esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Field Test Results
false alarms but the required SNR will inhibit the detection of valid targets. If the threshold is set too low, the large number of false alarms will mask the detection of valid targets. Based on this principle, we choose a false alarm rate as low as \(2\times 10^{-7}\) in the field test. Furthermore, the oversample rate in the Doppler axis is 2 and the down-sample rate in the time domain is 2, resulting in the size of the fusion RDM being 8192 \(\times\) 1024. Therefore, the expected false alarms in the detection region is approximately 2. False alarms are generated when thermal noise exceeds a pre-set threshold level. Due to the random characteristic of thermal noise, the false alarms probably occur in different range-Doppler cells in different trials. However, the real target will locate in the same or the adjacent range-Doppler cell in different observations. Accordingly, we can distinguish the false targets from the real ones by comparing two consecutive frames. The detection results in Figure 22 confirm the above analysis and the CFAR processing is able to control the false alarms. The ground truth of the target is obtained from the results without adding noise. We can find that the real detection is correlated while the false alarms are uncorrelated in different frames. Most importantly, the proposed method can successfully detect the target in two frames, while the MF-NCI-EL and MF-RFT without ME-PSO fail to detect the target. The performance of the MF-RFT-NCI is normal, as it can only detect the target in frame 1. For the sake of a better presentation of the detection results, the Doppler profile when \(R=36\) m is selected and zoomed at a range of \([-1000,\,-200]\) Hz, as shown in Figure 14:0 The detection threshold is also plotted in Figure 14:1 The performance of the MF-NCI-EL is the worst since the detection threshold is much higher than the Doppler profile. Therefore, the target cannot be successfully detected via MF-NCI-EL. The performance of the MF-RFT-NCI is acceptable, while the proposed MF-RFT-QCI achieves better performance as compared with the existing methods. These results confirm the effectiveness of the proposed method in the detection scenarios of weak MTD. Figure 14:2 The detection results based on different fusion methods when \(P_{fa}=2\times 10^{-7}\): (**a**) MF-NCI-EL; (**b**) MF-RFT-NCI; (**c**) MF-RFT without ME-PSO; (**d**) MF-RFT-QCI (proposed).
## 4 Methodology
### Nucleus-Based Cervical Lesion Detection Model  
The recognition of cervical lesions in cytological samples is performed by searching for cellular structures with abnormal morphological properties, typically associated with specific lesion levels [37]. Given the promising results obtained in [5] by a nucleus-based approach for automated adequacy assessment, that work was used as a reference starting point. In particular, this previous work provided a comparative analysis between three different meta-architecture/backbone combinations for the detection model: (i) SSD with a Mobilenet v2 backbone; (ii) RetinaNet with a Resnet50 backbone; and (iii) EfficientNet D0. The best-performing model for automatically detecting squamous nuclei was the RetinaNet Resnet50. This object detection model uses a ResNet50 backbone and a feature pyramid network (FPN) to capture multi-scale features, predicts object presence and class labels using anchor boxes at different feature map levels, and employs a focal loss to address class imbalance, giving more weight to challenging examples. Given this model's promising results and suitability for our target scenario, it was selected as the basis for the approach proposed in the present work. It should be noted that we aim to deploy and execute the model locally on the smartphone, so this model selection also took into account the lightness of the RetinaNet/ResNet50 architecture (i.e., it needs to be suitable for the computational power available on regular Android devices) and the availability of the model in the Tensorflow Object Detection API (to ensure model compatibility to run on Android devices by converting to TensorFlow Lite). Additionally, and similarly to [5], the momentum optimizer, the Huber localization loss, and the focal cross-entropy classification loss were also used to train the model in the new dataset of lesion nuclei described in  
Figure 4: General pipeline of the mobile-based solution for automated cervical cytology screening.  
Section 3.1. Details regarding the remaining training settings optimized in this work are provided in Sections 4.2.1 and 4.2.2.  
Moreover, considering the high impact of the model-centric optimizations that led to the best-performing model in [5], an analogous analysis was conducted for the trained nucleus-based lesion detection model, further explained in Sections 4.2.3 and 4.2.5.
## Appendix A  
Regarding \(\varepsilon>0\) and \(0<p<1,l_{1},l_{2}\) are defined in (25) and (26), correspondingly. With \(\beta_{1}>0\), in order to ensure \(\alpha_{1}>0\), \(\sigma_{1}\) and \(\sigma_{2}\) must be positive.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Tracking Error** & **Max** & **Min** & **RMS** \\ \hline \multirow{3}{*}{Proposed Controller} & \(e_{x}\) (m) & \(0.106\) & \(-0.017\) & \(0.068\) \\ \cline{2-5}  & \(e_{y}\) (m) & \(0.035\) & \(-0.1\) & \(0.016\) \\ \cline{2-5}  & \(e_{\theta}\) (rad) & \(0.134\) & \(-0.053\) & \(0.078\) \\ \hline \multirow{3}{*}{TRNC} & \(e_{x}\) (m) & \(0.17\) & \(-0.049\) & \(0.082\) \\ \cline{2-5}  & \(e_{y}\) (m) & \(0.106\) & \(-0.012\) & \(0.035\) \\ \cline{2-5}  & \(e_{\theta}\) (m) & \(0.18\) & \(-0.054\) & \(0.103\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative results between two controllers (max, min, and root mean square (RMS) value of tracking errors).  
**Proof:** For the case of \(\sigma_{1}\), we can obtain:  
\[\sigma_{1}=\frac{\varepsilon_{1}(2l_{1}-1)}{l_{1}}>0\] (A1)  
\[\Leftrightarrow\frac{(2l_{1}-1)}{l_{1}}>0\] (A2)  
\[\Leftrightarrow\left[\begin{array}{c}2l_{1}-1>0\\ l_{1}>0\\ 2l_{1}-1<0\\ l_{1}<0\\ \end{array},l_{1}\
eq 0\right.\] (A3)  
\[\Leftrightarrow\left[\begin{array}{c}l_{1}>\frac{1}{2}\\ l_{1}<0\\ \end{array}\right.\] (A4)  
From (25), we have  
\[(2-p)\varepsilon^{p-1}>\frac{1}{2}\] (A5)  
\[(2-p)\varepsilon^{p-1}<0\] (A6)  
It can be seen that (A6) is not satisfied. With \(\varepsilon>0\) and \(0<p<1\), to ensure that \(\sigma_{1}>0\) then,  
\[2(2-p)\varepsilon^{p-1}>1\] (A7)  
\[\Leftrightarrow 4\varepsilon^{p-1}>2(2-p)\varepsilon^{p-1}>2\varepsilon^{p-1}>1\] (A8)  
\[\Leftrightarrow\varepsilon^{p-1}>\frac{1}{2}\] (A9)  
For the case of \(\sigma_{2}\),  
\[\sigma_{1}=\frac{\varepsilon_{1}(2l_{1}-1)}{l_{1}}>0\] (A10)  
\[\Leftrightarrow\frac{(2l_{1}-1)}{l_{1}}>0\] (A11)  
\[\Leftrightarrow\left[\begin{array}{c}2l_{1}-1>0\\ l_{1}>0\\ 2l_{1}-1<0\\ l_{1}<0\\ \end{array},l_{1}\
eq 0\right.\] (A12)  
\[\Leftrightarrow\left[\begin{array}{c}l_{1}>\frac{1}{2}\\ l_{1}<0\\ \end{array}\right.\] (A13)  
From (26), it can be computed as  
\[\left[\begin{array}{c}(p-1)\varepsilon^{p-2}>\frac{1}{2}\\ (p-1)\varepsilon^{p-2}<0\\ \end{array}\right.\Leftrightarrow\left[\begin{array}{c}2(p-1)\varepsilon^{ p-2}>1\\ \varepsilon^{p-2}>0\\ \end{array}\right.\] (A14)  
With \(\varepsilon>0\) and \(0<p<1\),  
\[0<p<1\] (A15)  
\[\Leftrightarrow-2<2(p-1)<0\] (A16)  
\[\Leftrightarrow-2\varepsilon^{p-2}<2(p-1)\varepsilon^{p-2}<0\] (A17)  
To ensure that \(\sigma_{2}>0\),  
\[\varepsilon^{p-2}>0\] (A18)From (A9) and (A18), by choosing \(\varepsilon\) in order to meet \(\varepsilon^{p-1}>\frac{1}{2}\), \(\sigma_{1}\), \(\sigma_{2}\) will be larger than 0. \(\Box\)
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 4 Methodology
### Elimination of Problematic Articles  
This stage aims to develop a systematic pipeline to identify and filter out potentially problematic articles. Hence, we design an article elimination process following the similarity comparison to enhance the trustworthiness of our results, based on the observation that not all articles with high similarity scores necessarily represent reliable scientific contributions. We deploy a Random Forest classifier for this task, because of its ability to handle non-linear relationships and provide easy-to-understand feature importance for future analysis (see Layer 2 in Figure 1). Due to the characteristics of our data, the Random Forest classifier can help us solve the following problems. First, this model is effective at handling multiple features without making assumptions about the feature distributions. Second, this model demonstrates robustness against overfitting and shows the capability to capture complex interactions between different article metrics. Third, it provides feedback on feature importance to help identify the key indicators of article trustworthiness.  
In our design, the Random Forest classification model incorporates article metrics such as the journal impact factor, citation count (pre-retraction for retracted articles), and Altmetric scores. We split the dataset into training (80%) and testing (20%) sets, maintaining the class distribution through stratification. All features are then standardized using StandardScaler. We implement the model with 100 estimators and default parameters to establish a baseline, followed by hyperparameter fine-tuning using cross-validation to optimize model performance. To establish a filtering mechanism that could be applied to new articles during the user search interaction, the model is trained to classify articles into two categories: those likely to be problematic and those otherwise. To validate our approach, we conduct preliminary evaluations using standard machine learning metrics, such as precision, recall, F1-score, etc. Detailed performance metrics are provided in Section 5. Overall, this elimination process provides an additional layer of validation beyond the content similarity, creating a more robust and trustworthy result for the user.
## 3 Results and Analysis  
### Isolation and Screening of Cellulose-Degrading Bacteria  
A total of five strains with large transparent circle diameters were isolated from the piled white distillery lees (Table 2). The D/d of strain JZ2 is 2, and the ratio was the largest. The transparent circle produced by strain JZ2 was large and clear (Figure 1). Cellulase activity can be measured by the ratio of the transparent circle size to the colony size to a certain extent. However, further determination of cellulase activity was needed to rescreen. Measuring enzyme activity can provide a better indication of a strain's ability to produce cellulase.  
The strains selected via the initial screening were inoculated into a liquid enzyme-producing medium. We then determined endoglucanase activity, exoglucanase activity, and \(\beta\)-glucosidase activity in the fermentation broth. Table 3 shows the measurement results  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**The Level of**} \\ \cline{2-4}  & \(-\)**1** & **0** & **1** \\ \hline Time (d) & 1 & 2 & 3 \\ Rotation rate (\(\times\)\(g\)) & 170 & 180 & 190 \\ Temperature (\({}^{\circ}\)C) & 20 & 25 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Response surface factor level table.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**The Name of the**} & \multicolumn{1}{c}{**Diameter of Transparent**} & \multicolumn{1}{c}{**Diameter of the**} & \multirow{2}{*}{**Ratio (D/d)**} \\
**Strain** & **Ring. D/(mm)** & **Colony. d/(mm)** & \\ \hline JZ1 & 4.00 \(\pm\) 0.35 & 4.00 \(\pm\) 0.23 & 1.00 \(\pm\) 0.16 \({}^{\text{b}}\) \\ JZ2 & 16.00 \(\pm\) 0.47 & 8.00 \(\pm\) 0.51 & 2.00 \(\pm\) 0.08 \({}^{\text{a}}\) \\ JZ3 & 15.00 \(\pm\) 0.55 & 8.00 \(\pm\) 0.38 & 1.88 \(\pm\) 0.12 \({}^{\text{a}}\) \\ JZ4 & 20.00 \(\pm\) 0.42 & 18.00 \(\pm\) 0.44 & 1.11 \(\pm\) 0.17 \({}^{\text{b}}\) \\ JZ5 & 16.00 \(\pm\) 0.62 & 12.00 \(\pm\) 0.47 & 1.33 \(\pm\) 0.13 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Mean \(\pm\) SE, \({}^{\text{a,b}}\) Means with different superscripts within the same column are significantly different (\(p<\) 0.05).  
\end{table}
Table 2: Filter results of transparent circles.  
of enzyme activity. The enzyme activity of strain JZ2 was higher than in other strains. The values of exoglucanase activity, endoglucanase activity, and \(\beta\)-glucosidase active were 4.341 \(\pm\) 0.05 U/mL, 1.874 \(\pm\) 0.04 U/mL, and 0.739 \(\pm\) 0.02 U/mL, respectively. [PERSON] et al. [27] isolated and purified a strain of _Bacillus Vickers_ with cellulase production ability from distillery grains, producing exoglucanase, endoglucanase, and \(\beta\)-glucosidase activities of 2.66 U/mL, 0.60 U/mL, and 0.25 U/mL, respectively. The strains tested in this article had high enzyme activity, with the screened JZ2 strain having the highest activity. As a result, this has a huge advantage in the degradation of cellulose in distillery lees.
# Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising
###### Abstract  
Hyperspectral imaging (HSI) has been used in a wide range of applications in recent years. But in the process of image acquisition, hyperspectral images are subject to various types of noise interference. Noise reduction algorithms can be used to enhance the quality of images and make it easier to detect and analyze features of interest. To realize better image recovery, we propose a weighted group sparsity-regularized low-rank tensor ring decomposition (LRTRDGS) method for hyperspectral image recovery. Tensor ring decomposition can be utilized by this approach to investigate self-similarity and global spectral correlation. Furthermore, weighted group sparsity regularization can be employed to depict the sparsity structure of the group along the spectral dimension of the spatial difference image. Moreover, we solve the proposed model using a symmetric alternating direction method multiplier with the addition of a proximity term. The experimental data verify the effectiveness of our proposed method.  
HSI denoising; group sparsity; nonconvex relaxation; ADMM Article
## 2 Unified Modeling of LEO Satellite Constellation
### Model Architecture  
The architecture of the LEO constellation system model is shown in Figure 2, where each colored line with an arrow represents a specific type of interface [15].  
* Black solid lines represent the orbital information interface for all satellites, calculated by the constellation system model based the given constellation design parameters and then transmitted to the ground station and system control center via a satellite-to-ground link.
* Red solid lines represent the command interface of the satellite maneuvers, which are generated by the system control center according to the configuration changes of the constellation to control the on-off of the satellite orbital engine and maintain the constellation configuration.
* Blue solid lines represent the energy interface, through which the electrical power generated by the solar panel is sent to other subsystems.
* Brown solid lines mean the communication request interface, which contains the communication type and the latitude and longitude information of the originating terminal and the ending terminal.
* Orange dashed lines represent the link interface, which includes information such as link margin, time delay, bandwidth ratio, etc., calculated by the link models.  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Names** & **Functions** \\ \hline Ground station & Uplink and downlink data forwarding \\ System control center & Generating satellite maneuver command to maintain the stability of constellation configuration \\  & The simulation of the operational status of the four subsystems of satellite energy, control, propulsion and payload \\ User terminal & Generating communication requests and sending or receiving communication data \\ Satellite–ground link & The transmission of data between satellite and ground and the calculation of time delay and communication margin \\ Intersatellite link & The transmission of data between satellites and the calculation of time delay and communication margin \\ Constellation system & The calculation of the trajectories of each satellite within the constellation according to the constellation design parameters \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key components of satellite constellation system modeling.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 5 Performance Evaluation  
In this section, we evaluate the performance of the glider-assisted link disruption restoration mechanism. To verify the effectiveness of the glider-assisted link disruption restoration mechanism proposed in this paper, experiments will be conducted from the aspects of packet delivery rate, end-to-end delay, connectivity and communication energy consumption of the network. The glider-assisted link disruption restoration mechanism is compared with the classic underwater routing protocols vector-based forwarding (VBF) [26], hop-by-hop vector-based forwarding (HHVBF) [27] and OVAR [13]. In VBF, a vector from the source to the sink is build and acts as the axis of the \"routing pipe\". The data packet is transformed along the pipe. HH-VBF suggests the use of a routing vector for each individual forwarder in the network, instead of a single network-wide source-to-sink routing vector.
## 4 Proposed Model: RBF-Refined SVM Model
### Selection: Attribute  
The correlation feature evaluator evaluated and ranked the following sixteen features/attributes, as shown in Table 3, for selection: packet-sequence-number (#), time (s), bytes sent, sender latitude, sender longitude, sender speed (km/h), sender altitude (m), receiver latitude, receiver longitude, receiver speed, receiver altitude (m), packet received (Y/N), bytes received, signal strength, and noise strength. The ranking score was based on correlation with the attribute used to predict the class type, which confirmed whether the packets were received or not, with a Y/N or Boolean value. The ranking of attributes were ordered from maximum to minimum, based on their importance. The merit/ranking score ranged from 1 to 0, and was high (close to 1) in cases of certain attributes, as they had a higher correlation/association with the dependent variable. The choice of attribute usage was based on the ranking score, and attributes were given weight based on them predicting the dependent variable accordingly. The highest prioritized attribute, number of bytes received, was ranked with a merit score of 1 (strongly associated), and the next in priority was the signal strength attribute, with a score of 0.999 (strong association). Consequently, we had the noise strength attribute with a score of 0.982, and following this, an eventual decrease to 0 (weak association). The last three attributes in Table 3 could almost be totally neglected and were considered to be of no use. The use of these attributes in the prediction yielded no improvement in the outcomes.
## 4 Evaluation
### Performance Effect of Near Neighbor Entry Exchange  
Here, we evaluate the compression ratio (calculated by the compressed data size/the original data size) with varying distance \(d\) of the nearest neighbor entry exchange. Figure 6 shows the comparison of the compression ratio by varying \(d\) from 1 to 256. When \(d=256\), it is equivalent to the LRU (Least Recently Used) method. The smaller \(d\) provides much better complexity for hardware implementation, as we have discussed in the previous section. However, it is expected that the compression ratio should be better when the table operation is near the LRU method. According to the result, the effect of \(d\) is not large against compression ratio. The worst case shows about 8% difference between 1 and 256 of \(d\). This means that we can use \(d=1\) considering hardware implementation. Thus, \(d\) is not a serious parameter that affects the compression performance. From the next section, we use \(d=1\) and evaluate other sensitive parameters.
## 3 Results
### Model Estimations and Comparisons
#### 3.3.3 MEA-BPNN Model Prediction Results  
The threshold and weight optimization of BPNN were performed by using MEA. The parameters of BPNN were consistent with those in Section 3.3.2 to enable comparison. The prediction accuracy of metal Cr was the best, and its scatter distribution was closest to the 1:1 line. The prediction accuracies R\({}^{2}\) of Cr was 0.884 and the RPD value was 3.000. The relatively poor modelling results were detected in Cd and Pb, where R\({}^{2}\) values were 0.873, 0.857, and the RPD values were 2.114 and 2.560, respectively.
## 2 Materials and Methods
```
Input: Training validation set; hyperparameter \(\theta_{i}\), see a.3 in Algorithm 1 Output:\(H_{R}\) with hyperparameter \(\theta_{i}\), trained on training validation set  Randomly partition Training validation set\(V\) into L equal size subsets \(V=\{V_{1},V_{2},\ldots,V_{L}\}\) \(\text{a.3.1:}\) for\(l=1,2,\ldots,L\)do  Training set base model \(\gets V\smallsetminus V_{l}\) Test set base model \(\gets V_{l}\)  Training first level (base) models; T is the number of base models \(\text{a.3.1.1:}\) Repeat for \(t=1,2,\ldots,T\);  Train model \(h_{lt}\) on Training set base model  Construct a training set for second level meta-model \(\text{a.3.1.2:}\) Get predictions \(X_{metal}=\{h_{1l},h_{l2},\ldots,h_{lt}\}\) on Test set base model endfor a.3.2: Training second level meta-model  Train a new stacked regression \(H_{R}^{\cdot}\) on \(\text{collection}\{X_{metal},y_{l}\},l=1,2,\ldots,L\) \(\text{a.3.3:}\) Re-train first level base models \(\text{\bf for}\ t=1,2,\ldots,T\)do  Train first level base models \(h_{t}\) on Training_validation set \(V\) endfor a.3.4: Return \(H_{R}(x)=H_{R}^{\cdot}(h_{1}(x),h_{2}(x),\ldots,h_{T}(x))\)
```

**Algorithm 2** Extension of Step 1.1.a.3 of algorithm from Algorithm 1. Training stacked regression \(H_{R}\) with additional L fold cross validation

## 3 Experimental Evaluation

### Collected Data and Feature Engineering

Data collection was confined to a one-year period during which we aimed to collect as diverse data as possible. Consequently, data of 79 patients who underwent surgery were collected. In order to be included in the study, patients had to fully cooperate during the measurement of diaphragmatic movements. The presence of a primary cancer diagnosis was also necessary, as well as a complete assessment of the functional status and overall cardiorespiratory risk. Movements of both hemidiaphragms were measured radiographically and ultrasonography, along with muscle strength tests and respiratory function, preoperatively. In the initial stage of feature selection, we chose features that reflected the general medical state of the patient, including measurements related to diaphragm movement, as well as those necessary for prediction of FEV1 using traditional methods. Subsequently, domain experts performed additional filtration of the obtained feature set. This involved elimination of highly dependent features, as well as those already identified as lacking significant predictive value for FEV1. Further dimensionality reduction methods based on feature transformation (e.g., PCA) was not performed to maintain model interpretability. After data collection and feature selection, we were left with 25 most important variables, which were used to design the prediction model. Among them, 15 features were represented with absolute value, and the other 10 are represented with absolute and relative value, expressed as a percentage, see Table 1.