esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems
### MISO Systems
#### 3.2.1 Alternating Optimization with the Semidefinite Relaxation (SDR) Technique  
In [4], the transmit beamforming at the BS is designed together with the phase shifts at the RIS based on all BS-RIS, UE-RIS and UE-BS channels, to fully realize the network beamforming gain. They apply the semidefinite relaxation (SDR) technique to avoid the non-convex SINR limitations, as well as the signal unit-modulus limitations imposed by the MU passive phase shifters. Furthermore, to reduce the computational complexity, an efficient algorithm is used based on the alternating optimization of the phase and transmission shifts of the beamforming vector in an iterative manner, where their optimal solutions are derived in closed form with the other being fixed. For more details see Algorithm 1 in [4].
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 4 Training and Results
### Floating Point Operations per Second (FLOPs)  
Floating Point Operations per Second (FLOPs) is a metric that quantifies the number of floating-point calculations a computer system can perform in one second. Floating-point operations involve arithmetic calculations--such as addition, subtraction, multiplication, and division--on real numbers represented in a format that can accommodate a wide range of values. This capability is essential for applications requiring high precision and handling of very large or very small numbers.  
Table 5 shows that models employing the EPU activation function require fewer FLOPs (8.02 NMac) compared to those using ReLU (8.07 NMac), LeakyReLU (8.04 NMac), and ELU (8.07 NMac). This suggests that EPU is the most computationally efficient among the compared activation functions.  
While the differences in FLOPs presented in this table may seem minimal, they can become significant when processing large-scale images or deploying models in resource-constrained environments. In such scenarios, even slight reductions in computational requirements per operation can lead to substantial savings in processing time and energy  
\begin{table}
\begin{tabular}{c c} \hline
**Activation Function** & **Accuracy (\%)** \\ \hline EPU & 65.88 \\ ReLU & 65.2 \\ LeakyReLU & 65.5 \\ ELU & 65.42 \\ \hline \end{tabular}
\end{table}
Table 4: Top-1 accuracies on CINIC-10 for 250 epochs.  
Figure 12: Test accuracy for different activation functions on CINIC-10.  
consumption. Therefore, adopting the EPU activation function could enhance the efficiency and scalability of models, particularly in applications involving high-resolution images or large datasets.  
Table 6 presents a comparative analysis of the EPU model's performance metrics alongside several state-of-the-art convolutional neural networks (CNNs): ResNet-18, EfficientNet-B0, and VGG-11. The comparison focuses on two critical aspects: Floating Point Operations (FLOPs) and the number of parameters, providing insight into both the computational efficiency and model complexity.
# Investigating the Effect of Screw Size on the Stress Level in MERO Joint for Space Frame Structures
## 3 Results and Discussion
### Effect of Changing the Screw Size on The Maximum Stress Level in the MERO Joint  
The maximum stress levels in all three ball joints under application of four simultaneous tensile loads of 5 tons in the first layer of the ball joint and also four tensile or compressive loads of 5 tons in the second layer were calculated. Based on the geometry and dimensions of the MERO joint of diameter 98 mm, a maximum of three screw sizes of M12, M16, and M20 can be used for this joint as application of larger screws will result in their interference with each other in the joint. As shown in Figure 12, decreasing the screw size from M16 to M12 leads to a significant increase in the stress level. However, the maximum stress created in the ball joint for M16 and M20 was not substantially different. The reason is, even though increase in the screw size improves the stress distribution and decreases the stress values, interference of the stress fields overshadows these effects, hence leading to approximately a similar outcome.  
Regarding the geometry of the MERO joint with the diameter of 110 mm, five screw sizes of M12, M16, M20, M24, and M27 can be used. The stress variations caused by different screw sizes in the ball joint had a parabolic behavior, the minimum value of which was obtained using the M20 screw, as illustrated in Figure 13. Decreasing or increasing the screw size significantly increased the stress level under the same load. This  
Figure 12: Effect of the screw size on the maximum stress on the ball joint with the diameter of 98 mm.  
phenomenon can be explained as stress level in the MERO joint increases by the effects of stress concentration in smaller screw sizes, and the superposition of stress fields in larger sizes. As obvious in Figure 13, the effects of stress concentration were more significant than that of superposition of stress fields.  
Moreover, regarding the geometry of MERO joint with diameter of 132 mm, six sizes of M12, M16, M20, M24, M28, and M33 for screws can be applied. Similar to the ball joint of 110 mm diameter, the stress variations due to various screw sizes indicated a parabolic behavior in the ball joint, and the minimum value which was achieved for the M24 screw, as seen in Figure 14.  
Figure 14: Effect of the screw size on the maximum stress on the ball joint with the diameter of 132 mm.  
Figure 13: Effect of the screw size on the maximum stress on the ball joint with the diameter of 110 mm.  
Figure 15 superimposes and compares the results of the above-mentioned analyses, which indicates that a smaller ball joint will experience higher stress levels. Therefore, stronger materials should be utilized when using smaller ball joints. In addition, the parabolic behavior of the stress diagram proves that an optimal screw size can be recommended for each size of the MERO double-layer ball joint, for which the minimum stress level will be generated in the joint.
## 4 Discussion
### Practical Implication  
Previous studies have shown that AR interactions can quickly convey information and improve users' multitasking ability in immersive experiences, but the key is to reduce the perceptual conflicts between the real world and virtual information [8; 14; 15; 16]. The current study provides evidence that optimizing the presentation forms of 3D virtual objects could further improve user's behaviour of MAR interaction. The findings have important implications for the design of MAR interfaces: (1) Our findings suggest that the VWM capacity is closely related to the complexity and number of 3D virtual objects in augmented reality interactions, and that high complexity increases the amount of the allocated working memory resources to each virtual object, which directly in turn affects the user's performance. Therefore, designers should minimize the complexity of 3D objects in MAR and avoid using highly complex forms with high precision and multiple details. (2) When the interaction task is not time-sensitive and the details of the virtual objects are required (e.g., cartoon characters in many AR game applications), the designers can moderately decrease the number of 3D virtual objects appearing at the same time to reduce the memory load. These approaches provide insights for interface designers to further specify the optimal design form of 3D virtual objects in MAR interfaces to reduce cognitive friction and enhance user experience.
# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
###### Abstract  
Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image's quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns.  
onconcaled object detection; passive millimeter wave; bootstrap method; feature extraction; regularization; YOLOv8
# Deep Multimodal Emotion Recognition on Human Speech: A Review
## 4 Evaluation
### Performance Evaluation Metrics  
The performance of multimodal emotion recognition architectures is either measured on a class-based level or as an overall performance. For the former, binary accuracy and f1-score (\(f_{1}=2*(precision*recall)/(precision+recall)\)) are used for each emotion class, while for the latter, the accuracy over all emotions is monitored together with the weighted accuracy (\(WA\)), where, according to [86],  
\[WA=\frac{\text{TP}*\text{N}}{2\text{N}(\text{P}+\text{TN})} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Reference** &
\begin{tabular}{c} **Total** \\ **Duration** \\ **(h:ms)** \\ \end{tabular} & **Speakers** & **Modalities** & **Language(s)** \\ \hline IEMOCAP & [74] & 11:28:12 & 10 & \{A, T, V\} & English \\ CMU-MOSEI & [63] & 65:53:36 & 1000 & \{A, T, V\} & English \\ RECOLA & [75] & 03:50:00 & 46 & \{A, V\} & French \\ VAM & [76] & 12:00:00 & 20 & \{A, V\} & German \\  & & & & Chinese \\  & & & & & English \\ SEWA & [77] & 04:39:00 & 408 & \{A, V\} & German \\  & & & & Greek \\  & & & & Hungarian \\ HUMAINE & [78] & 04:11:00 & 4 & \{A, V\} & English \\  & & & & English \\ SEMAINE & [79] & 06:30:00 & 20 & \{A, V\} & Greek \\ AFEW & [80] & 02:28:03 & 330 & \{A, V\} & English \\ AFEW-VA & [81] & 00:40:00 & 240 & \{A, V\} & English \\ Mimicry & [82] & 11:00:00 & 48 & \{A, V\} & English \\ \hline \hline \end{tabular}
\end{table}
Table 1: Widely used multimodal emotion recognition datasets.  
where N is the total number of negatives, P is the total number of positives, TP is the true positives and TN is the true negatives.  
Here, we have to note that it is established to train and test the emotion recognition models on the four emotions of anger, happiness, sadness and neutral instead of the given nine (i.e., angry, excited, fear, sad, surprised, frustrated, happy, disappointed and neutral), while beginning with [87], some works merge the happiness and excitement classes.
## 4 Results and Discussion
### Performance of Batching vs. Pseudo-Batching  
In an attempt to speed up the model training mechanism, the pseudo-batching method (Section 3.3.4) was compared with the batching method (Section 3.3.5). It should be noted that this batching method only batches the input to the attention mechanism, and due to the complexity of the model selection for each line, it cannot be expected to achieve the same training speed improvements as batching of a conventional model. Furthermore, this experiment was run on an unrestricted prediction space for trajectory prediction, meaning that the model was able to predict any position along the trajectory in contrast to the other experiments discussed where the prediction was limited to points ahead of the last known position. Interestingly, this batching method did not achieve any improvement in training speed, but increased the average processing time required for each epoch by 24% and 4% for the westbound and eastbound directions, respectively (see Figure 14). Considering that the total number of journeys was the same for both conditions, this is a surprising finding but could be explained by the fact that the westbound data are less complex, meaning fewer data points from other lines are included. The eastbound dataset, on the other hand, has a higher proportion of data from other lines and is thus more complex, which could mean that the performance reduction using the GPU processing is less pronounced. It can be hypothesised that although performance was not improved in this dataset, with increasing complexity, GPU processing might become a more efficient training method. This could be useful in a setting that, for example, includes all vehicles at any time in the network.  
Although no training speed improvement was observed using this batching method, a benefit of using batching could be that applying batching to the attention mechanism could have a regularising effect on the model gradients [40]. This is due to the phenomenon that very small batch sizes, for example, a single sample as is the case in the pseudo-batching approach, can make the model unstable due to the high variance of the gradient estimates. It should be kept in mind that the line-based GRU models were not batched, and using a method that could apply batching to these models could further increase the model performance. However, this is not possible with the current model architecture.  
Figure 14: Figure shows boxplots of the time per epoch for both directions.
## 3 Analyzing Challenges
### Privacy  
Users' data, including location, personal information, and navigation trajectory, are considered as input for most data-driven machine learning methods. Data will be the most important thing in different types of AI in the next century. As explained in the next paragraph, this challenge can be viewed from two perspectives: data factors and human factors.  
Each fragment of data carries a different form of information which should be protected considering specific factors. In [49], three factors, persistence, repurposing, and spillovers related to data, are explained to highlight the characteristics of appropriate protection mechanisms in the AI era. On the other hand, data owner, data manipulator, and data visualizer are three typical roles that should be determined during the execution of machine learning methods. Preserving privacy considering different roles requires more effort and consideration by researchers [50]. Federated learning is one of these efforts [50]. This type of learning algorithm trains an algorithm across multiple decentralized computational resources, thus addressing critical issues such as data privacy, data security, and data access rights. During the design of an HLI-based agent, the definition of privacy issues may be somehow complex and complicated, whether humans accept privacy for other intelligent entities or not. How an HLI-based agent can be trained without the private data of humans is an important question.
## 4 A Distributed Mapping Service
### An Algorithm for Distributed Map Construction
#### 4.1.2 Cluster Head Computes Its Partial Map and Forwards It to the Sink  
The cluster head merges received data and compress it by removing redundant points. The data compression process makes the interpolation step faster. Given an up-to-date data set, the cluster head can easily generate a partial map. The cluster head caches two data representations: point data and graphical map. The former is used to build the local graphical map that is forwarded to the sink to be combined with data received from other clusters to build the complete network map. While the latter is used to respond quickly to user queries about information within the cluster area.
_Appl. Sci._**2012**, \(2\), 233-244; doi:10.3390/app2010233  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Optimization of Fluorine Plasma Treatment for Interface Improvement on HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As MOSFETs**  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON], [PERSON] and [PERSON]**  
Microelectronics Research Center, Department of Electrical and Computer Engineering,  
The University of Texas at Austin, TX 78758, USA; E-Mails: [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (F.X.); [EMAIL_ADDRESS] (F.Z.); [EMAIL_ADDRESS] (J.C.L.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-512-471-1627; Fax: +1-512-471-5625.  
_Received: 30 January 2012; in revised form: 20 February 2012 / Accepted: 10 March 2012 / Published: 19 March 2012_  
**Abstract:** This paper reports significant improvements in the electrical performance of In\({}_{0.53}\)Ga\({}_{0.47}\)As metal-oxide-semiconductor field-effect transistors (MOSFET) by a post-gate CF\({}_{4}\)/O\({}_{2}\) plasma treatment. The optimum condition of CF\({}_{4}\)/O\({}_{2}\) plasma treatment has been systematically studied and found to be 30 W for 3-5 min. Approximately 5\(\times\) reduction in interface trap density from 2.8 \(\times\) 10\({}^{12}\) to 4.9 \(\times\) 10\({}^{11}\) cm\({}^{-2}\)eV\({}^{-1}\) has been demonstrated with fluorine (F) incorporation. Subthreshold swing has been improved from 127 to 109 mV/dec. Effective channel mobility has been enhanced from 826 to 1,144 cm\({}^{2}\)/Vs.  
**Keywords:** InGaAs; HfO\({}_{2}\); fluorine plasma treatment; high-k dielectrics
## 2 The Elephant in the Room: Great Diversity
### Nb-IoT
#### 2.3.4 Frequency Band Utilization and Messaging  
The NB-IoT's strategic approach to frequency band utilization significantly enhances its applicability across a broad spectrum of IoT deployments. It operates in three distinct modes: (i) in-band, utilizing the LTE carrier blocks; (ii) guard-band, leveraging the unused spectrum in LTE guard bands; and (iii) stand-alone, repurposing existing GSM frequency bans; in so doing, the NB-IoT exhibits unparalleled flexibility in network implementation. This flexibility is critical in densely populated urban environments as well as in rural areas, where spectrum efficiency and coverage requirements vary greatly.  
The architecture of the NB-IoT was meticulously engineered to support the transmission of small data packets, corresponding to periodic updates from IoT devices. The NB-IoT's design specifically addresses IoT requirements, enabling its efficient management of compact data packets. This efficiency is critical for applications that require frequent updates from a vast array of devices, such as utility meters, environmental sensors, and health monitors, ensuring that the network remains uncluttered and responsive.  
The NB-IoT provides a solid foundation for wide-area IoT applications, which, combined with 1600-byte messages, makes the NB-IoT a highly versatile technology. It not only caters to dense, complex urban environments, but also extends its benefits to remote, underserved rural locations, where connectivity has traditionally been a challenge.
## 2 System Description
### Two-Indenture Fleet System Description  
A fleet system is thought to be made up of dozens or hundreds of homogeneous subsystems; each subsystem has the same structure and can be considered an independent system. In order to improve maintenance efficiency, the modular design is generally adopted. The fleet system structure would be divided into multiple indentures.  
The 2-level structure is shown in Figure 1, including Shop Replaceable Unit (SRU), which is the subset of Line Replaceable Unit (LRU). Moreover, at the location of maintenance for a system, SRU can fail and be repaired. [PERSON] has done a detailed analysis of the definition of LRU and SRU [19].  
The assumptions of the two-indenture fleet system are as follows [20]:  
1. In a given fleet system, the deterioration process of each homogenous component is independent and uniform, which indicates that each component randomly degenerates according to the same law over time.
2. The subcomponent will be replaced to repair system, if the corresponding component fails. Therefore, when a component should be maintained, each subcomponent at the same echelon level should be decided moved, discard, or repaired.
3. It takes little time on replacing a defective LRU, and the replacement belongs to operational availability. Therefore, compared with the supply and operational availability, we should focus on the former one (supply availability).
4. Discarding the LRU implies that its SRUs are discarded as well.
5. For each component at each location, an (\(s-1,s\)) continuous review inventory control policy (one for one replenishment) is used. That is, the replenishing and ordering strategy for one missing component is adopted to keep the stock at the level of \(s\). Because each component has multiple failure modes, and each mode has a different failure mechanism and failure rate, the system has different degrees of influence and consequences [14]. Two assumptions of multiple failure modes of components are added in this study:
6. The failure modes of the component appear randomly.
7. Minor fault mode could be repaired on base location; serious fault mode should be sent to the depot location for repair.