group.  
FDR encoding also consists of a prefix and a suffix, with the prefix encoding method being the same as [PERSON]'s. However, the length of the FDR suffix is variable and is equal to the prefix length, and the number of code words in each group is not equal, increasing exponentially. To optimize the FDR encoding process, [PERSON] proposed Extended Frequency-Directed Run-Length (EFDR) encoding [33], which adds an additional bit to the code word to distinguish between 0 runs and 1 runs. For EFDR encoding, [PERSON] [51] proposed the optimal filling method for the irrelevant bit X. It improves the compression efficiency of EFDR encoding without adding extra hardware resource occupation. When the number of 0 runs and 1 runs in the dataset is approximately equal, the efficiency of FDR encoding is limited. In this case, [PERSON] encoded 0 runs and 1 runs alternatively [52], forming the Adaptive Linear Feedback Register (ALT-FDR) encoding. This method is based on 0 and 1 dual runs, and does not require an additional identifier bit. On this basis, other researchers proposed symmetric run encoding [53], equal run encoding [54], Improved Frequency-Directed Run-Length (IFDR) encoding based on equal runs [55], Pattern Run-Length (PRL) encoding [56], and 2 nPRL encoding [57]. In the past decade, An adaptive Extended Frequency-Directed Run-Length (EFDR) code method for test data compression was presented by [PERSON] [58]. It can achieve a compression rate of 69.87%, 4.07% higher than the original EFDR code method. Meanwhile, the proposedCount Compatible Pattern Run-Length (CCPRL) coding compression method can achieve a compression ratio of 71.73% [59].  
Additionally, some researchers proposed Variable-Length Input Huffman Coding (VIHC) encoding [60], which considers only single runs, and RL-Huffman encoding [45], which considers dual runs, based on run encoding and Huffman encoding. In these cases, the code word length depends on the frequency of run lengths occurring.
# An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
## 4 Results and Discussion
### Recognizing the Existing Obstacles in the Development of SCs
#### 4.1.3 Utilities  
IoT-equipped SCs give more control to citizens over their home utilities, reducing overall bills and related costs [38]. By utilizing IoT technologies and effective approaches, such as smart meters for billing, monitoring consumption patterns, and remote monitoring, municipalities can achieve cost-effective connectivity to utility companies' IT systems. This helps customers consume energy and water based on improved monitoring and, therefore, presents better management services to the citizens [39]. Precooling optimization using system data (IoT), while preserving the thermal comfort of the inhabitants, has a direct influence on expenses and energy consumption (electricity costs) for cooling of a building by up to 30% percent, according to an Australian study [40]. Also, other research shows that, in Arabian Gulf countries, a smart energy management system using the Internet of Things can reduce costs, especially for air conditioning, which accounts for up to 60% of electricity consumption, while still meeting energy demand [41]. On the other hand, use of the IoT in utilities has a good effect on attainment of efficiency (management of large-scale solar photovoltaic systems) [42] and conservation of resources [43].
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 3 Proposed Scheme
### Receiving Antenna Decision Method
The frame format of Figure 3a consists of a legacy short training field (L-STF), a legacy long training field (L-LTF), and a legacy signal field (L-SIG) for backward compatibility, and a very high throughput signal field A (VHT-SIG-A), a VHT-STF, VHT-LTFs, a VHT-SIG-B, and data signals. When the downlink MU-MIMO-OFDM transmission is used, pre-coding is performed for the signal region from VHT-STF to the end of the data signals. On the other hand, pre-coding of the proposed method is performed for the signal region from L-STF to VHT-SIG-A. When a signal is received by the STA in Figure 3b, the STA calculates the received VHT-SIG-A and decides the allocated antenna using the powers calculated for the signal region from L-STF to the minimum received power among all the antennas. At the same time, the STA acquires information (modulation and coding rate scheme, frame length, and so on) necessary for demodulation from the signal captured by the non-allocated antenna. The STA then demodulates the signal after VHT-STF by using the decided antenna. By using the above frame format and receiving process, the proposed method can be realized. Figure 3: Frame format and receiving process for IEEE 802.11 ac. (**a**) Example frame format with the proposed pre-cording region and general pre-coding region; and (**b**) the receiving process with the concept of the proposed antenna decision method.
## 5 Conclusions  
The aim of the present work is to evaluate the main effects of tensile axial loads in the elastomeric bearings of the base-isolation system on the nonlinear seismic response of r.c. framed structures subjected to the horizontal and vertical components of near-fault earthquakes. To fill this gap, three base-isolated r.c. office buildings with three-, seven- and ten-storeys are designed in compliance with the Italian technical code NTC18, considering three values of the nominal stiffness ratio and assuming that all the buildings are located in a high-risk seismic zone. For each structure, incremental dynamic analysis is carried out with regard to three different near-fault EQs with prevailing horizontal component, prevailing vertical component, and comparable horizontal and vertical components, respectively. The following conclusions can be drawn from the results.  
* Ductility demands for beams under near-fault ground motions with prevailing horizontal component (e.g., Chi-Chi EQ) are more evident, especially at the end section of the lower storeys, while comparable ductility demands along the building height can be obtained when also the vertical component is significant (e.g., Northridge EQ). On the other hand, the highest ductility demands in the mid-span section of beams are recorded at the upper storeys when significant contribution of the vertical component is added (e.g., Northridge and Imperial Valley EQs).
* For all the examined test structures and typologies of near-fault ground motions, the lowest values of ductility demand of the r.c. frame members are generally obtained considering the CM2 CR model; moreover, the simplified VEL model of the HDRBs is generally the most conservative, producing upper-bound values of ductility demand, while overlapping results are provided by IDAs with the CM1 and CM1C models.
* Maximum values of the horizontal and total shear strains of the HDRBs are attained for near-fault ground motions characterized by prevailing horizontal component (e.g., Chi-Chi EQ), and precautionary estimation corresponds to the CM2 CR model while lower and similar results are provided by the other models.  
* Maximum values of the ratio between the compressive and critical axial load in the HDRBs are induced by earthquakes with significant vertical component acting alone (e.g., Imperial Valley EQ) or in combination (e.g., Northridge EQ) with a significant horizontal component. The CM2 CR and VEL models define the upper and lower bounds of this parameter, respectively, while the CM1 and CM2C models provide intermediate values of the same quantity.
* Maximum values of the ratio between the tensile and cavitation axial loads in the HDRBs are influenced by the nominal stiffness ratio of the isolation system; the most conservative results under the Imperial Valley and Northridge EQs could be provided by nonlinear models with different level of complexity rather than the CM2 CR model.
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 6 Discussion  
The experimental results clearly show the differences between the performance of the considered models and the setups in which they were applied. The numerical results presented in Table 3 show that the most accurate results were obtained when using a full 3D setup. This manifests itself by the highest Dice scores obtained for each of the considered models. On the other hand, the box-based setup seems to be less accurate, with the lowest Dice scores. It is somehow surprising since the amount of training data was largest in the box-based setup. However, these experiments show that the completeness of the information contained in the smaller amount of the whole volumes is more beneficial to the model than a large number of pieces of information sliced in the boxes. Probably for these reasons, the full 3D setup performed visibly better than the competitive frameworks.  
When the U-Net-based models are considered, MultiRes U-Net is the leader. This model performed best for all the considered setups, resulting in the most accurate segmentation for the slab-based approach. On the other hand, the original U-Net model performs worst, with the lowest Dice scores obtained for each of the considered frameworks. The training behavior observed in Figure 3 confirm the best performance of MultiRes U-Net.  
The above observations are also confirmed by the visual results presented in Figure 5. Mainly, the results of the U-Net model are visibly most noisy, with many small regions distant and not connected to the main vessels (see Figure 5b). On the other hand, the best visual representation of the liver vessels is obtained from MultiRes U-Net. From Figure 5d, one can see that that the resulting vessels are larger and more continuous, with the lowest amount of noise. Further, MultiRes U-Net provides the best segmentation of the largest branches. This latter fact is a significant advantage to automatically build the Couinaud representation of the liver, which is commonly used by clinicians of various specialties to describe the locations of hepatic lesions.  
However, MultiRes U-Net is the most expensive to train in terms of computer resources. In all considered setups, the time per epoch for this model was about twice  
Figure 5: 3D views of segmentations obtained from the first patient of IRCAD dataset, with the full 3D volume approach. The upper part of the visualization is also zoomed in (bottom).  
as much as that of the U-Net model, and slightly longer than the time required by Dense U-Net.
## 3 Results and Discussion
### Optimization of Corn Straw Epidermal Bonding Parameters
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Degree of Freedom** & **Mean Square** & \(F\) & \(p\) \\ \hline Model & 14 & 1318.29 & 5.06 & 0.0017 \\ \(X_{1}\) & 1 & 7444.80 & 28.59 & \(<\)0.0001 ** \\ \(X_{2}\) & 1 & 4074.22 & 15.65 & 0.0013 ** \\ \(X_{3}\) & 1 & 238.77 & 0.9170 & 0.3534 \\ \(X_{4}\) & 1 & 13.95 & 0.0536 & 0.8201 \\ \(X_{1}X_{2}\) & 1 & 877.64 & 3.37 & 0.0863 \\ \(X_{1}X_{3}\) & 1 & 3.15 & 0.0121 & 0.9139 \\ \(X_{1}X_{4}\) & 1 & 608.86 & 2.34 & 0.1470 \\ \(X_{2}X_{3}\) & 1 & 339.48 & 1.30 & 0.2714 \\ \(X_{2}X_{4}\) & 1 & 369.60 & 1.42 & 0.2520 \\ \(X_{3}X_{4}\) & 1 & 92.64 & 0.3558 & 0.5597 \\ \(X_{1}^{2}\) & 1 & 1364.48 & 5.24 & 0.0370 * \\ \(X_{2}^{2}\) & 1 & 100.65 & 0.3866 & 0.5434 \\ \(X_{3}^{2}\) & 1 & 2961.33 & 11.37 & 0.0442 * \\ \(X_{4}^{2}\) & 1 & 1182.38 & 4.54 & 0.0500 \\ Interecept & 15 & 260.37 & & \\ \(\mathrm{Lack}\) of fit & 10 & 350.03 & 4.32 & 0.0600 \\ Pure error & 5 & 81.05 & & \\ Cor total & 29 & & & \\ \hline \hline \end{tabular} Note. ** means highly significant (\(p<0.01\)), * means significant (\(0.01\leq p<0.05\)).  
\end{table} 
Table 5: Bending damage force response surface quadratic regression model analysis of variance. The calculation by the constraint solver tool of Design\(-\)Expert shows that when \(X_{1}=6.8571\times 10^{9}\) N/m and \(X_{2}=5.2297\times 10^{8}\) N/m, the simulated test damage force is 124.69 N, which is a 2.4% error from the average bending damage force of 127.8 N of the actual test. The bending damage curve of the epidermis is shown in Figure 11, which shows that the bending damage process of the epidermis is divided into three stages, including extrusion deformation, bending damage, and fracture stage. The epidermis in the actual test is tested by removing the inner flesh of the corn straw, retaining the outer bark of the straw can better ensure the mechanical properties of the epidermis itself cylindrical structure, and cutting the cylindrical structure of the destructive force at a greater force than the destructive force required for the lamellar structure. At the beginning of the test, the soft tissue of the epidermis is deformed, and the change in the damage force value is not obvious; with the increase in displacement, the epidermis is bent and damaged, and the damage force is linearly increased, and then the damage force increases slowly until it reaches the maximum value. At this stage, the internal tissues of the epidermis fractures successively until the bending damage force exceeds the binding strength of the vascular bundles of all epidermal tissues, causing the epidermis to fracture. The epidermal specimens used in this test are irregular hollow cylinders, and there are differences in the bending process in terms of the damage force applied to different parts, which may lead to fluctuations in the bending damage force and poor smoothness of the curve in the actual test.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 5 Experimental Results
### Special Case  
This map was U-shaped, with more rooms and entrances compared with the map with low complexity. When the same pathfinding algorithm was applied to the preprocessed topology map, the average number of nodes visited (n = 260 nodes) was reduced by 99.82% compared with the average of 143,376.6 nodes visited in the case of the existing image map (to search for a path to the destination). Although the image resolution did not increase compared with the map data with intermediate complexity, the time required for pathfinding through the image map increased, as there were more obstacles and the distance to be moved increased substantially. As a result, when only A* was used, it took an average of 557.6 ms. The average time required for preprocessing through the CNA was 582.8 ms, and when the same algorithm was applied to the topology map generated by the CNA, it took 2.8 ms, which means the overall execution time increased by 99.01%.