esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Scaffolds are thought to be a key element needed for successful cartilage repair treatments, and this prospective extension study aimed to evaluate long-term structural and clinical outcomes following osteochondral defect treatment with a cell-free biphasic scaffold. Structural outcomes were assessed using quantitative 3-D magnetic resonance imaging (MRI) and morphological segmentation to determine the percentage of defect filling and repair cartilage T2 relaxation times, and clinical outcomes were determined with the modified Cincinnati Rating System, and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Seventeen subjects with osteochondral defects in the knee were treated with ChondroMimetic scaffolds, from which 15 returned for long-term evaluation at a mean follow-up of \(7.9\pm 0.3\) years. The defects treated were trochlear donor sites for mosaicplasty in 13 subjects, and medial femoral condyle defects in 2 subjects. MRI analysis of scaffold-treated defects found a mean total defect filling of \(95.2\pm 3.6\%\), and a tissue mean T2 relaxation time of \(52.5\pm 4.8\) ms, which was identical to the T2 of ipsilateral control cartilage (\(52.3\pm 9.2\) ms). The overall modified Cincinnati Rating System score was statistically significant from baseline (\(p=0.0065\)), and KOOS subscales were equivalent to other cartilage repair techniques. ChondroMimetic treatment resulted in a consistently high degree of osteochondral defect filling with durable, cartilage-like repair tissue at 7.9 years, potentially associated with clinical improvement.  
biphasic scaffold; osteochondral defect; cartilage repair; quantitative MRI; calcium phosphate
# Synchrotron X-ray Radiation (SXR) in Medical Imaging: Current Status and Future Prospects
## 1 Introduction  
Synchrotron X-ray radiation (SXR) has a variety of features that compete with conventional X-ray imaging in the field of research with direct and indirect clinical advantages, from having a tunable monochromatic beam to an inherent coherent self-collimated X-ray beam, over a wide range of energies [1].  
The process of image formation in conventional radiology depends on the absorption and transmission differentiation of the primary X-ray beam, part of the X-ray beam is absorbed while the rest is transmitted through different organs and tissues. As the body consists of different tissues with different physical properties (e.g., thickness, density, atomic number, etc.), the beam is subjected to different attenuation as it travels, and an attenuation map is formed at the detector representing an image contrast of the anatomic area of interest [2]. The unavoidable limitation of this approach is the low flux available in the standard poly-energetic X-ray imaging system, poor detection of signals passing through the soft tissue. Because of these reasons, the poor soft tissue contrast provided by the system eventually affects the pathologic diagnosis and can only be conclusive with biopsy confirmation. Furthermore, the non-useful low energy of the conventional X-ray beam needs to be filtered out for two important reasons: (1) to reduce the amount of X-ray dose to the patient and (2) to reduce the scattering effect which is a prime source of reduction in contrast and signal to noise ratio (SNR).  
On the other hand, synchrotron generates very high flux of SXR. Such high flux enables the selection of a narrow energy band from a full radiation spectrum, whichallows researchers to characterize the structural and functional process of many diseases (e.g., breast cancer, lung disease, etc.) [3].  
The purpose of this review article is to explore the latest applications of SXR in medical imaging and to shed light on the advantages and drawbacks of this modality. The article highlights the latest developments in other fields that can greatly enhance the capability and applicability of SXR. The potentials of using machine and deep learning (DL)-based methods to generate synthetic images to use in regular clinics along with the use of energy counting X-ray detector, for the purpose of spectral medical imaging with SXR, are also discussed.
## 3 Results
### Effects of FF on Serum Lipid Metabolism  
Consumption of HFD is widely recognized to contribute to elevated serum TC levels [43]. Moreover, increased visceral fat accumulation resulting from HFD consumption influences serum TG concentration, further exacerbating lipid metabolism disturbances [44]. In this study, serum TC and TG levels were significantly increased in the HFD group compared to the NFD group, with TC levels significantly increasing from 1.21 \(\pm\) 0.06 \(\upmu\)g/uL (NFD) to 2.00 \(\pm\) 0.02 \(\upmu\)g/uL (HFD). However, TC levels exhibited a decrease in the FF group, with values of 1.90 \(\pm\) 0.04 \(\upmu\)g/uL (FF 50), 1.82 \(\pm\) 0.04 \(\upmu\)g/uL (FF 125), and 1.77 \(\pm\) 0.09 \(\upmu\)g/uL (FF 250) (Figure 6A). The TG levels exhibited a significant increase from 2.29 \(\pm\) 0.34 \(\upmu\)g/uL (NFD) to 3.11\(\pm\) 0.21 \(\upmu\)g/uL (HFD). However, a significant dose-dependent decrease was observed in the FF 250 group (2.29 \(\pm\) 0.35 \(\upmu\)g/uL) (Figure 6B). In the FF groups, LDL levels decreased in a dose-dependent manner to 0.44 \(\pm\) 0.04 \(\upmu\)g/uL (FF 50), 0.41 \(\pm\) 0.07 \(\upmu\)g/uL (FF 125), and 0.39 \(\pm\) 0.03 \(\upmu\)g/uL (FF 250) (Figure 6C). HDL levels showed a significant decrease in the HFD (0.49 \(\pm\) 0.08 \(\upmu\)g/uL) group compared to the NFD (0.90 \(\pm\) 0.05 \(\upmu\)g/uL) group. With FF, HDL levels increased in a dose-dependent manner to levels of 0.56 \(\pm\) 0.04 \(\upmu\)g/uL (FF 50), 0.66 \(\pm\) 0.07 \(\upmu\)g/uL (FF 125), and 0.74 \(\pm\) 0.02 \(\upmu\)g/uL (FF 250) (Figure 6D). These findings indicate the effectiveness of FF administration in improving lipid metabolism imbalances in the HFD-induced mice model.  
Figure 5: Effects of fermented fig (FF) on serum (**A**) C-peptide, (**B**) insulin, and (**C**) glucose levels in HFD-fed mice. NFD, mice treated with only DW; HFD, mice treated with HFD and DW; GTE, mice treated with HFD and green tea extract (GTE) 50 mg/kg; FF, mice treated with HFD and FF (50, 125, and 250 mg/kg). The results are expressed as mean \(\pm\) SEM (\(n\) = 8). \({}^{\text{a}}\)\({}^{\text{c}}\) The different variables were significantly different at \(p<\) 0.05 among the groups, as determined by [PERSON]’s analysis.
## 4 Technical Implementation
```
1:input: maximum allowable volume \(V_{max}\), calculated volume \(V\), maximum allowable plastic strain \(\overline{\varepsilon}_{max}^{pl}\), calculated equivalent plastic strain \(\overline{\varepsilon}^{pl}\), maximum allowable rotation \(\theta_{max}\), calculated rotation \(\theta\), minimum critical buckling load factor stab, calculated critical buckling load factor \(\overline{\lambda}\)
2:compute volume function : \(f(V)=\frac{V}{V_{max}}\)
3:if\(\overline{\varepsilon}^{pl}\leq\overline{\varepsilon}_{max}^{pl}\)then
4: evaluate penalty for equivalent plastic strain: \(p_{1}\Big{(}\overline{\varepsilon}^{pl}\Big{)}=0\)
5:else
6: evaluate penalty for equivalent plastic strain: \(p_{1}\Big{(}\overline{\varepsilon}^{pl}\Big{)}=1-\frac{\overline{\varepsilon }_{max}^{pl}}{\overline{\varepsilon}^{pl}}\)
7:if\(\theta\leq\theta_{max}\) then
8: evaluate penalty for joint rotation: \(p_{2}(\theta)=0\)
9:else
10: evaluate penalty for joint rotation: \(p_{2}(\theta)=1-\frac{\theta_{max}}{\theta}\)
11:if\(\overline{\lambda}\geq\lambda_{stab}\)then
12: evaluate penalty for critical buckling load factor: \(p_{3}(\overline{\lambda})=0\)
13:else
14: evaluate penalty for critical buckling load factor: \(p_{3}(\overline{\lambda})=1-\frac{\overline{\lambda}}{stab}\)
15: compute total fitness value: \(fitness=f(V)+p_{1}\Big{(}\overline{\varepsilon}^{pl}\Big{)}+p_{2}(\theta)+p_{3 }(\overline{\lambda})\)
16:output:\(fitness\)
```  
**Algorithm 1** Establish the fitness value of protentional solution.
## 3 Materials and Methods
### Limitations  
The dataset reveals a significant number of instances classified as \"allow\" (37.640 occurrences), indicating a prevalence of packets allowed by the firewall. Additionally, there are actual occurrences of samples classified as \"deny\" (14.987 occurrences) and \"drop\" (12.851 events), representing packets that were denied or dropped by the firewall, respectively. However, the subclassification class \"reset-both\" is severely underrepresented in the dataset, with only 54 occurrences. This class imbalance raises concerns about the model's performance and ability to predict the \"reset-both\" subclassification accurately. Moreover,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline SourcePort & \(4.94\times 10^{10}\) & \(1.53\times 10^{10}\) & 0.0 & 49,183.0 & 53,776.5 & 58,638.00 & \(6.55\times 10^{10}\) \\ \hline DestinationPort & \(1.06\times 10^{10}\) & \(1.85\times 10^{10}\) & 0.0 & 80.0 & 445.0 & 15,000.00 & \(6.55\times 10^{10}\) \\ \hline NATSourcePort & \(1.93\times 10^{10}\) & \(2.20\times 10^{10}\) & 0.0 & 0.0 & 8820.5 & 38,366.25 & \(6.55\times 10^{10}\) \\ \hline NATDestinationPort & \(2.67\times 10^{10}\) & \(9.74\times 10^{9}\) & 0.0 & 0.0 & 53.0 & 443.00 & \(6.55\times 10^{10}\) \\ \hline Bytes & \(9.71\times 10^{10}\) & \(5.62\times 10^{12}\) & 60.0 & 66.0 & 168.0 & 752.25 & \(1.27\times 10^{15}\) \\ \hline BytesSent & \(2.24\times 10^{10}\) & \(3.83\times 10^{12}\) & 60.0 & 66.0 & 90.0 & 210.00 & \(9.48\times 10^{14}\) \\ \hline BytesReceived & \(7.47\times 10^{10}\) & \(2.46\times 10^{12}\) & 0.0 & 0.0 & 79.0 & 449.00 & \(3.21\times 10^{14}\) \\ \hline Packets & \(1.03\times 10^{8}\) & \(5.13\times 10^{9}\) & 1.0 & 1.0 & 2.0 & 6.00 & \(1.04\times 10^{12}\) \\ \hline ElapsedTime (s) & \(6.58\times 10^{7}\) & \(3.02\times 10^{8}\) & 0.0 & 0.0 & 15.0 & 30.00 & \(1.08\times 10^{10}\) \\ \hline pkts\_sent & \(4.14\times 10^{7}\) & \(3.22\times 10^{9}\) & 1.0 & 1.0 & 1.0 & 3.00 & \(7.48\times 10^{11}\) \\ \hline pkts\_received & \(6.15\times 10^{7}\) & \(2.22\times 10^{10}\) & 0.0 & 0.0 & 1.0 & 2.00 & \(3.27\times 10^{11}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset information.  
Figure 1: Firewall dataset classification attribute data distribution.  
the restricted quantity of instances encapsulated in the \"reset-both\" class might constrain the model's capacity to generalize and apprehend the complexity inherent in real-world situations, particularly those where the firewall undertakes the reset of packets. Furthermore, within the methodological constraints of the study, the analysis was conducted solely using ANNs and SMOTE. The sequential process adhered to within this research study is depicted in Figure 2.
## 2 Materials and Methods
### Programming
#### 2.3.2 Software Coding and Flow Chart Design  
The program starts by identifying the main parameters and functions and by initialising the devices, which includes identifying the serial port, interrupt function, AD7606 setting, timer initialisation, direct memory access (DMA), motor, ADC, and LCD.  
The next step is to read the angle of the sensor and compare it with the original angle. If the current angle is greater than the original angle, then the motor is set to unfold mode; if the current angle is less than the original angle, then the motor is set to fold mode.  
This step is repeated until the current angle equals the original angle. Then, stop the motion of the motor and start the alarm signal that indicates the end of the process, as shown in Figure 5.  
After that, explain how the EMG signals is converted and sent to the PC: the STM32 sends a signal per millisecond to initiate the conversion operation of the AD7606. As soon as the AD7606 receives the signal, it starts the analogue-to-digital conversion function. Once it completes, it sends an interrupt signal to STM32, causing STM32 to turn to the interrupt function. In this function, the converted data are stored into the array, and when it is complete, the DMA mode is started to transmit the data to the PC by using the serial port cable as shown in Figure 6.  
Figure 5: Flowchart of the main functions.  
The final step is explaining the CPM machine's reciprocating motion. It starts by initialising the motor motion in stop mode and then delaying for 2 s using the timer function. After that, the movement instruction is set to fold or unfold mode to apply the CPM motion. Comparing the current angle and the receiving angle to stop the motor motion for 2 s, the motor moves in the opposite direction until it reaches the original angle. After that, the motor repeats this reciprocating motion until it receives a stop signal from the push button, as shown in Figure 7.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Blind Linear Equalizer Based on Genetic Algorithms  
#### 3.4.1 Architecture  
The Blind Linear Equalizer based on genetic algorithm (BLE-GA) is an adaptive blind block equalization scheme whose algorithm uses genetic algorithms as a linear stochastic problem solver. As shown in Figure 6, the BLE-GA operates by storing the received signal, \(u(k)\), in a buffer of length \(K\) and then constructing the matrices  
\[\mathbf{U}^{I}(k)=[u^{I}(k)\;\ldots\;u^{I}(k-K+1)] \tag{19}\]  
and  
\[\mathbf{U}^{Q}(k)=[u^{Q}(k)\;\ldots\;u^{Q}(k-K+1)]. \tag{20}\]  
The vectors \(\mathbf{U}^{I}(k)\) and \(\mathbf{U}^{Q}(k)\) are used by the genetic algorithm to optimize the equalizer weights, \(w(kk)\), in order to perform the channel equalization.
# Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry
## 1 Introduction
[37] further enhanced multiphysics modeling by incorporating gas-melt pool interactions into a deep learning framework, showing that physics-guided learning reduces data requirements while improving prediction transparency. [PERSON] et al. [38] integrated stochastic physical modeling with ML to detect multiple damage scenarios in digital twins. [PERSON] et al. [39] used FEA and ML to identify thermal patterns in directed energy deposition (DED). These studies highlight the potential of hybrid modeling in AM, particularly for low-volume, highly customized production. These hybrid strategies bridge the gap between computational modeling and experimental validation, providing more robust and interpretable models. In this work, an explicit-model-augmented machine learning framework is presented for predicting melt pool geometry in L-PBF. A physics-based analytical model is first calibrated using experimental data to simulate melt pool width and depth. This model is then used to generate physically consistent synthetic data to augment a limited dataset of 36 parameter combinations, covering conduction, transition, and keyhole mode. Three ML algorithms--multilayer perceptron (MLP), random forest, and XGBoost--are trained using fivefold cross-validation. The results show that the hybrid model improves predictive accuracy, reduces dependency on large datasets, and enhances physical interpretability. This study provides an efficient and generalizable approach for intelligent process optimization in metal additive manufacturing.
## 4 Field Experiments
### Experimental Procedure  
To enhance the confidence of the experimental results, each subject had 20 min of adaptive driving training. Furthermore, sampling frequency of EMG instruments and VBOX were adjusted to be as consistent as possible, both of which were 1024 Hz. Before the experiment, the maximum voluntary contraction (MVC) of each muscle working on steering wheel and pedal was calibrated in order to solve the muscle activation. During the experimental process, three subjects kept holding the steering wheel sensor at the \"3-9 o'clock\" position, and pressed the brake pedal sensor as shown in Figure 7. Each subject followed the trajectory three times, and the best instance was selected as the experimental data. Noteworthily, an emergency acceleration and braking scenario were designed in order to observe the fast response of the vehicle and muscle signals in S3 segments.
# Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities
## 3 Background
### XSS Vulnerability  
XSS is an injection attack that injects malicious scripts into benign and trusted websites [24]. Attackers can take advantage of this vulnerability to implant malicious code into every corner of the website. When users visit this website, malicious code will be automatically executed, and users will not sense any abnormality. This way, attackers can obtain users' private information, steal users' cookies, and spread webworms. Most of the XSS vulnerabilities appear in applications and websites with human-computer interaction, such as WordPad, sending email, message areas, and search bars. XSS vulnerabilities are divided into three types [25]: reflected XSS, stored XSS, and DOM-based XSS.  
* **Reflected XSS** directly reflects the user's input to the browser, causing the browser to execute some scripts. In this kind of attack, the attacker usually constructs the attack payload, combines the attack payload with the link to form a malicious link, and tricks the user into visiting the link. When the user visits the link, it causes the browser to execute malicious scripts.  
* **Stored XSS** stores the input data on the server side, and it will trigger when the data are displayed on the page visited by the user. Usually, the attacker uploads the attack payload on the page where text can be submitted (such as message boards, blogs, etc.), and when other users access the data, the attack vector will be activated.
* **DOM-based XSS** is a vulnerability that exploits the Document Object Model (DOM). The DOM allows scripts to dynamically access and update document content, structure, and styles, then display them on the page. This type of XSS vulnerability does not need to save the data to the server but executes the DOM data obtained by the client locally, which is also a reflected XSS in the strict sense.
## 2 Materials and Methods
### Antibacterial Activity Evaluation  
Antibacterial activity was evaluated following the ISO 22196:2011 standard [33], which measures antibacterial activity on plastic surfaces against _Staphylococcus aureus_ (NBRC12732). Polyethylene sheets, which do not have antibacterial properties, were used as controls. _S. aureus_ was cultured for 24 h on standard agar medium (5 g/L meat extract, 10 g/L peptone, 5 g/L NaCl, and 15 g/L agar). Colonies were then suspended in 1/500-diluted nutrient broth to achieve a bacterial concentration of 2.5-10 \(\times\) 10\({}^{5}\) colony forming units (CFU). This solution was applied to the reference and test PS samples, which were incubated at 35 \({}^{\circ}\)C for 24 h in 90% humidity. Samples were washed with soybean-casein-digest-lecithin-polysorbate medium (10 mL), serially diluted (10\(\times\), 10\({}^{2}\times\), 10\({}^{3}\times\), and 10\({}^{4}\times\)), and 1 mL of each dilution was plated on standard agar. After solidification, the agar plates were incubated at 35 \({}^{\circ}\)C for 40-48 h, and colonies were counted to calculate log (CFU/cm\({}^{2}\)). When the colony counts were zero, the log (CFU/cm\({}^{2}\)) was set to 0.05 for convenience.
# Power Transformer Inrush Current Analysis: Simulation, Measurement and Effects
## 1 Introduction
The theoretical model presented in this paper has as starting data the design data of the transformer. The models found in the literature approximate the magnetization curve through a hyperbolic function or determined either by experimental or numerical methods, in our case the magnetization curve was determined by calculation starting from the magnetization characteristics of the sheet from which the transformer coils are made. The presented model is very much based on the constructive data of the transformer, it is easy to use, it generates concrete information. The description of the magnetization curves is strongly influenced by the characteristics of the magnetic materials from which the coils that are integrated into the transformer core are made, they present a linear and saturation zone. In order to benefit from both a high value of the flux density and a high magnetic permeability, under normal operating conditions, the transformer operates in the vicinity of the saturation zone. In these conditions, the total flow can reach twice the nominal value,when the voltage passes through zero. The remaining existing flow represents a factor that can add a plus to the total flow [1; 3; 13]. For the energy industry, reducing the effects of this transient phenomenon, controlled connection (the connection angle of the supply voltage to 90\({}^{\circ}\)) have become the main objectives. Conventional methods have been used to reduce this inrush current [18; 20; 21; 23] as:  
* a resistor is inserted in series with the primary winding during the initial energization. This limits the initial current surge;
* a Negative Temperature Coefficient (NTC) thermistor is connected in series with the primary winding;
* the transformer core is partially magnetized before energization;
* careful design of the transformer core can reduce the inrush current;
* gradually increasing the voltage applied to the transformer;
* the transformer is energized at the point of minimum voltage;
* starting the transformer on a lower tap and then switching to the desired tap after the inrush current has subsided. Reducing and controlling the occurrence of transient currents has become a major focus for the power industry [22; 23; 24]. This paper deals with the transient phenomenon that occurs when the transformer is connected to the mains, i.e., the inrush current, and presents the analytical method for determining it according to the characteristics of the transformer, the modeling of the transient current, its measurement in laboratory tests and its effects in operation [1; 7; 25; 26; 27; 28; 29]. In addition to those listed, the analysis with the stationary wavelet transforms of the U_UV voltage (the phase on which the highest current was recorded) was also performed [30; 31; 32; 33].
## 2 Materials and Methods
### Reflection Coefficient  
The reflection coefficient \(k_{r}\) was calculated according to the method proposed by [PERSON] and [PERSON] [44] based on four wave gages (W2-W5 in Figure 1) installed on the offshore side of the flap in Figure 1. [PERSON] and [PERSON] [44] extended the method of [PERSON] and [PERSON] [45] to separate the free and bound waves of the incident and reflected waves based on four or more water level fluctuation data under the regular wave conditions. [PERSON] and [PERSON]'s method separates the first and \(n\)th harmonics of the free and bound waves of the incident and reflected waves using each water level fluctuation datum. Then, the reflection coefficient \(k_{r}\) was calculated by Equation (2), based on each amplitude of the separated incident and reflected waves.  
\[k_{r}=\frac{\sqrt{\left(\frac{a_{1}^{n}}{a_{1}^{2}}+\sum_{n\
eq 2}\left[ \left(a_{r,b}^{n}\right)^{2}+\left(a_{r,f}^{n}\right)^{2}\right]\right)}}{\sqrt {\left(a_{1}^{n}\right)^{2}+\sum_{n\
eq 2}\left[\left(a_{i,b}^{n}\right)^{2}+ \left(a_{i,f}^{n}\right)^{2}\right]}}, \tag{2}\]  
Figure 5: Schematic of a voltage divider circuit.  
where \(a\) is the amplitude and superscripts 1 and \(n\) are the first and \(n\)th harmonic waves, respectively. The first subscripts \(i\) and \(r\) denote the incident and reflected waves, and the second subscripts \(b\) and \(f\) denote the bound (locked mode) and free (free mode) waves, respectively. The separation method and process are specifically described in [PERSON] and [PERSON] (2004). As the decomposed incident wave heights tend to be overestimated under the relatively long period wave conditions, the results of \(k_{r}\), only when the decomposed incident wave height is within \(\pm\) 0.02 m of the targeted input wave height, are discussed in this paper.