esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 5 Publicly Available Datasets
### Yelp Dataset  
The Yelp dataset is a publicly available dataset that contains reviews, ratings, and other metadata related to businesses and services. It is a popular data source for recommendation systems, both in the context of federated learning and non-federated learning. In an FRS, the Yelp dataset can be used to build personalized user recommendations. Specifically, the dataset can be used to build a collaborative filtering model that considers user preferences and item ratings. The collaborative filtering model can then create personalized user recommendations based on his/her preferences and other related factors.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 4 Stability Analysis on Traffic Collision Risk
### Collision Risk String Stability Analysis
#### 4.3.2 Numerical Solutions' Results  
The numerical results are shown in Figure 4, where the difference between the criterion of traffic string stability and risk string stability is visualized. In Figure 3(a), the purple dots represent the numerical solutions satisfying both th traffic string stability criterion (Equation (8)) and the risk string stability criterion (Equation (31)). Meanwhile, in Figure 3(b), the red dots represent the numerical solutions satisfying only the traffic string stability criterion (Equation (8)) but not the risk string stability criterion. The orange dots denote both risk- and traffic-unstable conditions.  
To better unveil the similarities and differences between traffic string stability and risk stability, several 2-D diagrams are provided in Figure 5, which clearly s shows that when the vehicle platoon is risk-string-stable, it is also traffic-string-stable. Moreover, Figure 4(a),b reflect that the difference between risk and traffic string stability is rising with the growth of \(f_{\Delta x}\).  
Figure 4: Visualizaiton of the numerical solutions of risk string stability.  
Figures 4 and 5 show that if the platoon is traffic-risk-string-stable, it is also traffic-string-stable. However, if the platoon is traffic-string-stable, it is not necessarily risk-string-stable.  
It is important to note that the points illustrated in Figure 4 are not suggestive of distinct states \((f_{\Delta x},f_{\Delta x},f_{x})\) within the traffic dynamics. Rather, they are illustrative examples designed to demonstrate the potential behavior of the traffic dynamics under varying circumstances. Due to the complex nature of the system, correlations among the variables and their derivatives exist. Consequently, the diversity of points in Figure 4 serves to encapsulate a range of possibilities and to demonstrate the distinction between traffic flow stability and risk stability.
# A Deep Fusion Matching Network Semantic Reasoning Model
## 3 Methods
### Design of Reasoning Model Based on Deep Fusion Matching Network  
This paper proposes a semantic fusion deep matching network, referred to as SCFDMN. The core of the model is as follows: using the improved AF-DMN model to obtain the local inference information between sentences and help to obtain the deep reasoning information; using d-TBCNN to simulate the syntactic structure information of the sentence to improve the interpretability of the reasoning process; finally, the idea of the control gate is used to fuse the local inference information and syntactic structure information of sentences to form the global reasoning information of the reasoning model, thus expanding the reasoning depth and interpretability of the model.  
As shown in Figure 1, the whole matching network consists of five parts: coding layer, matching layer, dependency convolution layer, information aggregation layer, and inference prediction layer. The specific functions of each part are as follows:  
1. Coding layer: it mainly completes the transformation from natural language representation to sentence embedding representation, including sentence preprocessing, vectorization, semantic information coding, and embedded representation generation.
2. Matching layer and dependency convolution layer: they mainly complete the extraction of local inference information between sentences and syntactic structure inference information. Moreover, by extracting the interactive information between sentences, implicit logic is introduced into the reasoning process to improve the interpretability of the reasoning process.
3. Information aggregation layer: it mainly completes the integration of representation information, interactive reasoning information, and syntactic structure reasoning information. All information is integrated into fixed-length semantic information using cyclic neural networks and pooling in deep learning.
4. Reasoning and prediction layer: it mainly completes the output of prediction results of specific reasoning tasks. In general, linear function and multi-layer fully connected network are used to infer the global reasoning information after fusion to predict the implication relationship of a given sentence pair. The detailed structure and function of the sub-networks are given below.  
The detailed explanation and design of each part are given below.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 4 Discussion and Conclusions  
Intraluminal or interstitial photoacoustic imaging of tissues and organs requires the delivery of high peak power (MW) over the field of view of the ultrasound probe while minimizing the cross-sectional dimensions of the light delivery device for ease of access. As PA imaging requires the delivery of a large number of sub-micro-second pulses, the light  
Figure 5: PA imaging of polyurethane tubes filled with India ink embedded in chicken breast tissue. (**a**) Schematic diagram, (**b**) B-mode US image and (**c**) PA-mode image of the tube cross-sections with optical fiber perpendicular to the tubes (parallel to image plane), demonstrating the longitudinal illumination field, and (**d**) Schematic diagram, (**e**) B-mode US image, and (**f**) PA-mode image with the optical fiber parallel to the tubes (perpendicular to the plane) showing the angular illumination field; inset in (**f**) is zoomed-in view of PA signal from top and bottom surfaces of ink in the tube. Multi-plane imaging with 10 mm scan (**g**) schematic and (**h**) volume rendered PA-mode image showing both angular and longitudinal illumination field. Solid white arrows indicate tubes visible in PA-mode images; dotted white arrows indicate approximate orientation of the machined window and direction of the emitted laser beam.  
Figure 6: (**a**) Setup for imaging rat ear in vivo, with optical fiber placed 5 mm below the surface of a plastisiol block supporting the ear. Ultrasound gel couplant surrounds the ear. (**b**) Volume rendering of 3D B-mode and PA-mode scan of a rat ear. Pseudo-color PA-mode image (red) is overlaid on the grayscale B-mode image, and three perpendicular cross-sections are shown, intersecting near the middle of the ear. (**c**) The transverse view shows the same imaging plane as in (**b**). The image dimensions are 23 mm (lateral) \(\times\) 16 mm (depth), and the length of the imaged ear is 19 mm.  
delivery system must be resilient to high transient optical power densities. At the same time, the local fluence near the illumination device must be kept below safety limits. Since off-the-shelf cylindrical diffusers are not suitable due to their low power damage threshold, an elongated illuminator that can carry high peak power and scatter optical energy from the core is needed. Furthermore, the light emission should be directional and confined within the US imaging plane. In order to obtain light out from the side of the fiber, the modes in the core need to be disturbed. While structures or modifications on the fiber surface can redistribute energy from higher modes, alteration of the material close to the fiber center is required to redistribute the energy from lower modes and redirect the majority of the energy out of the fiber.
## 3 Principle of Encryption and Decryption
### Process of Decryption  
Figure 4 shows process of decryption. The detailed steps are as follows:  
**Step 1**: According to the Equation (13), we can get the complex amplitude U. The \(U\) is modulated by \(RPM^{*}\) after spherical diffraction. The combined image \(f\) (\(x,y\)) can be obtained by  
\[f(x,y)\ =\ ISpD[U(x,y)]RPM^{*}, \tag{17}\]  
where \(ISpD\) [\(\bullet\)] resents the inverse process of the spherical diffraction calculation. Then \(f\) (\(x\), \(y\)) is rounded to unify the data types.  
**Step 2**: Based on the Equation (11), image \(I_{N}^{\prime}(x,y)\) can be obtained by  
\[I_{N}^{\prime}(x,y)\ =\ f(x,y)\oplus P_{N}(x,y). \tag{18}\]  
**Step 3**: According to the Equation (8), the decrypted images can be obtained by the inverse chaotic scrambling and the inverse filtering diffusion in this scheme.
## 4 Result and Discussion
### Discussion  
This section provides insightful recommendations for enhancing the predictive model based on the obtained results and a comprehensive data analysis. Beyond the currently considered voltage and temperature parameters, there exists the potential to integrate additional features sourced from parameters such as battery current, state of health, and state of charge. Expanding the feature set allows the model to capture intricate relationships more effectively, thereby leading to more precise predictions. Moreover, optimizing these hyperparameters plays a crucial role in influencing the performance of the model. Further exploration and fine-tuning of these hyperparameters can significantly increase the accuracy of the model. Additionally, employing ensemble techniques by combining predictions from diverse models holds promise for further improving prediction accuracy and bolstering reliability, as it addresses the specific limitations of individual models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Smoothing Factor** & **RMSE** & **MAE** & **R-Squared** \\ \hline GPR & - & 0.0577 & 0.0386 & 0.9365 \\ SVR & - & 0.1392 & 0.0954 & 0.6461 \\ Random Forest & - & 0.0588 & 0.0386 & 0.9338 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance comparison without exponential smoothing.  
Figure 8: RMSE score comparison.
# Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles
###### Abstract  
Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called \"Assured Clear Distance Ahead\" (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. Experimental results show that Safe Driving Distance and Safe Driving Speed depend on several parameters such as weight of the vehicle, fires status, length of the vehicle, speed of the vehicle, type of road (snowy asphalt, wet asphalt, or dry asphalt or icy road) and the weather condition (clear or foggy). The study found that the technique is effective in calculating Safe Driving Distance, thereby resulting in forward collision avoidance by connected vehicles and maximizing road utilization by dynamically enforcing the minimum required safe separating gap as a function of the current values of the affecting parameters, including the speed of the surrounding vehicles, the road condition, and the weather condition.  
Asured Clear Distance Ahead; Safe Driving Distance; Safe Driving Speed; Internet of Vehicles; Intelligent Transportation Systems; smart cities; forward-collision avoidance +
Footnote †: journal: Sensors  
1 Computer Engineering Department, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia 12 Computer Science and Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), New Borg-El-Arab City 21934, Egypt 2
## 3 Systematic Review Results
### Frailty  
Frailty in older adults presents a complex challenge for technology developers and caregivers alike. Studies by [PERSON] [44] and [PERSON] et al. [40] highlight the physical limitations associated with frailty and the potential of assistive technologies to address them. However, the concept of frailty extends beyond the purely physical. Research by [PERSON] et al. [42] and [PERSON] et al. [55] suggests a strong correlation between perceived health and technology adoption, with individuals more likely to embrace technology when they perceive a direct benefit to their well-being. Interestingly, [PERSON] et al. [56] found that even those in good health are receptive to technologies like digital home assistants, indicating that adoption is driven by factors beyond just physical limitations.  
Technology can play a vital role in monitoring health and detecting changes in functional abilities. Studies by [PERSON] et al. [23] and [PERSON] et al. [57] showcase the potential of monitoring systems to not only support caregivers but also to identify early signs of cognitive decline. These advancements hold promise for proactive interventions and improved patient outcomes.  
While improving patient outcomes remains a primary focus, research also highlights the importance of influencing adoption behaviours. The concept of \"pre-frailty\" is particularly relevant, as studies by [47] suggest that targeting this group offers distinct advantages. By focusing on pragmatic functions, user-friendliness, and addressing concerns about privacy and accuracy, developers can create technologies that are not only appealing but also encourage adoption before the onset of frailty.  
Furthermore, research by [58] underscores the need for tailored interventions based on frailty status. Frail individuals may be influenced by fewer factors when considering technology adoption, suggesting a proactive approach is crucial. Understanding these distinctions is essential for developers and researchers alike, paving the way for more inclusive and effective technology solutions for older adults across the frailty spectrum.  
The multifaceted nature of frailty, encompassing physical, cognitive, and perceptual dimensions, demands a nuanced approach to technology design and implementation. These insights demonstrate that adoption is not solely driven by physical need but also by perceived benefits, health status, and the usability of technological solutions. This highlights the value of engaging older adults, particularly those in pre-frailty stages, in the co-design of technologies that are intuitive, respectful of privacy, and responsive to individual contexts. Tailoring interventions based on frailty status enables more precise alignment with users' capabilities and expectations, ultimately enhancing adoption.
###### Abstract
An active disturbance rejection controller (ADRC) is an excellent tool for total disturbance compensation, which only requires minimal model information and can sufficiently eliminate the model uncertainties as well as unknown disturbances. The most important aspect is the design of the extended state observer (ESO), which can estimate the total disturbance of the system in real time. [PERSON] in [16] expounded on the development from PID to ADRC in detail. Although ADRC has high feedback efficiency and tracking accuracy due to its nonlinear mechanism, it is not suitable for practical engineering applications. In [17; 18], the ADRC is linearized, which greatly simplifies the parameter adjustment method of the ADRC and makes it more suitable for practical engineering. Aiming at the weak anti-interference ability of a photovoltaic grid connected inverter management scheme, an improved LADRC controller was proposed by [PERSON]. [19; 20], which optimizes the parameters by observing and estimating the total interference by linear extended state observer (LESO), compensating the interference by the PD controller and adding adaption. The results showed that the improved method has better rapidity and robustness. The objective of [21] was to design a vibration isolation platform for payloads on spacecraft with a robust, wide bandwidth and multi-degree-of-freedom (MDOF), where the LADRC algorithm was utilized for the active control. An improved LADRC based on proportional differentiation was proposed in [22] in order to improve the control performance of the grid-side inverter of the energy storage system, where LESO was improved to a proportional differentiation link. In [23], a novel compound control scheme that combined the advantages of partial feedback linearization (PFL) and LADRC was presented for a pressurized water reactor (PWR), where the controller parameters were optimized by tuning bandwidth. Ref. [24] proposed a cascade control strategy based on the LADRC for a DC/DC boost converter, solving the problem of unstable output voltage of the boost converter. Motivated by the salient feature in model independence, the LADRC is employed for the tracking control of the radar position servo system to mitigate the undesirable impact of the internal nonlinear dynamics and unknown external disturbances.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 2 Related Works
### AMBTC Compression Technique  
AMBTC is a lossy compression technique [28] that uses low and high quantized values and a bitmap to represent a block. The proposed method is to embed the authentication codes into quantized values and bitmaps to protect the AMBTC images, and the limitations of AMBTC is that the compression ratio and image quality are both relatively low. Let \(I\) be the original image. Divide \(I\) into \(N\) blocks \(I=\{I_{i}\}_{i=0}^{N-1}\) of size \(n\times n\), and \(I_{i}=\{I_{i,j}\}_{j=0}^{n\times n-1}\), where \(I_{i,j}\) represents the \(j\)-th pixel of \(I_{i}\). Then, scan each block \(I_{i}\) of \(\{I_{i}\}_{i=0}^{N-1}\). To compress block \(I_{i}\), calculate the mean value \(m_{i}\) of \(I_{i}\). Average pixels in \(I_{i}\) that are smaller than \(m_{i}\), and record the result as the low quantized value \(a_{i}\). On the other hand, the high quantized value \(b_{i}\) is the average of pixels greater than or equal to \(m_{i}\). Compare \(m_{i}\) and the pixel \(I_{i,j}\) to obtain the bitmap \(B_{i}\) of block \(I_{i}\). If \(I_{i,j}\geq m_{i}\), then \(B_{i,j}=1\); otherwise, \(B_{i,j}=0\), where \(B_{i,j}\) represents the \(j\)-th bit of \(B_{i}\). Thus, the compressed code is \(C_{i}=(a_{i},\ b_{i},\ B_{i})\) of \(I_{i}\). All blocks are compressed in the same way, and the result is the AMBTC compressed codes of image \(I\), which is denoted by \(C=\{a_{i},\ b_{i},\ B_{i}\}_{i=0}^{N-1}\). To decompress \(C_{i}=(a_{i},\ b_{i},\ B_{i})\), prepare an empty block \(D_{i}\) with the same size of \(I_{i}\), and \(D_{i,j}\) is the \(j\)-th pixel of \(D_{i}\). If \(B_{i,j}=0\), then \(D_{i,j}=a_{i}\); otherwise, \(D_{i,j}=b_{i}\). All codes \(C=\{a_{i},\ b_{i},\ B_{i}\}_{i=0}^{N-1}\) are decompressed using the same procedures where the decompressed image is \(D=\{D_{i}\}_{i=0}^{N-1}\).  
A simple example is given to introduce the procedures of AMBTC compression technique. Let \(I_{i}=\)[36; 34; 41; 42; 37; 35; 43; 46; 35; 39; 44; 41; 36; 42; 48] be the original block of size \(4\times 4\). Calculate the mean value of \(I_{i}\), and \(m_{i}=40\) is obtained. Pixels in \(I_{i}\) less than 40 are 36, 34, 37, 35, 39 and 36, and the average of these pixels is \(a_{i}=36\). Similarly, \(b_{i}=43\) is calculated. To obtain \(B_{i}\), if \(I_{i,j}\geq 40\), then \(B_{i,j}=1\); otherwise, \(B_{i,j}=0\). Therefore, \(B_{i}=\)[0011; 0011; 0011; 0111]. Finally, the AMBTC compressed code \(C_{i}=\) (36, 43, [0011; 0011; 0111]) is obtained. To decompress \(C_{i}\), bits 0 and 1 in \(B_{i}\) are decoded by quantized values \(a_{i}=36\) and \(b_{i}=43\), respectively, and \(D_{i}=\)[36; 36; 43; 43; 36; 36; 43; 36; 36; 43; 36; 43; 36; 43; 36; 43; 36; 43; 36; 43; 36; 43; 36; 43; 33].
# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
## 3 Improved Lightweight Method for Clam Detection  
### RevColNet  
RevColNet (Reversible Connected Multi-Column Network) is a lightweight backbone architecture designed to enhance feature representation while reducing information loss. It achieves lossless cross-layer feature transmission through a multi-column design and reversible connection mechanism, improving the fusion of contextual information. This makes it particularly suitable for small object detection tasks, where preserving low-level details is crucial [16]. The macro structure of RevColNet is shown in Figure 4.  
The microarchitecture of RevColNet is depicted in Figure 5. As shown in Figure 5a, each layer performs feature extraction through a combination of downsampling and ConvNeXt blocks. Figure 5b illustrates the design of reversible connections between columns, which enable bidirectional information exchange and preserve feature integrity throughout  
Figure 4: RevColNet macro structure diagram.  
the network. Each layer receives two inputs: one from the preceding layer within the same column, and another from the corresponding layer of the adjacent column. The formal representations of these inputs are provided in Equations (1) and (2).  
\[X_{t}=F_{t}(X_{t-1},X_{t-m+1})+\gamma X_{t-m} \tag{1}\]  
\[X_{t-m}=\gamma^{(-1)}[X_{t}-F_{t}(X_{t-1},X_{t-m+1})] \tag{2}\]  
In the equations, \(X_{t}\) represents the feature map at layer \(t\), \(F_{t}(.)\) denotes the activation function, \(\gamma\) refers to the reversible operation, and \(\gamma^{-1}\) is its inverse. Each column consists of m feature maps within the group.