esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The Principles of Robustness Computation
#### 2.2.2 Principles of Image Cropping Attack  
Image cropping can be seen as a type of geometric attack. Part of the watermarked image is deleted randomly and watermark extraction is conducted in the remaining part. The attacking strength is defined as the percentage of the cropped data. Some examples of different attacking strength are shown in Figure 5.  
Figure 4: Illustration of Principle 2.  
Figure 5: \(Cont\).  
Combine the attack behavior of image cropping with Principle 1 and Principle 2, the analysis principles of attack are deduced as follows:  
* Principle 3: The watermark and its synchronization in the remaining part remain correct.  
The watermark information contained in the cropped part, obviously, cannot be further retrieved. According to Principle 2, the uniformity guarantees the effectiveness of Principle 3 for both watermarking algorithms based on the spatial domain and those based on the transformation domain. After image cropping, the watermark synchronization mechanism becomes the key to guarantee the robustness. It appears obvious that a well-designed watermarking algorithm will reduce the attack influence on the remaining parts to a minimal level. Because watermark synchronization has different types of implementations [6; 10; 34; 35], Principle 3 represents the ideal situation that all the watermark containing in the remaining part could be successfully extracted.  
* Principle 4: The cropped part of image is chosen randomly.  
As shown in Figure 5, the cropped part of the image can be anywhere, even under the same attack strength. Because the cropped part is not fixed to a specific area for each attack, hence it is reasonable to assume that the cropped part is randomly chosen. In addition, according to the uniformity distribution in Principle 2, cropping image randomly equals a random deletion of the watermark information. An example is shown in Figure 6 to illustrate the influence of image cropping attack on watermark.  
Figure 5: Examples of image cropping attack with different attack strengths: (**a**) Attack strength of 0.25; (**b**) Attack strength of 0.4; (**c**) Attack strength of 0.5; (**d**) Attack strength of 0.6.  
In Figure 6, the red blocks represent the deleted watermark bits, and the green blocks represent the correct. The deleted parts, the watermark information in cropped part of image, are distributed randomly in theory. According to Principle 3, the remaining parts of the watermark, which is denoted by green blocks, can then be correctly extracted.
## 3 B-Spline Neural Network
### B-Spline Basis Function Definition  
A B-spline curve differs from a Hermite or Berzier curve, because a B-spline curve usually consists of more than one curve segment. The B-spline curve is widely used in the field of computer graphics for its excellent performance. The B-spline basis function is the foundation of BSNN.  
**Definition 1** ([39]).: _Given a knot vector \(T=\{t_{0}\leq t_{1}\leq\cdots\leq t_{m}\}\), the \(i\)th B-spline basis function of degree \(k\) can be written as:_  
\[B_{i,0}(t)=\left\{\begin{array}{l}1,\;if\;t_{i}<=t<t_{i+1}\\ 0,\text{otherwise}\end{array}\right. \tag{14}\]  
\[B_{i,k}(t)=\frac{t-t_{i}}{t_{i+k-1}-t_{i}}\cdot B_{i,k-1}(t)+\frac{t_{i+k}-t} {t_{i+k}-t_{i+1}}\cdot B_{i+1,k-1}(t) \tag{15}\]  
**Remark 1**.: _If \(t_{i+1}-t_{i}(0\leq i\leq m-1)\) is a constant, the B-spline curve is called uniform; otherwise, it is called non-uniform._Definition 1 is usually referred to the Cox-de Boor recursion formula.  
The \(j\)th B-spline basis curve with degree \(k\) can be written as \(B^{i}_{j,k}(t)\) on an arbitrary vector \([\,t_{i},t_{i+1})\,\):  
\[B^{i}_{j,k}(t)=a^{i}_{j,0}+a^{i}_{j,1}t+a^{i}_{j,2}t^{2}+\cdots+a^{i}_{j,k}t^{k}= \left[\begin{array}{ccc}t^{k}&t^{k-1}&\cdots&1\end{array}\right]\left[ \begin{array}{c}a^{i}_{j,k}\\ \vdots\\ a^{i}_{j,1}\\ a^{i}_{j,0}\end{array}\right] \tag{16}\]  
An order \(k+1\) B-spline is formed by joining several pieces of polynomials of degree \(k\) with at most \(C^{k-1}\) continuity at the breakpoints [40], hence the following formula can be obtained:  
\[\left[\begin{array}{ccccc}t^{k}_{i}&t^{k-1}_{i,k}&\cdots&t_{i}&1\\ t_{i+1}^{k}&t_{i+1}^{k-1}&\cdots&t_{i}&1\\ kt_{i+1}^{k-1}&(k-1)t_{i+1}^{k-2}&\cdots&  
The coefficient matrixes \(M_{e}\) in Equation (20) and \(M_{e}^{\prime}\) in Equation (21) can be obtained by Equations (14), (15), (18) and (19).  
The B-spline curve is a linear combination of control points \(P_{i}\) and a B-spline basis function \(B_{i,k}(t)\). The definition is as follows:
## 3 Comparison between Circular and Slab Diffusion Models
### The Pre-Corrosion Initiation Time for the RC Circular Column  
The chloride threshold concentration \(C_{\rm cr}\) is closely related to the time required for corrosion initiation of a concrete structure. The JSCE (Japan Society of Civil Engineering) proposed a value of 1.2 kg/m\({}^{3}\) for the \(C_{\rm cr}\)[27], but [PERSON] et al. [24] emphasized that, according to numerous studies, the value ranges from 0.6 to 1.2 kg/m\({}^{3}\). Since this value depends on the steel material, concrete material composition, and external environment, the statistical variation in the property is unsurprising, but highlights the need for further research on the \(C_{\rm cr}\). The \(C_{\rm cr}\) applied in the durability design of the HZM project adopted here is summarized in Table 3.  
During the pre-corrosion initiation period, the chloride concentration at the rebar depth is equal to the \(C_{\rm cr}\). The time to corrosion initiation of RC circular column is calculated by modified slab diffusion model (Equation (15)), which, calculated by original slab diffusion model (Equation (11)), are expressed as:  
\[T_{\rm i\text{-}\text{cir}}=\left\{\frac{x^{2}}{4\left[erf^{-1}\left(1-\frac{C _{\rm cr}}{C_{\rm s}}\right)\right]^{2}}\cdot\frac{1-m}{D_{i}}\right\}^{\frac {1}{1-m}} \tag{17}\]  
\[T_{\rm i\text{-}slab}=\left\{\frac{x^{2}}{4\left[erf^{-1}\left(1-\frac{C_{\rm cr }}{C_{\rm s}}\right)\right]^{2}}\cdot\frac{1-m}{D_{\rm i}}\right\}^{\frac{1}{1 -m}} \tag{18}\]  
\(\Delta T_{\rm i}\) represents the difference value between \(T_{\rm i\text{-}\text{cir}}\) and \(T_{\rm i\text{-}\text{slab}}\). A sensitivity analysis of model parameters for \(\Delta T_{\rm i}\) is also performed. The ranges of the intervals for \(C_{0}\) and \(C_{\rm cr}\) are presented in Table 4, and the ranges of the interval of the other parameters in Table 2 are employed. In Table 4, the mean values of the model parameters are selected from the atmospheric zone, kept the same background with Table 2. The final analysis result is presented in Figure 11. Figure 11 reveals that higher \(C_{\rm cr}\), \(c\), \(x\), and \(m\) increase \(\Delta T_{\rm i}\), whereas higher \(C_{0}\), \(D_{28}\), and \(R\) decrease \(\Delta T_{\rm i}\). Compared with other parameters, \(R\) and \(D_{28}\) only mildly affect \(\Delta T_{\rm i}\).  
Figure 10: The effect of radius on \(\eta_{\rm c}\) for different concrete cover thicknesses.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Exposure Class** & **Distribution Type** & **Mean Value (\%:Binder)** & **STANDARD Deviation (\%:Binder)** \\ \hline Atmospheric zone & Lognormal distribution & 0.85 & 0.13 \\ \hline Splashing and tidal zone & Beta distribution (\(L=0.45\%\), \(U=1.25\%\)) & 0.75 & 0.23 \\ \hline Submerged zone & Beta distribution (\(L=1\%\), \(U=3.5\%\)) & 2 & 0.72 \\ \hline \end{tabular} Note: \(L\) represents lower limitation and \(U\) represents upper limitation.  
\end{table}
Table 3: The probability distribution of chloride threshold concentration \(C_{\rm cr}\).
_Algorithms_**2014**, \(7\), 405-417; doi:10.3390/a7030405  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Seminal Quality Prediction Using Clustering-Based Decision Forests**  
**[PERSON], [PERSON] and [PERSON] ***  
School of Mathematics & Statistics, Central South University, Changsha, Hunan, 410075, China;  
E-Mails: [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (Q.X.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-731-88660140.  
_Received: 14 May 2014; in revised form: 23 June 2014 / Accepted: 29 July 2014 / Published: 11 August 2014_  
**Abstract:** Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables' importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.  
**Keywords:** seminal prediction; imbalanced learning; variable importance
## 6 Analysis
### Resilience against Node Compromise Attacks on Key Management  
The proposed model has two stages that could be a target for node capture attacks. The first stage is a period between the distribution of sensor nodes to the target field, and establishment of pair-wise key. The second one is after broadcasting discovery messages and forming a connected pair-wise key network. The properties of two stages lead to different security levels.  
In the first stage, adversaries can capture enough sensor nodes in a cluster to interpolate polynomials, and then eavesdrops KSDM messages to get _nonce_ values. The number of sufficient compromised sensor nodes depends on the polynomial degree that is the memory for storing key materials. In this manner, attackers could generate all pair-wise keys in the cluster. So the security in this stage needs some consideration. But we need to remember that the time of this stage is quite short right after deployment, so a chance for an adversary to launch attacks is not highly possible.  
In the second stage, from (2) we can see that each pair-wise key depends not only on key-space information but also on random values associated with each node. Suppose that a node \(A\) is compromised and an adversary could get all information stored inside the \(A\)'s memory. In this case, she could only get pair-wise keys between \(A\) and its neighbors, and a fraction of polynomial \(f(N_{A},y)\). Even if she compromises a sufficiently large number of nodes like \(A\), then still it is not possible for an adversary to expose pair-wise key between non-compromised nodes \(B\) and \(C\). This is due to the fact that the adversary does not have the information about the random numbers stored on the non-compromised nodes. So, compared with other schemes such as [26]-[30], our scheme has not been affected by node capture attacks on the links between non-compromised nodes.
# Improvement of Electrical Performance in Heterostructure Junctions TFET Based on Dual Material Gate
###### Abstract  
In this paper, a dual metallic material gate heterostructure junctionless tunnel field-effect transistor (DMMG-HJLTFET) is proposed and investigated. We use the Si/SiGe heterostructure at the source/channel interface to improve the band to band tunneling (BTBT) rate, and introduce a sandwich stack (GaAs/Si/GaAs) at the drain region to suppress the OFF-state current and ambipolar current. Simultaneously, to further decrease ambipolar current, the gate electrode is divided into three parts namely auxiliary gate (M1), control gate (M2), and tunnel gate (M3) with workfunctions \(\Phi_{\text{MI}}\), \(\Phi_{\text{M2}}\) and \(\Phi_{\text{M3}}\), respectively, where \(\Phi_{\text{MI}}=\Phi_{\text{M3}}<\Phi_{\text{M2}}\). Simulation results indicate that DMMG-HJLTFET provides superior performance in terms of logic and analog/RF as compared with other possible combinations, the ON-state current of the DMMG-HJLTFET increases up to \(9.04\times 10^{-6}\) A/um, and the maximum \(\text{g}_{\text{m}}\) (which determine the analog performance of devices) of DMMG-HJLTFET is \(1.11\times 10^{-5}\) S/um at 1.0V drain-to-source voltage (Vds). Meanwhile, RF performance of devices depends on the cut-off frequency (f\({}_{\text{T}}\)) and gain bandwidth (GBW), and DMMG-HJLTFET could achieve a maximum f\({}_{\text{T}}\) of 5.84 GHz, and a maximum GBW of 0.39 GHz, respectively.  
gate material engineering; band-to-band tunneling (BTBTBT); a sandwich stack structure (GaAs/Si/GaAs); dual material gate heterostructure junctionless TFET (DMMG-HJLTFET)
###### Abstract  
Background: Post-activation performance enhancement (PAPE) protocols are increasingly being used to acutely boost strength and power in resistance training. The objective of this systematic review was to determine how PAPE interventions influence barbell velocity in the bench press among trained individuals and address the following research question: which conditioning activities (CAs), rest intervals, and loading strategies most effectively enhance bench press velocity parameters under PAPE conditions? Methods: This systematic review, conducted according to PRISMA guidelines, searched PubMed, Scopus, SpringerNature, EBSCO, and Web of Science up to 31 January 2025 for studies involving healthy adults (18-50 years) that employed bench press or related conditioning activities intended to induce PAPE, and assessed barbell velocity (peak or mean). From pool of 111 records, 7 studies (total n = 125 males, 23-29 years old, \(\geq\)2-year training experience) met the inclusion criteria. The methodological quality of included studies was moderate (5/10 on the PEDro scale). Results: Submaximal loads (70-80% 1 RM) applied for one to three short sets, with rest periods of around four to five minutes, frequently improved peak velocity (by up to 7%) and peak power (by up to 15.9%). Conversely, heavier loads (>85% 1 RM) and insufficient rest tended to offset these benefits due to fatigue. Blood flow restriction or ischemic preconditioning strategies produced positive velocity outcomes mainly at lower loads (20-50% 1 RM). Conclusions: These findings suggest that PAPE interventions can enhance bench press barbell velocity in trained individuals. Effectiveness, however, depends on training status, volume, load intensity, and recovery intervals. Future randomized controlled trials with larger samples, standardized reporting, and the inclusion of female athletes are recommended to refine and generalize PAPE applications for upper-body strength and power development.  
strength training; VBT; conditioning activity; speed; post-activation potentiation enhancement +
Footnote †: journal: Applied sciences
# Reliability of a New Portable Dynamometer for Assessing Hip and Lower Limb Strength
## 2 Materials and Methods
### Statistical Analysis  
The SPSS statistical package (version 25.0, IBM, New York, NY, USA) was used for statistical analysis. Descriptive statistics were calculated and reported as mean \(\pm\) standard deviation, in addition to the 95% confidence intervals. Shapiro-Wilk test was used to verify normality of data. To assess absolute reliability, the coefficient of variation (CV) was calculated as the quotient between the typical measurement error and the mean value of both sessions, expressed in %. The relative reliability was assessed with intra-class correlation coefficient, using a 2-way random model with absolute agreement (ICC2,k) and was interpreted as poor (<0.5), moderate (0.5-0.75), good (0.75-0.9), and excellent (>0.9) [17]. The systematic bias was analyzed by analysis of variance with repeated measurements. The level of statistical significance was set at \(p<0.05\).  
Figure 3: Knee extension (**A**) and ankle plantarflexion (**B**) strength measurements using MuscleBoard®. Knee extension isometric strength was measured using non-elastic strap that connected participant’s lower leg with a rigid rod mounted on both sensor braces on the superior part. Ankle plantarflexion isometric strength was measured using a rigid rod that was mounted on both sensor braces on the inferior part. For both tests, sum of forces from both load cells presented one unilateral measurement.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 2 Kalman Filter for Speech Enhancement
### Impact of Biased \(K_{0}(n)\) on KF-Based Speech Enhancement in WGN Condition
Technically, the real-life noise (colored/non-stationary) may contain time varying amplitudes, which impact (\(\{a_{i}\},\sigma_{0}^{2}\)) significantly as opposed to negligible impact of WGN in these parameters [20,21]. Therefore, the assumption of \(\sigma_{0}^{2}\
eq\sigma_{0}^{2}+\sigma_{0}^{2}\) made in [20,21] is invalid for real-life noise conditions. Moreover, the existing methods [20,21] do not analyze the impact of noise variance, \(\sigma_{0}^{2}\) on \(K_{0}(n)\). According to Equation (21), in addition to \(\alpha^{2}(n)\)  
Figure 1: Review of existing KF-based SEA: (**a,b**) spectrograms of the clean speech (utterance sp05) and the noisy speech (corrupt (**a**) with 5 dB WGN), (**c**) \(J_{2}(n)\) and \(J_{1}(n)\) metrics, (**d**) oracle and non-oracle \(K_{0}(n)\) with adjusted \(K_{0}^{\prime}(n)\) and \(K_{0}^{\prime\prime}(n)\), spectrogram of enhanced speech produced by: (**e**) KF-oracle method, (**f**) KF-non-oracle method, (**g,h**) methods in [20,21]. and \(\sigma_{w}^{2}\), \(\sigma_{v}^{2}\) is also an important parameter to compute \(K_{0}(n)\) accurately. In light of these observations, the methods in [20, 21] are not applicable for speech enhancement in real-life noise conditions. Therefore, we performed a detailed analysis of the biasing effect of \(K_{0}(n)\) on KF-based speech enhancement in real-life noise conditions.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### Power Management  
The PM stage limits and regulates the \(V_{REC}\) voltage provided by the HF section to power the temperature sensor. The PM is composed by a shunt regulator, a voltage/current  
Figure 4: Voltage buffer used between the temperature sensor and the ASK backscatter modulator.  
Figure 3: Small-signal model of an FVF cell.  
reference, and an LDO voltage regulator (blue-dashed line box in Figure 1). The shunt circuit works as a coarse voltage regulator, keeping the \(VDD\) voltage level below \(2 VDD\) by creating a current discharge path to ground. The voltage/current block provides a temperature-compensated \(V_{REF}\) voltage, a temperature-compensated \(I_{TC}\) current, and a PTAT \(I_{P}\) current. The LDO uses the \(V_{REF}\) voltage and \(I_{TC}\) current to bias its circuitry while provides a stable DC voltage level \(VDD_{2}\) against the current load transients generated by the temperature sensors and buffer of the LF stage.
## 4 Discussion
### Limitations and Uncertainty  
Three critical limitations warrant attention. First, detection error propagation occurs when the borehole deviation exceeds 10\({}^{\circ}\), causing a 15-30% error in \(D_{JS}\). Second, under high-velocity flow regimes where the Reynolds number (\(R_{e}\)) exceeds 2300, the Navier-Stokes simplification (Equation (25)) underestimates the flow by 18-27%. Third, the current model does not fully account for damage anisotropy in rock masses, as field data indicate fracture growth rates 40% higher along bedding planes. Future model refinements should incorporate orientation-dependent bond-breaking criteria.
## 3 Results
### Results of the Qualitative Study
#### 3.2.2 Organizational Perspective  
The organizational perspective included nine factors, six of which were identified during the interviews. Compatibility, trialability, and lock-in effect were not mentioned by any respondents. The remaining factors--external know-how, effort expectation, complexity, organization size, roles and responsibilities, and service controlling--were highlighted by at least some respondents.  
Most decision-makers, eight of them, emphasized the significance of the external know-how factor, considering it crucial for their companies. As SME1 stated, \"Jobs for which we cannot find suitable experts are outsourced to professionals or companies we partner with\". In the same context, six experts highlighted the effort expectation factor. For instance, SME8 noted, \"The costs of employing IT experts to maintain and develop IT infrastructure are significantly higher than the cost of cloud services. In case of an employee termination, we would have a significant challenge to find a replacement in a short period\".  
The size of the organization also influences the use of cloud services. Four respondents pointed out a high dependence on cloud service adoption, especially in larger companies, while representatives of small businesses classified their reliance on cloud services as low or medium. SME14 emphasized, \"The implementation of cloud services in our organization is critical. If their availability is compromised, the efficiency of our company's operations would be significantly endangered\".  
Additionally, four respondents highlighted the need to define access rights to cloud services based on roles within the company. SME9 explained, \"Our company uses cloud services for document management, and it is necessary to provide different access rights to files according to each employee's responsibilities and job descriptions. Therefore, we need cloud solutions that enable this, even though such solutions are sometimes much more expensive compared to other similar cloud services that do not offer role separation and access rights\".  
Only two respondents stressed the need for more significant monitoring and control of cloud services, primarily for security reasons. SME3 stated, \"Our security procedures require continuous monitoring and reporting of any security incidents. When choosing cloud services, we require additional monitoring and control over their use by our employees and partners. Cloud service providers often cannot provide such forms of control over their services\".