esa-sceva/satcom-chunk-collection huggingface dataset:
# Steering Pull Model and Its Sensitivity Analysis
## 5 Results and Verification
### Estimation Results  
For a steering pull estimation, a vehicle is designated and its design parameters such as tire properties, wheel alignment settings, mechanical characteristics of the steering system, and lower body linkages are adopted in the model. In addition to the vehicle, driving conditions are also prepared for the estimation. Typically, there are two types of test modes for steering pull evaluation--free control test and fixed control test [23]. In the case of the free control test, the driver drives straight at the constant speed of 80 km/h with one passenger onboard. When the vehicle reaches the target speed, the driver releases the steering wheel to have the vehicle in a free state. The vehicle starts to drift and when the driving distance reaches 100 m, the steering pull distance of the vehicle is measured as shown in Figure 9. In the case of the fixed control test, the steering wheel is not released but fixed by the driver. Instead, a torque sensor mounted in the steering column measures the torque generated by the steering pull.  
In this study, the free control test is employed for steering pull evaluation. The driving conditions and design parameters of the designated vehicle are described in Table 1.  
Figure 10 shows the steering pull estimation results. As expected, the steering pull distance increases as the vehicle moving distance increases and its increasing tendency is exponential rather than linearly proportional. When the vehicle reaches 100 m, the steering pull distances at 0.7 and 1.6 degrees of bank angle are 0.58 and 0.96 m, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Driving Conditions** & \\ \hline Vehicle speed (km/h) & 80 \\ Passengers & 2 (150 kg) \\ Driving distance (m) & 100 \\ Bank angle (deg) & 0.7, 1.6 \\ \hline \hline \multicolumn{2}{c}{Design parameters} \\ \hline Caster angle (deg) & 4.5 \\ Kingpin angle (deg) & 12.7 \\ Camber angle (deg) & \(-\)0.5 \\ Tire size & 215/55 R17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The estimation conditions of steering pull.  
Figure 9: Steering pull evaluation by free control test.
## 4 Conclusions  
We have presented a new version of the iTDS tongue-computer interface for individuals with severe physical disabilities. By being implemented as an arch-shaped dental retainer, the iTDS-2 preserves the intraoral space for tongue motion as well as the tactile feedback on the palate, both of which are expected to improve the system performance. The arch-shaped design also reduces the risk of interference with speech, which will be further evaluated in our future work. The iTDS-2 is equipped with a robust dual-band (27 MHz and 432 MHz) wireless link with increased output power compared to its predecessor (iTDS-1). A customized PIFA is fitted within the geometry of the iTDS-2 dental retainer and intraoral environment. It also monitors the link quality for each band in real time and automatically switches between them, if necessary. The built-in SAR-ADC and ultra-low power FPGA, which have replaced the MCU in the iTDS-1, increase the operating time of the iTDS-2 to ~27.3 hours per charge of a small 50 mAh Li-Ion battery. The iTDS-2 performance was evaluated by two healthy volunteers wearing their customized iTDS-2 dental retainers. The experimental results showed that subjects achieved 22% better performance, on average, when they used the iTDS-2, compared to the iTDS-1. The results also show that the eTDS still outperforms the iTDS-2 by 8%. In future, we plan to implement the iTDS on a flex-PCB to further reduce the retainer thickness and volume, thus improving the user comfort. Moreover, we will add a real time adaptive matching circuit to the antenna feed point to compensate for dynamic variations in the oral space and further decrease the power loss caused by Tx-antenna impedance mismatch.  
Like any other assistive technology or intraoral device medical that is meant for extended usage, safety is paramount in both functionality and performance of the iTDS, because it plays such an important role in the users' daily life, and it contains a rechargeable battery and other electronics. The current iTDS-2 prototype was developed only for acute experiments and coated in dental acrylic. Fortunately, hermetic sealing of the intraoral and implantable devices is well established. In the future versions of the iTDS, it will be coated with biocompatible polymers, such as Parylene-C, before being vacuum-molded in acrylic or thermoplastic. Additional safety and accelerated lifetime measurement tests are also necessary on the strength and the durability of the iTDS package and mechanical structure to avoid the possibility of crack formation or fragmentation under intraoral stress and aging.
## 2 Interference Signal Models
### Specific Steps of the Proposed Algorithm
#### 3.2.2 Steps for Initial Threshold Calculation  
1. Divide the data of the \(R\) segment into \(n\) segments, where each segment has a length of \(q\). Calculate the mean amplitude of each segment as follows: \[A^{\prime\prime}(k^{\prime})=\tfrac{1}{q}\sum\limits_{j=1}^{q}A[(R-1)\times l +q\times(k^{\prime}-1)+j]\quad k^{\prime}=1,2,\ldots,n\] (3)
2. Compute the differential value between the mean amplitude of two adjacent segments: \[D(q)=A^{\prime\prime}(k^{\prime}+1)-A^{\prime\prime}(k^{\prime})\quad q=1,2, \ldots,n-1\] (4)
3. Take the absolute value The process for initial threshold calculation is illustrated in Figure 3.
###### Abstract  
This article presents a novel approach to the estimation of motion parameters of objects in passive forward scattering radars (PFSR). In such systems, most frequency modulated signals which are used have parameters that depend on the geometry of a radar scene and an object's motion. Worth noting is that in bistatic (or multistatic) radars forward scattering geometry is present thus in this case only Doppler measurements are available while the range measurement is unambiguous. In this article the modulation factor, also called the Doppler rate, was determined based on the chirp rate (equivalent Doppler rate) estimation concept in the time-frequency (TF) domain. This approach utilizes the idea of the complex phase of the short-time Fourier transform (STFT) and its modification known from the literature. Mathematical dependencies were implemented and verified and the simulation results were described. The accuracy of the considered estimators were also verified using the Cramer-Rao lower bound (CRLB) to which simulated data for the considered estimators was compared. The proposed method was validated using a real-life signal collected from a radar operating in PFSR geometry. The Doppler rate provided by a car crossing the baseline between the receiver and the GSM transmitter was estimated. Finally, the concept of using CR estimation, which in the case of PFSR can be understood as Doppler rate, was confirmed on the basis of both simulated and real-life data.  
passive forward scattering radar; chirp rate estimation; passive radar; forward scattering radar; radar measurements; time-frequency analysis +
Footnote †: journal: Sensors
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 3 Materials and Methods
### Decomposition Approach  
Decomposing a higher-order digital filter into second-order sections (SOSs) provides a stable and numerically efficient means of realizing the transfer function. In this work, we use 2023b MATLAB's built-in function, which takes the filter's numerator and denominator polynomials (in transfer function form) and factors them into multiple second-order blocks. Each block corresponds to a pair of conjugate poles (and zeros), thereby reducing sensitivity to rounding errors and making it straightforward to manage overflow concerns. We opt for a cascade structure since it simplifies the allocation of scale factors for each block, ensuring a more uniform distribution of gain and helping maintain stability. Moreover, by breaking the filter into multiple SOSs, we can individually optimize or tweak specific sections as needed, offering flexibility for further performance refinements without rewriting the entire filter design.  
Figure 2 presents the magnitude response of the full 6 th-order low-pass elliptic filter (sampling frequency Fs-1 kHz; cutoff frequency Fc-100 Hz; passband ripple Rp-1 dB; stopband attenuation Rs-40 dB) and the magnitude responses of the three second-order sections.  
Figure 3 depicts the step responses of the full and SOS decomposition of the same low-pass elliptic filter.  
We propose to drive the focus to one of the sections and apply the time-varying coefficient methodology to reduce the transient time of the whole design.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 3 Problem Definition  
A heterogeneous information social network is an information network containing diverse node and link types [4], represented as an indirect graph \(G=(V,E)\). Here, \(V\) signifies the node set, encompassing \(N\) node types. \(E\) symbolizes the link set connecting nodes in \(V\), spanning \(K\) link types. Excluded unobserved links from \(E\) are denoted as \(U_{e}=V\times V-E\). Among these, \(U_{e}\) comprises testing links, selected at random with a ratio of \(\delta\) from the unobserved link set \(U\), while the rest are considered training links. The objective of link prediction in heterogeneous information social networks involves determining a predictive function \(F:(V,E,U)\to Y\), with \(Y=y(l_{1}),y(l_{2}),\ldots,y\Big{(}l_{|U|}\Big{)},y(l_{i})\in 0,1\), presenting the prediction results for the existence of testing links in the subsequent function. Taking an example link \(l\), we initially derive its feature representation by processing the node feature representation for each constituent node. Our task simplifies into constructing a model to predict the existence label \(y(l)\) for link \(l\). In the following section, we propose a combined link representation utilizing PME [12] and node2 vec to obtain precise link feature representation [23].
# Design and Implementation of Industrial Accident Detection Model Based on YOLOv4
## 2 Related Works
### Data Labeling Tool  
In general, the events of workers and accidents occurring at industrial sites are recorded and monitored by the CCTVs installed at industrial sites. In this study, because data collected from the workplace filmed by CCTV are being used, it is necessary to identify which event has occurred and which object has appeared in one frame in the video. This task is called data labeling or annotation. Various labeling tools exist; Table 1 compares the functions of the three tools [23; 24; 25].  
Table 1 indicates the functions provided by each labeling tool. DarkLabel is not open-source and works only on the Windows operating system, which is not web-based. In contrast, YOLO Mark and the \"Computer Vision Annotation Tool\" (CVAT) are open-source and work well with other operating systems. As CVAT is web-based, labeling can be  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DarkLabel [23]** & \
\begin{tabular}{c} **You Only Look Once** \\ **(YOLO) Mark [24]** \\ \end{tabular} &
\begin{tabular}{c} **Computer Vision** \\ **Annotation Tool (CVAT) [25]** \\ \end{tabular} \\ \hline Open-source & X & O & O \\ Web-based (Collaboration) & X & X & O \\ Platform dependencies & X (Windows only) & O & O \\ Shortcut, intuitive UI & O & \(\triangle\) & O \\ Automatic frame extraction & O & X & O \\ Automatic object labeling & \(\triangle\) (linear interpolation) & O (YOLOv2) & O (YOLO, etc.) \\ Segmentation & X & X & O \\ Associativity (Object detection & X & O (YOLO) & X \\ model) & & & \\ Various labeling format & O & \(\triangle\)(YOLO only) & O \\ Ease of installation & O & X (directly build) & \(\triangle\) (docker-based) \\  & & O: Supported, \(\triangle\): Not enough, X: Not supported \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature comparison of three annotations (labeling) tools.  
Figure 3: Number of injured (**left**) and number of deaths (**right**) by business size (number of workers).  
performed co-operatively. In addition, to consider the user's perspective, the UI or shortcut functions, labeling functions for automatically extracting frames or tracking objects, and the degree of difficulty of installation were investigated and displayed.
## 5 Evaluation
### Information Channel Access Management  
As described in the previous section, HTTPS is used for securing message exchanges between information providers, consumers and the IIP, by means of encryption. This will minimize the success rate of man-in-the-middle attacks. To maintain information privacy between applications, HTTP basic authentication credentials are directly used in IIP for authorizing applications to access different  
Figure 14: **(a)** The IIP web console’s main page; **(b)** A web page for information providers to list their information channels.  
information channels. Information consumers that are not listed in an information channel's allowed user list will not be able to access or subscribe to that particular channel. Figure 15a shows a test case where an HTTP POST request is sent from a test client application, acting as an information consumer, to the IIP for subscribing to an information channel with _infold_ info:829055923. The username, which is used by the information consumer, is listed in the information channel's allowed user list, and thus, a 200 OK response is returned by the IIP, containing a representation of the subscription in XML format in its body. Figure 15b shows another test case where similar HTTP POST request for subscription to the same information channel is sent from an information consumer. This time, however, the username used by the information consumer is not listed in the information channel's allowed user list, and therefore, the IIP returns a 403 Forbidden response with an XML-formatted message in its body, informing that the username is unauthorized to subscribe to the targeted information channel. All messages in both tests (Figure 15a,b) are captured using Wireshark, and HTTPS is not used (otherwise all packets are encrypted and cannot be interpreted). It can be concluded from the conducted tests that the implemented information channel access management scheme by directly utilizing HTTP basic credentials works as intended.  
The tests were conducted manually, since they were only intended to verify whether the access management implementation worked correctly. Automated unit testing could be used instead for a more formal way of verification, and protocol conformance testing could be applied, as well, to find out whether the implemented features comply with the chosen standards.  
Figure 15: Wireshark captures of: (**a**) An information consumer successfully subscribes to an information channel; (**b**) An information consumer is rejected when trying to subscribe to an information channel.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead for [PERSON] et al. [5]  
#### 5.2.1 Computation Cost on the Client Side  
1. Secret Key Sharing Cost: In this algorithm, the authors used secure aggregation. The client must send a part of the secret to the server to collect sufficient parts of the secret, and the updates are aggregated. This requires all clients to communicate with every other client and participate in FL. This makes the computational complexity the square of the total number of clients participating in FL. Because we assumed the number of clients to be 1000, the computational cost would be at least \(1000\times(1000-1)\text{ ms}\), which is relatively quite high.
2. Encryption Cost: The authors did not mention which algorithm they used for decryption. We can assume that they have used the same algorithm as ours for encryption. In this case, we assume that the cost of encryption is \(C_{ENC}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Number of CPU Cycles**} & \multicolumn{4}{c}{**Cost (s)**} \\ \cline{3-6}  & & \(t=50\) & \(t=100\) & \(t=150\) & \(t=1000\) \\ \hline [PERSON] et al. [24] & \(C_{SIG}+C_{VER}+t\times(C_{ENC}+C_{DEC}+C_{AGG})\) & 38.8 & 93.642 & 166.4 & 6889.3 \\ \hline [PERSON] et al. [1] & \(C_{SIG}+C_{VER}+t\times(3\times(C_{ENC}+C_{DEC})+C_{AGG})\) & 24.1 & 96.5 & 222.7 & 18,817.9 \\ \hline [PERSON] et al. [5] & \(C_{SIG}+C_{VER}+999000+t\times(C_{ENC}+C_{DEC}+C_{AGG})\) & 1007.2 & 1031.5 & 1073.72 & 7276.88 \\ \hline [PERSON] et al. [23] & \(C_{SIG}+C_{VER}+9990+t\times(C_{ENC}+C_{DEC}+C_{AGG})\) & 18.2 & 42.5 & 84.7 & 6287.9 \\ \hline [PERSON] et al. [22] & - & 961 & 3176 & 6703 & 15,760 \\ \hline Proposed & \(C_{\sigma}+C_{VER}+t\times(C_{ENC}+C_{DEC}+C_{AGG})\) & 8.11 & 32.25 & 74.3 & 6271.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational cost for multiple iterations.  
Signing Cost: Since they are not using a GS, their signature would be encrypted with the client's private key. Hence, the cost of signature is the cost of encryption \(C_{SIG}=C_{ENC}\).  
Hence, the computational cost of the client can be given as a formula given in Equation (23):  
\[C_{Client}=999,000\text{ ms}+C_{ENC}+C_{SIG}. \tag{23}\]
# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Textual Data
Machine learning methods regard sentiment analysis as a classification problem, using sentiment polarities as labels and employing various techniques, such as Recurrent Neural Network (RNN) [4], support vector machines [140; 141], conditional random field (CRF) [135], and neural networks [136], to construct classifiers with text features. In contrast, lexicon-based methods identify sentiment orientations by referring to pre-defined lexicons like LIWC, HowNet, and WordNet [141; 142]. These lexicons contain sentiment-related terms and their corresponding polarity. However, the quality of these lexicons is critical, and scholars are working towards improving the coverage, domain adaptation, and continuous updating of these resources. For instance, [PERSON] et al. [143] constructed a comprehensive lexicon by merging ten lexicons. [PERSON] et al. [144] proposed a sentiment classification model that uses semantic similarity measures and embedding representations, rather than keyword matching, to compute the semantic distance between the input word and lexicon word rather. [PERSON] et al. [145] analyzed several lexicons and how they complement each other. Lexicon collection can be divided into three categories: manual, dictionary-based, and corpus-based. The manual method is time-consuming and labor-intensive. The dictionary-based method uses a set of opinion words as seeds to search in existing dictionaries. The corpus-based method expands lexicons based on information from the corpus, resulting in domain-specific lexicons [146]. Machine learning methods offer higher accuracy, while lexicon-based methods are more general. Hybrid methods combine both. For instance, [PERSON] et al. [147] proposed a lexicon-enhanced approach for sentiment classification that combines machine learning and semantic-orientation methods. Their findings indicate that the hybrid method significantly improves sentiment classification performance. By reviewing literature related to product design based on textual data, we have found that it is an important research topic that has been addressed by many scholars in recent years. NLP technology is used to extract product attributes and sentiment features from text, which helps to understand user requirements, evaluate products, and grasp product development trends. While requirements and evaluates are the core foundation, it is important to note that the rest of the design process is equally crucial. Unfortunately, most existing research has overlooked this point, resulting in designs that are more of an improvement rather than being original and innovation. In other words, product design based on text still has vast potential for development. Undoubtedly, the emergence of textual data has great significance for product. Compared to traditional methods, textual data provides richer information of higher quality and can be acquired and processed much more quickly.
## 2 Materials and Methods
### 3D Interframe Displacement Estimation  
Displacement estimations were performed for the RF data beamformed on each of the four grids for each elevational position and time point for both probes. The resulting displacement estimates were labeled using an A or a B and a subscript. The A and B refer to the probe being used. The subscript refers to the acquisition angle and beamforming grid being used. Thus, A\({}_{-\alpha}\), refers to the displacements estimated using probe A and a beamforming grid and acquisition at steering angle \(-\alpha\).  
Displacement estimation was performed using a 2-step, normalized Cross-Correlation (CC) based method using 3D kernels defined in Table 2. In the first step, envelope (demodulated RF) data were used to find a global displacement estimate at sample accuracy (i.e., full samples). Subsequently,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Beamforming Grid (\(\mu\)m)**} & \multirow{2}{*}{**Displacement Grid (\(\mu\)m)**} \\ \cline{2-3}  & **Zero-Degree** & \(-\alpha\)**, +\(\alpha\), Orthogonal** & \\ \hline dx & 64.2 & 30.2 & 64.2 \\ dy & 10.1 & 10.7 & 9.1 \\ dz & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resolution of interlocked steered and non-steered grids.  
Figure 4: The first column depicts the grid configurations for both probes in which the overlay of all steered and non-steered grids results in a displacement grid consisting of coinciding sample-locations. Columns 2, 3, and 4 show the B-mode representations of the actual transmissions performed subsequently overlaid with a displacement grid and a single line indicating the direction of the reconstructed ultrasound beam in receive. The schematic drawing of the aperture indicates the +45\({}^{\circ}\) and \(-45^{\circ}\) angles of insonification relative to the phantom for probe A and B, respectively. The last column shows the RF data registration in which the axial direction of the probes serves as a native lateral ultrasound transmission of the opposite probe.  
subsample displacements were determined by 3D spline interpolation of the auto and cross-correlation function based on RF [42]. Median filtering was applied for all three directions using 3D kernels (see Table 2). For the elevational direction, additional regularization was applied by allowing only sub-plane displacement estimations.  
Since probe A and B are positioned orthogonally, and the axial and lateral displacement directions of probe A are equal to the lateral and axial displacement directions of probe B, respectively, and vice versa. The orientation of Probe A is chosen to be the primary orientation throughout the rest of this manuscript. The axial and lateral direction of probe A equals the vertical and horizontal direction of the accumulated displacement estimates as depicted in Figure 5.  
The displacement estimates from both probes for different directions are combined in three different ways used to derive strain estimates. These methods are described below and named: single-probe zero-degree, single-probe compounding, and Dual-probe reference.