esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Integrated circuits for high-speed applications are prone to electromagnetic interference (EMI) issues. For detecting these EMIs, near field electromagnetic measurement is an important tool, as it is helpful in determining the locations of high electromagnetic emission (EME hot spots) within the IC. As the 3D stack dies technology is increasingly important owing to its several advantages, the EMI issue within the stacked dies must be considered when the stack dies are high frequency and high power.  
However, near field electromagnetic measurement is ineffective to identify the hot spots of electromagnetic emission within the stacked dies, as illustrated in this work, and a computational method is developed to evaluate and identify the EME hot spots in integrated circuits with experimental verification. Using this method, we found that the EMI issue within the stack dies is a lot more severe. This work shows that the minimum value of the maximum magnetic field strength for 3D-stacked IC, as the distance between the stack increases, is about six times higher than an un-stacked IC. If the stack distance is smaller, the magnetic field strength can be as high as 16 times as compared with an unstacked IC. Thus, a minimum distance between the stack must be maintained. The actual minimum distance depends on the electromagnetic susceptible of the designed circuit. The method developed in this work can be done at the IC design stage before fabrication, enabling optimization of the stack distance as well as circuit design for reducing the EMI, and eliminating the significant cost of fabrication owing to improper design.  
Conceptualization, C.M.T.; Methodology, D.K.; Software, D.K.; Validation, D.K.; Formal analysis, V.S.; Investigation, D.K.; Resources, C.M.T.; Data curation, V.S.; Writing--original draft preparation, D.K.;  
Figure 13: Maximum magnetic field strength versus the distance between the layers of 3D-IC at 3 GHz operating frequency at the top surface of all the dies.  
Writing--review and editing, C.M.T.; Visualization, D.K.; Supervision, C.M.T.; Project administration, C.M.T.; Funding acquisition, C.M.T. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Chang Gung University research Grant QZRFD123 and CIRPD2F0024.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Laboratory Experimental Validation  
Due to in situ installation procedures, usage and aging, force plates accuracy may decrease, which can be propagated to the already calculated kinetic quantities. Based on these limitations, some research groups have developed systems to assess force plate accuracy using ad hoc designed in situ devices [8; 26]. In the current study, static calibrations were followed by dynamical calibrations performed with a rigid body falling procedure [26], revealing homogeneity of static calibration results, particularly in the time = 0 force time curve of the falling body. The simultaneous correlation coefficient between the posterior strain filtered signal (moving average of 32 samples) and the theory generated by a previous application in MatLab R2014 (MathWorks Inc., Natick, MA,USA) has shown a value of 0.95.
## 2 Principle
### Principle of CAP Modulation and Conventional CAP Receiver
(**b**) shows the details of the modified NN-based DD-LMS at the rear stage. where \(W^{1}\), \(W^{2}\), \(W^{3}\) and \(W^{4}\) represent the weight matrices for each layer respectively, \(b^{1}\), \(b^{2}\), \(b^{3}\) and \(b^{4}\) represent the bias, respectively. \(f(\cdot)\) denotes the nonlinear activation function (AF) Tanh(\(\cdot\)), which could be expressed as [30],  
\[\text{Tanh(x)}=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{8}\]  
After experiencing forward propagation, the minimum mean square error (MSE) is employed as the loss function to calculate the difference between the predicted symbols and transmitted symbols. The weight \(W\) and bias \(b\) can be obtained by the following Equation (9),  
\[W,b=\text{argmin}_{W,b}\frac{1}{m}\sum_{j=1}^{m}||\hat{y}(j)-y_{out}(j)||^{2} \tag{9}\]  
where, \(\hat{y}(j)\) is the \(j^{th}\) transmitted symbol, and \(y_{out}(j)\) is the corresponding predicted symbol; \(m\) is the number of all symbols in one batch. In this algorithm, we utilize Adam optimizer and ReduceLROnPlateau, which is a commonly used strategy to adjust the learning rate. The constellation points of 64 APSK and 64 QAM after the DNN-based waveform-to-symbol converter are displayed in Figure 1. There are a lot of red points that represent the wrongly decided symbols due to remaining ISI, linear and nonlinear distortions. Then the real part \(y_{r}(t)\) and imaginary part \(y_{i}(t)\) of outputs are sent to the modified NN-based DD-LMS algorithm after two slide windows, respectively. The \(k^{th}\) input \(X^{*}(k)\) of NN-based DD-LMS can be expressed as:  
\[X^{*}(k)=\left[y_{r}\bigg{(}k-\frac{M-1}{2}\bigg{)},y_{i}\bigg{(}k-\frac{M-1} {2}\bigg{)},\ldots,y_{r}(k),y_{i}(k),\ldots,y_{r}\bigg{(}k+\frac{M-1}{2}\bigg{)},y_{i}\bigg{(}k+\frac{M-1}{2}\bigg{)}\right]^{T} \tag{10}\]  
where \(M\) is the length of the sliding window on the real or imaginary part, and \(2M\) is the input length NN-based DD-LMS. According to our experiments, the input length of 42 would be the best. Then input signals are multiplied by the weight matrix \(H\) and connected to two output nodes that correspond to the real part \(y_{r}^{*}(k)\) and imaginary part \(y_{i}^{*}(k)\) of output. The \(k^{th}\) output can be expressed as,  
\[y_{out}^{*}=[y_{r}^{*}(k),y_{i}^{*}(k)]^{T} \tag{11}\]  
Therefore, the relationship between input and output can be given by,  
\[X(j)=\left\{x\left(j-\frac{l-1}{2}\right),\ldots,x(j-1),x(j),x(j+1),\ldots,x \left(j+\frac{l-1}{2}\right)\right\} \tag{5}\]0  
At the same time, \(y_
## 4 Multi-Module Measurement Validation of MANTiS-32
### Evaluation of Modal Analysis Performance Using a Scaled Cable-Stayed Bridge Model
#### 4.1.2 Finite Element Modal Analysis of the Scaled Cable-Stayed Bridge Model  
The dynamic characteristics of the scaled cable-stayed bridge model were analyzed using a finite element approach. Structural modeling was conducted in SIEMENS NX, and modal analysis was performed using the NASTRAN Normal Mode Dynamic Solver. As illustrated in Figure 19, the model was divided into key structural components (deck, pylons, cables, and lumped masses) to reflect the actual mechanical behavior of the bridge. The deck and pylons were modeled with two-dimensional beam elements, while the cables were represented using one-dimensional tension-only elements. Boundary conditions at the deck supports were simulated using rigid body elements to accurately constrain the model. This detailed FE modeling strategy enabled the precise evaluation of the bridge's natural frequencies and corresponding mode shapes, forming a foundation for validating the measurement system's dynamic sensing capabilities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & Steel & **Young’s modulus** & 2.15 \(\times\) 10\({}^{4}\) kgf/mm\({}^{2}\) \\ \hline
**Mass Block** & 1480 kg & **Yield Strength** & 40 kgf/mm\({}^{2}\) \\ \hline
**Shear Modulus** & 8.10 \(\times\) 10\({}^{3}\) kgf/mm\({}^{2}\) & **Poisson’s Ratio** & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Specification of cable-stayed bridge model.  
Figure 18: Configuration of the scaled cable-stayed bridge model.  
To enhance the dynamic flexibility of the scaled bridge model, twenty-two lumped masses of 40 kg each were placed at specific nodal locations along the central deck girders, excluding the pylon zones and both end spans. These mass additions were intended to replicate the inertial characteristics of a low-stiffness bridge deck. Boundary conditions were defined as follows: the bases of the pylons were fully fixed, while the left pylon and both ends of the deck were constrained with roller supports. The right pylon was connected to the deck via a pinned joint. Furthermore, 36 nodes at the center of the deck were left fully unconstrained in the x, y, and z directions to allow for free translational motion. Finite element modal analysis results demonstrate that, owing to the bridge's long and slender geometry, the first four vibration modes were dominated by global bending deformations, as shown in Figure 20. The corresponding natural frequencies were calculated as 3.502 Hz (1 st mode), 4.200 Hz (2 nd mode), 6.013 Hz (3 rd mode), and 7.078 Hz (4 th mode). All modes fell below the 10 Hz threshold, successfully capturing the dynamic behavior of a flexible, small-to-medium-span cable-stayed bridge.  
Figure 19: Finite element modeling components of the scaled cable-stayed bridge structure.  
Figure 20: Modal analysis results of the scaled cable-stayed bridge mode.
## 4 Proposed Method for Notch Fatigue Analysis
### Size Effect of Critical Distance  
Due to the existence of the critical distance size effect, the critical distance theory does not apply to some small notches [17; 18; 19]. The non-propagating crack will change the original position of the crack, causing a small change in the notch geometry, which in turn triggers the geometric size effect. Therefore, size effect of the critical distance might originate from the non-propagating cracks generated in the small notch. In view of this, the present study compared the proposed critical distance method with the model of [PERSON] et al. [21] and [PERSON] et al. [22], both of whom take into account the critical distance size effect. The critical distance point method expressions of [PERSON] and [PERSON] are respectively as follows:  
\[\text{[PERSON]: }L/2=Kt\times\frac{1}{2\pi}\bigg{(}\frac{K_{th}}{\sigma_{0}} \bigg{)}^{2} \tag{11}\]  
\[\text{[PERSON]: }L/2=\frac{1}{2\pi}\bigg{(}\frac{K_{th}}{0.66\sigma_{0}}\bigg{)}^{2} \tag{12}\]  
Table 4 presents critical distance values of notched Q345 qD specimens based on different critical distance point methods. Figure 11 shows the prediction results of three different critical distance methods. It can be seen from Figure 10 that the predictions of the proposed model and [PERSON]'s model fall within the \(\pm 10\%\) scattering band; [PERSON]'s model is within the \(\pm 15\%\) scattering band. The three methods have almost the same prediction accuracy. This shows that the proposed model can provide a reference for dealing with the critical distance size effect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen No.**} & \multicolumn{3}{c}{**Critical Distance (mm)**} \\ \cline{2-4}  & **[PERSON]** & **[PERSON]** & **Proposed** \\ \hline V1 & 0.725 & 0.572 & 0.620 \\ V2 & 0.657 & 0.572 & 0.620 \\ V3 & 0.563 & 0.572 & 0.620 \\ V4 & 0.496 & 0.572 & 0.620 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Critical distance of Q345 qD notched specimens based on different critical distance method.  
Figure 10: Scatter plots of experimental fatigue strength vs. predicted fatigue strength.
## 6 Conclusions  
In summary, an underwater contact-induced ionic current sensing system and a silent underwater information transmission method were proposed in this article. The generated ionic current signals were due to the potential difference between the sensing electrode and the composite working electrode. The magnitude and the direction of the ionic current signals depend on the material of the metal, water temperature, ionic concentration and electrode distance. The method proposed in this article is simple and self-powered, and it is promising in fields where silent underwater information transmission is a necessity.  
Conceptualization, J.Z.; methodology, B.S.; software, L.C.; validation, B.S. and Y.D.; formal analysis, B.S. and Y.D.; data curation, L.C.; writing--original draft preparation, B.S. and J.Z.; writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Open Fund of the National Center for International Research of Subsea Engineering Technology and Equipment (HG20240102) and the National Natural Science Foundation of China (52001049) to [PERSON].  
Not applicable.  
Figure 9: (**a**) Schematic diagram of silent information transmission in underwater conditions; (**b**) definition of English letters using “dot” and “dash” in Morse code; (**c**) definition of “dot” and “dash” signals using the ionic current signals; (**d**) demonstration of messages such as “SOS” and “UP” via Morse code; (**e**) display of instant messages on the screen of the decoder when translating the ionic current signals into intelligible English letters.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** We thank the anonymous reviewers for their comments and suggestions, which greatly improved the manuscript.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Inner Mongolia Company, State Power Investment Corporation Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Stability Reinforcement of Slopes Using Vegetation Considering the Existence of Soft Rock
## 2 Materials and Methods
### Numerical Model and Simulation  
#### 2.2.1 Reinforcement of Vegetation on Soil Strength  
The contribution of vegetation roots in the shear resistance of the soil is often taken as an additional cohesion of the soils [44; 48]. The increase of soil shear strength due to root reinforcement can be expressed as [48]:  
\[C=t_{R}(\cos\theta\tan\varphi+\sin\theta) \tag{1}\]  
where \(t_{R}\) denotes the unit tension strength of the vegetation root; \(\theta\) denotes the shear rotation angle, equaling to 90-slope angle (degree); \(\varphi\) denotes the friction angle of the soil. Four types of vegetation were considered in this study to reinforce the slope stability: grass, shrubs, young forest and mature forest, as shown in Figure 4a. The increase of soil shear strength caused by root reinforcement was represented as the increase of soil cohesion, which was further given as a function of depth to reflect its variations with depth. In this study, the influence of vegetation root on soils shear strength was considered only within a depth of 2.0 m, and a total of 6 sub-layers (0-0.25 m, 0.25-0.5 m, 0.5-0.75 m, 0.75-1.0 m, 1.0-1.5 m, 1.5-2.0 m) were divided with each of sub-layer characterized by a typical increase value of soil cohesion, similar to those in [44]. Such a subdivision of layers is to ensure quantitative consideration of reinforcement on soil strength by different types of vegetation and guarantees simultaneously the differences among them, as reported in [44]. The detailed increase value for the four vegetation was illustrated in Figure 4b. The influence depths of the grass, shrubs, young forest and mature forest are 0.25, 1.0, 1.5 and 2.0 m, respectively.  
Figure 3: Variations of properties of mudstone with sample depth determined from laboratory.
## 2 Materials and Methods
### Plasma Treatment of Cell Culture Media  
Serum-free DMEM with 1% Pen Strep was used throughout the experiments and is referred to as DMEM in the text. The medium underwent argon plasma treatment for 30 s (plasma-activated medium, PAM30) or 60 s (PAM60), as indicated for the specific experiment. For this purpose, 100 \(\upmu\)L of DMEM was supplied per well in a 96-well plate (Greiner Bio-One, Kremsmunster, Austria). The kINPen(r)09 was mounted vertically, and the quartz capillary was positioned at the top edge of each well in the 96-well plate. The distance between the tip of the quartz capillary and the 96-well plate did not exceed 1 mm. In this way, the argon plasma had immediate contact with the media [45, 80]. Argon gas treatment using the kINPen(r)09 without igniting the plasma for 60 s was used as a control (argon-activated medium, AAM). Untreated DMEM was included as a further control. After plasma treatment, the different samples and controls were pooled in separate tubes (1.5 mL; Eppendorf, Hamburg, Germany). For subsequent cell experiments, these media were stored at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) for 24 h to ensure that the cell effects observed were neither caused by the changed oxygen concentrations nor by the persistence of hydrogen peroxide; these were shown to be balanced over 24 h after plasma treatment [45].  
As a further control, suspended HaCaT cells were preincubated with hyaluronidase (HAdase, 600 units/mL, type IV-S, H-4272, Sigma, Steinheim, Germany) under slightly shaking for 30 min (37 \({}^{\circ}\)C) in serum-free DMEM with 1% Pen Strep (because HAdase is inactive in serum-containing media) and then seeded into DMEM with HAdase (600 units/mL, DMEM + HAdase) and cultivated for the appropriate times. Furthermore, HaCaT cells were incubated in DMEM supplemented with hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) to test the effect of a relatively long-lived reactive species, which usually arises from the interaction of CAP with a liquid medium. We used a concentration of 150 mg/L H\({}_{2}\)O\({}_{2}\) (from commercial stock solution, 30%, Fisher Scientific) in DMEM corresponding to the concentrations measured in an earlier study of our group [80].
## 3 Results and Discussion
### The Effect of pH
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Peak \#** & **Rt** **(min)** & **Molecular Ion** **(m/2)** & **Other** **Fragments** & **Tentative Identity** & **Formula** & **Qt*** & **Qt*** AsA**b & **Qt+ AsA**b & **Qt+ AsA**b & **Qt+ Sulfite**d \\ \hline
1 & 2.90 & 169 & 151 (\(\mathrm{H_{2}O}\)) &
\begin{tabular}{c} Phloroglucenol carboxylic \\ acid \\ acid \\ \end{tabular} & C\({}_{7}\)H\({}_{5}\)O\({}_{5}\) & + & + & + & + \\
2 & 5.16 & 153 & 109 (\(\mathrm{CO_{2}}\)) &
\begin{tabular}{c} Protocatechic acid \\ (2-4-dihydroxyhydroxybenzoic acid) \\ \end{tabular} & C\({}_{7}\)H\({}_{5}\)O\({}_{4}\) & + & + & + & + \\
3 & 12.10 & 179 & 119 & 3,4-dione-2-oxo-phenylacetic acid & C\({}_{4}\)H\({}_{5}\)O\({}_{3}\) & - & + & - & - \\
4 & 12.41 & 381 & - & Quercetin 3-sulfate & C\({}_{8}\)H\({}_{12}\)O\({}_{5}\) & - & - & - & + \\
5 & 14.22 & 379 & - & Quercetin 3-sulfate quinone & C\({}_{8}\)H\({}_{12}\)O\({}_{5}\) & - & - & - & + \\
6 & 20.27 & 301 & - & Quercetin & C\({}_{8}\)H\({}_{12}\)O\({}_{5}\) & - & + & + & + \\
7 & 21.66 & 299 & - & Quercetin quinone & C\({}_{8}\)H\({}_{12}\)O\({}_{5}\) & + & + & + & + \\ \hline \end{tabular}  
* Treatment of 100 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) Qt; b Treatment of 100 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) Qt + 200 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) AsA; c Treatment of 100 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) Qt + 200 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) CySH\({}_{4}\) d Treatment of 100 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) Qt + 200 \(\mathrm{\SIUnitSymbolMicro m}\) L\({}^{-1}\) sulfite.  
\end{table} 
Table 2: Mass spectral data of the major Qt degradation products, detected in Qt solutions after treatment for 50 min, at 90 \({}^{\circ}\)C.  
Figure 4: Total ion chromatogram (TIC) of a Qt solution (100 \(\mathrm{\SIUnitSymbolMicro m}\)ol L\({}^{-1}\)), treated at 90 \({}^{\circ}\)C, for 50 min, at pH 7.5. For peak assignment, see Table 2.
# Healthchain: A Privacy Protection System for Medical Data Based on Blockchain
## 4 Cryptographic Primitives
### Group Signature  
Group signature [31] is a relatively new signature concept proposed by [PERSON] and [PERSON] in 1991. This technology enables any member of a group to sign messages on behalf of the group. Like other digital signatures, the signature can be publicly verified. However, people other than the group manager cannot know which group member signed it, so the privacy of the group members is effectively protected. In this paper, we choose the scheme proposed by [32] because the size of its group public key and group signature are constant, which means that it does not increase with the increase of group members, which helps to reduce the system overhead. The group signature scheme is mainly composed of the following six algorithms:  
1. Setup (\(\lambda\)): The initialization algorithm acts as a randomization algorithm with security parameters \(\lambda\) as the input, the generated key sk is used to register group members, the  
Figure 4: RSA algorithm flow.  
key ok is used to identify the signer, and the group public key gpk is used to generate and verify group signatures. The key sk and ok are held by the group manager in secret, and the group public key is public to all.
2. Enroll (i, sk): The registration algorithm takes key sk as the input to provide user i with its private signature key gsk[i].
3. Gsig (gpk, gsk[i], M): The signature algorithm uses the key gsk[i] of user i and the group public key gpk to generate a signature \(\sigma\) on message M.
4. GVerify (gpk, M, \(\sigma\)): The verification algorithm is a deterministic algorithm, which takes the group public key gpk and the group signature \(\sigma\) on message M as inputs. It verifies whether the signature was generated by a group member.
5. Open (ok, M, \(\sigma\)): As a deterministic algorithm, the tracking algorithm uses the key ok to cancel the signature \(\sigma\)'s anonymity. That is, it can track which group member the signature \(\sigma\) comes from.
6. Rev: When necessary, the group manager can disclose the private key component Xi - Zi of the group members to the blacklist through the revocation algorithm to revoke the permissions of the group members.  
The algorithm flow is shown in Figure 5.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 3 Field Assessment
### Ids  
According to [PERSON] et al. [17], an IDS is a device or software application that monitors a network for malicious activity or policy violations.  
IDSs are essential tools for identifying unauthorized access or malicious activities in a system. An intrusion involves accessing a system without authentication or authorization, including activities like tampering with files, malware execution, or remote attempts to compromise a system. Basic protective measures such as antivirus software and firewalls are often insufficient, as malware signatures can be altered and firewall rules can be bypassed. An IDS provides comprehensive monitoring by analyzing incoming and outgoing network traffic as well as detecting intrusions, malicious packets, and policy violations. It records logs and alerts system administrators in real time, offering robust security for organizations. IDS solutions are also available as hardware platforms or software applications and are increasingly adopting machine learning algorithms to predict attacks and classify legitimate traffic. IDS systems are categorized into three main types: Network-based IDS (NIDS), Host-based IDS (HIDS), and Distributed IDS (DIDS) [18].  
IDSs can be classified by their detection methods, including signature-based analysis, which identifies known attack patterns; protocol-based analysis, which monitors compliance with protocol rules to detect violations; and anomaly-based analysis, which focuses on spotting unusual behavior to identify potential unknown threats. These methods collectively strengthen IDS capabilities in detecting and mitigating cyberattacks. The anomaly-based analysis addresses limitations in signature-based approaches by detecting unknown and known attacks through abnormal network behavior. Unlike relying on predefined signatures, this method uses heuristic rules or machine learning to classify traffic. It operates in two phases: training to learn the normal behavior of the system and testing to identify deviations indicating anomalies. Techniques such as neural networks, data mining, and artificial immune systems are used, supported by other tools. However, a notable drawback is the occurrence of false positives, where alarms are triggered without actual threats. Research continues to improve accuracy and reduce false alarms [18].