esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Preprocessing
### Binning  
Binning allowed for continuous data to be represented in various discrete categories or bins. In order to best characterize the data, the following attributes of the edge connections were binned: number of connections, average duration, and average bytes. In order to bin the data, the methodology outlined by the authors of [21] was utilized; however, a stationary mean was implemented instead of a moving mean. The standard deviation was first calculated by using the following formula:  
\[stddev=\sqrt{\sum_{i=1}^{n}\frac{(x_{i}-\overline{x})^{2}}{n}} \tag{1}\]where \(x\) is the attribute that is being binned, \(\overline{x}\) is the average of the attribute, and \(n\) is the number of data points. Six bins were then constructed using the calculated standard deviation, as follows:  
\[bin_{1}=(-\infty,\ \overline{x}-(2*stddev)) \tag{2}\]  
\[bin_{2}=[\ \overline{x}-(2*stddev),\ \overline{x}-stddev) \tag{3}\]  
\[bin_{3}=[\overline{x}-stddev,\ \overline{x}) \tag{4}\]  
\[bin_{4}=[\overline{x}\,\ \overline{x}+stddev) \tag{5}\]  
\[bin_{5}=[\overline{x}+stddev,\ \overline{x}+(2*stddev)) \tag{6}\]  
\[bin_{6}=[\overline{x}+(2*stddev),\ \infty) \tag{7}\]  
Each of the three edge attributes was assigned a bin, determined by which bin the attribute's value landed in. Because the data had a large variance, and thus a large deviation, the first two bins were negative for some of the attributes.  
After using Equation (1) to calculate the standard deviation for the count attribute for the full Reconnaissance dataset, Equations (2)-(7) were used to calculate the bins for the count attribute as follows:  
\[stddev=265,048.551,\ \ \overline{x}=16,963.973\]  
\[bin_{1}=(-\infty,\ \overline{x}-(2*stddev))=(-\infty,16,963.973-(2\ *265,048.551))=(-\infty,-513,133.129)\]  
\[bin_{2}=[\ \overline{x}-(2*stddev),\ \overline{x}-stddev)=[16,963.973-(2\ *265,048.551),\ 16,963.973-265,048.551))\]  
\[=[-513,133.129,-248,084.578)\]  
\[bin_{3}=[\ \overline{x}-stddev,\ \overline{x})=[16,963.973-265,048.551),\ 16,963.973\ )=[-248,084.578,\ 16,963.973)\]  
\[bin_{4}=[\ \overline{x},\overline{x}+stddev)=[16,963.973\,\ 16,963.973+265,048.551)=[16,963.973,\ 282,012.524)\]  
\[bin_{5}=[\overline{x}+stddev,\ \overline{x}+(2*stddev))=[16,963.973+265,048.55 1,\ 16,963.973+(2*265,048.551))\]  
\[=[282,012.524,\ 547,061.074)\]  
\[bin_{6}=[\overline{x}+(2*stddev),\infty)=[16,963.973+(2*265,048.551),\infty)=[ 547,061.074,\ \infty)\]  
To find which bin a value is in, the bin that overlaps the value is found. As an example, the value 1280 is between the values \(-\)248,084.578 and 16,963.973; therefore, this value resides in \(bin_{3}\).
## 3 Analysis of Factors Influencing the Results
### Effect of Slanted-Edge Angle on Slanted-Edge Method  
From Figure 11, it is evident that a smaller slanted-edge angle necessitates a greater number of pixels to represent the angle, leading to an increase in the image size in the height direction of the slanted edge. To explore the impact of the slanted-edge method, particularly how the slanted-edge angle, constrained by the height of the edge, affects the method's results, the imaging process of the imaging system was simulated as per Section 2.4. This simulation involved using a computer to generate 100 \(\times\) 100-pixel images of a slanted edge with angles increasing from 1\({}^{\circ}\) to 45\({}^{\circ}\). The grayscale values for the light and dark areas were set at 255 and 0, respectively. The slanted-edge method was then applied to calculate the MTF of the system at the Nyquist frequency. This paper's analysis includes comparing the results of the proposed algorithm with the theoretical values to evaluate the effectiveness of the slanted-edge method. The comparison of the theoretical and actual calculated values is presented in Figure 12.  
Figure 13 demonstrates that the relative error escalates notably when the slanted-edge angle is less than 6\({}^{\circ}\), suggesting that the solution results become highly volatile at  
Figure 11: Schematic diagram of slanted-edge discretization: (a) slanted-edge angle of 8.13\({}^{\circ}\); (b) slanted-edge angle of 14.036\({}^{\circ}\); (c) slanted-edge angle of 26.565\({}^{\circ}\).  
Figure 12: Relationship between slanted-edge angle and number of pixels.  
smaller angles. This is attributed to the inadequacy of the actual image's slanted-edge height in fully capturing the nuances of smaller angles. Conversely, when the angle exceeds 12\({}^{\circ}\), the relative error also increases as the angle widens. This rise in error is due to the increased width of the edge under a constant height, which introduces additional noise into the solution results. Therefore, the optimal range for the slanted-edge angle is between 6\({}^{\circ}\) and 12\({}^{\circ}\).  
When the slanted-edge angle ranges from 6\({}^{\circ}\) to 12\({}^{\circ}\), the relative error between the calculated and theoretical values remains below 0.01. This suggests that within this angle range, the slanted-edge method demonstrates optimal stability, making it the most ideal range for the slanted-edge angle.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
###### Abstract  
In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson-Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.  
Keywords:computed tomography (CT); iterative blind deconvolution; motion artifacts; Richardson-Lucy (RL) deconvolution; spatially adaptive total variation (SATV) regularization +
Footnote †: journal:
# A Systematic Review of Osteotomies to Correct Hallux Valgus in the First Metastasis
## 3 Results
### Distal Chevron Metastasis Osteotomy  
#### 3.2.1 Procedure and Analysis  
Distal Chevron metatarsal osteotomy (DCMO) was described by [PERSON] et al. [13]. First, a lateral incision is made to perform the initial lateral release of the first MT. After this, adductor tenotomy and the release of the MT-sesamoid capsule are completed for lateral soft tissue relief, followed by a varus brisement. A varus stress test of the first MTPJ is conducted to determine the appropriate extent of the lateral release, with around 20\({}^{\circ}\) of varus shift considered optimal. Next, a T-shaped incision is made at the medial capsule using the medial approach. Depending on the severity of the deformity, a 5-8 mm broad vertical limb of the capsule is excised. An oscillating microsaw is used to perform a bunionectomy, cutting parallel to the medial aspect of the first MT shaft. The 60\({}^{\circ}\) Chevron osteotomy's apex is positioned around 5-7 mm from the articular surface at the center of the first MT head. After the osteotomy, the MT head is laterally translated, as shown in Figure 6.  
In cases where there is a noticeable angle between the distal metatatarsal bone and the articular surface (DMA\(\bar{\text{a}}\)), the displaced fragment is corrected by rotating it medially. Two 1.1 mm Kirschner wires are inserted from a point 2 cm above the osteotomy site to fix the displaced fragment, and any remaining bony prominence is removed. If there is a defect in the cancellous bone, a bone cyst, or low cancellous bone density around the osteotomy site, an autologous bone graft is used, which is obtained from the bunionectomy bone or the removed fragments of the proximal bony prominence.  
Capsulorrhraphy is performed with the pronated toe in supination. If there is still deformity after distal Chevron MT osteotomy (DCMO) and accompanying soft tissue procedures, akin phalangeal osteotomy is performed based on the surgeon's judgment. The osteotomy is performed 7 mm below the proximal phalangeal base. Patients with no residual hallux pronation may undergo 1-3 mm medial closing wedge osteotomy, attempting to greenstick the lateral cortex. A complete osteotomy is performed for patients with residual toe pronation, followed by derotation.
## 3 Results and Reports
### Testing to Assess the Normality of Distribution
#### 3.5.1 Normality Test Using Statistical Metrics  
It is worth recalling that this process focused on data that fit the normal distribution. Otherwise, the first layer classification using SVM is sufficient to detect whether a medical entry is safe or considered as an anomaly. Therefore, in this case, the conclusions drawn during the 2 nd classification layer did not hold.  
The statistical algorithms listed below are efficient testing methods that return two measures: numerical values representing decision results called the \(p\)-value and s-value, which is equivalent to the critical statistical value of our distributions.  
* [PERSON] (AD);
* [PERSON] (SW);
* [PERSON] (KS);
* [PERSON] and Pearson (DP);  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **C** & **Gamma (y)** \\ \hline Dts\_1 & 10.1 & 0.01 \\ Dts\_2 & 20.2 & 0.01 \\ Dts\_3 & 40.4 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimized C and Gamma parameters for the radial basis function (RBF)-kernel.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **A1** & **A2** & **A3** & **A4** & **A5** & **Average (\%)** \\ \hline Dts\_1 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \(\pm\) (0.00) \\ Dts\_2 & 1.00 & 0.99 & 1.00 & 1.00 & 1.00 & 0.99 \(\pm\) (0.01) \\ Dts\_3 & 1.00 & 1.00 & 1.00 & 0.98 & 1.00 & 0.99 \(\pm\) (0.01) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Cross validation accuracies calculations.  
We applied all four of these algorithms in order to rule out the possibility that our data does not follow a normal distribution. Table 11 shows the results of testing of the patient samples.  
The results obtained from the normality test using AD, KS, SW and DP showed that there were no grounds to reject the null hypothesis that the data were normally distributed. The \(p\)-values returned by each statistical test could be interpreted as follows: Anderson-Darling (AD), Shapiro-Wilk (SW) and [PERSON] and Pearson (DP) must return a \(p\)-value greater than 0.05, while Kolmogorov-Smirnov (KS) must return a \(p\)-value smaller than 0.05. These were the standards that the \(p\)-value of each test must meet in order to validate our hypothesis.  
As can be seen from the table above, all four of the normality tests results show that our datasets fitted a normal distribution; the s-values and \(p\)-values indicate statistical significance and met the confirmation intervals
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 2 The Reliability Evaluation of PR Data by Comparisons with Ground-Based Instruments
### Comparisons of Quantitative Precipitation Estimation with Ground-Based Radars  
Quantitative precipitation estimation (QPE) can be used as criteria to evaluate the measurement of PR or GR since the QPE derived from reflectivity is a straight application of radar detection. Key information about the studies on QPE comparison of PR with GR is listed in Table 4.  
In general, the rain rates estimated by PR agreed well with GR on different spatial and temporal scales although PR missed the majority of light rains [41; 42; 43]. The major QPE error sources of PR included inaccurate \(Z\)-\(R\) relationship, Mie scattering, attenuation, and NUBF [44; 45].  
Figure 3: Scatterplots of radar reflectivity factors (top) and their probability density functions (bottom) at a height of 1.5 km derived from 210 TRMM overpasses over Melbourne for the period 1998-2007. “PR\({}_{\text{m}}\)” and “PR” refer to the PR-measured, and PR-corrected reflectivities, respectively. ([27] © American Meteorological Society. Used with permission.).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
## 4 Architecture Solution
### Tokenized Non-Fungible Asset Data Set Component  
Smart Contract transactions involving the OffChain Sequence for the NFT asset component involving RADAR data transfer are represented in Figure 7 for the initial bidding process, and the asset finalization process is explained as follows:  
* Data Demand Phase: In the initial phase, when the RADAR_OEM needs the RADAR data, it publishes the demand as an event notification transaction onto the network with the location, vehicle speed and acceptable price limit requirements. Vehicle_OEM can then respond to the event by broadcasting the demand to its vehicle clients for its participation contentment.
* Data Asset Offering and Bid Phase: Vehicle_OEM_X|Y responds individually by creating an ERC 721 token as an asset with asset criteria, floor, and roof price. Then, to maintain bid privacy, RADAR_OEM submits an encrypted bid with the public key of Vehicle_OEM_X|Y, respectively. Then, among the bids, one of RADAR_OEM_As is accepted by Vehicle_OEM_X as the bid of RADAR_OEM_B is not an acceptable price. The fund of RADAR_OEM_A for the bid is transferred to the escrow account as an intermediate transfer which is transferred to Vehicle_OEM_X when the bid criteria and the asset are respected along with necessary certification proof submissions.
* Data Aggregation and Certification: Vehicle_OEM_X starts the data collection job from its set of vehicle clients with the necessary criteria. Vehicles start recording RADAR data and submitting it with its signature hash as proof to the data pool and smart contract. Then, Vehicle_OEM_X processes the data removing the user's sensitive identification data, submits the final hash proof to the ledger, and generates a data access token to a middleware service to be retrieved later.
* OffChain Data Transfer and OAuth Access: The blockchain OffChain worker component then retrieves the OAuth access token from the middleware and submits it as an API POST request to RADAR_OEM_A if Vehicle_OEM_X submits all the certification proofs to the blockchain.
* Fund Transfer including Finalization of Asset Transfer: Then, the bid amount is transferred by the smart contract from escrow to the Vehicle_OEM_X account andVehicle_Accounts that participate in this monetization process such that the above condition of the access token and proof are respected.
* Review of transferred Asset: The RADAR_OEM_A that retrieves the processed data from the data pool utilizing the OAuth access token reviews the data. The result of the review process is submitted to the blockchain against the asset ERC721 token issued, which is a fidelity action to augment the price of the data issued by Vehicle_OEM_X in a later transaction or diminish if the review score is bad.
###### Abstract  
This paper aims to provide an experimental support on seismic performance evaluation of the steel braced truss-RC (reinforced concrete) column hybrid structure, which could be applied as the air-cooled supporting structural system in large-capacity thermal power plants located in strong earthquake prone regions. A series of pseudo-dynamic tests (PDTs) and quasi-static tests (QSTs) were performed on a 1/8-scaled sub-structure. The dynamic characteristics, lateral deformation patterns, deterioration behavior, hysteretic behavior and failure mechanisms were investigated. Test results showed that the first vibration mode is torsion, which is caused by the small torsional stiffness of this kind of hybrid structure. The lateral deformation shape is shear mode, and the drift ratio of the structure above the corbel is significantly less than that of the column below the corbel. Earthquake energy is mainly dissipated by the RC pipe columns where cracks mainly occurred at the bottom of column and lower part of corbel. The failure mechanisms were identified indicating that the steel braces improved the global stiffness and modified the load transfer mechanism. This study affirms that the steel braced truss-RC column hybrid structure has the sufficient ductility and good energy dissipation capacity to satisfy the design requirements in high seismic regions.  
steel-concrete hybrid structure; steel diagonal brace; pseudo-dynamic test; quasi-static test; seismic behavior +
Footnote †: journal: Applied Sciences
###### Abstract  
Soil heavy metals along roadsides pose a great threat to ecosystems while their spatial variations and influencing factors still remain unclear in some regions, especially in tropical areas with complex landscape characteristics. Our study was carried out to determine how the land use, vegetation characteristics, topographical factors and distance to the road affect the soil heavy metal distribution. Taking Jinghong county in Yunnan Province, Southwest China as a case, soil samples were collected at different distances off roads and canonical correspondence analysis (CCA) methods were used to determine the relative importance of different factors. Our results showed that heavy metal sources were obtained mainly from the road, based on the principle component analysis (PCA) identification. There were no obvious trends of soil quality index (SQI) with distance to the road in natural soils, while SQI nutrients and SQI metals in farmlands had a decreasing and increasing trend, respectively, which could both be expressed by logarithm models. However, soil properties showed little differences for road levels while they showed significant differences under land use types. The CCA further showed that heavy metal variations in natural soils were jointly affected by distance, plant coverage, relative elevation and soil properties in decreasing order.  
land use; heavy metal; CCA method; spatial variation +
Footnote †: journal: Applied Sciences
# Few-Shot Relation Extraction on Ancient Chinese Documents
## 2 Related Work
### Sentence Encoding  
Sentence encoding helps a machine encode a sentence from the original word embedding. The four types of encoder are as follows: a recurrent neural network (RNN)-based encoder [38], a CNN-based encoder [39], a transformer encoder [40], and their combinations. Encoders, such as LSTM with attention [41] or ConvS2S [42], can easily surpass the basic phrase-based encoder [43] with larger N-gram language models [44]. Meanwhile, ConvS2S is slightly better than the LSTM models. The transformer encoder [45] has better performance on language modeling but worse performance on a long-range dependency problem. These models can capture only temporal or spatial features of a sentence, which forces researchers to adopt a composite model.  
Composite models can capture more information. The CNN-RNN structured networks outperform conventional CNNs and BLSTM in terms of text semantic modeling. For example, the LSTM-CNN network [46] used a convolution network to form character-level representation. Then, the character-level representation was concatenated with word embedding to construct an LSTM input. The output result of the LSTM input was used as the final sequence feature, and it was sent to the CRF layer to accomplish a NER task. The performance of the LSTM-CNN network broke the record on the Penn Treebank WSJ and CoNLL-2003 datasets.  
However, the traditional RNN-based model can suffer from its sequential structure when dealing with long-term dependencies [47]. Accordingly, the Simultaneously Self-Attending model [48] adopts a novel encoder called BRAN, which is similar to the Conformer [49], to fix the above problems by replacing RNN with a self-attention mechanism. This encoder uses a transformer encoder structure with a convolution module and multi-head attention as each block component. An instance sentence passes through the novel encoder and is sent to two MLPs to produce head or tail position-specified sentence representation. The relation between the head and the tail is calculated by performing a bi-affine operation of two sentence representations. This method achieved state-of-the-art performance on the biocreative V chemical disease relation dataset.  
Although BRAN eliminates the problem that RNN brings in, this model still has problems. First, the original BRAN model uses a sequential combination of multi-head attention and convolution modules, which may lead to the loss of information during data transfer between two different modules. In our experimentation, the original model showed drawbacks on few-shot learning tasks. Our proposed MASCOT encoder relieved this problem through a parallel convolution and multi-head attention structure. In comparison with BRAN, MASCOT has better performance under the deeper layer, faster training speed through parallel structure, and stronger capability on capturing and storing features.
## 3 Methodology
### Implementation
#### 3.5.6 Baseline Method IV: Combined Historical and Calculated  
The authors propose a fourth, novel method. This method is a hybrid form and combines a highest x out of y (historical) baseline with a calculated correction based on the solar irradiance at the time. The highest x out of y baseline is implemented similar to baseline method III, taking the highest 3 out of 5 historical days. In addition, for each timestep, the historical measurements of the irradiance are captured. The average irradiance, corresponding with the highest 3 out of 5 measurements is then used to scale the baseline with the measurement at the timestep of activation.  
Equations (8) and (9) describe the baselining procedure, where \(x\) and \(y\) represent the highest x (i.e., 3) out of y (i.e., 5), \(T_{act}\) represents the list of activated timesteps, \(T_{max}\) is the list of timesteps (day, hour) of the highest \(x\) out of \(y\) days, \(l_{t}\) the irradiance at timestep \(t\), \(\overline{l}_{t}\) the average irradiance at timestep \(t\), and the average power \(\overline{P}_{t}\) is determined using Equation (6). Figure 6 illustrates this method.  
\[\overline{l_{t}}=\frac{1}{x}\sum\limits_{k=0}^{x}I_{k,jl}\quad(j,l)\in T_{max} \tag{8}\]  
\[P_{t}{}^{base}=\left\{\begin{array}{ll}\frac{l_{t}\overline{P}_{t}}{l_{t}} &if\qquad\quad t\in T_{act}\\ P_{t}{}^{meas}&otherwise\end{array}\right. \tag{9}\]  
Figure 5: Illustration of baseline III and its variables. The necessary data is taken per timestep, for the previous y days. As only x out of y historical days are included, not all historical values are needed.