esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Shear Effect on Deflection of RC Beams
## 5 Conclusions  
In this study, an experiment was performed to evaluate the effect of shear on the deflection of RC beams subjected to combined bending and shear. As a result, the following conclusions were drawn:From flexural tests of RC beams with tension reinforcement ratios of 0.3\(\rho_{b}\) and 0.6\(\rho_{b}\) and shear span-to-depth ratios of 2.5 and 4.0, when the amount of shear reinforcement recommended in the ACI code was satisfied, no additional deflection due to the change in the shear capacity ratio was observed.
2. When the shear span-to-depth ratio was small or the tension reinforcement ratio was high, it was observed that the number of inclined shear cracks increased and their spacing narrowed because a relatively large shear force was applied to the shear span of the specimens.
3. From the moment versus effective moment of inertia relationships, it was confirmed that the accuracy of ACI 318-19 formula decreased as the effect of shear increased. In other words, when the shear span-to-depth ratio was low or the tension reinforcement ratio was high, the difference between the analytical results using ACI equation and the experimental results obtained from LVDT combined shear-bending component increased.
4. The deflection at the yield moment obtained using ACI equation almost coincided with the deflection obtained using the curvature measured in the pure bending region, regardless of the shear span-to-depth ratio and tension reinforcement ratio of the specimens, with an average of 1.0. On the other hand, the ACI equation underestimated the experimental results measured by LVDT combined shear-bending component by a maximum of 1.6 times. This is because the ACI equation does not consider the effect of shear on deflection of RC beams.  
[PERSON]-W.K., [PERSON]-S.H. and [PERSON]-H.K. conceived and performed the experiments and analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript. This work was supported by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) founded by the Ministry of Education (2019R1A6A1A03032988); this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) founded by the Ministry of Education (2018R1A2B3001656).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiment Results
### Breakdown Strength Analysis  
This section presents the results of the RT-BDS and ST-BDS measurements and determine whether the breakdown data set complies with the normal and Weibull distributions [18,19]. These probability distributions are known as the most commonly used models for analyzing the BDS of dielectrics. They serve as useful models in designing and maintaining power equipment by improving the predictability of insulation performance [35].  
To verify which probability law the experimental data followed, the hypothesis tests that were introduced in Section 3.1 were applied. The Shapiro-Wilk test [19], Anderson-Darling test [20], Ryan-Joiner test [21], and Kolmogorov-Smirnov test [22] were used to analyze whether the data followed the normal distribution law. Additionally, to determine if the data followed the Weibull distribution law, the Anderson-Darling test [20] was utilized. The statistical analysis was performed using R software [31] and Minitab19 software [32]. The \(p\)-value represents the probability of evidence against the null hypothesis. After calculating the \(p\)-value using the software, the same statistical procedure was followed by comparing the \(p\)-value to a significance level of 5% to determine if the experimental results conformed to the statistical distribution. If the \(p\)-value is smaller than the significance level, the null hypothesis is rejected, indicating that the sample data do not conform to the considered statistical distribution law [35]. A smaller \(p\)-value provides stronger evidence for rejecting the null hypothesis, and a larger Anderson-Darling statistic (A-D) indicates that the data do not follow the analyzed distribution.
# Multi-Objective Optimization of Production Objectives Based on Surrogate Model
## 2 Background of Multi-Objective Optimization Based on a Surrogate Model
### Multi-Objective Optimization Problem (MOOP)
It is a different but very natural and therefore a widely spread approach to handle with the MOOP solving by its decomposition to a set of single-objective optimization problems. This strategy transforms a complex multi-objective problem in a set of simple subproblems. Decomposition methods utilize a scalarizing parametric function to aggregate all the objectives into a single scalar objective function [8]. Different setting weight vectors and other parameters relating to the individual functions lead to yielding different optimal points belonging to a Pareto front. For the simple scalarization through the Weighted Sum Method (WSM) under an assumption of the convexconstraints, it is proved that the solution is Pareto optimal if the value of the corresponding scalar multi-objective function is minimal [7]. The known disadvantage of this method can be easily explained by the geometry point of view. A scalar multi-objective function is expressed as a linear combination of individual objective functions; therefore, it offers the capability of reaching the Pareto optimal solutions only in the convex part of the Pareto front.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Computation Complexity  
Computation complexity is an important metric in evaluating encryption algorithms as it determines the computational requirements to perform the encryption and decryption operations. To evaluate an encryption algorithm's computation complexity, researchers assess the key generation complexity and the encryption-decryption process complexity. Furthermore, in the evaluation of image encryption algorithms, _runtime_ is an important consideration (other than security) in adopting an image encryption algorithm for a particular domain. For example, many edge devices have limited computing power [62]; thus, image encryption algorithms with lower computational complexity are suitable for such edge devices. Similarly, many applications are time-sensitive (e.g., surveillance applications [63]) and, thus, image encryption algorithms that can be executed in real time are more befitting for such applications compared to highly compute-intensive encryption algorithms.
## 3 Vegetation Phenology
### Phenological Patterns Derived from Remote Sensing
#### 3.2.2 Monitoring Forest Phenology
Although [PERSON] _et al._[134] observed a positive correlation between EVI with leaf flushing measured in the field in central-east Amazonia, EVI was relatively insensitive to changes in LAI. The increase in EVI during the dry season has also been attributed to solar illumination effects rather than changes in LAI; and changes in canopy foliage detected from MODIS LAI data were not consistent with LAI estimates from hemispherical photographs for a site in southern Amazonia [135]. Another criticism to the results presented by [66, 74, 133] is the fact that areas that exhibited higher indices during the dry season in their analysis are located in regions with dissimilar onset and end of the dry/wet season [136]. For example, areas that presented higher indices in their study are located in the north hemisphere in the Amazon region--north-east Amazonian forests--and have the dry season from September to November (sample 1, Figure 3); in center-east Brazilian Amazon--the Tapajos region, low rainfall occurs from July to October (sample 2, Figure 3), and central-west Brazilian Amazon presents low rainfall in August (sample 3, Figure 3). These contrasting results show that the large-scale extrapolation of a data collected from one site and with a reasonable agreement with the satellite image may not be valid for different regions. In addition, saturation and noise in the remote sensing dataset may cover the detection of changes in such dense canopy forests. Figure 3: Rainfall derived from TRMM (Tropical Rainfall Measuring Mission), product 3B43-v6, at 0.25\({}^{\circ}\) spatial resolution. Dry season is defined here when rainfall is lower than the estimated tropical forests canopy transpiration (\(\sim\)100 mm\(\cdot\)month\({}^{-1}\)). (**a**) Location of the samples sites; (**b**) Averaged rainfall values for each sample, from 1998 to 2010, and the standard deviation as error bars.
## 3 Cyber-Attacks on SF and PA
This category of attacks can be divided into the following subcategories [75]. * **Data Leakage[2, 76]*
* refers to the illegal transmission of an organization's data by a source (a person or a device) within the organization to an unauthorized external destination. This kind of attack violates the confidentiality of the data. * **Ransomware[37, 72]*
* encrypts files, partitions or entire storage devices, and keeps the key secret to make the owner [77] pay a ransom. These attacks violate the privacy, trust, and integrity. * **Cloud Data Leakage[76, 78]*
* is the exposure of data related to the users of an organization or the provided services, which violates the privacy of users or parties. * **False Data Injection[2, 79]*
* is a title for attacks that try to feed malicious information or control commands into the system. This kind of attack targets the integrity of the data. * is the action of configuring the SF or the PA reporting systems in a way that reflects invalid information regarding the managed farm, which can lead to costly, disruptive decisions and actions from the farmers. Misconfiguration attacks violate the integrity. * **Attacks on Code (applications):*
* **Software Update Attacks[2]*
* violate the integrity and the availability of the system via disrupting the update process of the installed software. * **Malware Injection[2, 72, 80]*
* refers to attacks that infect nodes and devices by malicious codes. This kind of attack violates integrity. * **Buffer overflow[72, 80]*
* is a software coding error or vulnerability that hackers can exploit to gain unauthorized access to corporate systems. This kind of attack violates availability. * **Indirect Attacks (SQL Injection)[2, 81]*
* use code injection techniques in order to mislead the database server to run malicious SQL codes injected into entry fields of the database. Indirect attacks violate trust. * **Attacks on Support Chain:*
* are designed to hit different components of the support chain. * **Third Party Attacks[2, 80]*
* occurs when an adversary infiltrates a system via an outside partner or provider who has access to the system and/or the data. Third party attacks can violate the confidentiality or the integrity of the system. * **Data Fabrication[82]*
* involves the creation of malicious data or processes misusing an access provided for another purpose. It can lead to the violation of the system's integrity. * **Misuse Attacks:*
* include attacks that misuse SF and PA physical resources in order to conduct attacks on other entities such as people or institutes. * may use IoT systems and cyber-physical devices to attack people or premises from afar. This can lead to the violation of trust in SF and PA systems. * **Invalidation and /compliance[2, 83]*
* refers to disruptions in the certification process created by fabricated false data. These attacks target the integrity of the system. Figure 2 demonstrates the above classification of attacks on SF and PA and maps each class of attacks to the related security aspects.
## 4 Discussion
Overall, the study results indicate that while all forms of taping may provide arch support and contribute to static alignment, elastic anti-pronation taping demonstrated superior outcomes in dorsiflexion strength and stride length. However, inelastic taping also showed benefits, especially for step length, and should not be overlooked. The lack of improvement in balance measures underscores the complexity of postural control and suggests that sensory or motor training may be needed in addition to mechanical intervention. This study has several limitations. First, the sample included only young women, limiting the generalizability of the results to males and older adults. Second, the interventions were tested over short time intervals without assessing the long-term effects or adherence. Third, while strength and balance were measured using reliable tools, electromyography and three-dimensional motion analysis can provide more comprehensive insights into neuromuscular and biomechanical changes. Fourth, baseline data on the participants' physical activity levels were not collected, which is a limitation as regular physical activity could act as a confounding variable influencing ankle strength, gait, and balance outcomes. Finally, although the order of the taping interventions was randomized, neither the participants nor the assessors were blinded to the specific taping method being applied, which could have introduced potential performance or measurement bias. Given the significant improvement observed in dorsiflexion strength following elastic taping, future research could explore its application in populations requiring explosive or stabilizing ankle control, such as female athletes with flexible flat feet or individuals with chronic ankle instability. Investigating the effects of taping on jump mechanics, dynamic balance, and injury risk may provide clinically meaningful insights for sports rehabilitation.
# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 3 Results  
### Overview of Included Studies  
A total of 810 publications were retrieved following a computer-assisted search (Figure 1). Following the removal of duplicates, 519 articles were screened by title and abstract to assess eligibility, resulting in the exclusion of 305 articles. The remaining 214 articles progressed to full-text analysis in which 190 articles were excluded: 156 due to wrong population of interest, 12 due to wrong intervention, 12 due to wrong study design, 7 due to wrong outcomes and 1 article due to retraction. A total of 24 articles were included in this scoping review (Table 1). Of the 24 articles, 23 were retrospective observational studies utilising NLP tools within various fields of orthopaedics. The remaining article was a case study. All retrospective observational studies were of level III evidence and the case study was of level IV evidence based on the Oxford Centre for Evidence-Based Medicine guidelines. Of the included studies, one study reported on the use of NLP for intraoperative monitoring, six for detection of adverse events, five for establishing orthopaedic diagnoses, two for assessing the patient experience, two as an informative resource for patients, one for predicting readmission, one for triaging, five for auditing and one for billing and coding (Figure 2). Figure 1: Search strategy and workflow in accordance with PRISMA guidelines. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Study Type** & **Location** & **Intervention** & **Cohort** & **Level of Evidence (CEBM)** \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{2022} & Retrospective & \multirow{4}{*}{United Kingdom} & NLP (Bio\_ClinicalBERT) for clinical data extraction, identifying intraoperative neuromonitoring in spine surgery, compared against traditional codes. & 13,718 patients who had spinal surgeries, with 23,243 operative reports in total & \multirow{4}{*}{III} \\  & & & NLP (Generalised Linear Model, K-NN, Random Forest, SVM, Shallow Neural Network, Multilayer Bidirectional Long Short-term Memory (BiLSTM) and Convolutional Neural Network (CNN)) for clinical data extraction, identifying adverse events in free-text clinical notes. & \multirow{4}{*}{2021} \\ \cline{1-1} \cline{5-5}  & & Retrospective & \multirow{4}{*}{United States} & NLP (MedTaggerIE) for clinical data extraction, identifying & \multirow{4}{*}{1179 surgeries.} & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of included studies.
## 3 Results
### Stringent Conditions Activate rsm sRNAs Genes Expression  
The expression of the four _rsm_ genes _rsmX-1_, _rsmX-2_, _rsmY_ and _rsmZ_ increases over time until a maximum is reached in stationary phase (24 h of growth), when nutrients become limited. We therefore wondered whether carbon deprivation could activate the _rsm_ expression, as previously reported for the Csr system [33]. To assess the biological relevance of these observations, we monitored the expression level of the four _rsm_ genes under different nutrient level conditions. Here, bacteria were grown in either undiluted TSB medium (considered as a rich medium), 10-fold diluted TSB (TSB/10), or 20-fold diluted TSB (TSB/20) (which was considered as a nutrient-poor medium in which _P. brassicacarum_ growth may occur). Using transcriptional fusions for the promoter from each _rsm_ gene, we found a greater increase in _rsm_ expression when nutrient availability declines (Figure 5). Thus, even if growth decreases when nutrients are scarce, as indicated by OD\({}_{600\text{ nm}}\) monitoring in tables beneath each graph of Figure 5, the expression of the four _rsm_ genes highly increases. The greatest effect was observed for _rsmZ_ (Figure 5). Nutrient starvation conditions influenced the expression levels of _rsm_ genes and also certain targets of RsmA/E synthesis, as indicated by an enhancement of protease activity (Figure S1).  
Figure 4: _rsmX-2_ restores GacA-dependent phenotypes. Restoration of wild-type phenotype by overexpression of _rsmX-2_ from a plasmid under the control of _Plac_ promoter in _gacA_ cells as determined by (**A**) Protease activity on skimmed milk-containing medium, (**B**) Colony morphology on PAF medium and (**C**) Biofilm formation.
# Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent
## 4 Results
### XOR Gate Dataset
#### 4.1.4 Analysis  
The Hardware implementations generated a large increase in the SVM training execution speed, as seen on Tables 2 and 5. Moreover, the impact of the acceleration provided becomes even clearer when analyzing speedup, that is, how many times faster the hardware implementation is in relation to the software implementation. The acceleration value is obtained by dividing the runtime value in software by the runtime value on FPGA.  
It can be seen that the use of FPGA decreased execution time by more than three orders of magnitude, reaching an increase in the execution speed of up to 10,205\(\times\) compared to the implementation in Python. This higher execution speed provided by the reconfigurable hardware implementation is an important factor considering the amount of data presented in massive data analysis.  
It is possible to draw a superior margin on the floating point hardware implementation execution speed, analyzing around 10 million samples per second. Since each sample consists of 8 bytes, it would be possible to compute 1 GB of information in at least 12.5 s using 2 kernels. On fixed-point, with the same number of kernels, it is possible to analyze more than 19 million of samples of 4.5 bytes per second, resulting in the analysis of 1 GB in at least 5.8 s. Meanwhile, the implementation of Python would take about 1 hour to analyze the same amount of data.  
Despite the increased throughput, the speed of floating-point hardware design decreases significantly with the number of kernels used. This is caused by the increase in the sum tree depth on the AM. However, this effect can be reduced by applying pipeline to each of the sum stages, adding a delay in the circuit response but increasing the SVM throughput.
# Bluetooth Communication Leveraging Ultra-Low Power Radio Design
## 3 BLE Radio Design Trends and Considerations
### Transmitter Design Trends
#### 3.2.2 Digitization of BLE Transmitters  
Another trend of BLE transmitter design is digitization. There are three major building blocks in a BLE transmitter: baseband processor, frequency synthesizer and power amplifier (PA). The baseband processor is a digital block that generates data packets for modulation and transmission. The frequency synthesizer converts the baseband signal into RF frequency at its target channel and applies modulation to the data. In addition, finally, the PA amplifies the signal for transmission. All-digital PLLs have been used more and more in recent designs [37, 51, 52]. Due to CMOS process scaling, digitally controlled capacitor banks can now reach very fine resolution. Thus, the modulation signal, which used to be applied to the varactor in the LC-VCO in the analog charge pump PLL, is now applied to these digitally controlled capacitor banks in the ADPLL [37]. Recently, [54] proposed the first reported ring-oscillator based all-digital BLE transmitter design which has successfully removed the LC-VCO as the last analog circuit block in the frequency synthesizer block. The power amplifier has also gone digital as well. The class-D switched-capacitor digital power amplifier [55] has been adopted more and more in recent publications. Digital architectures benefit more from process scaling and could eventually be automatically synthesized using digital IC design tools [56]. This will result in significant reduction in production time and overall cost for circuit design.