esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Analyzing the Influence of Using Islands
The _p_-values of the comparison of the 3-island method with respect to 4, 2, and 1 islands respectively are 0.3400, 0.4008, 0.4647 respectively, and [PERSON]'s procedure requires that these values are iteratively lower than 0.0167, 0.0250 and 0.0500, respectively. Since the condition does not hold for the first comparison, the three null hypotheses are accepted, so all methods with 4, 2, and 1 islands are pairwise considered equivalent to using 3 islands. Note that, although we cannot find relevant  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **1 Island** & **2 Islands** & **3 Islands** & **4 Islands** \\ \hline \(f_{1}(x)\) & \(1.02\times 10^{-4}\) & \(2.91\times 10^{-4}\) & \(9.54\times 10^{-4}\) & \(1.22\times 10^{-3}\) \\ \(f_{2}(x)\) & \(8.29\times 10^{-3}\) & \(1.15\times 10^{-2}\) & \(1.18\times 10^{-2}\) & \(1.47\times 10^{-2}\) \\ \(f_{3}(x)\) & \(1.93\times 10^{-5}\) & \(5.40\times 10^{-5}\) & \(6.55\times 10^{-5}\) & \(1.57\times 10^{-4}\) \\ \(f_{4}(x)\) & \(1.45\times 10^{-3}\) & \(1.69\times 10^{-3}\) & \(1.57\times 10^{-3}\) & \(1.60\times 10^{-3}\) \\ \(f_{5}(x)\) & 26.57 & 8.56 & 12.96 & 14.01 \\ \(f_{6}(x)\) & 0 & 0 & 0 & 0 \\ \(f_{8}(x)\) & \(-9686.99\) & \(-9699.22\) & \(-9892.67\) & \(-10,126.50\) \\ \(f_{9}(x)\) & \(6.97\times 10^{-8}\) & \(2.28\times 10^{-7}\) & \(1.19\times 10^{-6}\) & \(2.04\times 10^{-6}\) \\ \(f_{10}(x)\) & \(2.41\times 10^{-3}\) & \(3.98\times 10^{-3}\) & \(3.98\times 10^{-3}\) & \(6.65\times 10^{-3}\) \\ \(f_{11}(x)\) & \(4.69\times 10^{-3}\) & \(5.59\times 10^{-3}\) & \(5.88\times 10^{-3}\) & \(5.86\times 10^{-3}\) \\differences when comparing them pairwise, when we consider the four methods together the differences are found by the test. Now we consider the Quade test. This time the \(p\)-value is 0.0228, so the test concludes that using 1, 2, 3 or 4 islands is not (globally) equivalent. The ranks given by Quade test to these methods are 2.3125, 2.4522, 2.3346 and 2.9007, respectively. Thus, the control method is 1 island. The \(p\)-values of the comparison of 1 island with 4, 3, and 2 islands are 0.3550, 0.8261 and 0.9723, and [PERSON]'s procedure requires that these values are lower than 0.0167, 0.0250 and 0.0500, respectively. Again, the first comparison fails, so all null hypotheses are accepted. Thus, methods with 4, 3 and 2 islands are pairwise considered equivalent to using 1 island.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 3 Advances in Physical Modeling Method
### n-g Modeling  
According to [PERSON] et al. (2015) [66], geotechnical centrifuge modeling technology has undergone multiple stages of development over the previous century. The first documented tests were performed in the United States and the Soviet Union in the 1930s, and across the next four decades, early pioneers from all across the world made significant achievements. In the years between 1970 and 1980, rapid developments were made in centrifuge instruments and modeling methods, resulting in increased application and understanding of centrifuge modeling potential in the wider civil engineering domain. Centrifuge modeling resolved a significant difficulty in geotechnical structure modeling by maintaining true stress conditions, as most soil attributes rely on effective confining stress. Models evaluated at 1-g provided only qualitative details because their soil attributes (such as stiffness, strength, and dilatancy) varies greatly from full-scale situations. Increasing the gravitation field in centrifuge tests assured that the model and full-scale prototype have similar stress responses, making the model's response to diverse loadings more reflective of real-world situations. Some examples of recently performed centrifuge modeling tests to simulate open caisson shafts are discussed in detail in the upcoming sections.
# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
## 6 Conclusions and Prospect  
This paper proposes a TCAG model that uses a convolutional neural network to extract the spatio-temporal features, uses topological data analysis to extract the topological features, and then combines the spatio-temporal features and topological features to train the attention GRU neural network model. Finally, according to the prediction results of the GRU model, the dynamic threshold interval was calculated for the early fault warning of  
Figure 12: GRU model prediction results.  
Figure 13: Dynamic threshold interval diagram.  
the information equipment. Based on the experimental results, the following conclusions can be drawn:  
1. The proposed dynamic threshold-setting mechanism solves the problem of early fault warning in AIOps. The mechanism calculates the threshold interval automatically adjusted with scene change according to the prediction results and can fully consider the prediction error caused by step growth, which has great practical significance.
2. Traditional feature extraction methods are difficult to adapt to instability and nonlinear structures in a time series. To solve this challenge, spatiotemporal features and topological features were combined to train the attention GRU neural network model. Compared with the original, TDA, ICA, and TSNE features, the TCAG model was able to extract the most concise and effective feature expression in the data and significantly improve the prediction effect of the model.
3. To verify the prediction performance of the TCAG model, comparative experiments were carried out using MLP, TCN, LSTM, and GRU models. The results showed that the TCAG model is superior to the benchmark model in various evaluation indexes and obtains the best prediction accuracy and robust performance. It is worth mentioning that the average absolute percentage error increased by 8.96% compared with that of the MLP model.  
In this study, the early fault warning of AIOps was extensively studied and achieved ideal results. Future work will explore other directions of AIOps (abnormal monitoring, fault self-healing, etc.) and will continue to contribute to the improvement of the intelligent degree of operation and maintenance.  
Methodology, Z.C.; Supervision, L.Z.; Writing--original draft, K.W.; Writing--review & editing, Y.T. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by The Major Program of the National Natural Science Foundation of China (71633006): Technological Innovation 2030-Subproject of \"New Generation Artificial Intelligence\" Major Project (2020 AAA0109605); State Grid Ningxia Electric Power Co., LTD. Science and Technology project \"Research on key technologies of AIOps for intelligent system based on composite AI algorithm\" (project number: 5229 XT20003T).  
Not applicable.  
The authors declare no conflict of interest.
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
###### Abstract  
Floating-point division is indispensable and becoming increasingly important in many modern applications. To improve speed performance of floating-point division in actual microprocessors, this paper proposes a low-latency architecture with a multi-precision architecture for floating-point division which will meet the IEEE-754 standard. There are three parts in the floating-point division design: pre-configuration, mantissa division, and quotient normalization. In the part of mantissa division, based on the fast division algorithm, a Predict-Correct algorithm is employed which brings about more partial quotient bits per cycle without consuming too much circuit area. Detailed analysis is presented to support the guaranteed accuracy per cycle with no restriction to specific parameters. In the synthesis using TSMC, 90 nm standard cell library, the results show that the proposed architecture has \(\approx\)63.6% latency, \(\approx\)30.23% total time (latency \(\times\) period), \(\approx\)31.8% total energy (power \(\times\) latency \(\times\) period), and \(\approx\)44.6% efficient average energy (power \(\times\) latency \(\times\) period/efficient length) overhead over the latest floating-point division structure. In terms of latency, the proposed division architecture is much faster than several classic processors.  
arithmetic; floating-point division; low latency; hardware configurable architecture +
Footnote †: journal: Applied  
1  
[PERSON]  
1 Shenzhen Key Laboratory of IoT Key Technology, Harbin Institute of Technology, Shenzhen 518000, China; [EMAIL_ADDRESS] (J.X.); [EMAIL_ADDRESS] (W.F)  
School of Microelectronics, Shenzhen Institute of Information Technology, Shenzhen 518000, China  
Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (M.W.); 1-86-0755-8655-5455 (M.W).1
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 3 Path Segmentation-Based Hybrid Caching
### Caching in Non-Edge Area
#### 3.4.1 Migration Node Selection  
We select the migration node by calculating and comparing the cache pressure value \(CPV_{i}\) for each cache node \(i\) in the non-edge area. When calculating the cache pressure value for the cache node, we consider three parameters: cache space utilization, cache utilization, and cache replacement rate. Cache space utilization is the main indicator of the node cache status. The cache spaces of most cache nodes are close to saturation when the network is stable; because of this, we combine the cache space utilization with the cache replacement rate (the ratio of the content replaced to the total cache in a period) to measure the node cache status, and at the same time, we combine the cache utilization (the ratio of the hit content to the total cache in a period) to calculate cache pressure value \(CPV_{i}\). The calculation formula of the cache pressure value \(CPV_{i}\) of node \(i\) is as follows:  
\[CPV_{i}=\frac{UA(i)}{OP(i)+RP(i)} \tag{4}\]  
where \(UA(i)=\frac{\sum_{j}^{n,\mathit{min}}c(j)}{CS(i)}\), \(OP(i)=\frac{\sum_{j}^{n,\mathit{cache}}c(j)}{CS(i)}\), and \(RP(i)=\frac{\sum_{j}^{n,\mathit{replace}}c(j)}{CS(i)}\) represent cache utilization, cache space utilization, and cache replacement rate, respectively. \(CS(i)\) is the total cache size, and \(c(j)\) is the size of each content.  
When the data packet passes from the hit node to the first node in the non-edge area, its address and cache pressure value are added to the data packet. As described in Algorithm 2, the subsequent cache node in the non-edge area calculates its cache pressure value and compares it with the cache pressure value recorded in the data packet, which updates the corresponding information of the packet record if smaller. In this way, when the data packet is delivered to the user-edge area, the packet will record the node address that is most suitable for migration. This path competition-based migration node selection avoids additional communication overhead relative to other migration node selection mechanisms [16; 17; 18].
```
current node: \(i\)
1: for any incoming content \(k\)
2:\(CPV_{i}=\frac{UA(i)}{OP(i)+RP(i)}\)
3:if\(CPV_{i}<k.cache_pressure\)then
4:\(k.cache_pressure=CPV_{i}\)
5:\(k.migrate_node_address=i\)
6:endif
```
**Algorithm 2** Migration Node Selection
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Mobility Management  
In upcoming HetNets, mobility control of drones will be a critical aspect that requires thorough analysis. A major risk exists since drones rapidly move in three dimensions with high speed and different characterizations [114; 137; 138]. This increases the handover probability and may lead to increasing the handover ping-pong effect and RLFs. Another significant issue during drone movement is the use of the mm-wave spectrum and terahertz band; this use in next-generation networks is discussed in [141]. The rapid development and massive growth of drones and mobile networks will further exacerbate the problem since load balancing will be a critical factor, necessitating an appropriate solution. The case becomes more critical if no optimal and efficient handover mechanisms are used. Therefore, managing the connection during drone mobility must be adequately emphasized in future networks.
## 2 Related Work
### ID-Based BP  
The third group comprises schemes that utilize an identity (ID) approach based on a bilinear pair (BP) to overcome the member revocation problem of GS-based approaches [25; 26; 27; 28; 29; 30]. This approach supports a batch verification process to verify multiple messages simultaneously. However, the operations of bilinear pairs in this approach are time-consuming and complex, which introduce huge system overheads in signing and verifying messages.
## 3 Main Technical Research
### Study on Multi-Modal Data Fusion Technology for Obstacle Avoidance System  
In order to quickly and intuitively view the warning information, the fusion display of point cloud and camera data is particularly important. Therefore, how to effectively merge point cloud and camera data is the focus of this research. This paper proposes a multi-modal data fusion scheme based on time synchronization and spatial registration, addressing the issue of fusing LiDAR and camera data within the system.  
Figure 8: The triangular frame point cloud before and after the OBB algorithm. (**a**) Triangular frame point cloud without OBB algorithm. (**b**) Triangular frame point cloud with OBB algorithm.  
Figure 7: Flowchart of Euclidean Clustering Algorithm.  
In terms of time synchronization, due to the different data acquisition rates of LiDAR and cameras, it is not feasible to employ multi-camera synchronization strategies typically found in the field of machine vision. Instead, this paper adopts a method of adding timestamps, thus actively searching for the nearest neighbor data in the data stream for synchronization. Specifically, a nearest-time matching strategy is implemented for time fusion. The time of the LiDAR data frame is taken as the baseline, matching the image closest to it in time to meet the system's application requirements.  
In spatial registration, LiDAR point cloud data and image pixel points need to be matched one-to-one to determine the coordinate transformation relationship between the LiDAR and the camera. The transformation relationship between the LiDAR's three-dimensional world coordinates and the image pixel coordinates is then solved based on the camera's intrinsic and extrinsic parameters. This paper uses the Zhang Zhengyou calibration method [34] to correct camera distortion and employs an algorithm based on a calibration board to calculate the extrinsic parameters of the camera and LiDAR using the feature point matching of 3D LiDAR point cloud and camera images. Through combined calibration and spatial registration, the 3D LiDAR point cloud targets can be remapped to the image target position according to the extrinsic parameters.  
Figure 9 shows the remapping image of point clouds and images in the KITTI dataset. After temporal synchronization and spatial registration, the matching error reaches to the centimeter level, which meets the requirements of the system.  
Figure 9: Point cloud remapping images before and after time synchronization and space registration. (a) 3D Point Cloud Image. (b) Camera Image. (c) Fused image.  
By implementing the above methods, the advantages of 3D LiDAR and image data are complementary, enabling more accurate detection and localization of tunnel obstacles.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Electrical Model  
Electrical power system (EPS) architectures can have control in the solar panels, although the so-called direct energy transfer (DET) cannot [23; 32]. In these architectures, the solar panels' voltage depends on the battery voltage, and this varies depending on the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \(R_{T_{w=1,..,7}}\) & 100 K/W \\ \(m_{w=1,..,6}\) & 0.05 kg \\ \(v_{w=1,...,6}\) & 1000 J/(kg K) \\ \(m_{w=7}\) & 0.06 kg \\ \(v_{w=7}\) & 800 J/(kg K) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the thermal model.  
power budget. Therefore, heaters' power consumption affects battery voltage and energy harvesting, impacting the satellite's energy balance.  
The directly coupled architecture is a DET topology, which is the simplest energy harvesting model [24]. Figure 5 shows a simplified directly coupled circuit diagram, where it has a single block representing solar panels. However, there are six solar panels, which are connected in parallel. This architecture requires that the solar panels, loads, and batteries have compatible voltage. As it was already exposed, the solar panels operate at almost the same voltage as the battery voltage, not always on their maximum power point. In this analysis, the passive components between the solar panels and the battery were not considered in the circuit. Figure 5 shows how to calculate the battery current \(I_{B}\):  
\[I_{B}(t)=I_{H}(t)+I_{L}(t)-\sum_{w=1}^{6}I_{\text{PV}_{w}}(t) \tag{40}\]  
\(I_{L}\) is the load current, \(I_{H}\) is the heater current, and \(I_{\text{PV}}\) is the photovoltaic panels' current. The heater current will be expressed in Equation (41) with a resistance for the heater \(R_{H}\) and a duty cycle \(D\) of a PWM signal control, already introduced in Section 2.5.  
\[I_{H}(t)=\frac{V_{B}(t)D(t)}{R_{H}} \tag{41}\]  
The load current \(I_{L}\) depends on the load power \(P_{L}\), defined as:  
\[I_{L}(t)=\frac{P_{L}(L)}{V_{B}(t)} \tag{42}\]  
The solar panel and battery models are required in the above expressions, and they will be evaluated in the following sections.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 3 Phenomena and Problems Caused by Photodarkening
### Fluorescence Lifetime Reduction  
In 2013, [PERSON] et al. [70] found that the fluorescence intensity and lifetime of Yb\({}^{3+}\) decreased significantly before and after the PD experiment, which was significantly related to the measured PD loss. In 2014, [PERSON] et al. [32] studied the effect of PD on the fluorescence lifetime of Yb and found that the additional loss in equilibrium state caused by PD has a linear relationship with the reduction of Yb fluorescence lifetime. In the same year, [PERSON] et al. [69] measured the fluorescence power attenuation spectrum of darkened and nondarkened Yb-doped fiber, and obtained that the fluorescence lifetime of the darkened fiber decreased by 12%, while the fluorescence life was linearly related to the laser efficiency, consequently, the laser efficiency decreased by 12%.
# Randomized Parameterized Algorithms for the Kidney Exchange Problem
## 3 A Randomized Algorithm Based on Random Partitioning Technique
Let \(P_{2}\) and \(P_{3}\) be the vertex sets of \(k_{2}\) vertex-disjoint 2-cycles and \(k_{3}\) vertex disjoint 3-cycles, respectively. Thus, \(|P_{2}|=2k_{2}\) and \(|P_{3}|=3k_{3}\). By random partitioning, if the vertex set \(V\) can be correctly divided into \(V_{2}\) and \(V_{3}\) such that \(P_{2}\subseteq V_{2}\) and \(P_{3}\subseteq V_{3}\), then we can obtain \(P_{2}\) by maximum matching algorithm for \(V_{2}\) in line 8 and obtain \(P_{3}\) by 3-set-packing algorithm for \(V_{3}\) in line 12. Thus in line 16, \(P_{2}\cup P_{3}\) can be correctly returned. We analyze the probability that \(\text{RKEP}(D,k_{2},k_{3})\) fails to find a packing \(P\) in \(D\). Since each vertex \(v\in V\) is put into \(V_{2}\) or \(V_{3}\) with probability \(1/2\) in each while loop, the vertices \(V_{2}\) and \(V_{3}\) can be correctly partitioned in line 5-7 with probability \(\left(\frac{1}{2}\right)^{2k_{2}}\cdot\left(\frac{1}{2}\right)^{3k_{3}}=2^{-2k _{2}-3k_{3}}\). Thus, the probability in each loop iteration is \(1-2^{-2k_{2}-3k_{3}}\) such that there exists at least one vertex \(v\in P_{2}\) which is put into \(V_{3}\) or at least one vertex \(u\in P_{3}\) which is put into \(V_{2}\). Hence, after executing \(c\cdot 2^{2k_{2}+3k_{3}}\) loops, the probability that \(P_{2}\) and \(P_{3}\) are not divided correctly is \((1-2^{-2k_{2}-3k_{3}})\cdot c^{2k_{2}+3k_{3}}=((1-\frac{1}{2^{2k_{2}+3k_{3}}}) ^{2k_{2}+3k_{3}})^{c}\leq\frac{1}{c^{2}}\). Therefore, by the random partitioning operation, there exist \(P_{2}\subseteq V_{2}\) and \(P_{3}\subseteq V_{3}\) with a probability at least \(1-\frac{1}{c^{2}}\). We analyze the running time of \(\text{RKEP}(D,k_{2},k_{3})\). In each loop iteration, lines 5-7 run in \(O(|V(D)|)\) time. For line 8, we transform it into the maximum matching problem of an undirected graph \(G\). Graph \(G\) can be constructed in time \(O(|V_{2}|^{2})\) in the following way: Let \(V(G)=V_{2}\). For \(v_{i},v_{j}\in V_{2}\), if \((v_{i},v_{j})\in A(D[V_{2}])\) and \((v_{j},v_{i})\in A(D[V_{2}])\), then there is an edge \(\{v_{i},v_{j}\}\in E(G)\). Line 8 takes \(O(|E(G)|\sqrt{|V_{2}|})\) time by applying [PERSON] algorithm [39] for the maximum matching problem of graph \(G\). For line 12, we find a packing of \(k_{3}\) vertex-disjoint 3-cycles in \(V_{3}\) by transforming into the 3-set-packing problem and invoking the corresponding algorithm in \(O^{*}(2.3^{3k_{3}})\)[34]. Thus, the running time of Algorithm 1 \(\text{RKEP}(D,k_{2},k_{3})\) is \(O^{*}(4.6^{3k_{3}}\cdot 2^{2k_{2}})\).
## 7 bpMRI vs. mpMRI Accuracy in VI-RADS  
As the MRI is gaining a dominant role in BC staging, several studies have focused on the best combination of anatomic sequences and the possibility to omit DCE sequences. During the past decade, commonly used bladder MRI protocols included anatomic sequences of T1w- and T2-w imaging, and functional sequences of DCE and DWI. Comparative, multi-reader studies [35; 36] have been published to assess the accuracy of the mpMRI and the biparametric (bp) MRI protocols in detecting MIBC using the VI-RADS criteria, with no significant differences shown in diagnostic performance between the two protocols and excellent agreement among the readers as well as high diagnostic accuracy, sensitivity and specificity. Furthermore, in both MRI protocols, diagnostic performance did not appear to be significantly affected by the reader's experience when using the VI-RADS criteria [35], regardless of the use of DCE sequences [37]. According to [PERSON] et al., only the T2w-imaging independently contributed to the diagnosis of high-grade and MIBC upon multivariate logistic regression [38], accurately showing the characteristics of BC without the necessity of a full multiparametric approach. Similarly, DWI-based VI-RADS scoring had the highest sensitivity (90.3-90.3%), specificity (99.1-95.8%), accuracy (97.3-94%), and AUC (0.947-0.914) in estimating MIBC for readers in the study of [PERSON] et al. [35]. Moreover, [PERSON] et al. [39] developed a modified VI-RADS without DCE imaging, defined \"non-contrast-enhanced VI-RADS (NCE-VI-RADS), which achieved a predictive accuracy for muscle invasion comparable to that of standard VI-RADS reporting. On the other hand, some authors underline the importance of DCE MRI, when disparities between sequences are found [9; 12]. In a small-cohort multi-reader prospective study, [PERSON] et al. advocated carefulness when interpreting DCE sequences by less experienced readers, as a confounding factor such as inflammatory changes could potentially lead to an overestimation of the tumour stage [40]; however, most importantly, the non-contrast MRI protocol had the same diagnostic accuracy as the standard mpMRI in detecting muscle invasion, whatever the reader's experience [41].