esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 2 Materials and Methods
### Physical Examination  
During admission to the hospital, a detailed medical history was taken and a physical examination was performed for each patient. Each physical examination adhered to identical protocol. MRI was not shown to the examining surgeon at the time.  
Afterwards, the patient was stripped to their underwent and their gait was evaluated. Such factors as muscle atrophy, limb alignment and passive and active knee range of motion (ROM) were noted. Every joint was palpated in order to detect tenderness. After a general evaluation, a detailed knee examination with the use of dedicated ACL and meniscal tests was performed. Classical tests used to detect meniscal pathologies were performed, including McMurray [13], Apley [14] and Thessaly [15] tests, along with a palpation of the joint line. The detection of ACL tears was based on four tests including Lachman [16], anterior drawer [17], pivot shift (PS) [18] and lever sign [19]. To the best of our knowledge, this study is one of the first to evaluate the diagnostic accuracy of lever sign in ACL tears. Accuracy of the diagnostic tests was determined by the specificity and sensitivity of each test in detecting meniscal, chondral and ACL lesions. For each test, the number of false negative, false positive, true positive and true negative results was counted. The results were compared with the use of the receiver operating characteristic (ROC) curve.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \\ \hline Age, year (mean \(\pm\) SD) & 45 \(\pm\) 16 \\ Sex, n (\%) & Females 49 (51\%), Males 47 (49\%) \\ Duration of symptoms, months & 43.9 \(\pm\) 70.8 \\ History of trauma, n (\%) & 60 (62.5\%) \\ Involvement of the dominant extremity, n (\%) & 5 (31.25\%), 50 (48\%) \\ Opposite extremity involvement, n (\%) & 14 (14.5\%) \\ Physiotherapy prior to surgery, n (\%) & 60 (62.5\%) \\ Medial meniscus lesions, n (\%) & 45 (46.8\%) \\ Lateral meniscus lesions, n (\%) & 17 (17.7\%) \\ Bilateral meniscus lesions, n (\%) & 3 (3.125\%) \\ ACL tear, n (\%) & 30 (31.25\%) \\ Isolated ACL tear, n, (\% of ACL tears) & 3 (10\%) \\ ACL tear and MM lesion, n, (\% of ACL tears) & 4 (13.3\%) \\ ACL tear and LM lesion, n, (\%of ACL tears) & 3 (10\%) \\ ACL tear, MM and LM lesion, n, (\% of ACL tears) & 0 (0\%) \\ ACL tear and isolated chondral defect
## 2 Materials and Methods
### Signal Format for Alpha Navigation System  
Currently, the Alpha navigation system is the only operational long-range land-based navigation system based on VLF signals [7]. The Alpha navigation system contains five stations, including one main station and four secondary stations, and the working area covers approximately 70% of the world. The transmitter power of each station is approximately 200-250 kW, and the antenna radiation power ranges from 50 to 80 kW [24; 25]. The positioning accuracy of the system is in the range of 2-4 n mile. To solve the signal propagation delay correction problem, Russia established 31 Alpha signal propagation measurement stations in the operating area of the Alpha system, and the positioning accuracy can be improved to 200 m from 1000 m by using the propagation delay correction. The distribution of Alpha navigation system stations is shown in Figure 1[26; 27]. As can be observed from the distribution of stations, the system is a resource that China can use for research on VLF timing and navigation.  
The five stations in the Alpha navigation system broadcast three frequency signals: \(f_{1}\), \(f_{2}\), and \(f_{3}\). The stations are distinguished based on the transmission order of the three signals. Table 1 lists the transmission frequency and sequence of signals at each station  
Figure 1: Schematic diagram of Alpha navigation system distribution.  
in the system [5; 9]. The time taken for each station to broadcast a signal frame was 3.6 s, and each frame consisted of six time periods with a length of 600 ms [7; 17]. According to the signal broadcasting format designed by Alpha, although multiple signals of the same frequency will not be received at any location or time within the coverage range of the system, signals of different frequencies will be received simultaneously.
## 4 Results
### Soil Spectral Profile Characterization  
The visualization results of the original soil spectral data compared to the original spectral curve are shown in Figure 6. From the spectral curves of the soil samples, it can be observed that within the wavelength range of 900 to 1700 nm, the reflectance curves exhibit a similar overall trend for all of the soil samples. The spectral reflectance values of the soil samples range between 0.15 and 0.6. As shown in Figure 6, the spectral reflectance continuously increases in the range of 900-1400 nm. This may be due to the interaction between the chemical bonds in the molecular structure of soil components and near-infrared light. The wavelength range of 900-1400 nm has a strong penetration capability. The organic matter molecules in the soil exhibit strong absorption of near-infrared light. The spectral reflectance remains relatively stable in the range of 1420-1680 nm. This is mainly because the spectral characteristics within this range have weak interactions with the functional groups and chemical bonds in the molecular structure of soil organic matter. Additionally,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistics** & \begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Minimum** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Mean Value** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Coefficient of** \\ **Variation** \\ \end{tabular} \\ \hline Total sample & 85 & 19.40 & 3.68 & 9.77 & 3.80 & 38.94 \\ Training sample & 59 & 18.50 & 5.03 & 9.68 & 3.53 & 36.56 \\ Testing sample & 26 & 19.40 & 3.68 & 9.81 & 3.97 & 40.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of SOM content/(g/kg).  
Figure 5: Distribution map of soil organic matter content.  
the spectral characteristics in this range are influenced by water and other soil components, leading to higher stability in the spectral reflectance of the SOM. Moreover, there is a prominent absorption peak at 1420 nm, which is primarily influenced by the O-H molecules in the soil. The vibration of the C-H bonds in the organic matter molecules generates a typical 1.4 \(\upmu\)m absorption band, which can be detected using spectroscopic instruments and manifested as a distinct absorption peak.
## 2 Material and Methods  
### Experimental Studies  
#### 2.1.1 Influencing Factors  
The stress of the rivet joint with an equal riveting distance was investigated in this chapter. In Figure 1 the stress condition of the lap plate was shown, including the single-rivet small section joint and the double-row rivet small section joint. When a riveted plate is subjected to tension, the plate is also subjected to tension, the rivet is subjected to shear,and the plate is subjected to both shear and squeeze forces at the rivets. To prevent the plate from being badly pulled, squeezed, or sheared, it is necessary to set the breaking tension, breaking squeezing force, and breaking shear force of the plate equally. The expressions for the breaking tensile force, \(P_{1}\), the damage squeeze force, \(P_{2}\), and the damage shear force, \(P_{3}\), of the plate are given in Equations (1)-(3), respectively. In Equations (1)-(3), \(t\), \(c\), \(\delta\), and \(d\) are the rivet spacing, the edge distance, the plate thickness, and the rivet diameter, respectively, while \(\sigma_{b}\), \(\tau_{b}\), and \(\sigma_{1}\) represent the tensile strength, the shear strength, and the extrusion strength of the plate, respectively.  
\[P_{1}=(t-d)\delta\cdot\sigma_{b}, \tag{1}\]  
\[P_{2}=d\cdot\delta\cdot\sigma_{1}, \tag{2}\]  
\[P_{3}=2(c-\frac{d}{2})\cdot\delta\cdot\tau_{b}. \tag{3}\]  
When the damage squeeze force, \(P_{2}\), at the rivet hole is equal to the damage shear force, \(P_{3}\), at the edge of the plate, then Equation (4) applies:  
\[d\cdot\delta\cdot\sigma_{1}=2(c-\frac{d}{2})\cdot\delta\cdot\tau_{b},c=\frac {d}{2}(1+\frac{\sigma_{1}}{\tau_{b}}). \tag{4}\]  
For the m-row rivets, when the breaking tension, \(P_{1}\), of the plate is equal to the damage squeeze force of the plate, then Equation (5) applies:  
\[(t-d)\delta\cdot\sigma_{b}=m\cdot d\cdot\delta\cdot\sigma_{1},t=d(1+1.8m). \tag{5}\]  
The lap strength of a riveted component is closely related to the rivet spacing and the edge distance. In addition, the lap strength is related to the number of rivets, the number of rivet rows, the rivet arrangement, and the row spacing. Therefore, experiments must be conducted to study these parameters.
## 2 Materials and Methods
### Principal Component Background Suppression with Peak Threshold Detection
#### 2.2.3 Peak and Amplitude Filtering  
Once the data was background suppressed, target pixel identification proceeded. This identification was done by first peak filtering the background suppressed maximum value frequency spectrum. A peak prominence of three standard deviations was used to identify statistically significant frequencies. A threshold of three standard deviations above the mean was then applied to the amplitudes of those selected frequencies, individually, to identify the pixels with significant frequency content as target pixels. Three standard deviations were used as a bench mark to ensure that, after the background suppression, only the most significant frequencies and only the pixels strongly associated with those frequencies were identified with high confidence.  
While the statistical filtering process does pick out individual frequencies, the values of those frequencies are less relevant to detection. Instead, this method simply focuses on which pixels have significant frequency content after the background suppression. This technique allows for normal FFT issues like phase differences, spectral leakage, aliasing, and frequency energy shifts caused by the binning to be relatively inconsequential. The limited relevance of the actual target frequencies and their phase differences relative to the overwhelming sources is associated with adequate target sampling by the EVS. If the target is the same frequency as the overwhelming source and in phase, the EVS will most likely not see that target. Additionally, if target frequencies are too low, they may not generate enough events between the overwhelming source peaks for the FFT to capture a frequency. The target frequencies in this work were selected to represent signals that may be too low in frequency for sufficient sampling, align with the overwhelming source (though not necessarily in phase), and were high enough to produce events between the peaks of the overwhelming source (see Section 2.1).
# Toward Sustainability of the Aqueous Phase Reforming of Wastewater: Heat Recovery and Integration
## 3 Results and Discussion
### Process Heat Balance at Different APR Operating Conditions
Table 3 shows the maximum HC achievable, in the whole pressure interval studied and for an organic matter concentration in the feedstock of 1 and 2%, for total conversion at reforming reaction and extreme values of methanation reaction conversion. Due to the higher gas production, higher values of HC were obtained when 2% inlet concentration was considered, reaching HC values well above 20%. After HC vs. pressure sensitivity studies, while best results in heat balance were obtained operating at 35 bar for 1% inlet concentration, the highest result was reached at 45 bar when the concentration was increased to 2%. In any case, since the compression of wastewater is not a high-cost operation, higher operating pressure could be achieved and would be largely compensated by the increase of HC. The values of operating conditions found as optimal are in agreement with those used in most reported works on the APR of sorbitol and similar polyols (xylitol, etc. ), i.e., 30-50 bar pressure range and organic matter concentration below 10% [14; 23; 27]. Likewise, the results are concordant with those reported for the simulation of the APR of 10% sorbitol [23]. The authors found similar results in terms of process heat balance but concluded that the main limitation for process feasibility was the cost of feedstock, amounting to around 98% of total operating cost. The obtained HC results indicated that the application could be extended to a wider range of operating conditions. In this sense, when no cost was considered for the feedstock and an adequate strategy was used for heat recovery, it was possible to achieve the process of self-covering and use the net heat excess to decrease operating costs of steam generation or other usual alternatives in process engineering, as long as the operating conditions are above certain values depending on the feedstock composition. Figure 7: HC for different reforming and methanation conversion values.
# An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks
## 6 Discussion
### Ability to Enforce Security Principles (Dual Control and Split Knowledge)  
To the best of our knowledge, no approach deals with the psychological depression of the victim regarding vishing attacks by enforcing the organization's policies. Our proposed model enforces the organization's rules by implementing two security principles: split knowledge and dual control. The main idea is to subject suspected SE attacks to double or greater verification. The enforcement of security policies is enabled through blockchain deployment. It manages dual control through separation of the request (smart contract) sent by the client (recipient) and the required verification by peers (background verifiers) to endorse (accept) or reject it. Consequently, split knowledge is enforced, since the information known by the recipient is not sensitive based on the organization's data sensitivity classification, as explained in Section 4.1.  
The blockchain has the power to coordinate between the different parties without negatively impacting the business process. Moreover, blockchain technology will not delay the response to the request, since we adopt a private blockchain network, which does not require proof of work to create a new block. In addition, the proposed approach will not cause any business disruption by requiring more entities to verify customers' requests since the organization can determine the minimum number of endorsers needed to create a new block according to the sensitivity of the transaction and the business requirements.  
The ability to avoid a single point of failure is a consequence of security principles enforcement since any potential transaction is dually verified.
## 4 Results and Discussion
### Elastic Inversion Results
To assess the model's performance and ensure control, we utilized metrics such as mean square error (MSE) and the coefficient (\(R^{2}\)) in each step of the process. The aim was to evaluate the results and enhance the quality of each iteration. These metrics helped us identify and choose the most favorable results of the Bayesian linearized inversion (BLI) process. The \(R^{2}\) metric is commonly used in machine learning to measure how well the model fits the observed data in regression analysis results. These control measures were applied to attain optimal hyperparameter values by iteratively refining the models used. These metrics together imply that, within this hypothetical scenario, Bayesian inversion is  
Figure 9: A comparative analysis is presented between the results of the AVO and BLI inversion processes concerning the V\({}_{P}\) well log. The black line represents the original data, the blue line represents the BLI inversion results, and the red line represents the AVO inversion results. The results highlight the superior accuracy achieved through Bayesian inversion. Additionally, the results illustrate the shift between the initial and final states of the 95% confidence interval (CI). Image (**a**) corresponds to data from well No-6608-10-02, while image (**b**) displays data from well No-6608-10-04. the option that provides a better definition of the inversion compared to the AVO method. Figure 10 we can see that, specifically, both the amplitude versus offset inversion (AVO) method and the Bayesian linear inversion (BLI) method exhibit an exceptional ability to recover values closely aligned with those of the original velocity model. However, the BLI inversion process outperforms the AVO method regarding recovery accuracy, as demonstrated by the results presented in Figure 10. The comparison is especially notable at higher values where the shape of the horizon is distorted, showing that the BLI inversion accurately captures the main features of the model. In this study, we incorporate prior knowledge by using prior information from the low-frequency components of the low-frequency model to establish constraints for the model. To improve quality, we use a likelihood function to evaluate how well the model describes the observed data, considering assumptions and limitations based on what we already know. The objective was to maximize this likelihood function to determine the values of the model parameters that maximize the probability of obtaining the observed data. During the data inversion stage, the study used two different inversion processes: AVO and BLI inversion. To facilitate this, in the initial phase, a synthetic model was selected to apply the inversion processes.
## 3 Methods  
### Model Architecture  
The architecture of the WGAN-GP-based color correction model involves two adversarial components, the Generator (G) and the Critic (C), which are trained simultaneously. The Generator (G) takes, as input, a pair of images, consisting of a reference image (\(x_{ref}\)) and a target image (\(x_{target}\)), and aims to realistically generate an image (\(G(x_{ref}\), \(.\)\(x_{target}\))) that resembles the reference image (\(x_{ref}\)). Meanwhile, the Critic (C) determines whether the input image is the reference image (\(x_{ref}\)) or the generated image (\(G(x_{ref}\), \(.\)\(x_{target}\))). The architecture of the WGAN-GP model is illustrated in Figure 1.  
The G takes a reference image (\(x_{ref}\)) and a target image (\(x_{target}\)) as inputs and combines them to create a new image. The architecture of the G used in this study is organized as follows.  
To simplify, the G first combines the two input images and applies a series of convolutional layers and activation functions to gradually extract features. In more detail, the G starts by passing the combined images through a 3 \(\times\) 3 convolutional layer with ReLU activation, which is repeated twice to capture basic patterns. MaxPooling is then applied to reduce the dimensions, summarizing key features. This process is repeated twice, with the number of filters increasing each time to capture more complex details as the resolution decreases. In the next stage, G uses up-sampling to restore resolution, followed by further convolutional layers and ReLU activation to refine the image output. In the final step, a sigmoid function is applied to ensure the output values are within the range [0, 1], as shown in Figure 2a.  
The C is responsible for determining whether the input image is a real image (reference image, \(x_{ref}\)) or a generated image (\(G(x_{ref},.\ x_{target})\)). The architecture of C used in this study is organized as follows.  
The C follows a similar approach but focuses on differentiating between real and generated images. The input is passed through a 3 \(\times\) 3 convolutional layer with ReLU activation, followed by MaxPooling to reduce dimensions and extract essential features. This process is repeated three times, with increasing filter numbers to enhance the detection of patterns. Finally, the output is flattened into a vector through a Flatten layer, and a final decision is made, as shown in Figure 2b. To make this process more intuitive, a flowchart explaining each step visually has been included in Figure 1.  
Figure 1: An overview of the architecture of the WGAN-GP model.  
Figure 2: Architecture of the Generator and Critic in the WGAN-GP model. **(a)** Architecture of the Generator **(b)** Architecture of the Critic.
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 3 Proposed Coordinated Control Strategy of CU-MTDC under Abnormal Conditions  
### Basic Concept  
As shown in Figures 1 and 3, if DC unbalanced power exceeds the regulation capacity of the master converter and auxiliary converter, DC voltage will be out of control until the APC converters change into droop control. Therefore, the active power of the APC converter reduces rapidly, and the load ratio of the interconnected critical line increases. As a result, the coordinated power redistribution strategy is required to guarantee power supply security.  
In the traditional method, all the normal converters are involved in power distribution. Total regulation capacity is fully utilized, and the security of all the critical AC lines is confirmed. However, the DC power variation impacts the power flow among all areas of the AC system. Meanwhile, the constant active power supply of the APC converter is broken,  
Figure 3: \(U_{dc}\)-\(P\) characteristics of the master-auxiliary control strategy. (**a**) \(U_{dc}\)-\(P\) characteristics of the master converter. (**b**) \(U_{dc}\)-\(P\) characteristics of the auxiliary converter. (**c**) \(U_{dc}\)-\(P\) characteristics of the active power converter. (**d**) \(U_{dc}\)-\(P\) characteristics of the wind farm converter.  
and some unnecessary costs occur, considering the requirement of the electricity market. As a result, dynamically scheduling the number of the regulation converter according to the value of DC unbalanced power is a feasible solution method when the unbalanced power is less than a certain value. Generally, there exist two basic scenarios of unbalanced power coordinated distribution under abnormal conditions.  
Scenario 1: The first scenario is that the DC unbalanced power is absorbed by one or more converters, which is referred to as the priority control mode.  
Scenario 2: The second scenario is that the DC unbalanced power will be absorbed by all the normal APC converters, which is referred to as the shared control mode. Under this condition, the active power output value of every regulation converter should be further confirmed by a specific objective. In this paper, the control objective is all the critical AC lines that are interconnected with the APC converters have the same load ratio.  
It should be noticed that offshore wind power directly affects DC unbalanced power. Therefore, the influence of offshore wind power on control can be generalized into the effect of the unbalanced power.  
As shown in Figure 3c, two active power values of the APC converter are very important for the objective control implementation. The active power reference value \(P_{\textit{refn}}\) and the actual active power output value \(P_{\textit{n}}\) are calculated according to the proposed method, which is described in Sections 3.2 and 3.3.
## 2 Methodological and Operational Framework for CDTs
### Application AreasReference Scenarios
#### 3.3.2 Virtual Supply Chain Production Lines  
This responds to the need of supply chain alignment. The main idea is to create a virtual production line at the supply chain where all actors (supplier, manufacturer, warehouses) are modelled as CDTs and planning is performed at the supply chain level similar to intra-factory. Events such as machine breakdowns, maintenance activities and other potential delays are shared among the supply chain and through the CDTs we can simulate the impact in different individual production plans and re-optimize them.