esa-sceva/satcom-chunk-collection huggingface dataset:
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 4 Experimental Results
### CNN Testing Evaluation  
When testing the CNNs, it was observed from the evaluation metrics presented in Table 4 that the CNN based on ResNet50V2 obtained the highest accuracy with 99.71% \(\pm\) 0.1% average, followed by VGG16 with 99.39% \(\pm\) 0.2% average. At the same time, the ResNet50V2-based CNN had the highest Recall with 99.47% \(\pm\) 0.2% average for the Drowsy class.  
The overall accuracy and recall of the Drowsy class of 10 experiments for each of the three networks is presented in Figure 11. Where, in Figure 11a, it was observed that the ResNet50V2-based network had the best performance with a median of 99.71% accuracy. Meanwhile, in Figure 11b, it can be seen that the VGG16-based network and ResNet50V2-based network had the same median value with a 99.41% recall in class Drowsy. It can also be observed that the network based on VGG16 presents an optimal performance compared to the training results presented in Figure 9.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**CNN Based on** & **Class Name** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{3}{*}{InceptionV3} & Not drowsy & 0.9908 \(\pm\) 0.003 & 0.9957 \(\pm\) 0.002 & 0.9928 \(\pm\) 0.001 & \multirow{3}{*}{0.9927 \(\pm\) 0.001} \\  & Drowsy & 0.9957 \(\pm\) 0.002 & 0.9908 \(\pm\) 0.003 & 0.9927 \(\pm\) 0.001 & \\ \hline \multirow{3}{*}{VGG16} & Not drowsy & 0.9937 \(\pm\) 0.003 & 0.9941 \(\pm\) 0.005 & 0.9939 \(\pm\) 0.002 & \multirow{3}{*}{0.9939 \(\pm\) 0.002} \\  & Drowsy & 0.9941 \(\pm\) 0.005 & 0.9937 \(\pm\) 0.003 & 0.9939 \(\pm\) 0.002 & \\ \hline \multirow{3}{*}{ResNet50V2} & Not drowsy & 0.9948 \(\pm\) 0.002 & 0.9994 \(\pm\) 0.001 & 0.9971 \(\pm\) 0.001 & \multirow{3}{*}{0.9971 \(\pm\) 0.001} \\  & Drowsy & 0.9994 \(\pm\) 0.001 & 0.9947 \(\pm\) 0.002 & 0.9971 \(\pm\) 0.001 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metrics in Testing (Test).  
Figure 11: Boxplot of two evaluation metrics in test. (**a**) Accuracy of the three networks. (**b**) Recall of Drowsy class of the three networks.  
Figure 10: Radial behavior of two training metrics. (**a**) Accuracy of the three networks in radial. (**b**) Recall of Drowsy class of the three networks in radial.  
The radial behavior of the 10 test experiments are shown in Figure 12, showing the two evaluation metrics of overall accuracy and recall of the Drowsy class. Where, in Figure 12a, the ResNet50V2-based network performed better than the other two networks. Whereas, in Figure 12b, it was observed that the ResNet50V2 and VGG16-based networks performed similarly in the results of the 10 experiments. Thus, the performance of the VGG16-based network was significantly improved compared to the training results presented in Figure 10.
## 3 The Proposed Architecture
### Architecture Implementation  
The proposed knowledge-based cognitive architecture was implemented using Amazon Web Services (AWS). AWS provides many of the building blocks required to build a secure, flexible, cost-effective lambda architecture in the cloud. A lambda architecture describes a system consisting of three layers: (1) a batch layer runs a batch job iteratively, and data arriving after a job starts is processed by the next job; (2) a speed layer directly retrieves data streams from data sources, processes them, and updates the results into the real-time views in the database in the serving layer, and it does not keep any history records, and typically uses main memory-based technologies to analyse the incoming data; and (3) a serving layer is to provide responses to users' queries. Hence, in the serving layer, users can be allowed to input their points of view in terms of the views from the speed and the batch layers, and the system fuses them to output final decisions. Therefore, we can create our proposed architecture by combining AWS services with Apache Spark Streaming and Spark SQL for anomaly detection based on live data streams. Figure 4 illustrates the implementation our knowledge-based cognitive architecture under the lambda architecture. In the batch layer, the batched data were pre-processed, the pre-processed data (features) are input to the trained models in the speed layer, and the outputs of the models are sensed-checked with the rules in the knowledge base. Finally, the results are shown to users for evaluation.
## 4 Discussion  
This study proposed to computationally evaluate the effect of pad shape and material stiffness on plantar pressure distribution and the reduction in diabetic foot with neuropathy using the FEA. The current study found a softer material used in the heel-forefoot pad tends to reduce the frictional stresses at the forefoot and hindfoot compared with the heel pad and the insole without a pad. The heel pad designed from a softer material distributed the plantar pressure from the hindfoot compared with stiffer materials and the heel-forefoot pad fabricated from soft to stiff materials. Likewise, the peak contact pressure at the medial and central forefoot was reduced by the soft heel-forefoot pad compared with the soft heel pad only and the insole without a pad. The principles of the reduction in peak contact pressure in a diabetic foot with neuropathy require the consideration of various designs and material selections with the inclusion of frictional stress. A CMI is beneficial for a diabetic neuropathic foot for the reduction in peak plantar pressure, especially at the forefoot [37; 38; 39; 40]. However, some patients require a further reduction in peak plantar pressure either at the forefoot or hindfoot to prevent ulceration and to reduce the occurrence of pain. Experimental and FEA studies have highlighted the heel as one of the most frequently encountered regions with pain in all populations, including athletes and diabetic patients with a high risk of foot ulceration [41; 42]. Metatarsal pads and small plugs are used to reduce the peak plantar pressure under metatarsal heads [37; 43]. Using a CMI causes comfort in the foot due to the reduction and uniform distribution of the plantar pressure [44]. A previous experiment indicated that the CMI material and its modifications significantly lowered peak contact pressure at the forefoot and hindfoot, including shifting contact pressure to the midfoot [45]. A similar trend was found in this current study with a reduced peak planar pressure with the use of softer pads compared with the insole without a pad, firm pads, and voids. Appropriate CMI material selection and pad design play a major impact on contact pressure reduction and redistribution under the hindfoot, and forefoot compared with a flat insole and no insole. The thickness and stiffness of the CMI material reduced the plantar pressure distribution in [46]. In our work, a softer material sandwiched between stiffer materials in addition to varying pad designs fabricated from a low Young's modulus material could further reduce the peak contact pressure, especially at the heel. A similar trend of reduction was found with an appropriate selection of CMI material and thickness for people with prolonged standing at work [15; 17; 42]. By changing the designs of CMIs including pads, we can further reduce and redistribute the peak contact pressure in a diabetic neuropathic foot. Evidence suggests using a CMI with a metatatarsal pad and a metatarsal bar can redistribute the plantar pressure. However, whether the metatarsal pad
# Dynamic Response Analysis of Parallel Twin Tunnels Under Different Train Loads
## 2 Background Project and Vibration of Train Type A  
The engineering background is a super-close twin tunnel in Wuhan designed for high-capacity type A trains [17] that is suitable for an exploration of overload issues. The tunnel segment is precast by C60 concrete, with an outer diameter of \(D\) = 6.2 m and a segment thickness of 0.35 m. The net distance between the two tunnels is 0.25 \(D\), with a buried depth of 1.5 \(D\). The soil layer crossed is mainly silty sand, characterized by a bulk density of 20 kN/m\({}^{3}\), compressive modulus of 21.9 MPa, friction of 31.9\({}^{\circ}\), cohesion of 7.0 kPa, and Poisson's ratio of 0.49. The dynamic responses of the structure and the surroundings of the right tunnel (T2) are investigated when type A trains with different loads pass through the left tunnel (T1). The weight of the carriage is 38 tons. Based on crowd weights of 0 t, 6 t, 12 t, 18 t, 24 t, 30 t, 36 t, and 42 t, eight conditions (A\({}_{1}\)-A\({}_{8}\)) are defined. Crowd weights of 0-6 tons are classified as normal load conditions, while all other conditions are considered overload conditions. The load vibration curve of the type A train can be expressed by a sinusoidal function with time [18], as shown in Figure 1.
## 2 Least Mean Squares Algorithm  
An adaptive digital filtering system is generally used for active control of noise and vibration. The least mean squares (LMS) used in the system shown in Figure 1 below is the most commonly used feedforward algorithm [12].  
The formula for updating the weights of an LMS algorithm is defined as  
\[\begin{split} w(k+1)&=w(k)+1/2\cdot\mu[-\
abla]\\ &=w(k)+\mu\cdot\left[x^{T}(k)\cdot d(k)-x(k)\cdot x^{T}(k)\cdot w( k)\right]\\ &=w(k)+\mu\cdot\left[x^{T}(k)\cdot\left(d(k)-x(k)\cdot w(k) \right)\right]\\ &=w(k)+\mu\cdot x^{T}(k)\cdot e(k)\end{split} \tag{1}\]  
Here, \(\mu\) is a factor that determines the convergence speed of an LMS algorithm. It is referred to as step size and is related to the update of the weights. When \(\mu\) is relatively small, it takes a long time to find the weight that minimizes the slope of the squared error curve because the change in the weight is small at each sampling time. However, when \(\mu\) is relatively large, the control system becomes unstable and fails to find the correct value. Therefore, \(\mu\) is critical to the performance of an LMS algorithm.  
The range of \(\mu\) for the convergence of an LMS algorithm is as follows, where \(\lambda_{\max}\) is the maximum eigenvalue of the autocorrelation matrix consisting of input signal \(x(k)\).  
\[0<\mu<\frac{1}{\lambda_{\max}} \tag{2}\]  
As described above, an LMS adaptive filter involves simple calculations. However, since the convergence speed is relatively less, it takes a long time to approach the optimum filter coefficient. In addition, the convergence characteristic is very dynamic. Particularly, since the step size is fixed, a drastic change in the target signal cannot be tracked properly. To overcome these limitations, several adaptive filter algorithms with variable step size have been studied recently. Most of these algorithms use variable steps represented by complex expressions, and therefore require extensive computations, although they use optimal step size to adapt to the magnitude and direction of the target signal. For example, when the response signal of the system changes greatly as a transient state at the beginning of a signal input to the system, a relatively large step size is applied. After a certain time, the weight of the adaptive filter becomes an optimal value, the error signal becomes smaller, and consequently, the step size becomes relatively small. Finally, in the steady state, the error value becomes close to 0, and the step size can be used as a fixed value without a large change.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 3 Proposed Algorithm  
### SINR and MCS  
We consider wireless networks consisting of \(N\) nodes in a two-dimensional Euclidean space. For the calculation of the signal strengths and SINRs of received packets, we use a simple radio transmission model similar to the one used in [28] (Equation (1)) except for the random channel gains for simplicity in analysis: We assume that a node of interest is located at \(x_{0}\) without loss of generality, and calculates the signal strength (\(P_{i}\)) of a received packet sent from node \(i\) located at \(x_{i}\) as  
\[P_{i}=A\cdot P_{i}\|x_{0}-x_{i}\|^{-\alpha}, \tag{1}\]  
where \(i=1,2,3,\ldots,N-1\), \(A\) is a constant to represent radio environments such as antenna gains, \(P_{i}\) is the transmit power of nodes, \(\alpha\) is the path loss exponent parameter, and \(\|\cdot\|\) represents the Euclidian norm (When the distance between a sender and a receiver is less than 1 meter, the Euclidian norm is set to 1 to prevent possible overestimation of the received signal strengths from the arbitrary proximity of nodes owing to the property of PPP). In this paper, \(A\) is set to \(\sim\)5.06, \(P_{i}\) is set to 100 mW, \(\alpha\) is set to 4, and we do not consider short-term fading effects such as shadowing.  
By using Equation (1) and the definition of SINR, when the node at \(x_{0}\) is communicating with node \(i\), the SINR of a received packet from node \(i\), \(SINR_{i}\), is calculated as  
\[SINR_{i}=\frac{P_{i}}{\sum_{j\
eq i}A\cdot P_{i}\|x_{0}-x_{j}\|^{-\alpha} \frac{1}{\{j\text{\,interferes with }i\}}+W}, \tag{2}\]  
where \(x_{j}\) is for locations of nodes whose packets interfere with desired packets from node \(i\), and \(\mathbb{1}\) is the indicator function. Thus, \(\sum_{j\
eq i}A\cdot P_{i}\|x_{0}-x_{j}\|^{-\alpha}\mathbb{1}_{\{j\text{\, interferes with }i\}}\) represents the cumulative power (i.e., interference) of interfering packets while the desired packet from node \(i\) is being received. \(W\) is the thermal noise power at receivers and it is assumed to be \(-126\) dBW in this paper.  
In IEEE 802.11 standards, such as IEEE 802.11a, multiple MCS levels are defined [14]. Each MCS level requires satisfaction of the minimum SINR for correct demodulation of received packets of the MCS level. For example, the IEEE 802.11 Ext module in ns-2, which we use for the evaluation of the proposed algorithm, supports four MCS levels [29], as listed in Table 1 (Note that ACK frames are also  
Figure 1: Changes of cumulative power (Figure 5.1 in [27]/CC BY).  
sent using the MCS levels). In Table 1, we also list the (physical layer) transmission speed and the minimum SINR requirement (\(\tau_{k}\)) of each MCS level (\(k\)).
# Timed-Release Silica Microcapsules for Consistent Fragrance Release in Topical Formulations
## 3 Results and Discussion
### Synthesis of Sol-Gel Microcapsules with Cationic Emulsifier Suitable for Skin
#### 3.1.3 Evaluation of Fragrance-Release Profiles Based on Microcapsule Size  
Dispersions of the microcapsules were diluted in DI water at a ratio of 1:9, and the fragrance evaporation rate from the silica microcapsules was determined by measuring the residual fragrance at 36 \({}^{\circ}\)C for 24 h. As shown in Figure 1B, the non-encapsulated fragrance completely evaporated within 6 h at 36 \({}^{\circ}\)C. However, for the microcapsules prepared with 0.1% emulsifier, 35% of the fragrance remained after 24 h, meaning that 65% was gradually released over this period. In contrast, the microcapsules prepared with 0.025% emulsifier released over 70% of the fragrance in the first 8 h, with less than 10% released during the subsequent 16 h. These microcapsules were unsuitable for timed-release applications, as the fragrance became too weak to be perceived after 8 h following drying.  
Microcapsules prepared with emulsifier concentrations of 0.4% and 0.8% exhibited more rapid fragrance evaporation than those prepared with 0.1% emulsifier. In particular, microcapsules with 0.8% emulsifier showed significant evaporation, with over 90% of the fragrance evaporating within 8 h.  
It has been reported that microcapsules with a uniform wall thickness exhibit greater stability as their size decreases, allowing for prolonged retention of active ingredients [41; 42]. This effect is demonstrated in melamine-based and polyurethane polymers [41; 42], and similar results are anticipated for silica microcapsules. In this study, we only assessed the stability of the silica microcapsules prepared with 0.1% emulsifier. It is hypothesized that the accelerated formation of silica, likely driven by the high concentration of the cationic emulsifier (which acts as a catalyst in the sol-gel reaction), resulted in a porous microcapsule wall structure, leading to rapid fragrance evaporation [10; 43]. Furthermore, at emulsifier concentrations exceeding 0.8%, the sol-gel reaction proceeded rapidly, resulting in the formation of silica nanoparticles rather than core-shell microcapsules [43].
## 4 Results
### Datasets  
We experiment on four popular action recognition datasets: the widely used as a benchmark dataset Kinetics400 [30], the skeleton-based widely used benchmark dataset NTU RGB+D 60 [31], NTU RGB+D 120 [32], and the fine-grained dataset FineGym-99 [33].  
Figure 5: The details of the classification token fusion method.  
In Kinetics400, there are some videos that are not human-centric, so it is impossible or difficult to extract skeleton data from these videos. After weeding out some invalid videos, Kinetics400 consists of 239K training videos and 20K validation videos of 400 action classes. The videos are sampled by stratified sampling to ensure a uniform distribution between the training and test sets.  
NTU RGB+D is a large-scale human action recognition dataset collected indoors. NTU RGB+D 60 contains 57K videos of 60 action classes. NTU RGB+D 120 contains 114K videos of 120 human actions from 106 subjects. Both datasets provide 3D skeleton sequences collected by Microsoft Kinect v2 sensors [34] for these videos. In order to be consistent with other datasets, we still use the Top-Down pose estimator, HRNet, to obtain skeleton data. Meanwhile, we use the Cross-subject (X-sub) splits through all experiments which split the dataset as reported by the dataset authors. Similarly, we weed out the videos where skeleton data is missing.  
FineGym99 has 29k videos of 99 fine-grained gymnastic action categories. The same as [10], for the skeleton stream, during skeleton extraction, we use the ground truth bounding boxed for the athletes in all frames. After stratified sampling, we used 23K videos for training and 6K videos for testing.
## 8 Computational Time Estimation  
A practical objective quality metric should not only correlate with subjective quality perception but should also assess the quality as fast as possible. Unfortunately, the SSIM metric, as defined in [11], introduces high computational and memory bandwidth complexity. Assuming the default size of the Gaussian weighting mask (\(\omega\)) equal to 11 \(\times\) 11, the calculation of SSIM requires 242 memory accesses and \(\sim\)1000 multiplications for each component of a pixel. To mitigate the mentioned complexity, the proposed IV-SSIM metric was designed to be parallelizable by spreading the metric calculation across multiple CPU cores.  
The computational performance was evaluated using our experimental, parallel implementation of \(\text{SSIM}_{\text{Y}}\), \(\text{SSIM}_{\text{YUV}}\), and IV-SSIM. This experimental implementation is not fully optimized and leaves possibilities for prospective performance improvements. We expect that fully optimized and vectorized implementation would be noticeably faster, but production-quality software implementation is out of the scope of this paper.  
Figure 6 compares IV-SSIM with 16 evaluated state-of-the-art metrics in terms of the computational time required for assessing the quality of a single frame of a 4K \(\times\) 4K video. These results are presented together with SROCC values estimated in the immersive video experiment (cf. Section 6).  
As presented in Figure 6, quality assessment of a single frame of a high-resolution sequence requires only 1.3 s (in a multi-thread implementation), which is faster than most tested implementations of state-of-the-art metrics. Simultaneously, IV-SSIM provides the highest correlation with MOS (mean opinion score).  
Figure 5: SROCC and PLCC values for all considered metrics; TID2013 database.
## 4 Experiments
### Evaluation Indicators  
In this study, we utilize mean Average Precision (mAP), the total number of model parameters, and GFLOPs as evaluation metrics.  
* mAP (mean Average Precision): mAP represents the weighted mean of the Average Precision (AP) values for each class, acting as a metric to assess the model's overall performance in detecting across all categories. The formula for its calculation is shown in Equation (16). \[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\] (16) where \(AP_{i}\) denotes the AP for the class indexed by \(i\). N stands for the total number of object classes in the training dataset (in this study, N = 10). mAP0.5 refers to the mAP of the detection model when the IOU threshold is set to 0.5, while mAP0.5:0.95 indicates the mAP when IOU values range from 0.5 to 0.95 in steps of 0.05.
* AP (Average Precision):
* Average precision is the weighted average of precision (P) over all recall (R) values, with its calculation method outlined in Equation (17). \[\text{AP}= \int_{0}^{1}P(R)d(R)\] (17)
* Precision (P): Precision is the ratio of the number of true positive predictions to the total number of detected samples, and the formula for its calculation is shown in Equation (18). \[P=\frac{TP}{TP+FP}\] (18) where TP is true positive, where a predicted positive sample is indeed positive, and FP is false positive, where a predicted positive sample is actually negative.
* Recall (R): Recall is the ratio of the number of true positive predictions to the total number of actual samples, and the formula for its calculation is shown in Equation (19). \[\text{R}= \frac{TP}{TP+FN}\] (19) where FN is false negative, where a predicted negative sample is actually positive.
## 4 Numerical Examples
### Forward Modeling  
In this experiment, we verify the accuracy of forward modeling of the wave equation based on PIRNN and demonstrate the enhanced modeling precision achieved through high-order staggered-grid finite-difference methods and the implementation of PML boundaries. A homogeneous velocity model is selected for forward modeling, with a Ricker wavelet employed as the source function. The specific parameter settings are provided in Table 2.  
Using the parameter settings in Table 2, we plotted wavefield images with and without PML layers, as well as wavefields with different finite-difference orders (Figure 8). Figure 8,b illustrate the wavefield at \(t=0.5\) s with and without PML layers, respectively. without PML layers, acoustic waves exhibit significant reflections at the boundaries during propagation. These boundary reflections adversely impact shot-gather recordings in VSP observation systems, thereby degrading the resolution of FWI results. Another factor influencing shot-gather quality is dispersion. Higher wavelet frequencies improve shot-gather resolution but are more susceptible to dispersion. Figure 8,d demonstrate the impact of different staggered-grid finite-difference orders on the wavefield at a wavelet frequency of \(f=40\) Hz. Figure 8 employs 10 th-order central differences, while Figure 8d uses 4 th-order central differences. Evidently, at higher wavelet frequencies, Figure 8d exhibits noticeable dispersion due to the insufficient difference order. This dispersion leads to the reception of numerous false reflection signals in VSP observation systems, consequently affecting inversion performance. In conclusion, we achieved high-precision wavefield forward modeling by incorporating PML layers and high-order staggered-grid finite-difference methods into our PIRNN model, providing a robust foundation for inversion.