esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 4 Experimental Results and Analysis
### Performance of Feature Selection  
First, the ML-ReliefF, RF-ML and UML-ReliefF algorithms are evaluated, and the experimental results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature ID** & **Feature Name** & **ML-ReliefF** & **RF-ML** & **UML-ReliefF** \\ \hline
9 & Website Name & 0.332 & 0.457 & 2.593 \\
10 & Website IP & 0.247 & 0.802 & 2.344 \\
3 & TAC & 0.271 & 0.473 & 1.377 \\
13 & Phone Model & 0.402 & 1.861 & 0.734 \\
11 & DNS IP & 0.065 & 0.185 & 0.453 \\
12 & Phone ID & 0.473 & 2.491 & 0.381 \\
4 & CellID & 0.407 & 2.484 & 0.348 \\
6 & Latitude & 0.062 & 0.889 & 0.348 \\
5 & Longitude & 0.054 & 1.507 & 0.283 \\
7 & RSRP & 0.054 & 0.765 & 0.122 \\
1 & Date & 0.034 & 0.52 & 0.119 \\
8 & RSRQ & 0.029 & 0.567 & 0.102 \\
2 & Time & 0.035 & 0.359 & 0.088 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of feature weights of the feature selection (FS) algorithms.  
Figure 2: Spatial distribution of data samples.  
In Table 1, the feature weights of UML-ReliefF are listed in the descending order. It can be seen that the weights achieved by different algorithms are significantly different, in both the ranking and weight concentration aspects. By summarizing the weights of Top N features (as shown in Table 2), it is found that the weighting concentration of UML-ReliefF is the highest. For example, the total weight of Top 4 features, i.e., the features with ID = [9, 10, 3, 13], exceeds 75%.  
In addition, the three FS algorithms also have different convergence rates during the weight iterations (as shown in Figure 3). It can be seen that RF-ML converges after about 500 iterations, which is the fastest, while UML-ReliefF converges at about 1000 iterations, and ML-ReliefF does not converge even after 2000 iterations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Features** & **ML-ReliefF** & **RF-ML** & **UML-ReliefF** \\ \hline
1 & 19.19 & 18.65 & 27.91 \\
4 & 65.48 & 62.45 & 75.86 \\
7 & 89.13 & 80.83 & 88.59 \\
10 & 96.03 & 92.50 & 96.70 \\
13 & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weight concentration of top N features (%).  
Figure 3: _Cont._  
The impact of feature selection algorithms on the KQI prediction is evaluated and analyzed by the following experiments.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 5 Algorithm Design
### Sub-Policy Forwarding Phase  
As shown in Algorithm 2, when a node carrying copies of a message encounters a new node, it adopts a different forwarding strategy until it reaches the destination node, depending on whether the new node contains the destination node in its Relation Tree:  
1. If the Relation Tree of the new node does not contain the destination node, half of the message copies carried by themselves (rounded down) are forwarded to the new node. This forwarding strategy uses halving propagation to improve the efficiency of breadth-first forwarding among unfamiliar nodes.
2. If the Relation Tree of the new node contains the destination node, all the message copies carried are copied and forwarded to the new node, and the survival time (\(TTL_{new}\)) of these newly copied generated message copies is shown in Equation (3). Where \(M<T\), ensuring \(TTL_{new}<TTL_{old}\), because the destination node is in the Relation Tree of the new node, the encounter expectation is higher, and the message copies generated by the new replication only need a smaller survival time to avoid wasting network resources. This forwarding strategy can effectively accelerate the forwarding efficiency in the final stage of messages by exploiting the flooding effect while keeping the negative impact of the flooding effect in a localized range within a short period of time.  
\[TTL_{new}=TTL_{old}*\frac{M}{T} \tag{3}\]  
The purpose of using different forwarding policies is to perform more targeted forwarding based on the social attributes of each node to reduce the impact of flooding and reduce network overhead while improving forwarding efficiency and reducing delay.  
```
1:\(V=\{v_{i}|1<i<n\}\), set of nodes in the network \(N(v_{i},v_{j})\big{|}v_{i}\in V,v_{j}\in V\big{)}\), the number of times \(v_{i}\) and \(v_{j}\) meet in a memory cycle T \(RT_{i}\), the relation tree of node \(v_{i}\) \(RT_{j}\), the relation tree of node \(v_{j}\) M, relationship threshold T, memory cycle \(L_{i}(m_{a})\), the number of copies of \(m_{a}\) carried by node \(v_{i}\)
1:if\(v_{i}\) encounters \(v_{j}\)then
2:for each message \(m_{a}\) in \(v_{i}\)do
3:if\(m_{a}.TTL>0\)then
4:\(v_{d}=m_{d}^{\prime}\) destination
5:if\(v_{j}==v_{d}\)then
6:\(v_{i}\) directly forwards \(m_{a}\) to \(v_{j}\)
7:elseif\(v_{d}\in RT_{j}\)then
8:\(L_{j}(m_{a})=L_{i}(m_{a})\)
9:\(v_{j}.m_{a}.TTL=v_{j}.m_{a}.TTL*M/T\)
10:else
11:\(L_{j}(m_{a})=\lfloor L_{i}(m_{a})/2\rfloor\)
12:\(L_{j}(m_{a})=\lceil L_{i}(m_{a})/2\rceil\)
13:endif
14:endif
15:endfor
16:endif
```  
**Algorithm 2** Forwarding Phase
## 5 Effects of ABH Parameters and Damping Layer
### Effect of the ABH Orientation  
Due to the axial symmetry, one side of the uniform thickness plate facing to the anechoic chamber is the same as the other side which is facing to the reverberation chamber. However, in the analysis of the sound transmission loss of the circular plate embedded with ABH, since the circular plate no longer had axial symmetry, the side with the ABH region could be placed on one side of the anechoic chamber or on the side of the reverberation chamber. For each plate of a different ABH diameter, the sound transmission loss values of the circular plates when the ABH region was toward the side of the anechoic chamber were compared with those values of the plates when the ABH region was toward the side of the reverberation chamber, as shown in Figure 10. It can be seen from Figure 10 that for the circular plate with the acoustic black hole with the diameter of 10 mm-60 mm in the center, the effect of ABH orientation change on the TL of the circular plate embedded with ABH is below 0.05 dB, and in the range of 100-3000 Hz, the TL change caused by the orientation of the ABH region is within 0.02 dB. It can be further concluded that for a circular plate structure embedded with single ABH, the orientation of the ABH region has little effect on the sound transmission loss of the plate.  
Figure 9: TL of circular plates with different ABH diameters: (**a**) ABH diameter of 10–20 mm; (**b**) ABH diameter of 30–50 mm.
## 4 Results and Discussion  
### Experimental Setup  
This section describes the dataset used and the evaluation methodology used to obtain the results of the next section. Our data set contains 47 patients (27 KRAS+ and 20 KRAS-) from the CHUS University hospital in Santiago de Compostela. The inclusion criteria were as follows: (1) patients with colorectal cancer with determination of the KRAS mutation by biopsy between 2016 and 2017; (2) selection of those patients whose intravenous contrast CT dated before any treatment; (3) obtaining the CT images with a thickness of slice of 5 mm. From each patient, an experienced radiologist selects the slice with the largest transverse diameter of the tumour (central slice) and the slices immediately above and below it. From these three slices, the radiologist takes four images of each of the slices, two of which are manually outlined. The total number of images (slices) is 141 (47 patients multiplied by 3 cuts per patient).  
As mentioned, we use the LOPO (leave-one-patient-out) cross-validation approach. To develop the experiment, \(N\) partitions were created, being \(N\) the number of patients, each including a training, a validation and a test set. For each partition \(i=1,\ldots,N\), the three slices belonging to the \(i\)-th patient are used as test set, and the remaining patients (three slices per patient) are distributed into the training and validation sets (50% of patients each one). All the inputs, that correspond to the different texture feature vectors extracted from the image, are pre-processed to have zero mean and standard deviation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **KRAS+** & **KRAS-** \\ \hline \multirow{2}{*}{**True class**} & **KRAS+** & TP & FP \\  & **KRAS-** & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representation of a confusion matrix used to visualize the classifier performance. Each column in the matrix represents the instances in the predicted class while each row represents the instances in the true class.  
one. For each partition, \(i=1,\ldots,N\), the classifier is trained on the training set for each combination of hyper-parameter values and tested on its corresponding validation set. The combination that achieves the highest kappa on the validation set is selected. Then, the classifier is trained using jointly the training and validation sets, using the selected hyper-parameter values, and tested on the test set, composed by the three images (slices) of the patient that has been left out. The classifier prediction for this patient is the class that has been most voted over the three slices. This process is repeated for the \(N\) partitions to achieve the predicted class (KRAS+ or KRAS-) for each patient. Once this prediction is known, the kappa statistic of the classifier is calculated to measure its agreement with the true class labels.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 3 Emulator-Based Approach for Investigating TCP Performance in a Wi-Fi Network
### Congestion Control Algorithms  
A TCP sender uses a CCA to detect congestion and control the rate at which packets are sent in a network. The sending rate is adjusted following the congestion control window (i.e., cwnd). If there are overprovisioned packets, cwnd is reduced to suppress the traffic. TCP can reduce the number of lost packets and effectively utilize the available bandwidth. TCP CCAs can be roughly classified into three types: loss-based, delay-based, and hybrid algorithms. Algorithms of the first type, which detect congestion based on packet loss, reduce cwnd when packet loss occurs. Delay-based algorithms recognize congestion occurrence based on a delay-related parameter (i.e., the RTT). Hybrid algorithms identify congestion using both RTT and packet loss. Since the adjustment of cwnd differs for different CCAs, the algorithm with the best performance may change if the network changes. This work investigates 14 algorithms, including six loss-based CCAs, three delay-based CCAs, and five hybrid CCAs.
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
## 6 Conclusions  
This paper introduces an internal BP architecture for port-multiplexed crosspoint-buffered switches. Within the constraints of limited hardware resources, the approach consolidates multiple input/output ports onto a singular internal bus, enabling interconnection among various input/output modules via at the crosspoints. Subsequently, multiple BP schemes are integrated, positioned within the buffer queues attached at the egress ports. When several data streams concurrently transmit data towards a single queue, reaching a state of saturation, a BP signal is sent to the input side to avert data overflow. This dynamic adjustment of network traffic load alleviates the congestion issues, preventing buffer queue overload. This paper describes the primary structural design of the system in great detail and presents a systematic analysis of the performance of different BP mechanisms by simulations. Incorporating the dynamic threshold-based queue management strategy, under a non-uniform traffic distribution model, the BP load balance mechanism achieves a reduction in packet loss ranging from 12% to 44%, and the throughput rate can reach 89%, at the cost of merely a 3.5% increase in packet delay. This scheme observably improves the traffic congestion issues blocked by buffer overload as a consequence of port multiplexing. In future research, we will explore BP schemes under different switching architectures, such as three-stage CLOS switching networks or higher-radix switching architectures. Additionally, we will further investigate the impact of traffic duration on BP mechanisms.  
Figure 10: (**a**) The influence of \(\omega\) on the packet loss rate under different queue management mechanisms. (**b**) The influence of \(\omega\) on the throughput rate under different queue management mechanisms.  
**Author Contributions:** Conceptualization, L.Z., W.W. and Y.F.; methodology, L.Z. and W.W.; software, L.Z. and W.W.; validation, L.Z. and W.W.; investigation, L.Z. and W.W.; resources, W.P.; writing--original draft preparation, W.W.; writing--review and editing, L.Z. and W.W.; visualization, L.Z. and Y.F.; supervision, Y.F. and W.P.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62102314 and the Natural Science Basic Research Program of Shaanxi Province under Grant 2021 JQ-708 and 2022 JQ-635.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Datasets  
#### 2.1.1 CINEDE Dataset  
The CINEDE dataset used in this work consists of 124 patients with Cine and DE MRI, as shown in Figure 1.  
The exams were acquired using 1.5 T and 3 T magnets (Siemens Healthineers, Erlangen, Germany) with a phased thoracic coil. DE MRI was acquired 10 min after the injection of a gadolinium-based contrast agent at a concentration of 0.1 or 0.2 mmol/kg. A T1-weighted phase-sensitive inversion recovery (PSIR) sequence was used (TR = 3.5 ms, TE = 1.42 ms, TI = 400 ms, flip angle = 20\({}^{\circ}\)). Cine MRI was performed using a breath-hold SSFP sequence (TR = 3.7 ms, TE = 1.6 ms, flip angle = 20\({}^{\circ}\)). The number of slices in short-axis orientation per volume varies between 7 and 12, with a total of 984 2D slices in short-axisorientation. The median voxel spacing in the x, y, and z axes for the Cine and DE modalities are \([1.367,1.367,10]\) mm\({}^{3}\) and \([1.875,1.875,10]\) mm\({}^{3}\), respectively. All images have their corresponding manual annotations (ground truth) with two labels: left ventricle cavity and myocardium, corresponding to the epicardial border of the muscle.  
The dataset represents cases from five different conditions, namely, normal examinations, dilated cardiomyopathy, myocarditis, myocardial infarction, hypertrophic cardiomyopathy, and other pathology. This diversity in the dataset translates to a large set of distinct features that the model must learn as they might impact how the anatomical structures appear, thus increasing the difficulty of the segmentation task. The sample distribution among different classes is uneven.
# Seismic Response of Aeolian Sand High Embankment Slopes in Shaking Table Tests
###### Abstract  
Aeolian sand high embankments are always damaged by earthquakes; however, little research has addressed this so far. In this study, shaking table tests were conducted on three aeolian sand high embankment models. Based on the shear failure mechanism of aeolian sand, the seismic responses of model embankments were analyzed. When seismic waves were inputted, the horizontal acceleration magnification (HAM) of three models always exceeded 1.0, and showed an increasing trend with height. Furthermore, according to the HAM change rules of three models under different input peak accelerations, the destruction of model embankments under earthquakes includes three stages: the reflected wave emergence (RWE) stage, the reflected wave strengthening (RWS) stage, and the acceleration magnification attenuation (AMA) stage. According to this definition, models with slopes of 1/1.2 and 1/0.8 experienced all three stages during tests, and the critical horizontal acceleration transform from the RWS stage to the AMA stage appeared. The model with a slope of 1/1.5 only experienced RWE and RWS stages during the test. At the end of the tests, the macroscopic instability mechanisms of all three models were studied, which were found to match the distribution law of HAM during tests and the destruction stage definition.  
Keywords:seismic response; aeolian sand high embankment; shaking table test; instability mechanism +
Footnote †: journal: Applied Sciences
# Effect of Nonlinear Hysteresis Details of Isolation System on In-Structure Response Spectra
## 4 ISRS According to the Initial Stiffness of Isolator
### ISRS for Different Seismic Intensities  
Because the maximum displacement varies depending on the seismic intensity and may affect the ISRS due to changes in the hysteresis behavior, the ISRS variability was analyzed by inputting different seismic intensities to the structure to which an isolator with a stiffness ratio of 0.01 was applied. The ISRS results analyzed by inputting motions with PGAs of 0.2 g, 0.3 g, 0.4 g and 0.5 g are shown in Figure 16. As confirmed in the previous results, large responses occurred at stories far from the middle story, the center of rotation of the structure, so only responses of 0-1F and 5F were depicted. As the seismic intensity increased, the response in the low-frequency range, including the isolation frequency increased significantly. Although the response increased with the seismic intensity in the high-frequency range, the natural frequency of the structure was not affected by the seismic intensity. As can be seen in Figure 17, as the seismic intensity increases, the hysteresis behavior increases and the isolation frequency decreases, so the response in the low-frequency range increased. And because the force required in the elastic range before yielding was the same, the response at the natural frequency of the structure appears to be the same. That is, it can be seen that the seismic intensity has a large effect in the low-frequency range where the isolation frequency is included, but at the high-frequency range where the natural frequency of the structure is included, the initial stiffness acts as a larger variable. Figure 18 is a graph showing the ISRS according to the seismic intensity for each stiffness ratio at 0F and 5F. In the low-frequency range, the same variability was observed depending on the intensity regardless of the stiffness ratio. In the high-frequency range, except for the stiffness ratio of 0.01, the response increased as the intensity increased, but the variability was less than that of the low-frequency range. In the case of the stiffness ratio of 0.01 that the initial stiffness was very large, the response at the natural frequency of the structure was similar regardless of the intensity, and it was confirmed again that the initial stiffness had a significant influence on the high-frequency range. Although the frequency with a peak in the low-frequency range tends to change and the response increases with the seismic intensity, this appears to be enveloped by the largest intensity. It is judged that even if the response in the low-frequency range is significantly affected by the intensity, it does not need to be considered when developing the design ISRS.  
Figure 16: ISRS according to seismic intensity (\(k_{d}/k_{u}=0.01\)).
## 1 Introduction
The issue of a large number of alarms not only exists in the wind power domain but in many other domains, such as network management [13], IT security [14], and process control in manufacturing systems [15]. Researchers and vendors proposed many approaches to alarm reduction and correlation [16]. A similarity analysis is one of the most common methods used in analyzing alarms. It aims to reduce the total number of alarms by aggregating them using their similarities. This strategy is based on the assumption that similar alarms tend to have the same root causes. The key step in a similarity analysis is to define appropriate similarity measures. An analysis method was proposed in [17] to investigate similar alarm floods from historic alarms. The dissimilarity score between each pair of alarm floods was calculated using the Jaccard distance. The similar floods were clustered into groups using the Agglomerative Hierarchical Clustering algorithm. The results showed that the grouping of alarm floods could be used to eliminate sequential alarms and ascertain rationalization suggestions. An online algorithm was proposed in [18] to provide an early prediction of an incoming alarm flood. The alarm sequences were clustered into similar groups based on their similarity scores. A pattern database of the common patterns was formed for each cluster. The online alarm sequence was matched with these patterns. A method to extract a fault template from a set of alarm lists, raised on the occurrence of several faults, was proposed in [19; 20]. The fault template could be used to extract relevant information on the alarm system and by operators as a guideline for fault diagnosis. The proposed fault isolation method was based on a weighted sequential similarity measure. Motivated by the shortcomings of the existing methods in the wind power domain, this paper proposes an online fault diagnosis method for SCADA alarms based on a similarity analysis. The proposed method does not require a time-consuming training procedure and is accessed via an easy online application. Moreover, it is not over-reliant on the available data. As the data increase, the performance of the proposed method becomes self-optimizing. Our main contributions are as follows:  
1. We segment alarms into alarm lists by an information alarm and represent alarm lists by vectors to simplify alarms;We extract the feature vector from alarm vectors as a unique signature representing an occurring fault;  
We define the weights of alarms to establish the coupling correspondence between alarms and faults. The remainder of this paper is organized as follows: Section 2 introduces SCADA alarms and maintenance logs; Section 3 presents the online root fault identification method; Section 4 presents the results and the discussion; and Section 5 presents the conclusions.