esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny
## 2 Materials and Methods
### Structured Pruning for Eye State Detection  
To optimize the YOLOv7-tiny model for the specialized task of detecting eye states, a method known as structured pruning is employed. This approach elaborately targets and eliminates less-essential scales of feature maps that do not significantly contribute to the accuracy of detecting small to medium-sized objects such as eyes, which are critical in the context of image resolution and camera proximity.  
Structured pruning involves the strategic removal of layers or channels associated with these non-essential scales. For example, coarser 20 \(\times\) 20 feature maps, which typically provide less detail required for detecting eye states, can be pruned to reduce redundancy. This reduction reallocates computational resources to more relevant feature map scales, thereby enhancing both the detection speed and accuracy.  
This study begins with the baseline configuration of the YOLOv7-tiny model. The primary objective here is to identify fine details such as the open or closed state of eyes, necessitating higher resolution feature maps like the 80 \(\times\) 80 scale. These maps are more capable of capturing the intricate details essential for accurate eye state analysis. Consequently, we introduce the YOLOv7-tiny-First-Prune architecture, which eschews the model's original components tied to the 20 \(\times\) 20 feature map scale, as depicted in Figure 2. This strategic decision is predicated on the understanding that the task of eye state detection does not necessitate the discernment of less detailed spatial information, often associated  
Figure 1: Architectural overview of YOLOv7-tiny: integrating ELAN and SPPCSPC for enhanced object detection.  
with the detection of larger objects. Progressing further, the YOLOv7-tiny-Second-Prune architecture streamlines the network even more by also excising elements contributing to the 40 \(\times\) 40 feature map scale, as illustrated in Figure 3. This refinement suggests that the network can forgo processing details of larger and medium-sized objects, which are extraneous for the detection of eye states. The impact of these pruning interventions on the model's performance is meticulously evaluated to ascertain that the optimized model maintains its efficacy for the intended detection task. This approach underscores a concerted effort to balance model complexity with performance efficiency, ensuring the YOLOv7-tiny model remains a viable and effective tool for real-time eye state detection applications.
## 4 Experimental Results
### Statistical Significance Analysis  
To validate the statistical significance of our improvements over baseline models, we conducted paired t-tests on AUC, EER, and BPCER\({}_{10}\) across 10 repeated trials. As shown in Table 8, our method achieves significantly better performance than U-OCFR in all three metrics (\(p\) < 0.05), and outperforms PPAD in BPCER (\(p\) = 0.0420). These results support the robustness and consistency of the proposed method.
## 4 Evaluation of Model Performance
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Net**} & \multicolumn{4}{c}{**Melanoma (\%)**} & \multicolumn{4}{c}{**Seborrheic Keratosis (\%)**} & \multicolumn{1}{c}{**REF**} \\ \cline{2-10}  & **ACC** & **AUC** & **SENS** & **SPEC** & **ACC** & **AUC** & **SENS** & **SPEC** & \\ \hline ResNet-50 & 83.0 & 80.4 & 43.6 & 92.5 & 87.7 & 88.0 & 62.2 & 92.2 & \\ \hline DenseNet-121 & 82.8 & 83.5 & 58.1 & 88.8 & 88.3 & 91.0 & 66.7 & 92.2 & \\ \hline ResNet-50 \& LBP & 83.8 & 78.4 & 52.1 & 88.6 & 90.5 & 90.9 & 72.2 & 91.3 & \\ \hline DenseNet-121 \& LBP & 85.8 & 83.2 & 61.5 & 91.7 & 91.7 & 93.4 & 72.2 & 95.1 & \\ \hline \multicolumn{2}{c}{_Our Method_} & **89.3** & **87.1** & **89.0** & **89.3** & **86.3** & **91.6** & **86.0** & **86.3** & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation results of the proposed method and different DCNN models for the ISIC 2017 dataset; ACC (accuracy), SENS (sensitivity), SPEC (specificity). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**REF** & **Team/Authors** & \multicolumn{4}{c}{**Melanoma (\%)**} & \multicolumn{4}{c}{**Seborrheic Keratosis (\%)**} \\ \cline{3-10}  & **ACC** & **AUC** & **SENS** & **SPEC** & **ACC** & **AUC** & **SENS** & **SPEC** \\ \hline
[31] & [PERSON] et al. & 83.0 & 83.0 & 43.6 & 92.5 & 91.7 & 94.2 & 70.0 & 99.5 \\ \hline
[37] & [PERSON] et al. & - & 85.3 & 67.5 & 90.9 & - & 95.5 & 88.9 & 92.8 \\ \hline
[38] & [PERSON] et al. & 83.7 & 85.9 & 59.0 & 89.6 & 90.8 & 95.1 & 77.8 & 93.1 \\ \hline
[39] & [PERSON] et al. & 85.8 & 83.2 & 61.5 & 91.7 & 97.7 & 93.4 & 72.2 & 95.1 \\ \hline - & _Our Method_ & _89.3_ & _87.1_ & _89.0_ & _89.3_ & _86.3_ & _91.6_ & _86.0_ & _86.3_ \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison result with a previous related references for the ISIC 2017 dataset; ACC (accuracy), SENS (sensitivity), SPEC (specificity).
## 2 Related Work
An experimental demonstration of a low-cost OCC system with a 2D-constellation-assisted 4-CSK transmission scheme is proposed in paper [18]. The system could achieve a BER of \(3.8\times 10^{-3}\) for the distance of 0.8m and a data rate of 10.74 kb/s. A novel CSK-based OCC system is proposed in paper [19], which uses an 8-CSK modulation pattern to obtain pixel efficiency of 3.75 pixels per bit. Here, Pixel efficiency is a performance evaluation parameter for an OCC system, which is the minimum number of pixels required to represent a bit. In paper [20] an LED-to-camera Communication system called ColorBars is proposed, which addresses major challenges about color flicker, inter-frame data loss, and receiver diversity. It was observed that when ColorBars use lower CSK modulation, reliable communication is guaranteed due to the extremely low symbol error rate. These works aided metrics selection for performance evaluation of our proposed technique. These works have provided us with sufficient insight and motivation to explore the OCC-based LiFi domain further. We have tried to contribute to this growing domain with our proposed testbed and modulation technique. Our work is inspired by these related works and attempts to extract, combine, and modify them to create a better solution.
# Tantalum Alloy Welding: Does the Thermal Cycle Influence the Microstructure?
## 2 Materials and Methods  
### Sample and Welding  
For the study, it is necessary to consider the characteristics of the material to be evaluated. Table 1 shows the standard physical properties of pure tantalum, assimilated in this work to those of the alloy B 52 (TBC), according to the Alloy Casting Institute (ACI).  
To perform the welded joint, three specimens of the tantalum alloys were prepared as butt joints with square edges of 1.5 mm thickness \(\times\) 32 mm outside diameter pipes (150 mm of length each of them). Figure 1 shows the schematic picture of the welding test samples. AWS standards suggest the use of the WT 2% thorium tungsten electrode, as it has an oxide quantity between 1.7-2.2% ThO\({}_{2}\), which enhances arc ignition and reduces welding puddle contamination. Table 3 reproduces the welding characteristics and parameters.  
The authors in [11] insist on special care with welding environment, pre-welding cleaning, and welding protection, due to the welding characteristics of tantalum materials. Strict technological measurements in cutting, groove processing, and GTAW process preparation were used to minimize the effect of foreign variables. No vacuum chamber was used. No current pulses were used during welding. Authors, such as [12], suggest a grain refinement process when temperatures in the melted pool decrease for a short time (to be raised again the next instant), creating nucleation by partial solidification.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Analysis of Results
#### 4.3.4 Detection Ratio % vs. No of Nodes  
Figure 9 provides the results of the detection ratio % of the proposed SBAODV scheme compared to other routing protocols in IoMT networks. The detection ratio is an essential parameter that measures the capability of the routing protocol to detect malicious nodes present in the network. We can observe from Figure 9 that when the number of nodes in the network increases, the chances of malicious devices also increase. Therefore, it is crucial to detect them and ensure the safety of the network.  
The Figure illustrates that as the number of nodes increases, the detection capability of SBAODV increases as well, reaching up to 95% at 100 nodes. The proposed SBAODV scheme outperforms other routing protocols with its excellent detection capability. This is due to the Stackelberg game theory algorithm, which observes the behavior of all devices and UAVs in case it recognizes any abnormal behavior. It utilizes the strong Stackelberg strategy to tackle the black hole attackers. Moreover, our proposed scheme can utilize pure and mixed strategies to  
Figure 8: Throughput (kbps) vs. no of nodes.  
Figure 9: Detection ratio vs. no of nodes.  
tackle the black hole attackers, providing a high detection ratio compared to all other schemes. On the other hand, DADRP provides the second-best results, with its detection ratio decreasing gradually as the number of nodes increases. The results of this study provide insight into the effectiveness of different routing protocols in detecting malicious nodes in IoMT networks, which can help improve the security of these networks.
# Patients' Perceptions of Sound and Noise Dimensions in the Dental Clinic Soundscape
## 4 Discussion
### Preferences of Dental Patients Relative to Music Playing in the Dental Soundscape  
This study further examines participants' music preferences and anxiety-alleviating behaviors, revealing a range of choices. Preferences for relaxing instrumental, foreign pop, and classical music align with the literature suggesting their anxiolytic effects in healthcare settings [14; 21]. However, the relatively low irritation reported due to the absence of music in the dental clinic suggests that while music is appreciated, it might not be universally expected. A significant majority of participants prefer relaxing instrumental music while waiting, as suggested by previous studies [57; 58; 27], aiming to establish a healthy acoustic environment [45]. Yet, it remains unclear what elements should be prioritized for a healthy dental acoustic soundscape [44]. Additionally, many participants expressed a desire for random radio music, indicating the potential value of offering diverse music options to cater to varied preferences in healthcare settings [27].
## 4 Numerical Simulations and Discussion
### Uniform Circular Array (UCA)  
Some applications such as radar and tracking systems require estimation of both elevation and azimuth angles in order accurately to track targets that are moving in three dimensions. Hence, the proposed method is next applied to the circular array, with a range of azimuth angles [0, 360\({}^{\circ}\)] and elevation angles [0, 90\({}^{\circ}\)]. The type of received signals is BPSK with carrier frequency f\({}_{\rm c}\) = 10 GHz and the signals are distorted with AWGN. The other simulation parameters are the number of sensors \(M\) = 15, the number of samples is 100, SNR = 10 dB, and \(d\) = 0.5\(\lambda\). Three cases are considered in this simulation, with the directions of arrival generated randomly.  
In the first scenario, two signals incident on the sensor array from different directions are considered  
Figure 4: Performance estimation of the PDDA method with two AOAs. (**a**) 3D plot of the PDDA method; (**b**) 2D plot of the PDDA method.  
Figure 5: Performance estimation of the PDDA method with three AOAs. (**a**) 3D plot of the PDDA method; (**b**) 2D plot of the PDDA method.
## 5 Experiments and Result
### Results
The experimental results indicate that, in the proposed framework, unlike the baseline system, a large batch size is effective and exhibits the best performance when the batch size and number of utterances per speaker are 1920 and 4, respectively. These results can be posited as follows: the proposed method works stably when performing metric learning in large-scale batches using more accurate embedding from the temporal averaging model, MT. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Consistency Loss**} & \multirow{2}{*}{**NP**} & \multirow{2}{*}{**BC**} & \multirow{2}{*}{**LT**} & \multicolumn{2}{c}{**Vox1-O**} & \multicolumn{2}{c}{**Vox1-E**} & \multicolumn{1}{c}{**Vox1-H**} \\ \cline{6-8}  & & & & & **EER (\%)** & **EER (\%)** & **EER (\%)** \\ \hline \#1 (Org\_MT) & & \(\times\) & S & P & 4.98 & 4.86 & 8.61 \\ \#2 & \multirow{2}{*}{MSE} & \(\times\) & S & E & 3.56 & 3.15 & 5.61 \\ \#3 & & \(\times\) & D & E & 2.37 & 2.18 & 4.35 \\ \#4 & & \(\checkmark\) & D & E & 2.28 & 2.13 & 4.22 \\ \hline \#5 & GE2E & \(\checkmark\) & D & E & 2.27 & 2.21 & 4.3 \\ \hline \#6 & AP & \(\checkmark\) & D & E & 2.18 & 2.05 & 4.11 \\ \hline \#7 (Proposed) & GE2E-H & \(\checkmark\) & D & E & 1.98 & 1.88 & 3.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison and ablation experiment results of the proposed method. NP: negative pair, BC: batch configuration, S: same batch, D: different batch, LT: learning target, P: prediction, E: embedding.
## 3 Numerical Results
### Case of a Mattress of 100 mm on Reinforced Soil (R100)
#### 3.2.2 Settlements  
Figure 17 shows a comparison between the experimental and numerical settlements at the LTP base in the monotonic test M_R100. The numerical results predict well the settlements when compared to the experimental ones. With respect to the given pre-consolidation pressure \(p_{c}=2\times a_{0}=6\) kPa, a good fitting of the elastic to plastic transition (where \(p\) ranges 0 to 8 kPa) between the proposed the numerical results and measured data is showed. Additionally, both the numerical slopes of the normal consolidation line (where \(p\) ranges 8 to 30 kPa) are parallel to the experimental one. In addition, the HYP model curve is closer to the experimental curve than the CYsoil one.  
Figures 18 and 19 present the experimental and numerical cumulative settlements at the LTP base under different cyclic loading tests. The measured cumulative settlements after 50 cycles in the C1 and C2 tests are equal to 6.5 and 1.3 mm, respectively. Additionally, the higher the number of load cycles, the larger the cumulative settlements. The cumulative settlements of the system are well modeled by using the HYP model for the LTP. The HYP model models well the cumulative settlements within the first 10 cycles while the CYsoil model always gives very high values. The stiffness and the transition between the intergranular strain and grain rearrangement deformations allows the HYP model to eliminate ratcheting (i.e., an excessive cumulation of deformations for small stress cycles) [16]. Similarly to the cyclic loading tests with a slab, the cumulative settlements  
Figure 16: Experimental and numerical efficacies in test C2_R100.  
Figure 15: Experimental and numerical efficacies in test C1_R100.  
of the test C2 are smaller than the C1 ones. Less cumulative settlements in the mattress-reinforced soil system (R100) than in the slab-mattress-reinforced soil (D100) are observed (about two-thirds of reduction).
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Security Issues
#### 3.1.3 Availability  
Availability and consistency in BIOT are tightly linked together in a reversal relationship. If the availability is increased, then the consistency will be decreased, and vice versa. For example, Ethereum is considered faster than Bitcoin as shown clearly in the quicker block time, whereas it is less secure as the process of adding mined blocks requires multiple confirmations by many BC applications to prevent transactions from a double-spending attack, which happens when an attacker obtains service or commodities from the account holder, and then manages to restructure the transaction ledger so that the crediting transaction is reversed [75; 112]. Moreover, BIOT is exposed to a 51% attack which will cause the availability to be controlled by the attackers. Among the main attacks is the distributed denial of service (DDoS) which can abuse the process of authentication and trust by harming the system with an out-of-control number of requests to update and insert new records [124; 4].  
During information transfer, sophisticated security procedures are frequently ignored. As a result, attackers have the chance to intrude while data are being sent. Once this occurs, it will have an impact on the IoT system, leading user rights to be violated and the system to fail to execute specific duties [76]. The IoT captures substantial data and stores substantial personal and private information about users, such as passwords and personal preferences. All countermeasures should be examined about the stored data, including strategies to prevent data theft and destruction, as well as ways to prepare in advance if the aforesaid circumstance arises. Furthermore, the IoT system would appear to malfunction if data are maliciously combined with \"dirty\" data. As a result, it is also important to think about dealing with data cleaning and other events [76].
## 2 System Overview
### Communications Workflow: Relaying Data to the Base Station
Therefore, it has a double purpose: on the one hand, showing, storing and processing collected information in order to analyze relevant factors (tendency, evolutions, critical points, _etc._) in a simple way. On the other hand, providing a  
Figure 9: Arduino system employed for communication with the base station, likewise for message delivery. user-friendly tool to manage missions that only requires selecting targeted parcels by clicking over them in the map, and defining the mission parameters (such us sample time per parcel, flight altitude or elapsed time between repetitions). This information is directly configured in the air drone.
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 4 Performance Evaluation
### Experiment Settings
In contrast, for the multi-hop path selection algorithm, the ratio reflects the proportion of valid multi-hop paths to valid single-hop paths. The path number above also includes underlay path number and overlay path number, which reflect the diversity of physical transmission paths and the flexibility in overlay path selection, respectively. The network performance is evaluated through the ns-3 simulator using the Germany50 topology. Nodes A, B, and C are pre-selected as overlay and endpoint nodes for the transmissions. MOP and TAO then place an additional 17 of the 20 overlay nodes, respectively. Next-hop sets are generated using Loop-Free Invariant (LFI). Instead of utilizing centralized source-based methods with a limited number of paths, a hashing method is adopted as the comparison scheme for ONHS. This method hashes the header of the packets to select paths at intermediate nodes, which is referred to as HI. The underlay link capacity is set to 100 Mbps with a 1 ms latency. Traffic is generated between applications A->B and A->C. The traffic pattern follows that of a previous study [37] on inter-datacenter multipath transmission in WANs. The request arrival times are modeled as a Poisson process, with the expected transfer rate following an exponential distribution with a mean of 6 Mbps. The request arrival rate is set to 50 requests per second and 80 requests per second. The experiment runs for 24 s, with requests generated during the first 2 s.  
The innovation of this study primarily lies in the network layer. The impact of out-of-order delivery on the reliable transmission mechanism of the TCP protocol prevents the reflection of path diversity at the network layer. This study opts to use the UDP protocol. Additionally, in the ns-3 simulation environment, a flow control mechanism is introduced that incorporates network-layer available bandwidth awareness and additive increase/multiple decrease (AIMD) to regulate the transmission rate. The simulation evaluates the throughput of all the transfers in combinations of MOP + ONHS, MOP + HI and TAO + ONHS. The simulations are conducted on a PC equipped with 6 Intel Core i5-12500 2.60 GHz CPUs, 16 GB of memory, and a 512 GB hard disk, running the Ubuntu 22 operating system. The node placement algorithm and overlay multipath selection algorithm are simulated with Python 3.10. The transmission performance evaluation is simulated using ns3-3.40.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 4 Methodology
### Attention-Based Transformer Encoder
#### 4.3.1 Transformer Encoder Layer  
We initially map \(\mathbf{E}\) to the hidden dimension \(d_{m}\) of the Graph Transformer encoder using a learnable linear projection,  
\[\mathbf{H}^{(0)}=[\mathbf{E}_{0}\mathbf{W}_{0};\mathbf{E}_{1}\mathbf{W}_{1}; \ldots;\mathbf{E}_{K}\mathbf{W}_{K}], \tag{7}\]  
where \(\mathbf{W}_{i}\in\mathbb{R}^{d\times d_{m}}\) is a learnable weight, and \(\mathbf{H}_{0}\in\mathbb{R}^{|E|\times(K+1)\times d_{m}}\) is then fed into the Transformer encoder.  
A Transformer encoder is composed of a series of Transformer layers, each comprising a multi-head self-attention (_MSA_) mechanism and a position-wise feed-forward network (_FFN_). The primary objective of the _MSA_ layer is to capture the semantic dependencies among entities within KGs. For instance, given an input \(\mathbf{H}\), the single-head self-attention (_SA_) begins by mapping \(\mathbf{H}\) into three distinct parallel spaces,  
\[\mathbf{Q}=\mathbf{H}\mathbf{W}^{Q},\mathbf{K}=\mathbf{H}\mathbf{W}^{K}, \mathbf{V}=\mathbf{H}\mathbf{W}^{V}, \tag{8}\]  
where \(\mathbf{W}^{Q}\in\mathbb{R}^{d\times d_{k}},\mathbf{W}^{K}\in\mathbb{R}^{d \times d_{k}},\mathbf{W}^{V}\in\mathbb{R}^{d\times d_{o}}\) are the learnable weights. Then, the output of a single-headed self-attention is  
\[SA(\mathbf{H})=\text{softmax}\left(\frac{\mathbf{Q}\mathbf{K}^{\top}}{\sqrt{d _{k}}}\right)\mathbf{V}, \tag{9}\]  
where \(\frac{1}{\sqrt{d_{k}}}\) is the scaling factor. The _MSA_ layer emphasizes information from various representation subspaces across different locations, with the output derived from the concatenation of multiple single-head attention results,  
\[MSA(\mathbf{H})=[SA(\mathbf{H})_{1}||SA(\mathbf{H})_{2}||\ldots||SA(\mathbf{H })_{h}]. \tag{10}\]  
It follows that the value of the previous dimension is given by \(d_{k}=d_{v}=\frac{d_{m}}{h}\), where \(d_{m}\) represents the hidden layer dimension.  
Furthermore, we apply LayerNorm (_LN_) prior to each Transformer encoder layer, in accordance with the implementation outlined in [50] for the vanilla Graph Transformer. The final _FFN_ layer comprises two linear layers with GELU nonlinearity, as described below:  
\[\begin{split}\mathbf{H}^{\prime(l)}&=MSA(LN( \mathbf{H}^{(l-1)}))+\mathbf{H}^{(l-1)},\\ \mathbf{H}^{(l)}&=FFN(LN(\mathbf{H}^{\prime(l)}))+ \mathbf{H}^{\prime(l)},\end{split} \tag{11}\]  
where \(l\in[1,L]\), and \(L\) indicates the number of Transformer encoder's layers.