esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 3 IEEE 802.11 ah Overview
### Restricted Access Window (RAW)
Stations determine the index of their assigned RAW slots as follows [2]:  
\[i_{slot}=(x+N_{offset}\text{ mod }N_{RAW}), \tag{2}\]  
where \(i_{slot}\) is the index of the RAW slot to which the station is mapped, \(N_{RAW}\) is the total number of RAW slots in the corresponding RAW group, \(N_{offset}\) is the offset value in the mapping function to improve fairness among the stations in a RAW, and \(x\) is the position index of the AID of the station if the RAW is restricted to stations whose AID bits in the TIM element are equal to 1, otherwise it is the AID itself. When the RAW is not restricted to stations with DL indication in the TIM element (paged stations), all stations that belong to a RAW group are allowed to access the medium in that RAW. Otherwise, only paged stations are allowed to access the medium in the RAW. After receiving a TIM element, the paged station starts to contend for the medium not earlier than its allocated RAW slot defined as the function of the station's position in the TIM element and the RAW group information in the RPS element, whereas non-paged stations are not allowed to access the RAW. The RAW slot assignment procedure for both paged and non-paged stations is illustrated in Figure 4.  
The RAW feature also introduced two back-off states for Enhanced Distributed Channel Access (EDCA) to manage transmissions inside and outside their assigned RAW slot, respectively. Lastly, RAW has two more operation modes: operation with Resource Allocation (RA) and Periodic RAW (PRAW) operation, which are beyond the scope of this paper. For more information, we refer the readers to [2].
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 2 Method
### Watermark Embedding  
The goal of our watermarking scheme is to enable the extraction of the embedded watermark under various attacks and to provide high fidelity of the original image. To achieve this goal, we generate random watermark information \(\mathbf{M}=[m_{0},m_{1},\dots,m_{k-1}]\), \(m_{i}\in\{0,1\}\) and orthonormal code vectors \(\mathbf{P_{i}}=[p_{i0},p_{i1},\dots,p_{i(n-1)}]\), \(n=2k\) and \(i\in[0,k-1]\). The modulation process for generating the spread spectrum watermark \(\mathbf{W}=[w_{0},w_{1},\dots,w_{n-1}]\) can be simplified to:  
\[w_{j}=\sum_{i=0}^{k-1}((2m_{i}-1)\cdot p_{ij}), \tag{4}\]  
where \(j=0,1,\dots,n-1\). To embed the watermark \(\mathbf{W}\), the clean image \(I_{ori}\in\mathbb{R}^{3\times W\times H}\) is mapped to the latent space using the VAE encoder to obtain the latent vector \(z_{0}\in\mathbb{R}^{4\times W/f\times H/f}\), where \(f\) is the scale factor set to 8 in this work. Estimated noise is applied to \(z_{0}\) in \(T\) time steps, yielding \(z_{T}\in\mathbb{R}^{4\times W/f\times H/f}\):  
\[z_{t+1}=\sqrt{\bar{\pi}_{t+1}}z_{0}^{t}+\sqrt{1-\bar{\pi}_{t+1}}\epsilon_{ \theta}(z_{t}), \tag{5}\]  
where \(\bar{\pi}_{t}=\prod_{i=0}^{t}(1-\beta_{t})\) and \(\epsilon_{\theta}(\cdot)\) is a noise estimator. Then, the noisy latent vector \(z_{T}\) is split into four slices along the channel dimension. It is empirically found that embedding the watermark in the fourth channel leads to better image quality, in spite of the fact that the embedding capacity would profit from using all the four channels. Considering the invisibility of watermarks, watermark embedding is performed in 8 x 8 DCT blocks of \(z_{T}\). We extract the seventh high-frequency coefficients on the main diagonal from each block and concatenate them to construct the embedding matrix \(\mathbf{F_{co}}=[F_{0},F_{1},\dots,F_{n-1}]\). The watermark embedding is depicted as:  
\[\mathbf{F_{w}}=\mathbf{F_{co}}+S\cdot\mathbf{W}, \tag{6}\]  
where \(S\) is the strength factor of the embedding. IDCT is applied to \(\mathbf{F_{w}}\) to update \(z_{T}\) as \(z_{T}^{\prime}\). Finally, the watermarked image \(I_{gen}\) is yielded by  
\[I_{gen}=Dec(e_{\theta}(z_{T}^{\prime},T)), \tag{7}\]  
where \(Dec(\cdot)\) is the VAE decoder to transform the latent vector back into image space.  
To further improve the image quality, the original image is partially added to the generated image to obtain the watermarked image \(I_{w}\). The enhancement is achieved by  
\[I_{w}=\lambda I_{ori}+(1-\lambda)I_{gen}, \tag{8}\]  
where \(\lambda\) is a weighting parameter.
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
###### Abstract  
Bridge cables under traffic loads are more prone to failure during the service life due to the corrosion-fatigue coupling effect. In this study, a novel lifespan model based on the equivalent initial flaw size (EIFS) theory is established to analyze the various stages of the lifespan of steel wires. Additionally, a comprehensive corrosion-fatigue lifespan calculation method for parallel steel wire cable is established based on the series-parallel model. A case study of the Runyang Suspension Bridge is conducted to evaluate the evolution of corrosion-fatigue damage in bridge cables during the service life. The results indicate that under the action of corrosion-fatigue, steel wires are more prone to crack initiation, and their fracture toughness is further reduced. In cases where the corrosion level is relatively low, the steel wires of the bridge cables experience no corrosion-fatigue fracture. When the steel wires have initial defects and are subject to corrosion-fatigue conditions, their fracture lifespan is dependent on the severity of the corrosive medium. The reduction in the service life of the cables under the corrosion environment is much greater than that under heavy loads. This research may contribute to the understanding of corrosion-fatigue damage in bridge cables, involving assessment, maintenance, and replacement for bridge cables.  
bridge cables; EIFS; corrosion fatigue; crack initiation; lifespan assessment
## 6 Discussion and Conclusions  
### Discussion  
Cyclic pushover and seismic fragility analyses were performed to investigate a corroded bridge system reinforced by steel plates under mainshock-aftershock sequences; meaningful results were obtained. The findings in this work can be used to guide the design of reinforcements by selecting the rational thickness of steel plates to enhance the structural capacity. In addition, through the proposed framework, the failure probability of a corroded structure reinforced by steel plates under mainshock-aftershock sequences can be calculated efficiently. In this work, steel plates with different thicknesses were used for the reinforcement; this work aimed to use a minimum thickness steel plate that could cover the decreased capacity of the bridge pier induced by corrosion, and thus steel plates with thicknesses of 1-5 mm were utilized. In reality, other than the five kinds of steel plates mentioned, thicker steel plates can also be used for reinforcement.  
Through cyclic pushover analysis, it can be observed by comparing the skeleton curves that pitting corrosion had a large and significant influence on the structural capacity of the bridge pier. At the same corrosion level, the capacity of the bridge pier with pitting corrosion was less than that of the bridge pier with uniform corrosion. Therefore, structures with pitting corrosion are prone to vulnerability to natural hazards, such as earthquake  
Figure 6: Fragile curves of the bridge system with 20% pitting corrosion strengthened by steel plates. (**a**) Slight damage, (**b**) moderate damage, (**c**) major damage, (**d**) collapse.  
loads. In this study, pitting corrosion was simulated by reducing the mechanical properties of the steel bars using the model proposed by [PERSON] and [PERSON] [3]. In future research, we suggest creating a solid model to simulate pitting corrosion and investigate its impact on the performance of the structures. For simplification, in this study, we only show the fragility curves for pitting corrosion with a 20% corrosion ratio; the probability of failure at other corrosion levels for pitting or uniform corrosion of the bridge before and after reinforcement can be calculated using the same framework. There are many kinds of bridges constructed worldwide, such as arch bridges, cable-stayed bridges, or suspension bridges. In this work, a two-span-RC bridge was used as an example bridge to evaluate its failure probability under both corrosion and mainshock-aftershock sequences. For future research, it is suggested that more types of bridges in the marine environment are assessed.
# A Virtual Reality Soldier Simulator with Body Area Networks for Team Training
## 5 System Operating Procedures
### Virtual Environment  
The immersive environment was designed for a rescue mission in an enemy-held building (Figure 15). In addition, three hostages were being guarded on the top floor of a three-storied building which was controlled by 15 enemies. To ensure that the virtual environment was consistent with an actual training facility, we simulated a real military training site, including the urban street, the enemy-held building, and so on. All enemies controlled automatically by the system were capable of shooting, evading attacks, and team striking. When participants were immersed in the virtual environment, they could interact with other participants not only through gesture tracking, but also through VoIP communication technology.  
As mentioned above, sensing measurement errors greatly affect the sensor nodes, which are attached to a human body. The integration of the inertial sensors, including sensor signals and drift errors, is performed on the basis of the kinematics of the human body. Therefore, sensing errors will be accumulated in quaternion form. As shown in Figure 16, the sensing measurement errors are calibrated when the T-pose calibration is performed. In the first step, we normalize the accelerometer, gyroscope and magnetometer in all of the sensor nodes and compensate for bias errors. In the following step, a complementary filter is used to mitigate the accumulated errors based on the advantages of long-term observation and short-term observation respectively. In the final step, T-pose calibration is performed to align the orientation of the sensor nodes with respect to the body segments, after which the sensor node is able to capture body movement accurately in the virtual environment.
## 4 Experiments
### Ablation Studies  
Ablation experiments are conducted to evaluate the contribution of each component in the proposed CCD-Net. The impact is analyzed by systematically removing different modules of the network: (I) CCD-Net removing the color-correction branch; (II) CCD-Net removing the loss of the Lab color space \(L_{color}\); and (III) our proposed CCD-Net. In the second case, we only use the L1 loss in the RGB space to verify the effect of the loss \(L_{color}\) in the Lab space. Table 4 and Figures 12 and 13 present the quantitative and visual results of the ablation experiments on different datasets separately.  
Figure 12 shows that when only the dehazing branch is used, it can be seen from the visualization results (a) to (b) that although the fog is removed, the overall image still appears gray, the color is cold and lacks saturation, the detail performance is not clear enough, and there is still a slight color bias in local areas, especially the blurring of the edges, especially in the corners where the light is not uniform. When the color-correction branch is added, the color bias is effectively reduced. At this point, the brightness of the image is improved, the texture of the room carpet is more prominent, and the color saturation and contrast are improved. After further adding the color loss function \(L_{color}\), it shows that the color restoration is most effective. At this time, the color of the image is more natural, and the white wall no longer has a gray-yellow residual haze, achieving an overall effect that is remarkably close to the real image. The details are crisp and well defined, with a clear sense of layering. Notably, the finer details in locally zoomed-in areas have been exceptionally restored.  
Similarly, in outdoor situations, the base dehaze branch of shows the limited performance of the base model. Visualization result (b) in Figure 13 shows that although the haze has been removed, the overall image is still grayish and the red buildings are dark, which cannot accurately restore the real colors. After adding the color-correction branch, the color saturation of the image is improved, the red buildings become clearer, the green advertising is brighter, and the overall image contrast is enhanced. However, some regions still have uneven colors. After further introducing the color loss function \(L_{color}\), the red color of the building and the green color of the advertisement are clearly defined, and even almost indistinguishable from the real image, which indicates that its ability in terms of detail retention and color reproduction has been significantly improved. In conclusion, the image recovered by CCD-Net is closest to the ground-truth image.
# The Impact of Freeze Drying on Bioactivity and Physical Properties of Food Products
## 5 Impact on Physical Properties and Flavor
Also, the most significant degradations of the bioactive components of the sample were acquired via spray drying and hot air drying. In contrast, microwave drying highly increased the retention of the bioactive components and preserved the color, taste, and aroma of the tea while reducing the drying time. Due to these outcomes, microwave drying was considered the best drying technique to preserve the tea. On the other hand, FD can be applied to protect the quality of mushrooms due to the high retention capacity of FD on the components of the sample [144]. In a study, vacuum FD, hot air drying, microwave rolling-bed drying, hot-air microwave rolling-bed drying, and microwave vacuum rolling-bed drying techniques were applied to Pleurotus eryngii mushrooms, and these drying techniques were compared with each other according to their drying suitability for the preservation of flavor and physicochemical properties of the sample [140]. When the results were examined, FD time was effectively reduced due to microwave rolling-bed drying and hot-air microwave rolling-bed drying. However, the least drying time was obtained, with color and flavor protection of the mushrooms achieved using microwave vacuum rolling-bed drying compared with other drying techniques. As a result, the study explained that microwave vacuum rolling-bed drying and vacuum FD gave similar outcomes regarding the bioactive properties, color, and taste of _Pleurotus eryngii_. However, microwave vacuum rolling-bed drying was chosen as the best drying technique among other techniques instead of vacuum FD due to its short drying time with a high-quality final product. Additionally, FD is used to improve the shelf life of various industrial products, while decreasing the degradation of their bioactive properties and physical properties, especially flavor [143]. For example, in the case of coconut drinks, FD maintained the Mg and Fe content but lowered the Na content [141]. Furthermore, FD preserved the sensory properties, flavor, and taste, though some minor degradation was observed in the sweetness and fermented aroma of the coconut drink. However, these degradations were minimal. When comparing the freeze-dried coconut drink with the fresh coconut drink, no significant changes were observed in several minerals, amino acids, and fatty acids in the freeze-dried sample. As a result of this experiment, the shelf life of the sample was successfully improved. More examples of the effect of FD on the samples, including fruits, vegetables, mushrooms, and flowers, were examined [145; 146; 147; 148; 149; 150].
## 2 Experimental Dataset  
The block diagram of the bearing fault simulator is demonstrated in Figure 1. Based on this figure, this fault simulator has three main parts: a three-phase induction motor, a gearbox, and acoustic emission sensors. To transfer the load to the shaft, a gearbox is used. The acoustic emission sensors are used to collect the bearing data. Based on this figure, a FAG NJ206-E-TVP2 bearing is attached in the shaft. Moreover, a wideband frequency AE sensor (PAC WS\(\alpha\)) was used to acquire data when placed on the top of nondrive end shaft bearing at a 21.48 mm displacement under normal and abnormal conditions when the sampling rate is 250 kHz [9]. To simulate the faulty conditions, a bearing with 3-mm and 6-mm crack sizes in length and 0.3-mm crack size in depth and 0.35 mm in width is used [25; 26; 27]. Figure 2 shows our lab's testbed, which was used to collect bearing data in normal (NS) and seven abnormal conditions. Moreover, Table 1 shows information about the AE sensors used for data collection [25; 26; 27]. Figure 3 illustrates the seven abnormal conditions in the bearing, including ball (BS), inner (IS), outer (OS), inner-ball (IBS), outer-ball (OBS), inner-outer (IOS), and inner-outer-ball (IOBS).  
The ball faults are the anomalies found on the balls, the outer faults are the faults found on the outside of the balls, and the inner faults are the faults found on the inside of the balls. Moreover, the other faults are the combination of ball, inner, and outer faults. Furthermore, because fault diagnosis on a low-speed motor is difficult, we considered motor rotation speeds of 300, 400, 450, and 500 RPM. The information of the bearing dataset, comprising the working conditions, motor speed, and crack size of the bearings, is illustrated in Table 2.  
Figure 1: Bearing fault simulator.  
Figure 2: Experimental test for data collection in bearings: (**a**) data generation and (**b**) two-channel acoustic emission PCI board for data acquisition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AE Sensor (PAC WS\(\alpha\)) Information** & **PCI Board with Two-Channel AE Sensor Information** \\ \hline Peak sensitivity [\(V/\upmu\)bar]: \(-62\) dB & A/D conversion: 18-bit 40 MHz \\ Operational frequency range: 100–900 kHz & AE sensor: 2 channels (one has a 10 M samples/s rate, \\ Directionality: \(\pm 1.5\) dB & and the other has a 5 M samples/s rate; the two channels are used simultaneously) \\ Resonant frequency: 650 kHz & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor and data collection system information [9].
## 4 Theoretical Background and Research Model
### Privacy Concerns  
As computers have become so prevalent in daily life and there is a huge amount of data stored on computer devices, privacy and security have evolved into public concern [74]. Hence, the notion of informational privacy has been spotlighted in many disciplines as a key research topic, including justice, marketing, economics, psychology, and particularly in the Information Systems discipline [3]. The idea that privacy concerns relate to protective behavior is grounded in [75] who proposed that people try to use behavior strategies to achieve desired levels of privacy. Extensive research indicates that information privacy concerns are strong predictors of risk beliefs and trust in the context of electronic commerce [76] as well as the antecedents of protective behavior to disclose Internet personal information [77]. Also, privacy concerns are positively related to privacy protection, and those worried about their online privacy prefer to follow behaviors that regulate their disclosure and online privacy risks [78, 79, 80]. Further to this, many theories and studies argue that users adopt protective behaviors, if they feel they are endangered and/or if the risk is considered severe. Also, we may expect that people who are concerned about their privacy may feel more vulnerable to privacy threats and they may perceive the consequences of privacy violations to be more severe. Based on the above, we propose the following hypotheses:  
**Hypothesis 7 (H7).**_Privacy concerns positively affect perceived vulnerability._  
**Hypothesis 8 (H8).**_Privacy concerns positively affect perceived severity._  
We also expect that people who are concerned about privacy may be more willing to learn how to use technologies that provide protection, such as BBCA technologies, and may perceive their efficacy to use these technologies to be high.  
**Hypothesis 9 (H9).**_Privacy concerns positively affect self-efficacy._  
Privacy was demonstrated as a core value that drives users to adopt a protective behavior [81]. In order to apply protective behavior, users should have faith in the specific protective behavior's ability to protect them from the threat. We may expect that when users are concerned about their privacy, they may have a more positive view of protective technologies and, consequently, they may perceive their efficacy to be higher. Thus, we hypothesize:  
**Hypothesis 10 (H10).**_Privacy concerns positively affect perceived response efficacy._  
Response cost includes the inconvenience caused by the use of extra protective technology, i.e., (BBCA), as well as the perceived cost of behavioural biometrics data collection. The latter is directly associated with the privacy concerns of the user. If users are concerned about their privacy, they may also be worried that the BBCA tool may misuse their personal data. Thus, we hypothesize that:  
**Hypothesis 11 (H11).**_Privacy concerns positively affect perceived response cost_.